_/plugins/slicer/MONAILabel (You can drag the slicer/MONAILabel folder to the module panel.)
-- _**Restart**_ 3D Slicer
-
-
-
-
-
-### Sample Data
-
-Prepare some sample data to start with:
-
-Download MSD pancreas dataset as the sample dataset using monailabel API.
-The task is the volumetric (3D) segmentation of the pancreas from CT image.
-The dataset is from the 2018 MICCAI challenge.
-
-```bash
-monailabel datasets --download --name Task07_Pancreas --output .
-```
-
-### Start MONAI Label Server with VISTA Model
-
-Specify the sample app and sample datasets' path in the following command:
-
-```bash
-monailabel start_server --app monaivista --studies ./Task07_Pancreas/imagesTs --conf models vista_point_2pt5
-```
-
-- Open 3D Slicer and MONAI VISTA-Label plugin.
-
-
-- Connect to the monailabel server, start annotating!
-
-
-## Community
-
-Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join
-our [Slack channel](https://projectmonai.slack.com/archives/C031QRE0M1C).
-
-Ask and answer questions on [MONAI VISTA's GitHub discussions tab](https://github.com/Project-MONAI/VISTA/discussions).
-
-## License
-
-The model is licensed under the Apache 2.0 license.
-
-## Reference
-
-The current model is trained and developed based on [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything). Check the 3rd party license for reference.
-
-We greatly appreciate the authors of [`Segment Anything`](https://github.com/facebookresearch/segment-anything) and [`TotalSegmentator`](https://github.com/wasserth/TotalSegmentator) for releasing their work under a permissive license to the community.
-
-```
-@article{kirillov2023segany,
- title={Segment Anything},
- author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
- journal={arXiv:2304.02643},
- year={2023}
- }
-@article{wasserthal2022totalsegmentator,
- title={TotalSegmentator: robust segmentation of 104 anatomical structures in CT images},
- author={Wasserthal, Jakob and Meyer, Manfred and Breit, Hanns-Christian and Cyriac, Joshy and Yang, Shan and Segeroth, Martin},
- journal={arXiv preprint arXiv:2208.05868},
- year={2022}
- }
-```
-
-This integration is based on MONAI Label:
-
-```bash
-@article{diaz2022monai,
- title={Monai label: A framework for ai-assisted interactive labeling of 3d medical images},
- author={Diaz-Pinto, Andres and Alle, Sachidanand and Nath, Vishwesh and Tang, Yucheng and Ihsani, Alvin and Asad, Muhammad and P{\'e}rez-Garc{\'\i}a, Fernando and Mehta, Pritesh and Li, Wenqi and Flores, Mona and others},
- journal={arXiv preprint arXiv:2203.12362},
- year={2022}
-}
-```
+# MONAI VISTA Repository
+This is the repository for VISTA3D and VISTA2D For the older VISTA2.5d code, please checkout the vista2.5d branch
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diff --git a/monailabel/monaivista/README.md b/monailabel/monaivista/README.md
deleted file mode 100644
index d896418..0000000
--- a/monailabel/monaivista/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
-
-
-The MONAI VISTA app contains several tasks:
-
-- Inferencing tasks: These tasks allow end-users to invoke pre-trained models for image analysis.
-- Training and fine-tuning tasks: (coming soon)
-
-#### Implementing an Inference Task
-To implement an inference task, developers must inherit the [InferTask](https://github.com/Project-MONAI/MONAILabel/blob/main/monailabel/tasks/infer/basic_infer.py) interface, which specifies a list of pre- and post-transforms and an inferer.
-
-The code snippet below demonstrates an example implementation of `InferTask`. In this example, the image is pre-processed to a Numpy array and input into the `SimpleInferer`. The resulting output is post-processed by applying sigmoid activation with binary discretization.
-
-
-from monai.inferers import SimpleInferer
-from monai.transforms import (LoadImaged, ToNumpyd, Activationsd AsDiscreted, ToNumpyd)
-from monailabel.interfaces.tasks import InferTask
-
-class MyInfer(InferTask):
- def pre_transforms(self, data=None):
- return [
- LoadImaged(keys="image"),
- ToNumpyd(keys="image"),
- ]
- def inferer(self, data=None):
- return SimpleInferer()
-
- def post_transforms(self, data=None):
- return [
- Activationsd(keys="pred", sigmoid=True),
- AsDiscreted(keys="pred", threshold=0.5),
- ToNumpyd(keys="pred"),
- ]
-
-
-Note that `inferer` needs to be defined by developers.
diff --git a/monailabel/monaivista/lib/activelearning/__init__.py b/monailabel/monaivista/lib/activelearning/__init__.py
deleted file mode 100644
index b24dd5b..0000000
--- a/monailabel/monaivista/lib/activelearning/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .last import Last
diff --git a/monailabel/monaivista/lib/activelearning/last.py b/monailabel/monaivista/lib/activelearning/last.py
deleted file mode 100644
index d62863c..0000000
--- a/monailabel/monaivista/lib/activelearning/last.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-
-from monailabel.interfaces.datastore import Datastore
-from monailabel.interfaces.tasks.strategy import Strategy
-
-logger = logging.getLogger(__name__)
-
-
-class Last(Strategy):
- """
- Consider implementing a first strategy for active learning
- """
-
- def __init__(self):
- super().__init__("Get Last Sample")
-
- def __call__(self, request, datastore: Datastore):
- images = datastore.get_unlabeled_images()
- if not len(images):
- return None
-
- images.sort()
- image = images[-1]
-
- logger.info(f"First: Selected Image: {image}")
- return {"id": image}
diff --git a/monailabel/monaivista/lib/basic_infer.py b/monailabel/monaivista/lib/basic_infer.py
deleted file mode 100644
index 0d913fc..0000000
--- a/monailabel/monaivista/lib/basic_infer.py
+++ /dev/null
@@ -1,664 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import copy
-import logging
-import os
-import time
-from abc import abstractmethod
-from enum import Enum
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
-
-import torch
-from monai.data import decollate_batch
-from monai.inferers import Inferer, SimpleInferer, SlidingWindowInferer
-from monailabel.interfaces.exception import MONAILabelError, MONAILabelException
-from monailabel.interfaces.tasks.infer_v2 import InferTask, InferType
-from monailabel.interfaces.utils.transform import dump_data, run_transforms
-from monailabel.transform.cache import CacheTransformDatad
-
-# from monailabel.transform.writer import ClassificationWriter, DetectionWriter, Writer
-from monailabel.utils.others.generic import device_list, device_map, name_to_device
-
-from .writer import ClassificationWriter, DetectionWriter, Writer
-
-logger = logging.getLogger(__name__)
-
-
-class CallBackTypes(str, Enum):
- PRE_TRANSFORMS = "PRE_TRANSFORMS"
- INFERER = "INFERER"
- INVERT_TRANSFORMS = "INVERT_TRANSFORMS"
- POST_TRANSFORMS = "POST_TRANSFORMS"
- WRITER = "WRITER"
-
-
-class BasicInferTask(InferTask):
- """
- Basic Inference Task Helper
- """
-
- def __init__(
- self,
- path: Union[None, str, Sequence[str]],
- network: Union[None, Any],
- type: Union[str, InferType],
- labels: Union[str, None, Sequence[str], Dict[Any, Any]],
- dimension: int,
- description: str,
- model_state_dict: str = "model",
- input_key: str = "image",
- output_label_key: str = "pred",
- output_json_key: str = "result",
- config: Union[None, Dict[str, Any]] = None,
- load_strict: bool = False,
- roi_size=None,
- preload=False,
- train_mode=False,
- skip_writer=False,
- ):
- """
- :param path: Model File Path. Supports multiple paths to support versions (Last item will be picked as latest)
- :param network: Model Network (e.g. monai.networks.xyz). None in case if you use TorchScript (torch.jit).
- :param type: Type of Infer (segmentation, deepgrow etc..)
- :param labels: Labels associated to this Infer
- :param dimension: Input dimension
- :param description: Description
- :param model_state_dict: Key for loading the model state from checkpoint
- :param input_key: Input key for running inference
- :param output_label_key: Output key for storing result/label of inference
- :param output_json_key: Output key for storing result/label of inference
- :param config: K,V pairs to be part of user config
- :param load_strict: Load model in strict mode
- :param roi_size: ROI size for scanning window inference
- :param preload: Preload model/network on all available GPU devices
- :param train_mode: Run in Train mode instead of eval (when network has dropouts)
- :param skip_writer: Skip Writer and return data dictionary
- """
-
- super().__init__(type, labels, dimension, description, config)
-
- self.path = [] if not path else [path] if isinstance(path, str) else path
- self.network = network
- self.model_state_dict = model_state_dict
- self.input_key = input_key
- self.output_label_key = output_label_key
- self.output_json_key = output_json_key
- self.load_strict = load_strict
- self.roi_size = roi_size
- self.train_mode = train_mode
- self.skip_writer = skip_writer
-
- self._networks: Dict = {}
- self._cachedData: Dict = {}
- self._cachedEmbedding: Dict = {}
-
- self.computeEmbedding = False
-
- self._config.update(
- {
- "device": device_list(),
- # "result_extension": None,
- # "result_dtype": None,
- # "result_compress": False
- # "roi_size": self.roi_size,
- # "sw_batch_size": 1,
- # "sw_overlap": 0.25,
- }
- )
-
- if config:
- self._config.update(config)
-
- if preload:
- for device in device_map().values():
- logger.info(f"Preload Network for device: {device}")
- self._get_network(device, None)
-
- def info(self) -> Dict[str, Any]:
- return {
- "type": self.type,
- "labels": self.labels,
- "dimension": self.dimension,
- "description": self.description,
- "config": self.config(),
- }
-
- def config(self) -> Dict[str, Any]:
- return self._config
-
- def is_valid(self) -> bool:
- if self.network or self.type == InferType.SCRIBBLES:
- return True
-
- paths = self.path
- for path in reversed(paths):
- if path and os.path.exists(path):
- return True
- return False
-
- def get_path(self, validate=True):
- if not self.path:
- return None
-
- paths = self.path
- for path in reversed(paths):
- if path:
- if not validate or os.path.exists(path):
- return path
- return None
-
- def add_cache_transform(self, t, data, keys=("image", "image_meta_dict"), hash_key=("image_path", "model")):
- if data and data.get("cache_transforms", False):
- in_memory = data.get("cache_transforms_in_memory", True)
- ttl = data.get("cache_transforms_ttl", 300)
-
- t.append(CacheTransformDatad(keys=keys, hash_key=hash_key, in_memory=in_memory, ttl=ttl))
-
- @abstractmethod
- def pre_transforms(self, data=None) -> Sequence[Callable]:
- """
- Provide List of pre-transforms
-
- :param data: current data dictionary/request which can be helpful to define the transforms per-request basis
-
- For Example::
-
- return [
- monai.transforms.LoadImaged(keys='image'),
- monai.transforms.AddChanneld(keys='image'),
- monai.transforms.Spacingd(keys='image', pixdim=[1.0, 1.0, 1.0]),
- monai.transforms.ScaleIntensityRanged(keys='image',
- a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),
- ]
-
- """
- pass
-
- def inverse_transforms(self, data=None) -> Union[None, Sequence[Callable]]:
- """
- Provide List of inverse-transforms. They are normally subset of pre-transforms.
- This task is performed on output_label (using the references from input_key)
-
- :param data: current data dictionary/request which can be helpful to define the transforms per-request basis
-
- Return one of the following.
- - None: Return None to disable running any inverse transforms (default behavior).
- - Empty: Return [] to run all applicable pre-transforms which has inverse method
- - list: Return list of specific pre-transforms names/classes to run inverse method
-
- For Example::
-
- return [
- monai.transforms.Spacingd,
- ]
-
- """
- return None
-
- @abstractmethod
- def post_transforms(self, data=None) -> Sequence[Callable]:
- """
- Provide List of post-transforms
-
- :param data: current data dictionary/request which can be helpful to define the transforms per-request basis
-
- For Example::
-
- return [
- monai.transforms.AddChanneld(keys='pred'),
- monai.transforms.Activationsd(keys='pred', softmax=True),
- monai.transforms.AsDiscreted(keys='pred', argmax=True),
- monai.transforms.SqueezeDimd(keys='pred', dim=0),
- monai.transforms.ToNumpyd(keys='pred'),
- monailabel.interface.utils.Restored(keys='pred', ref_image='image'),
- monailabel.interface.utils.ExtremePointsd(keys='pred', result='result', points='points'),
- monailabel.interface.utils.BoundingBoxd(keys='pred', result='result', bbox='bbox'),
- ]
-
- """
- pass
-
- def inferer(self, data=None) -> Inferer:
- input_shape = data[self.input_key].shape if data else None
-
- roi_size = data.get("roi_size", self.roi_size) if data else self.roi_size
- sw_batch_size = data.get("sw_batch_size", 1) if data else 1
- sw_overlap = data.get("sw_overlap", 0.25) if data else 0.25
- device = data.get("device")
-
- sliding = False
- if input_shape and roi_size:
- for i in range(len(roi_size)):
- if input_shape[-i] > roi_size[-i]:
- sliding = True
-
- if sliding:
- return SlidingWindowInferer(
- roi_size=roi_size,
- overlap=sw_overlap,
- sw_batch_size=sw_batch_size,
- sw_device=device,
- device=device,
- )
- return SimpleInferer()
-
- def detector(self, data=None) -> Optional[Callable]:
- return None
-
- def __call__(
- self, request, callbacks: Union[Dict[CallBackTypes, Any], None] = None
- ) -> Union[Dict, Tuple[str, Dict[str, Any]]]:
- """
- It provides basic implementation to run the following in order
- - Run Pre Transforms
- - Run Inferer
- - Run Invert Transforms
- - Run Post Transforms
- - Run Writer to save the label mask and result params
-
- You can provide callbacks which can be useful while writing pipelines to consume intermediate outputs
- Callback function should consume data and return data (modified/updated) e.g. `def my_cb(data): return data`
-
- Returns: Label (File Path) and Result Params (JSON)
- """
- begin = time.time()
- req = copy.deepcopy(self._config)
- req.update(request)
-
- # device
- device = name_to_device(req.get("device", "cuda"))
- req["device"] = device
-
- logger.setLevel(req.get("logging", "INFO").upper())
- if req.get("image") is not None and isinstance(req.get("image"), str):
- logger.info(f"Infer Request (final): {req}")
- data = copy.deepcopy(req)
- data.update({"image_path": req.get("image")})
- else:
- dump_data(req, logger.level)
- data = req
-
- self.class_prompts = request.get("class_prompts", None)
- self.point_prompts = request.get("point_prompts", None)
- self.computeEmbedding = request.get("computeEmbedding", False)
-
- # callbacks useful in case of pipeliens to consume intermediate output from each of the following stages
- # callback function should consume data and returns data (modified/updated)
- callbacks = callbacks if callbacks else {}
- callback_run_pre_transforms = callbacks.get(CallBackTypes.PRE_TRANSFORMS)
- callback_run_inferer = callbacks.get(CallBackTypes.INFERER)
- callback_run_invert_transforms = callbacks.get(CallBackTypes.INVERT_TRANSFORMS)
- callback_run_post_transforms = callbacks.get(CallBackTypes.POST_TRANSFORMS)
- callback_writer = callbacks.get(CallBackTypes.WRITER)
-
- start = time.time()
- pre_transforms = self.pre_transforms(data)
- data = self.run_pre_transforms(data, pre_transforms)
- if callback_run_pre_transforms:
- data = callback_run_pre_transforms(data)
- latency_pre = time.time() - start
-
- start = time.time()
-
- self._cachedData = (
- {}
- if self._cachedData and data.get("image_path") != self._cachedData.get("image_path")
- else self._cachedData or {}
- )
-
- if self.type == InferType.DETECTION:
- data = self.run_detector(data, device=device)
- else:
- data = self.run_inferer(data, self._cachedEmbedding, self._cachedData, device=device)
-
- if not self.computeEmbedding:
- self._cachedData = data.copy()
-
- if callback_run_inferer:
- data = callback_run_inferer(data)
- latency_inferer = time.time() - start
-
- start = time.time()
- data = self.run_invert_transforms(data, pre_transforms, self.inverse_transforms(data))
- if callback_run_invert_transforms:
- data = callback_run_invert_transforms(data)
- latency_invert = time.time() - start
-
- start = time.time()
- data = self.run_post_transforms(data, self.post_transforms(data))
- if callback_run_post_transforms:
- data = callback_run_post_transforms(data)
- latency_post = time.time() - start
-
- if self.skip_writer:
- return dict(data)
-
- start = time.time()
- result_file_name, result_json = self.writer(data)
- if callback_writer:
- data = callback_writer(data)
- latency_write = time.time() - start
-
- latency_total = time.time() - begin
- logger.info(
- "++ Latencies => Total: {:.4f}; "
- "Pre: {:.4f}; Inferer: {:.4f}; Invert: {:.4f}; Post: {:.4f}; Write: {:.4f}".format(
- latency_total,
- latency_pre,
- latency_inferer,
- latency_invert,
- latency_post,
- latency_write,
- )
- )
-
- result_json["label_names"] = self.labels
- result_json["latencies"] = {
- "pre": round(latency_pre, 2),
- "infer": round(latency_inferer, 2),
- "invert": round(latency_invert, 2),
- "post": round(latency_post, 2),
- "write": round(latency_write, 2),
- "total": round(latency_total, 2),
- "transform": data.get("latencies"),
- }
-
- if result_file_name is not None and isinstance(result_file_name, str):
- logger.info(f"Result File: {result_file_name}")
- logger.info(f"Result Json Keys: {list(result_json.keys())}")
- return result_file_name, result_json
- else:
- # result_file_name, result_json = self.writer(data, embedding=True)
-
- self._cachedEmbedding = data
- result_json = {"Cache": "embedding"}
- result_file_name = "CacheEmbedding"
- # logger.info(f"Computed image mebedding: {data}")
- self.computeEmbedding = False
- self._cachedData = {}
- return result_file_name, result_json
-
- def run_pre_transforms(self, data: Dict[str, Any], transforms):
- pre_cache: List[Any] = []
- post_cache: List[Any] = []
-
- current = pre_cache
- cache_t = None
- for t in transforms:
- if isinstance(t, CacheTransformDatad):
- cache_t = t
- current = post_cache
- else:
- current.append(t)
-
- if cache_t is not None:
-
- class LoadFromCache:
- def __call__(self, data):
- return cache_t.load(data)
-
- d = run_transforms(data, [LoadFromCache()], log_prefix="PRE", use_compose=False)
-
- # Failed/Cache-Miss (run everything)
- if d is None:
- return run_transforms(data, transforms, log_prefix="PRE", use_compose=False)
- return run_transforms(d, post_cache, log_prefix="PRE", use_compose=False) if post_cache else d
-
- return run_transforms(data, transforms, log_prefix="PRE", use_compose=False)
-
- def run_invert_transforms(self, data: Dict[str, Any], pre_transforms, names):
- if names is None:
- return data
-
- pre_names = dict()
- transforms = []
- for t in reversed(pre_transforms):
- if hasattr(t, "inverse"):
- pre_names[t.__class__.__name__] = t
- transforms.append(t)
-
- # Run only selected/given
- if len(names) > 0:
- transforms = [pre_transforms[n if isinstance(n, str) else n.__name__] for n in names]
-
- d = copy.deepcopy(dict(data))
- d[self.input_key] = data[self.output_label_key]
-
- d = run_transforms(d, transforms, inverse=True, log_prefix="INV")
- data[self.output_label_key] = d[self.input_key]
- return data
-
- def run_post_transforms(self, data: Dict[str, Any], transforms):
- return run_transforms(data, transforms, log_prefix="POST")
-
- def clear_cache(self):
- self._networks.clear()
-
- def _get_network(self, device, data):
- path = self.get_path()
- logger.info(f"Infer model path: {path}")
-
- if data and self._config.get("model_filename"):
- model_filename = data.get("model_filename")
- model_filename = model_filename if isinstance(model_filename, str) else model_filename[0]
- user_path = os.path.join(os.path.dirname(self.path[0]), model_filename)
- if user_path and os.path.exists(user_path):
- path = user_path
- logger.info(f"Using provided model_file: {user_path}")
- else:
- logger.info(f"Ignoring provided model_file (not valid): {user_path}")
-
- if not path and not self.network:
- if self.type == InferType.SCRIBBLES:
- return None
-
- raise MONAILabelException(
- MONAILabelError.INFERENCE_ERROR,
- f"Model Path ({self.path}) does not exist/valid",
- )
-
- cached = self._networks.get(device)
- statbuf = os.stat(path) if path else None
- network = None
- if cached:
- if statbuf and statbuf.st_mtime == cached[1]:
- network = cached[0]
- elif statbuf:
- logger.warning(f"Reload model from cache. Prev ts: {cached[1]}; Current ts: {statbuf.st_mtime}")
-
- if network is None:
- if self.network:
- network = copy.deepcopy(self.network)
- network.to(torch.device(device))
-
- if path:
- model_state_dict = torch.load(path, map_location=torch.device(device))
- load_dict = model_state_dict["state_dict"]
-
- network.load_state_dict(load_dict, strict=True)
-
- else:
- network = torch.jit.load(path, map_location=torch.device(device))
-
- if self.train_mode:
- network.train()
- else:
- network.eval()
-
- self._networks[device] = (network, statbuf.st_mtime if statbuf else 0)
-
- return network
-
- def run_inferer(self, data: Dict[str, Any], cachedEmbedding, cachedData, convert_to_batch=True, device="cuda"):
- """
- Run Inferer over pre-processed Data. Derive this logic to customize the normal behavior.
- In some cases, you want to implement your own for running chained inferers over pre-processed data
-
- :param data: pre-processed data
- :param convert_to_batch: convert input to batched input
- :param device: device type run load the model and run inferer
- :return: updated data with output_key stored that will be used for post-processing
- """
- inferer = self.inferer(data)
- logger.info(f"Inferer:: {device} => {inferer.__class__.__name__} => {inferer.__dict__}")
-
- image_meta_dict = data.get("image_meta_dict", None)
- if image_meta_dict:
- original_affine = image_meta_dict.get("original_affine", None)
- else:
- original_affine = None
-
- network = self._get_network(device, data)
- if network:
- inputs = data[self.input_key]
- inputs = inputs if torch.is_tensor(inputs) else torch.from_numpy(inputs)
- inputs = inputs[None] if convert_to_batch else inputs
- inputs = inputs.to(torch.device(device))
-
- with torch.no_grad():
- if self.computeEmbedding:
- outputs = inferer(
- inputs, network, computeEmbedding=self.computeEmbedding, labels=self.labels, device=device
- )
- return outputs
- else:
- outputs = inferer(
- inputs,
- network,
- class_prompts=self.class_prompts,
- point_prompts=self.point_prompts,
- cachedEmbedding=cachedEmbedding,
- cached_data=cachedData,
- computeEmbedding=self.computeEmbedding,
- labels=self.labels,
- device=device,
- original_affine=original_affine,
- )
-
- if device.startswith("cuda"):
- torch.cuda.empty_cache()
-
- if convert_to_batch:
- if isinstance(outputs, dict):
- outputs_d = decollate_batch(outputs)
- outputs = outputs_d[0]
- else:
- outputs = outputs[0]
-
- data[self.output_label_key] = outputs
- else:
- # consider them as callable transforms
- data = run_transforms(data, inferer, log_prefix="INF", log_name="Inferer")
- return data
-
- def run_detector(self, data: Dict[str, Any], convert_to_batch=True, device="cuda"):
- """
- Run Detector over pre-processed Data. Derive this logic to customize the normal behavior.
- In some cases, you want to implement your own for running chained inferers over pre-processed data
-
- :param data: pre-processed data
- :param convert_to_batch: convert input to batched input
- :param device: device type run load the model and run inferer
- :return: updated data with output_key stored that will be used for post-processing
- """
-
- """
- Run Detector over pre-processed Data. Derive this logic to customize the normal behavior.
- In some cases, you want to implement your own for running chained detector ops over pre-processed data
-
- :param data: pre-processed data
- :param device: device type run load the model and run inferer
- :return: updated data with output_key stored that will be used for post-processing
- """
- detector = self.detector(data)
- if detector is None:
- raise ValueError("Detector is Not Provided")
-
- if hasattr(detector, "inferer"):
- logger.info(
- f"Detector Inferer:: {device} => {detector.inferer.__class__.__name__} => "
- f"{detector.inferer.__dict__}"
- )
-
- network = self._get_network(device, data)
- if network:
- inputs = data[self.input_key]
- inputs = inputs if torch.is_tensor(inputs) else torch.from_numpy(inputs)
- inputs = inputs[None] if convert_to_batch else inputs
- inputs = inputs.to(torch.device(device))
-
- if hasattr(detector, "network"):
- detector.network = network # type: ignore
- else:
- logger.warning("Detector has no 'network' attribute defined; Running without pretrained network")
-
- with torch.no_grad():
- if callable(getattr(detector, "eval", None)):
- detector.eval() # type: ignore
- network.eval()
- outputs = detector(inputs, use_inferer=True)
-
- if device.startswith("cuda"):
- torch.cuda.empty_cache()
-
- if convert_to_batch:
- if isinstance(outputs, dict):
- outputs_d = decollate_batch(outputs)
- outputs = outputs_d[0]
- else:
- outputs = outputs[0]
-
- if isinstance(outputs, dict):
- data.update(outputs)
- else:
- data[self.output_label_key] = outputs
- return data
-
- def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]:
- """
- You can provide your own writer. However, this writer saves the prediction/label mask to file
- and fetches result json
-
- :param data: typically it is post processed data
- :param extension: output label extension
- :param dtype: output label dtype
- :return: tuple of output_file and result_json
- """
- logger.info("Writing Result...")
- if extension is not None:
- data["result_extension"] = extension
- if dtype is not None:
- data["result_dtype"] = dtype
- if self.labels is not None:
- data["labels"] = self.labels
-
- if self.type == InferType.CLASSIFICATION:
- if isinstance(self.labels, dict):
- label_names = {v: k for k, v in self.labels.items()}
- else:
- label_names = {v: k for v, k in enumerate(self.labels)} if isinstance(self.labels, Sequence) else None
-
- cw = ClassificationWriter(label=self.output_label_key, label_names=label_names)
- return cw(data)
-
- if self.type == InferType.DETECTION:
- dw = DetectionWriter()
- return dw(data)
-
- writer = Writer(label=self.output_label_key, json=self.output_json_key)
- return writer(data)
-
- def clear(self):
- self._networks.clear()
-
- def set_loglevel(self, level: str):
- logger.setLevel(level.upper())
diff --git a/monailabel/monaivista/lib/class_utils.py b/monailabel/monaivista/lib/class_utils.py
deleted file mode 100644
index bb8c49a..0000000
--- a/monailabel/monaivista/lib/class_utils.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import glob
-import importlib.util
-import inspect
-import logging
-import os
-import sys
-from distutils.util import strtobool
-from typing import List
-
-from monailabel.interfaces.exception import MONAILabelError, MONAILabelException
-
-logger = logging.getLogger(__name__)
-
-
-def unload_module(name):
- modules = []
- for m in sorted(sys.modules):
- if m == name or m.startswith(f"{name}."):
- modules.append(m)
-
- if modules and strtobool(os.environ.get("MONAI_LABEL_RELOAD_APP_LIB", "true")):
- logger.info(f"Remove/Reload previous Modules: {modules}")
- for m in modules:
- del sys.modules[m]
-
-
-def module_from_file(module_name, file_path):
- app_dir = os.path.dirname(file_path)
- sys.path.insert(0, app_dir)
- unload_module("lib")
-
- spec = importlib.util.spec_from_file_location(module_name, file_path)
- module = importlib.util.module_from_spec(spec)
- spec.loader.exec_module(module)
-
- sys.path.remove(app_dir)
- logger.debug(f"module: {module}")
- return module
-
-
-def is_subclass(n, o, class_c):
- if inspect.isclass(o) and n != class_c:
- b = [cls.__name__ for cls in o.__bases__]
- logger.debug(f"Base classes => {b}")
- if class_c in b:
- logger.info(f"Subclass for {class_c} Found: {o}")
- return True
- return False
-
-
-def get_class_of_subclass(module, class_c):
- logger.debug(f"{module} => {class_c}")
- for n, o in inspect.getmembers(module):
- if not inspect.isclass(o):
- continue
-
- logger.debug(f"{n} => {o}")
- if is_subclass(n, o, class_c):
- return o
- return None
-
-
-def get_class_of_subclass_from_file(module_name, file_path, class_c):
- return get_class_of_subclass(module_from_file(module_name, file_path), class_c)
-
-
-def to_expression(class_path, class_args):
- key_val = []
- for key in class_args:
- val = class_args[key]
- if isinstance(val, str):
- val = f"'{val}'"
- elif isinstance(val, tuple) or isinstance(val, list):
- vals = []
- for v in val:
- if isinstance(v, str):
- v = f"'{v}'"
- else:
- v = str(v)
- vals.append(v)
- if isinstance(val, tuple):
- val = f"({', '.join(vals)})"
- else:
- val = f"[{', '.join(vals)}]"
- else:
- val = str(val)
- key_val.append(f"{key}={val}")
- return f"{class_path}({', '.join(key_val)})"
-
-
-def class_args_to_exp(c, mappings=None):
- class_name = c["name"]
- class_name = mappings.get(class_name, class_name) if mappings else class_name
- class_args = c.get("args", {})
- return to_expression(class_name, class_args)
-
-
-def get_class_info(exp, handle_bool=True):
- if isinstance(exp, dict):
- return exp["name"], exp["args"]
- if exp.find("(") == -1:
- return exp, {}
-
- def foo(**kwargs):
- return kwargs
-
- if handle_bool:
- exp = exp.replace("=true", "=True").replace("=false", "=False") # safe to assume
- exp = exp.replace(" true", " True").replace(" false", " False")
- class_path = exp[: exp.find("(")]
- class_args = exp[exp.find("(") + 1 : -1] if exp.find("(") >= 0 else None
-
- logger.debug(f"Eval Input:: {class_path} => {class_args}")
- class_args = eval("foo(" + class_args + ")") if class_args else None
-
- logger.debug(f"{class_path} => {class_args}")
- return class_path, class_args
-
-
-def init_class(class_path, class_args):
- if "." not in class_path:
- raise MONAILabelException(
- MONAILabelError.CLASS_INIT_ERROR, "Class path need to be in the form [module/file].[class_name]."
- )
- module_name, class_name = class_path.rsplit(".", 1)
-
- m = importlib.import_module(module_name)
- importlib.reload(m)
- c = getattr(m, class_name)
- return c(**class_args) if class_args else c()
-
-
-def init_class_from_exp(exp):
- class_path, class_args = get_class_info(exp)
- return init_class(class_path, class_args)
-
-
-def get_class_names(p, subclass=None) -> List[str]:
- logger = logging.getLogger(__name__)
-
- result = []
- logger.debug(f"Module File Path: {p.__file__}")
-
- if os.path.basename(p.__file__).startswith("__"):
- current_dir = os.path.dirname(p.__file__)
- current_module_name = p.__package__
-
- for file in glob.glob(current_dir + "/*.py*"):
- name = os.path.splitext(os.path.basename(file))[0]
- if name.startswith("__"):
- continue
-
- module = importlib.import_module("." + name, package=current_module_name)
- # print("!!! 0: {}".format(module))
-
- for m in dir(module):
- c = getattr(module, m)
- if not c or inspect.isabstract(c):
- continue
-
- # if "lib.configs.samm_clipUM" in module.__name__:
- # # print("!!! 1: {}".format(m))
-
- if (
- inspect.isclass(c)
- and c.__module__ == module.__name__
- and (not subclass or is_subclass(c.__name__, c, subclass))
- ):
- result.append(c.__module__ + "." + c.__name__)
-
- else:
- for m in dir(p):
- c = getattr(p, m)
- if not c or inspect.isabstract(c):
- continue
- print("!!! 1: {}".format(p.__name__))
- print("!!! 2: {}".format(c.__module__))
-
- if (
- inspect.isclass(c)
- and c.__module__ == p.__name__
- and (not subclass or is_subclass(c.__name__, c, subclass))
- ):
- result.append(c.__module__ + "." + c.__name__)
-
- return result
diff --git a/monailabel/monaivista/lib/configs/vista_point_2pt5.py b/monailabel/monaivista/lib/configs/vista_point_2pt5.py
deleted file mode 100644
index 0ff4b4d..0000000
--- a/monailabel/monaivista/lib/configs/vista_point_2pt5.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-from typing import Any, Dict, Optional, Union
-
-import lib.infers
-import lib.trainers
-from lib.model.vista_point_2pt5.model_2pt5 import sam_model_registry
-from monailabel.interfaces.config import TaskConfig
-from monailabel.interfaces.tasks.infer_v2 import InferTask
-from monailabel.interfaces.tasks.train import TrainTask
-from monailabel.utils.others.generic import download_file, strtobool
-
-logger = logging.getLogger(__name__)
-
-
-class VISTAPOINT2PT5(TaskConfig):
- def __init__(self):
- super().__init__()
-
- def init(self, name: str, model_dir: str, conf: Dict[str, str], planner: Any, **kwargs):
- super().init(name, model_dir, conf, planner, **kwargs)
-
- # Labels
- self.labels = {
- "spleen": 1,
- "kidney_right": 2,
- "kidney_left": 3,
- "gallbladder": 4,
- "liver": 5,
- "stomach": 6,
- "aorta": 7,
- "inferior_vena_cava": 8,
- "portal_vein_and_splenic_vein": 9,
- "pancreas": 10,
- "adrenal_gland_right": 11,
- "adrenal_gland_left": 12,
- "lung_upper_lobe_left": 13,
- "lung_lower_lobe_left": 14,
- "lung_upper_lobe_right": 15,
- "lung_middle_lobe_right": 16,
- "lung_lower_lobe_right": 17,
- "vertebrae_L5": 18,
- "vertebrae_L4": 19,
- "vertebrae_L3": 20,
- "vertebrae_L2": 21,
- "vertebrae_L1": 22,
- "vertebrae_T12": 23,
- "vertebrae_T11": 24,
- "vertebrae_T10": 25,
- "vertebrae_T9": 26,
- "vertebrae_T8": 27,
- "vertebrae_T7": 28,
- "vertebrae_T6": 29,
- "vertebrae_T5": 30,
- "vertebrae_T4": 31,
- "vertebrae_T3": 32,
- "vertebrae_T2": 33,
- "vertebrae_T1": 34,
- "vertebrae_C7": 35,
- "vertebrae_C6": 36,
- "vertebrae_C5": 37,
- "vertebrae_C4": 38,
- "vertebrae_C3": 39,
- "vertebrae_C2": 40,
- "vertebrae_C1": 41,
- "esophagus": 42,
- "trachea": 43,
- "heart_myocardium": 44,
- "heart_atrium_left": 45,
- "heart_ventricle_left": 46,
- "heart_atrium_right": 47,
- "heart_ventricle_right": 48,
- "pulmonary_artery": 49,
- "brain": 50,
- "iliac_artery_left": 51,
- "iliac_artery_right": 52,
- "iliac_vena_left": 53,
- "iliac_vena_right": 54,
- "small_bowel": 55,
- "duodenum": 56,
- "colon": 57,
- "rib_left_1": 58,
- "rib_left_2": 59,
- "rib_left_3": 60,
- "rib_left_4": 61,
- "rib_left_5": 62,
- "rib_left_6": 63,
- "rib_left_7": 64,
- "rib_left_8": 65,
- "rib_left_9": 66,
- "rib_left_10": 67,
- "rib_left_11": 68,
- "rib_left_12": 69,
- "rib_right_1": 70,
- "rib_right_2": 71,
- "rib_right_3": 72,
- "rib_right_4": 73,
- "rib_right_5": 74,
- "rib_right_6": 75,
- "rib_right_7": 76,
- "rib_right_8": 77,
- "rib_right_9": 78,
- "rib_right_10": 79,
- "rib_right_11": 80,
- "rib_right_12": 81,
- "humerus_left": 82,
- "humerus_right": 83,
- "scapula_left": 84,
- "scapula_right": 85,
- "clavicula_left": 86,
- "clavicula_right": 87,
- "femur_left": 88,
- "femur_right": 89,
- "hip_left": 90,
- "hip_right": 91,
- "sacrum": 92,
- "face": 93,
- "gluteus_maximus_left": 94,
- "gluteus_maximus_right": 95,
- "gluteus_medius_left": 96,
- "gluteus_medius_right": 97,
- "gluteus_minimus_left": 98,
- "gluteus_minimus_right": 99,
- "autochthon_left": 100,
- "autochthon_right": 101,
- "iliopsoas_left": 102,
- "iliopsoas_right": 103,
- "urinary_bladder": 104,
- }
-
- # Model Files
- self.path = [
- os.path.join(self.model_dir, f"pretrained_{name}.pt"), # pretrained
- os.path.join(self.model_dir, f"{name}.pt"), # published
- ]
-
- # Download PreTrained Model
- if strtobool(self.conf.get("use_pretrained_model", "true")):
- url = f"{self.conf.get('pretrained_path', self.PRE_TRAINED_PATH)}"
- url = f"{url}/monaivista_point_104_sam_2pt5.pt"
- download_file(url, self.path[0])
-
- self.target_spacing = (1.5, 1.5, 1.5) # target space for image
- # Setting ROI size - This is for the image padding
- self.roi_size = (96, 96, 96)
-
- self.network = sam_model_registry["vit_b"](checkpoint=None, image_size=1024, encoder_in_chans=9 * 3)
-
- def infer(self) -> Union[InferTask, Dict[str, InferTask]]:
- task: InferTask = lib.infers.VISTAPOINT2PT5(
- path=self.path,
- network=self.network,
- labels=self.labels,
- preload=strtobool(self.conf.get("preload", "false")),
- )
- return task
-
- def trainer(self) -> Optional[TrainTask]:
- None
diff --git a/monailabel/monaivista/lib/infers/__init__.py b/monailabel/monaivista/lib/infers/__init__.py
deleted file mode 100644
index 83dda98..0000000
--- a/monailabel/monaivista/lib/infers/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .vista_point_2pt5 import VISTAPOINT2PT5
diff --git a/monailabel/monaivista/lib/infers/vista_point_2pt5.py b/monailabel/monaivista/lib/infers/vista_point_2pt5.py
deleted file mode 100644
index ee35286..0000000
--- a/monailabel/monaivista/lib/infers/vista_point_2pt5.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, Sequence
-
-from lib.basic_infer import BasicInferTask
-from lib.model.vista_point_2pt5.inferer import VISTASliceInferer
-from monai.inferers import Inferer
-from monai.transforms import (
- EnsureChannelFirstd,
- EnsureTyped,
- LoadImaged,
- Orientationd,
- ScaleIntensityRanged,
-)
-from monailabel.interfaces.tasks.infer_v2 import InferType
-from monailabel.transform.post import Restored
-
-
-class VISTAPOINT2PT5(BasicInferTask):
- """
- This provides Inference Engine for pre-trained VISTA segmentation model.
- """
-
- def __init__(
- self,
- path,
- network=None,
- target_spacing=(1.5, 1.5, 1.5),
- type=InferType.SEGMENTATION,
- labels=None,
- dimension=2,
- description="A pre-trained model for volumetric (2.5D) segmentation of the monai vista",
- **kwargs,
- ):
- super().__init__(
- path=path,
- network=network,
- type=type,
- labels=labels,
- dimension=dimension,
- description=description,
- **kwargs,
- )
- self.target_spacing = target_spacing
-
- def is_valid(self) -> bool:
- return True
-
- def pre_transforms(self, data=None) -> Sequence[Callable]:
- return [
- LoadImaged(keys="image"),
- EnsureChannelFirstd(keys="image"),
- Orientationd(keys="image", axcodes="RAS"),
- ScaleIntensityRanged(keys="image", a_min=-1024, a_max=1024, b_min=0.0, b_max=1.0, clip=True),
- ]
-
- def inferer(self, data=None) -> Inferer:
- return VISTASliceInferer(device=data.get("device") if data else None)
-
- def inverse_transforms(self, data=None):
- return []
-
- def post_transforms(self, data=None) -> Sequence[Callable]:
- return [
- EnsureTyped(keys="pred", device=data.get("device") if data else None),
- Restored(keys="pred", ref_image="image"),
- ]
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/__init__.py b/monailabel/monaivista/lib/model/vista_point_2pt5/__init__.py
deleted file mode 100644
index 1e97f89..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/inferer.py b/monailabel/monaivista/lib/model/vista_point_2pt5/inferer.py
deleted file mode 100644
index 77e52f7..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/inferer.py
+++ /dev/null
@@ -1,327 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from collections.abc import Callable, Sequence
-from typing import Any
-
-import torch
-import torch.nn.functional as F
-from monai.apps.utils import get_logger
-from monai.data.meta_tensor import MetaTensor
-from monai.inferers import Inferer
-from monai.transforms import Activations, AsDiscrete, Compose
-from monai.utils import convert_to_dst_type
-from torch.cuda.amp import autocast
-
-from .utils.utils import prepare_sam_val_input
-
-logger = get_logger(__name__)
-
-
-class VISTASliceInferer(Inferer):
- def __init__(
- self,
- device: torch.device | str | None = None,
- progress: bool = False,
- cpu_thresh: int | None = None,
- ) -> None:
- super().__init__()
- self.device = device
- self.progress = progress
- self.cpu_thresh = cpu_thresh
-
- def __call__(
- self,
- inputs: torch.Tensor,
- network: Callable[..., torch.Tensor | Sequence[torch.Tensor] | dict[Any, torch.Tensor]],
- device: torch.device | str | None = None,
- n_z_slices: int = 9,
- *args: Any,
- **kwargs: Any,
- ) -> torch.Tensor | tuple[torch.Tensor, ...] | dict[Any, torch.Tensor]:
- """
-
- Args:
- inputs: model input data for inference.
- network: target model to execute inference.
- supports callables such as ``lambda x: my_torch_model(x, additional_config)``
- args: optional args to be passed to ``network``.
- kwargs: optional keyword args to be passed to ``network``.
-
- """
-
- device = kwargs.pop("device", self.device)
-
- if device is None and self.cpu_thresh is not None and inputs.shape[2:].numel() > self.cpu_thresh:
- device = "cpu" # stitch in cpu memory if image is too large
-
- return vista_slice_inference(
- inputs,
- network,
- device,
- n_z_slices,
- *args,
- **kwargs,
- )
-
-
-def vista_slice_inference(
- inputs: torch.Tensor | MetaTensor,
- predictor: Callable[..., torch.Tensor | Sequence[torch.Tensor] | dict[Any, torch.Tensor]],
- device: torch.device | str | None = None,
- n_z_slices: int = 9,
- *args: Any,
- **kwargs: Any,
-) -> torch.Tensor | tuple[torch.Tensor, ...] | dict[Any, torch.Tensor]:
- temp_meta = None
- if isinstance(inputs, MetaTensor):
- temp_meta = MetaTensor([]).copy_meta_from(inputs, copy_attr=False)
-
- labels = kwargs.pop("labels")
- num_classes = len(labels)
-
- inputs_l = inputs
- pred_volume = torch.repeat_interleave(torch.zeros_like(inputs_l), num_classes + 1, dim=1).float()
-
- inputs_l = inputs_l.squeeze()
- n_z_before_pad = inputs_l.shape[-1]
- # pad the z direction, so we can easily extract 2.5D input and predict labels for the center slice
-
- pd = (n_z_slices // 2, n_z_slices // 2)
- inputs_l = F.pad(inputs_l, pd, "constant", 0)
-
- computeEmbedding = kwargs.pop("computeEmbedding")
-
- if computeEmbedding:
- embedding = compute_embedding(n_z_slices, n_z_before_pad, inputs_l, predictor)
- return embedding
-
- post_pred = Compose([Activations(sigmoid=True)])
- post_pred_slice = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
-
- class_prompts = kwargs.pop("class_prompts")
- point_prompts = kwargs.pop("point_prompts")
- cached_data = kwargs.pop("cached_data")
- cached_pred = cached_data["pred"] if cached_data else None
-
- cachedEmbedding = kwargs.pop("cachedEmbedding")
- cachedEmbedding = cachedEmbedding if cachedEmbedding else None
- original_affine = kwargs.pop("original_affine")
-
- if (class_prompts is None) and (point_prompts is None):
- # Everything button: no class, no point prompts: iterate all slices
- class_prompts = [i for i in range(num_classes)]
- point_prompts = {"foreground": [], "background": []}
- pred_volume = iterate_all(
- pred_volume,
- n_z_slices,
- n_z_before_pad,
- inputs_l,
- class_prompts,
- point_prompts,
- predictor,
- post_pred,
- cachedEmbedding,
- cached_pred,
- device,
- )
- elif (point_prompts is None) and (class_prompts is not None):
- if class_prompts:
- # class prompts only: need to iterate all slices
- point_prompts = {"foreground": [], "background": []}
- pred_volume = iterate_all(
- pred_volume,
- n_z_slices,
- n_z_before_pad,
- inputs_l,
- class_prompts,
- point_prompts,
- predictor,
- post_pred,
- cachedEmbedding,
- cached_pred,
- device,
- )
- else:
- pred_volume = pred_volume.argmax(1).unsqueeze(1)
- elif (class_prompts is None) and (point_prompts is not None):
- class_prompts = []
- pred_volume = update_slice(
- pred_volume,
- n_z_slices,
- n_z_before_pad,
- inputs_l,
- class_prompts,
- point_prompts,
- predictor,
- post_pred_slice,
- cached_pred,
- num_classes,
- original_affine,
- device,
- )
- else:
- pred_volume = update_slice(
- pred_volume,
- n_z_slices,
- n_z_before_pad,
- inputs_l,
- class_prompts,
- point_prompts,
- predictor,
- post_pred_slice,
- cached_pred,
- num_classes,
- original_affine,
- device,
- )
-
- if temp_meta is not None:
- final_output = convert_to_dst_type(pred_volume, temp_meta, device=device)[0]
- else:
- final_output = convert_to_dst_type(pred_volume, inputs, device=device)[0]
-
- return final_output # type: ignore
-
-
-def compute_embedding(n_z_slices, n_z_before_pad, inputs_l, predictor):
- # image_embedding_dict saves the image embedding for each slice.
- # The key (int) is the index of center slice in original volume (before padding), e.g., 0,1,2,...n if the
- # original volume has n slices.
- # The value (torch.tensor) is the corresponding image embedding.
- image_embedding_dict = {}
- # get image embedding from the predictor (network) forward function
- for start_idx in range((n_z_slices // 2), (n_z_slices // 2 + n_z_before_pad)):
- inputs = inputs_l[..., start_idx - (n_z_slices // 2) : start_idx + (n_z_slices // 2) + 1].permute(2, 0, 1)
- # Here, the batch size is 1 (it is possible to increase batch size if the device has enough memory).
- data = [{"image": inputs}]
- with autocast():
- image_embeddings = predictor.get_image_embeddings(data) # (1, C, H, W)
- # Save image embedding for each slice to RAM
- image_embedding_dict[start_idx - (n_z_slices // 2)] = image_embeddings.cpu()
-
- return image_embedding_dict
-
-
-def update_slice(
- pred_volume,
- n_z_slices,
- n_z_before_pad,
- inputs_l,
- class_prompts,
- point_prompts,
- predictor,
- post_pred_slice,
- cached_pred,
- num_classes,
- original_affine,
- device,
-):
- z_indices = [p[2] + (9 // 2) for p in point_prompts["foreground"]]
- z_indices.extend([p[2] + (9 // 2) for p in point_prompts["background"]])
- z_indices = list(set(z_indices))
-
- pred_volume = pred_volume.argmax(1).unsqueeze(1)
-
- for start_idx in z_indices:
- if start_idx < (n_z_slices // 2):
- continue
-
- inputs = inputs_l[..., start_idx - (n_z_slices // 2) : start_idx + (n_z_slices // 2) + 1].permute(2, 0, 1)
- if device and (device == "cuda" or isinstance(device, torch.device) and device.type == "cuda"):
- inputs = inputs.cuda()
- data, unique_labels = prepare_sam_val_input(
- inputs, class_prompts, point_prompts, start_idx, original_affine, device=device
- )
-
- predictor.eval()
- if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
- with torch.cuda.amp.autocast():
- outputs = predictor(data)
- logit = outputs[0]["high_res_logits"]
- else:
- with torch.cpu.amp.autocast():
- outputs = predictor(data)
- logit = outputs[0]["high_res_logits"]
-
- out_list = torch.unbind(logit, dim=0)
- y_pred = torch.stack(post_pred_slice(out_list)).float()
-
- pred_volume = pred_volume.float()
- idx = torch.where(y_pred[0] == 1)
- z_idx = start_idx - (n_z_slices // 2)
-
- if cached_pred is not None:
- if class_prompts:
- cached_pred_idx = torch.where(cached_pred[:, :, :, z_idx] == class_prompts[0] + 1)
- cached_pred[:, :, :, z_idx][cached_pred_idx] = 0
- cached_pred[:, :, :, z_idx][idx] = class_prompts[0] + 1
- else:
- cached_pred[:, :, :, z_idx][idx] = num_classes + 1
- else:
- pred_volume[0, :, :, :, z_idx][idx] = class_prompts[0] + 1 if class_prompts else num_classes + 1
-
- if cached_pred is not None:
- pred_volume[0] = cached_pred.float()
-
- return pred_volume
-
-
-def iterate_all(
- pred_volume,
- n_z_slices,
- n_z_before_pad,
- inputs_l,
- class_prompts,
- point_prompts,
- predictor,
- post_pred,
- cachedEmbedding,
- cached_pred,
- device,
-):
- start_range = (
- range(n_z_slices // 2, min((n_z_slices // 2 + n_z_before_pad), len(cachedEmbedding)))
- if cachedEmbedding
- else range(n_z_slices // 2, n_z_slices // 2 + n_z_before_pad)
- )
- for start_idx in start_range:
- inputs = inputs_l[..., start_idx - n_z_slices // 2 : start_idx + n_z_slices // 2 + 1].permute(2, 0, 1)
- if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
- inputs = inputs.cuda()
- data, unique_labels = prepare_sam_val_input(inputs, class_prompts, point_prompts, start_idx, device=device)
- predictor = predictor.eval()
- with autocast():
- if cachedEmbedding:
- curr_embedding = cachedEmbedding[start_idx]
- if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
- curr_embedding = curr_embedding.cuda()
- outputs = predictor.get_mask_prediction(data, curr_embedding)
- else:
- outputs = predictor(data)
- logit = outputs[0]["high_res_logits"]
-
- out_list = torch.unbind(logit, dim=0)
- y_pred = torch.stack(post_pred(out_list)).float()
- pred_idx = start_idx - (n_z_slices // 2) if not cachedEmbedding else start_idx
- pred_volume[0, unique_labels, ..., pred_idx] = y_pred
-
- pred_volume = pred_volume.argmax(1).unsqueeze(1).cpu()
- pred_volume = pred_volume.float()
-
- if cached_pred is not None:
- pred_volume_idx = torch.where(pred_volume[0] != 0)
- cached_pred[pred_volume_idx] = pred_volume[0][pred_volume_idx]
- pred_volume[0] = cached_pred.float()
-
- return pred_volume
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/model_2pt5.py b/monailabel/monaivista/lib/model/vista_point_2pt5/model_2pt5.py
deleted file mode 100644
index 08759ec..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/model_2pt5.py
+++ /dev/null
@@ -1,439 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-from typing import Any, Dict, List, Tuple
-
-import monai
-import torch
-from segment_anything.modeling import TwoWayTransformer
-from segment_anything.modeling.mask_decoder import MaskDecoder
-from torch import nn
-from torch.nn import functional as F
-
-from .vista_2pt5_image_encoder import VistaImageEncoderViT
-from .vista_2pt5_prompt_encoder import VistaPromptEncoder
-
-
-class Vista2pt5D(nn.Module):
- mask_threshold: float = 0.5
- image_format: str = "RGB"
-
- def __init__(
- self,
- image_encoder: VistaImageEncoderViT,
- prompt_encoder: VistaPromptEncoder,
- mask_decoder: MaskDecoder,
- pixel_mean: List[float] = [123.675, 116.28, 103.53],
- pixel_std: List[float] = [58.395, 57.12, 57.375],
- ) -> None:
- """
- SAM predicts object masks from an image and input prompts.
-
- Arguments:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
- prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
- pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
- pixel_std (list(float)): Std values for normalizing pixels in the input image.
- """
- super().__init__()
- self.image_encoder = image_encoder
- self.prompt_encoder = prompt_encoder
- self.mask_decoder = mask_decoder
- self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
- @property
- def device(self) -> Any:
- return self.pixel_mean.device
-
- def get_image_embeddings(
- self,
- batched_input: List[Dict[str, Any]],
- ):
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
- return image_embeddings
-
- def get_mask_prediction(
- self, batched_input: List[Dict[str, Any]], image_embeddings, multimask_output: bool = False
- ):
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- class_labels=image_record.get("labels", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
-
- high_res_masks = self.postprocess_masks(
- low_res_masks,
- original_size=image_record["original_size"],
- )
- masks = high_res_masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- "high_res_logits": high_res_masks,
- }
- )
- return outputs
-
- def forward(
- self,
- batched_input: List[Dict[str, Any]],
- multimask_output: bool = False,
- is_train: bool = False,
- ) -> List[Dict[str, torch.Tensor]]:
- """
- Predicts masks end-to-end from provided images and prompts.
- If prompts are not known in advance, using SamPredictor is
- recommended over calling the model directly.
-
- Arguments:
- batched_input (list(dict)): A list over input images, each a
- dictionary with the following keys. A prompt key can be
- excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'labels': (torch.Tensor) Batched labels for class-label prompt,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
- multimask_output (bool): Whether the model should predict multiple
- disambiguating masks, or return a single mask.
-
- Returns:
- (list(dict)): A list over input images, where each element is
- as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
- """
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
-
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- class_labels=image_record.get("labels", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
- if is_train:
- outputs.append(
- {
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- }
- )
- else:
- high_res_masks = self.postprocess_masks(
- low_res_masks,
- # input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = high_res_masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- "high_res_logits": high_res_masks,
- }
- )
- return outputs
-
- def postprocess_masks(
- self,
- masks: torch.Tensor,
- original_size: Tuple[int, ...],
- ) -> torch.Tensor:
- """
- Remove padding and upscale masks to the original image size.
-
- Arguments:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
-
- Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
- """
- # make it high resolution
- masks = F.interpolate(
- masks,
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- )
- # resize it back to the longest dim (square image)
- masks = F.interpolate(masks, max(original_size), mode="bilinear", align_corners=False)
- # remove padding
- masks = masks[..., : original_size[0], : original_size[1]]
- return masks
-
- def preprocess(self, x: torch.Tensor, is_input=True) -> torch.Tensor:
- """Normalize pixel values and pad to a square input."""
- if is_input:
- if x.shape[0] == 1:
- # Normalize colors map the values in [0,1] to [0,255] for input images and then using
- # original pixel_mean and pixel_std to do normalization
- x = (x * 255.0 - self.pixel_mean) / self.pixel_std
- else:
- # for other 2.5d data, we normalize each input slice
- x = torch.cat(
- [(x[i].unsqueeze(0) * 255.0 - self.pixel_mean) / self.pixel_std for i in range(x.shape[0])], dim=0
- )
-
- # Pad image and make it a square image
- h, w = x.shape[-2:]
- # find the longest dim
- target_length = max(h, w)
- padh = target_length - h
- padw = target_length - w
- x = F.pad(x, (0, padw, 0, padh))
- if is_input:
- # Resize it to self.image_encoder.img_size
- x = F.interpolate(
- x.unsqueeze(0),
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- ).squeeze(0)
- else:
- # Resize it to self.image_encoder.img_size // 4 (for labels). the size is same as low-res logit
- x = F.interpolate(
- x.unsqueeze(0), (self.image_encoder.img_size // 4, self.image_encoder.img_size // 4), mode="nearest"
- ).squeeze(0)
- return x
-
-
-def _build_vista2pt5d(
- encoder_in_chans,
- encoder_embed_dim,
- encoder_depth,
- encoder_num_heads,
- encoder_global_attn_indexes,
- checkpoint=None,
- image_size=1024,
- clip_class_label_prompt=False,
- patch_embed_3d=False,
-):
- prompt_embed_dim = 256
- image_size = image_size
- vit_patch_size = 16
- image_embedding_size = image_size // vit_patch_size
- sam = Vista2pt5D(
- image_encoder=VistaImageEncoderViT(
- in_chans=encoder_in_chans,
- depth=encoder_depth,
- embed_dim=encoder_embed_dim,
- img_size=image_size,
- mlp_ratio=4,
- norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
- num_heads=encoder_num_heads,
- patch_size=vit_patch_size,
- qkv_bias=True,
- use_rel_pos=True,
- global_attn_indexes=encoder_global_attn_indexes,
- window_size=14,
- out_chans=prompt_embed_dim,
- patch_embed_3d=patch_embed_3d,
- ),
- prompt_encoder=VistaPromptEncoder(
- embed_dim=prompt_embed_dim,
- image_embedding_size=(image_embedding_size, image_embedding_size),
- input_image_size=(image_size, image_size),
- mask_in_chans=16,
- clip_class_label_prompt=clip_class_label_prompt,
- ),
- mask_decoder=MaskDecoder(
- num_multimask_outputs=3, # TODO: only predict one binary mask
- transformer=TwoWayTransformer(
- depth=2,
- embedding_dim=prompt_embed_dim,
- mlp_dim=2048,
- num_heads=8,
- ),
- transformer_dim=prompt_embed_dim,
- iou_head_depth=3,
- iou_head_hidden_dim=256,
- ),
- pixel_mean=[123.675, 116.28, 103.53],
- pixel_std=[58.395, 57.12, 57.375],
- )
-
- if checkpoint is not None:
- with open(checkpoint, "rb") as f:
- state_dict = torch.load(f)
-
- if image_size == 1024:
- # we try to use all pretrained weights
- new_dict = state_dict
- else:
- new_dict = {}
- for k, v in state_dict.items():
- # skip weights in position embedding and learned relative positional embeddings
- # due to the change of input size
- if ("pos_embed" in k and k.startswith("image_encoder")) or (
- "attn.rel_pos" in k and k.startswith("image_encoder")
- ):
- continue
- else:
- new_dict[k] = v
-
- if encoder_in_chans != 3:
- new_dict.pop("image_encoder.patch_embed.proj.weight")
- new_dict.pop("image_encoder.patch_embed.proj.bias")
-
- sam.load_state_dict(new_dict, strict=False)
- print(f"Load {len(new_dict)} keys from checkpoint {checkpoint}, current model has {len(sam.state_dict())} keys")
-
- total_params = []
- image_encoder_params = []
- prompt_encoder_params = []
- mask_decoder_params = []
- for name, param in sam.named_parameters():
- n_param = param.numel()
- total_params.append(n_param)
- if name.startswith("image_encoder"):
- image_encoder_params.append(n_param)
- elif name.startswith("prompt_encoder"):
- prompt_encoder_params.append(n_param)
- elif name.startswith("mask_decoder"):
- mask_decoder_params.append(n_param)
-
- print(
- f"{sam.__class__.__name__} has {sum(total_params) * 1.e-6:.2f} M params, "
- f"{sum(image_encoder_params) * 1.e-6:.2f} M params in image encoder,"
- f"{sum(prompt_encoder_params) * 1.e-6:.2f} M params in prompt encoder,"
- f"{sum(mask_decoder_params) * 1.e-6:.2f} M params in mask decoder."
- )
-
- total_trainable_params = sum(p.numel() if p.requires_grad else 0 for p in sam.parameters())
- print(f"{sam.__class__.__name__} has {total_trainable_params * 1.e-6:.2f} M trainable params.")
- return sam
-
-
-def build_vista2pt5d_vit_h(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=1280,
- encoder_depth=32,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[7, 15, 23, 31],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-def build_vista2pt5d_vit_l(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=1024,
- encoder_depth=24,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[5, 11, 17, 23],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-def build_vista2pt5d_vit_b(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-sam_model_registry = {
- "default": build_vista2pt5d_vit_h,
- "vit_h": build_vista2pt5d_vit_h,
- "vit_l": build_vista2pt5d_vit_l,
- "vit_b": build_vista2pt5d_vit_b,
-}
-
-
-if __name__ == "__main__":
- model = build_vista2pt5d_vit_b()
- model.cuda()
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/__init__.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/__init__.py
deleted file mode 100644
index 718fdc3..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .automatic_mask_generator import SamAutomaticMaskGenerator
-from .build_sam import (
- build_sam,
- build_sam_vit_b,
- build_sam_vit_h,
- build_sam_vit_l,
- sam_model_registry,
-)
-from .predictor import SamPredictor
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/automatic_mask_generator.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/automatic_mask_generator.py
deleted file mode 100644
index 2cd252d..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/automatic_mask_generator.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-from torchvision.ops.boxes import batched_nms, box_area # type: ignore
-
-from .modeling import Sam
-from .predictor import SamPredictor
-from .utils.amg import (
- MaskData,
- area_from_rle,
- batch_iterator,
- batched_mask_to_box,
- box_xyxy_to_xywh,
- build_all_layer_point_grids,
- calculate_stability_score,
- coco_encode_rle,
- generate_crop_boxes,
- is_box_near_crop_edge,
- mask_to_rle_pytorch,
- remove_small_regions,
- rle_to_mask,
- uncrop_boxes_xyxy,
- uncrop_masks,
- uncrop_points,
-)
-
-
-class SamAutomaticMaskGenerator:
- def __init__(
- self,
- model: Sam,
- points_per_side: Optional[int] = 32,
- points_per_batch: int = 64,
- pred_iou_thresh: float = 0.88,
- stability_score_thresh: float = 0.95,
- stability_score_offset: float = 1.0,
- box_nms_thresh: float = 0.7,
- crop_n_layers: int = 0,
- crop_nms_thresh: float = 0.7,
- crop_overlap_ratio: float = 512 / 1500,
- crop_n_points_downscale_factor: int = 1,
- point_grids: Optional[List[np.ndarray]] = None,
- min_mask_region_area: int = 0,
- output_mode: str = "binary_mask",
- ) -> None:
- """
- Using a SAM model, generates masks for the entire image.
- Generates a grid of point prompts over the image, then filters
- low quality and duplicate masks. The default settings are chosen
- for SAM with a ViT-H backbone.
-
- Arguments:
- model (Sam): The SAM model to use for mask prediction.
- points_per_side (int or None): The number of points to be sampled
- along one side of the image. The total number of points is
- points_per_side**2. If None, 'point_grids' must provide explicit
- point sampling.
- points_per_batch (int): Sets the number of points run simultaneously
- by the model. Higher numbers may be faster but use more GPU memory.
- pred_iou_thresh (float): A filtering threshold in [0,1], using the
- model's predicted mask quality.
- stability_score_thresh (float): A filtering threshold in [0,1], using
- the stability of the mask under changes to the cutoff used to binarize
- the model's mask predictions.
- stability_score_offset (float): The amount to shift the cutoff when
- calculated the stability score.
- box_nms_thresh (float): The box IoU cutoff used by non-maximal
- suppression to filter duplicate masks.
- crops_n_layers (int): If >0, mask prediction will be run again on
- crops of the image. Sets the number of layers to run, where each
- layer has 2**i_layer number of image crops.
- crops_nms_thresh (float): The box IoU cutoff used by non-maximal
- suppression to filter duplicate masks between different crops.
- crop_overlap_ratio (float): Sets the degree to which crops overlap.
- In the first crop layer, crops will overlap by this fraction of
- the image length. Later layers with more crops scale down this overlap.
- crop_n_points_downscale_factor (int): The number of points-per-side
- sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
- point_grids (list(np.ndarray) or None): A list over explicit grids
- of points used for sampling, normalized to [0,1]. The nth grid in the
- list is used in the nth crop layer. Exclusive with points_per_side.
- min_mask_region_area (int): If >0, postprocessing will be applied
- to remove disconnected regions and holes in masks with area smaller
- than min_mask_region_area. Requires opencv.
- output_mode (str): The form masks are returned in. Can be 'binary_mask',
- 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
- For large resolutions, 'binary_mask' may consume large amounts of
- memory.
- """
-
- assert (points_per_side is None) != (
- point_grids is None
- ), "Exactly one of points_per_side or point_grid must be provided."
- if points_per_side is not None:
- self.point_grids = build_all_layer_point_grids(
- points_per_side,
- crop_n_layers,
- crop_n_points_downscale_factor,
- )
- elif point_grids is not None:
- self.point_grids = point_grids
- else:
- raise ValueError("Can't have both points_per_side and point_grid be None.")
-
- assert output_mode in [
- "binary_mask",
- "uncompressed_rle",
- "coco_rle",
- ], f"Unknown output_mode {output_mode}."
- if output_mode == "coco_rle":
- from pycocotools import mask as mask_utils # type: ignore # noqa: F401
-
- if min_mask_region_area > 0:
- import cv2 # type: ignore # noqa: F401
-
- self.predictor = SamPredictor(model)
- self.points_per_batch = points_per_batch
- self.pred_iou_thresh = pred_iou_thresh
- self.stability_score_thresh = stability_score_thresh
- self.stability_score_offset = stability_score_offset
- self.box_nms_thresh = box_nms_thresh
- self.crop_n_layers = crop_n_layers
- self.crop_nms_thresh = crop_nms_thresh
- self.crop_overlap_ratio = crop_overlap_ratio
- self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
- self.min_mask_region_area = min_mask_region_area
- self.output_mode = output_mode
-
- @torch.no_grad()
- def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
- """
- Generates masks for the given image.
-
- Arguments:
- image (np.ndarray): The image to generate masks for, in HWC uint8 format.
-
- Returns:
- list(dict(str, any)): A list over records for masks. Each record is
- a dict containing the following keys:
- segmentation (dict(str, any) or np.ndarray): The mask. If
- output_mode='binary_mask', is an array of shape HW. Otherwise,
- is a dictionary containing the RLE.
- bbox (list(float)): The box around the mask, in XYWH format.
- area (int): The area in pixels of the mask.
- predicted_iou (float): The model's own prediction of the mask's
- quality. This is filtered by the pred_iou_thresh parameter.
- point_coords (list(list(float))): The point coordinates input
- to the model to generate this mask.
- stability_score (float): A measure of the mask's quality. This
- is filtered on using the stability_score_thresh parameter.
- crop_box (list(float)): The crop of the image used to generate
- the mask, given in XYWH format.
- """
-
- # Generate masks
- mask_data = self._generate_masks(image)
-
- # Filter small disconnected regions and holes in masks
- if self.min_mask_region_area > 0:
- mask_data = self.postprocess_small_regions(
- mask_data,
- self.min_mask_region_area,
- max(self.box_nms_thresh, self.crop_nms_thresh),
- )
-
- # Encode masks
- if self.output_mode == "coco_rle":
- mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
- elif self.output_mode == "binary_mask":
- mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
- else:
- mask_data["segmentations"] = mask_data["rles"]
-
- # Write mask records
- curr_anns = []
- for idx in range(len(mask_data["segmentations"])):
- ann = {
- "segmentation": mask_data["segmentations"][idx],
- "area": area_from_rle(mask_data["rles"][idx]),
- "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
- "predicted_iou": mask_data["iou_preds"][idx].item(),
- "point_coords": [mask_data["points"][idx].tolist()],
- "stability_score": mask_data["stability_score"][idx].item(),
- "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
- }
- curr_anns.append(ann)
-
- return curr_anns
-
- def _generate_masks(self, image: np.ndarray) -> MaskData:
- orig_size = image.shape[:2]
- crop_boxes, layer_idxs = generate_crop_boxes(orig_size, self.crop_n_layers, self.crop_overlap_ratio)
-
- # Iterate over image crops
- data = MaskData()
- for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
- crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
- data.cat(crop_data)
-
- # Remove duplicate masks between crops
- if len(crop_boxes) > 1:
- # Prefer masks from smaller crops
- scores = 1 / box_area(data["crop_boxes"])
- scores = scores.to(data["boxes"].device)
- keep_by_nms = batched_nms(
- data["boxes"].float(),
- scores,
- torch.zeros(len(data["boxes"])), # categories
- iou_threshold=self.crop_nms_thresh,
- )
- data.filter(keep_by_nms)
-
- data.to_numpy()
- return data
-
- def _process_crop(
- self,
- image: np.ndarray,
- crop_box: List[int],
- crop_layer_idx: int,
- orig_size: Tuple[int, ...],
- ) -> MaskData:
- # Crop the image and calculate embeddings
- x0, y0, x1, y1 = crop_box
- cropped_im = image[y0:y1, x0:x1, :]
- cropped_im_size = cropped_im.shape[:2]
- self.predictor.set_image(cropped_im)
-
- # Get points for this crop
- points_scale = np.array(cropped_im_size)[None, ::-1]
- points_for_image = self.point_grids[crop_layer_idx] * points_scale
-
- # Generate masks for this crop in batches
- data = MaskData()
- for (points,) in batch_iterator(self.points_per_batch, points_for_image):
- batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
- data.cat(batch_data)
- del batch_data
- self.predictor.reset_image()
-
- # Remove duplicates within this crop.
- keep_by_nms = batched_nms(
- data["boxes"].float(),
- data["iou_preds"],
- torch.zeros(len(data["boxes"])), # categories
- iou_threshold=self.box_nms_thresh,
- )
- data.filter(keep_by_nms)
-
- # Return to the original image frame
- data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
- data["points"] = uncrop_points(data["points"], crop_box)
- data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
-
- return data
-
- def _process_batch(
- self,
- points: np.ndarray,
- im_size: Tuple[int, ...],
- crop_box: List[int],
- orig_size: Tuple[int, ...],
- ) -> MaskData:
- orig_h, orig_w = orig_size
-
- # Run model on this batch
- transformed_points = self.predictor.transform.apply_coords(points, im_size)
- in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
- in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
- masks, iou_preds, _ = self.predictor.predict_torch(
- in_points[:, None, :],
- in_labels[:, None],
- multimask_output=True,
- return_logits=True,
- )
-
- # Serialize predictions and store in MaskData
- data = MaskData(
- masks=masks.flatten(0, 1),
- iou_preds=iou_preds.flatten(0, 1),
- points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
- )
- del masks
-
- # Filter by predicted IoU
- if self.pred_iou_thresh > 0.0:
- keep_mask = data["iou_preds"] > self.pred_iou_thresh
- data.filter(keep_mask)
-
- # Calculate stability score
- data["stability_score"] = calculate_stability_score(
- data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
- )
- if self.stability_score_thresh > 0.0:
- keep_mask = data["stability_score"] >= self.stability_score_thresh
- data.filter(keep_mask)
-
- # Threshold masks and calculate boxes
- data["masks"] = data["masks"] > self.predictor.model.mask_threshold
- data["boxes"] = batched_mask_to_box(data["masks"])
-
- # Filter boxes that touch crop boundaries
- keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
- if not torch.all(keep_mask):
- data.filter(keep_mask)
-
- # Compress to RLE
- data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
- data["rles"] = mask_to_rle_pytorch(data["masks"])
- del data["masks"]
-
- return data
-
- @staticmethod
- def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData:
- """
- Removes small disconnected regions and holes in masks, then reruns
- box NMS to remove any new duplicates.
-
- Edits mask_data in place.
-
- Requires open-cv as a dependency.
- """
- if len(mask_data["rles"]) == 0:
- return mask_data
-
- # Filter small disconnected regions and holes
- new_masks = []
- scores = []
- for rle in mask_data["rles"]:
- mask = rle_to_mask(rle)
-
- mask, changed = remove_small_regions(mask, min_area, mode="holes")
- unchanged = not changed
- mask, changed = remove_small_regions(mask, min_area, mode="islands")
- unchanged = unchanged and not changed
-
- new_masks.append(torch.as_tensor(mask).unsqueeze(0))
- # Give score=0 to changed masks and score=1 to unchanged masks
- # so NMS will prefer ones that didn't need postprocessing
- scores.append(float(unchanged))
-
- # Recalculate boxes and remove any new duplicates
- masks = torch.cat(new_masks, dim=0)
- boxes = batched_mask_to_box(masks)
- keep_by_nms = batched_nms(
- boxes.float(),
- torch.as_tensor(scores),
- torch.zeros(len(boxes)), # categories
- iou_threshold=nms_thresh,
- )
-
- # Only recalculate RLEs for masks that have changed
- for i_mask in keep_by_nms:
- if scores[i_mask] == 0.0:
- mask_torch = masks[i_mask].unsqueeze(0)
- mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
- mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
- mask_data.filter(keep_by_nms)
-
- return mask_data
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/build_sam.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/build_sam.py
deleted file mode 100644
index fef9980..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/build_sam.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-
-import torch
-
-from .modeling import (
- ImageEncoderViT,
- MaskDecoder,
- PromptEncoder,
- Sam,
- TwoWayTransformer,
-)
-
-
-def build_sam_vit_h(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=1280,
- encoder_depth=32,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[7, 15, 23, 31],
- checkpoint=checkpoint,
- )
-
-
-build_sam = build_sam_vit_h
-
-
-def build_sam_vit_l(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=1024,
- encoder_depth=24,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[5, 11, 17, 23],
- checkpoint=checkpoint,
- )
-
-
-def build_sam_vit_b(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=checkpoint,
- )
-
-
-sam_model_registry = {
- "default": build_sam,
- "vit_h": build_sam,
- "vit_l": build_sam_vit_l,
- "vit_b": build_sam_vit_b,
-}
-
-
-def _build_sam(
- encoder_embed_dim,
- encoder_depth,
- encoder_num_heads,
- encoder_global_attn_indexes,
- checkpoint=None,
-):
- prompt_embed_dim = 256
- image_size = 1024
- vit_patch_size = 16
- image_embedding_size = image_size // vit_patch_size
- sam = Sam(
- image_encoder=ImageEncoderViT(
- depth=encoder_depth,
- embed_dim=encoder_embed_dim,
- img_size=image_size,
- mlp_ratio=4,
- norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
- num_heads=encoder_num_heads,
- patch_size=vit_patch_size,
- qkv_bias=True,
- use_rel_pos=True,
- global_attn_indexes=encoder_global_attn_indexes,
- window_size=14,
- out_chans=prompt_embed_dim,
- ),
- prompt_encoder=PromptEncoder(
- embed_dim=prompt_embed_dim,
- image_embedding_size=(image_embedding_size, image_embedding_size),
- input_image_size=(image_size, image_size),
- mask_in_chans=16,
- ),
- mask_decoder=MaskDecoder(
- num_multimask_outputs=3,
- transformer=TwoWayTransformer(
- depth=2,
- embedding_dim=prompt_embed_dim,
- mlp_dim=2048,
- num_heads=8,
- ),
- transformer_dim=prompt_embed_dim,
- iou_head_depth=3,
- iou_head_hidden_dim=256,
- ),
- pixel_mean=[123.675, 116.28, 103.53],
- pixel_std=[58.395, 57.12, 57.375],
- )
- sam.eval()
- if checkpoint is not None:
- with open(checkpoint, "rb") as f:
- state_dict = torch.load(f)
- sam.load_state_dict(state_dict)
- return sam
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/__init__.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/__init__.py
deleted file mode 100644
index 088af38..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-from .sam import Sam
-from .transformer import TwoWayTransformer
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/common.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/common.py
deleted file mode 100644
index e872781..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/common.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Type
-
-import torch
-import torch.nn as nn
-
-
-class MLPBlock(nn.Module):
- def __init__(
- self,
- embedding_dim: int,
- mlp_dim: int,
- act: Type[nn.Module] = nn.GELU,
- ) -> None:
- super().__init__()
- self.lin1 = nn.Linear(embedding_dim, mlp_dim)
- self.lin2 = nn.Linear(mlp_dim, embedding_dim)
- self.act = act()
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.lin2(self.act(self.lin1(x)))
-
-
-# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
-# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
-class LayerNorm2d(nn.Module):
- def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
- super().__init__()
- self.weight = nn.Parameter(torch.ones(num_channels))
- self.bias = nn.Parameter(torch.zeros(num_channels))
- self.eps = eps
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- u = x.mean(1, keepdim=True)
- s = (x - u).pow(2).mean(1, keepdim=True)
- x = (x - u) / torch.sqrt(s + self.eps)
- x = self.weight[:, None, None] * x + self.bias[:, None, None]
- return x
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/image_encoder.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/image_encoder.py
deleted file mode 100644
index 4e4b63b..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/image_encoder.py
+++ /dev/null
@@ -1,440 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional, Tuple, Type
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .common import LayerNorm2d, MLPBlock
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class ImageEncoderViT(nn.Module):
- def __init__(
- self,
- img_size: int = 1024,
- patch_size: int = 16,
- in_chans: int = 3,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- out_chans: int = 256,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_abs_pos: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- global_attn_indexes: Tuple[int, ...] = (),
- ) -> None:
- """
- Args:
- img_size (int): Input image size.
- patch_size (int): Patch size.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- depth (int): Depth of ViT.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_abs_pos (bool): If True, use absolute positional embeddings.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks.
- global_attn_indexes (list): Indexes for blocks using global attention.
- """
- super().__init__()
- self.img_size = img_size
-
- # if in_chans == 3:
- # self.patch_embed = PatchEmbed(
- # kernel_size=(patch_size, patch_size),
- # stride=(patch_size, patch_size),
- # in_chans=in_chans,
- # embed_dim=embed_dim,
- # )
- # else:
- # self.patch_embed = PatchEmbed2pt5D(
- # kernel_size=(patch_size, patch_size, in_chans//3),
- # stride=(patch_size, patch_size, in_chans//3),
- # in_chans=3,
- # embed_dim=embed_dim,
- # )
- self.patch_embed = PatchEmbed(
- kernel_size=(patch_size, patch_size),
- stride=(patch_size, patch_size),
- in_chans=in_chans,
- embed_dim=embed_dim,
- )
-
- self.pos_embed: Optional[nn.Parameter] = None
- if use_abs_pos:
- # Initialize absolute positional embedding with pretrain image size.
- self.pos_embed = nn.Parameter(torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))
-
- self.blocks = nn.ModuleList()
- for i in range(depth):
- block = Block(
- dim=embed_dim,
- num_heads=num_heads,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- norm_layer=norm_layer,
- act_layer=act_layer,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- window_size=window_size if i not in global_attn_indexes else 0,
- input_size=(img_size // patch_size, img_size // patch_size),
- )
- self.blocks.append(block)
-
- self.neck = nn.Sequential(
- nn.Conv2d(
- embed_dim,
- out_chans,
- kernel_size=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- nn.Conv2d(
- out_chans,
- out_chans,
- kernel_size=3,
- padding=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.patch_embed(x)
- if self.pos_embed is not None:
- x = x + self.pos_embed
-
- for blk in self.blocks:
- x = blk(x)
-
- x = self.neck(x.permute(0, 3, 1, 2))
-
- return x
-
-
-class Block(nn.Module):
- """Transformer blocks with support of window attention and residual propagation blocks"""
-
- def __init__(
- self,
- dim: int,
- num_heads: int,
- mlp_ratio: float = 4.0,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks. If it equals 0, then
- use global attention.
- input_size (int or None): Input resolution for calculating the relative positional
- parameter size.
- """
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads,
- qkv_bias=qkv_bias,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- input_size=input_size if window_size == 0 else (window_size, window_size),
- )
-
- self.norm2 = norm_layer(dim)
- self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
-
- self.window_size = window_size
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- shortcut = x
- x = self.norm1(x)
- # Window partition
- if self.window_size > 0:
- H, W = x.shape[1], x.shape[2]
- x, pad_hw = window_partition(x, self.window_size)
-
- x = self.attn(x)
- # Reverse window partition
- if self.window_size > 0:
- x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-
- x = shortcut + x
- x = x + self.mlp(self.norm2(x))
-
- return x
-
-
-class Attention(nn.Module):
- """Multi-head Attention block with relative position embeddings."""
-
- def __init__(
- self,
- dim: int,
- num_heads: int = 8,
- qkv_bias: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- qkv_bias (bool: If True, add a learnable bias to query, key, value.
- rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- input_size (int or None): Input resolution for calculating the relative positional
- parameter size.
- """
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = head_dim**-0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.proj = nn.Linear(dim, dim)
-
- self.use_rel_pos = use_rel_pos
- if self.use_rel_pos:
- assert input_size is not None, "Input size must be provided if using relative positional encoding."
- # initialize relative positional embeddings
- self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
- self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, H, W, _ = x.shape
- # qkv with shape (3, B, nHead, H * W, C)
- qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- # q, k, v with shape (B * nHead, H * W, C)
- q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
-
- attn = (q * self.scale) @ k.transpose(-2, -1)
-
- if self.use_rel_pos:
- attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
-
- attn = attn.softmax(dim=-1)
- x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
- x = self.proj(x)
-
- return x
-
-
-def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
- """
- Partition into non-overlapping windows with padding if needed.
- Args:
- x (tensor): input tokens with [B, H, W, C].
- window_size (int): window size.
-
- Returns:
- windows: windows after partition with [B * num_windows, window_size, window_size, C].
- (Hp, Wp): padded height and width before partition
- """
- B, H, W, C = x.shape
-
- pad_h = (window_size - H % window_size) % window_size
- pad_w = (window_size - W % window_size) % window_size
- if pad_h > 0 or pad_w > 0:
- x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
- Hp, Wp = H + pad_h, W + pad_w
-
- x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows, (Hp, Wp)
-
-
-def window_unpartition(
- windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
-) -> torch.Tensor:
- """
- Window unpartition into original sequences and removing padding.
- Args:
- x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
- window_size (int): window size.
- pad_hw (Tuple): padded height and width (Hp, Wp).
- hw (Tuple): original height and width (H, W) before padding.
-
- Returns:
- x: unpartitioned sequences with [B, H, W, C].
- """
- Hp, Wp = pad_hw
- H, W = hw
- B = windows.shape[0] // (Hp * Wp // window_size // window_size)
- x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-
- if Hp > H or Wp > W:
- x = x[:, :H, :W, :].contiguous()
- return x
-
-
-def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
- """
- Get relative positional embeddings according to the relative positions of
- query and key sizes.
- Args:
- q_size (int): size of query q.
- k_size (int): size of key k.
- rel_pos (Tensor): relative position embeddings (L, C).
-
- Returns:
- Extracted positional embeddings according to relative positions.
- """
- max_rel_dist = int(2 * max(q_size, k_size) - 1)
- # Interpolate rel pos if needed.
- if rel_pos.shape[0] != max_rel_dist:
- # Interpolate rel pos.
- rel_pos_resized = F.interpolate(
- rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
- size=max_rel_dist,
- mode="linear",
- )
- rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
- else:
- rel_pos_resized = rel_pos
-
- # Scale the coords with short length if shapes for q and k are different.
- q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
- k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
- relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
-
- return rel_pos_resized[relative_coords.long()]
-
-
-def add_decomposed_rel_pos(
- attn: torch.Tensor,
- q: torch.Tensor,
- rel_pos_h: torch.Tensor,
- rel_pos_w: torch.Tensor,
- q_size: Tuple[int, int],
- k_size: Tuple[int, int],
-) -> torch.Tensor:
- """
- Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
- https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
- Args:
- attn (Tensor): attention map.
- q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
- rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
- rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
- q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
- k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
-
- Returns:
- attn (Tensor): attention map with added relative positional embeddings.
- """
- q_h, q_w = q_size
- k_h, k_w = k_size
- Rh = get_rel_pos(q_h, k_h, rel_pos_h)
- Rw = get_rel_pos(q_w, k_w, rel_pos_w)
-
- B, _, dim = q.shape
- r_q = q.reshape(B, q_h, q_w, dim)
- rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
- rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-
- attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
- B, q_h * q_w, k_h * k_w
- )
-
- return attn
-
-
-class PatchEmbed(nn.Module):
- """
- Image to Patch Embedding.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int] = (16, 16),
- stride: Tuple[int, int] = (16, 16),
- padding: Tuple[int, int] = (0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.proj(x)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
-
-
-class PatchEmbed2pt5D(nn.Module):
- """
- Image to Patch Embedding.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int, int] = (16, 16, 1),
- stride: Tuple[int, int, int] = (16, 16, 1),
- padding: Tuple[int, int, int] = (0, 0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- # got restore RGB channel dim and the depth dim
- c = x.shape[1]
- x = torch.stack(x.chunk(c // 3, dim=1), dim=-1)
- x = self.proj(x)
- # remove dummy depth dim to make it 2d
- x = x.squeeze(-1)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/mask_decoder.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/mask_decoder.py
deleted file mode 100644
index dfacfe5..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/mask_decoder.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import List, Tuple, Type
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .common import LayerNorm2d
-
-
-class MaskDecoder(nn.Module):
- def __init__(
- self,
- *,
- transformer_dim: int,
- transformer: nn.Module,
- num_multimask_outputs: int = 3,
- activation: Type[nn.Module] = nn.GELU,
- iou_head_depth: int = 3,
- iou_head_hidden_dim: int = 256,
- ) -> None:
- """
- Predicts masks given an image and prompt embeddings, using a
- tranformer architecture.
-
- Arguments:
- transformer_dim (int): the channel dimension of the transformer
- transformer (nn.Module): the transformer used to predict masks
- num_multimask_outputs (int): the number of masks to predict
- when disambiguating masks
- activation (nn.Module): the type of activation to use when
- upscaling masks
- iou_head_depth (int): the depth of the MLP used to predict
- mask quality
- iou_head_hidden_dim (int): the hidden dimension of the MLP
- used to predict mask quality
- """
- super().__init__()
- self.transformer_dim = transformer_dim
- self.transformer = transformer
-
- self.num_multimask_outputs = num_multimask_outputs
-
- self.iou_token = nn.Embedding(1, transformer_dim)
- self.num_mask_tokens = num_multimask_outputs + 1
- self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
- self.output_upscaling = nn.Sequential(
- nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
- LayerNorm2d(transformer_dim // 4),
- activation(),
- nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
- activation(),
- )
- self.output_hypernetworks_mlps = nn.ModuleList(
- [MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for i in range(self.num_mask_tokens)]
- )
-
- self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)
-
- def forward(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- multimask_output: bool,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Predict masks given image and prompt embeddings.
-
- Arguments:
- image_embeddings (torch.Tensor): the embeddings from the image encoder
- image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
- sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
- dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
- multimask_output (bool): Whether to return multiple masks or a single
- mask.
-
- Returns:
- torch.Tensor: batched predicted masks
- torch.Tensor: batched predictions of mask quality
- """
- masks, iou_pred = self.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=image_pe,
- sparse_prompt_embeddings=sparse_prompt_embeddings,
- dense_prompt_embeddings=dense_prompt_embeddings,
- )
-
- # Select the correct mask or masks for output
- if multimask_output:
- mask_slice = slice(1, None)
- else:
- mask_slice = slice(0, 1)
- masks = masks[:, mask_slice, :, :]
- iou_pred = iou_pred[:, mask_slice]
-
- # Prepare output
- return masks, iou_pred
-
- def predict_masks(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Predicts masks. See 'forward' for more details."""
- # Concatenate output tokens
- output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
- output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
- tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
-
- # Expand per-image data in batch direction to be per-mask
- src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
- src = src + dense_prompt_embeddings
- pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
- b, c, h, w = src.shape
-
- # Run the transformer
- hs, src = self.transformer(src, pos_src, tokens)
- iou_token_out = hs[:, 0, :]
- mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
-
- # Upscale mask embeddings and predict masks using the mask tokens
- src = src.transpose(1, 2).view(b, c, h, w)
- upscaled_embedding = self.output_upscaling(src)
- hyper_in_list: List[torch.Tensor] = []
- for i in range(self.num_mask_tokens):
- hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
- hyper_in = torch.stack(hyper_in_list, dim=1)
- b, c, h, w = upscaled_embedding.shape
-
- masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
-
- # Generate mask quality predictions
- iou_pred = self.iou_prediction_head(iou_token_out)
-
- return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
- def __init__(
- self,
- input_dim: int,
- hidden_dim: int,
- output_dim: int,
- num_layers: int,
- sigmoid_output: bool = False,
- ) -> None:
- super().__init__()
- self.num_layers = num_layers
- h = [hidden_dim] * (num_layers - 1)
- self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
- self.sigmoid_output = sigmoid_output
-
- def forward(self, x):
- for i, layer in enumerate(self.layers):
- x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
- if self.sigmoid_output:
- x = F.sigmoid(x)
- return x
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/prompt_encoder.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/prompt_encoder.py
deleted file mode 100644
index 7a7da9a..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/prompt_encoder.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from torch import nn
-
-from .common import LayerNorm2d
-
-
-class PromptEncoder(nn.Module):
- def __init__(
- self,
- embed_dim: int,
- image_embedding_size: Tuple[int, int],
- input_image_size: Tuple[int, int],
- mask_in_chans: int,
- activation: Type[nn.Module] = nn.GELU,
- n_classes: int = 512,
- ) -> None:
- """
- Encodes prompts for input to SAM's mask decoder.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- n_classes (int): The number of pre-defined classes.
- """
- super().__init__()
- self.embed_dim = embed_dim
- self.input_image_size = input_image_size
- self.image_embedding_size = image_embedding_size
- self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
-
- self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
- point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
- self.point_embeddings = nn.ModuleList(point_embeddings)
- self.not_a_point_embed = nn.Embedding(1, embed_dim)
-
- self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
- self.mask_downscaling = nn.Sequential(
- nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans // 4),
- activation(),
- nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans),
- activation(),
- nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
- )
- self.no_mask_embed = nn.Embedding(1, embed_dim)
-
- # Add support for onehot vector embedding for pre-defined classes
- self.label_embeddings = nn.Embedding(n_classes, embed_dim)
- self.no_label_embed = nn.Embedding(1, embed_dim)
-
- def get_dense_pe(self) -> torch.Tensor:
- """
- Returns the positional encoding used to encode point prompts,
- applied to a dense set of points the shape of the image encoding.
-
- Returns:
- torch.Tensor: Positional encoding with shape
- 1x(embed_dim)x(embedding_h)x(embedding_w)
- """
- return self.pe_layer(self.image_embedding_size).unsqueeze(0)
-
- def _embed_points(
- self,
- points: torch.Tensor,
- labels: torch.Tensor,
- pad: bool,
- ) -> torch.Tensor:
- """Embeds point prompts."""
- points = points + 0.5 # Shift to center of pixel
- if pad:
- padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
- padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
- points = torch.cat([points, padding_point], dim=1)
- labels = torch.cat([labels, padding_label], dim=1)
- point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
- point_embedding[labels == -1] = 0.0
- point_embedding[labels == -1] += self.not_a_point_embed.weight
- point_embedding[labels == 0] += self.point_embeddings[0].weight
- point_embedding[labels == 1] += self.point_embeddings[1].weight
- return point_embedding
-
- def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
- """Embeds box prompts."""
- boxes = boxes + 0.5 # Shift to center of pixel
- coords = boxes.reshape(-1, 2, 2)
- corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
- corner_embedding[:, 0, :] += self.point_embeddings[2].weight
- corner_embedding[:, 1, :] += self.point_embeddings[3].weight
- return corner_embedding
-
- def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
- """Embeds mask inputs."""
- mask_embedding = self.mask_downscaling(masks)
- return mask_embedding
-
- def _embed_labels(self, labels: torch.Tensor) -> torch.Tensor:
- """Embeds onehot vector inputs."""
- # Add support for onehot vector embedding for pre-defined classes
- label_embedding = self.label_embeddings(labels)
- return label_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- labels: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- elif labels is not None:
- return labels.shape[0]
- else:
- return 1
-
- def _get_device(self) -> torch.device:
- return self.point_embeddings[0].weight.device
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- class_labels: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks, class_labels)
-
- # Add support for onehot vector embedding for pre-defined classes
- if class_labels is not None:
- label_embeddings = self._embed_labels(class_labels)
- else:
- label_embeddings = self.no_label_embed.weight.reshape(1, 1, -1).expand(bs, -1, -1)
-
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
-
- # Add support for onehot vector embedding for pre-defined classes
- sparse_embeddings = torch.cat([sparse_embeddings, label_embeddings], dim=1)
-
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
-
-
-class PositionEmbeddingRandom(nn.Module):
- """
- Positional encoding using random spatial frequencies.
- """
-
- def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
- super().__init__()
- if scale is None or scale <= 0.0:
- scale = 1.0
- self.register_buffer(
- "positional_encoding_gaussian_matrix",
- scale * torch.randn((2, num_pos_feats)),
- )
-
- def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
- """Positionally encode points that are normalized to [0,1]."""
- # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
- coords = 2 * coords - 1
- coords = coords @ self.positional_encoding_gaussian_matrix
- coords = 2 * np.pi * coords
- # outputs d_1 x ... x d_n x C shape
- return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
-
- def forward(self, size: Tuple[int, int]) -> torch.Tensor:
- """Generate positional encoding for a grid of the specified size."""
- h, w = size
- device: Any = self.positional_encoding_gaussian_matrix.device
- grid = torch.ones((h, w), device=device, dtype=torch.float32)
- y_embed = grid.cumsum(dim=0) - 0.5
- x_embed = grid.cumsum(dim=1) - 0.5
- y_embed = y_embed / h
- x_embed = x_embed / w
-
- pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
- return pe.permute(2, 0, 1) # C x H x W
-
- def forward_with_coords(self, coords_input: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
- """Positionally encode points that are not normalized to [0,1]."""
- coords = coords_input.clone()
- coords[:, :, 0] = coords[:, :, 0] / image_size[1]
- coords[:, :, 1] = coords[:, :, 1] / image_size[0]
- return self._pe_encoding(coords.to(torch.float)) # B x N x C
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/sam.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/sam.py
deleted file mode 100644
index c1ce4ec..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/sam.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Dict, List, Tuple
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-
-
-class Sam(nn.Module):
- mask_threshold: float = 0.0
- image_format: str = "RGB"
-
- def __init__(
- self,
- image_encoder: ImageEncoderViT,
- prompt_encoder: PromptEncoder,
- mask_decoder: MaskDecoder,
- pixel_mean: List[float] = [123.675, 116.28, 103.53],
- pixel_std: List[float] = [58.395, 57.12, 57.375],
- ) -> None:
- """
- SAM predicts object masks from an image and input prompts.
-
- Arguments:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
- prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
- pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
- pixel_std (list(float)): Std values for normalizing pixels in the input image.
- """
- super().__init__()
- self.image_encoder = image_encoder
- self.prompt_encoder = prompt_encoder
- self.mask_decoder = mask_decoder
- self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
- @property
- def device(self) -> Any:
- return self.pixel_mean.device
-
- @torch.no_grad()
- def forward(
- self,
- batched_input: List[Dict[str, Any]],
- multimask_output: bool,
- ) -> List[Dict[str, torch.Tensor]]:
- """
- Predicts masks end-to-end from provided images and prompts.
- If prompts are not known in advance, using SamPredictor is
- recommended over calling the model directly.
-
- Arguments:
- batched_input (list(dict)): A list over input images, each a
- dictionary with the following keys. A prompt key can be
- excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
- multimask_output (bool): Whether the model should predict multiple
- disambiguating masks, or return a single mask.
-
- Returns:
- (list(dict)): A list over input images, where each element is
- as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
- """
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
-
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
- masks = self.postprocess_masks(
- low_res_masks,
- input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- }
- )
- return outputs
-
- def postprocess_masks(
- self,
- masks: torch.Tensor,
- input_size: Tuple[int, ...],
- original_size: Tuple[int, ...],
- ) -> torch.Tensor:
- """
- Remove padding and upscale masks to the original image size.
-
- Arguments:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
-
- Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
- """
- masks = F.interpolate(
- masks,
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- )
- masks = masks[..., : input_size[0], : input_size[1]]
- masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
- return masks
-
- def preprocess(self, x: torch.Tensor) -> torch.Tensor:
- """Normalize pixel values and pad to a square input."""
- # Normalize colors
- x = (x - self.pixel_mean) / self.pixel_std
-
- # Pad
- h, w = x.shape[-2:]
- padh = self.image_encoder.img_size - h
- padw = self.image_encoder.img_size - w
- x = F.pad(x, (0, padw, 0, padh))
- return x
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/transformer.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/transformer.py
deleted file mode 100644
index 5bdd753..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/modeling/transformer.py
+++ /dev/null
@@ -1,232 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from typing import Tuple, Type
-
-import torch
-from torch import Tensor, nn
-
-from .common import MLPBlock
-
-
-class TwoWayTransformer(nn.Module):
- def __init__(
- self,
- depth: int,
- embedding_dim: int,
- num_heads: int,
- mlp_dim: int,
- activation: Type[nn.Module] = nn.ReLU,
- attention_downsample_rate: int = 2,
- ) -> None:
- """
- A transformer decoder that attends to an input image using
- queries whose positional embedding is supplied.
-
- Args:
- depth (int): number of layers in the transformer
- embedding_dim (int): the channel dimension for the input embeddings
- num_heads (int): the number of heads for multihead attention. Must
- divide embedding_dim
- mlp_dim (int): the channel dimension internal to the MLP block
- activation (nn.Module): the activation to use in the MLP block
- """
- super().__init__()
- self.depth = depth
- self.embedding_dim = embedding_dim
- self.num_heads = num_heads
- self.mlp_dim = mlp_dim
- self.layers = nn.ModuleList()
-
- for i in range(depth):
- self.layers.append(
- TwoWayAttentionBlock(
- embedding_dim=embedding_dim,
- num_heads=num_heads,
- mlp_dim=mlp_dim,
- activation=activation,
- attention_downsample_rate=attention_downsample_rate,
- skip_first_layer_pe=(i == 0),
- )
- )
-
- self.final_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
- self.norm_final_attn = nn.LayerNorm(embedding_dim)
-
- def forward(
- self,
- image_embedding: Tensor,
- image_pe: Tensor,
- point_embedding: Tensor,
- ) -> Tuple[Tensor, Tensor]:
- """
- Args:
- image_embedding (torch.Tensor): image to attend to. Should be shape
- B x embedding_dim x h x w for any h and w.
- image_pe (torch.Tensor): the positional encoding to add to the image. Must
- have the same shape as image_embedding.
- point_embedding (torch.Tensor): the embedding to add to the query points.
- Must have shape B x N_points x embedding_dim for any N_points.
-
- Returns:
- torch.Tensor: the processed point_embedding
- torch.Tensor: the processed image_embedding
- """
- # BxCxHxW -> BxHWxC == B x N_image_tokens x C
- bs, c, h, w = image_embedding.shape
- image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
- image_pe = image_pe.flatten(2).permute(0, 2, 1)
-
- # Prepare queries
- queries = point_embedding
- keys = image_embedding
-
- # Apply transformer blocks and final layernorm
- for layer in self.layers:
- queries, keys = layer(
- queries=queries,
- keys=keys,
- query_pe=point_embedding,
- key_pe=image_pe,
- )
-
- # Apply the final attenion layer from the points to the image
- q = queries + point_embedding
- k = keys + image_pe
- attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
- queries = queries + attn_out
- queries = self.norm_final_attn(queries)
-
- return queries, keys
-
-
-class TwoWayAttentionBlock(nn.Module):
- def __init__(
- self,
- embedding_dim: int,
- num_heads: int,
- mlp_dim: int = 2048,
- activation: Type[nn.Module] = nn.ReLU,
- attention_downsample_rate: int = 2,
- skip_first_layer_pe: bool = False,
- ) -> None:
- """
- A transformer block with four layers: (1) self-attention of sparse
- inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
- block on sparse inputs, and (4) cross attention of dense inputs to sparse
- inputs.
-
- Arguments:
- embedding_dim (int): the channel dimension of the embeddings
- num_heads (int): the number of heads in the attention layers
- mlp_dim (int): the hidden dimension of the mlp block
- activation (nn.Module): the activation of the mlp block
- skip_first_layer_pe (bool): skip the PE on the first layer
- """
- super().__init__()
- self.self_attn = Attention(embedding_dim, num_heads)
- self.norm1 = nn.LayerNorm(embedding_dim)
-
- self.cross_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
- self.norm2 = nn.LayerNorm(embedding_dim)
-
- self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
- self.norm3 = nn.LayerNorm(embedding_dim)
-
- self.norm4 = nn.LayerNorm(embedding_dim)
- self.cross_attn_image_to_token = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
-
- self.skip_first_layer_pe = skip_first_layer_pe
-
- def forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor) -> Tuple[Tensor, Tensor]:
- # Self attention block
- if self.skip_first_layer_pe:
- queries = self.self_attn(q=queries, k=queries, v=queries)
- else:
- q = queries + query_pe
- attn_out = self.self_attn(q=q, k=q, v=queries)
- queries = queries + attn_out
- queries = self.norm1(queries)
-
- # Cross attention block, tokens attending to image embedding
- q = queries + query_pe
- k = keys + key_pe
- attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
- queries = queries + attn_out
- queries = self.norm2(queries)
-
- # MLP block
- mlp_out = self.mlp(queries)
- queries = queries + mlp_out
- queries = self.norm3(queries)
-
- # Cross attention block, image embedding attending to tokens
- q = queries + query_pe
- k = keys + key_pe
- attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
- keys = keys + attn_out
- keys = self.norm4(keys)
-
- return queries, keys
-
-
-class Attention(nn.Module):
- """
- An attention layer that allows for downscaling the size of the embedding
- after projection to queries, keys, and values.
- """
-
- def __init__(
- self,
- embedding_dim: int,
- num_heads: int,
- downsample_rate: int = 1,
- ) -> None:
- super().__init__()
- self.embedding_dim = embedding_dim
- self.internal_dim = embedding_dim // downsample_rate
- self.num_heads = num_heads
- assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
-
- self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
- self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
- self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
- self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
-
- def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
- b, n, c = x.shape
- x = x.reshape(b, n, num_heads, c // num_heads)
- return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
-
- def _recombine_heads(self, x: Tensor) -> Tensor:
- b, n_heads, n_tokens, c_per_head = x.shape
- x = x.transpose(1, 2)
- return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
-
- def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
- # Input projections
- q = self.q_proj(q)
- k = self.k_proj(k)
- v = self.v_proj(v)
-
- # Separate into heads
- q = self._separate_heads(q, self.num_heads)
- k = self._separate_heads(k, self.num_heads)
- v = self._separate_heads(v, self.num_heads)
-
- # Attention
- _, _, _, c_per_head = q.shape
- attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
- attn = attn / math.sqrt(c_per_head)
- attn = torch.softmax(attn, dim=-1)
-
- # Get output
- out = attn @ v
- out = self._recombine_heads(out)
- out = self.out_proj(out)
-
- return out
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/predictor.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/predictor.py
deleted file mode 100644
index d80ca28..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/predictor.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional, Tuple
-
-import numpy as np
-import torch
-
-from .modeling import Sam
-from .utils.transforms import ResizeLongestSide
-
-
-class SamPredictor:
- def __init__(
- self,
- sam_model: Sam,
- ) -> None:
- """
- Uses SAM to calculate the image embedding for an image, and then
- allow repeated, efficient mask prediction given prompts.
-
- Arguments:
- sam_model (Sam): The model to use for mask prediction.
- """
- super().__init__()
- self.model = sam_model
- self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
- self.reset_image()
-
- def set_image(
- self,
- image: np.ndarray,
- image_format: str = "RGB",
- ) -> None:
- """
- Calculates the image embeddings for the provided image, allowing
- masks to be predicted with the 'predict' method.
-
- Arguments:
- image (np.ndarray): The image for calculating masks. Expects an
- image in HWC uint8 format, with pixel values in [0, 255].
- image_format (str): The color format of the image, in ['RGB', 'BGR'].
- """
- assert image_format in [
- "RGB",
- "BGR",
- ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
- if image_format != self.model.image_format:
- image = image[..., ::-1]
-
- # Transform the image to the form expected by the model
- input_image = self.transform.apply_image(image)
- input_image_torch = torch.as_tensor(input_image, device=self.device)
- input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
-
- self.set_torch_image(input_image_torch, image.shape[:2])
-
- @torch.no_grad()
- def set_torch_image(
- self,
- transformed_image: torch.Tensor,
- original_image_size: Tuple[int, ...],
- ) -> None:
- """
- Calculates the image embeddings for the provided image, allowing
- masks to be predicted with the 'predict' method. Expects the input
- image to be already transformed to the format expected by the model.
-
- Arguments:
- transformed_image (torch.Tensor): The input image, with shape
- 1x3xHxW, which has been transformed with ResizeLongestSide.
- original_image_size (tuple(int, int)): The size of the image
- before transformation, in (H, W) format.
- """
- assert (
- len(transformed_image.shape) == 4
- and transformed_image.shape[1] == 3
- and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
- ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
- self.reset_image()
-
- self.original_size = original_image_size
- self.input_size = tuple(transformed_image.shape[-2:])
- input_image = self.model.preprocess(transformed_image)
- self.features = self.model.image_encoder(input_image)
- self.is_image_set = True
-
- def predict(
- self,
- point_coords: Optional[np.ndarray] = None,
- point_labels: Optional[np.ndarray] = None,
- box: Optional[np.ndarray] = None,
- mask_input: Optional[np.ndarray] = None,
- multimask_output: bool = True,
- return_logits: bool = False,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Predict masks for the given input prompts, using the currently set image.
-
- Arguments:
- point_coords (np.ndarray or None): A Nx2 array of point prompts to the
- model. Each point is in (X,Y) in pixels.
- point_labels (np.ndarray or None): A length N array of labels for the
- point prompts. 1 indicates a foreground point and 0 indicates a
- background point.
- box (np.ndarray or None): A length 4 array given a box prompt to the
- model, in XYXY format.
- mask_input (np.ndarray): A low resolution mask input to the model, typically
- coming from a previous prediction iteration. Has form 1xHxW, where
- for SAM, H=W=256.
- multimask_output (bool): If true, the model will return three masks.
- For ambiguous input prompts (such as a single click), this will often
- produce better masks than a single prediction. If only a single
- mask is needed, the model's predicted quality score can be used
- to select the best mask. For non-ambiguous prompts, such as multiple
- input prompts, multimask_output=False can give better results.
- return_logits (bool): If true, returns un-thresholded masks logits
- instead of a binary mask.
-
- Returns:
- (np.ndarray): The output masks in CxHxW format, where C is the
- number of masks, and (H, W) is the original image size.
- (np.ndarray): An array of length C containing the model's
- predictions for the quality of each mask.
- (np.ndarray): An array of shape CxHxW, where C is the number
- of masks and H=W=256. These low resolution logits can be passed to
- a subsequent iteration as mask input.
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
- # Transform input prompts
- coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
- if point_coords is not None:
- assert point_labels is not None, "point_labels must be supplied if point_coords is supplied."
- point_coords = self.transform.apply_coords(point_coords, self.original_size)
- coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
- labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
- coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
- if box is not None:
- box = self.transform.apply_boxes(box, self.original_size)
- box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
- box_torch = box_torch[None, :]
- if mask_input is not None:
- mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
- mask_input_torch = mask_input_torch[None, :, :, :]
-
- masks, iou_predictions, low_res_masks = self.predict_torch(
- coords_torch,
- labels_torch,
- box_torch,
- mask_input_torch,
- multimask_output,
- return_logits=return_logits,
- )
-
- masks = masks[0].detach().cpu().numpy()
- iou_predictions = iou_predictions[0].detach().cpu().numpy()
- low_res_masks = low_res_masks[0].detach().cpu().numpy()
- return masks, iou_predictions, low_res_masks
-
- @torch.no_grad()
- def predict_torch(
- self,
- point_coords: Optional[torch.Tensor],
- point_labels: Optional[torch.Tensor],
- boxes: Optional[torch.Tensor] = None,
- mask_input: Optional[torch.Tensor] = None,
- multimask_output: bool = True,
- return_logits: bool = False,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Predict masks for the given input prompts, using the currently set image.
- Input prompts are batched torch tensors and are expected to already be
- transformed to the input frame using ResizeLongestSide.
-
- Arguments:
- point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
- model. Each point is in (X,Y) in pixels.
- point_labels (torch.Tensor or None): A BxN array of labels for the
- point prompts. 1 indicates a foreground point and 0 indicates a
- background point.
- box (np.ndarray or None): A Bx4 array given a box prompt to the
- model, in XYXY format.
- mask_input (np.ndarray): A low resolution mask input to the model, typically
- coming from a previous prediction iteration. Has form Bx1xHxW, where
- for SAM, H=W=256. Masks returned by a previous iteration of the
- predict method do not need further transformation.
- multimask_output (bool): If true, the model will return three masks.
- For ambiguous input prompts (such as a single click), this will often
- produce better masks than a single prediction. If only a single
- mask is needed, the model's predicted quality score can be used
- to select the best mask. For non-ambiguous prompts, such as multiple
- input prompts, multimask_output=False can give better results.
- return_logits (bool): If true, returns un-thresholded masks logits
- instead of a binary mask.
-
- Returns:
- (torch.Tensor): The output masks in BxCxHxW format, where C is the
- number of masks, and (H, W) is the original image size.
- (torch.Tensor): An array of shape BxC containing the model's
- predictions for the quality of each mask.
- (torch.Tensor): An array of shape BxCxHxW, where C is the number
- of masks and H=W=256. These low res logits can be passed to
- a subsequent iteration as mask input.
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
- if point_coords is not None:
- points = (point_coords, point_labels)
- else:
- points = None
-
- # Embed prompts
- sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
- points=points,
- boxes=boxes,
- masks=mask_input,
- )
-
- # Predict masks
- low_res_masks, iou_predictions = self.model.mask_decoder(
- image_embeddings=self.features,
- image_pe=self.model.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
-
- # Upscale the masks to the original image resolution
- masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
-
- if not return_logits:
- masks = masks > self.model.mask_threshold
-
- return masks, iou_predictions, low_res_masks
-
- def get_image_embedding(self) -> torch.Tensor:
- """
- Returns the image embeddings for the currently set image, with
- shape 1xCxHxW, where C is the embedding dimension and (H,W) are
- the embedding spatial dimension of SAM (typically C=256, H=W=64).
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) to generate an embedding.")
- assert self.features is not None, "Features must exist if an image has been set."
- return self.features
-
- @property
- def device(self) -> torch.device:
- return self.model.device
-
- def reset_image(self) -> None:
- """Resets the currently set image."""
- self.is_image_set = False
- self.features = None
- self.orig_h = None
- self.orig_w = None
- self.input_h = None
- self.input_w = None
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/__init__.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/__init__.py
deleted file mode 100644
index 5277f46..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/amg.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/amg.py
deleted file mode 100644
index 1c9c491..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/amg.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from copy import deepcopy
-from itertools import product
-from typing import Any, Dict, Generator, ItemsView, List, Tuple
-
-import numpy as np
-import torch
-
-
-class MaskData:
- """
- A structure for storing masks and their related data in batched format.
- Implements basic filtering and concatenation.
- """
-
- def __init__(self, **kwargs) -> None:
- for v in kwargs.values():
- assert isinstance(
- v, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats = dict(**kwargs)
-
- def __setitem__(self, key: str, item: Any) -> None:
- assert isinstance(
- item, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats[key] = item
-
- def __delitem__(self, key: str) -> None:
- del self._stats[key]
-
- def __getitem__(self, key: str) -> Any:
- return self._stats[key]
-
- def items(self) -> ItemsView[str, Any]:
- return self._stats.items()
-
- def filter(self, keep: torch.Tensor) -> None:
- for k, v in self._stats.items():
- if v is None:
- self._stats[k] = None
- elif isinstance(v, torch.Tensor):
- self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
- elif isinstance(v, np.ndarray):
- self._stats[k] = v[keep.detach().cpu().numpy()]
- elif isinstance(v, list) and keep.dtype == torch.bool:
- self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
- elif isinstance(v, list):
- self._stats[k] = [v[i] for i in keep]
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def cat(self, new_stats: "MaskData") -> None:
- for k, v in new_stats.items():
- if k not in self._stats or self._stats[k] is None:
- self._stats[k] = deepcopy(v)
- elif isinstance(v, torch.Tensor):
- self._stats[k] = torch.cat([self._stats[k], v], dim=0)
- elif isinstance(v, np.ndarray):
- self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
- elif isinstance(v, list):
- self._stats[k] = self._stats[k] + deepcopy(v)
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def to_numpy(self) -> None:
- for k, v in self._stats.items():
- if isinstance(v, torch.Tensor):
- self._stats[k] = v.detach().cpu().numpy()
-
-
-def is_box_near_crop_edge(
- boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
-) -> torch.Tensor:
- """Filter masks at the edge of a crop, but not at the edge of the original image."""
- crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
- orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
- boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
- near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
- near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
- near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
- return torch.any(near_crop_edge, dim=1)
-
-
-def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
- box_xywh = deepcopy(box_xyxy)
- box_xywh[2] = box_xywh[2] - box_xywh[0]
- box_xywh[3] = box_xywh[3] - box_xywh[1]
- return box_xywh
-
-
-def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
- assert len(args) > 0 and all(
- len(a) == len(args[0]) for a in args
- ), "Batched iteration must have inputs of all the same size."
- n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
- for b in range(n_batches):
- yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
-
-
-def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
- """
- Encodes masks to an uncompressed RLE, in the format expected by
- pycoco tools.
- """
- # Put in fortran order and flatten h,w
- b, h, w = tensor.shape
- tensor = tensor.permute(0, 2, 1).flatten(1)
-
- # Compute change indices
- diff = tensor[:, 1:] ^ tensor[:, :-1]
- change_indices = diff.nonzero()
-
- # Encode run length
- out = []
- for i in range(b):
- cur_idxs = change_indices[change_indices[:, 0] == i, 1]
- cur_idxs = torch.cat(
- [
- torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
- cur_idxs + 1,
- torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
- ]
- )
- btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
- counts = [] if tensor[i, 0] == 0 else [0]
- counts.extend(btw_idxs.detach().cpu().tolist())
- out.append({"size": [h, w], "counts": counts})
- return out
-
-
-def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
- """Compute a binary mask from an uncompressed RLE."""
- h, w = rle["size"]
- mask = np.empty(h * w, dtype=bool)
- idx = 0
- parity = False
- for count in rle["counts"]:
- mask[idx : idx + count] = parity
- idx += count
- parity ^= True
- mask = mask.reshape(w, h)
- return mask.transpose() # Put in C order
-
-
-def area_from_rle(rle: Dict[str, Any]) -> int:
- return sum(rle["counts"][1::2])
-
-
-def calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor:
- """
- Computes the stability score for a batch of masks. The stability
- score is the IoU between the binary masks obtained by thresholding
- the predicted mask logits at high and low values.
- """
- # One mask is always contained inside the other.
- # Save memory by preventing unnecesary cast to torch.int64
- intersections = (masks > (mask_threshold + threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
- unions = (masks > (mask_threshold - threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
- return intersections / unions
-
-
-def build_point_grid(n_per_side: int) -> np.ndarray:
- """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
- offset = 1 / (2 * n_per_side)
- points_one_side = np.linspace(offset, 1 - offset, n_per_side)
- points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
- points_y = np.tile(points_one_side[:, None], (1, n_per_side))
- points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
- return points
-
-
-def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> List[np.ndarray]:
- """Generates point grids for all crop layers."""
- points_by_layer = []
- for i in range(n_layers + 1):
- n_points = int(n_per_side / (scale_per_layer**i))
- points_by_layer.append(build_point_grid(n_points))
- return points_by_layer
-
-
-def generate_crop_boxes(
- im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
-) -> Tuple[List[List[int]], List[int]]:
- """
- Generates a list of crop boxes of different sizes. Each layer
- has (2**i)**2 boxes for the ith layer.
- """
- crop_boxes, layer_idxs = [], []
- im_h, im_w = im_size
- short_side = min(im_h, im_w)
-
- # Original image
- crop_boxes.append([0, 0, im_w, im_h])
- layer_idxs.append(0)
-
- def crop_len(orig_len, n_crops, overlap):
- return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
-
- for i_layer in range(n_layers):
- n_crops_per_side = 2 ** (i_layer + 1)
- overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
-
- crop_w = crop_len(im_w, n_crops_per_side, overlap)
- crop_h = crop_len(im_h, n_crops_per_side, overlap)
-
- crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
- crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
-
- # Crops in XYWH format
- for x0, y0 in product(crop_box_x0, crop_box_y0):
- box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
- crop_boxes.append(box)
- layer_idxs.append(i_layer + 1)
-
- return crop_boxes, layer_idxs
-
-
-def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
- # Check if boxes has a channel dimension
- if len(boxes.shape) == 3:
- offset = offset.unsqueeze(1)
- return boxes + offset
-
-
-def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0]], device=points.device)
- # Check if points has a channel dimension
- if len(points.shape) == 3:
- offset = offset.unsqueeze(1)
- return points + offset
-
-
-def uncrop_masks(masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int) -> torch.Tensor:
- x0, y0, x1, y1 = crop_box
- if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
- return masks
- # Coordinate transform masks
- pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
- pad = (x0, pad_x - x0, y0, pad_y - y0)
- return torch.nn.functional.pad(masks, pad, value=0)
-
-
-def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tuple[np.ndarray, bool]:
- """
- Removes small disconnected regions and holes in a mask. Returns the
- mask and an indicator of if the mask has been modified.
- """
- import cv2 # type: ignore
-
- assert mode in ["holes", "islands"]
- correct_holes = mode == "holes"
- working_mask = (correct_holes ^ mask).astype(np.uint8)
- n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
- sizes = stats[:, -1][1:] # Row 0 is background label
- small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
- if len(small_regions) == 0:
- return mask, False
- fill_labels = [0] + small_regions
- if not correct_holes:
- fill_labels = [i for i in range(n_labels) if i not in fill_labels]
- # If every region is below threshold, keep largest
- if len(fill_labels) == 0:
- fill_labels = [int(np.argmax(sizes)) + 1]
- mask = np.isin(regions, fill_labels)
- return mask, True
-
-
-def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
- from pycocotools import mask as mask_utils # type: ignore
-
- h, w = uncompressed_rle["size"]
- rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
- rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json
- return rle
-
-
-def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
- """
- Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
- an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
- """
- # torch.max below raises an error on empty inputs, just skip in this case
- if torch.numel(masks) == 0:
- return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
-
- # Normalize shape to CxHxW
- shape = masks.shape
- h, w = shape[-2:]
- if len(shape) > 2:
- masks = masks.flatten(0, -3)
- else:
- masks = masks.unsqueeze(0)
-
- # Get top and bottom edges
- in_height, _ = torch.max(masks, dim=-1)
- in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
- bottom_edges, _ = torch.max(in_height_coords, dim=-1)
- in_height_coords = in_height_coords + h * (~in_height)
- top_edges, _ = torch.min(in_height_coords, dim=-1)
-
- # Get left and right edges
- in_width, _ = torch.max(masks, dim=-2)
- in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
- right_edges, _ = torch.max(in_width_coords, dim=-1)
- in_width_coords = in_width_coords + w * (~in_width)
- left_edges, _ = torch.min(in_width_coords, dim=-1)
-
- # If the mask is empty the right edge will be to the left of the left edge.
- # Replace these boxes with [0, 0, 0, 0]
- empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
- out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
- out = out * (~empty_filter).unsqueeze(-1)
-
- # Return to original shape
- if len(shape) > 2:
- out = out.reshape(*shape[:-2], 4)
- else:
- out = out[0]
-
- return out
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/onnx.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/onnx.py
deleted file mode 100644
index 9cd17c7..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/onnx.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Tuple
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from ..modeling import Sam
-from .amg import calculate_stability_score
-
-
-class SamOnnxModel(nn.Module):
- """
- This model should not be called directly, but is used in ONNX export.
- It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
- with some functions modified to enable model tracing. Also supports extra
- options controlling what information. See the ONNX export script for details.
- """
-
- def __init__(
- self,
- model: Sam,
- return_single_mask: bool,
- use_stability_score: bool = False,
- return_extra_metrics: bool = False,
- ) -> None:
- super().__init__()
- self.mask_decoder = model.mask_decoder
- self.model = model
- self.img_size = model.image_encoder.img_size
- self.return_single_mask = return_single_mask
- self.use_stability_score = use_stability_score
- self.stability_score_offset = 1.0
- self.return_extra_metrics = return_extra_metrics
-
- @staticmethod
- def resize_longest_image_size(input_image_size: torch.Tensor, longest_side: int) -> torch.Tensor:
- input_image_size = input_image_size.to(torch.float32)
- scale = longest_side / torch.max(input_image_size)
- transformed_size = scale * input_image_size
- transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
- return transformed_size
-
- def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
- point_coords = point_coords + 0.5
- point_coords = point_coords / self.img_size
- point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
- point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
-
- point_embedding = point_embedding * (point_labels != -1)
- point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1)
-
- for i in range(self.model.prompt_encoder.num_point_embeddings):
- point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[i].weight * (
- point_labels == i
- )
-
- return point_embedding
-
- def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
- mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
- mask_embedding = mask_embedding + (1 - has_mask_input) * self.model.prompt_encoder.no_mask_embed.weight.reshape(
- 1, -1, 1, 1
- )
- return mask_embedding
-
- def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
- masks = F.interpolate(
- masks,
- size=(self.img_size, self.img_size),
- mode="bilinear",
- align_corners=False,
- )
-
- prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size)
- masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])]
-
- orig_im_size = orig_im_size.to(torch.int64)
- h, w = orig_im_size[0], orig_im_size[1]
- masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
- return masks
-
- def select_masks(
- self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- # Determine if we should return the multiclick mask or not from the number of points.
- # The reweighting is used to avoid control flow.
- score_reweight = torch.tensor([[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]).to(
- iou_preds.device
- )
- score = iou_preds + (num_points - 2.5) * score_reweight
- best_idx = torch.argmax(score, dim=1)
- masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
- iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
-
- return masks, iou_preds
-
- @torch.no_grad()
- def forward(
- self,
- image_embeddings: torch.Tensor,
- point_coords: torch.Tensor,
- point_labels: torch.Tensor,
- mask_input: torch.Tensor,
- has_mask_input: torch.Tensor,
- orig_im_size: torch.Tensor,
- ):
- sparse_embedding = self._embed_points(point_coords, point_labels)
- dense_embedding = self._embed_masks(mask_input, has_mask_input)
-
- masks, scores = self.model.mask_decoder.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=self.model.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embedding,
- dense_prompt_embeddings=dense_embedding,
- )
-
- if self.use_stability_score:
- scores = calculate_stability_score(masks, self.model.mask_threshold, self.stability_score_offset)
-
- if self.return_single_mask:
- masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
-
- upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
-
- if self.return_extra_metrics:
- stability_scores = calculate_stability_score(
- upscaled_masks, self.model.mask_threshold, self.stability_score_offset
- )
- areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
- return upscaled_masks, scores, stability_scores, areas, masks
-
- return upscaled_masks, scores, masks
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/transforms.py b/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/transforms.py
deleted file mode 100644
index 96a4ed6..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/segment_anything/utils/transforms.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from copy import deepcopy
-from typing import Tuple
-
-import numpy as np
-import torch
-from torch.nn import functional as F
-from torchvision.transforms.functional import resize, to_pil_image # type: ignore
-
-
-class ResizeLongestSide:
- """
- Resizes images to longest side 'target_length', as well as provides
- methods for resizing coordinates and boxes. Provides methods for
- transforming both numpy array and batched torch tensors.
- """
-
- def __init__(self, target_length: int) -> None:
- self.target_length = target_length
-
- def apply_image(self, image: np.ndarray) -> np.ndarray:
- """
- Expects a numpy array with shape HxWxC in uint8 format.
- """
- target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
- return np.array(resize(to_pil_image(image), target_size))
-
- def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array of length 2 in the final dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
- coords = deepcopy(coords).astype(float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array shape Bx4. Requires the original image size
- in (H, W) format.
- """
- boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
- """
- Expects batched images with shape BxCxHxW and float format. This
- transformation may not exactly match apply_image. apply_image is
- the transformation expected by the model.
- """
- # Expects an image in BCHW format. May not exactly match apply_image.
- target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
- return F.interpolate(image, target_size, mode="bilinear", align_corners=False, antialias=True)
-
- def apply_coords_torch(self, coords: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
- """
- Expects a torch tensor with length 2 in the last dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
- coords = deepcopy(coords).to(torch.float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes_torch(self, boxes: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
- """
- Expects a torch tensor with shape Bx4. Requires the original image
- size in (H, W) format.
- """
- boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- @staticmethod
- def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
- """
- Compute the output size given input size and target long side length.
- """
- scale = long_side_length * 1.0 / max(oldh, oldw)
- newh, neww = oldh * scale, oldw * scale
- neww = int(neww + 0.5)
- newh = int(newh + 0.5)
- return (newh, neww)
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/utils/data_utils.py b/monailabel/monaivista/lib/model/vista_point_2pt5/utils/data_utils.py
deleted file mode 100644
index 02bf82c..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/utils/data_utils.py
+++ /dev/null
@@ -1,257 +0,0 @@
-# Copyright 2020 - 2022 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import copy
-import math
-import os
-
-import numpy as np
-import torch
-from monai import data, transforms
-from monai.transforms import ScaleIntensityRanged
-
-
-class Sampler(torch.utils.data.Sampler):
- def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, make_even=True):
- if num_replicas is None:
- if not torch.distributed.is_available():
- raise RuntimeError("Requires distributed package to be available")
- num_replicas = torch.distributed.get_world_size()
- if rank is None:
- if not torch.distributed.is_available():
- raise RuntimeError("Requires distributed package to be available")
- rank = torch.distributed.get_rank()
- self.shuffle = shuffle
- self.make_even = make_even
- self.dataset = dataset
- self.num_replicas = num_replicas
- self.rank = rank
- self.epoch = 0
- self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
- self.total_size = self.num_samples * self.num_replicas
- indices = list(range(len(self.dataset)))
- self.valid_length = len(indices[self.rank : self.total_size : self.num_replicas])
-
- def __iter__(self):
- if self.shuffle:
- g = torch.Generator()
- g.manual_seed(self.epoch)
- indices = torch.randperm(len(self.dataset), generator=g).tolist()
- else:
- indices = list(range(len(self.dataset)))
- if self.make_even:
- if len(indices) < self.total_size:
- if self.total_size - len(indices) < len(indices):
- indices += indices[: (self.total_size - len(indices))]
- else:
- extra_ids = np.random.randint(low=0, high=len(indices), size=self.total_size - len(indices))
- indices += [indices[ids] for ids in extra_ids]
- assert len(indices) == self.total_size
- indices = indices[self.rank : self.total_size : self.num_replicas]
- self.num_samples = len(indices)
- return iter(indices)
-
- def __len__(self):
- return self.num_samples
-
- def set_epoch(self, epoch):
- self.epoch = epoch
-
-
-def get_loader(args):
- # min_val = -1024
- # max_val = 1024
- train_files, val_files, test_files = split_data(args)
-
- train_transform = transforms.Compose(
- [
- transforms.LoadImaged(keys=["image", "label"]),
- transforms.EnsureChannelFirstd(keys=["image", "label"]),
- transforms.Orientationd(keys=["image", "label"], axcodes="RAS"),
- ScaleIntensityRanged(
- keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True
- ),
- # transforms.Resized(keys=["image", "label"], spatial_size=[512, 512, -1], mode=["trilinear", "nearest"]),
- # SpatialPadd(keys=["image", "label"], spatial_size=[256, 256, -1]),
- # transforms.CropForegroundd(keys=["image", "label"], source_key="image")
- # transforms.Spacingd(keys=["image", "label"], pixdim=[3.0, 3.0, 3.0], mode=["bilinear", "nearest"])
- # transforms.SpatialPadd
- ]
- )
-
- val_transform = transforms.Compose(
- [
- transforms.LoadImaged(keys=["image", "label"]),
- transforms.EnsureChannelFirstd(keys=["image", "label"]),
- transforms.Orientationd(keys=["image", "label"], axcodes="RAS"),
- ScaleIntensityRanged(
- keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True
- ),
- # transforms.CropForegroundd(keys=["image", "label"], source_key="image"),
- # transforms.Spacingd(keys=["image", "label"], pixdim=[1.0, 1.0, 1.0], mode=["bilinear", "nearest"])
- ]
- )
-
- if args.test_mode:
- pass
- else:
- datalist = train_files
- if args.use_normal_dataset:
- train_ds = data.Dataset(data=datalist[:1], transform=train_transform)
- else:
- if args.distributed:
- datalist = data.partition_dataset(
- data=datalist,
- shuffle=True,
- num_partitions=args.world_size,
- even_divisible=True,
- )[args.rank]
-
- train_ds = data.CacheDataset(
- data=datalist,
- transform=train_transform,
- cache_rate=1.0,
- num_workers=args.workers,
- )
- # train_sampler = Sampler(train_ds) if args.distributed else None
- train_sampler = None
-
- train_loader = data.DataLoader(
- train_ds,
- batch_size=args.batch_size,
- shuffle=(train_sampler is None),
- num_workers=args.workers,
- sampler=train_sampler,
- pin_memory=True,
- )
- val_files = val_files
- if args.distributed:
- val_files = data.partition_dataset(
- data=val_files,
- shuffle=False,
- num_partitions=args.world_size,
- even_divisible=False,
- )[args.rank]
- val_ds = data.CacheDataset(
- data=val_files,
- transform=val_transform,
- cache_rate=1.0,
- num_workers=args.workers,
- )
- val_sampler = None # Sampler(val_ds, shuffle=False) if args.distributed else None
- val_loader = data.DataLoader(
- val_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=val_sampler, pin_memory=True
- )
- loader = [train_loader, val_loader]
-
- return loader
-
-
-def split_data(args):
- data_dir = args.data_dir
- import json
-
- with open(args.json_list, "r") as f:
- json_data = json.load(f)
-
- list_train = []
- list_valid = []
- if "validation" in json_data.keys():
- list_train = json_data["training"]
- list_valid = json_data["validation"]
- list_test = json_data["testing"]
- else:
- for item in json_data["training"]:
- if item["fold"] == args.fold:
- item.pop("fold", None)
- list_valid.append(item)
- else:
- item.pop("fold", None)
- list_train.append(item)
- if "testing" in json_data.keys() and "label" in json_data["testing"][0]:
- list_test = json_data["testing"]
- else:
- list_test = copy.deepcopy(list_valid)
- if args.splitval > 0:
- list_train = sorted(list_train, key=lambda x: x["image"])
- l = int((len(list_train) + len(list_valid)) * args.splitval)
- list_valid = list_train[-l:]
- list_train = list_train[:-l]
-
- if hasattr(args, "rank") and args.rank == 0:
- print("train files", len(list_train), [os.path.basename(_["image"]).split(".")[0] for _ in list_train])
- print("val files", len(list_valid), [os.path.basename(_["image"]).split(".")[0] for _ in list_valid])
- print("test files", len(list_test), [os.path.basename(_["image"]).split(".")[0] for _ in list_test])
-
- # training data
- files = []
- for _i in range(len(list_train)):
- str_img = os.path.join(data_dir, list_train[_i]["image"])
- str_seg = os.path.join(data_dir, list_train[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
-
- train_files = copy.deepcopy(files)
-
- files = []
- for _i in range(len(list_valid)):
- str_img = os.path.join(data_dir, list_valid[_i]["image"])
- str_seg = os.path.join(data_dir, list_valid[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
- val_files = copy.deepcopy(files)
-
- files = []
- for _i in range(len(list_test)):
- str_img = os.path.join(data_dir, list_test[_i]["image"])
- str_seg = os.path.join(data_dir, list_test[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
- test_files = copy.deepcopy(files)
- # return train_files[:2], val_files[:2], test_files
- return train_files, val_files, test_files
-
-
-if __name__ == "__main__":
- import argparse
-
- parser = argparse.ArgumentParser(description="dummy parser")
- args = parser.parse_args()
- args.data_dir = "/mnt/3td1/dummy_totalsegmentator_104"
- args.json_list = "/home/pengfeig/code/samm_2pt5/dummy_totalsegmentator_104organs_folds_v2.json"
- args.fold = 0
- args.splitval = 0
- train_files, val_files, test_files = split_data(args=args)
- args.use_normal_dataset = True
- args.test_mode = False
- args.distributed = False
- args.batch_size = 1
- args.workers = 0
- loaders = get_loader(args)
- d = next(iter(loaders[0]))
- volume = torch.squeeze(d["image"])
- import matplotlib
-
- matplotlib.use("TkAgg")
- import matplotlib.pyplot as plt
-
- plt.imshow(volume[..., 50], cmap="gray")
- plt.show()
- print()
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/utils/utils.py b/monailabel/monaivista/lib/model/vista_point_2pt5/utils/utils.py
deleted file mode 100644
index 695aeee..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/utils/utils.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# Copyright 2020 - 2022 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import scipy.ndimage as ndimage
-import torch
-
-SAM_IMAGE_SIZE = 1024
-
-
-def resample_3d(img, target_size):
- imx, imy, imz = img.shape
- tx, ty, tz = target_size
- zoom_ratio = (float(tx) / float(imx), float(ty) / float(imy), float(tz) / float(imz))
- img_resampled = ndimage.zoom(img, zoom_ratio, order=0, prefilter=False)
- return img_resampled
-
-
-def dice(x, y):
- intersect = np.sum(np.sum(np.sum(x * y)))
- y_sum = np.sum(np.sum(np.sum(y)))
- if y_sum == 0:
- return 0.0
- x_sum = np.sum(np.sum(np.sum(x)))
- return 2 * intersect / (x_sum + y_sum)
-
-
-class AverageMeter(object):
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
-
-
-def distributed_all_gather(
- tensor_list, valid_batch_size=None, out_numpy=False, world_size=None, no_barrier=False, is_valid=None
-):
- if world_size is None:
- world_size = torch.distributed.get_world_size()
- if valid_batch_size is not None:
- valid_batch_size = min(valid_batch_size, world_size)
- elif is_valid is not None:
- is_valid = torch.tensor(bool(is_valid), dtype=torch.bool, device=tensor_list[0].device)
- if not no_barrier:
- torch.distributed.barrier()
- tensor_list_out = []
- with torch.no_grad():
- if is_valid is not None:
- is_valid_list = [torch.zeros_like(is_valid) for _ in range(world_size)]
- torch.distributed.all_gather(is_valid_list, is_valid)
- is_valid = [x.item() for x in is_valid_list]
- for tensor in tensor_list:
- gather_list = [torch.zeros_like(tensor) for _ in range(world_size)]
- torch.distributed.all_gather(gather_list, tensor)
- if valid_batch_size is not None:
- gather_list = gather_list[:valid_batch_size]
- elif is_valid is not None:
- gather_list = [g for g, v in zip(gather_list, is_valid_list) if v]
- if out_numpy:
- gather_list = [t.cpu().numpy() for t in gather_list]
- tensor_list_out.append(gather_list)
- return tensor_list_out
-
-
-def prepare_sam_val_input(inputs, class_prompts, point_prompts, start_idx, original_affine=None, device=None):
- # Don't exclude background in val but will ignore it in metric calculation
- H, W = inputs.shape[1:]
- foreground_all = point_prompts["foreground"]
- background_all = point_prompts["background"]
-
- class_list = [[i + 1] for i in class_prompts]
- unique_labels = torch.tensor(class_list).long()
- if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
- unique_labels = unique_labels.cuda()
-
- volume_point_coords = [cp for cp in foreground_all]
- volume_point_labels = [1] * len(foreground_all)
-
- for cp in background_all:
- volume_point_coords.append(cp)
- volume_point_labels.append(0)
-
- point_coords = [[]]
- point_labels = [[]]
-
- # Reoriente point coord if not in RAS
- if original_affine is not None:
- IJK2orientation = np.diag(original_affine[:3, :3])
- negative_indices = np.where(IJK2orientation < 0)[0]
- if len(negative_indices) > 0:
- for idx, c in enumerate(volume_point_coords):
- volume_point_coords[idx][negative_indices[0]] = H - volume_point_coords[idx][negative_indices[0]]
- volume_point_coords[idx][negative_indices[1]] = W - volume_point_coords[idx][negative_indices[1]]
-
- for idx, cp in enumerate(volume_point_coords):
- if cp[2] + 4 == start_idx:
- new_H = cp[0] * (SAM_IMAGE_SIZE / H)
- new_W = cp[1] * (SAM_IMAGE_SIZE / W)
- point_coords[0].append([new_H, new_W])
- point_labels[0].append(volume_point_labels[idx])
-
- if len(point_coords[0]) == 0:
- point_coords = None
- point_labels = None
-
- prepared_input = [{"image": inputs, "original_size": tuple(inputs.shape[1:])}]
-
- if len(class_prompts) == 0:
- class_enabled = False
- else:
- class_enabled = True
- if class_enabled:
- prepared_input[0].update({"labels": unique_labels})
-
- if point_coords:
- point_coords = torch.tensor(point_coords).long()
- point_labels = torch.tensor(point_labels).long()
- if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
- point_coords = point_coords.cuda()
- point_labels = point_labels.cuda()
-
- prepared_input[0].update({"point_coords": point_coords, "point_labels": point_labels})
-
- return prepared_input, unique_labels
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/vista_2pt5_image_encoder.py b/monailabel/monaivista/lib/model/vista_point_2pt5/vista_2pt5_image_encoder.py
deleted file mode 100644
index 166794e..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/vista_2pt5_image_encoder.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Tuple, Type
-
-import torch
-import torch.nn as nn
-from segment_anything.modeling.image_encoder import ImageEncoderViT, PatchEmbed
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py
-class VistaImageEncoderViT(ImageEncoderViT):
- def __init__(
- self,
- img_size: int = 1024,
- patch_size: int = 16,
- in_chans: int = 3,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- out_chans: int = 256,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_abs_pos: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- global_attn_indexes: Tuple[int, ...] = (),
- patch_embed_3d: bool = False,
- ) -> None:
- """
- Args:
- img_size (int): Input image size.
- patch_size (int): Patch size.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- depth (int): Depth of ViT.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_abs_pos (bool): If True, use absolute positional embeddings.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks.
- global_attn_indexes (list): Indexes for blocks using global attention.
- patch_embed_3d (bool): If True, use 3D Patch Embedding.
- """
- super().__init__(
- img_size,
- patch_size,
- in_chans,
- embed_dim,
- depth,
- num_heads,
- mlp_ratio,
- out_chans,
- qkv_bias,
- norm_layer,
- act_layer,
- use_abs_pos,
- use_rel_pos,
- rel_pos_zero_init,
- window_size,
- global_attn_indexes,
- )
-
- self.img_size = img_size
-
- if in_chans > 3 and patch_embed_3d:
- print("ImageEncoderViT: Using 3D PatchEmbed")
- self.patch_embed = PatchEmbed2pt5D(
- kernel_size=(patch_size, patch_size, in_chans // 3),
- stride=(patch_size, patch_size, in_chans // 3),
- in_chans=3,
- embed_dim=embed_dim,
- )
- else:
- self.patch_embed = PatchEmbed(
- kernel_size=(patch_size, patch_size),
- stride=(patch_size, patch_size),
- in_chans=in_chans,
- embed_dim=embed_dim,
- )
-
-
-class PatchEmbed2pt5D(nn.Module):
- """
- Image to Patch Embedding by 3D Conv.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int, int] = (16, 16, 1),
- stride: Tuple[int, int, int] = (16, 16, 1),
- padding: Tuple[int, int, int] = (0, 0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- # got restore RGB channel dim and the depth dim
- c = x.shape[1]
- x = torch.stack(x.chunk(c // 3, dim=1), dim=-1)
- x = self.proj(x)
- # remove dummy depth dim to make it 2d
- x = x.squeeze(-1)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/monailabel/monaivista/lib/model/vista_point_2pt5/vista_2pt5_prompt_encoder.py b/monailabel/monaivista/lib/model/vista_point_2pt5/vista_2pt5_prompt_encoder.py
deleted file mode 100644
index 16958e2..0000000
--- a/monailabel/monaivista/lib/model/vista_point_2pt5/vista_2pt5_prompt_encoder.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from segment_anything.modeling.common import LayerNorm2d
-from segment_anything.modeling.prompt_encoder import PromptEncoder
-from torch import nn
-
-
-class VistaPromptEncoder(PromptEncoder):
- def __init__(
- self,
- embed_dim: int,
- image_embedding_size: Tuple[int, int],
- input_image_size: Tuple[int, int],
- mask_in_chans: int,
- activation: Type[nn.Module] = nn.GELU,
- n_classes: int = 512,
- clip_class_label_prompt: bool = False,
- ) -> None:
- """
- Encodes prompts for input to Segment Anything Model's mask decoder.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- n_classes (int): The number of pre-defined classes.
- clip_class_label_prompt (bool): Using clip txt features
- as class label prompt.
- """
- super().__init__(embed_dim, image_embedding_size, input_image_size, mask_in_chans, activation)
-
- self.clip_class_label_prompt = clip_class_label_prompt
- # Add support for onehot vector embedding for pre-defined classes
- if self.clip_class_label_prompt:
- raise NotImplementedError
- else:
- self.label_embeddings = nn.Embedding(n_classes, embed_dim)
- self.no_label_embed = nn.Embedding(1, embed_dim)
-
- def _embed_labels(self, labels: torch.Tensor) -> torch.Tensor:
- """Embeds onehot vector inputs."""
- if self.clip_class_label_prompt:
- raise NotImplementedError
- else:
- # Add support for onehot vector embedding for pre-defined classes
- label_embedding = self.label_embeddings(labels)
- return label_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- labels: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- elif labels is not None:
- return labels.shape[0]
- else:
- return 1
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- class_labels: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
- class_labels (torch.Tensor or none): labels to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks, class_labels)
-
- # Add support for onehot vector embedding for pre-defined classes
- if class_labels is not None:
- label_embeddings = self._embed_labels(class_labels)
- else:
- label_embeddings = self.no_label_embed.weight.reshape(1, 1, -1).expand(bs, -1, -1)
-
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
-
- # Add support for onehot vector embedding for pre-defined classes
- sparse_embeddings = torch.cat([sparse_embeddings, label_embeddings], dim=1)
-
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
diff --git a/monailabel/monaivista/lib/trainers/__init__.py b/monailabel/monaivista/lib/trainers/__init__.py
deleted file mode 100644
index 1e97f89..0000000
--- a/monailabel/monaivista/lib/trainers/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/monailabel/monaivista/lib/transforms/__init__.py b/monailabel/monaivista/lib/transforms/__init__.py
deleted file mode 100644
index 1e97f89..0000000
--- a/monailabel/monaivista/lib/transforms/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/monailabel/monaivista/lib/transforms/transforms.py b/monailabel/monaivista/lib/transforms/transforms.py
deleted file mode 100644
index 65a509b..0000000
--- a/monailabel/monaivista/lib/transforms/transforms.py
+++ /dev/null
@@ -1,528 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import copy
-import logging
-from typing import Dict, Hashable, Mapping
-
-import numpy as np
-import torch
-from monai.config import KeysCollection, NdarrayOrTensor
-from monai.transforms import (
- CropForeground,
- GaussianSmooth,
- Randomizable,
- Resize,
- ScaleIntensity,
- SpatialCrop,
-)
-from monai.transforms.transform import MapTransform, Transform
-
-logger = logging.getLogger(__name__)
-
-THRESHOLD_DIC = {
- "Spleen": 0.5,
- "Right Kidney": 0.5,
- "Left Kidney": 0.5,
- "Gall Bladder": 0.5,
- "Esophagus": 0.5,
- "Liver": 0.5,
- "Stomach": 0.5,
- "Arota": 0.5,
- "Postcava": 0.5,
- "Portal Vein and Splenic Vein": 0.5,
- "Pancreas": 0.5,
- "Right Adrenal Gland": 0.5,
- "Left Adrenal Gland": 0.5,
- "Duodenum": 0.5,
- "Hepatic Vessel": 0.5,
- "Right Lung": 0.5,
- "Left Lung": 0.5,
- "Colon": 0.5,
- "Intestine": 0.5,
- "Rectum": 0.5,
- "Bladder": 0.5,
- "Prostate": 0.5,
- "Left Head of Femur": 0.5,
- "Right Head of Femur": 0.5,
- "Celiac Truck": 0.5,
- "Kidney Tumor": 0.5,
- "Liver Tumor": 0.5,
- "Pancreas Tumor": 0.5,
- "Hepatic Vessel Tumor": 0.5,
- "Lung Tumor": 0.5,
- "Colon Tumor": 0.5,
- "Kidney Cyst": 0.5,
-}
-
-
-THRESHOLD_DIC_PRO = {
- "spleen": 0.5,
- "Right Kidney": 0.5,
- "Left Kidney": 0.5,
- "Gall Bladder": 0.5,
- "Esophagus": 0.5,
- "Liver": 0.5,
- "Stomach": 0.5,
- "Arota": 0.5,
- "Postcava": 0.5,
- "Portal Vein and Splenic Vein": 0.5,
- "Pancreas": 0.5,
- "Right Adrenal Gland": 0.5,
- "Left Adrenal Gland": 0.5,
- "Duodenum": 0.5,
- "Hepatic Vessel": 0.5,
- "Right Lung": 0.5,
- "Left Lung": 0.5,
- "Colon": 0.5,
- "Intestine": 0.5,
- "Rectum": 0.5,
- "Bladder": 0.5,
- "Prostate": 0.5,
- "Left Head of Femur": 0.5,
- "Right Head of Femur": 0.5,
- "Celiac Truck": 0.5,
- "Kidney Tumor": 0.5,
- "Liver Tumor": 0.5,
- "Pancreas Tumor": 0.5,
- "Hepatic Vessel Tumor": 0.5,
- "Lung Tumor": 0.5,
- "Colon Tumor": 0.5,
- "Kidney Cyst": 0.5,
- "Left Lung Upper Lobe": 0.5,
- "Left Lung Lower Lobe": 0.5,
- "Right Lung Upper Lobe": 0.5,
- "Right Lung Middle Lobe": 0.5,
- "Right Lung Lower Lobe": 0.5,
- "Vertebrae L5": 0.5,
- "Vertebrae L4": 0.5,
- "Vertebrae L3": 0.5,
- "Vertebrae L2": 0.5,
- "Vertebrae L1": 0.5,
- "Vertebrae T12": 0.5,
- "Vertebrae T11": 0.5,
- "Vertebrae T10": 0.5,
- "Vertebrae T9": 0.5,
- "Vertebrae T8": 0.5,
- "Vertebrae T7": 0.5,
- "Vertebrae T6": 0.5,
- "Vertebrae T5": 0.5,
- "Vertebrae T4": 0.5,
- "Vertebrae T3": 0.5,
- "Vertebrae T2": 0.5,
- "Vertebrae T1": 0.5,
- "Vertebrae C7": 0.5,
- "Vertebrae C6": 0.5,
- "Vertebrae C5": 0.5,
- "Vertebrae C4": 0.5,
- "Vertebrae C3": 0.5,
- "Vertebrae C2": 0.5,
- "Vertebrae C1": 0.5,
- "Trachea": 0.5,
- "Heart Myocardium": 0.5,
- "Left Heart Atrium": 0.5,
- "Left Heart Ventricle": 0.5,
- "Right Heart Atrium": 0.5,
- "Right Heart Ventricle": 0.5,
- "Pulmonary Artery": 0.5,
- "Brain": 0.5,
- "Left Iliac Artery": 0.5,
- "Right Iliac Artery": 0.5,
- "Left Iliac Vena": 0.5,
- "Right Iliac Vena": 0.5,
- "Small Bowel": 0.5,
- "Left Rib 1": 0.5,
- "Left Rib 2": 0.5,
- "Left Rib 3": 0.5,
- "Left Rib 4": 0.5,
- "Left Rib 5": 0.5,
- "Left Rib 6": 0.5,
- "Left Rib 7": 0.5,
- "Left Rib 8": 0.5,
- "Left Rib 9": 0.5,
- "Left Rib 10": 0.5,
- "Left Rib 11": 0.5,
- "Left Rib 12": 0.5,
- "Right Rib 1": 0.5,
- "Right Rib 2": 0.5,
- "Right Rib 3": 0.5,
- "Right Rib 4": 0.5,
- "Right Rib 5": 0.5,
- "Right Rib 6": 0.5,
- "Right Rib 7": 0.5,
- "Right Rib 8": 0.5,
- "Right Rib 9": 0.5,
- "Right Rib 10": 0.5,
- "Right Rib 11": 0.5,
- "Right Rib 12": 0.5,
- "Left Humerus": 0.5,
- "Right Humerus": 0.5,
- "Left Scapula": 0.5,
- "Right Scapula": 0.5,
- "Left Clavicula": 0.5,
- "Right Clavicula": 0.5,
- "Left Hip": 0.5,
- "Right Hip": 0.5,
- "Sacrum": 0.5,
- "Face": 0.5,
- "Left Gluteus Maximus": 0.5,
- "Right Gluteus Maximus": 0.5,
- "Left Gluteus Medius": 0.5,
- "Right Gluteus Medius": 0.5,
- "Left Gluteus Minimus": 0.5,
- "Right Gluteus Minimus": 0.5,
- "Left Autochthon": 0.5,
- "Right Autochthon": 0.5,
- "Left iliopsoas": 0.5,
- "Right iliopsoas": 0.5,
-}
-
-
-class BinaryMaskd(MapTransform):
- def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
- """
- Convert to single label - This should actually create the heat map for the first stage
-
- :param keys: The ``keys`` parameter will be used to get and set the actual data item to transform
-
- """
- super().__init__(keys, allow_missing_keys)
-
- def __call__(self, data):
- d: Dict = dict(data)
- for key in self.key_iterator(d):
- d[key][d[key] > 0] = 1
- return d
-
-
-class PlaceCroppedAread(MapTransform):
- def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
- """
- Place the ROI predicted in the full image
-
- :param keys: The ``keys`` parameter will be used to get and set the actual data item to transform
-
- """
- super().__init__(keys, allow_missing_keys)
-
- def __call__(self, data):
- d: Dict = dict(data)
- for _ in self.key_iterator(d):
- final_pred = np.zeros(
- (1, d["original_size"][-3], d["original_size"][-2], d["original_size"][-1]), dtype=np.float32
- )
- # Undo/invert the resize of d["pred"] #
- d["pred"] = Resize(spatial_size=d["cropped_size"], mode="nearest")(d["pred"])
- final_pred[
- :,
- d["slices_cropped"][-3][0] : d["slices_cropped"][-3][1],
- d["slices_cropped"][-2][0] : d["slices_cropped"][-2][1],
- d["slices_cropped"][-1][0] : d["slices_cropped"][-1][1],
- ] = d["pred"]
- d["pred"] = final_pred * int(d["current_label"])
- return d
-
-
-class CropAndCreateSignald(MapTransform):
- def __init__(self, keys: KeysCollection, signal_key, allow_missing_keys: bool = False):
- """
- Based on the centroids:
-
- 1/ Crop the image around the centroid,
- 2/ Create Gaussian smoothed signal
-
- :param keys: The ``keys`` parameter will be used to get and set the actual data item to transform
-
- """
- super().__init__(keys, allow_missing_keys)
- self.signal_key = signal_key
-
- def __call__(self, data):
- d: Dict = dict(data)
- for key in self.key_iterator(d):
- ###########
- # Crop the image
- ###########
- d["current_label"] = list(d["centroids"][0].values())[0][-4]
-
- (
- x,
- y,
- z,
- ) = (
- list(d["centroids"][0].values())[0][-3],
- list(d["centroids"][0].values())[0][-2],
- list(d["centroids"][0].values())[0][-1],
- )
- current_size = d[key].shape[1:]
- original_size = d[key].meta["spatial_shape"]
- x = int(x * current_size[0] / original_size[0])
- y = int(y * current_size[1] / original_size[1])
- z = int(z * current_size[2] / original_size[2])
-
- # Cropping
- cropper = SpatialCrop(roi_center=[x, y, z], roi_size=(96, 96, 64))
-
- slices_cropped = [
- [cropper.slices[-3].start, cropper.slices[-3].stop],
- [cropper.slices[-2].start, cropper.slices[-2].stop],
- [cropper.slices[-1].start, cropper.slices[-1].stop],
- ]
- d["slices_cropped"] = slices_cropped
- d[key] = cropper(d[key])
-
- cropped_size = d[key].shape[1:]
- d["cropped_size"] = cropped_size
-
- #################################
- # Create signal based on centroid
- #################################
- signal = torch.zeros_like(d[key])
- signal[:, cropped_size[0] // 2, cropped_size[1] // 2, cropped_size[2] // 2] = 1.0
-
- sigma = 1.6 + (d["current_label"] - 1.0) * 0.1
- signal = GaussianSmooth(sigma)(signal)
- d[self.signal_key] = signal
-
- return d
-
-
-class GetOriginalInformation(MapTransform):
- def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
- """
- Get information from original image
-
- """
- super().__init__(keys, allow_missing_keys)
-
- def __call__(self, data):
- d: Dict = dict(data)
- for key in self.key_iterator(d):
- d["original_size"] = d[key].shape[-3], d[key].shape[-2], d[key].shape[-1]
- return d
-
-
-class AddCentroidFromClicks(Transform, Randomizable):
- def __init__(self, label_names, key_label="label", key_clicks="foreground", key_centroids="centroids"):
- self.label_names = label_names
- self.key_label = key_label
- self.key_clicks = key_clicks
- self.key_centroids = key_centroids
-
- def __call__(self, data):
- d: Dict = dict(data)
-
- clicks = d.get(self.key_clicks, [])
- if clicks:
- label = d.get(self.key_label, "label")
- label_idx = self.label_names.get(label, 0)
- for click in clicks:
- d[self.key_centroids] = [{f"label_{label_idx}": [label_idx, click[-3], click[-2], click[-1]]}]
-
- logger.info(f"Using Centroid: {label} => {d[self.key_centroids]}")
- return d
-
-
-class CacheObjectd(MapTransform):
- def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
- d: Dict = dict(data)
- for key in self.key_iterator(d):
- cache_key = f"{key}_cached"
- if d.get(cache_key) is None:
- d[cache_key] = copy.deepcopy(d[key])
- return d
-
-
-class ThreshMergeLabeld(MapTransform):
- def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
- """
- Normalize label values according to label names dictionary
-
- Args:
- keys: The ``keys`` parameter will be used to get and set the actual data item to transform
- label_names: all label names
- """
- super().__init__(keys, allow_missing_keys)
-
- def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
- d: Dict = dict(data)
- class_prompts = d.get("class_prompts", None)
- for key in self.key_iterator(d):
- C, W, H, D = d[key].shape
-
- threshold_list = []
- if class_prompts:
- for organ in class_prompts:
- for idx, k in enumerate(THRESHOLD_DIC.keys()):
- if organ == idx:
- threshold_list.append(THRESHOLD_DIC[k])
- else:
- for k, value in THRESHOLD_DIC.items():
- threshold_list.append(value)
- threshold_list = torch.tensor(threshold_list).repeat(1).reshape(len(threshold_list), 1, 1, 1).cuda()
-
- pred_hard = d[key] > threshold_list
-
- print("in post transform before merge pred shape: {}".format(pred_hard.shape))
- new_pred = torch.zeros(1, W, H, D)
-
- if class_prompts:
- for idx, item in enumerate(class_prompts):
- new_pred[0][pred_hard[idx] == 1] = item + 1
- else:
- for i in range(d[key].shape[0]):
- new_pred[0][pred_hard[i] == 1] = i + 1
-
- print("in post transform after merge pred shape: {}".format(new_pred.shape))
- print("Hey values: {}".format(np.unique(new_pred)))
-
- d[key] = new_pred
- return d
-
-
-class ThreshMergeLabelProd(MapTransform):
- def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
- """
- Normalize label values according to label names dictionary
-
- Args:
- keys: The ``keys`` parameter will be used to get and set the actual data item to transform
- label_names: all label names
- """
- super().__init__(keys, allow_missing_keys)
-
- def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
- d: Dict = dict(data)
- class_prompts = d.get("class_prompts", None)
- for key in self.key_iterator(d):
- C, W, H, D = d[key].shape
-
- threshold_list = []
- if class_prompts:
- for organ in class_prompts:
- for idx, k in enumerate(THRESHOLD_DIC_PRO.keys()):
- if organ == idx:
- threshold_list.append(THRESHOLD_DIC_PRO[k])
- else:
- for k, value in THRESHOLD_DIC_PRO.items():
- threshold_list.append(value)
- threshold_list = torch.tensor(threshold_list).repeat(1).reshape(len(threshold_list), 1, 1, 1).cuda()
-
- pred_hard = d[key] > threshold_list
-
- print("in post transform before merge pred shape: {}".format(pred_hard.shape))
- new_pred = torch.zeros(1, W, H, D)
-
- if class_prompts:
- for idx, item in enumerate(class_prompts):
- new_pred[0][pred_hard[idx] == 1] = item + 1
- else:
- for i in range(d[key].shape[0]):
- new_pred[0][pred_hard[i] == 1] = i + 1
-
- print("in post transform after merge pred shape: {}".format(new_pred.shape))
- print("Hey values: {}".format(np.unique(new_pred)))
-
- d[key] = new_pred
- return d
-
-
-class ThreshMergeLabeld_bk(MapTransform):
- def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
- """
- Normalize label values according to label names dictionary
-
- Args:
- keys: The ``keys`` parameter will be used to get and set the actual data item to transform
- label_names: all label names
- """
- super().__init__(keys, allow_missing_keys)
-
- def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
- d: Dict = dict(data)
- class_prompts = None
- for key in self.key_iterator(d):
- C, W, H, D = d[key].shape
-
- threshold_list = []
-
- if class_prompts:
- for organ in class_prompts:
- for idx, k in enumerate(THRESHOLD_DIC.keys()):
- if organ == idx:
- threshold_list.append(THRESHOLD_DIC[k])
- else:
- for k, value in THRESHOLD_DIC.items():
- threshold_list.append(value)
- threshold_list = torch.tensor(threshold_list).repeat(1).reshape(len(threshold_list), 1, 1, 1).cuda()
-
- pred_hard = d[key] > threshold_list
-
- print("in post transform before merge pred shape: {}".format(pred_hard.shape))
- new_pred = torch.zeros(1, W, H, D)
-
- if class_prompts:
- for idx, item in enumerate(class_prompts):
- new_pred[0][pred_hard[idx] == 1] = item + 1
- else:
- for i in range(d[key].shape[0]):
- new_pred[0][pred_hard[i] == 1] = i + 1
-
- print("in post transform after merge pred shape: {}".format(new_pred.shape))
-
- d[key].array = new_pred
- return d
-
-
-class ThreshMergeLabelPFd(MapTransform):
- def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
- """
- Normalize label values according to label names dictionary
-
- Args:
- keys: The ``keys`` parameter will be used to get and set the actual data item to transform
- label_names: all label names
- """
- super().__init__(keys, allow_missing_keys)
-
- def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
- d: Dict = dict(data)
- class_prompts = d.get("class_prompts", None)
- for key in self.key_iterator(d):
- C, W, H, D = d[key].shape
-
- threshold_list = []
- if class_prompts:
- for organ in class_prompts:
- for idx, k in enumerate(THRESHOLD_DIC_PRO.keys()):
- if organ == idx:
- threshold_list.append(THRESHOLD_DIC_PRO[k])
- else:
- for k, value in THRESHOLD_DIC_PRO.items():
- threshold_list.append(value)
- threshold_list = torch.tensor(threshold_list).repeat(1).reshape(len(threshold_list), 1, 1, 1).cuda()
-
- pred_hard = d[key] > threshold_list
-
- print("in post transform before merge pred shape: {}".format(pred_hard.shape))
- new_pred = torch.zeros(1, W, H, D)
-
- if class_prompts:
- for idx, item in enumerate(class_prompts):
- new_pred[0][pred_hard[idx] == 1] = item + 1
- else:
- for i in range(d[key].shape[0]):
- new_pred[0][pred_hard[i] == 1] = i + 1
-
- print("in post transform after merge pred shape: {}".format(new_pred.shape))
- print("Hey values: {}".format(np.unique(new_pred)))
-
- d[key] = new_pred
- return d
diff --git a/monailabel/monaivista/lib/writer.py b/monailabel/monaivista/lib/writer.py
deleted file mode 100644
index b193225..0000000
--- a/monailabel/monaivista/lib/writer.py
+++ /dev/null
@@ -1,386 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import tempfile
-from typing import Any, Dict, Iterable, List, Optional, Tuple
-
-import itk
-import nrrd
-import numpy as np
-import torch
-from monai.data import MetaTensor
-from monai.data.image_writer import NibabelWriter
-from monailabel.utils.others.detection import create_slicer_detection_json
-from monailabel.utils.others.generic import file_ext
-from monailabel.utils.others.pathology import (
- create_asap_annotations_xml,
- create_dsa_annotations_json,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def write_nifti(data, filename, affine=None, original_affine=None, output_dtype=np.float32):
- writer = NibabelWriter(output_dtype=output_dtype)
- writer.set_data_array(data, channel_dim=None, spatial_ndim=None)
- writer.set_metadata({"affine": affine, "original_affine": original_affine})
- writer.write(filename)
-
-
-def write_itk(image_np, output_file, affine, dtype, compress):
- if isinstance(image_np, torch.Tensor):
- image_np = image_np.numpy()
- if isinstance(affine, torch.Tensor):
- affine = affine.numpy()
- if len(image_np.shape) >= 2:
- image_np = image_np.transpose().copy()
- if dtype:
- image_np = image_np.astype(dtype)
-
- result_image = itk.image_from_array(image_np)
- logger.debug(f"ITK Image size: {itk.size(result_image)}")
-
- # https://github.com/RSIP-Vision/medio/blob/master/medio/metadata/affine.py#L108-L121
- if affine is not None:
- convert_aff_mat = np.diag([-1, -1, 1, 1])
- if affine.shape[0] == 3: # Handle RGB (2D Image)
- convert_aff_mat = np.diag([-1, -1, 1])
- affine = convert_aff_mat @ affine
-
- dim = affine.shape[0] - 1
- _origin_key = (slice(-1), -1)
- _m_key = (slice(-1), slice(-1))
-
- origin = affine[_origin_key]
- spacing = np.linalg.norm(affine[_m_key] @ np.eye(dim), axis=0)
- direction = affine[_m_key] @ np.diag(1 / spacing)
-
- logger.debug(f"Affine: {affine}")
- logger.debug(f"Origin: {origin}")
- logger.debug(f"Spacing: {spacing}")
- logger.debug(f"Direction: {direction}")
-
- result_image.SetDirection(itk.matrix_from_array(direction))
- result_image.SetSpacing(spacing)
- result_image.SetOrigin(origin)
-
- itk.imwrite(result_image, output_file, compress)
-
-
-def write_seg_nrrd(
- image_np: np.ndarray,
- output_file: str,
- dtype: type,
- affine: np.ndarray,
- labels: List[str],
- color_map: Optional[Dict[str, List[float]]] = None,
- index_order: str = "C",
- space: str = "left-posterior-superior",
-) -> None:
- """Write multi-channel seg.nrrd file.
-
- Args:
- image_np: Image as numpy ndarray
- output_file: Output file path that the seg.nrrd file should be saved to
- dtype: numpy type e.g. float32
- affine: Affine matrix
- labels: Labels of image segment which will be written to the nrrd header
- color_map: Mapping from segment_name(str) to it's color e.g. {'heart': [255/255, 244/255, 209/255]}
- index_order: Either 'C' or 'F' (see nrrd.write() documentation)
-
- Raises:
- ValueError: In case affine is not provided
- ValueError: In case labels are not provided
- """
- if isinstance(image_np, torch.Tensor):
- image_np = image_np.numpy()
- if isinstance(affine, torch.Tensor):
- affine = affine.numpy()
- image_np = image_np.transpose().copy()
- if dtype:
- image_np = image_np.astype(dtype)
-
- if not isinstance(labels, Iterable):
- raise ValueError("Labels have to be defined, e.g. as a list")
-
- header: Dict[str, Any] = {}
- for i, segment_name in enumerate(labels):
- header.update(
- {
- f"Segment{i}_ID": segment_name,
- f"Segment{i}_Name": segment_name,
- }
- )
- if color_map is not None:
- header[f"Segment{i}_Color"] = " ".join(list(map(str, color_map[segment_name])))
-
- if affine is None:
- raise ValueError("Affine matrix has to be defined")
-
- kinds = ["list", "domain", "domain", "domain"]
-
- convert_aff_mat = np.diag([-1, -1, 1, 1])
- affine = convert_aff_mat @ affine
-
- _origin_key = (slice(-1), -1)
- origin = affine[_origin_key]
-
- space_directions = np.array(
- [
- [np.nan, np.nan, np.nan],
- affine[0, :3],
- affine[1, :3],
- affine[2, :3],
- ]
- )
-
- header.update(
- {
- "kinds": kinds,
- "space directions": space_directions,
- "space origin": origin,
- "space": space,
- }
- )
- nrrd.write(
- output_file,
- image_np,
- header=header,
- index_order=index_order,
- )
-
-
-class Writer:
- def __init__(
- self,
- label="pred",
- json=None,
- ref_image=None,
- key_extension="result_extension",
- key_dtype="result_dtype",
- key_compress="result_compress",
- key_write_to_file="result_write_to_file",
- meta_key_postfix="meta_dict",
- nibabel=False,
- embedding=False,
- ):
- self.label = label
- self.json = json
- self.ref_image = ref_image if ref_image else label
-
- # User can specify through params
- self.key_extension = key_extension
- self.key_dtype = key_dtype
- self.key_compress = key_compress
- self.key_write_to_file = key_write_to_file
- self.meta_key_postfix = meta_key_postfix
- self.nibabel = nibabel
- self.embedding = embedding
-
- def __call__(self, data) -> Tuple[Any, Any]:
- logger.setLevel(data.get("logging", "INFO").upper())
-
- path = data.get("image_path")
- ext = file_ext(path) if path else None
- dtype = data.get(self.key_dtype, None)
- compress = data.get(self.key_compress, False)
- write_to_file = data.get(self.key_write_to_file, True)
-
- ext = data.get(self.key_extension) if data.get(self.key_extension) else ext
- write_to_file = write_to_file if ext else False
- logger.info(f"Result ext: {ext}; write_to_file: {write_to_file}; dtype: {dtype}")
-
- if isinstance(data[self.label], MetaTensor):
- image_np = data[self.label].array
- else:
- image_np = data[self.label]
-
- # Always using Restored as the last transform before writing
- meta_dict = data.get(f"{self.ref_image}_{self.meta_key_postfix}")
- affine = meta_dict.get("affine") if meta_dict else None
- if affine is None and isinstance(data[self.ref_image], MetaTensor):
- affine = data[self.ref_image].affine
-
- logger.debug(f"Image: {image_np.shape}; Data Image: {data[self.label].shape}")
-
- output_file = None
- output_json = data.get(self.json, {})
-
- if self.embedding:
- write_to_file = False
- output_file = tempfile.NamedTemporaryFile(suffix=ext).name
- write_h5(image_np, output_file)
-
- if write_to_file:
- output_file = tempfile.NamedTemporaryFile(suffix=ext).name
- logger.debug(f"Saving Image to: {output_file}")
-
- if self.is_multichannel_image(image_np):
- if ext != ".seg.nrrd":
- logger.warning(
- f"Using extension '{ext}' with multi-channel 4D label will probably fail"
- + "Consider to use extension '.seg.nrrd'"
- )
- labels = data.get("labels")
- color_map = data.get("color_map")
- logger.debug("Using write_seg_nrrd...")
- write_seg_nrrd(image_np, output_file, dtype, affine, labels, color_map)
- # Issue with slicer:: https://discourse.itk.org/t/saving-non-orthogonal-volume-in-nifti-format/2760/22
- elif self.nibabel and ext and ext.lower() in [".nii", ".nii.gz"]:
- logger.debug("Using MONAI write_nifti...")
- write_nifti(image_np, output_file, affine=affine, output_dtype=dtype)
- else:
- write_itk(image_np, output_file, affine if len(image_np.shape) > 2 else None, dtype, compress)
- else:
- output_file = image_np
-
- return output_file, output_json
-
- def is_multichannel_image(self, image_np: np.ndarray) -> bool:
- """Check if the provided image contains multiple channels
-
- Args:
- image_np : Expected shape (channels, width, height, batch)
-
- Returns:
- bool: If this is a multi-channel image or not
- """
- return len(image_np.shape) == 4 and image_np.shape[0] > 1
-
-
-def write_h5(data, filename, affine=None, original_affine=None, output_dtype=np.float32):
- writer = NibabelWriter(output_dtype=output_dtype)
- writer.set_data_array(data, channel_dim=None, spatial_ndim=None)
- writer.set_metadata({"affine": affine, "original_affine": original_affine})
- writer.write(filename)
-
-
-class ClassificationWriter:
- def __init__(self, label="pred", label_names=None):
- self.label = label
- self.label_names = label_names
-
- def __call__(self, data) -> Tuple[Any, Any]:
- logger.info(data[self.label].array)
-
- result = []
- for idx, score in enumerate(data[self.label]):
- name = f"label_{idx}"
- name = self.label_names.get(idx) if self.label_names else name
- if name:
- result.append({"idx": idx, "label": name, "score": float(score)})
-
- return None, {"prediction": result}
-
-
-class PolygonWriter:
- def __init__(
- self,
- label="pred",
- json="result",
- key_write_to_file="result_write_to_file",
- key_annotations="annotations",
- key_label_colors="label_colors",
- key_output_format="output",
- ):
- self.label = label
- self.json = json
- self.key_write_to_file = key_write_to_file
- self.key_annotations = key_annotations
- self.key_label_colors = key_label_colors
- self.key_output_format = key_output_format
- self.format = format
-
- def __call__(self, data) -> Tuple[Any, Any]:
- loglevel = data.get("logging", "INFO").upper()
- logger.setLevel(loglevel)
-
- output = data.get(self.key_output_format, "dsa")
- logger.info(f"+++ Output Type: {output}")
-
- output_json = data.get(self.json, {})
- write_to_file = data.get(self.key_write_to_file, True)
- if not write_to_file:
- return None, output_json
-
- res_json = {
- "name": f"MONAILabel Annotations - {data.get('model')}",
- "description": data.get("description"),
- "model": data.get("model"),
- "location": data.get("location"),
- "size": data.get("size"),
- "annotations": [output_json],
- "latencies": data.get("latencies"),
- }
-
- output_file = None
- if output == "asap":
- logger.info("+++ Generating ASAP XML Annotation")
- output_file, _ = create_asap_annotations_xml(res_json, loglevel=loglevel)
- elif output == "dsa":
- logger.info("+++ Generating DSA JSON Annotation")
- output_file, _ = create_dsa_annotations_json(res_json, loglevel=loglevel)
- else:
- logger.info("+++ Return Default JSON Annotation")
-
- return output_file, res_json
-
-
-class DetectionWriter:
- def __init__(
- self,
- json="result",
- pred_box_key="box",
- pred_label_key="label",
- key_write_to_file="result_write_to_file",
- key_output_format="slicer",
- ):
- self.json = json
- self.pred_box_key = pred_box_key
- self.pred_label_key = pred_label_key
- self.key_write_to_file = key_write_to_file
- self.key_output_format = key_output_format
- self.format = format
-
- def __call__(self, data) -> Tuple[Any, Any]:
- loglevel = data.get("logging", "INFO").upper()
- logger.setLevel(loglevel)
-
- output = data.get(self.key_output_format, "slicer")
- logger.info(f"+++ Output Type: {output}")
-
- output_json = data.get(self.json, {})
- write_to_file = data.get(self.key_write_to_file, True)
- if not write_to_file:
- return None, output_json
-
- res_json = {
- "name": f"MONAILabel Annotations - {data.get('model')}",
- "description": data.get("description"),
- "model": data.get("model"),
- "location": data.get("location"),
- "size": data.get("size"),
- "box": data.get(self.pred_box_key).cpu().detach().tolist(),
- "label": data.get(self.pred_label_key).cpu().detach().tolist(),
- "image": data.get("image_path", None),
- "latencies": data.get("latencies"),
- }
-
- output_files = None
-
- if output == "slicer":
- logger.info("+++ Generating Slicer Detection ROI Node JSON file")
- output_files, _ = create_slicer_detection_json(res_json, loglevel=loglevel)
- else:
- logger.info("+++ Return Default JSON Annotation")
-
- return output_files, res_json
diff --git a/monailabel/monaivista/main.py b/monailabel/monaivista/main.py
deleted file mode 100644
index 963b1bd..0000000
--- a/monailabel/monaivista/main.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import logging
-import os
-from typing import Dict
-
-import lib.configs
-from lib.class_utils import get_class_names
-from monailabel.interfaces.app import MONAILabelApp
-from monailabel.interfaces.config import TaskConfig
-from monailabel.interfaces.datastore import Datastore
-from monailabel.interfaces.tasks.infer_v2 import InferTask
-from monailabel.interfaces.tasks.strategy import Strategy
-from monailabel.interfaces.tasks.train import TrainTask
-from monailabel.tasks.activelearning.first import First
-from monailabel.tasks.activelearning.random import Random
-
-# bundle
-from monailabel.tasks.infer.bundle import BundleInferTask
-from monailabel.tasks.train.bundle import BundleTrainTask
-
-# from monailabel.utils.others.class_utils import get_class_names
-from monailabel.utils.others.generic import get_bundle_models, strtobool
-from monailabel.utils.others.planner import HeuristicPlanner
-
-import monailabel
-
-logger = logging.getLogger(__name__)
-
-
-class MyApp(MONAILabelApp):
- def __init__(self, app_dir, studies, conf):
- self.model_dir = os.path.join(app_dir, "model")
-
- configs = {}
- for c in get_class_names(lib.configs, "TaskConfig"):
- # print("!!!!!!:{}".format(c))
- name = c.split(".")[-2].lower()
- configs[name] = c
-
- configs = {k: v for k, v in sorted(configs.items())}
-
- # Load models from app model implementation, e.g., --conf models
- models = conf.get("models")
- if not models:
- print("")
- print("---------------------------------------------------------------------------------------")
- print("Provide --conf models ")
- print("Following are the available models. You can pass comma (,) seperated names to pass multiple")
- print(f" {', '.join(configs.keys())}")
- print("---------------------------------------------------------------------------------------")
- print("")
- exit(-1)
-
- models = models.split(",")
- models = [m.strip() for m in models]
- invalid = [m for m in models if m != "all" and not configs.get(m)]
- if invalid:
- print("")
- print("---------------------------------------------------------------------------------------")
- print(f"Invalid Model(s) are provided: {invalid}")
- print("Following are the available models. You can pass comma (,) seperated names to pass multiple")
- print(f" {', '.join(configs.keys())}")
- print("---------------------------------------------------------------------------------------")
- print("")
- exit(-1)
-
- # Use Heuristic Planner to determine target spacing and spatial size based on dataset+gpu
- spatial_size = json.loads(conf.get("spatial_size", "[48, 48, 32]"))
- target_spacing = json.loads(conf.get("target_spacing", "[1.0, 1.0, 1.0]"))
- self.heuristic_planner = strtobool(conf.get("heuristic_planner", "false"))
- self.planner = HeuristicPlanner(spatial_size=spatial_size, target_spacing=target_spacing)
-
- # app models
- self.models: Dict[str, TaskConfig] = {}
- for n in models:
- for k, v in configs.items():
- if self.models.get(k):
- continue
- if n == k or n == "all":
- logger.info(f"+++ Adding Model: {k} => {v}")
- self.models[k] = eval(f"{v}()")
- self.models[k].init(k, self.model_dir, conf, self.planner)
- logger.info(f"+++ Using Models: {list(self.models.keys())}")
-
- # Load models from bundle config files, local or released in Model-Zoo, e.g., --conf bundles
- self.bundles = get_bundle_models(app_dir, conf, conf_key="bundles") if conf.get("bundles") else None
-
- super().__init__(
- app_dir=app_dir,
- studies=studies,
- conf=conf,
- name=f"MONAILabel - monaivista ({monailabel.__version__})",
- description="MONAI VISTA: A foundational model for medical image segmentation",
- version=monailabel.__version__,
- )
-
- def init_datastore(self) -> Datastore:
- datastore = super().init_datastore()
- if self.heuristic_planner:
- self.planner.run(datastore)
- return datastore
-
- def init_infers(self) -> Dict[str, InferTask]:
- infers: Dict[str, InferTask] = {}
-
- #################################################
- # Models
- #################################################
- for n, task_config in self.models.items():
- c = task_config.infer()
- c = c if isinstance(c, dict) else {n: c}
- for k, v in c.items():
- logger.info(f"+++ Adding Inferer:: {k} => {v}")
- infers[k] = v
-
- #################################################
- # Bundle Models
- #################################################
- if self.bundles:
- for n, b in self.bundles.items():
- i = BundleInferTask(b, self.conf)
- logger.info(f"+++ Adding Bundle Inferer:: {n} => {i}")
- infers[n] = i
-
- logger.info(infers)
- return infers
-
- def init_trainers(self) -> Dict[str, TrainTask]:
- trainers: Dict[str, TrainTask] = {}
- if strtobool(self.conf.get("skip_trainers", "false")):
- return trainers
- #################################################
- # Models
- #################################################
- for n, task_config in self.models.items():
- t = task_config.trainer()
- if not t:
- continue
-
- logger.info(f"+++ Adding Trainer:: {n} => {t}")
- trainers[n] = t
-
- #################################################
- # Bundle Models
- #################################################
- if self.bundles:
- for n, b in self.bundles.items():
- t = BundleTrainTask(b, self.conf)
- if not t or not t.is_valid():
- continue
-
- logger.info(f"+++ Adding Bundle Trainer:: {n} => {t}")
- trainers[n] = t
-
- return trainers
-
- def init_strategies(self) -> Dict[str, Strategy]:
- strategies: Dict[str, Strategy] = {
- "random": Random(),
- "first": First(),
- }
-
- if strtobool(self.conf.get("skip_strategies", "true")):
- return strategies
-
- for n, task_config in self.models.items():
- s = task_config.strategy()
- if not s:
- continue
- s = s if isinstance(s, dict) else {n: s}
- for k, v in s.items():
- logger.info(f"+++ Adding Strategy:: {k} => {v}")
- strategies[k] = v
-
- logger.info(f"Active Learning Strategies:: {list(strategies.keys())}")
- return strategies
-
-
-def main():
- """
- Example to run train/infer/scoring task(s) locally without actually running MONAI Label Server
- """
- import argparse
- import shutil
- from pathlib import Path
-
- from monailabel.utils.others.generic import device_list, file_ext
-
- os.putenv("MASTER_ADDR", "127.0.0.1")
- os.putenv("MASTER_PORT", "1234")
-
- logging.basicConfig(
- level=logging.INFO,
- format="[%(asctime)s] [%(process)s] [%(threadName)s] [%(levelname)s] (%(name)s:%(lineno)d) - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- force=True,
- )
-
- home = str(Path.home())
- studies = f"{home}/Dataset/Radiology/WholeBodyCTDataset/test"
-
- parser = argparse.ArgumentParser()
- parser.add_argument("-s", "--studies", default=studies)
- parser.add_argument("-m", "--model", default="vista_point_2pt5")
- parser.add_argument("-t", "--test", default="infer", choices=("train", "infer"))
- args = parser.parse_args()
-
- app_dir = os.path.dirname(__file__)
- studies = args.studies
- conf = {
- "models": args.model,
- "preload": "false",
- }
-
- app = MyApp(app_dir, studies, conf)
-
- # Infer
- if args.test == "infer":
- sample = app.next_sample(request={"strategy": "first"})
- image_id = sample["id"]
- image_path = sample["path"]
-
- # Run on all devices
- for device in device_list():
- res = app.infer(request={"model": args.model, "image": image_id, "device": device})
- label = res["file"]
- label_json = res["params"]
- test_dir = os.path.join(args.studies, "test_labels")
- os.makedirs(test_dir, exist_ok=True)
-
- label_file = os.path.join(test_dir, image_id + file_ext(image_path))
- shutil.move(label, label_file)
-
- print(label_json)
- print(f"++++ Image File: {image_path}")
- print(f"++++ Label File: {label_file}")
- break
- return
-
- # Train
- app.train(
- request={
- "model": args.model,
- "max_epochs": 10,
- "dataset": "Dataset", # PersistentDataset, CacheDataset
- "train_batch_size": 1,
- "val_batch_size": 1,
- "multi_gpu": False,
- "val_split": 0.1,
- },
- )
-
-
-if __name__ == "__main__":
- # export PYTHONPATH=~/Projects/MONAILabel:`pwd`
- # python main.py
- main()
diff --git a/monailabel/monaivista/requirements.txt b/monailabel/monaivista/requirements.txt
deleted file mode 100644
index 1e97f89..0000000
--- a/monailabel/monaivista/requirements.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/monailabel/plugins/slicer/.gitignore b/monailabel/plugins/slicer/.gitignore
deleted file mode 100644
index 1e97f89..0000000
--- a/monailabel/plugins/slicer/.gitignore
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/monailabel/plugins/slicer/CMakeLists.txt b/monailabel/plugins/slicer/CMakeLists.txt
deleted file mode 100644
index e6bf749..0000000
--- a/monailabel/plugins/slicer/CMakeLists.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-cmake_minimum_required(VERSION 3.13.4)
-
-project(MONAILabel)
-
-#-----------------------------------------------------------------------------
-# Extension meta-information
-set(EXTENSION_HOMEPAGE "https://github.com/Project-MONAI/MONAILabel/tree/main/plugins/slicer/MONAILabel")
-set(EXTENSION_CATEGORY "Active Learning")
-set(EXTENSION_CONTRIBUTORS "NVIDIA, KCL")
-set(EXTENSION_DESCRIPTION "This is Active Learning solution developed under project MONAILabel")
-set(EXTENSION_ICONURL "https://github.com/Project-MONAI/MONAILabel/raw/main/plugins/slicer/MONAILabel/Resources/Icons/MONAILabel.png")
-set(EXTENSION_SCREENSHOTURLS "https://github.com/Project-MONAI/MONAILabel/raw/main/plugins/slicer/MONAILabel/Screenshots/1.png https://github.com/Project-MONAI/MONAILabel/raw/main/plugins/slicer/MONAILabel/Screenshots/2.png")
-set(EXTENSION_DEPENDS "NA") # Specified as a list or "NA" if no dependencies
-
-#-----------------------------------------------------------------------------
-# Extension dependencies
-find_package(Slicer REQUIRED)
-include(${Slicer_USE_FILE})
-
-#-----------------------------------------------------------------------------
-# Extension modules
-add_subdirectory(MONAILabel)
-add_subdirectory(MONAILabelReviewer)
-## NEXT_MODULE
-
-#-----------------------------------------------------------------------------
-include(${Slicer_EXTENSION_GENERATE_CONFIG})
-include(${Slicer_EXTENSION_CPACK})
diff --git a/monailabel/plugins/slicer/MONAI-Label.png b/monailabel/plugins/slicer/MONAI-Label.png
deleted file mode 100644
index 614a736..0000000
Binary files a/monailabel/plugins/slicer/MONAI-Label.png and /dev/null differ
diff --git a/monailabel/plugins/slicer/MONAILabel/CMakeLists.txt b/monailabel/plugins/slicer/MONAILabel/CMakeLists.txt
deleted file mode 100644
index 7d0f07b..0000000
--- a/monailabel/plugins/slicer/MONAILabel/CMakeLists.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#-----------------------------------------------------------------------------
-set(MODULE_NAME MONAILabel)
-
-#-----------------------------------------------------------------------------
-set(MODULE_PYTHON_SCRIPTS
- ${MODULE_NAME}.py
- ${MODULE_NAME}Lib/__init__.py
- ${MODULE_NAME}Lib/client.py
- ${MODULE_NAME}Lib/labelcolors.py
- )
-
-set(MODULE_PYTHON_RESOURCES
- Resources/Icons/${MODULE_NAME}.png
- Resources/Icons/refresh-icon.png
- Resources/Icons/download.png
- Resources/Icons/save.png
- Resources/Icons/segment.png
- Resources/Icons/stop.png
- Resources/Icons/training.png
- Resources/Icons/upload.svg
- Resources/Icons/contour.svg
- Resources/Icons/paint.png
- Resources/Icons/eraser.png
- Resources/Icons/fg_green.png
- Resources/Icons/bg_red.png
- Resources/UI/${MODULE_NAME}.ui
- )
-
-#-----------------------------------------------------------------------------
-slicerMacroBuildScriptedModule(
- NAME ${MODULE_NAME}
- SCRIPTS ${MODULE_PYTHON_SCRIPTS}
- RESOURCES ${MODULE_PYTHON_RESOURCES}
- WITH_GENERIC_TESTS
- )
-
-#-----------------------------------------------------------------------------
-if(BUILD_TESTING)
-
- # Register the unittest subclass in the main script as a ctest.
- # Note that the test will also be available at runtime.
- slicer_add_python_unittest(SCRIPT ${MODULE_NAME}.py)
-
- # Additional build-time testing
- add_subdirectory(Testing)
-endif()
diff --git a/monailabel/plugins/slicer/MONAILabel/MONAILabel.py b/monailabel/plugins/slicer/MONAILabel/MONAILabel.py
deleted file mode 100644
index 7056586..0000000
--- a/monailabel/plugins/slicer/MONAILabel/MONAILabel.py
+++ /dev/null
@@ -1,2665 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import logging
-import os
-import shutil
-import tempfile
-import time
-import traceback
-from collections import OrderedDict
-from urllib.parse import quote_plus
-
-import ctk
-import qt
-import SampleData
-import SimpleITK as sitk
-import sitkUtils
-import slicer
-import vtk
-import vtkSegmentationCore
-from MONAILabelLib import GenericAnatomyColors, MONAILabelClient
-from slicer.ScriptedLoadableModule import *
-from slicer.util import VTKObservationMixin
-
-
-class MONAILabel(ScriptedLoadableModule):
- def __init__(self, parent):
- ScriptedLoadableModule.__init__(self, parent)
- self.parent.title = "MONAILabel"
- self.parent.categories = ["Active Learning"]
- self.parent.dependencies = []
- self.parent.contributors = ["NVIDIA, KCL"]
- self.parent.helpText = """
-Active Learning solution.
-See more information in module documentation .
-"""
- self.parent.acknowledgementText = """
-Developed by NVIDIA, KCL
-"""
-
- # Additional initialization step after application startup is complete
- slicer.app.connect("startupCompleted()", self.initializeAfterStartup)
-
- def initializeAfterStartup(self):
- if not slicer.app.commandOptions().noMainWindow:
- self.settingsPanel = MONAILabelSettingsPanel()
- slicer.app.settingsDialog().addPanel("MONAI Label", self.settingsPanel)
-
-
-class _ui_MONAILabelSettingsPanel:
- def __init__(self, parent):
- vBoxLayout = qt.QVBoxLayout(parent)
-
- # settings
- groupBox = ctk.ctkCollapsibleGroupBox()
- groupBox.title = "MONAI Label Server"
- groupLayout = qt.QFormLayout(groupBox)
-
- serverUrl = qt.QLineEdit()
- groupLayout.addRow("Server address:", serverUrl)
- parent.registerProperty("MONAILabel/serverUrl", serverUrl, "text", str(qt.SIGNAL("textChanged(QString)")))
-
- serverUrlHistory = qt.QLineEdit()
- groupLayout.addRow("Server address history:", serverUrlHistory)
- parent.registerProperty(
- "MONAILabel/serverUrlHistory", serverUrlHistory, "text", str(qt.SIGNAL("textChanged(QString)"))
- )
-
- fileExtension = qt.QLineEdit()
- fileExtension.setText(".nii.gz")
- fileExtension.toolTip = "Default extension for uploading images/labels"
- groupLayout.addRow("File Extension:", fileExtension)
- parent.registerProperty(
- "MONAILabel/fileExtension", fileExtension, "text", str(qt.SIGNAL("textChanged(QString)"))
- )
-
- clientId = qt.QLineEdit()
- clientId.setText("user-xyz")
- clientId.toolTip = "Client/User ID that will be sent to MONAI Label server for reference"
- groupLayout.addRow("Client/User-ID:", clientId)
- parent.registerProperty("MONAILabel/clientId", clientId, "text", str(qt.SIGNAL("textChanged(QString)")))
-
- autoRunSegmentationCheckBox = qt.QCheckBox()
- autoRunSegmentationCheckBox.checked = False
- autoRunSegmentationCheckBox.toolTip = (
- "Enable this option to auto run segmentation if pre-trained model exists when Next Sample is fetched"
- )
- groupLayout.addRow("Auto-Run Pre-Trained Model:", autoRunSegmentationCheckBox)
- parent.registerProperty(
- "MONAILabel/autoRunSegmentationOnNextSample",
- ctk.ctkBooleanMapper(autoRunSegmentationCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- autoFetchNextSampleCheckBox = qt.QCheckBox()
- autoFetchNextSampleCheckBox.checked = False
- autoFetchNextSampleCheckBox.toolTip = "Enable this option to fetch Next Sample after saving the label"
- groupLayout.addRow("Auto-Fetch Next Sample:", autoFetchNextSampleCheckBox)
- parent.registerProperty(
- "MONAILabel/autoFetchNextSample",
- ctk.ctkBooleanMapper(autoFetchNextSampleCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- autoUpdateModelCheckBox = qt.QCheckBox()
- autoUpdateModelCheckBox.checked = False
- autoUpdateModelCheckBox.toolTip = "Enable this option to auto update model after submitting the label"
- groupLayout.addRow("Auto-Update Model:", autoUpdateModelCheckBox)
- parent.registerProperty(
- "MONAILabel/autoUpdateModelV2",
- ctk.ctkBooleanMapper(autoUpdateModelCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- askForUserNameCheckBox = qt.QCheckBox()
- askForUserNameCheckBox.checked = False
- askForUserNameCheckBox.toolTip = (
- "Enable this option to ask for the user name every time the MONAILabel "
- + "extension is loaded for the first time"
- )
- groupLayout.addRow("Ask For User Name:", askForUserNameCheckBox)
- parent.registerProperty(
- "MONAILabel/askForUserName",
- ctk.ctkBooleanMapper(askForUserNameCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- allowOverlapCheckBox = qt.QCheckBox()
- allowOverlapCheckBox.checked = False
- allowOverlapCheckBox.toolTip = "Enable this option to allow overlapping segmentations"
- groupLayout.addRow("Allow Overlapping Segmentations:", allowOverlapCheckBox)
- parent.registerProperty(
- "MONAILabel/allowOverlappingSegments",
- ctk.ctkBooleanMapper(allowOverlapCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
- allowOverlapCheckBox.connect("toggled(bool)", self.onUpdateAllowOverlap)
-
- originalLabelCheckBox = qt.QCheckBox()
- originalLabelCheckBox.checked = True
- originalLabelCheckBox.toolTip = "Enable this option to first read original label (predictions)"
- groupLayout.addRow("Original Labels:", originalLabelCheckBox)
- parent.registerProperty(
- "MONAILabel/originalLabel",
- ctk.ctkBooleanMapper(originalLabelCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- developerModeCheckBox = qt.QCheckBox()
- developerModeCheckBox.checked = True
- developerModeCheckBox.toolTip = "Enable this option to find options tab etc..."
- groupLayout.addRow("Developer Mode:", developerModeCheckBox)
- parent.registerProperty(
- "MONAILabel/developerMode",
- ctk.ctkBooleanMapper(developerModeCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- preComputeEmbeddingCheckBox = qt.QCheckBox()
- preComputeEmbeddingCheckBox.checked = True
- preComputeEmbeddingCheckBox.toolTip = "Enable this option to automatically pre-compute image embedding"
- groupLayout.addRow("Pre-Compute Embedding:", preComputeEmbeddingCheckBox)
- parent.registerProperty(
- "MONAILabel/preComputeEmbeddingMode",
- ctk.ctkBooleanMapper(preComputeEmbeddingCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- showSegmentsIn3DCheckBox = qt.QCheckBox()
- showSegmentsIn3DCheckBox.checked = False
- showSegmentsIn3DCheckBox.toolTip = "Enable this option to show segments in 3D (slow) after mask update..."
- groupLayout.addRow("Show Segments In 3D:", showSegmentsIn3DCheckBox)
- parent.registerProperty(
- "MONAILabel/showSegmentsIn3D",
- ctk.ctkBooleanMapper(showSegmentsIn3DCheckBox, "checked", str(qt.SIGNAL("toggled(bool)"))),
- "valueAsInt",
- str(qt.SIGNAL("valueAsIntChanged(int)")),
- )
-
- vBoxLayout.addWidget(groupBox)
- vBoxLayout.addStretch(1)
-
- def onUpdateAllowOverlap(self):
- if slicer.util.settingsValue("MONAILabel/allowOverlappingSegments", True, converter=slicer.util.toBool):
- if slicer.util.settingsValue("MONAILabel/fileExtension", None) != ".seg.nrrd":
- slicer.util.warningDisplay(
- "Overlapping segmentations are only availabel with the '.seg.nrrd' file extension! "
- + "Consider changing MONAILabel file extension."
- )
-
-
-class MONAILabelSettingsPanel(ctk.ctkSettingsPanel):
- def __init__(self, *args, **kwargs):
- ctk.ctkSettingsPanel.__init__(self, *args, **kwargs)
- self.ui = _ui_MONAILabelSettingsPanel(self)
-
-
-class MONAILabelWidget(ScriptedLoadableModuleWidget, VTKObservationMixin):
- def __init__(self, parent=None):
- """
- Called when the user opens the module the first time and the widget is initialized.
- """
- ScriptedLoadableModuleWidget.__init__(self, parent)
- VTKObservationMixin.__init__(self) # needed for parameter node observation
-
- self.logic = None
- self._parameterNode = None
- self._volumeNode = None
- self._segmentNode = None
- self._scribblesROINode = None
- self._volumeNodes = []
- self._updatingGUIFromParameterNode = False
-
- self.info = {}
- self.models = OrderedDict()
- self.trainers = OrderedDict()
- self.config = OrderedDict()
- self.current_sample = None
- self.samples = {}
- self.state = {
- "SegmentationModel": "",
- "DeepgrowModel": "",
- "ScribblesMethod": "",
- "CurrentStrategy": "",
- "CurrentTrainer": "",
- }
- self.file_ext = ".nii.gz"
-
- self.dgPositivePointListNode = None
- self.dgPositivePointListNodeObservers = []
- self.dgNegativePointListNode = None
- self.dgNegativePointListNodeObservers = []
- self.ignorePointListNodeAddEvent = False
-
- self.progressBar = None
- self.tmpdir = None
- self.timer = None
-
- self.scribblesMode = None
- self.ignoreScribblesLabelChangeEvent = False
- self.deepedit_multi_label = False
-
- self.optionsSectionIndex = 0
- self.optionsNameIndex = 0
-
- self.sammPositivePointListNode = None
- self.sammPositivePointListNodeObservers = []
- self.sammNegativePointListNode = None
- self.sammNegativePointListNodeObservers = []
- self.ignoreSAMMPointListNodeAddEvent = False
-
- def setup(self):
- """
- Called when the user opens the module the first time and the widget is initialized.
- """
- ScriptedLoadableModuleWidget.setup(self)
-
- # Load widget from .ui file (created by Qt Designer).
- # Additional widgets can be instantiated manually and added to self.layout.
- uiWidget = slicer.util.loadUI(self.resourcePath("UI/MONAILabel.ui"))
- self.layout.addWidget(uiWidget)
- self.ui = slicer.util.childWidgetVariables(uiWidget)
-
- # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's
- # "mrmlSceneChanged(vtkMRMLScene*)" signal in is connected to each MRML widget's.
- # "setMRMLScene(vtkMRMLScene*)" slot.
- uiWidget.setMRMLScene(slicer.mrmlScene)
-
- # These connections ensure that we update parameter node when scene is closed
- self.addObserver(slicer.mrmlScene, slicer.mrmlScene.StartCloseEvent, self.onSceneStartClose)
- self.addObserver(slicer.mrmlScene, slicer.mrmlScene.EndCloseEvent, self.onSceneEndClose)
- self.addObserver(slicer.mrmlScene, slicer.mrmlScene.NodeAddedEvent, self.onSceneEndImport)
-
- # Create logic class. Logic implements all computations that should be possible to run
- # in batch mode, without a graphical user interface.
- self.tmpdir = slicer.util.tempDirectory("slicer-monai-label")
- self.logic = MONAILabelLogic(self.tmpdir, resourcePath=self.resourcePath)
-
- # Set icons and tune widget properties
- self.ui.serverComboBox.lineEdit().setPlaceholderText("enter server address or leave empty to use default")
- self.ui.anatomyComboBox.setPlaceholderText("enter and search anatomy or leave empty")
-
- self.ui.fetchServerInfoButton.setIcon(self.icon("refresh-icon.png"))
- self.ui.sammButton.setIcon(self.icon("samm_everything.png"))
- self.ui.nextSampleButton.setIcon(self.icon("segment.png"))
- self.ui.saveLabelButton.setIcon(self.icon("save.png"))
- self.ui.trainingButton.setIcon(self.icon("training.png"))
- self.ui.stopTrainingButton.setIcon(self.icon("stop.png"))
- self.ui.uploadImageButton.setIcon(self.icon("upload.svg"))
- self.ui.importLabelButton.setIcon(self.icon("download.png"))
- self.ui.pointSegButton.setIcon(self.icon("samm_points.png"))
- self.ui.promptSegButton.setIcon(self.icon("samm_class.png"))
- self.ui.uncheckPromptButton.setIcon(self.icon("samm_reset.png"))
-
- self.ui.sammPosPlacementWidget.setMRMLScene(slicer.mrmlScene)
- self.ui.sammPosPlacementWidget.placeButton().toolTip = "Select +ve points"
- self.ui.sammPosPlacementWidget.buttonsVisible = True
- self.ui.sammPosPlacementWidget.placeButton().show()
- self.ui.sammPosPlacementWidget.deleteButton().show()
- # self.ui.sammPosPlacementWidget.setEnabled(True)
- # self.ui.sammPosPlacementWidget.setPlaceModeEnabled(True)
-
- self.ui.sammNegPlacementWidget.setMRMLScene(slicer.mrmlScene)
- self.ui.sammNegPlacementWidget.placeButton().toolTip = "Select -ve points"
- self.ui.sammNegPlacementWidget.buttonsVisible = True
- self.ui.sammNegPlacementWidget.placeButton().show()
- self.ui.sammNegPlacementWidget.deleteButton().show()
- # self.ui.sammNegPlacementWidget.setEnabled(True)
- # self.ui.sammNegPlacementWidget.setPlaceModeEnabled(True)
-
- self.ui.dgPositiveControlPointPlacementWidget.setMRMLScene(slicer.mrmlScene)
- self.ui.dgPositiveControlPointPlacementWidget.placeButton().toolTip = "Select +ve points"
- self.ui.dgPositiveControlPointPlacementWidget.buttonsVisible = False
- self.ui.dgPositiveControlPointPlacementWidget.placeButton().show()
- self.ui.dgPositiveControlPointPlacementWidget.deleteButton().show()
-
- self.ui.dgNegativeControlPointPlacementWidget.setMRMLScene(slicer.mrmlScene)
- self.ui.dgNegativeControlPointPlacementWidget.placeButton().toolTip = "Select -ve points"
- self.ui.dgNegativeControlPointPlacementWidget.buttonsVisible = False
- self.ui.dgNegativeControlPointPlacementWidget.placeButton().show()
- self.ui.dgNegativeControlPointPlacementWidget.deleteButton().show()
-
- self.ui.dgUpdateButton.setIcon(self.icon("segment.png"))
-
- # Connections
- self.ui.fetchServerInfoButton.connect("clicked(bool)", self.onClickFetchInfo)
- self.ui.serverComboBox.connect("currentIndexChanged(int)", self.onClickFetchInfo)
- self.ui.sammModelSelector.connect("currentIndexChanged(int)", self.updateParameterNodeFromGUI)
- self.ui.sammButton.connect("clicked(bool)", self.onClickSegmentation)
- self.ui.deepgrowModelSelector.connect("currentIndexChanged(int)", self.updateParameterNodeFromGUI)
- self.ui.nextSampleButton.connect("clicked(bool)", self.onNextSampleButton)
- # self.ui.trainingButton.connect("clicked(bool)", self.onTraining)
- self.ui.stopTrainingButton.connect("clicked(bool)", self.onStopTraining)
- self.ui.saveLabelButton.connect("clicked(bool)", self.onSaveLabel)
- self.ui.uploadImageButton.connect("clicked(bool)", self.onUploadImage)
- self.ui.importLabelButton.connect("clicked(bool)", self.onImportLabel)
- self.ui.labelComboBox.connect("currentIndexChanged(int)", self.onSelectLabel)
- self.ui.scribLabelComboBox.connect("currentIndexChanged(int)", self.onSelectScribLabel)
- self.ui.dgUpdateButton.connect("clicked(bool)", self.onUpdateDeepgrow)
- self.ui.dgUpdateCheckBox.setStyleSheet("padding-left: 10px;")
- self.ui.optionsSection.connect("currentIndexChanged(int)", self.onSelectOptionsSection)
- self.ui.optionsName.connect("currentIndexChanged(int)", self.onSelectOptionsName)
-
- self.ui.promptSegButton.connect("clicked(bool)", self.onSetPromptSegmentation)
- self.ui.uncheckPromptButton.connect("clicked(bool)", self.updateClassPromptTable)
-
- self.ui.pointSegButton.connect("clicked(bool)", self.onPointPromptSegmentation)
-
- self.ui.anatomyComboBox.textChanged.connect(self.filterClassPromptTable)
-
- # self.ui.sammModelSelector.connect("currentIndexChanged(int)", self.updateParameterNodeFromGUI)
-
- # Scribbles
- # brush and eraser icon from: https://tablericons.com/
- self.ui.scribblesMethodSelector.connect("currentIndexChanged(int)", self.updateParameterNodeFromGUI)
-
- self.ui.paintScribblesButton.setIcon(self.icon("paint.png"))
- self.ui.paintScribblesButton.setToolTip("Paint scribbles for selected scribble layer")
- self.ui.eraseScribblesButton.setIcon(self.icon("eraser.png"))
- self.ui.eraseScribblesButton.setToolTip("Erase scribbles for selected scribble layer")
- self.ui.updateScribblesButton.setIcon(self.icon("segment.png"))
- self.ui.updateScribblesButton.setToolTip(
- "Update label by sending scribbles to server to apply selected post processing method"
- )
-
- self.ui.brushSizeSlider.connect("valueChanged(double)", self.updateBrushSize)
- self.ui.brushSizeSlider.setToolTip("Change brush size for scribbles tool")
- self.ui.brush3dCheckbox.stateChanged.connect(self.on3dBrushCheckbox)
- self.ui.brush3dCheckbox.setToolTip("Use 3D brush to paint/erase in multiple slices in 3D")
- self.ui.updateScribblesButton.clicked.connect(self.onUpdateScribbles)
- self.ui.paintScribblesButton.clicked.connect(self.onPaintScribbles)
- self.ui.eraseScribblesButton.clicked.connect(self.onEraseScribbles)
- self.ui.scribblesSelector.connect("currentIndexChanged(int)", self.onSelectScribblesLabel)
-
- # creating editable combo box
- self.ui.scribblesSelector.addItem(self.icon("fg_green.png"), "Foreground")
- self.ui.scribblesSelector.addItem(self.icon("bg_red.png"), "Background")
- self.ui.scribblesSelector.setCurrentIndex(0)
-
- # ROI placement for scribbles
- self.ui.scribblesPlaceWidget.setButtonsVisible(False)
- self.ui.scribblesPlaceWidget.placeButton().show()
- self.ui.scribblesPlaceWidget.setMRMLScene(slicer.mrmlScene)
-
- # start with scribbles section disabled
- self.ui.scribblesCollapsibleButton.setEnabled(False)
- self.ui.scribblesCollapsibleButton.collapsed = True
-
- # embedded segment editor
- self.ui.embeddedSegmentEditorWidget.setMRMLScene(slicer.mrmlScene)
- self.ui.embeddedSegmentEditorWidget.setSegmentationNodeSelectorVisible(False)
- self.ui.embeddedSegmentEditorWidget.setSourceVolumeNodeSelectorVisible(False)
- self.ui.embeddedSegmentEditorWidget.setMRMLSegmentEditorNode(self.logic.get_segment_editor_node())
-
- # options section
- self.ui.optionsSection.addItem("infer")
- self.ui.optionsSection.addItem("train")
- self.ui.optionsSection.addItem("activelearning")
- self.ui.optionsSection.addItem("scoring")
-
- self.initializeParameterNode()
- self.updateServerUrlGUIFromSettings()
- # self.onClickFetchInfo()
-
- if slicer.util.settingsValue("MONAILabel/askForUserName", False, converter=slicer.util.toBool):
- text = qt.QInputDialog().getText(
- self.parent,
- "User Name",
- "Please enter your name:",
- qt.QLineEdit.Normal,
- slicer.util.settingsValue("MONAILabel/clientId", None),
- )
- if text:
- settings = qt.QSettings()
- settings.setValue("MONAILabel/clientId", text)
-
- def cleanup(self):
- self.removeObservers()
- shutil.rmtree(self.tmpdir, ignore_errors=True)
-
- def enter(self):
- self.initializeParameterNode()
- if self._segmentNode:
- self.updateGUIFromParameterNode()
-
- def exit(self):
- self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)
-
- def onSceneStartClose(self, caller, event):
- self.state = {
- "SegmentationModel": self.ui.sammModelSelector.currentText,
- "DeepgrowModel": self.ui.deepgrowModelSelector.currentText,
- "ScribblesMethod": self.ui.scribblesMethodSelector.currentText,
- "CurrentStrategy": self.ui.strategyBox.currentText,
- "CurrentTrainer": self.ui.trainerBox.currentText,
- # "SAMMModel": self.ui.sammModelSelector.currentText,
- }
-
- self._volumeNode = None
- self._segmentNode = None
- self._volumeNodes.clear()
- self.setParameterNode(None)
- self.current_sample = None
- self.samples.clear()
- self._scribblesROINode = None
-
- self.resetPointList(
- self.ui.dgPositiveControlPointPlacementWidget,
- self.dgPositivePointListNode,
- self.dgPositivePointListNodeObservers,
- )
- self.dgPositivePointListNode = None
- self.resetPointList(
- self.ui.dgNegativeControlPointPlacementWidget,
- self.dgNegativePointListNode,
- self.dgNegativePointListNodeObservers,
- )
- self.dgNegativePointListNode = None
-
- self.sammPositivePointListNode = None
- self.resetPointList(
- self.ui.sammPosPlacementWidget,
- self.sammPositivePointListNode,
- self.sammPositivePointListNodeObservers,
- )
- self.sammNegativePointListNode = None
- self.resetPointList(
- self.ui.sammNegPlacementWidget,
- self.sammNegativePointListNode,
- self.sammNegativePointListNodeObservers,
- )
-
- self.onResetScribbles()
-
- def resetPointList(self, markupsPlaceWidget, pointListNode, pointListNodeObservers):
- if markupsPlaceWidget.placeModeEnabled:
- markupsPlaceWidget.setPlaceModeEnabled(False)
-
- if pointListNode:
- slicer.mrmlScene.RemoveNode(pointListNode)
- self.removePointListNodeObservers(pointListNode, pointListNodeObservers)
-
- def onSceneEndClose(self, caller, event):
- if self.parent.isEntered:
- self.initializeParameterNode()
-
- def onSceneEndImport(self, caller, event):
- if not self._volumeNode:
- self.updateGUIFromParameterNode()
-
- def initializeParameterNode(self):
- self.setParameterNode(self.logic.getParameterNode())
-
- # Select default input nodes if nothing is selected yet to save a few clicks for the user
- if not self._parameterNode.GetNodeReference("InputVolume"):
- firstVolumeNode = slicer.mrmlScene.GetFirstNodeByClass("vtkMRMLScalarVolumeNode")
- if firstVolumeNode:
- self._parameterNode.SetNodeReferenceID("InputVolume", firstVolumeNode.GetID())
-
- def setParameterNode(self, inputParameterNode):
- if inputParameterNode:
- self.logic.setDefaultParameters(inputParameterNode)
-
- if self._parameterNode is not None:
- self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)
- self._parameterNode = inputParameterNode
- if self._parameterNode is not None:
- self.addObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)
-
- # Initial GUI update
- self.updateGUIFromParameterNode()
-
- def monitorTraining(self):
- status = self.isTrainingRunning(check_only=False)
- if status and status.get("status") == "RUNNING":
- info = self.logic.info()
- train_stats = info.get("train_stats")
- if not train_stats:
- return
-
- train_stats = next(iter(train_stats.values()))
-
- current = 0 if train_stats.get("total_time") else train_stats.get("epoch", 1)
- total = train_stats.get("total_epochs", 1)
- percent = max(1, 100 * current / total)
- if self.ui.trainingProgressBar.value != percent:
- self.ui.trainingProgressBar.setValue(percent)
- self.ui.trainingProgressBar.setToolTip(f"{current}/{total} epoch is completed")
-
- dice = train_stats.get("best_metric", 0)
- self.updateAccuracyBar(dice)
- return
-
- print("Training completed")
- self.ui.trainingProgressBar.setValue(100)
- self.timer.stop()
- self.timer = None
- self.ui.trainingProgressBar.setToolTip(f"Training: {status.get('status', 'DONE')}")
-
- self.ui.trainingButton.setEnabled(True)
- self.ui.stopTrainingButton.setEnabled(False)
- self.fetchInfo()
-
- def updateGUIFromParameterNode(self, caller=None, event=None):
- if self._parameterNode is None or self._updatingGUIFromParameterNode:
- return
-
- # Make sure GUI changes do not call updateParameterNodeFromGUI (it could cause infinite loop)
- self._updatingGUIFromParameterNode = True
-
- file_ext = slicer.util.settingsValue("MONAILabel/fileExtension", self.file_ext)
- self.file_ext = file_ext if file_ext else self.file_ext
-
- # Update node selectors and sliders
- self.ui.inputSelector.clear()
- for v in self._volumeNodes:
- self.ui.inputSelector.addItem(v.GetName())
- self.ui.inputSelector.setToolTip(self.current_sample.get("name", "") if self.current_sample else "")
- if self._volumeNode:
- self.ui.inputSelector.setCurrentIndex(self.ui.inputSelector.findText(self._volumeNode.GetName()))
- self.ui.inputSelector.setEnabled(False) # Allow only one active scene
-
- self.ui.uploadImageButton.setEnabled(False)
- if self.info and slicer.mrmlScene.GetFirstNodeByClass("vtkMRMLScalarVolumeNode") and self._volumeNode is None:
- self._volumeNode = slicer.mrmlScene.GetFirstNodeByClass("vtkMRMLScalarVolumeNode")
- self.initSample({"id": self._volumeNode.GetName(), "session": True}, autosegment=False)
- self.ui.inputSelector.setEnabled(False)
-
- self.ui.uploadImageButton.setEnabled(self.current_sample and self.current_sample.get("session"))
-
- self.updateSelector(self.ui.sammModelSelector, ["segmentation", "detection"], "SegmentationModel", 0)
- self.updateSelector(self.ui.deepgrowModelSelector, ["deepgrow", "deepedit"], "DeepgrowModel", 0)
- self.updateSelector(self.ui.scribblesMethodSelector, ["scribbles"], "ScribblesMethod", 0)
- # self.updateSelector(self.ui.sammModelSelector, ["segmentation", "detection"], "SegmentationModel", 0)
-
- if self.models and [k for k, v in self.models.items() if v["type"] in ("segmentation", "detection")]:
- self.ui.promptCollapsibleButton.collapsed = False
- self.ui.promptCollapsibleButton.show()
- self.ui.segmentationCollapsibleButton.hide()
-
- # self.ui.segmentationCollapsibleButton.collapsed = False
- # self.ui.segmentationCollapsibleButton.show()
- else:
- self.ui.segmentationCollapsibleButton.hide()
-
- if self.models and [k for k, v in self.models.items() if v["type"] in ("deepgrow", "deepedit")]:
- self.ui.deepgrowCollapsibleButton.collapsed = False
- self.ui.deepgrowCollapsibleButton.show()
- else:
- self.ui.deepgrowCollapsibleButton.hide()
-
- if self.models and [k for k, v in self.models.items() if v["type"] == "scribbles"]:
- self.ui.scribblesCollapsibleButton.collapsed = False
- self.ui.scribblesCollapsibleButton.show()
- else:
- self.ui.scribblesCollapsibleButton.hide()
-
- if self.info.get("trainers", {}):
- self.ui.trainWidget.show()
- else:
- self.ui.trainWidget.hide()
-
- self.ignoreScribblesLabelChangeEvent = True
- self.ui.labelComboBox.clear()
- self.ui.scribLabelComboBox.clear()
- if self._segmentNode:
- segmentation = self._segmentNode.GetSegmentation()
- totalSegments = segmentation.GetNumberOfSegments()
- segmentIds = [segmentation.GetNthSegmentID(i) for i in range(totalSegments)]
- for idx, segmentId in enumerate(segmentIds):
- segment = segmentation.GetSegment(segmentId)
- label = segment.GetName()
- if label not in ["foreground_scribbles", "background_scribbles"]:
- self.ui.labelComboBox.addItem(label)
- if label not in ["background", "foreground_scribbles", "background_scribbles"]:
- self.ui.scribLabelComboBox.addItem(label)
- else:
- for label in self.info.get("labels", {}):
- self.ui.labelComboBox.addItem(label)
- if label != "background":
- self.ui.scribLabelComboBox.addItem(label)
-
- currentLabel = self._parameterNode.GetParameter("CurrentLabel")
- idx = self.ui.labelComboBox.findText(currentLabel) if currentLabel else 0
- idx = 0 if idx < 0 < self.ui.labelComboBox.count else idx
- self.ui.labelComboBox.setCurrentIndex(idx)
-
- currentScribbleLabel = self._parameterNode.GetParameter("CurrentScribLabel")
- idx = self.ui.scribLabelComboBox.findText(currentScribbleLabel) if currentScribbleLabel else 0
- idx = 0 if idx < 0 < self.ui.scribLabelComboBox.count else idx
- self.ui.scribLabelComboBox.setCurrentIndex(idx)
- self.ignoreScribblesLabelChangeEvent = False
-
- self.ui.appComboBox.clear()
- self.ui.appComboBox.addItem(self.info.get("name", ""))
-
- datastore_stats = self.info.get("datastore", {})
- current = datastore_stats.get("completed", 0)
- total = datastore_stats.get("total", 0)
- self.ui.activeLearningProgressBar.setValue(current / max(total, 1) * 100)
- self.ui.activeLearningProgressBar.setToolTip(f"{current}/{total} samples are labeled")
-
- train_stats = self.info.get("train_stats", {})
- train_stats = next(iter(train_stats.values())) if train_stats else train_stats
-
- dice = train_stats.get("best_metric", 0)
- self.updateAccuracyBar(dice)
-
- self.ui.strategyBox.clear()
- for strategy in self.info.get("strategies", {}):
- self.ui.strategyBox.addItem(strategy)
- currentStrategy = self._parameterNode.GetParameter("CurrentStrategy")
- currentStrategy = currentStrategy if currentStrategy else self.state["CurrentStrategy"]
- self.ui.strategyBox.setCurrentIndex(self.ui.strategyBox.findText(currentStrategy) if currentStrategy else 0)
-
- self.ui.trainerBox.clear()
- trainers = self.info.get("trainers", {})
- for t in trainers:
- self.ui.trainerBox.addItem(t)
- if trainers:
- currentTrainer = self._parameterNode.GetParameter("CurrentTrainer")
- currentTrainer = currentTrainer if currentTrainer else self.state["CurrentTrainer"]
- self.ui.trainerBox.setCurrentIndex(self.ui.trainerBox.findText(currentTrainer) if currentTrainer else 0)
-
- developer_mode = slicer.util.settingsValue("MONAILabel/developerMode", True, converter=slicer.util.toBool)
- self.ui.optionsCollapsibleButton.setVisible(developer_mode)
- self.ui.promptCollapsibleButton.setVisible(developer_mode)
-
- self.computeEmbedding = slicer.util.settingsValue(
- "MONAILabel/preComputeEmbeddingMode", True, converter=slicer.util.toBool
- )
-
- # Enable/Disable
- self.ui.nextSampleButton.setEnabled(self.ui.strategyBox.count)
-
- is_training_running = True if self.info and self.isTrainingRunning() else False
- self.ui.trainingButton.setEnabled(self.info and not is_training_running and current)
- self.ui.stopTrainingButton.setEnabled(is_training_running)
- if is_training_running and self.timer is None:
- self.timer = qt.QTimer()
- self.timer.setInterval(5000)
- self.timer.connect("timeout()", self.monitorTraining)
- self.timer.start()
-
- self.ui.sammButton.setEnabled(self.ui.sammModelSelector.currentText and self._volumeNode is not None)
-
- self.ui.pointSegButton.setEnabled(self.ui.sammModelSelector.currentText and self._volumeNode is not None)
-
- # self.ui.sammButton.setEnabled(
- # self.ui.sammModelSelector.currentText and self._volumeNode is not None
- # )
-
- self.ui.promptSegButton.setEnabled(self.ui.sammModelSelector.currentText and self._volumeNode is not None)
- self.ui.uncheckPromptButton.setEnabled(self.ui.sammModelSelector.currentText and self._volumeNode is not None)
- # self.ui.uncheckPromptButton.setEnabled()
-
- self.ui.saveLabelButton.setEnabled(self._segmentNode is not None)
- self.ui.importLabelButton.setEnabled(self._segmentNode is not None)
-
- # Create empty markup point list node for deep grow +ve and -ve
- if self._segmentNode:
- if not self.dgPositivePointListNode:
- self.dgPositivePointListNode, self.dgPositivePointListNodeObservers = self.createPointListNode(
- "P", self.onDeepGrowPointListNodeModified, [0.5, 1, 0.5]
- )
- self.ui.dgPositiveControlPointPlacementWidget.setCurrentNode(self.dgPositivePointListNode)
- self.ui.dgPositiveControlPointPlacementWidget.setPlaceModeEnabled(False)
-
- if not self.dgNegativePointListNode:
- self.dgNegativePointListNode, self.dgNegativePointListNodeObservers = self.createPointListNode(
- "N", self.onDeepGrowPointListNodeModified, [0.5, 0.5, 1]
- )
- self.ui.dgNegativeControlPointPlacementWidget.setCurrentNode(self.dgNegativePointListNode)
- self.ui.dgNegativeControlPointPlacementWidget.setPlaceModeEnabled(False)
-
- self.ui.scribblesCollapsibleButton.setEnabled(self.ui.scribblesMethodSelector.count)
- self.ui.scribblesCollapsibleButton.collapsed = False
-
- if not self.sammPositivePointListNode:
- self.sammPositivePointListNode, self.sammPositivePointListNodeObservers = self.createPointListNode(
- "SAMM Pos", self.onSAMMPointListNodeModified, [0.5, 1, 0.5]
- )
- sammPosCPDisplayNode = self.sammPositivePointListNode.GetDisplayNode()
- sammPosCPDisplayNode.SetGlyphScale(0.8)
-
- self.ui.sammPosPlacementWidget.setCurrentNode(self.sammPositivePointListNode)
- self.ui.sammPosPlacementWidget.setPlaceModeEnabled(False)
-
- if not self.sammNegativePointListNode:
- self.sammNegativePointListNode, self.sammNegativePointListNodeObservers = self.createPointListNode(
- "SAMM Neg", self.onSAMMPointListNodeModified, [0.4, 0.4, 0.9]
- )
- sammNegCPDisplayNode = self.sammNegativePointListNode.GetDisplayNode()
- sammNegCPDisplayNode.SetGlyphScale(0.8)
- self.ui.sammNegPlacementWidget.setCurrentNode(self.sammNegativePointListNode)
- self.ui.sammNegPlacementWidget.setPlaceModeEnabled(False)
-
- self.ui.dgPositiveControlPointPlacementWidget.setEnabled(self.ui.deepgrowModelSelector.currentText)
- self.ui.dgNegativeControlPointPlacementWidget.setEnabled(self.ui.deepgrowModelSelector.currentText)
-
- self.ui.sammPosPlacementWidget.setEnabled(True)
- self.ui.sammNegPlacementWidget.setEnabled(True)
-
- self.deepedit_multi_label = False
- m = self.models.get(self.ui.deepgrowModelSelector.currentText) if self.models else None
- self.deepedit_multi_label = m and m.get("type") == "deepedit" and len(m.get("labels")) > 0
-
- if self.deepedit_multi_label:
- self.ui.dgLabelBackground.hide()
- self.ui.dgNegativeControlPointPlacementWidget.hide()
- self.ui.freezeUpdateCheckBox.show()
- self.ui.dgLabelForeground.setText("Landmarks:")
- else:
- self.ui.dgNegativeControlPointPlacementWidget.show()
- self.ui.freezeUpdateCheckBox.hide()
- self.ui.dgLabelForeground.setText("Foreground:")
-
- self.ui.dgUpdateCheckBox.setEnabled(self.ui.deepgrowModelSelector.currentText and self._segmentNode)
- self.ui.dgUpdateButton.setEnabled(self.ui.deepgrowModelSelector.currentText and self._segmentNode)
-
- # All the GUI updates are done
- self._updatingGUIFromParameterNode = False
-
- def updateParameterNodeFromGUI(self, caller=None, event=None):
- if self._parameterNode is None or self._updatingGUIFromParameterNode:
- return
-
- wasModified = self._parameterNode.StartModify() # Modify all properties in a single batch
-
- segmentationModelIndex = self.ui.sammModelSelector.currentIndex
- if segmentationModelIndex >= 0:
- segmentationModel = self.ui.sammModelSelector.itemText(segmentationModelIndex)
- self._parameterNode.SetParameter("SegmentationModel", segmentationModel)
-
- # sammModelIndex = self.ui.sammModelSelector.currentIndex
- # if sammModelIndex >= 0:
- # segmentationModel = self.ui.sammModelSelector.itemText(sammModelIndex)
- # self._parameterNode.SetParameter("SegmentationModel", segmentationModel)
-
- deepgrowModelIndex = self.ui.deepgrowModelSelector.currentIndex
- if deepgrowModelIndex >= 0:
- deepgrowModel = self.ui.deepgrowModelSelector.itemText(deepgrowModelIndex)
- self._parameterNode.SetParameter("DeepgrowModel", deepgrowModel)
-
- scribblesMethodIndex = self.ui.scribblesMethodSelector.currentIndex
- if scribblesMethodIndex >= 0:
- scribblesMethod = self.ui.scribblesMethodSelector.itemText(scribblesMethodIndex)
- self._parameterNode.SetParameter("ScribblesMethod", scribblesMethod)
-
- currentLabelIndex = self.ui.labelComboBox.currentIndex
- if currentLabelIndex >= 0:
- currentLabel = self.ui.labelComboBox.itemText(currentLabelIndex)
- self._parameterNode.SetParameter("CurrentLabel", currentLabel)
-
- currentScribLabelIndex = self.ui.scribLabelComboBox.currentIndex
- if currentScribLabelIndex >= 0:
- currentScribLabel = self.ui.scribLabelComboBox.itemText(currentScribLabelIndex)
- self._parameterNode.SetParameter("CurrentScribLabel", currentScribLabel)
-
- currentStrategyIndex = self.ui.strategyBox.currentIndex
- if currentStrategyIndex >= 0:
- currentStrategy = self.ui.strategyBox.itemText(currentStrategyIndex)
- self._parameterNode.SetParameter("CurrentStrategy", currentStrategy)
-
- currentTrainerIndex = self.ui.trainerBox.currentIndex
- if currentTrainerIndex >= 0:
- currentTrainer = self.ui.trainerBox.itemText(currentTrainerIndex)
- self._parameterNode.SetParameter("CurrentTrainer", currentTrainer)
-
- self._parameterNode.EndModify(wasModified)
-
- def updateSelector(self, selector, model_types, param, defaultIndex=0):
- wasSelectorBlocked = selector.blockSignals(True)
- selector.clear()
-
- for model_name, model in self.models.items():
- if model["type"] in model_types:
- selector.addItem(model_name)
- selector.setItemData(selector.count - 1, model["description"], qt.Qt.ToolTipRole)
-
- model = self._parameterNode.GetParameter(param)
- model = model if model else self.state.get(param, "")
- modelIndex = selector.findText(model)
- modelIndex = defaultIndex if modelIndex < 0 < selector.count else modelIndex
- selector.setCurrentIndex(modelIndex)
-
- try:
- modelInfo = self.models[model]
- selector.setToolTip(modelInfo["description"])
- except BaseException:
- selector.setToolTip("")
- selector.blockSignals(wasSelectorBlocked)
-
- def getSelectedOptionSection(self, index=-1):
- optionsSectionIndex = index if index >= 0 else self.ui.optionsSection.currentIndex
- optionsSectionIndex = optionsSectionIndex if optionsSectionIndex > 0 else 0
- optionsSection = self.ui.optionsSection.itemText(optionsSectionIndex)
-
- logging.info(f"Current Selection Options Section: {optionsSection}")
- mapping = {"infer": "models", "train": "trainers", "activelearning": "strategies", "scoring": "scoring"}
-
- return mapping.get(optionsSection)
-
- def getSelectedOptionName(self, index=-1):
- optionsNameIndex = index if index >= 0 else self.ui.optionsName.currentIndex
- optionsNameIndex = optionsNameIndex if optionsNameIndex > 0 else 0
- optionsName = self.ui.optionsName.itemText(optionsNameIndex)
-
- logging.info(f"Current Selection Options Name: {optionsName}")
- return optionsName
-
- def invalidateConfigTable(self, selection=-1, name=-1):
- section = self.getSelectedOptionSection(selection)
- name = self.getSelectedOptionName(name)
- if not section or not name:
- return
-
- mapping = {"infer": "models", "train": "trainers", "activelearning": "strategies", "scoring": "scoring"}
- section = mapping.get(section, section)
- for row in range(self.ui.configTable.rowCount):
- key = str(self.ui.configTable.item(row, 0).text())
- value = self.ui.configTable.item(row, 1)
-
- v = self.info.get(section, {}).get(name, {}).get("config", {}).get(key, {})
- if value is None:
- value = self.ui.configTable.cellWidget(row, 1)
- if isinstance(value, qt.QCheckBox):
- value = True if value.checked else False
- else:
- value = value.currentText
- else:
- value = str(value.text())
-
- if isinstance(v, bool):
- value = True if value else False
- elif isinstance(v, int):
- value = int(value) if value else 0
- elif isinstance(v, float):
- value = float(value) if value else 0.0
- elif isinstance(v, list):
- v.remove(value)
- v.insert(0, value)
- value = v
-
- logging.info(f"Invalidate:: {section} => {name} => {key} => {value} => {type(v)}")
- self.info.get(section, {}).get(name, {}).get("config", {})[key] = value
-
- def updateConfigTable(self, refresh=True):
- logging.info(f"updateConfigTable => refresh:{refresh}")
- section = self.getSelectedOptionSection()
- sectionConfig = self.info.get(section, {})
- if refresh:
- self.ui.optionsName.blockSignals(True)
- self.ui.optionsName.clear()
- for k in sectionConfig.keys():
- if sectionConfig[k].get("config"):
- self.ui.optionsName.addItem(k)
- if self.ui.optionsName.count:
- self.ui.optionsName.setCurrentIndex(0)
- self.ui.optionsName.blockSignals(False)
-
- name = self.getSelectedOptionName()
- nameConfig = sectionConfig.get(name, {}).get("config", {})
-
- table = self.ui.configTable
- table.clear()
- headers = ["key", "value"]
- table.setColumnCount(len(headers))
- table.setHorizontalHeaderLabels(headers)
- table.setColumnWidth(0, 250)
- table.setRowCount(len(nameConfig))
-
- n = 0
- for key, val in nameConfig.items():
- item = qt.QTableWidgetItem(key)
- table.setItem(n, 0, item)
- item.setFlags(item.flags() & ~qt.Qt.ItemIsEditable)
-
- if isinstance(val, dict) or isinstance(val, list):
- combo = qt.QComboBox()
- for m, v in enumerate(val):
- combo.addItem(v)
- combo.setCurrentIndex(0)
- table.setCellWidget(n, 1, combo)
- elif isinstance(val, bool):
- checkbox = qt.QCheckBox()
- checkbox.setChecked(val)
- table.setCellWidget(n, 1, checkbox)
- else:
- table.setItem(n, 1, qt.QTableWidgetItem(str(val) if val else ""))
-
- logging.info(f"{n} => {section} => {name} => {key} => {val}")
- n = n + 1
-
- def updateAccuracyBar(self, dice):
- self.ui.accuracyProgressBar.setValue(dice * 100)
- css = ["stop: 0 red"]
- if dice > 0.5:
- css.append(f"stop: {0.5 / dice} orange")
- if dice > 0.6:
- css.append(f"stop: {0.6 / dice} yellow")
- if dice > 0.7:
- css.append(f"stop: {0.7 / dice} lightgreen")
- if dice > 0.8:
- css.append(f"stop: {0.8 / dice} green")
- if dice > 0.9:
- css.append(f"stop: {0.9 / dice} darkgreen")
- self.ui.accuracyProgressBar.setStyleSheet(
- "QProgressBar {text-align: center;} "
- "QProgressBar::chunk {background-color: "
- "qlineargradient(x0: 0, x2: 1, " + ",".join(css) + ")}"
- )
- self.ui.accuracyProgressBar.setToolTip(f"Accuracy: {dice:.4f}")
-
- def getParamsFromConfig(self, section, name):
- self.invalidateConfigTable()
-
- mapping = {"infer": "models", "train": "trainers", "activelearning": "strategies", "scoring": "scoring"}
- section = mapping.get(section, section)
- sectionConfig = self.info.get(section, {})
- nameConfig = sectionConfig.get(name, {}).get("config", {})
-
- return {k: v[0] if isinstance(v, list) else v for k, v in nameConfig.items()}
-
- def onDeepGrowPointListNodeModified(self, observer, eventid):
- logging.debug("Deepgrow Point Event!!")
-
- if self.ignorePointListNodeAddEvent:
- return
-
- markupsNode = observer
- movingMarkupIndex = markupsNode.GetDisplayNode().GetActiveControlPoint()
- logging.debug(f"Markup point added; point ID = {movingMarkupIndex}")
-
- current_point = self.getControlPointXYZ(markupsNode, movingMarkupIndex)
-
- if not self.ui.dgUpdateCheckBox.checked:
- self.onClickDeepgrow(current_point, skip_infer=True)
- return
-
- self.onClickDeepgrow(current_point)
-
- self.ignorePointListNodeAddEvent = True
- self.onEditControlPoints(self.dgPositivePointListNode, "MONAILabel.ForegroundPoints")
- self.onEditControlPoints(self.dgNegativePointListNode, "MONAILabel.BackgroundPoints")
- self.ignorePointListNodeAddEvent = False
-
- def onSAMMPointListNodeModified(self, observer, eventid):
- logging.debug("SAMM Point Event!")
-
- if self.ignoreSAMMPointListNodeAddEvent:
- return
-
- markupsNode = observer
- movingMarkupIndex = markupsNode.GetDisplayNode().GetActiveControlPoint()
- logging.debug(f"Markup point added; point ID = {movingMarkupIndex}")
-
- self.getControlPointXYZ(markupsNode, movingMarkupIndex)
-
- # if not self.ui.dgUpdateCheckBox.checked:
- # self.onClickDeepgrow(current_point, skip_infer=True)
- # return
-
- # self.onClickSAMMPoint(current_point)
-
- self.ignoreSAMMPointListNodeAddEvent = True
- # self.onEditControlPoints(self.sammPositivePointListNode, "MONAILabel.ForegroundPoints")
- # self.onEditControlPoints(self.sammNegativePointListNode, "MONAILabel.BackgroundPoints")
- self.ignoreSAMMPointListNodeAddEvent = False
-
- def getControlPointsXYZ(self, pointListNode, name):
- v = self._volumeNode
- RasToIjkMatrix = vtk.vtkMatrix4x4()
- v.GetRASToIJKMatrix(RasToIjkMatrix)
-
- point_set = []
- n = pointListNode.GetNumberOfControlPoints()
- for i in range(n):
- coord = pointListNode.GetNthControlPointPosition(i)
-
- world = [0, 0, 0]
- pointListNode.GetNthControlPointPositionWorld(i, world)
-
- p_Ras = [coord[0], coord[1], coord[2], 1.0]
- p_Ijk = RasToIjkMatrix.MultiplyDoublePoint(p_Ras)
- p_Ijk = [round(i) for i in p_Ijk]
-
- logging.debug(f"RAS: {coord}; WORLD: {world}; IJK: {p_Ijk}")
- point_set.append(p_Ijk[0:3])
-
- logging.info(f"{name} => Current control points: {point_set}")
- return point_set
-
- def getControlPointXYZ(self, pointListNode, index):
- v = self._volumeNode
- RasToIjkMatrix = vtk.vtkMatrix4x4()
- v.GetRASToIJKMatrix(RasToIjkMatrix)
-
- coord = pointListNode.GetNthControlPointPosition(index)
-
- world = [0, 0, 0]
- pointListNode.GetNthControlPointPositionWorld(index, world)
-
- p_Ras = [coord[0], coord[1], coord[2], 1.0]
- p_Ijk = RasToIjkMatrix.MultiplyDoublePoint(p_Ras)
- p_Ijk = [round(i) for i in p_Ijk]
-
- logging.debug(f"RAS: {coord}; WORLD: {world}; IJK: {p_Ijk}")
- return p_Ijk[0:3]
-
- def onEditControlPoints(self, pointListNode, tagName):
- if pointListNode is None:
- return
-
- pointListNode.RemoveAllControlPoints()
- segmentId, segment = self.currentSegment()
- if segment and segmentId:
- v = self._volumeNode
- IjkToRasMatrix = vtk.vtkMatrix4x4()
- v.GetIJKToRASMatrix(IjkToRasMatrix)
-
- fPosStr = vtk.mutable("")
- segment.GetTag(tagName, fPosStr)
- pointset = str(fPosStr)
- logging.debug(f"{segmentId} => {segment.GetName()} Control points are: {pointset}")
-
- if fPosStr is not None and len(pointset) > 0:
- points = json.loads(pointset)
- for p in points:
- p_Ijk = [p[0], p[1], p[2], 1.0]
- p_Ras = IjkToRasMatrix.MultiplyDoublePoint(p_Ijk)
- logging.debug(f"Add Control Point: {p_Ijk} => {p_Ras}")
- pointListNode.AddControlPoint(p_Ras[0:3])
-
- def currentSegment(self):
- segmentation = self._segmentNode.GetSegmentation()
- segmentId = segmentation.GetSegmentIdBySegmentName(self.ui.labelComboBox.currentText)
- segment = segmentation.GetSegment(segmentId)
-
- logging.debug(f"Current SegmentID: {segmentId}; Segment: {segment}")
- return segmentId, segment
-
- def currentScribSegment(self):
- segmentation = self._segmentNode.GetSegmentation()
- segmentId = segmentation.GetSegmentIdBySegmentName(self.ui.scribLabelComboBox.currentText)
- segment = segmentation.GetSegment(segmentId)
-
- logging.debug(f"Current SegmentID: {segmentId}; Segment: {segment}")
- return segmentId, segment
-
- def onSelectLabel(self, caller=None, event=None):
- self.updateParameterNodeFromGUI(caller, event)
-
- self.ignorePointListNodeAddEvent = True
- self.onEditControlPoints(self.dgPositivePointListNode, "MONAILabel.ForegroundPoints")
- self.onEditControlPoints(self.dgNegativePointListNode, "MONAILabel.BackgroundPoints")
- self.ignorePointListNodeAddEvent = False
-
- def onSelectOptionsSection(self, index, caller=None, event=None):
- self.updateParameterNodeFromGUI(caller, event)
- logging.info(f"Options Section Selection Changed.... current:{index}; prev: {self.optionsSectionIndex}")
-
- self.invalidateConfigTable(self.optionsSectionIndex, self.optionsNameIndex)
- self.optionsSectionIndex = index
- self.optionsNameIndex = 0
- self.updateConfigTable()
-
- def onSelectOptionsName(self, index, caller=None, event=None):
- self.updateParameterNodeFromGUI(caller, event)
- logging.info(f"Options Name Selection Changed.... current:{index}; prev: {self.optionsNameIndex}")
-
- self.invalidateConfigTable(self.optionsSectionIndex, self.optionsNameIndex)
- self.optionsNameIndex = index
- self.updateConfigTable(refresh=False)
-
- def onSelectScribLabel(self, caller=None, event=None):
- if self.scribblesLayersPresent() and not self.ignoreScribblesLabelChangeEvent:
- if not slicer.util.confirmOkCancelDisplay(
- "This will clear current scribbles session.\n" "Are you sure to continue?"
- ):
- # undo changes to combobox
- currentScribLabel = self._parameterNode.GetParameter("CurrentScribLabel")
- logging.info(f"Cancel: reverting to original selection {currentScribLabel}")
- self.ignoreScribblesLabelChangeEvent = True
- self.ui.scribLabelComboBox.setCurrentIndex(self.ui.scribLabelComboBox.findText(currentScribLabel))
- self.ignoreScribblesLabelChangeEvent = False
- return
- self.onClearScribbles()
-
- self.updateParameterNodeFromGUI(caller, event)
-
- def icon(self, name="MONAILabel.png"):
- # It should not be necessary to modify this method
- iconPath = os.path.join(os.path.dirname(__file__), "Resources", "Icons", name)
- if os.path.exists(iconPath):
- return qt.QIcon(iconPath)
- return qt.QIcon()
-
- def updateServerSettings(self):
- self.logic.setServer(self.serverUrl())
- self.logic.setClientId(slicer.util.settingsValue("MONAILabel/clientId", "user-xyz"))
- self.saveServerUrl()
-
- def serverUrl(self):
- serverUrl = self.ui.serverComboBox.currentText
- if not serverUrl:
- serverUrl = "http://127.0.0.1:8000"
- return serverUrl.rstrip("/")
-
- def saveServerUrl(self):
- self.updateParameterNodeFromGUI()
-
- # Save selected server URL
- settings = qt.QSettings()
- serverUrl = self.ui.serverComboBox.currentText
- settings.setValue("MONAILabel/serverUrl", serverUrl)
-
- # Save current server URL to the top of history
- serverUrlHistory = settings.value("MONAILabel/serverUrlHistory")
- if serverUrlHistory:
- serverUrlHistory = serverUrlHistory.split(";")
- else:
- serverUrlHistory = []
- try:
- serverUrlHistory.remove(serverUrl)
- except ValueError:
- pass
-
- serverUrlHistory.insert(0, serverUrl)
- serverUrlHistory = serverUrlHistory[:10] # keep up to first 10 elements
- settings.setValue("MONAILabel/serverUrlHistory", ";".join(serverUrlHistory))
-
- self.updateServerUrlGUIFromSettings()
-
- def onClickFetchInfo(self):
- self.fetchInfo()
- self.updateConfigTable()
- self.updateClassPromptTable()
-
- def fetchInfo(self, showInfo=False):
- if not self.logic:
- return
-
- start = time.time()
- try:
- self.updateServerSettings()
- info = self.logic.info()
- self.info = info
- if self.info.get("config"):
- slicer.util.errorDisplay(
- "Please upgrade the monai server to latest version",
- detailedText=traceback.format_exc(),
- )
- return
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- "Failed to fetch models from remote server. "
- "Make sure server address is correct and /info/ "
- f"is accessible in browser.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- return
-
- self.models.clear()
- self.config = info.get("config", {})
-
- model_count = {}
- models = info.get("models", {})
- for k, v in models.items():
- model_type = v.get("type", "segmentation")
- model_count[model_type] = model_count.get(model_type, 0) + 1
-
- logging.debug(f"{k} = {model_type}")
- self.models[k] = v
-
- self.updateGUIFromParameterNode()
-
- msg = ""
- msg += "-----------------------------------------------------\t\n"
- msg += "Total Models Available: \t" + str(len(models)) + "\t\n"
- msg += "-----------------------------------------------------\t\n"
- for model_type in model_count.keys():
- msg += model_type.capitalize() + " Models: \t" + str(model_count[model_type]) + "\t\n"
- msg += "-----------------------------------------------------\t\n"
-
- if showInfo:
- qt.QMessageBox.information(slicer.util.mainWindow(), "MONAI Label", msg)
- logging.info(msg)
- logging.info(f"Time consumed by fetch info: {time.time() - start:3.1f}")
-
- def setProgressBarLabelText(self, label):
- if not self.progressBar:
- self.progressBar = slicer.util.createProgressDialog(windowTitle="Wait...", maximum=100)
- self.progressBar.labelText = label
-
- def reportProgress(self, progressPercentage):
- if not self.progressBar:
- self.progressBar = slicer.util.createProgressDialog(windowTitle="Wait...", maximum=100)
- self.progressBar.show()
- self.progressBar.activateWindow()
- self.progressBar.setValue(progressPercentage)
- slicer.app.processEvents()
-
- def onTraining(self):
- start = time.time()
- status = None
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
- self.updateServerSettings()
-
- model = self.ui.trainerBox.currentText
- if not model:
- slicer.util.errorDisplay(
- "No Model selected is to run the training", detailedText=traceback.format_exc()
- )
- return
-
- params = self.getParamsFromConfig("train", model)
- status = self.logic.train_start(model, params)
-
- self.ui.trainingProgressBar.setValue(1)
- self.ui.trainingProgressBar.setToolTip("Training: STARTED")
-
- time.sleep(1)
- self.updateGUIFromParameterNode()
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to run training in MONAI Label Server.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
-
- if status:
- msg = "ID: {}\nStatus: {}\nStart Time: {}\n".format(
- status.get("id"),
- status.get("status"),
- status.get("start_ts"),
- )
- # slicer.util.infoDisplay(msg, detailedText=json.dumps(status, indent=2))
- logging.info(msg)
-
- logging.info(f"Time consumed by training: {time.time() - start:3.1f}")
-
- def onStopTraining(self):
- start = time.time()
- status = None
- if not slicer.util.confirmOkCancelDisplay(
- "This will kill/stop current Training task. Are you sure to continue?"
- ):
- return
-
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
- self.updateServerSettings()
- status = self.logic.train_stop()
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to stop Training Task.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
-
- if status:
- msg = "Status: {}\nStart Time: {}\nEnd Time: {}\nResult: {}".format(
- status.get("status"),
- status.get("start_ts"),
- status.get("end_ts"),
- status.get("result", status.get("details", [])[-1]),
- )
- # slicer.util.infoDisplay(msg, detailedText=json.dumps(status, indent=2))
- logging.info(msg)
- self.updateGUIFromParameterNode()
-
- logging.info(f"Time consumed by stop training: {time.time() - start:3.1f}")
-
- def isTrainingRunning(self, check_only=True):
- if not self.logic:
- return False
- self.updateServerSettings()
- return self.logic.train_status(check_only)
-
- def onNextSampleButton(self):
- if not self.logic:
- return
-
- if self._volumeNode or len(slicer.util.getNodesByClass("vtkMRMLScalarVolumeNode")):
- if not slicer.util.confirmOkCancelDisplay(
- "This will close current scene. Please make sure you have saved your current work.\n"
- "Are you sure to continue?"
- ):
- return
- self.onResetScribbles()
- slicer.mrmlScene.Clear(0)
-
- start = time.time()
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
-
- self.updateServerSettings()
- strategy = self.ui.strategyBox.currentText
- if not strategy:
- slicer.util.errorDisplay("No Strategy Found/Selected\t")
- return
-
- sample = self.logic.next_sample(strategy, self.getParamsFromConfig("activelearning", strategy))
- logging.debug(sample)
- if not sample.get("id"):
- slicer.util.warningDisplay(
- "Unlabeled samples or images not found at server.\n"
- "Instead please go to File -> Add Data to load image."
- )
- return
-
- if self.samples.get(sample["id"]) is not None:
- self.current_sample = self.samples[sample["id"]]
- name = self.current_sample["VolumeNodeName"]
- index = self.ui.inputSelector.findText(name)
- self.ui.inputSelector.setCurrentIndex(index)
- return
-
- logging.info(sample)
- image_id = sample["id"]
- image_file = sample.get("path")
- image_name = sample.get("name", image_id)
- node_name = sample.get("PatientID", sample.get("name", image_id))
- checksum = sample.get("checksum")
- local_exists = image_file and os.path.exists(image_file)
-
- logging.info(f"Check if file exists/shared locally: {image_file} => {local_exists}")
- if local_exists:
- self._volumeNode = slicer.util.loadVolume(image_file)
- self._volumeNode.SetName(node_name)
- else:
- download_uri = f"{self.serverUrl()}/datastore/image?image={quote_plus(image_id)}"
- logging.info(download_uri)
-
- sampleDataLogic = SampleData.SampleDataLogic()
- self._volumeNode = sampleDataLogic.downloadFromURL(
- nodeNames=node_name, fileNames=image_name, uris=download_uri, checksums=checksum
- )[0]
-
- self.computeEmbedding = True
- if self.computeEmbedding:
- model = self.ui.sammModelSelector.currentText
- # image_file = self.current_sample["id"]
- params = self.getParamsFromConfig("infer", model)
- params.update({"computeEmbedding": True})
-
- if not self.progressBar:
- self.progressBar = slicer.util.createProgressDialog(
- windowTitle="Extracting 3D embedding...", maximum=100
- )
- self.progressBar.setLabelText("Initializing...")
- self.progressBar.setFixedSize(400, 100)
- self.progressBar.show()
- # self.progressBar.activateWindow()
- self.progressBar.setValue(0)
- self.progressBar.setCancelButton(None)
-
- slicer.app.processEvents()
-
- _, params = self.logic.infer(model, image_file, params, session_id=self.getSessionId())
-
- if slicer.util.settingsValue("MONAILabel/originalLabel", True, converter=slicer.util.toBool):
- try:
- datastore = self.logic.datastore()
- label_info = datastore["objects"][image_id]["labels"]["original"]["info"]
- labels = label_info.get("params", {}).get("label_names", {})
-
- if labels:
- # labels are available in original label info
- labels = labels.keys()
- else:
- # labels not available
- # assume labels in app info are valid for original label file
- labels = self.logic.info().get("labels")
-
- # ext = datastore['objects'][image_id]['labels']['original']['ext']
- maskFile = self.logic.download_label(image_id, "original")
- self.updateSegmentationMask(maskFile, list(labels))
- print("Original label uploaded! ")
-
- except BaseException as e:
- print(f"Original label not found ... {e}")
-
- self.initSample(sample)
-
- except BaseException as e:
- print(e)
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to fetch Sample from MONAI Label Server.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
-
- self.updateGUIFromParameterNode()
- logging.info(f"Time consumed by next_sample: {time.time() - start:3.1f}")
-
- def initSample(self, sample, autosegment=True):
- sample["VolumeNodeName"] = self._volumeNode.GetName()
- self.current_sample = sample
- self.samples[sample["id"]] = sample
- self._volumeNodes.append(self._volumeNode)
-
- # Create Empty Segments for all labels for this node
- self.createSegmentNode()
- self.ui.embeddedSegmentEditorWidget.setSegmentationNode(self._segmentNode)
- self.ui.embeddedSegmentEditorWidget.setSourceVolumeNode(self._volumeNode)
-
- self.createScribblesROINode()
- self.ui.scribblesPlaceWidget.setCurrentNode(self._scribblesROINode)
-
- # check if user allows overlapping segments
- if slicer.util.settingsValue("MONAILabel/allowOverlappingSegments", False, converter=slicer.util.toBool):
- # set segment editor to allow overlaps
- self.logic.get_segment_editor_node().SetOverwriteMode(slicer.vtkMRMLSegmentEditorNode.OverwriteNone)
-
- if self.info.get("labels"):
- self.updateSegmentationMask(None, self.info.get("labels"))
-
- # Check if user wants to run auto-segmentation on new sample
- if autosegment and slicer.util.settingsValue(
- "MONAILabel/autoRunSegmentationOnNextSample", True, converter=slicer.util.toBool
- ):
- for label in self.info.get("labels", []):
- for name, model in self.models.items():
- if label in model.get("labels", []):
- qt.QApplication.restoreOverrideCursor()
- self.ui.sammModelSelector.currentText = name
- self.onClickSegmentation()
- return
-
- def getPermissionForImageDataUpload(self):
- return slicer.util.confirmOkCancelDisplay(
- "Source volume - without any additional patient information -"
- " will be sent to remote data processing server: {}.\n\n"
- "Click 'OK' to proceed with the segmentation.\n"
- "Click 'Cancel' to not upload any data and cancel segmentation.\n".format(self.serverUrl()),
- dontShowAgainSettingsKey="MONAILabel/showImageDataSendWarning",
- )
-
- def onUploadImage(self, init_sample=True, session=False):
- volumeNode = slicer.mrmlScene.GetFirstNodeByClass("vtkMRMLScalarVolumeNode")
- image_id = volumeNode.GetName()
-
- if not self.getPermissionForImageDataUpload():
- return False
-
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
- in_file = tempfile.NamedTemporaryFile(suffix=self.file_ext, dir=self.tmpdir).name
- self.reportProgress(5)
-
- start = time.time()
- slicer.util.saveNode(volumeNode, in_file)
- logging.info(f"Saved Input Node into {in_file} in {time.time() - start:3.1f}s")
- self.reportProgress(30)
-
- if session:
- self.current_sample["session_id"] = self.logic.create_session(in_file)["session_id"]
- else:
- self.logic.upload_image(in_file, image_id)
- self.current_sample["session"] = False
- self.reportProgress(100)
-
- self._volumeNode = volumeNode
- if init_sample:
- self.initSample({"id": image_id}, autosegment=False)
- qt.QApplication.restoreOverrideCursor()
-
- self.updateGUIFromParameterNode()
- return True
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- self.reportProgress(100)
- qt.QApplication.restoreOverrideCursor()
- if session:
- slicer.util.errorDisplay(
- "Server Error:: Session creation Failed\nPlease upgrade to latest monailable version (> 0.2.0)",
- detailedText=traceback.format_exc(),
- )
- self.current_sample["session"] = None
- else:
- slicer.util.errorDisplay(
- f"Failed to upload volume to Server.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- return False
-
- def onImportLabel(self):
- if not self.ui.labelPathLineEdit.currentPath or not os.path.exists(self.ui.labelPathLineEdit.currentPath):
- slicer.util.warningDisplay("Label File not selected")
- return
-
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
- self.updateSegmentationMask(self.ui.labelPathLineEdit.currentPath, self.info["labels"])
- qt.QApplication.restoreOverrideCursor()
- except BaseException:
- qt.QApplication.restoreOverrideCursor()
- slicer.util.errorDisplay("Failed to import label", detailedText=traceback.format_exc())
-
- def onSaveLabel(self):
- start = time.time()
- labelmapVolumeNode = None
- result = None
- self.onResetScribbles()
-
- if self.current_sample.get("session"):
- if not self.onUploadImage(init_sample=False):
- return
-
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
- model = self.ui.sammModelSelector.currentText
-
- if model and self.models[model]["type"] == "detection":
- label_in, label_info = self.onGenerateJSONFromMulipltROIs()
- self.reportProgress(30)
- else:
- segmentationNode = self._segmentNode
- segmentation = segmentationNode.GetSegmentation()
- totalSegments = segmentation.GetNumberOfSegments()
- segmentIds = [segmentation.GetNthSegmentID(i) for i in range(totalSegments)]
-
- # remove background and scribbles labels
- label_info = []
- save_segment_ids = vtk.vtkStringArray()
- for idx, segmentId in enumerate(segmentIds):
- segment = segmentation.GetSegment(segmentId)
- if segment.GetName() in ["background", "foreground_scribbles", "background_scribbles"]:
- logging.info(f"Removing segment {segmentId}: {segment.GetName()}")
- continue
-
- save_segment_ids.InsertNextValue(segmentId)
- label_info.append({"name": segment.GetName(), "idx": idx + 1})
- # label_info.append({"color": segment.GetColor()})
-
- # export labelmap
- labelmapVolumeNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLLabelMapVolumeNode")
- slicer.modules.segmentations.logic().ExportSegmentsToLabelmapNode(
- segmentationNode, save_segment_ids, labelmapVolumeNode, self._volumeNode
- )
-
- label_in = tempfile.NamedTemporaryFile(suffix=self.file_ext, dir=self.tmpdir).name
- self.reportProgress(5)
-
- if (
- slicer.util.settingsValue("MONAILabel/allowOverlappingSegments", True, converter=slicer.util.toBool)
- and slicer.util.settingsValue("MONAILabel/fileExtension", self.file_ext) == ".seg.nrrd"
- ):
- slicer.util.saveNode(segmentationNode, label_in)
- else:
- slicer.util.saveNode(labelmapVolumeNode, label_in)
- self.reportProgress(30)
-
- self.updateServerSettings()
- result = self.logic.save_label(self.current_sample["id"], label_in, {"label_info": label_info})
- self.fetchInfo()
-
- if slicer.util.settingsValue("MONAILabel/autoUpdateModelV2", False, converter=slicer.util.toBool):
- try:
- if self.isTrainingRunning(check_only=True):
- self.logic.train_stop()
- except BaseException:
- logging.info("Failed to stop training; or already stopped")
- self.onTraining()
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to save Label to MONAI Label Server.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
- self.reportProgress(100)
-
- if labelmapVolumeNode:
- slicer.mrmlScene.RemoveNode(labelmapVolumeNode)
- if result:
- slicer.util.infoDisplay(
- "Label-Mask saved into MONAI Label Server\t\t", detailedText=json.dumps(result, indent=2)
- )
-
- if slicer.util.settingsValue("MONAILabel/autoFetchNextSample", False, converter=slicer.util.toBool):
- slicer.mrmlScene.Clear(0)
- self.onNextSampleButton()
-
- logging.info(f"Time consumed by save label: {time.time() - start:3.1f}")
-
- def getSessionId(self):
- session_id = None
- if self.current_sample.get("session", False):
- session_id = self.current_sample.get("session_id")
- if not session_id or not self.logic.get_session(session_id):
- self.onUploadImage(init_sample=False, session=True)
- session_id = self.current_sample["session_id"]
- return session_id
-
- def onClickSegmentation(self):
- if not self.current_sample:
- return
-
- start = time.time()
- result_file = None
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
-
- self.updateServerSettings()
-
- model = self.ui.sammModelSelector.currentText
- image_file = self.current_sample["id"]
- params = self.getParamsFromConfig("infer", model)
-
- result_file, params = self.logic.infer(model, image_file, params, session_id=self.getSessionId())
- print(f"Result Params for Segmentation: {params}")
-
- labels = (
- params.get("label_names") if params and params.get("label_names") else self.models[model].get("labels")
- )
- if labels and isinstance(labels, dict):
- labels = [k for k, _ in sorted(labels.items(), key=lambda item: item[1])]
- self.updateSegmentationMask(result_file, labels)
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to run inference in MONAI Label Server.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
- if result_file and os.path.exists(result_file):
- os.unlink(result_file)
-
- self.updateGUIFromParameterNode()
- logging.info(f"Time consumed by segmentation: {time.time() - start:3.1f}")
-
- def onUpdateDeepgrow(self):
- self.onClickDeepgrow(None)
-
- def onClickDeepgrow(self, current_point, skip_infer=False):
- model = self.ui.deepgrowModelSelector.currentText
- if not model:
- slicer.util.warningDisplay("Please select a deepgrow model")
- return
-
- _, segment = self.currentSegment()
- if not segment:
- slicer.util.warningDisplay("Please add the required label to run deepgrow")
- return
-
- foreground_all = self.getControlPointsXYZ(self.dgPositivePointListNode, "foreground")
- background_all = self.getControlPointsXYZ(self.dgNegativePointListNode, "background")
-
- segment.SetTag("MONAILabel.ForegroundPoints", json.dumps(foreground_all))
- segment.SetTag("MONAILabel.BackgroundPoints", json.dumps(background_all))
- if skip_infer:
- return
-
- # use model info "deepgrow" to determine
- deepgrow_3d = False if self.models[model].get("dimension", 3) == 2 else True
- print(f"Is DeepGrow 3D: {deepgrow_3d}")
- start = time.time()
-
- label = segment.GetName()
- operationDescription = f"Run Deepgrow for segment: {label}; model: {model}; 3d {deepgrow_3d}"
- logging.debug(operationDescription)
-
- if not current_point:
- if not foreground_all and not deepgrow_3d:
- slicer.util.warningDisplay(operationDescription + " - points not added")
- return
- current_point = foreground_all[-1] if foreground_all else background_all[-1] if background_all else None
-
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
-
- sliceIndex = None
- if self.deepedit_multi_label:
- params = {}
- segmentation = self._segmentNode.GetSegmentation()
- for name in self.info.get("labels", []):
- points = []
- segmentId = segmentation.GetSegmentIdBySegmentName(name)
- segment = segmentation.GetSegment(segmentId) if segmentId else None
- if segment:
- fPosStr = vtk.mutable("")
- segment.GetTag("MONAILabel.ForegroundPoints", fPosStr)
- pointset = str(fPosStr)
- print(f"{segmentId} => {name} Control points are: {pointset}")
- if fPosStr is not None and len(pointset) > 0:
- points = json.loads(pointset)
-
- params[name] = points
- params["label"] = label
- labels = None
- else:
- sliceIndex = current_point[2] if current_point else None
- print(f"Slice Index: {sliceIndex}")
-
- if deepgrow_3d or not sliceIndex:
- foreground = foreground_all
- background = background_all
- else:
- foreground = [x for x in foreground_all if x[2] == sliceIndex]
- background = [x for x in background_all if x[2] == sliceIndex]
-
- logging.debug(f"Foreground: {foreground}")
- logging.debug(f"Background: {background}")
- logging.debug(f"Current point: {current_point}")
-
- params = {
- "label": label,
- "foreground": foreground,
- "background": background,
- }
- labels = [label]
-
- params["label"] = label
- params.update(self.getParamsFromConfig("infer", model))
- print(f"Request Params for Deepgrow/Deepedit: {params}")
-
- image_file = self.current_sample["id"]
- result_file, params = self.logic.infer(model, image_file, params, session_id=self.getSessionId())
- print(f"Result Params for Deepgrow/Deepedit: {params}")
- if labels is None:
- labels = (
- params.get("label_names")
- if params and params.get("label_names")
- else self.models[model].get("labels")
- )
- if labels and isinstance(labels, dict):
- labels = [k for k, _ in sorted(labels.items(), key=lambda item: item[1])]
-
- freeze = label if self.ui.freezeUpdateCheckBox.checked else None
- self.updateSegmentationMask(result_file, labels, None if deepgrow_3d else sliceIndex, freeze=freeze)
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- operationDescription + f" - unexpected error.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
-
- self.updateGUIFromParameterNode()
- logging.info(f"Time consumed by Deepgrow: {time.time() - start:3.1f}")
-
- def updateClassPromptTable(self, refresh=True):
- logging.info(f"updateClassPromptTable => refresh:{refresh}")
- model = self.ui.sammModelSelector.currentText
- params = self.getParamsFromConfig("infer", model)
- print(params)
- labels = params.get("label_names") if params and params.get("label_names") else self.models[model].get("labels")
-
- class_table = self.ui.classPromptsTable
- class_table.clear()
- headers = ["Anatomy", "Prompt"]
- class_table.setColumnCount(len(headers))
- class_table.setHorizontalHeaderLabels(headers)
- class_table.setColumnWidth(0, 250)
- class_table.setRowCount(len(labels))
-
- n = 0
- for key in labels:
- item = qt.QTableWidgetItem(key)
- class_table.setItem(n, 0, item)
- item.setFlags(item.flags() & ~qt.Qt.ItemIsEditable)
-
- checkbox = qt.QCheckBox()
- # checkbox.setChecked(val)
- class_table.setCellWidget(n, 1, checkbox)
-
- logging.info(f"{n} => Add class checkbox => {key}")
- n = n + 1
-
- def getClassPromptTable(self):
- class_prompts = []
- for row in range(self.ui.classPromptsTable.rowCount):
- key = str(self.ui.classPromptsTable.item(row, 0).text())
- value = self.ui.classPromptsTable.item(row, 1)
-
- if value is None:
- value = self.ui.classPromptsTable.cellWidget(row, 1)
- if isinstance(value, qt.QCheckBox):
- value = True if value.checked else False
- else:
- value = value.currentText
- else:
- value = str(value.text())
-
- value = True if value else False
-
- if value is True:
- class_prompts.append(key)
-
- logging.info(f"Class prompts:: => {class_prompts}")
- return class_prompts
-
- def filterClassPromptTable(self, text):
- self.ui.classPromptsTable.setUpdatesEnabled(False)
-
- for row in range(self.ui.classPromptsTable.rowCount):
- key = str(self.ui.classPromptsTable.item(row, 0).text())
- showRow = text.lower() in key.lower()
- self.ui.classPromptsTable.setRowHidden(row, not showRow)
-
- self.ui.classPromptsTable.setUpdatesEnabled(True)
-
- def onSetPromptSegmentation(self):
- if not self.current_sample:
- return
-
- start = time.time()
- result_file = None
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
- self.updateServerSettings()
- model = self.ui.sammModelSelector.currentText
- image_file = self.current_sample["id"]
-
- # get class prompt from checkbox
- params = self.getParamsFromConfig("infer", model)
- labels = (
- params.get("label_names") if params and params.get("label_names") else self.models[model].get("labels")
- )
- class_names = self.getClassPromptTable()
-
- class_prompts = []
-
- for k in class_names:
- class_prompts.append(labels[k] - 1)
-
- # params = {
- # "class_prompts": class_prompts,
- # }
- # -------
- params.update({"class_prompts": class_prompts})
-
- result_file, params = self.logic.infer(model, image_file, params, session_id=self.getSessionId())
- print(f"Result Params for Segmentation: {params}")
-
- if labels and isinstance(labels, dict):
- labels = [k for k, _ in sorted(labels.items(), key=lambda item: item[1])]
- self.updateSegmentationMask(result_file, labels)
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to run inference in MONAI Label Server.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
- if result_file and os.path.exists(result_file):
- os.unlink(result_file)
-
- self.updateGUIFromParameterNode()
- logging.info(f"Time consumed by segmentation: {time.time() - start:3.1f}")
-
- def onPointPromptSegmentation(self):
- if not self.current_sample:
- return
- start = time.time()
- result_file = None
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
- self.updateServerSettings()
- model = self.ui.sammModelSelector.currentText
- image_file = self.current_sample["id"]
-
- # get class prompt from checkbox
- params = self.getParamsFromConfig("infer", model)
- labels = (
- params.get("label_names") if params and params.get("label_names") else self.models[model].get("labels")
- )
- class_names = self.getClassPromptTable()
-
- class_prompts = []
-
- for k in class_names:
- class_prompts.append(labels[k] - 1)
-
- foreground_all = self.getControlPointsXYZ(self.sammPositivePointListNode, "foreground")
- background_all = self.getControlPointsXYZ(self.sammNegativePointListNode, "background")
- foreground = foreground_all
- background = background_all
-
- logging.debug(f"Foreground: {foreground}")
- logging.debug(f"Background: {background}")
- point_prompts = {
- "foreground": foreground,
- "background": background,
- }
-
- params.update({"class_prompts": class_prompts, "point_prompts": point_prompts})
-
- result_file, params = self.logic.infer(model, image_file, params, session_id=self.getSessionId())
- print(f"Result Params for Segmentation: {params}")
-
- if labels and isinstance(labels, dict):
- labels = [k for k, _ in sorted(labels.items(), key=lambda item: item[1])]
- self.updateSegmentationMask(result_file, labels)
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to run inference in MONAI Label Server.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
- if result_file and os.path.exists(result_file):
- os.unlink(result_file)
-
- self.updateGUIFromParameterNode()
- logging.info(f"Time consumed by segmentation: {time.time() - start:3.1f}")
-
- def createCursor(self, widget):
- return slicer.util.mainWindow().cursor
-
- def createSegmentNode(self):
- if self._volumeNode is None:
- return
- if self._segmentNode is None:
- name = "segmentation_" + self._volumeNode.GetName()
- self._segmentNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLSegmentationNode")
- self._segmentNode.SetReferenceImageGeometryParameterFromVolumeNode(self._volumeNode)
- self._segmentNode.SetName(name)
-
- def createScribblesROINode(self):
- if self._volumeNode is None:
- return
- if self._scribblesROINode is None:
- scribblesROINode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLMarkupsROINode")
- scribblesROINode.SetName("Scribbles ROI")
- scribblesROINode.CreateDefaultDisplayNodes()
- scribblesROINode.GetDisplayNode().SetFillOpacity(0.4)
- scribblesROINode.GetDisplayNode().SetSelectedColor(1, 1, 1)
- scribblesROINode.GetDisplayNode().SetColor(1, 1, 1)
- scribblesROINode.GetDisplayNode().SetActiveColor(1, 1, 1)
- self._scribblesROINode = scribblesROINode
-
- def getLabelColor(self, name):
- color = GenericAnatomyColors.get(name.lower())
- return [c / 255.0 for c in color] if color else None
-
- def updateSegmentationMask(self, in_file, labels, sliceIndex=None, freeze=None):
- # TODO:: Add ROI Node (for Bounding Box if provided in the result)
- start = time.time()
- logging.debug(f"Update Segmentation Mask from: {in_file}")
- if in_file and not os.path.exists(in_file):
- return False
-
- segmentationNode = self._segmentNode
- segmentation = segmentationNode.GetSegmentation()
-
- if in_file is None:
- for label in labels:
- if not segmentation.GetSegmentIdBySegmentName(label):
- segmentation.AddEmptySegment(label, label, self.getLabelColor(label))
- return True
-
- if in_file.endswith(".seg.nrrd") and self.file_ext == ".seg.nrrd":
- source_node = slicer.modules.segmentations.logic().LoadSegmentationFromFile(in_file, False)
- destination_node = segmentationNode
- destination_segmentations = destination_node.GetSegmentation()
- source_segmentations = source_node.GetSegmentation()
-
- destination_segmentations.DeepCopy(source_segmentations)
-
- if self._volumeNode:
- destination_node.SetReferenceImageGeometryParameterFromVolumeNode(self._volumeNode)
-
- slicer.mrmlScene.RemoveNode(source_node)
- elif in_file.endswith(".json"):
- # Add bounding box ROI nodes, load multiple ROI nodes in the same scene.
- logging.info("Update Detection ROI Bounding Box")
- slicer.util.loadMarkups(in_file)
- detectionROIs = slicer.mrmlScene.GetNodesByClass("vtkMRMLMarkupsROINode") # Get all ROI node from scene
- numNodes = detectionROIs.GetNumberOfItems()
- for i in range(numNodes):
- ROINode = detectionROIs.GetItemAsObject(i)
- if ROINode.GetName() != "Scribbles ROI":
- ROINode.SetName(f"Detection ROI - {i}")
- ROINode.GetDisplayNode().SetInteractionHandleScale(0.7) # set handle size
- else:
- labels = [label for label in labels if label != "background"]
- logging.info(f"Update Segmentation Mask using Labels: {labels}")
-
- # segmentId, segment = self.currentSegment()
- labelImage = sitk.ReadImage(in_file)
- labelmapVolumeNode = sitkUtils.PushVolumeToSlicer(labelImage, None, className="vtkMRMLLabelMapVolumeNode")
- logging.info(f"Time consumed by Import LabelMask: {time.time() - start:3.1f}")
-
- freeze = [freeze] if freeze and isinstance(freeze, str) else freeze
- logging.info(f"Import only Freezed label: {freeze}")
-
- if sliceIndex is None and not freeze:
- # List of segments to import
- segmentIds = vtk.vtkStringArray()
- for label in labels:
- segmentIds.InsertNextValue(label)
-
- # faster import (based on selected segmentIds)
- slicer.modules.segmentations.logic().ImportLabelmapToSegmentationNode(
- labelmapVolumeNode, segmentationNode, segmentIds
- )
- slicer.mrmlScene.RemoveNode(labelmapVolumeNode)
- else:
- existingCount = segmentation.GetNumberOfSegments()
- existing_label_ids = {}
- for label in labels:
- id = segmentation.GetSegmentIdBySegmentName(label)
- if id:
- existing_label_ids[label] = id
-
- # slower import (import all - use only when you have to update one particular slice for 2D)
- slicer.modules.segmentations.logic().ImportLabelmapToSegmentationNode(
- labelmapVolumeNode, segmentationNode
- )
- slicer.mrmlScene.RemoveNode(labelmapVolumeNode)
-
- addedCount = segmentation.GetNumberOfSegments() - existingCount
- addedSegmentIds = [segmentation.GetNthSegmentID(existingCount + i) for i in range(addedCount)]
-
- self.ui.embeddedSegmentEditorWidget.setSegmentationNode(segmentationNode)
- self.ui.embeddedSegmentEditorWidget.setSourceVolumeNode(self._volumeNode)
-
- for i, segmentId in enumerate(addedSegmentIds):
- label = labels[i] if i < len(labels) else f"unknown {i}"
- if freeze and label not in freeze:
- logging.info(f"Discard label update for: {label}")
- else:
- segment = segmentation.GetSegment(segmentId)
- logging.info(f"select segmentation with id: {segmentId} => {segment.GetName()} => {label}")
- if label in existing_label_ids:
- l_start = time.time()
- label_id = existing_label_ids[label]
-
- self.ui.embeddedSegmentEditorWidget.setCurrentSegmentID(label_id)
- effect = self.ui.embeddedSegmentEditorWidget.effectByName("Logical operators")
-
- if sliceIndex is not None:
- selectedSegmentLabelmap = effect.selectedSegmentLabelmap()
- dims = selectedSegmentLabelmap.GetDimensions()
- for x in range(dims[0]):
- for y in range(dims[1]):
- selectedSegmentLabelmap.SetScalarComponentFromDouble(x, y, sliceIndex, 0, 0)
-
- logging.info(f"{label} - Time to Clean the slice: {time.time() - l_start:3.1f}")
-
- l_start = time.time()
- newLabelmap = slicer.vtkOrientedImageData()
- segmentationNode.GetBinaryLabelmapRepresentation(segmentId, newLabelmap)
- op = (
- slicer.qSlicerSegmentEditorAbstractEffect.ModificationModeSet
- if sliceIndex is None
- else slicer.qSlicerSegmentEditorAbstractEffect.ModificationModeAdd
- )
- effect.modifySelectedSegmentByLabelmap(newLabelmap, op)
- logging.info(f"{label} - Time to Update the segment: {time.time() - l_start:3.1f}")
-
- segmentationNode.RemoveSegment(segmentId)
- logging.info(f"Time consumed until Import Segment => {label}: {time.time() - start:3.1f}")
-
- if slicer.util.settingsValue("MONAILabel/showSegmentsIn3D", False, converter=slicer.util.toBool):
- self.showSegmentationsIn3D()
-
- logging.info(f"Time consumed by updateSegmentationMask: {time.time() - start:3.1f}")
- return True
-
- def showSegmentationsIn3D(self):
- # add closed surface representation
- if self._segmentNode:
- self._segmentNode.CreateClosedSurfaceRepresentation()
- view = slicer.app.layoutManager().threeDWidget(0).threeDView()
- view.resetFocalPoint()
-
- def updateServerUrlGUIFromSettings(self):
- # Save current server URL to the top of history
- settings = qt.QSettings()
- serverUrlHistory = settings.value("MONAILabel/serverUrlHistory")
-
- wasBlocked = self.ui.serverComboBox.blockSignals(True)
- self.ui.serverComboBox.clear()
- if serverUrlHistory:
- self.ui.serverComboBox.addItems(serverUrlHistory.split(";"))
- self.ui.serverComboBox.setCurrentText(settings.value("MONAILabel/serverUrl"))
- self.ui.serverComboBox.blockSignals(wasBlocked)
-
- def createPointListNode(self, name, onMarkupNodeModified, color):
- displayNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLMarkupsDisplayNode")
- displayNode.SetTextScale(0)
- displayNode.SetSelectedColor(color)
-
- pointListNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLMarkupsFiducialNode")
- pointListNode.SetName(name)
- pointListNode.SetAndObserveDisplayNodeID(displayNode.GetID())
-
- pointListNodeObservers = []
- self.addPointListNodeObserver(pointListNode, onMarkupNodeModified)
- return pointListNode, pointListNodeObservers
-
- def removePointListNodeObservers(self, pointListNode, pointListNodeObservers):
- if pointListNode and pointListNodeObservers:
- for observer in pointListNodeObservers:
- pointListNode.RemoveObserver(observer)
-
- def addPointListNodeObserver(self, pointListNode, onMarkupNodeModified):
- pointListNodeObservers = []
- if pointListNode:
- eventIds = [slicer.vtkMRMLMarkupsNode.PointPositionDefinedEvent]
- for eventId in eventIds:
- pointListNodeObservers.append(pointListNode.AddObserver(eventId, onMarkupNodeModified))
- return pointListNodeObservers
-
- def scribblesLayersPresent(self):
- scribbles_exist = False
- if self._segmentNode is not None:
- segmentationNode = self._segmentNode
- segmentation = segmentationNode.GetSegmentation()
- numSegments = segmentation.GetNumberOfSegments()
- segmentIds = [segmentation.GetNthSegmentID(i) for i in range(numSegments)]
- scribbles_exist = sum(int("scribbles" in sid) for sid in segmentIds) > 0
- return scribbles_exist
-
- def ensureScribblesLayersPresent(self):
- if (not self._segmentNode) or self.scribblesLayersPresent():
- return
-
- # add background, layer index = -2 [2], color = red
- self._segmentNode.GetSegmentation().AddEmptySegment(
- "background_scribbles", "background_scribbles", [1.0, 0.0, 0.0]
- )
-
- # add foreground, layer index = -1 [3], color = green
- self._segmentNode.GetSegmentation().AddEmptySegment(
- "foreground_scribbles", "foreground_scribbles", [0.0, 1.0, 0.0]
- )
-
- # change segmentation display properties to "see through" the scribbles
- # further explanation at:
- # https://apidocs.slicer.org/main/classvtkMRMLSegmentationDisplayNode.html
- segmentationDisplayNode = self._segmentNode.GetDisplayNode()
-
- # background
- opacity = 0.2
- segmentationDisplayNode.SetSegmentOpacity2DFill("background_scribbles", opacity)
- segmentationDisplayNode.SetSegmentOpacity2DOutline("background_scribbles", opacity)
-
- # foreground
- segmentationDisplayNode.SetSegmentOpacity2DFill("foreground_scribbles", opacity)
- segmentationDisplayNode.SetSegmentOpacity2DOutline("foreground_scribbles", opacity)
-
- def onUpdateScribbles(self):
- logging.info("Scribbles update event")
- scribblesMethod = self.ui.scribblesMethodSelector.currentText
- scribbles_in = None
- result_file = None
-
- try:
- qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
-
- # get scribbles + label
- segmentationNode = self._segmentNode
- labelmapVolumeNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLLabelMapVolumeNode")
- save_segment_ids = vtk.vtkStringArray()
- segmentationNode.GetSegmentation().GetSegmentIDs(save_segment_ids)
- slicer.modules.segmentations.logic().ExportSegmentsToLabelmapNode(
- segmentationNode, save_segment_ids, labelmapVolumeNode, self._volumeNode
- )
- segmentation = segmentationNode.GetSegmentation()
- totalSegments = segmentation.GetNumberOfSegments()
- segmentIds = [segmentation.GetNthSegmentID(i) for i in range(totalSegments)]
-
- label_info = []
- for idx, segmentId in enumerate(segmentIds):
- segment = segmentation.GetSegment(segmentId)
- label_info.append({"name": segment.GetName(), "id": idx + 1})
-
- scribbles_in = tempfile.NamedTemporaryFile(suffix=self.file_ext, dir=self.tmpdir).name
- self.reportProgress(5)
-
- # save scribbles + label to file
- slicer.util.saveNode(labelmapVolumeNode, scribbles_in)
- slicer.mrmlScene.RemoveNode(labelmapVolumeNode)
- self.reportProgress(30)
- self.updateServerSettings()
- self.reportProgress(60)
-
- # try to get roi if placed
- roiNode = self.ui.scribblesPlaceWidget.currentNode()
- selected_roi = []
- if roiNode and roiNode.GetControlPointPlacementComplete():
- selected_roi = self.getROIPointsXYZ(roiNode)
-
- # send scribbles + label to server along with selected scribbles method
- params = self.getParamsFromConfig("infer", scribblesMethod)
- params.update({"roi": selected_roi})
- params.update({"label_info": label_info})
- _, segment = self.currentScribSegment()
- selected_label_name = segment.GetName()
- params.update({"selected_label_name": selected_label_name})
-
- image_file = self.current_sample["id"]
- result_file, params = self.logic.infer(
- scribblesMethod, image_file, params, scribbles_in, session_id=self.getSessionId()
- )
-
- # display result from server
- self.reportProgress(90)
- self.updateSegmentationMask(result_file, [selected_label_name])
- except BaseException as e:
- msg = f"Message:: {e.msg}" if hasattr(e, "msg") else ""
- slicer.util.errorDisplay(
- f"Failed to post process label on MONAI Label Server using {scribblesMethod}.\n{msg}",
- detailedText=traceback.format_exc(),
- )
- finally:
- qt.QApplication.restoreOverrideCursor()
- self.reportProgress(100)
-
- # clear all temporary files
- if scribbles_in and os.path.exists(scribbles_in):
- os.unlink(scribbles_in)
-
- if result_file and os.path.exists(result_file):
- os.unlink(result_file)
-
- def getROIPointsXYZ(self, roiNode):
- if roiNode is None:
- return []
-
- v = self._volumeNode
- RasToIjkMatrix = vtk.vtkMatrix4x4()
- v.GetRASToIJKMatrix(RasToIjkMatrix)
-
- roi_points_ras = [0.0] * 6
- center = [0] * 3
- roiNode.GetCenter(center)
- roi_points_ras = [(x - s / 2, x + s / 2) for x, s in zip(center, roiNode.GetSize())]
- roi_points_ras = [item for sublist in roi_points_ras for item in sublist]
-
- min_points_ras = [roi_points_ras[0], roi_points_ras[2], roi_points_ras[4], 1.0]
- max_points_ras = [roi_points_ras[0 + 1], roi_points_ras[2 + 1], roi_points_ras[4 + 1], 1.0]
-
- min_points_ijk = RasToIjkMatrix.MultiplyDoublePoint(min_points_ras)
- max_points_ijk = RasToIjkMatrix.MultiplyDoublePoint(max_points_ras)
-
- min_points_ijk = [round(i) for i in min_points_ijk]
- max_points_ijk = [round(i) for i in max_points_ijk]
-
- roi_points_ijk = [val for pair in zip(min_points_ijk[0:3], max_points_ijk[0:3]) for val in pair]
- logging.debug(f"RAS: {roi_points_ras}; IJK: {roi_points_ijk}")
- # print("RAS: {}; IJK: {}".format(roi_points_ras, roi_points_ijk))
-
- return roi_points_ijk
-
- def onClearScribblesSegmentNodes(self):
- # more explanation on this at:
- # https://discourse.slicer.org/t/how-to-clear-segmentation/7433/4
- # clear "scribbles" segment before saving the label
- if not self._segmentNode:
- return
-
- segmentation = self._segmentNode
- num_segments = segmentation.GetSegmentation().GetNumberOfSegments()
- for i in range(num_segments):
- segmentId = segmentation.GetSegmentation().GetNthSegmentID(i)
- if "scribbles" in segmentId:
- logging.info(f"clearing {segmentId}")
- labelMapRep = slicer.vtkOrientedImageData()
- segmentation.GetBinaryLabelmapRepresentation(segmentId, labelMapRep)
- vtkSegmentationCore.vtkOrientedImageDataResample.FillImage(labelMapRep, 0, labelMapRep.GetExtent())
- slicer.vtkSlicerSegmentationsModuleLogic.SetBinaryLabelmapToSegment(
- labelMapRep, segmentation, segmentId, slicer.vtkSlicerSegmentationsModuleLogic.MODE_REPLACE
- )
-
- # refresh segmentation view to clear scribbles segmentations
- # help from: https://discourse.slicer.org/t/refresh-volume-rendering/11847/6
- segmentation.SetDisplayVisibility(False)
- segmentation.SetDisplayVisibility(True)
-
- def resetScribblesROI(self):
- if self._scribblesROINode:
- self._scribblesROINode.RemoveAllControlPoints()
-
- def onClearScribbles(self):
- # for clearing scribbles and resetting tools to default
- # remove "scribbles" segments from label
- self.onClearScribblesSegmentNodes()
-
- self.resetScribblesROI()
-
- self.ui.paintScribblesButton.setChecked(True)
- self.ui.eraseScribblesButton.setChecked(False)
-
- self.ui.scribblesSelector.setCurrentIndex(0)
-
- def onResetScribbles(self):
- # reset scribbles mode
- self.scribblesMode = None
-
- # remove "scribbles" segments from label
- self.onClearScribblesSegmentNodes()
-
- self.ui.paintScribblesButton.setChecked(False)
- self.ui.eraseScribblesButton.setChecked(False)
-
- self.ui.scribblesSelector.setCurrentIndex(0)
- self.ignoreScribblesLabelChangeEvent = True
- self.ui.scribLabelComboBox.setCurrentIndex(0)
- self.ignoreScribblesLabelChangeEvent = False
-
- def updateScribToolLayerFromMode(self):
- if not self._segmentNode:
- return
-
- logging.info(f"Scribbles mode {self.scribblesMode} ")
-
- if self.scribblesMode is None:
- self.changeScribblesMode(tool="Paint", layer="foreground_scribbles")
- self.updateScribToolLayerFromMode()
-
- # update tool/layer select for scribblesEditorWidget
- tool, layer = self.getToolAndLayerFromScribblesMode()
- if self.scribblesMode is not None:
- self.ensureScribblesLayersPresent()
-
- # adding new scribbles can overwrite a new one-hot vector, hence erase any existing
- # labels - this is not a desired behaviour hence we swith to overlay mode that enables drawing
- # scribbles without changing existing labels. Further explanation at:
- # https://discourse.slicer.org/t/how-can-i-set-masking-settings-on-a-segment-editor-effect-in-python/4406/7
- self.logic.get_segment_editor_node().SetOverwriteMode(slicer.vtkMRMLSegmentEditorNode.OverwriteNone)
-
- self.ui.embeddedSegmentEditorWidget.setActiveEffectByName(tool)
- self.ui.embeddedSegmentEditorWidget.setCurrentSegmentID(layer)
-
- # update brush type from checkbox
- if tool in ("Paint", "Erase"):
- is3dbrush = self.ui.brush3dCheckbox.checkState()
- self.on3dBrushCheckbox(state=is3dbrush)
-
- # update brush size from slider
- brushSize = self.ui.brushSizeSlider.value
- self.updateBrushSize(value=brushSize)
-
- def getToolAndLayerFromScribblesMode(self):
- if self.scribblesMode is not None:
- return self.scribblesMode.split("+")
- else:
- # default modes
- return "Paint", "foreground_scribbles"
-
- def changeScribblesMode(self, tool=None, layer=None):
- ctool, clayer = self.getToolAndLayerFromScribblesMode()
-
- ctool = tool if tool is not None else ctool
- clayer = layer if layer is not None else clayer
-
- self.scribblesMode = "+".join([ctool, clayer])
-
- def onPaintScribbles(self):
- if not self._segmentNode:
- return
-
- if self.ui.eraseScribblesButton.checked:
- self.ui.eraseScribblesButton.setChecked(False)
-
- self.changeScribblesMode(tool="Paint" if self.ui.paintScribblesButton.checked else "None")
- self.updateScribToolLayerFromMode()
-
- def onEraseScribbles(self):
- if not self._segmentNode:
- return
-
- if self.ui.paintScribblesButton.checked:
- self.ui.paintScribblesButton.setChecked(False)
-
- self.changeScribblesMode(tool="Erase" if self.ui.eraseScribblesButton.checked else "None")
- self.updateScribToolLayerFromMode()
-
- def onSelectScribblesLabel(self):
- if not self._segmentNode:
- return
-
- index = self.ui.scribblesSelector.currentIndex
- index = 0 if index < 0 else index
- selected = self.ui.scribblesSelector.itemText(index)
-
- layer = "foreground_scribbles" if selected == "Foreground" else "background_scribbles"
- self.changeScribblesMode(layer=layer)
- self.updateScribToolLayerFromMode()
-
- def on3dBrushCheckbox(self, state):
- logging.info(f"3D brush update {state}")
- # enable scribbles in 3d using a sphere brush
- effect = self.ui.embeddedSegmentEditorWidget.effectByName("Paint")
- effect.setParameter("BrushSphere", state)
-
- def updateBrushSize(self, value):
- logging.info(f"brush size update {value}")
- effect = self.ui.embeddedSegmentEditorWidget.effectByName("Paint")
- effect.setParameter("BrushAbsoluteDiameter", value)
-
- def onGenerateJSONFromMulipltROIs(self):
- """
- The functon to generate output JSON label fils with multiple ROI nodes.
- """
- detectionROIs = slicer.mrmlScene.GetNodesByClass("vtkMRMLMarkupsROINode")
- numNodes = detectionROIs.GetNumberOfItems()
-
- label_in = tempfile.NamedTemporaryFile(suffix=".json", dir=self.tmpdir).name
- total_count = 0
- with open(label_in, "w") as fp:
- fp.write("{\n")
- fp.write(
- ' "@schema": "https://raw.githubusercontent.com/slicer/slicer/master/Modules/Loadable/Markups/Resources/Schema/markups-schema-v1.0.3.json#",\n'
- )
- fp.write(' "markups": [\n')
- for i in range(numNodes):
- ROINode = detectionROIs.GetItemAsObject(i)
- if ROINode.GetName() != "Scribbles ROI":
- box_label = tempfile.NamedTemporaryFile(suffix=".json", dir=self.tmpdir).name
- slicer.util.saveNode(ROINode, box_label)
- f = open(box_label)
- jsob = json.load(f)
- markupsDict = jsob["markups"][0]
- if total_count > 0:
- fp.write(",\n")
- fp.write(f" {json.dumps(markupsDict)}")
- total_count += 1
- f.close()
- fp.write("]\n") # close elements
- fp.write("}") # end of root
- label_info = []
- return label_in, label_info
-
-
-class MONAILabelLogic(ScriptedLoadableModuleLogic):
- def __init__(self, tmpdir=None, server_url=None, progress_callback=None, client_id=None, resourcePath=None):
- ScriptedLoadableModuleLogic.__init__(self)
-
- self.server_url = server_url
- self.tmpdir = slicer.util.tempDirectory("slicer-monai-label") if tmpdir is None else tmpdir
- self.client_id = client_id
- self.resourcePath = resourcePath
- self.username = None
- self.password = None
- self.auth_token = None
-
- self.volumeToSessions = dict()
- self.progress_callback = progress_callback
-
- def setDefaultParameters(self, parameterNode):
- if not parameterNode.GetParameter("SegmentationModel"):
- parameterNode.SetParameter("SegmentationModel", "")
- if not parameterNode.GetParameter("DeepgrowModel"):
- parameterNode.SetParameter("DeepgrowModel", "")
- if not parameterNode.GetParameter("ScribblesMethod"):
- parameterNode.SetParameter("ScribblesMethod", "")
-
- def __del__(self):
- shutil.rmtree(self.tmpdir, ignore_errors=True)
-
- def setServer(self, server_url=None):
- if self.server_url != server_url:
- self.username = None
- self.password = None
- self.auth_token = None
-
- self.server_url = server_url if server_url else "http://127.0.0.1:8000"
-
- def setClientId(self, client_id):
- self.client_id = client_id if client_id else "user-xyz"
-
- def setProgressCallback(self, progress_callback=None):
- self.progress_callback = progress_callback
-
- def reportProgress(self, progress):
- if self.progress_callback:
- self.progress_callback(progress)
-
- def get_segment_editor_node(self):
- # Use the Segment Editor module's parameter node for the embedded segment editor widget.
- # This ensures that if the user switches to the Segment Editor then the selected
- # segmentation node, volume node, etc. are the same.
- segmentEditorSingletonTag = "SegmentEditor"
- segmentEditorNode = slicer.mrmlScene.GetSingletonNode(segmentEditorSingletonTag, "vtkMRMLSegmentEditorNode")
- if segmentEditorNode is None:
- segmentEditorNode = slicer.mrmlScene.CreateNodeByClass("vtkMRMLSegmentEditorNode")
- segmentEditorNode.UnRegister(None)
- segmentEditorNode.SetSingletonTag(segmentEditorSingletonTag)
- segmentEditorNode = slicer.mrmlScene.AddNode(segmentEditorNode)
- return segmentEditorNode
-
- def _client(self):
- mc = MONAILabelClient(self.server_url, self.tmpdir, self.client_id)
- if mc.auth_enabled():
- if not self.username or not self.password:
- dlg = LoginDialog(username=self.client_id, password="", resourcePath=self.resourcePath)
- dlg.exec()
-
- self.username = dlg.ui.username.text
- self.password = dlg.ui.password.text
-
- if self.auth_token:
- mc.update_auth(self.auth_token)
-
- # TODO:: JWT token can be validated (with additional py dependencies) to avoid further calls to server
- if not self.auth_token or not mc.auth_valid_token():
- try:
- print(f"Fetching new Token for: {self.username}")
- self.auth_token = mc.auth_token(self.username, self.password)
- mc.update_auth(self.auth_token)
- except BaseException:
- self.username = None
- self.password = None
- self.auth_token = None
- return mc
-
- def info(self):
- return self._client().info()
-
- def datastore(self):
- return self._client().datastore()
-
- def download_label(self, label_id, tag):
- return self._client().download_label(label_id, tag)
-
- def next_sample(self, strategy, params={}):
- return self._client().next_sample(strategy, params)
-
- def create_session(self, image_in):
- return self._client().create_session(image_in)
-
- def get_session(self, session_id):
- return self._client().get_session(session_id)
-
- def remove_session(self, session_id):
- return self._client().remove_session(session_id)
-
- def upload_image(self, image_in, image_id=None):
- return self._client().upload_image(image_in, image_id)
-
- def save_label(self, image_in, label_in, params):
- return self._client().save_label(image_in, label_in, params=params)
-
- def infer(self, model, image_in, params={}, label_in=None, file=None, session_id=None):
- logging.debug("Preparing input data for segmentation")
- self.reportProgress(0)
-
- client = self._client()
- params["result_extension"] = ".nrrd" # expect .nrrd
- params["result_dtype"] = "uint8"
- result_file, params = client.infer(model, image_in, params, label_in, file, session_id)
-
- logging.debug(f"Image Response: {result_file}")
- logging.debug(f"JSON Response: {params}")
-
- self.reportProgress(100)
- return result_file, params
-
- def train_start(self, model=None, params={}):
- return self._client().train_start(model, params)
-
- def train_status(self, check_if_running):
- return self._client().train_status(check_if_running)
-
- def train_stop(self):
- return self._client().train_stop()
-
-
-class LoginDialog(qt.QDialog):
- def __init__(self, username, password, resourcePath):
- super().__init__()
- self.setWindowTitle("User Login")
-
- layout = qt.QVBoxLayout()
- uiWidget = slicer.util.loadUI(resourcePath("UI/LoginDialog.ui"))
- layout.addWidget(uiWidget)
-
- self.ui = slicer.util.childWidgetVariables(uiWidget)
- self.setLayout(layout)
- self.ui.username.setText(username)
- self.ui.password.setText(password if password else "")
- self.ui.loginButton.connect("clicked(bool)", self.onLogin)
-
- def onLogin(self):
- self.close()
-
-
-class MONAILabelTest(ScriptedLoadableModuleTest):
- def setUp(self):
- slicer.mrmlScene.Clear()
-
- def runTest(self):
- self.setUp()
- self.test_MONAILabel1()
-
- def test_MONAILabel1(self):
- self.delayDisplay("Test passed")
diff --git a/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/__init__.py b/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/__init__.py
deleted file mode 100644
index b59d6bb..0000000
--- a/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .client import *
-from .labelcolors import *
diff --git a/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/client.py b/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/client.py
deleted file mode 100644
index 920dda2..0000000
--- a/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/client.py
+++ /dev/null
@@ -1,672 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import cgi
-import http.client
-import json
-import logging
-import mimetypes
-import os
-import re
-import ssl
-import tempfile
-from pathlib import Path
-from urllib.parse import quote_plus, unquote, urlencode, urlparse
-
-import requests
-
-logger = logging.getLogger(__name__)
-
-
-def bytes_to_str(b):
- return b.decode("utf-8") if isinstance(b, bytes) else b
-
-
-class MONAILabelClient:
- """
- Basic MONAILabel Client to invoke infer/train APIs over http/https
- """
-
- def __init__(self, server_url, tmpdir=None, client_id=None):
- """
- :param server_url: Server URL for MONAILabel (e.g. http://127.0.0.1:8000)
- :param tmpdir: Temp directory to save temporary files. If None then it uses tempfile.tempdir
- :param client_id: Client ID that will be added for all basic requests
- """
-
- self._server_url = server_url.rstrip("/").strip()
- self._tmpdir = tmpdir if tmpdir else tempfile.tempdir if tempfile.tempdir else "/tmp"
- self._client_id = client_id
- self._headers = {}
-
- def _update_client_id(self, params):
- if params:
- params["client_id"] = self._client_id
- else:
- params = {"client_id": self._client_id}
- return params
-
- def update_auth(self, token):
- if token:
- self._headers["Authorization"] = f"{token['token_type']} {token['access_token']}"
-
- def get_server_url(self):
- """
- Return server url
-
- :return: the url for monailabel server
- """
- return self._server_url
-
- def set_server_url(self, server_url):
- """
- Set url for monailabel server
-
- :param server_url: server url for monailabel
- """
- self._server_url = server_url.rstrip("/").strip()
-
- def auth_enabled(self) -> bool:
- """
- Check if Auth is enabled
-
- """
- selector = "/auth/"
- status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector)
- if status != 200:
- return False
-
- response = bytes_to_str(response)
- logger.debug(f"Response: {response}")
- enabled = json.loads(response).get("enabled", False)
- return True if enabled else False
-
- def auth_token(self, username, password):
- """
- Fetch Auth Token. Currently only basic authentication is supported.
-
- :param username: UserName for basic authentication
- :param password: Password for basic authentication
- """
- selector = "/auth/token"
- data = urlencode({"username": username, "password": password, "grant_type": "password"})
- status, response, _, _ = MONAILabelUtils.http_method(
- "POST", self._server_url, selector, data, None, "application/x-www-form-urlencoded"
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- response = bytes_to_str(response)
- logger.debug(f"Response: {response}")
- return json.loads(response)
-
- def auth_valid_token(self) -> bool:
- selector = "/auth/token/valid"
- status, _, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers)
- return True if status == 200 else False
-
- def info(self):
- """
- Invoke /info/ request over MONAILabel Server
-
- :return: json response
- """
- selector = "/info/"
- status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers)
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def next_sample(self, strategy, params):
- """
- Get Next sample
-
- :param strategy: Name of strategy to be used for fetching next sample
- :param params: Additional JSON params as part of strategy request
- :return: json response which contains information about next image selected for annotation
- """
- params = self._update_client_id(params)
- selector = f"/activelearning/{MONAILabelUtils.urllib_quote_plus(strategy)}"
- status, response, _, _ = MONAILabelUtils.http_method(
- "POST", self._server_url, selector, params, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def create_session(self, image_in, params=None):
- """
- Create New Session
-
- :param image_in: filepath for image to be sent to server as part of session creation
- :param params: additional JSON params as part of session reqeust
- :return: json response which contains session id and other details
- """
- selector = "/session/"
- params = self._update_client_id(params)
-
- status, response, _ = MONAILabelUtils.http_upload(
- "PUT", self._server_url, selector, params, [image_in], headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def get_session(self, session_id):
- """
- Get Session
-
- :param session_id: Session Id
- :return: json response which contains more details about the session
- """
- selector = f"/session/{MONAILabelUtils.urllib_quote_plus(session_id)}"
- status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers)
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def remove_session(self, session_id):
- """
- Remove any existing Session
-
- :param session_id: Session Id
- :return: json response
- """
- selector = f"/session/{MONAILabelUtils.urllib_quote_plus(session_id)}"
- status, response, _, _ = MONAILabelUtils.http_method(
- "DELETE", self._server_url, selector, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def upload_image(self, image_in, image_id=None, params=None):
- """
- Upload New Image to MONAILabel Datastore
-
- :param image_in: Image File Path
- :param image_id: Force Image ID; If not provided then Server it auto generate new Image ID
- :param params: Additional JSON params
- :return: json response which contains image id and other details
- """
- selector = f"/datastore/?image={MONAILabelUtils.urllib_quote_plus(image_id)}"
-
- files = {"file": image_in}
- params = self._update_client_id(params)
- fields = {"params": json.dumps(params) if params else "{}"}
-
- status, response, _, _ = MONAILabelUtils.http_multipart(
- "PUT", self._server_url, selector, fields, files, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR,
- f"Status: {status}; Response: {bytes_to_str(response)}",
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def save_label(self, image_id, label_in, tag="", params=None):
- """
- Save/Submit Label
-
- :param image_id: Image Id for which label needs to saved/submitted
- :param label_in: Label File path which shall be saved/submitted
- :param tag: Save label against tag in datastore
- :param params: Additional JSON params for the request
- :return: json response
- """
- selector = f"/datastore/label?image={MONAILabelUtils.urllib_quote_plus(image_id)}"
- if tag:
- selector += f"&tag={MONAILabelUtils.urllib_quote_plus(tag)}"
-
- params = self._update_client_id(params)
- fields = {
- "params": json.dumps(params),
- }
- files = {"label": label_in}
-
- status, response, _, _ = MONAILabelUtils.http_multipart(
- "PUT", self._server_url, selector, fields, files, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR,
- f"Status: {status}; Response: {bytes_to_str(response)}",
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def datastore(self):
- selector = "/datastore/?output=all"
- status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers)
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def download_label(self, label_id, tag):
- selector = "/datastore/label?label={}&tag={}".format(
- MONAILabelUtils.urllib_quote_plus(label_id), MONAILabelUtils.urllib_quote_plus(tag)
- )
- status, response, _, headers = MONAILabelUtils.http_method(
- "GET", self._server_url, selector, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response
- )
-
- content_disposition = headers.get("content-disposition")
-
- if not content_disposition:
- logging.warning("Filename not found. Fall back to no loaded labels")
- file_name = MONAILabelUtils.get_filename(content_disposition)
-
- file_ext = "".join(Path(file_name).suffixes)
- local_filename = tempfile.NamedTemporaryFile(dir=self._tmpdir, suffix=file_ext).name
- with open(local_filename, "wb") as f:
- f.write(response)
-
- return local_filename
-
- def infer(self, model, image_id, params, label_in=None, file=None, session_id=None):
- """
- Run Infer
-
- :param model: Name of Model
- :param image_id: Image Id
- :param params: Additional configs/json params as part of Infer request
- :param label_in: File path for label mask which is needed to run Inference (e.g. In case of Scribbles)
- :param file: File path for Image (use raw image instead of image_id)
- :param session_id: Session ID (use existing session id instead of image_id)
- :return: response_file (label mask), response_body (json result/output params)
- """
- selector = "/infer/{}?image={}".format(
- MONAILabelUtils.urllib_quote_plus(model),
- MONAILabelUtils.urllib_quote_plus(image_id),
- )
- if session_id:
- selector += f"&session_id={MONAILabelUtils.urllib_quote_plus(session_id)}"
-
- params = self._update_client_id(params)
- fields = {"params": json.dumps(params) if params else "{}"}
- files = {"label": label_in} if label_in else {}
- files.update({"file": file} if file and not session_id else {})
-
- status, form, files, _ = MONAILabelUtils.http_multipart(
- "POST", self._server_url, selector, fields, files, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR,
- f"Status: {status}; Response: {bytes_to_str(form)}",
- )
-
- form = json.loads(form) if isinstance(form, str) else form
- params = form.get("params") if files else form
- params = json.loads(params) if isinstance(params, str) else params
-
- image_out = MONAILabelUtils.save_result(files, self._tmpdir)
- return image_out, params
-
- def wsi_infer(self, model, image_id, body=None, output="dsa", session_id=None):
- """
- Run WSI Infer in case of Pathology App
-
- :param model: Name of Model
- :param image_id: Image Id
- :param body: Additional configs/json params as part of Infer request
- :param output: Output File format (dsa|asap|json)
- :param session_id: Session ID (use existing session id instead of image_id)
- :return: response_file (None), response_body
- """
- selector = "/infer/wsi/{}?image={}".format(
- MONAILabelUtils.urllib_quote_plus(model),
- MONAILabelUtils.urllib_quote_plus(image_id),
- )
- if session_id:
- selector += f"&session_id={MONAILabelUtils.urllib_quote_plus(session_id)}"
- if output:
- selector += f"&output={MONAILabelUtils.urllib_quote_plus(output)}"
-
- body = self._update_client_id(body if body else {})
- status, form, _, _ = MONAILabelUtils.http_method("POST", self._server_url, selector, body)
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR,
- f"Status: {status}; Response: {bytes_to_str(form)}",
- )
-
- return None, form
-
- def train_start(self, model, params):
- """
- Run Train Task
-
- :param model: Name of Model
- :param params: Additional configs/json params as part of Train request
- :return: json response
- """
- params = self._update_client_id(params)
-
- selector = "/train/"
- if model:
- selector += MONAILabelUtils.urllib_quote_plus(model)
-
- status, response, _, _ = MONAILabelUtils.http_method(
- "POST", self._server_url, selector, params, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR,
- f"Status: {status}; Response: {bytes_to_str(response)}",
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def train_stop(self):
- """
- Stop any running Train Task(s)
-
- :return: json response
- """
- selector = "/train/"
- status, response, _, _ = MONAILabelUtils.http_method(
- "DELETE", self._server_url, selector, headers=self._headers
- )
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR,
- f"Status: {status}; Response: {bytes_to_str(response)}",
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
- def train_status(self, check_if_running=False):
- """
- Check Train Task Status
-
- :param check_if_running: Fast mode. Only check if training is Running
- :return: boolean if check_if_running is enabled; else json response that contains of full details
- """
- selector = "/train/"
- if check_if_running:
- selector += "?check_if_running=true"
- status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers)
- if check_if_running:
- return status == 200
-
- if status != 200:
- raise MONAILabelClientException(
- MONAILabelError.SERVER_ERROR,
- f"Status: {status}; Response: {bytes_to_str(response)}",
- )
-
- response = bytes_to_str(response)
- logging.debug(f"Response: {response}")
- return json.loads(response)
-
-
-class MONAILabelError:
- """
- Type of Inference Model
-
- Attributes:
- SERVER_ERROR - Server Error
- SESSION_EXPIRED - Session Expired
- UNKNOWN - Unknown Error
- """
-
- SERVER_ERROR = 1
- SESSION_EXPIRED = 2
- UNKNOWN = 3
-
-
-class MONAILabelClientException(Exception):
- """
- MONAILabel Client Exception
- """
-
- __slots__ = ["error", "msg"]
-
- def __init__(self, error, msg, status_code=None, response=None):
- """
- :param error: Error code represented by MONAILabelError
- :param msg: Error message
- :param status_code: HTTP Response code
- :param response: HTTP Response
- """
- self.error = error
- self.msg = msg
- self.status_code = status_code
- self.response = response
-
-
-class MONAILabelUtils:
- @staticmethod
- def http_method(method, server_url, selector, body=None, headers=None, content_type=None):
- logging.debug(f"{method} {server_url}{selector}")
-
- parsed = urlparse(server_url)
- path = parsed.path.rstrip("/")
- selector = path + "/" + selector.lstrip("/")
- logging.debug(f"URI Path: {selector}")
-
- parsed = urlparse(server_url)
- if parsed.scheme == "https":
- logger.debug("Using HTTPS mode")
- # noinspection PyProtectedMember
- conn = http.client.HTTPSConnection(parsed.hostname, parsed.port, context=ssl._create_unverified_context())
- else:
- conn = http.client.HTTPConnection(parsed.hostname, parsed.port)
-
- headers = headers if headers else {}
- if body:
- if not content_type:
- if isinstance(body, dict):
- body = json.dumps(body)
- content_type = "application/json"
- else:
- content_type = "text/plain"
- headers.update({"content-type": content_type, "content-length": str(len(body))})
-
- conn.request(method, selector, body=body, headers=headers)
- return MONAILabelUtils.send_response(conn)
-
- @staticmethod
- def http_upload(method, server_url, selector, fields, files, headers=None):
- logging.debug(f"{method} {server_url}{selector}")
-
- url = server_url.rstrip("/") + "/" + selector.lstrip("/")
- logging.debug(f"URL: {url}")
-
- files = [("files", (os.path.basename(f), open(f, "rb"))) for f in files]
- headers = headers if headers else {}
- response = (
- requests.post(url, files=files, headers=headers)
- if method == "POST"
- else requests.put(url, files=files, data=fields, headers=headers)
- )
- return response.status_code, response.text, None
-
- @staticmethod
- def http_multipart(method, server_url, selector, fields, files, headers={}):
- logging.debug(f"{method} {server_url}{selector}")
-
- content_type, body = MONAILabelUtils.encode_multipart_formdata(fields, files)
- headers = headers if headers else {}
- headers.update({"content-type": content_type, "content-length": str(len(body))})
-
- parsed = urlparse(server_url)
- path = parsed.path.rstrip("/")
- selector = path + "/" + selector.lstrip("/")
- logging.debug(f"URI Path: {selector}")
-
- if parsed.scheme == "https":
- logger.debug("Using HTTPS mode")
- # noinspection PyProtectedMember
- conn = http.client.HTTPSConnection(parsed.hostname, parsed.port, context=ssl._create_unverified_context())
- else:
- conn = http.client.HTTPConnection(parsed.hostname, parsed.port)
-
- conn.request(method, selector, body, headers)
- return MONAILabelUtils.send_response(conn, content_type)
-
- @staticmethod
- def send_response(conn, content_type="application/json"):
- response = conn.getresponse()
- logging.debug(f"HTTP Response Code: {response.status}")
- logging.debug(f"HTTP Response Message: {response.reason}")
- logging.debug(f"HTTP Response Headers: {response.getheaders()}")
-
- response_content_type = response.getheader("content-type", content_type)
- logging.debug(f"HTTP Response Content-Type: {response_content_type}")
-
- if "multipart" in response_content_type:
- if response.status == 200:
- form, files = MONAILabelUtils.parse_multipart(response.fp if response.fp else response, response.msg)
- logging.debug(f"Response FORM: {form}")
- logging.debug(f"Response FILES: {files.keys()}")
- return response.status, form, files, response.headers
- else:
- return response.status, response.read(), None, response.headers
-
- logging.debug("Reading status/content from simple response!")
- return response.status, response.read(), None, response.headers
-
- @staticmethod
- def save_result(files, tmpdir):
- if files is None:
- return
- for name in files:
- data = files[name]
- result_file = os.path.join(tmpdir, name)
-
- logging.debug(f"Saving {name} to {result_file}; Size: {len(data)}")
- dir_path = os.path.dirname(os.path.realpath(result_file))
- if not os.path.exists(dir_path):
- os.makedirs(dir_path)
-
- with open(result_file, "wb") as f:
- if isinstance(data, bytes):
- f.write(data)
- else:
- f.write(data.encode("utf-8"))
-
- # Currently only one file per response supported
- return result_file
-
- @staticmethod
- def encode_multipart_formdata(fields, files):
- limit = "----------lImIt_of_THE_fIle_eW_$"
- lines = []
- for key, value in fields.items():
- lines.append("--" + limit)
- lines.append('Content-Disposition: form-data; name="%s"' % key)
- lines.append("")
- lines.append(value)
- for key, filename in files.items():
- lines.append("--" + limit)
- lines.append(f'Content-Disposition: form-data; name="{key}"; filename="{filename}"')
- lines.append("Content-Type: %s" % MONAILabelUtils.get_content_type(filename))
- lines.append("")
- with open(filename, mode="rb") as f:
- data = f.read()
- lines.append(data)
- lines.append("--" + limit + "--")
- lines.append("")
-
- body = bytearray()
- for line in lines:
- body.extend(line if isinstance(line, bytes) else line.encode("utf-8"))
- body.extend(b"\r\n")
-
- content_type = "multipart/form-data; boundary=%s" % limit
- return content_type, body
-
- @staticmethod
- def get_content_type(filename):
- return mimetypes.guess_type(filename)[0] or "application/octet-stream"
-
- @staticmethod
- def parse_multipart(fp, headers):
- fs = cgi.FieldStorage(
- fp=fp,
- environ={"REQUEST_METHOD": "POST"},
- headers=headers,
- keep_blank_values=True,
- )
- form = {}
- files = {}
- if hasattr(fs, "list") and isinstance(fs.list, list):
- for f in fs.list:
- logger.debug(f"FILE-NAME: {f.filename}; NAME: {f.name}; SIZE: {len(f.value)}")
- if f.filename:
- files[f.filename] = f.value
- else:
- form[f.name] = f.value
- return form, files
-
- @staticmethod
- def urllib_quote_plus(s):
- return quote_plus(s)
-
- @staticmethod
- def get_filename(content_disposition):
- file_name = re.findall(r"filename\*=([^;]+)", content_disposition, flags=re.IGNORECASE)
- if not file_name:
- file_name = re.findall('filename="(.+)"', content_disposition, flags=re.IGNORECASE)
- if "utf-8''" in file_name[0].lower():
- file_name = re.sub("utf-8''", "", file_name[0], flags=re.IGNORECASE)
- file_name = unquote(file_name)
- else:
- file_name = file_name[0]
- return file_name
diff --git a/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/labelcolors.py b/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/labelcolors.py
deleted file mode 100644
index ad9ee36..0000000
--- a/monailabel/plugins/slicer/MONAILabel/MONAILabelLib/labelcolors.py
+++ /dev/null
@@ -1,323 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-GenericAnatomyColors = {
- "background": (0, 0, 0),
- "tissue": (128, 174, 128),
- "bone": (241, 214, 145),
- "skin": (177, 122, 101),
- "connective tissue": (111, 184, 210),
- "blood": (216, 101, 79),
- "organ": (221, 130, 101),
- "mass": (144, 238, 144),
- "muscle": (192, 104, 88),
- "foreign object": (220, 245, 20),
- "waste": (78, 63, 0),
- "teeth": (255, 250, 220),
- "fat": (230, 220, 70),
- "gray matter": (200, 200, 235),
- "white matter": (250, 250, 210),
- "nerve": (244, 214, 49),
- "vein": (0, 151, 206),
- "artery": (216, 101, 79),
- "capillary": (183, 156, 220),
- "ligament": (183, 214, 211),
- "tendon": (152, 189, 207),
- "cartilage": (111, 184, 210),
- "meniscus": (178, 212, 242),
- "lymph node": (68, 172, 100),
- "lymphatic vessel": (111, 197, 131),
- "cerebro-spinal fluid": (85, 188, 255),
- "bile": (0, 145, 30),
- "urine": (214, 230, 130),
- "feces": (78, 63, 0),
- "gas": (218, 255, 255),
- "fluid": (170, 250, 250),
- "edema": (140, 224, 228),
- "bleeding": (188, 65, 28),
- "necrosis": (216, 191, 216),
- "clot": (145, 60, 66),
- "embolism": (150, 98, 83),
- "head": (177, 122, 101),
- "central nervous system": (244, 214, 49),
- "brain": (250, 250, 225),
- "gray matter of brain": (200, 200, 215),
- "telencephalon": (68, 131, 98),
- "cerebral cortex": (128, 174, 128),
- "right frontal lobe": (83, 146, 164),
- "left frontal lobe": (83, 146, 164),
- "right temporal lobe": (162, 115, 105),
- "left temporal lobe": (162, 115, 105),
- "right parietal lobe": (141, 93, 137),
- "left parietal lobe": (141, 93, 137),
- "right occipital lobe": (182, 166, 110),
- "left occipital lobe": (182, 166, 110),
- "right insular lobe": (188, 135, 166),
- "left insular lobe": (188, 135, 166),
- "right limbic lobe": (154, 150, 201),
- "left limbic lobe": (154, 150, 201),
- "right striatum": (177, 140, 190),
- "left striatum": (177, 140, 190),
- "right caudate nucleus": (30, 111, 85),
- "left caudate nucleus": (30, 111, 85),
- "right putamen": (210, 157, 166),
- "left putamen": (210, 157, 166),
- "right pallidum": (48, 129, 126),
- "left pallidum": (48, 129, 126),
- "right amygdaloid complex": (98, 153, 112),
- "left amygdaloid complex": (98, 153, 112),
- "diencephalon": (69, 110, 53),
- "thalamus": (166, 113, 137),
- "right thalamus": (122, 101, 38),
- "left thalamus": (122, 101, 38),
- "pineal gland": (253, 135, 192),
- "midbrain": (145, 92, 109),
- "substantia nigra": (46, 101, 131),
- "right substantia nigra": (0, 108, 112),
- "left substantia nigra": (0, 108, 112),
- "cerebral white matter": (250, 250, 225),
- "right superior longitudinal fasciculus": (127, 150, 88),
- "left superior longitudinal fasciculus": (127, 150, 88),
- "right inferior longitudinal fasciculus": (159, 116, 163),
- "left inferior longitudinal fasciculus": (159, 116, 163),
- "right arcuate fasciculus": (125, 102, 154),
- "left arcuate fasciculus": (125, 102, 154),
- "right uncinate fasciculus": (106, 174, 155),
- "left uncinate fasciculus": (106, 174, 155),
- "right cingulum bundle": (154, 146, 83),
- "left cingulum bundle": (154, 146, 83),
- "projection fibers": (126, 126, 55),
- "right corticospinal tract": (201, 160, 133),
- "left corticospinal tract": (201, 160, 133),
- "right optic radiation": (78, 152, 141),
- "left optic radiation": (78, 152, 141),
- "right medial lemniscus": (174, 140, 103),
- "left medial lemniscus": (174, 140, 103),
- "right superior cerebellar peduncle": (139, 126, 177),
- "left superior cerebellar peduncle": (139, 126, 177),
- "right middle cerebellar peduncle": (148, 120, 72),
- "left middle cerebellar peduncle": (148, 120, 72),
- "right inferior cerebellar peduncle": (186, 135, 135),
- "left inferior cerebellar peduncle": (186, 135, 135),
- "optic chiasm": (99, 106, 24),
- "right optic tract": (156, 171, 108),
- "left optic tract": (156, 171, 108),
- "right fornix": (64, 123, 147),
- "left fornix": (64, 123, 147),
- "commissural fibers": (138, 95, 74),
- "corpus callosum": (97, 113, 158),
- "posterior commissure": (126, 161, 197),
- "cerebellar white matter": (194, 195, 164),
- "CSF space": (85, 188, 255),
- "ventricles of brain": (88, 106, 215),
- "right lateral ventricle": (88, 106, 215),
- "left lateral ventricle": (88, 106, 215),
- "right third ventricle": (88, 106, 215),
- "left third ventricle": (88, 106, 215),
- "cerebral aqueduct": (88, 106, 215),
- "fourth ventricle": (88, 106, 215),
- "subarachnoid space": (88, 106, 215),
- "spinal cord": (244, 214, 49),
- "gray matter of spinal cord": (200, 200, 215),
- "white matter of spinal cord": (250, 250, 225),
- "endocrine system of brain": (82, 174, 128),
- "pituitary gland": (57, 157, 110),
- "adenohypophysis": (60, 143, 83),
- "neurohypophysis": (92, 162, 109),
- "meninges": (255, 244, 209),
- "dura mater": (255, 244, 209),
- "arachnoid": (255, 244, 209),
- "pia mater": (255, 244, 209),
- "muscles of head": (201, 121, 77),
- "salivary glands": (70, 163, 117),
- "lips": (188, 91, 95),
- "nose": (177, 122, 101),
- "tongue": (166, 84, 94),
- "soft palate": (182, 105, 107),
- "right inner ear": (229, 147, 118),
- "left inner ear": (229, 147, 118),
- "right external ear": (174, 122, 90),
- "left external ear": (174, 122, 90),
- "right middle ear": (201, 112, 73),
- "left middle ear": (201, 112, 73),
- "right eyeball": (194, 142, 0),
- "left eyeball": (194, 142, 0),
- "skull": (241, 213, 144),
- "right frontal bone": (203, 179, 77),
- "left frontal bone": (203, 179, 77),
- "right parietal bone": (229, 204, 109),
- "left parietal bone": (229, 204, 109),
- "right temporal bone": (255, 243, 152),
- "left temporal bone": (255, 243, 152),
- "right sphenoid bone": (209, 185, 85),
- "left sphenoid bone": (209, 185, 85),
- "right ethmoid bone": (248, 223, 131),
- "left ethmoid bone": (248, 223, 131),
- "occipital bone": (255, 230, 138),
- "maxilla": (196, 172, 68),
- "right zygomatic bone": (255, 255, 167),
- "right lacrimal bone": (255, 250, 160),
- "vomer bone": (255, 237, 145),
- "right palatine bone": (242, 217, 123),
- "left palatine bone": (242, 217, 123),
- "mandible": (222, 198, 101),
- "neck": (177, 122, 101),
- "muscles of neck": (213, 124, 109),
- "pharynx": (184, 105, 108),
- "larynx": (150, 208, 243),
- "thyroid gland": (62, 162, 114),
- "right parathyroid glands": (62, 162, 114),
- "left parathyroid glands": (62, 162, 114),
- "skeleton of neck": (242, 206, 142),
- "hyoid bone": (250, 210, 139),
- "cervical vertebral column": (255, 255, 207),
- "thorax": (177, 122, 101),
- "trachea": (182, 228, 255),
- "bronchi": (175, 216, 244),
- "right lung": (197, 165, 145),
- "left lung": (197, 165, 180),
- "superior lobe of right lung": (172, 138, 115),
- "superior lobe of left lung": (172, 138, 115),
- "middle lobe of right lung": (202, 164, 140),
- "inferior lobe of right lung": (224, 186, 162),
- "inferior lobe of left lung": (224, 186, 162),
- "pleura": (255, 245, 217),
- "heart": (206, 110, 84),
- "right atrium": (210, 115, 89),
- "left atrium": (203, 108, 81),
- "atrial septum": (233, 138, 112),
- "ventricular septum": (195, 100, 73),
- "right ventricle of heart": (181, 85, 57),
- "left ventricle of heart": (152, 55, 13),
- "mitral valve": (159, 63, 27),
- "tricuspid valve": (166, 70, 38),
- "aortic valve": (218, 123, 97),
- "pulmonary valve": (225, 130, 104),
- "aorta": (224, 97, 76),
- "pericardium": (255, 244, 209),
- "pericardial cavity": (184, 122, 154),
- "esophagus": (211, 171, 143),
- "thymus": (47, 150, 103),
- "mediastinum": (255, 244, 209),
- "skin of thoracic wall": (173, 121, 88),
- "muscles of thoracic wall": (188, 95, 76),
- "skeleton of thorax": (255, 239, 172),
- "thoracic vertebral column": (226, 202, 134),
- "ribs": (253, 232, 158),
- "sternum": (244, 217, 154),
- "right clavicle": (205, 179, 108),
- "left clavicle": (205, 179, 108),
- "abdominal cavity": (186, 124, 161),
- "abdomen": (177, 122, 101),
- "peritoneum": (255, 255, 220),
- "omentum": (234, 234, 194),
- "peritoneal cavity": (204, 142, 178),
- "retroperitoneal space": (180, 119, 153),
- "stomach": (216, 132, 105),
- "duodenum": (255, 253, 229),
- "small bowel": (205, 167, 142),
- "colon": (204, 168, 143),
- "anus": (255, 224, 199),
- "liver": (221, 130, 101),
- "biliary tree": (0, 145, 30),
- "gallbladder": (139, 150, 98),
- "pancreas": (249, 180, 111),
- "spleen": (157, 108, 162),
- "urinary system": (203, 136, 116),
- "right kidney": (185, 102, 83),
- "left kidney": (185, 102, 140),
- "right ureter": (247, 182, 164),
- "left ureter": (247, 182, 164),
- "urinary bladder": (222, 154, 132),
- "urethra": (124, 186, 223),
- "right adrenal gland": (249, 186, 150),
- "left adrenal gland": (249, 186, 195),
- "female internal genitalia": (244, 170, 147),
- "uterus": (255, 181, 158),
- "right fallopian tube": (255, 190, 165),
- "left fallopian tube": (227, 153, 130),
- "right ovary": (213, 141, 113),
- "left ovary": (213, 141, 113),
- "vagina": (193, 123, 103),
- "male internal genitalia": (216, 146, 127),
- "prostate": (230, 158, 140),
- "right seminal vesicle": (245, 172, 147),
- "left seminal vesicle": (245, 172, 147),
- "right deferent duct": (241, 172, 151),
- "left deferent duct": (241, 172, 151),
- "skin of abdominal wall": (177, 124, 92),
- "muscles of abdominal wall": (171, 85, 68),
- "skeleton of abdomen": (217, 198, 131),
- "lumbar vertebral column": (212, 188, 102),
- "female external genitalia": (185, 135, 134),
- "male external genitalia": (185, 135, 134),
- "skeleton of upper limb": (198, 175, 125),
- "muscles of upper limb": (194, 98, 79),
- "right upper limb": (177, 122, 101),
- "left upper limb": (177, 122, 101),
- "right shoulder": (177, 122, 101),
- "left shoulder": (177, 122, 101),
- "right arm": (177, 122, 101),
- "left arm": (177, 122, 101),
- "right elbow": (177, 122, 101),
- "left elbow": (177, 122, 101),
- "right forearm": (177, 122, 101),
- "left forearm": (177, 122, 101),
- "right wrist": (177, 122, 101),
- "left wrist": (177, 122, 101),
- "right hand": (177, 122, 101),
- "left hand": (177, 122, 101),
- "skeleton of lower limb": (255, 238, 170),
- "muscles of lower limb": (206, 111, 93),
- "right lower limb": (177, 122, 101),
- "left lower limb": (177, 122, 101),
- "right hip": (177, 122, 101),
- "left hip": (177, 122, 101),
- "right thigh": (177, 122, 101),
- "left thigh": (177, 122, 101),
- "right knee": (177, 122, 101),
- "left knee": (177, 122, 101),
- "right leg": (177, 122, 101),
- "left leg": (177, 122, 101),
- "right foot": (177, 122, 101),
- "left foot": (177, 122, 101),
- "peripheral nervous system": (216, 186, 0),
- "autonomic nerve": (255, 226, 77),
- "sympathetic trunk": (255, 243, 106),
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- "region 2": (251, 159, 255),
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- "region 14": (0, 0, 255),
- "region 15": (157, 0, 0),
- "unknown": (100, 100, 130),
- "cyst": (205, 205, 100),
-}
diff --git a/monailabel/plugins/slicer/MONAILabel/Resources/Icons/MONAILabel.png b/monailabel/plugins/slicer/MONAILabel/Resources/Icons/MONAILabel.png
deleted file mode 100644
index 614a736..0000000
Binary files a/monailabel/plugins/slicer/MONAILabel/Resources/Icons/MONAILabel.png and /dev/null differ
diff --git a/monailabel/plugins/slicer/MONAILabel/Resources/Icons/bg_red.png b/monailabel/plugins/slicer/MONAILabel/Resources/Icons/bg_red.png
deleted file mode 100644
index 0b52f69..0000000
Binary files a/monailabel/plugins/slicer/MONAILabel/Resources/Icons/bg_red.png and /dev/null differ
diff --git a/monailabel/plugins/slicer/MONAILabel/Resources/Icons/contour.svg b/monailabel/plugins/slicer/MONAILabel/Resources/Icons/contour.svg
deleted file mode 100644
index 54fad6b..0000000
--- a/monailabel/plugins/slicer/MONAILabel/Resources/Icons/contour.svg
+++ /dev/null
@@ -1 +0,0 @@
-Upload
- Dialog
-
-
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- 0
- 0
- 375
- 76
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- 375
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diff --git a/monailabel/plugins/slicer/MONAILabel/Resources/UI/MONAILabel.ui b/monailabel/plugins/slicer/MONAILabel/Resources/UI/MONAILabel.ui
deleted file mode 100644
index a10dde5..0000000
--- a/monailabel/plugins/slicer/MONAILabel/Resources/UI/MONAILabel.ui
+++ /dev/null
@@ -1,1067 +0,0 @@
-
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- MONAILabel
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- 0
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- 1055
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- Select Node
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- Qt::ElideMiddle
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- true
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- Submit Label
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-
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- Model:
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-
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- SmartEdit / Deepgrow
-
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- true
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- Update
-
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- 0
- 0
-
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- Select Deepgrow/DeepEdit Model
-
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- Label:
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- 0
- 0
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- false
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- 16777215
- 16777211
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-
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- Foreground:
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- 0
- 0
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- false
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- Everything
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- Point prompts:
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- Foreground:
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- 0
- 0
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-
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- qSlicerMarkupsPlaceWidget::ForcePlaceMultipleMarkups
-
-
-
-
-
-
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-
-
- -
-
-
- Background:
-
-
-
- -
-
-
-
- 0
- 0
-
-
-
- qSlicerMarkupsPlaceWidget::ForcePlaceMultipleMarkups
-
-
-
- -
-
-
- false
-
-
- Points
-
-
-
-
-
-
-
-
-
-
- -
-
-
- Class prompts:
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-
-
-
-
-
- -
-
-
- -
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-
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-
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- false
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- Reset
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- false
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- Classes
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- Scribbles
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- Paint
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- true
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-
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-
- Erase
-
-
- true
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- Size:
-
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- false
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- ROI:
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- qSlicerMarkupsPlaceWidget
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- qMRMLSegmentEditorWidget
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-
-
-
diff --git a/monailabel/plugins/slicer/MONAILabel/Screenshots/1.png b/monailabel/plugins/slicer/MONAILabel/Screenshots/1.png
deleted file mode 100644
index 2da5a92..0000000
Binary files a/monailabel/plugins/slicer/MONAILabel/Screenshots/1.png and /dev/null differ
diff --git a/monailabel/plugins/slicer/MONAILabel/Screenshots/2.png b/monailabel/plugins/slicer/MONAILabel/Screenshots/2.png
deleted file mode 100644
index 39a60b5..0000000
Binary files a/monailabel/plugins/slicer/MONAILabel/Screenshots/2.png and /dev/null differ
diff --git a/monailabel/plugins/slicer/MONAILabel/Screenshots/3.png b/monailabel/plugins/slicer/MONAILabel/Screenshots/3.png
deleted file mode 100644
index edb54d0..0000000
Binary files a/monailabel/plugins/slicer/MONAILabel/Screenshots/3.png and /dev/null differ
diff --git a/monailabel/plugins/slicer/MONAILabel/Testing/CMakeLists.txt b/monailabel/plugins/slicer/MONAILabel/Testing/CMakeLists.txt
deleted file mode 100644
index b3dd1c9..0000000
--- a/monailabel/plugins/slicer/MONAILabel/Testing/CMakeLists.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-add_subdirectory(Python)
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/.qt_for_python/uic/MONAILabelReviewer.py b/monailabel/plugins/slicer/MONAILabelReviewer/.qt_for_python/uic/MONAILabelReviewer.py
deleted file mode 100644
index 104649f..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/.qt_for_python/uic/MONAILabelReviewer.py
+++ /dev/null
@@ -1,706 +0,0 @@
-################################################################################
-# Form generated from reading UI file 'MONAILabelReviewer.ui'
-#
-# Created by: Qt User Interface Compiler version 5.15.2
-#
-# WARNING! All changes made in this file will be lost when recompiling UI file!
-################################################################################
-
-from ctkCollapsibleButton import ctkCollapsibleButton
-from PySide2.QtCore import *
-from PySide2.QtGui import *
-from PySide2.QtWidgets import *
-
-
-class Ui_MONAILabelReviewer:
- def setupUi(self, MONAILabelReviewer):
- if not MONAILabelReviewer.objectName():
- MONAILabelReviewer.setObjectName("MONAILabelReviewer")
- MONAILabelReviewer.resize(517, 752)
- self.gridLayout = QGridLayout(MONAILabelReviewer)
- self.gridLayout.setObjectName("gridLayout")
- self.horizontalLayout_9 = QHBoxLayout()
- self.horizontalLayout_9.setObjectName("horizontalLayout_9")
- self.btn_basic_mode = QPushButton(MONAILabelReviewer)
- self.btn_basic_mode.setObjectName("btn_basic_mode")
- self.btn_basic_mode.setStyleSheet("background-color: rgb(118, 214, 255);")
- self.btn_basic_mode.setCheckable(True)
- self.btn_basic_mode.setChecked(True)
-
- self.horizontalLayout_9.addWidget(self.btn_basic_mode)
-
- self.btn_reviewers_mode = QPushButton(MONAILabelReviewer)
- self.btn_reviewers_mode.setObjectName("btn_reviewers_mode")
- self.btn_reviewers_mode.setStyleSheet("background-color: rgb(255, 126, 121);")
- self.btn_reviewers_mode.setCheckable(True)
-
- self.horizontalLayout_9.addWidget(self.btn_reviewers_mode)
-
- self.gridLayout.addLayout(self.horizontalLayout_9, 2, 0, 1, 1)
-
- self.collapsibleButton_search_image = ctkCollapsibleButton(MONAILabelReviewer)
- self.collapsibleButton_search_image.setObjectName("collapsibleButton_search_image")
- self.collapsibleButton_search_image.setEnabled(False)
- self.collapsibleButton_search_image.setCollapsed(True)
- self.horizontalLayout = QHBoxLayout(self.collapsibleButton_search_image)
- self.horizontalLayout.setObjectName("horizontalLayout")
- self.verticalLayout_15 = QVBoxLayout()
- self.verticalLayout_15.setObjectName("verticalLayout_15")
- self.tabWidget = QTabWidget(self.collapsibleButton_search_image)
- self.tabWidget.setObjectName("tabWidget")
- self.ById = QWidget()
- self.ById.setObjectName("ById")
- self.ById.setMinimumSize(QSize(252, 0))
- self.verticalLayout_4 = QVBoxLayout(self.ById)
- self.verticalLayout_4.setObjectName("verticalLayout_4")
- self.label_18 = QLabel(self.ById)
- self.label_18.setObjectName("label_18")
-
- self.verticalLayout_4.addWidget(self.label_18)
-
- self.textEdit_search = QTextEdit(self.ById)
- self.textEdit_search.setObjectName("textEdit_search")
-
- self.verticalLayout_4.addWidget(self.textEdit_search)
-
- self.btn_search = QPushButton(self.ById)
- self.btn_search.setObjectName("btn_search")
- self.btn_search.setStyleSheet("background-color: rgb(146, 146, 146);")
-
- self.verticalLayout_4.addWidget(self.btn_search)
-
- self.tabWidget.addTab(self.ById, "")
- self.tab_8 = QWidget()
- self.tab_8.setObjectName("tab_8")
- self.verticalLayout_6 = QVBoxLayout(self.tab_8)
- self.verticalLayout_6.setObjectName("verticalLayout_6")
- self.label = QLabel(self.tab_8)
- self.label.setObjectName("label")
-
- self.verticalLayout_6.addWidget(self.label)
-
- self.comboBox_search_annotator = QComboBox(self.tab_8)
- self.comboBox_search_annotator.setObjectName("comboBox_search_annotator")
-
- self.verticalLayout_6.addWidget(self.comboBox_search_annotator)
-
- self.label_3 = QLabel(self.tab_8)
- self.label_3.setObjectName("label_3")
-
- self.verticalLayout_6.addWidget(self.label_3)
-
- self.comboBox_search_reviewer = QComboBox(self.tab_8)
- self.comboBox_search_reviewer.setObjectName("comboBox_search_reviewer")
-
- self.verticalLayout_6.addWidget(self.comboBox_search_reviewer)
-
- self.checkBox_search_approved = QCheckBox(self.tab_8)
- self.checkBox_search_approved.setObjectName("checkBox_search_approved")
-
- self.verticalLayout_6.addWidget(self.checkBox_search_approved)
-
- self.checkBox_search_flagged = QCheckBox(self.tab_8)
- self.checkBox_search_flagged.setObjectName("checkBox_search_flagged")
-
- self.verticalLayout_6.addWidget(self.checkBox_search_flagged)
-
- self.verticalSpacer_2 = QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding)
-
- self.verticalLayout_6.addItem(self.verticalSpacer_2)
-
- self.btn_search_annotator_reviewer = QPushButton(self.tab_8)
- self.btn_search_annotator_reviewer.setObjectName("btn_search_annotator_reviewer")
- self.btn_search_annotator_reviewer.setStyleSheet("background-color: rgb(146, 146, 146);")
-
- self.verticalLayout_6.addWidget(self.btn_search_annotator_reviewer)
-
- self.tabWidget.addTab(self.tab_8, "")
- self.tab = QWidget()
- self.tab.setObjectName("tab")
- self.verticalLayout_7 = QVBoxLayout(self.tab)
- self.verticalLayout_7.setObjectName("verticalLayout_7")
- self.label_2 = QLabel(self.tab)
- self.label_2.setObjectName("label_2")
-
- self.verticalLayout_7.addWidget(self.label_2)
-
- self.verticalSpacer_3 = QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding)
-
- self.verticalLayout_7.addItem(self.verticalSpacer_3)
-
- self.checkBox_search_easy = QCheckBox(self.tab)
- self.checkBox_search_easy.setObjectName("checkBox_search_easy")
-
- self.verticalLayout_7.addWidget(self.checkBox_search_easy)
-
- self.checkBox_search_medium = QCheckBox(self.tab)
- self.checkBox_search_medium.setObjectName("checkBox_search_medium")
-
- self.verticalLayout_7.addWidget(self.checkBox_search_medium)
-
- self.checkBox_search_hard = QCheckBox(self.tab)
- self.checkBox_search_hard.setObjectName("checkBox_search_hard")
-
- self.verticalLayout_7.addWidget(self.checkBox_search_hard)
-
- self.verticalSpacer_4 = QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding)
-
- self.verticalLayout_7.addItem(self.verticalSpacer_4)
-
- self.btn_search_level = QPushButton(self.tab)
- self.btn_search_level.setObjectName("btn_search_level")
- self.btn_search_level.setStyleSheet("background-color: rgb(146, 146, 146);")
-
- self.verticalLayout_7.addWidget(self.btn_search_level)
-
- self.tabWidget.addTab(self.tab, "")
-
- self.verticalLayout_15.addWidget(self.tabWidget)
-
- self.horizontalLayout.addLayout(self.verticalLayout_15)
-
- self.verticalLayout_16 = QVBoxLayout()
- self.verticalLayout_16.setObjectName("verticalLayout_16")
- self.label_search_result = QLabel(self.collapsibleButton_search_image)
- self.label_search_result.setObjectName("label_search_result")
-
- self.verticalLayout_16.addWidget(self.label_search_result)
-
- self.tableWidge_imageMeta = QTableWidget(self.collapsibleButton_search_image)
- if self.tableWidge_imageMeta.columnCount() < 3:
- self.tableWidge_imageMeta.setColumnCount(3)
- font = QFont()
- font.setPointSize(10)
- __qtablewidgetitem = QTableWidgetItem()
- __qtablewidgetitem.setTextAlignment(Qt.AlignCenter)
- __qtablewidgetitem.setFont(font)
- self.tableWidge_imageMeta.setHorizontalHeaderItem(0, __qtablewidgetitem)
- __qtablewidgetitem1 = QTableWidgetItem()
- __qtablewidgetitem1.setTextAlignment(Qt.AlignCenter)
- __qtablewidgetitem1.setFont(font)
- self.tableWidge_imageMeta.setHorizontalHeaderItem(1, __qtablewidgetitem1)
- __qtablewidgetitem2 = QTableWidgetItem()
- self.tableWidge_imageMeta.setHorizontalHeaderItem(2, __qtablewidgetitem2)
- self.tableWidge_imageMeta.setObjectName("tableWidge_imageMeta")
- self.tableWidge_imageMeta.setSortingEnabled(True)
-
- self.verticalLayout_16.addWidget(self.tableWidge_imageMeta)
-
- self.btn_show_image = QPushButton(self.collapsibleButton_search_image)
- self.btn_show_image.setObjectName("btn_show_image")
- self.btn_show_image.setEnabled(False)
-
- self.verticalLayout_16.addWidget(self.btn_show_image)
-
- self.horizontalLayout.addLayout(self.verticalLayout_16)
-
- self.gridLayout.addWidget(self.collapsibleButton_search_image, 8, 0, 1, 1)
-
- self.CollapsibleButton = ctkCollapsibleButton(MONAILabelReviewer)
- self.CollapsibleButton.setObjectName("CollapsibleButton")
- self.gridLayout_2 = QGridLayout(self.CollapsibleButton)
- self.gridLayout_2.setObjectName("gridLayout_2")
- self.gridLayout_3 = QGridLayout()
- self.gridLayout_3.setObjectName("gridLayout_3")
- self.comboBox_server_url = QComboBox(self.CollapsibleButton)
- self.comboBox_server_url.setObjectName("comboBox_server_url")
- self.comboBox_server_url.setEditable(True)
-
- self.gridLayout_3.addWidget(self.comboBox_server_url, 1, 1, 1, 1)
-
- self.label_idx_seg_image = QLabel(self.CollapsibleButton)
- self.label_idx_seg_image.setObjectName("label_idx_seg_image")
- self.label_idx_seg_image.setAlignment(Qt.AlignCenter)
-
- self.gridLayout_3.addWidget(self.label_idx_seg_image, 3, 2, 1, 1)
-
- self.label_idx_appr_image = QLabel(self.CollapsibleButton)
- self.label_idx_appr_image.setObjectName("label_idx_appr_image")
- self.label_idx_appr_image.setAlignment(Qt.AlignCenter)
-
- self.gridLayout_3.addWidget(self.label_idx_appr_image, 4, 2, 1, 1)
-
- self.btn_connect_monai = QPushButton(self.CollapsibleButton)
- self.btn_connect_monai.setObjectName("btn_connect_monai")
- self.btn_connect_monai.setStyleSheet("background-color: rgba(0, 144, 81, 1);")
-
- self.gridLayout_3.addWidget(self.btn_connect_monai, 1, 2, 1, 1)
-
- self.label_8 = QLabel(self.CollapsibleButton)
- self.label_8.setObjectName("label_8")
-
- self.gridLayout_3.addWidget(self.label_8, 3, 0, 1, 1)
-
- self.progressBar_approved_total = QProgressBar(self.CollapsibleButton)
- self.progressBar_approved_total.setObjectName("progressBar_approved_total")
- self.progressBar_approved_total.setStyleSheet("selection-background-color: rgb(255, 147, 0);")
- self.progressBar_approved_total.setValue(0)
-
- self.gridLayout_3.addWidget(self.progressBar_approved_total, 4, 1, 1, 1)
-
- self.label_17 = QLabel(self.CollapsibleButton)
- self.label_17.setObjectName("label_17")
-
- self.gridLayout_3.addWidget(self.label_17, 4, 0, 1, 1)
-
- self.progressBar_segmentation = QProgressBar(self.CollapsibleButton)
- self.progressBar_segmentation.setObjectName("progressBar_segmentation")
- self.progressBar_segmentation.setValue(0)
-
- self.gridLayout_3.addWidget(self.progressBar_segmentation, 3, 1, 1, 1)
-
- self.label_19 = QLabel(self.CollapsibleButton)
- self.label_19.setObjectName("label_19")
-
- self.gridLayout_3.addWidget(self.label_19, 1, 0, 1, 1)
-
- self.comboBox_reviewers = QComboBox(self.CollapsibleButton)
- self.comboBox_reviewers.setObjectName("comboBox_reviewers")
- self.comboBox_reviewers.setEnabled(True)
- self.comboBox_reviewers.setEditable(True)
-
- self.gridLayout_3.addWidget(self.comboBox_reviewers, 2, 1, 1, 1)
-
- self.label_20 = QLabel(self.CollapsibleButton)
- self.label_20.setObjectName("label_20")
-
- self.gridLayout_3.addWidget(self.label_20, 2, 0, 1, 1)
-
- self.gridLayout_2.addLayout(self.gridLayout_3, 0, 0, 1, 1)
-
- self.gridLayout.addWidget(self.CollapsibleButton, 4, 0, 1, 1)
-
- self.collapsibleButton_dicom_evaluation = ctkCollapsibleButton(MONAILabelReviewer)
- self.collapsibleButton_dicom_evaluation.setObjectName("collapsibleButton_dicom_evaluation")
- self.collapsibleButton_dicom_evaluation.setEnabled(False)
- self.collapsibleButton_dicom_evaluation.setCollapsed(False)
- self.verticalLayout_12 = QVBoxLayout(self.collapsibleButton_dicom_evaluation)
- self.verticalLayout_12.setObjectName("verticalLayout_12")
- self.verticalLayout_11 = QVBoxLayout()
- self.verticalLayout_11.setObjectName("verticalLayout_11")
- self.verticalLayout_2 = QVBoxLayout()
- self.verticalLayout_2.setObjectName("verticalLayout_2")
- self.label_level_difficulty = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_level_difficulty.setObjectName("label_level_difficulty")
-
- self.verticalLayout_2.addWidget(self.label_level_difficulty)
-
- self.horizontalLayout_5 = QHBoxLayout()
- self.horizontalLayout_5.setObjectName("horizontalLayout_5")
- self.btn_easy = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_easy.setObjectName("btn_easy")
- self.btn_easy.setStyleSheet("background-color: rgb(0, 250, 146);")
-
- self.horizontalLayout_5.addWidget(self.btn_easy)
-
- self.btn_medium = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_medium.setObjectName("btn_medium")
- self.btn_medium.setStyleSheet("background-color: rgba(255, 251, 0, 179);")
-
- self.horizontalLayout_5.addWidget(self.btn_medium)
-
- self.btn_hard = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_hard.setObjectName("btn_hard")
- self.btn_hard.setStyleSheet("background-color: rgba(255, 38, 0, 179);")
-
- self.horizontalLayout_5.addWidget(self.btn_hard)
-
- self.verticalLayout_2.addLayout(self.horizontalLayout_5)
-
- self.verticalLayout_11.addLayout(self.verticalLayout_2)
-
- self.horizontalLayout_3 = QHBoxLayout()
- self.horizontalLayout_3.setObjectName("horizontalLayout_3")
- self.btn_previous = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_previous.setObjectName("btn_previous")
- self.btn_previous.setStyleSheet("background-color: rgb(255, 147, 0);")
-
- self.horizontalLayout_3.addWidget(self.btn_previous)
-
- self.btn_next = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_next.setObjectName("btn_next")
- self.btn_next.setStyleSheet("background-color: rgb(118, 214, 255);")
-
- self.horizontalLayout_3.addWidget(self.btn_next)
-
- self.btn_mark_revision = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_mark_revision.setObjectName("btn_mark_revision")
-
- self.horizontalLayout_3.addWidget(self.btn_mark_revision)
-
- self.btn_approved = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_approved.setObjectName("btn_approved")
-
- self.horizontalLayout_3.addWidget(self.btn_approved)
-
- self.verticalLayout_11.addLayout(self.horizontalLayout_3)
-
- self.verticalLayout = QVBoxLayout()
- self.verticalLayout.setObjectName("verticalLayout")
- self.label_idx_image = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_idx_image.setObjectName("label_idx_image")
-
- self.verticalLayout.addWidget(self.label_idx_image)
-
- self.horizontalSlider_image_idx = QSlider(self.collapsibleButton_dicom_evaluation)
- self.horizontalSlider_image_idx.setObjectName("horizontalSlider_image_idx")
- self.horizontalSlider_image_idx.setEnabled(False)
- self.horizontalSlider_image_idx.setOrientation(Qt.Horizontal)
-
- self.verticalLayout.addWidget(self.horizontalSlider_image_idx)
-
- self.verticalLayout_11.addLayout(self.verticalLayout)
-
- self.verticalLayout_12.addLayout(self.verticalLayout_11)
-
- self.verticalLayout_10 = QVBoxLayout()
- self.verticalLayout_10.setObjectName("verticalLayout_10")
- self.label_version_labels = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_version_labels.setObjectName("label_version_labels")
-
- self.verticalLayout_10.addWidget(self.label_version_labels)
-
- self.splitter = QSplitter(self.collapsibleButton_dicom_evaluation)
- self.splitter.setObjectName("splitter")
- self.splitter.setOrientation(Qt.Horizontal)
- self.comboBox_label_version = QComboBox(self.splitter)
- self.comboBox_label_version.setObjectName("comboBox_label_version")
- self.splitter.addWidget(self.comboBox_label_version)
- self.btn_edit_label = QPushButton(self.splitter)
- self.btn_edit_label.setObjectName("btn_edit_label")
- self.btn_edit_label.setStyleSheet("background-color: rgb(0, 150, 255);")
- self.splitter.addWidget(self.btn_edit_label)
-
- self.verticalLayout_10.addWidget(self.splitter)
-
- self.splitter_3 = QSplitter(self.collapsibleButton_dicom_evaluation)
- self.splitter_3.setObjectName("splitter_3")
- self.splitter_3.setOrientation(Qt.Horizontal)
- self.btn_overwrite_version = QPushButton(self.splitter_3)
- self.btn_overwrite_version.setObjectName("btn_overwrite_version")
- self.btn_overwrite_version.setStyleSheet("")
- self.splitter_3.addWidget(self.btn_overwrite_version)
- self.btn_save_new_version = QPushButton(self.splitter_3)
- self.btn_save_new_version.setObjectName("btn_save_new_version")
- self.btn_save_new_version.setStyleSheet("")
- self.splitter_3.addWidget(self.btn_save_new_version)
- self.btn_delete_version = QPushButton(self.splitter_3)
- self.btn_delete_version.setObjectName("btn_delete_version")
- self.splitter_3.addWidget(self.btn_delete_version)
-
- self.verticalLayout_10.addWidget(self.splitter_3)
-
- self.btn_update_version = QPushButton(self.collapsibleButton_dicom_evaluation)
- self.btn_update_version.setObjectName("btn_update_version")
- self.btn_update_version.setStyleSheet("background-color: rgb(115, 250, 121);")
-
- self.verticalLayout_10.addWidget(self.btn_update_version)
-
- self.verticalLayout_12.addLayout(self.verticalLayout_10)
-
- self.horizontalLayout_4 = QHBoxLayout()
- self.horizontalLayout_4.setObjectName("horizontalLayout_4")
- self.horizontalLayout_2 = QHBoxLayout()
- self.horizontalLayout_2.setObjectName("horizontalLayout_2")
- self.verticalLayout_9 = QVBoxLayout()
- self.verticalLayout_9.setObjectName("verticalLayout_9")
- self.label_12 = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_12.setObjectName("label_12")
-
- self.verticalLayout_9.addWidget(self.label_12)
-
- self.label_13 = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_13.setObjectName("label_13")
-
- self.verticalLayout_9.addWidget(self.label_13)
-
- self.label_15 = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_15.setObjectName("label_15")
-
- self.verticalLayout_9.addWidget(self.label_15)
-
- self.label_16 = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_16.setObjectName("label_16")
-
- self.verticalLayout_9.addWidget(self.label_16)
-
- self.label_14 = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_14.setObjectName("label_14")
-
- self.verticalLayout_9.addWidget(self.label_14)
-
- self.label_5 = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_5.setObjectName("label_5")
-
- self.verticalLayout_9.addWidget(self.label_5)
-
- self.label_11 = QLabel(self.collapsibleButton_dicom_evaluation)
- self.label_11.setObjectName("label_11")
-
- self.verticalLayout_9.addWidget(self.label_11)
-
- self.horizontalLayout_2.addLayout(self.verticalLayout_9)
-
- self.verticalLayout_8 = QVBoxLayout()
- self.verticalLayout_8.setObjectName("verticalLayout_8")
- self.lineEdit_image_id = QLineEdit(self.collapsibleButton_dicom_evaluation)
- self.lineEdit_image_id.setObjectName("lineEdit_image_id")
- self.lineEdit_image_id.setEnabled(False)
-
- self.verticalLayout_8.addWidget(self.lineEdit_image_id)
-
- self.lineEdit_segmentator = QLineEdit(self.collapsibleButton_dicom_evaluation)
- self.lineEdit_segmentator.setObjectName("lineEdit_segmentator")
- self.lineEdit_segmentator.setEnabled(False)
-
- self.verticalLayout_8.addWidget(self.lineEdit_segmentator)
-
- self.lineEdit_date = QLineEdit(self.collapsibleButton_dicom_evaluation)
- self.lineEdit_date.setObjectName("lineEdit_date")
- self.lineEdit_date.setEnabled(False)
-
- self.verticalLayout_8.addWidget(self.lineEdit_date)
-
- self.lineEdit_level = QLineEdit(self.collapsibleButton_dicom_evaluation)
- self.lineEdit_level.setObjectName("lineEdit_level")
- self.lineEdit_level.setEnabled(False)
-
- self.verticalLayout_8.addWidget(self.lineEdit_level)
-
- self.lineEdit_status = QLineEdit(self.collapsibleButton_dicom_evaluation)
- self.lineEdit_status.setObjectName("lineEdit_status")
- self.lineEdit_status.setEnabled(False)
-
- self.verticalLayout_8.addWidget(self.lineEdit_status)
-
- self.lineEdit_editor = QLineEdit(self.collapsibleButton_dicom_evaluation)
- self.lineEdit_editor.setObjectName("lineEdit_editor")
-
- self.verticalLayout_8.addWidget(self.lineEdit_editor)
-
- self.lineEdit_editing_date = QLineEdit(self.collapsibleButton_dicom_evaluation)
- self.lineEdit_editing_date.setObjectName("lineEdit_editing_date")
-
- self.verticalLayout_8.addWidget(self.lineEdit_editing_date)
-
- self.horizontalLayout_2.addLayout(self.verticalLayout_8)
-
- self.horizontalLayout_4.addLayout(self.horizontalLayout_2)
-
- self.plainText_comment = QPlainTextEdit(self.collapsibleButton_dicom_evaluation)
- self.plainText_comment.setObjectName("plainText_comment")
-
- self.horizontalLayout_4.addWidget(self.plainText_comment)
-
- self.verticalLayout_12.addLayout(self.horizontalLayout_4)
-
- self.gridLayout.addWidget(self.collapsibleButton_dicom_evaluation, 7, 0, 1, 1)
-
- self.collapsibleButton_dicom_stream = ctkCollapsibleButton(MONAILabelReviewer)
- self.collapsibleButton_dicom_stream.setObjectName("collapsibleButton_dicom_stream")
- self.collapsibleButton_dicom_stream.setEnabled(False)
- self.collapsibleButton_dicom_stream.setCollapsed(True)
- self.gridLayout_5 = QGridLayout(self.collapsibleButton_dicom_stream)
- self.gridLayout_5.setObjectName("gridLayout_5")
- self.gridLayout_4 = QGridLayout()
- self.gridLayout_4.setObjectName("gridLayout_4")
- self.btn_load = QPushButton(self.collapsibleButton_dicom_stream)
- self.btn_load.setObjectName("btn_load")
-
- self.gridLayout_4.addWidget(self.btn_load, 0, 2, 1, 1)
-
- self.progressBar_approved_client = QProgressBar(self.collapsibleButton_dicom_stream)
- self.progressBar_approved_client.setObjectName("progressBar_approved_client")
- self.progressBar_approved_client.setStyleSheet("selection-background-color: rgba(255, 147, 0, 209);")
- self.progressBar_approved_client.setValue(0)
-
- self.gridLayout_4.addWidget(self.progressBar_approved_client, 2, 1, 1, 1)
-
- self.comboBox_clients = QComboBox(self.collapsibleButton_dicom_stream)
- self.comboBox_clients.setObjectName("comboBox_clients")
-
- self.gridLayout_4.addWidget(self.comboBox_clients, 0, 1, 1, 1)
-
- self.label_10 = QLabel(self.collapsibleButton_dicom_stream)
- self.label_10.setObjectName("label_10")
-
- self.gridLayout_4.addWidget(self.label_10, 2, 0, 1, 1)
-
- self.label_9 = QLabel(self.collapsibleButton_dicom_stream)
- self.label_9.setObjectName("label_9")
-
- self.gridLayout_4.addWidget(self.label_9, 1, 0, 1, 1)
-
- self.label_7 = QLabel(self.collapsibleButton_dicom_stream)
- self.label_7.setObjectName("label_7")
-
- self.gridLayout_4.addWidget(self.label_7, 0, 0, 1, 1)
-
- self.progressBar_segmented_client = QProgressBar(self.collapsibleButton_dicom_stream)
- self.progressBar_segmented_client.setObjectName("progressBar_segmented_client")
- self.progressBar_segmented_client.setStyleSheet("selection-background-color: rgba(78, 157, 246, 209);")
- self.progressBar_segmented_client.setValue(0)
- self.progressBar_segmented_client.setTextVisible(True)
-
- self.gridLayout_4.addWidget(self.progressBar_segmented_client, 1, 1, 1, 1)
-
- self.label_6 = QLabel(self.collapsibleButton_dicom_stream)
- self.label_6.setObjectName("label_6")
-
- self.gridLayout_4.addWidget(self.label_6, 3, 0, 1, 1)
-
- self.horizontalLayout_6 = QHBoxLayout()
- self.horizontalLayout_6.setObjectName("horizontalLayout_6")
- self.verticalLayout_3 = QVBoxLayout()
- self.verticalLayout_3.setObjectName("verticalLayout_3")
- self.checkBox_not_segmented = QCheckBox(self.collapsibleButton_dicom_stream)
- self.checkBox_not_segmented.setObjectName("checkBox_not_segmented")
- self.checkBox_not_segmented.setEnabled(False)
-
- self.verticalLayout_3.addWidget(self.checkBox_not_segmented)
-
- self.checkBox_flagged = QCheckBox(self.collapsibleButton_dicom_stream)
- self.checkBox_flagged.setObjectName("checkBox_flagged")
-
- self.verticalLayout_3.addWidget(self.checkBox_flagged)
-
- self.horizontalLayout_6.addLayout(self.verticalLayout_3)
-
- self.verticalLayout_5 = QVBoxLayout()
- self.verticalLayout_5.setObjectName("verticalLayout_5")
- self.checkBox_segmented = QCheckBox(self.collapsibleButton_dicom_stream)
- self.checkBox_segmented.setObjectName("checkBox_segmented")
-
- self.verticalLayout_5.addWidget(self.checkBox_segmented)
-
- self.checkBox_approved = QCheckBox(self.collapsibleButton_dicom_stream)
- self.checkBox_approved.setObjectName("checkBox_approved")
-
- self.verticalLayout_5.addWidget(self.checkBox_approved)
-
- self.horizontalLayout_6.addLayout(self.verticalLayout_5)
-
- self.gridLayout_4.addLayout(self.horizontalLayout_6, 3, 1, 1, 1)
-
- self.label_idx_seg_image_client = QLabel(self.collapsibleButton_dicom_stream)
- self.label_idx_seg_image_client.setObjectName("label_idx_seg_image_client")
- self.label_idx_seg_image_client.setAlignment(Qt.AlignCenter)
-
- self.gridLayout_4.addWidget(self.label_idx_seg_image_client, 1, 2, 1, 1)
-
- self.label_idx_appr_image_client = QLabel(self.collapsibleButton_dicom_stream)
- self.label_idx_appr_image_client.setObjectName("label_idx_appr_image_client")
- self.label_idx_appr_image_client.setAlignment(Qt.AlignCenter)
-
- self.gridLayout_4.addWidget(self.label_idx_appr_image_client, 2, 2, 1, 1)
-
- self.gridLayout_5.addLayout(self.gridLayout_4, 0, 0, 1, 1)
-
- self.gridLayout.addWidget(self.collapsibleButton_dicom_stream, 6, 0, 1, 1)
-
- self.verticalSpacer = QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding)
-
- self.gridLayout.addItem(self.verticalSpacer, 9, 0, 1, 1)
-
- self.retranslateUi(MONAILabelReviewer)
-
- self.tabWidget.setCurrentIndex(2)
-
- QMetaObject.connectSlotsByName(MONAILabelReviewer)
-
- # setupUi
-
- def retranslateUi(self, MONAILabelReviewer):
- self.btn_basic_mode.setText(QCoreApplication.translate("MONAILabelReviewer", "Basic mode", None))
- self.btn_reviewers_mode.setText(QCoreApplication.translate("MONAILabelReviewer", "Reviewer's mode", None))
- self.collapsibleButton_search_image.setText(
- QCoreApplication.translate("MONAILabelReviewer", "Search Images", None)
- )
- self.label_18.setText(QCoreApplication.translate("MONAILabelReviewer", "Image Ids", None))
- self.textEdit_search.setPlaceholderText(
- QCoreApplication.translate("MONAILabelReviewer", "imageId_1, imageId2, ...", None)
- )
- self.btn_search.setText(QCoreApplication.translate("MONAILabelReviewer", "Search", None))
- self.tabWidget.setTabText(
- self.tabWidget.indexOf(self.ById), QCoreApplication.translate("MONAILabelReviewer", "Ids", None)
- )
- self.label.setText(QCoreApplication.translate("MONAILabelReviewer", "Select annotator", None))
- self.label_3.setText(QCoreApplication.translate("MONAILabelReviewer", "Select reviewer", None))
- self.checkBox_search_approved.setText(QCoreApplication.translate("MONAILabelReviewer", "approved", None))
- self.checkBox_search_flagged.setText(QCoreApplication.translate("MONAILabelReviewer", "flagged", None))
- self.btn_search_annotator_reviewer.setText(QCoreApplication.translate("MONAILabelReviewer", "Search", None))
- self.tabWidget.setTabText(
- self.tabWidget.indexOf(self.tab_8),
- QCoreApplication.translate("MONAILabelReviewer", "Annotator/Reviewer", None),
- )
- self.label_2.setText(QCoreApplication.translate("MONAILabelReviewer", "Select level of difficulty", None))
- self.checkBox_search_easy.setText(QCoreApplication.translate("MONAILabelReviewer", "easy", None))
- self.checkBox_search_medium.setText(QCoreApplication.translate("MONAILabelReviewer", "medium", None))
- self.checkBox_search_hard.setText(QCoreApplication.translate("MONAILabelReviewer", "hard", None))
- self.btn_search_level.setText(QCoreApplication.translate("MONAILabelReviewer", "Search", None))
- self.tabWidget.setTabText(
- self.tabWidget.indexOf(self.tab), QCoreApplication.translate("MONAILabelReviewer", "Quality", None)
- )
- self.label_search_result.setText(QCoreApplication.translate("MONAILabelReviewer", "Result:", None))
- ___qtablewidgetitem = self.tableWidge_imageMeta.horizontalHeaderItem(0)
- ___qtablewidgetitem.setText(QCoreApplication.translate("MONAILabelReviewer", "Image Id", None))
- ___qtablewidgetitem1 = self.tableWidge_imageMeta.horizontalHeaderItem(1)
- ___qtablewidgetitem1.setText(QCoreApplication.translate("MONAILabelReviewer", "found", None))
- ___qtablewidgetitem2 = self.tableWidge_imageMeta.horizontalHeaderItem(2)
- ___qtablewidgetitem2.setText(QCoreApplication.translate("MONAILabelReviewer", "segmented", None))
- self.btn_show_image.setText(QCoreApplication.translate("MONAILabelReviewer", "Show", None))
- self.CollapsibleButton.setText(QCoreApplication.translate("MONAILabelReviewer", "Server", None))
- self.label_idx_seg_image.setText("")
- self.label_idx_appr_image.setText("")
- self.btn_connect_monai.setText(QCoreApplication.translate("MONAILabelReviewer", "Connect", None))
- self.label_8.setText(QCoreApplication.translate("MONAILabelReviewer", "Segmented", None))
- self.label_17.setText(QCoreApplication.translate("MONAILabelReviewer", "Approved", None))
- self.label_19.setText(QCoreApplication.translate("MONAILabelReviewer", "Server IP:", None))
- self.label_20.setText(QCoreApplication.translate("MONAILabelReviewer", "Reviewer:", None))
- self.collapsibleButton_dicom_evaluation.setText(
- QCoreApplication.translate("MONAILabelReviewer", "Data Evaluation", None)
- )
- self.label_level_difficulty.setText(
- QCoreApplication.translate("MONAILabelReviewer", "Level of difficulty", None)
- )
- self.btn_easy.setText(QCoreApplication.translate("MONAILabelReviewer", "Easy", None))
- self.btn_medium.setText(QCoreApplication.translate("MONAILabelReviewer", "Medium", None))
- self.btn_hard.setText(QCoreApplication.translate("MONAILabelReviewer", "Hard", None))
- self.btn_previous.setText(QCoreApplication.translate("MONAILabelReviewer", "Previous", None))
- self.btn_next.setText(QCoreApplication.translate("MONAILabelReviewer", "Next", None))
- self.btn_mark_revision.setText(QCoreApplication.translate("MONAILabelReviewer", "Flag", None))
- self.btn_approved.setText(QCoreApplication.translate("MONAILabelReviewer", "Approve", None))
- self.label_idx_image.setText(QCoreApplication.translate("MONAILabelReviewer", "Image: x/y", None))
- self.label_version_labels.setText(QCoreApplication.translate("MONAILabelReviewer", "Version of labels", None))
- self.btn_edit_label.setText(QCoreApplication.translate("MONAILabelReviewer", "Start label edit", None))
- self.btn_overwrite_version.setText(
- QCoreApplication.translate("MONAILabelReviewer", "Overwrite this version", None)
- )
- self.btn_save_new_version.setText(QCoreApplication.translate("MONAILabelReviewer", "Save as new version", None))
- self.btn_delete_version.setText(QCoreApplication.translate("MONAILabelReviewer", "Delete this version", None))
- self.btn_update_version.setText(QCoreApplication.translate("MONAILabelReviewer", "Confirm", None))
- self.label_12.setText(QCoreApplication.translate("MONAILabelReviewer", "Image Id: ", None))
- self.label_13.setText(QCoreApplication.translate("MONAILabelReviewer", "Annotator:", None))
- self.label_15.setText(QCoreApplication.translate("MONAILabelReviewer", "Annotation Date:", None))
- self.label_16.setText(QCoreApplication.translate("MONAILabelReviewer", "Difficulty Level:", None))
- self.label_14.setText(QCoreApplication.translate("MONAILabelReviewer", "Status:", None))
- self.label_5.setText(QCoreApplication.translate("MONAILabelReviewer", "Editor: ", None))
- self.label_11.setText(QCoreApplication.translate("MONAILabelReviewer", "Editing Date:", None))
- self.plainText_comment.setPlaceholderText(QCoreApplication.translate("MONAILabelReviewer", "Add Comment", None))
- self.collapsibleButton_dicom_stream.setText(
- QCoreApplication.translate("MONAILabelReviewer", "Data Set Explorer", None)
- )
- self.btn_load.setText(QCoreApplication.translate("MONAILabelReviewer", "Load", None))
- self.label_10.setText(QCoreApplication.translate("MONAILabelReviewer", "Approved", None))
- self.label_9.setText(QCoreApplication.translate("MONAILabelReviewer", "Segmented", None))
- self.label_7.setText(QCoreApplication.translate("MONAILabelReviewer", "Annotator", None))
- self.label_6.setText(QCoreApplication.translate("MONAILabelReviewer", "Filter", None))
- self.checkBox_not_segmented.setText(QCoreApplication.translate("MONAILabelReviewer", "not segmented", None))
- self.checkBox_flagged.setText(QCoreApplication.translate("MONAILabelReviewer", "flagged", None))
- self.checkBox_segmented.setText(QCoreApplication.translate("MONAILabelReviewer", "segmented", None))
- self.checkBox_approved.setText(QCoreApplication.translate("MONAILabelReviewer", "approved", None))
- self.label_idx_seg_image_client.setText("")
- self.label_idx_appr_image_client.setText("")
- pass
-
- # retranslateUi
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/CMakeLists.txt b/monailabel/plugins/slicer/MONAILabelReviewer/CMakeLists.txt
deleted file mode 100644
index 5e3dd65..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/CMakeLists.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#-----------------------------------------------------------------------------
-set(MODULE_NAME MONAILabelReviewer)
-
-#-----------------------------------------------------------------------------
-set(MODULE_PYTHON_SCRIPTS
- ${MODULE_NAME}.py
- ${MODULE_NAME}Lib/__init__
- ${MODULE_NAME}Lib/DataStoreKeys.py
- ${MODULE_NAME}Lib/ImageData.py
- ${MODULE_NAME}Lib/ImageDataController.py
- ${MODULE_NAME}Lib/ImageDataExtractor.py
- ${MODULE_NAME}Lib/ImageDataStatistics.py
- ${MODULE_NAME}Lib/JsonParser.py
- ${MODULE_NAME}Lib/MONAILabelReviewerEnum.py
- ${MODULE_NAME}Lib/MonaiServerREST.py
- ${MODULE_NAME}Lib/SegmentationMeta.py
- )
-
-set(MODULE_PYTHON_RESOURCES
- Resources/Icons/${MODULE_NAME}.png
- Resources/UI/${MODULE_NAME}.ui
- )
-
-#-----------------------------------------------------------------------------
-slicerMacroBuildScriptedModule(
- NAME ${MODULE_NAME}
- SCRIPTS ${MODULE_PYTHON_SCRIPTS}
- RESOURCES ${MODULE_PYTHON_RESOURCES}
- WITH_GENERIC_TESTS
- )
-
-#-----------------------------------------------------------------------------
-if(BUILD_TESTING)
-
- # Register the unittest subclass in the main script as a ctest.
- # Note that the test will also be available at runtime.
- slicer_add_python_unittest(SCRIPT ${MODULE_NAME}.py)
-
- # Additional build-time testing
- add_subdirectory(Testing)
-endif()
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewer.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewer.py
deleted file mode 100644
index 3b7b7ee..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewer.py
+++ /dev/null
@@ -1,1772 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import datetime
-import logging
-import os
-import re
-import tempfile
-from typing import Dict, List
-
-import qt
-import requests
-import SampleData
-import slicer
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.ImageDataController import (
- ImageDataController,
- ImageDataStatistics,
-)
-from MONAILabelReviewerLib.MONAILabelReviewerEnum import Label, Level, SegStatus
-from slicer.ScriptedLoadableModule import *
-from slicer.util import VTKObservationMixin
-
-
-class MONAILabelReviewer(ScriptedLoadableModule):
- """Uses ScriptedLoadableModule base class, available at:
- https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py
- """
-
- def __init__(self, parent):
- ScriptedLoadableModule.__init__(self, parent)
- self.parent.title = "MONAILabel Reviewer"
- self.parent.categories = ["Active Learning"]
- self.parent.dependencies = []
- self.parent.contributors = ["Minh Duc, Do (rAIdiance)"]
- self.parent.helpText = """
-This module provides the user to review on segmentations on X-Ray-dicom images.
-See more information in module documentation .
-"""
- self.parent.acknowledgementText = """
-Developed by rAiDiance, and funded by Berlin Institute of Health (BIH).
-"""
-
-
-class MONAILabelReviewerWidget(ScriptedLoadableModuleWidget, VTKObservationMixin):
- """Uses ScriptedLoadableModuleWidget base class, available at:
- https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py
- """
-
- def __init__(self, parent=None):
- """
- Called when the user opens the module the first time and the widget is initialized.
- """
- ScriptedLoadableModuleWidget.__init__(self, parent)
- VTKObservationMixin.__init__(self) # needed for parameter node observation
-
- # Color set
- self.colorGreenPressedButton = "background-color : rgb(0, 250, 146)"
- self.colorDarkGrayButton = "background-color : rgb(169, 169, 169)"
- self.colorGreenButtonAfterSuccessfulLoad = "background-color : rgb(0, 144, 81)"
- self.colorGreenEasyButton = "background-color : rgb(0, 250, 146)"
- self.colorYellowMediumButton = "background-color : rgba(255, 251, 0, 179)"
- self.colorRedHardButton = "background-color : rgba(255, 38, 0, 179)"
- self.colorLightGreenButton = "background-color : rgb(115, 250, 121)"
- self.colorRedReviewerModeButton = "background-color : rgb(255, 126, 121)"
- self.colorBlueBasicModeButton = "background-color : rgb(118, 214, 255)"
- self.colorRed = "color: red"
- self.colorGreen = "color: green"
- self.colorLightYellow = "background-color : rgb(255,255,153)"
-
- self.logic = None
- self._parameterNode = None
- self._updatingGUIFromParameterNode = False
-
- self.STATUS = SegStatus()
- self.LEVEL = Level()
- self.LABEL = Label()
-
- self.selectedReviewer: str = ""
- self.selectedClientId: str = ""
- self.currentImageId: str = ""
- self.currentLabelVersion: str = ""
- self.listImageData: List[ImageData] = None
- self.imageCounter: int = 0
- self.currentImageData: ImageData = None
- self.idToimageData: Dict[str, ImageData] = None
-
- # Meta Information
- self.finalStatus: str = ""
- self.finalLevel: str = ""
- self.finalComment: str = ""
- self.tmpdir = ""
-
- self.reviewersModeIsActive = False
- self.isSelectableByLabelVersion = False
-
- self.mapFiltersToBool: Dict[str, bool] = {
- "segmented": False,
- "notSegemented": False,
- "approved": False,
- "flagged": False,
- }
-
- def setup(self):
- """
- Called when the user opens the module the first time and the widget is initialized.
- """
- ScriptedLoadableModuleWidget.setup(self)
-
- # Load widget from .ui file (created by Qt Designer).
- # Additional widgets can be instantiated manually and added to self.layout.
- uiWidget = slicer.util.loadUI(self.resourcePath("UI/MONAILabelReviewer.ui"))
- self.layout.addWidget(uiWidget)
- self.ui = slicer.util.childWidgetVariables(uiWidget)
-
- # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's
- # "mrmlSceneChanged(vtkMRMLScene*)" signal in is connected to each MRML widget's.
- # "setMRMLScene(vtkMRMLScene*)" slot.
- uiWidget.setMRMLScene(slicer.mrmlScene)
-
- # Create logic class. Logic implements all computations that should be possible to run
- # in batch mode, without a graphical user interface.
- self.logic = MONAILabelReviewerLogic()
-
- # set segmentator editor
- self.segmentEditorWidget = slicer.qMRMLSegmentEditorWidget()
- self.addSegmentator()
- self.setLightVersion()
-
- self.ui.verticalLayout_10.addWidget(self.segmentEditorWidget)
- self.loadServerSelection()
-
- # Section: Widget Elements
- self.ui.btn_connect_monai.clicked.connect(self.init_dicom_stream)
- self.ui.btn_load.clicked.connect(self.loadImageData)
-
- self.ui.btn_approved.clicked.connect(self.approveSegmentation)
- self.ui.btn_mark_revision.clicked.connect(self.flagSegmentation)
-
- self.ui.btn_next.clicked.connect(self.getNextSegmentation)
- self.ui.btn_previous.clicked.connect(self.getPreviousSegmenation)
-
- self.ui.btn_easy.clicked.connect(self.setEasy)
- self.ui.btn_medium.clicked.connect(self.setMedium)
- self.ui.btn_hard.clicked.connect(self.setHard)
-
- self.ui.btn_search.clicked.connect(self.search)
- self.ui.btn_search_annotator_reviewer.clicked.connect(self.searchByAnnotatorReviewer)
- self.ui.btn_search_level.clicked.connect(self.searchByLevel)
-
- self.ui.checkBox_search_approved.clicked.connect(self.checkedAppprovedSearch)
- self.ui.checkBox_search_flagged.clicked.connect(self.checkedFlaggedSearch)
-
- self.ui.btn_show_image.clicked.connect(self.showSearchedImage)
-
- self.ui.checkBox_flagged.clicked.connect(self.checkedFlagged)
- self.ui.checkBox_approved.clicked.connect(self.checkApproved)
- self.ui.checkBox_not_segmented.clicked.connect(self.checkNotSegmented)
- self.ui.checkBox_segmented.clicked.connect(self.checkSegmented)
-
- self.ui.btn_basic_mode.clicked.connect(self.setLightVersion)
- self.ui.btn_reviewers_mode.clicked.connect(self.setReviewerVersion)
- self.ui.comboBox_clients.currentIndexChanged.connect(self.index_changed)
- self.ui.comboBox_reviewers.currentIndexChanged.connect(self.indexReviewerchanged)
-
- self.ui.comboBox_label_version.currentIndexChanged.connect(self.indexLabelVersionChanged)
- self.ui.btn_save_new_version.clicked.connect(self.setSaveAsNewVersion)
- self.ui.btn_overwrite_version.clicked.connect(self.setOverwriteCurrentVersion)
- self.ui.btn_update_version.clicked.connect(self.updateAfterEditingSegmentation)
- self.ui.btn_edit_label.clicked.connect(self.displayEditorTools)
- self.ui.btn_delete_version.clicked.connect(self.setDeleteVersion)
-
- def getCurrentTime(self):
- return datetime.datetime.now()
-
- def getCurrentMetaStatus(self) -> str:
- if not self.finalStatus:
- return ""
- return self.finalStatus
-
- def setCurrentMetaStatus(self, status=""):
- self.finalStatus = status
-
- def getCurrentMetaLevel(self) -> str:
- if not self.finalLevel:
- return ""
- return self.finalLevel
-
- def setCurrentMetaLevel(self, level=""):
- self.finalLevel = level
-
- def getCurrentComment(self) -> str:
- comment = self.ui.plainText_comment.toPlainText()
- if not comment:
- return ""
- return comment
-
- def setCurrentComment(self, comment=""):
- self.finalComment = comment
-
- def getSelectedReviewer(self) -> str:
- selectedReviewer = self.ui.comboBox_reviewers.currentText
- if not selectedReviewer:
- return ""
- return selectedReviewer
-
- def getSelectedClientFromComboBox(self) -> str:
- selectedClient = self.ui.comboBox_clients.currentText
- if not selectedClient:
- return ""
- return selectedClient
-
- def cleanup(self):
- """
- Called when the application closes and the module widget is destroyed.
- """
- self.removeObservers()
-
- def indexReviewerchanged(self, index):
- logging.info(f"{self.getCurrentTime()}: Selected reviewer: '{self.ui.comboBox_reviewers.currentText}'")
- self.selectedReviewer = self.ui.comboBox_reviewers.currentText
-
- def index_changed(self, index):
- self.loadImageData()
-
- def indexLabelVersionChanged(self, index):
- logging.warn(
- f"{self.getCurrentTime()}: Selected labal version: '{self.getCurrentLabelVersionFromComboBox()}', "
- f"is enabled '{self.isSelectableByLabelVersion}'"
- )
- self.displayAdditionalMetaIfEdited(self.getCurrentLabelVersionFromComboBox())
- if self.isSelectableByLabelVersion:
- self.disableDifficultyButtons(tag=self.getCurrentLabelVersion())
- self.loadNextImage(imageData=self.currentImageData, tag=self.getCurrentLabelVersion())
-
- def getCurrentLabelVersionFromComboBox(self) -> str:
- labelString = self.ui.comboBox_label_version.currentText
- return self.parseSelectedVersionFromComboBox(labelString)
-
- def getCurrentLabelVersion(self) -> str:
- label = self.getCurrentLabelVersionFromComboBox()
- if label == "":
- label = self.LABEL.FINAL
- return label
-
- def disableDifficultyButtons(self, tag: str):
- if self.LABEL.VERSION in tag:
- self.ui.btn_easy.hide()
- self.ui.btn_medium.hide()
- self.ui.btn_hard.hide()
- else:
- self.ui.btn_easy.show()
- self.ui.btn_medium.show()
- self.ui.btn_hard.show()
-
- def setSaveAsNewVersion(self) -> bool:
- setToSave = bool(self.ui.btn_save_new_version.isChecked())
- self.ui.btn_update_version.enabled = True
- if setToSave:
- self.ui.btn_overwrite_version.setChecked(False)
- self.ui.btn_overwrite_version.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.btn_delete_version.setChecked(False)
- self.ui.btn_delete_version.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.btn_save_new_version.setStyleSheet(self.colorGreenPressedButton)
- self.ui.btn_update_version.setText("Confirm: Saving")
- return setToSave
-
- def setOverwriteCurrentVersion(self) -> bool:
- setToOverwrite = bool(self.ui.btn_overwrite_version.isChecked())
- self.ui.btn_update_version.enabled = True
- if setToOverwrite:
- self.ui.btn_save_new_version.setChecked(False)
- self.ui.btn_save_new_version.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.btn_delete_version.setChecked(False)
- self.ui.btn_delete_version.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.btn_overwrite_version.setStyleSheet(self.colorGreenPressedButton)
- self.ui.btn_update_version.setText("Confirm: Overwriting")
-
- return setToOverwrite
-
- def setDeleteVersion(self) -> bool:
- setToDelete = bool(self.ui.btn_delete_version.isChecked())
- self.ui.btn_update_version.enabled = True
- if setToDelete:
- self.ui.btn_save_new_version.setChecked(False)
- self.ui.btn_save_new_version.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.btn_overwrite_version.setChecked(False)
- self.ui.btn_overwrite_version.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.btn_delete_version.setStyleSheet(self.colorGreenPressedButton)
- self.ui.btn_update_version.setText("Confirm: Deletion")
-
- return setToDelete
-
- def setButtonColorReviewerOrBasicMode(self, isReviewerMode: bool):
- if isReviewerMode:
- self.ui.btn_reviewers_mode.setStyleSheet(self.colorRedReviewerModeButton)
- self.ui.btn_basic_mode.setStyleSheet(self.colorDarkGrayButton)
- else:
- self.ui.btn_reviewers_mode.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_basic_mode.setStyleSheet(self.colorBlueBasicModeButton)
-
- def setReviewerVersion(self):
- self.reviewersModeIsActive = True
- self.setButtonColorReviewerOrBasicMode(isReviewerMode=self.reviewersModeIsActive)
- # section: Server
- # Reviewer Field
- self.ui.label_20.show()
- self.ui.comboBox_reviewers.show()
-
- # Approved bar
- self.ui.label_17.show()
- self.ui.progressBar_approved_total.show()
- self.ui.label_idx_appr_image.show()
-
- # section: Data set explorer
-
- # Approved bar
- self.ui.label_10.show()
- self.ui.progressBar_approved_client.show()
- self.ui.label_idx_appr_image_client.show()
-
- # filter option
- self.ui.label_6.show()
- self.ui.checkBox_not_segmented.show()
- self.ui.checkBox_flagged.show()
- self.ui.checkBox_segmented.show()
- self.ui.checkBox_approved.show()
-
- # section: Data evaluation
- self.ui.btn_easy.show()
- self.ui.btn_medium.show()
- self.ui.btn_hard.show()
- self.ui.label_level_difficulty.show()
- self.ui.btn_mark_revision.show()
- self.ui.btn_approved.show()
-
- # imag information
- self.ui.label_14.show()
- self.ui.lineEdit_status.show()
- self.ui.label_16.show()
- self.ui.lineEdit_level.show()
- self.ui.plainText_comment.show()
- if self.ui.btn_basic_mode.isChecked():
- self.ui.btn_basic_mode.setChecked(False)
-
- self.collapseAllSecions()
-
- self.activateSegmentatorEditor(activated=False)
- self.hideEditingSelectionOption(isHidden=False)
-
- # Section: Light version Option
- def setLightVersion(self):
- self.reviewersModeIsActive = False
- self.setButtonColorReviewerOrBasicMode(isReviewerMode=self.reviewersModeIsActive)
- # section: Server
- # Reviewer Field
- self.ui.label_20.hide()
- self.ui.comboBox_reviewers.hide()
-
- # Approved bar
- self.ui.label_17.hide()
- self.ui.progressBar_approved_total.hide()
- self.ui.label_idx_appr_image.hide()
-
- # section: Data set explorer
- # Approved bar
- self.ui.label_10.hide()
- self.ui.progressBar_approved_client.hide()
- self.ui.label_idx_appr_image_client.hide()
-
- # filter option
- self.ui.label_6.hide()
- self.ui.checkBox_not_segmented.hide()
- self.ui.checkBox_flagged.hide()
- self.ui.checkBox_segmented.hide()
- self.ui.checkBox_approved.hide()
-
- # section: Data evaluation
- self.ui.btn_easy.hide()
- self.ui.btn_medium.hide()
- self.ui.btn_hard.hide()
- self.ui.label_level_difficulty.hide()
- self.ui.btn_mark_revision.hide()
- self.ui.btn_approved.hide()
-
- # imag information
- self.ui.label_14.hide()
- self.ui.lineEdit_status.hide()
- self.ui.label_16.hide()
- self.ui.lineEdit_level.hide()
- self.ui.plainText_comment.hide()
- if self.ui.btn_reviewers_mode.isChecked():
- self.ui.btn_reviewers_mode.setChecked(False)
-
- self.collapseAllSecions()
-
- self.activateSegmentatorEditor(activated=False)
- self.hideEditingSelectionOption(isHidden=True)
-
- def cleanCache(self):
- self.logic = MONAILabelReviewerLogic()
- self.selectedReviewer = ""
- self.selectedClientId = ""
-
- self.listImageData = None
- self.imageCounter = 0
- self.currentImageData = None
- self.idToimageData = None
-
- # Meta Information
- self.setCurrentMetaStatus(status="")
- self.setCurrentMetaLevel(level="")
- self.setCurrentComment(comment="")
-
- logging.info(f"{self.getCurrentTime()}: Cache is cleaned")
-
- # Section: Server
- def loadServerSelection(self):
- settings = qt.QSettings()
- serverUrlHistory = settings.value("MONAILabel/serverUrlHistory")
-
- self.ui.comboBox_server_url.clear()
- self.ui.comboBox_server_url.addItems(serverUrlHistory.split(";"))
-
- def init_dicom_stream(self):
- """
- initiates connection to monai server
- Default: client listens on "http://127.0.0.1:8000"
- """
- # Check Connection
- self.cleanCache()
- serverUrl: str = self.ui.comboBox_server_url.currentText
- isConnected: bool = self.logic.connectToMonaiServer(serverUrl)
- if not isConnected:
- warningMessage = f"Connection to server failed \ndue to invalid ip '{serverUrl}'"
- slicer.util.warningDisplay(warningMessage)
- return
- self.ui.btn_connect_monai.setStyleSheet(self.colorGreenButtonAfterSuccessfulLoad)
- self.processDataStoreRecords()
- self.initUI()
-
- def collapseAllSecions(self):
- self.ui.collapsibleButton_search_image.enabled = False
- self.ui.collapsibleButton_dicom_stream.enabled = False
- self.ui.collapsibleButton_dicom_evaluation.enabled = False
-
- self.ui.collapsibleButton_search_image.collapsed = True
- self.ui.collapsibleButton_dicom_stream.collapsed = True
- self.ui.collapsibleButton_dicom_evaluation.collapsed = True
-
- def initUI(self):
- self.selectedReviewer = self.ui.comboBox_reviewers.currentText
- if self.reviewersModeIsActive and self.selectedReviewer == "":
- warningMessage = "Missing reviewer's name.\nPlease enter your id or name in the reviewer's field!"
- slicer.util.warningDisplay(warningMessage)
- return
- self.ui.collapsibleButton_search_image.enabled = True
- self.ui.collapsibleButton_dicom_stream.enabled = True
-
- # set Segmentation progress bar
- self.setProgessBar()
-
- # fill combobox
- self.fillComboBoxes()
-
- # set up buttons
- self.setButtons()
-
- self.selectedClientId = ""
-
- def setButtons(self):
- self.ui.btn_approved.setCheckable(True)
- self.ui.btn_mark_revision.setCheckable(True)
- self.ui.btn_easy.setCheckable(True)
- self.ui.btn_medium.setCheckable(True)
- self.ui.btn_hard.setCheckable(True)
- self.ui.btn_reviewers_mode.setCheckable(True)
- self.ui.btn_basic_mode.setCheckable(True)
-
- self.ui.btn_edit_label.setCheckable(True)
- self.ui.btn_save_new_version.setCheckable(True)
- self.ui.btn_overwrite_version.setCheckable(True)
- self.ui.btn_delete_version.setCheckable(True)
- self.ui.btn_update_version.setCheckable(True)
-
- self.ui.btn_delete_version.hide()
- self.ui.btn_save_new_version.hide()
- self.ui.btn_overwrite_version.hide()
- self.ui.btn_update_version.hide()
- self.ui.btn_update_version.enabled = False
-
- self.ui.btn_show_image.enabled = False
-
- def setProgessBar(self):
- statistics = self.logic.getStatistics()
-
- self.ui.progressBar_segmentation.setProperty("value", statistics.getSegmentationProgress())
- self.ui.label_idx_seg_image.setText(statistics.getIdxTotalSegmented())
- self.ui.label_idx_appr_image.setText(statistics.getIdxTotalApproved())
- self.ui.progressBar_approved_total.setProperty("value", statistics.getProgressPercentage())
-
- def fillComboBoxes(self):
- # clients
- clientIds = self.logic.getClientIds()
-
- self.ui.comboBox_clients.clear()
- self.ui.comboBox_clients.addItem("All")
- for clientId in clientIds:
- self.ui.comboBox_clients.addItem(str(clientId))
-
- # combobox in search section
- self.ui.comboBox_search_annotator.clear()
- self.ui.comboBox_search_annotator.addItem("All")
- for clientId in clientIds:
- self.ui.comboBox_search_annotator.addItem(str(clientId))
-
- # reviewers
- self.ui.comboBox_reviewers.clear()
- reviewers = self.logic.getReviewers()
- self.ui.comboBox_reviewers.addItem(self.selectedReviewer)
-
- for reviewer in reviewers:
- if reviewer == self.selectedReviewer:
- continue
- self.ui.comboBox_reviewers.addItem(str(reviewer))
- self.ui.comboBox_reviewers.setCurrentText(self.selectedReviewer)
-
- # combobox in search section
- self.ui.comboBox_search_reviewer.clear()
- self.ui.comboBox_search_reviewer.addItem("All")
- for reviewer in reviewers:
- self.ui.comboBox_search_reviewer.addItem(str(reviewer))
-
- def cleanDicomStreamSection(self):
- self.setCurrentMetaStatus(status="")
- self.setCurrentMetaLevel(level="")
- self.setCurrentComment(comment="")
-
- self.selectedClientId = None
- self.imageCounter = 0
- self.currentImageData = None
- self.idToimageData = None
- self.listImageData = None
-
- self.cleanProgressBarDicomStreamSection()
- self.cleanCheckBoxes()
- self.resetHorizontalSlider()
-
- # Section: Loading images
- def loadImageData(self):
- if (self.selectedClientId == self.getSelectedClientFromComboBox()) and (self.isDifferentFilter() is False):
- return
- self.imageCounter = 0
-
- self.cleanSearchSection()
- # select segmentator: ALL
- self.selectedClientId = self.getSelectedClientFromComboBox()
- if self.selectedClientId == "All":
- self.listImageData = self.loadImageDataWithFilter(selectedClientId="")
- self.ui.checkBox_segmented.setEnabled(True)
- self.ui.checkBox_not_segmented.setEnabled(True)
- self.setProgressBarOfAll()
-
- # select segmentator: client was selected
- if self.selectedClientId != "All":
- self.listImageData = self.loadImageDataWithFilter(selectedClientId=self.selectedClientId)
- self.setCheckBoxesClient()
- self.setProgressBarOfClient(self.selectedClientId)
-
- logging.info(
- "{}: Successfully loaded Image data [total = {}, category = '{}']".format(
- self.getCurrentTime(), len(self.listImageData), self.selectedClientId
- )
- )
-
- if len(self.listImageData) > 0:
- self.currentImageData = self.listImageData[self.imageCounter]
- self.loadNextImage(self.currentImageData)
-
- self.ui.collapsibleButton_dicom_evaluation.enabled = True
- self.ui.collapsibleButton_dicom_evaluation.collapsed = False
- self.setHorizontalSlider(len(self.listImageData))
- self.collectFilters()
- self.setLoadButtonColor(reload=False)
-
- def loadImageDataWithFilter(self, selectedClientId: str) -> list:
- isApproved = bool(self.ui.checkBox_approved.isChecked())
- isFlagged = bool(self.ui.checkBox_flagged.isChecked())
- isNotSegmented = bool(self.ui.checkBox_not_segmented.isChecked())
- segmented = bool(self.ui.checkBox_segmented.isChecked())
- logging.info(
- "{}: Selected filters: segmented= {} | isNotSegmented= {} | isApproved= {} | isFlagged= {}".format(
- self.getCurrentTime(), segmented, isNotSegmented, isApproved, isFlagged
- )
- )
- if selectedClientId == "":
- return self.logic.getAllImageData(segmented, isNotSegmented, isApproved, isFlagged)
- return self.logic.getImageDataByClientId(selectedClientId, isApproved, isFlagged)
-
- def setProgressBarOfAll(self):
- statistics: ImageDataStatistics = self.logic.getStatistics()
- # Progress bar: Segmented/TotalImage
- self.ui.progressBar_segmented_client.setProperty("value", statistics.getSegmentationProgressAllPercentage())
- self.ui.label_idx_seg_image_client.setText(statistics.getIdxTotalSegmented())
- # Progress bar: approvalCount/TotalImage
- self.ui.progressBar_approved_client.setProperty("value", statistics.getApprovalProgressPercentage())
- self.ui.label_idx_appr_image_client.setText(statistics.getIdxTotalApproved())
-
- def cleanProgressBarDicomStreamSection(self):
- self.ui.progressBar_segmented_client.setProperty("value", 0)
- self.ui.progressBar_approved_client.setProperty("value", 0)
- self.ui.label_idx_seg_image_client.setText("x/y")
- self.ui.label_idx_appr_image_client.setText("x/y")
-
- def setLoadButtonColor(self, reload: bool):
- if reload: # reload required
- self.ui.btn_load.setStyleSheet(self.colorDarkGrayButton)
- return
- self.ui.btn_load.setStyleSheet(self.colorGreenButtonAfterSuccessfulLoad)
-
- def setProgressBarOfClient(self, selectedClientId: str):
- percentageApprovedOfClient, idxApprovedOfClient = self.logic.getPercentageApproved(selectedClientId)
- self.ui.progressBar_approved_client.setProperty("value", percentageApprovedOfClient)
- self.ui.label_idx_appr_image_client.setText(idxApprovedOfClient)
-
- percentageSemgmentedByClient, idxSegmentedByClient = self.logic.getPercentageSemgmentedByClient(
- selectedClientId
- )
- self.ui.progressBar_segmented_client.setProperty("value", percentageSemgmentedByClient)
- self.ui.label_idx_seg_image_client.setText(idxSegmentedByClient)
-
- def setHorizontalSlider(self, loadesImageCount: int):
- self.ui.horizontalSlider_image_idx.setMinimum(0)
- self.ui.horizontalSlider_image_idx.setMaximum(loadesImageCount - 1)
- idxImage = f"Image: {self.imageCounter + 1}/{len(self.listImageData)}"
- self.ui.label_idx_image.setText(idxImage)
-
- def updateHorizontalSlider(self):
- self.ui.horizontalSlider_image_idx.setValue(self.imageCounter)
- idxImage = f"Image: {self.imageCounter + 1}/{len(self.listImageData)}"
- self.ui.label_idx_image.setText(idxImage)
-
- def resetHorizontalSlider(self):
- self.ui.horizontalSlider_image_idx.setValue(1)
- self.ui.label_idx_image.setText("Image:")
-
- # Section: Filter
- def collectFilters(self):
- self.mapFiltersToBool["segmented"] = self.ui.checkBox_segmented.isChecked()
- self.mapFiltersToBool["notSegemented"] = self.ui.checkBox_not_segmented.isChecked()
- self.mapFiltersToBool["approved"] = self.ui.checkBox_approved.isChecked()
- self.mapFiltersToBool["flagged"] = self.ui.checkBox_flagged.isChecked()
-
- def isDifferentFilter(self) -> bool:
- if self.mapFiltersToBool["segmented"] != self.ui.checkBox_segmented.isChecked():
- return True
- if self.mapFiltersToBool["notSegemented"] != self.ui.checkBox_not_segmented.isChecked():
- return True
- if self.mapFiltersToBool["approved"] != self.ui.checkBox_approved.isChecked():
- return True
- if self.mapFiltersToBool["flagged"] != self.ui.checkBox_flagged.isChecked():
- return True
- return False
-
- # CheckBox: clean
- def cleanCheckBoxes(self):
- self.ui.checkBox_segmented.setChecked(False)
- self.ui.checkBox_not_segmented.setChecked(False)
- self.ui.checkBox_flagged.setChecked(False)
- self.ui.checkBox_approved.setChecked(False)
-
- # CheckBox: flagged
- def setCheckBoxesClient(self):
- self.setLoadButtonColor(reload=True)
- self.ui.checkBox_not_segmented.setEnabled(False)
- self.ui.checkBox_segmented.setChecked(True)
- self.ui.checkBox_segmented.setEnabled(False)
-
- # CheckBox: flagged
- def checkedFlagged(self):
- self.setLoadButtonColor(reload=True)
- self.ui.checkBox_segmented.setChecked(True)
- if self.ui.checkBox_approved.isChecked():
- self.ui.checkBox_approved.setChecked(False)
- if self.ui.checkBox_not_segmented.isChecked():
- self.ui.checkBox_not_segmented.setChecked(False)
-
- # CheckBox: approved
- def checkApproved(self):
- self.setLoadButtonColor(reload=True)
- self.ui.checkBox_segmented.setChecked(True)
- if self.ui.checkBox_flagged.isChecked():
- self.ui.checkBox_flagged.setChecked(False)
- if self.ui.checkBox_not_segmented.isChecked():
- self.ui.checkBox_not_segmented.setChecked(False)
-
- # CheckBox: NOT segmented
- def checkNotSegmented(self):
- self.setLoadButtonColor(reload=True)
- if self.ui.checkBox_approved.isChecked():
- self.ui.checkBox_approved.setChecked(False)
- if self.ui.checkBox_flagged.isChecked():
- self.ui.checkBox_flagged.setChecked(False)
- if self.ui.checkBox_segmented.isChecked():
- self.ui.checkBox_segmented.setChecked(False)
-
- # CheckBox: segmented
- def checkSegmented(self):
- self.setLoadButtonColor(reload=True)
- if self.ui.checkBox_segmented.isChecked() is False:
- self.ui.checkBox_approved.setChecked(False)
- self.ui.checkBox_flagged.setChecked(False)
- return
-
- if self.ui.checkBox_not_segmented.isChecked():
- self.ui.checkBox_not_segmented.setChecked(False)
-
- # Section: Search Image
- def cleanSearchSection(self):
- self.ui.tableWidge_imageMeta.setRowCount(0)
- self.ui.tableWidge_imageMeta.clearContents()
- self.ui.textEdit_search.clear()
-
- def search(self):
- """
- After triggering search button, load images and segmentation by input ids
- """
- self.cleanDicomStreamSection()
-
- if self.ui.textEdit_search.toPlainText() == "":
- logging.info(f"{self.getCurrentTime()}: Search input field is empty")
- return
-
- idsStr = self.ui.textEdit_search.toPlainText()
- idList = self.getIdsFromString(idsStr)
-
- self.idToimageData = self.logic.getMultImageDataByIds(idList)
- self.listImageData = [*self.idToimageData.values()]
-
- foundIdList = [imageData.getName() for imageData in self.listImageData]
- notFoundIdList = [id for id in idList if (id not in foundIdList)]
- self.loadSearchImageMetaInTable(self.listImageData, notFoundIdList)
-
- self.ui.collapsibleButton_dicom_evaluation.enabled = True
- self.setHorizontalSlider(len(foundIdList))
- if len(foundIdList) > 0:
- self.setSearchResultMessage(numOfFound=len(foundIdList))
- self.loadFirstImage()
- else:
- self.setSearchResultMessage(numOfFound=0)
-
- def searchByAnnotatorReviewer(self):
- selectedAnnotator: str = self.ui.comboBox_search_annotator.currentText
- selectedReviewer: str = self.ui.comboBox_search_reviewer.currentText
- isApproved: bool = bool(self.ui.checkBox_search_approved.isChecked())
- isFlagged: bool = bool(self.ui.checkBox_search_flagged.isChecked())
- logging.warn(
- f"{self.getCurrentTime()}: Search by annontator: '{selectedAnnotator}' | reviewer: '{selectedReviewer}' "
- f"| isApproved: '{isApproved}' | isFlagged: '{isFlagged}'"
- )
-
- self.idToimageData = self.logic.searchByAnnotatorReviewer(
- selectedAnnotator, selectedReviewer, isApproved, isFlagged
- )
- self.listImageData = [*self.idToimageData.values()]
-
- self.loadSearchImageMetaInTable(self.listImageData, [])
- if len(self.listImageData) > 0:
- self.ui.collapsibleButton_dicom_evaluation.enabled = True
- self.setSearchResultMessage(numOfFound=len(self.idToimageData))
- self.setHorizontalSlider(len(self.idToimageData))
- self.loadFirstImage()
- else:
- self.setSearchResultMessage(numOfFound=0)
-
- def searchByLevel(self):
- isEasy: bool = bool(self.ui.checkBox_search_easy.isChecked())
- isMedium: bool = bool(self.ui.checkBox_search_medium.isChecked())
- isHard: bool = bool(self.ui.checkBox_search_hard.isChecked())
-
- self.idToimageData = self.logic.searchByLevel(isEasy, isMedium, isHard)
- self.listImageData = [*self.idToimageData.values()]
-
- self.loadSearchImageMetaInTable(self.listImageData, [])
- if len(self.listImageData) > 0:
- self.ui.collapsibleButton_dicom_evaluation.enabled = True
- self.setSearchResultMessage(numOfFound=len(self.idToimageData))
- self.setHorizontalSlider(len(self.idToimageData))
- self.loadFirstImage()
- else:
- self.setSearchResultMessage(numOfFound=0)
-
- def setSearchResultMessage(self, numOfFound: int):
- if numOfFound == 0:
- self.ui.label_search_result.setText("Result: No images found.")
- self.ui.label_search_result.setStyleSheet(self.colorRed)
- else:
- resultMessage = f"Result: {numOfFound} images found."
- self.ui.label_search_result.setText(resultMessage)
- self.ui.label_search_result.setStyleSheet(self.colorGreen)
-
- def checkedAppprovedSearch(self):
- isFlagged = bool(self.ui.checkBox_search_flagged.isChecked())
- if isFlagged:
- self.ui.checkBox_search_flagged.setChecked(False)
-
- def checkedFlaggedSearch(self):
- isApproved = bool(self.ui.checkBox_search_approved.isChecked())
- if isApproved:
- self.ui.checkBox_search_approved.setChecked(False)
-
- def loadSearchImageMetaInTable(self, foundlist: List[ImageData], notFoundIdList: List[str]):
- """
- Set table content after triggering button "show"
- Parameters:
- foundlist (list): list contains found ids
- notFoundIdList (list): list contains not found ids
- """
- rowCount = len(foundlist) + len(notFoundIdList)
- self.ui.tableWidge_imageMeta.setRowCount(rowCount)
- rowCounter = 0
- for row, imageData in enumerate(foundlist):
- self.ui.tableWidge_imageMeta.setItem(row, 0, qt.QTableWidgetItem(imageData.getName()))
- self.ui.tableWidge_imageMeta.setItem(row, 1, qt.QTableWidgetItem("Yes"))
- self.ui.tableWidge_imageMeta.setItem(row, 2, qt.QTableWidgetItem(str(imageData.isSegemented())))
- rowCounter += 1
-
- for row, notFoundId in enumerate(notFoundIdList):
- self.ui.tableWidge_imageMeta.setItem(rowCounter, 0, qt.QTableWidgetItem(notFoundId))
- self.ui.tableWidge_imageMeta.setItem(rowCounter, 1, qt.QTableWidgetItem("No"))
- self.ui.tableWidge_imageMeta.setItem(rowCounter, 2, qt.QTableWidgetItem("No"))
- rowCounter += 1
-
- self.ui.btn_show_image.enabled = True
-
- def loadFirstImage(self):
- self.imageCounter = 0
- self.currentImageData = self.listImageData[self.imageCounter]
- self.loadNextImage(self.currentImageData)
- self.updateHorizontalSlider()
-
- def showSearchedImage(self):
- """
- displays dicom & segmentation to corresponding selected row in listed ids
- """
- selectedRow = self.ui.tableWidge_imageMeta.currentRow()
- if selectedRow == -1:
- logging.warn(f"{self.getCurrentTime()}: Selected row [row number = {selectedRow}]is not valid")
- return
- selectedImageId = self.ui.tableWidge_imageMeta.item(selectedRow, 0).text()
-
- if selectedImageId not in self.idToimageData:
- logging.info(f"{self.getCurrentTime()}: Selected image id [id = {selectedImageId}] was not found")
- return
- self.currentImageData = self.idToimageData[selectedImageId]
- self.loadNextImage(self.currentImageData)
-
- def removeAllWhiteSpaces(self, strChain) -> str:
- """
- removes white spaces within string
- """
- pattern = r"\s+"
- return re.sub(pattern, "", strChain)
-
- def getIdsFromString(self, idStr: str) -> List[str]:
- """
- parses string which contains comma seperated ids
- Parameters:
- idStr (str): string which contains comma seperated ids
- Returns:
- list: contains ids
- """
- cleanedStr = self.removeAllWhiteSpaces(idStr)
- idsList = cleanedStr.split(",")
- return list(dict.fromkeys(idsList)) # remove all duplicates
-
- # Section: Dicom stream
- # Button: Approve
- def approveSegmentation(self):
- statusApproved = self.ui.btn_approved.isChecked()
- statusFlagged = self.ui.btn_mark_revision.isChecked()
-
- if statusFlagged or self.getCurrentMetaStatus() == self.STATUS.FLAGGED:
- self.ui.btn_mark_revision.setChecked(False)
- self.ui.btn_mark_revision.setDown(False)
- self.ui.btn_mark_revision.setStyleSheet(self.colorDarkGrayButton)
-
- if statusApproved:
- self.setCurrentMetaStatus(status=self.STATUS.APPROVED)
- self.ui.btn_approved.setChecked(True)
- self.ui.btn_approved.setStyleSheet(self.colorLightGreenButton)
- self.ui.btn_mark_revision.setStyleSheet(self.colorDarkGrayButton)
- self.ui.lineEdit_status.setStyleSheet(self.colorLightGreenButton)
- else:
- self.setCurrentMetaStatus(status="")
- self.resetButtonsOfApproveOrFlag()
- self.updateDisplayImageMetaData()
-
- # Button: Flagge
- def flagSegmentation(self):
- statusApproved = self.ui.btn_approved.isChecked()
- statusFlagged = self.ui.btn_mark_revision.isChecked()
-
- if statusApproved or self.getCurrentMetaStatus() == self.STATUS.APPROVED:
- self.ui.btn_approved.setChecked(False)
- self.ui.btn_approved.setDown(False)
- self.ui.btn_approved.setStyleSheet(self.colorDarkGrayButton)
- if statusFlagged:
- self.setCurrentMetaStatus(status=self.STATUS.FLAGGED)
- self.ui.btn_mark_revision.setChecked(True)
- self.ui.btn_mark_revision.setStyleSheet(self.colorLightGreenButton)
- self.ui.btn_approved.setStyleSheet(self.colorDarkGrayButton)
- self.ui.lineEdit_status.setStyleSheet(self.colorLightYellow)
- else:
- self.setCurrentMetaStatus(status="")
- self.resetButtonsOfApproveOrFlag()
- self.updateDisplayImageMetaData()
-
- def resetButtonsOfApproveOrFlag(self):
- self.ui.btn_mark_revision.setStyleSheet("")
- self.ui.btn_approved.setStyleSheet("")
- self.ui.lineEdit_status.setStyleSheet("")
-
- # Button: Clear
- def clearButtons(self):
- self.ui.btn_mark_revision.setChecked(False)
- self.ui.btn_approved.setChecked(False)
-
- self.ui.btn_mark_revision.setDown(False)
- self.ui.btn_approved.setDown(False)
-
- self.resetButtonsOfApproveOrFlag()
-
- self.ui.btn_easy.setChecked(False)
- self.ui.btn_medium.setChecked(False)
- self.ui.btn_hard.setChecked(False)
-
- self.ui.btn_easy.setDown(False)
- self.ui.btn_medium.setDown(False)
- self.ui.btn_hard.setDown(False)
-
- self.resetButtonOfDifficulty()
-
- def disableButtons(self):
- self.ui.btn_easy.setDown(False)
- self.ui.btn_medium.setDown(False)
- self.ui.btn_hard.setDown(False)
-
- def setDifficultyButtonAccordingColorAndChecked(self, difficulty: str):
- if difficulty == self.LEVEL.EASY:
- self.ui.btn_easy.setStyleSheet(self.colorGreenEasyButton)
- self.ui.lineEdit_level.setStyleSheet(self.colorGreenEasyButton)
-
- self.ui.btn_medium.setChecked(False)
- self.ui.btn_hard.setChecked(False)
-
- self.ui.btn_medium.setDown(False)
- self.ui.btn_hard.setDown(False)
-
- self.ui.btn_medium.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_hard.setStyleSheet(self.colorDarkGrayButton)
-
- elif difficulty == self.LEVEL.MEDIUM:
- self.ui.btn_medium.setStyleSheet(self.colorYellowMediumButton)
- self.ui.lineEdit_level.setStyleSheet(self.colorYellowMediumButton)
-
- self.ui.btn_easy.setChecked(False)
- self.ui.btn_hard.setChecked(False)
-
- self.ui.btn_easy.setDown(False)
- self.ui.btn_hard.setDown(False)
-
- self.ui.btn_easy.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_hard.setStyleSheet(self.colorDarkGrayButton)
-
- elif difficulty == self.LEVEL.HARD:
- self.ui.btn_hard.setStyleSheet(self.colorRedHardButton)
- self.ui.lineEdit_level.setStyleSheet(self.colorRedHardButton)
-
- self.ui.btn_easy.setChecked(False)
- self.ui.btn_medium.setChecked(False)
-
- self.ui.btn_easy.setDown(False)
- self.ui.btn_medium.setDown(False)
-
- self.ui.btn_easy.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_medium.setStyleSheet(self.colorDarkGrayButton)
-
- # Button: Easy
- def setEasy(self):
- levelEasy = self.ui.btn_easy.isChecked()
- if levelEasy:
- self.setCurrentMetaLevel(level=self.LEVEL.EASY)
- self.setDifficultyButtonAccordingColorAndChecked(difficulty=self.LEVEL.EASY)
- self.ui.lineEdit_level.setText(self.getCurrentMetaLevel())
-
- if levelEasy is False and self.getCurrentMetaLevel() == self.LEVEL.EASY:
- self.setCurrentMetaLevel(level="")
- self.resetButtonOfDifficulty()
- self.ui.lineEdit_level.setStyleSheet("")
-
- self.updateDisplayImageMetaData()
-
- # Button: Medium
- def setMedium(self):
- levelMedium = self.ui.btn_medium.isChecked()
- if levelMedium:
- self.setCurrentMetaLevel(level=self.LEVEL.MEDIUM)
- self.setDifficultyButtonAccordingColorAndChecked(difficulty=self.LEVEL.MEDIUM)
- self.ui.lineEdit_level.setText(self.getCurrentMetaLevel())
-
- if levelMedium is False and self.getCurrentMetaLevel() == self.LEVEL.MEDIUM:
- self.setCurrentMetaLevel(level="")
- self.resetButtonOfDifficulty()
- self.ui.lineEdit_level.setStyleSheet("")
-
- self.updateDisplayImageMetaData()
-
- # Button: Hard
- def setHard(self):
- levelHard = self.ui.btn_hard.isChecked()
-
- if levelHard:
- self.setCurrentMetaLevel(level=self.LEVEL.HARD)
- self.setDifficultyButtonAccordingColorAndChecked(difficulty=self.LEVEL.HARD)
- self.ui.lineEdit_level.setText(self.getCurrentMetaLevel())
-
- if levelHard is False and self.getCurrentMetaLevel() == self.LEVEL.HARD:
- self.setCurrentMetaLevel(level="")
- self.resetButtonOfDifficulty()
- self.ui.lineEdit_level.setStyleSheet("")
-
- self.updateDisplayImageMetaData()
-
- def resetButtonOfDifficulty(self):
- self.ui.btn_easy.setStyleSheet(self.colorGreenEasyButton)
- self.ui.btn_medium.setStyleSheet(self.colorYellowMediumButton)
- self.ui.btn_hard.setStyleSheet(self.colorRedHardButton)
-
- # Button: Next
- def getNextSegmentation(self):
- """
- after triggering next button:
- 1. persist meta data in monai server
- 2. update progess bar
- 3. load next dicom & segmentation
- """
-
- self.persistMetaInMonaiServer()
-
- # Re process Meta Data after image data was persisted
- self.reloadOverallStatistic()
- # Request Next Image
- self.imageCounter += 1
-
- if self.imageCounter >= len(self.listImageData):
- message = f"{self.getCurrentTime()}: End of list has been reached."
- slicer.util.warningDisplay(message)
- self.imageCounter = len(self.listImageData) - 1
- return
- self.updateHorizontalSlider()
- self.currentImageData = self.listImageData[self.imageCounter]
-
- # Displays Next Image
- self.loadNextImage(self.currentImageData)
- self.resetSegmentationEditorTools()
- self.activateSegmentatorEditor(activated=False)
-
- # Monai Server: Put
- def persistMetaInMonaiServer(self):
- """
- Sends the updated meta data of dicom and segmentation to monai-server
- Monai-server incorporates that information into datastore.json file
- """
- self.logic.updateLabelInfo(
- imageData=self.currentImageData,
- versionTag=self.getCurrentLabelVersion(),
- status=self.getCurrentMetaStatus(),
- level=self.getCurrentMetaLevel(),
- approvedBy=self.selectedReviewer,
- comment=self.getCurrentComment(),
- )
-
- # Button: Previouse
- def getPreviousSegmenation(self):
- """
- Loads the previous dicom and corresponding segmentation
- after useres tiggers Previous-Button
- """
- self.imageCounter -= 1
- if self.imageCounter < 0:
- message = f"{self.getCurrentTime()}: Lower limit of data set has been reached."
- slicer.util.warningDisplay(message)
- self.imageCounter = 0
- return
- self.updateHorizontalSlider()
- self.currentImageData = self.listImageData[self.imageCounter]
- self.currentImageData.display()
-
- self.fillComboBoxLabelVersions(self.currentImageData)
- approvedOrLatestVersionTag = self.currentImageData.getApprovedVersionTagElseReturnLatestVersion()
- self.loadNextImage(self.currentImageData, tag=approvedOrLatestVersionTag)
- self.resetSegmentationEditorTools()
-
- def cleanLineEditsContainingSegMeta(self):
- self.ui.lineEdit_image_id.setText("")
- self.ui.lineEdit_status.setText("")
- self.ui.lineEdit_segmentator.setText("")
- self.ui.lineEdit_level.setText("")
- self.ui.lineEdit_level.setStyleSheet("")
- self.ui.lineEdit_date.setText("")
- self.ui.plainText_comment.setPlainText("")
-
- def displayImageMetaData(self, imageData: ImageData, currentLabelVersion: str):
- """
- Displays meta info of dicom and segmentation in the info box on slicer
-
- Parameters:
- imageData (ImageData): Contains meta data (of dicom and segmenation)
- """
- self.cleanLineEditsContainingSegMeta()
- self.clearButtons()
-
- self.setCurrentMetaStatus(status=imageData.getStatus(currentLabelVersion))
-
- self.fillLineEditsWithSegmenationMeta(imageData, currentLabelVersion)
- self.setMetaButtonsAccordingToImageData(imageData, currentLabelVersion)
-
- self.setCurrentMetaLevel(level=imageData.getLevel(currentLabelVersion))
-
- def setMetaButtonsAccordingToImageData(self, imageData: ImageData, currentLabelVersion: str):
- finalLevel = imageData.getLevel(currentLabelVersion)
- if finalLevel != "":
- self.activateBtnLevelOfDifficulty(finalLevel)
-
- if imageData.isApprovedVersion(currentLabelVersion):
- self.activateBtnApproved(True)
-
- if imageData.isFlagged(currentLabelVersion):
- self.activateBtnApproved(False)
-
- def fillLineEditsWithSegmenationMeta(self, imageData: ImageData, currentLabelVersion: str):
- logging.info(f"==== currentLabelVersion: {currentLabelVersion}")
- logging.info(f"==== getName: {imageData.getName()}")
- logging.info(f"==== getClientId: {imageData.getClientId(currentLabelVersion)}")
- logging.info(f"==== getTime: {imageData.getTimeOfAnnotation()}")
- logging.info(f"==== getStatus: {imageData.getStatus(currentLabelVersion)}")
- logging.info(f"==== getComment: {imageData.getComment(currentLabelVersion)}")
- logging.info(f"==== getLevel: {imageData.getLevel(currentLabelVersion)}")
- logging.info(f"==== edtitingTme: {imageData.getTimeOfEditing(currentLabelVersion)}")
-
- name = imageData.getName()
- annotator = imageData.getClientId(currentLabelVersion)
- editor = imageData.getApprovedBy(currentLabelVersion)
- edtitingTme = imageData.getTimeOfEditing(currentLabelVersion)
- annotationTime = imageData.getTimeOfAnnotation()
- status = imageData.getStatus(currentLabelVersion)
- comment = imageData.getComment(currentLabelVersion)
-
- self.ui.lineEdit_image_id.setText(name)
- self.ui.lineEdit_segmentator.setText(annotator)
- self.ui.lineEdit_editor.setText(editor)
- self.ui.lineEdit_editing_date.setText(edtitingTme)
- self.ui.lineEdit_date.setText(annotationTime)
- self.ui.lineEdit_status.setText(status)
- self.ui.plainText_comment.setPlainText(comment)
-
- finalLevel = imageData.getLevel(currentLabelVersion)
- self.ui.lineEdit_level.setText(finalLevel)
-
- def activateBtnLevelOfDifficulty(self, finalLevel):
- if finalLevel == self.LEVEL.EASY:
- self.ui.btn_easy.setDown(True)
- self.ui.btn_easy.setChecked(True)
-
- self.ui.btn_easy.setStyleSheet(self.colorGreenEasyButton)
- self.ui.btn_medium.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_hard.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.lineEdit_level.setStyleSheet(self.colorGreenEasyButton)
- self.setEasy()
-
- elif finalLevel == self.LEVEL.MEDIUM:
- self.ui.btn_medium.setDown(True)
- self.ui.btn_medium.setChecked(True)
-
- self.ui.btn_easy.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_medium.setStyleSheet(self.colorYellowMediumButton)
- self.ui.btn_hard.setStyleSheet(self.colorDarkGrayButton)
-
- self.ui.lineEdit_level.setStyleSheet(self.colorYellowMediumButton)
- self.setMedium()
-
- elif finalLevel == self.LEVEL.HARD:
- self.ui.btn_hard.setDown(True)
- self.ui.btn_hard.setChecked(True)
-
- self.ui.btn_easy.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_medium.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_hard.setStyleSheet(self.colorRedHardButton)
-
- self.ui.lineEdit_level.setStyleSheet(self.colorRedHardButton)
- self.setHard()
-
- def activateBtnApproved(self, activated: bool):
- self.ui.btn_mark_revision.setChecked(not activated)
- self.ui.btn_mark_revision.setDown(not activated)
- self.ui.btn_approved.setChecked(activated)
- self.ui.btn_approved.setDown(activated)
- if activated:
- self.ui.btn_approved.setStyleSheet(self.colorLightGreenButton)
- self.ui.btn_mark_revision.setStyleSheet(self.colorDarkGrayButton)
- self.ui.lineEdit_status.setStyleSheet(self.colorLightGreenButton)
- else:
- self.ui.btn_approved.setStyleSheet(self.colorDarkGrayButton)
- self.ui.btn_mark_revision.setStyleSheet(self.colorLightGreenButton)
- self.ui.lineEdit_status.setStyleSheet(self.colorLightYellow)
-
- def updateDisplayImageMetaData(self):
- """
- Displays updated level (easy, medium, hard)
- in the info box on slicer
- """
- self.ui.lineEdit_status.setText(self.getCurrentMetaStatus())
-
- def loadNextImage(self, imageData: ImageData, tag=""):
- """
- Loads original Dicom image and Segmentation into slicer window
- Parameters:
- imageData (ImageData): Contains meta data (of dicom and segmenation)
- which is required for rest request to monai server
- in order to get dicom and segmenation (.nrrd).
- """
- slicer.mrmlScene.Clear()
- self.clearInformationFields()
-
- if tag == "":
- tag = self.currentImageData.getApprovedVersionTagElseReturnLatestVersion()
- if tag == "":
- tag = self.getCurrentLabelVersion()
- logging.warn(f"{self.getCurrentTime()} Loading image (id='{imageData.getName()}', tag='{tag}')")
- self.disableDifficultyButtons(tag=tag)
- self.displayImageMetaData(imageData, tag)
-
- self.logic.loadDicomAndSegmentation(imageData, tag)
-
- if imageData.getStatus() != self.STATUS.NOT_SEGMENTED:
- self.displayLabelOfSegmentation()
-
- if self.currentImageId is not imageData.getName():
- self.currentImageId = imageData.getName()
- self.fillComboBoxLabelVersions(imageData)
-
- def fillComboBoxLabelVersions(self, imageData: ImageData):
- self.isSelectableByLabelVersion = False
- self.ui.comboBox_label_version.clear()
- labelVersions = imageData.getVersionNames()
- approvedVersion = ""
- labelVersion = ""
- for labelVersion in labelVersions:
- if imageData.isApprovedVersion(versionTag=labelVersion) is True:
- labelVersion = "{} ({})".format(labelVersion, "approved")
- approvedVersion = labelVersion
- self.ui.comboBox_label_version.addItem(labelVersion)
- if approvedVersion != "":
- self.setVersionTagInComboBox(approvedVersion)
- elif labelVersion != "":
- self.setVersionTagInComboBox(labelVersion)
- self.isSelectableByLabelVersion = True
-
- def setVersionTagInComboBox(self, versionTag="", currentImageData=None):
- if self.isBlank(versionTag):
- return
- if (currentImageData is not None) and (currentImageData.isApprovedVersion(versionTag=versionTag) is True):
- versionTag = "{} ({})".format(versionTag, "approved")
- self.ui.comboBox_label_version.setCurrentText(versionTag)
-
- def parseSelectedVersionFromComboBox(self, versionTagString: str) -> str:
- if self.isBlank(versionTagString):
- return ""
- array = versionTagString.split()
- if len(array) == 0:
- return ""
- return array[0]
-
- # Sub Section: Display version selection option
- def activateSegmentatorEditor(self, activated=False):
- self.segmentEditorWidget.setSourceVolumeNodeSelectorVisible(activated)
- self.segmentEditorWidget.setSegmentationNodeSelectorVisible(activated)
- self.segmentEditorWidget.setSwitchToSegmentationsButtonVisible(activated)
- self.segmentEditorWidget.unorderedEffectsVisible = activated
- self.segmentEditorWidget.setReadOnly(not activated)
-
- def displayEditorTools(self):
- isCheckedForEdit = self.ui.btn_edit_label.isChecked()
- if isCheckedForEdit:
- self.clearButtons()
- self.ui.btn_edit_label.setText("Reset current label edit")
- self.cleanLineEditsContainingSegMetaWhenStartEditing()
- self.activatedEditorTools()
- else:
- self.deactivatedEditorTools()
- self.loadNextImage(imageData=self.currentImageData, tag=self.getCurrentLabelVersion())
-
- def cleanLineEditsContainingSegMetaWhenStartEditing(self):
- self.setCurrentMetaStatus(status="")
- self.setCurrentComment(comment="")
- self.ui.lineEdit_status.setText("")
- self.ui.lineEdit_editing_date.setText("")
- self.ui.plainText_comment.setPlainText("")
-
- def clearInformationFields(self):
- self.setCurrentMetaStatus(status="")
- self.setCurrentComment(comment="")
- self.setCurrentMetaLevel(level="")
-
- def activatedEditorTools(self):
- self.activateSegmentatorEditor(activated=True)
- self.ui.btn_save_new_version.setChecked(False)
- self.ui.btn_overwrite_version.setChecked(False)
- self.ui.btn_delete_version.setChecked(False)
-
- self.ui.btn_save_new_version.show()
- self.ui.btn_save_new_version.setStyleSheet("")
-
- self.ui.btn_overwrite_version.show()
- self.ui.btn_overwrite_version.setStyleSheet("")
-
- self.ui.btn_delete_version.show()
- self.ui.btn_delete_version.setStyleSheet("")
- self.ui.btn_update_version.show()
-
- self.ui.btn_next.enabled = False
- self.ui.btn_previous.enabled = False
- self.ui.btn_easy.enabled = False
- self.ui.btn_medium.enabled = False
- self.ui.btn_hard.enabled = False
- self.ui.btn_mark_revision.enabled = False
- self.ui.btn_approved.enabled = False
-
- def deactivatedEditorTools(self):
- self.activateSegmentatorEditor(activated=False)
- self.ui.btn_save_new_version.setChecked(False)
- self.ui.btn_overwrite_version.setChecked(False)
- self.ui.btn_delete_version.setChecked(False)
-
- self.ui.btn_save_new_version.hide()
- self.ui.btn_overwrite_version.hide()
- self.ui.btn_delete_version.hide()
- self.ui.btn_update_version.hide()
- self.ui.btn_update_version.enabled = False
-
- self.ui.btn_next.enabled = True
- self.ui.btn_previous.enabled = True
- self.ui.btn_easy.enabled = True
- self.ui.btn_medium.enabled = True
- self.ui.btn_hard.enabled = True
- self.ui.btn_mark_revision.enabled = True
- self.ui.btn_approved.enabled = True
-
- self.ui.btn_update_version.setText("Confirm")
- self.ui.btn_edit_label.setText("Start label edit")
-
- def resetSegmentationEditorTools(self):
- self.ui.btn_edit_label.setChecked(False)
- self.deactivatedEditorTools()
-
- def hideEditingSelectionOption(self, isHidden: bool):
- if isHidden:
- self.ui.btn_edit_label.hide()
- else:
- self.ui.btn_edit_label.show()
-
- # Section: Display label
- def addSegmentator(self):
- self.segmentEditorWidget.setMRMLScene(slicer.mrmlScene)
- self.selectParameterNode()
- self.segmentEditorWidget.setEffectNameOrder([])
-
- def displayLabelOfSegmentation(self):
- self.selectParameterNode()
- self.getDefaultSourceVolumeNodeID()
- self.segmentEditorWidget.SegmentationNodeComboBox.setCurrentNodeIndex(0)
- self.segmentEditorWidget.SourceVolumeNodeComboBox.setCurrentNodeIndex(0)
-
- def selectParameterNode(self):
- # Select parameter set node if one is found in the scene, and create one otherwise
- segmentEditorSingletonTag = "SegmentEditor"
- segmentEditorNode = slicer.mrmlScene.GetSingletonNode(segmentEditorSingletonTag, "vtkMRMLSegmentEditorNode")
- if segmentEditorNode is None:
- segmentEditorNode = slicer.mrmlScene.CreateNodeByClass("vtkMRMLSegmentEditorNode")
- segmentEditorNode.UnRegister(None)
- segmentEditorNode.SetSingletonTag(segmentEditorSingletonTag)
- segmentEditorNode = slicer.mrmlScene.AddNode(segmentEditorNode)
- self.segmentEditorWidget.setMRMLSegmentEditorNode(segmentEditorNode)
-
- def getDefaultSourceVolumeNodeID(self):
- layoutManager = slicer.app.layoutManager()
- firstForegroundVolumeID = None
- # Use first background volume node in any of the displayed layouts.
- # If no beackground volume node is in any slice view then use the first
- # foreground volume node.
- for sliceViewName in layoutManager.sliceViewNames():
- sliceWidget = layoutManager.sliceWidget(sliceViewName)
- if not sliceWidget:
- continue
- compositeNode = sliceWidget.mrmlSliceCompositeNode()
- if compositeNode.GetBackgroundVolumeID():
- return compositeNode.GetBackgroundVolumeID()
- if compositeNode.GetForegroundVolumeID() and not firstForegroundVolumeID:
- firstForegroundVolumeID = compositeNode.GetForegroundVolumeID()
- # No background volume was found, so use the foreground volume (if any was found)
- return firstForegroundVolumeID
-
- def getVersionName(self) -> str:
- if self.setOverwriteCurrentVersion():
- return self.getCurrentLabelVersion()
- else:
- return self.currentImageData.getNewVersionName()
-
- def persistEditedSegmentation(self, newVersionName: str):
- self.currentImageData.updateSegmentationMetaByVerionTag(
- tag=newVersionName,
- status=self.getCurrentMetaStatus(),
- level=self.getCurrentMetaLevel(),
- approvedBy=self.selectedReviewer,
- comment=self.getCurrentComment(),
- )
-
- segmentationNode = self.segmentEditorWidget.segmentationNode()
- self.tmpdir = slicer.util.tempDirectory("slicer-monai-reviewer")
- label_in = tempfile.NamedTemporaryFile(suffix=".nrrd", dir=self.tmpdir).name
- slicer.util.saveNode(segmentationNode, label_in)
-
- self.logic.saveLabelInMonaiServer(imageData=self.currentImageData, label_in=label_in, tag=newVersionName)
-
- def deleteLabelByVersionTag(self):
- imageVersionTag = self.getCurrentLabelVersion()
- if self.isBlank(imageVersionTag):
- return
- if self.currentImageData.hasVersionTag(versionTag=imageVersionTag) is False:
- return
- self.logic.deleteLabelByVersionTag(imageData=self.currentImageData, versionTag=imageVersionTag)
-
- def updateAfterEditingSegmentation(self):
- imageVersionTag = self.getCurrentLabelVersion()
- setToSave = bool(self.ui.btn_save_new_version.isChecked())
- setToOverwrite = bool(self.ui.btn_overwrite_version.isChecked())
- setToDelete = bool(self.ui.btn_delete_version.isChecked())
- newLabelNameCreated = ""
- if setToSave:
- newLabelNameCreated = self.currentImageData.getNewVersionName()
- self.persistEditedSegmentation(newVersionName=newLabelNameCreated)
-
- elif setToOverwrite:
- if (imageVersionTag == self.LABEL.FINAL) or (imageVersionTag == self.LABEL.ORIGINAL):
- warningMessage: str = (
- "Initial Segmentation with label 'final' or 'original' \n"
- "cannot be overwritten.\n Please save current edit as new version."
- )
- slicer.util.warningDisplay(warningMessage)
- logging.warn(warningMessage)
- return
-
- self.persistEditedSegmentation(newVersionName=imageVersionTag)
-
- elif setToDelete:
- if (imageVersionTag == self.LABEL.FINAL) or (imageVersionTag == self.LABEL.ORIGINAL):
- warningMessage: str = "Initial Segmentation with label 'final' or 'original' \ncannot be deleted."
- slicer.util.warningDisplay(warningMessage)
- logging.warn(warningMessage)
- return
-
- self.deleteLabelByVersionTag()
-
- self.resetSegmentationEditorTools()
- self.reloadImageAfterEditingLabel()
- if newLabelNameCreated != "":
- self.setVersionTagInComboBox(versionTag=newLabelNameCreated)
-
- def reloadImageAfterEditingLabel(self):
- imageId = self.currentImageData.getFileName()
- latestVersion = self.currentImageData.getLatestVersionTag()
- logging.info(f"{self.getCurrentTime()}: Loading image (id='{imageId}') with version tag = '{latestVersion}'")
- self.loadNextImage(imageData=self.currentImageData, tag=latestVersion)
- self.fillComboBoxLabelVersions(self.currentImageData)
-
- def processDataStoreRecords(self):
- serverUrl: str = self.ui.comboBox_server_url.currentText
- result: bool = self.logic.initMetaDataProcessing()
- if result is False:
- warningMessage = (
- "Request for datastore-info failed.\nPlease check if server address is correct \n('{}')!".format(
- serverUrl
- )
- )
- slicer.util.warningDisplay(warningMessage)
- logging.warn(warningMessage)
- return
- logging.info(f"{self.getCurrentTime()}: Successfully processed all records in datastore.")
-
- def reloadOverallStatistic(self):
- if self.reviewersModeIsActive:
- self.processDataStoreRecords()
- self.setProgessBar()
- self.setProgressBarOfAll()
-
- def displayAdditionalMetaIfEdited(self, tag: str):
- if tag == self.LABEL.FINAL or tag == self.LABEL.ORIGINAL:
- self.ui.lineEdit_editor.hide()
- self.ui.lineEdit_editing_date.hide()
- self.ui.label_editor.hide()
- self.ui.label_editing_date.hide()
- else:
- self.ui.lineEdit_editor.show()
- self.ui.lineEdit_editing_date.show()
- self.ui.label_editor.show()
- self.ui.label_editing_date.show()
-
- def isBlank(self, string) -> bool:
- return not (string and string.strip())
-
-
-class MONAILabelReviewerLogic(ScriptedLoadableModuleLogic):
- """This class should implement all the actual
- computation done by module. The interface
- should be such that other python code can import
- this class and make use of the functionality without
- requiring an instance of the Widget.
- Uses ScriptedLoadableModuleLogic base class, available at:
- https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py
- """
-
- def __init__(self):
- """
- Called when the logic class is instantiated. Can be used for initializing member variables.
- """
- ScriptedLoadableModuleLogic.__init__(self)
- self.temp_dir = None
- self.imageDataController: ImageDataController = ImageDataController()
-
- # Section: Server
- def getServerUrl(self) -> str:
- return self.imageDataController.getServerUrl()
-
- def getCurrentTime(self) -> datetime:
- return datetime.datetime.now()
-
- def connectToMonaiServer(self, serverUrl: str) -> bool:
- return self.imageDataController.connectToMonaiServer(serverUrl)
-
- def getMapIdToImageData(self) -> Dict[str, ImageData]:
- """
- Returns dictionary (Dict[str:ImageData]) which maps id to Imagedata-object
- """
- return self.imageDataController.getMapIdToImageData()
-
- def initMetaDataProcessing(self) -> bool:
- return self.imageDataController.initMetaDataProcessing()
-
- def getStatistics(self) -> ImageDataStatistics:
- return self.imageDataController.getStatistics()
-
- def getClientIds(self) -> List[str]:
- return self.imageDataController.getClientIds()
-
- def getReviewers(self) -> List[str]:
- return self.imageDataController.getReviewers()
-
- # Section: Loading images
- def getAllImageData(
- self, segmented: str, isNotSegmented: str, isApproved: bool, isFlagged: bool
- ) -> List[ImageData]:
- return self.imageDataController.getAllImageData(segmented, isNotSegmented, isApproved, isFlagged)
-
- def getImageDataByClientId(self, selectedClientId: str, isApproved: bool, isFlagged: bool) -> List[ImageData]:
- return self.imageDataController.getImageDataByClientId(selectedClientId, isApproved, isFlagged)
-
- def getPercentageApproved(self, selectedClientId: str):
- percentageApprovedOfClient, idxApprovedOfClient = self.imageDataController.getPercentageApproved(
- selectedClientId
- )
- return percentageApprovedOfClient, idxApprovedOfClient
-
- def getPercentageSemgmentedByClient(self, selectedClientId: str):
- percentageSemgmentedByClient, idxSegmentedByClient = self.imageDataController.getPercentageSemgmentedByClient(
- selectedClientId
- )
- return percentageSemgmentedByClient, idxSegmentedByClient
-
- # Section: Search Image
- def getMultImageDataByIds(self, idList: List[str]) -> Dict[str, ImageData]:
- return self.imageDataController.getMultImageDataByIds(idList)
-
- def searchByAnnotatorReviewer(
- self, selectedAnnotator: str, selectedReviewer: str, isApproved: bool, isFlagged: bool
- ) -> Dict[str, ImageData]:
- return self.imageDataController.searchByAnnotatorReviewer(
- selectedAnnotator, selectedReviewer, isApproved, isFlagged
- )
-
- def searchByLevel(self, isEasy: bool, isMedium: bool, isHard: bool) -> Dict[str, ImageData]:
- return self.imageDataController.getImageDataByLevel(isEasy=isEasy, isMedium=isMedium, isHard=isHard)
-
- def updateImageData(
- self, imageData: ImageData, versionTag: str, status: str, level: str, approvedBy: str, comment: str
- ) -> dict:
- """
- update meta data in information box
- Returns: jsonDict: json dictionary which contains updated meta data
- """
- imageId = imageData.getName()
- isEqual = imageData.isEqualSegmentationMeta(
- tag=versionTag, status=status, level=level, approvedBy=approvedBy, comment=comment
- )
- if isEqual:
- logging.info(f"{self.getCurrentTime()}: No changes for image (id='{imageId}')")
- return ""
-
- imageData.updateSegmentationMetaByVerionTag(
- tag=versionTag, status=status, level=level, approvedBy=approvedBy, comment=comment
- )
- jsonDict = imageData.getMetaByVersionTag(tag=versionTag)
-
- if jsonDict is None:
- logging.info(f"{self.getCurrentTime()}: No update for Image (id='{imageId}')")
- return ""
- logging.info(f"{self.getCurrentTime()}: Successfully updated Image (id='{imageId}')")
- return jsonDict
-
- # Section: Dicom stream
- def updateLabelInfo(
- self, imageData: ImageData, versionTag: str, status: str, level: str, approvedBy: str, comment: str
- ):
- imageId = imageData.getName()
- updatedMetaJson = self.updateImageData(imageData, versionTag, status, level, approvedBy, comment)
- if updatedMetaJson == "":
- logging.info(
- "{} : Image update (id='{}', version tag='{}') is empty".format(
- self.getCurrentTime(), imageId, versionTag
- )
- )
- return
-
- logging.info(f"{self.getCurrentTime()} : Image update (id='{imageId}', version tag='{versionTag}')")
- logging.info(updatedMetaJson)
- self.imageDataController.updateLabelInfoOfAllVersionTags(
- imageData=imageData, versionTag=versionTag, level=level, updatedMetaJson=updatedMetaJson
- )
-
- def loadDicomAndSegmentation(self, imageData: ImageData, tag: str):
- """
- Loads original Dicom image and Segmentation into slicer window
- Parameters:
- imageData (ImageData): Contains meta data (of dicom and segmenation)
- which is required for rest request to monai server
- in order to get dicom and segmenation (.nrrd).
- """
- # Request dicom
- image_name = imageData.getFileName()
- image_id = imageData.getName()
- node_name = imageData.getNodeName()
- logging.info(
- "{}: Request Data image_name='{}', node_name='{}', image_id='{}'".format(
- self.getCurrentTime(), image_name, node_name, image_id
- )
- )
-
- self.requestDicomImage(image_id, image_name, node_name)
- self.setTempFolderDir()
-
- # Request segmentation
- if imageData.isSegemented():
- segmentationFileName = imageData.getSegmentationFileName()
- img_blob = self.imageDataController.reuqestSegmentation(image_id, tag)
- destination = self.storeSegmentation(img_blob, segmentationFileName, self.temp_dir.name)
- self.displaySegmention(destination)
- os.remove(destination)
- logging.info(f"{self.getCurrentTime()}: Removed file at {destination}")
-
- def storeSegmentation(
- self, response: requests.models.Response, segmentationFileName: str, tempDirectory: str
- ) -> str:
- """
- stores loaded segmenation temporarily in local directory
- Parameters:
- response (requests.models.Response): contains segmentation data
- image_id (str): image id of segmentation
- """
- segmentation = response.content
- destination = self.getPathToStore(segmentationFileName, tempDirectory)
- with open(destination, "wb") as img_file:
- img_file.write(segmentation)
- logging.info(f"{self.getCurrentTime()}: Image segmentation is stored temoparily in: {destination}")
- return destination
-
- def getPathToStore(self, segmentationFileName: str, tempDirectory: str) -> str:
- return tempDirectory + "/" + segmentationFileName
-
- def displaySegmention(self, destination: str):
- """
- Displays the segmentation in slicer window
- """
- slicer.util.loadSegmentation(destination)
-
- def requestDicomImage(self, image_id: str, image_name: str, node_name: str):
- download_uri = self.imageDataController.getDicomDownloadUri(image_id)
- SampleData.SampleDataLogic().downloadFromURL(nodeNames=node_name, fileNames=image_name, uris=download_uri)
-
- def setTempFolderDir(self):
- """
- Create temporary dirctory to store the downloaded segmentation (.nrrd)
- """
- if self.temp_dir is None:
- self.temp_dir = tempfile.TemporaryDirectory()
- logging.info(f"{self.getCurrentTime()}: Temporary Directory: '{self.temp_dir.name}'")
-
- def saveLabelInMonaiServer(self, imageData: ImageData, label_in: str, tag: str):
- imageName = imageData.getName()
- params = imageData.obtainUpdatedParams(tag)
- self.imageDataController.saveLabelInMonaiServer(imageName, label_in, tag, params)
-
- def deleteLabelByVersionTag(self, imageData: ImageData, versionTag: str) -> bool:
- imageId = imageData.getName()
- imageData.deleteVersionName(versionTag)
- successfullyDeleted: bool = self.imageDataController.deleteLabelByVersionTag(imageId, versionTag)
- return successfullyDeleted
-
-
-class MONAILabelReviewerTest(ScriptedLoadableModuleTest):
- """
- This is the test case for your scripted module.
- Uses ScriptedLoadableModuleTest base class, available at:
- https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py
- """
-
- def setUp(self):
- """Do whatever is needed to reset the state - typically a scene clear will be enough."""
- slicer.mrmlScene.Clear()
-
- def runTest(self):
- """Run as few or as many tests as needed here."""
- self.setUp()
- self.test_MONAILabelReviewer1()
-
- def test_MONAILabelReviewer1(self):
- """Ideally you should have several levels of tests. At the lowest level
- tests should exercise the functionality of the logic with different inputs
- (both valid and invalid). At higher levels your tests should emulate the
- way the user would interact with your code and confirm that it still works
- the way you intended.
- One of the most important features of the tests is that it should alert other
- developers when their changes will have an impact on the behavior of your
- module. For example, if a developer removes a feature that you depend on,
- your test should break so they know that the feature is needed.
- """
-
- self.delayDisplay("Starting the test")
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/DataStoreKeys.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/DataStoreKeys.py
deleted file mode 100644
index 5c38878..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/DataStoreKeys.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List
-
-
-class DataStoreKeys:
- """
- DataStoreKeys contains arrays which represent the structure of datastore.json
- That provides an overview of keys which are relevant for the access the key-value-pair
- within json file.
- Below please have a look on an example of such entry in datastore.json.
- """
-
- def __init__(self):
- self.OBJECT = "objects"
- self.FINAL = "final"
- self.ORIGINAL = "original"
- self.ANNOTATE = "annotate"
- self.RANDOM = "Random"
- self.INFO = "info"
- self.LABEL_INFO = "label_info"
-
- self.IMAGE_INFO = ["image", "info"]
- self.FILENAME = ["image", "info", "name"]
- self.NODE_NAME = ["image", "info", "name"]
-
- self.TIMESTAMP = ["image", "info", "ts"]
- self.TIMESTAMP_ANNOTATE = ["image", "info", "strategy", "annotate", "ts"]
- self.TIMESTAMP_RANDOM = ["image", "info", "strategy", "Random", "ts"]
-
- self.STRATEGY = ["image", "info", "strategy"]
- self.CLIENT_ID_BY_ANNOTATE = ["image", "info", "strategy", "annotate", "client_id"]
- self.CLIENT_ID_BY_RANDOM = ["image", "info", "strategy", "Random", "client_id"]
- self.CLIENT_ID = ["labels", "final", "info", "client_id"]
-
- self.LABELS = ["labels"]
- self.LABELS_FINAL = ["labels", "final"]
- self.LABELS_FINAL_INFO = ["labels", "final", "info"]
- self.LABELS_INFO = ["labels", "original", "info", "label_info"]
- self.LABELS_FINAL_INFO_LABELS_INFO = ["labels", "final", "info", "label_info"]
- self.SEGMENTATION_NAME_BY_FINAL = ["labels", "final", "info", "name"]
- self.SEGMENTATION_NAME_BY_ORIGINAL = ["labels", "original", "info", "name"]
-
- # Additional entries in json file which contains meta data
- self.META = "segmentationMeta"
- self.META_STATUS = "status"
- self.META_LEVEL = "level"
- self.APPROVED_BY = "approvedBy"
-
- self.META_EDIT_TIME = "editTime"
- self.META_COMMENT = "comment"
-
- def getMeta(self, key: str, label: str) -> List[str]:
- return ["labels"] + [label] + ["info", "segmentationMeta"] + [key]
-
- def getMetaStatus(self, label: str):
- return self.getMeta(key=self.META_STATUS, label=label)
-
- def getMetaLevel(self, label: str):
- return self.getMeta(key=self.META_LEVEL, label=label)
-
- def getMetaApprovedBy(self, label: str):
- return self.getMeta(key=self.APPROVED_BY, label=label)
-
- def getMetaEditTime(self, label: str):
- return self.getMeta(key=self.META_EDIT_TIME, label=label)
-
- def getMetaComment(self, label: str):
- return self.getMeta(key=self.META_COMMENT, label=label)
-
- def getInfoInLabels(self, label: str):
- return ["labels"] + [label] + ["info"]
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageData.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageData.py
deleted file mode 100644
index 895c396..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageData.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-from datetime import datetime
-from typing import Dict, List
-
-from MONAILabelReviewerLib.MONAILabelReviewerEnum import SegStatus
-from MONAILabelReviewerLib.SegmentationMeta import SegmentationMeta
-
-
-class ImageData:
- """
- ImageData is a container for each segmentation/image.
- Such ImageData contains the meta data of corresponding segmentation/image (e.g. fileName, checkSum, comment, etc.)
- Each change (regarding the review process) will be monitored within ImageData.
- Once a user select the next segmentation during review the information in ImageData will be send to MONAI-Server in order
- to persist the data in datastore_v2.json file.
-
- """
-
- def __init__(self, name, fileName, nodeName, segmented, timeStamp, comment=""):
- self.name: str = name # equals imageId
- self.fileName: str = fileName
- self.nodeName: str = nodeName
- self.segmented: bool = segmented
- self.timeStamp: int = timeStamp
- self.comment: str = comment
-
- self.versionNames: List[str] = [] # equals to labelNames
- self.labelContent: dict = {}
- """
- example of 'labelContent'
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- """
- self.segmentationMetaDict: Dict[str, SegmentationMeta] = {}
-
- self.STATUS = SegStatus()
-
- self.client_id: str = None
- self.segmentationFileName: str = None
- self.tempDirectory: str = None
- self.prefixVersion = "version_"
- self.FINAL = "final"
- self.ORIGIN = "origin"
-
- def setVersionNames(self, versionNames: List[str]):
- self.versionNames: List[str] = versionNames
-
- def setLabelContent(self, labelContent: dict):
- self.labelContent: dict = labelContent
-
- def setSegmentationMetaDict(self, segmentationMetaDict: Dict[str, SegmentationMeta]):
- self.segmentationMetaDict = segmentationMetaDict
-
- def getName(self) -> str:
- return self.name
-
- def getFileName(self) -> str:
- return self.fileName
-
- def getNodeName(self) -> str:
- return self.nodeName
-
- def getsegmentationMetaDict(self) -> dict:
- return self.segmentationMetaDict
-
- def getClientId(self, versionTag="final") -> str:
- return self.client_id
-
- def getTimeStamp(self) -> int:
- return self.timeStamp
-
- def formatTimeStamp(self, timeStamp) -> str:
- if type(timeStamp) == str:
- return timeStamp
- return str(datetime.fromtimestamp(timeStamp))
-
- def getTimeOfAnnotation(self) -> str:
- return self.formatTimeStamp(self.timeStamp)
-
- def getTimeOfEditing(self, versionTag="final"):
- if self.isSegemented() is False or self.hasSegmentationMeta(tag=versionTag) is False:
- return ""
-
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=versionTag)
- if segmentationMeta is None:
- return ""
-
- formattedTime = self.formatTimeStamp(segmentationMeta.getEditTime())
- return formattedTime
-
- def isSegemented(self) -> bool:
- return self.segmented
-
- def getLabelContent(self) -> dict:
- return self.labelContent
-
- def getComment(self, versionTag="final") -> str:
- if self.isSegemented() is False or self.hasSegmentationMeta(tag=versionTag) is False:
- return ""
-
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=versionTag)
- if segmentationMeta is None:
- return ""
-
- return segmentationMeta.getComment()
-
- def getSegmentationMetaDict(self) -> dict:
- return self.segmentationMetaDict
-
- def getStatus(self, versionTag="final") -> str:
- if self.isSegemented() is False:
- return self.STATUS.NOT_SEGMENTED
-
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=versionTag)
- if segmentationMeta is None:
- return ""
-
- return segmentationMeta.getStatus()
-
- def getApprovedBy(self, versionTag="final") -> str:
- if self.isSegemented() is False or self.hasSegmentationMeta(tag=versionTag) is False:
- return ""
-
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=versionTag)
- if segmentationMeta is None:
- return ""
-
- return segmentationMeta.getApprovedBy()
-
- def isApprovedVersion(self, versionTag="final") -> bool:
- if self.isSegemented() is False or self.hasSegmentationMeta(tag=versionTag) is False:
- return False
-
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=versionTag)
- if segmentationMeta is None:
- return False
-
- status = segmentationMeta.getStatus()
- if status == self.STATUS.APPROVED:
- return True
-
- return False
-
- def isApproved(self, versionTag="final") -> bool:
- if self.isSegemented() is False:
- return False
-
- for segmentationMeta in self.segmentationMetaDict.values():
- status = segmentationMeta.getStatus()
- if status == self.STATUS.APPROVED:
- return True
- return False
-
- def isFlagged(self, versionTag="final") -> bool:
- if self.isSegemented() is False or self.hasSegmentationMeta(tag=versionTag) is False:
- return False
-
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=versionTag)
- if segmentationMeta is None:
- return False
-
- status = segmentationMeta.getStatus()
- if status == self.STATUS.FLAGGED:
- return True
-
- return False
-
- def getLevel(self, versionTag="final") -> str:
- if self.isSegemented() is False or self.hasSegmentationMeta(tag=versionTag) is False:
- return ""
-
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=versionTag)
- if segmentationMeta is None:
- return ""
- return segmentationMeta.getLevel()
-
- def setSegmentationFileName(self, fileName: str):
- self.segmentationFileName = fileName
-
- def getSegmentationFileName(self) -> str:
- return self.segmentationFileName
-
- def setClientId(self, client_id: str):
- self.client_id = client_id
-
- def addNewSegmentationMeta(self, tag: str, status: str, level: str, approvedBy: str, comment: str):
- segmentationMeta = SegmentationMeta()
- segmentationMeta.build(status=status, level=level, approvedBy=approvedBy, comment=comment, editTime="")
- segmentationMeta.setVersionNumber(versionTag=tag)
- self.segmentationMetaDict[tag] = segmentationMeta
-
- def getSegmentationMetaByVersionTag(self, tag: str):
- if tag not in self.segmentationMetaDict:
- return None
- return self.segmentationMetaDict[tag]
-
- def isEqualSegmentationMeta(self, tag: str, status: str, level: str, approvedBy: str, comment: str) -> bool:
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag)
- if (
- segmentationMeta is None
- and self.isBlank(status)
- and self.isBlank(level)
- and self.isBlank(approvedBy)
- and self.isBlank(comment)
- ):
- return True
-
- if segmentationMeta is None:
- self.addNewSegmentationMeta(tag, status, level, approvedBy, comment)
- return False
-
- return segmentationMeta.isEqual(status=status, level=level, approvedBy=approvedBy, comment=comment)
-
- def isBlank(self, string) -> bool:
- return not (string and string.strip())
-
- def getMetaByVersionTag(self, tag: str) -> dict:
- if tag not in self.segmentationMetaDict:
- return {}
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag)
- return segmentationMeta.getMeta()
-
- def hasSegmentationMeta(self, tag="final") -> bool:
- segmentationMeta = self.getSegmentationMetaByVersionTag(tag=tag)
- if segmentationMeta is None:
- return False
- return True
-
- def addSegementationMetaByVersionTag(self, tag="", status="", level="", approvedBy="", comment=""):
- segmentationMeta = SegmentationMeta()
- segmentationMeta.build(status=status, level=level, approvedBy=approvedBy, comment=comment)
- segmentationMeta.setVersionNumber(versionTag=tag)
- self.segmentationMetaDict[tag] = segmentationMeta
-
- def getSegementationMetaByVersionTag(self, tag: str) -> SegmentationMeta:
- if self.isBlank(tag):
- return None
- if tag not in self.segmentationMetaDict.keys():
- return None
- return self.segmentationMetaDict[tag]
-
- def obtainUpdatedParams(self, tag: str) -> dict:
- params = self.labelContent.copy()
- segementationMeta = self.getSegementationMetaByVersionTag(tag=tag)
- if segementationMeta is None:
- return params
- segementationMeta.setEditTime()
- metaData = segementationMeta.getMeta()
- if len(metaData) > 0:
- params["segmentationMeta"] = metaData["segmentationMeta"]
- return params
-
- def updateSegmentationMetaByVerionTag(self, tag="", status="", level="", approvedBy="", comment="") -> bool:
- if self.isBlank(tag):
- return False
- segmentationMeta = self.getSegementationMetaByVersionTag(tag=tag)
- if segmentationMeta is None:
- segmentationMeta = SegmentationMeta()
- segmentationMeta.build(status=status, level=level, approvedBy=approvedBy, comment=comment)
- segmentationMeta.setVersionNumber(versionTag=tag)
- else:
- segmentationMeta.update(status=status, level=level, approvedBy=approvedBy, comment=comment)
- segmentationMeta.setEditTime()
- self.segmentationMetaDict[tag] = segmentationMeta
-
- return True
-
- def updateApprovedStatusOfOtherThanSubjectedVersion(
- self, subjectedTag: str, difficultyLevel: str
- ) -> Dict[str, dict]:
- tagToSegmentationMetaJson = {}
- for tag, segmentationMeta in self.segmentationMetaDict.items():
- if subjectedTag == tag:
- continue
-
- updated = False
- if segmentationMeta.getStatus() == self.STATUS.APPROVED:
- segmentationMeta.setStatus("")
- updated = True
-
- if segmentationMeta.getLevel != difficultyLevel:
- segmentationMeta.setLevel(difficultyLevel)
- updated = True
-
- if updated:
- self.segmentationMetaDict[tag] = segmentationMeta
- tagToSegmentationMetaJson[tag] = segmentationMeta.getMeta()
- return tagToSegmentationMetaJson
-
- def getApprovedVersionTagElseReturnLatestVersion(self) -> str:
- latest = 0
- latestVersion = ""
-
- if len(self.segmentationMetaDict) == 1:
- return [*self.segmentationMetaDict.keys()][0]
-
- for tag, segmentationMeta in self.segmentationMetaDict.items():
- if segmentationMeta.getStatus() == self.STATUS.APPROVED:
- return tag
-
- version = segmentationMeta.getVersionNumber()
-
- if latest < version:
- latest = version
- latestVersion = tag
-
- return latestVersion
-
- # methods dealing with versions
-
- def getLatestVersionTag(self) -> str:
- if len(self.versionNames) == 0:
- return ""
- return self.versionNames[len(self.versionNames) - 1]
-
- def getOldestVersion(self) -> str:
- if len(self.versionNames) == 0:
- return ""
- return self.versionNames[0]
-
- def getNewVersionName(self) -> str:
- subsequentIndex = self.obtainSubsequentIndexFromVersionName(self.versionNames)
- newVersionName = self.obtainNextVersionName(subsequentIndex)
- self.versionNames.append(newVersionName)
- return newVersionName
-
- def getNumberOfVersions(self) -> int:
- return len(self.versionNames)
-
- def getVersionName(self, version: int) -> str:
- if version >= len(self.versionNames):
- return ""
-
- return self.versionNames[version]
-
- def hasVersionTag(self, versionTag: str):
- return versionTag in self.versionNames
-
- def getVersionNames(self) -> List[str]:
- return self.versionNames
-
- def obtainNextVersionName(self, index: int) -> str:
- return self.prefixVersion + str(index)
-
- def deleteVersionName(self, versionTag: str):
- if versionTag not in self.versionNames:
- return
-
- self.versionNames.remove(versionTag)
-
- if versionTag in self.segmentationMetaDict.items():
- self.segmentationMetaDict.pop(versionTag)
-
- def obtainSubsequentIndexFromVersionName(self, versionNames) -> int:
- if len(versionNames) == 0:
- return 1
- lastVersionTag = versionNames[len(versionNames) - 1]
- if lastVersionTag == self.FINAL or lastVersionTag == self.ORIGIN:
- return 1
- try:
- indexOfDelimeter = lastVersionTag.index("_")
- except BaseException:
- exceptionIndex = len(versionNames) + 100
- logging.info(
- f"Version name is incorrect. Format should be like 'version_1' but was {lastVersionTag}. "
- f"Hence, following id will be used {exceptionIndex}."
- )
- return exceptionIndex
-
- lastCharIndex = len(lastVersionTag)
- versionTagIndex = lastVersionTag[indexOfDelimeter + 1 : lastCharIndex]
- return int(versionTagIndex) + 1
-
- def display(self):
- print("name: ", self.name)
- print("fileName: ", self.fileName)
- print("nodeName: ", self.nodeName)
- print("isSegmented: ", self.segmented)
- print("getTimeStamp: ", self.getTimeOfAnnotation())
- print("=== Version labels ====")
- for version in self.versionNames:
- print("version: ", version)
- if self.isSegemented():
- print("Client Id: ", self.client_id)
- print("segmentationFileName: ", self.segmentationFileName)
- print("=== Segmentation Meta ====")
-
- if self.hasSegmentationMeta():
- for k, segmentationMeta in self.segmentationMetaDict.items():
- print("version: ", k)
- segmentationMeta.display()
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataController.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataController.py
deleted file mode 100644
index a027fc5..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataController.py
+++ /dev/null
@@ -1,247 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import datetime
-import logging
-from typing import Dict, List
-
-import requests
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.ImageDataExtractor import ImageDataExtractor
-from MONAILabelReviewerLib.ImageDataStatistics import ImageDataStatistics
-from MONAILabelReviewerLib.JsonParser import JsonParser
-from MONAILabelReviewerLib.MonaiServerREST import MonaiServerREST
-
-
-class ImageDataController:
- """
- ImageDataController manages all data processing and data transactions via
-
- 1. MonaiServerREST (requests and peristency of meta data, image data, segmentation data from monai server)
- 2. ImageDataExtractor (handling of logical operations coming from set of imageData)
- 3. JsonParser (parsing information from datastore_v2.json)
- 4. ImageData (container which caches information of single image and corresponding segmenation information and meta data)
-
- content of meta data:
- 1. "status" (flagged or approved)
- 2. "approvedBy" (name of reviewer)
- 3. "level" (level of difficulty of segmentation: easy, medium, hard)
- 4. "comment" (any comment on image and segmenation)
- 5. "editTime"
-
- list of meta information can be extanded
-
- """
-
- def __init__(self):
- self.monaiServerREST: MonaiServerREST = None
- self.imageDataExtractor: ImageDataExtractor = None
- self.temp_dir = None
-
- def getCurrentTime(self) -> datetime:
- return datetime.datetime.now()
-
- # ImageDataExtractor methods
-
- def initMetaDataProcessing(self) -> bool:
- """
- Passes mapIdToImageData to ImageDataExtractor object in order to process the meta information
- for each imageData
-
- returns True if it was successful
- else False
- """
- mapIdToImageData = self.getMapIdToImageData()
- if mapIdToImageData is None:
- return False
- self.imageDataExtractor = ImageDataExtractor(mapIdToImageData)
- self.imageDataExtractor.init()
- return True
-
- # returns only client id of those images which are segemented
- def getClientIds(self) -> List[str]:
- return self.imageDataExtractor.getClientIds()
-
- def getReviewers(self) -> List[str]:
- return self.imageDataExtractor.getReviewers()
-
- def getStatistics(self) -> ImageDataStatistics:
- """
- returns a map which contains statistical values which are comming from ImageDataExtractor object
- """
- statistics = ImageDataStatistics()
-
- statistics.build(
- segmentationProgress=self.imageDataExtractor.getSegmentationProgessInPercentage(),
- idxTotalSegmented=self.imageDataExtractor.getSegmentationVsTotalStr(),
- idxTotalApproved=self.imageDataExtractor.getApprovalVsTotal(),
- progressPercentage=self.imageDataExtractor.getApprovalProgressInPercentage(),
- segmentationProgressAllPercentage=self.imageDataExtractor.getSegmentationProgessInPercentage(),
- approvalProgressPercentage=self.imageDataExtractor.getApprovalProgressInPercentage(),
- )
-
- return statistics
-
- # Section: Loading images
- def getAllImageData(self, segmented, isNotSegmented, isApproved, isFlagged) -> List[ImageData]:
- return self.imageDataExtractor.getAllImageData(
- segmented=segmented, notSegmented=isNotSegmented, approved=isApproved, flagged=isFlagged
- )
-
- def getImageDataByClientId(self, selectedClientId, isApproved, isFlagged) -> List[ImageData]:
- return self.imageDataExtractor.getImageDataByClientId(
- clientId=selectedClientId, approved=isApproved, flagged=isFlagged
- )
-
- def getPercentageApproved(self, selectedClientId):
- percentageApprovedOfClient, idxApprovedOfClient = self.imageDataExtractor.getPercentageApproved(
- selectedClientId
- )
- return percentageApprovedOfClient, idxApprovedOfClient
-
- def getPercentageSemgmentedByClient(self, selectedClientId):
- percentageSemgmentedByClient, idxSegmentedByClient = self.imageDataExtractor.getPercentageSemgmentedByClient(
- selectedClientId
- )
- return percentageSemgmentedByClient, idxSegmentedByClient
-
- # Section: Search Image
- def getMultImageDataByIds(self, imageIds) -> Dict[str, ImageData]:
- return self.imageDataExtractor.getMultImageDataByIds(imageIds)
-
- def searchByAnnotatorReviewer(
- self, selectedAnnotator: str, selectedReviewer: str, isApproved: bool, isFlagged: bool
- ) -> Dict[str, ImageData]:
- """
- returns set of imageData (imageId mapped to ImageData) according to given filter options
- """
- idToImageData: Dict[str, ImageData] = {}
- imageIdsOfAnnotator = None
- if selectedAnnotator == "All" and selectedReviewer != "All":
- imageIdsOfAnnotator = self.imageDataExtractor.getImageDataByReviewer(
- selectedReviewer, isApproved, isFlagged
- )
-
- if selectedAnnotator != "All" and selectedReviewer == "All":
- imageIdsOfAnnotator = self.imageDataExtractor.getImageDataByClientId(
- selectedAnnotator, isApproved, isFlagged
- )
-
- if selectedReviewer == "All" and selectedAnnotator == "All":
- imageIdsOfAnnotator = self.imageDataExtractor.getAllImageData(
- segmented=True, notSegmented=False, approved=isApproved, flagged=isFlagged
- )
-
- if selectedReviewer != "All" and selectedAnnotator != "All":
- imageIdsOfAnnotator = self.imageDataExtractor.getImageDataByClientAndReviewer(
- selectedAnnotator, selectedReviewer, isApproved, isFlagged
- )
-
- if imageIdsOfAnnotator is None:
- return idToImageData
-
- for imageData in imageIdsOfAnnotator:
- idToImageData[imageData.getName()] = imageData
-
- return idToImageData
-
- def getImageDataByLevel(self, isEasy: bool, isMedium: bool, isHard: bool) -> Dict[str, ImageData]:
- """
- returns set of imageData (imageId mapped to ImageData) according to given level of difficulty
- """
- imageIdsOfAnnotator = self.imageDataExtractor.getImageDataByLevel(
- isEasy=isEasy, isMedium=isMedium, isHard=isHard
- )
- return imageIdsOfAnnotator
-
- # MONAI server methods
-
- def getServerUrl(self) -> str:
- return self.monaiServerREST.getServerUrl()
-
- def setMonaiServer(self, serverUrl: str):
- self.monaiServerREST = MonaiServerREST(serverUrl)
-
- def connectToMonaiServer(self, serverUrl: str) -> bool:
- self.setMonaiServer(serverUrl)
- return self.monaiServerREST.checkServerConnection()
-
- def getMapIdToImageData(self) -> Dict[str, ImageData]:
- """
- Returns dictionary (Dict[str:ImageData]) which maps id to Imagedata-object
- """
- jsonObj = self.monaiServerREST.requestDataStoreInfo()
- if jsonObj is None:
- return None
-
- # Parse json file to ImageData object
- jsonParser = JsonParser(jsonObj)
- jsonParser.init()
- mapIdToImageData = jsonParser.getMapIdToImageData()
- return mapIdToImageData
-
- # Section: Dicom stream
- def updateLabelInfoOfAllVersionTags(
- self, imageData: ImageData, versionTag: str, level: str, updatedMetaJson: dict
- ) -> bool:
- imageId = imageData.getName()
- self.updateLabelInfo(imageId, versionTag, updatedMetaJson)
-
- tagToSegmentationMetaJson = imageData.updateApprovedStatusOfOtherThanSubjectedVersion(
- subjectedTag=versionTag, difficultyLevel=level
- )
- for tag, segmentationMetaJson in tagToSegmentationMetaJson.items():
- self.updateLabelInfo(imageId, tag, segmentationMetaJson)
-
- def updateLabelInfo(self, imageId, versionTag, updatedMetaJson) -> bool:
- """
- sends meta information via http request to monai server
- in order to perist the information in datastore_v2.json file
-
- returns True if successfully sent http request
- else False
- """
- repsonseCode = self.monaiServerREST.updateLabelInfo(image_id=imageId, tag=versionTag, params=updatedMetaJson)
- if repsonseCode == 200:
- logging.info(f"{self.getCurrentTime()}: Successfully persist meta data for image (id='{imageId}')")
- return True
- else:
- logging.info(
- "{}: Failed meta date persistence for image (id='{}', response code = '{}')".format(
- self.getCurrentTime(), imageId, repsonseCode
- )
- )
- return False
-
- def reuqestSegmentation(self, image_id: str, tag: str) -> requests.models.Response:
- """
- after sending request to monai server
- rerturns response body (img_blob) which contains the segmentation data
- """
- img_blob = self.monaiServerREST.requestSegmentation(image_id, tag)
- logging.info(
- "{}: Segmentation successfully requested from MONAIServer (image id: {})".format(
- self.getCurrentTime(), image_id
- )
- )
- return img_blob
-
- def getDicomDownloadUri(self, image_id: str) -> str:
- return self.monaiServerREST.getDicomDownloadUri(image_id)
-
- def saveLabelInMonaiServer(self, image_in: str, label_in: str, tag: str, params: Dict):
- self.monaiServerREST.saveLabel(image_in, label_in, tag, params)
-
- def deleteLabelByVersionTag(self, imageId: str, versionTag: str) -> bool:
- reponseCode = self.monaiServerREST.deleteLabelByVersionTag(imageId, versionTag)
- if reponseCode == 200:
- return True
- return False
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataExtractor.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataExtractor.py
deleted file mode 100644
index aae2d41..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataExtractor.py
+++ /dev/null
@@ -1,378 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import datetime
-import logging
-from typing import Dict, List
-
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.MONAILabelReviewerEnum import Level
-
-
-class ImageDataExtractor:
- """
- ImageDataExtractor gets dictionary (mapping from id to ImageData from JsonParser) and caches
- Mapping:
- - imageIds TO ImageData,
- - client TO list of imageIds
- List:
- - imageIds of all images which are not segemented yet
- - imageIds of all images which are approved
- - all reviewers
-
- Each modification during review process will be stored in corresponding ImageData
- ImageDataExtractor provides the meta data across all ImageData-Containers when the user selects the filter option
- """
-
- def __init__(self, nameToImageData: dict):
- self.LEVEL = Level()
- self.nameToImageData: Dict[str, ImageData] = nameToImageData
-
- self.clientToImageIds: Dict[str, list] = {}
- self.idsOfNotSegmented: List[str] = []
- self.idsOfApprovedSementations: List[str] = []
- self.reviewers: List[str] = []
-
- def init(self):
- self.groupImageDataByClientId()
- self.extractAllReviewers()
- self.extractNotSegmentedImageIds()
-
- def getCurrentTime(self) -> datetime:
- return datetime.datetime.now()
-
- def groupImageDataByClientId(self):
- for imageId, imageData in self.nameToImageData.items():
- if imageData.isSegemented():
- clientId = imageData.getClientId()
- if clientId:
- if clientId not in self.clientToImageIds:
- self.clientToImageIds[clientId] = []
- self.clientToImageIds[clientId].append(imageId)
-
- def extractAllReviewers(self):
- for imageData in self.nameToImageData.values():
- if imageData.isSegemented():
- reviewer = imageData.getApprovedBy()
- if reviewer not in self.reviewers and reviewer != "":
- self.reviewers.append(reviewer)
-
- def extractNotSegmentedImageIds(self):
- for imageId, imageData in self.nameToImageData.items():
- if imageData.isSegemented() is False:
- self.idsOfNotSegmented.append(imageId)
-
- def getTotalNumImages(self) -> int:
- return len(self.nameToImageData)
-
- def getImageDataIds(self) -> List[str]:
- return [*self.nameToImageData.keys()]
-
- def getClientIds(self) -> List[str]:
- return [*self.clientToImageIds.keys()]
-
- def getReviewers(self) -> List[str]:
- return self.reviewers
-
- def getImageDataNotsegmented(self) -> List[ImageData]:
- """
- returns list of ImageData of corresponingd image studies wich has not been segemeted
- """
- notSegmented = []
- for id in self.idsOfNotSegmented:
- imageData = self.nameToImageData[id]
- notSegmented.append(imageData)
- return notSegmented
-
- def getNumOfNotSegmented(self) -> int:
- return len(self.idsOfNotSegmented)
-
- def getNumOfSegmented(self) -> int:
- count = 0
- for idList in self.clientToImageIds.values():
- count += len(idList)
- return count
-
- def getSegmentationProgessInPercentage(self) -> int:
- """
- returns percentage of already segmented images out of all available images
- """
- segmentedCount = self.getNumOfSegmented()
- float_Num = segmentedCount / self.getTotalNumImages()
- return int(float_Num * 100)
-
- def getSegmentationVsTotalStr(self) -> str:
- """
- returns the index of subjected imageData within imageData data set
- """
- segmentedCount = self.getNumOfSegmented()
- idxTotalSegmented = f"{segmentedCount}/{self.getTotalNumImages()}"
- return idxTotalSegmented
-
- def getApprovalProgressInPercentage(self) -> int:
- """
- returns percentage of already approved imageData out of all available imageData
- """
- approvalCount = self.getNumApprovedSegmentation()
- fraction = approvalCount / self.getTotalNumImages()
- return int(fraction * 100)
-
- def getApprovalVsTotal(self) -> str:
- approvalCount = self.getNumApprovedSegmentation()
- idxTotalApproved = f"{approvalCount}/{self.getTotalNumImages()}"
- return idxTotalApproved
-
- def invalidFilterCombination(self, segmented: bool, notSegmented: bool, approved: bool, flagged: bool) -> bool:
- return (
- (notSegmented is True and segmented is True)
- or (approved is True and flagged is True)
- or (notSegmented is True and approved is True)
- or (notSegmented is True and flagged is True)
- )
-
- def getAllImageData(self, segmented=False, notSegmented=False, approved=False, flagged=False) -> List[ImageData]:
- """
- returns fitered list of imageData which are filtered according to input parameters
- """
- if self.invalidFilterCombination(segmented, notSegmented, approved, flagged):
- logging.warning(
- f"{self.getCurrentTime()}: Selected filter options are not valid: segmented='{segmented}' | "
- f"notSegmented='{notSegmented}' | approved='{approved}' | flagged='{flagged}')"
- )
- return None
-
- if notSegmented is False and segmented is False and approved is False and flagged is False:
- return [*self.nameToImageData.values()]
-
- selectedImageData = []
- for imagedata in self.nameToImageData.values():
- if notSegmented is True and segmented is False and imagedata.isSegemented() is False:
- selectedImageData.append(imagedata)
- continue
-
- if imagedata.isSegemented() is segmented and imagedata.isApproved() is True and approved is True:
- selectedImageData.append(imagedata)
- continue
-
- if (
- imagedata.isSegemented() is segmented
- # and imagedata.isApproved() is approved
- and imagedata.isFlagged() is True
- and flagged is True
- ):
- selectedImageData.append(imagedata)
- continue
-
- return selectedImageData
-
- def getImageDataByClientId(self, clientId: str, approved=False, flagged=False) -> List[ImageData]:
- """
- returns fitered list of imageData which are filtered according to client (=annotator) and parameters (approved, flagged)
- """
- if clientId == "":
- return None
- if approved and flagged:
- logging.warning(
- "{}: Selected filter options are not valid: approved='{}' and flagged='{}')".format(
- self.getCurrentTime(), approved, flagged
- )
- )
- return None
-
- imageIds = self.clientToImageIds[clientId]
-
- if approved is False and flagged is False:
- return self.extractImageDataByIds(imageIds)
- else:
- return self.extractImageDataByApprovedAndFlaggedStatus(clientId, approved, flagged, imageIds)
-
- def extractImageDataByIds(self, imageIds: List[str]) -> List[ImageData]:
- imageDataList = []
- for id in imageIds:
- imageData = self.nameToImageData[id]
- imageDataList.append(imageData)
- return imageDataList
-
- def extractImageDataByApprovedAndFlaggedStatus(
- self, clientId: str, approved: bool, flagged: bool, imageIds: List[str]
- ) -> List[ImageData]:
- imageDataList = []
- for id in imageIds:
- if id not in self.nameToImageData:
- logging.error(
- "{}: Image data [id = {}] not found for [clientId = {}] ".format(
- self.getCurrentTime(), id, clientId
- )
- )
- continue
- imageData = self.nameToImageData[id]
- if imageData.hasSegmentationMeta() is False:
- continue
- if approved and imageData.isApproved() is False:
- continue
- if flagged and imageData.isFlagged() is False:
- continue
-
- imageDataList.append(imageData)
- return imageDataList
-
- def getImageDataByClientAndReviewer(
- self, clientId: str, reviewerId: str, approved=False, flagged=False
- ) -> List[ImageData]:
- """
- returns fitered list of imageData which are filtered according to client (=annotator) and
- reviewer and parameters (approved, flagged)
- """
-
- imageDatas = self.getImageDataByClientId(clientId, approved, flagged)
- filteredByRewiewer = list(filter(lambda imageData: (imageData.getApprovedBy() == reviewerId), imageDatas))
- return filteredByRewiewer
-
- def getImageDataByReviewer(self, reviewerId: str, approved=False, flagged=False) -> List[ImageData]:
- if reviewerId == "":
- return None
- if approved and flagged:
- logging.warning(
- "{}: Selected filter options are not valid: approved='{}' and flagged='{}')".format(
- self.getCurrentTime(), approved, flagged
- )
- )
- return None
-
- filteredImageDataList = []
-
- for imageData in self.nameToImageData.values():
- if imageData.isSegemented() is False:
- continue
- if approved and imageData.isApproved() is False:
- continue
- if flagged and imageData.isFlagged() is False:
- continue
- if imageData.getApprovedBy() == reviewerId:
- filteredImageDataList.append(imageData)
-
- return filteredImageDataList
-
- def getImageDataByLevel(self, isEasy: bool, isMedium: bool, isHard: bool) -> Dict[str, ImageData]:
- """
- returns filtered list of imageData which are filtered according to level of difficulty
- (regarding segmentation): easy, medium, hard
- """
- filteredImageData = {}
- for id, imagedata in self.nameToImageData.items():
- if imagedata is None:
- continue
- if imagedata.isSegemented() is False:
- continue
- if isEasy and imagedata.getLevel() == self.LEVEL.EASY:
- filteredImageData[id] = imagedata
- continue
-
- if isMedium and imagedata.getLevel() == self.LEVEL.MEDIUM:
- filteredImageData[id] = imagedata
- continue
-
- if isHard and imagedata.getLevel() == self.LEVEL.HARD:
- filteredImageData[id] = imagedata
- return filteredImageData
-
- def getSingleImageDataById(self, imageId: str) -> ImageData:
- """
- returns imageData by given imageId
- """
- if self.isBlank(imageId):
- return None
- if imageId not in self.nameToImageData:
- logging.warning(f"{self.getCurrentTime()}: Image data for requested id [{imageId}] not found")
- return None
- return self.nameToImageData[imageId]
-
- def getMultImageDataByIds(self, ids: List[str]) -> Dict[str, ImageData]:
- """
- returns multiple imageData by given list of imageId
- """
- idToimageData: Dict[str, ImageData] = {}
- if len(ids) == 0:
- logging.warning(f"{self.getCurrentTime()}: Given id list is empty.")
- return {}
- for id in ids:
- imageData = self.getSingleImageDataById(id)
- if imageData is None:
- continue
- idToimageData[imageData.getName()] = imageData
- return idToimageData
-
- def getNumApprovedSegmentation(self) -> int:
- """
- returns total number of imageData which are approved
- """
- count = self.countApprovedSegmentation(self.nameToImageData.values())
- return count
-
- def countApprovedSegmentation(self, imageDatas: List[ImageData]) -> int:
- if imageDatas is None:
- return 0
- approvedCount = 0
- for imageData in imageDatas:
- if imageData is None:
- continue
- if imageData.isApproved():
- approvedCount += 1
- return approvedCount
-
- def getPercentageApproved(self, clientId: str):
- """
- returns the percentage of images that have already been approved by given client (=Annotator)
- and the value: (total number of images approved by given client (=Annotator))/(total number of imageData)
- """
- listImageData = self.getImageDataByClientId(clientId=clientId)
- approvedCount = self.countApprovedSegmentation(listImageData)
- if len(listImageData) == 0:
- logging.warning(f"{self.getCurrentTime()}: There are no images")
- return 0
- fraction = approvedCount / len(listImageData)
- precentage = int(fraction * 100)
- idxApprovedOfClient: str = f"{approvedCount}/{len(listImageData)}"
- return precentage, idxApprovedOfClient
-
- def getPercentageSemgmentedByClient(self, clientId: str):
- """
- returns the percentage of images that have already been segmented by given client (=Annotator)
- and the value: (total number of images segmented by given client (=Annotator))/(total number of imageData)
- """
- numSegementedByClient = len(self.clientToImageIds[clientId])
- fraction = numSegementedByClient / self.getTotalNumImages()
- precentage = int(fraction * 100)
- idxSegmentedByClient: str = f"{numSegementedByClient}/{self.getTotalNumImages()}"
- return precentage, idxSegmentedByClient
-
- def getApprovedSegmentationIds(self) -> List[str]:
- """
- returns list of ids of all approved imageData
- """
- idsOfApprovedSementations = []
- for imageId, imageData in self.nameToImageData.items():
- if imageData.isApproved():
- idsOfApprovedSementations.append(imageId)
- return idsOfApprovedSementations
-
- def getSegmentedImageIds(self) -> List[str]:
- """
- returns list of ids of all segmented imageData
- """
- idsOfSegmented = []
- for imageId, imageData in self.nameToImageData.items():
- if imageData.isSegemented():
- idsOfSegmented.append(imageId)
- return idsOfSegmented
-
- def isBlank(self, string) -> bool:
- return not (string and string.strip())
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataStatistics.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataStatistics.py
deleted file mode 100644
index 423584a..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/ImageDataStatistics.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-class ImageDataStatistics:
- def __init__(self):
- self.segmentationProgress: int = 0
- self.idxTotalSegmented: str = ""
- self.idxTotalApproved: str = ""
- self.progressPercentage: int = 0
-
- self.segmentationProgressAllPercentage: int = 0
- self.approvalProgressPercentage: int = 0
-
- def build(
- self,
- segmentationProgress=0,
- idxTotalSegmented="",
- idxTotalApproved="",
- progressPercentage=0,
- segmentationProgressAllPercentage=0,
- approvalProgressPercentage=0,
- ):
- self.segmentationProgress = segmentationProgress
- self.idxTotalSegmented = idxTotalSegmented
- self.idxTotalApproved = idxTotalApproved
- self.progressPercentage = progressPercentage
- self.segmentationProgressAllPercentage = segmentationProgressAllPercentage
- self.approvalProgressPercentage = approvalProgressPercentage
-
- def getSegmentationProgress(self) -> int:
- return self.segmentationProgress
-
- def getIdxTotalSegmented(self) -> str:
- return self.idxTotalSegmented
-
- def getIdxTotalApproved(self) -> str:
- return self.idxTotalApproved
-
- def getProgressPercentage(self) -> int:
- return self.progressPercentage
-
- def getSegmentationProgressAllPercentage(self) -> int:
- return self.segmentationProgressAllPercentage
-
- def getApprovalProgressPercentage(self) -> int:
- return self.approvalProgressPercentage
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/JsonParser.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/JsonParser.py
deleted file mode 100644
index 4b4a976..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/JsonParser.py
+++ /dev/null
@@ -1,240 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Dict, List
-
-from MONAILabelReviewerLib.DataStoreKeys import DataStoreKeys
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.MONAILabelReviewerEnum import Label
-from MONAILabelReviewerLib.SegmentationMeta import SegmentationMeta
-
-
-class JsonParser:
- """
- JsonParser parses the datastore.json file
- and caches the information in dictionary: Mapping from id to ImageData
- """
-
- def __init__(self, jsonObject: dict):
- self.LABEL = Label()
- self.dataStoreKeys = DataStoreKeys()
-
- self.jsonObject = jsonObject
- self.mapIdToImageData: Dict[str, ImageData] = {}
-
- def init(self):
- self.parseJsonToImageData()
-
- def getValueByKey(self, keyArr: List[str], jsonObj: dict):
- if len(keyArr) == 0:
- return ""
- for key in keyArr:
- if key not in jsonObj:
- return ""
- jsonObj = jsonObj[key]
- return jsonObj
-
- def getFileName(self, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.FILENAME, obj)
-
- def getNodeName(self, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.NODE_NAME, obj)
-
- def getTimeStamp(self, obj: dict) -> int:
- if self.hasKeyAnnotate(obj):
- return self.getValueByKey(self.dataStoreKeys.TIMESTAMP_ANNOTATE, obj)
- if self.hasKeyRandom(obj):
- return self.getValueByKey(self.dataStoreKeys.TIMESTAMP_RANDOM, obj)
- return self.getValueByKey(self.dataStoreKeys.TIMESTAMP, obj)
-
- def getInfo(self, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.IMAGE_INFO, obj)
-
- def getInfoInLabels(self, label: str, obj: dict) -> Dict[str, str]:
- keys = self.dataStoreKeys.getInfoInLabels(label)
- return self.getValueByKey(keys, obj)
-
- def hasLabels(self, obj: dict) -> bool:
- lablesSection = self.getValueByKey(self.dataStoreKeys.LABELS, obj)
- if len(lablesSection) == 0:
- return False
- return True
-
- def hasSegemantatorsId(self, obj: dict) -> bool:
- labels = self.getValueByKey(self.dataStoreKeys.LABELS, obj)
-
- if len(labels) == 0:
- return False
-
- if self.dataStoreKeys.FINAL not in labels:
- return False
-
- final = self.getValueByKey(self.dataStoreKeys.LABELS_FINAL, obj)
- if self.dataStoreKeys.INFO not in final:
- return False
-
- info = self.getValueByKey(self.dataStoreKeys.LABELS_FINAL_INFO, obj)
- if self.dataStoreKeys.LABEL_INFO not in info:
- return False
-
- labelsInfo = self.getValueByKey(self.dataStoreKeys.LABELS_FINAL_INFO_LABELS_INFO, obj)
- if len(labelsInfo) == 0:
- return False
- return True
-
- def extractLabels(self, obj: dict) -> dict:
- return self.getValueByKey(self.dataStoreKeys.LABELS, obj)
-
- def extractLabelNames(self, labelsDict: dict) -> List[str]:
- return list(labelsDict.keys())
-
- def extractLabelContentByName(self, labels: dict, labelName="final") -> Dict[str, str]:
- if labelName not in labels:
- return {}
- content = labels[labelName][self.dataStoreKeys.INFO]
-
- if self.dataStoreKeys.LABEL_INFO not in content:
- return {}
-
- labelDict = {}
- labelDict[self.dataStoreKeys.LABEL_INFO] = content[self.dataStoreKeys.LABEL_INFO]
- return labelDict
-
- def extractSegmentationMetaOfVersion(self, labels: dict, labelName: str) -> dict:
- if labelName not in labels:
- return {}
- content = labels[labelName][self.dataStoreKeys.INFO]
-
- if self.dataStoreKeys.META not in content:
- return {}
- return content[self.dataStoreKeys.META]
-
- def getAllSegmentationMetaOfAllLabels(self, labels: dict, labelNames: List[str]) -> Dict[str, SegmentationMeta]:
- if len(labelNames) == 0:
- return {}
-
- allSegMetaOfLabels = {}
- for labelName in labelNames:
- segMetaSingle = self.extractSegmentationMetaOfVersion(labels, labelName)
- if len(segMetaSingle) == 0:
- continue
- segmentationMeta = self.produceSegementationData(segMetaSingle)
- segmentationMeta.setVersionNumber(versionTag=labelName)
- allSegMetaOfLabels[labelName] = segmentationMeta
- return allSegMetaOfLabels
-
- def produceSegementationData(self, segmenatationDict: dict) -> SegmentationMeta:
- segmentationMeta = SegmentationMeta()
- segmentationMeta.build(
- status=segmenatationDict[self.dataStoreKeys.META_STATUS],
- level=segmenatationDict[self.dataStoreKeys.META_LEVEL],
- approvedBy=segmenatationDict[self.dataStoreKeys.APPROVED_BY],
- comment=segmenatationDict[self.dataStoreKeys.META_COMMENT],
- editTime=segmenatationDict[self.dataStoreKeys.META_EDIT_TIME],
- )
-
- return segmentationMeta
-
- def isSegmented(self, obj: dict) -> bool:
- labels = self.getValueByKey(self.dataStoreKeys.LABELS, obj)
- if len(labels) == 0:
- return False
- if self.dataStoreKeys.FINAL not in labels:
- return False
- return True
-
- def hasKeyFinal(self, obj: dict) -> bool:
- labelsDict = self.getValueByKey(self.dataStoreKeys.LABELS, obj)
- return self.dataStoreKeys.FINAL in labelsDict
-
- def hasKeyOriginal(self, obj: dict) -> bool:
- labelsDict = self.getValueByKey(self.dataStoreKeys.LABELS, obj)
- return self.dataStoreKeys.ORIGINAL in labelsDict
-
- def getSegmentationName(self, obj: dict) -> dict:
- if self.hasKeyFinal(obj):
- return self.getValueByKey(self.dataStoreKeys.SEGMENTATION_NAME_BY_FINAL, obj)
- if self.hasKeyOriginal(obj):
- return self.getValueByKey(self.dataStoreKeys.SEGMENTATION_NAME_BY_ORIGINAL, obj)
-
- def hasKeyAnnotate(self, obj: dict) -> bool:
- strategyDict = self.getValueByKey(self.dataStoreKeys.STRATEGY, obj)
- return self.dataStoreKeys.ANNOTATE in strategyDict
-
- def hasKeyRandom(self, obj: dict) -> bool:
- strategyDict = self.getValueByKey(self.dataStoreKeys.STRATEGY, obj)
- return self.dataStoreKeys.RANDOM in strategyDict
-
- def getClientId(self, obj: dict) -> str:
- if self.hasKeyAnnotate(obj):
- return self.getValueByKey(self.dataStoreKeys.CLIENT_ID_BY_ANNOTATE, obj)
- if self.hasKeyRandom(obj):
- return self.getValueByKey(self.dataStoreKeys.CLIENT_ID_BY_RANDOM, obj)
- if self.hasSegemantatorsId(obj):
- return self.getValueByKey(self.dataStoreKeys.CLIENT_ID, obj)
- return "Segmented without annotator's id"
-
- def getMetaStatus(self, label: str, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.getMetaStatus(label), obj)
-
- def getMetaLevel(self, label: str, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.getMetaLevel(label), obj)
-
- def getMetaApprovedBy(self, label: str, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.getMetaApprovedBy(label), obj)
-
- def getMetaEditTime(self, label: str, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.getMetaEditTime(label), obj)
-
- def getMetaComment(self, label: str, obj: dict) -> str:
- return self.getValueByKey(self.dataStoreKeys.getMetaComment(label), obj)
-
- def parseJsonToImageData(self):
- objects = self.jsonObject[self.dataStoreKeys.OBJECT]
- for key, value in objects.items():
- imageData = self.jsonToImageData(key, value)
- self.mapIdToImageData[key] = imageData
-
- def jsonToImageData(self, key: str, value: dict) -> ImageData:
- imageData = ImageData(
- name=key,
- fileName=self.getFileName(value),
- nodeName=self.getNodeName(value),
- segmented=self.isSegmented(value),
- timeStamp=self.getTimeStamp(value),
- )
-
- if self.isSegmented(value):
- segName = self.getSegmentationName(value)
- imageData.setSegmentationFileName(segName)
-
- clientId = self.getClientId(value)
- imageData.setClientId(clientId)
-
- if self.hasLabels(value):
- labelsDict = self.extractLabels(value)
- labelNames = self.extractLabelNames(labelsDict)
- labelContent = self.extractLabelContentByName(labelsDict)
- labelSegmentationMeta: Dict[str, SegmentationMeta] = self.getAllSegmentationMetaOfAllLabels(
- labelsDict, labelNames
- )
-
- imageData.setVersionNames(labelNames)
- imageData.setLabelContent(labelContent)
- imageData.setSegmentationMetaDict(labelSegmentationMeta)
-
- return imageData
-
- def hasSegmentationMeta(self, info: dict) -> bool:
- return self.dataStoreKeys.META in info.keys()
-
- def getMapIdToImageData(self) -> Dict[str, ImageData]:
- return self.mapIdToImageData
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/MONAILabelReviewerEnum.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/MONAILabelReviewerEnum.py
deleted file mode 100644
index ce33c8d..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/MONAILabelReviewerEnum.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-class SegStatus:
- def __init__(self):
- self.NOT_SEGMENTED = "not segmented"
- self.APPROVED = "approved"
- self.SEGMENTED = "segmented"
- self.FLAGGED = "flagged"
-
-
-class Level:
- def __init__(self):
- self.EASY = "easy"
- self.MEDIUM = "medium"
- self.HARD = "hard"
-
-
-class Label:
- def __init__(self):
- self.ORIGINAL = "original"
- self.FINAL = "final"
- self.VERSION = "version"
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/MonaiServerREST.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/MonaiServerREST.py
deleted file mode 100644
index 3527a95..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/MonaiServerREST.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import datetime
-import json
-import logging
-import os
-from urllib.parse import quote_plus
-
-import requests
-from requests.structures import CaseInsensitiveDict
-
-
-class MonaiServerREST:
- """
- MonaiServerREST provides the REST endpoints to the MONAIServer
- """
-
- def __init__(self, serverUrl: str):
- self.PARAMS_PREFIX_REST_REQUEST = "params"
- self.serverUrl = serverUrl
-
- def getServerUrl(self) -> str:
- return self.serverUrl
-
- def getCurrentTime(self) -> datetime:
- return datetime.datetime.now()
-
- def requestDataStoreInfo(self) -> dict:
- download_uri = f"{self.serverUrl}/datastore/?output=all"
-
- try:
- response = requests.get(download_uri, timeout=5)
- except Exception as exception:
- logging.warning(f"{self.getCurrentTime()}: Request for DataStoreInfo failed due to '{exception}'")
- return None
- if response.status_code != 200:
- logging.warning(
- "{}: Request for datastore-info failed (url: '{}'). Response code is {}".format(
- self.getCurrentTime(), download_uri, response.status_code
- )
- )
- return None
-
- return response.json()
-
- def getDicomDownloadUri(self, image_id: str) -> str:
- download_uri = f"{self.serverUrl}/datastore/image?image={quote_plus(image_id)}"
- logging.info(f"{self.getCurrentTime()}: REST: request dicom image '{download_uri}'")
- return download_uri
-
- def requestSegmentation(self, image_id: str, tag: str) -> requests.models.Response:
- if tag == "":
- tag = "final"
- download_uri = f"{self.serverUrl}/datastore/label?label={quote_plus(image_id)}&tag={quote_plus(tag)}"
- logging.info(f"{self.getCurrentTime()}: REST: request segmentation '{download_uri}'")
-
- try:
- response = requests.get(download_uri, timeout=5)
- except Exception as exception:
- logging.warning(
- "{}: Segmentation request (image id: '{}') failed due to '{}'".format(
- self.getCurrentTime(), image_id, exception
- )
- )
- return None
- if response.status_code != 200:
- logging.warn(
- "{}: Segmentation request (image id: '{}') failed due to response code: '{}'".format(
- self.getCurrentTime(), image_id, response.status_code
- )
- )
- return None
-
- return response
-
- def checkServerConnection(self) -> bool:
- if not self.serverUrl:
- self.serverUrl = "http://127.0.0.1:8000"
- url = self.serverUrl.rstrip("/")
-
- try:
- response = requests.get(url, timeout=5)
- except Exception as exception:
- logging.warning(f"{self.getCurrentTime()}: Connection to Monai Server failed due to '{exception}'")
- return False
- if response.status_code != 200:
- logging.warn(
- "{}: Server connection Failed. (response code = {}) ".format(
- self.getCurrentTime(), response.status_code
- )
- )
- return False
-
- logging.info(f"{self.getCurrentTime()}: Successfully connected to server (server url: '{url}').")
- return True
-
- def updateLabelInfo(self, image_id: str, tag: str, params: dict) -> int:
- """
- the image_id is the unique ID of an radiographic image
- If the image has a label/segmentation, its label/label_id corresponds to its image_id
- """
- embeddedParams = self.embeddedLabelContentInParams(params)
- logging.info(f"Sending updated label info: {embeddedParams}")
-
- url = f"{self.serverUrl}/datastore/updatelabelinfo?label={quote_plus(image_id)}&tag={quote_plus(tag)}"
- headers = CaseInsensitiveDict()
- headers["Content-Type"] = "application/x-www-form-urlencoded"
- headers["accept"] = "application/json"
-
- try:
- response = requests.put(url, headers=headers, data=embeddedParams)
- except Exception as exception:
- logging.warning(
- "{}: Update meta data (image id: '{}') failed due to '{}'".format(
- self.getCurrentTime(), image_id, exception
- )
- )
- return None
- if response.status_code != 200:
- logging.warn(
- "{}: Update meta data (image id: '{}') failed due to response code = {}) ".format(
- self.getCurrentTime(), image_id, response.status_code
- )
- )
- return response.status_code
-
- logging.info(f"{self.getCurrentTime()}: Meta data was updated successfully (image id: '{image_id}').")
- return response.status_code
-
- def embeddedLabelContentInParams(self, labelContent: dict) -> dict:
- params = {}
- params[self.PARAMS_PREFIX_REST_REQUEST] = json.dumps(labelContent)
- return params
-
- def saveLabel(self, imageId: str, labelDirectory: str, tag: str, params: dict):
- if params is not None:
- embeddedParams = self.embeddedLabelContentInParams(params)
- logging.info(f"{self.getCurrentTime()}: Label and Meta data (image id: '{imageId}'): '{embeddedParams}'")
-
- url = f"{self.serverUrl}/datastore/label?image={imageId}"
- if tag:
- url += f"&tag={tag}"
-
- try:
- with open(os.path.abspath(labelDirectory), "rb") as f:
- response = requests.put(url, data=embeddedParams, files={"label": (imageId + ".nrrd", f)})
-
- except Exception as exception:
- logging.error(
- "{}: Label and Meta data update failed (image id: '{}', meta data: '{}', due to '{}'".format(
- self.getCurrentTime(), imageId, embeddedParams, exception
- )
- )
-
- if response.status_code == 200:
- logging.info(
- f"{self.getCurrentTime()}: Label and Meta data was updated successfully (image id: '{imageId}')."
- )
- logging.warn(f"{self.getCurrentTime()}: Meta : '{embeddedParams}'")
- else:
- logging.warn(
- "{}: Update label (image id: '{}') failed due to response code = {}) ".format(
- self.getCurrentTime(), imageId, response.status_code
- )
- )
- return response.status_code
-
- def deleteLabelByVersionTag(self, imageId: str, versionTag: str) -> int:
- url = f"{self.serverUrl}/datastore/label?id={imageId}&tag={versionTag}"
- try:
- response = requests.delete(url)
- except Exception as exception:
- logging.error(
- "{}: Label and Meta data deletion failed (image id: '{}', version tag: '{}') due to '{}'".format(
- self.getCurrentTime(), imageId, versionTag, exception
- )
- )
-
- if response.status_code == 200:
- logging.info(
- f"{self.getCurrentTime()}: Label and Meta data was deleted successfully (image id: '{imageId}') "
- f"| tae: '{versionTag}'."
- )
- else:
- logging.warn(
- "{}: Deletion of label (image id: '{}') failed due to response code = {}) ".format(
- self.getCurrentTime(), imageId, response.status_code
- )
- )
- return response.status_code
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/SegmentationMeta.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/SegmentationMeta.py
deleted file mode 100644
index 87baebf..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/SegmentationMeta.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import time
-
-from MONAILabelReviewerLib.MONAILabelReviewerEnum import Label
-
-
-class SegmentationMeta:
- """
- SegmentationMeta stores all the meta data of its corresponding ImageData
- The class returns a json string which will be send to MONAI-Server to persist the
- information in datastore.json
- """
-
- def __init__(self):
- self.preFix = "params="
- self.LABEL = Label()
-
- self.status: str = ""
- self.level: str = ""
- self.approvedBy: str = ""
- self.editTime: str = ""
- self.comment: str = ""
-
- self.versionNumber: int = 0
-
- def build(self, status="", level="", approvedBy="", comment="", editTime=""):
- self.setEditTime()
- self.status = status
- self.level = level
- self.approvedBy = approvedBy
- self.comment = comment
- self.editTime = editTime
-
- def setVersionNumber(self, versionTag: str):
- if versionTag == self.LABEL.FINAL or versionTag == self.LABEL.ORIGINAL:
- self.versionNumber = 0
- else:
- self.versionNumber = self.parsNumberFromVersionTagString(versionTag=versionTag)
-
- def parsNumberFromVersionTagString(self, versionTag: str) -> int:
- lastCharIndex = len(versionTag)
- indexOfDelimeter = versionTag.index("_")
- versionTagIndex = versionTag[indexOfDelimeter + 1 : lastCharIndex]
- return int(versionTagIndex)
-
- def getVersionNumber(self) -> int:
- return self.versionNumber
-
- def update(self, status="", level="", approvedBy="", comment="") -> bool:
- logging.warn("=============== HEER ==============")
- logging.warn(f"status={status}, level={level}, approvedBy={approvedBy}, comment={comment}")
- isChanged = False
- if self.isBlank(status) is False and status != self.status:
- self.status = status
- isChanged = True
-
- if self.isBlank(level) is False and level != self.level:
- self.level = level
- isChanged = True
-
- if self.isBlank(comment) is False and comment != self.comment:
- self.comment = comment
- isChanged = True
-
- if isChanged:
- if self.isBlank(approvedBy) is False and approvedBy != self.approvedBy:
- self.approvedBy = approvedBy
-
- return isChanged
-
- def setApprovedBy(self, approvedBy: str):
- self.approvedBy = approvedBy
-
- def setStatus(self, status: str):
- self.status = status
-
- def setLevel(self, level: str):
- self.level = level
-
- def setComment(self, comment: str):
- self.comment = comment
-
- def setEditTime(self):
- self.editTime = int(time.time())
-
- def getMeta(self) -> dict:
- metaJson = {
- "segmentationMeta": {
- "status": self.status,
- "approvedBy": self.approvedBy,
- "level": self.level,
- "comment": self.comment,
- "editTime": self.editTime,
- }
- }
- return metaJson
-
- def getStatus(self) -> str:
- return self.status
-
- def getLevel(self) -> str:
- return self.level
-
- def getApprovedBy(self) -> str:
- return self.approvedBy
-
- def getComment(self) -> str:
- return self.comment
-
- def getEditTime(self) -> str:
- return self.editTime
-
- def isEqual(self, status="", level="", approvedBy="", comment=""):
- if status != self.status:
- return False
- if approvedBy != self.approvedBy:
- return False
- if level != self.level:
- return False
- if comment != self.comment:
- return False
- return True
-
- def isBlank(self, string) -> bool:
- return not (string and string.strip())
-
- def display(self):
- print("versionNumber: ", self.getVersionNumber)
- print("status: ", self.status)
- print("level: ", self.level)
- print("approvedBy: ", self.approvedBy)
- print("editTime: ", self.editTime)
- print("comment: ", self.comment)
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/__init__.py b/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/__init__.py
deleted file mode 100644
index 80d5f98..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/MONAILabelReviewerLib/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .DataStoreKeys import DataStoreKeys
-from .ImageData import ImageData
-from .ImageDataController import ImageDataController
-from .ImageDataExtractor import ImageDataExtractor
-from .JsonParser import JsonParser
-from .MONAILabelReviewerEnum import *
-from .MonaiServerREST import MonaiServerREST
-from .SegmentationMeta import SegmentationMeta
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/README.md b/monailabel/plugins/slicer/MONAILabelReviewer/README.md
deleted file mode 100644
index 2774ce1..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/README.md
+++ /dev/null
@@ -1,130 +0,0 @@
-
-
-# Purpose
-Radiologists have different levels of experience reading X-ray images.
-Therefore, agreement of several radiologists on X-ray segmentations (especially in difficult cases) is required to increase the overall quality of a data set, which is then used to model a neural network.
-MONAILABELReviewer is a tool for research groups to check the quality of the segmentation of their data sets.
-
-
-
-
-# Import MONAILABELReviewer into 3D Slicer
-1. Select "Edit"
-2. Select "Application Settings"
-3. Select "Modules"
-4. Select "Add"
-5. Within Browser select folder "MONAILabelReviewer"
-
-
-
-# MonaiLabelReviewer UI
-MonaiLabelReviewer has two usage modes, "Reviewer Mode" and "Basic Mode". The latter can be enabled by checking the checkbox next to "Basic Mode".
-"Reviewer Mode" provides advanced features such as filer options and segmentation classification by difficulty (see subsection "UI in Reviewer Mode" for further description)
-
-# UI in basic mode
-1. If checkbox is selected, "basic mode" is activated (just for streaming through the segmentations)
-2. Progress bar displays how many images have already been segmented in total.
-3. Progress bar displays how many images have already been segmented by the selected annotator
-4. Combobox: Selection of annotator (if option "All" is selected, the dataset includes segmentations of all annotators)
-5. Slide bar: Displays currently index of image within the selected dataset
-6. Lines which displays the meta data: imageId, annotator's name, date
-7. Segmentation selection box: Hide/Show-option of segmentation layers
-
-
-
-
-#Required extensions in MonaiLabel
-
-In order to persist the information created by MonaiLabelReviewer during the review process, an additional rest endpoint needs to be introduced into MonaiLabel.
-In particular, the following methods (see below) need to be added in the datastore.py file.
-(We will apply these changes in the monai community, so the workflow in MonaiLabelReviewer will be available without any additional manual changes in MonaiLabel.)
-
-├── MONAILAIBEL
- ├── monailabel
- ├── endpoints
- ├── datastore.py
-
-```
-@router.put("/updatelabelinfo", summary="Update label info")
-async def api_update_label_info(label_id: str, label_tag : str, params: str = Form("{}")):
- return update_label_info(label_id, label_tag, params)
-
-def update_label_info(label_id: str, label_tag : str, params: str = Form("{}")):
- save_params: Dict[str, Any] = json.loads(params) if params else {}
- instance: MONAILabelApp = app_instance()
- instance.datastore().update_label_info(label_id=label_id, label_tag=label_tag, info=save_params)
- return {}
-```
-
-# UI in Reviewer mode
-1. If checkbox is not selected, "Reviewer Mode" is activated (the enables all feature for reviewing the segmentations)
-2. Selection of reviewer's name or add new reviewer
-3. Progress bar displays how many images have already been approved in total.
-4. Progress bar displays how many images of selected Annotator have already been approved
-5. Buttons (Easy, Medium, Hard) allows reviewer to classify the difficulty of segmentation to Easy, Medium, Hard
-6. "Approve" Button: After the reviewer approves the segmentation, it can be included in the neural network modelling dataset
-7. "Flag" Button allows the reviewer to mark a segmentation for later evaluation by another radiologist
-8. Comment field: Reviewer can add comment into that box regarding the segmentation. If commented review is flagged additionally, the annotator can improve the segmentation according to comment
-9. Filter options allows the reviewer to select a subset of image data set (not segmented, segmented, flagged, approved)
-
-
-
-
-# Search by Image Id
-After entering a list of comma-separated image IDs in the left field, the right field displays a list of IDs of the corresponding found images.
-That data set can be reviewed using the "Next"-"Previous"-Button.
-
-
-
-# Editing/Improving segmentation & Version control
-1. After clicking the combo box, a list of segmentation version tags will appear.
-(The tag of the initial segmentation is "final". The tag of all subsequent improved/edited segmentations starts with "version_" (followed by a number).
-
-2. By clicking the "Start Label Editing" button, the user can start improving the subjected segmentation.
-
-
- MONAILabelReviewer
-
-
-
- 0
- 0
- 555
- 752
-
-
-
- -
-
- -
-
-
- background-color: rgb(118, 214, 255);
-
-
- Basic mode
-
-
- true
-
-
- true
-
-
-
- -
-
-
- background-color: rgb(255, 126, 121);
-
-
- Reviewer's mode
-
-
- true
-
-
-
-
-
- -
-
-
- false
-
-
- Search Images
-
-
- true
-
-
- -
-
- -
-
-
- 0
-
-
-
-
- 252
- 0
-
-
-
- Ids
-
-
- -
-
-
- Image Ids
-
-
-
- -
-
-
- imageId_1, imageId2, ...
-
-
-
- -
-
-
- background-color: rgb(146, 146, 146);
-
-
- Search
-
-
-
-
-
-
-
- Annotator/Reviewer
-
-
- -
-
-
- Select annotator
-
-
-
- -
-
- -
-
-
- Select reviewer
-
-
-
- -
-
- -
-
-
- approved
-
-
-
- -
-
-
- flagged
-
-
-
- -
-
-
- Qt::Vertical
-
-
-
- 20
- 40
-
-
-
-
- -
-
-
- background-color: rgb(146, 146, 146);
-
-
- Search
-
-
-
-
-
-
-
- Quality
-
-
- -
-
-
- Select level of difficulty
-
-
-
- -
-
-
- Qt::Vertical
-
-
-
- 20
- 40
-
-
-
-
- -
-
-
- easy
-
-
-
- -
-
-
- medium
-
-
-
- -
-
-
- hard
-
-
-
- -
-
-
- Qt::Vertical
-
-
-
- 20
- 40
-
-
-
-
- -
-
-
- background-color: rgb(146, 146, 146);
-
-
- Search
-
-
-
-
-
-
-
-
-
- -
-
- -
-
-
- Result:
-
-
-
- -
-
-
- true
-
-
-
- Image Id
-
-
-
- 10
-
-
-
- AlignCenter
-
-
-
-
- found
-
-
-
- 10
-
-
-
- AlignCenter
-
-
-
-
- segmented
-
-
-
-
- -
-
-
- false
-
-
- Show
-
-
-
-
-
-
-
-
- -
-
-
- Server
-
-
- -
-
- -
-
-
- true
-
-
-
- -
-
-
-
-
- Qt::AlignCenter
-
-
-
- -
-
-
-
-
- Qt::AlignCenter
-
-
-
- -
-
-
- background-color: rgb(169, 169, 169)
-
-
- Connect
-
-
-
- -
-
-
- Segmented
-
-
-
- -
-
-
- selection-background-color: rgb(255, 147, 0);
-
-
- 0
-
-
-
- -
-
-
- Approved
-
-
-
- -
-
-
- 0
-
-
-
- -
-
-
- Server Url
-
-
-
- -
-
-
- true
-
-
- true
-
-
-
- -
-
-
- Reviewer
-
-
-
-
-
-
-
-
- -
-
-
- false
-
-
- Data Evaluation
-
-
- false
-
-
- -
-
- -
-
- -
-
-
- Level of difficulty
-
-
-
- -
-
- -
-
-
- background-color: rgb(0, 250, 146);
-
-
- Easy
-
-
-
- -
-
-
- background-color: rgba(255, 251, 0, 179);
-
-
- Medium
-
-
-
- -
-
-
- background-color: rgba(255, 38, 0, 179);
-
-
- Hard
-
-
-
-
-
-
-
- -
-
- -
-
-
- background-color: rgb(255, 147, 0);
-
-
- Previous
-
-
-
- -
-
-
- background-color: rgb(118, 214, 255);
-
-
- Next
-
-
-
- -
-
-
- Flag
-
-
-
- -
-
-
- Approve
-
-
-
-
-
- -
-
- -
-
-
- Image: x/y
-
-
-
- -
-
-
- false
-
-
- Qt::Horizontal
-
-
-
-
-
-
-
- -
-
- -
-
-
- Version of labels
-
-
-
- -
-
-
- Qt::Horizontal
-
-
-
- background-color: rgb(0, 150, 255);
-
-
- Start label edit
-
-
-
-
- -
-
-
- Qt::Horizontal
-
-
-
-
-
- Overwrite this version
-
-
-
-
-
-
- Save as new version
-
-
-
-
- Delete this version
-
-
-
-
- -
-
-
- background-color: rgb(115, 250, 121);
-
-
- Confirm
-
-
-
-
-
- -
-
- -
-
- -
-
- -
-
-
- Image Id:
-
-
-
- -
-
-
- Annotator:
-
-
-
- -
-
-
- Annotation Date:
-
-
-
- -
-
-
- Difficulty Level:
-
-
-
- -
-
-
- Status:
-
-
-
- -
-
-
- Editor:
-
-
-
- -
-
-
- Editing Date:
-
-
-
-
-
- -
-
- -
-
-
- false
-
-
-
- -
-
-
- false
-
-
-
- -
-
-
- false
-
-
-
- -
-
-
- false
-
-
-
- -
-
-
- false
-
-
-
- -
-
- -
-
-
-
-
-
- -
-
-
- Add Comment
-
-
-
-
-
-
-
-
- -
-
-
- false
-
-
- Data Set Explorer
-
-
- true
-
-
- -
-
- -
-
-
- Load
-
-
-
- -
-
-
- selection-background-color: rgba(255, 147, 0, 209);
-
-
- 0
-
-
-
- -
-
- -
-
-
- Approved
-
-
-
- -
-
-
- Segmented
-
-
-
- -
-
-
- Annotator
-
-
-
- -
-
-
- selection-background-color: rgba(78, 157, 246, 209);
-
-
- 0
-
-
- true
-
-
-
- -
-
-
- Filter
-
-
-
- -
-
- -
-
- -
-
-
- false
-
-
- not segmented
-
-
-
- -
-
-
- flagged
-
-
-
-
-
- -
-
- -
-
-
- segmented
-
-
-
- -
-
-
- approved
-
-
-
-
-
-
-
- -
-
-
-
-
- Qt::AlignCenter
-
-
-
- -
-
-
-
-
- Qt::AlignCenter
-
-
-
-
-
-
-
-
- -
-
-
- Qt::Vertical
-
-
-
- 20
- 40
-
-
-
-
-
-
-
-
- ctkCollapsibleButton
- QWidget
-
- 1
-
-
- qMRMLWidget
- QWidget
-
- 1
-
-
-
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/Testing/CMakeLists.txt b/monailabel/plugins/slicer/MONAILabelReviewer/Testing/CMakeLists.txt
deleted file mode 100644
index b3dd1c9..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/Testing/CMakeLists.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-add_subdirectory(Python)
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/Testing/Python/CMakeLists.txt b/monailabel/plugins/slicer/MONAILabelReviewer/Testing/Python/CMakeLists.txt
deleted file mode 100644
index d7649ab..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/Testing/Python/CMakeLists.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-#slicer_add_python_unittest(SCRIPT ${MODULE_NAME}ModuleTest.py)
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataControllerTest.py b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataControllerTest.py
deleted file mode 100644
index 3db960d..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataControllerTest.py
+++ /dev/null
@@ -1,402 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-import sys
-import unittest
-from typing import Dict
-from unittest.mock import Mock, patch
-
-# sys.path.append("..")
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.ImageDataController import ImageDataController
-from MONAILabelReviewerLib.MONAILabelReviewerEnum import Level, SegStatus
-from MONAILabelReviewerLib.MonaiServerREST import MonaiServerREST
-
-
-class ImageDataControllerTest(unittest.TestCase):
- @classmethod
- def setUp(self):
- self.STATUS = SegStatus()
- self.LEVEL = Level()
- self.url = "http://127.0.0.1:8000"
- self.controller = ImageDataController()
-
- self.nameToImageData: Dict[str, ImageData] = {}
- self.createImageData()
- self.testDataStore_V2_json = ""
- self.loadJsonStr()
-
- @classmethod
- def loadJsonStr(self) -> str:
- with open(os.path.join(sys.path[0], "TestDataSet/test_json_datastore_v2.json")) as f:
- self.testDataStore_V2_json = json.dumps(json.load(f))
-
- @classmethod
- def createImageData(self):
- # is segmented
- imageDataTest_1 = ImageData(
- name="6667571",
- fileName="6667571.dcm",
- nodeName="6667571.dcm",
- checkSum="SHA256:2a454e9ab8a33dc74996784163a362a53e04adcee2fd73a8b6299bf0ce5060d3",
- segmented=True,
- timeStamp=1639985938,
- comment="",
- )
- imageDataTest_1.setClientId("Test-Radiolgist-Segmented")
- imageDataTest_1.setSegmentationFileName("6667571.seg.nrrd")
- imageDataTest_1.addNewSegmentationMeta(
- tag="final", status=self.STATUS.APPROVED, level=self.LEVEL.HARD, approvedBy="Test-Reviewer", comment=""
- )
-
- # is segmented
- imageDataTest_2 = ImageData(
- name="imageId_2",
- fileName="fileName_2",
- nodeName="nodeName_2",
- checkSum="checkSum_2",
- segmented=True,
- timeStamp=1640171961,
- comment="comment_2",
- )
- imageDataTest_2.setClientId("client_id_1")
- imageDataTest_2.setSegmentationFileName("testSegementation_2.nrrd")
- imageDataTest_2.addNewSegmentationMeta(
- tag="final",
- status=self.STATUS.FLAGGED,
- level=self.LEVEL.MEDIUM,
- approvedBy="theRadologist_2",
- comment="comment_2",
- )
-
- # is not segmented
- imageDataTest_3 = ImageData(
- name="6213798",
- fileName="6213798.dcm",
- nodeName="6213798.dcm",
- checkSum="SHA256:5ca275af76a8fe88939058f9c91ecb72ce96bd5f012198fc366e4ef0214849b9",
- segmented=False,
- timeStamp=1642170835,
- comment="",
- )
-
- self.nameToImageData["6667571"] = imageDataTest_1
- self.nameToImageData["imageId_2"] = imageDataTest_2
- self.nameToImageData["6213798"] = imageDataTest_3
-
- @classmethod
- def areEqual(self, imageData_1: ImageData, imageData_2: ImageData) -> bool:
- if (
- imageData_1.getClientId() == imageData_2.getClientId()
- and imageData_1.getName() == imageData_2.getName()
- and imageData_1.getFileName() == imageData_2.getFileName()
- and imageData_1.getNodeName() == imageData_2.getNodeName()
- and imageData_1.isSegemented() == imageData_2.isSegemented()
- ):
- if imageData_1.isSegemented() is False:
- return True
-
- if (
- imageData_1.getSegmentationFileName() == imageData_2.getSegmentationFileName()
- and imageData_1.isApproved() == imageData_2.isApproved()
- and imageData_1.isFlagged() == imageData_2.isFlagged()
- and imageData_1.getLevel() == imageData_2.getLevel()
- and imageData_1.getApprovedBy() == imageData_2.getApprovedBy()
- ):
- return True
-
- return False
-
- @patch.object(MonaiServerREST, "getServerUrl", return_value="http://127.0.0.1:8000")
- def test_returnedUrl(self, getServerUrl):
- self.controller.setMonaiServer(self.url)
- url = self.controller.getServerUrl()
- getServerUrl.assert_called_once()
- self.assertEqual(self.url, url)
-
- @patch.object(MonaiServerREST, "checkServerConnection", return_value=True)
- def test_connectToMonaiServer(self, checkServerConnection):
- isConnected = self.controller.connectToMonaiServer(self.url)
- checkServerConnection.assert_called_once()
- self.assertTrue(isConnected)
-
- def test_getMapIdToImageData(self):
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
- returnedMap = self.controller.getMapIdToImageData()
-
- selectedImageData = returnedMap["6213798"]
- expectedImageData = self.nameToImageData["6213798"]
-
- self.assertTrue(self.areEqual(expectedImageData, selectedImageData))
-
- def test_initMetaDataProcessing(self):
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- def test_getStatistics(self):
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- statistics = self.controller.getStatistics()
-
- self.assertEqual(50, statistics["segmentationProgress"])
- self.assertEqual("2/4", statistics["idxTotalSegmented"])
- self.assertEqual("1/4", statistics["idxTotalApproved"])
- self.assertEqual(25, statistics["progressPercentage"])
- self.assertEqual(50, statistics["segmentationProgressAllPercentage"])
- self.assertEqual(25, statistics["approvalProgressPercentage"])
-
- def test_getClientIds(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- clientIds = self.controller.getClientIds()
-
- # Verify
- self.assertListEqual(clientIds, ["Test-Radiolgist-Segmented"])
-
- def test_getReviewers(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- clientIds = self.controller.getReviewers()
-
- # Verify
- self.assertListEqual(clientIds, ["Test-Reviewer"])
-
- def test_getAllImageData(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- imageDatas = self.controller.getAllImageData(
- segmented=True, isNotSegmented=False, isApproved=True, isFlagged=False
- )
-
- # Verify
- self.assertEqual(1, len(imageDatas))
- returnedImageData = imageDatas[0]
- expectedImageData = self.nameToImageData["6667571"]
- self.assertTrue(self.areEqual(expectedImageData, returnedImageData))
-
- def test_getImageDataByClientId(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- imageDatas = self.controller.getImageDataByClientId(
- selectedClientId="Test-Radiolgist-Segmented", isApproved=True, isFlagged=False
- )
-
- # Verify
- self.assertEqual(1, len(imageDatas))
- returnedImageData = imageDatas[0]
- expectedImageData = self.nameToImageData["6667571"]
- self.assertTrue(self.areEqual(expectedImageData, returnedImageData))
-
- def test_getPercentageApproved(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- percentageApprovedOfClient, idxApprovedOfClient = self.controller.getPercentageApproved(
- selectedClientId="Test-Radiolgist-Segmented"
- )
-
- # Verify
- self.assertEqual(50, percentageApprovedOfClient)
- self.assertEqual("1/2", idxApprovedOfClient)
-
- def test_getPercentageSemgmentedByClient(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- percentageSemgmentedByClient, idxSegmentedByClient = self.controller.getPercentageSemgmentedByClient(
- selectedClientId="Test-Radiolgist-Segmented"
- )
-
- # Verify
- self.assertEqual(50, percentageSemgmentedByClient)
- self.assertEqual("2/4", idxSegmentedByClient)
-
- def test_getMultImageDataByIds(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- imageIdDummy = "1234567"
- idToImage: Dict[str, ImageData] = self.controller.getMultImageDataByIds(
- imageIds=["6213798", "6667571", imageIdDummy]
- )
-
- # Verify
- self.assertTrue("6213798" in idToImage.keys())
- self.assertTrue("6667571" in idToImage.keys())
- self.assertTrue(imageIdDummy not in idToImage.keys())
-
- expectedImageData_1 = self.nameToImageData["6213798"]
- expectedImageData_2 = self.nameToImageData["6667571"]
-
- self.assertTrue(self.areEqual(expectedImageData_1, idToImage["6213798"]))
- self.assertTrue(self.areEqual(expectedImageData_2, idToImage["6667571"]))
-
- def test_searchByAnnotatorReviewer_selectedReviewer_is_all(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- idToImage: Dict[str, ImageData] = self.controller.searchByAnnotatorReviewer(
- selectedAnnotator="Test-Radiolgist-Segmented", selectedReviewer="All", isApproved=True, isFlagged=False
- )
-
- # Verify
- self.assertTrue("6667571" in idToImage.keys())
- expectedImageData = self.nameToImageData["6667571"]
- self.assertTrue(self.areEqual(expectedImageData, idToImage["6667571"]))
-
- def test_searchByAnnotatorReviewer_selectedReviewer_is_all_and_selectedReviewer_is_all(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- # Test
- idToImage: Dict[str, ImageData] = self.controller.searchByAnnotatorReviewer(
- selectedAnnotator="All", selectedReviewer="All", isApproved=True, isFlagged=False
- )
-
- # Verify
- self.assertTrue("6667571" in idToImage.keys())
- expectedImageData = self.nameToImageData["6667571"]
- self.assertTrue(self.areEqual(expectedImageData, idToImage["6667571"]))
-
- def test_updateLabelInfo_successfully_update_label_info(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- mockObject.updateLabelInfo = Mock(return_value=200)
-
- # Test
- successfullyUpdate = self.controller.updateLabelInfo(imageId="6667571", updatedMetaJson="")
-
- # Verify
- self.assertTrue(successfullyUpdate)
-
- def test_updateLabelInfo_failed_update_label_info(self):
- # Set up
- json_with_segmentation = json.loads(self.testDataStore_V2_json)
-
- self.controller.monaiServerREST = MonaiServerREST(self.url)
- mockObject = self.controller.monaiServerREST
- mockObject.requestDataStoreInfo = Mock(return_value=json_with_segmentation)
-
- success = self.controller.initMetaDataProcessing()
- self.assertTrue(success)
-
- mockObject.updateLabelInfo = Mock(return_value=400)
-
- # Test
- failedUpdate = self.controller.updateLabelInfo(imageId="6667571", updatedMetaJson="")
-
- # Verify
- self.assertFalse(failedUpdate)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataExtractorTest.py b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataExtractorTest.py
deleted file mode 100644
index c8836e7..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataExtractorTest.py
+++ /dev/null
@@ -1,361 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import sys
-import unittest
-from typing import Dict, List
-
-# sys.path.append("..")
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.ImageDataExtractor import ImageDataExtractor
-from MONAILabelReviewerLib.MONAILabelReviewerEnum import Level, SegStatus
-
-
-class ImageDataExtractorTest(unittest.TestCase):
- @classmethod
- def setUp(self):
- self.STATUS = SegStatus()
- self.LEVEL = Level()
- self.createImageData()
-
- @classmethod
- def createImageData(self):
- # is segmented
- imageDataTest_1 = ImageData(
- name="imageId_1",
- fileName="fileName_1",
- nodeName="nodeName_1",
- checkSum="checkSum_1",
- segmented=True,
- timeStamp=1640171961,
- comment="comment_1",
- )
- imageDataTest_1.setClientId("client_id_1")
- imageDataTest_1.setSegmentationFileName("testSegementation_1.nrrd")
- imageDataTest_1.addNewSegmentationMeta(
- tag="final",
- status=self.STATUS.APPROVED,
- level=self.LEVEL.HARD,
- approvedBy="theRadologist_1",
- comment="comment_1",
- )
-
- # is segmented
- imageDataTest_2 = ImageData(
- name="imageId_2",
- fileName="fileName_2",
- nodeName="nodeName_2",
- checkSum="checkSum_2",
- segmented=True,
- timeStamp=1640171961,
- comment="comment_2",
- )
- imageDataTest_2.setClientId("client_id_1")
- imageDataTest_2.setSegmentationFileName("testSegementation_2.nrrd")
- imageDataTest_2.addNewSegmentationMeta(
- tag="final",
- status=self.STATUS.FLAGGED,
- level=self.LEVEL.MEDIUM,
- approvedBy="theRadologist_2",
- comment="comment_2",
- )
-
- # is not segmented
- imageDataTest_3 = ImageData(
- name="imageId_3",
- fileName="fileName_3",
- nodeName="nodeName_3",
- checkSum="checkSum_3",
- segmented=False,
- timeStamp=1640171961,
- comment="comment_3",
- )
- imageDataTest_3.setClientId("client_id_3")
-
- self.nameToImageData: Dict[str, ImageData] = {}
- self.nameToImageData["imageId_1"] = imageDataTest_1
- self.nameToImageData["imageId_2"] = imageDataTest_2
- self.nameToImageData["imageId_3"] = imageDataTest_3
- self.imageDataExtractor = ImageDataExtractor(nameToImageData=self.nameToImageData)
- self.imageDataExtractor.init()
-
- @classmethod
- def areEqual(self, imageData_1: ImageData, imageData_2: ImageData) -> bool:
- if (
- imageData_1.getClientId() == imageData_2.getClientId()
- and imageData_1.getName() == imageData_2.getName()
- and imageData_1.getFileName() == imageData_2.getFileName()
- and imageData_1.getNodeName() == imageData_2.getNodeName()
- and imageData_1.isSegemented() == imageData_2.isSegemented()
- and imageData_1.getSegmentationFileName() == imageData_2.getSegmentationFileName()
- and imageData_1.isApproved() == imageData_2.isApproved()
- and imageData_1.isFlagged() == imageData_2.isFlagged()
- and imageData_1.getLevel() == imageData_2.getLevel()
- and imageData_1.getApprovedBy() == imageData_2.getApprovedBy()
- ):
- return True
- return False
-
- def test_getTotalNumImages(self):
- totalNumOfImages = self.imageDataExtractor.getTotalNumImages()
- print(totalNumOfImages)
- self.assertEqual(len(self.nameToImageData), totalNumOfImages)
-
- def test_getImageDataIds(self):
- ids = self.imageDataExtractor.getImageDataIds()
- expectedIds = [*self.nameToImageData.keys()]
- containsAll = all(id in ids for id in expectedIds)
- self.assertEqual(True, containsAll)
-
- def test_getClientIds(self):
- expectedClientIds = ["client_id_1"]
- clientIds = self.imageDataExtractor.getClientIds()
- self.assertEqual(len(expectedClientIds), len(clientIds))
- containsClients = all(id in clientIds for id in expectedClientIds)
- self.assertEqual(True, containsClients)
-
- def test_getReviewers(self):
- expectedReviewersIds = ["theRadologist_1", "theRadologist_2"]
- reviewerIds = self.imageDataExtractor.getReviewers()
- self.assertEqual(len(expectedReviewersIds), len(reviewerIds))
- containsReviewers = all(id in reviewerIds for id in expectedReviewersIds)
- self.assertEqual(True, containsReviewers)
-
- def test_getImageDataNotsegmented(self):
- notSegmentedImages: List[ImageData] = self.imageDataExtractor.getImageDataNotsegmented()
- self.assertEqual(1, len(notSegmentedImages))
- notSegementedImage = notSegmentedImages[0]
- self.assertEqual("client_id_3", notSegementedImage.getClientId())
- self.assertEqual(False, notSegementedImage.isSegemented())
-
- def test_getNumOfNotSegmented(self):
- numOfNotSegmented = self.imageDataExtractor.getNumOfNotSegmented()
- self.assertEqual(1, numOfNotSegmented)
-
- def test_getNumOfSegmented(self):
- numOfNotSegmented = self.imageDataExtractor.getNumOfSegmented()
- self.assertEqual(2, numOfNotSegmented)
-
- def test_getSegmentationProgessInPercentage(self):
- percentage = self.imageDataExtractor.getSegmentationProgessInPercentage()
- self.assertEqual(66, percentage)
-
- def test_getSegmentationProgessInPercentage_as_fraction(self):
- idxTotalSegmented = self.imageDataExtractor.getSegmentationVsTotalStr()
- self.assertEqual("2/3", idxTotalSegmented)
-
- def test_getApprovalProgressInPercentage(self):
- fraction = self.imageDataExtractor.getApprovalProgressInPercentage()
- self.assertEqual(33, fraction)
-
- def test_getApprovalVsTotal(self):
- idxTotalApproved = self.imageDataExtractor.getApprovalVsTotal()
- self.assertEqual("1/3", idxTotalApproved)
-
- def test_getAllImageData_segmented_is_true_approved_is_true(self):
- imageDatas = self.imageDataExtractor.getAllImageData(
- segmented=True, notSegmented=False, approved=True, flagged=False
- )
- self.assertEqual(1, len(imageDatas))
- expectedImageData = self.nameToImageData["imageId_1"]
- self.assertEqual(True, self.areEqual(expectedImageData, imageDatas[0]))
-
- def test_getAllImageData_segmented_is_true_flagges_is_true(self):
- imageDatas = self.imageDataExtractor.getAllImageData(
- segmented=True, notSegmented=False, approved=False, flagged=True
- )
- self.assertEqual(1, len(imageDatas))
- expectedImageData = self.nameToImageData["imageId_2"]
- self.assertEqual(True, self.areEqual(expectedImageData, imageDatas[0]))
-
- def test_getAllImageData_isNotSegmented_is_false_approved_is_false(self):
- imageDatas = self.imageDataExtractor.getAllImageData(
- segmented=False, notSegmented=True, approved=False, flagged=False
- )
- self.assertEqual(1, len(imageDatas))
- expectedImageData = self.nameToImageData["imageId_3"]
- self.assertEqual(True, self.areEqual(expectedImageData, imageDatas[0]))
-
- def test_getImageDataByClientId_approved_is_true(self):
- imageDataTest_4 = ImageData(
- name="imageId_4",
- fileName="fileName_4",
- nodeName="nodeName_4",
- checkSum="checkSum_4",
- segmented=True,
- timeStamp=1640171961,
- comment="comment_4",
- )
- imageDataTest_4.setClientId("client_id_1")
- imageDataTest_4.setSegmentationFileName("testSegementation_4.nrrd")
- imageDataTest_4.addNewSegmentationMeta(
- tag="final",
- status=self.STATUS.APPROVED,
- level=self.LEVEL.MEDIUM,
- approvedBy="theRadologist_4",
- comment="comment_4",
- )
- self.nameToImageData["imageId_4"] = imageDataTest_4
-
- imageDataExtractor = ImageDataExtractor(nameToImageData=self.nameToImageData)
- imageDataExtractor.init()
- returnedImageDatas = imageDataExtractor.getImageDataByClientId(
- clientId="client_id_1", approved=True, flagged=False
- )
- self.assertEqual(2, len(returnedImageDatas))
- returnedImageData_1 = list(filter(lambda image: (image.getName() == "imageId_1"), returnedImageDatas))
- returnedImageData_4 = list(filter(lambda image: (image.getName() == "imageId_4"), returnedImageDatas))
-
- self.assertEqual(True, self.areEqual(self.nameToImageData["imageId_1"], returnedImageData_1[0]))
- self.assertEqual(True, self.areEqual(self.nameToImageData["imageId_4"], returnedImageData_4[0]))
-
- def test_getImageDataByClientAndReviewer_approved_is_true(self):
- imageDataExtractor = ImageDataExtractor(nameToImageData=self.nameToImageData)
- imageDataExtractor.init()
- returnedImageDatas = imageDataExtractor.getImageDataByClientAndReviewer(
- clientId="client_id_1", reviewerId="theRadologist_1", approved=True, flagged=False
- )
- for imageData in returnedImageDatas:
- imageData.display()
-
- self.assertEqual(1, len(returnedImageDatas))
- imageData = returnedImageDatas[0]
- self.assertEqual(True, self.areEqual(self.nameToImageData["imageId_1"], imageData))
-
- def test_getImageDataByClientId_flagged_is_true(self):
- returnedImageDatas = self.imageDataExtractor.getImageDataByClientId(
- clientId="client_id_1", approved=False, flagged=True
- )
- self.assertEqual(1, len(returnedImageDatas))
- returnedImageData = returnedImageDatas[0]
- expectedImageData = self.nameToImageData["imageId_2"]
- self.assertEqual(True, self.areEqual(expectedImageData, returnedImageData))
-
- def test_getImageDataByReviewer_approved_is_true(self):
- returnedImageDatas = self.imageDataExtractor.getImageDataByReviewer(
- reviewerId="theRadologist_1", approved=True, flagged=False
- )
- self.assertEqual(1, len(returnedImageDatas))
- returnedImageData = returnedImageDatas[0]
- expectedImageData = self.nameToImageData["imageId_1"]
- self.assertEqual(True, self.areEqual(expectedImageData, returnedImageData))
-
- def test_getImageDataByReviewer_flagged_is_true(self):
- returnedImageDatas = self.imageDataExtractor.getImageDataByReviewer(
- reviewerId="theRadologist_2", approved=False, flagged=True
- )
- self.assertEqual(1, len(returnedImageDatas))
- returnedImageData = returnedImageDatas[0]
- expectedImageData = self.nameToImageData["imageId_2"]
- self.assertEqual(True, self.areEqual(expectedImageData, returnedImageData))
-
- def test_getImageDataByLevel_level_hard(self):
- returnedImageDatas = self.imageDataExtractor.getImageDataByLevel(isEasy=False, isMedium=False, isHard=True)
- self.assertEqual(1, len(returnedImageDatas))
- returnedImageData = returnedImageDatas["imageId_1"]
- expectedImageData = self.nameToImageData["imageId_1"]
- self.assertEqual(True, self.areEqual(expectedImageData, returnedImageData))
-
- def test_getSingleImageDataById_found_ImageData(self):
- returnedImageData = self.imageDataExtractor.getSingleImageDataById(imageId="imageId_1")
- expectedImageData = self.nameToImageData["imageId_1"]
- self.assertTrue(self.areEqual(expectedImageData, returnedImageData))
-
- def test_getSingleImageDataById_isBlank(self):
- returnedImageData = self.imageDataExtractor.getSingleImageDataById(imageId=" ")
- self.assertIsNone(returnedImageData)
-
- def test_getMultImageDataByIds(self):
- returnedImageDatas: Dict[str, ImageData] = self.imageDataExtractor.getMultImageDataByIds(
- ids=["imageId_1", "imageId_3", "dummy"]
- )
- self.assertEqual(2, len(returnedImageDatas))
-
- imageDataWithimageId_1 = returnedImageDatas["imageId_1"]
- imageDataWithimageId_3 = returnedImageDatas["imageId_3"]
- self.assertTrue(self.areEqual(self.nameToImageData["imageId_1"], imageDataWithimageId_1))
- self.assertTrue(self.areEqual(self.nameToImageData["imageId_3"], imageDataWithimageId_3))
-
- def test_getMultImageDataByIds_given_empty_idList(self):
- returnedImageDatas: Dict[str, ImageData] = self.imageDataExtractor.getMultImageDataByIds(ids=[])
- self.assertEqual(0, len(returnedImageDatas))
-
- def test_getNumApprovedSegmentation(self):
- numApprovedSegmentation = self.imageDataExtractor.getNumApprovedSegmentation()
- self.assertEqual(1, numApprovedSegmentation)
-
- def test_getPercentageApproved(self):
- imageDataTest_4 = ImageData(
- name="imageId_4",
- fileName="fileName_4",
- nodeName="nodeName_4",
- checkSum="checkSum_4",
- segmented=True,
- timeStamp=1640171961,
- comment="comment_4",
- )
- imageDataTest_4.setClientId("client_id_1")
- imageDataTest_4.setSegmentationFileName("testSegementation_4.nrrd")
- imageDataTest_4.addNewSegmentationMeta(
- tag="final",
- status=self.STATUS.APPROVED,
- level=self.LEVEL.MEDIUM,
- approvedBy="theRadologist_4",
- comment="comment_4",
- )
- self.nameToImageData["imageId_4"] = imageDataTest_4
-
- imageDataExtractor = ImageDataExtractor(nameToImageData=self.nameToImageData)
- imageDataExtractor.init()
-
- precentage, idxApprovedOfClient = imageDataExtractor.getPercentageApproved(clientId="client_id_1")
- self.assertEqual(66, precentage)
- self.assertEqual("2/3", idxApprovedOfClient)
-
- def test_getPercentageSemgmentedByClient(self):
- imageDataTest_4 = ImageData(
- name="imageId_4",
- fileName="fileName_4",
- nodeName="nodeName_4",
- checkSum="checkSum_4",
- segmented=True,
- timeStamp=1640171961,
- comment="comment_4",
- )
- imageDataTest_4.setClientId("client_id_1")
- imageDataTest_4.setSegmentationFileName("testSegementation_4.nrrd")
- imageDataTest_4.addNewSegmentationMeta(
- tag="final",
- status=self.STATUS.APPROVED,
- level=self.LEVEL.MEDIUM,
- approvedBy="theRadologist_4",
- comment="comment_4",
- )
- self.nameToImageData["imageId_4"] = imageDataTest_4
-
- imageDataExtractor = ImageDataExtractor(nameToImageData=self.nameToImageData)
- imageDataExtractor.init()
-
- precentage, idxSegmentedByClien = imageDataExtractor.getPercentageSemgmentedByClient(clientId="client_id_1")
- self.assertEqual(75, precentage)
- self.assertEqual("3/4", idxSegmentedByClien)
-
- def test_getApprovedSegmentationIds(self):
- idsOfApprovedSementations = self.imageDataExtractor.getApprovedSegmentationIds()
- self.assertEqual(1, len(idsOfApprovedSementations))
-
- def test_getSegmentedImageIds(self):
- idsOfSegmented = self.imageDataExtractor.getSegmentedImageIds()
- self.assertEqual(2, len(idsOfSegmented))
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataTest.py b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataTest.py
deleted file mode 100644
index 4541a75..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/ImageDataTest.py
+++ /dev/null
@@ -1,267 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-import sys
-import unittest
-
-# sys.path.append("..")
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.JsonParser import JsonParser
-from MONAILabelReviewerLib.SegmentationMeta import SegmentationMeta
-
-
-class ImageDataTest(unittest.TestCase):
- @classmethod
- def setUp(self):
- self.jsonParser = JsonParser(None)
- self.imageData = self.createTestImageData()
-
- fileNameWithMultipleVersions = "test_datastore_v2_image_with_multiple_versions.json"
- self.parsedImageDataWithMultiVersions = self.parseJsonToImageData(
- path=fileNameWithMultipleVersions, fileName="lan.dcm"
- )
-
- fileNameWithSingleVersion = "test_datastore_v2_image_with_segmentation.json"
- self.parsedImageDataWithSingleVersions = self.parseJsonToImageData(
- path=fileNameWithSingleVersion, fileName="mai.dcm"
- )
-
- @classmethod
- def parseJsonToImageData(self, path: str, fileName: str) -> ImageData:
- path_str = self.loadJsonStr(path)
- json_str = json.loads(path_str)
- return self.jsonParser.jsonToImageData(fileName, json_str)
-
- @classmethod
- def loadJsonStr(self, fileName: str) -> str:
- with open(os.path.join(sys.path[0], "TestDataSet/" + fileName)) as f:
- data = json.dumps(json.load(f))
- return data
-
- @classmethod
- def createTestImageData(self) -> ImageData:
- name = "6667571"
- fileName = "6667571.dcm"
- nodeName = "6667571.dcm"
- checkSum = "SHA256:2a454e9ab8a33dc74996784163a362a53e04adcee2fd73a8b6299bf0ce5060d3"
- isSegmented = True
- timeStamp = 1639647550
- comment = "test-comment"
-
- imageData = ImageData(
- name=name,
- fileName=fileName,
- nodeName=nodeName,
- checkSum=checkSum,
- segmented=isSegmented,
- timeStamp=timeStamp,
- comment=comment,
- )
-
- # imageData.addNewSegmentationMeta(status="flagged", level="hard", approvedBy="Dr.Faust",
- # comment="Damit ich erkenne, was die Menchenwelt im Innerstern zusammenhaelt")
- imageData.setSegmentationFileName("6662775.seg.nrrd")
- imageData.setClientId("segmentator")
- imageData.setVersionNames(["final", "version_1", "version_2"])
-
- segMeta_final = self.createSegMeta(
- status="flagged",
- level="hard",
- approvedBy="annotator",
- comment="Errare humanum est",
- editTime="Thu Jan 13 12:21:01 2022",
- )
- segMeta_version1 = self.createSegMeta(
- status="flagged",
- level="medium",
- approvedBy="radiologist_1",
- comment="Irren ist menschlisch",
- editTime="Thu Jan 14 12:21:01 2022",
- )
- segMeta_version2 = self.createSegMeta(
- status="flagged",
- level="easy",
- approvedBy="radiologist_2",
- comment="Menschlische Gebrechen sühnet reine Menschlichkeit",
- editTime="Thu Jan 15 12:21:01 2022",
- )
-
- segmentationMetaDict = {}
- segmentationMetaDict["final"] = segMeta_final
- segmentationMetaDict["version_1"] = segMeta_version1
- segmentationMetaDict["version_2"] = segMeta_version2
-
- imageData.setSegmentationMetaDict(segmentationMetaDict)
-
- return imageData
-
- @classmethod
- def createSegMeta(self, status, level, approvedBy, comment, editTime) -> SegmentationMeta:
- segmentationMeta = SegmentationMeta()
- segmentationMeta.build(status=status, level=level, approvedBy=approvedBy, comment=comment, editTime=editTime)
- return segmentationMeta
-
- def test_getStatus(self):
- status = self.imageData.getStatus(versionTag="final")
- self.assertEqual(status, "flagged")
-
- def test_getLatestVersionTag(self):
- latestVersion = self.imageData.getLatestVersionTag()
- self.assertEqual(latestVersion, "version_2")
-
- def test_getLatestVersionTag_remove_last_version(self):
- self.imageData.deleteVersionName("version_2")
- versionNames = self.imageData.getVersionNames()
- self.assertListEqual(["final", "version_1"], versionNames)
-
- def test_getLatestVersionTag_remove_version_inbetween(self):
- self.imageData.deleteVersionName("version_1")
- versionNames = self.imageData.getVersionNames()
- self.assertListEqual(["final", "version_2"], versionNames)
-
- def test_getMetaByVersionTag(self):
- segMeta_version2 = self.imageData.getMetaByVersionTag("version_2")
-
- exspectMeta = {
- "segmentationMeta": {
- "status": "flagged",
- "approvedBy": "radiologist_2",
- "level": "easy",
- "comment": "Menschlische Gebrechen sühnet reine Menschlichkeit",
- "editTime": "Thu Jan 15 12:21:01 2022",
- }
- }
-
- self.assertDictEqual(exspectMeta, segMeta_version2)
-
- def test_obtainUpdatedParams(self):
- params = self.parsedImageDataWithMultiVersions.obtainUpdatedParams("version_3")
- exspectedParams = {
- "label_info": [
- {"name": "Lung", "idx": 1},
- {"name": "Heart", "idx": 2},
- {"name": "Trachea", "idx": 3},
- {"name": "Mediastinum", "idx": 4},
- {"name": "Clavicle", "idx": 5},
- ],
- "segmentationMeta": {
- "status": "self.status_3",
- "approvedBy": "self.approvedBy_3",
- "level": "self.level_3",
- "comment": "self.comment_3",
- },
- }
-
- self.assertEqual(exspectedParams["label_info"], params["label_info"])
- self.assertEqual(exspectedParams["segmentationMeta"]["status"], params["segmentationMeta"]["status"])
- self.assertEqual(exspectedParams["segmentationMeta"]["approvedBy"], params["segmentationMeta"]["approvedBy"])
- self.assertEqual(exspectedParams["segmentationMeta"]["level"], params["segmentationMeta"]["level"])
- self.assertEqual(exspectedParams["segmentationMeta"]["comment"], params["segmentationMeta"]["comment"])
-
- def test_isEqualSegmentationMeta(self):
- isEqual = self.parsedImageDataWithMultiVersions.isEqualSegmentationMeta(
- tag="version_3",
- status="self.status_3",
- level="self.level_3",
- approvedBy="self.approvedBy_3",
- comment="self.comment_3",
- )
- self.assertTrue(isEqual)
-
- def test_isEqualSegmentationMeta_when_segmentationmetadata_does_not_exit_add(self):
- isEqual = self.parsedImageDataWithMultiVersions.isEqualSegmentationMeta(
- tag="version_4",
- status="self.status_4",
- level="self.level_4",
- approvedBy="self.approvedBy_4",
- comment="self.comment_4",
- )
-
- self.assertFalse(isEqual)
- metas = self.parsedImageDataWithMultiVersions.getsegmentationMetaDict()
- self.assertTrue("version_4" in metas)
-
- def test_getClientId_when_request_init_segmentation(self):
- clientId = self.parsedImageDataWithMultiVersions.getClientId("final")
- self.assertEqual("user-xyz", clientId)
-
- def test_getClientId_when_request_edit_version(self):
- clientId = self.parsedImageDataWithMultiVersions.getClientId("version_3")
- self.assertEqual("user-xyz", clientId)
-
- def test_getComment_when_request_init_segmentation(self):
- comment = self.parsedImageDataWithMultiVersions.getComment("final")
- self.assertEqual("self.comment_final", comment)
-
- def test_getComment_when_request_edit_version(self):
- comment = self.parsedImageDataWithMultiVersions.getComment("version_3")
- self.assertEqual("self.comment_3", comment)
-
- def test_getApprovedBy_when_request_init_segmentation(self):
- approvedBy = self.parsedImageDataWithMultiVersions.getApprovedBy("final")
- self.assertEqual("self.approvedBy_final", approvedBy)
-
- def test_getApprovedBy_when_request_edit_version(self):
- approvedBy = self.parsedImageDataWithMultiVersions.getApprovedBy("version_3")
- self.assertEqual("self.approvedBy_3", approvedBy)
-
- def test_isFlagged_when_segmentation_is_not_flagged(self):
- isFlagged = self.parsedImageDataWithMultiVersions.isFlagged("version_3")
- self.assertFalse(isFlagged)
-
- def test_isFlagged_when_segmentation_is_flagged(self):
- isFlagged = self.imageData.isFlagged("version_1")
- self.assertTrue(isFlagged)
-
- def test_hasSegmentationMeta_when_has_segmentation(self):
- hasSegmentationMeta = self.imageData.hasSegmentationMeta("version_1")
- self.assertTrue(hasSegmentationMeta)
-
- def test_hasSegmentationMeta_when_version_does_not_exit(self):
- hasSegmentationMeta = self.imageData.hasSegmentationMeta("version_4")
- self.assertFalse(hasSegmentationMeta)
-
- def test_getTimeOfEditing(self):
- editTime = self.parsedImageDataWithMultiVersions.getTimeOfEditing("version_1")
- self.assertEqual("2022-06-27 08:43:00", editTime)
-
- def test_getSegmentationMetaByVersionTag(self):
- segmentationData: SegmentationMeta = self.parsedImageDataWithMultiVersions.getSegmentationMetaByVersionTag(
- "version_1"
- )
- self.assertEqual("self.status_1", segmentationData.getStatus())
- self.assertEqual("self.level_1", segmentationData.getLevel())
- self.assertEqual("self.approvedBy_1", segmentationData.getApprovedBy())
- self.assertEqual(1656312180, segmentationData.getEditTime())
- self.assertEqual("self.comment_1", segmentationData.getComment())
-
- def test_isApproved(self):
- isApproved = self.parsedImageDataWithMultiVersions.isApproved()
- self.assertTrue(isApproved)
-
- def test_getApprovedVersionTagElseReturnLatestVersion_imagedata_with_multiple_versions(self):
- self.parsedImageDataWithMultiVersions.getsegmentationMetaDict()
- latestVersion = self.parsedImageDataWithMultiVersions.getApprovedVersionTagElseReturnLatestVersion()
- self.assertEqual("version_4", latestVersion)
-
- def test_getApprovedVersionTagElseReturnLatestVersion_imagedata_with_single_version(self):
- metas = self.parsedImageDataWithSingleVersions.getsegmentationMetaDict()
- for k, v in metas.items():
- print("key: ", k)
- v.display()
- latestVersion = self.parsedImageDataWithSingleVersions.getApprovedVersionTagElseReturnLatestVersion()
- self.assertEqual("final", latestVersion)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/JsonParserTest.py b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/JsonParserTest.py
deleted file mode 100644
index 106e30a..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/JsonParserTest.py
+++ /dev/null
@@ -1,220 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-import sys
-import unittest
-
-# sys.path.append("..")
-from MONAILabelReviewerLib.ImageData import ImageData
-from MONAILabelReviewerLib.JsonParser import JsonParser
-
-
-class JsonParserTest(unittest.TestCase):
- @classmethod
- def setUp(self):
- self.jsonParser = JsonParser(None)
-
- fileNameWithOutSegmentation = "test_datastore_v2_image_without_segmentation.json"
- json_without_segmentation_str = self.loadJsonStr(fileNameWithOutSegmentation)
-
- self.json_without_segmentation = json.loads(json_without_segmentation_str)
-
- fileNameWithSegmentation = "test_datastore_v2_image_with_segmentation.json"
- json_with_segmentation_str = self.loadJsonStr(fileNameWithSegmentation)
- self.json_with_segmentation = json.loads(json_with_segmentation_str)
-
- fileNameWithMultipleVersions = "test_datastore_v2_image_with_multiple_versions.json"
- fileNameWithMultipleVersions_str = self.loadJsonStr(fileNameWithMultipleVersions)
- self.json_with_multiple_versions = json.loads(fileNameWithMultipleVersions_str)
-
- @classmethod
- def loadJsonStr(self, fileName: str) -> str:
- with open(os.path.join(sys.path[0], "TestDataSet/" + fileName)) as f:
- data = json.dumps(json.load(f))
- return data
-
- def test_getFileName(self):
- name = self.jsonParser.getFileName(self.json_without_segmentation)
- self.assertEqual(name, "6245968.dcm")
-
- def test_getNodeName(self):
- name = self.jsonParser.getNodeName(self.json_without_segmentation)
- self.assertEqual(name, "6245968.dcm")
-
- def test_getCheckSum(self):
- name = self.jsonParser.getCheckSum(self.json_without_segmentation)
- self.assertEqual(name, "SHA256:f1f8ef13433b1f0966e589818f3180750606eff69b3bc4a55e0181d7a9da8da1")
-
- def test_getTimeStamp(self):
- name = self.jsonParser.getTimeStamp(self.json_without_segmentation)
- self.assertEqual(name, 1642371057)
-
- def test_getInfo(self):
- name = self.jsonParser.getInfo(self.json_without_segmentation)
- info = (
- '{"ts": 1642170799,'
- '"checksum": "SHA256:f1f8ef13433b1f0966e589818f3180750606eff69b3bc4a55e0181d7a9da8da1",'
- '"name": "6245968.dcm", "strategy": {"Random": {"ts": 1642371057,'
- '"client_id": "Dr Radiologist"}}}'
- )
- infoDict = json.loads(info)
- self.assertEqual(name, infoDict)
-
- def test_isSegmented(self):
- name = self.jsonParser.isSegmented(self.json_without_segmentation)
- self.assertEqual(False, name)
-
- def test_getSegmentationName(self):
- name = self.jsonParser.getSegmentationName(self.json_without_segmentation)
- self.assertEqual(name, "6245968.seg.nrrd")
-
- def test_hasKeyAnnotate(self):
- result = self.jsonParser.hasKeyAnnotate(self.json_without_segmentation)
- self.assertEqual(result, False)
-
- def test_hasKeyRandom(self):
- result = self.jsonParser.hasKeyRandom(self.json_without_segmentation)
- self.assertEqual(result, True)
-
- def test_getClientId(self):
- result = self.jsonParser.getClientId(self.json_without_segmentation)
- self.assertEqual(result, "Dr Radiologist")
-
- def test_getMetaStatus(self):
- result = self.jsonParser.getMetaStatus("final", self.json_with_segmentation)
- self.assertEqual(result, "flagged")
-
- def test_getMetaLevel(self):
- result = self.jsonParser.getMetaLevel("final", self.json_with_segmentation)
- self.assertEqual(result, "easy")
-
- def test_getMetaApprovedBy(self):
- result = self.jsonParser.getMetaApprovedBy("final", self.json_with_segmentation)
- self.assertEqual(result, "Prof Radiogolist")
-
- def test_getMetaEditTime(self):
- result = self.jsonParser.getMetaEditTime("final", self.json_with_segmentation)
- self.assertEqual(result, "Thu Jan 13 12:21:01 2022")
-
- def test_getMetaComment(self):
- result = self.jsonParser.getMetaComment("final", self.json_with_segmentation)
- self.assertEqual(result, "Segementation was not easy")
-
- def test_jsonToImageData(self):
- imageData: ImageData = self.jsonParser.jsonToImageData("6662775.dcm", self.json_with_segmentation)
- self.assertEqual(imageData.getName(), "6662775.dcm")
- self.assertEqual(imageData.getFileName(), "6662775.dcm")
- self.assertEqual(
- imageData.getCheckSum(), "SHA256:1b474d23bda3de0c28f4287a7c0380d461e9f71d0dc468e64f336412cb575327"
- )
- self.assertEqual(imageData.isSegemented(), True)
- self.assertEqual(imageData.getClientId(), "Annotator")
- self.assertEqual(imageData.getStatus(), "flagged")
- self.assertEqual(imageData.getLevel(), "easy")
- self.assertEqual(imageData.getApprovedBy(), "Prof Radiogolist")
- self.assertEqual(imageData.getTimeOfAnnotation(), "2021-12-16 10:35:51")
-
- def test_extractLabelNames(self):
- labelsDict = self.jsonParser.extractLabels(self.json_with_multiple_versions)
- labelNames = self.jsonParser.extractLabelNames(labelsDict)
- self.assertListEqual(labelNames, ["final", "version_1", "version_2", "version_3", "version_4"])
-
- def test_jsonToImageData_with_multiple_versions(self):
- imageData: ImageData = self.jsonParser.jsonToImageData("lan.dcm", self.json_with_multiple_versions)
-
- self.assertEqual(imageData.getName(), "lan.dcm")
- self.assertEqual(imageData.getFileName(), "lan.dcm")
- self.assertEqual(
- imageData.getCheckSum(), "SHA256:a48c454592a36c1d2895322320e5ab5479eb5e93d9d4f3e16825625033875d6f"
- )
-
- self.assertEqual(imageData.isSegemented(), True)
- self.assertEqual(imageData.getClientId(), "user-xyz")
- self.assertEqual(imageData.getTimeOfAnnotation(), "2022-01-02 17:52:47")
- self.assertListEqual(imageData.getVersionNames(), ["final", "version_1", "version_2", "version_3", "version_4"])
- # dictMeta = imageData.getsegmentationMetaDict()
- # for k,v in dictMeta.items():
- # print("------- key: ", k)
- # v.display()
-
- def test_extractLabelContentByName(self):
- labelsDict = self.jsonParser.extractLabels(self.json_with_multiple_versions)
- labelContent = self.jsonParser.extractLabelContentByName(labelsDict)
- exspectedLabelContent = {
- "label_info": [
- {"name": "Lung", "idx": 1},
- {"name": "Heart", "idx": 2},
- {"name": "Trachea", "idx": 3},
- {"name": "Mediastinum", "idx": 4},
- {"name": "Clavicle", "idx": 5},
- ]
- }
- self.assertDictEqual(exspectedLabelContent, labelContent)
-
- def test_extractSegmentationMetaOfVersion(self):
- labelsDict = self.jsonParser.extractLabels(self.json_with_multiple_versions)
- labelContent = self.jsonParser.extractSegmentationMetaOfVersion(labelsDict, labelName="version_3")
- segmentationMeta = self.jsonParser.produceSegementationData(labelContent)
-
- self.assertEqual("self.status_3", segmentationMeta.getStatus())
- self.assertEqual("self.level_3", segmentationMeta.getLevel())
- self.assertEqual("self.approvedBy_3", segmentationMeta.getApprovedBy())
- self.assertEqual("self.comment_3", segmentationMeta.getComment())
- self.assertEqual(1656312200, segmentationMeta.getEditTime())
-
- def test_extractSegmentationMetaOfVersion_final_as_label(self):
- labelsDict = self.jsonParser.extractLabels(self.json_with_multiple_versions)
- labelContent = self.jsonParser.extractSegmentationMetaOfVersion(labelsDict, labelName="final")
- segmentationMeta = self.jsonParser.produceSegementationData(labelContent)
-
- self.assertEqual("self.status_final", segmentationMeta.getStatus())
- self.assertEqual("self.level_final", segmentationMeta.getLevel())
- self.assertEqual("self.approvedBy_final", segmentationMeta.getApprovedBy())
- self.assertEqual("self.comment_final", segmentationMeta.getComment())
- self.assertEqual(1656312100, segmentationMeta.getEditTime())
-
- def test_getAllSegmentationMetaOfAllLabels(self):
- labelsDict = self.jsonParser.extractLabels(self.json_with_multiple_versions)
- labelNames = self.jsonParser.extractLabelNames(labelsDict)
- segmentationMetaDict = self.jsonParser.getAllSegmentationMetaOfAllLabels(labelsDict, labelNames)
-
- self.assertNotIn("version_2", segmentationMetaDict)
- self.assertListEqual(list(segmentationMetaDict.keys()), ["final", "version_1", "version_3", "version_4"])
-
- self.assertEqual("self.status_final", segmentationMetaDict["final"].getStatus())
- self.assertEqual("self.level_final", segmentationMetaDict["final"].getLevel())
- self.assertEqual("self.approvedBy_final", segmentationMetaDict["final"].getApprovedBy())
- self.assertEqual("self.comment_final", segmentationMetaDict["final"].getComment())
- self.assertEqual(1656312100, segmentationMetaDict["final"].getEditTime())
-
- self.assertEqual("self.status_1", segmentationMetaDict["version_1"].getStatus())
- self.assertEqual("self.level_1", segmentationMetaDict["version_1"].getLevel())
- self.assertEqual("self.approvedBy_1", segmentationMetaDict["version_1"].getApprovedBy())
- self.assertEqual("self.comment_1", segmentationMetaDict["version_1"].getComment())
- self.assertEqual(1656312180, segmentationMetaDict["version_1"].getEditTime())
-
- self.assertEqual("self.status_3", segmentationMetaDict["version_3"].getStatus())
- self.assertEqual("self.level_3", segmentationMetaDict["version_3"].getLevel())
- self.assertEqual("self.approvedBy_3", segmentationMetaDict["version_3"].getApprovedBy())
- self.assertEqual("self.comment_3", segmentationMetaDict["version_3"].getComment())
- self.assertEqual(1656312200, segmentationMetaDict["version_3"].getEditTime())
-
- self.assertEqual("approved", segmentationMetaDict["version_4"].getStatus())
- self.assertEqual("self.level_4", segmentationMetaDict["version_4"].getLevel())
- self.assertEqual("self.approvedBy_4", segmentationMetaDict["version_4"].getApprovedBy())
- self.assertEqual("self.comment_4", segmentationMetaDict["version_4"].getComment())
- self.assertEqual(1656312200, segmentationMetaDict["version_4"].getEditTime())
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/MonaiServerRESTTest.py b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/MonaiServerRESTTest.py
deleted file mode 100644
index 874d031..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/MonaiServerRESTTest.py
+++ /dev/null
@@ -1,257 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import json
-import logging
-import os
-import sys
-import time
-import unittest
-
-import requests
-from mockserver_friendly import (
- MockServerFriendlyClient,
- json_equals,
- request,
- response,
- times,
-)
-
-# sys.path.append("..")
-from MONAILabelReviewerLib.MonaiServerREST import MonaiServerREST
-from python_on_whales import docker
-
-
-class MonaiServerRESTTest(unittest.TestCase):
- @classmethod
- def setUpClass(cls) -> None:
- logging.info("Start test sets of MonaiServerREST-Test-class")
- cls.MOCK_SERVER_URL = "http://localhost:1080"
- cls.strJson = cls.loadJsonStr("test_json_datastore_v2.json")
- cls.mockServerContainer = docker.run(
- "jamesdbloom/mockserver:mockserver-5.4.1", publish=[(1080, 1080)], detach=True
- )
- if cls.mockServerContainer.state.running is True:
- logging.info(f"Mock server started successfully. Listening on port {1080}")
- else:
- logging.info("Starting mockserver failed")
- time.sleep(8)
- return super().setUpClass()
-
- @classmethod
- def tearDownClass(cls) -> None:
- cls.mockServerContainer.stop()
- if cls.mockServerContainer.state.running is False:
- logging.info("Mock server terminated successfully")
- cls.mockServerContainer.remove()
- return super().tearDownClass()
-
- @classmethod
- def loadJsonStr(cls, fileName: str) -> str:
- with open(os.path.join(sys.path[0], "TestDataSet/" + fileName)) as f:
- data = json.dumps(json.load(f))
- return data
-
- def test_requestDataStoreInfo(cls):
- client = MockServerFriendlyClient(cls.MOCK_SERVER_URL)
- urlToBeTested = "http://localhost:1080/datastore?output=all"
- client.expect(
- request(method="GET", path="/datastore", querystring={"output": "all"}, body=json_equals(cls.strJson)),
- response(code=200),
- times(1),
- )
-
- result = requests.get(
- cls.MOCK_SERVER_URL + "/datastore",
- params={"output": "all"},
- json=cls.strJson,
- headers={"Content-Type": "application/json"},
- )
-
- cls.assertEqual(200, result.status_code)
- cls.assertEqual(urlToBeTested, result.url)
- client.reset()
-
- def test_request_for_image_with_segmentation_meta_data(cls):
- client = MockServerFriendlyClient(cls.MOCK_SERVER_URL)
-
- urlToBeTested = cls.MOCK_SERVER_URL + "/datastore/updatelabelinfo?label=6662775"
- body = {
- "segmentationMeta": {
- "status": "approved",
- "approvedBy": "",
- "level": "hard",
- "comment": "",
- "editTime": "Fri May 27 07:41:08 2022",
- }
- }
-
- client.expect(
- request(
- method="PUT",
- path="/datastore/updatelabelinfo",
- querystring={"label": "6662775"},
- headers={"content-Type": "application/json"},
- body=json_equals(body),
- ),
- response(code=200),
- times(1),
- )
-
- result = requests.put(
- cls.MOCK_SERVER_URL + "/datastore/updatelabelinfo",
- params={"label": "6662775"},
- json=body,
- headers={"content-Type": "application/json"},
- )
-
- cls.assertEqual(200, result.status_code)
- cls.assertEqual(urlToBeTested, result.url)
- client.reset()
-
- def test_updateLabelInfo(cls):
- client = MockServerFriendlyClient(cls.MOCK_SERVER_URL)
-
- urlToBeTested = "{}/datastore/updatelabelinfo?label={}&tag={}".format(
- cls.MOCK_SERVER_URL, str(6662775), "final"
- )
- body = {
- "segmentationMeta": {
- "status": "approved",
- "approvedBy": "",
- "level": "hard",
- "comment": "",
- "editTime": "Fri May 27 07:41:08 2022",
- }
- }
-
- client.expect(
- request(
- method="PUT",
- path="/datastore/updatelabelinfo",
- querystring={"label": "6662775", "tag": "final"},
- headers={"content-Type": "application/json"},
- body=json_equals(body),
- ),
- response(code=200),
- times(1),
- )
-
- result = requests.put(
- cls.MOCK_SERVER_URL + "/datastore/updatelabelinfo",
- params={"label": "6662775", "tag": "final"},
- json=body,
- headers={"content-Type": "application/json"},
- )
- cls.assertEqual(200, result.status_code)
-
- cls.assertEqual(urlToBeTested, result.url)
- client.reset()
-
- def test_saveLabel(cls):
- client = MockServerFriendlyClient(cls.MOCK_SERVER_URL)
- urlToBeTested = "{}/datastore/label?image={}&tag={}".format(cls.MOCK_SERVER_URL, 6662775, "version_1")
- body = {
- "params": {
- "label_info": [
- {"name": "Lung", "idx": 1},
- {"name": "Heart", "idx": 2},
- {"name": "Trachea", "idx": 3},
- {"name": "Mediastinum", "idx": 4},
- {"name": "Clavicle", "idx": 5},
- ],
- "segmentationMeta": {
- "status": "approved",
- "approvedBy": "Approver",
- "level": "hard",
- "comment": "the_comment",
- "editTime": 1660488836,
- },
- }
- }
-
- client.expect(
- request(
- method="PUT",
- path="/datastore/label",
- querystring={"image": "6662775", "tag": "version_1"},
- headers={"content-Type": "application/json"},
- body=json_equals(body),
- ),
- response(code=200),
- times(1),
- )
-
- result = requests.put(
- cls.MOCK_SERVER_URL + "/datastore/label",
- params={"image": "6662775", "tag": "version_1"},
- json=body,
- headers={"content-Type": "application/json"},
- )
-
- cls.assertEqual(200, result.status_code)
- cls.assertEqual(urlToBeTested, result.url)
- client.reset()
-
- def test_deleteLabelByVersionTag(cls):
- client = MockServerFriendlyClient(cls.MOCK_SERVER_URL)
-
- urlToBeTested = "{}/datastore/label?id={}&tag={}".format(cls.MOCK_SERVER_URL, "6662775", "final")
-
- client.expect(
- request(method="DELETE", path="/datastore/label", querystring={"id": "6662775", "tag": "final"}),
- response(code=200),
- times(1),
- )
-
- result = requests.delete(
- cls.MOCK_SERVER_URL + "/datastore/label",
- params={"id": "6662775", "tag": "final"},
- )
- cls.assertEqual(200, result.status_code)
-
- cls.assertEqual(urlToBeTested, result.url)
- client.reset()
-
- def test_checkServerConnection(cls):
- client = MockServerFriendlyClient(cls.MOCK_SERVER_URL)
- client.expect(request(method="GET"), response(code=200), times(1))
-
- result = requests.get(cls.MOCK_SERVER_URL)
-
- cls.assertEqual(200, result.status_code)
- cls.assertEqual(cls.MOCK_SERVER_URL + "/", result.url)
-
- def test_requestSegmentation(cls):
- urlToBeTested = cls.MOCK_SERVER_URL + "/datastore/label?label=6662775&tag=final"
- client = MockServerFriendlyClient(cls.MOCK_SERVER_URL)
-
- client.expect(
- request(method="GET", path="/datastore/label", querystring={"label": "6662775", "tag": "final"}),
- response(code=200),
- times(1),
- )
-
- result = requests.get(cls.MOCK_SERVER_URL + "/datastore/label", params={"label": "6662775", "tag": "final"})
-
- cls.assertEqual(200, result.status_code)
- cls.assertEqual(urlToBeTested, result.url)
-
- def test_getDicomDownloadUri(cls):
- monaiServerREST = MonaiServerREST(cls.MOCK_SERVER_URL)
- imageId = "6662775"
- url = monaiServerREST.getDicomDownloadUri(imageId)
- cls.assertEqual("http://localhost:1080/datastore/image?image=6662775", url)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_with_multiple_versions.json b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_with_multiple_versions.json
deleted file mode 100644
index 36ecec3..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_with_multiple_versions.json
+++ /dev/null
@@ -1,204 +0,0 @@
-{
- "image": {
- "ext": ".dcm",
- "info": {
- "ts": 1640005767,
- "checksum": "SHA256:a48c454592a36c1d2895322320e5ab5479eb5e93d9d4f3e16825625033875d6f",
- "name": "lan.dcm",
- "strategy": {
- "annotate": {
- "ts": 1641142367,
- "client_id": "user-xyz"
- }
- },
- "segmentationMeta": {
- "status": "approved",
- "approvedBy": "",
- "level": "medium",
- "comment": "",
- "editTime": 1641142567
- }
- }
- },
- "labels": {
- "final": {
- "ext": ".nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "client_id": "user-xyz",
- "ts": 1641142448,
- "checksum": "SHA256:46f01fd17dbe3e812e03ca3495b0282e90c7224d02ec85e63e9b5b4da83ba5af",
- "name": "lan.nrrd",
- "segmentationMeta": {
- "status": "self.status_final",
- "approvedBy": "self.approvedBy_final",
- "level": "self.level_final",
- "comment": "self.comment_final",
- "editTime": 1656312100
- }
- }
- },
- "version_1": {
- "ext": ".nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "segmentationMeta": {
- "status": "self.status_1",
- "approvedBy": "self.approvedBy_1",
- "level": "self.level_1",
- "comment": "self.comment_1",
- "editTime": 1656312180
- },
- "ts": 1656312180,
- "checksum": "SHA256:6abdbcacccd5dabaeaf1c933802b6964c4a021a04d56cbe1a342cc04d6049cb5",
- "name": "lan.nrrd"
- }
- },
- "version_2": {
- "ext": ".nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "ts": 1656312196,
- "checksum": "SHA256:6abdbcacccd5dabaeaf1c933802b6964c4a021a04d56cbe1a342cc04d6049cb5",
- "name": "lan.nrrd"
- }
- },
- "version_3": {
- "ext": ".nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "segmentationMeta": {
- "status": "self.status_3",
- "approvedBy": "self.approvedBy_3",
- "level": "self.level_3",
- "comment": "self.comment_3",
- "editTime": 1656312200
- },
- "ts": 1656312200,
- "checksum": "SHA256:6abdbcacccd5dabaeaf1c933802b6964c4a021a04d56cbe1a342cc04d6049cb5",
- "name": "lan.nrrd"
- }
- },
- "version_4": {
- "ext": ".nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "segmentationMeta": {
- "status": "approved",
- "approvedBy": "self.approvedBy_4",
- "level": "self.level_4",
- "comment": "self.comment_4",
- "editTime": 1656312200
- },
- "ts": 1656312200,
- "checksum": "SHA256:6abdbcacccd5dabaeaf1c933802b6964c4a021a04d56cbe1a342cc04d6049cb5",
- "name": "lan.nrrd"
- }
- }
- }
-}
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_with_segmentation.json b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_with_segmentation.json
deleted file mode 100644
index 0d65231..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_with_segmentation.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
- "image": {
- "ext": ".dcm",
- "info": {
- "ts": 1639519453,
- "checksum": "SHA256:1b474d23bda3de0c28f4287a7c0380d461e9f71d0dc468e64f336412cb575327",
- "name": "6662775.dcm",
- "strategy": {
- "annotate": {
- "ts": 1639647351,
- "client_id": "Annotator"
- }
- }
- }
- },
- "labels": {
- "final": {
- "ext": ".seg.nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "client_id": "Annotator",
- "ts": 1639647550,
- "checksum": "SHA256:7563c2dda16ac2db37fdcc0587f6bbf7c3c6646dcb5676891a264388cfb1c8ba",
- "name": "6662775.seg.nrrd",
- "segmentationMeta": {
- "status": "flagged",
- "approvedBy": "Prof Radiogolist",
- "level": "easy",
- "comment": "Segementation was not easy",
- "editTime": "Thu Jan 13 12:21:01 2022"
- }
- }
- }
- }
-}
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_without_segmentation.json b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_without_segmentation.json
deleted file mode 100644
index 25f1311..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_datastore_v2_image_without_segmentation.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
- "image": {
- "ext": ".dcm",
- "info": {
- "ts": 1642170799,
- "checksum": "SHA256:f1f8ef13433b1f0966e589818f3180750606eff69b3bc4a55e0181d7a9da8da1",
- "name": "6245968.dcm",
- "strategy": {
- "Random": {
- "ts": 1642371057,
- "client_id": "Dr Radiologist"
- }
- }
- }
- },
- "labels": {
- "original": {
- "ext": ".seg.nrrd",
- "info": {
- "label_names": [
- "Background",
- "Mediastinum",
- "Lung",
- "Heart",
- "Trachea",
- "Clavicle"
- ],
- "ts": 1642371078,
- "checksum": "SHA256:18f67b39871c68c7d62292b0b8bbec97dafeee66c607cf823d67303b8a59c953",
- "name": "6245968.seg.nrrd"
- }
- }
- }
-}
diff --git a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_json_datastore_v2.json b/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_json_datastore_v2.json
deleted file mode 100644
index 2852485..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/UnitTests/TestDataSet/test_json_datastore_v2.json
+++ /dev/null
@@ -1,171 +0,0 @@
-{
- "name": "new-dataset",
- "description": "New Dataset",
- "images_dir": ".",
- "labels_dir": "labels",
- "objects": {
- "6213798": {
- "image": {
- "ext": ".dcm",
- "info": {
- "ts": 1642170799,
- "checksum": "SHA256:5ca275af76a8fe88939058f9c91ecb72ce96bd5f012198fc366e4ef0214849b9",
- "name": "6213798.dcm",
- "strategy": {
- "Random": {
- "ts": 1642170835,
- "client_id": "Test-Radiologist-not-segmented"
- }
- }
- }
- },
- "labels": {}
- },
- "6245968": {
- "image": {
- "ext": ".dcm",
- "info": {
- "ts": 1642170799,
- "checksum": "SHA256:f1f8ef13433b1f0966e589818f3180750606eff69b3bc4a55e0181d7a9da8da1",
- "name": "6245968.dcm",
- "strategy": {
- "Random": {
- "ts": 1642371057,
- "client_id": "Test-Radiologist"
- }
- }
- }
- },
- "labels": {
- "original": {
- "ext": ".seg.nrrd",
- "info": {
- "label_names": [
- "Background",
- "Mediastinum",
- "Lung",
- "Heart",
- "Trachea",
- "Clavicle"
- ],
- "ts": 1642371078,
- "checksum": "SHA256:18f67b39871c68c7d62292b0b8bbec97dafeee66c607cf823d67303b8a59c953",
- "name": "6245968.seg.nrrd"
- }
- }
- }
- },
- "6667571": {
- "image": {
- "ext": ".dcm",
- "info": {
- "ts": 1639519453,
- "checksum": "SHA256:2a454e9ab8a33dc74996784163a362a53e04adcee2fd73a8b6299bf0ce5060d3",
- "name": "6667571.dcm",
- "strategy": {
- "annotate": {
- "ts": 1639985938,
- "client_id": "Test-Radiolgist-Segmented"
- }
- }
- }
- },
- "labels": {
- "final": {
- "ext": ".seg.nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "client_id": "Test-Radiolgist-Segmented",
- "ts": 1639986085,
- "checksum": "SHA256:d58542b4ac321b137dd33edc86cd69a84aa6c1fd1c2cb704a124756ef8788e79",
- "name": "6667571.seg.nrrd",
- "segmentationMeta": {
- "status": "approved",
- "approvedBy": "Test-Reviewer",
- "level": "hard",
- "comment": "",
- "editTime": "Thu Apr 28 16:56:36 2022"
- }
- }
- }
- }
- },
- "8887571": {
- "image": {
- "ext": ".dcm",
- "info": {
- "ts": 1639519453,
- "checksum": "SHA256:2a454e9ab8a33dc74996784163a362a53e04adcee2fd73a8b6299bf0ce5060d3",
- "name": "8887571.dcm",
- "strategy": {
- "annotate": {
- "ts": 1639985938,
- "client_id": "Test-Radiolgist-Segmented"
- }
- }
- }
- },
- "labels": {
- "final": {
- "ext": ".seg.nrrd",
- "info": {
- "label_info": [
- {
- "name": "Lung",
- "idx": 1
- },
- {
- "name": "Heart",
- "idx": 2
- },
- {
- "name": "Trachea",
- "idx": 3
- },
- {
- "name": "Mediastinum",
- "idx": 4
- },
- {
- "name": "Clavicle",
- "idx": 5
- }
- ],
- "client_id": "Test-Radiolgist-Segmented",
- "ts": 1639986085,
- "checksum": "SHA256:d58542b4ac321b137dd33edc86cd69a84aa6c1fd1c2cb704a124756ef8788e79",
- "name": "8887571.seg.nrrd",
- "segmentationMeta": {
- "status": "flagged",
- "approvedBy": "Test-Reviewer",
- "level": "hard",
- "comment": "",
- "editTime": "Thu Apr 28 16:56:36 2022"
- }
- }
- }
- }
- }
- }
-}
diff --git a/monailabel/plugins/slicer/README.md b/monailabel/plugins/slicer/README.md
deleted file mode 100644
index 404246e..0000000
--- a/monailabel/plugins/slicer/README.md
+++ /dev/null
@@ -1,54 +0,0 @@
-
-
-## MONAI Label Plugin for 3D Slicer
-
-3D Slicer is a free, open-source software for visualization, processing, segmentation, registration, and other 3D images and meshes. MONAI Label supports 3D Slicer with radiology and monaibundle applications. With its advanced features, 3D Slicer is a mature and well-tested viewer for radiology studies and algorithms.
-
-_/plugins/slicer/MONAILabel
-- _**Restart**_ 3D Slicer
-
-### Plugin Settings
-You can change some default behaviors for the MONAI Label plugin by following these steps:
-
-1. Go to **Edit** -> **Application Settings** -> **MONAI Label**
-2. Customize the settings as per your requirement.
-
- link .
-
-# Data Preparation
-
-Figure source from the TotalSegmentator [2].
-
-The training data is from the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator) [2].
-
-- Target: 104 anatomical structures.
-- Task: Segmentation
-- Modality: CT
-- Size: 1204 3D volumes
-- Spacing: [1.5, 1.5, 1.5]
-
-More details about preprocessing this dataset can be found at
- link .
-
-The json file containing the data list that is used to train our models can be downloaded from
- link .
-
-
-Note that you need to provide the location of your dataset directory and json file by using ```--data_dir``` and ```--json_list```.
-
-# Training
-
-A MONAI VISTA 2.5D model (ViT-B base) with standard hyperparameters is defined as:
-
-```py
-_build_vista2pt5d(
- encoder_in_chans=27,
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=None,
- image_size=1024,
- clip_class_label_prompt=False,
- patch_embed_3d=False,
- )
-```
-
-Or, you may directly call:
-
-```py
-build_vista2pt5d_vit_b()
-```
-
-The above VISTA 2.5D model is used for CT images (9 slices 2.5D) with input spacing size ```(1.5, 1.5, 1.5)``` and for ```104``` class promptable segmentation.
-
-Using the default values for hyperparameters,
-the following command can be used to initiate training using PyTorch native AMP package:
-
-``` bash
-python main_2pt5d.py --max_epochs 100 --val_every 1 --optim_lr 0.000005 \
---num_patch 24 --num_prompt 32 \
---json_list ./totalsegmentator_104organs_folds_v2.json \
---data_dir /data/ \
---roi_z_iter 9 --save_checkpoint \
---sam_base_model vit_b \
---logdir finetune_ckpt_example --point_prompt --label_prompt --distributed --seed 12346 \
---iterative_training_warm_up_epoch 50 --reuse_img_embedding \
---label_prompt_warm_up_epoch 25 \
---checkpoint ./runs/9s_2dembed_model.pt
-```
-
-Above command will start the finetune training for the provided pre-trained weights
-(50 epochs single-step training and 50 epochs iterative training).
-
-# Evaluation
-
-To evaluate the `VISTA 2.5D model` using MONAI Label, please find the detailed instructions from
- here .
-
-
-# Reference
-
-```
-[1]: @article{kirillov2023segany,
- title={Segment Anything},
- author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
- journal={arXiv:2304.02643},
- year={2023}
- }
-
-[2]: @article{wasserthal2022totalsegmentator,
- title={TotalSegmentator: robust segmentation of 104 anatomical structures in CT images},
- author={Wasserthal, Jakob and Meyer, Manfred and Breit, Hanns-Christian and Cyriac, Joshy and Yang, Shan and Segeroth, Martin},
- journal={arXiv preprint arXiv:2208.05868},
- year={2022}
- }
-```
diff --git a/training/example_train_script.sh b/training/example_train_script.sh
deleted file mode 100644
index 4976c56..0000000
--- a/training/example_train_script.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-python main_2pt5d.py --max_epochs 100 --val_every 1 --optim_lr 0.000005 \
---num_patch 24 --num_prompt 32 \
---json_list ./totalsegmentator_104organs_folds_v2.json \
---data_dir /data/ \
---roi_z_iter 9 --save_checkpoint \
---sam_base_model vit_b \
---logdir finetune_ckpt_example --point_prompt --label_prompt --distributed --seed 12346 \
---iterative_training_warm_up_epoch 50 --reuse_img_embedding \
---label_prompt_warm_up_epoch 25 \
---checkpoint ./runs/9s_2dembed_model.pt
diff --git a/training/main_2pt5d.py b/training/main_2pt5d.py
deleted file mode 100644
index 46f3637..0000000
--- a/training/main_2pt5d.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright 2020 - 2022 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import sys
-import warnings
-from subprocess import Popen
-
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-import torch.nn.parallel
-import torch.utils.data.distributed
-from monai.losses import DiceCELoss
-from monai.metrics import DiceMetric
-from monai.transforms import Activations, AsDiscrete, Compose
-from monai.utils import set_determinism
-from monai.utils.enums import MetricReduction
-from optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
-from trainer_2pt5d import run_training
-from utils.data_utils import get_loader
-from vista_2pt5d.model import sam_model_registry
-
-warnings.filterwarnings("ignore", category=UserWarning, module="monai")
-warnings.filterwarnings("ignore", category=UserWarning, module="torch")
-warnings.filterwarnings("ignore", category=UserWarning, module="nibabel")
-parser = argparse.ArgumentParser(description="Swin UNETR segmentation pipeline")
-parser.add_argument("--checkpoint", default=None, help="start training from saved checkpoint")
-parser.add_argument("--logdir", default="vista2pt5d", type=str, help="directory to save the tensorboard logs")
-parser.add_argument("--data_dir", default="/dataset/dataset0/", type=str, help="dataset directory")
-parser.add_argument("--json_list", default="dataset_0.json", type=str, help="dataset json file")
-parser.add_argument("--save_checkpoint", action="store_true", help="save checkpoint during training")
-parser.add_argument("--max_epochs", default=1600, type=int, help="max number of training epochs")
-parser.add_argument("--batch_size", default=1, type=int, help="number of batch size")
-parser.add_argument("--optim_lr", default=1e-4, type=float, help="optimization learning rate")
-parser.add_argument("--optim_name", default="adamw", type=str, help="optimization algorithm")
-parser.add_argument("--reg_weight", default=1e-5, type=float, help="regularization weight")
-parser.add_argument("--momentum", default=0.99, type=float, help="momentum")
-parser.add_argument("--noamp", action="store_true", help="do NOT use amp for training")
-parser.add_argument("--val_every", default=1, type=int, help="validation frequency")
-parser.add_argument("--distributed", action="store_true", help="start distributed training")
-parser.add_argument("--world_size", default=1, type=int, help="number of nodes for distributed training")
-parser.add_argument("--rank", default=0, type=int, help="node rank for distributed training")
-parser.add_argument("--dist-url", default="tcp://127.0.0.1:23456", type=str, help="distributed url")
-parser.add_argument("--dist-backend", default="nccl", type=str, help="distributed backend")
-parser.add_argument("--workers", default=8, type=int, help="number of workers")
-parser.add_argument("--use_normal_dataset", action="store_true", help="use monai Dataset class")
-parser.add_argument("--a_min", default=-1024, type=float, help="a_min in ScaleIntensityRanged")
-parser.add_argument("--a_max", default=1024, type=float, help="a_max in ScaleIntensityRanged")
-parser.add_argument("--b_min", default=0.0, type=float, help="b_min in ScaleIntensityRanged")
-parser.add_argument("--b_max", default=1.0, type=float, help="b_max in ScaleIntensityRanged")
-parser.add_argument("--fold", default=0, type=int, help="fold")
-parser.add_argument(
- "--splitval", default=0, type=float, help="if not zero, split the last portion to validation and validation to test"
-)
-parser.add_argument("--roi_z_iter", default=9, type=int, help="roi size in z direction")
-parser.add_argument("--roi_z_iter_dilation", default=0, type=int, help="dilation size in z direction")
-parser.add_argument("--lrschedule", default="No", type=str, help="type of learning rate scheduler")
-parser.add_argument("--warmup_epochs", default=50, type=int, help="number of warmup epochs")
-parser.add_argument("--resume_ckpt", action="store_true", help="resume training from pretrained checkpoint")
-parser.add_argument("--num_patch", default=4, type=int, help="number of patches in each volume")
-parser.add_argument("--num_patch_val", default=30, type=int, help="number of patches in each volume during validation")
-parser.add_argument("--num_prompt", default=8, type=int, help="number of prompts for each training instance")
-parser.add_argument("--clip", default=None, type=float, help="gradient clip")
-parser.add_argument("--seed", default=-1, type=int, help="seed")
-parser.add_argument("--sam_pretrain_ckpt", type=str, default=None, help="sam_pretrain_ckpt")
-parser.add_argument("--sam_base_model", type=str, default="vit_b", help="sam_pretrain_ckpt")
-parser.add_argument("--sam_image_size", type=int, default=1024, help="sam input res")
-parser.add_argument("--label_prompt", action="store_true", help="using class label prompt in training")
-parser.add_argument("--drop_label_prob", default=0.5, type=float, help="prob for dropping label prompt in training")
-parser.add_argument(
- "--label_prompt_warm_up_epoch",
- default=20,
- type=int,
- help="before this number of epoch, we will drop label prompt with low prob.",
-)
-parser.add_argument("--point_prompt", action="store_true", help="using point prompt in training")
-parser.add_argument("--drop_point_prob", default=0.5, type=float, help="prob for dropping point prompt in training")
-parser.add_argument(
- "--max_points",
- default=8,
- type=int,
- help="max number of point prompts in training for the first ponit prompt generation",
-)
-parser.add_argument("--points_val_pos", default=1, type=int, help="number of positive point prompts in evaluation")
-parser.add_argument("--points_val_neg", default=0, type=int, help="number of negative point prompts in evaluation")
-parser.add_argument("--num_iterative_step", default=5, type=int, help="number of iterative step in training")
-parser.add_argument("--reuse_img_embedding", action="store_true", help="reuse image embedding in iterative training")
-parser.add_argument(
- "--no_more_points_for_cp_only",
- action="store_true",
- help="if no point prompt at the first prompt generation we will not add "
- "more additional pointa during iterative training.",
-)
-parser.add_argument(
- "--iterative_training_warm_up_epoch",
- default=100,
- type=int,
- help="before this number of epoch, we will not start iterative_training_.",
-)
-parser.add_argument("--data_aug", action="store_true", help="using data augmentation in training")
-parser.add_argument("--pop_pos_embed", action="store_true", help="remove pos embedding when load checkpoint")
-parser.add_argument("--pop_point_embed", action="store_true", help="remove point embedding when load checkpoint")
-parser.add_argument("--skip_bk", action="store_true", help="skip background (0) during training")
-parser.add_argument("--patch_embed_3d", action="store_true", help="using 3d patch embedding layer")
-
-
-def start_tb(log_dir):
- cmd = ["tensorboard", "--logdir", log_dir]
- Popen(cmd, stderr=sys.stderr, stdout=sys.stdout, shell=False)
-
-
-def main():
- args = parser.parse_args()
- args.amp = not args.noamp
- args.logdir = "./runs/" + args.logdir
- # start_tb(args.logdir)
- if args.seed > -1:
- set_determinism(seed=args.seed)
- if args.distributed:
- args.ngpus_per_node = torch.cuda.device_count()
- print("Found total gpus", args.ngpus_per_node)
- args.world_size = args.ngpus_per_node * args.world_size
- mp.spawn(main_worker, nprocs=args.ngpus_per_node, args=(args,))
- else:
- main_worker(gpu=0, args=args)
-
-
-def main_worker(gpu, args):
- if args.distributed:
- torch.multiprocessing.set_start_method("fork", force=True)
- np.set_printoptions(formatter={"float": "{: 0.3f}".format}, suppress=True)
- args.gpu = gpu
- if args.distributed:
- args.rank = args.rank * args.ngpus_per_node + gpu
- dist.init_process_group(
- backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
- )
- torch.cuda.set_device(args.gpu)
- torch.backends.cudnn.benchmark = True
- args.test_mode = False
- loader = get_loader(args)
- print(args.rank, " gpu", args.gpu)
- if args.rank == 0:
- print("Batch size is:", args.batch_size, "epochs", args.max_epochs)
-
- model = sam_model_registry[args.sam_base_model](
- args.sam_pretrain_ckpt,
- image_size=args.sam_image_size,
- encoder_in_chans=args.roi_z_iter * 3,
- patch_embed_3d=args.patch_embed_3d,
- )
-
- dice_loss = DiceCELoss(sigmoid=True)
-
- post_label = AsDiscrete(to_onehot=105)
- post_pred = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
- dice_acc = DiceMetric(include_background=False, reduction=MetricReduction.MEAN, get_not_nans=True)
-
- pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
- print("Total trainable parameters count", pytorch_total_params * 1.0e-6, "M")
-
- best_acc = 0
- start_epoch = 0
- optimizer_state = None
-
- if args.checkpoint is not None:
- checkpoint = torch.load(args.checkpoint, map_location="cpu")
- from collections import OrderedDict
-
- new_state_dict = OrderedDict()
- for k, v in checkpoint["state_dict"].items():
- new_state_dict[k] = v
- if args.pop_pos_embed:
- print("pop_pos_embed")
- new_state_dict.pop("image_encoder.patch_embed.proj.weight")
- new_state_dict.pop("image_encoder.patch_embed.proj.bias")
- model.load_state_dict(new_state_dict, strict=False)
- elif args.pop_point_embed:
- print("pop_point_embed")
- new_state_dict.pop("prompt_encoder.point_embeddings.0.weight")
- new_state_dict.pop("prompt_encoder.point_embeddings.1.weight")
- new_state_dict.pop("prompt_encoder.point_embeddings.2.weight")
- new_state_dict.pop("prompt_encoder.point_embeddings.3.weight")
- model.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=True)
- if args.resume_ckpt:
- if "epoch" in checkpoint:
- start_epoch = checkpoint["epoch"]
- if "best_acc" in checkpoint:
- best_acc = checkpoint["best_acc"]
- if "optimizer" in checkpoint:
- optimizer_state = checkpoint["optimizer"]
- print("=> loaded checkpoint '{}' (epoch {}) (bestacc {})".format(args.checkpoint, start_epoch, best_acc))
-
- model.cuda(args.gpu)
-
- if args.distributed:
- torch.cuda.set_device(args.gpu)
- model.cuda(args.gpu)
- model = torch.nn.parallel.DistributedDataParallel(
- model, device_ids=[args.gpu], output_device=args.gpu, find_unused_parameters=True
- )
- if args.optim_name == "adam":
- optimizer = torch.optim.Adam(model.parameters(), lr=args.optim_lr, weight_decay=args.reg_weight)
- elif args.optim_name == "adamw":
- optimizer = torch.optim.AdamW(model.parameters(), lr=args.optim_lr, weight_decay=args.reg_weight)
- elif args.optim_name == "sgd":
- optimizer = torch.optim.SGD(
- model.parameters(), lr=args.optim_lr, momentum=args.momentum, nesterov=True, weight_decay=args.reg_weight
- )
- else:
- raise ValueError("Unsupported Optimization Procedure: " + str(args.optim_name))
-
- if optimizer_state is not None:
- optimizer.load_state_dict(optimizer_state)
- # override lr by the given value
- for param_group in optimizer.param_groups:
- param_group["lr"] = args.optim_lr
-
- if args.lrschedule == "warmup_cosine":
- scheduler = LinearWarmupCosineAnnealingLR(
- optimizer, warmup_epochs=args.warmup_epochs, max_epochs=args.max_epochs
- )
- elif args.lrschedule == "cosine_anneal":
- scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.max_epochs)
- if args.checkpoint is not None:
- scheduler.step(epoch=start_epoch)
- else:
- scheduler = None
- accuracy = run_training(
- model=model,
- train_loader=loader[0],
- val_loader=loader[1],
- optimizer=optimizer,
- loss_func=dice_loss,
- acc_func=dice_acc,
- args=args,
- scheduler=scheduler,
- start_epoch=start_epoch,
- post_label=post_label,
- post_pred=post_pred,
- )
- return accuracy
-
-
-if __name__ == "__main__":
- main()
diff --git a/training/optimizers/__init__.py b/training/optimizers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/training/optimizers/lr_scheduler.py b/training/optimizers/lr_scheduler.py
deleted file mode 100644
index 0c35292..0000000
--- a/training/optimizers/lr_scheduler.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright 2020 - 2021 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-import warnings
-from typing import List
-
-from torch import nn as nn
-from torch.optim import Adam, Optimizer
-from torch.optim.lr_scheduler import LambdaLR, _LRScheduler
-
-__all__ = ["LinearLR", "ExponentialLR"]
-
-
-class _LRSchedulerMONAI(_LRScheduler):
- """Base class for increasing the learning rate between two boundaries over a number
- of iterations"""
-
- def __init__(self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1) -> None:
- """
- Args:
- optimizer: wrapped optimizer.
- end_lr: the final learning rate.
- num_iter: the number of iterations over which the test occurs.
- last_epoch: the index of last epoch.
- Returns:
- None
- """
- self.end_lr = end_lr
- self.num_iter = num_iter
- super(_LRSchedulerMONAI, self).__init__(optimizer, last_epoch)
-
-
-class LinearLR(_LRSchedulerMONAI):
- """Linearly increases the learning rate between two boundaries over a number of
- iterations.
- """
-
- def get_lr(self):
- r = self.last_epoch / (self.num_iter - 1)
- return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
-
-
-class ExponentialLR(_LRSchedulerMONAI):
- """Exponentially increases the learning rate between two boundaries over a number of
- iterations.
- """
-
- def get_lr(self):
- r = self.last_epoch / (self.num_iter - 1)
- return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
-
-
-class WarmupCosineSchedule(LambdaLR):
- """Linear warmup and then cosine decay.
- Based on https://huggingface.co/ implementation.
- """
-
- def __init__(
- self, optimizer: Optimizer, warmup_steps: int, t_total: int, cycles: float = 0.5, last_epoch: int = -1
- ) -> None:
- """
- Args:
- optimizer: wrapped optimizer.
- warmup_steps: number of warmup iterations.
- t_total: total number of training iterations.
- cycles: cosine cycles parameter.
- last_epoch: the index of last epoch.
- Returns:
- None
- """
- self.warmup_steps = warmup_steps
- self.t_total = t_total
- self.cycles = cycles
- super(WarmupCosineSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch)
-
- def lr_lambda(self, step):
- if step < self.warmup_steps:
- return float(step) / float(max(1.0, self.warmup_steps))
- progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps))
- return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.cycles) * 2.0 * progress)))
-
-
-class LinearWarmupCosineAnnealingLR(_LRScheduler):
- def __init__(
- self,
- optimizer: Optimizer,
- warmup_epochs: int,
- max_epochs: int,
- warmup_start_lr: float = 0.0,
- eta_min: float = 0.0,
- last_epoch: int = -1,
- ) -> None:
- """
- Args:
- optimizer (Optimizer): Wrapped optimizer.
- warmup_epochs (int): Maximum number of iterations for linear warmup
- max_epochs (int): Maximum number of iterations
- warmup_start_lr (float): Learning rate to start the linear warmup. Default: 0.
- eta_min (float): Minimum learning rate. Default: 0.
- last_epoch (int): The index of last epoch. Default: -1.
- """
- self.warmup_epochs = warmup_epochs
- self.max_epochs = max_epochs
- self.warmup_start_lr = warmup_start_lr
- self.eta_min = eta_min
-
- super(LinearWarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch)
-
- def get_lr(self) -> List[float]:
- """
- Compute learning rate using chainable form of the scheduler
- """
- if not self._get_lr_called_within_step:
- warnings.warn(
- "To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", UserWarning
- )
-
- if self.last_epoch == 0:
- return [self.warmup_start_lr] * len(self.base_lrs)
- elif self.last_epoch < self.warmup_epochs:
- return [
- group["lr"] + (base_lr - self.warmup_start_lr) / (self.warmup_epochs - 1)
- for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
- ]
- elif self.last_epoch == self.warmup_epochs:
- return self.base_lrs
- elif (self.last_epoch - 1 - self.max_epochs) % (2 * (self.max_epochs - self.warmup_epochs)) == 0:
- return [
- group["lr"]
- + (base_lr - self.eta_min) * (1 - math.cos(math.pi / (self.max_epochs - self.warmup_epochs))) / 2
- for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
- ]
-
- return [
- (1 + math.cos(math.pi * (self.last_epoch - self.warmup_epochs) / (self.max_epochs - self.warmup_epochs)))
- / (
- 1
- + math.cos(
- math.pi * (self.last_epoch - self.warmup_epochs - 1) / (self.max_epochs - self.warmup_epochs)
- )
- )
- * (group["lr"] - self.eta_min)
- + self.eta_min
- for group in self.optimizer.param_groups
- ]
-
- def _get_closed_form_lr(self) -> List[float]:
- """
- Called when epoch is passed as a param to the `step` function of the scheduler.
- """
- if self.last_epoch < self.warmup_epochs:
- return [
- self.warmup_start_lr + self.last_epoch * (base_lr - self.warmup_start_lr) / (self.warmup_epochs - 1)
- for base_lr in self.base_lrs
- ]
-
- return [
- self.eta_min
- + 0.5
- * (base_lr - self.eta_min)
- * (1 + math.cos(math.pi * (self.last_epoch - self.warmup_epochs) / (self.max_epochs - self.warmup_epochs)))
- for base_lr in self.base_lrs
- ]
diff --git a/training/requirements.txt b/training/requirements.txt
deleted file mode 100644
index 4666771..0000000
--- a/training/requirements.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-monai>=1.2.0
-nibabel
-fvcore
-tqdm
-einops
-tensorboardX
-scipy
-positional-encodings[pytorch]
-SimpleITK
-tensorboard
-scipy
-scikit-learn
-scikit-image
-ptflops
-pillow
-timm
-nnunet
-h5py
-torch
-torchvision
diff --git a/training/segment_anything/__init__.py b/training/segment_anything/__init__.py
deleted file mode 100644
index 718fdc3..0000000
--- a/training/segment_anything/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .automatic_mask_generator import SamAutomaticMaskGenerator
-from .build_sam import (
- build_sam,
- build_sam_vit_b,
- build_sam_vit_h,
- build_sam_vit_l,
- sam_model_registry,
-)
-from .predictor import SamPredictor
diff --git a/training/segment_anything/automatic_mask_generator.py b/training/segment_anything/automatic_mask_generator.py
deleted file mode 100644
index 2cd252d..0000000
--- a/training/segment_anything/automatic_mask_generator.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-from torchvision.ops.boxes import batched_nms, box_area # type: ignore
-
-from .modeling import Sam
-from .predictor import SamPredictor
-from .utils.amg import (
- MaskData,
- area_from_rle,
- batch_iterator,
- batched_mask_to_box,
- box_xyxy_to_xywh,
- build_all_layer_point_grids,
- calculate_stability_score,
- coco_encode_rle,
- generate_crop_boxes,
- is_box_near_crop_edge,
- mask_to_rle_pytorch,
- remove_small_regions,
- rle_to_mask,
- uncrop_boxes_xyxy,
- uncrop_masks,
- uncrop_points,
-)
-
-
-class SamAutomaticMaskGenerator:
- def __init__(
- self,
- model: Sam,
- points_per_side: Optional[int] = 32,
- points_per_batch: int = 64,
- pred_iou_thresh: float = 0.88,
- stability_score_thresh: float = 0.95,
- stability_score_offset: float = 1.0,
- box_nms_thresh: float = 0.7,
- crop_n_layers: int = 0,
- crop_nms_thresh: float = 0.7,
- crop_overlap_ratio: float = 512 / 1500,
- crop_n_points_downscale_factor: int = 1,
- point_grids: Optional[List[np.ndarray]] = None,
- min_mask_region_area: int = 0,
- output_mode: str = "binary_mask",
- ) -> None:
- """
- Using a SAM model, generates masks for the entire image.
- Generates a grid of point prompts over the image, then filters
- low quality and duplicate masks. The default settings are chosen
- for SAM with a ViT-H backbone.
-
- Arguments:
- model (Sam): The SAM model to use for mask prediction.
- points_per_side (int or None): The number of points to be sampled
- along one side of the image. The total number of points is
- points_per_side**2. If None, 'point_grids' must provide explicit
- point sampling.
- points_per_batch (int): Sets the number of points run simultaneously
- by the model. Higher numbers may be faster but use more GPU memory.
- pred_iou_thresh (float): A filtering threshold in [0,1], using the
- model's predicted mask quality.
- stability_score_thresh (float): A filtering threshold in [0,1], using
- the stability of the mask under changes to the cutoff used to binarize
- the model's mask predictions.
- stability_score_offset (float): The amount to shift the cutoff when
- calculated the stability score.
- box_nms_thresh (float): The box IoU cutoff used by non-maximal
- suppression to filter duplicate masks.
- crops_n_layers (int): If >0, mask prediction will be run again on
- crops of the image. Sets the number of layers to run, where each
- layer has 2**i_layer number of image crops.
- crops_nms_thresh (float): The box IoU cutoff used by non-maximal
- suppression to filter duplicate masks between different crops.
- crop_overlap_ratio (float): Sets the degree to which crops overlap.
- In the first crop layer, crops will overlap by this fraction of
- the image length. Later layers with more crops scale down this overlap.
- crop_n_points_downscale_factor (int): The number of points-per-side
- sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
- point_grids (list(np.ndarray) or None): A list over explicit grids
- of points used for sampling, normalized to [0,1]. The nth grid in the
- list is used in the nth crop layer. Exclusive with points_per_side.
- min_mask_region_area (int): If >0, postprocessing will be applied
- to remove disconnected regions and holes in masks with area smaller
- than min_mask_region_area. Requires opencv.
- output_mode (str): The form masks are returned in. Can be 'binary_mask',
- 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
- For large resolutions, 'binary_mask' may consume large amounts of
- memory.
- """
-
- assert (points_per_side is None) != (
- point_grids is None
- ), "Exactly one of points_per_side or point_grid must be provided."
- if points_per_side is not None:
- self.point_grids = build_all_layer_point_grids(
- points_per_side,
- crop_n_layers,
- crop_n_points_downscale_factor,
- )
- elif point_grids is not None:
- self.point_grids = point_grids
- else:
- raise ValueError("Can't have both points_per_side and point_grid be None.")
-
- assert output_mode in [
- "binary_mask",
- "uncompressed_rle",
- "coco_rle",
- ], f"Unknown output_mode {output_mode}."
- if output_mode == "coco_rle":
- from pycocotools import mask as mask_utils # type: ignore # noqa: F401
-
- if min_mask_region_area > 0:
- import cv2 # type: ignore # noqa: F401
-
- self.predictor = SamPredictor(model)
- self.points_per_batch = points_per_batch
- self.pred_iou_thresh = pred_iou_thresh
- self.stability_score_thresh = stability_score_thresh
- self.stability_score_offset = stability_score_offset
- self.box_nms_thresh = box_nms_thresh
- self.crop_n_layers = crop_n_layers
- self.crop_nms_thresh = crop_nms_thresh
- self.crop_overlap_ratio = crop_overlap_ratio
- self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
- self.min_mask_region_area = min_mask_region_area
- self.output_mode = output_mode
-
- @torch.no_grad()
- def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
- """
- Generates masks for the given image.
-
- Arguments:
- image (np.ndarray): The image to generate masks for, in HWC uint8 format.
-
- Returns:
- list(dict(str, any)): A list over records for masks. Each record is
- a dict containing the following keys:
- segmentation (dict(str, any) or np.ndarray): The mask. If
- output_mode='binary_mask', is an array of shape HW. Otherwise,
- is a dictionary containing the RLE.
- bbox (list(float)): The box around the mask, in XYWH format.
- area (int): The area in pixels of the mask.
- predicted_iou (float): The model's own prediction of the mask's
- quality. This is filtered by the pred_iou_thresh parameter.
- point_coords (list(list(float))): The point coordinates input
- to the model to generate this mask.
- stability_score (float): A measure of the mask's quality. This
- is filtered on using the stability_score_thresh parameter.
- crop_box (list(float)): The crop of the image used to generate
- the mask, given in XYWH format.
- """
-
- # Generate masks
- mask_data = self._generate_masks(image)
-
- # Filter small disconnected regions and holes in masks
- if self.min_mask_region_area > 0:
- mask_data = self.postprocess_small_regions(
- mask_data,
- self.min_mask_region_area,
- max(self.box_nms_thresh, self.crop_nms_thresh),
- )
-
- # Encode masks
- if self.output_mode == "coco_rle":
- mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
- elif self.output_mode == "binary_mask":
- mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
- else:
- mask_data["segmentations"] = mask_data["rles"]
-
- # Write mask records
- curr_anns = []
- for idx in range(len(mask_data["segmentations"])):
- ann = {
- "segmentation": mask_data["segmentations"][idx],
- "area": area_from_rle(mask_data["rles"][idx]),
- "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
- "predicted_iou": mask_data["iou_preds"][idx].item(),
- "point_coords": [mask_data["points"][idx].tolist()],
- "stability_score": mask_data["stability_score"][idx].item(),
- "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
- }
- curr_anns.append(ann)
-
- return curr_anns
-
- def _generate_masks(self, image: np.ndarray) -> MaskData:
- orig_size = image.shape[:2]
- crop_boxes, layer_idxs = generate_crop_boxes(orig_size, self.crop_n_layers, self.crop_overlap_ratio)
-
- # Iterate over image crops
- data = MaskData()
- for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
- crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
- data.cat(crop_data)
-
- # Remove duplicate masks between crops
- if len(crop_boxes) > 1:
- # Prefer masks from smaller crops
- scores = 1 / box_area(data["crop_boxes"])
- scores = scores.to(data["boxes"].device)
- keep_by_nms = batched_nms(
- data["boxes"].float(),
- scores,
- torch.zeros(len(data["boxes"])), # categories
- iou_threshold=self.crop_nms_thresh,
- )
- data.filter(keep_by_nms)
-
- data.to_numpy()
- return data
-
- def _process_crop(
- self,
- image: np.ndarray,
- crop_box: List[int],
- crop_layer_idx: int,
- orig_size: Tuple[int, ...],
- ) -> MaskData:
- # Crop the image and calculate embeddings
- x0, y0, x1, y1 = crop_box
- cropped_im = image[y0:y1, x0:x1, :]
- cropped_im_size = cropped_im.shape[:2]
- self.predictor.set_image(cropped_im)
-
- # Get points for this crop
- points_scale = np.array(cropped_im_size)[None, ::-1]
- points_for_image = self.point_grids[crop_layer_idx] * points_scale
-
- # Generate masks for this crop in batches
- data = MaskData()
- for (points,) in batch_iterator(self.points_per_batch, points_for_image):
- batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
- data.cat(batch_data)
- del batch_data
- self.predictor.reset_image()
-
- # Remove duplicates within this crop.
- keep_by_nms = batched_nms(
- data["boxes"].float(),
- data["iou_preds"],
- torch.zeros(len(data["boxes"])), # categories
- iou_threshold=self.box_nms_thresh,
- )
- data.filter(keep_by_nms)
-
- # Return to the original image frame
- data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
- data["points"] = uncrop_points(data["points"], crop_box)
- data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
-
- return data
-
- def _process_batch(
- self,
- points: np.ndarray,
- im_size: Tuple[int, ...],
- crop_box: List[int],
- orig_size: Tuple[int, ...],
- ) -> MaskData:
- orig_h, orig_w = orig_size
-
- # Run model on this batch
- transformed_points = self.predictor.transform.apply_coords(points, im_size)
- in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
- in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
- masks, iou_preds, _ = self.predictor.predict_torch(
- in_points[:, None, :],
- in_labels[:, None],
- multimask_output=True,
- return_logits=True,
- )
-
- # Serialize predictions and store in MaskData
- data = MaskData(
- masks=masks.flatten(0, 1),
- iou_preds=iou_preds.flatten(0, 1),
- points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
- )
- del masks
-
- # Filter by predicted IoU
- if self.pred_iou_thresh > 0.0:
- keep_mask = data["iou_preds"] > self.pred_iou_thresh
- data.filter(keep_mask)
-
- # Calculate stability score
- data["stability_score"] = calculate_stability_score(
- data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
- )
- if self.stability_score_thresh > 0.0:
- keep_mask = data["stability_score"] >= self.stability_score_thresh
- data.filter(keep_mask)
-
- # Threshold masks and calculate boxes
- data["masks"] = data["masks"] > self.predictor.model.mask_threshold
- data["boxes"] = batched_mask_to_box(data["masks"])
-
- # Filter boxes that touch crop boundaries
- keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
- if not torch.all(keep_mask):
- data.filter(keep_mask)
-
- # Compress to RLE
- data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
- data["rles"] = mask_to_rle_pytorch(data["masks"])
- del data["masks"]
-
- return data
-
- @staticmethod
- def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData:
- """
- Removes small disconnected regions and holes in masks, then reruns
- box NMS to remove any new duplicates.
-
- Edits mask_data in place.
-
- Requires open-cv as a dependency.
- """
- if len(mask_data["rles"]) == 0:
- return mask_data
-
- # Filter small disconnected regions and holes
- new_masks = []
- scores = []
- for rle in mask_data["rles"]:
- mask = rle_to_mask(rle)
-
- mask, changed = remove_small_regions(mask, min_area, mode="holes")
- unchanged = not changed
- mask, changed = remove_small_regions(mask, min_area, mode="islands")
- unchanged = unchanged and not changed
-
- new_masks.append(torch.as_tensor(mask).unsqueeze(0))
- # Give score=0 to changed masks and score=1 to unchanged masks
- # so NMS will prefer ones that didn't need postprocessing
- scores.append(float(unchanged))
-
- # Recalculate boxes and remove any new duplicates
- masks = torch.cat(new_masks, dim=0)
- boxes = batched_mask_to_box(masks)
- keep_by_nms = batched_nms(
- boxes.float(),
- torch.as_tensor(scores),
- torch.zeros(len(boxes)), # categories
- iou_threshold=nms_thresh,
- )
-
- # Only recalculate RLEs for masks that have changed
- for i_mask in keep_by_nms:
- if scores[i_mask] == 0.0:
- mask_torch = masks[i_mask].unsqueeze(0)
- mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
- mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
- mask_data.filter(keep_by_nms)
-
- return mask_data
diff --git a/training/segment_anything/build_sam.py b/training/segment_anything/build_sam.py
deleted file mode 100644
index fef9980..0000000
--- a/training/segment_anything/build_sam.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-
-import torch
-
-from .modeling import (
- ImageEncoderViT,
- MaskDecoder,
- PromptEncoder,
- Sam,
- TwoWayTransformer,
-)
-
-
-def build_sam_vit_h(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=1280,
- encoder_depth=32,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[7, 15, 23, 31],
- checkpoint=checkpoint,
- )
-
-
-build_sam = build_sam_vit_h
-
-
-def build_sam_vit_l(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=1024,
- encoder_depth=24,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[5, 11, 17, 23],
- checkpoint=checkpoint,
- )
-
-
-def build_sam_vit_b(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=checkpoint,
- )
-
-
-sam_model_registry = {
- "default": build_sam,
- "vit_h": build_sam,
- "vit_l": build_sam_vit_l,
- "vit_b": build_sam_vit_b,
-}
-
-
-def _build_sam(
- encoder_embed_dim,
- encoder_depth,
- encoder_num_heads,
- encoder_global_attn_indexes,
- checkpoint=None,
-):
- prompt_embed_dim = 256
- image_size = 1024
- vit_patch_size = 16
- image_embedding_size = image_size // vit_patch_size
- sam = Sam(
- image_encoder=ImageEncoderViT(
- depth=encoder_depth,
- embed_dim=encoder_embed_dim,
- img_size=image_size,
- mlp_ratio=4,
- norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
- num_heads=encoder_num_heads,
- patch_size=vit_patch_size,
- qkv_bias=True,
- use_rel_pos=True,
- global_attn_indexes=encoder_global_attn_indexes,
- window_size=14,
- out_chans=prompt_embed_dim,
- ),
- prompt_encoder=PromptEncoder(
- embed_dim=prompt_embed_dim,
- image_embedding_size=(image_embedding_size, image_embedding_size),
- input_image_size=(image_size, image_size),
- mask_in_chans=16,
- ),
- mask_decoder=MaskDecoder(
- num_multimask_outputs=3,
- transformer=TwoWayTransformer(
- depth=2,
- embedding_dim=prompt_embed_dim,
- mlp_dim=2048,
- num_heads=8,
- ),
- transformer_dim=prompt_embed_dim,
- iou_head_depth=3,
- iou_head_hidden_dim=256,
- ),
- pixel_mean=[123.675, 116.28, 103.53],
- pixel_std=[58.395, 57.12, 57.375],
- )
- sam.eval()
- if checkpoint is not None:
- with open(checkpoint, "rb") as f:
- state_dict = torch.load(f)
- sam.load_state_dict(state_dict)
- return sam
diff --git a/training/segment_anything/modeling/__init__.py b/training/segment_anything/modeling/__init__.py
deleted file mode 100644
index 088af38..0000000
--- a/training/segment_anything/modeling/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-from .sam import Sam
-from .transformer import TwoWayTransformer
diff --git a/training/segment_anything/modeling/common.py b/training/segment_anything/modeling/common.py
deleted file mode 100644
index e872781..0000000
--- a/training/segment_anything/modeling/common.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Type
-
-import torch
-import torch.nn as nn
-
-
-class MLPBlock(nn.Module):
- def __init__(
- self,
- embedding_dim: int,
- mlp_dim: int,
- act: Type[nn.Module] = nn.GELU,
- ) -> None:
- super().__init__()
- self.lin1 = nn.Linear(embedding_dim, mlp_dim)
- self.lin2 = nn.Linear(mlp_dim, embedding_dim)
- self.act = act()
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.lin2(self.act(self.lin1(x)))
-
-
-# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
-# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
-class LayerNorm2d(nn.Module):
- def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
- super().__init__()
- self.weight = nn.Parameter(torch.ones(num_channels))
- self.bias = nn.Parameter(torch.zeros(num_channels))
- self.eps = eps
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- u = x.mean(1, keepdim=True)
- s = (x - u).pow(2).mean(1, keepdim=True)
- x = (x - u) / torch.sqrt(s + self.eps)
- x = self.weight[:, None, None] * x + self.bias[:, None, None]
- return x
diff --git a/training/segment_anything/modeling/image_encoder.py b/training/segment_anything/modeling/image_encoder.py
deleted file mode 100644
index 31360d3..0000000
--- a/training/segment_anything/modeling/image_encoder.py
+++ /dev/null
@@ -1,389 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional, Tuple, Type
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .common import LayerNorm2d, MLPBlock
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class ImageEncoderViT(nn.Module):
- def __init__(
- self,
- img_size: int = 1024,
- patch_size: int = 16,
- in_chans: int = 3,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- out_chans: int = 256,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_abs_pos: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- global_attn_indexes: Tuple[int, ...] = (),
- ) -> None:
- """
- Args:
- img_size (int): Input image size.
- patch_size (int): Patch size.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- depth (int): Depth of ViT.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_abs_pos (bool): If True, use absolute positional embeddings.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks.
- global_attn_indexes (list): Indexes for blocks using global attention.
- """
- super().__init__()
- self.img_size = img_size
-
- self.patch_embed = PatchEmbed(
- kernel_size=(patch_size, patch_size),
- stride=(patch_size, patch_size),
- in_chans=in_chans,
- embed_dim=embed_dim,
- )
-
- self.pos_embed: Optional[nn.Parameter] = None
- if use_abs_pos:
- # Initialize absolute positional embedding with pretrain image size.
- self.pos_embed = nn.Parameter(torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))
-
- self.blocks = nn.ModuleList()
- for i in range(depth):
- block = Block(
- dim=embed_dim,
- num_heads=num_heads,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- norm_layer=norm_layer,
- act_layer=act_layer,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- window_size=window_size if i not in global_attn_indexes else 0,
- input_size=(img_size // patch_size, img_size // patch_size),
- )
- self.blocks.append(block)
-
- self.neck = nn.Sequential(
- nn.Conv2d(
- embed_dim,
- out_chans,
- kernel_size=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- nn.Conv2d(
- out_chans,
- out_chans,
- kernel_size=3,
- padding=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.patch_embed(x)
- if self.pos_embed is not None:
- x = x + self.pos_embed
-
- for blk in self.blocks:
- x = blk(x)
-
- x = self.neck(x.permute(0, 3, 1, 2))
-
- return x
-
-
-class Block(nn.Module):
- """Transformer blocks with support of window attention and residual propagation blocks"""
-
- def __init__(
- self,
- dim: int,
- num_heads: int,
- mlp_ratio: float = 4.0,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks. If it equals 0, then
- use global attention.
- input_size (tuple(int, int) or None): Input resolution for calculating the relative
- positional parameter size.
- """
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads,
- qkv_bias=qkv_bias,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- input_size=input_size if window_size == 0 else (window_size, window_size),
- )
-
- self.norm2 = norm_layer(dim)
- self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
-
- self.window_size = window_size
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- shortcut = x
- x = self.norm1(x)
- # Window partition
- if self.window_size > 0:
- H, W = x.shape[1], x.shape[2]
- x, pad_hw = window_partition(x, self.window_size)
-
- x = self.attn(x)
- # Reverse window partition
- if self.window_size > 0:
- x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-
- x = shortcut + x
- x = x + self.mlp(self.norm2(x))
-
- return x
-
-
-class Attention(nn.Module):
- """Multi-head Attention block with relative position embeddings."""
-
- def __init__(
- self,
- dim: int,
- num_heads: int = 8,
- qkv_bias: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- input_size (tuple(int, int) or None): Input resolution for calculating the relative
- positional parameter size.
- """
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = head_dim**-0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.proj = nn.Linear(dim, dim)
-
- self.use_rel_pos = use_rel_pos
- if self.use_rel_pos:
- assert input_size is not None, "Input size must be provided if using relative positional encoding."
- # initialize relative positional embeddings
- self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
- self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, H, W, _ = x.shape
- # qkv with shape (3, B, nHead, H * W, C)
- qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- # q, k, v with shape (B * nHead, H * W, C)
- q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
-
- attn = (q * self.scale) @ k.transpose(-2, -1)
-
- if self.use_rel_pos:
- attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
-
- attn = attn.softmax(dim=-1)
- x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
- x = self.proj(x)
-
- return x
-
-
-def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
- """
- Partition into non-overlapping windows with padding if needed.
- Args:
- x (tensor): input tokens with [B, H, W, C].
- window_size (int): window size.
-
- Returns:
- windows: windows after partition with [B * num_windows, window_size, window_size, C].
- (Hp, Wp): padded height and width before partition
- """
- B, H, W, C = x.shape
-
- pad_h = (window_size - H % window_size) % window_size
- pad_w = (window_size - W % window_size) % window_size
- if pad_h > 0 or pad_w > 0:
- x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
- Hp, Wp = H + pad_h, W + pad_w
-
- x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows, (Hp, Wp)
-
-
-def window_unpartition(
- windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
-) -> torch.Tensor:
- """
- Window unpartition into original sequences and removing padding.
- Args:
- windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
- window_size (int): window size.
- pad_hw (Tuple): padded height and width (Hp, Wp).
- hw (Tuple): original height and width (H, W) before padding.
-
- Returns:
- x: unpartitioned sequences with [B, H, W, C].
- """
- Hp, Wp = pad_hw
- H, W = hw
- B = windows.shape[0] // (Hp * Wp // window_size // window_size)
- x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-
- if Hp > H or Wp > W:
- x = x[:, :H, :W, :].contiguous()
- return x
-
-
-def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
- """
- Get relative positional embeddings according to the relative positions of
- query and key sizes.
- Args:
- q_size (int): size of query q.
- k_size (int): size of key k.
- rel_pos (Tensor): relative position embeddings (L, C).
-
- Returns:
- Extracted positional embeddings according to relative positions.
- """
- max_rel_dist = int(2 * max(q_size, k_size) - 1)
- # Interpolate rel pos if needed.
- if rel_pos.shape[0] != max_rel_dist:
- # Interpolate rel pos.
- rel_pos_resized = F.interpolate(
- rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
- size=max_rel_dist,
- mode="linear",
- )
- rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
- else:
- rel_pos_resized = rel_pos
-
- # Scale the coords with short length if shapes for q and k are different.
- q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
- k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
- relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
-
- return rel_pos_resized[relative_coords.long()]
-
-
-def add_decomposed_rel_pos(
- attn: torch.Tensor,
- q: torch.Tensor,
- rel_pos_h: torch.Tensor,
- rel_pos_w: torch.Tensor,
- q_size: Tuple[int, int],
- k_size: Tuple[int, int],
-) -> torch.Tensor:
- """
- Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
- https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
- Args:
- attn (Tensor): attention map.
- q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
- rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
- rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
- q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
- k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
-
- Returns:
- attn (Tensor): attention map with added relative positional embeddings.
- """
- q_h, q_w = q_size
- k_h, k_w = k_size
- Rh = get_rel_pos(q_h, k_h, rel_pos_h)
- Rw = get_rel_pos(q_w, k_w, rel_pos_w)
-
- B, _, dim = q.shape
- r_q = q.reshape(B, q_h, q_w, dim)
- rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
- rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-
- attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
- B, q_h * q_w, k_h * k_w
- )
-
- return attn
-
-
-class PatchEmbed(nn.Module):
- """
- Image to Patch Embedding.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int] = (16, 16),
- stride: Tuple[int, int] = (16, 16),
- padding: Tuple[int, int] = (0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.proj(x)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/training/segment_anything/modeling/mask_decoder.py b/training/segment_anything/modeling/mask_decoder.py
deleted file mode 100644
index dfacfe5..0000000
--- a/training/segment_anything/modeling/mask_decoder.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import List, Tuple, Type
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .common import LayerNorm2d
-
-
-class MaskDecoder(nn.Module):
- def __init__(
- self,
- *,
- transformer_dim: int,
- transformer: nn.Module,
- num_multimask_outputs: int = 3,
- activation: Type[nn.Module] = nn.GELU,
- iou_head_depth: int = 3,
- iou_head_hidden_dim: int = 256,
- ) -> None:
- """
- Predicts masks given an image and prompt embeddings, using a
- tranformer architecture.
-
- Arguments:
- transformer_dim (int): the channel dimension of the transformer
- transformer (nn.Module): the transformer used to predict masks
- num_multimask_outputs (int): the number of masks to predict
- when disambiguating masks
- activation (nn.Module): the type of activation to use when
- upscaling masks
- iou_head_depth (int): the depth of the MLP used to predict
- mask quality
- iou_head_hidden_dim (int): the hidden dimension of the MLP
- used to predict mask quality
- """
- super().__init__()
- self.transformer_dim = transformer_dim
- self.transformer = transformer
-
- self.num_multimask_outputs = num_multimask_outputs
-
- self.iou_token = nn.Embedding(1, transformer_dim)
- self.num_mask_tokens = num_multimask_outputs + 1
- self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
- self.output_upscaling = nn.Sequential(
- nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
- LayerNorm2d(transformer_dim // 4),
- activation(),
- nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
- activation(),
- )
- self.output_hypernetworks_mlps = nn.ModuleList(
- [MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for i in range(self.num_mask_tokens)]
- )
-
- self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)
-
- def forward(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- multimask_output: bool,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Predict masks given image and prompt embeddings.
-
- Arguments:
- image_embeddings (torch.Tensor): the embeddings from the image encoder
- image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
- sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
- dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
- multimask_output (bool): Whether to return multiple masks or a single
- mask.
-
- Returns:
- torch.Tensor: batched predicted masks
- torch.Tensor: batched predictions of mask quality
- """
- masks, iou_pred = self.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=image_pe,
- sparse_prompt_embeddings=sparse_prompt_embeddings,
- dense_prompt_embeddings=dense_prompt_embeddings,
- )
-
- # Select the correct mask or masks for output
- if multimask_output:
- mask_slice = slice(1, None)
- else:
- mask_slice = slice(0, 1)
- masks = masks[:, mask_slice, :, :]
- iou_pred = iou_pred[:, mask_slice]
-
- # Prepare output
- return masks, iou_pred
-
- def predict_masks(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Predicts masks. See 'forward' for more details."""
- # Concatenate output tokens
- output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
- output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
- tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
-
- # Expand per-image data in batch direction to be per-mask
- src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
- src = src + dense_prompt_embeddings
- pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
- b, c, h, w = src.shape
-
- # Run the transformer
- hs, src = self.transformer(src, pos_src, tokens)
- iou_token_out = hs[:, 0, :]
- mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
-
- # Upscale mask embeddings and predict masks using the mask tokens
- src = src.transpose(1, 2).view(b, c, h, w)
- upscaled_embedding = self.output_upscaling(src)
- hyper_in_list: List[torch.Tensor] = []
- for i in range(self.num_mask_tokens):
- hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
- hyper_in = torch.stack(hyper_in_list, dim=1)
- b, c, h, w = upscaled_embedding.shape
-
- masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
-
- # Generate mask quality predictions
- iou_pred = self.iou_prediction_head(iou_token_out)
-
- return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
- def __init__(
- self,
- input_dim: int,
- hidden_dim: int,
- output_dim: int,
- num_layers: int,
- sigmoid_output: bool = False,
- ) -> None:
- super().__init__()
- self.num_layers = num_layers
- h = [hidden_dim] * (num_layers - 1)
- self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
- self.sigmoid_output = sigmoid_output
-
- def forward(self, x):
- for i, layer in enumerate(self.layers):
- x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
- if self.sigmoid_output:
- x = F.sigmoid(x)
- return x
diff --git a/training/segment_anything/modeling/prompt_encoder.py b/training/segment_anything/modeling/prompt_encoder.py
deleted file mode 100644
index 1887c8d..0000000
--- a/training/segment_anything/modeling/prompt_encoder.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from torch import nn
-
-from .common import LayerNorm2d
-
-
-class PromptEncoder(nn.Module):
- def __init__(
- self,
- embed_dim: int,
- image_embedding_size: Tuple[int, int],
- input_image_size: Tuple[int, int],
- mask_in_chans: int,
- activation: Type[nn.Module] = nn.GELU,
- ) -> None:
- """
- Encodes prompts for input to SAM's mask decoder.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- """
- super().__init__()
- self.embed_dim = embed_dim
- self.input_image_size = input_image_size
- self.image_embedding_size = image_embedding_size
- self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
-
- self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
- point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
- self.point_embeddings = nn.ModuleList(point_embeddings)
- self.not_a_point_embed = nn.Embedding(1, embed_dim)
-
- self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
- self.mask_downscaling = nn.Sequential(
- nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans // 4),
- activation(),
- nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans),
- activation(),
- nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
- )
- self.no_mask_embed = nn.Embedding(1, embed_dim)
-
- def get_dense_pe(self) -> torch.Tensor:
- """
- Returns the positional encoding used to encode point prompts,
- applied to a dense set of points the shape of the image encoding.
-
- Returns:
- torch.Tensor: Positional encoding with shape
- 1x(embed_dim)x(embedding_h)x(embedding_w)
- """
- return self.pe_layer(self.image_embedding_size).unsqueeze(0)
-
- def _embed_points(
- self,
- points: torch.Tensor,
- labels: torch.Tensor,
- pad: bool,
- ) -> torch.Tensor:
- """Embeds point prompts."""
- points = points + 0.5 # Shift to center of pixel
- if pad:
- padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
- padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
- points = torch.cat([points, padding_point], dim=1)
- labels = torch.cat([labels, padding_label], dim=1)
- point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
- point_embedding[labels == -1] = 0.0
- point_embedding[labels == -1] += self.not_a_point_embed.weight
- point_embedding[labels == 0] += self.point_embeddings[0].weight
- point_embedding[labels == 1] += self.point_embeddings[1].weight
- return point_embedding
-
- def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
- """Embeds box prompts."""
- boxes = boxes + 0.5 # Shift to center of pixel
- coords = boxes.reshape(-1, 2, 2)
- corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
- corner_embedding[:, 0, :] += self.point_embeddings[2].weight
- corner_embedding[:, 1, :] += self.point_embeddings[3].weight
- return corner_embedding
-
- def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
- """Embeds mask inputs."""
- mask_embedding = self.mask_downscaling(masks)
- return mask_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- else:
- return 1
-
- def _get_device(self) -> torch.device:
- return self.point_embeddings[0].weight.device
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks)
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
-
-
-class PositionEmbeddingRandom(nn.Module):
- """
- Positional encoding using random spatial frequencies.
- """
-
- def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
- super().__init__()
- if scale is None or scale <= 0.0:
- scale = 1.0
- self.register_buffer(
- "positional_encoding_gaussian_matrix",
- scale * torch.randn((2, num_pos_feats)),
- )
-
- def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
- """Positionally encode points that are normalized to [0,1]."""
- # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
- coords = 2 * coords - 1
- coords = coords @ self.positional_encoding_gaussian_matrix
- coords = 2 * np.pi * coords
- # outputs d_1 x ... x d_n x C shape
- return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
-
- def forward(self, size: Tuple[int, int]) -> torch.Tensor:
- """Generate positional encoding for a grid of the specified size."""
- h, w = size
- device: Any = self.positional_encoding_gaussian_matrix.device
- grid = torch.ones((h, w), device=device, dtype=torch.float32)
- y_embed = grid.cumsum(dim=0) - 0.5
- x_embed = grid.cumsum(dim=1) - 0.5
- y_embed = y_embed / h
- x_embed = x_embed / w
-
- pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
- return pe.permute(2, 0, 1) # C x H x W
-
- def forward_with_coords(self, coords_input: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
- """Positionally encode points that are not normalized to [0,1]."""
- coords = coords_input.clone()
- coords[:, :, 0] = coords[:, :, 0] / image_size[1]
- coords[:, :, 1] = coords[:, :, 1] / image_size[0]
- return self._pe_encoding(coords.to(torch.float)) # B x N x C
diff --git a/training/segment_anything/modeling/sam.py b/training/segment_anything/modeling/sam.py
deleted file mode 100644
index c1ce4ec..0000000
--- a/training/segment_anything/modeling/sam.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Dict, List, Tuple
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-
-
-class Sam(nn.Module):
- mask_threshold: float = 0.0
- image_format: str = "RGB"
-
- def __init__(
- self,
- image_encoder: ImageEncoderViT,
- prompt_encoder: PromptEncoder,
- mask_decoder: MaskDecoder,
- pixel_mean: List[float] = [123.675, 116.28, 103.53],
- pixel_std: List[float] = [58.395, 57.12, 57.375],
- ) -> None:
- """
- SAM predicts object masks from an image and input prompts.
-
- Arguments:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
- prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
- pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
- pixel_std (list(float)): Std values for normalizing pixels in the input image.
- """
- super().__init__()
- self.image_encoder = image_encoder
- self.prompt_encoder = prompt_encoder
- self.mask_decoder = mask_decoder
- self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
- @property
- def device(self) -> Any:
- return self.pixel_mean.device
-
- @torch.no_grad()
- def forward(
- self,
- batched_input: List[Dict[str, Any]],
- multimask_output: bool,
- ) -> List[Dict[str, torch.Tensor]]:
- """
- Predicts masks end-to-end from provided images and prompts.
- If prompts are not known in advance, using SamPredictor is
- recommended over calling the model directly.
-
- Arguments:
- batched_input (list(dict)): A list over input images, each a
- dictionary with the following keys. A prompt key can be
- excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
- multimask_output (bool): Whether the model should predict multiple
- disambiguating masks, or return a single mask.
-
- Returns:
- (list(dict)): A list over input images, where each element is
- as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
- """
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
-
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
- masks = self.postprocess_masks(
- low_res_masks,
- input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- }
- )
- return outputs
-
- def postprocess_masks(
- self,
- masks: torch.Tensor,
- input_size: Tuple[int, ...],
- original_size: Tuple[int, ...],
- ) -> torch.Tensor:
- """
- Remove padding and upscale masks to the original image size.
-
- Arguments:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
-
- Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
- """
- masks = F.interpolate(
- masks,
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- )
- masks = masks[..., : input_size[0], : input_size[1]]
- masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
- return masks
-
- def preprocess(self, x: torch.Tensor) -> torch.Tensor:
- """Normalize pixel values and pad to a square input."""
- # Normalize colors
- x = (x - self.pixel_mean) / self.pixel_std
-
- # Pad
- h, w = x.shape[-2:]
- padh = self.image_encoder.img_size - h
- padw = self.image_encoder.img_size - w
- x = F.pad(x, (0, padw, 0, padh))
- return x
diff --git a/training/segment_anything/predictor.py b/training/segment_anything/predictor.py
deleted file mode 100644
index fb252a4..0000000
--- a/training/segment_anything/predictor.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional, Tuple
-
-import numpy as np
-import torch
-from segment_anything.modeling import Sam
-
-from .utils.transforms import ResizeLongestSide
-
-
-class SamPredictor:
- def __init__(
- self,
- sam_model: Sam,
- ) -> None:
- """
- Uses SAM to calculate the image embedding for an image, and then
- allow repeated, efficient mask prediction given prompts.
-
- Arguments:
- sam_model (Sam): The model to use for mask prediction.
- """
- super().__init__()
- self.model = sam_model
- self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
- self.reset_image()
-
- def set_image(
- self,
- image: np.ndarray,
- image_format: str = "RGB",
- ) -> None:
- """
- Calculates the image embeddings for the provided image, allowing
- masks to be predicted with the 'predict' method.
-
- Arguments:
- image (np.ndarray): The image for calculating masks. Expects an
- image in HWC uint8 format, with pixel values in [0, 255].
- image_format (str): The color format of the image, in ['RGB', 'BGR'].
- """
- assert image_format in [
- "RGB",
- "BGR",
- ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
- if image_format != self.model.image_format:
- image = image[..., ::-1]
-
- # Transform the image to the form expected by the model
- input_image = self.transform.apply_image(image)
- input_image_torch = torch.as_tensor(input_image, device=self.device)
- input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
-
- self.set_torch_image(input_image_torch, image.shape[:2])
-
- @torch.no_grad()
- def set_torch_image(
- self,
- transformed_image: torch.Tensor,
- original_image_size: Tuple[int, ...],
- ) -> None:
- """
- Calculates the image embeddings for the provided image, allowing
- masks to be predicted with the 'predict' method. Expects the input
- image to be already transformed to the format expected by the model.
-
- Arguments:
- transformed_image (torch.Tensor): The input image, with shape
- 1x3xHxW, which has been transformed with ResizeLongestSide.
- original_image_size (tuple(int, int)): The size of the image
- before transformation, in (H, W) format.
- """
- assert (
- len(transformed_image.shape) == 4
- and transformed_image.shape[1] == 3
- and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
- ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
- self.reset_image()
-
- self.original_size = original_image_size
- self.input_size = tuple(transformed_image.shape[-2:])
- input_image = self.model.preprocess(transformed_image)
- self.features = self.model.image_encoder(input_image)
- self.is_image_set = True
-
- def predict(
- self,
- point_coords: Optional[np.ndarray] = None,
- point_labels: Optional[np.ndarray] = None,
- box: Optional[np.ndarray] = None,
- mask_input: Optional[np.ndarray] = None,
- multimask_output: bool = True,
- return_logits: bool = False,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Predict masks for the given input prompts, using the currently set image.
-
- Arguments:
- point_coords (np.ndarray or None): A Nx2 array of point prompts to the
- model. Each point is in (X,Y) in pixels.
- point_labels (np.ndarray or None): A length N array of labels for the
- point prompts. 1 indicates a foreground point and 0 indicates a
- background point.
- box (np.ndarray or None): A length 4 array given a box prompt to the
- model, in XYXY format.
- mask_input (np.ndarray): A low resolution mask input to the model, typically
- coming from a previous prediction iteration. Has form 1xHxW, where
- for SAM, H=W=256.
- multimask_output (bool): If true, the model will return three masks.
- For ambiguous input prompts (such as a single click), this will often
- produce better masks than a single prediction. If only a single
- mask is needed, the model's predicted quality score can be used
- to select the best mask. For non-ambiguous prompts, such as multiple
- input prompts, multimask_output=False can give better results.
- return_logits (bool): If true, returns un-thresholded masks logits
- instead of a binary mask.
-
- Returns:
- (np.ndarray): The output masks in CxHxW format, where C is the
- number of masks, and (H, W) is the original image size.
- (np.ndarray): An array of length C containing the model's
- predictions for the quality of each mask.
- (np.ndarray): An array of shape CxHxW, where C is the number
- of masks and H=W=256. These low resolution logits can be passed to
- a subsequent iteration as mask input.
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
- # Transform input prompts
- coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
- if point_coords is not None:
- assert point_labels is not None, "point_labels must be supplied if point_coords is supplied."
- point_coords = self.transform.apply_coords(point_coords, self.original_size)
- coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
- labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
- coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
- if box is not None:
- box = self.transform.apply_boxes(box, self.original_size)
- box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
- box_torch = box_torch[None, :]
- if mask_input is not None:
- mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
- mask_input_torch = mask_input_torch[None, :, :, :]
-
- masks, iou_predictions, low_res_masks = self.predict_torch(
- coords_torch,
- labels_torch,
- box_torch,
- mask_input_torch,
- multimask_output,
- return_logits=return_logits,
- )
-
- masks = masks[0].detach().cpu().numpy()
- iou_predictions = iou_predictions[0].detach().cpu().numpy()
- low_res_masks = low_res_masks[0].detach().cpu().numpy()
- return masks, iou_predictions, low_res_masks
-
- @torch.no_grad()
- def predict_torch(
- self,
- point_coords: Optional[torch.Tensor],
- point_labels: Optional[torch.Tensor],
- boxes: Optional[torch.Tensor] = None,
- mask_input: Optional[torch.Tensor] = None,
- multimask_output: bool = True,
- return_logits: bool = False,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Predict masks for the given input prompts, using the currently set image.
- Input prompts are batched torch tensors and are expected to already be
- transformed to the input frame using ResizeLongestSide.
-
- Arguments:
- point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
- model. Each point is in (X,Y) in pixels.
- point_labels (torch.Tensor or None): A BxN array of labels for the
- point prompts. 1 indicates a foreground point and 0 indicates a
- background point.
- box (np.ndarray or None): A Bx4 array given a box prompt to the
- model, in XYXY format.
- mask_input (np.ndarray): A low resolution mask input to the model, typically
- coming from a previous prediction iteration. Has form Bx1xHxW, where
- for SAM, H=W=256. Masks returned by a previous iteration of the
- predict method do not need further transformation.
- multimask_output (bool): If true, the model will return three masks.
- For ambiguous input prompts (such as a single click), this will often
- produce better masks than a single prediction. If only a single
- mask is needed, the model's predicted quality score can be used
- to select the best mask. For non-ambiguous prompts, such as multiple
- input prompts, multimask_output=False can give better results.
- return_logits (bool): If true, returns un-thresholded masks logits
- instead of a binary mask.
-
- Returns:
- (torch.Tensor): The output masks in BxCxHxW format, where C is the
- number of masks, and (H, W) is the original image size.
- (torch.Tensor): An array of shape BxC containing the model's
- predictions for the quality of each mask.
- (torch.Tensor): An array of shape BxCxHxW, where C is the number
- of masks and H=W=256. These low res logits can be passed to
- a subsequent iteration as mask input.
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
- if point_coords is not None:
- points = (point_coords, point_labels)
- else:
- points = None
-
- # Embed prompts
- sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
- points=points,
- boxes=boxes,
- masks=mask_input,
- )
-
- # Predict masks
- low_res_masks, iou_predictions = self.model.mask_decoder(
- image_embeddings=self.features,
- image_pe=self.model.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
-
- # Upscale the masks to the original image resolution
- masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
-
- if not return_logits:
- masks = masks > self.model.mask_threshold
-
- return masks, iou_predictions, low_res_masks
-
- def get_image_embedding(self) -> torch.Tensor:
- """
- Returns the image embeddings for the currently set image, with
- shape 1xCxHxW, where C is the embedding dimension and (H,W) are
- the embedding spatial dimension of SAM (typically C=256, H=W=64).
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) to generate an embedding.")
- assert self.features is not None, "Features must exist if an image has been set."
- return self.features
-
- @property
- def device(self) -> torch.device:
- return self.model.device
-
- def reset_image(self) -> None:
- """Resets the currently set image."""
- self.is_image_set = False
- self.features = None
- self.orig_h = None
- self.orig_w = None
- self.input_h = None
- self.input_w = None
diff --git a/training/segment_anything/utils/__init__.py b/training/segment_anything/utils/__init__.py
deleted file mode 100644
index 5277f46..0000000
--- a/training/segment_anything/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/training/segment_anything/utils/amg.py b/training/segment_anything/utils/amg.py
deleted file mode 100644
index 1c9c491..0000000
--- a/training/segment_anything/utils/amg.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from copy import deepcopy
-from itertools import product
-from typing import Any, Dict, Generator, ItemsView, List, Tuple
-
-import numpy as np
-import torch
-
-
-class MaskData:
- """
- A structure for storing masks and their related data in batched format.
- Implements basic filtering and concatenation.
- """
-
- def __init__(self, **kwargs) -> None:
- for v in kwargs.values():
- assert isinstance(
- v, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats = dict(**kwargs)
-
- def __setitem__(self, key: str, item: Any) -> None:
- assert isinstance(
- item, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats[key] = item
-
- def __delitem__(self, key: str) -> None:
- del self._stats[key]
-
- def __getitem__(self, key: str) -> Any:
- return self._stats[key]
-
- def items(self) -> ItemsView[str, Any]:
- return self._stats.items()
-
- def filter(self, keep: torch.Tensor) -> None:
- for k, v in self._stats.items():
- if v is None:
- self._stats[k] = None
- elif isinstance(v, torch.Tensor):
- self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
- elif isinstance(v, np.ndarray):
- self._stats[k] = v[keep.detach().cpu().numpy()]
- elif isinstance(v, list) and keep.dtype == torch.bool:
- self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
- elif isinstance(v, list):
- self._stats[k] = [v[i] for i in keep]
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def cat(self, new_stats: "MaskData") -> None:
- for k, v in new_stats.items():
- if k not in self._stats or self._stats[k] is None:
- self._stats[k] = deepcopy(v)
- elif isinstance(v, torch.Tensor):
- self._stats[k] = torch.cat([self._stats[k], v], dim=0)
- elif isinstance(v, np.ndarray):
- self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
- elif isinstance(v, list):
- self._stats[k] = self._stats[k] + deepcopy(v)
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def to_numpy(self) -> None:
- for k, v in self._stats.items():
- if isinstance(v, torch.Tensor):
- self._stats[k] = v.detach().cpu().numpy()
-
-
-def is_box_near_crop_edge(
- boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
-) -> torch.Tensor:
- """Filter masks at the edge of a crop, but not at the edge of the original image."""
- crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
- orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
- boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
- near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
- near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
- near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
- return torch.any(near_crop_edge, dim=1)
-
-
-def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
- box_xywh = deepcopy(box_xyxy)
- box_xywh[2] = box_xywh[2] - box_xywh[0]
- box_xywh[3] = box_xywh[3] - box_xywh[1]
- return box_xywh
-
-
-def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
- assert len(args) > 0 and all(
- len(a) == len(args[0]) for a in args
- ), "Batched iteration must have inputs of all the same size."
- n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
- for b in range(n_batches):
- yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
-
-
-def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
- """
- Encodes masks to an uncompressed RLE, in the format expected by
- pycoco tools.
- """
- # Put in fortran order and flatten h,w
- b, h, w = tensor.shape
- tensor = tensor.permute(0, 2, 1).flatten(1)
-
- # Compute change indices
- diff = tensor[:, 1:] ^ tensor[:, :-1]
- change_indices = diff.nonzero()
-
- # Encode run length
- out = []
- for i in range(b):
- cur_idxs = change_indices[change_indices[:, 0] == i, 1]
- cur_idxs = torch.cat(
- [
- torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
- cur_idxs + 1,
- torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
- ]
- )
- btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
- counts = [] if tensor[i, 0] == 0 else [0]
- counts.extend(btw_idxs.detach().cpu().tolist())
- out.append({"size": [h, w], "counts": counts})
- return out
-
-
-def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
- """Compute a binary mask from an uncompressed RLE."""
- h, w = rle["size"]
- mask = np.empty(h * w, dtype=bool)
- idx = 0
- parity = False
- for count in rle["counts"]:
- mask[idx : idx + count] = parity
- idx += count
- parity ^= True
- mask = mask.reshape(w, h)
- return mask.transpose() # Put in C order
-
-
-def area_from_rle(rle: Dict[str, Any]) -> int:
- return sum(rle["counts"][1::2])
-
-
-def calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor:
- """
- Computes the stability score for a batch of masks. The stability
- score is the IoU between the binary masks obtained by thresholding
- the predicted mask logits at high and low values.
- """
- # One mask is always contained inside the other.
- # Save memory by preventing unnecesary cast to torch.int64
- intersections = (masks > (mask_threshold + threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
- unions = (masks > (mask_threshold - threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
- return intersections / unions
-
-
-def build_point_grid(n_per_side: int) -> np.ndarray:
- """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
- offset = 1 / (2 * n_per_side)
- points_one_side = np.linspace(offset, 1 - offset, n_per_side)
- points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
- points_y = np.tile(points_one_side[:, None], (1, n_per_side))
- points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
- return points
-
-
-def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> List[np.ndarray]:
- """Generates point grids for all crop layers."""
- points_by_layer = []
- for i in range(n_layers + 1):
- n_points = int(n_per_side / (scale_per_layer**i))
- points_by_layer.append(build_point_grid(n_points))
- return points_by_layer
-
-
-def generate_crop_boxes(
- im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
-) -> Tuple[List[List[int]], List[int]]:
- """
- Generates a list of crop boxes of different sizes. Each layer
- has (2**i)**2 boxes for the ith layer.
- """
- crop_boxes, layer_idxs = [], []
- im_h, im_w = im_size
- short_side = min(im_h, im_w)
-
- # Original image
- crop_boxes.append([0, 0, im_w, im_h])
- layer_idxs.append(0)
-
- def crop_len(orig_len, n_crops, overlap):
- return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
-
- for i_layer in range(n_layers):
- n_crops_per_side = 2 ** (i_layer + 1)
- overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
-
- crop_w = crop_len(im_w, n_crops_per_side, overlap)
- crop_h = crop_len(im_h, n_crops_per_side, overlap)
-
- crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
- crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
-
- # Crops in XYWH format
- for x0, y0 in product(crop_box_x0, crop_box_y0):
- box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
- crop_boxes.append(box)
- layer_idxs.append(i_layer + 1)
-
- return crop_boxes, layer_idxs
-
-
-def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
- # Check if boxes has a channel dimension
- if len(boxes.shape) == 3:
- offset = offset.unsqueeze(1)
- return boxes + offset
-
-
-def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0]], device=points.device)
- # Check if points has a channel dimension
- if len(points.shape) == 3:
- offset = offset.unsqueeze(1)
- return points + offset
-
-
-def uncrop_masks(masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int) -> torch.Tensor:
- x0, y0, x1, y1 = crop_box
- if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
- return masks
- # Coordinate transform masks
- pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
- pad = (x0, pad_x - x0, y0, pad_y - y0)
- return torch.nn.functional.pad(masks, pad, value=0)
-
-
-def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tuple[np.ndarray, bool]:
- """
- Removes small disconnected regions and holes in a mask. Returns the
- mask and an indicator of if the mask has been modified.
- """
- import cv2 # type: ignore
-
- assert mode in ["holes", "islands"]
- correct_holes = mode == "holes"
- working_mask = (correct_holes ^ mask).astype(np.uint8)
- n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
- sizes = stats[:, -1][1:] # Row 0 is background label
- small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
- if len(small_regions) == 0:
- return mask, False
- fill_labels = [0] + small_regions
- if not correct_holes:
- fill_labels = [i for i in range(n_labels) if i not in fill_labels]
- # If every region is below threshold, keep largest
- if len(fill_labels) == 0:
- fill_labels = [int(np.argmax(sizes)) + 1]
- mask = np.isin(regions, fill_labels)
- return mask, True
-
-
-def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
- from pycocotools import mask as mask_utils # type: ignore
-
- h, w = uncompressed_rle["size"]
- rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
- rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json
- return rle
-
-
-def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
- """
- Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
- an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
- """
- # torch.max below raises an error on empty inputs, just skip in this case
- if torch.numel(masks) == 0:
- return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
-
- # Normalize shape to CxHxW
- shape = masks.shape
- h, w = shape[-2:]
- if len(shape) > 2:
- masks = masks.flatten(0, -3)
- else:
- masks = masks.unsqueeze(0)
-
- # Get top and bottom edges
- in_height, _ = torch.max(masks, dim=-1)
- in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
- bottom_edges, _ = torch.max(in_height_coords, dim=-1)
- in_height_coords = in_height_coords + h * (~in_height)
- top_edges, _ = torch.min(in_height_coords, dim=-1)
-
- # Get left and right edges
- in_width, _ = torch.max(masks, dim=-2)
- in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
- right_edges, _ = torch.max(in_width_coords, dim=-1)
- in_width_coords = in_width_coords + w * (~in_width)
- left_edges, _ = torch.min(in_width_coords, dim=-1)
-
- # If the mask is empty the right edge will be to the left of the left edge.
- # Replace these boxes with [0, 0, 0, 0]
- empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
- out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
- out = out * (~empty_filter).unsqueeze(-1)
-
- # Return to original shape
- if len(shape) > 2:
- out = out.reshape(*shape[:-2], 4)
- else:
- out = out[0]
-
- return out
diff --git a/training/segment_anything/utils/onnx.py b/training/segment_anything/utils/onnx.py
deleted file mode 100644
index 9cd17c7..0000000
--- a/training/segment_anything/utils/onnx.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Tuple
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from ..modeling import Sam
-from .amg import calculate_stability_score
-
-
-class SamOnnxModel(nn.Module):
- """
- This model should not be called directly, but is used in ONNX export.
- It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
- with some functions modified to enable model tracing. Also supports extra
- options controlling what information. See the ONNX export script for details.
- """
-
- def __init__(
- self,
- model: Sam,
- return_single_mask: bool,
- use_stability_score: bool = False,
- return_extra_metrics: bool = False,
- ) -> None:
- super().__init__()
- self.mask_decoder = model.mask_decoder
- self.model = model
- self.img_size = model.image_encoder.img_size
- self.return_single_mask = return_single_mask
- self.use_stability_score = use_stability_score
- self.stability_score_offset = 1.0
- self.return_extra_metrics = return_extra_metrics
-
- @staticmethod
- def resize_longest_image_size(input_image_size: torch.Tensor, longest_side: int) -> torch.Tensor:
- input_image_size = input_image_size.to(torch.float32)
- scale = longest_side / torch.max(input_image_size)
- transformed_size = scale * input_image_size
- transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
- return transformed_size
-
- def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
- point_coords = point_coords + 0.5
- point_coords = point_coords / self.img_size
- point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
- point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
-
- point_embedding = point_embedding * (point_labels != -1)
- point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1)
-
- for i in range(self.model.prompt_encoder.num_point_embeddings):
- point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[i].weight * (
- point_labels == i
- )
-
- return point_embedding
-
- def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
- mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
- mask_embedding = mask_embedding + (1 - has_mask_input) * self.model.prompt_encoder.no_mask_embed.weight.reshape(
- 1, -1, 1, 1
- )
- return mask_embedding
-
- def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
- masks = F.interpolate(
- masks,
- size=(self.img_size, self.img_size),
- mode="bilinear",
- align_corners=False,
- )
-
- prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size)
- masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])]
-
- orig_im_size = orig_im_size.to(torch.int64)
- h, w = orig_im_size[0], orig_im_size[1]
- masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
- return masks
-
- def select_masks(
- self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- # Determine if we should return the multiclick mask or not from the number of points.
- # The reweighting is used to avoid control flow.
- score_reweight = torch.tensor([[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]).to(
- iou_preds.device
- )
- score = iou_preds + (num_points - 2.5) * score_reweight
- best_idx = torch.argmax(score, dim=1)
- masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
- iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
-
- return masks, iou_preds
-
- @torch.no_grad()
- def forward(
- self,
- image_embeddings: torch.Tensor,
- point_coords: torch.Tensor,
- point_labels: torch.Tensor,
- mask_input: torch.Tensor,
- has_mask_input: torch.Tensor,
- orig_im_size: torch.Tensor,
- ):
- sparse_embedding = self._embed_points(point_coords, point_labels)
- dense_embedding = self._embed_masks(mask_input, has_mask_input)
-
- masks, scores = self.model.mask_decoder.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=self.model.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embedding,
- dense_prompt_embeddings=dense_embedding,
- )
-
- if self.use_stability_score:
- scores = calculate_stability_score(masks, self.model.mask_threshold, self.stability_score_offset)
-
- if self.return_single_mask:
- masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
-
- upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
-
- if self.return_extra_metrics:
- stability_scores = calculate_stability_score(
- upscaled_masks, self.model.mask_threshold, self.stability_score_offset
- )
- areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
- return upscaled_masks, scores, stability_scores, areas, masks
-
- return upscaled_masks, scores, masks
diff --git a/training/segment_anything/utils/transforms.py b/training/segment_anything/utils/transforms.py
deleted file mode 100644
index 96a4ed6..0000000
--- a/training/segment_anything/utils/transforms.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from copy import deepcopy
-from typing import Tuple
-
-import numpy as np
-import torch
-from torch.nn import functional as F
-from torchvision.transforms.functional import resize, to_pil_image # type: ignore
-
-
-class ResizeLongestSide:
- """
- Resizes images to longest side 'target_length', as well as provides
- methods for resizing coordinates and boxes. Provides methods for
- transforming both numpy array and batched torch tensors.
- """
-
- def __init__(self, target_length: int) -> None:
- self.target_length = target_length
-
- def apply_image(self, image: np.ndarray) -> np.ndarray:
- """
- Expects a numpy array with shape HxWxC in uint8 format.
- """
- target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
- return np.array(resize(to_pil_image(image), target_size))
-
- def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array of length 2 in the final dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
- coords = deepcopy(coords).astype(float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array shape Bx4. Requires the original image size
- in (H, W) format.
- """
- boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
- """
- Expects batched images with shape BxCxHxW and float format. This
- transformation may not exactly match apply_image. apply_image is
- the transformation expected by the model.
- """
- # Expects an image in BCHW format. May not exactly match apply_image.
- target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
- return F.interpolate(image, target_size, mode="bilinear", align_corners=False, antialias=True)
-
- def apply_coords_torch(self, coords: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
- """
- Expects a torch tensor with length 2 in the last dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
- coords = deepcopy(coords).to(torch.float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes_torch(self, boxes: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
- """
- Expects a torch tensor with shape Bx4. Requires the original image
- size in (H, W) format.
- """
- boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- @staticmethod
- def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
- """
- Compute the output size given input size and target long side length.
- """
- scale = long_side_length * 1.0 / max(oldh, oldw)
- newh, neww = oldh * scale, oldw * scale
- neww = int(neww + 0.5)
- newh = int(newh + 0.5)
- return (newh, neww)
diff --git a/training/trainer_2pt5d.py b/training/trainer_2pt5d.py
deleted file mode 100644
index 1d1e906..0000000
--- a/training/trainer_2pt5d.py
+++ /dev/null
@@ -1,654 +0,0 @@
-# Copyright 2020 - 2023 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-import random
-import time
-from copy import deepcopy
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.nn.parallel
-import torch.utils.data.distributed
-from monai.data import decollate_batch
-from monai.metrics import compute_dice
-from tensorboardX import SummaryWriter
-from torch.cuda.amp import GradScaler, autocast
-from utils.utils import AverageMeter, distributed_all_gather
-
-
-def apply_coords_torch(coords, original_size, sam_image_size) -> np.ndarray:
- """
- Expects a numpy array of length 2 in the final dimension. Requires the
- original image size in (H, W) format.
- """
- old = original_size
- new = sam_image_size
- coords = deepcopy(coords).float()
- # Here, we can apply a same scale factor to h and w, because we first pad the input to a square image along the
- # longest side then resize it to sam_image_size. In other words, the scale factor is determined by the longest side.
- coords[..., 0] = coords[..., 0] * (new / old)
- coords[..., 1] = coords[..., 1] * (new / old)
- return coords
-
-
-def sample_points(labelpoints, n_points):
- idx = torch.randperm(len(labelpoints), dtype=torch.long, device=labelpoints.device)[:n_points]
- return [labelpoints[idx]]
-
-
-def generate_point_prompt(batch_labels_, args, points_pos=None, points_neg=None, previous_pred=None):
- max_point = args.max_points
- Np = (
- points_pos
- if points_pos is not None
- else min(max_point, int(np.abs(random.gauss(mu=0, sigma=max_point // 2))) + 1)
- )
- Nn = points_neg if points_neg is not None else min(max_point, int(np.abs(random.gauss(mu=0, sigma=max_point // 2))))
- # To follow original SAM, with equal probability either a foreground point
- # is selected randomly for the target mask
- _point = []
- _point_label = []
- b, h, w = batch_labels_.shape
- device = batch_labels_.device
- for i in range(b):
- plabels = batch_labels_[i, ...]
- nlabels = (plabels == 0.0).float()
- if previous_pred is not None:
- ppred = previous_pred[i, 0, ...]
- npred = (previous_pred[i, 0, ...] == 0.0).float()
-
- # False positive mask (pixels that are predicted as positive but are actually negative)
- fp_mask = torch.logical_and(nlabels, ppred)
- # False negative mask (pixels that are predicted as negative but are actually positive)
- fn_mask = torch.logical_and(plabels, npred)
- # we sample positive points from false negative pred.
- # we sample negative points from false positive pred.
- plabelpoints = torch.nonzero(fn_mask)
- nlabelpoints = torch.nonzero(fp_mask)
-
- else:
- plabelpoints = torch.nonzero(plabels)
- nlabelpoints = torch.nonzero(nlabels)
- # 1 indicates a foreground point and 0 indicates a background point.
- # -1 indicates a dummy non-point as the placeholder.
- n_placeholder = Np + Nn - min(len(plabelpoints), Np) - min(len(nlabelpoints), Nn)
-
- # Use torch.randperm to generate indices on a GPU tensor
- _point.append(
- torch.cat(
- sample_points(plabelpoints, min(len(plabelpoints), Np))
- + sample_points(nlabelpoints, min(len(nlabelpoints), Nn))
- + [torch.zeros((1, 2), device=device)] * n_placeholder,
- dim=0,
- )
- )
- _point_label.append(
- torch.tensor([1] * min(len(plabelpoints), Np) + [0] * min(len(nlabelpoints), Nn) + [-1] * n_placeholder).to(
- device
- )
- )
-
- point = torch.stack(_point)
- point_label = torch.stack(_point_label)
- point_coords = apply_coords_torch(point, max(h, w), args.sam_image_size)
-
- return point_coords, point_label
-
-
-def prepare_sam_training_input(inputs, labels, args, model):
- unique_labels = torch.unique(labels).as_tensor().long()
-
- if args.skip_bk:
- unique_labels = unique_labels[1:]
-
- if len(unique_labels) == 0:
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
- batch_labels = torch.zeros(1, 1, args.sam_image_size // 4, args.sam_image_size // 4).cuda(args.rank)
- skip = True
- return prepared_input, batch_labels, None, skip
-
- # random sample args.num_prompt prompts, this will help to manage the GPU memory upper bound.
- if len(unique_labels) > args.num_prompt:
- idxs = random.sample(range(len(unique_labels)), args.num_prompt)
- idxs = torch.tensor(idxs)
- unique_labels = unique_labels[idxs]
- if len(unique_labels) < args.num_prompt:
- while len(unique_labels) < args.num_prompt:
- unique_labels = torch.cat([unique_labels, unique_labels], 0)
- unique_labels = unique_labels[: args.num_prompt]
-
- # add 4 background labels to every batch
- background_labels = list(set([i for i in range(1, 105)]) - set(unique_labels.cpu().numpy()))
- random.shuffle(background_labels)
- unique_labels = torch.cat([unique_labels, torch.tensor(background_labels[:4]).cuda(args.rank)])
-
- # preprocess make the size of label same as low_res_logit
- batch_labels_ = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
-
- if args.distributed:
- batch_labels = model.module.preprocess(batch_labels_, is_input=False)
- else:
- batch_labels = model.preprocess(batch_labels_, is_input=False)
-
- # TODO: we currently only use class-label and points prompt.
-
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
- if args.label_prompt:
- labels_prompt = unique_labels.unsqueeze(-1)
- prepared_input[0].update({"labels": labels_prompt})
-
- if args.point_prompt:
- point_coords, point_labels = generate_point_prompt(batch_labels_, args)
- prepared_input[0].update({"point_coords": point_coords, "point_labels": point_labels})
-
- if args.label_prompt and args.point_prompt:
- # if we use both two kinds of prompts, then we randomly drop one kind.
- if random.uniform(0, 1) < args.drop_label_prob:
- prepared_input[0].pop("labels")
- else:
- if random.uniform(0, 1) < args.drop_point_prob:
- prepared_input[0].pop("point_coords")
- prepared_input[0].pop("point_labels")
-
- return prepared_input, batch_labels.unsqueeze(1).cuda(args.rank), batch_labels_, False
-
-
-def train_epoch(model, loader, optimizer, scaler, epoch, loss_func, args):
- model.train()
- start_time = time.time()
- run_loss = AverageMeter()
- # we need to make sure the number of 2.5D input is an odd number.
- assert args.roi_z_iter % 2 == 1
- for idx, batch_data in enumerate(loader):
- # only take 1 batch
- inputs_l = batch_data["image"]
- labels_l = batch_data["label"]
- # TODO: we only support batch_size = 1 for data loader.
- inputs_l = inputs_l.squeeze()
- labels_l = labels_l.squeeze()
- n_z_before_pad = labels_l.shape[-1]
-
- n_slice = args.roi_z_iter
- # pad the z direction, so we can easily extract 2.5D input and predict labels for the center slice
- pd = (n_slice // 2, n_slice // 2)
- inputs_l = F.pad(inputs_l, pd, "constant", 0)
- labels_l = F.pad(labels_l, pd, "constant", 0)
- _loss = torch.tensor(0.0).cuda(args.rank)
-
- for _k in range(args.num_patch):
- # Return random integers from `low` (inclusive) to `high` (exclusive).
- start_idx = int(np.random.randint(low=n_slice // 2, high=(n_slice // 2 + n_z_before_pad)))
-
- inputs = inputs_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1].permute(2, 0, 1)
-
- # we only need the label for the center slice
- labels = labels_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1][..., n_slice // 2]
-
- data, target, target_original, skip = prepare_sam_training_input(
- inputs.cuda(args.rank), labels.cuda(args.rank), args, model
- )
-
- for param in model.parameters():
- param.grad = None
-
- with autocast(enabled=args.amp):
- outputs = model(data, is_train=True)
- loss = loss_func(outputs[0]["low_res_logits"], target)
-
- if skip:
- loss = loss * 0.0
-
- if args.amp:
- scaler.scale(loss).backward()
- if args.clip is not None:
- scaler.unscale_(optimizer)
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- scaler.step(optimizer)
- scaler.update()
- else:
- loss.backward()
- if args.clip is not None:
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- optimizer.step()
-
- _loss += loss.detach()
- _loss /= min(args.num_patch, n_z_before_pad)
- if args.distributed:
- loss_list = distributed_all_gather(
- [_loss],
- out_numpy=True,
- )
- run_loss.update(
- np.mean(np.mean(np.stack(loss_list, axis=0), axis=0), axis=0), n=args.batch_size * args.world_size
- )
- else:
- run_loss.update(_loss.item(), n=args.num_patch)
- if args.rank == 0:
- print(
- "Epoch {}/{} {}/{}".format(epoch, args.max_epochs, idx, len(loader)),
- "loss: {:.4f}".format(run_loss.avg),
- "time {:.2f}s".format(time.time() - start_time),
- )
- start_time = time.time()
- for param in model.parameters():
- param.grad = None
- return run_loss.avg
-
-
-def train_epoch_iterative(model, loader, optimizer, scaler, epoch, loss_func, args):
- model.train()
- start_time = time.time()
- run_loss = AverageMeter()
- # we need to make sure the number of 2.5D input is an odd number.
- assert args.roi_z_iter % 2 == 1
- for idx, batch_data in enumerate(loader):
- # only take 1 batch
- inputs_l = batch_data["image"]
- labels_l = batch_data["label"]
- # TODO: we only support batch_size = 1 for data loader.
- inputs_l = inputs_l.squeeze()
- labels_l = labels_l.squeeze()
- n_z_before_pad = labels_l.shape[-1]
-
- n_slice = args.roi_z_iter
- # pad the z direction, so we can easily extract 2.5D input and predict labels for the center slice
- pd = (n_slice // 2, n_slice // 2)
- inputs_l = F.pad(inputs_l, pd, "constant", 0)
- labels_l = F.pad(labels_l, pd, "constant", 0)
- _loss = torch.tensor(0.0).cuda(args.rank)
- for _k in range(min(args.num_patch, n_z_before_pad)):
- # Return random integers from `low` (inclusive) to `high` (exclusive).
- start_idx = int(np.random.randint(low=n_slice // 2, high=(n_slice // 2 + n_z_before_pad)))
-
- inputs = inputs_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1].permute(2, 0, 1)
-
- # we only need the label for the center slice
- labels = labels_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1][..., n_slice // 2]
-
- data, target, target_original, skip = prepare_sam_training_input(
- inputs.cuda(args.rank), labels.cuda(args.rank), args, model
- )
- for param in model.parameters():
- param.grad = None
-
- with autocast(enabled=args.amp):
- if args.distributed:
- image_embeddings = model.module.get_image_embeddings(data)
- else:
- image_embeddings = model.get_image_embeddings(data)
-
- if skip:
- with autocast(enabled=args.amp):
- if args.distributed:
- outputs = model.module.get_mask_prediction(data, image_embeddings)
- else:
- outputs = model.get_mask_prediction(data, image_embeddings)
- loss = loss_func(outputs[0]["low_res_logits"], target) * 0.0
- else:
- # iterative training
- loss = 0
- drop_iter = random.randint(0, args.num_iterative_step - 2)
- for i in range(args.num_iterative_step):
- with autocast(enabled=args.amp):
- if args.distributed:
- outputs = model.module.get_mask_prediction(data, image_embeddings)
- else:
- outputs = model.get_mask_prediction(data, image_embeddings)
- loss += loss_func(outputs[0]["low_res_logits"], target)
- if i == args.num_iterative_step - 1:
- # no need to perform the following operations after the last step
- continue
- # we also supply the mask prediction from the previous iteration
- # as an additional prompt to our model (follow original SAM).
- data[0]["mask_inputs"] = outputs[0]["low_res_logits"].detach()
- if i == drop_iter:
- # for drop iter, no additional points are sampled (follow original SAM).
- continue
-
- previous_point_coords = data[0].get("point_coords", None)
- previous_point_labels = data[0].get("point_labels", None)
-
- if previous_point_coords is None and args.no_more_points_for_cp_only:
- # if no point prompt at the first prompt generation,
- # we will not add more additional pointa during iterative training.
- continue
-
- # sample one pos and on neg point based on previous prediction
- previous_pred = (F.sigmoid(outputs[0]["high_res_logits"].detach()) > 0.5).float()
- point_coords, point_labels = generate_point_prompt(
- target_original, args=args, points_pos=1, points_neg=1, previous_pred=previous_pred
- )
-
- if previous_point_coords is not None:
- data[0]["point_coords"] = torch.cat([previous_point_coords, point_coords], dim=1)
- data[0]["point_labels"] = torch.cat([previous_point_labels, point_labels], dim=1)
- else:
- data[0]["point_coords"] = point_coords
- data[0]["point_labels"] = point_labels
-
- if args.amp:
- scaler.scale(loss).backward()
- if args.clip is not None:
- scaler.unscale_(optimizer)
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- scaler.step(optimizer)
- scaler.update()
- else:
- loss.backward()
- if args.clip is not None:
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- optimizer.step()
-
- _loss += loss.detach() / args.num_iterative_step
- _loss /= min(args.num_patch, n_z_before_pad)
- if args.distributed:
- loss_list = distributed_all_gather(
- [_loss],
- out_numpy=True,
- )
- run_loss.update(
- np.mean(np.mean(np.stack(loss_list, axis=0), axis=0), axis=0), n=args.batch_size * args.world_size
- )
- else:
- run_loss.update(_loss.item(), n=args.num_patch)
- if args.rank == 0:
- print(
- "Epoch {}/{} {}/{}".format(epoch, args.max_epochs, idx, len(loader)),
- "loss: {:.4f}".format(run_loss.avg),
- "time {:.2f}s".format(time.time() - start_time),
- )
- start_time = time.time()
- for param in model.parameters():
- param.grad = None
- return run_loss.avg
-
-
-def prepare_sam_test_input(inputs, labels, args, previous_pred=None):
- unique_labels = torch.tensor([i for i in range(1, 105)]).cuda(args.rank)
-
- # preprocess make the size of lable same as high_res_logit
- batch_labels = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
-
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
- if args.label_prompt:
- labels_prompt = unique_labels.unsqueeze(-1)
- prepared_input[0].update({"labels": labels_prompt})
-
- if args.point_prompt:
- point_coords, point_labels = generate_point_prompt(
- batch_labels,
- args,
- points_pos=args.points_val_pos,
- points_neg=args.points_val_neg,
- previous_pred=previous_pred,
- )
- prepared_input[0].update({"point_coords": point_coords, "point_labels": point_labels})
-
- return prepared_input, batch_labels.unsqueeze(1).cuda(args.rank), unique_labels
-
-
-def prepare_sam_val_input_cp_only(inputs, labels, args):
- # Don't exclude background in val but will ignore it in metric calculation
- unique_labels = torch.tensor([i for i in range(1, 105)]).cuda(args.rank)
-
- # preprocess make the size of lable same as high_res_logit
- batch_labels = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
-
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
-
- labels_prompt = unique_labels.unsqueeze(-1)
- prepared_input[0].update({"labels": labels_prompt})
-
- return prepared_input, batch_labels.unsqueeze(1).cuda(args.rank), unique_labels
-
-
-def val_epoch(model, loader, epoch, acc_func, args, iterative=False, post_label=None, post_pred=None):
- model.eval()
- run_acc = AverageMeter()
- start_time = time.time()
- with torch.no_grad():
- for idx, batch_data in enumerate(loader):
- # only take 1 batch
- inputs_l = batch_data["image"]
- labels_l = batch_data["label"]
- labels_l.shape[-1]
- # assert n_z_before_pad >= args.num_patch_val + args.roi_z_iter
-
- # TODO: we only support batch_size = 1 for data loader.
- inputs_l = inputs_l.squeeze()
- labels_l = labels_l.squeeze()
-
- n_slice = args.roi_z_iter
- # pad the z direction, so we can easily extract 2.5D input and predict labels for the center slice
- pd = (n_slice // 2, n_slice // 2)
-
- inputs_l = F.pad(inputs_l, pd, "constant", 0)
- labels_l = F.pad(labels_l, pd, "constant", 0)
- n_z_after_pad = labels_l.shape[-1]
-
- acc_sum_total = 0.0
- not_nans_total = 0.0
- # We only loop the center args.num_patch_val slices to save val time
- for start_idx in range(
- n_z_after_pad // 2 - args.num_patch_val // 2, n_z_after_pad // 2 + args.num_patch_val // 2
- ):
- inputs = inputs_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1].permute(2, 0, 1)
-
- # we only need the label for the center slice
- labels = labels_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1][..., n_slice // 2]
-
- data, target, _ = prepare_sam_val_input_cp_only(inputs.cuda(args.rank), labels.cuda(args.rank), args)
-
- with autocast(enabled=args.amp):
- outputs = model(data)
- logit = outputs[0]["high_res_logits"]
-
- y_pred = torch.stack(post_pred(decollate_batch(logit)), 0)
-
- # TODO: we compute metric for each prompt for simplicity in validation.
- acc_batch = compute_dice(y_pred=y_pred, y=target)
- acc_sum, not_nans = (
- torch.nansum(acc_batch).item(),
- 104 - torch.sum(torch.isnan(acc_batch).float()).item(),
- )
- acc_sum_total += acc_sum
- not_nans_total += not_nans
-
- acc, not_nans = acc_sum_total / not_nans_total, not_nans_total
- f_name = batch_data["image"].meta["filename_or_obj"]
- print(f"Rank: {args.rank}, Case: {f_name}, Acc: {acc:.4f}, N_prompts: {int(not_nans)} ")
-
- acc = torch.tensor(acc).cuda(args.rank)
- not_nans = torch.tensor(not_nans).cuda(args.rank)
-
- if args.distributed:
- acc_list, not_nans_list = distributed_all_gather([acc, not_nans], out_numpy=True)
- for al, nl in zip(acc_list, not_nans_list):
- run_acc.update(al, n=nl)
-
- else:
- run_acc.update(acc.cpu().numpy(), n=not_nans.cpu().numpy())
-
- if args.rank == 0:
- avg_acc = np.mean(run_acc.avg)
- print(
- "Val {}/{} {}/{}".format(epoch, args.max_epochs, idx + 1, len(loader)),
- "acc",
- avg_acc,
- "time {:.2f}s".format(time.time() - start_time),
- )
- start_time = time.time()
- return run_acc.avg
-
-
-def save_checkpoint(model, epoch, args, filename="model.pt", best_acc=0, optimizer=None, scheduler=None):
- state_dict = model.state_dict() if not args.distributed else model.module.state_dict()
- save_dict = {"epoch": epoch, "best_acc": best_acc, "state_dict": state_dict}
- if optimizer is not None:
- save_dict["optimizer"] = optimizer.state_dict()
- if scheduler is not None:
- save_dict["scheduler"] = scheduler.state_dict()
- filename = os.path.join(args.logdir, filename)
- torch.save(save_dict, filename)
- print("Saving checkpoint", filename)
-
-
-def run_training(
- model,
- train_loader,
- val_loader,
- optimizer,
- loss_func,
- acc_func,
- args,
- scheduler=None,
- start_epoch=0,
- post_label=None,
- post_pred=None,
-):
- writer = None
- if args.logdir is not None and args.rank == 0:
- writer = SummaryWriter(log_dir=args.logdir)
- if args.rank == 0:
- print("Writing Tensorboard logs to ", args.logdir)
- scaler = None
- if args.amp:
- scaler = GradScaler()
- val_acc_max = 0.0
- best_epoch = -1
- val_MA = None
- best_log = {}
- for epoch in range(start_epoch, args.max_epochs):
- if args.distributed:
- torch.distributed.barrier()
- print(args.rank, time.ctime(), "Epoch:", epoch)
- epoch_time = time.time()
- if args.rank == 0:
- if scheduler is not None:
- print("Current lr:", scheduler.get_last_lr())
- else:
- print("Current lr:", optimizer.param_groups[0]["lr"])
-
- if args.label_prompt and args.point_prompt:
- if epoch < args.label_prompt_warm_up_epoch:
- # during warm up, we drop class label prompt embedding with less prob,
- # since class label prompt embedding layer is trained from scratch.
- args.drop_label_prob = 0.2
- args.drop_point_prob = 0.5
- else:
- # after warmp up, we evenly drop two kinds of prompts
- args.drop_label_prob = 0.5
- args.drop_point_prob = 0.5
- print(
- "rank:",
- args.rank,
- "label_prompt (train):",
- args.label_prompt,
- ", label_drop_prob:",
- args.drop_label_prob,
- "| point_prompt (train):",
- args.point_prompt,
- ", point_drop_prob:",
- args.drop_point_prob,
- )
-
- # we don't perform iterative training for the first args.iterative_training_warm_up_epoch epochs
- if epoch > args.iterative_training_warm_up_epoch:
- if args.reuse_img_embedding:
- if args.rank == 0:
- print("Iterative Training: Reuse image embedding!")
- train_loss = train_epoch_iterative(
- model, train_loader, optimizer, scaler=scaler, epoch=epoch, loss_func=loss_func, args=args
- )
- else:
- if args.rank == 0:
- print("Iterative Training: Don't reuse image embedding!")
- raise NotImplementedError
- else:
- print(f" Rank: {args.rank} Single-step Training")
- train_loss = train_epoch(
- model, train_loader, optimizer, scaler=scaler, epoch=epoch, loss_func=loss_func, args=args
- )
-
- if args.rank == 0:
- print(
- "Final training {}/{}".format(epoch, args.max_epochs - 1),
- "loss: {:.4f}".format(train_loss),
- "time {:.2f}s".format(time.time() - epoch_time),
- )
- if args.rank == 0 and writer is not None:
- writer.add_scalar("train_loss", train_loss, epoch)
-
- if (epoch + 1) % args.val_every == 0:
- if args.distributed:
- torch.distributed.barrier()
- if args.rank == 0:
- print("Start validation")
- print("label_prompt (val):", args.label_prompt, "point_prompt (val):", args.point_prompt)
- epoch_time = time.time()
- val_avg_acc = val_epoch(
- model,
- val_loader,
- iterative=False,
- epoch=epoch,
- acc_func=acc_func,
- args=args,
- post_label=post_label,
- post_pred=post_pred,
- )
-
- val_avg_acc = np.mean(val_avg_acc)
- if val_MA is None:
- val_MA = val_avg_acc
- else:
- val_MA = 0.9 * val_MA + 0.1 * val_avg_acc
- if args.rank == 0:
- print(
- "Final validation {}/{},".format(epoch, args.max_epochs - 1),
- f"Acc {val_avg_acc:.4f},",
- f"mv Acc {val_MA:.4f},",
- "Previous Best validation at epoch {} is {:.4f},".format(best_epoch, val_acc_max),
- "time {:.2f}s".format(time.time() - epoch_time),
- )
- if writer is not None:
- writer.add_scalar("val_acc", val_avg_acc, epoch)
- if val_avg_acc > val_acc_max:
- print("new best ({:.6f} --> {:.6f}). ".format(val_acc_max, val_avg_acc))
- val_acc_max = val_avg_acc
- best_log[epoch] = float(val_acc_max)
- best_epoch = epoch
- if args.rank == 0 and args.logdir is not None and args.save_checkpoint:
- save_checkpoint(
- model,
- epoch,
- args,
- best_acc=val_acc_max,
- filename="model_best.pt",
- optimizer=optimizer,
- scheduler=scheduler,
- )
- with open(os.path.join(args.logdir, "train.log"), "w") as f:
- json.dump(best_log, f)
- if args.rank == 0 and args.logdir is not None and args.save_checkpoint:
- save_checkpoint(model, epoch, args, best_acc=val_acc_max, filename="model_final.pt")
-
- if scheduler is not None:
- scheduler.step()
-
- if args.rank == 0 and writer is not None:
- writer.close()
-
- print("Training Finished !, Best Accuracy: ", val_acc_max, "at epoch", best_epoch)
-
- return val_acc_max
diff --git a/training/utils/__init__.py b/training/utils/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/training/utils/data_utils.py b/training/utils/data_utils.py
deleted file mode 100644
index 5d4064b..0000000
--- a/training/utils/data_utils.py
+++ /dev/null
@@ -1,251 +0,0 @@
-# Copyright 2020 - 2023 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import copy
-import math
-import os
-
-import numpy as np
-import torch
-from monai import data, transforms
-from monai.transforms import (
- EnsureChannelFirstd,
- LoadImaged,
- Orientationd,
- RandRotate90d,
- RandShiftIntensityd,
- ScaleIntensityRanged,
- Spacingd,
-)
-
-
-class Sampler(torch.utils.data.Sampler):
- def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, make_even=True):
- if num_replicas is None:
- if not torch.distributed.is_available():
- raise RuntimeError("Requires distributed package to be available")
- num_replicas = torch.distributed.get_world_size()
- if rank is None:
- if not torch.distributed.is_available():
- raise RuntimeError("Requires distributed package to be available")
- rank = torch.distributed.get_rank()
- self.shuffle = shuffle
- self.make_even = make_even
- self.dataset = dataset
- self.num_replicas = num_replicas
- self.rank = rank
- self.epoch = 0
- self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
- self.total_size = self.num_samples * self.num_replicas
- indices = list(range(len(self.dataset)))
- self.valid_length = len(indices[self.rank : self.total_size : self.num_replicas])
-
- def __iter__(self):
- if self.shuffle:
- g = torch.Generator()
- g.manual_seed(self.epoch)
- indices = torch.randperm(len(self.dataset), generator=g).tolist()
- else:
- indices = list(range(len(self.dataset)))
- if self.make_even:
- if len(indices) < self.total_size:
- if self.total_size - len(indices) < len(indices):
- indices += indices[: (self.total_size - len(indices))]
- else:
- extra_ids = np.random.randint(low=0, high=len(indices), size=self.total_size - len(indices))
- indices += [indices[ids] for ids in extra_ids]
- assert len(indices) == self.total_size
- indices = indices[self.rank : self.total_size : self.num_replicas]
- self.num_samples = len(indices)
- return iter(indices)
-
- def __len__(self):
- return self.num_samples
-
- def set_epoch(self, epoch):
- self.epoch = epoch
-
-
-def get_loader(args):
- train_files, val_files, test_files = split_data(args)
-
- random_transforms = (
- [
- RandRotate90d(
- keys=["image", "label"],
- prob=0.10,
- max_k=3,
- ),
- RandShiftIntensityd(
- keys=["image"],
- offsets=0.10,
- prob=0.10,
- ),
- ]
- if args.data_aug
- else []
- )
-
- if args.data_aug:
- print("using data augmentation")
- else:
- print("No data augmentation")
-
- train_transform = transforms.Compose(
- [
- LoadImaged(keys=["image", "label"], image_only=True),
- EnsureChannelFirstd(keys=["image", "label"]),
- Orientationd(keys=["image", "label"], axcodes="RAS"),
- Spacingd(keys=["image", "label"], pixdim=(1.5, 1.5, 1.5), mode=("bilinear", "nearest")),
- ScaleIntensityRanged(
- keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True
- ),
- ]
- + random_transforms
- )
-
- val_transform = transforms.Compose(
- [
- LoadImaged(keys=["image", "label"], image_only=True),
- EnsureChannelFirstd(keys=["image", "label"]),
- Orientationd(keys=["image", "label"], axcodes="RAS"),
- Spacingd(keys=["image", "label"], pixdim=(1.5, 1.5, 1.5), mode=("bilinear", "nearest")),
- ScaleIntensityRanged(
- keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True
- ),
- ]
- )
-
- if args.test_mode:
- pass
- else:
- datalist = train_files
- if args.use_normal_dataset:
- train_ds = data.Dataset(data=datalist[:1], transform=train_transform)
- else:
- if args.distributed:
- datalist = data.partition_dataset(
- data=datalist,
- shuffle=True,
- num_partitions=args.world_size,
- even_divisible=True,
- )[args.rank]
-
- train_ds = data.CacheDataset(
- data=datalist,
- transform=train_transform,
- cache_rate=1.0,
- num_workers=args.workers,
- )
- train_sampler = None
-
- train_loader = data.DataLoader(
- train_ds,
- batch_size=args.batch_size,
- shuffle=(train_sampler is None),
- num_workers=args.workers,
- sampler=train_sampler,
- pin_memory=True,
- )
- val_files = val_files
- if args.distributed:
- val_files = data.partition_dataset(
- data=val_files,
- shuffle=False,
- num_partitions=args.world_size,
- even_divisible=False,
- )[args.rank]
- val_ds = data.CacheDataset(
- data=val_files,
- transform=val_transform,
- cache_rate=1.0,
- num_workers=args.workers,
- )
- val_sampler = None
- val_loader = data.DataLoader(
- val_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=val_sampler, pin_memory=True
- )
- loader = [train_loader, val_loader]
-
- return loader
-
-
-def split_data(args):
- data_dir = args.data_dir
- import json
-
- with open(args.json_list, "r") as f:
- json_data = json.load(f)
-
- list_train = []
- list_valid = []
- if "validation" in json_data.keys():
- list_train = json_data["training"]
- list_valid = json_data["validation"]
- list_test = json_data["testing"]
- else:
- for item in json_data["training"]:
- if item["fold"] == args.fold:
- item.pop("fold", None)
- list_valid.append(item)
- else:
- item.pop("fold", None)
- list_train.append(item)
- if "testing" in json_data.keys() and "label" in json_data["testing"][0]:
- list_test = json_data["testing"]
- else:
- list_test = copy.deepcopy(list_valid)
- if args.splitval > 0:
- list_train = sorted(list_train, key=lambda x: x["image"])
- l = int((len(list_train) + len(list_valid)) * args.splitval)
- list_valid = list_train[-l:]
- list_train = list_train[:-l]
-
- if hasattr(args, "rank") and args.rank == 0:
- print("train files", len(list_train), [os.path.basename(_["image"]).split(".")[0] for _ in list_train])
- print("val files", len(list_valid), [os.path.basename(_["image"]).split(".")[0] for _ in list_valid])
- print("test files", len(list_test), [os.path.basename(_["image"]).split(".")[0] for _ in list_test])
-
- # training data
- files = []
- for _i in range(len(list_train)):
- str_img = os.path.join(data_dir, list_train[_i]["image"])
- str_seg = os.path.join(data_dir, list_train[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
-
- train_files = copy.deepcopy(files)
-
- files = []
- for _i in range(len(list_valid)):
- str_img = os.path.join(data_dir, list_valid[_i]["image"])
- str_seg = os.path.join(data_dir, list_valid[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
- val_files = copy.deepcopy(files)
-
- files = []
- for _i in range(len(list_test)):
- str_img = os.path.join(data_dir, list_test[_i]["image"])
- str_seg = os.path.join(data_dir, list_test[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
- test_files = copy.deepcopy(files)
- return train_files, val_files, test_files
diff --git a/training/utils/utils.py b/training/utils/utils.py
deleted file mode 100644
index 8ac31c6..0000000
--- a/training/utils/utils.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright 2020 - 2022 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import scipy.ndimage as ndimage
-import torch
-
-
-def resample_3d(img, target_size):
- imx, imy, imz = img.shape
- tx, ty, tz = target_size
- zoom_ratio = (float(tx) / float(imx), float(ty) / float(imy), float(tz) / float(imz))
- img_resampled = ndimage.zoom(img, zoom_ratio, order=0, prefilter=False)
- return img_resampled
-
-
-def dice(x, y):
- intersect = np.sum(np.sum(np.sum(x * y)))
- y_sum = np.sum(np.sum(np.sum(y)))
- if y_sum == 0:
- return 0.0
- x_sum = np.sum(np.sum(np.sum(x)))
- return 2 * intersect / (x_sum + y_sum)
-
-
-class AverageMeter(object):
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
-
-
-def distributed_all_gather(
- tensor_list, valid_batch_size=None, out_numpy=False, world_size=None, no_barrier=False, is_valid=None
-):
- if world_size is None:
- world_size = torch.distributed.get_world_size()
- if valid_batch_size is not None:
- valid_batch_size = min(valid_batch_size, world_size)
- elif is_valid is not None:
- is_valid = torch.tensor(bool(is_valid), dtype=torch.bool, device=tensor_list[0].device)
- if not no_barrier:
- torch.distributed.barrier()
- tensor_list_out = []
- with torch.no_grad():
- if is_valid is not None:
- is_valid_list = [torch.zeros_like(is_valid) for _ in range(world_size)]
- torch.distributed.all_gather(is_valid_list, is_valid)
- is_valid = [x.item() for x in is_valid_list]
- for tensor in tensor_list:
- gather_list = [torch.zeros_like(tensor) for _ in range(world_size)]
- torch.distributed.all_gather(gather_list, tensor)
- if valid_batch_size is not None:
- gather_list = gather_list[:valid_batch_size]
- elif is_valid is not None:
- gather_list = [g for g, v in zip(gather_list, is_valid_list) if v]
- if out_numpy:
- gather_list = [t.cpu().numpy() for t in gather_list]
- tensor_list_out.append(gather_list)
- return tensor_list_out
diff --git a/training/vista_2pt5d/__init__.py b/training/vista_2pt5d/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/training/vista_2pt5d/model.py b/training/vista_2pt5d/model.py
deleted file mode 100644
index 1df8097..0000000
--- a/training/vista_2pt5d/model.py
+++ /dev/null
@@ -1,441 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-from typing import Any, Dict, List, Tuple
-
-import monai
-import torch
-from segment_anything.modeling import TwoWayTransformer
-from segment_anything.modeling.mask_decoder import MaskDecoder
-from torch import nn
-from torch.nn import functional as F
-from vista_2pt5d.vista_image_encoder import VistaImageEncoderViT
-from vista_2pt5d.vista_prompt_encoder import VistaPromptEncoder
-
-
-class Vista2pt5D(nn.Module):
- mask_threshold: float = 0.5
- image_format: str = "RGB"
-
- def __init__(
- self,
- image_encoder: VistaImageEncoderViT,
- prompt_encoder: VistaPromptEncoder,
- mask_decoder: MaskDecoder,
- pixel_mean: List[float] = [123.675, 116.28, 103.53],
- pixel_std: List[float] = [58.395, 57.12, 57.375],
- ) -> None:
- """
- SAM predicts object masks from an image and input prompts.
-
- Arguments:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
- prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
- pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
- pixel_std (list(float)): Std values for normalizing pixels in the input image.
- """
- super().__init__()
- self.image_encoder = image_encoder
- self.prompt_encoder = prompt_encoder
- self.mask_decoder = mask_decoder
- self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
- @property
- def device(self) -> Any:
- return self.pixel_mean.device
-
- def get_image_embeddings(
- self,
- batched_input: List[Dict[str, Any]],
- ):
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
- return image_embeddings
-
- def get_mask_prediction(
- self, batched_input: List[Dict[str, Any]], image_embeddings, multimask_output: bool = False
- ):
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- # raise NotImplementedError
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- class_labels=image_record.get("labels", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
-
- high_res_masks = self.postprocess_masks(
- low_res_masks,
- # input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = high_res_masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- "high_res_logits": high_res_masks,
- }
- )
- return outputs
-
- def forward(
- self,
- batched_input: List[Dict[str, Any]],
- multimask_output: bool = False,
- is_train: bool = False,
- ) -> List[Dict[str, torch.Tensor]]:
- """
- Predicts masks end-to-end from provided images and prompts.
- If prompts are not known in advance, using SamPredictor is
- recommended over calling the model directly.
-
- Arguments:
- batched_input (list(dict)): A list over input images, each a
- dictionary with the following keys. A prompt key can be
- excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'labels': (torch.Tensor) Batched labels for class-label prompt,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
- multimask_output (bool): Whether the model should predict multiple
- disambiguating masks, or return a single mask.
-
- Returns:
- (list(dict)): A list over input images, where each element is
- as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
- """
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
-
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- # raise NotImplementedError
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- class_labels=image_record.get("labels", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
- if is_train:
- outputs.append(
- {
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- }
- )
- else:
- high_res_masks = self.postprocess_masks(
- low_res_masks,
- # input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = high_res_masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- "high_res_logits": high_res_masks,
- }
- )
- return outputs
-
- def postprocess_masks(
- self,
- masks: torch.Tensor,
- # input_size: Tuple[int, ...],
- original_size: Tuple[int, ...],
- ) -> torch.Tensor:
- """
- Remove padding and upscale masks to the original image size.
-
- Arguments:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
-
- Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
- """
- # make it high resolution
- masks = F.interpolate(
- masks,
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- )
- # resize it back to the longest dim (square image)
- masks = F.interpolate(masks, max(original_size), mode="bilinear", align_corners=False)
- # remove padding
- masks = masks[..., : original_size[0], : original_size[1]]
- return masks
-
- def preprocess(self, x: torch.Tensor, is_input=True) -> torch.Tensor:
- """Normalize pixel values and pad to a square input."""
- if is_input:
- if x.shape[0] == 1:
- # Normalize colors map the values in [0,1] to [0,255] for input images and then using
- # original pixel_mean and pixel_std to do normalization
- x = (x * 255.0 - self.pixel_mean) / self.pixel_std
- else:
- # for other 2.5d data, we normalize each input slice
- x = torch.cat(
- [(x[i].unsqueeze(0) * 255.0 - self.pixel_mean) / self.pixel_std for i in range(x.shape[0])], dim=0
- )
-
- # Pad image and make it a square image
- h, w = x.shape[-2:]
- # find the longest dim
- target_length = max(h, w)
- padh = target_length - h
- padw = target_length - w
- x = F.pad(x, (0, padw, 0, padh))
- if is_input:
- # Resize it to self.image_encoder.img_size
- x = F.interpolate(
- x.unsqueeze(0),
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- ).squeeze(0)
- else:
- # Resize it to self.image_encoder.img_size // 4 (for labels). the size is same as low-res logit
- x = F.interpolate(
- x.unsqueeze(0), (self.image_encoder.img_size // 4, self.image_encoder.img_size // 4), mode="nearest"
- ).squeeze(0)
- return x
-
-
-def _build_vista2pt5d(
- encoder_in_chans,
- encoder_embed_dim,
- encoder_depth,
- encoder_num_heads,
- encoder_global_attn_indexes,
- checkpoint=None,
- image_size=1024,
- clip_class_label_prompt=False,
- patch_embed_3d=False,
-):
- prompt_embed_dim = 256
- image_size = image_size # TODO: Shall we try to adapt model to 512x512 ?
- vit_patch_size = 16
- image_embedding_size = image_size // vit_patch_size
- sam = Vista2pt5D(
- image_encoder=VistaImageEncoderViT(
- in_chans=encoder_in_chans,
- depth=encoder_depth,
- embed_dim=encoder_embed_dim,
- img_size=image_size,
- mlp_ratio=4,
- norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
- num_heads=encoder_num_heads,
- patch_size=vit_patch_size,
- qkv_bias=True,
- use_rel_pos=True,
- global_attn_indexes=encoder_global_attn_indexes,
- window_size=14,
- out_chans=prompt_embed_dim,
- patch_embed_3d=patch_embed_3d,
- ),
- prompt_encoder=VistaPromptEncoder(
- embed_dim=prompt_embed_dim,
- image_embedding_size=(image_embedding_size, image_embedding_size),
- input_image_size=(image_size, image_size),
- mask_in_chans=16,
- clip_class_label_prompt=clip_class_label_prompt,
- ),
- mask_decoder=MaskDecoder(
- num_multimask_outputs=3, # TODO: only predict one binary mask
- transformer=TwoWayTransformer(
- depth=2,
- embedding_dim=prompt_embed_dim,
- mlp_dim=2048,
- num_heads=8,
- ),
- transformer_dim=prompt_embed_dim,
- iou_head_depth=3,
- iou_head_hidden_dim=256,
- ),
- pixel_mean=[123.675, 116.28, 103.53],
- pixel_std=[58.395, 57.12, 57.375],
- )
-
- if checkpoint is not None:
- with open(checkpoint, "rb") as f:
- state_dict = torch.load(f)
-
- if image_size == 1024:
- # we try to use all pretrained weights
- new_dict = state_dict
- else:
- new_dict = {}
- for k, v in state_dict.items():
- # skip weights in position embedding and learned relative positional embeddings
- # due to the change of input size
- if ("pos_embed" in k and k.startswith("image_encoder")) or (
- "attn.rel_pos" in k and k.startswith("image_encoder")
- ):
- continue
- else:
- new_dict[k] = v
-
- if encoder_in_chans != 3:
- new_dict.pop("image_encoder.patch_embed.proj.weight")
- new_dict.pop("image_encoder.patch_embed.proj.bias")
-
- sam.load_state_dict(new_dict, strict=False)
- print(f"Load {len(new_dict)} keys from checkpoint {checkpoint}, current model has {len(sam.state_dict())} keys")
-
- total_params = []
- image_encoder_params = []
- prompt_encoder_params = []
- mask_decoder_params = []
- for name, param in sam.named_parameters():
- n_param = param.numel()
- total_params.append(n_param)
- if name.startswith("image_encoder"):
- image_encoder_params.append(n_param)
- elif name.startswith("prompt_encoder"):
- prompt_encoder_params.append(n_param)
- elif name.startswith("mask_decoder"):
- mask_decoder_params.append(n_param)
-
- print(
- f"{sam.__class__.__name__} has {sum(total_params) * 1.e-6:.2f} M params, "
- f"{sum(image_encoder_params) * 1.e-6:.2f} M params in image encoder,"
- f"{sum(prompt_encoder_params) * 1.e-6:.2f} M params in prompt encoder,"
- f"{sum(mask_decoder_params) * 1.e-6:.2f} M params in mask decoder."
- )
-
- total_trainable_params = sum(p.numel() if p.requires_grad else 0 for p in sam.parameters())
- print(f"{sam.__class__.__name__} has {total_trainable_params * 1.e-6:.2f} M trainable params.")
- return sam
-
-
-def build_vista2pt5d_vit_h(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=1280,
- encoder_depth=32,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[7, 15, 23, 31],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-def build_vista2pt5d_vit_l(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=1024,
- encoder_depth=24,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[5, 11, 17, 23],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-def build_vista2pt5d_vit_b(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-sam_model_registry = {
- "default": build_vista2pt5d_vit_h,
- "vit_h": build_vista2pt5d_vit_h,
- "vit_l": build_vista2pt5d_vit_l,
- "vit_b": build_vista2pt5d_vit_b,
-}
-
-if __name__ == "__main__":
- model = build_vista2pt5d_vit_b()
- model.cuda()
diff --git a/training/vista_2pt5d/vista_image_encoder.py b/training/vista_2pt5d/vista_image_encoder.py
deleted file mode 100644
index 4382425..0000000
--- a/training/vista_2pt5d/vista_image_encoder.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Tuple, Type
-
-import torch
-import torch.nn as nn
-from segment_anything.modeling.image_encoder import ImageEncoderViT, PatchEmbed
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class VistaImageEncoderViT(ImageEncoderViT):
- def __init__(
- self,
- img_size: int = 1024,
- patch_size: int = 16,
- in_chans: int = 3,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- out_chans: int = 256,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_abs_pos: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- global_attn_indexes: Tuple[int, ...] = (),
- patch_embed_3d: bool = False,
- ) -> None:
- """
- Args:
- img_size (int): Input image size.
- patch_size (int): Patch size.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- depth (int): Depth of ViT.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_abs_pos (bool): If True, use absolute positional embeddings.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks.
- global_attn_indexes (list): Indexes for blocks using global attention.
- patch_embed_3d (bool): If True, use 3D Patch Embedding.
- """
- super().__init__(
- img_size,
- patch_size,
- in_chans,
- embed_dim,
- depth,
- num_heads,
- mlp_ratio,
- out_chans,
- qkv_bias,
- norm_layer,
- act_layer,
- use_abs_pos,
- use_rel_pos,
- rel_pos_zero_init,
- window_size,
- global_attn_indexes,
- )
-
- self.img_size = img_size
-
- if in_chans > 3 and patch_embed_3d:
- print("ImageEncoderViT: Using 3D PatchEmbed")
- self.patch_embed = PatchEmbed2pt5D(
- kernel_size=(patch_size, patch_size, in_chans // 3),
- stride=(patch_size, patch_size, in_chans // 3),
- in_chans=3,
- embed_dim=embed_dim,
- )
- else:
- self.patch_embed = PatchEmbed(
- kernel_size=(patch_size, patch_size),
- stride=(patch_size, patch_size),
- in_chans=in_chans,
- embed_dim=embed_dim,
- )
-
-
-class PatchEmbed2pt5D(nn.Module):
- """
- Image to Patch Embedding by 3D Conv.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int, int] = (16, 16, 1),
- stride: Tuple[int, int, int] = (16, 16, 1),
- padding: Tuple[int, int, int] = (0, 0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- # got restore RGB channel dim and the depth dim
- c = x.shape[1]
- x = torch.stack(x.chunk(c // 3, dim=1), dim=-1)
- x = self.proj(x)
- # remove dummy depth dim to make it 2d
- x = x.squeeze(-1)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/training/vista_2pt5d/vista_prompt_encoder.py b/training/vista_2pt5d/vista_prompt_encoder.py
deleted file mode 100644
index 42568f8..0000000
--- a/training/vista_2pt5d/vista_prompt_encoder.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from segment_anything.modeling.common import LayerNorm2d
-from segment_anything.modeling.prompt_encoder import PromptEncoder
-from torch import nn
-
-
-class VistaPromptEncoder(PromptEncoder):
- def __init__(
- self,
- embed_dim: int,
- image_embedding_size: Tuple[int, int],
- input_image_size: Tuple[int, int],
- mask_in_chans: int,
- activation: Type[nn.Module] = nn.GELU,
- n_classes: int = 512,
- clip_class_label_prompt: bool = False,
- ) -> None:
- """
- Encodes prompts for input to SAM's mask decoder.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- n_classes (int): The number of pre-defined classes.
- clip_class_label_prompt (bool): Using clip txt features
- as class label prompt.
- """
- super().__init__(embed_dim, image_embedding_size, input_image_size, mask_in_chans, activation)
-
- self.clip_class_label_prompt = clip_class_label_prompt
- # Add support for onehot vector embedding for pre-defined classes
- if self.clip_class_label_prompt:
- raise NotImplementedError
- else:
- self.label_embeddings = nn.Embedding(n_classes, embed_dim)
- self.no_label_embed = nn.Embedding(1, embed_dim)
-
- def _embed_labels(self, labels: torch.Tensor) -> torch.Tensor:
- """Embeds onehot vector inputs."""
- if self.clip_class_label_prompt:
- raise NotImplementedError
- else:
- # Add support for onehot vector embedding for pre-defined classes
- label_embedding = self.label_embeddings(labels)
- return label_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- labels: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- elif labels is not None:
- return labels.shape[0]
- else:
- return 1
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- class_labels: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
- class_labels (torch.Tensor or none): labels to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks, class_labels)
-
- # Add support for onehot vector embedding for pre-defined classes
- if class_labels is not None:
- label_embeddings = self._embed_labels(class_labels)
- else:
- label_embeddings = self.no_label_embed.weight.reshape(1, 1, -1).expand(bs, -1, -1)
-
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
-
- # Add support for onehot vector embedding for pre-defined classes
- sparse_embeddings = torch.cat([sparse_embeddings, label_embeddings], dim=1)
-
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
diff --git a/LICENSE b/vista3d/LICENSE
similarity index 100%
rename from LICENSE
rename to vista3d/LICENSE
diff --git a/vista3d/README.md b/vista3d/README.md
new file mode 100644
index 0000000..1ac5bc9
--- /dev/null
+++ b/vista3d/README.md
@@ -0,0 +1,146 @@
+
+
+# MONAI **V**ersatile **I**maging **S**egmen**T**ation and **A**nnotation
+[[`Paper`](https://arxiv.org/pdf/2406.05285)] [[`Demo`](https://build.nvidia.com/nvidia/vista-3d)] [[`Container`](https://docs.nvidia.com/ai-enterprise/nim-medical-imaging/latest/vista-3d.html)]
+## Overview
+
+The **VISTA3D** is a foundation model trained systematically on 11,454 volumes encompassing 127 types of human anatomical structures and various lesions. It provides accurate out-of-the-box segmentation that matches state-of-the-art supervised models which are trained on each dataset. The model also achieves state-of-the-art zero-shot interactive segmentation in 3D, representing a promising step toward developing a versatile medical image foundation model.
+
+
+### Out-of box automatic segmentation
+For supported 127 classes, the model can perform highly accurate out-of-box segmentation. The fully automated process adopts a patch-based sliding-window inference and only requires a class prompt.
+Compared to supervised segmentation models trained on each dataset separately, VISTA3D showed comparable out-of-box performances and strong generalizability ('VISTA3D auto' in Table.1).
+
+
+
+ NIM Demo supports "Segment Everything"
+
+
+
+ Specify a supported class and edit the automatic results
+
+
+
+ Interactive supported class segmentation without specifying class
+
+
+
+ Add a new unseen class and do annotation
+
+
+
+## Usage
+
+### Installation
+The code requires `monai>=1.3`. Download the [model checkpoint](xxxx) and save it at ./models/model.pt.
+```
+docker pull projectmonai/monai:1.3.2
+```
+
+
+### Inference
+We provide two ways to use the model for inference.
+1. We recommend users to use the optimized and standardized [MONAI bundle]() model. The bundle provides a unified API for inference.
+The [VISTA3D NVIDIA Inference Microservices (NIM)]() deploys the bundle with an interactive front-end.
+2. For quick debugging and model development purposes, we also provide the `infer.py` script and its light-weight front-end `debugger.py`. `python -m scripts.debugger run`. Note we will prioritize [NIM]() and [monai bundle]() developments and those functions will be deprecated in the future.
+```
+export CUDA_VISIBLE_DEVICES=0; python -m scripts.infer --config_file 'configs/infer.yaml' - infer --image_file 'example-1.nii.gz' --label_prompt [1] --save_mask true
+export CUDA_VISIBLE_DEVICES=0; python -m scripts.infer --config_file 'configs/infer.yaml' - infer_everything --image_file 'example-1.nii.gz'
+```
+
+### Training
+#### Dataset and SuperVoxel Curation
+All dataset must contain a json data list file. We provide the json lists for all our training data in `data/jsons`. More details can be found [here](./data/README.md). For datasets used in VISTA3D training, we already included the json splits and registered their data specific label index to the global index as [label_mapping](./data/jsons/label_mappings.json) and their data path coded in `./data/datasets.py`. The supported global class index is defined in [label_dict](./data/jsons/label_dict.json). To generate supervoxels, refer to the [instruction](./data/README.md).
+#### Execute training
+VISTA3D has four stages training. The configurations represents the training procedure but may not fully reproduce the weights of VISTA3D since each stage has multiple rounds with slightly varying configuration changes.
+```
+export CUDA_VISIBLE_DEVICES=0; python -m scripts.train run --config_file "['configs/train/hyper_parameters_stage1.yaml']"
+```
+
+Execute multi-GPU model training (the codebase also supports multi-node training):
+
+```
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7;torchrun --nnodes=1 --nproc_per_node=8 -m scripts.train run --config_file "['configs/train/hyper_parameters_stage1.yaml']"
+```
+### Evaluation
+We provide code for supported class fully automatic dice score evaluation (val_multigpu_point_patch), point click only (val_multigpu_point_patch), and auto + point (val_multigpu_autopoint_patch).
+
+```
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7;torchrun --nnodes=1 --nproc_per_node=8 -m scripts.validation.val_multigpu_point_patch run --config_file "['configs/supported_eval/infer_patch_auto.yaml']" --dataset_name 'xxxx'
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7;torchrun --nnodes=1 --nproc_per_node=8 -m scripts.validation.val_multigpu_point_patch run --config_file "['configs/supported_eval/infer_patch_point.yaml']" --dataset_name 'xxxx'
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7;torchrun --nnodes=1 --nproc_per_node=8 -m scripts.validation.val_multigpu_autopoint_patch run --config_file "['configs/supported_eval/infer_patch_autopoint.yaml']" --dataset_name 'xxxx'
+```
+For zero-shot, we perform iterative point sampling. To create a new zero-shot evaluation dataset, user only need to change `label_set` in the json config to match the class indexes in the original groundtruth.
+```
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7;torchrun --nnodes=1 --nproc_per_node=8 -m scripts.validation.val_multigpu_point_iterative run --config_file "['configs/zeroshot_eval/infer_iter_point_hcc.yaml']"
+```
+### Finetune
+For finetuning, user need to change `label_set` and `mapped_label_set` in the json config, where `label_set` matches the index values in the groundtruth files. The `mapped_label_set` can be random selected but we recommend pick the most related global index defined in [label_dict](./data/jsons/label_dict.json). User should modify the transforms, resolutions, patch sizes e.t.c regarding to their dataset for optimal finetuning performances, we recommend using configs generated by auto3dseg. The learning rate 5e-5 should be good enough for finetuning purposes.
+```
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7;torchrun --nnodes=1 --nproc_per_node=8 -m scripts.train_finetune run --config_file "['configs/finetune/train_finetune_word.yaml']"
+```
+
+
+## Community
+
+Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join
+our [Slack channel](https://projectmonai.slack.com/archives/C031QRE0M1C).
+
+Ask and answer questions on [MONAI VISTA's GitHub discussions tab](https://github.com/Project-MONAI/VISTA/discussions).
+
+## License
+
+The codebase is under Apache 2.0 Licence. The model weight is under special NVIDIA license.
+
+## Reference
+
+```
+@article{he2024vista3d,
+ title={VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography},
+ author={He, Yufan and Guo, Pengfei and Tang, Yucheng and Myronenko, Andriy and Nath, Vishwesh and Xu, Ziyue and Yang, Dong and Zhao, Can and Simon, Benjamin and Belue, Mason and others},
+ journal={arXiv preprint arXiv:2406.05285},
+ year={2024}
+}
+```
+
+## Acknowledgement
+- [segment-anything](https://github.com/facebookresearch/segment-anything)
+- [TotalSegmentator](https://github.com/wasserth/TotalSegmentator)
diff --git a/assets/imgs/demo_gif.gif b/vista3d/assets/imgs/demo_gif.gif
similarity index 100%
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diff --git a/assets/imgs/wholeBody.png b/vista3d/assets/imgs/wholeBody.png
similarity index 100%
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diff --git a/vista3d/assets/imgs/zeroshot.gif b/vista3d/assets/imgs/zeroshot.gif
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diff --git a/vista3d/configs/finetune/infer_patch_auto_murine.yaml b/vista3d/configs/finetune/infer_patch_auto_murine.yaml
new file mode 100644
index 0000000..965dbf0
--- /dev/null
+++ b/vista3d/configs/finetune/infer_patch_auto_murine.yaml
@@ -0,0 +1,43 @@
+amp: true
+output_path: "$'/workspace/vista3d/work_dir_finetune_murine_' + str(@train_number)"
+ckpt: "$@output_path + '/model_fold0/best_metric_model.pt'"
+dataset_name: "murine"
+label_set: [0,1,2,3,4]
+mapped_label_set: [0,115,121,30,28]
+val_auto: true
+overlap: 0.625
+data_file_base_dir: '/data/micro-ct-murine/1_nativeCTdata_nifti/'
+data_list_file_path: './data/external/micro-ct-murine-native_5_folds.json'
+log_output_file: "$@output_path + '/test_set.log'"
+list_key: 'testing'
+five_fold: true
+fold: 0
+train_number: 89
+argmax_first: false
+input_channels: 1
+image_key: image
+label_key: label
+pixdim: [1,1,1]
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/finetune/infer_patch_auto_word.yaml b/vista3d/configs/finetune/infer_patch_auto_word.yaml
new file mode 100644
index 0000000..2db3cb9
--- /dev/null
+++ b/vista3d/configs/finetune/infer_patch_auto_word.yaml
@@ -0,0 +1,48 @@
+amp: true
+output_path: "$'/workspace/vista3d/work_dir_finetune_word_' + str(@train_number)"
+ckpt: "$@output_path + '/model_fold0/best_metric_model.pt'"
+dataset_name: "WORD"
+label_set: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
+mapped_label_set: [0,1,3,14,5,12,10,11,4,13,62,19,8,18,15,93,94]
+val_auto: true
+overlap: 0.625
+data_file_base_dir: '/data/WORD'
+data_list_file_path: './data/external/WORD.json'
+log_output_file: "$@output_path + '/test_set.log'"
+list_key: 'testing'
+five_fold: false
+fold: 0
+train_number: 100
+argmax_first: false
+input_channels: 1
+image_key: image
+label_key: label
+pixdim: [1,1,1]
+patch_size: [224, 224, 144]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: Spacingd
+ keys: ["@image_key",'@label_key']
+ pixdim: '@pixdim'
+ mode: [bilinear,nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/finetune/train_finetune_murine.yaml b/vista3d/configs/finetune/train_finetune_murine.yaml
new file mode 100644
index 0000000..3aa68ed
--- /dev/null
+++ b/vista3d/configs/finetune/train_finetune_murine.yaml
@@ -0,0 +1,163 @@
+amp: true
+train_number: 89
+comment: 'finetune on murine datasets.'
+bundle_root: $'./work_dir_ft_final_finetune_murine_' + str(@train_number)
+label_set: [0,1,2,3,4]
+mapped_label_set: [0,115,121,30,28]
+model: "vista3d_segresnet_d"
+use_folds: true
+data_file_base_dir: '/data/micro-ct-murine/1_nativeCTdata_nifti/'
+data_list_file_path: './data/external/micro-ct-murine-native_5_folds.json'
+ckpt_path: $@bundle_root + '/model_fold' + str(@fold)
+drop_label_prob: 0
+drop_point_prob: 1
+finetune: {activate: true, exclude_vars: null, pretrained_ckpt_name: $'/workspace/vista3d/models/model.pt'}
+fold: 0
+image_key: image
+input_channels: 1
+iter_num: 5
+label_key: label
+learning_rate: 0.00005
+log_output_file: $@bundle_root + '/model_fold' + str(@fold) + '/finetune_word.log'
+loss: {_target_: DiceCELoss, include_background: false, sigmoid: true, smooth_dr: 1.0e-05, smooth_nr: 0, softmax: false, squared_pred: true,
+ to_onehot_y: false}
+lr_scheduler: {_target_: monai.optimizers.WarmupCosineSchedule, optimizer: $@optimizer,
+ t_total: $@num_epochs+1, warmup_multiplier: 0.1, warmup_steps: 0}
+max_backprompt: null
+max_foreprompt: null
+ignore_labelset: false
+max_point: 3
+max_prompt: null
+num_epochs: 200
+freeze_epoch: 0
+freeze_head: 'point'
+save_last: false
+save_all: false
+num_epochs_per_validation: 5
+num_images_per_batch: 1
+num_patches_per_image: 2
+num_patches_per_iter: 1
+optimizer: {_target_: torch.optim.AdamW, lr: '@learning_rate', weight_decay: 1.0e-05}
+output_classes: 133
+overlap_ratio: 0.625
+patch_size: [224, 224, 144]
+random_seed: 0
+skip_iter_prob: 1
+transforms_train:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ - _target_: CropForegroundd
+ allow_smaller: true
+ end_coord_key: null
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ start_coord_key: null
+ allow_missing_keys: true
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ track_meta: false
+ - _target_: SpatialPadd
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ mode: [constant, constant]
+ spatial_size: '@patch_size'
+ - _target_: RandCropByLabelClassesd
+ keys:
+ - '@image_key'
+ - '@label_key'
+ label_key: '@label_key'
+ num_classes: 5
+ num_samples: '@num_patches_per_image'
+ spatial_size: '@patch_size'
+ ratios: $tuple(float(i >= 0) for i in range(5))
+ warn: false
+ allow_missing_keys: true
+ - _target_: RandZoomd
+ keys:
+ - '@image_key'
+ - '@label_key'
+ min_zoom: 0.8
+ max_zoom: 1.2
+ mode:
+ - trilinear
+ - nearest
+ prob: 0.2
+ allow_missing_keys: true
+ - _target_: RandSimulateLowResolutiond
+ keys:
+ - '@image_key'
+ zoom_range:
+ - 0.3
+ - 1
+ prob: 0.2
+ allow_missing_keys: true
+ - _target_: RandGaussianSmoothd
+ keys:
+ - '@image_key'
+ prob: 0.2
+ sigma_x:
+ - 0.5
+ - 1
+ sigma_y:
+ - 0.5
+ - 1
+ sigma_z:
+ - 0.5
+ - 1
+ - _target_: RandScaleIntensityd
+ keys:
+ - '@image_key'
+ factors: 0.1
+ prob: 0.2
+ - _target_: RandShiftIntensityd
+ keys:
+ - '@image_key'
+ offsets: 0.1
+ prob: 0.2
+ - _target_: RandGaussianNoised
+ keys:
+ - '@image_key'
+ prob: 0.2
+ mean: 0
+ std: 0.2
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.int32]
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+
+transforms_validate:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key', '@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ track_meta: true
+
+transforms_infer: $@transforms_validate
diff --git a/vista3d/configs/finetune/train_finetune_word.yaml b/vista3d/configs/finetune/train_finetune_word.yaml
new file mode 100644
index 0000000..b4a11b0
--- /dev/null
+++ b/vista3d/configs/finetune/train_finetune_word.yaml
@@ -0,0 +1,173 @@
+amp: true
+train_number: 100
+bundle_root: $'./work_dir_finetune_word_' + str(@train_number)
+comment: 'finetune on WORD datasets.'
+label_set: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
+mapped_label_set: [0,1,3,14,5,12,10,11,4,13,62,19,8,18,15,93,94]
+model: "vista3d_segresnet_d"
+data_file_base_dir: '/data/WORD'
+data_list_file_path: './data/external/WORD.json'
+ckpt_path: $@bundle_root + '/model_fold' + str(@fold)
+drop_label_prob: 0
+drop_point_prob: 1
+finetune: {activate: true, exclude_vars: null, pretrained_ckpt_name: $'/workspace/vista3d/models/model.pt'}
+fold: 0
+image_key: image
+input_channels: 1
+iter_num: 5
+label_key: label
+learning_rate: 0.00005
+log_output_file: $@bundle_root + '/model_fold' + str(@fold) + '/finetune_word.log'
+loss: {_target_: DiceCELoss, include_background: false, sigmoid: true, smooth_dr: 1.0e-05, smooth_nr: 0, softmax: false, squared_pred: true,
+ to_onehot_y: false}
+lr_scheduler: {_target_: monai.optimizers.WarmupCosineSchedule, optimizer: $@optimizer,
+ t_total: $@num_epochs+1, warmup_multiplier: 0.1, warmup_steps: 0}
+max_backprompt: null
+max_foreprompt: null
+ignore_labelset: false
+max_point: 3
+max_prompt: null
+num_epochs: 200
+freeze_epoch: 0
+freeze_head: 'point'
+save_last: false
+save_all: false
+num_epochs_per_validation: 1
+num_images_per_batch: 1
+num_patches_per_image: 2
+num_patches_per_iter: 1
+optimizer: {_target_: torch.optim.AdamW, lr: '@learning_rate', weight_decay: 1.0e-05}
+output_classes: 133
+overlap_ratio: 0.625
+patch_size: [224, 224, 144]
+random_seed: 0
+resample_to_spacing: [1., 1., 1.]
+skip_iter_prob: 1
+transforms_train:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ - _target_: CropForegroundd
+ allow_smaller: true
+ end_coord_key: null
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ start_coord_key: null
+ allow_missing_keys: true
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key"]
+ pixdim: '@resample_to_spacing'
+ mode: [bilinear, nearest]
+ align_corners: [true, true]
+ allow_missing_keys: true
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ track_meta: false
+ - _target_: SpatialPadd
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+ mode: [constant, constant]
+ spatial_size: '@patch_size'
+ - _target_: RandCropByLabelClassesd
+ keys:
+ - '@image_key'
+ - '@label_key'
+ label_key: '@label_key'
+ num_classes: 133
+ num_samples: '@num_patches_per_image'
+ spatial_size: '@patch_size'
+ ratios: $tuple(float(i >= 0) for i in range(133))
+ warn: false
+ allow_missing_keys: true
+ - _target_: RandZoomd
+ keys:
+ - '@image_key'
+ - '@label_key'
+ min_zoom: 0.8
+ max_zoom: 1.2
+ mode:
+ - trilinear
+ - nearest
+ prob: 0.2
+ allow_missing_keys: true
+ - _target_: RandSimulateLowResolutiond
+ keys:
+ - '@image_key'
+ zoom_range:
+ - 0.3
+ - 1
+ prob: 0.2
+ allow_missing_keys: true
+ - _target_: RandGaussianSmoothd
+ keys:
+ - '@image_key'
+ prob: 0.2
+ sigma_x:
+ - 0.5
+ - 1
+ sigma_y:
+ - 0.5
+ - 1
+ sigma_z:
+ - 0.5
+ - 1
+ - _target_: RandScaleIntensityd
+ keys:
+ - '@image_key'
+ factors: 0.1
+ prob: 0.2
+ - _target_: RandShiftIntensityd
+ keys:
+ - '@image_key'
+ offsets: 0.1
+ prob: 0.2
+ - _target_: RandGaussianNoised
+ keys:
+ - '@image_key'
+ prob: 0.2
+ mean: 0
+ std: 0.2
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.int32]
+ keys: ['@image_key', '@label_key']
+ allow_missing_keys: true
+
+transforms_validate:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key"]
+ pixdim: '@resample_to_spacing'
+ mode: [bilinear, nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key', '@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ track_meta: true
+transforms_infer: $@transforms_validate
diff --git a/vista3d/configs/infer.yaml b/vista3d/configs/infer.yaml
new file mode 100644
index 0000000..89d018b
--- /dev/null
+++ b/vista3d/configs/infer.yaml
@@ -0,0 +1,37 @@
+amp: true
+input_channels: 1
+patch_size: [128, 128, 128]
+bundle_root: './models'
+fold: 0
+infer: {ckpt_name: $@bundle_root + '/model.pt',
+ output_path: $@bundle_root + '/prediction',
+ log_output_file: $@bundle_root + '/inference.log'}
+resample_to_spacing: [1.5, 1.5, 1.5]
+model: "vista3d_segresnet_d"
+image_key: "image"
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ keys: "@image_key"
+ image_only: True
+ - _target_: EnsureChannelFirstd
+ keys: "@image_key"
+ - _target_: ScaleIntensityRanged
+ a_max: 1053.678477684517
+ a_min: -963.8247715525971
+ b_max: 1.0
+ b_min: 0.0
+ clip: true
+ keys: '@image_key'
+ - _target_: Orientationd
+ keys: "@image_key"
+ axcodes: RAS
+ - _target_: Spacingd
+ keys: ["@image_key"]
+ pixdim: "@resample_to_spacing"
+ mode: [bilinear]
+ align_corners: [true]
+ - _target_: CastToTyped
+ keys: "@image_key"
+ dtype: "$torch.float32"
diff --git a/vista3d/configs/supported_eval/infer_patch_auto.yaml b/vista3d/configs/supported_eval/infer_patch_auto.yaml
new file mode 100644
index 0000000..5b36dfc
--- /dev/null
+++ b/vista3d/configs/supported_eval/infer_patch_auto.yaml
@@ -0,0 +1,51 @@
+amp: true
+exps: models
+output_path: "$'/workspace/vista3d/' + @exps"
+ckpt: "$@output_path + '/model.pt'"
+dataset_name: "TotalSegmentatorV2"
+label_set: null
+overlap: 0.625
+json_name: "$@dataset_name + '_5_folds.json'"
+data_file_base_dir: "$'/data/' + @dataset_name"
+data_list_file_path: "$'./data/jsons/' + @json_name"
+log_output_file: "$@output_path + '/validation_auto_' + @dataset_name + '.log'"
+list_key: 'testing'
+val_auto: true
+argmax_first: false
+fold: 0
+input_channels: 1
+image_key: image
+label_key: label
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: CropForegroundd
+ allow_smaller: true
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ - _target_: Spacingd
+ keys: ["@image_key",'@label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear,nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/supported_eval/infer_patch_autopoint.yaml b/vista3d/configs/supported_eval/infer_patch_autopoint.yaml
new file mode 100644
index 0000000..35ab27b
--- /dev/null
+++ b/vista3d/configs/supported_eval/infer_patch_autopoint.yaml
@@ -0,0 +1,52 @@
+amp: true
+exps: models
+output_path: "$'/workspace/vista3d/' + @exps"
+ckpt: "$@output_path + '/model.pt'"
+dataset_name: "TotalSegmentatorV2"
+label_set: null
+overlap: 0.625
+json_name: "$@dataset_name + '_5_folds.json'"
+data_file_base_dir: "$'/data/' + @dataset_name"
+data_list_file_path: "$'./data/jsons/' + @json_name"
+log_output_file: "$@output_path + '/validation_autopoint_patch_' + @dataset_name + '.log'"
+list_key: 'testing'
+save_metric: false
+argmax_first: false
+val_auto: false
+fold: 0
+input_channels: 1
+image_key: image
+label_key: label
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: CropForegroundd
+ allow_smaller: true
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ - _target_: Spacingd
+ keys: ["@image_key",'@label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear,nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/supported_eval/infer_patch_point.yaml b/vista3d/configs/supported_eval/infer_patch_point.yaml
new file mode 100644
index 0000000..22ef399
--- /dev/null
+++ b/vista3d/configs/supported_eval/infer_patch_point.yaml
@@ -0,0 +1,52 @@
+amp: true
+exps: models
+output_path: "$'/workspace/vista3d/' + @exps"
+ckpt: "$@output_path + '/model.pt'"
+dataset_name: "TotalSegmentatorV2"
+label_set: null
+overlap: 0.625
+json_name: "$@dataset_name + '_5_folds.json'"
+data_file_base_dir: "$'/data/' + @dataset_name"
+data_list_file_path: "$'./data/jsons/' + @json_name"
+log_output_file: "$@output_path + '/validation_1point_' + @dataset_name + '.log'"
+list_key: 'testing'
+save_metric: false
+argmax_first: false
+val_auto: false
+fold: 0
+input_channels: 1
+image_key: image
+label_key: label
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: CropForegroundd
+ allow_smaller: true
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ - _target_: Spacingd
+ keys: ["@image_key",'@label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear,nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/train/hyper_parameters_stage1.yaml b/vista3d/configs/train/hyper_parameters_stage1.yaml
new file mode 100644
index 0000000..1c4e0a0
--- /dev/null
+++ b/vista3d/configs/train/hyper_parameters_stage1.yaml
@@ -0,0 +1,119 @@
+amp: true
+bundle_root: ./work_dir_stage1
+comments: "After training for several epoch, remove the unlabeled dataset from train_datasets."
+json_dir: ./data/jsons
+ckpt_path: $@bundle_root + '/model_fold' + str(@fold)
+model: "vista3d_segresnet_d"
+weighted_sampling: false
+drop_label_prob: 1
+drop_point_prob: 0
+finetune: {activate: true, exclude_vars: null, pretrained_ckpt_name: $'/workspace/vista3d/models/model.pt'}
+fold: 0
+image_key: image
+input_channels: 1
+iter_num: 5
+label_key: label
+label_sv_key: label_sv
+pseudo_label_key: pseudo_label
+learning_rate: 0.00002
+log_output_file: $@bundle_root + '/model_fold' + str(@fold) + '/training.log'
+loss: {_target_: DiceCELoss, include_background: false, sigmoid: true, smooth_dr: 1.0e-05, smooth_nr: 0, softmax: false, squared_pred: true,
+ to_onehot_y: false}
+lr_scheduler: {_target_: monai.optimizers.WarmupCosineSchedule, optimizer: $@optimizer,
+ t_total: $@num_epochs+1, warmup_multiplier: 0.1, warmup_steps: 0}
+max_backprompt: 0
+max_foreprompt: 4
+max_point: 3
+max_prompt: null
+num_epochs: 300
+freeze_epoch: 0
+freeze_head: 'auto'
+save_last: true
+save_all: false
+num_epochs_per_validation: 1
+num_images_per_batch: 1
+num_patches_per_image: 2
+num_patches_per_iter: 1
+optimizer: {_target_: torch.optim.AdamW, lr: '@learning_rate', weight_decay: 1.0e-05}
+output_classes: 133
+overlap_ratio: 0.5
+patch_size: [128, 128, 128]
+random_seed: 0
+resample_to_spacing: [1.5, 1.5, 1.5]
+skip_iter_prob: 0
+train_datasets: [CTPelvic1K-CLINIC, AbdomenCT-1K, AeroPath, AMOS22,
+ BTCV-Abdomen, BTCV-Cervix, CT-ORG, FLARE22, Multi-organ-Abdominal-CT-btcv,
+ Multi-organ-Abdominal-CT-tcia, Pancreas-CT, Task03, Task06, Task07,
+ Task08, Task09, Task10, VerSe, CRLM-CT, TotalSegmentatorV2, NLST, LIDC, StonyBrook-CT, TCIA_Colon, Covid19]
+val_datasets: [TotalSegmentatorV2]
+transforms_train:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: CropForegroundd
+ allow_smaller: true
+ end_coord_key: null
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ margin: 10
+ source_key: '@image_key'
+ start_coord_key: null
+ allow_missing_keys: true
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key", '@label_sv_key', '@pseudo_label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest, nearest, nearest]
+ align_corners: [true, true, true, true]
+ allow_missing_keys: true
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ track_meta: false
+ - _target_: SpatialPadd
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ mode: [constant, constant, constant, constant]
+ spatial_size: '@patch_size'
+ - "Placeholder for dataset-specific transform"
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.int32, $torch.int32, $torch.int32]
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+transforms_validate:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ - _target_: CropForegroundd
+ allow_smaller: true
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key"]
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key', '@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ track_meta: true
+ - "Placeholder for dataset-specific transform"
diff --git a/vista3d/configs/train/hyper_parameters_stage2.yaml b/vista3d/configs/train/hyper_parameters_stage2.yaml
new file mode 100644
index 0000000..abd566b
--- /dev/null
+++ b/vista3d/configs/train/hyper_parameters_stage2.yaml
@@ -0,0 +1,118 @@
+amp: true
+bundle_root: ./work_dir_stage2
+json_dir: ./data/jsons
+ckpt_path: $@bundle_root + '/model_fold' + str(@fold)
+model: "vista3d_segresnet_d"
+weighted_sampling: false
+drop_label_prob: 1
+drop_point_prob: 0
+finetune: {activate: true, exclude_vars: null, pretrained_ckpt_name: $'/workspace/vista3d/models/model.pt'}
+fold: 0
+image_key: image
+input_channels: 1
+iter_num: 5
+label_key: label
+label_sv_key: label_sv
+pseudo_label_key: pseudo_label
+learning_rate: 0.00002
+log_output_file: $@bundle_root + '/model_fold' + str(@fold) + '/training.log'
+loss: {_target_: DiceCELoss, include_background: false, sigmoid: true, smooth_dr: 1.0e-05, smooth_nr: 0, softmax: false, squared_pred: true,
+ to_onehot_y: false}
+lr_scheduler: {_target_: monai.optimizers.WarmupCosineSchedule, optimizer: $@optimizer,
+ t_total: $@num_epochs+1, warmup_multiplier: 0.1, warmup_steps: 0}
+max_backprompt: 0
+max_foreprompt: 4
+max_point: 3
+max_prompt: null
+num_epochs: 300
+freeze_epoch: 0
+freeze_head: 'auto'
+save_last: true
+save_all: true
+num_epochs_per_validation: 1
+num_images_per_batch: 1
+num_patches_per_image: 2
+num_patches_per_iter: 1
+optimizer: {_target_: torch.optim.AdamW, lr: '@learning_rate', weight_decay: 1.0e-05}
+output_classes: 133
+overlap_ratio: 0.5
+patch_size: [128, 128, 128]
+random_seed: 0
+resample_to_spacing: [1.5, 1.5, 1.5]
+skip_iter_prob: 0
+train_datasets: [CTPelvic1K-CLINIC, AbdomenCT-1K, AeroPath, AMOS22, BTCV-Abdomen,
+ BTCV-Cervix, CT-ORG, FLARE22, Multi-organ-Abdominal-CT-btcv, Multi-organ-Abdominal-CT-tcia,
+ Pancreas-CT, Task03, Task06, Task07, Task08, Task09, Task10, VerSe, CRLM-CT,
+ TotalSegmentatorV2]
+val_datasets: ['CRLM-CT', 'AeroPath', 'Task03','Task06','Task07','Task08','Task10']
+transforms_train:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: CropForegroundd
+ allow_smaller: true
+ end_coord_key: null
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ margin: 10
+ source_key: '@image_key'
+ start_coord_key: null
+ allow_missing_keys: true
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key", '@label_sv_key', '@pseudo_label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest, nearest, nearest]
+ align_corners: [true, true, true, true]
+ allow_missing_keys: true
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ track_meta: false
+ - _target_: SpatialPadd
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ mode: [constant, constant, constant, constant]
+ spatial_size: '@patch_size'
+ - "Placeholder for dataset-specific transform"
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.int32, $torch.int32, $torch.int32]
+ keys: ['@image_key', '@label_key', '@label_sv_key', '@pseudo_label_key']
+ allow_missing_keys: true
+transforms_validate:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ - _target_: CropForegroundd
+ allow_smaller: true
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key"]
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key', '@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ track_meta: true
+ - "Placeholder for dataset-specific transform"
diff --git a/vista3d/configs/train/hyper_parameters_stage3.yaml b/vista3d/configs/train/hyper_parameters_stage3.yaml
new file mode 100644
index 0000000..b7d7e69
--- /dev/null
+++ b/vista3d/configs/train/hyper_parameters_stage3.yaml
@@ -0,0 +1,121 @@
+amp: true
+bundle_root: ./work_dir_stage3
+comments: "After training for several epoch, remove the unlabeled dataset from train_datasets."
+json_dir: ./data/jsons
+ckpt_path: $@bundle_root + '/model_fold' + str(@fold)
+model: "vista3d_segresnet_d"
+weighted_sampling: false
+drop_label_prob: 0
+drop_point_prob: 1
+finetune: {activate: true, exclude_vars: null, pretrained_ckpt_name: $'/workspace/vista3d/models/model.pt'}
+fold: 0
+image_key: image
+input_channels: 1
+iter_num: 5
+label_key: label
+label_sv_key: label_sv
+pseudo_label_key: pseudo_label
+learning_rate: 0.00002
+log_output_file: $@bundle_root + '/model_fold' + str(@fold) + '/training.log'
+loss: {_target_: DiceCELoss, include_background: false, sigmoid: true, smooth_dr: 1.0e-05, smooth_nr: 0, softmax: false, squared_pred: true,
+ to_onehot_y: false}
+lr_scheduler: {_target_: monai.optimizers.WarmupCosineSchedule, optimizer: $@optimizer,
+ t_total: $@num_epochs+1, warmup_multiplier: 0.1, warmup_steps: 0}
+max_backprompt: 4
+max_foreprompt: 32
+max_point: 3
+max_prompt: null
+num_epochs: 200
+freeze_epoch: 1000
+freeze_head: 'point'
+save_last: false
+save_all: false
+num_epochs_per_validation: 5
+num_images_per_batch: 1
+num_patches_per_image: 2
+num_patches_per_iter: 1
+optimizer: {_target_: torch.optim.AdamW, lr: '@learning_rate', weight_decay: 1.0e-05}
+output_classes: 133
+overlap_ratio: 0.5
+patch_size: [128, 128, 128]
+random_seed: 0
+resample_to_spacing: [1.5, 1.5, 1.5]
+skip_iter_prob: 1
+train_datasets: [CTPelvic1K-CLINIC, AbdomenCT-1K, AeroPath, AMOS22, BTCV-Abdomen,
+ BTCV-Cervix, CT-ORG, FLARE22, Multi-organ-Abdominal-CT-btcv, Multi-organ-Abdominal-CT-tcia,
+ Pancreas-CT, Task03, Task06, Task07, Task08, Task09, Task10, VerSe, CRLM-CT,
+ TotalSegmentatorV2, NLST, LIDC, StonyBrook-CT, TCIA_Colon]
+val_datasets: ['TotalSegmentatorV2']
+transforms_train:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: DeleteItemsd
+ keys: ['@label_sv_key']
+ - _target_: CropForegroundd
+ allow_smaller: true
+ end_coord_key: null
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ margin: 10
+ source_key: '@image_key'
+ start_coord_key: null
+ allow_missing_keys: true
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key", '@pseudo_label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest, nearest]
+ align_corners: [true, true, true]
+ allow_missing_keys: true
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ track_meta: false
+ - _target_: SpatialPadd
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ mode: [constant, constant, constant]
+ spatial_size: '@patch_size'
+ - "Placeholder for dataset-specific transform"
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.int32, $torch.int32]
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+transforms_validate:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ - _target_: CropForegroundd
+ allow_smaller: true
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key"]
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key', '@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ track_meta: true
+ - "Placeholder for dataset-specific transform"
diff --git a/vista3d/configs/train/hyper_parameters_stage4.yaml b/vista3d/configs/train/hyper_parameters_stage4.yaml
new file mode 100644
index 0000000..1b8a127
--- /dev/null
+++ b/vista3d/configs/train/hyper_parameters_stage4.yaml
@@ -0,0 +1,121 @@
+amp: true
+bundle_root: ./work_dir_stage4
+json_dir: ./data/jsons
+balance_gt: true
+ckpt_path: $@bundle_root + '/model_fold' + str(@fold)
+model: "vista3d_segresnet_d"
+weighted_sampling: false
+drop_label_prob: 0
+drop_point_prob: 1
+finetune: {activate: true, exclude_vars: null, pretrained_ckpt_name: $'/workspace/vista3d/models/model.pt'}
+fold: 0
+image_key: image
+input_channels: 1
+iter_num: 5
+label_key: label
+label_sv_key: label_sv
+pseudo_label_key: pseudo_label
+learning_rate: 0.00002
+log_output_file: $@bundle_root + '/model_fold' + str(@fold) + '/training.log'
+loss: {_target_: DiceCELoss, include_background: false, sigmoid: true, smooth_dr: 1.0e-05, smooth_nr: 0, softmax: false, squared_pred: true,
+ to_onehot_y: false}
+lr_scheduler: {_target_: monai.optimizers.WarmupCosineSchedule, optimizer: $@optimizer,
+ t_total: $@num_epochs+1, warmup_multiplier: 0.1, warmup_steps: 0}
+max_backprompt: 4
+max_foreprompt: 32
+max_point: 3
+max_prompt: null
+num_epochs: 200
+freeze_epoch: 1000
+freeze_head: 'point'
+save_last: true
+save_all: true
+num_epochs_per_validation: 1
+num_images_per_batch: 1
+num_patches_per_image: 2
+num_patches_per_iter: 1
+optimizer: {_target_: torch.optim.AdamW, lr: '@learning_rate', weight_decay: 1.0e-05}
+output_classes: 133
+overlap_ratio: 0.5
+patch_size: [128, 128, 128]
+random_seed: 0
+resample_to_spacing: [1.5, 1.5, 1.5]
+skip_iter_prob: 1
+train_datasets: [CTPelvic1K-CLINIC, AbdomenCT-1K, AeroPath, AMOS22, BTCV-Abdomen,
+ BTCV-Cervix, CT-ORG, FLARE22, Multi-organ-Abdominal-CT-btcv, Multi-organ-Abdominal-CT-tcia,
+ Pancreas-CT, Task03, Task06, Task07, Task08, Task09, Task10, VerSe, CRLM-CT,
+ TotalSegmentatorV2]
+val_datasets: ['CRLM-CT', 'AeroPath', 'Task03','Task06','Task07','Task08','Task10','Bone-NIH']
+transforms_train:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: DeleteItemsd
+ keys: ['@label_sv_key']
+ - _target_: CropForegroundd
+ allow_smaller: true
+ end_coord_key: null
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ margin: 10
+ source_key: '@image_key'
+ start_coord_key: null
+ allow_missing_keys: true
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key", '@pseudo_label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest, nearest]
+ align_corners: [true, true, true]
+ allow_missing_keys: true
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ track_meta: false
+ - _target_: SpatialPadd
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+ mode: [constant, constant, constant]
+ spatial_size: '@patch_size'
+ - "Placeholder for dataset-specific transform"
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.int32, $torch.int32]
+ keys: ['@image_key', '@label_key', '@pseudo_label_key']
+ allow_missing_keys: true
+transforms_validate:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key', '@label_key']
+ - _target_: CropForegroundd
+ allow_smaller: true
+ keys: ['@image_key', '@label_key']
+ margin: 10
+ source_key: '@image_key'
+ - _target_: Spacingd
+ keys: ["@image_key", "@label_key"]
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear, nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key', '@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key', '@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key', '@label_key']
+ track_meta: true
+ - "Placeholder for dataset-specific transform"
diff --git a/vista3d/configs/zeroshot_eval/infer_iter_point_adrenal.yaml b/vista3d/configs/zeroshot_eval/infer_iter_point_adrenal.yaml
new file mode 100644
index 0000000..91ac868
--- /dev/null
+++ b/vista3d/configs/zeroshot_eval/infer_iter_point_adrenal.yaml
@@ -0,0 +1,45 @@
+amp: true
+exps: models
+output_path: "$'/workspace/vista3d/' + @exps"
+ckpt: "$@output_path + '/model.pt'"
+dataset_name: "Adrenal_Ki67"
+label_set: [0,1]
+max_iter: 80
+overlap: 0.625
+json_name: "$@dataset_name + '_5_folds.json'"
+data_file_base_dir: "$'/data/' + @dataset_name"
+data_list_file_path: "$'./data/external/' + @json_name"
+log_output_file: $@output_path + '/inference_adrenal.log'
+list_key: 'all'
+fold: 0
+input_channels: 1
+image_key: image
+label_key: label
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: Spacingd
+ keys: ["@image_key",'@label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear,nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/zeroshot_eval/infer_iter_point_hcc.yaml b/vista3d/configs/zeroshot_eval/infer_iter_point_hcc.yaml
new file mode 100644
index 0000000..a168df7
--- /dev/null
+++ b/vista3d/configs/zeroshot_eval/infer_iter_point_hcc.yaml
@@ -0,0 +1,45 @@
+amp: true
+exps: models
+output_path: "$'/workspace/vista3d/' + @exps"
+ckpt: "$@output_path + '/model.pt'"
+dataset_name: "HCC-TACE-Seg"
+label_set: [0,2]
+max_iter: 80
+overlap: 0.625
+json_name: "$@dataset_name + '_5_folds.json'"
+data_file_base_dir: "$'/data/' + @dataset_name"
+data_list_file_path: "$'./data/external/' + @json_name"
+log_output_file: $@output_path + '/inference_hcc.log'
+list_key: 'all'
+fold: 0
+input_channels: 1
+image_key: image
+label_key: label
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: Spacingd
+ keys: ["@image_key",'@label_key']
+ pixdim: [1.5, 1.5, 1.5]
+ mode: [bilinear,nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/zeroshot_eval/infer_iter_point_kits.yaml b/vista3d/configs/zeroshot_eval/infer_iter_point_kits.yaml
new file mode 100644
index 0000000..f59b076
--- /dev/null
+++ b/vista3d/configs/zeroshot_eval/infer_iter_point_kits.yaml
@@ -0,0 +1,45 @@
+amp: true
+exps: models
+output_path: "$'/workspace/vista3d/' + @exps"
+ckpt: "$@output_path + '/model.pt'"
+dataset_name: "C4KC-KiTS"
+label_set: [0,2]
+max_iter: 80
+overlap: 0.625
+json_name: "$@dataset_name + '_5_folds.json'"
+data_file_base_dir: "$'/data/' + @dataset_name + '/nifti'"
+data_list_file_path: "$'./data/external/' + @json_name"
+log_output_file: $@output_path + '/inference_kits.log'
+list_key: 'all'
+fold: 0
+input_channels: 1
+image_key: image
+label_key: label
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - _target_: Spacingd
+ keys: ["@image_key",'@label_key']
+ pixdim: [1., 1., 1.]
+ mode: [bilinear,nearest]
+ align_corners: [true, true]
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/configs/zeroshot_eval/infer_iter_point_murine.yaml b/vista3d/configs/zeroshot_eval/infer_iter_point_murine.yaml
new file mode 100644
index 0000000..de3ae1d
--- /dev/null
+++ b/vista3d/configs/zeroshot_eval/infer_iter_point_murine.yaml
@@ -0,0 +1,40 @@
+amp: true
+exps: models
+output_path: "$'/workspace/vista3d/' + @exps"
+ckpt: "$@output_path + '/model.pt'"
+dataset_name: "micro-ct-murine-native"
+label_set: [0,1,2,3,4]
+max_iter: 80
+overlap: 0.625
+json_name: "$@dataset_name + '_5_folds.json'"
+data_file_base_dir: '/data/micro-ct-murine/1_nativeCTdata_nifti'
+data_list_file_path: "$'./data/external/' + @json_name"
+log_output_file: $@output_path + '/inference_murine.log'
+list_key: 'all'
+fold: 0
+input_channels: 1
+image_key: image
+label_key: label
+patch_size: [128, 128, 128]
+transforms_infer:
+ _target_: Compose
+ transforms:
+ - _target_: LoadImaged
+ ensure_channel_first: true
+ image_only: true
+ keys: ['@image_key','@label_key']
+ - _target_: CopyItemsd
+ names: 'label_gt'
+ keys: '@label_key'
+ - {_target_: ScaleIntensityRanged, a_max: 1053.678477684517, a_min: -963.8247715525971,
+ b_max: 1.0, b_min: 0.0, clip: true, keys: '@image_key'}
+ - _target_: Orientationd
+ axcodes: RAS
+ keys: ['@image_key','@label_key']
+ - _target_: CastToTyped
+ dtype: [$torch.float32, $torch.uint8]
+ keys: ['@image_key','@label_key']
+ - _target_: EnsureTyped
+ keys: ['@image_key','@label_key']
+ track_meta: true
+model: "vista3d_segresnet_d"
diff --git a/vista3d/data/README.md b/vista3d/data/README.md
new file mode 100644
index 0000000..03354c3
--- /dev/null
+++ b/vista3d/data/README.md
@@ -0,0 +1,123 @@
+#### Aggregating multiple datasets
+
+The training workflow requires one or multiple dataset JSON files to specifiy the image and segmentation pairs as well as dataset preprocessing transformations.
+Example files are located in the `data/jsons` folder.
+
+The JSON file has the following structure:
+```python
+{
+ "training": [
+ {
+ "image": "img1.nii.gz", # relative path to the primary image file
+ "label": "label1.nii.gz", # optional relative path to the primary label file
+ "pseudo_label": "p_label1.nii.gz", # optional relative path to the pseudo label file
+ "pseudo_label_reliability": 1 # optional reliability score for pseudo label
+ "label_sv": "label_sv1.nii.gz", # optional relative path to the supervoxel label file
+ "fold": 0 # optional fold index for cross validation, fold 0 is used for training
+ },
+
+ ...
+ ],
+ "training_transform": [
+ # a set of monai transform configuration for dataset-specific loading
+ ],
+ "original_label_dict": {"1": "liver", ...},
+ "label_dict": {"1": "liver", ...}
+}
+```
+
+During training, the JSON files will be consumed along with additional configurations, for example:
+```py
+from data.datasets import get_datalist_with_dataset_name_and_transform
+
+train_files, _, dataset_specific_transforms, dataset_specific_transforms_val = \
+ get_datalist_with_dataset_name_and_transform(
+ datasets=train_datasets,
+ fold_idx=fold,
+ image_key=image_key,
+ label_key=label_key,
+ label_sv_key=label_sv_key,
+ pseudo_label_key=pseudo_label_key,
+ num_patches_per_image=parser.get_parsed_content("num_patches_per_image"),
+ patch_size=parser.get_parsed_content("patch_size"),
+ json_dir=json_dir)
+```
+
+The following steps are necessary for creating a multi-dataset data loader for model training.
+Step 1 and 2 generate persistent JSON files based on the original dataset (the `image` and `label` pairs; without the additional pseudo label or supervoxel-based label), and only need to be run once when the JSON files don't exist.
+
+##### 1. Generate data list JSON file
+```
+python -m data.make_datalists
+```
+
+This script reads image and label folders, lists all the nii.gz files,
+creates a JSON file in a format:
+
+```json
+{
+ "training": [
+ {"image": "img0001.nii.gz", "label": "label0001.nii.gz", "fold": 0},
+ {"image": "img0002.nii.gz", "label": "label0002.nii.gz", "fold": 2},
+ ...
+ ],
+ "testing": [
+ {"image": "img0003.nii.gz", "label": "label0003.nii.gz"},
+ {"image": "img0004.nii.gz", "label": "label0004.nii.gz"},
+ ...
+ ]
+ "original_label_dict": {"1": "liver", ...},
+ "label_dict": {"1": "liver", ...}
+}
+```
+
+This step includes a 5-fold cross validation splitting and
+some logic for 80-20 training/testing splitting. User need to modify the code in make_datalists.py for their own dataset. Meanwhile, the "training_transform" should manually added for each dataset.
+
+The `original_label_dict` corresponds to the original dataset label definitions.
+The `label_dict` modifies `original_label_dict` by simply rephrasing the terms.
+For example in Task06, `cancer` is renamed to `lung tumor`.
+The output of this step is multiple JSON files, each file corresponds
+to one dataset.
+
+##### 2. Add label_dict.json and label_mapping.json
+Add new class indexes to `label_dict.json` and the local to global mapping to `label_mapping.json`.
+
+## SupverVoxel Generation
+1. Download the segment anything repo and download the ViT-H weights
+```
+git clone https://github.com/facebookresearch/segment-anything.git
+mv segment-anything/segment_anything/ segment_anything/
+wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
+```
+2. Modify the code for supervoxel generation
+- Add this function to `predictor.py/SamPredictor`
+```python
+@torch.no_grad()
+def get_feature_upsampled(self, input_image=None):
+ if input_image is None:
+ image_embeddings = self.model.mask_decoder.predict_masks_noprompt(self.features)
+ else:
+ image_embeddings = self.model.mask_decoder.predict_masks_noprompt(self.model.image_encoder(input_image))
+ return image_embeddings
+```
+- Add this function to `modeling/mask_decoder.py/MaskDecoder`
+```python
+def predict_masks_noprompt(
+ self,
+ image_embeddings: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Predicts masks. See 'forward' for more details."""
+ # Concatenate output tokens
+
+ # Expand per-image data in batch direction to be per-mask
+ src = image_embeddings
+ # Upscale mask embeddings and predict masks using the mask tokens
+ upscaled_embedding = self.output_upscaling(src)
+
+ return upscaled_embedding
+```
+3. Run the supervoxel generation script. The processsing time is over 10 minutes, use `batch_infer` and multi-gpu for speed up.
+```
+python -m scripts.slic_process_sam infer --image_file xxxx
+```
diff --git a/monailabel/monaivista/__init__.py b/vista3d/data/__init__.py
similarity index 100%
rename from monailabel/monaivista/__init__.py
rename to vista3d/data/__init__.py
diff --git a/vista3d/data/dataset_weights.yaml b/vista3d/data/dataset_weights.yaml
new file mode 100644
index 0000000..7a67ecf
--- /dev/null
+++ b/vista3d/data/dataset_weights.yaml
@@ -0,0 +1,24 @@
+# This is the weights for weighted sampling in stage2 and stage4
+{
+ "CTPelvic1K-CLINIC": 1.3333333333333333,
+ "AbdomenCT-1K": 0.15625,
+ "AeroPath": 5.882352941176471,
+ "AMOS22": 0.5208333333333334,
+ "BTCV-Abdomen": 5.2631578947368425,
+ "BTCV-Cervix": 5.2631578947368425,
+ "CT-ORG": 1.1494252873563218,
+ "FLARE22": 3.125,
+ "Multi-organ-Abdominal-CT-btcv": 3.3333333333333335,
+ "Multi-organ-Abdominal-CT-tcia": 3.7037037037037037,
+ "Pancreas-CT": 1.9607843137254901,
+ "Task03": 1.1904761904761905,
+ "Task06": 2.5,
+ "Task07": 0.5555555555555556,
+ "Task08": 0.5181347150259067,
+ "Task09": 3.8461538461538463,
+ "Task10": 1.25,
+ "VerSe": 0.41841004184100417,
+ "Bone-NIH": 0.5291005291005291,
+ "CRLM-CT": 0.7936507936507936,
+ "TotalSegmentatorV2": 0.12755102040816327
+}
diff --git a/vista3d/data/datasets.py b/vista3d/data/datasets.py
new file mode 100644
index 0000000..8d793d1
--- /dev/null
+++ b/vista3d/data/datasets.py
@@ -0,0 +1,268 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from pprint import pformat
+
+from monai import transforms
+from monai.apps import get_logger
+from monai.auto3dseg.utils import datafold_read
+from monai.bundle import ConfigParser
+from monai.utils import ensure_tuple, look_up_option
+
+# Define the root path to the orignal manual label files, supervoxels, and pseudolabels.
+all_base_dirs = {
+ "AbdomenCT-1K": "/data/AbdomenCT-1K",
+ "FLARE22": "/data/AbdomenCT-1K/FLARE22Train",
+ "AMOS22": "/data/AMOS22",
+ "BTCV-Abdomen": "/data/BTCV/Abdomen",
+ "BTCV-Cervix": "/data/BTCV/Cervix",
+ "CT-ORG": "/data/CT-ORG",
+ "Multi-organ-Abdominal-CT-btcv": "/data/Multi-organ-Abdominal-CT/res_1.0mm_relabeled2",
+ "Multi-organ-Abdominal-CT-tcia": "/data/Multi-organ-Abdominal-CT/res_1.0mm_relabeled2",
+ "Pancreas-CT": "/data/Pancreas-CT",
+ "Task06": "/data/Task06",
+ "Task07": "/data/Task07",
+ "Task08": "/data/Task08",
+ "Task09": "/data/Task09",
+ "Task10": "/data/Task10",
+ "TotalSegmentator": "/data/TotalSegmentator",
+ "TotalSegmentatorV2": "/data/TotalSegmentatorV2",
+ "Task03": "/data/Task03",
+ "Bone-NIH": "/data/Bone-NIH",
+ "CRLM-CT": "/data/CRLM-CT/nifti",
+ "VerSe": "/data/VerSe/",
+ "AeroPath": "/data/AeroPath/",
+ "CTPelvic1K-CLINIC": "/data/CTPelvic1K-CLINIC",
+ "NLST": "/data/NLST",
+ "LIDC": "/data/LIDC",
+ "Covid19": "/data/Covid19",
+ "TCIA_Colon": "/data/TCIA_Colon",
+ "StonyBrook-CT": "/data/StonyBrook-CT",
+}
+# Notice the root path to supervoxel and pseudolabel has the same sub-folder structure as all_base_dirs
+# The path is generated by replacing json_base with supervoxel_base/pl_base in get_datalist_with_dataset_name
+json_base = "/data/"
+supervoxel_base = "/workspace_infer/supervoxel_sam/"
+pl_base = "/workspace_infer/V2_pseudo_12Feb2024/"
+
+
+cur_json_dir = os.path.join(os.path.dirname(__file__), "jsons")
+logger = get_logger(__name__)
+
+
+def get_json_files_k_folds(json_dir=None, base_dirs=None, k=5):
+ """the json files are generated by data/make_datalists.py, stored at `json_dir`"""
+ if json_dir is None:
+ json_dir = cur_json_dir
+ if base_dirs is None:
+ base_dirs = all_base_dirs
+ output_dict = {
+ item: os.path.join(json_dir, f"{item}_{k}_folds.json") for item in base_dirs
+ }
+ logger.debug(pformat(output_dict))
+ return output_dict
+
+
+def get_class_names(json_dir=None):
+ """
+ the list of class names, background is at 0
+ """
+ parser = ConfigParser.load_config_file(os.path.join(json_dir, "label_dict.json"))
+ label_dict = dict(parser)
+ label_dict["unspecified region"] = 0
+ inv_label_dict = {v: k for k, v in label_dict.items()}
+ label_list = []
+ for i in range(len(label_dict)):
+ label_list.append(inv_label_dict[i])
+ return label_list
+
+
+def get_datalist_with_dataset_name(
+ datasets=None, fold_idx=-1, key="training", json_dir=None, base_dirs=None
+):
+ """
+ when `datasets` is None, it returns a list of all data from all datasets.
+ when `datasets` is a list of dataset names, it returns a list of all data from the specified datasets.
+
+ train_list's item format::
+
+ {"image": image_file_path, "label": label_file_path, "dataset_name": dataset_name, "fold": fold_id}
+
+ """
+ if base_dirs is None:
+ base_dirs = all_base_dirs # all_base_dirs is the broader set
+ # get the list of training/validation files (absolute path)
+ json_files = get_json_files_k_folds(json_dir=json_dir, base_dirs=base_dirs)
+ if datasets is None:
+ loading_dict = json_files.copy()
+ else:
+ loading_dict = {
+ k: look_up_option(k, json_files) for k in ensure_tuple(datasets)
+ }
+ train_list, val_list = [], []
+ for k, j in loading_dict.items():
+ t, v = datafold_read(j, basedir=all_base_dirs[k], fold=fold_idx, key=key)
+ for item in t:
+ item["dataset_name"] = k
+ if "label_sv" in item.keys():
+ item["label_sv"] = item["label_sv"].replace(
+ json_base, supervoxel_base, 1
+ )
+ if "pseudo_label" in item.keys():
+ item["pseudo_label"] = item["pseudo_label"].replace(
+ json_base, pl_base, 1
+ )
+ train_list += t
+ for item in v:
+ item["dataset_name"] = k
+ if "label_sv" in item.keys():
+ item["label_sv"] = item["label_sv"].replace(
+ json_base, supervoxel_base, 1
+ )
+ if "pseudo_label" in item.keys():
+ item["pseudo_label"] = item["pseudo_label"].replace(
+ json_base, pl_base, 1
+ )
+
+ val_list += v
+ logger.warning(
+ f"data list from datasets={datasets} fold={fold_idx}: train={len(train_list)}, val={len(val_list)}"
+ )
+ return ensure_tuple(train_list), ensure_tuple(val_list)
+
+
+def get_datalist_with_dataset_name_and_transform(
+ image_key,
+ label_key,
+ label_sv_key,
+ pseudo_label_key,
+ num_patches_per_image,
+ patch_size,
+ datasets=None,
+ fold_idx=-1,
+ key="training",
+ json_dir=None,
+ base_dirs=None,
+):
+ """
+ when `datasets` is None, it returns a list of all data from all datasets.
+ when `datasets` is a list of dataset names, it returns a list of all data from the specified datasets.
+ Return file lists and specific transforms for each dataset.
+
+ """
+ if base_dirs is None:
+ base_dirs = all_base_dirs # all_base_dirs is the broader set
+ train_list, val_list = get_datalist_with_dataset_name(
+ datasets=datasets,
+ fold_idx=fold_idx,
+ key=key,
+ json_dir=json_dir,
+ base_dirs=base_dirs,
+ )
+ # get the list of training/validation files (absolute path)
+ json_files = get_json_files_k_folds(json_dir=json_dir, base_dirs=base_dirs)
+ if datasets is None:
+ loading_dict = json_files.copy()
+ else:
+ loading_dict = {
+ k: look_up_option(k, json_files) for k in ensure_tuple(datasets)
+ }
+
+ dataset_transforms = {}
+ dataset_transforms_val = {}
+ for k, j in loading_dict.items():
+ parser = ConfigParser()
+ parser.read_config(j)
+ # those parameters are required to initiate the transforms
+ parser.update(
+ pairs={
+ "image_key": image_key,
+ "label_key": label_key,
+ "label_sv_key": label_sv_key,
+ "pseudo_label_key": pseudo_label_key,
+ "num_patches_per_image": num_patches_per_image,
+ "patch_size": patch_size,
+ }
+ )
+ transform = parser.get_parsed_content("training_transform")
+ dataset_transforms[k] = transforms.Compose(transform)
+ transform_val = parser.get_parsed_content("validation_transform", default=None)
+ dataset_transforms_val[k] = (
+ transforms.Compose(transform_val) if transform_val is not None else None
+ )
+ return (
+ ensure_tuple(train_list),
+ ensure_tuple(val_list),
+ dataset_transforms,
+ dataset_transforms_val,
+ )
+
+
+def compute_dataset_weights(datalist, weight_path="./data/dataset_weights.yaml"):
+ """based on class-wise weight, assign a weight to each training sample"""
+ cfg = ConfigParser.load_config_file(weight_path)
+ w = []
+ for item in datalist:
+ fg_w = cfg[item["dataset_name"]]
+ w.append(fg_w)
+ item["w"] = fg_w
+ return w
+
+
+def calculate_dataset_weights(datalist):
+ dataset_name = []
+ dataset_counts = {}
+ for item in datalist:
+ dn = item["dataset_name"]
+ if dn in dataset_name:
+ dataset_counts[dn] += 1
+ else:
+ dataset_name.append(dn)
+ dataset_counts[dn] = 1
+ dataset_weights = {}
+ non_tumor_count = 0
+ tumor_count = 0
+ for item in dataset_name:
+ if item not in ["Task03", "Task06", "Task07", "Task08", "Task10", "Bone-NIH"]:
+ non_tumor_count += dataset_counts[item]
+ else:
+ tumor_count += dataset_counts[item]
+
+ for item in dataset_name:
+ if item not in ["Task03", "Task06", "Task07", "Task08", "Task10", "Bone-NIH"]:
+ dataset_weights[item] = 100 / dataset_counts[item] # non_tumor_count
+ else:
+ dataset_weights[item] = 100 / dataset_counts[item] # tumor_count
+
+ dataset_prob = {}
+ total_prob = 0
+ for item in dataset_name:
+ dataset_prob[item] = dataset_weights[item] * dataset_counts[item]
+ total_prob += dataset_prob[item]
+ for item in dataset_name:
+ dataset_prob[item] /= total_prob
+
+ import json
+
+ with open("./dataset_counts.yaml", "w") as f:
+ json.dump(dataset_counts, f, indent=4)
+ with open("./dataset_weights.yaml", "w") as f:
+ json.dump(dataset_weights, f, indent=4)
+ with open("./dataset_prob.yaml", "w") as f:
+ json.dump(dataset_prob, f, indent=4)
+
+
+if __name__ == "__main__":
+ from monai.utils import optional_import
+
+ fire, _ = optional_import("fire")
+ fire.Fire()
diff --git a/vista3d/data/external/Adrenal_Ki67_5_folds.json b/vista3d/data/external/Adrenal_Ki67_5_folds.json
new file mode 100644
index 0000000..30f5b86
--- /dev/null
+++ b/vista3d/data/external/Adrenal_Ki67_5_folds.json
@@ -0,0 +1,266 @@
+{
+ "training": [
+ {
+ "image": "Adrenal_Ki67_Seg_026-Adrenal_Ki67_Seg_026/8-_OMNIPAQUE_350_VL100_FC12_ORG_8.nii.gz",
+ "label": "Adrenal_Ki67_Seg_026-Adrenal_Ki67_Seg_026/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_007-Adrenal_Ki67_Seg_007/2-AX_W_2.nii.gz",
+ "label": "Adrenal_Ki67_Seg_007-Adrenal_Ki67_Seg_007/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_027-Adrenal_Ki67_Seg_027/14-No_series_description_14.nii.gz",
+ "label": "Adrenal_Ki67_Seg_027-Adrenal_Ki67_Seg_027/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_017-Adrenal_Ki67_Seg_017/5-ABD_(2.5MM_SOFT)_5.nii.gz",
+ "label": "Adrenal_Ki67_Seg_017-Adrenal_Ki67_Seg_017/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_042-Adrenal_Ki67_Seg_042/10-No_series_description_10.nii.gz",
+ "label": "Adrenal_Ki67_Seg_042-Adrenal_Ki67_Seg_042/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_037-Adrenal_Ki67_Seg_037/2-ABD_PEL_W_2.nii.gz",
+ "label": "Adrenal_Ki67_Seg_037-Adrenal_Ki67_Seg_037/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_039-Adrenal_Ki67_Seg_039/5-3_AX_VENOUS_5.nii.gz",
+ "label": "Adrenal_Ki67_Seg_039-Adrenal_Ki67_Seg_039/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_004-Adrenal_Ki67_Seg_004/6-Venous_3.0_B31f_6.nii.gz",
+ "label": "Adrenal_Ki67_Seg_004-Adrenal_Ki67_Seg_004/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_034-Adrenal_Ki67_Seg_034/930-CAP_W_5mm_(SafeCT)_930.nii.gz",
+ "label": "Adrenal_Ki67_Seg_034-Adrenal_Ki67_Seg_034/seg-1__fix.nii.gz",
+ "fold": 0
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_031-Adrenal_Ki67_Seg_031/5-Abd_Pel_with_5.0_B30f_ST_5.nii.gz",
+ "label": "Adrenal_Ki67_Seg_031-Adrenal_Ki67_Seg_031/seg-1__fix.nii.gz",
+ "fold": 1
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_020-Adrenal_Ki67_Seg_020/5-Recon_2_ABD_PLVS_W_CONTRAST_5.nii.gz",
+ "label": "Adrenal_Ki67_Seg_020-Adrenal_Ki67_Seg_020/seg-1__fix.nii.gz",
+ "fold": 1
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_052-Adrenal_Ki67_Seg_052/10-No_series_description_10.nii.gz",
+ "label": "Adrenal_Ki67_Seg_052-Adrenal_Ki67_Seg_052/seg-1__fix.nii.gz",
+ "fold": 1
+ },
+ {
+ "image": "Adrenal_Ki67_Seg_049-Adrenal_Ki67_Seg_049/3-CAP_W_O_5.0_I30f_3_3.nii.gz",
+ "label": "Adrenal_Ki67_Seg_049-Adrenal_Ki67_Seg_049/seg-1__fix.nii.gz",
+ "fold": 1
+ },
+ {
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+}
diff --git a/vista3d/data/external/C4KC-KiTS_5_folds.json b/vista3d/data/external/C4KC-KiTS_5_folds.json
new file mode 100644
index 0000000..043db03
--- /dev/null
+++ b/vista3d/data/external/C4KC-KiTS_5_folds.json
@@ -0,0 +1,1022 @@
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+ "label": "KiTS-00160/mask.nii.gz",
+ "fold": 3
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+ "label": "KiTS-00099/mask.nii.gz",
+ "fold": 3
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+ "image": "KiTS-00127/5_arterial.nii.gz",
+ "label": "KiTS-00127/mask.nii.gz",
+ "fold": 3
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+ "label": "KiTS-00188/mask.nii.gz",
+ "fold": 3
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+ "label": "KiTS-00195/mask.nii.gz",
+ "fold": 3
+ },
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+ "label": "KiTS-00128/mask.nii.gz",
+ "fold": 3
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+ "label": "KiTS-00154/mask.nii.gz",
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+ "label": "KiTS-00032/mask.nii.gz",
+ "fold": 3
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+ "label": "KiTS-00083/mask.nii.gz",
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+ "label": "KiTS-00073/mask.nii.gz",
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+ },
+ {
+ "image": "KiTS-00049/6_arterial.nii.gz",
+ "label": "KiTS-00049/mask.nii.gz",
+ "fold": 3
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+ {
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+ "label": "KiTS-00141/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00164/2_arterial.nii.gz",
+ "label": "KiTS-00164/mask.nii.gz",
+ "fold": 3
+ },
+ {
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+ "label": "KiTS-00200/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00121/2_arterial.nii.gz",
+ "label": "KiTS-00121/mask.nii.gz",
+ "fold": 3
+ },
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+ "label": "KiTS-00139/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00084/6_arterial.nii.gz",
+ "label": "KiTS-00084/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00137/5_arterial.nii.gz",
+ "label": "KiTS-00137/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00150/3_arterial.nii.gz",
+ "label": "KiTS-00150/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00074/3_arterial.nii.gz",
+ "label": "KiTS-00074/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00039/2_arterial.nii.gz",
+ "label": "KiTS-00039/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00020/2_arterial.nii.gz",
+ "label": "KiTS-00020/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00108/8_arterial.nii.gz",
+ "label": "KiTS-00108/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00038/2_arterial.nii.gz",
+ "label": "KiTS-00038/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00117/4_arterial.nii.gz",
+ "label": "KiTS-00117/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00004/7_arterial.nii.gz",
+ "label": "KiTS-00004/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00030/4_arterial.nii.gz",
+ "label": "KiTS-00030/mask.nii.gz",
+ "fold": 3
+ },
+ {
+ "image": "KiTS-00100/6_arterial.nii.gz",
+ "label": "KiTS-00100/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00113/2_arterial.nii.gz",
+ "label": "KiTS-00113/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00114/11_arterial.nii.gz",
+ "label": "KiTS-00114/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00105/9_arterial.nii.gz",
+ "label": "KiTS-00105/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00050/3_arterial.nii.gz",
+ "label": "KiTS-00050/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00159/7_arterial.nii.gz",
+ "label": "KiTS-00159/mask.nii.gz",
+ "fold": 4
+ },
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+ "image": "KiTS-00097/2_arterial.nii.gz",
+ "label": "KiTS-00097/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00013/2_arterial.nii.gz",
+ "label": "KiTS-00013/mask.nii.gz",
+ "fold": 4
+ },
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+ "image": "KiTS-00155/7_arterial.nii.gz",
+ "label": "KiTS-00155/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00120/7_arterial.nii.gz",
+ "label": "KiTS-00120/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00207/2_arterial.nii.gz",
+ "label": "KiTS-00207/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00125/2_arterial.nii.gz",
+ "label": "KiTS-00125/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00033/3_arterial.nii.gz",
+ "label": "KiTS-00033/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00112/3_arterial.nii.gz",
+ "label": "KiTS-00112/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00181/2_arterial.nii.gz",
+ "label": "KiTS-00181/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00056/2_arterial.nii.gz",
+ "label": "KiTS-00056/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00111/2_arterial.nii.gz",
+ "label": "KiTS-00111/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00162/3_arterial.nii.gz",
+ "label": "KiTS-00162/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00144/2_arterial.nii.gz",
+ "label": "KiTS-00144/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00043/3_arterial.nii.gz",
+ "label": "KiTS-00043/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00096/7_arterial.nii.gz",
+ "label": "KiTS-00096/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00076/6_arterial.nii.gz",
+ "label": "KiTS-00076/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00185/6_arterial.nii.gz",
+ "label": "KiTS-00185/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00065/5_arterial.nii.gz",
+ "label": "KiTS-00065/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00062/2_arterial.nii.gz",
+ "label": "KiTS-00062/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00163/3_arterial.nii.gz",
+ "label": "KiTS-00163/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00176/3_arterial.nii.gz",
+ "label": "KiTS-00176/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00161/5_arterial.nii.gz",
+ "label": "KiTS-00161/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00101/6_arterial.nii.gz",
+ "label": "KiTS-00101/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00028/2_arterial.nii.gz",
+ "label": "KiTS-00028/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00009/5_arterial.nii.gz",
+ "label": "KiTS-00009/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00199/2_arterial.nii.gz",
+ "label": "KiTS-00199/mask.nii.gz",
+ "fold": 4
+ },
+ {
+ "image": "KiTS-00143/2_arterial.nii.gz",
+ "label": "KiTS-00143/mask.nii.gz",
+ "fold": 4
+ }
+ ],
+ "testing": [
+ {
+ "image": "KiTS-00002/10_arterial.nii.gz",
+ "label": "KiTS-00002/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00003/7_arterial.nii.gz",
+ "label": "KiTS-00003/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00133/2_arterial.nii.gz",
+ "label": "KiTS-00133/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00052/7_arterial.nii.gz",
+ "label": "KiTS-00052/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00136/8_arterial.nii.gz",
+ "label": "KiTS-00136/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00180/5_arterial.nii.gz",
+ "label": "KiTS-00180/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00126/2_arterial.nii.gz",
+ "label": "KiTS-00126/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00102/7_arterial.nii.gz",
+ "label": "KiTS-00102/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00072/2_arterial.nii.gz",
+ "label": "KiTS-00072/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00138/301_arterial.nii.gz",
+ "label": "KiTS-00138/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00090/5_arterial.nii.gz",
+ "label": "KiTS-00090/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00147/6_arterial.nii.gz",
+ "label": "KiTS-00147/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00135/8_arterial.nii.gz",
+ "label": "KiTS-00135/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00109/3_arterial.nii.gz",
+ "label": "KiTS-00109/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00190/2_arterial.nii.gz",
+ "label": "KiTS-00190/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00183/8_arterial.nii.gz",
+ "label": "KiTS-00183/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00107/2_arterial.nii.gz",
+ "label": "KiTS-00107/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00082/2_arterial.nii.gz",
+ "label": "KiTS-00082/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00014/3_arterial.nii.gz",
+ "label": "KiTS-00014/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00146/6_arterial.nii.gz",
+ "label": "KiTS-00146/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00034/3_arterial.nii.gz",
+ "label": "KiTS-00034/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00158/6_arterial.nii.gz",
+ "label": "KiTS-00158/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00209/2_arterial.nii.gz",
+ "label": "KiTS-00209/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00058/2_arterial.nii.gz",
+ "label": "KiTS-00058/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00054/2_arterial.nii.gz",
+ "label": "KiTS-00054/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00106/2_arterial.nii.gz",
+ "label": "KiTS-00106/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00169/4_arterial.nii.gz",
+ "label": "KiTS-00169/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00184/2_arterial.nii.gz",
+ "label": "KiTS-00184/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00025/3_arterial.nii.gz",
+ "label": "KiTS-00025/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00201/3_arterial.nii.gz",
+ "label": "KiTS-00201/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00145/4_arterial_soft.nii.gz",
+ "label": "KiTS-00145/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00017/2_arterial.nii.gz",
+ "label": "KiTS-00017/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00086/4_arterial.nii.gz",
+ "label": "KiTS-00086/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00182/2_arterial.nii.gz",
+ "label": "KiTS-00182/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00170/4_arterial.nii.gz",
+ "label": "KiTS-00170/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00027/8_arterial.nii.gz",
+ "label": "KiTS-00027/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00119/2_arterial.nii.gz",
+ "label": "KiTS-00119/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00171/2_arterial.nii.gz",
+ "label": "KiTS-00171/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00174/5_arterial.nii.gz",
+ "label": "KiTS-00174/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00173/2_arterial.nii.gz",
+ "label": "KiTS-00173/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00095/6_arterial.nii.gz",
+ "label": "KiTS-00095/mask.nii.gz"
+ },
+ {
+ "image": "KiTS-00021/6_arterial.nii.gz",
+ "label": "KiTS-00021/mask.nii.gz"
+ }
+ ],
+ "label_dict": {
+ "1": "kidney",
+ "2": "kidney mass"
+ },
+ "original_label_dict": {
+ "1": "Kidney",
+ "2": "Mass"
+ }
+}
diff --git a/vista3d/data/external/HCC-TACE-Seg_5_folds.json b/vista3d/data/external/HCC-TACE-Seg_5_folds.json
new file mode 100644
index 0000000..73e65c4
--- /dev/null
+++ b/vista3d/data/external/HCC-TACE-Seg_5_folds.json
@@ -0,0 +1,516 @@
+{
+ "training": [
+ {
+ "image": "HCC_076-HCC_076/6-Recon_3_LIVER_2_PHASE_(C_A_P)_6.nii.gz",
+ "label": "HCC_076-HCC_076/seg__fix.nii.gz",
+ "fold": 0
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+ "image": "HCC_022-HCC_022/5-Recon_3_LIVER_3_PHASE_(C_A_P)_5.nii.gz",
+ "label": "HCC_022-HCC_022/seg__fix.nii.gz"
+ }
+ ],
+ "label_dict": {
+ "2": "hepatic tumor"
+ },
+ "original_label_dict": {
+ "2": "hepatic tumor"
+ }
+}
diff --git a/vista3d/data/external/WORD.json b/vista3d/data/external/WORD.json
new file mode 100644
index 0000000..a475765
--- /dev/null
+++ b/vista3d/data/external/WORD.json
@@ -0,0 +1,725 @@
+{
+ "name": "WORD-V0.1.0",
+ "description": "Whole abdomen ORgan segmentation Dataset (WORD), just for research use !!!",
+ "reference": "WORD: Revisiting Organs Segmentation in the Whole Abdominal Region, link:https://arxiv.org/pdf/2111.02403.pdf, https://github.com/HiLab-git/WORD",
+ "licence": "GNU General Public License v3.0",
+ "release": "v0.1.0 10/11/2021",
+ "tensorImageSize": "3D",
+ "modality": {
+ "0": "CT"
+ },
+ "labels": {
+ "0": "background",
+ "1": "liver",
+ "2": "spleen",
+ "3": "left_kidney",
+ "4": "right_kidney",
+ "5": "stomach",
+ "6": "gallbladder",
+ "7": "esophagus",
+ "8": "pancreas",
+ "9": "duodenum",
+ "10": "colon",
+ "11": "intestine",
+ "12": "adrenal",
+ "13": "rectum",
+ "14": "bladder",
+ "15": "Head_of_femur_L",
+ "16": "Head_of_femur_R"
+ },
+ "numTraining": 100,
+ "numValidation": 20,
+ "numTest": 30,
+ "training": [
+ {
+ "image": "./imagesTr/word_0096.nii.gz",
+ "label": "./labelsTr/word_0096.nii.gz"
+ },
+ {
+ "image": "./imagesTr/word_0010.nii.gz",
+ "label": "./labelsTr/word_0010.nii.gz"
+ },
+ {
+ "image": "./imagesTr/word_0078.nii.gz",
+ "label": "./labelsTr/word_0078.nii.gz"
+ },
+ {
+ "image": "./imagesTr/word_0109.nii.gz",
+ "label": "./labelsTr/word_0109.nii.gz"
+ },
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+ "label": "./labelsTr/word_0051.nii.gz"
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+ "label": "./labelsTr/word_0067.nii.gz"
+ },
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+ "label": "./labelsTr/word_0107.nii.gz"
+ },
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+ "label": "./labelsTr/word_0105.nii.gz"
+ },
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+ "label": "./labelsTr/word_0065.nii.gz"
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+ "label": "./labelsTr/word_0009.nii.gz"
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+ "label": "./labelsTr/word_0101.nii.gz"
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+ "image": "imagesTs/word_0131.nii.gz",
+ "label": "labelsTs/word_0131.nii.gz"
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+ ],
+ "addition_validation_from_LiTS": [
+ "addition_validation_from_LiTS/imagesTs",
+ "addition_validation_from_LiTS/labelsTs"
+ ],
+ "lits_testing": [
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+ "label": "addition_validation_from_LiTS/labelsTs/liver_10_word_label.nii.gz"
+ },
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+ "label": "addition_validation_from_LiTS/labelsTs/liver_11_word_label.nii.gz"
+ },
+ {
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+ "label": "addition_validation_from_LiTS/labelsTs/liver_12_word_label.nii.gz"
+ },
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+ "label": "addition_validation_from_LiTS/labelsTs/liver_17_word_label.nii.gz"
+ },
+ {
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+ "label": "addition_validation_from_LiTS/labelsTs/liver_19_word_label.nii.gz"
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diff --git a/vista3d/data/external/micro-ct-murine-native_5_folds.json b/vista3d/data/external/micro-ct-murine-native_5_folds.json
new file mode 100644
index 0000000..e6feba2
--- /dev/null
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new file mode 100644
index 0000000..626d90d
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diff --git a/vista3d/data/jsons/AbdomenCT-1K_5_folds.json b/vista3d/data/jsons/AbdomenCT-1K_5_folds.json
new file mode 100644
index 0000000..85e8ff3
--- /dev/null
+++ b/vista3d/data/jsons/AbdomenCT-1K_5_folds.json
@@ -0,0 +1,7151 @@
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+ "image": "AbdomenCT-1K-ImagePart2/Case_00494_0000.nii.gz",
+ "label": "Mask/Case_00494.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00169_0000.nii.gz",
+ "label": "Mask/Case_00169.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00025_0000.nii.gz",
+ "label": "Mask/Case_00025.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00332_0000.nii.gz",
+ "label": "Mask/Case_00332.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00601_0000.nii.gz",
+ "label": "Mask/Case_00601.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00507_0000.nii.gz",
+ "label": "Mask/Case_00507.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00653_0000.nii.gz",
+ "label": "Mask/Case_00653.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00453_0000.nii.gz",
+ "label": "Mask/Case_00453.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00749_0000.nii.gz",
+ "label": "Mask/Case_00749.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00031_0000.nii.gz",
+ "label": "Mask/Case_00031.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00243_0000.nii.gz",
+ "label": "Mask/Case_00243.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00487_0000.nii.gz",
+ "label": "Mask/Case_00487.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00467_0000.nii.gz",
+ "label": "Mask/Case_00467.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00156_0000.nii.gz",
+ "label": "Mask/Case_00156.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart3/Case_00846_0000.nii.gz",
+ "label": "Mask/Case_00846.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart3/Case_00915_0000.nii.gz",
+ "label": "Mask/Case_00915.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00193_0000.nii.gz",
+ "label": "Mask/Case_00193.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart3/Case_01001_0000.nii.gz",
+ "label": "Mask/Case_01001.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart3/Case_00856_0000.nii.gz",
+ "label": "Mask/Case_00856.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00393_0000.nii.gz",
+ "label": "Mask/Case_00393.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00732_0000.nii.gz",
+ "label": "Mask/Case_00732.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart2/Case_00459_0000.nii.gz",
+ "label": "Mask/Case_00459.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart3/Case_01004_0000.nii.gz",
+ "label": "Mask/Case_01004.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart1/Case_00106_0000.nii.gz",
+ "label": "Mask/Case_00106.nii.gz"
+ },
+ {
+ "image": "AbdomenCT-1K-ImagePart3/Case_00850_0000.nii.gz",
+ "label": "Mask/Case_00850.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/AeroPath_5_folds.json b/vista3d/data/jsons/AeroPath_5_folds.json
new file mode 100644
index 0000000..26f9c47
--- /dev/null
+++ b/vista3d/data/jsons/AeroPath_5_folds.json
@@ -0,0 +1,297 @@
+{
+ "training": [
+ {
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+ "pseudo_label": "8/8_CT_HR.nii.gz",
+ "label": "8/8_CT_HR_label.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 0
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+ {
+ "image": "6/6_CT_HR.nii.gz",
+ "pseudo_label": "6/6_CT_HR.nii.gz",
+ "label": "6/6_CT_HR_label.nii.gz",
+ "fold": 0,
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+ "pseudo_label": "14/14_CT_HR.nii.gz",
+ "label": "14/14_CT_HR_label.nii.gz",
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+ "label": "16/16_CT_HR_label.nii.gz",
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+ }
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+ "_target_": "RandCropByLabelClassesd",
+ "keys": [
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+ "@label_key",
+ "@pseudo_label_key",
+ "@label_sv_key"
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+ "num_samples": "@num_patches_per_image",
+ "spatial_size": "@patch_size",
+ "ratios": "$tuple(float(i >= 0) for i in range(133))",
+ "warn": false,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandZoomd",
+ "keys": [
+ "@image_key",
+ "@label_key",
+ "@pseudo_label_key",
+ "@label_sv_key"
+ ],
+ "min_zoom": 0.8,
+ "max_zoom": 1.2,
+ "mode": [
+ "trilinear",
+ "nearest",
+ "nearest",
+ "nearest"
+ ],
+ "prob": 0.2,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandSimulateLowResolutiond",
+ "keys": [
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+ "zoom_range": [
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+ "prob": 0.2,
+ "allow_missing_keys": true
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+ {
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+ "keys": [
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+ "prob": 0.2,
+ "sigma_x": [
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+ "sigma_y": [
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+ "sigma_z": [
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+ 1.0
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+ "_target_": "RandScaleIntensityd",
+ "keys": [
+ "@image_key"
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+ "factors": 0.1,
+ "prob": 0.2
+ },
+ {
+ "_target_": "RandShiftIntensityd",
+ "keys": [
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+ "offsets": 0.1,
+ "prob": 0.2
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+ "keys": [
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+ "std": 0.2
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+ "label_dict": {
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+ "original_label_dict": {
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+ "2": "airway"
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+ "testing": [
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+ "label": "19/19_CT_HR_label.nii.gz"
+ },
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+ "label": "2/2_CT_HR_label.nii.gz"
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+ "label": "22/22_CT_HR_label.nii.gz"
+ },
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+ "label": "21/21_CT_HR_label.nii.gz"
+ },
+ {
+ "image": "4/4_CT_HR.nii.gz",
+ "label": "4/4_CT_HR_label.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/BTCV-Abdomen_5_folds.json b/vista3d/data/jsons/BTCV-Abdomen_5_folds.json
new file mode 100644
index 0000000..55b1067
--- /dev/null
+++ b/vista3d/data/jsons/BTCV-Abdomen_5_folds.json
@@ -0,0 +1,340 @@
+{
+ "training": [
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+ "pseudo_label": "RawData/Training/img/img0039.nii.gz",
+ "label": "RawData/Training/label/label0039.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 1
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+ "pseudo_label": "RawData/Training/img/img0037.nii.gz",
+ "label": "RawData/Training/label/label0037.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 1
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+ "label": "RawData/Training/label/label0008.nii.gz",
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+ "label_sv": "/workspace_infer/supervoxel_sam/BTCV-Abdomen_100/img0023/img0023_seg.nii.gz"
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+ "label_sv": "/workspace_infer/supervoxel_sam/BTCV-Abdomen_100/img0031/img0031_seg.nii.gz"
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+ "9": "inferior vena cava",
+ "10": "portal vein and splenic vein",
+ "11": "pancreas",
+ "12": "right adrenal gland",
+ "13": "left adrenal gland"
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+ "3": "left kidney",
+ "4": "gallbladder",
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+ "9": "inferior vena cava",
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+ "11": "pancreas",
+ "12": "right adrenal gland",
+ "13": "left adrenal gland"
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+ },
+ {
+ "image": "RawData/Training/img/img0032.nii.gz",
+ "label": "RawData/Training/label/label0032.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/BTCV-Cervix_5_folds.json b/vista3d/data/jsons/BTCV-Cervix_5_folds.json
new file mode 100644
index 0000000..5bfd9a7
--- /dev/null
+++ b/vista3d/data/jsons/BTCV-Cervix_5_folds.json
@@ -0,0 +1,318 @@
+{
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+ "warn": false,
+ "allow_missing_keys": true
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+ "_target_": "RandZoomd",
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+ "2": "uterus",
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+ },
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+ "image": "FixedData/Training/img/5664630-Image.nii.gz",
+ "label": "FixedDataV2/Training/label/5664630-Mask.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/CRLM-CT_5_folds.json b/vista3d/data/jsons/CRLM-CT_5_folds.json
new file mode 100644
index 0000000..a28223d
--- /dev/null
+++ b/vista3d/data/jsons/CRLM-CT_5_folds.json
@@ -0,0 +1,1491 @@
+{
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+ "5": "Tumor"
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+ "label": "CRLM-CT-1022/mask.nii.gz"
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+}
diff --git a/vista3d/data/jsons/CT-ORG_5_folds.json b/vista3d/data/jsons/CT-ORG_5_folds.json
new file mode 100644
index 0000000..b5e6817
--- /dev/null
+++ b/vista3d/data/jsons/CT-ORG_5_folds.json
@@ -0,0 +1,1059 @@
+{
+ "training": [
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+ "pseudo_label": "OrganSegmentations/volume-128.nii.gz",
+ "label": "OrganSegmentations/labels-128.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 1
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+ "label": "OrganSegmentations/labels-27.nii.gz",
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+ "label_sv": "/workspace_infer/supervoxel_sam/CT-ORG_100/volume-43/volume-43_seg.nii.gz"
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+ "label_sv": "/workspace_infer/supervoxel_sam/CT-ORG_100/volume-20/volume-20_seg.nii.gz"
+ },
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+ },
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+ },
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+ "label": "OrganSegmentations/labels-55.nii.gz",
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+ },
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+ "label": "OrganSegmentations/labels-115.nii.gz",
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+ },
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+ "label_sv": "/workspace_infer/supervoxel_sam/CT-ORG_100/volume-18/volume-18_seg.nii.gz"
+ },
+ {
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+ "label": "fixed_affine/labels-32.nii.gz",
+ "fold": 4,
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+ {
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+ "label": "OrganSegmentations/labels-91.nii.gz",
+ "fold": 4,
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+ "label_sv": "/workspace_infer/supervoxel_sam/CT-ORG_100/volume-91/volume-91_seg.nii.gz"
+ },
+ {
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+ "label": "fixed_affine/labels-45.nii.gz",
+ "fold": 4,
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+ {
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+ "label_sv": "/workspace_infer/supervoxel_sam/CT-ORG_100/volume-134/volume-134_seg.nii.gz"
+ },
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+ "label": "fixed_affine/labels-29.nii.gz",
+ "fold": 4,
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+ {
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+ "label": "OrganSegmentations/labels-13.nii.gz",
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+ "label": "OrganSegmentations/labels-118.nii.gz",
+ "fold": 4,
+ "pseudo_label_reliability": 1,
+ "label_sv": "/workspace_infer/supervoxel_sam/CT-ORG_100/volume-118/volume-118_seg.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-9.nii.gz",
+ "pseudo_label": "OrganSegmentations/volume-9.nii.gz",
+ "label": "OrganSegmentations/labels-9.nii.gz",
+ "fold": 4,
+ "pseudo_label_reliability": 1
+ },
+ {
+ "image": "fixed_affine/volume-135.nii.gz",
+ "pseudo_label": "fixed_affine/volume-135.nii.gz",
+ "label": "fixed_affine/labels-135.nii.gz",
+ "fold": 4,
+ "pseudo_label_reliability": 1,
+ "label_sv": "/workspace_infer/supervoxel_sam/CT-ORG_100/volume-135/volume-135_seg.nii.gz"
+ }
+ ],
+ "training_transform": [
+ {
+ "_target_": "RandCropByLabelClassesd",
+ "keys": [
+ "@image_key",
+ "@label_key",
+ "@pseudo_label_key",
+ "@label_sv_key"
+ ],
+ "label_key": "@pseudo_label_key",
+ "num_classes": 133,
+ "num_samples": "@num_patches_per_image",
+ "spatial_size": "@patch_size",
+ "ratios": "$tuple(float(i >= 0) for i in range(133))",
+ "warn": false,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandZoomd",
+ "keys": [
+ "@image_key",
+ "@label_key",
+ "@pseudo_label_key",
+ "@label_sv_key"
+ ],
+ "min_zoom": 0.8,
+ "max_zoom": 1.2,
+ "mode": [
+ "trilinear",
+ "nearest",
+ "nearest",
+ "nearest"
+ ],
+ "prob": 0.2,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandSimulateLowResolutiond",
+ "keys": [
+ "@image_key"
+ ],
+ "zoom_range": [
+ 0.3,
+ 1
+ ],
+ "prob": 0.2,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandGaussianSmoothd",
+ "keys": [
+ "@image_key"
+ ],
+ "prob": 0.2,
+ "sigma_x": [
+ 0.5,
+ 1.0
+ ],
+ "sigma_y": [
+ 0.5,
+ 1.0
+ ],
+ "sigma_z": [
+ 0.5,
+ 1.0
+ ]
+ },
+ {
+ "_target_": "RandScaleIntensityd",
+ "keys": [
+ "@image_key"
+ ],
+ "factors": 0.1,
+ "prob": 0.2
+ },
+ {
+ "_target_": "RandShiftIntensityd",
+ "keys": [
+ "@image_key"
+ ],
+ "offsets": 0.1,
+ "prob": 0.2
+ },
+ {
+ "_target_": "RandGaussianNoised",
+ "keys": [
+ "@image_key"
+ ],
+ "prob": 0.2,
+ "mean": 0.0,
+ "std": 0.2
+ }
+ ],
+ "label_dict": {
+ "1": "liver",
+ "2": "bladder",
+ "3": "lung",
+ "4": "kidney",
+ "5": "bone",
+ "6": "brain"
+ },
+ "original_label_dict": {
+ "1": "liver",
+ "2": "bladder",
+ "3": "lungs",
+ "4": "kidneys",
+ "5": "bone",
+ "6": "brain"
+ },
+ "testing": [
+ {
+ "image": "OrganSegmentations/volume-104.nii.gz",
+ "label": "OrganSegmentations/labels-104.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-73.nii.gz",
+ "label": "OrganSegmentations/labels-73.nii.gz"
+ },
+ {
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+ "label": "OrganSegmentations/labels-89.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-23.nii.gz",
+ "label": "OrganSegmentations/labels-23.nii.gz"
+ },
+ {
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+ "label": "OrganSegmentations/labels-81.nii.gz"
+ },
+ {
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+ "label": "OrganSegmentations/labels-71.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-48.nii.gz",
+ "label": "OrganSegmentations/labels-48.nii.gz"
+ },
+ {
+ "image": "fixed_affine/volume-31.nii.gz",
+ "label": "fixed_affine/labels-31.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-56.nii.gz",
+ "label": "OrganSegmentations/labels-56.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-110.nii.gz",
+ "label": "OrganSegmentations/labels-110.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-114.nii.gz",
+ "label": "OrganSegmentations/labels-114.nii.gz"
+ },
+ {
+ "image": "fixed_affine/volume-44.nii.gz",
+ "label": "fixed_affine/labels-44.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-129.nii.gz",
+ "label": "OrganSegmentations/labels-129.nii.gz"
+ },
+ {
+ "image": "fixed_affine/volume-47.nii.gz",
+ "label": "fixed_affine/labels-47.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-125.nii.gz",
+ "label": "OrganSegmentations/labels-125.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-99.nii.gz",
+ "label": "OrganSegmentations/labels-99.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-26.nii.gz",
+ "label": "OrganSegmentations/labels-26.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-22.nii.gz",
+ "label": "OrganSegmentations/labels-22.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-59.nii.gz",
+ "label": "OrganSegmentations/labels-59.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-7.nii.gz",
+ "label": "OrganSegmentations/labels-7.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-120.nii.gz",
+ "label": "OrganSegmentations/labels-120.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-113.nii.gz",
+ "label": "OrganSegmentations/labels-113.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-51.nii.gz",
+ "label": "OrganSegmentations/labels-51.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-122.nii.gz",
+ "label": "OrganSegmentations/labels-122.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-84.nii.gz",
+ "label": "OrganSegmentations/labels-84.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-6.nii.gz",
+ "label": "OrganSegmentations/labels-6.nii.gz"
+ },
+ {
+ "image": "OrganSegmentations/volume-117.nii.gz",
+ "label": "OrganSegmentations/labels-117.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/CTPelvic1K-CLINIC_5_folds.json b/vista3d/data/jsons/CTPelvic1K-CLINIC_5_folds.json
new file mode 100644
index 0000000..b759eb2
--- /dev/null
+++ b/vista3d/data/jsons/CTPelvic1K-CLINIC_5_folds.json
@@ -0,0 +1,928 @@
+{
+ "training": [
+ {
+ "image": "CTPelvic1K_dataset7_data/dataset7_CLINIC_metal_0005_data.nii.gz",
+ "pseudo_label": "CTPelvic1K_dataset7_data/dataset7_CLINIC_metal_0005_data.nii.gz",
+ "label": "CTPelvic1K_dataset7_mask/CLINIC_metal_0005_mask_4label.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 1
+ },
+ {
+ "image": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0076_data.nii.gz",
+ "pseudo_label": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0076_data.nii.gz",
+ "label": "ipcai2021_dataset6_Anonymized/dataset6_CLINIC_0076_mask_4label.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 0
+ },
+ {
+ "image": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0080_data.nii.gz",
+ "pseudo_label": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0080_data.nii.gz",
+ "label": "ipcai2021_dataset6_Anonymized/dataset6_CLINIC_0080_mask_4label.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 0
+ },
+ {
+ "image": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0065_data.nii.gz",
+ "pseudo_label": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0065_data.nii.gz",
+ "label": "ipcai2021_dataset6_Anonymized/dataset6_CLINIC_0065_mask_4label.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 1
+ },
+ {
+ "image": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0045_data.nii.gz",
+ "pseudo_label": "CTPelvic1K_dataset6_data/dataset6_CLINIC_0045_data.nii.gz",
+ "label": "ipcai2021_dataset6_Anonymized/dataset6_CLINIC_0045_mask_4label.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 1
+ },
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+ "label": "ipcai2021_dataset6_Anonymized/dataset6_CLINIC_0022_mask_4label.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/Covid19_5_folds.json b/vista3d/data/jsons/Covid19_5_folds.json
new file mode 100644
index 0000000..a664250
--- /dev/null
+++ b/vista3d/data/jsons/Covid19_5_folds.json
@@ -0,0 +1,3765 @@
+{
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+}
diff --git a/vista3d/data/jsons/FLARE22_5_folds.json b/vista3d/data/jsons/FLARE22_5_folds.json
new file mode 100644
index 0000000..8c2a45a
--- /dev/null
+++ b/vista3d/data/jsons/FLARE22_5_folds.json
@@ -0,0 +1,480 @@
+{
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+ "keys": [
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+ "@pseudo_label_key",
+ "@label_sv_key"
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+ "ratios": "$tuple(float(i >= 0) for i in range(133))",
+ "warn": false,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandZoomd",
+ "keys": [
+ "@image_key",
+ "@label_key",
+ "@pseudo_label_key",
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+ "min_zoom": 0.8,
+ "max_zoom": 1.2,
+ "mode": [
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+ "nearest",
+ "nearest",
+ "nearest"
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+ "prob": 0.2,
+ "allow_missing_keys": true
+ },
+ {
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+ "keys": [
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+ "zoom_range": [
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+ "prob": 0.2,
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+ "keys": [
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+ "sigma_z": [
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+ 1.0
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+ },
+ {
+ "_target_": "RandScaleIntensityd",
+ "keys": [
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+ "factors": 0.1,
+ "prob": 0.2
+ },
+ {
+ "_target_": "RandShiftIntensityd",
+ "keys": [
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+ "offsets": 0.1,
+ "prob": 0.2
+ },
+ {
+ "_target_": "RandGaussianNoised",
+ "keys": [
+ "@image_key"
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+ "prob": 0.2,
+ "mean": 0.0,
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+ "label_dict": {
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+ "2": "right kidney",
+ "3": "spleen",
+ "4": "pancreas",
+ "5": "aorta",
+ "6": "inferior vena cava",
+ "7": "right adrenal gland",
+ "8": "left adrenal gland",
+ "9": "gallbladder",
+ "10": "esophagus",
+ "11": "stomach",
+ "12": "duodenum",
+ "13": "left kidney"
+ },
+ "original_label_dict": {
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+ "2": "right kidney",
+ "3": "spleen",
+ "4": "pancreas",
+ "5": "aorta",
+ "6": "inferior vena cava",
+ "7": "right adrenal gland",
+ "8": "left adrenal gland",
+ "9": "gallbladder",
+ "10": "esophagus",
+ "11": "stomach",
+ "12": "duodenum",
+ "13": "left kidney"
+ },
+ "testing": [
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+ "label": "labels/FLARE22_Tr_0042.nii.gz"
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+ "label": "labels/FLARE22_Tr_0028.nii.gz"
+ },
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+ "label": "labels/FLARE22_Tr_0044.nii.gz"
+ },
+ {
+ "image": "images/FLARE22_Tr_0047_0000.nii.gz",
+ "label": "labels/FLARE22_Tr_0047.nii.gz"
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+ {
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+ "label": "labels/FLARE22_Tr_0046.nii.gz"
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+ "label": "labels/FLARE22_Tr_0032.nii.gz"
+ },
+ {
+ "image": "images/FLARE22_Tr_0022_0000.nii.gz",
+ "label": "labels/FLARE22_Tr_0022.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/LIDC_5_folds.json b/vista3d/data/jsons/LIDC_5_folds.json
new file mode 100644
index 0000000..b8385be
--- /dev/null
+++ b/vista3d/data/jsons/LIDC_5_folds.json
@@ -0,0 +1,2943 @@
+{
+ "training": [
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+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0587_0.nii.gz",
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+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0587_0/LIDC-IDRI-0587_0_seg.nii.gz"
+ },
+ {
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+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0560_0.nii.gz",
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+ },
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+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0390_0.nii.gz",
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+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0390_0/LIDC-IDRI-0390_0_seg.nii.gz"
+ },
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+ },
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+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0385_0.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
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+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0385_0/LIDC-IDRI-0385_0_seg.nii.gz"
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+ "dataset": "/data/LIDC/LIDC",
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+ },
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+ "dataset": "/data/LIDC/LIDC",
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+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0468_0/LIDC-IDRI-0468_0_seg.nii.gz"
+ },
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+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0374_0.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
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+ },
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+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0713_0/LIDC-IDRI-0713_0_seg.nii.gz"
+ },
+ {
+ "image": "Image_LIDC/LIDC-IDRI-0273_1.nii.gz",
+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0273_1.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0273_1/LIDC-IDRI-0273_1_seg.nii.gz"
+ },
+ {
+ "image": "Image_LIDC/LIDC-IDRI-0185_1.nii.gz",
+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0185_1.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0185_1/LIDC-IDRI-0185_1_seg.nii.gz"
+ },
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+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0735_0/LIDC-IDRI-0735_0_seg.nii.gz"
+ },
+ {
+ "image": "Image_LIDC/LIDC-IDRI-0329_0.nii.gz",
+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0329_0.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0329_0/LIDC-IDRI-0329_0_seg.nii.gz"
+ },
+ {
+ "image": "Image_LIDC/LIDC-IDRI-0393_0.nii.gz",
+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0393_0.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0393_0/LIDC-IDRI-0393_0_seg.nii.gz"
+ },
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+ "image": "Image_LIDC/LIDC-IDRI-0061_1.nii.gz",
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+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0061_1/LIDC-IDRI-0061_1_seg.nii.gz"
+ },
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+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0032_1.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0032_1/LIDC-IDRI-0032_1_seg.nii.gz"
+ },
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+ "image": "Image_LIDC/LIDC-IDRI-0073_0.nii.gz",
+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0073_0.nii.gz",
+ "dataset": "/data/LIDC/LIDC",
+ "region": "/data/LIDC/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/lidc_100/LIDC-IDRI-0073_0/LIDC-IDRI-0073_0_seg.nii.gz"
+ },
+ {
+ "image": "Image_LIDC/LIDC-IDRI-0394_0.nii.gz",
+ "pseudo_label": "Image_LIDC/LIDC-IDRI-0394_0.nii.gz",
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+ "allow_missing_keys": true
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+ {
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+ "keys": [
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+ "mean": 0.0,
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+}
diff --git a/vista3d/data/jsons/Multi-organ-Abdominal-CT-btcv_5_folds.json b/vista3d/data/jsons/Multi-organ-Abdominal-CT-btcv_5_folds.json
new file mode 100644
index 0000000..fbe3747
--- /dev/null
+++ b/vista3d/data/jsons/Multi-organ-Abdominal-CT-btcv_5_folds.json
@@ -0,0 +1,461 @@
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+ "training_transform": [
+ {
+ "_target_": "RandCropByLabelClassesd",
+ "keys": [
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+ ],
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+ "warn": false,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandZoomd",
+ "keys": [
+ "@image_key",
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+ "nearest",
+ "nearest"
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+ "allow_missing_keys": true
+ },
+ {
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+ "keys": [
+ "@image_key"
+ ],
+ "zoom_range": [
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+ 1
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+ "prob": 0.2,
+ "allow_missing_keys": true
+ },
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+ "_target_": "RandGaussianSmoothd",
+ "keys": [
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+ 1.0
+ ]
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+ "prob": 0.2
+ },
+ {
+ "_target_": "RandShiftIntensityd",
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+ "prob": 0.2
+ },
+ {
+ "_target_": "RandGaussianNoised",
+ "keys": [
+ "@image_key"
+ ],
+ "prob": 0.2,
+ "mean": 0.0,
+ "std": 0.2
+ }
+ ],
+ "label_dict": {
+ "1": "spleen",
+ "2": "right kidney",
+ "3": "left kidney",
+ "4": "gallbladder",
+ "5": "esophagus",
+ "6": "liver",
+ "7": "stomach",
+ "8": "aorta",
+ "9": "inferior vena cava",
+ "10": "portal vein and splenic vein",
+ "11": "pancreas",
+ "12": "right adrenal gland",
+ "13": "left adrenal gland",
+ "14": "duodenum"
+ },
+ "original_label_dict": {
+ "1": "spleen",
+ "2": "right kidney",
+ "3": "left kidney",
+ "4": "gallbladder",
+ "5": "esophagus",
+ "6": "liver",
+ "7": "stomach",
+ "8": "aorta",
+ "9": "inferior vena cava",
+ "10": "portal vein and splenic vein",
+ "11": "pancreas",
+ "12": "right adrenal gland",
+ "13": "left adrenal gland",
+ "14": "duodenum"
+ }
+}
diff --git a/vista3d/data/jsons/Multi-organ-Abdominal-CT-tcia_5_folds.json b/vista3d/data/jsons/Multi-organ-Abdominal-CT-tcia_5_folds.json
new file mode 100644
index 0000000..0060f1e
--- /dev/null
+++ b/vista3d/data/jsons/Multi-organ-Abdominal-CT-tcia_5_folds.json
@@ -0,0 +1,415 @@
+{
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+ },
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+ },
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+ "label": "label_tcia_multiorgan+rkidney/label0019.nii",
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+ "label": "label_tcia_multiorgan+rkidney/label0048.nii",
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+ },
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+ "label": "label_tcia_multiorgan+rkidney/label0033.nii",
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+ },
+ {
+ "image": "images_tcia/PANCREAS_0007.nii",
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+ "label_sv": "/workspace_infer/supervoxel_sam/Multi-organ-Abdominal-CT-tcia_100/PANCREAS_0007/PANCREAS_0007_seg.nii.gz"
+ },
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+ "label": "label_tcia_multiorgan+rkidney/label0002.nii",
+ "fold": 4,
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+ "label_sv": "/workspace_infer/supervoxel_sam/Multi-organ-Abdominal-CT-tcia_100/PANCREAS_0002/PANCREAS_0002_seg.nii.gz"
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+ "pseudo_label": "images_tcia/PANCREAS_0018.nii.gz",
+ "label": "label_tcia_multiorgan+rkidney/label0018.nii",
+ "fold": 4,
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+ "label_sv": "/workspace_infer/supervoxel_sam/Multi-organ-Abdominal-CT-tcia_100/PANCREAS_0018/PANCREAS_0018_seg.nii.gz"
+ }
+ ],
+ "training_transform": [
+ {
+ "_target_": "RandCropByLabelClassesd",
+ "keys": [
+ "@image_key",
+ "@label_key",
+ "@pseudo_label_key",
+ "@label_sv_key"
+ ],
+ "label_key": "@pseudo_label_key",
+ "num_classes": 133,
+ "num_samples": "@num_patches_per_image",
+ "spatial_size": "@patch_size",
+ "ratios": "$tuple(float(i >= 0) for i in range(133))",
+ "warn": false,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandZoomd",
+ "keys": [
+ "@image_key",
+ "@label_key",
+ "@pseudo_label_key",
+ "@label_sv_key"
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+ "min_zoom": 0.8,
+ "max_zoom": 1.2,
+ "mode": [
+ "trilinear",
+ "nearest",
+ "nearest",
+ "nearest"
+ ],
+ "prob": 0.2,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandSimulateLowResolutiond",
+ "keys": [
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+ ],
+ "zoom_range": [
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+ 1
+ ],
+ "prob": 0.2,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandGaussianSmoothd",
+ "keys": [
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+ "prob": 0.2,
+ "sigma_x": [
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+ 1.0
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+ 1.0
+ ],
+ "sigma_z": [
+ 0.5,
+ 1.0
+ ]
+ },
+ {
+ "_target_": "RandScaleIntensityd",
+ "keys": [
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+ ],
+ "factors": 0.1,
+ "prob": 0.2
+ },
+ {
+ "_target_": "RandShiftIntensityd",
+ "keys": [
+ "@image_key"
+ ],
+ "offsets": 0.1,
+ "prob": 0.2
+ },
+ {
+ "_target_": "RandGaussianNoised",
+ "keys": [
+ "@image_key"
+ ],
+ "prob": 0.2,
+ "mean": 0.0,
+ "std": 0.2
+ }
+ ],
+ "label_dict": {
+ "1": "spleen",
+ "2": "right kidney",
+ "3": "left kidney",
+ "4": "gallbladder",
+ "5": "esophagus",
+ "6": "liver",
+ "7": "stomach",
+ "8": "pancreas",
+ "9": "duodenum"
+ },
+ "original_label_dict": {
+ "1": "spleen",
+ "2": "right kidney",
+ "3": "left kidney",
+ "4": "gallbladder",
+ "5": "esophagus",
+ "6": "liver",
+ "7": "stomach",
+ "8": "pancreas",
+ "9": "duodenum"
+ },
+ "testing": [
+ {
+ "image": "images_tcia/PANCREAS_0029.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0029.nii"
+ },
+ {
+ "image": "images_tcia/PANCREAS_0041.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0041.nii"
+ },
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+ "label": "label_tcia_multiorgan+rkidney/label0012.nii"
+ },
+ {
+ "image": "images_tcia/PANCREAS_0044.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0044.nii"
+ },
+ {
+ "image": "images_tcia/PANCREAS_0020.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0020.nii"
+ },
+ {
+ "image": "images_tcia/PANCREAS_0026.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0026.nii"
+ },
+ {
+ "image": "images_tcia/PANCREAS_0031.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0031.nii"
+ },
+ {
+ "image": "images_tcia/PANCREAS_0035.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0035.nii"
+ },
+ {
+ "image": "images_tcia/PANCREAS_0025.nii",
+ "label": "label_tcia_multiorgan+rkidney/label0025.nii"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/NLST_5_folds.json b/vista3d/data/jsons/NLST_5_folds.json
new file mode 100644
index 0000000..619d414
--- /dev/null
+++ b/vista3d/data/jsons/NLST_5_folds.json
@@ -0,0 +1,21888 @@
+{
+ "training": [
+ {
+ "image": "102913/4_1opasevzoomb30f37021206030na.nii.gz",
+ "pseudo_label": "102913/4_1opasevzoomb30f37021206030na.nii.gz",
+ "dataset": "/data/NLST/NLST",
+ "region": "/data/NLST/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/nlst/102913/4_1opasevzoomb30f37021206030na/4_1opasevzoomb30f37021206030na_seg.nii.gz"
+ },
+ {
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+ "pseudo_label": "102913/2_0opasevzoomb50f36021206030na.nii.gz",
+ "dataset": "/data/NLST/NLST",
+ "region": "/data/NLST/chest",
+ "label_sv": "/workspace_infer/supervoxel_sam/nlst/102913/2_0opasevzoomb50f36021206030na/2_0opasevzoomb50f36021206030na_seg.nii.gz"
+ },
+ {
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+ "pseudo_label": "102913/2_2opasevzoomb50f37021206030na.nii.gz",
+ "dataset": "/data/NLST/NLST",
+ "region": "/data/NLST/chest",
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+ },
+ {
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+ "pseudo_label": "102913/3_1opasevzoomb50f37021206030na.nii.gz",
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diff --git a/vista3d/data/jsons/Pancreas-CT_5_folds.json b/vista3d/data/jsons/Pancreas-CT_5_folds.json
new file mode 100644
index 0000000..81772c5
--- /dev/null
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diff --git a/vista3d/data/jsons/StonyBrook-CT_5_folds.json b/vista3d/data/jsons/StonyBrook-CT_5_folds.json
new file mode 100644
index 0000000..bb8614d
--- /dev/null
+++ b/vista3d/data/jsons/StonyBrook-CT_5_folds.json
@@ -0,0 +1,9008 @@
+{
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diff --git a/vista3d/data/jsons/TCIA_Colon_5_folds.json b/vista3d/data/jsons/TCIA_Colon_5_folds.json
new file mode 100644
index 0000000..1be8ef3
--- /dev/null
+++ b/vista3d/data/jsons/TCIA_Colon_5_folds.json
@@ -0,0 +1,9751 @@
+{
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+ "keys": [
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diff --git a/vista3d/data/jsons/Task03_5_folds.json b/vista3d/data/jsons/Task03_5_folds.json
new file mode 100644
index 0000000..e2f92e9
--- /dev/null
+++ b/vista3d/data/jsons/Task03_5_folds.json
@@ -0,0 +1,1030 @@
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diff --git a/vista3d/data/jsons/Task06_5_folds.json b/vista3d/data/jsons/Task06_5_folds.json
new file mode 100644
index 0000000..5f1d4ce
--- /dev/null
+++ b/vista3d/data/jsons/Task06_5_folds.json
@@ -0,0 +1,547 @@
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diff --git a/vista3d/data/jsons/Task07_5_folds.json b/vista3d/data/jsons/Task07_5_folds.json
new file mode 100644
index 0000000..83cd67e
--- /dev/null
+++ b/vista3d/data/jsons/Task07_5_folds.json
@@ -0,0 +1,2083 @@
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+ "warn": false,
+ "allow_missing_keys": true
+ },
+ {
+ "_target_": "RandZoomd",
+ "keys": [
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+ "@pseudo_label_key",
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+ "min_zoom": 0.8,
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+ "mode": [
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+ "nearest",
+ "nearest",
+ "nearest"
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+ "allow_missing_keys": true
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+ {
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+ "zoom_range": [
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+ {
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+ {
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+ "label": "labelsTr/pancreas_416.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/Task08_5_folds.json b/vista3d/data/jsons/Task08_5_folds.json
new file mode 100644
index 0000000..9c779eb
--- /dev/null
+++ b/vista3d/data/jsons/Task08_5_folds.json
@@ -0,0 +1,2206 @@
+{
+ "training": [
+ {
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+ "pseudo_label": "imagesTr/hepaticvessel_372.nii.gz",
+ "label": "labelsTr/hepaticvessel_372.nii.gz",
+ "fold": 0,
+ "pseudo_label_reliability": 0
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+ "pseudo_label": "imagesTr/hepaticvessel_165.nii.gz",
+ "label": "labelsTr/hepaticvessel_165.nii.gz",
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diff --git a/vista3d/data/jsons/Task09_5_folds.json b/vista3d/data/jsons/Task09_5_folds.json
new file mode 100644
index 0000000..95d2fc0
--- /dev/null
+++ b/vista3d/data/jsons/Task09_5_folds.json
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new file mode 100644
index 0000000..0299fb2
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diff --git a/vista3d/data/jsons/TotalSegmentatorV2_5_folds.json b/vista3d/data/jsons/TotalSegmentatorV2_5_folds.json
new file mode 100644
index 0000000..c359561
--- /dev/null
+++ b/vista3d/data/jsons/TotalSegmentatorV2_5_folds.json
@@ -0,0 +1,7991 @@
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+ "9": "portal vein and splenic vein",
+ "10": "pancreas",
+ "11": "right adrenal gland",
+ "12": "left adrenal gland",
+ "13": "left lung upper lobe",
+ "14": "left lung lower lobe",
+ "15": "right lung upper lobe",
+ "16": "right lung middle lobe",
+ "17": "right lung lower lobe",
+ "18": "vertebrae L5",
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+ "20": "vertebrae L3",
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+ "22": "vertebrae L1",
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+ "25": "vertebrae T10",
+ "26": "vertebrae T9",
+ "27": "vertebrae T8",
+ "28": "vertebrae T7",
+ "29": "vertebrae T6",
+ "30": "vertebrae T5",
+ "31": "vertebrae T4",
+ "32": "vertebrae T3",
+ "33": "vertebrae T2",
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+ "36": "vertebrae C6",
+ "37": "vertebrae C5",
+ "38": "vertebrae C4",
+ "39": "vertebrae C3",
+ "40": "vertebrae C2",
+ "41": "vertebrae C1",
+ "42": "esophagus",
+ "43": "trachea",
+ "44": "brain",
+ "45": "left iliac artery",
+ "46": "right iliac artery",
+ "47": "left iliac vena",
+ "48": "right iliac vena",
+ "49": "small bowel",
+ "50": "duodenum",
+ "51": "colon",
+ "52": "left rib 1",
+ "53": "left rib 2",
+ "54": "left rib 3",
+ "55": "left rib 4",
+ "56": "left rib 5",
+ "57": "left rib 6",
+ "58": "left rib 7",
+ "59": "left rib 8",
+ "60": "left rib 9",
+ "61": "left rib 10",
+ "62": "left rib 11",
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+ "64": "right rib 1",
+ "65": "right rib 2",
+ "66": "right rib 3",
+ "67": "right rib 4",
+ "68": "right rib 5",
+ "69": "right rib 6",
+ "70": "right rib 7",
+ "71": "right rib 8",
+ "72": "right rib 9",
+ "73": "right rib 10",
+ "74": "right rib 11",
+ "75": "right rib 12",
+ "76": "left humerus",
+ "77": "right humerus",
+ "78": "left scapula",
+ "79": "right scapula",
+ "80": "left clavicula",
+ "81": "right clavicula",
+ "82": "left femur",
+ "83": "right femur",
+ "84": "left hip",
+ "85": "right hip",
+ "86": "sacrum",
+ "87": "left gluteus maximus",
+ "88": "right gluteus maximus",
+ "89": "left gluteus medius",
+ "90": "right gluteus medius",
+ "91": "left gluteus minimus",
+ "92": "right gluteus minimus",
+ "93": "left autochthon",
+ "94": "right autochthon",
+ "95": "left iliopsoas",
+ "96": "right iliopsoas",
+ "97": "bladder",
+ "98": "left atrial appendage",
+ "99": "brachiocephalic trunk",
+ "100": "left brachiocephalic vein",
+ "101": "right brachiocephalic vein",
+ "102": "left common carotid artery",
+ "103": "right common carotid artery",
+ "104": "costal cartilages",
+ "105": "heart",
+ "106": "left kidney cyst",
+ "107": "right kidney cyst",
+ "108": "prostate",
+ "109": "pulmonary vein",
+ "110": "skull",
+ "111": "spinal cord",
+ "112": "sternum",
+ "113": "left subclavian artery",
+ "114": "right subclavian artery",
+ "115": "superior vena cava",
+ "116": "thyroid gland",
+ "117": "vertebrae S1"
+ },
+ "original_label_dict": {
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+ "2": "kidney_right",
+ "3": "kidney_left",
+ "4": "gallbladder",
+ "5": "liver",
+ "6": "stomach",
+ "7": "aorta",
+ "8": "inferior_vena_cava",
+ "9": "portal_vein_and_splenic_vein",
+ "10": "pancreas",
+ "11": "adrenal_gland_right",
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diff --git a/vista3d/data/jsons/VerSe_5_folds.json b/vista3d/data/jsons/VerSe_5_folds.json
new file mode 100644
index 0000000..cf1fe75
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+ "label": "dataset-01training/derivatives/sub-verse564/sub-verse564_dir-iso_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-03test/rawdata/sub-verse546/sub-verse546_dir-ax_ct.nii.gz",
+ "label": "dataset-03test/derivatives/sub-verse546/sub-verse546_dir-ax_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-01training/rawdata/sub-verse527/sub-verse527_dir-ax_ct.nii.gz",
+ "label": "dataset-01training/derivatives/sub-verse527/sub-verse527_dir-ax_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-verse19training/rawdata/sub-verse403/sub-verse403_split-verse255_ct.nii.gz",
+ "label": "dataset-verse19training/derivatives/sub-verse403/sub-verse403_split-verse255_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-01training/rawdata/sub-verse818/sub-verse818_dir-ax_ct.nii.gz",
+ "label": "dataset-01training/derivatives/sub-verse818/sub-verse818_dir-ax_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-02validation/rawdata/sub-verse635/sub-verse635_ct.nii.gz",
+ "label": "dataset-02validation/derivatives/sub-verse635/sub-verse635_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-03test/rawdata/sub-verse648/sub-verse648_dir-iso_ct.nii.gz",
+ "label": "dataset-03test/derivatives/sub-verse648/sub-verse648_dir-iso_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-01training/rawdata/sub-verse535/sub-verse535_dir-iso_ct.nii.gz",
+ "label": "dataset-01training/derivatives/sub-verse535/sub-verse535_dir-iso_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-verse19test/rawdata/sub-verse081/sub-verse081_ct.nii.gz",
+ "label": "dataset-verse19test/derivatives/sub-verse081/sub-verse081_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-03test/rawdata/sub-gl217/sub-gl217_dir-ax_ct.nii.gz",
+ "label": "dataset-03test/derivatives/sub-gl217/sub-gl217_dir-ax_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-verse19training/rawdata/sub-verse063/sub-verse063_ct.nii.gz",
+ "label": "dataset-verse19training/derivatives/sub-verse063/sub-verse063_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-verse19validation/rawdata/sub-verse026/sub-verse026_ct.nii.gz",
+ "label": "dataset-verse19validation/derivatives/sub-verse026/sub-verse026_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-03test/rawdata/sub-verse626/sub-verse626_dir-iso_ct.nii.gz",
+ "label": "dataset-03test/derivatives/sub-verse626/sub-verse626_dir-iso_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-verse19training/rawdata/sub-verse091/sub-verse091_ct.nii.gz",
+ "label": "dataset-verse19training/derivatives/sub-verse091/sub-verse091_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-03test/rawdata/sub-verse590/sub-verse590_dir-iso_ct.nii.gz",
+ "label": "dataset-03test/derivatives/sub-verse590/sub-verse590_dir-iso_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-02validation/rawdata/sub-verse601/sub-verse601_ct.nii.gz",
+ "label": "dataset-02validation/derivatives/sub-verse601/sub-verse601_seg-vert_msk.nii.gz"
+ },
+ {
+ "image": "dataset-verse19training/rawdata/sub-verse072/sub-verse072_ct.nii.gz",
+ "label": "dataset-verse19training/derivatives/sub-verse072/sub-verse072_seg-vert_msk.nii.gz"
+ }
+ ]
+}
diff --git a/vista3d/data/jsons/label_dict.json b/vista3d/data/jsons/label_dict.json
new file mode 100644
index 0000000..6fb98b5
--- /dev/null
+++ b/vista3d/data/jsons/label_dict.json
@@ -0,0 +1,129 @@
+{
+ "liver": 1,
+ "kidney": 2,
+ "spleen": 3,
+ "pancreas": 4,
+ "right kidney": 5,
+ "aorta": 6,
+ "inferior vena cava": 7,
+ "right adrenal gland": 8,
+ "left adrenal gland": 9,
+ "gallbladder": 10,
+ "esophagus": 11,
+ "stomach": 12,
+ "duodenum": 13,
+ "left kidney": 14,
+ "bladder": 15,
+ "portal vein and splenic vein": 17,
+ "small bowel": 19,
+ "lung": 20,
+ "bone": 21,
+ "brain": 22,
+ "lung tumor": 23,
+ "pancreatic tumor": 24,
+ "hepatic vessel": 25,
+ "hepatic tumor": 26,
+ "colon cancer primaries": 27,
+ "left lung upper lobe": 28,
+ "left lung lower lobe": 29,
+ "right lung upper lobe": 30,
+ "right lung middle lobe": 31,
+ "right lung lower lobe": 32,
+ "vertebrae L5": 33,
+ "vertebrae L4": 34,
+ "vertebrae L3": 35,
+ "vertebrae L2": 36,
+ "vertebrae L1": 37,
+ "vertebrae T12": 38,
+ "vertebrae T11": 39,
+ "vertebrae T10": 40,
+ "vertebrae T9": 41,
+ "vertebrae T8": 42,
+ "vertebrae T7": 43,
+ "vertebrae T6": 44,
+ "vertebrae T5": 45,
+ "vertebrae T4": 46,
+ "vertebrae T3": 47,
+ "vertebrae T2": 48,
+ "vertebrae T1": 49,
+ "vertebrae C7": 50,
+ "vertebrae C6": 51,
+ "vertebrae C5": 52,
+ "vertebrae C4": 53,
+ "vertebrae C3": 54,
+ "vertebrae C2": 55,
+ "vertebrae C1": 56,
+ "trachea": 57,
+ "left iliac artery": 58,
+ "right iliac artery": 59,
+ "left iliac vena": 60,
+ "right iliac vena": 61,
+ "colon": 62,
+ "left rib 1": 63,
+ "left rib 2": 64,
+ "left rib 3": 65,
+ "left rib 4": 66,
+ "left rib 5": 67,
+ "left rib 6": 68,
+ "left rib 7": 69,
+ "left rib 8": 70,
+ "left rib 9": 71,
+ "left rib 10": 72,
+ "left rib 11": 73,
+ "left rib 12": 74,
+ "right rib 1": 75,
+ "right rib 2": 76,
+ "right rib 3": 77,
+ "right rib 4": 78,
+ "right rib 5": 79,
+ "right rib 6": 80,
+ "right rib 7": 81,
+ "right rib 8": 82,
+ "right rib 9": 83,
+ "right rib 10": 84,
+ "right rib 11": 85,
+ "right rib 12": 86,
+ "left humerus": 87,
+ "right humerus": 88,
+ "left scapula": 89,
+ "right scapula": 90,
+ "left clavicula": 91,
+ "right clavicula": 92,
+ "left femur": 93,
+ "right femur": 94,
+ "left hip": 95,
+ "right hip": 96,
+ "sacrum": 97,
+ "left gluteus maximus": 98,
+ "right gluteus maximus": 99,
+ "left gluteus medius": 100,
+ "right gluteus medius": 101,
+ "left gluteus minimus": 102,
+ "right gluteus minimus": 103,
+ "left autochthon": 104,
+ "right autochthon": 105,
+ "left iliopsoas": 106,
+ "right iliopsoas": 107,
+ "left atrial appendage": 108,
+ "brachiocephalic trunk": 109,
+ "left brachiocephalic vein": 110,
+ "right brachiocephalic vein": 111,
+ "left common carotid artery": 112,
+ "right common carotid artery": 113,
+ "costal cartilages": 114,
+ "heart": 115,
+ "left kidney cyst": 116,
+ "right kidney cyst": 117,
+ "prostate": 118,
+ "pulmonary vein": 119,
+ "skull": 120,
+ "spinal cord": 121,
+ "sternum": 122,
+ "left subclavian artery": 123,
+ "right subclavian artery": 124,
+ "superior vena cava": 125,
+ "thyroid gland": 126,
+ "vertebrae S1": 127,
+ "bone lesion": 128,
+ "airway": 132
+}
diff --git a/vista3d/data/jsons/label_mappings.json b/vista3d/data/jsons/label_mappings.json
new file mode 100644
index 0000000..50b5d0d
--- /dev/null
+++ b/vista3d/data/jsons/label_mappings.json
@@ -0,0 +1,1006 @@
+{
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+ "BTCV-Cervix": [
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+ [
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+ "Task10": [
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+ ],
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+ ],
+ "AeroPath": [
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+ ]
+ ],
+ "Autopet23": [
+ [
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+ 133
+ ]
+ ],
+ "LIDC-IDRI": [
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+ 1,
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+ ]
+ ],
+ "CTPelvic1K-CLINIC": [
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+ 1,
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+ ],
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+ ],
+ [
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+ ]
+ ],
+ "COLON_ACRIN6664": [
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+ ]
+}
diff --git a/vista3d/data/make_datalists.py b/vista3d/data/make_datalists.py
new file mode 100644
index 0000000..80469c5
--- /dev/null
+++ b/vista3d/data/make_datalists.py
@@ -0,0 +1,2286 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+# Step 1.
+# reading image and label folders, listing all the nii.gz files,
+# creating a data_list.json for training and validation
+# the data_list.json format is like ('testing' labels are optional):
+# {
+# "training": [
+# {"image": "img0001.nii.gz", "label": "label0001.nii.gz", "fold": 0},
+# {"image": "img0002.nii.gz", "label": "label0002.nii.gz", "fold": 2},
+# ...
+# ],
+# "testing": [
+# {"image": "img0003.nii.gz", "label": "label0003.nii.gz"},
+# {"image": "img0004.nii.gz", "label": "label0004.nii.gz"},
+# ...
+# ]
+# }
+
+import os
+import re
+from glob import glob
+from pprint import pprint
+
+from monai.apps import get_logger
+from monai.bundle import ConfigParser
+from monai.data.utils import partition_dataset
+
+logger = get_logger(__name__)
+test_ratio = 0.2 # test split
+n_folds = 5 # training and validation split
+seed = 20230808 # random seed for data partition_dataset reproducibility
+output_json_dir = os.path.join(os.path.dirname(__file__), "jsons")
+
+
+def register_make(func):
+ """
+ register the function to make the data list
+ """
+ global _make_funcs
+ if "_make_funcs" not in globals():
+ _make_funcs = {}
+ if func.__name__ in _make_funcs:
+ raise ValueError(f"{func.__name__} already exists.")
+ _make_funcs[func.__name__] = func
+ return func
+
+
+def search_image_files(base_dir, ext, regex=None):
+ """returns a list of relative filenames with given extension in `base_dir`"""
+ print(f"searching ext={ext} from base_dir={base_dir}")
+ images = []
+ for root, _, files in os.walk(base_dir):
+ images.extend(
+ os.path.join(root, filename) for filename in files if filename.endswith(ext)
+ )
+ if regex is not None:
+ images = [x for x in images if re.compile(regex).search(x) is not None]
+ print(f"found {len(images)} *.{ext} files")
+ return sorted(images)
+
+
+def create_splits_and_write_json(
+ images,
+ labels,
+ ratio,
+ num_folds,
+ json_name,
+ rng_seed,
+ label_dict,
+ original_label_dict=None,
+):
+ """
+ first generate training/test split, then from the training part,
+ generate training/validation num_folds
+ """
+ items = [{"image": img, "label": lab} for img, lab in zip(images, labels)]
+ train_test = partition_dataset(
+ items, ratios=[1 - ratio, ratio], shuffle=True, seed=rng_seed
+ )
+ print(f"training: {len(train_test[0])}, testing: {len(train_test[1])}")
+ train_val = partition_dataset(
+ train_test[0], num_partitions=num_folds, shuffle=True, seed=rng_seed
+ )
+ print(f"training validation folds sizes: {[len(x) for x in train_val]}")
+ training = []
+ for f, x in enumerate(train_val):
+ for item in x:
+ item["fold"] = f
+ training.append(item)
+
+ # write json
+ parser = ConfigParser({})
+ parser["training"] = training
+ parser["testing"] = train_test[1]
+
+ parser["label_dict"] = label_dict
+ parser["original_label_dict"] = original_label_dict or label_dict
+
+ print(f"writing {json_name}\n\n")
+ if os.path.exists(json_name):
+ logger.warning(f"rewrite existing datalist file: {json_name}")
+ ConfigParser.export_config_file(parser.config, json_name, indent=4)
+
+
+def filtering_files(base_url, image_names, label_names, idx=-1):
+ """
+ check the idx-th item in the lists of image and label filenames, remove:
+
+ - image files without corresponding label files
+
+ """
+ _tmp_img = os.path.join(base_url, image_names[idx])
+ _tmp_lab = os.path.join(base_url, label_names[idx])
+ if not (os.path.exists(_tmp_img) and os.path.exists(_tmp_lab)):
+ if not os.path.exists(_tmp_img):
+ logger.warning(f"image file {_tmp_img} pair does not exist")
+ if not os.path.exists(_tmp_lab):
+ logger.warning(f"label file {_tmp_lab} pair does not exist")
+ image_names.pop(idx)
+ label_names.pop(idx)
+
+
+####
+@register_make
+def make_abdomenct_1k():
+ base_url = "/data/AbdomenCT-1K"
+ dataset_name = "AbdomenCT-1K"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "Mask"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"Case_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"Case_{idx}_0000.nii.gz"
+ for f in [
+ "AbdomenCT-1K-ImagePart1",
+ "AbdomenCT-1K-ImagePart2",
+ "AbdomenCT-1K-ImagePart3",
+ ]:
+ if os.path.exists(os.path.join(base_url, f, img_name)):
+ images.append(os.path.join(f, img_name))
+ break
+ # print(f"image: {images[-1]}, label: {labels[-1]}")
+ filtering_files(base_url, images, labels)
+ label_dict = {1: "liver", 2: "kidney", 3: "spleen", 4: "pancreas"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_flare22():
+ base_url = "/data/AbdomenCT-1K/FLARE22Train"
+ dataset_name = "FLARE22"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labels"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"FLARE22_Tr_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"FLARE22_Tr_{idx}_0000.nii.gz"
+ images.append(os.path.join("images", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {
+ 1: "liver",
+ 2: "right kidney",
+ 3: "spleen",
+ 4: "pancreas",
+ 5: "aorta",
+ 6: "inferior vena cava",
+ 7: "right adrenal gland",
+ 8: "left adrenal gland",
+ 9: "gallbladder",
+ 10: "esophagus",
+ 11: "stomach",
+ 12: "duodenum",
+ 13: "left kidney",
+ }
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_amos22():
+ base_url = "/data/AMOS22"
+ dataset_name = "AMOS22"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ masks += search_image_files(os.path.join(base_url, "labelsVa"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"amos_(\d+).nii.gz").search(rel_mask)[1]
+ if int(idx) >= 500: # skip the MRI cases
+ labels.pop()
+ continue
+ img_name = f"amos_{idx}.nii.gz"
+ for f in ["imagesTr", "imagesVa"]:
+ if os.path.exists(os.path.join(base_url, f, img_name)):
+ images.append(os.path.join(f, img_name))
+ break
+ # print(f"image: {images[-1]}, label: {labels[-1]}")
+ filtering_files(base_url, images, labels)
+ original_label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "esophagus",
+ 6: "liver",
+ 7: "stomach",
+ 8: "aorta",
+ 9: "postcava",
+ 10: "pancreas",
+ 11: "right adrenal gland",
+ 12: "left adrenal gland",
+ 13: "duodenum",
+ 14: "bladder",
+ 15: "prostate or uterus",
+ }
+ label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "esophagus",
+ 6: "liver",
+ 7: "stomach",
+ 8: "aorta",
+ 9: "inferior vena cava",
+ 10: "pancreas",
+ 11: "right adrenal gland",
+ 12: "left adrenal gland",
+ 13: "duodenum",
+ 14: "bladder",
+ 15: "prostate or uterus",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_btcv_abdomen():
+ base_url = "/data/BTCV/Abdomen"
+ dataset_name = "BTCV-Abdomen"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "RawData", "Training", "label"), ".nii.gz"
+ )
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"label(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"img{idx}.nii.gz"
+ images.append(os.path.join("RawData", "Training", "img", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "esophagus",
+ 6: "liver",
+ 7: "stomach",
+ 8: "aorta",
+ 9: "inferior vena cava",
+ 10: "portal vein and splenic vein",
+ 11: "pancreas",
+ 12: "right adrenal gland",
+ 13: "left adrenal gland",
+ }
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_btcv_cervix():
+ base_url = "/data/BTCV/Cervix"
+ dataset_name = "BTCV-Cervix"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "FixedDataV2", "Training", "label"), ".nii.gz"
+ )
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"(\d+)-Mask.nii.gz").search(rel_mask)[1]
+ img_name = f"{idx}-Image.nii.gz"
+ images.append(os.path.join("FixedData", "Training", "img", img_name))
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "bladder", 2: "uterus", 3: "rectum", 4: "small bowel"}
+ label_dict = {1: "bladder", 2: "prostate or uterus", 3: "rectum", 4: "small bowel"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_chaos():
+ base_url = "/data/CHAOS"
+ dataset_name = "CHAOS"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "Train_Sets_nifti_ct"), "segmentation.nii.gz"
+ )
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"(\d+)_segmentation.nii.gz").search(rel_mask)[1]
+ img_name = f"{idx}_image.nii.gz"
+ images.append(os.path.join("Train_Sets_nifti_ct", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {1: "liver"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_ct_org():
+ base_url = "/data/CT-ORG"
+ dataset_name = "CT-ORG"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "OrganSegmentations"), ".nii.gz", regex=r"labels"
+ )
+ images, labels = [], []
+ for mask in masks:
+ idx = re.compile(r"labels-(\d+).nii.gz").search(mask)[1]
+ if idx in {"19", "70", "74", "76"}:
+ continue # these are problematic cases
+ _fixed_mask = os.path.join(base_url, "fixed_affine", f"labels-{idx}.nii.gz")
+ img_name = f"volume-{idx}.nii.gz"
+ mask_name = f"labels-{idx}.nii.gz"
+ if os.path.exists(_fixed_mask): # there are newer fixed files
+ images.append(os.path.join("fixed_affine", img_name))
+ labels.append(os.path.join("fixed_affine", mask_name))
+ else:
+ images.append(os.path.join("OrganSegmentations", img_name))
+ labels.append(os.path.join("OrganSegmentations", mask_name))
+ filtering_files(base_url, images, labels)
+ original_label_dict = {
+ 1: "liver",
+ 2: "bladder",
+ 3: "lungs",
+ 4: "kidneys",
+ 5: "bone",
+ 6: "brain",
+ }
+ label_dict = {
+ 1: "liver",
+ 2: "bladder",
+ 3: "lung",
+ 4: "kidney",
+ 5: "bone",
+ 6: "brain",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_kits23():
+ base_url = "/data/KiTS23/dataset"
+ dataset_name = "KiTS23"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "segmentation.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"case_(\d+)").search(rel_mask)[1]
+ img_name = f"case_{idx}"
+ images.append(os.path.join(img_name, "imaging.nii.gz"))
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "kidney", 2: "tumor", 3: "cyst"}
+ label_dict = {1: "kidney", 2: "kidney tumor", 3: "kidney cyst"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_lits23():
+ base_url = "/data/LiTS/Training_Batch"
+ dataset_name = "LiTS"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, ".nii", regex=r"segmentation")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"segmentation-(\d+).nii").search(rel_mask)[1]
+ img_name = f"volume-{idx}.nii"
+ images.append(img_name)
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "liver", 2: "tumor"}
+ label_dict = {1: "liver", 2: "liver tumor"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_multi_organ_btcv():
+ base_url = "/data/Multi-organ-Abdominal-CT/res_1.0mm_relabeled2"
+ dataset_name = "Multi-organ-Abdominal-CT-btcv"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "label_btcv_multiorgan"), ".nii")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"label(\d+).nii").search(rel_mask)[1]
+ img_name = f"img{idx}.nii"
+ images.append(os.path.join("images_btcv", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "esophagus",
+ 6: "liver",
+ 7: "stomach",
+ 8: "aorta",
+ 9: "inferior vena cava",
+ 10: "portal vein and splenic vein",
+ 11: "pancreas",
+ 12: "right adrenal gland",
+ 13: "left adrenal gland",
+ 14: "duodenum",
+ }
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_multi_organ_tcia():
+ base_url = "/data/Multi-organ-Abdominal-CT/res_1.0mm_relabeled2"
+ dataset_name = "Multi-organ-Abdominal-CT-tcia"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "label_tcia_multiorgan+rkidney"), ".nii"
+ )
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"label(\d+).nii").search(rel_mask)[1]
+ img_name = f"PANCREAS_{idx}.nii"
+ images.append(os.path.join("images_tcia", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "esophagus",
+ 6: "liver",
+ 7: "stomach",
+ 8: "pancreas",
+ 9: "duodenum",
+ }
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_pancreas_ct():
+ base_url = "/data/Pancreas-CT"
+ dataset_name = "Pancreas-CT"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "TCIA_pancreas_labels-02-05-2017"), ".nii.gz"
+ )
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"label(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"PANCREAS_{idx}.nii.gz"
+ images.append(os.path.join("manifest-1599750808610", "nifti", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {1: "pancreas"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_task06():
+ base_url = "/data/Task06"
+ dataset_name = "Task06"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"lung_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"lung_{idx}.nii.gz"
+ images.append(os.path.join("imagesTr", img_name))
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "cancer"}
+ label_dict = {1: "lung tumor"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_task07():
+ base_url = "/data/Task07"
+ dataset_name = "Task07"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"pancreas_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"pancreas_{idx}.nii.gz"
+ images.append(os.path.join("imagesTr", img_name))
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "pancreas", 2: "cancer"}
+ label_dict = {1: "pancreas", 2: "pancreatic tumor"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_task08():
+ base_url = "/data/Task08"
+ dataset_name = "Task08"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"hepaticvessel_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"hepaticvessel_{idx}.nii.gz"
+ images.append(os.path.join("imagesTr", img_name))
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "Vessel", 2: "Tumour"}
+ label_dict = {1: "hepatic vessel", 2: "hepatic tumor"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_task09():
+ base_url = "/data/Task09"
+ dataset_name = "Task09"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"spleen_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"spleen_{idx}.nii.gz"
+ images.append(os.path.join("imagesTr", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {1: "spleen"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_task10():
+ base_url = "/data/Task10"
+ dataset_name = "Task10"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"colon_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"colon_{idx}.nii.gz"
+ images.append(os.path.join("imagesTr", img_name))
+ filtering_files(base_url, images, labels)
+ label_dict = {1: "colon cancer primaries"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_total_segmentator():
+ base_url = "/data/TotalSegmentator"
+ dataset_name = "TotalSegmentator"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labels"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"s(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"s{idx}.nii.gz"
+ images.append(os.path.join("images", img_name))
+ filtering_files(base_url, images, labels)
+ original_label_dict = { # https://github.com/wasserth/TotalSegmentator/blob/master/totalsegmentator/map_to_binary.py
+ 1: "spleen",
+ 2: "kidney_right",
+ 3: "kidney_left",
+ 4: "gallbladder",
+ 5: "liver",
+ 6: "stomach",
+ 7: "aorta",
+ 8: "inferior_vena_cava",
+ 9: "portal_vein_and_splenic_vein",
+ 10: "pancreas",
+ 11: "adrenal_gland_right",
+ 12: "adrenal_gland_left",
+ 13: "lung_upper_lobe_left",
+ 14: "lung_lower_lobe_left",
+ 15: "lung_upper_lobe_right",
+ 16: "lung_middle_lobe_right",
+ 17: "lung_lower_lobe_right",
+ 18: "vertebrae_L5",
+ 19: "vertebrae_L4",
+ 20: "vertebrae_L3",
+ 21: "vertebrae_L2",
+ 22: "vertebrae_L1",
+ 23: "vertebrae_T12",
+ 24: "vertebrae_T11",
+ 25: "vertebrae_T10",
+ 26: "vertebrae_T9",
+ 27: "vertebrae_T8",
+ 28: "vertebrae_T7",
+ 29: "vertebrae_T6",
+ 30: "vertebrae_T5",
+ 31: "vertebrae_T4",
+ 32: "vertebrae_T3",
+ 33: "vertebrae_T2",
+ 34: "vertebrae_T1",
+ 35: "vertebrae_C7",
+ 36: "vertebrae_C6",
+ 37: "vertebrae_C5",
+ 38: "vertebrae_C4",
+ 39: "vertebrae_C3",
+ 40: "vertebrae_C2",
+ 41: "vertebrae_C1",
+ 42: "esophagus",
+ 43: "trachea",
+ 44: "heart_myocardium",
+ 45: "heart_atrium_left",
+ 46: "heart_ventricle_left",
+ 47: "heart_atrium_right",
+ 48: "heart_ventricle_right",
+ 49: "pulmonary_artery",
+ 50: "brain",
+ 51: "iliac_artery_left",
+ 52: "iliac_artery_right",
+ 53: "iliac_vena_left",
+ 54: "iliac_vena_right",
+ 55: "small_bowel",
+ 56: "duodenum",
+ 57: "colon",
+ 58: "rib_left_1",
+ 59: "rib_left_2",
+ 60: "rib_left_3",
+ 61: "rib_left_4",
+ 62: "rib_left_5",
+ 63: "rib_left_6",
+ 64: "rib_left_7",
+ 65: "rib_left_8",
+ 66: "rib_left_9",
+ 67: "rib_left_10",
+ 68: "rib_left_11",
+ 69: "rib_left_12",
+ 70: "rib_right_1",
+ 71: "rib_right_2",
+ 72: "rib_right_3",
+ 73: "rib_right_4",
+ 74: "rib_right_5",
+ 75: "rib_right_6",
+ 76: "rib_right_7",
+ 77: "rib_right_8",
+ 78: "rib_right_9",
+ 79: "rib_right_10",
+ 80: "rib_right_11",
+ 81: "rib_right_12",
+ 82: "humerus_left",
+ 83: "humerus_right",
+ 84: "scapula_left",
+ 85: "scapula_right",
+ 86: "clavicula_left",
+ 87: "clavicula_right",
+ 88: "femur_left",
+ 89: "femur_right",
+ 90: "hip_left",
+ 91: "hip_right",
+ 92: "sacrum",
+ 93: "face",
+ 94: "gluteus_maximus_left",
+ 95: "gluteus_maximus_right",
+ 96: "gluteus_medius_left",
+ 97: "gluteus_medius_right",
+ 98: "gluteus_minimus_left",
+ 99: "gluteus_minimus_right",
+ 100: "autochthon_left",
+ 101: "autochthon_right",
+ 102: "iliopsoas_left",
+ 103: "iliopsoas_right",
+ 104: "urinary_bladder",
+ }
+ label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "liver",
+ 6: "stomach",
+ 7: "aorta",
+ 8: "inferior vena cava",
+ 9: "portal vein and splenic vein",
+ 10: "pancreas",
+ 11: "right adrenal gland",
+ 12: "left adrenal gland",
+ 13: "left lung upper lobe",
+ 14: "left lung lower lobe",
+ 15: "right lung upper lobe",
+ 16: "right lung middle lobe",
+ 17: "right lung lower lobe",
+ 18: "vertebrae L5",
+ 19: "vertebrae L4",
+ 20: "vertebrae L3",
+ 21: "vertebrae L2",
+ 22: "vertebrae L1",
+ 23: "vertebrae T12",
+ 24: "vertebrae T11",
+ 25: "vertebrae T10",
+ 26: "vertebrae T9",
+ 27: "vertebrae T8",
+ 28: "vertebrae T7",
+ 29: "vertebrae T6",
+ 30: "vertebrae T5",
+ 31: "vertebrae T4",
+ 32: "vertebrae T3",
+ 33: "vertebrae T2",
+ 34: "vertebrae T1",
+ 35: "vertebrae C7",
+ 36: "vertebrae C6",
+ 37: "vertebrae C5",
+ 38: "vertebrae C4",
+ 39: "vertebrae C3",
+ 40: "vertebrae C2",
+ 41: "vertebrae C1",
+ 42: "esophagus",
+ 43: "trachea",
+ 44: "heart myocardium",
+ 45: "left heart atrium",
+ 46: "left heart ventricle",
+ 47: "right heart atrium",
+ 48: "right heart ventricle",
+ 49: "pulmonary artery",
+ 50: "brain",
+ 51: "left iliac artery",
+ 52: "right iliac artery",
+ 53: "left iliac vena",
+ 54: "right iliac vena",
+ 55: "small bowel",
+ 56: "duodenum",
+ 57: "colon",
+ 58: "left rib 1",
+ 59: "left rib 2",
+ 60: "left rib 3",
+ 61: "left rib 4",
+ 62: "left rib 5",
+ 63: "left rib 6",
+ 64: "left rib 7",
+ 65: "left rib 8",
+ 66: "left rib 9",
+ 67: "left rib 10",
+ 68: "left rib 11",
+ 69: "left rib 12",
+ 70: "right rib 1",
+ 71: "right rib 2",
+ 72: "right rib 3",
+ 73: "right rib 4",
+ 74: "right rib 5",
+ 75: "right rib 6",
+ 76: "right rib 7",
+ 77: "right rib 8",
+ 78: "right rib 9",
+ 79: "right rib 10",
+ 80: "right rib 11",
+ 81: "right rib 12",
+ 82: "left humerus",
+ 83: "right humerus",
+ 84: "left scapula",
+ 85: "right scapula",
+ 86: "left clavicula",
+ 87: "right clavicula",
+ 88: "left femur",
+ 89: "right femur",
+ 90: "left hip",
+ 91: "right hip",
+ 92: "sacrum",
+ 93: "face",
+ 94: "left gluteus maximus",
+ 95: "right gluteus maximus",
+ 96: "left gluteus medius",
+ 97: "right gluteus medius",
+ 98: "left gluteus minimus",
+ 99: "right gluteus minimus",
+ 100: "left autochthon",
+ 101: "right autochthon",
+ 102: "left iliopsoas",
+ 103: "right iliopsoas",
+ 104: "bladder",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_word():
+ base_url = "/data/WORD"
+ dataset_name = "WORD"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ masks += search_image_files(os.path.join(base_url, "labelsTs"), ".nii.gz")
+ masks += search_image_files(os.path.join(base_url, "labelsVal"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"word_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"word_{idx}.nii.gz"
+ for f in ["imagesTr", "imagesTs", "imagesVal"]:
+ if os.path.exists(os.path.join(base_url, f, img_name)):
+ images.append(os.path.join(f, img_name))
+ break
+ filtering_files(base_url, images, labels)
+ original_label_dict = {
+ 1: "liver",
+ 2: "spleen",
+ 3: "left_kidney",
+ 4: "right_kidney",
+ 5: "stomach",
+ 6: "gallbladder",
+ 7: "esophagus",
+ 8: "pancreas",
+ 9: "duodenum",
+ 10: "colon",
+ 11: "intestine",
+ 12: "adrenal",
+ 13: "rectum",
+ 14: "bladder",
+ 15: "Head_of_femur_L",
+ 16: "Head_of_femur_R",
+ }
+ label_dict = {
+ 1: "liver",
+ 2: "spleen",
+ 3: "left kidney",
+ 4: "right kidney",
+ 5: "stomach",
+ 6: "gallbladder",
+ 7: "esophagus",
+ 8: "pancreas",
+ 9: "duodenum",
+ 10: "colon",
+ 11: "intestine",
+ 12: "adrenal gland",
+ 13: "rectum",
+ 14: "bladder",
+ 15: "left head of femur",
+ 16: "right head of femur",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_task03():
+ base_url = "/data/Task03"
+ dataset_name = "Task03"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"liver_(\d+).nii.gz").search(rel_mask)[1]
+ img_name = f"liver_{idx}.nii.gz"
+ images.append(os.path.join("imagesTr", img_name))
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "liver", 2: "cancer"}
+ label_dict = {1: "liver", 2: "hepatic tumor"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_bone():
+ base_url = "/data/Bone-NIH"
+ dataset_name = "Bone-NIH"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "enriched_3-class.nii.gz")
+ full_ct = search_image_files(base_url, "CT.nii.gz")
+ for x in full_ct:
+ if x.endswith(os.path.join("anon_data", "BONE-017", "CT.nii.gz")):
+ full_ct.remove(x)
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ idx = re.compile(r"BONE-(\d+)").search(rel_mask)[1]
+ if idx in {"066"}:
+ continue
+ img_name = os.path.join(f"BONE-{idx}", "CT.nii.gz")
+ for x in full_ct:
+ if x.endswith(img_name):
+ images.append(os.path.relpath(x, base_url))
+ full_ct.remove(x)
+ break
+ labels.append(rel_mask)
+ filtering_files(base_url, images, labels)
+ if len(full_ct) > 1:
+ raise ValueError("Remaining items in the full ct set.")
+ original_label_dict = {1: "lesion 1", 2: "lesion 2"}
+ label_dict = {1: "bone lesion", 2: "bone lesion"} # merging 1 and 2
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_total_segmentator_v2():
+ base_url = "/data/TotalSegmentatorV2"
+ dataset_name = "TotalSegmentatorV2"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "seg.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ if "s1104" in rel_mask or "s0992" in rel_mask or "s0248" in rel_mask:
+ # using totalseg v2.0.0 list, although in v2.0.1 this has been fixed
+ continue
+ labels.append(rel_mask)
+ img_name = os.path.join(os.path.dirname(rel_mask), "ct.nii.gz")
+ images.append(img_name)
+ filtering_files(base_url, images, labels)
+ original_label_dict = {
+ 1: "spleen",
+ 2: "kidney_right",
+ 3: "kidney_left",
+ 4: "gallbladder",
+ 5: "liver",
+ 6: "stomach",
+ 7: "aorta",
+ 8: "inferior_vena_cava",
+ 9: "portal_vein_and_splenic_vein",
+ 10: "pancreas",
+ 11: "adrenal_gland_right",
+ 12: "adrenal_gland_left",
+ 13: "lung_upper_lobe_left",
+ 14: "lung_lower_lobe_left",
+ 15: "lung_upper_lobe_right",
+ 16: "lung_middle_lobe_right",
+ 17: "lung_lower_lobe_right",
+ 18: "vertebrae_L5",
+ 19: "vertebrae_L4",
+ 20: "vertebrae_L3",
+ 21: "vertebrae_L2",
+ 22: "vertebrae_L1",
+ 23: "vertebrae_T12",
+ 24: "vertebrae_T11",
+ 25: "vertebrae_T10",
+ 26: "vertebrae_T9",
+ 27: "vertebrae_T8",
+ 28: "vertebrae_T7",
+ 29: "vertebrae_T6",
+ 30: "vertebrae_T5",
+ 31: "vertebrae_T4",
+ 32: "vertebrae_T3",
+ 33: "vertebrae_T2",
+ 34: "vertebrae_T1",
+ 35: "vertebrae_C7",
+ 36: "vertebrae_C6",
+ 37: "vertebrae_C5",
+ 38: "vertebrae_C4",
+ 39: "vertebrae_C3",
+ 40: "vertebrae_C2",
+ 41: "vertebrae_C1",
+ 42: "esophagus",
+ 43: "trachea",
+ 44: "brain",
+ 45: "iliac_artery_left",
+ 46: "iliac_artery_right",
+ 47: "iliac_vena_left",
+ 48: "iliac_vena_right",
+ 49: "small_bowel",
+ 50: "duodenum",
+ 51: "colon",
+ 52: "rib_left_1",
+ 53: "rib_left_2",
+ 54: "rib_left_3",
+ 55: "rib_left_4",
+ 56: "rib_left_5",
+ 57: "rib_left_6",
+ 58: "rib_left_7",
+ 59: "rib_left_8",
+ 60: "rib_left_9",
+ 61: "rib_left_10",
+ 62: "rib_left_11",
+ 63: "rib_left_12",
+ 64: "rib_right_1",
+ 65: "rib_right_2",
+ 66: "rib_right_3",
+ 67: "rib_right_4",
+ 68: "rib_right_5",
+ 69: "rib_right_6",
+ 70: "rib_right_7",
+ 71: "rib_right_8",
+ 72: "rib_right_9",
+ 73: "rib_right_10",
+ 74: "rib_right_11",
+ 75: "rib_right_12",
+ 76: "humerus_left",
+ 77: "humerus_right",
+ 78: "scapula_left",
+ 79: "scapula_right",
+ 80: "clavicula_left",
+ 81: "clavicula_right",
+ 82: "femur_left",
+ 83: "femur_right",
+ 84: "hip_left",
+ 85: "hip_right",
+ 86: "sacrum",
+ 87: "gluteus_maximus_left",
+ 88: "gluteus_maximus_right",
+ 89: "gluteus_medius_left",
+ 90: "gluteus_medius_right",
+ 91: "gluteus_minimus_left",
+ 92: "gluteus_minimus_right",
+ 93: "autochthon_left",
+ 94: "autochthon_right",
+ 95: "iliopsoas_left",
+ 96: "iliopsoas_right",
+ 97: "urinary_bladder",
+ 98: "atrial_appendage_left",
+ 99: "brachiocephalic_trunk",
+ 100: "brachiocephalic_vein_left",
+ 101: "brachiocephalic_vein_right",
+ 102: "common_carotid_artery_left",
+ 103: "common_carotid_artery_right",
+ 104: "costal_cartilages",
+ 105: "heart",
+ 106: "kidney_cyst_left",
+ 107: "kidney_cyst_right",
+ 108: "prostate",
+ 109: "pulmonary_vein",
+ 110: "skull",
+ 111: "spinal_cord",
+ 112: "sternum",
+ 113: "subclavian_artery_left",
+ 114: "subclavian_artery_right",
+ 115: "superior_vena_cava",
+ 116: "thyroid_gland",
+ 117: "vertebrae_S1",
+ }
+ label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "liver",
+ 6: "stomach",
+ 7: "aorta",
+ 8: "inferior vena cava",
+ 9: "portal vein and splenic vein",
+ 10: "pancreas",
+ 11: "right adrenal gland",
+ 12: "left adrenal gland",
+ 13: "left lung upper lobe",
+ 14: "left lung lower lobe",
+ 15: "right lung upper lobe",
+ 16: "right lung middle lobe",
+ 17: "right lung lower lobe",
+ 18: "vertebrae L5",
+ 19: "vertebrae L4",
+ 20: "vertebrae L3",
+ 21: "vertebrae L2",
+ 22: "vertebrae L1",
+ 23: "vertebrae T12",
+ 24: "vertebrae T11",
+ 25: "vertebrae T10",
+ 26: "vertebrae T9",
+ 27: "vertebrae T8",
+ 28: "vertebrae T7",
+ 29: "vertebrae T6",
+ 30: "vertebrae T5",
+ 31: "vertebrae T4",
+ 32: "vertebrae T3",
+ 33: "vertebrae T2",
+ 34: "vertebrae T1",
+ 35: "vertebrae C7",
+ 36: "vertebrae C6",
+ 37: "vertebrae C5",
+ 38: "vertebrae C4",
+ 39: "vertebrae C3",
+ 40: "vertebrae C2",
+ 41: "vertebrae C1",
+ 42: "esophagus",
+ 43: "trachea",
+ 44: "brain",
+ 45: "left iliac artery",
+ 46: "right iliac artery",
+ 47: "left iliac vena",
+ 48: "right iliac vena",
+ 49: "small bowel",
+ 50: "duodenum",
+ 51: "colon",
+ 52: "left rib 1",
+ 53: "left rib 2",
+ 54: "left rib 3",
+ 55: "left rib 4",
+ 56: "left rib 5",
+ 57: "left rib 6",
+ 58: "left rib 7",
+ 59: "left rib 8",
+ 60: "left rib 9",
+ 61: "left rib 10",
+ 62: "left rib 11",
+ 63: "left rib 12",
+ 64: "right rib 1",
+ 65: "right rib 2",
+ 66: "right rib 3",
+ 67: "right rib 4",
+ 68: "right rib 5",
+ 69: "right rib 6",
+ 70: "right rib 7",
+ 71: "right rib 8",
+ 72: "right rib 9",
+ 73: "right rib 10",
+ 74: "right rib 11",
+ 75: "right rib 12",
+ 76: "left humerus",
+ 77: "right humerus",
+ 78: "left scapula",
+ 79: "right scapula",
+ 80: "left clavicula",
+ 81: "right clavicula",
+ 82: "left femur",
+ 83: "right femur",
+ 84: "left hip",
+ 85: "right hip",
+ 86: "sacrum",
+ 87: "left gluteus maximus",
+ 88: "right gluteus maximus",
+ 89: "left gluteus medius",
+ 90: "right gluteus medius",
+ 91: "left gluteus minimus",
+ 92: "right gluteus minimus",
+ 93: "left autochthon",
+ 94: "right autochthon",
+ 95: "left iliopsoas",
+ 96: "right iliopsoas",
+ 97: "bladder",
+ 98: "left atrial appendage",
+ 99: "brachiocephalic trunk",
+ 100: "left brachiocephalic vein",
+ 101: "right brachiocephalic vein",
+ 102: "left common carotid artery",
+ 103: "right common carotid artery",
+ 104: "costal cartilages",
+ 105: "heart",
+ 106: "left kidney cyst",
+ 107: "right kidney cyst",
+ 108: "prostate",
+ 109: "pulmonary vein",
+ 110: "skull",
+ 111: "spinal cord",
+ 112: "sternum",
+ 113: "left subclavian artery",
+ 114: "right subclavian artery",
+ 115: "superior vena cava",
+ 116: "thyroid gland",
+ 117: "vertebrae S1",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_c4kc_kits():
+ base_url = "/data/C4KC-KiTS/nifti"
+ dataset_name = "C4KC-KiTS"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "mask.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ img_folder = os.path.dirname(mask)
+ img_name = [
+ s
+ for s in sorted(glob(os.path.join(img_folder, "*.nii.gz")))
+ if not ("seg" in os.path.basename(s) or "mask" in os.path.basename(s))
+ ][0]
+ rel_img = os.path.relpath(img_name, base_url)
+ images.append(rel_img)
+ labels.append(rel_mask)
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "Kidney", 2: "Mass"}
+ label_dict = {1: "kidney", 2: "kidney mass"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_crlm_ct():
+ base_url = "/data/CRLM-CT/nifti"
+ dataset_name = "CRLM-CT"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "mask.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ img_folder = os.path.dirname(mask)
+ img_name = [
+ s
+ for s in sorted(glob(os.path.join(img_folder, "*.nii.gz")))
+ if not ("seg" in os.path.basename(s) or "mask" in os.path.basename(s))
+ ][0]
+ rel_img = os.path.relpath(img_name, base_url)
+ images.append(rel_img)
+ labels.append(rel_mask)
+ filtering_files(base_url, images, labels)
+ original_label_dict = {
+ 1: "Liver",
+ 2: "Liver Remnant",
+ 3: "Hepatic Vein",
+ 4: "Portal Vein",
+ 5: "Tumor",
+ }
+ label_dict = {
+ 3: "hepatic vessel",
+ 4: "portal vein and splenic vein",
+ 5: "liver tumor",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_verse():
+ base_url = "/data/VerSe/"
+ dataset_name = "VerSe"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "dataset-01training"), "_msk.nii.gz"
+ )
+ masks += search_image_files(
+ os.path.join(base_url, "dataset-02validation"), "_msk.nii.gz"
+ )
+ masks += search_image_files(os.path.join(base_url, "dataset-03test"), "_msk.nii.gz")
+ masks += search_image_files(
+ os.path.join(base_url, "dataset-verse19test"), "_msk.nii.gz"
+ )
+ masks += search_image_files(
+ os.path.join(base_url, "dataset-verse19training"), "_msk.nii.gz"
+ )
+ masks += search_image_files(
+ os.path.join(base_url, "dataset-verse19validation"), "_msk.nii.gz"
+ )
+ masks = sorted(masks)
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ img_name = f"{mask}".replace("derivatives", "rawdata").replace(
+ "_seg-vert_msk", "_ct"
+ )
+ rel_img = os.path.relpath(img_name, base_url)
+ images.append(rel_img)
+ labels.append(rel_mask)
+ filtering_files(base_url, images, labels)
+ label_dict = {
+ 1: "vertebrae C1",
+ 2: "vertebrae C2",
+ 3: "vertebrae C3",
+ 4: "vertebrae C4",
+ 5: "vertebrae C5",
+ 6: "vertebrae C6",
+ 7: "vertebrae C7",
+ 8: "vertebrae T1",
+ 9: "vertebrae T2",
+ 10: "vertebrae T3",
+ 11: "vertebrae T4",
+ 12: "vertebrae T5",
+ 13: "vertebrae T6",
+ 14: "vertebrae T7",
+ 15: "vertebrae T8",
+ 16: "vertebrae T9",
+ 17: "vertebrae T10",
+ 18: "vertebrae T11",
+ 19: "vertebrae T12",
+ 20: "vertebrae L1",
+ 21: "vertebrae L2",
+ 22: "vertebrae L3",
+ 23: "vertebrae L4",
+ 24: "vertebrae L5",
+ 25: "vertebrae L6",
+ }
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_aeropath():
+ base_url = "/data/AeroPath/"
+ dataset_name = "AeroPath"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "_label.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ img_folder = os.path.dirname(mask)
+ img_name = [
+ s
+ for s in sorted(glob(os.path.join(img_folder, "*.nii.gz")))
+ if "label" not in os.path.basename(s)
+ ][0]
+ rel_img = os.path.relpath(img_name, base_url)
+ images.append(rel_img)
+ labels.append(rel_mask)
+ filtering_files(base_url, images, labels)
+ original_label_dict = {1: "lung", 2: "airway"}
+ label_dict = {2: "airway"}
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_autopet23():
+ base_url = "/data/Autopet23/"
+ dataset_name = "Autopet23"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "SEG.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ img_folder = os.path.dirname(mask)
+ img_name = os.path.join(img_folder, "CTres.nii.gz")
+ rel_img = os.path.relpath(img_name, base_url)
+ images.append(rel_img)
+ labels.append(rel_mask)
+ filtering_files(base_url, images, labels)
+ label_dict = {1: "FDG-avid lesion"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_LIDC_IDRI():
+ base_url = "/data/LIDC-IDRI/"
+ dataset_name = "LIDC-IDRI"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "Mask"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ if "0985" in mask:
+ continue
+ rel_mask = os.path.relpath(mask, base_url)
+ rel_img = rel_mask.replace("Mask", "Image")
+ images.append(rel_img)
+ labels.append(rel_mask)
+ filtering_files(base_url, images, labels)
+ label_dict = {1: "lung nodule"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_ctpelvic1k_clinic():
+ base_url = "/data/CTPelvic1K-CLINIC"
+ dataset_name = "CTPelvic1K-CLINIC"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(base_url, "ipcai2021_dataset6_Anonymized"), ".nii.gz"
+ )
+ masks += search_image_files(
+ os.path.join(base_url, "CTPelvic1K_dataset7_mask"), ".nii.gz"
+ )
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ if "dataset7" in mask:
+ img = (
+ mask.replace("mask", "data")
+ .replace("CLINIC_metal", "dataset7_CLINIC_metal")
+ .replace("_4label", "")
+ )
+ if "dataset6" in mask:
+ img = (
+ mask.replace("ipcai2021", "CTPelvic1K")
+ .replace("_Anonymized", "_data")
+ .replace("mask_4label", "data")
+ )
+ images.append(img)
+ label_dict = {1: "sacrum", 2: "left hip", 3: "right hip", 4: "lumbar spine"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_colon_acrin6664():
+ base_url = "/data/COLON_ACRIN6664"
+ dataset_name = "COLON_ACRIN6664"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "mask"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img = "_".join(mask.split("_")[1:3]) + ".nii.gz"
+ img = os.path.join("nifti", img)
+ images.append(img)
+ label_dict = {1: "sacrum", 2: "left hip", 3: "right hip", 4: "lumbar spine"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_adrenal_ki67():
+ base_url = "/data/Adrenal_Ki67"
+ dataset_name = "Adrenal_Ki67"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "seg-1__fix.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_folder = os.path.join(base_url, os.path.dirname(mask))
+ img_name = [
+ s
+ for s in sorted(glob(os.path.join(img_folder, "*.nii.gz")))
+ if "seg" not in os.path.basename(s)
+ ][0]
+ img_name = os.path.relpath(img_name, base_url)
+ if "Ki67_Seg_049" in img_name:
+ img_name = os.path.join(
+ "Adrenal_Ki67_Seg_049-Adrenal_Ki67_Seg_049",
+ "3-CAP_W_O_5.0_I30f_3_3.nii.gz",
+ )
+ if "Ki67_Seg_053" in img_name:
+ img_name = os.path.join(
+ "Adrenal_Ki67_Seg_053-Adrenal_Ki67_Seg_053", "7-ABD_AX_3_PV_7.nii.gz"
+ )
+ images.append(img_name)
+ label_dict = {1: "adrenocortical tumor"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_hcc_tace():
+ base_url = "/data/HCC-TACE-Seg"
+ dataset_name = "HCC-TACE-Seg"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "seg__fix.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ if "089" in mask:
+ mask = mask.replace("seg__fix.nii.gz", "seg_ori__fix_2.nii.gz", 1)
+ labels.append(mask)
+ img_folder = os.path.join(base_url, os.path.dirname(mask))
+ img_name = [
+ s
+ for s in sorted(glob(os.path.join(img_folder, "*.nii.gz")))
+ if "seg" not in os.path.basename(s)
+ ][0]
+ img_name = os.path.relpath(img_name, base_url)
+ images.append(img_name)
+ label_dict = {2: "hepatic tumor"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_micro_ct_murine_native():
+ base_url = "/data/micro-ct-murine/1_nativeCTdata_nifti"
+ dataset_name = "micro-ct-murine-native"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "seg.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_name = mask.replace("seg.nii.gz", "CT140.nii.gz")
+ images.append(img_name)
+ label_dict = {1: "heart", 2: "spinal cord", 3: "right lung", 4: "left lung"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_micro_ct_murine_contrast():
+ base_url = "/data/micro-ct-murine/2_contrast-enhancedCTdata_nifti"
+ dataset_name = "micro-ct-murine-contrast"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "seg.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_name = mask.replace("seg.nii.gz", "CT140.nii.gz")
+ images.append(img_name)
+ label_dict = {1: "heart", 2: "spinal cord", 3: "right lung", 4: "left lung"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_segrap2023_task1():
+ base_url = "/data/segrap23/"
+ dataset_name = "segrap23-task1"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(
+ os.path.join(
+ base_url, "SegRap2023_Training_Set_120cases_OneHot_Labels", "Task001"
+ ),
+ ".nii.gz",
+ )
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ s_id = os.path.basename(mask)[: -len(".nii.gz")]
+ img_name = os.path.join(
+ "SegRap2023_Training_Set_120cases", f"{s_id}", "image.nii.gz"
+ )
+ images.append(img_name)
+ original_label_dict = {
+ "1": "Brain",
+ "2": "BrainStem",
+ "3": "Chiasm",
+ "4": "TemporalLobe_L",
+ "5": "TemporalLobe_R",
+ "6": "TemporalLobe_Hippocampus_OverLap_L",
+ "7": "TemporalLobe_Hippocampus_OverLap_R",
+ "8": "Hippocampus_L",
+ "9": "Hippocampus_R",
+ "10": "Eye_L",
+ "11": "Eye_R",
+ "12": "Lens_L",
+ "13": "Lens_R",
+ "14": "OpticNerve_L",
+ "15": "OpticNerve_R",
+ "16": "MiddleEar_L",
+ "17": "MiddleEar_R",
+ "18": "IAC_L",
+ "19": "IAC_R",
+ "20": "MiddleEar_TympanicCavity_OverLap_L",
+ "21": "MiddleEar_TympanicCavity_OverLap_R",
+ "22": "TympanicCavity_L",
+ "23": "TympanicCavity_R",
+ "24": "MiddleEar_VestibulSemi_OverLap_L",
+ "25": "MiddleEar_VestibulSemi_OverLap_R",
+ "26": "VestibulSemi_L",
+ "27": "VestibulSemi_R",
+ "28": "Cochlea_L",
+ "29": "Cochlea_R",
+ "30": "MiddleEar_ETbone_OverLap_L",
+ "31": "MiddleEar_ETbone_OverLap_R",
+ "32": "ETbone_L",
+ "33": "ETbone_R",
+ "34": "Pituitary",
+ "35": "OralCavity",
+ "36": "Mandible_L",
+ "37": "Mandible_R",
+ "38": "Submandibular_L",
+ "39": "Submandibular_R",
+ "40": "Parotid_L",
+ "41": "Parotid_R",
+ "42": "Mastoid_L",
+ "43": "Mastoid_R",
+ "44": "TMjoint_L",
+ "45": "TMjoint_R",
+ "46": "SpinalCord",
+ "47": "Esophagus",
+ "48": "Larynx",
+ "49": "Larynx_Glottic",
+ "50": "Larynx_Supraglot",
+ "51": "Larynx_PharynxConst_OverLap",
+ "52": "PharynxConst",
+ "53": "Thyroid",
+ "54": "Trachea",
+ }
+ label_dict = {
+ "1": "brain",
+ "2": "brain stem",
+ "3": "optic chiasm",
+ "4": "left temporal lobe",
+ "5": "right temporal lobe",
+ "6": "left temporal lobe hippocampus overlap",
+ "7": "right temporal lobe hippocampus overlap",
+ "8": "left hippocampus",
+ "9": "right hippocampus",
+ "10": "left eye",
+ "11": "right eye",
+ "12": "left lens",
+ "13": "right lens",
+ "14": "left optic nerve",
+ "15": "right optic nerve",
+ "16": "left middle ear",
+ "17": "right middle ear",
+ "18": "left internal auditory canal",
+ "19": "right internal auditory canal",
+ "20": "left middle ear tympanic cavity overlap",
+ "21": "right middle ear tympanic cavity overlap",
+ "22": "left tympanic cavity",
+ "23": "right tympanic cavity",
+ "24": "left middle ear vestibular semicircular canal overlap",
+ "25": "right middle ear vestibular semicircular canal overlap",
+ "26": "left vestibular semicircular canal",
+ "27": "right vestibular semicircular canal",
+ "28": "left cochlea",
+ "29": "right cochlea",
+ "30": "left middle ear eustachian tube bone overlap",
+ "31": "right middle ear eustachian tube bone overlap",
+ "32": "left eustachian tube bone",
+ "33": "right eustachian tube bone",
+ "34": "pituitary",
+ "35": "oral cavity",
+ "36": "left mandible",
+ "37": "right mandible",
+ "38": "left submandibular",
+ "39": "right submandibular",
+ "40": "left parotid",
+ "41": "right parotid",
+ "42": "left mastoid",
+ "43": "right mastoid",
+ "44": "left temporomandibular joint",
+ "45": "right temporomandibular joint",
+ "46": "spinal cord",
+ "47": "esophagus",
+ "48": "larynx",
+ "49": "larynx glottic",
+ "50": "larynx supraglottic",
+ "51": "larynx pharynx const overlap",
+ "52": "pharynx const",
+ "53": "thyroid",
+ "54": "trachea",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_pediatric_ct_seg():
+ base_url = "/data/Pediatric-CT-SEG/"
+ dataset_name = "Pediatric-CT-SEG"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(base_url, "seg.nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_name = mask.replace("seg.nii.gz", "image.nii.gz")
+ images.append(img_name)
+ original_label_dict = {
+ 1: "bladder",
+ 2: "rectum",
+ 3: "gonads",
+ 4: "prostate",
+ 5: "uterocervix",
+ 6: "femoral-head-lef",
+ 7: "femoral-head-rig",
+ 8: "small-intestine",
+ 9: "large-intestine",
+ 10: "spinal-canal",
+ 11: "gall-bladder",
+ 12: "kidney-left",
+ 13: "kidney-right",
+ 14: "spleen",
+ 15: "liver",
+ 16: "stomach",
+ 17: "pancreas",
+ 18: "duodenum",
+ 19: "adrenal-left",
+ 20: "adrenal-right",
+ 21: "heart",
+ 22: "esophagus",
+ 23: "lung_l",
+ 24: "lung_r",
+ 25: "breast-left",
+ 26: "breast-right",
+ 27: "thymus",
+ 28: "skin",
+ 29: "bones",
+ }
+
+ label_dict = {
+ 1: "bladder",
+ 2: "rectum",
+ 3: "gonads",
+ 4: "prostate",
+ 5: "uterocervix",
+ 6: "left femoral head",
+ 7: "right femoral head",
+ 8: "small intestine",
+ 9: "large intestine",
+ 10: "spinal canal",
+ 11: "gallbladder",
+ 12: "left kidney",
+ 13: "right kidney",
+ 14: "spleen",
+ 15: "liver",
+ 16: "stomach",
+ 17: "pancreas",
+ 18: "duodenum",
+ 19: "left adrenal",
+ 20: "right adrenal",
+ 21: "heart",
+ 22: "esophagus",
+ 23: "left lung",
+ 24: "right lung",
+ 25: "left breast",
+ 26: "right breast",
+ 27: "thymus",
+ 28: "skin",
+ 29: "bones",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+####
+@register_make
+def make_autopet_atlas():
+ base_url = "/data/"
+ dataset_name = "AutoPET-Atlas"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "AutoPET-Atlas"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ pat_id = mask.split("_")[1]
+ img_folder = os.path.join(
+ base_url, "Autopet23", "FDG-PET-CT-Lesions", f"PETCT_{pat_id}"
+ )
+ image_nii = search_image_files(img_folder, "CT.nii.gz")[0]
+ img_name = os.path.relpath(image_nii, base_url)
+ images.append(img_name)
+ label_dict = {
+ 2: "muscles",
+ 3: "fat",
+ 4: "abdominal tissue",
+ 5: "mediastinal tissue",
+ 6: "esophagus",
+ 7: "stomach",
+ 8: "small bowel",
+ 9: "duodenum",
+ 10: "colon",
+ 11: "rectum",
+ 12: "gallbladder",
+ 13: "liver",
+ 14: "pancreas",
+ 15: "left kidney",
+ 16: "right kidney",
+ 17: "bladder",
+ 18: "gonads",
+ 19: "prostate",
+ 20: "uterocervix",
+ 21: "uterus",
+ 22: "left breast",
+ 23: "right breast",
+ 24: "spinal canal",
+ 25: "brain",
+ 26: "spleen",
+ 27: "left adrenal gland",
+ 28: "right adrenal gland",
+ 29: "left thyroid",
+ 30: "right thyroid",
+ 31: "thymus",
+ 32: "left gluteus maximus",
+ 33: "right gluteus maximus",
+ 34: "left gluteus medius",
+ 35: "right gluteus medius",
+ 36: "left gluteus minimus",
+ 37: "right gluteus minimus",
+ 38: "left iliopsoas",
+ 39: "right iliopsoas",
+ 40: "left autochthon",
+ 41: "right autochthon",
+ 42: "skin",
+ 43: "vertebrae C1",
+ 44: "vertebrae C2",
+ 45: "vertebrae C3",
+ 46: "vertebrae C4",
+ 47: "vertebrae C5",
+ 48: "vertebrae C6",
+ 49: "vertebrae C7",
+ 50: "vertebrae T1",
+ 51: "vertebrae T2",
+ 52: "vertebrae T3",
+ 53: "vertebrae T4",
+ 54: "vertebrae T5",
+ 55: "vertebrae T6",
+ 56: "vertebrae T7",
+ 57: "vertebrae T8",
+ 58: "vertebrae T9",
+ 59: "vertebrae T10",
+ 60: "vertebrae T11",
+ 61: "vertebrae T12",
+ 62: "vertebrae L1",
+ 63: "vertebrae L2",
+ 64: "vertebrae L3",
+ 65: "vertebrae L4",
+ 66: "vertebrae L5",
+ 67: "left costa 1",
+ 68: "right costa 1",
+ 69: "left costa 2",
+ 70: "right costa 2",
+ 71: "left costa 3",
+ 72: "right costa 3",
+ 73: "left costa 4",
+ 74: "right costa 4",
+ 75: "left costa 5",
+ 76: "right costa 5",
+ 77: "left costa 6",
+ 78: "right costa 6",
+ 79: "left costa 7",
+ 80: "right costa 7",
+ 81: "left costa 8",
+ 82: "right costa 8",
+ 83: "left costa 9",
+ 84: "right costa 9",
+ 85: "left costa 10",
+ 86: "right costa 10",
+ 87: "left costa 11",
+ 88: "right costa 11",
+ 89: "left costa 12",
+ 90: "right costa 12",
+ 91: "rib_cartilage",
+ 92: "sternum",
+ 93: "left clavicle",
+ 94: "right clavicle",
+ 95: "left scapula",
+ 96: "right scapula",
+ 97: "left humerus",
+ 98: "right humerus",
+ 99: "skull",
+ 100: "left hip",
+ 101: "right hip",
+ 102: "sacrum",
+ 103: "left femur",
+ 104: "right femur",
+ 105: "heart ",
+ 106: "left heart atrium",
+ 107: "heart tissue",
+ 108: "right heart atrium",
+ 109: "heart myocardium",
+ 110: "left heart ventricle",
+ 111: "right heart ventricle",
+ 112: "left iliac artery",
+ 113: "right iliac artery",
+ 114: "aorta",
+ 115: "left iliac vena",
+ 116: "right iliac vena",
+ 117: "inferior vena cava",
+ 118: "portal vein and splenic vein",
+ 119: "celiac trunk",
+ 120: "left lung lower lobe",
+ 121: "left lung upper lobe",
+ 122: "right lung lower lobe",
+ 123: "right lung middle lobe",
+ 124: "right lung upper lobe",
+ 125: "bronchie",
+ 126: "trachea",
+ 127: "pulmonary artery",
+ 128: "left cheek",
+ 129: "right cheek",
+ 130: "left eyeball",
+ 131: "right eyeball",
+ }
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_uls23():
+ base_url = "/data/ULS23"
+ dataset_name = "ULS23_DeepLesion"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ # data/ULS23/ULS23_annotations/processed_data/partially_annotated/DeepLesion/labels_grabcut
+ seg_folder = [
+ base_url,
+ "ULS23_annotations",
+ "processed_data",
+ "partially_annotated",
+ "DeepLesion",
+ "labels_grabcut",
+ ]
+ masks = search_image_files(os.path.join(*seg_folder), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_name = mask.replace("ULS23_annotations", "ULS23").replace(
+ "labels_grabcut", "images"
+ )
+ images.append(img_name)
+ label_dict = {1: "lesion"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_uls23_deeplesion3d():
+ base_url = "/data/ULS23"
+ dataset_name = "ULS23_DeepLesion3D"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ # /data/ULS23/ULS23_annotations/novel_data/ULS23_DeepLesion3D/labels
+ seg_folder = [
+ base_url,
+ "ULS23_annotations",
+ "novel_data",
+ "ULS23_DeepLesion3D",
+ "labels",
+ ]
+ masks = search_image_files(os.path.join(*seg_folder), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_name = mask.replace("ULS23_annotations", "ULS23").replace(
+ "labels", "images"
+ )
+ images.append(img_name)
+ label_dict = {1: "lesion"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_uls23_bone():
+ base_url = "/data/ULS23"
+ dataset_name = "ULS23_Bone"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ # /data/ULS23/ULS23_annotations/novel_data/ULS23_Radboudumc_Bone/labels
+ seg_folder = [
+ base_url,
+ "ULS23_annotations",
+ "novel_data",
+ "ULS23_Radboudumc_Bone",
+ "labels",
+ ]
+ masks = search_image_files(os.path.join(*seg_folder), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_name = mask.replace("ULS23_annotations", "ULS23").replace(
+ "labels", "images"
+ )
+ images.append(img_name)
+ label_dict = {1: "lesion"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_uls23_pancreas():
+ base_url = "/data/ULS23"
+ dataset_name = "ULS23_Pancreas"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ # /data/ULS23/ULS23_annotations/novel_data/ULS23_Radboudumc_Pancreas/labels
+ seg_folder = [
+ base_url,
+ "ULS23_annotations",
+ "novel_data",
+ "ULS23_Radboudumc_Pancreas",
+ "labels",
+ ]
+ masks = search_image_files(os.path.join(*seg_folder), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ mask = os.path.relpath(mask, base_url)
+ labels.append(mask)
+ img_name = mask.replace("ULS23_annotations", "ULS23").replace(
+ "labels", "images"
+ )
+ images.append(img_name)
+ label_dict = {1: "lesion"}
+ create_splits_and_write_json(
+ images, labels, test_ratio, n_folds, json_name, seed, label_dict
+ )
+
+
+####
+@register_make
+def make_mr_amos22():
+ base_url = "/data/AMOS22"
+ dataset_name = "MR_AMOS22"
+ json_name = os.path.join(output_json_dir, f"{dataset_name}_{n_folds}_folds.json")
+ masks = search_image_files(os.path.join(base_url, "labelsTr"), ".nii.gz")
+ masks += search_image_files(os.path.join(base_url, "labelsVa"), ".nii.gz")
+ images, labels = [], []
+ for mask in masks:
+ rel_mask = os.path.relpath(mask, base_url)
+ labels.append(rel_mask)
+ idx = re.compile(r"amos_(\d+).nii.gz").search(rel_mask)[1]
+ if int(idx) < 500: # skip the CT cases
+ labels.pop()
+ continue
+ img_name = f"amos_{idx}.nii.gz"
+ for f in ["imagesTr", "imagesVa"]:
+ if os.path.exists(os.path.join(base_url, f, img_name)):
+ images.append(os.path.join(f, img_name))
+ break
+ # print(f"image: {images[-1]}, label: {labels[-1]}")
+ filtering_files(base_url, images, labels)
+ original_label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "esophagus",
+ 6: "liver",
+ 7: "stomach",
+ 8: "aorta",
+ 9: "postcava",
+ 10: "pancreas",
+ 11: "right adrenal gland",
+ 12: "left adrenal gland",
+ 13: "duodenum",
+ 14: "bladder",
+ 15: "prostate or uterus",
+ }
+ label_dict = {
+ 1: "spleen",
+ 2: "right kidney",
+ 3: "left kidney",
+ 4: "gallbladder",
+ 5: "esophagus",
+ 6: "liver",
+ 7: "stomach",
+ 8: "aorta",
+ 9: "inferior vena cava",
+ 10: "pancreas",
+ 11: "right adrenal gland",
+ 12: "left adrenal gland",
+ 13: "duodenum",
+ 14: "bladder",
+ 15: "prostate or uterus",
+ }
+ create_splits_and_write_json(
+ images,
+ labels,
+ test_ratio,
+ n_folds,
+ json_name,
+ seed,
+ label_dict,
+ original_label_dict,
+ )
+
+
+if __name__ == "__main__":
+ pprint(_make_funcs)
+ for func_name, f in _make_funcs.items():
+ print(f"running {func_name}")
+ f()
diff --git a/monailabel/monaivista/lib/__init__.py b/vista3d/scripts/__init__.py
similarity index 100%
rename from monailabel/monaivista/lib/__init__.py
rename to vista3d/scripts/__init__.py
diff --git a/vista3d/scripts/debugger.py b/vista3d/scripts/debugger.py
new file mode 100644
index 0000000..b2568f4
--- /dev/null
+++ b/vista3d/scripts/debugger.py
@@ -0,0 +1,240 @@
+import copy
+from tkinter import Tk, filedialog, messagebox
+
+import fire
+import matplotlib.pyplot as plt
+import nibabel as nib
+import numpy as np
+from matplotlib.widgets import Button, TextBox
+
+from .infer import InferClass
+from .utils.workflow_utils import get_point_label
+
+inferer = InferClass(config_file=["./configs/infer.yaml"])
+
+
+class samm_visualizer:
+ def __init__(self):
+ self.clicked_points = []
+ self.data = None
+ self.mask_plot = None
+ self.mask = None
+ self.data_path = None
+ self.circle_artists = []
+ self.class_label = None
+
+ def select_data_file(self):
+ root = Tk()
+ root.withdraw()
+ file_path = filedialog.askopenfilename(
+ title="Select Data File", initialfile=self.data_path
+ )
+ if not file_path:
+ print("No file selected.")
+ exit()
+ # Load data from NIfTI file
+ try:
+ nifti_img = nib.load(file_path)
+ data = nifti_img.get_fdata()
+ if len(data.shape) == 4:
+ data = data[..., 0] # Extract last element along the 4th dimension
+ except FileNotFoundError:
+ print("File not found.")
+ exit()
+ except nib.filebasedimages.ImageFileError:
+ print("Invalid NIfTI file.")
+ exit()
+ self.data = data
+ self.data_path = file_path
+
+ def generate_mask(self):
+ point = []
+ point_label = []
+ self.class_label = self.text_box.text
+ if len(self.class_label) == 0:
+ messagebox.showwarning(
+ "Warning",
+ "Label prompt is not specified. Assuming the point is for supported class. \
+ For zero-shot, input random number > 132",
+ )
+ label_prompt = None
+ prompt_class = None
+ neg_id, pos_id = get_point_label(1)
+ else:
+ if self.class_label in [2, 20, 21]:
+ messagebox.showwarning(
+ "Warning",
+ "Current debugger skip kidney (2), lung (20), and bone (21). Use their subclasses.",
+ )
+ return
+ label_prompt = int(self.class_label)
+ neg_id, pos_id = get_point_label(label_prompt)
+ label_prompt = np.array([label_prompt])[np.newaxis, ...]
+ prompt_class = copy.deepcopy(label_prompt)
+ # if zero-shot
+ if label_prompt is not None and label_prompt[0] > 132:
+ label_prompt = None
+ for p in self.clicked_points:
+ point.append([p[1], p[0], p[2]])
+ point_label.append(pos_id if p[3] == 1 else neg_id)
+ if len(point) == 0:
+ point = None
+ point_label = None
+ else:
+ point = np.array(point)[np.newaxis, ...]
+ point_label = np.array(point_label)[np.newaxis, ...]
+ mask = inferer.infer(
+ {"image": self.data_path},
+ point,
+ point_label,
+ label_prompt,
+ prompt_class,
+ save_mask=True,
+ point_start=self.point_start,
+ )[0]
+ nan_mask = np.isnan(mask)
+ mask = mask.data.cpu().numpy() > 0.5
+ mask = mask.astype(np.float32)
+ mask[mask == 0] = np.nan
+ if self.mask is None:
+ self.mask = mask
+ else:
+ self.mask[~nan_mask] = mask[~nan_mask]
+
+ def display_3d_slices(self):
+ fig, ax = plt.subplots()
+ assert self.data is not None, "Load data first."
+ ax.volume = self.data
+ ax.index = self.data.shape[2] // 2
+ ax.imshow(self.data[:, :, ax.index], cmap="gray")
+ ax.set_title(f"Slice {ax.index}")
+ self.update_slice(ax)
+ fig.canvas.mpl_connect("scroll_event", self.process_scroll)
+ fig.canvas.mpl_connect("button_press_event", self.process_click)
+ # Add numerical input box for slice index
+ text_ax = plt.axes([0.45, 0.01, 0.2, 0.05]) # Position of the text box
+ self.text_box = TextBox(text_ax, "Class prompt", initial=self.class_label)
+ # Add a button
+ button_ax = plt.axes([0.05, 0.01, 0.2, 0.05]) # Position of the button
+ button = Button(button_ax, "Run")
+
+ def on_button_click(event, ax=ax):
+ # Define what happens when the button is clicked
+ print("-- segmenting ---")
+ self.generate_mask()
+ print("-- done ---")
+ print(
+ "-- Note: Point only prompts will only do 128 cubic segmentation, a cropping artefact will be observed. ---"
+ )
+ print(
+ "-- Note: Point without class will be treated as supported class, which has worse zero-shot ability. Try class > 132 to perform better zeroshot. ---"
+ )
+ print("-- Note: CTRL + Right Click will be adding negative points. ---")
+ print(
+ "-- Note: Click points on different foreground class will cause segmentation conflicts. Clear first. ---"
+ )
+ print(
+ "-- Note: Click points not matching class prompts will also cause confusion. ---"
+ )
+
+ self.update_slice(ax)
+ # self.point_start = len(self.clicked_points)
+
+ button.on_clicked(on_button_click)
+
+ button_ax_clear = plt.axes([0.75, 0.01, 0.2, 0.05]) # Position of the button
+ button_clear = Button(button_ax_clear, "Clear")
+
+ def on_button_click_clear(event, ax=ax):
+ # Define what happens when the button is clicked
+ inferer.clear_cache()
+ # clear points
+ self.clicked_points = []
+ self.point_start = 0
+ self.mask = None
+ self.mask_plot.remove()
+ self.mask_plot = None
+ self.update_slice(ax)
+
+ button_clear.on_clicked(on_button_click_clear)
+
+ plt.show()
+
+ def process_scroll(self, event):
+ ax = event.inaxes
+ try:
+ if event.button == "up":
+ self.previous_slice(ax)
+ elif event.button == "down":
+ self.next_slice(ax)
+ except BaseException:
+ pass
+
+ def previous_slice(self, ax):
+ if ax is None:
+ return
+ ax.index = (ax.index - 1) % ax.volume.shape[2]
+ self.update_slice(ax)
+
+ def next_slice(self, ax):
+ if ax is None:
+ return
+ ax.index = (ax.index + 1) % ax.volume.shape[2]
+ self.update_slice(ax)
+
+ def update_slice(self, ax):
+ # remove circles
+ while len(self.circle_artists) > 0:
+ ca = self.circle_artists.pop()
+ ca.remove()
+ # plot circles
+ for x, y, z, label in self.clicked_points:
+ if z == ax.index:
+ color = "red" if (label == 1 or label == 3) else "blue"
+ circle_artist = plt.Circle((x, y), 1, color=color, fill=False)
+ self.circle_artists.append(circle_artist)
+ ax.add_artist(circle_artist)
+ ax.images[0].set_array(ax.volume[:, :, ax.index])
+ if self.mask is not None and self.mask_plot is None:
+ self.mask_plot = ax.imshow(
+ np.zeros_like(self.mask[:, :, ax.index]) * np.nan,
+ cmap="viridis",
+ alpha=0.5,
+ )
+ if self.mask is not None and self.mask_plot is not None:
+ self.mask_plot.set_data(self.mask[:, :, ax.index])
+ self.mask_plot.set_visible(True)
+ ax.set_title(f"Slice {ax.index}")
+ ax.figure.canvas.draw()
+
+ def process_click(self, event):
+ try:
+ ax = event.inaxes
+ if ax is not None:
+ x = int(event.xdata)
+ y = int(event.ydata)
+ z = ax.index
+ print(f"Clicked coordinates: x={x}, y={y}, z={z}")
+ if event.key == "control":
+ point_label = 0
+ else:
+ point_label = 1
+ self.clicked_points.append((x, y, z, point_label))
+ self.update_slice(ax)
+ except BaseException:
+ pass
+
+ def run(self):
+ # File selection
+ self.select_data_file()
+ inferer.clear_cache()
+ self.point_start = 0
+ self.display_3d_slices()
+
+
+if __name__ == "__main__":
+ from monai.utils import optional_import
+
+ fire, _ = optional_import("fire")
+ # using python -m interactive run
+ fire.Fire(samm_visualizer)
diff --git a/vista3d/scripts/infer.py b/vista3d/scripts/infer.py
new file mode 100644
index 0000000..924a5ab
--- /dev/null
+++ b/vista3d/scripts/infer.py
@@ -0,0 +1,328 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import sys
+from functools import partial
+
+import monai
+import numpy as np
+import torch
+import torch.distributed as dist
+from monai import transforms
+from monai.apps.auto3dseg.auto_runner import logger
+from monai.auto3dseg.utils import datafold_read
+from monai.bundle import ConfigParser
+from monai.bundle.scripts import _pop_args, _update_args
+from monai.data import decollate_batch, list_data_collate, partition_dataset
+from monai.utils import optional_import
+
+from vista3d import vista_model_registry
+
+from .sliding_window import point_based_window_inferer, sliding_window_inference
+from .train import CONFIG
+from .utils.trans_utils import VistaPostTransform, get_largest_connected_component_point
+
+rearrange, _ = optional_import("einops", name="rearrange")
+sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
+IGNORE_PROMPT = set(
+ [
+ 2, # kidney
+ 16, # prostate or uterus
+ 18, # rectum
+ 20, # lung
+ 21, # bone
+ 23, # lung tumor
+ 24, # pancreatic tumor
+ 25, # hepatic vessel
+ 26, # hepatic tumor
+ 27, # colon cancer primaries
+ 128, # bone lesion
+ 129, # kidney mass
+ 130, # liver tumor
+ 131, # vertebrae L6
+ 132,
+ ]
+) # airway
+EVERYTHING_PROMPT = list(set([i + 1 for i in range(133)]) - IGNORE_PROMPT)
+
+
+def infer_wrapper(inputs, model, **kwargs):
+ outputs = model(input_images=inputs, **kwargs)
+ return outputs.transpose(1, 0)
+
+
+class InferClass:
+ def __init__(self, config_file="./configs/infer.yaml", **override):
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+ _args = _update_args(config_file=config_file, **override)
+ config_file_ = _pop_args(_args, "config_file")[0]
+
+ parser = ConfigParser()
+ parser.read_config(config_file_)
+ parser.update(pairs=_args)
+
+ self.amp = parser.get_parsed_content("amp")
+ input_channels = parser.get_parsed_content("input_channels")
+ patch_size = parser.get_parsed_content("patch_size")
+ self.patch_size = patch_size
+
+ ckpt_name = parser.get_parsed_content("infer")["ckpt_name"]
+ output_path = parser.get_parsed_content("infer")["output_path"]
+ if not os.path.exists(output_path):
+ os.makedirs(output_path, exist_ok=True)
+
+ CONFIG["handlers"]["file"]["filename"] = parser.get_parsed_content("infer")[
+ "log_output_file"
+ ]
+ logging.config.dictConfig(CONFIG)
+ self.infer_transforms = parser.get_parsed_content("transforms_infer")
+
+ self.device = torch.device("cuda:0")
+ model_registry = parser.get_parsed_content("model")
+ model = vista_model_registry[model_registry](
+ in_channels=input_channels, image_size=patch_size
+ )
+ self.model = model.to(self.device)
+
+ pretrained_ckpt = torch.load(ckpt_name, map_location=self.device)
+ self.model.load_state_dict(pretrained_ckpt, strict=False)
+ logger.debug(f"[debug] checkpoint {ckpt_name:s} loaded")
+ post_transforms = [
+ VistaPostTransform(keys="pred"),
+ transforms.Invertd(
+ keys="pred",
+ transform=self.infer_transforms,
+ orig_keys="image",
+ meta_keys="pred_meta_dict",
+ orig_meta_keys="image_meta_dict",
+ meta_key_postfix="meta_dict",
+ nearest_interp=True,
+ to_tensor=True,
+ ),
+ ]
+
+ # For Vista3d, sigmoid is always used, but for visualization, argmax is needed
+ save_transforms = [
+ transforms.SaveImaged(
+ keys="pred",
+ meta_keys="pred_meta_dict",
+ output_dir=output_path,
+ output_postfix="seg",
+ resample=False,
+ data_root_dir=None,
+ print_log=False,
+ )
+ ]
+ self.post_transforms = transforms.Compose(post_transforms)
+ self.save_transforms = transforms.Compose(save_transforms)
+ self.prev_mask = None
+ self.batch_data = None
+ return
+
+ def clear_cache(self):
+ self.prev_mask = None
+ self.batch_data = None
+
+ def transform_points(self, point, affine):
+ """transform point to the coordinates of the transformed image
+ point: numpy array [bs, N, 3]
+ """
+ bs, N = point.shape[:2]
+ point = np.concatenate((point, np.ones((bs, N, 1))), axis=-1)
+ point = rearrange(point, "b n d -> d (b n)")
+ point = affine @ point
+ point = rearrange(point, "d (b n)-> b n d", b=bs)[:, :, :3]
+ return point
+
+ @torch.no_grad()
+ def infer(
+ self,
+ image_file,
+ point=None,
+ point_label=None,
+ label_prompt=None,
+ prompt_class=None,
+ save_mask=False,
+ point_start=0,
+ ):
+ """Infer a single image_file. If save_mask is true, save the argmax prediction to disk. If false,
+ do not save and return the probability maps (usually used by autorunner emsembler). point_start is
+ used together with prev_mask. If prev_mask is generated by N points, point_start should be N+1 to save
+ time and avoid repeated inference. This is by default disabled.
+ """
+ self.model.eval()
+ if not isinstance(image_file, dict):
+ image_file = {"image": image_file}
+ if self.batch_data is not None:
+ batch_data = self.batch_data
+ else:
+ batch_data = self.infer_transforms(image_file)
+ if label_prompt is not None:
+ batch_data["label_prompt"] = label_prompt
+ batch_data = list_data_collate([batch_data])
+ self.batch_data = batch_data
+ if point is not None:
+ if type(point) is list:
+ point = np.array(point)[np.newaxis, ...]
+ point_label = np.array(point_label)[np.newaxis, ...]
+ point = self.transform_points(
+ point,
+ np.linalg.inv(batch_data["image"].affine[0])
+ @ batch_data["image"].meta["original_affine"][0].numpy(),
+ )
+ self.sliding_window_inferer = partial(
+ point_based_window_inferer, point_start=point_start
+ )
+ else:
+ self.sliding_window_inferer = sliding_window_inference
+ device_list_input = [self.device, self.device, "cpu"]
+ device_list_output = [self.device, "cpu", "cpu"]
+ for _device_in, _device_out in zip(device_list_input, device_list_output):
+ try:
+ with torch.cuda.amp.autocast(enabled=self.amp):
+ batch_data["pred"] = self.sliding_window_inferer(
+ inputs=batch_data["image"].to(_device_in),
+ roi_size=self.patch_size,
+ sw_batch_size=1,
+ predictor=partial(infer_wrapper, model=self.model),
+ mode="gaussian",
+ overlap=0.625,
+ progress=True,
+ sw_device=self.device,
+ device=_device_out,
+ point_coords=(
+ torch.tensor(point).to(_device_in)
+ if point is not None
+ else None
+ ),
+ point_labels=(
+ torch.tensor(point_label).to(_device_in)
+ if point_label is not None
+ else None
+ ),
+ class_vector=(
+ torch.tensor(label_prompt).to(_device_in)
+ if label_prompt is not None
+ else None
+ ),
+ prompt_class=(
+ torch.tensor(prompt_class).to(_device_in)
+ if prompt_class is not None
+ else None
+ ),
+ prev_mask=(
+ torch.tensor(self.prev_mask).to(_device_in)
+ if self.prev_mask is not None
+ else None
+ ),
+ )
+
+ if not hasattr(batch_data["pred"], "meta"):
+ batch_data["pred"] = monai.data.MetaTensor(
+ batch_data["pred"],
+ affine=batch_data["image"].meta["affine"],
+ meta=batch_data["image"].meta,
+ )
+ self.prev_mask = batch_data["pred"]
+ if label_prompt is None and point is not None:
+ batch_data["pred"] = get_largest_connected_component_point(
+ batch_data["pred"], point_coords=point, point_labels=point_label
+ )
+ batch_data["image"] = batch_data["image"].to("cpu")
+ batch_data["pred"] = batch_data["pred"].to("cpu")
+ torch.cuda.empty_cache()
+ batch_data = [
+ self.post_transforms(i) for i in decollate_batch(batch_data)
+ ]
+ if save_mask:
+ batch_data = [self.save_transforms(i) for i in batch_data]
+
+ finished = True
+ except RuntimeError as e:
+ if not any(x in str(e).lower() for x in ("memory", "cuda", "cudnn")):
+ raise e
+ finished = False
+ if finished:
+ break
+ if not finished:
+ raise RuntimeError("Infer not finished due to OOM.")
+ return batch_data[0]["pred"]
+
+ @torch.no_grad()
+ def infer_everything(self, image_file, label_prompt=EVERYTHING_PROMPT, rank=0):
+ self.model.eval()
+ device = f"cuda:{rank}"
+ if not isinstance(image_file, dict):
+ image_file = {"image": image_file}
+ batch_data = self.infer_transforms(image_file)
+ batch_data["label_prompt"] = label_prompt
+ batch_data = list_data_collate([batch_data])
+ device_list_input = [device, device, "cpu"]
+ device_list_output = [device, "cpu", "cpu"]
+ for _device_in, _device_out in zip(device_list_input, device_list_output):
+ try:
+ with torch.cuda.amp.autocast(enabled=self.amp):
+ batch_data["pred"] = sliding_window_inference(
+ inputs=batch_data["image"].to(_device_in),
+ roi_size=self.patch_size,
+ sw_batch_size=1,
+ predictor=partial(infer_wrapper, model=self.model),
+ mode="gaussian",
+ overlap=0.625,
+ sw_device=device,
+ device=_device_out,
+ class_vector=torch.tensor(label_prompt).to(_device_in),
+ )
+ if not hasattr(batch_data["pred"], "meta"):
+ batch_data["pred"] = monai.data.MetaTensor(
+ batch_data["pred"],
+ affine=batch_data["image"].meta["affine"],
+ meta=batch_data["image"].meta,
+ )
+ torch.cuda.empty_cache()
+ batch_data = [
+ self.post_transforms(i) for i in decollate_batch(batch_data)
+ ]
+ batch_data = [self.save_transforms(i) for i in batch_data]
+ finished = True
+ except RuntimeError as e:
+ if not any(x in str(e).lower() for x in ("memory", "cuda", "cudnn")):
+ raise e
+ finished = False
+ if finished:
+ break
+ if not finished:
+ raise RuntimeError("Infer not finished due to OOM.")
+
+ @torch.no_grad()
+ def batch_infer_everything(self, datalist=str, basedir=str):
+ train_files, _ = datafold_read(datalist=datalist, basedir=basedir, fold=0)
+ train_files = [_["image"] for _ in train_files]
+ dist.init_process_group(backend="nccl", init_method="env://")
+ world_size = dist.get_world_size()
+ rank = dist.get_rank()
+ # no need to wrap model with DistributedDataParallel
+ self.model = self.model.to(f"cuda:{rank}")
+ infer_files = partition_dataset(
+ data=train_files,
+ shuffle=False,
+ num_partitions=world_size,
+ even_divisible=False,
+ )[rank]
+ self.infer(infer_files, label_prompt=EVERYTHING_PROMPT, rank=rank)
+
+
+if __name__ == "__main__":
+ fire, _ = optional_import("fire")
+ fire.Fire(InferClass)
diff --git a/vista3d/scripts/slic_process_sam.py b/vista3d/scripts/slic_process_sam.py
new file mode 100644
index 0000000..c73b69c
--- /dev/null
+++ b/vista3d/scripts/slic_process_sam.py
@@ -0,0 +1,219 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import sys
+import time
+
+import monai
+import numpy as np
+import torch
+import torch.distributed as dist
+from monai import transforms
+from monai.auto3dseg.utils import datafold_read
+from monai.data import partition_dataset
+from monai.utils import optional_import
+from segment_anything import SamPredictor, sam_model_registry
+from skimage.segmentation import slic
+from tqdm import tqdm
+
+from .train import CONFIG
+from .utils.trans_utils import dilate3d, erode3d
+
+rearrange, _ = optional_import("einops", name="rearrange")
+sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
+
+
+def pad_to_divisible_by_16(image):
+ # Get the dimensions of the input image
+ depth, height, width = image.shape[-3:]
+
+ # Calculate the padding required to make the dimensions divisible by 16
+ pad_depth = (16 - (depth % 16)) % 16
+ pad_height = (16 - (height % 16)) % 16
+ pad_width = (16 - (width % 16)) % 16
+
+ # Create a tuple with the padding values for each dimension
+ padding = (0, pad_width, 0, pad_height, 0, pad_depth)
+
+ # Pad the image
+ padded_image = torch.nn.functional.pad(image, padding)
+
+ return padded_image, padding
+
+
+class InferClass:
+ def __init__(self):
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+ output_path = "./supervoxel_sam"
+ if not os.path.exists(output_path):
+ os.makedirs(output_path, exist_ok=True)
+ self.amp = True
+ CONFIG["handlers"]["file"]["filename"] = f"{output_path}/log.log"
+ logging.config.dictConfig(CONFIG)
+ self.device = torch.device("cuda:0")
+ self.sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth").to(
+ self.device
+ )
+ self.model = SamPredictor(self.sam)
+ return
+
+ @torch.no_grad()
+ @torch.cuda.amp.autocast()
+ def infer(
+ self,
+ image_file="example/s1238.nii.gz",
+ rank=0,
+ output_dir="./supervoxel_sam/",
+ data_root_dir=None,
+ n_segments=400,
+ ):
+ """Infer a single image_file. If save_mask is true, save the argmax prediction to disk. If false,
+ do not save and return the probability maps (usually used by autorunner emsembler).
+ """
+ pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(1, 3, 1, 1)
+ pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(1, 3, 1, 1)
+ if not isinstance(image_file, list):
+ image_file = [image_file]
+
+ permute_pairs = [
+ [(2, 0, 1), None],
+ [(1, 0, 2), (0, 1, 3, 2)],
+ [(0, 1, 2), (0, 3, 1, 2)],
+ ]
+ for file in image_file:
+ if data_root_dir is not None:
+ savefolder = os.path.join(
+ output_dir,
+ file.replace(data_root_dir, "").split("/")[0],
+ file.replace(data_root_dir, "")
+ .split("/")[1]
+ .replace(".nii.gz", ""),
+ )
+ else:
+ savefolder = os.path.join(
+ output_dir, file.split("/")[-1].replace(".nii.gz", "")
+ )
+ if os.path.isdir(savefolder):
+ print(f"{file} already exist. Skipped")
+ continue
+ try:
+ batch_data = None
+ batch_data = transforms.LoadImage(image_only=True)(file)
+ orig_data = batch_data.clone()
+ batch_data = transforms.ScaleIntensityRange(
+ a_max=1000, a_min=-1000, b_max=255, b_min=0, clip=True
+ )(batch_data)
+ print(f"[{rank}] working on {file}")
+ outputs = None
+ torch.cuda.empty_cache()
+ features_ = 0
+ for views in permute_pairs:
+ data = batch_data.permute(*views[0])
+ features = []
+ max_slice = 8
+ for i in tqdm(range(int(np.ceil(data.shape[0] / max_slice)))):
+ idx = (i * max_slice, min((i + 1) * max_slice, data.shape[0]))
+ image = data[idx[0] : idx[1]]
+ d, h, w = image.shape
+ pad_h = 0 if h > w else w - h
+ pad_w = 0 if w > h else h - w
+ image = torch.nn.functional.pad(
+ image, (0, pad_w, 0, pad_h, 0, 0)
+ )
+ image = monai.transforms.Resize(
+ [d, 1024, 1024], mode="bilinear"
+ )(image.unsqueeze(0)).squeeze(0)
+ image = (
+ torch.stack([image, image, image], -1)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+ image = (image - pixel_mean) / pixel_std
+ feature = self.model.get_feature_upsampled(
+ image.to(f"cuda:{rank}")
+ )
+ feature = monai.transforms.Resize(
+ [h + pad_h, w + pad_w, d], mode="bilinear"
+ )(feature.permute(1, 2, 3, 0))[:, :h, :w]
+ features.append(feature.cpu())
+ features = torch.cat(features, -1)
+ if views[1] is not None:
+ features = features.permute(*views[1])
+ features_ += features
+ features = None
+ start = time.time()
+ outputs = slic(
+ features_.numpy(),
+ channel_axis=0,
+ compactness=0.01,
+ n_segments=n_segments,
+ sigma=3,
+ )
+ features_ = None
+ outputs = torch.from_numpy(outputs).cuda()
+ print("slic took", time.time() - start)
+ mask = monai.transforms.utils.get_largest_connected_component_mask(
+ orig_data < -800, connectivity=None, num_components=1
+ ).cuda()
+ mask = dilate3d(mask, erosion=3)
+ mask = erode3d(mask, erosion=3)
+ outputs[mask.to(torch.bool)] = 0
+ outputs = monai.data.MetaTensor(
+ outputs, affine=batch_data.affine, meta=batch_data.meta
+ )
+ monai.transforms.SaveImage(
+ output_dir=output_dir,
+ output_postfix="seg",
+ data_root_dir=data_root_dir,
+ )(outputs.unsqueeze(0).cpu().to(torch.int16))
+ except BaseException:
+ print(f"{file} failed. Skipped.")
+
+ @torch.no_grad()
+ @torch.cuda.amp.autocast()
+ def batch_infer(
+ self,
+ datalist=str,
+ basedir=str,
+ output_dir="./supervoxel_sam/",
+ data_root_dir=None,
+ n_segments=400,
+ ):
+ train_files, _ = datafold_read(datalist=datalist, basedir=basedir, fold=0)
+ train_files = [_["image"] for _ in train_files]
+ dist.init_process_group(backend="nccl", init_method="env://")
+ world_size = dist.get_world_size()
+ rank = dist.get_rank()
+ # no need to wrap model with DistributedDataParallel
+ self.model = SamPredictor(self.sam.to(f"cuda:{rank}"))
+ infer_files = partition_dataset(
+ data=train_files,
+ shuffle=False,
+ num_partitions=world_size,
+ even_divisible=False,
+ )[rank]
+ self.infer(
+ infer_files,
+ rank=rank,
+ output_dir=output_dir,
+ data_root_dir=data_root_dir,
+ n_segments=n_segments,
+ )
+
+
+if __name__ == "__main__":
+ from monai.utils import optional_import
+
+ inferer = InferClass()
+ fire, _ = optional_import("fire")
+ fire.Fire(inferer)
diff --git a/vista3d/scripts/sliding_window.py b/vista3d/scripts/sliding_window.py
new file mode 100644
index 0000000..67b2061
--- /dev/null
+++ b/vista3d/scripts/sliding_window.py
@@ -0,0 +1,666 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import copy
+import itertools
+from collections.abc import Callable, Mapping, Sequence
+from typing import Any, Iterable
+
+import monai
+import numpy as np
+import torch
+import torch.nn.functional as F
+from monai.data.meta_tensor import MetaTensor
+from monai.data.utils import (
+ compute_importance_map,
+ dense_patch_slices,
+ get_valid_patch_size,
+)
+from monai.utils import (
+ BlendMode,
+ PytorchPadMode,
+ convert_data_type,
+ convert_to_dst_type,
+ ensure_tuple,
+ ensure_tuple_rep,
+ fall_back_tuple,
+ look_up_option,
+ optional_import,
+ pytorch_after,
+)
+
+tqdm, _ = optional_import("tqdm", name="tqdm")
+_nearest_mode = "nearest-exact" if pytorch_after(1, 11) else "nearest"
+
+__all__ = ["sliding_window_inference", "point_based_window_inferer"]
+
+
+def get_window_idx_c(p, roi, s):
+ if p - roi // 2 < 0:
+ left, right = 0, roi
+ elif p + roi // 2 > s:
+ left, right = s - roi, s
+ else:
+ left, right = int(p) - roi // 2, int(p) + roi // 2
+ return left, right
+
+
+def get_window_idx(p, roi, s, center_only=True, margin=5):
+ left, right = get_window_idx_c(p, roi, s)
+ if center_only:
+ return [left], [right]
+ left_most = max(0, p - roi + margin)
+ right_most = min(s, p + roi - margin)
+ left = [left_most, right_most - roi, left]
+ right = [left_most + roi, right_most, right]
+ return left, right
+
+
+def pad_previous_mask(inputs, roi_size, padvalue=0):
+ pad_size = []
+ for k in range(len(inputs.shape) - 1, 1, -1):
+ diff = max(roi_size[k - 2] - inputs.shape[k], 0)
+ half = diff // 2
+ pad_size.extend([half, diff - half])
+ if any(pad_size):
+ inputs = torch.nn.functional.pad(
+ inputs, pad=pad_size, mode="constant", value=padvalue
+ )
+ return inputs, pad_size
+
+
+def point_based_window_inferer(
+ inputs,
+ roi_size,
+ sw_batch_size,
+ predictor,
+ mode,
+ overlap,
+ sw_device,
+ device,
+ point_coords,
+ point_labels,
+ class_vector,
+ prompt_class,
+ prev_mask,
+ point_mask=None,
+ point_start=0,
+ **kwargs,
+):
+ """Point based window inferer, crop a patch centered at the point, and perform inference. Different patches are combined with gaussian weighted weights.
+ Args:
+ predictor: partial(infer_wrapper, model). infer_wrapper transpose the model output. The model output is [B, 1, H, W, D] which needs to be transposed to [1, B, H, W, D]
+ point_coords: [B, N, 3]
+ point_labels: [B, N]
+ class_vector: [B]
+ prev_mask: [1, B, H, W, D], THE VALUE IS BEFORE SIGMOID!
+ Returns:
+ stitched_output: [1, B, H, W, D]. The value is before sigmoid.
+ Notice: The function currently only supports SINGLE OBJECT INFERENCE with B=1.
+ """
+ assert point_coords.shape[0] == 1, "Only supports single object point click"
+ image, pad = pad_previous_mask(copy.deepcopy(inputs), roi_size)
+ point_coords = point_coords + torch.tensor([pad[-2], pad[-4], pad[-6]]).to(
+ point_coords.device
+ )
+ prev_mask = (
+ pad_previous_mask(copy.deepcopy(prev_mask), roi_size)[0]
+ if prev_mask is not None
+ else None
+ )
+ stitched_output = None
+ center_only = True
+ for p in point_coords[0][point_start:]:
+ lx_, rx_ = get_window_idx(
+ p[0], roi_size[0], image.shape[-3], center_only=center_only, margin=5
+ )
+ ly_, ry_ = get_window_idx(
+ p[1], roi_size[1], image.shape[-2], center_only=center_only, margin=5
+ )
+ lz_, rz_ = get_window_idx(
+ p[2], roi_size[2], image.shape[-1], center_only=center_only, margin=5
+ )
+ for i in range(len(lx_)):
+ for j in range(len(ly_)):
+ for k in range(len(lz_)):
+ lx, rx, ly, ry, lz, rz = (
+ lx_[i],
+ rx_[i],
+ ly_[j],
+ ry_[j],
+ lz_[k],
+ rz_[k],
+ )
+ unravel_slice = [
+ slice(None),
+ slice(None),
+ slice(int(lx), int(rx)),
+ slice(int(ly), int(ry)),
+ slice(int(lz), int(rz)),
+ ]
+ batch_image = image[unravel_slice]
+ # ball = get_gaussian_ball(batch_image.shape[-3:])
+ output = predictor(
+ batch_image,
+ point_coords=point_coords,
+ point_labels=point_labels,
+ class_vector=class_vector,
+ prompt_class=prompt_class,
+ patch_coords=unravel_slice,
+ prev_mask=prev_mask,
+ **kwargs,
+ )
+ if stitched_output is None:
+ stitched_output = torch.zeros(
+ [
+ 1,
+ output.shape[1],
+ image.shape[-3],
+ image.shape[-2],
+ image.shape[-1],
+ ],
+ device="cpu",
+ )
+ stitched_mask = torch.zeros(
+ [
+ 1,
+ output.shape[1],
+ image.shape[-3],
+ image.shape[-2],
+ image.shape[-1],
+ ],
+ device="cpu",
+ )
+ stitched_output[unravel_slice] += output.to("cpu")
+ stitched_mask[unravel_slice] = 1
+ # if stitched_mask is 0, then NaN value
+ stitched_output = stitched_output / stitched_mask
+ # revert padding
+ stitched_output = stitched_output[
+ :,
+ :,
+ pad[4] : image.shape[-3] - pad[5],
+ pad[2] : image.shape[-2] - pad[3],
+ pad[0] : image.shape[-1] - pad[1],
+ ]
+ stitched_mask = stitched_mask[
+ :,
+ :,
+ pad[4] : image.shape[-3] - pad[5],
+ pad[2] : image.shape[-2] - pad[3],
+ pad[0] : image.shape[-1] - pad[1],
+ ]
+ if prev_mask is not None:
+ prev_mask = prev_mask[
+ :,
+ :,
+ pad[4] : image.shape[-3] - pad[5],
+ pad[2] : image.shape[-2] - pad[3],
+ pad[0] : image.shape[-1] - pad[1],
+ ]
+ prev_mask = prev_mask.to("cpu")
+ # for un-calculated place, use previous mask
+ stitched_output[stitched_mask < 1] = prev_mask[stitched_mask < 1]
+ if not hasattr(stitched_output, "meta"):
+ stitched_output = monai.data.MetaTensor(
+ stitched_output, affine=inputs.meta["affine"], meta=inputs.meta
+ )
+ return stitched_output
+
+
+def sliding_window_inference(
+ inputs: torch.Tensor | MetaTensor,
+ roi_size: Sequence[int] | int,
+ sw_batch_size: int,
+ predictor: Callable[
+ ..., torch.Tensor | Sequence[torch.Tensor] | dict[Any, torch.Tensor]
+ ],
+ overlap: Sequence[float] | float = 0.25,
+ mode: BlendMode | str = BlendMode.CONSTANT,
+ sigma_scale: Sequence[float] | float = 0.125,
+ padding_mode: PytorchPadMode | str = PytorchPadMode.CONSTANT,
+ cval: float = 0.0,
+ sw_device: torch.device | str | None = None,
+ device: torch.device | str | None = None,
+ progress: bool = False,
+ roi_weight_map: torch.Tensor | None = None,
+ process_fn: Callable | None = None,
+ buffer_steps: int | None = None,
+ buffer_dim: int = -1,
+ *args: Any,
+ **kwargs: Any,
+) -> torch.Tensor | tuple[torch.Tensor, ...] | dict[Any, torch.Tensor]:
+ """
+ Sliding window inference on `inputs` with `predictor`.
+
+ The outputs of `predictor` could be a tensor, a tuple, or a dictionary of tensors.
+ Each output in the tuple or dict value is allowed to have different resolutions with respect to the input.
+ e.g., the input patch spatial size is [128,128,128], the output (a tuple of two patches) patch sizes
+ could be ([128,64,256], [64,32,128]).
+ In this case, the parameter `overlap` and `roi_size` need to be carefully chosen to ensure the output ROI is still
+ an integer. If the predictor's input and output spatial sizes are not equal, we recommend choosing the parameters
+ so that `overlap*roi_size*output_size/input_size` is an integer (for each spatial dimension).
+
+ When roi_size is larger than the inputs' spatial size, the input image are padded during inference.
+ To maintain the same spatial sizes, the output image will be cropped to the original input size.
+
+ Args:
+ inputs: input image to be processed (assuming NCHW[D])
+ roi_size: the spatial window size for inferences.
+ When its components have None or non-positives, the corresponding inputs dimension will be used.
+ if the components of the `roi_size` are non-positive values, the transform will use the
+ corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted
+ to `(32, 64)` if the second spatial dimension size of img is `64`.
+ sw_batch_size: the batch size to run window slices.
+ predictor: given input tensor ``patch_data`` in shape NCHW[D],
+ The outputs of the function call ``predictor(patch_data)`` should be a tensor, a tuple, or a dictionary
+ with Tensor values. Each output in the tuple or dict value should have the same batch_size, i.e. NM'H'W'[D'];
+ where H'W'[D'] represents the output patch's spatial size, M is the number of output channels,
+ N is `sw_batch_size`, e.g., the input shape is (7, 1, 128,128,128),
+ the output could be a tuple of two tensors, with shapes: ((7, 5, 128, 64, 256), (7, 4, 64, 32, 128)).
+ In this case, the parameter `overlap` and `roi_size` need to be carefully chosen
+ to ensure the scaled output ROI sizes are still integers.
+ If the `predictor`'s input and output spatial sizes are different,
+ we recommend choosing the parameters so that ``overlap*roi_size*zoom_scale`` is an integer for each dimension.
+ overlap: Amount of overlap between scans along each spatial dimension, defaults to ``0.25``.
+ mode: {``"constant"``, ``"gaussian"``}
+ How to blend output of overlapping windows. Defaults to ``"constant"``.
+
+ - ``"constant``": gives equal weight to all predictions.
+ - ``"gaussian``": gives less weight to predictions on edges of windows.
+
+ sigma_scale: the standard deviation coefficient of the Gaussian window when `mode` is ``"gaussian"``.
+ Default: 0.125. Actual window sigma is ``sigma_scale`` * ``dim_size``.
+ When sigma_scale is a sequence of floats, the values denote sigma_scale at the corresponding
+ spatial dimensions.
+ padding_mode: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}
+ Padding mode for ``inputs``, when ``roi_size`` is larger than inputs. Defaults to ``"constant"``
+ See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
+ cval: fill value for 'constant' padding mode. Default: 0
+ sw_device: device for the window data.
+ By default the device (and accordingly the memory) of the `inputs` is used.
+ Normally `sw_device` should be consistent with the device where `predictor` is defined.
+ device: device for the stitched output prediction.
+ By default the device (and accordingly the memory) of the `inputs` is used. If for example
+ set to device=torch.device('cpu') the gpu memory consumption is less and independent of the
+ `inputs` and `roi_size`. Output is on the `device`.
+ progress: whether to print a `tqdm` progress bar.
+ roi_weight_map: pre-computed (non-negative) weight map for each ROI.
+ If not given, and ``mode`` is not `constant`, this map will be computed on the fly.
+ process_fn: process inference output and adjust the importance map per window
+ buffer_steps: the number of sliding window iterations along the ``buffer_dim``
+ to be buffered on ``sw_device`` before writing to ``device``.
+ (Typically, ``sw_device`` is ``cuda`` and ``device`` is ``cpu``.)
+ default is None, no buffering. For the buffer dim, when spatial size is divisible by buffer_steps*roi_size,
+ (i.e. no overlapping among the buffers) non_blocking copy may be automatically enabled for efficiency.
+ buffer_dim: the spatial dimension along which the buffers are created.
+ 0 indicates the first spatial dimension. Default is -1, the last spatial dimension.
+ args: optional args to be passed to ``predictor``.
+ kwargs: optional keyword args to be passed to ``predictor``.
+
+ Note:
+ - input must be channel-first and have a batch dim, supports N-D sliding window.
+
+ """
+ buffered = buffer_steps is not None and buffer_steps > 0
+ num_spatial_dims = len(inputs.shape) - 2
+ if buffered:
+ if buffer_dim < -num_spatial_dims or buffer_dim > num_spatial_dims:
+ raise ValueError(
+ f"buffer_dim must be in [{-num_spatial_dims}, {num_spatial_dims}], got {buffer_dim}."
+ )
+ if buffer_dim < 0:
+ buffer_dim += num_spatial_dims
+ overlap = ensure_tuple_rep(overlap, num_spatial_dims)
+ for o in overlap:
+ if o < 0 or o >= 1:
+ raise ValueError(f"overlap must be >= 0 and < 1, got {overlap}.")
+ compute_dtype = inputs.dtype
+
+ # determine image spatial size and batch size
+ # Note: all input images must have the same image size and batch size
+ batch_size, _, *image_size_ = inputs.shape
+ device = device or inputs.device
+ sw_device = sw_device or inputs.device
+
+ temp_meta = None
+ if isinstance(inputs, MetaTensor):
+ temp_meta = MetaTensor([]).copy_meta_from(inputs, copy_attr=False)
+ inputs = convert_data_type(inputs, torch.Tensor, wrap_sequence=True)[0]
+ roi_size = fall_back_tuple(roi_size, image_size_)
+
+ # in case that image size is smaller than roi size
+ image_size = tuple(
+ max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims)
+ )
+ pad_size = []
+ for k in range(len(inputs.shape) - 1, 1, -1):
+ diff = max(roi_size[k - 2] - inputs.shape[k], 0)
+ half = diff // 2
+ pad_size.extend([half, diff - half])
+ if any(pad_size):
+ inputs = F.pad(
+ inputs,
+ pad=pad_size,
+ mode=look_up_option(padding_mode, PytorchPadMode),
+ value=cval,
+ )
+ if "labels" in kwargs.keys() and kwargs["labels"] is not None:
+ kwargs["labels"] = F.pad(
+ kwargs["labels"],
+ pad=pad_size,
+ mode=look_up_option(padding_mode, PytorchPadMode),
+ value=cval,
+ )
+ if "prev_mask" in kwargs.keys() and kwargs["prev_mask"] is not None:
+ kwargs["prev_mask"] = F.pad(
+ kwargs["prev_mask"],
+ pad=pad_size,
+ mode=look_up_option(padding_mode, PytorchPadMode),
+ value=cval,
+ )
+ if "point_coords" in kwargs.keys() and kwargs["point_coords"] is not None:
+ kwargs["point_coords"] = kwargs["point_coords"] + torch.tensor(
+ [pad_size[-2], pad_size[-4], pad_size[-6]]
+ ).to(kwargs["point_coords"].device)
+
+ # Store all slices
+ scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)
+ slices = dense_patch_slices(
+ image_size, roi_size, scan_interval, return_slice=not buffered
+ )
+
+ num_win = len(slices) # number of windows per image
+ total_slices = num_win * batch_size # total number of windows
+ windows_range: Iterable
+ if not buffered:
+ non_blocking = False
+ windows_range = range(0, total_slices, sw_batch_size)
+ else:
+ slices, n_per_batch, b_slices, windows_range = _create_buffered_slices(
+ slices, batch_size, sw_batch_size, buffer_dim, buffer_steps
+ )
+ non_blocking, _ss = torch.cuda.is_available(), -1
+ for x in b_slices[:n_per_batch]:
+ if x[1] < _ss: # detect overlapping slices
+ non_blocking = False
+ break
+ _ss = x[2]
+
+ # Create window-level importance map
+ valid_patch_size = get_valid_patch_size(image_size, roi_size)
+ if valid_patch_size == roi_size and (roi_weight_map is not None):
+ importance_map_ = roi_weight_map
+ else:
+ try:
+ valid_p_size = ensure_tuple(valid_patch_size)
+ importance_map_ = compute_importance_map(
+ valid_p_size,
+ mode=mode,
+ sigma_scale=sigma_scale,
+ device=sw_device,
+ dtype=compute_dtype,
+ )
+ if len(importance_map_.shape) == num_spatial_dims and not process_fn:
+ importance_map_ = importance_map_[
+ None, None
+ ] # adds batch, channel dimensions
+ except Exception as e:
+ raise RuntimeError(
+ f"patch size {valid_p_size}, mode={mode}, sigma_scale={sigma_scale}, device={device}\n"
+ "Seems to be OOM. Please try smaller patch size or mode='constant' instead of mode='gaussian'."
+ ) from e
+ importance_map_ = convert_data_type(
+ importance_map_, torch.Tensor, device=sw_device, dtype=compute_dtype
+ )[0]
+
+ # stores output and count map
+ output_image_list, count_map_list, sw_device_buffer, b_s, b_i = [], [], [], 0, 0 # type: ignore
+ # for each patch
+ for slice_g in tqdm(windows_range) if progress else windows_range:
+ slice_range = range(
+ slice_g,
+ min(
+ slice_g + sw_batch_size, b_slices[b_s][0] if buffered else total_slices
+ ),
+ )
+ unravel_slice = [
+ [slice(idx // num_win, idx // num_win + 1), slice(None)]
+ + list(slices[idx % num_win])
+ for idx in slice_range
+ ]
+ if sw_batch_size > 1:
+ win_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(
+ sw_device
+ )
+ else:
+ win_data = inputs[unravel_slice[0]].to(sw_device)
+
+ seg_prob_out = predictor(
+ win_data, patch_coords=unravel_slice[0], *args, **kwargs
+ ) # batched patch
+
+ # convert seg_prob_out to tuple seg_tuple, this does not allocate new memory.
+ dict_keys, seg_tuple = _flatten_struct(seg_prob_out)
+ if process_fn:
+ seg_tuple, w_t = process_fn(seg_tuple, win_data, importance_map_)
+ else:
+ w_t = importance_map_
+ if len(w_t.shape) == num_spatial_dims:
+ w_t = w_t[None, None]
+ w_t = w_t.to(dtype=compute_dtype, device=sw_device)
+ if buffered:
+ c_start, c_end = b_slices[b_s][1:]
+ if not sw_device_buffer:
+ k = seg_tuple[0].shape[1] # len(seg_tuple) > 1 is currently ignored
+ sp_size = list(image_size)
+ sp_size[buffer_dim] = c_end - c_start
+ sw_device_buffer = [
+ torch.zeros(
+ size=[1, k, *sp_size], dtype=compute_dtype, device=sw_device
+ )
+ ]
+ for p, s in zip(seg_tuple[0], unravel_slice):
+ offset = s[buffer_dim + 2].start - c_start
+ s[buffer_dim + 2] = slice(offset, offset + roi_size[buffer_dim])
+ s[0] = slice(0, 1)
+ sw_device_buffer[0][s] += p * w_t
+ b_i += len(unravel_slice)
+ if b_i < b_slices[b_s][0]:
+ continue
+ else:
+ sw_device_buffer = list(seg_tuple)
+
+ for ss in range(len(sw_device_buffer)):
+ b_shape = sw_device_buffer[ss].shape
+ seg_chns, seg_shape = b_shape[1], b_shape[2:]
+ z_scale = None
+ if not buffered and seg_shape != roi_size:
+ z_scale = [
+ out_w_i / float(in_w_i)
+ for out_w_i, in_w_i in zip(seg_shape, roi_size)
+ ]
+ w_t = F.interpolate(w_t, seg_shape, mode=_nearest_mode)
+ if len(output_image_list) <= ss:
+ output_shape = [batch_size, seg_chns]
+ output_shape += (
+ [int(_i * _z) for _i, _z in zip(image_size, z_scale)]
+ if z_scale
+ else list(image_size)
+ )
+ # allocate memory to store the full output and the count for overlapping parts
+ new_tensor: Callable = torch.empty if non_blocking else torch.zeros # type: ignore
+ output_image_list.append(
+ new_tensor(output_shape, dtype=compute_dtype, device=device)
+ )
+ count_map_list.append(
+ torch.zeros(
+ [1, 1] + output_shape[2:], dtype=compute_dtype, device=device
+ )
+ )
+ w_t_ = w_t.to(device)
+ for __s in slices:
+ if z_scale is not None:
+ __s = tuple(
+ slice(int(_si.start * z_s), int(_si.stop * z_s))
+ for _si, z_s in zip(__s, z_scale)
+ )
+ count_map_list[-1][(slice(None), slice(None), *__s)] += w_t_
+ if buffered:
+ o_slice = [slice(None)] * len(inputs.shape)
+ o_slice[buffer_dim + 2] = slice(c_start, c_end)
+ img_b = b_s // n_per_batch # image batch index
+ o_slice[0] = slice(img_b, img_b + 1)
+ if non_blocking:
+ output_image_list[0][o_slice].copy_(
+ sw_device_buffer[0], non_blocking=non_blocking
+ )
+ else:
+ output_image_list[0][o_slice] += sw_device_buffer[0].to(
+ device=device
+ )
+ else:
+ sw_device_buffer[ss] *= w_t
+ sw_device_buffer[ss] = sw_device_buffer[ss].to(device)
+ _compute_coords(
+ unravel_slice, z_scale, output_image_list[ss], sw_device_buffer[ss]
+ )
+ sw_device_buffer = []
+ if buffered:
+ b_s += 1
+
+ if non_blocking:
+ torch.cuda.current_stream().synchronize()
+
+ # account for any overlapping sections
+ for ss in range(len(output_image_list)):
+ output_image_list[ss] /= count_map_list.pop(0)
+
+ # remove padding if image_size smaller than roi_size
+ if any(pad_size):
+ for ss, output_i in enumerate(output_image_list):
+ zoom_scale = [
+ _shape_d / _roi_size_d
+ for _shape_d, _roi_size_d in zip(output_i.shape[2:], roi_size)
+ ]
+ final_slicing: list[slice] = []
+ for sp in range(num_spatial_dims):
+ si = num_spatial_dims - sp - 1
+ slice_dim = slice(
+ int(round(pad_size[sp * 2] * zoom_scale[si])),
+ int(round((pad_size[sp * 2] + image_size_[si]) * zoom_scale[si])),
+ )
+ final_slicing.insert(0, slice_dim)
+ output_image_list[ss] = output_i[(slice(None), slice(None), *final_slicing)]
+
+ final_output = _pack_struct(output_image_list, dict_keys)
+ if temp_meta is not None:
+ final_output = convert_to_dst_type(final_output, temp_meta, device=device)[0]
+ else:
+ final_output = convert_to_dst_type(final_output, inputs, device=device)[0]
+
+ return final_output # type: ignore
+
+
+def _create_buffered_slices(
+ slices, batch_size, sw_batch_size, buffer_dim, buffer_steps
+):
+ """rearrange slices for buffering"""
+ slices_np = np.asarray(slices)
+ slices_np = slices_np[np.argsort(slices_np[:, buffer_dim, 0], kind="mergesort")]
+ slices = [tuple(slice(c[0], c[1]) for c in i) for i in slices_np]
+ slices_np = slices_np[:, buffer_dim]
+
+ _, _, _b_lens = np.unique(slices_np[:, 0], return_counts=True, return_index=True)
+ b_ends = np.cumsum(_b_lens).tolist() # possible buffer flush boundaries
+ x = [0, *b_ends][:: min(len(b_ends), int(buffer_steps))]
+ if x[-1] < b_ends[-1]:
+ x.append(b_ends[-1])
+ n_per_batch = len(x) - 1
+ windows_range = [
+ range(b * x[-1] + x[i], b * x[-1] + x[i + 1], sw_batch_size)
+ for b in range(batch_size)
+ for i in range(n_per_batch)
+ ]
+ b_slices = []
+ for _s, _r in enumerate(windows_range):
+ s_s = slices_np[windows_range[_s - 1].stop % len(slices) if _s > 0 else 0, 0]
+ s_e = slices_np[(_r.stop - 1) % len(slices), 1]
+ b_slices.append((_r.stop, s_s, s_e)) # buffer index, slice start, slice end
+ windows_range = itertools.chain(*windows_range) # type: ignore
+ return slices, n_per_batch, b_slices, windows_range
+
+
+def _compute_coords(coords, z_scale, out, patch):
+ """sliding window batch spatial scaling indexing for multi-resolution outputs."""
+ for original_idx, p in zip(coords, patch):
+ idx_zm = list(original_idx) # 4D for 2D image, 5D for 3D image
+ if z_scale:
+ for axis in range(2, len(idx_zm)):
+ idx_zm[axis] = slice(
+ int(original_idx[axis].start * z_scale[axis - 2]),
+ int(original_idx[axis].stop * z_scale[axis - 2]),
+ )
+ out[idx_zm] += p
+
+
+def _get_scan_interval(
+ image_size: Sequence[int],
+ roi_size: Sequence[int],
+ num_spatial_dims: int,
+ overlap: Sequence[float],
+) -> tuple[int, ...]:
+ """
+ Compute scan interval according to the image size, roi size and overlap.
+ Scan interval will be `int((1 - overlap) * roi_size)`, if interval is 0,
+ use 1 instead to make sure sliding window works.
+
+ """
+ if len(image_size) != num_spatial_dims:
+ raise ValueError(
+ f"len(image_size) {len(image_size)} different from spatial dims {num_spatial_dims}."
+ )
+ if len(roi_size) != num_spatial_dims:
+ raise ValueError(
+ f"len(roi_size) {len(roi_size)} different from spatial dims {num_spatial_dims}."
+ )
+
+ scan_interval = []
+ for i, o in zip(range(num_spatial_dims), overlap):
+ if roi_size[i] == image_size[i]:
+ scan_interval.append(int(roi_size[i]))
+ else:
+ interval = int(roi_size[i] * (1 - o))
+ scan_interval.append(interval if interval > 0 else 1)
+ return tuple(scan_interval)
+
+
+def _flatten_struct(seg_out):
+ dict_keys = None
+ seg_probs: tuple[torch.Tensor, ...]
+ if isinstance(seg_out, torch.Tensor):
+ seg_probs = (seg_out,)
+ elif isinstance(seg_out, Mapping):
+ dict_keys = sorted(seg_out.keys()) # track predictor's output keys
+ seg_probs = tuple(seg_out[k] for k in dict_keys)
+ else:
+ seg_probs = ensure_tuple(seg_out)
+ return dict_keys, seg_probs
+
+
+def _pack_struct(seg_out, dict_keys=None):
+ if dict_keys is not None:
+ return dict(zip(dict_keys, seg_out))
+ if isinstance(seg_out, (list, tuple)) and len(seg_out) == 1:
+ return seg_out[0]
+ return ensure_tuple(seg_out)
diff --git a/vista3d/scripts/train.py b/vista3d/scripts/train.py
new file mode 100644
index 0000000..e9beb8e
--- /dev/null
+++ b/vista3d/scripts/train.py
@@ -0,0 +1,1028 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+import math
+import os
+import sys
+import time
+import warnings
+from datetime import timedelta
+from functools import partial
+from typing import Optional, Sequence, Union
+
+import monai
+import nibabel as nib
+import numpy as np
+import torch
+import torch.distributed as dist
+import yaml
+from data.datasets import (
+ compute_dataset_weights,
+ get_class_names,
+ get_datalist_with_dataset_name,
+ get_datalist_with_dataset_name_and_transform,
+)
+from monai import transforms
+from monai.apps.auto3dseg.auto_runner import logger
+from monai.apps.utils import DEFAULT_FMT
+from monai.bundle import ConfigParser
+from monai.bundle.scripts import _pop_args, _update_args
+from monai.data import DataLoader, DistributedSampler, DistributedWeightedRandomSampler
+from monai.metrics import compute_dice
+from monai.networks.utils import copy_model_state
+from monai.utils import optional_import, set_determinism
+from torch.nn.parallel import DistributedDataParallel
+from torch.utils.data.sampler import RandomSampler, WeightedRandomSampler
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+from vista3d import vista_model_registry
+
+from .sliding_window import sliding_window_inference
+from .utils.sample_utils import Point_sampler
+from .utils.trans_utils import DatasetSelectTansformd, RelabelD
+from .utils.workflow_utils import (
+ MERGE_LIST,
+ USE_SV_GT_LIST,
+ generate_prompt_pairs,
+ get_next_points,
+ none_cat,
+ sample_points_patch_val,
+)
+
+nib.imageglobals.logger.setLevel(40)
+RankFilter, _ = optional_import("monai.utils", name="RankFilter")
+CONFIG = {
+ "version": 1,
+ "disable_existing_loggers": False,
+ "formatters": {"monai_default": {"format": DEFAULT_FMT}},
+ "loggers": {
+ "monai.apps.auto3dseg.auto_runner": {
+ "handlers": ["file", "console"],
+ "level": "DEBUG",
+ "propagate": False,
+ },
+ "data.analyzer": {
+ "handlers": ["file", "console"],
+ "level": "INFO",
+ "propagate": False,
+ },
+ "data.datasets": {
+ "handlers": ["file", "console"],
+ "level": "INFO",
+ "propagate": False,
+ },
+ },
+ "filters": {"rank_filter": {"()": RankFilter}},
+ "handlers": {
+ "file": {
+ "class": "logging.FileHandler",
+ "filename": "runner.log",
+ "mode": "a", # append or overwrite
+ "level": "DEBUG",
+ "formatter": "monai_default",
+ "filters": ["rank_filter"],
+ },
+ "console": {
+ "class": "logging.StreamHandler",
+ "level": "INFO",
+ "formatter": "monai_default",
+ "filters": ["rank_filter"],
+ },
+ },
+}
+
+
+def infer_wrapper(inputs, model, **kwargs):
+ """VISTA3D output is [B, 1, H, W, D], which segment B foregrounds and stacked at the batch dimension.
+ Sliding-window inferer requires [1, B, H, W, D]. The function is used only for sliding-window inference.
+ """
+ outputs = model(input_images=inputs, **kwargs)
+ return outputs.transpose(1, 0)
+
+
+def loss_wrapper(pred, label, loss_function):
+ return loss_function(pred, label)
+
+
+def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
+ # Initialize distributed and scale parameters based on GPU memory
+ if torch.cuda.device_count() > 1:
+ dist.init_process_group(
+ backend="nccl", init_method="env://", timeout=timedelta(seconds=3600)
+ )
+ world_size = dist.get_world_size()
+ dist.barrier()
+ else:
+ world_size = 1
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+ if isinstance(config_file, str) and "," in config_file:
+ config_file = config_file.split(",")
+ _args = _update_args(config_file=config_file, **override)
+ config_file_ = _pop_args(_args, "config_file")[0]
+
+ parser = ConfigParser()
+ parser.read_config(config_file_)
+ parser.update(pairs=_args)
+
+ # loggings and experiment pathes
+ start_time = time.time()
+ bundle_root = parser.get_parsed_content("bundle_root")
+ ckpt_path = parser.get_parsed_content("ckpt_path")
+ os.makedirs(ckpt_path, exist_ok=True)
+ if world_size == 1 or dist.get_rank() == 0:
+ writer = SummaryWriter(log_dir=os.path.join(ckpt_path, "Events"))
+ with open(os.path.join(ckpt_path, "accuracy_history.csv"), "a") as f:
+ f.write("epoch\tmetric\tloss\tlr\ttime\titer\n")
+ metric_dim = (
+ parser.get_parsed_content("output_classes") - 1
+ ) # only affect dice calculation
+ random_seed = parser.get_parsed_content("random_seed")
+ save_last = parser.get_parsed_content(
+ "save_last", default=False
+ ) # save the last checkpoint
+ save_all = parser.get_parsed_content(
+ "save_all", default=False
+ ) # save all val-checkpoint
+ if world_size == 1 or dist.get_rank() == 0:
+ config_yaml = os.path.join(bundle_root, "configs.yaml")
+ ConfigParser.export_config_file(
+ parser.get(), config_yaml, fmt="yaml", default_flow_style=None
+ )
+ if random_seed is not None and (
+ isinstance(random_seed, int) or isinstance(random_seed, float)
+ ):
+ set_determinism(seed=random_seed)
+ CONFIG["handlers"]["file"]["filename"] = parser.get_parsed_content(
+ "log_output_file"
+ )
+ logging.config.dictConfig(CONFIG)
+ logging.getLogger("torch.distributed.distributed_c10d").setLevel(logging.WARNING)
+
+ # training hyperparameters - workflow
+ device = (
+ torch.device(f"cuda:{os.environ['LOCAL_RANK']}")
+ if world_size > 1
+ else torch.device("cuda:0")
+ )
+ amp = parser.get_parsed_content("amp")
+ if amp:
+ from torch.cuda.amp import GradScaler, autocast
+
+ scaler = GradScaler()
+ logger.debug("Amp enabled")
+ finetune = parser.get_parsed_content("finetune")
+ num_epochs = parser.get_parsed_content("num_epochs")
+ num_epochs_per_validation = parser.get_parsed_content("num_epochs_per_validation")
+ weighted_sampling = parser.get_parsed_content(
+ "weighted_sampling"
+ ) # perform dataset weighted sample using dataset_weights.yaml
+ skip_iter_prob = parser.get_parsed_content(
+ "skip_iter_prob"
+ ) # prob. to skip iterative point sampling, 1 for auto-training
+ iter_num = parser.get_parsed_content(
+ "iter_num"
+ ) # total iter number in point branch training
+ freeze_epoch = parser.get_parsed_content(
+ "freeze_epoch", default=-1
+ ) # freeze the whole branch epoch
+ freeze_head = parser.get_parsed_content(
+ "freeze_head", default="auto"
+ ) # freeze which branch, "auto" or "point". We freeze point for auto-training.
+ logger.debug(f"World_size: {world_size}")
+ logger.debug(f"num_epochs: {num_epochs}")
+ logger.debug(f"num_epochs_per_validation: {num_epochs_per_validation}")
+
+ # training hyperparameters - model and optimizer
+ input_channels = parser.get_parsed_content("input_channels")
+ model_registry = parser.get_parsed_content("model")
+ patch_size = parser.get_parsed_content("patch_size")
+ model = vista_model_registry[model_registry](
+ in_channels=input_channels, image_size=patch_size
+ )
+ model = model.to(device)
+ optimizer_part = parser.get_parsed_content("optimizer", instantiate=False)
+ optimizer = optimizer_part.instantiate(params=model.parameters())
+ lr_scheduler_part = parser.get_parsed_content("lr_scheduler", instantiate=False)
+ lr_scheduler = lr_scheduler_part.instantiate(optimizer=optimizer)
+ if world_size > 1:
+ model = DistributedDataParallel(
+ model, device_ids=[device], find_unused_parameters=True
+ )
+ if finetune["activate"] and os.path.isfile(finetune["pretrained_ckpt_name"]):
+ logger.debug(
+ "Fine-tuning pre-trained checkpoint {:s}".format(
+ finetune["pretrained_ckpt_name"]
+ )
+ )
+ pretrained_ckpt = torch.load(
+ finetune["pretrained_ckpt_name"], map_location=device
+ )
+ copy_model_state(
+ model, pretrained_ckpt, exclude_vars=finetune.get("exclude_vars")
+ )
+ del pretrained_ckpt
+ else:
+ logger.debug("Training from scratch")
+
+ # training hyperparameters - sample
+ num_images_per_batch = parser.get_parsed_content("num_images_per_batch")
+ num_patches_per_iter = parser.get_parsed_content("num_patches_per_iter")
+ overlap_ratio = parser.get_parsed_content("overlap_ratio") # sliding window overlap
+ max_prompt = parser.get_parsed_content("max_prompt", default=96)
+ max_backprompt = parser.get_parsed_content("max_backprompt", default=96)
+ max_foreprompt = parser.get_parsed_content("max_foreprompt", default=96)
+ drop_label_prob = parser.get_parsed_content("drop_label_prob")
+ drop_point_prob = parser.get_parsed_content("drop_point_prob")
+ max_point = parser.get_parsed_content("max_point")
+ balance_gt = parser.get_parsed_content("balance_gt", default=False)
+
+ # training hyperparameters - data and transforms
+ fold = parser.get_parsed_content("fold")
+ json_dir = parser.get_parsed_content(
+ "json_dir", default="./data/jsons"
+ ) # path to json datalists
+ train_datasets = parser.get_parsed_content("train_datasets", default=None)
+ val_datasets = parser.get_parsed_content("val_datasets", default=None)
+ train_transforms = parser.get_parsed_content(
+ "transforms_train#transforms", default=None
+ )
+ val_transforms = parser.get_parsed_content(
+ "transforms_validate#transforms", default=None
+ )
+ post_pred = transforms.Compose(
+ [
+ transforms.EnsureType(),
+ transforms.AsDiscrete(threshold=0.0, dtype=torch.uint8),
+ ]
+ )
+ class_names = get_class_names(json_dir=json_dir)
+ label_mappings = dict(
+ ConfigParser.load_config_file(os.path.join(json_dir, "label_mappings.json"))
+ )
+ image_key, label_key, label_sv_key, pseudo_label_key = (
+ parser.get_parsed_content("image_key", default="image"),
+ parser.get_parsed_content("label_key", default="label"),
+ parser.get_parsed_content("label_sv_key", default="label_sv"),
+ parser.get_parsed_content("pseudo_label_key", default="pseudo_label"),
+ )
+ train_files, _, dataset_specific_transforms, dataset_specific_transforms_val = (
+ get_datalist_with_dataset_name_and_transform(
+ datasets=train_datasets,
+ fold_idx=fold,
+ image_key=image_key,
+ label_key=label_key,
+ label_sv_key=label_sv_key,
+ pseudo_label_key=pseudo_label_key,
+ num_patches_per_image=parser.get_parsed_content("num_patches_per_image"),
+ patch_size=parser.get_parsed_content("patch_size"),
+ json_dir=json_dir,
+ )
+ )
+
+ _, val_files = get_datalist_with_dataset_name(
+ datasets=val_datasets, fold_idx=fold, json_dir=json_dir
+ )
+ if world_size > 1:
+ if len(val_files) < world_size:
+ val_files = list(val_files) * math.ceil(
+ float(world_size) / float(len(val_files))
+ )
+ logger.debug(f"Train_files: {len(train_files)}")
+ logger.debug(f"Val_files: {len(val_files)}")
+
+ if train_transforms is not None:
+ # Add dataset-specific transforms to over-sample certain labels
+ dataset_select_transform = DatasetSelectTansformd(
+ [image_key, label_key], dataset_specific_transforms
+ )
+ train_transforms[
+ train_transforms.index("Placeholder for dataset-specific transform")
+ ] = dataset_select_transform
+ logger.debug("using dataset-specific transforms")
+ for k, v in dataset_specific_transforms.items():
+ logger.debug(k)
+ logger.debug(v.transforms)
+ train_transforms.append(
+ RelabelD(label_key, label_mappings=label_mappings, dtype=torch.int32)
+ )
+ train_transforms = transforms.Compose(train_transforms)
+
+ if val_transforms is not None:
+ dataset_select_transform_val = DatasetSelectTansformd(
+ [image_key, label_key], dataset_specific_transforms_val
+ )
+ val_transforms[
+ val_transforms.index("Placeholder for dataset-specific transform")
+ ] = dataset_select_transform_val
+ logger.debug("using dataset-specific transforms for validation")
+ for k, v in dataset_specific_transforms_val.items():
+ logger.debug(k)
+ try:
+ logger.debug(v.transforms)
+ except BaseException:
+ logger.debug(v)
+ val_transforms = transforms.Compose(val_transforms)
+
+ with warnings.catch_warnings():
+ warnings.simplefilter(action="ignore", category=FutureWarning)
+ warnings.simplefilter(action="ignore", category=Warning)
+ train_ds, val_ds = None, None
+ train_ds = monai.data.Dataset(
+ data=train_files * num_epochs_per_validation, transform=train_transforms
+ )
+ val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
+
+ train_sampler, val_sampler = None, None
+ train_w = None
+ if weighted_sampling:
+ train_w = (
+ compute_dataset_weights(
+ train_files, weight_path="./data/dataset_weights.yaml"
+ )
+ * num_epochs_per_validation
+ )
+ logger.debug("using uniform sample")
+ if world_size > 1:
+ train_sampler = DistributedWeightedRandomSampler(train_ds, train_w)
+ val_sampler = DistributedSampler(
+ val_ds, shuffle=False, even_divisible=False
+ )
+ else:
+ train_sampler = WeightedRandomSampler(train_w, len(train_files))
+ else:
+ if world_size > 1:
+ train_sampler = DistributedSampler(train_ds, shuffle=True)
+ val_sampler = DistributedSampler(
+ val_ds, shuffle=False, even_divisible=False
+ )
+ else:
+ train_sampler = RandomSampler(train_ds)
+ train_loader = DataLoader(
+ train_ds,
+ num_workers=4,
+ batch_size=num_images_per_batch,
+ shuffle=(train_sampler is None),
+ persistent_workers=True,
+ pin_memory=True,
+ sampler=train_sampler,
+ prefetch_factor=1,
+ )
+ val_loader = DataLoader(
+ val_ds,
+ num_workers=4,
+ batch_size=1,
+ shuffle=False,
+ sampler=val_sampler,
+ prefetch_factor=1,
+ persistent_workers=False,
+ )
+
+ # --------- Start training ---------
+ """ Notes: The training script is directly modified from auto3dseg.
+ To increase speed, the training script is not based on epoch, but based on validation rounds.
+ In each batch, num_images_per_batch=2 whole 3D images are loaded into CPU for data transformation
+ num_patches_per_image=2*num_patches_per_iter is extracted from each 3D image, in each iteration,
+ num_patches_per_iter patches is used for training (real batch size on each GPU).
+ """
+ num_rounds = int(np.ceil(float(num_epochs) // float(num_epochs_per_validation)))
+ best_metric = -1
+ best_metric_epoch = -1
+ idx_iter = 0
+ if num_rounds == 0:
+ raise RuntimeError(
+ "num_epochs_per_validation > num_epochs, modify hyper_parameters.yaml"
+ )
+
+ if world_size == 1 or dist.get_rank() == 0:
+ progress_bar = tqdm(
+ range(num_rounds),
+ desc=f"{os.path.basename(bundle_root)} - training ...",
+ unit="round",
+ )
+ for _round in (
+ range(num_rounds) if world_size > 1 and dist.get_rank() != 0 else progress_bar
+ ):
+ model.train()
+ epoch_loss = 0
+ loss_torch = torch.zeros(2, dtype=torch.float, device=device) + 1e-5
+ step = 0
+ e_time = time.time()
+ if world_size > 1:
+ train_loader.sampler.set_epoch(_round)
+ for batch_data in train_loader:
+ # for batch_data in cycle_k(train_loader):
+ s_time = time.time()
+ # if step % (len(train_loader)) == 0:
+ if step % (len(train_loader) // num_epochs_per_validation) == 0:
+ epoch = _round * num_epochs_per_validation + step // (
+ len(train_loader) // num_epochs_per_validation
+ )
+ lr_scheduler.step()
+ lr = lr_scheduler.get_last_lr()[0]
+ if freeze_epoch > epoch:
+ # if automatic branch is frozen, drop label prompts
+ if freeze_head == "auto":
+ drop_label_prob_train = 1
+ drop_point_prob_train = 0
+ auto_freeze = True
+ point_freeze = False
+ elif freeze_head == "point":
+ drop_label_prob_train = 0
+ drop_point_prob_train = 1
+ auto_freeze = False
+ point_freeze = True
+ try:
+ model.module.set_auto_grad(
+ auto_freeze=auto_freeze, point_freeze=point_freeze
+ )
+ except BaseException:
+ model.set_auto_grad(
+ auto_freeze=auto_freeze, point_freeze=point_freeze
+ )
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"Auto freeze {auto_freeze}, point freeze {point_freeze} at epoch {epoch}!"
+ )
+ else:
+ drop_label_prob_train = drop_label_prob
+ drop_point_prob_train = drop_point_prob
+ try:
+ model.module.set_auto_grad(
+ auto_freeze=False, point_freeze=False
+ )
+ except BaseException:
+ model.set_auto_grad(auto_freeze=False, point_freeze=False)
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"Auto freeze {False}, point freeze {False} at epoch {epoch}!"
+ )
+
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug("----------")
+ logger.debug(f"epoch {epoch}/{num_epochs}")
+ logger.debug(f"Learning rate is set to {lr}")
+ step += 1
+ inputs_l = batch_data["image"].as_subclass(torch.Tensor)
+ if "label" not in batch_data:
+ # this will only happen for unlabeled dataset, converting pseudo-label to manual label
+ batch_data["label"] = batch_data.pop("pseudo_label")
+ labels_l = batch_data["label"].as_subclass(torch.Tensor)
+ labels_sv_l = (
+ batch_data["label_sv"].as_subclass(torch.Tensor)
+ if "label_sv" in batch_data
+ else None
+ )
+ labels_p_l = (
+ batch_data["pseudo_label"].as_subclass(torch.Tensor)
+ if "pseudo_label" in batch_data
+ else None
+ )
+
+ # if pseudo_label_reliability does not exist, treat as reliable
+ if labels_p_l is not None:
+ pl_reliability_l = batch_data.get(
+ "pseudo_label_reliability", torch.ones(labels_p_l.shape[0], 1)
+ )
+ else:
+ pl_reliability_l = None
+ ds_l = batch_data["dataset_name"]
+
+ if len(inputs_l) > 1:
+ _idx = torch.randperm(inputs_l.shape[0])
+ inputs_l = inputs_l[_idx]
+ labels_l = labels_l[_idx]
+ labels_sv_l = labels_sv_l[_idx] if labels_sv_l is not None else None
+ labels_p_l = labels_p_l[_idx] if labels_p_l is not None else None
+ pl_reliability_l = (
+ pl_reliability_l[_idx] if pl_reliability_l is not None else None
+ )
+ ds_l = [ds_l[int(_d_i)] for _d_i in _idx]
+
+ for _k in range(inputs_l.shape[0] // num_patches_per_iter):
+ inputs = inputs_l[
+ _k * num_patches_per_iter : (_k + 1) * num_patches_per_iter, ...
+ ]
+ labels = labels_l[
+ _k * num_patches_per_iter : (_k + 1) * num_patches_per_iter, ...
+ ]
+ labels_sv = (
+ labels_sv_l[
+ _k * num_patches_per_iter : (_k + 1) * num_patches_per_iter, ...
+ ]
+ if labels_sv_l is not None
+ else None
+ )
+
+ inputs = inputs.to(device)
+ labels = labels.to(device)
+ labels_sv = labels_sv.to(device) if labels_sv is not None else None
+
+ ds_name = ds_l[_k]
+ # label_mapping does not contain unlabeled dataset, use totalsegv2's labelmapping for unlabeled datasets.
+ try:
+ train_label_set = {_xx[1] for _xx in label_mappings[ds_name]}
+ except BaseException:
+ train_label_set = {
+ _xx[1] for _xx in label_mappings["TotalSegmentatorV2"]
+ }
+ # hepatic vessel and airway are generated in pseudolabel generation
+ train_label_set_pseudo = {
+ _xx[1] for _xx in label_mappings["TotalSegmentatorV2"]
+ } | {25, 132}
+
+ pl_reliability = (
+ pl_reliability_l[
+ _k * num_patches_per_iter : (_k + 1) * num_patches_per_iter, ...
+ ]
+ if pl_reliability_l is not None
+ else None
+ )
+ labels_p = None
+ if pl_reliability is not None and pl_reliability > 0:
+ labels_p = (
+ labels_p_l[
+ _k * num_patches_per_iter : (_k + 1) * num_patches_per_iter,
+ ...,
+ ]
+ if labels_p_l is not None
+ else None
+ )
+ labels_p = labels_p.to(device) if labels_p is not None else None
+ # decide if use iterative training and sync across all ranks
+ if world_size > 1:
+ if dist.get_rank() == 0:
+ skip_iter = (torch.rand(1) < skip_iter_prob).to(
+ dtype=torch.float, device=device
+ )
+ else:
+ skip_iter = torch.empty(1).to(dtype=torch.float, device=device)
+ dist.broadcast(skip_iter, src=0)
+ else:
+ skip_iter = (torch.rand(1) < skip_iter_prob).float()
+ if skip_iter > 0:
+ # if not using iterative
+ num_iters = 1
+ else:
+ # if use iterative training
+ num_iters = max(iter_num, 1)
+
+ point_sampler = None
+ point_sampler_pseudo = None
+ # for dataset other than totalseg, use pseudolabel for zero-shot. for totalseg, if labels_p exist, use labels_p for zero-shot,
+ # gt for regular sample. If labels_p does not exist, use gt for zero-shot.
+ if labels_sv is not None:
+ if labels_p is not None:
+ point_sampler_pseudo = Point_sampler(
+ label=labels_p[0, 0],
+ label_sv=labels_sv[0, 0],
+ map_shift=512,
+ )
+ elif ds_name in USE_SV_GT_LIST:
+ point_sampler = Point_sampler(
+ label=labels[0, 0], label_sv=labels_sv[0, 0], map_shift=512
+ )
+
+ label_prompt, point, point_label, prompt_class = generate_prompt_pairs(
+ labels,
+ train_label_set,
+ max_point=max_point,
+ max_prompt=max_prompt,
+ max_backprompt=max_backprompt,
+ max_foreprompt=max_foreprompt,
+ drop_label_prob=drop_label_prob_train,
+ drop_point_prob=drop_point_prob_train,
+ point_sampler=point_sampler,
+ )
+ (
+ label_prompt_pseudo,
+ point_pseudo,
+ point_label_pseudo,
+ prompt_class_pseudo,
+ ) = (None, None, None, None)
+
+ if labels_p is not None:
+ (
+ label_prompt_pseudo,
+ point_pseudo,
+ point_label_pseudo,
+ prompt_class_pseudo,
+ ) = generate_prompt_pairs(
+ labels_p,
+ train_label_set_pseudo,
+ max_point=max_point,
+ max_prompt=max_prompt,
+ max_backprompt=max_backprompt,
+ max_foreprompt=max_foreprompt,
+ drop_label_prob=drop_label_prob_train,
+ drop_point_prob=drop_point_prob_train,
+ point_sampler=point_sampler_pseudo,
+ )
+ # point sampler updates the labels internally.
+ if point_sampler is not None and prompt_class is not None:
+ # update the labels. The shifted prompt index means zero-shot index.
+ labels = point_sampler.label.unsqueeze(0).unsqueeze(0)
+ shifted = point_sampler.shifted
+ for i, p in enumerate(prompt_class):
+ if p in list(shifted.keys()):
+ prompt_class[i] = shifted[p.item()]
+ del point_sampler
+ if point_sampler_pseudo is not None and prompt_class_pseudo is not None:
+ # update the labels. The shifted prompt index means zero-shot index.
+ labels_p = point_sampler_pseudo.label.unsqueeze(0).unsqueeze(0)
+ shifted = point_sampler_pseudo.shifted
+ for i, p in enumerate(prompt_class_pseudo):
+ if p in list(shifted.keys()):
+ prompt_class_pseudo[i] = shifted[p.item()]
+ del point_sampler_pseudo
+ torch.cuda.empty_cache()
+ # Skip the training if prompts are both None
+ skip_update = torch.zeros(1, device=device)
+ if (
+ label_prompt is None
+ and point is None
+ and label_prompt_pseudo is None
+ and point_pseudo is None
+ ):
+ logger.debug(f"Iteration skipped due to None prompts at {ds_name}")
+ skip_update = torch.ones(1, device=device)
+ if world_size > 1:
+ dist.all_reduce(skip_update, op=dist.ReduceOp.SUM)
+ if skip_update[0] > 0:
+ continue # some rank has no foreground, skip this batch
+ # clear image_embedding
+ try:
+ model.module.clear_cache()
+ except BaseException:
+ model.clear_cache()
+ for click_indx in range(num_iters):
+ outputs = None
+ inputs = inputs.to(device)
+ labels = labels.to(device)
+ labels_p = labels_p.to(device) if labels_p is not None else None
+ # only sinlge point prompt case activate multi-mask output
+ loss_function = partial(
+ loss_wrapper, loss_function=parser.get_parsed_content("loss")
+ )
+ with autocast():
+ outputs = model(
+ input_images=inputs,
+ point_coords=none_cat(point, point_pseudo),
+ point_labels=none_cat(point_label, point_label_pseudo),
+ class_vector=none_cat(label_prompt, label_prompt_pseudo),
+ prompt_class=none_cat(prompt_class, prompt_class_pseudo),
+ )
+ # cumulate loss
+ loss, loss_n = torch.tensor(0.0, device=device), torch.tensor(
+ 0.0, device=device
+ )
+ ps_start = len(prompt_class) if prompt_class is not None else 0
+ if prompt_class is not None:
+ for idx in range(len(prompt_class)):
+ if prompt_class[idx] == 0:
+ continue # skip background class
+ loss_n += 1.0
+ gt = labels == prompt_class[idx]
+ if prompt_class[idx].item() in MERGE_LIST.keys():
+ for m in MERGE_LIST[prompt_class[idx].item()]:
+ gt = torch.logical_or(gt, labels == m)
+ loss += loss_function(outputs[[idx]].float(), gt)
+
+ if prompt_class_pseudo is not None:
+ if balance_gt:
+ multiplier = len(prompt_class_pseudo) / len(prompt_class)
+ loss *= multiplier
+ for idx in range(len(prompt_class_pseudo)):
+ if prompt_class_pseudo[idx] == 0:
+ continue # skip background class
+ loss_n += 1.0
+ gt = labels_p == prompt_class_pseudo[idx]
+ if prompt_class_pseudo[idx].item() in MERGE_LIST.keys():
+ for m in MERGE_LIST[prompt_class_pseudo[idx].item()]:
+ gt = torch.logical_or(gt, labels_p == m)
+ loss += loss_function(outputs[[idx + ps_start]].float(), gt)
+
+ loss /= max(loss_n, 1.0)
+ print(loss, ds_name)
+ if num_iters > 1:
+ if click_indx != num_iters - 1: # do not sample at last iter
+ outputs.sigmoid_()
+ if prompt_class is not None:
+ point, point_label = get_next_points(
+ outputs[: len(prompt_class)],
+ labels,
+ prompt_class,
+ point,
+ point_label,
+ )
+ if prompt_class_pseudo is not None:
+ point_pseudo, point_label_pseudo = get_next_points(
+ outputs[ps_start:],
+ labels_p,
+ prompt_class_pseudo,
+ point_pseudo,
+ point_label_pseudo,
+ )
+ # stop iterative if no new points are added.
+ skip_this_iter = torch.tensor(False, device=device)
+ if prompt_class is not None:
+ if torch.all(point_label[:, -1] == -1) and torch.all(
+ point_label[:, -2] == -1
+ ):
+ skip_this_iter = torch.tensor(True, device=device)
+ if prompt_class_pseudo is not None:
+ if torch.all(
+ point_label_pseudo[:, -1] == -1
+ ) and torch.all(point_label_pseudo[:, -2] == -1):
+ skip_this_iter = torch.tensor(True, device=device)
+
+ if world_size > 1:
+ dist.all_reduce(
+ skip_this_iter, op=dist.ReduceOp.PRODUCT
+ )
+ skip_this_iter = bool(skip_this_iter.item())
+ if skip_this_iter:
+ print(f"iteration end at {click_indx}")
+ logger.info(f"iteration end at {click_indx}")
+ break
+ del outputs
+ torch.cuda.empty_cache()
+
+ for param in model.parameters():
+ param.grad = None
+ inputs = inputs.to("cpu")
+ labels = labels.to("cpu")
+ labels_p = labels_p.to("cpu") if labels_p is not None else None
+ scaler.scale(loss).backward()
+ scaler.unscale_(optimizer)
+ scaler.step(optimizer)
+ scaler.update()
+
+ epoch_loss += loss.item()
+ loss_torch[0] += loss.item()
+ loss_torch[1] += 1.0
+ epoch_len = len(train_loader)
+ idx_iter += 1
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"{time.time() - s_time:.4f} {step}/{epoch_len}, train_loss: {loss.item():.4f}"
+ )
+ writer.add_scalar(
+ "train/loss", loss.item(), epoch_len * _round + step
+ )
+
+ if world_size > 1:
+ dist.all_reduce(loss_torch, op=torch.distributed.ReduceOp.SUM)
+
+ loss_torch = loss_torch.tolist()
+ if world_size == 1 or dist.get_rank() == 0:
+ loss_torch_epoch = loss_torch[0] / loss_torch[1]
+ logger.debug(
+ f"{time.time() - e_time:.4f} Epoch {epoch} average loss: {loss_torch_epoch:.4f}, "
+ f"best mean dice: {best_metric:.4f} at epoch {best_metric_epoch}"
+ )
+ try:
+ del inputs, labels, inputs_l, labels_l, batch_data, labels_sv_l
+ except BaseException:
+ pass
+ torch.cuda.empty_cache()
+
+ # --------- Start Validation ---------
+ """ Note:
+ In training transform, labels are mapped to global index with Relabel transform. However, there could be local index that are not used since it can excluded
+ from label_mapping definition. In training sample generation, training pairs will only be sampled from label_set. In validation, the label_prompt
+ will use global mapping, but the val label is not mapped to global index, so we need the val_orig_set. Notice the compute_dice assume gt label starts
+ from 0,1,2,3,4,.... If some are index are not used (not defined in label_mapping.json thus label_set does not include them), compute_dice directly will give wrong
+ number. We calculate dice for each class with a for loop.
+ """
+ model.eval()
+ model_inferer = partial(infer_wrapper, model=model)
+ with torch.no_grad():
+ # for metric, index 2*c is the dice for class c, and 2*c + 1 is the not-nan counts for class c
+ metric = torch.zeros(metric_dim * 2, dtype=torch.float, device=device)
+ for _index, val_data in enumerate(val_loader):
+ val_filename = val_data["image"].meta["filename_or_obj"][0]
+ val_data["label"] = val_data["label"].as_subclass(torch.Tensor)
+ val_data["image"] = val_data["image"].as_subclass(torch.Tensor)
+ ds_l = val_data["dataset_name"][0] # assume batch_size=1
+ val_label_set = [0] + [_xx[1] for _xx in label_mappings[ds_l]]
+ val_orig_set = [0] + [_xx[0] for _xx in label_mappings[ds_l]]
+ # special handling of Bone lesion dataset
+ if ds_l == "Bone-NIH":
+ val_label_set = val_label_set[:-1]
+ val_orig_set = val_orig_set[:-1]
+ # merge bone lesion 1 and 2
+ val_data["label"][val_data["label"] == 2] = 1
+
+ for _device_in, _device_out in zip(
+ [device, device, "cpu"], [device, "cpu", "cpu"]
+ ):
+ try:
+ label_prompt = (
+ torch.tensor(val_label_set).to(device).unsqueeze(0)
+ )
+ prompt_class = torch.ones(len(val_orig_set), 1).to(
+ device
+ ) # supported class
+ if drop_point_prob_train > 0.99 or (
+ freeze_head == "point" and freeze_epoch > 0
+ ):
+ point = None
+ point_label = None
+ if drop_label_prob_train > 0.99 or (
+ freeze_head == "auto" and freeze_epoch > 0
+ ):
+ label_prompt = None
+ with autocast(enabled=amp):
+ val_outputs = None
+ torch.cuda.empty_cache()
+ val_outputs = sliding_window_inference(
+ inputs=val_data["image"].to(_device_in),
+ roi_size=patch_size,
+ sw_batch_size=1,
+ predictor=model_inferer,
+ mode="gaussian",
+ overlap=overlap_ratio,
+ sw_device=device,
+ device=_device_out,
+ point_coords=None,
+ point_labels=None,
+ class_vector=label_prompt,
+ prompt_class=prompt_class,
+ labels=val_data["label"].to(_device_in),
+ label_set=val_orig_set,
+ val_point_sampler=partial(
+ sample_points_patch_val,
+ mapped_label_set=val_label_set,
+ max_ppoint=1,
+ use_center=True,
+ ),
+ )
+ try:
+ val_outputs = post_pred(val_outputs[0, ...])
+ except BaseException:
+ val_outputs = post_pred(val_outputs[0, ...].to("cpu"))
+ finished = True
+
+ except RuntimeError as e:
+ if not any(
+ x in str(e).lower() for x in ("memory", "cuda", "cudnn")
+ ):
+ raise e
+ logger.warning(e)
+ finished = False
+
+ if finished:
+ break
+
+ if finished:
+ del val_data["image"]
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ val_outputs = val_outputs[None, ...]
+ value_l = []
+ for i in range(1, len(val_orig_set)):
+ gt = val_data["label"].to(val_outputs.device) == val_orig_set[i]
+ value_l.append(
+ compute_dice(
+ y_pred=val_outputs[:, [i]],
+ y=gt,
+ include_background=False,
+ )
+ )
+ value_l = torch.hstack(value_l).to(device)
+ for _v_i, _v_c in enumerate(value_l[0]):
+ value[0, val_label_set[_v_i + 1] - 1] = _v_c
+ else:
+ # During training, allow validation OOM for some big data to avoid crush.
+ logger.debug(
+ f"{val_filename} is skipped due to OOM, using NaN dice values"
+ )
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+
+ # remove temp variables to save memory.
+ val_outputs, val_data = None, None
+ torch.cuda.empty_cache()
+
+ logger.debug(
+ f"{_index + 1} / {len(val_loader)} / {val_filename}: {value}"
+ )
+
+ for _c in range(metric_dim):
+ val0 = torch.nan_to_num(value[0, _c], nan=0.0)
+ val1 = 1.0 - torch.isnan(value[0, _c]).float()
+ metric[2 * _c] += val0
+ metric[2 * _c + 1] += val1
+
+ if world_size > 1:
+ dist.all_reduce(metric, op=torch.distributed.ReduceOp.SUM)
+
+ metric = metric.tolist()
+ metric_class = np.zeros(metric_dim)
+ if world_size == 1 or dist.get_rank() == 0:
+ avg_metric = 0
+ valid = 0
+ for _c in range(metric_dim):
+ if metric[2 * _c + 1] > 0:
+ v = metric[2 * _c] / metric[2 * _c + 1]
+ avg_metric += v
+ valid += 1
+ else:
+ v = torch.nan
+ metric_class[_c] = v
+ try:
+ writer.add_scalar(
+ f"val_class/acc_{class_names[_c + 1]}", v, epoch
+ )
+ logger.debug(
+ f"Evaluation metric - class {_c + 1} {class_names[_c + 1]}: {v:.4f}"
+ )
+ except BaseException:
+ writer.add_scalar(f"val_class/acc_{_c}", v, epoch)
+ logger.debug(f"Evaluation metric - class {_c + 1} : {v:.4f}")
+
+ avg_metric = avg_metric / valid
+ logger.debug(f"Avg_metric: {avg_metric}")
+
+ writer.add_scalar("val/acc", avg_metric, epoch)
+ if avg_metric > best_metric or save_last:
+ best_metric = avg_metric
+ best_metric_epoch = epoch
+ if save_all:
+ ckpt_name = f"best_metric_model_{epoch}.pt"
+ else:
+ ckpt_name = "best_metric_model.pt"
+ if world_size > 1:
+ torch.save(
+ model.module.state_dict(),
+ os.path.join(ckpt_path, ckpt_name),
+ )
+ else:
+ torch.save(
+ model.state_dict(), os.path.join(ckpt_path, ckpt_name)
+ )
+ logger.debug("Saved new best metric model")
+
+ dict_file = {}
+ dict_file["best_avg_dice_score"] = float(best_metric)
+ dict_file["best_avg_dice_score_epoch"] = int(best_metric_epoch)
+ dict_file["best_avg_dice_score_iteration"] = int(idx_iter)
+ with open(
+ os.path.join(ckpt_path, "progress.yaml"), "a"
+ ) as out_file:
+ yaml.dump([dict_file], stream=out_file)
+
+ logger.debug(
+ "Current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}".format(
+ epoch, avg_metric, best_metric, best_metric_epoch
+ )
+ )
+
+ current_time = time.time()
+ elapsed_time = (current_time - start_time) / 60.0
+ with open(os.path.join(ckpt_path, "accuracy_history.csv"), "a") as f:
+ f.write(
+ "{:d}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.1f}\t{:d}\n".format(
+ epoch,
+ avg_metric,
+ loss_torch_epoch,
+ lr,
+ elapsed_time,
+ idx_iter,
+ )
+ )
+
+ if world_size > 1:
+ dist.barrier()
+
+ torch.cuda.empty_cache()
+
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"Training completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}."
+ )
+
+ writer.flush()
+ writer.close()
+
+ logger.warning(f"{os.path.basename(bundle_root)} - training: finished")
+
+ if world_size > 1:
+ dist.destroy_process_group()
+
+ return
+
+
+if __name__ == "__main__":
+ fire, _ = optional_import("fire")
+ fire.Fire()
diff --git a/vista3d/scripts/train_finetune.py b/vista3d/scripts/train_finetune.py
new file mode 100644
index 0000000..bb945ee
--- /dev/null
+++ b/vista3d/scripts/train_finetune.py
@@ -0,0 +1,817 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import copy
+import logging
+import math
+import os
+import sys
+import time
+import warnings
+from datetime import timedelta
+from functools import partial
+from typing import Optional, Sequence, Union
+
+import monai
+import nibabel as nib
+import numpy as np
+import torch
+import torch.distributed as dist
+import yaml
+from monai import transforms
+from monai.apps.auto3dseg.auto_runner import logger
+from monai.auto3dseg.utils import datafold_read
+from monai.bundle import ConfigParser
+from monai.bundle.scripts import _pop_args, _update_args
+from monai.data import DataLoader, DistributedSampler
+from monai.metrics import compute_dice
+from monai.networks.utils import copy_model_state
+from monai.utils import set_determinism
+from torch.nn.parallel import DistributedDataParallel
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+from vista3d import vista_model_registry
+
+from .sliding_window import sliding_window_inference
+from .train import CONFIG, infer_wrapper, loss_wrapper
+from .utils.workflow_utils import (
+ MERGE_LIST,
+ generate_prompt_pairs,
+ get_next_points,
+ sample_points_patch_val,
+)
+
+nib.imageglobals.logger.setLevel(40)
+
+
+def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
+ # Initialize distributed and scale parameters based on GPU memory
+ if torch.cuda.device_count() > 1:
+ dist.init_process_group(
+ backend="nccl", init_method="env://", timeout=timedelta(seconds=3600)
+ )
+ world_size = dist.get_world_size()
+ dist.barrier()
+ else:
+ world_size = 1
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+ if isinstance(config_file, str) and "," in config_file:
+ config_file = config_file.split(",")
+ _args = _update_args(config_file=config_file, **override)
+ config_file_ = _pop_args(_args, "config_file")[0]
+
+ parser = ConfigParser()
+ parser.read_config(config_file_)
+ parser.update(pairs=_args)
+
+ # loggings and experiment pathes
+ start_time = time.time()
+ bundle_root = parser.get_parsed_content("bundle_root")
+ ckpt_path = parser.get_parsed_content("ckpt_path")
+ os.makedirs(ckpt_path, exist_ok=True)
+ if world_size == 1 or dist.get_rank() == 0:
+ writer = SummaryWriter(log_dir=os.path.join(ckpt_path, "Events"))
+ with open(os.path.join(ckpt_path, "accuracy_history.csv"), "a") as f:
+ f.write("epoch\tmetric\tloss\tlr\ttime\titer\n")
+ random_seed = parser.get_parsed_content("random_seed")
+ save_last = parser.get_parsed_content(
+ "save_last", default=False
+ ) # save the last checkpoint
+ save_all = parser.get_parsed_content(
+ "save_all", default=False
+ ) # save all val-checkpoint
+ if world_size == 1 or dist.get_rank() == 0:
+ config_yaml = os.path.join(bundle_root, "configs.yaml")
+ ConfigParser.export_config_file(
+ parser.get(), config_yaml, fmt="yaml", default_flow_style=None
+ )
+ if random_seed is not None and (
+ isinstance(random_seed, int) or isinstance(random_seed, float)
+ ):
+ set_determinism(seed=random_seed)
+ CONFIG["handlers"]["file"]["filename"] = parser.get_parsed_content(
+ "log_output_file"
+ )
+ logging.config.dictConfig(CONFIG)
+ logging.getLogger("torch.distributed.distributed_c10d").setLevel(logging.WARNING)
+
+ # training hyperparameters - workflow
+ device = (
+ torch.device(f"cuda:{os.environ['LOCAL_RANK']}")
+ if world_size > 1
+ else torch.device("cuda:0")
+ )
+ amp = parser.get_parsed_content("amp")
+ if amp:
+ from torch.cuda.amp import GradScaler, autocast
+
+ scaler = GradScaler()
+ logger.debug("Amp enabled")
+ finetune = parser.get_parsed_content("finetune")
+ num_epochs = parser.get_parsed_content("num_epochs")
+ num_epochs_per_validation = parser.get_parsed_content("num_epochs_per_validation")
+ skip_iter_prob = parser.get_parsed_content(
+ "skip_iter_prob"
+ ) # prob. to skip iterative point sampling, 1 for auto-training
+ iter_num = parser.get_parsed_content(
+ "iter_num"
+ ) # total iter number in point branch training
+ freeze_epoch = parser.get_parsed_content(
+ "freeze_epoch", default=-1
+ ) # freeze the whole branch epoch
+ freeze_head = parser.get_parsed_content(
+ "freeze_head", default="auto"
+ ) # freeze which branch, "auto" or "point". We freeze point for auto-training.
+ logger.debug(f"World_size: {world_size}")
+ logger.debug(f"num_epochs: {num_epochs}")
+ logger.debug(f"num_epochs_per_validation: {num_epochs_per_validation}")
+
+ # training hyperparameters - model and optimizer
+ input_channels = parser.get_parsed_content("input_channels")
+ model_registry = parser.get_parsed_content("model")
+ patch_size = parser.get_parsed_content("patch_size")
+ model = vista_model_registry[model_registry](
+ in_channels=input_channels, image_size=patch_size
+ )
+ model = model.to(device)
+ optimizer_part = parser.get_parsed_content("optimizer", instantiate=False)
+ optimizer = optimizer_part.instantiate(params=model.parameters())
+ lr_scheduler_part = parser.get_parsed_content("lr_scheduler", instantiate=False)
+ lr_scheduler = lr_scheduler_part.instantiate(optimizer=optimizer)
+ if world_size > 1:
+ model = DistributedDataParallel(
+ model, device_ids=[device], find_unused_parameters=True
+ )
+ if finetune["activate"] and os.path.isfile(finetune["pretrained_ckpt_name"]):
+ logger.debug(
+ "Fine-tuning pre-trained checkpoint {:s}".format(
+ finetune["pretrained_ckpt_name"]
+ )
+ )
+ pretrained_ckpt = torch.load(
+ finetune["pretrained_ckpt_name"], map_location=device
+ )
+ copy_model_state(
+ model, pretrained_ckpt, exclude_vars=finetune.get("exclude_vars")
+ )
+ del pretrained_ckpt
+ else:
+ logger.debug("Training from scratch")
+
+ # training hyperparameters - sample
+ num_images_per_batch = parser.get_parsed_content("num_images_per_batch")
+ num_patches_per_iter = parser.get_parsed_content("num_patches_per_iter")
+ overlap_ratio = parser.get_parsed_content("overlap_ratio") # sliding window overlap
+ max_prompt = parser.get_parsed_content("max_prompt", default=96)
+ max_backprompt = parser.get_parsed_content("max_backprompt", default=96)
+ max_foreprompt = parser.get_parsed_content("max_foreprompt", default=96)
+ drop_label_prob = parser.get_parsed_content("drop_label_prob")
+ drop_point_prob = parser.get_parsed_content("drop_point_prob")
+ max_point = parser.get_parsed_content("max_point")
+
+ # training hyperparameters - data and transforms
+ label_set = parser.get_parsed_content("label_set", default=None)
+ mapped_label_set = parser.get_parsed_content(
+ "mapped_label_set", default=copy.deepcopy(label_set)
+ )
+ # user can define class names in the json config
+ class_names = parser.get_parsed_content("class_names", default=None)
+ label_mapping = {label_set[i]: mapped_label_set[i] for i in range(len(label_set))}
+ metric_dim = len(label_set) - 1 # only affect dice calculation
+ fold = parser.get_parsed_content("fold")
+ use_folds = parser.get_parsed_content("use_folds", default=False)
+ post_pred = transforms.Compose(
+ [
+ transforms.EnsureType(),
+ transforms.AsDiscrete(threshold=0.0, dtype=torch.uint8),
+ ]
+ )
+ data_file_base_dir = parser.get_parsed_content("data_file_base_dir")
+ data_list_file_path = parser.get_parsed_content("data_list_file_path")
+ train_number = parser.get_parsed_content("train_number", default=-1)
+ train_transforms, val_transforms = None, None
+ train_transforms = parser.get_parsed_content("transforms_train", default=None)
+ val_transforms = parser.get_parsed_content("transforms_validate", default=None)
+ post_transform = transforms.Invertd(
+ keys="pred",
+ transform=val_transforms,
+ orig_keys="image",
+ meta_keys="pred_meta_dict",
+ orig_meta_keys="image_meta_dict",
+ meta_key_postfix="meta_dict",
+ nearest_interp=False,
+ to_tensor=True,
+ )
+ if use_folds:
+ train_files, val_files = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=fold,
+ key="training",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ else:
+ train_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="training",
+ )
+ val_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="validation",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ train_files = train_files[:train_number]
+ if world_size > 1:
+ if len(val_files) < world_size:
+ val_files = list(val_files) * math.ceil(
+ float(world_size) / float(len(val_files))
+ )
+ logger.debug(f"Train_files: {len(train_files)}")
+ logger.debug(f"Val_files: {len(val_files)}")
+ with warnings.catch_warnings():
+ warnings.simplefilter(action="ignore", category=FutureWarning)
+ warnings.simplefilter(action="ignore", category=Warning)
+ train_ds, val_ds, test_ds = None, None, None
+ # for pact 118 use normal dataset to avoid crash.
+ train_ds = monai.data.Dataset(
+ data=train_files * num_epochs_per_validation, transform=train_transforms
+ )
+ val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
+ test_ds = monai.data.Dataset(data=test_files, transform=val_transforms)
+ train_sampler, val_sampler, test_sampler = None, None, None
+ if world_size > 1:
+ if train_ds is not None:
+ train_sampler = DistributedSampler(train_ds, shuffle=True)
+ if val_ds is not None:
+ val_sampler = DistributedSampler(
+ val_ds, shuffle=False, even_divisible=False
+ )
+ if test_ds is not None:
+ test_sampler = DistributedSampler(
+ test_ds, shuffle=False, even_divisible=False
+ )
+ train_loader = DataLoader(
+ train_ds,
+ num_workers=4,
+ batch_size=num_images_per_batch,
+ shuffle=(train_sampler is None),
+ persistent_workers=True,
+ pin_memory=True,
+ sampler=train_sampler,
+ prefetch_factor=1,
+ )
+ val_loader = DataLoader(
+ val_ds,
+ num_workers=4,
+ batch_size=1,
+ shuffle=False,
+ sampler=val_sampler,
+ prefetch_factor=1,
+ persistent_workers=False,
+ )
+ test_loader = DataLoader(
+ test_ds,
+ num_workers=4,
+ batch_size=1,
+ shuffle=False,
+ sampler=test_sampler,
+ prefetch_factor=1,
+ persistent_workers=False,
+ )
+
+ # --------- Start training ---------
+ """ Notes: The training script is directly modified from auto3dseg.
+ To increase speed, the training script is not based on epoch, but based on validation rounds.
+ In each batch, num_images_per_batch=2 whole 3D images are loaded into CPU for data transformation
+ num_patches_per_image=2*num_patches_per_iter is extracted from each 3D image, in each iteration,
+ num_patches_per_iter patches is used for training (real batch size on each GPU).
+ """
+ num_rounds = int(np.ceil(float(num_epochs) // float(num_epochs_per_validation)))
+ best_metric = -1
+ best_metric_epoch = -1
+ idx_iter = 0
+ if num_rounds == 0:
+ raise RuntimeError(
+ "num_epochs_per_validation > num_epochs, modify hyper_parameters.yaml"
+ )
+
+ if world_size == 1 or dist.get_rank() == 0:
+ progress_bar = tqdm(
+ range(num_rounds),
+ desc=f"{os.path.basename(bundle_root)} - training ...",
+ unit="round",
+ )
+ for _round in (
+ range(num_rounds) if world_size > 1 and dist.get_rank() != 0 else progress_bar
+ ):
+ model.train()
+ epoch_loss = 0
+ loss_torch_epoch = 0
+ epoch = 0
+ loss_torch = torch.zeros(2, dtype=torch.float, device=device) + 1e-5
+ step = 0
+ e_time = time.time()
+ if world_size > 1:
+ train_loader.sampler.set_epoch(_round)
+ for batch_data in train_loader:
+ # for batch_data in cycle_k(train_loader):
+ s_time = time.time()
+ # if step % (len(train_loader)) == 0:
+ if step % (len(train_loader) // num_epochs_per_validation) == 0:
+ epoch = _round * num_epochs_per_validation + step // (
+ len(train_loader) // num_epochs_per_validation
+ )
+ lr_scheduler.step()
+ lr = lr_scheduler.get_last_lr()[0]
+ if freeze_epoch > epoch:
+ # if automatic branch is frozen, drop label prompts
+ if freeze_head == "auto":
+ drop_label_prob_train = 1
+ drop_point_prob_train = 0
+ auto_freeze = True
+ point_freeze = False
+ elif freeze_head == "point":
+ drop_label_prob_train = 0
+ drop_point_prob_train = 1
+ auto_freeze = False
+ point_freeze = True
+ try:
+ model.module.set_auto_grad(
+ auto_freeze=auto_freeze, point_freeze=point_freeze
+ )
+ except BaseException:
+ model.set_auto_grad(
+ auto_freeze=auto_freeze, point_freeze=point_freeze
+ )
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"Auto freeze {auto_freeze}, point freeze {point_freeze} at epoch {epoch}!"
+ )
+ else:
+ drop_label_prob_train = drop_label_prob
+ drop_point_prob_train = drop_point_prob
+ try:
+ model.module.set_auto_grad(
+ auto_freeze=False, point_freeze=False
+ )
+ except BaseException:
+ model.set_auto_grad(auto_freeze=False, point_freeze=False)
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"Auto freeze {False}, point freeze {False} at epoch {epoch}!"
+ )
+
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug("----------")
+ logger.debug(f"epoch {epoch}/{num_epochs}")
+ logger.debug(f"Learning rate is set to {lr}")
+ step += 1
+ inputs_l = batch_data["image"].as_subclass(torch.Tensor)
+ if "label" not in batch_data:
+ # this will only happen for unlabeled dataset
+ batch_data["label"] = batch_data.pop("pseudo_label")
+ labels_l = batch_data["label"].as_subclass(torch.Tensor)
+
+ if len(inputs_l) > 1:
+ _idx = torch.randperm(inputs_l.shape[0])
+ inputs_l = inputs_l[_idx]
+ labels_l = labels_l[_idx]
+
+ for _k in range(inputs_l.shape[0] // num_patches_per_iter):
+ inputs = inputs_l[
+ _k * num_patches_per_iter : (_k + 1) * num_patches_per_iter, ...
+ ]
+ labels = labels_l[
+ _k * num_patches_per_iter : (_k + 1) * num_patches_per_iter, ...
+ ]
+ inputs = inputs.to(device)
+ labels = labels.to(device)
+ train_label_set = label_set
+ if world_size > 1:
+ if dist.get_rank() == 0:
+ skip_iter = (torch.rand(1) < skip_iter_prob).to(
+ dtype=torch.float, device=device
+ )
+ else:
+ skip_iter = torch.empty(1).to(dtype=torch.float, device=device)
+ dist.broadcast(skip_iter, src=0)
+ else:
+ skip_iter = (torch.rand(1) < skip_iter_prob).float()
+ if skip_iter > 0:
+ # if not using iterative
+ num_iters = 1
+ else:
+ # if use iterative training
+ num_iters = max(iter_num, 1)
+
+ # for dataset other than totalseg, use pseudolabel for zero-shot. for totalseg, if labels_p exist, use labels_p for zero-shot,
+ # gt for regular sample. If labels_p does not exist, use gt for zero-shot.
+ label_prompt, point, point_label, prompt_class = generate_prompt_pairs(
+ labels,
+ train_label_set,
+ max_point=max_point,
+ max_prompt=max_prompt,
+ max_backprompt=max_backprompt,
+ max_foreprompt=max_foreprompt,
+ drop_label_prob=drop_label_prob_train,
+ drop_point_prob=drop_point_prob_train,
+ )
+ # Skip the training if prompts are both None
+ skip_update = torch.zeros(1, device=device)
+ if label_prompt is None and point is None:
+ logger.debug("Iteration skipped due to None prompts")
+ skip_update = torch.ones(1, device=device)
+ if world_size > 1:
+ dist.all_reduce(skip_update, op=dist.ReduceOp.SUM)
+ if skip_update[0] > 0:
+ continue # some rank has no foreground, skip this batch
+ # clear image_embedding
+ try:
+ model.module.clear_cache()
+ except BaseException:
+ model.clear_cache()
+ for click_indx in range(num_iters):
+ outputs = None
+ inputs = inputs.to(device)
+ labels = labels.to(device)
+ # only sinlge point prompt case activate multi-mask output
+ loss_function = partial(
+ loss_wrapper, loss_function=parser.get_parsed_content("loss")
+ )
+
+ # need to convert label_prompt to global index
+ label_prompt_global = []
+ for i in label_prompt[:, 0].cpu().tolist():
+ label_prompt_global.append(label_mapping[i])
+ with autocast():
+ outputs = model(
+ input_images=inputs,
+ point_coords=point,
+ point_labels=point_label,
+ class_vector=torch.tensor(label_prompt_global)
+ .to(device)
+ .unsqueeze(1),
+ prompt_class=prompt_class,
+ )
+ # cumulate loss
+ loss, loss_n = torch.tensor(0.0, device=device), torch.tensor(
+ 0.0, device=device
+ )
+ for idx in range(len(prompt_class)):
+ if prompt_class[idx] == 0:
+ continue # skip background class
+ loss_n += 1.0
+ gt = labels == prompt_class[idx]
+ if prompt_class[idx].item() in MERGE_LIST.keys():
+ for m in MERGE_LIST[prompt_class[idx].item()]:
+ gt = torch.logical_or(gt, labels == m)
+ loss += loss_function(outputs[[idx]].float(), gt)
+
+ loss /= max(loss_n, 1.0)
+
+ if num_iters > 1:
+ if click_indx != num_iters - 1: # do not sample at last iter
+ outputs.sigmoid_()
+ if prompt_class is not None:
+ point, point_label = get_next_points(
+ outputs[: len(prompt_class)],
+ labels,
+ prompt_class,
+ point,
+ point_label,
+ )
+ # stop iterative if no new points are added.
+ skip_this_iter = torch.tensor(False, device=device)
+ if prompt_class is not None:
+ if torch.all(point_label[:, -1] == -1) and torch.all(
+ point_label[:, -2] == -1
+ ):
+ skip_this_iter = torch.tensor(True, device=device)
+ if world_size > 1:
+ dist.all_reduce(
+ skip_this_iter, op=dist.ReduceOp.PRODUCT
+ )
+ skip_this_iter = bool(skip_this_iter.item())
+ if skip_this_iter:
+ print(f"iteration end at {click_indx}")
+ logger.info(f"iteration end at {click_indx}")
+ break
+ del outputs
+ torch.cuda.empty_cache()
+
+ for param in model.parameters():
+ param.grad = None
+ inputs = inputs.to("cpu")
+ labels = labels.to("cpu")
+ scaler.scale(loss).backward()
+ scaler.unscale_(optimizer)
+ # clip_grad_norm_(model.parameters(), 0.5)
+ scaler.step(optimizer)
+ scaler.update()
+
+ epoch_loss += loss.item()
+ loss_torch[0] += loss.item()
+ loss_torch[1] += 1.0
+ epoch_len = len(train_loader) # * num_epochs_per_validation
+ idx_iter += 1
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"{time.time() - s_time:.4f} {step}/{epoch_len}, train_loss: {loss.item():.4f}"
+ )
+ writer.add_scalar(
+ "train/loss", loss.item(), epoch_len * _round + step
+ )
+
+ if world_size > 1:
+ dist.all_reduce(loss_torch, op=torch.distributed.ReduceOp.SUM)
+
+ loss_torch = loss_torch.tolist()
+ if world_size == 1 or dist.get_rank() == 0:
+ loss_torch_epoch = loss_torch[0] / loss_torch[1]
+ logger.debug(
+ f"{time.time() - e_time:.4f} Epoch {epoch} average loss: {loss_torch_epoch:.4f}, "
+ f"best mean dice: {best_metric:.4f} at epoch {best_metric_epoch}"
+ )
+ try:
+ del inputs, labels, inputs_l, labels_l, batch_data
+ except BaseException:
+ pass
+ torch.cuda.empty_cache()
+
+ # --------- Start Validation ---------
+ """ Note:
+ In training transform, labels are mapped to global index with Relabel transform. However, there could be local index that are not used since it can excluded
+ from label_mapping definition. In training sample generation, training pairs will only be sampled from label_set. In validation, the label_prompt
+ will use global mapping, but the val label is not mapped to global index, so we need the val_orig_set. Notice the compute_dice assume gt label starts
+ from 0,1,2,3,4,.... If some are index are not used (not defined in label_mapping.json thus label_set does not include them), compute_dice directly will give wrong
+ number. We calculate dice for each class with a for loop.
+ """
+ model.eval()
+ model_inferer = partial(infer_wrapper, model=model)
+ data_loader = [val_loader, test_loader]
+ log_info = ["Validation", "Testing"]
+ for val_times in [0]:
+ with torch.no_grad():
+ # for metric, index 2*c is the dice for class c, and 2*c + 1 is the not-nan counts for class c
+ metric = torch.zeros(metric_dim * 2, dtype=torch.float, device=device)
+ val_data = None
+ torch.cuda.empty_cache()
+ for _index, val_data in enumerate(data_loader[val_times]):
+ val_filename = val_data["image"].meta["filename_or_obj"][0]
+ # one difference is that label_set in train.py does not include 0 but here we require user add 0 in
+ # the json config.
+ val_label_set = mapped_label_set
+ val_orig_set = label_set
+
+ for _device_in, _device_out in zip(
+ [device, device, "cpu"], [device, "cpu", "cpu"]
+ ):
+ try:
+ label_prompt = (
+ torch.tensor(val_label_set).to(device).unsqueeze(0)
+ )
+ promot_class = torch.ones(len(label_set), 1).to(
+ device
+ ) # supported class
+ if drop_label_prob > 0.99:
+ label_prompt = None
+ with autocast(enabled=amp):
+ val_outputs = None
+ torch.cuda.empty_cache()
+ val_outputs = sliding_window_inference(
+ inputs=val_data["image"].to(_device_in),
+ roi_size=patch_size,
+ sw_batch_size=1,
+ predictor=model_inferer,
+ mode="gaussian",
+ overlap=overlap_ratio,
+ sw_device=device,
+ device=_device_out,
+ point_coords=None,
+ point_labels=None,
+ class_vector=label_prompt,
+ prompt_class=promot_class,
+ labels=val_data["label"].to(_device_in),
+ label_set=val_orig_set,
+ val_point_sampler=partial(
+ sample_points_patch_val,
+ mapped_label_set=val_label_set,
+ max_ppoint=1,
+ use_center=True,
+ ),
+ )
+ try:
+ val_outputs = post_pred(val_outputs[0, ...])
+ except BaseException:
+ val_outputs = post_pred(val_outputs[0, ...].to("cpu"))
+ finished = True
+
+ except RuntimeError as e:
+ if not any(
+ x in str(e).lower() for x in ("memory", "cuda", "cudnn")
+ ):
+ raise e
+ logger.warning(e)
+ finished = False
+
+ if finished:
+ break
+
+ if finished:
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ val_outputs = val_outputs[None, ...]
+ if val_times > 0:
+ try:
+ val_outputs = post_transform(
+ {
+ "image": val_data["image"][0],
+ "pred": val_outputs[0],
+ }
+ )["pred"][None, ...]
+ except BaseException:
+ print(val_filename, "OOM", val_data["label_gt"].shape)
+ val_data["image"] = val_data["image"].cpu()
+ val_outputs = val_outputs.cpu()
+ val_outputs = post_transform(
+ {
+ "image": val_data["image"][0],
+ "pred": val_outputs[0],
+ }
+ )["pred"][None, ...]
+ print("finished OOM")
+ del val_data["image"]
+ value_l = []
+ for i in range(1, len(val_orig_set)):
+ if val_times == 0:
+ gt = (
+ val_data["label"].to(val_outputs.device)
+ == val_orig_set[i]
+ )
+ else:
+ gt = (
+ val_data["label_gt"].to(val_outputs.device)
+ == val_orig_set[i]
+ )
+ value_l.append(
+ compute_dice(
+ y_pred=val_outputs[:, [i]],
+ y=gt,
+ include_background=False,
+ )
+ )
+ value = torch.hstack(value_l).to(device)
+ else:
+ # During training, allow validation OOM for some big data to avoid crush.
+ logger.debug(
+ f"{val_filename} is skipped due to OOM, using NaN dice values"
+ )
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+
+ # remove temp variables to save memory.
+ val_outputs, val_data = None, None
+ torch.cuda.empty_cache()
+
+ logger.debug(
+ f"{_index + 1} / {len(val_loader)} / {val_filename}: {value}"
+ )
+
+ for _c in range(metric_dim):
+ val0 = torch.nan_to_num(value[0, _c], nan=0.0)
+ val1 = 1.0 - torch.isnan(value[0, _c]).float()
+ metric[2 * _c] += val0
+ metric[2 * _c + 1] += val1
+
+ if world_size > 1:
+ dist.all_reduce(metric, op=torch.distributed.ReduceOp.SUM)
+
+ metric = metric.tolist()
+ metric_class = np.zeros(metric_dim)
+ if world_size == 1 or dist.get_rank() == 0:
+ avg_metric = 0
+ valid = 0
+ for _c in range(metric_dim):
+ if metric[2 * _c + 1] > 0:
+ v = metric[2 * _c] / metric[2 * _c + 1]
+ avg_metric += v
+ valid += 1
+ else:
+ v = torch.nan
+ metric_class[_c] = v
+ try:
+ writer.add_scalar(
+ f"val_class/acc_{class_names[_c + 1]}", v, epoch
+ )
+ logger.debug(
+ f"Evaluation metric - class {_c + 1} {class_names[_c + 1]}: {v:.4f}"
+ )
+ except BaseException:
+ writer.add_scalar(f"val_class/acc_{_c}", v, epoch)
+ logger.debug(
+ f"Evaluation metric - class {_c + 1} : {v:.4f}"
+ )
+
+ avg_metric = avg_metric / valid
+ logger.debug(f"{log_info[val_times]} Avg_metric: {avg_metric}")
+ if val_times > 0:
+ continue
+ writer.add_scalar("val/acc", avg_metric, epoch)
+ if avg_metric > best_metric or save_last:
+ best_metric = avg_metric
+ best_metric_epoch = epoch
+ if save_all:
+ ckpt_name = f"best_metric_model_{epoch}.pt"
+ else:
+ ckpt_name = "best_metric_model.pt"
+ if world_size > 1:
+ torch.save(
+ model.module.state_dict(),
+ os.path.join(ckpt_path, ckpt_name),
+ )
+ else:
+ torch.save(
+ model.state_dict(), os.path.join(ckpt_path, ckpt_name)
+ )
+ logger.debug("Saved new best metric model")
+
+ dict_file = {}
+ dict_file["best_avg_dice_score"] = float(best_metric)
+ dict_file["best_avg_dice_score_epoch"] = int(best_metric_epoch)
+ dict_file["best_avg_dice_score_iteration"] = int(idx_iter)
+ with open(
+ os.path.join(ckpt_path, "progress.yaml"), "a"
+ ) as out_file:
+ yaml.dump([dict_file], stream=out_file)
+
+ logger.debug(
+ "Current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}".format(
+ epoch, avg_metric, best_metric, best_metric_epoch
+ )
+ )
+
+ current_time = time.time()
+ elapsed_time = (current_time - start_time) / 60.0
+ with open(
+ os.path.join(ckpt_path, "accuracy_history.csv"), "a"
+ ) as f:
+ f.write(
+ "{:d}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.1f}\t{:d}\n".format(
+ epoch,
+ avg_metric,
+ loss_torch_epoch,
+ lr,
+ elapsed_time,
+ idx_iter,
+ )
+ )
+
+ if world_size > 1:
+ dist.barrier()
+
+ torch.cuda.empty_cache()
+
+ if world_size == 1 or dist.get_rank() == 0:
+ logger.debug(
+ f"Training completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}."
+ )
+
+ writer.flush()
+ writer.close()
+
+ logger.warning(f"{os.path.basename(bundle_root)} - training: finished")
+
+ if world_size > 1:
+ dist.destroy_process_group()
+
+ return
+
+
+if __name__ == "__main__":
+ from monai.utils import optional_import
+
+ fire, _ = optional_import("fire")
+ fire.Fire()
diff --git a/vista3d/scripts/utils/sample_utils.py b/vista3d/scripts/utils/sample_utils.py
new file mode 100644
index 0000000..24973f0
--- /dev/null
+++ b/vista3d/scripts/utils/sample_utils.py
@@ -0,0 +1,348 @@
+import copy
+import random
+
+import monai
+import numpy as np
+import torch
+from skimage import measure
+
+from .trans_utils import dilate3d, erode3d
+from .workflow_utils import ENABLE_SPECIAL, SPECIAL_INDEX, get_point_label
+
+
+def open_lcc(plabels):
+ plabels_org = plabels.clone()
+ plabels = erode3d(plabels, erosion=3)
+ plabels = monai.transforms.utils.get_largest_connected_component_mask(plabels)
+ return dilate3d(plabels, erosion=3).to(torch.bool) * plabels_org
+
+
+def find_lcc_label(plabels, region):
+ plabels_org = plabels.clone()
+ plabels = erode3d(plabels, erosion=3)
+ label = measure.label
+ features, num_features = label(
+ plabels.cpu().numpy(), connectivity=3, return_num=True
+ )
+ features = torch.from_numpy(features).to(region.device)
+ max_cc = torch.zeros_like(region)
+ max_cc_count = 0
+ region = dilate3d(region, erosion=5).to(torch.bool)
+ for i in range(1, num_features):
+ cc = features == i
+ if torch.logical_and(cc, region).any():
+ cc_sum = cc.sum()
+ if cc_sum > max_cc_count:
+ max_cc = cc
+ max_cc_count = cc_sum
+ return dilate3d(max_cc, erosion=5).to(torch.bool) * plabels_org
+
+
+class Point_sampler:
+ """Point sampler will take original manual label and supervoxel to perform augmentation.
+ Args:
+ label: manual label
+ label_sv: supervoxel
+ map_shift: this value must be larger than the last_supported value in vista3d.point_head. When a
+ mask is given a shift of id + map_shift, it will be identified as zero-shot.
+ offset: remove patch boundary samples
+ vrange: the probability range for different augmentations.
+ """
+
+ def __init__(
+ self,
+ label,
+ label_sv,
+ map_shift=512,
+ offset=10,
+ vrange=[0.6, 0.7, 0.8, 0.9],
+ ):
+ self.label = label
+ self.label_sv = label_sv
+ self.map_shift = map_shift
+ self.shifted = {}
+ self.device = self.label.device
+ self.label_ = label.clone()
+ self.window = torch.ones_like(label, dtype=torch.bool)
+ self.window[offset:-offset, offset:-offset, offset:-offset] = False
+ self.vrange = vrange
+ self.zshot_rate = 0.5
+
+ def reset(self):
+ self.label = self.label_.clone()
+
+ def skip_aug(self, id):
+ if id in SPECIAL_INDEX and ENABLE_SPECIAL:
+ return True
+ return False
+
+ def __call__(self, unique_labels, Np=1, Nn=0):
+ _point = []
+ _point_label = []
+ vrange = self.vrange
+ self.read_only_id = copy.deepcopy(unique_labels)
+ for id in unique_labels:
+ if self.skip_aug(id):
+ v = 0
+ print(f"{id} will use regular")
+ else:
+ v = np.random.rand()
+ if v < vrange[0] or self.label_sv is None:
+ _p, _pl = self.regular(id, Np, Nn)
+ if v >= vrange[0] and v < vrange[1]:
+ _p, _pl = self.organ_sub(id, Np, Nn)
+ if v >= vrange[1] and v < vrange[2]:
+ _p, _pl = self.organ_add(id, Np, Nn)
+ if v >= vrange[2] and v < vrange[3]:
+ _p, _pl = self.zeroshot_unseen(id, Np, Nn)
+ if v >= vrange[3]:
+ _p, _pl = self.zeroshot_random(id, Np, Nn)
+ _point.append(_p)
+ _point_label.append(_pl)
+ return self._padding(_point, _point_label)
+
+ def _padding(self, point, point_label):
+ if len(point) > 0:
+ max_len = max([len(_) for _ in point])
+ point = [
+ torch.stack(
+ _ + [torch.tensor([0, 0, 0]).to(self.device)] * (max_len - len(_))
+ )
+ for _ in point
+ ]
+ point_label = [
+ torch.tensor(_ + [-1] * (max_len - len(_))).to(self.device)
+ for _ in point_label
+ ]
+ # print(point, point_label)
+ return point, point_label
+
+ def regular(self, id, Np=1, Nn=0):
+ print("regular")
+ plabels = self.label == int(id)
+ _plabels = erode3d(plabels)
+ _plabels[self.window] = 0
+ plabelpoints = torch.nonzero(_plabels)
+ kp = min(len(plabelpoints), Np)
+ if kp == 0:
+ plabelpoints = torch.nonzero(plabels)
+ kp = min(len(plabelpoints), Np)
+ _point = random.choices(plabelpoints, k=kp)
+ neg_id, pos_id = get_point_label(id)
+ _point_label = [pos_id] * kp
+ if Nn > 0:
+ nlabels = ~plabels
+ nlabels = erode3d(nlabels)
+ nlabelpoints = torch.nonzero(nlabels)
+ kn = min(len(nlabelpoints), Nn)
+ if kn > 0:
+ _point += random.choices(nlabelpoints, k=kn)
+ _point_label += [neg_id] * kn
+ return _point, _point_label
+
+ def zeroshot_random(self, id, Np=1, Nn=0):
+ min_size = 20 * 20 * 20
+ region = self.label == 0
+ sregion = self.label_sv[region]
+ sid = torch.unique(sregion).cpu().numpy()
+ random.shuffle(sid)
+ for sid_p in sid:
+ plabels = self.label_sv == sid_p
+ plabels = open_lcc(plabels)
+ ids = self.label[plabels].unique().cpu().numpy()
+ if np.array([i in ids for i in self.read_only_id]).any():
+ continue
+ if plabels.sum() < min_size:
+ continue
+ _point = []
+ _point_label = []
+ random.shuffle(ids)
+ count = 0
+ max_merge = len(ids)
+ for i in ids:
+ if i == 0:
+ continue
+ if count >= max_merge:
+ break
+ fg = self.label == i
+ overlap = torch.logical_and(plabels, fg)
+ if 0.1 * fg.sum() < overlap.sum():
+ plabels = torch.logical_or(plabels, fg)
+ _plabels = erode3d(overlap)
+ _plabels[self.window] = 0
+ plabelpoints = torch.nonzero(_plabels)
+ kp = min(len(plabelpoints), 1)
+ if kp == 0:
+ continue
+ _point += random.choices(plabelpoints, k=kp)
+ _point_label += [1] * kp
+ count += 1
+ _plabels = torch.logical_and(plabels, region)
+ _plabels = erode3d(_plabels)
+ _plabels[self.window] = 0
+ plabelpoints = torch.nonzero(_plabels)
+ kp = min(len(plabelpoints), Np)
+ if kp == 0:
+ continue
+ _point += random.choices(plabelpoints, k=kp)
+ _point_label += [1] * kp
+ self.label[plabels.to(torch.bool)] = id + self.map_shift
+ self.shifted[id] = id + self.map_shift
+ self.read_only_id.append(id + self.map_shift)
+ print("zeroshot_random")
+ return _point, _point_label
+ return self.regular(id, Np, Nn)
+
+ def zeroshot_unseen(self, id, Np=1, Nn=0):
+ # PASSED
+ min_size = 20 * 20 * 20
+ region = self.label == 0
+ sregion = self.label_sv[region]
+ sid = torch.unique(sregion).cpu().numpy()
+ random.shuffle(sid)
+ npoint = None
+ for sid_p in sid:
+ plabels = torch.logical_and(self.label_sv == sid_p, region)
+ plabels = open_lcc(plabels)
+ if plabels.sum() < min_size:
+ continue
+ ids = self.label[plabels].unique().cpu().numpy().tolist()
+ if np.array([i in ids for i in self.read_only_id]).any():
+ continue
+ _plabels = erode3d(plabels)
+ _plabels[self.window] = 0
+ plabelpoints = torch.nonzero(_plabels)
+ kp = min(len(plabelpoints), Np)
+ if kp == 0:
+ continue
+ _point = random.choices(plabelpoints, k=kp)
+ _point_label = [1] * kp
+ if Nn > 0:
+ nlabels = torch.logical_and(~plabels, region)
+ nlabels = erode3d(nlabels)
+ nlabelpoints = torch.nonzero(nlabels)
+ kn = min(len(nlabelpoints), Nn)
+ if kn > 0:
+ _point += random.choices(nlabelpoints, k=kn)
+ _point_label += [0] * kn
+ if npoint is not None:
+ _point += npoint
+ _point_label += [0]
+ self.label[plabels.to(torch.bool)] = id + self.map_shift
+ self.shifted[id] = id + self.map_shift
+ self.read_only_id.append(id + self.map_shift)
+ print("zeroshot_unseen")
+ return _point, _point_label
+ return self.regular(id, Np, Nn)
+
+ def remove_outside(self, plabels, region):
+ """Remove the points that does not lay within region slices (3 views). Avoid sample far away points"""
+ ps = torch.stack(torch.nonzero(region, as_tuple=True)).transpose(1, 0)
+ index = torch.ones_like(plabels).to(torch.bool)
+ index[
+ ps[:, 0].min() : ps[:, 0].max(),
+ ps[:, 1].min() : ps[:, 1].max(),
+ ps[:, 2].min() : ps[:, 2].max(),
+ ] = False
+ plabels[index] = 0
+ return plabels
+
+ def organ_add(self, id, Np=1, Nn=0):
+ """For class id, find a supvervoxel index sid that mostly inside id."""
+ lower_size = 0.1 # times
+ region = self.label == id
+ region_size = region.sum()
+ all_region = self.label == 0
+ sid = torch.unique(self.label_sv[region]).cpu().numpy()
+ random.shuffle(sid)
+ for sid_p in sid:
+ plabels = torch.logical_and(self.label_sv == int(sid_p), all_region)
+ if not plabels.any():
+ continue
+ plabels = find_lcc_label(plabels, region)
+ ids = self.label[plabels].unique().cpu().numpy().tolist()
+ if np.array([i in ids for i in self.read_only_id]).any():
+ continue
+ psize = plabels.sum()
+ if psize < lower_size * region_size:
+ continue
+ _plabels = erode3d(plabels)
+ _plabels[self.window] = 0
+ _plabels = self.remove_outside(_plabels, region)
+ # only pick points at the slice with the organ
+ plabelpoints = torch.nonzero(_plabels)
+ kp = min(len(plabelpoints), Np)
+ if kp == 0:
+ continue
+ _point = random.choices(plabelpoints, k=kp)
+ plabels_ = erode3d(region)
+ plabels_[self.window] = 0
+ plabelpoints = torch.nonzero(plabels_)
+ kp2 = min(len(plabelpoints), Np)
+ if kp2 == 0:
+ plabelpoints = torch.nonzero(region)
+ kp2 = min(len(plabelpoints), Np)
+ _point += random.choices(plabelpoints, k=kp2)
+ self.label[plabels] = id
+ _point_label = [1] * (kp + kp2)
+ if np.random.rand() < self.zshot_rate:
+ self.label[self.label == id] = id + self.map_shift
+ self.shifted[id] = id + self.map_shift
+ self.read_only_id.append(id + self.map_shift)
+ print("organ add")
+ return _point, _point_label
+ return self.regular(id, Np, Nn)
+
+ def organ_sub(self, id, Np=1, Nn=0):
+ """Substract a sid that is not too big or too small. 10%-50% of the id area size. At least 1 pos in remaining region in id and 1 neg
+ in subtracted region.
+ """
+ upper_size = 0.9 # times
+ lower_size = 0.1 # times
+ region = self.label == id
+ region_size = region.sum()
+ sregion = self.label_sv[region]
+ sid = torch.unique(sregion).cpu().numpy()
+ random.shuffle(sid)
+ use_zs = np.random.rand() < 0.5
+ for sid_p in sid:
+ # must contain 2 points within id and 1 point within sid_p
+ if use_zs:
+ plabels = torch.logical_and(self.label_sv == int(sid_p), region)
+ plabels = open_lcc(plabels)
+ psize = plabels.sum()
+ if psize < lower_size * region_size or psize > upper_size * region_size:
+ continue
+ nlabels = torch.logical_and(~plabels, region)
+ else:
+ nlabels = torch.logical_and(self.label_sv == int(sid_p), region)
+ nlabels = open_lcc(nlabels)
+ nsize = nlabels.sum()
+ if nsize < lower_size * region_size or nsize > upper_size * region_size:
+ continue
+ plabels = torch.logical_and(~nlabels, region)
+
+ _plabels = erode3d(plabels)
+ _plabels[self.window] = 0
+ plabelpoints = torch.nonzero(_plabels)
+ _nlabels = erode3d(nlabels)
+ _nlabels[self.window] = 0
+ nlabelpoints = torch.nonzero(_nlabels)
+ kp = min(len(plabelpoints), Np)
+ kn = min(len(nlabelpoints), max(1, Nn))
+ if kp == 0 or kn == 0:
+ continue
+ _point = random.choices(plabelpoints, k=kp) + random.choices(
+ nlabelpoints, k=kn
+ )
+ _point_label = [1] * kp + [0] * kn
+ if use_zs or np.random.rand() < self.zshot_rate:
+ self.label[plabels.to(torch.bool)] = id + self.map_shift
+ self.shifted[id] = id + self.map_shift
+ self.read_only_id.append(id + self.map_shift)
+ else:
+ self.label[nlabels.to(torch.bool)] = 0
+ print("organ_sub")
+ return _point, _point_label
+ _point, _point_label = self.regular(id, Np, Nn)
+ return _point, _point_label
diff --git a/vista3d/scripts/utils/trans_utils.py b/vista3d/scripts/utils/trans_utils.py
new file mode 100644
index 0000000..ec3ac4d
--- /dev/null
+++ b/vista3d/scripts/utils/trans_utils.py
@@ -0,0 +1,497 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from collections.abc import Hashable, Mapping
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from monai.config import DtypeLike, KeysCollection
+from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
+from monai.data.meta_obj import get_track_meta
+from monai.data.meta_tensor import MetaTensor
+from monai.transforms import (
+ MapLabelValue,
+ MapTransform,
+ RandCropByLabelClasses,
+ RandCropByLabelClassesd,
+ SpatialCrop,
+)
+from monai.utils import ImageMetaKey as Key
+from monai.utils import (
+ convert_data_type,
+ ensure_tuple_rep,
+ fall_back_tuple,
+ look_up_option,
+ min_version,
+ optional_import,
+)
+from monai.utils.type_conversion import convert_to_dst_type
+
+measure, has_measure = optional_import("skimage.measure", "0.14.2", min_version)
+morphology, has_morphology = optional_import("skimage.morphology")
+ndimage, _ = optional_import("scipy.ndimage")
+cp, has_cp = optional_import("cupy")
+cp_ndarray, _ = optional_import("cupy", name="ndarray")
+exposure, has_skimage = optional_import("skimage.exposure")
+
+__all__ = [
+ "get_largest_connected_component_mask",
+ "RandCropByLabelClassesShiftd",
+ "erode3d",
+ "erode2d",
+ "dilate3d",
+ "VistaPostTransform",
+ "convert_points_to_disc",
+]
+
+
+def convert_points_to_disc(image_size, point, point_label, radius=2, disc=False):
+ # [b, N, 3], [b, N]
+ # generate masks [b,2,h,w,d]
+ if not torch.is_tensor(point):
+ point = torch.from_numpy(point)
+ masks = torch.zeros(
+ [point.shape[0], 2, image_size[0], image_size[1], image_size[2]],
+ device=point.device,
+ )
+ row_array = torch.arange(
+ start=0, end=image_size[0], step=1, dtype=torch.float32, device=point.device
+ )
+ col_array = torch.arange(
+ start=0, end=image_size[1], step=1, dtype=torch.float32, device=point.device
+ )
+ z_array = torch.arange(
+ start=0, end=image_size[2], step=1, dtype=torch.float32, device=point.device
+ )
+ coord_rows, coord_cols, coord_z = torch.meshgrid(z_array, col_array, row_array)
+ # [1,3,h,w,d] -> [b, 2, 3, h,w,d]
+ coords = (
+ torch.stack((coord_rows, coord_cols, coord_z), dim=0)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ .repeat(point.shape[0], 2, 1, 1, 1, 1)
+ )
+ for b in range(point.shape[0]):
+ for n in range(point.shape[1]):
+ if point_label[b, n] > -1:
+ channel = 0 if (point_label[b, n] == 0 or point_label[b, n] == 2) else 1
+ if disc:
+ masks[b, channel] += (
+ torch.pow(
+ coords[b, channel]
+ - point[b, n].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1),
+ 2,
+ ).sum(0)
+ < radius**2
+ )
+ else:
+ masks[b, channel] += torch.exp(
+ -torch.pow(
+ coords[b, channel]
+ - point[b, n].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1),
+ 2,
+ ).sum(0)
+ / (2 * radius**2)
+ )
+ # masks[masks>1] = 1
+ return masks
+
+
+def erode3d(input_tensor, erosion=3):
+ # Define the structuring element
+ erosion = ensure_tuple_rep(erosion, 3)
+ structuring_element = torch.ones(1, 1, erosion[0], erosion[1], erosion[2]).to(
+ input_tensor.device
+ )
+
+ # Pad the input tensor to handle border pixels
+ input_padded = F.pad(
+ input_tensor.float().unsqueeze(0).unsqueeze(0),
+ (
+ erosion[2] // 2,
+ erosion[2] // 2,
+ erosion[1] // 2,
+ erosion[1] // 2,
+ erosion[0] // 2,
+ erosion[0] // 2,
+ ),
+ mode="constant",
+ value=1.0,
+ )
+
+ # Apply erosion operation
+ output = F.conv3d(input_padded, structuring_element, padding=0)
+
+ # Set output values based on the minimum value within the structuring element
+ output = torch.where(output == torch.sum(structuring_element), 1.0, 0.0)
+
+ return output.squeeze(0).squeeze(0)
+
+
+def erode2d(input_tensor, erosion=3):
+ # Define the structuring element
+ erosion = ensure_tuple_rep(erosion, 2)
+ structuring_element = torch.ones(1, 1, erosion[0], erosion[1]).to(
+ input_tensor.device
+ )
+
+ # Pad the input tensor to handle border pixels
+ input_padded = F.pad(
+ input_tensor.float().unsqueeze(0).unsqueeze(0),
+ (erosion[1] // 2, erosion[1] // 2, erosion[0] // 2, erosion[0] // 2),
+ mode="constant",
+ value=1.0,
+ )
+
+ # Apply erosion operation
+ output = F.conv2d(input_padded, structuring_element, padding=0)
+
+ # Set output values based on the minimum value within the structuring element
+ output = torch.where(output == torch.sum(structuring_element), 1.0, 0.0)
+
+ return output.squeeze(0).squeeze(0)
+
+
+def dilate3d(input_tensor, erosion=3):
+ # Define the structuring element
+ erosion = ensure_tuple_rep(erosion, 3)
+ structuring_element = torch.ones(1, 1, erosion[0], erosion[1], erosion[2]).to(
+ input_tensor.device
+ )
+
+ # Pad the input tensor to handle border pixels
+ input_padded = F.pad(
+ input_tensor.float().unsqueeze(0).unsqueeze(0),
+ (
+ erosion[2] // 2,
+ erosion[2] // 2,
+ erosion[1] // 2,
+ erosion[1] // 2,
+ erosion[0] // 2,
+ erosion[0] // 2,
+ ),
+ mode="constant",
+ value=0.0,
+ )
+
+ # Apply erosion operation
+ output = F.conv3d(input_padded, structuring_element, padding=0)
+
+ # Set output values based on the minimum value within the structuring element
+ output = torch.where(output > 0, 1.0, 0.0)
+
+ return output.squeeze(0).squeeze(0)
+
+
+def get_largest_connected_component_point(
+ img: NdarrayTensor, point_coords=None, point_labels=None
+) -> NdarrayTensor:
+ """
+ Gets the largest connected component mask of an image. img is before post process! And will include NaN values.
+ Args:
+ img: [1, B, H, W, D]
+ point_coords [B, N, 3]
+ point_labels [B, N]
+ """
+ outs = torch.zeros_like(img)
+ for c in range(len(point_coords)):
+ if not ((point_labels[c] == 3).any() or (point_labels[c] == 1).any()):
+ continue
+ coords = (
+ point_coords[c, point_labels[c] == 3].tolist()
+ + point_coords[c, point_labels[c] == 1].tolist()
+ )
+ not_nan_mask = ~torch.isnan(img[0, c])
+ img_ = torch.nan_to_num(img[0, c] > 0, 0)
+ img_, *_ = convert_data_type(img_, np.ndarray)
+ label = measure.label
+ features = label(img_, connectivity=3)
+ pos_mask = torch.from_numpy(img_).to(img.device) > 0
+ # if num features less than max desired, nothing to do.
+ features = torch.from_numpy(features).to(img.device)
+ # generate a map with all pos points
+ idx = []
+ for p in coords:
+ idx.append(features[round(p[0]), round(p[1]), round(p[2])].item())
+ idx = list(set(idx))
+ for i in idx:
+ if i == 0:
+ continue
+ outs[0, c] += features == i
+ outs = outs > 0
+ # find negative mean value
+ fill_in = img[0, c][torch.logical_and(~outs[0, c], not_nan_mask)].mean()
+ img[0, c][torch.logical_and(pos_mask, ~outs[0, c])] = fill_in
+ return img
+
+
+def get_largest_connected_component_mask(
+ img_pos: NdarrayTensor,
+ img_neg: NdarrayTensor,
+ connectivity: int | None = None,
+ num_components: int = 1,
+ point_coords=None,
+ point_labels=None,
+ margins=3,
+) -> NdarrayTensor:
+ """
+ Gets the largest connected component mask of an image that include the point_coords.
+ Args:
+ img_pos: [1, B, H, W, D]
+ point_coords [B, N, 3]
+ point_labels [B, N]
+ """
+ # use skimage/cucim.skimage and np/cp depending on whether packages are
+ # available and input is non-cpu torch.tensor
+ # cucim, has_cucim = optional_import("cucim")
+
+ # use_cp = has_cp and has_cucim and isinstance(img_pos, torch.Tensor) and img_pos.device != torch.device("cpu")
+ # if use_cp:
+ # img_pos_ = convert_to_cupy(img_pos.short()) # type: ignore
+ # img_neg_ = convert_to_cupy(img_neg.short()) # type: ignore
+ # label = cucim.skimage.measure.label
+ # lib = cp
+ # else:
+ # if not has_measure:
+ # raise RuntimeError("Skimage.measure required.")
+ # img_pos_, *_ = convert_data_type(img_pos, np.ndarray)
+ # img_neg_, *_ = convert_data_type(img_neg, np.ndarray)
+ # label = measure.label
+ # lib = np
+ img_pos_, *_ = convert_data_type(img_pos, np.ndarray)
+ img_neg_, *_ = convert_data_type(img_neg, np.ndarray)
+ label = measure.label
+ lib = np
+ # features will be an image -- 0 for background and then each different
+ # feature will have its own index.
+ # features, num_features = label(img_, connectivity=connectivity, return_num=True)
+
+ features_pos, num_features = label(img_pos_, connectivity=3, return_num=True)
+ features_neg, num_features = label(img_neg_, connectivity=3, return_num=True)
+ # if num features less than max desired, nothing to do.
+ outs = np.zeros_like(img_pos_)
+ for bs in range(point_coords.shape[0]):
+ for i, p in enumerate(point_coords[bs]):
+ if point_labels[bs, i] == 1 or point_labels[bs, i] == 3:
+ features = features_pos
+ elif point_labels[bs, i] == 0 or point_labels[bs, i] == 2:
+ features = features_neg
+ else:
+ # if -1 padding point, skip
+ continue
+ for margin in range(margins):
+ left, right = max(p[0].round().int().item() - margin, 0), min(
+ p[0].round().int().item() + margin + 1, features.shape[-3]
+ )
+ t, d = max(p[1].round().int().item() - margin, 0), min(
+ p[1].round().int().item() + margin + 1, features.shape[-2]
+ )
+ f, b = max(p[2].round().int().item() - margin, 0), min(
+ p[2].round().int().item() + margin + 1, features.shape[-1]
+ )
+ if (features[bs, 0, left:right, t:d, f:b] > 0).any():
+ index = features[bs, 0, left:right, t:d, f:b].max()
+ outs[[bs]] += lib.isin(features[[bs]], index)
+ break
+ outs[outs > 1] = 1
+ outs = convert_to_dst_type(outs, dst=img_pos, dtype=outs.dtype)[0]
+ return outs
+
+
+class VistaPostTransform(MapTransform):
+ def __init__(
+ self,
+ keys: KeysCollection,
+ allow_missing_keys: bool = False,
+ ) -> None:
+ """
+ Args:
+ keys: keys of the corresponding items to be transformed.
+ dataset_transforms: a dictionary specifies the transform for corresponding dataset:
+ key: dataset name, value: list of data transforms.
+ dataset_key: key to get the dataset name from the data dictionary, default to "dataset_name".
+ allow_missing_keys: don't raise exception if key is missing.
+
+ """
+ super().__init__(keys, allow_missing_keys)
+
+ def __call__(
+ self, data: Mapping[Hashable, NdarrayOrTensor]
+ ) -> dict[Hashable, NdarrayOrTensor]:
+ for keys in self.keys:
+ if keys in data:
+ pred = data[keys]
+ object_num = pred.shape[0]
+ # device = pred.device
+ pred[pred < 0] = 0.0
+ # if it's multichannel, perform argmax
+ if object_num > 1:
+ # concate background channel. Make sure user did not provide 0 as prompt.
+ is_bk = torch.all(pred <= 0, dim=0, keepdim=True)
+ pred = pred.argmax(0).unsqueeze(0).float() + 1.0
+ pred[is_bk] = 0.0
+ else:
+ # AsDiscrete will remove NaN
+ # pred = monai.transforms.AsDiscrete(threshold=0.5)(pred)
+ pred[pred > 0] = 1.0
+ if "label_prompt" in data and data["label_prompt"] is not None:
+ pred += 0.5 # inplace mapping to avoid cloning pred
+ for i in range(1, object_num + 1):
+ frac = i + 0.5
+ pred[pred == frac] = torch.tensor(
+ data["label_prompt"][i - 1]
+ ).to(pred.dtype)
+ pred[pred == 0.5] = 0.0
+ data[keys] = pred
+ return data
+
+
+class RandCropByLabelClassesShift(RandCropByLabelClasses):
+ def __call__(
+ self,
+ img: torch.Tensor,
+ label: torch.Tensor | None = None,
+ image: torch.Tensor | None = None,
+ indices: list[NdarrayOrTensor] | None = None,
+ randomize: bool = True,
+ lazy: bool | None = None,
+ ) -> list[torch.Tensor]:
+ """
+ Args:
+ img: input data to crop samples from based on the ratios of every class, assumes `img` is a
+ channel-first array.
+ label: the label image that is used for finding indices of every class, if None, use `self.label`.
+ image: optional image data to help select valid area, can be same as `img` or another image array.
+ use ``image > image_threshold`` to select the centers only in valid region. if None, use `self.image`.
+ indices: list of indices for every class in the image, used to randomly select crop centers.
+ randomize: whether to execute the random operations, default to `True`.
+ lazy: a flag to override the lazy behaviour for this call, if set. Defaults to None.
+ """
+ if image is None:
+ image = self.image
+ if randomize:
+ if label is None:
+ label = self.label
+ self.randomize(label, indices, image)
+ results: list[torch.Tensor] = []
+ if self.centers is not None:
+ img_shape = (
+ img.peek_pending_shape()
+ if isinstance(img, MetaTensor)
+ else img.shape[1:]
+ )
+ roi_size = fall_back_tuple(self.spatial_size, default=img_shape)
+ lazy_ = self.lazy if lazy is None else lazy
+ for i, center in enumerate(self.centers):
+ for i in range(3):
+ center[i] = min(
+ img_shape[i],
+ max(
+ 0,
+ np.random.randint(-roi_size[i] // 3, roi_size[i] // 3)
+ + center[i],
+ ),
+ )
+ cropper = SpatialCrop(
+ roi_center=tuple(center), roi_size=roi_size, lazy=lazy_
+ )
+ cropped = cropper(img)
+ if get_track_meta():
+ ret_: MetaTensor = cropped # type: ignore
+ ret_.meta[Key.PATCH_INDEX] = i
+ ret_.meta["crop_center"] = center
+ self.push_transform(ret_, replace=True, lazy=lazy_)
+ results.append(cropped)
+
+ return results
+
+
+class RandCropByLabelClassesShiftd(RandCropByLabelClassesd):
+ backend = RandCropByLabelClassesShift.backend
+
+
+class RelabelD(MapTransform):
+ def __init__(
+ self,
+ keys: KeysCollection,
+ label_mappings: dict[str, list[tuple[int, int]]],
+ dtype: DtypeLike = np.int16,
+ dataset_key: str = "dataset_name",
+ allow_missing_keys: bool = False,
+ ) -> None:
+ """
+ Args:
+ keys: keys of the corresponding items to be transformed.
+ label_mappings: a dictionary specifies how local dataset class indices are mapped to the
+ global class indices, format:
+ key: dataset name, value: list of (local label, global label) pairs
+ set this argument to "{}" to disable relabeling.
+ dtype: convert the output data to dtype, default to float32.
+ dataset_key: key to get the dataset name from the data dictionary, default to "dataset_name".
+ allow_missing_keys: don't raise exception if key is missing.
+
+ """
+ super().__init__(keys, allow_missing_keys)
+ self.mappers = {}
+ self.dataset_key = dataset_key
+ for name, mapping in label_mappings.items():
+ self.mappers[name] = MapLabelValue(
+ orig_labels=[pair[0] for pair in mapping],
+ target_labels=[pair[1] for pair in mapping],
+ dtype=dtype,
+ )
+
+ def __call__(
+ self, data: Mapping[Hashable, NdarrayOrTensor]
+ ) -> dict[Hashable, NdarrayOrTensor]:
+ d = dict(data)
+ dataset_name = d.get(self.dataset_key, "default")
+ _m = look_up_option(dataset_name, self.mappers, default=None)
+ if not _m:
+ return d
+ for key in self.key_iterator(d):
+ d[key] = _m(d[key])
+ return d
+
+
+class DatasetSelectTansformd(MapTransform):
+ def __init__(
+ self,
+ keys: KeysCollection,
+ dataset_transforms,
+ dataset_key: str = "dataset_name",
+ allow_missing_keys: bool = False,
+ ) -> None:
+ """
+ Args:
+ keys: keys of the corresponding items to be transformed.
+ dataset_transforms: a dictionary specifies the transform for corresponding dataset:
+ key: dataset name, value: list of data transforms.
+ dataset_key: key to get the dataset name from the data dictionary, default to "dataset_name".
+ allow_missing_keys: don't raise exception if key is missing.
+
+ """
+ super().__init__(keys, allow_missing_keys)
+ self.dataset_transforms = dataset_transforms
+ self.dataset_key = dataset_key
+
+ def __call__(
+ self, data: Mapping[Hashable, NdarrayOrTensor]
+ ) -> dict[Hashable, NdarrayOrTensor]:
+ d = dict(data)
+ dataset_name = d[self.dataset_key]
+ _m = self.dataset_transforms[dataset_name]
+ if _m is None:
+ return d
+ return _m(d)
diff --git a/vista3d/scripts/utils/workflow_utils.py b/vista3d/scripts/utils/workflow_utils.py
new file mode 100644
index 0000000..49692f9
--- /dev/null
+++ b/vista3d/scripts/utils/workflow_utils.py
@@ -0,0 +1,609 @@
+import copy
+import random
+
+import monai
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from .trans_utils import erode3d
+
+ENABLE_SPECIAL = True
+SPECIAL_INDEX = [23, 24, 25, 26, 27, 57, 128]
+MERGE_LIST = {
+ 1: [25, 26], # hepatic tumor and vessel merge into liver
+ 4: [24], # pancreatic tumor merge into pancreas
+ 132: [57], # overlap with trachea merge into airway
+}
+USE_SV_GT_LIST = [
+ "TotalSegmentatorV2",
+ "Covid19",
+ "NLST",
+ "LIDC",
+ "StonyBrook-CT",
+ "TCIA_Colon",
+]
+
+
+def get_point_label(id):
+ """Get point label from class index"""
+ # [B, N]
+ if id in SPECIAL_INDEX and ENABLE_SPECIAL:
+ return 2, 3
+ else:
+ return 0, 1
+
+
+def convert_point_label(point_label, label_set=None):
+ if label_set is None or not ENABLE_SPECIAL:
+ return point_label
+ assert point_label.shape[0] == len(label_set)
+ for i in range(len(label_set)):
+ if label_set[i] in SPECIAL_INDEX:
+ for j in range(len(point_label[i])):
+ point_label[i, j] = (
+ point_label[i, j] + 2
+ if point_label[i, j] > -1
+ else point_label[i, j]
+ )
+ return point_label
+
+
+def none_cat(point, point_pseudo):
+ """Concatenate point and point_pseudo and allow None input and padding."""
+ _point = None
+ if point is not None:
+ if point_pseudo is not None:
+ if len(point.shape) == 3:
+ pad_n = max(point.shape[1], point_pseudo.shape[1])
+ point = F.pad(point, (0, 0, 0, pad_n - point.shape[1], 0, 0))
+ point_pseudo = F.pad(
+ point_pseudo, (0, 0, 0, pad_n - point_pseudo.shape[1], 0, 0)
+ )
+ elif len(point.shape) == 2:
+ pad_n = max(point.shape[1], point_pseudo.shape[1])
+ point = F.pad(point, (0, pad_n - point.shape[1], 0, 0), value=-1)
+ point_pseudo = F.pad(
+ point_pseudo, (0, pad_n - point_pseudo.shape[1], 0, 0), value=-1
+ )
+ elif len(point.shape) == 1:
+ pad_n = max(point.shape[0], point_pseudo.shape[0])
+ point = F.pad(point, (0, pad_n - point.shape[1]), value=-1)
+ point_pseudo = F.pad(
+ point_pseudo, (0, pad_n - point_pseudo.shape[1]), value=-1
+ )
+ _point = torch.cat([point, point_pseudo], dim=0)
+ else:
+ _point = point
+ elif point_pseudo is not None:
+ _point = point_pseudo
+ return _point
+
+
+def sample_points_patch_val(
+ labels,
+ patch_coords,
+ label_set,
+ use_center=True,
+ mapped_label_set=None,
+ max_ppoint=1,
+ max_npoint=0,
+ **kwargs
+):
+ """Sample points for patch during sliding window validation. Only used for point only validation.
+ Args:
+ labels: [1, 1, H, W, D]
+ patch_coords: sliding window slice object
+ label_set: local index, must match values in labels
+ use_center: sample points from the center
+ mapped_label_set: global index, it is used to identify special classes.
+ max_ppoint/max_npoint: positive points and negative points to sample.
+ """
+ point_coords, point_labels = generate_prompt_pairs_val(
+ labels[patch_coords],
+ label_set,
+ max_ppoint=max_ppoint,
+ max_npoint=max_npoint,
+ device=labels.device,
+ use_center=use_center,
+ )
+ point_labels = convert_point_label(point_labels, mapped_label_set)
+ return (
+ point_coords,
+ point_labels,
+ torch.tensor(mapped_label_set).to(point_coords.device).unsqueeze(-1),
+ )
+
+
+def generate_prompt_pairs_val(
+ labels, label_set=None, max_ppoint=1, max_npoint=0, device="cpu", use_center=False
+):
+ """Sample points from labels. This function is only used for validation and did not map point label to 2, 3.
+ For zero-shot point evaluation, this function will be called directly. Otherwise see sample_points_patch_val.
+ Args:
+ labels: [1, 1, H, W, D]
+ label_set: local index, must match values in labels
+ Returns:
+ point: [B, N, 3]
+ point_label: [B, N]
+ """
+ assert labels.shape[0] == 1, "only support batch size 1"
+ labels = labels[0, 0]
+ unique_labels = labels.unique().cpu().numpy().tolist()
+ _point = []
+ _point_label = []
+ Nn = max_npoint
+ Np = max_ppoint
+ for id in label_set:
+ if id in unique_labels:
+ plabels = labels == int(id)
+ nlabels = ~plabels
+ _plabels = erode3d(plabels)
+ _plabels = monai.transforms.utils.get_largest_connected_component_mask(
+ _plabels
+ )
+ plabelpoints = torch.nonzero(_plabels).to(device)
+ if len(plabelpoints) == 0:
+ plabelpoints = torch.nonzero(plabels).to(device)
+ nlabelpoints = torch.nonzero(nlabels).to(device)
+ if use_center:
+ pmean = plabelpoints.float().mean(0)
+ pdis = ((plabelpoints - pmean) ** 2).sum(-1)
+ _, sorted_indices = torch.sort(pdis)
+ _point.append(
+ torch.stack(
+ [
+ plabelpoints[sorted_indices[i]]
+ for i in range(min(len(plabelpoints), Np))
+ ]
+ + random.choices(nlabelpoints, k=min(len(nlabelpoints), Nn))
+ + [torch.tensor([0, 0, 0], device=device)]
+ * (
+ Np
+ + Nn
+ - min(len(plabelpoints), Np)
+ - min(len(nlabelpoints), Nn)
+ )
+ )
+ )
+ _point_label.append(
+ torch.tensor(
+ [1] * min(len(plabelpoints), Np)
+ + [0.0] * min(len(nlabelpoints), Nn)
+ + [-1]
+ * (
+ Np
+ + Nn
+ - min(len(plabelpoints), Np)
+ - min(len(nlabelpoints), Nn)
+ )
+ ).to(device)
+ )
+
+ else:
+ _point.append(
+ torch.stack(
+ random.choices(plabelpoints, k=min(len(plabelpoints), Np))
+ + random.choices(nlabelpoints, k=min(len(nlabelpoints), Nn))
+ + [torch.tensor([0, 0, 0], device=device)]
+ * (
+ Np
+ + Nn
+ - min(len(plabelpoints), Np)
+ - min(len(nlabelpoints), Nn)
+ )
+ )
+ )
+ _point_label.append(
+ torch.tensor(
+ [1] * min(len(plabelpoints), Np)
+ + [0.0] * min(len(nlabelpoints), Nn)
+ + [-1]
+ * (
+ Np
+ + Nn
+ - min(len(plabelpoints), Np)
+ - min(len(nlabelpoints), Nn)
+ )
+ ).to(device)
+ )
+ else:
+ # pad the background labels
+ _point.append(torch.zeros(Np + Nn, 3).to(device)) # all 0
+ _point_label.append(torch.zeros(Np + Nn).to(device) - 1) # -1 not a point
+ point = torch.stack(_point)
+ point_label = torch.stack(_point_label)
+ return point, point_label
+
+
+def generate_prompt_pairs(
+ labels,
+ label_set=None,
+ max_prompt=None,
+ max_foreprompt=None,
+ max_backprompt=1,
+ max_point=20,
+ include_background=False,
+ drop_label_prob=0.2,
+ drop_point_prob=0.2,
+ point_sampler=None,
+):
+ """This is the main function sampling training pairs for point branch. Only used in training.
+ Args:
+ labels: [1, 1, H, W, D]
+ label_set: the label list for the specific dataset.
+ max_prompt: int, max number of total prompt, including foreground and background.
+ max_foreprompt: int, max number of prompt from foreground.
+ max_backprompt: int, max number of prompt from background.
+ max_point: maximum number of points for each object
+ include_background: if include label=0 into training prompt. May casue issue in partial label
+ trainig.
+ drop_label_prob: probablity to drop label prompt
+ drop_point_prob: probablity to drop point prompt
+ point_sampler: sampler to augment masks with supervoxel.
+ Returns:
+ label_prompt: [b, 1]
+ point: [b, N, 3]
+ point_label: [b, N]
+ prompt_class: [b, 1], exactly the same with label_prompt for label indexing for training loss.
+
+ """
+ # class label number
+ assert labels.shape[0] == 1, "only support batch size 1"
+ labels = labels[0, 0]
+ device = labels.device
+ unique_labels = labels.unique().cpu().numpy().tolist()
+ if include_background:
+ unique_labels = list(set(unique_labels) - (set(unique_labels) - set(label_set)))
+ else:
+ unique_labels = list(
+ set(unique_labels) - (set(unique_labels) - set(label_set)) - set([0])
+ )
+ background_labels = list(set(label_set) - set(unique_labels))
+ # during training, balance background and foreground prompts
+ if max_backprompt is not None:
+ if len(background_labels) > max_backprompt:
+ random.shuffle(background_labels)
+ background_labels = background_labels[:max_backprompt]
+
+ if max_foreprompt is not None:
+ if len(unique_labels) > max_foreprompt:
+ random.shuffle(unique_labels)
+ unique_labels = unique_labels[:max_foreprompt]
+
+ if max_prompt is not None:
+ if len(unique_labels) + len(background_labels) > max_prompt:
+ if len(unique_labels) > max_prompt:
+ unique_labels = random.sample(unique_labels, max_prompt)
+ background_labels = []
+ else:
+ background_labels = random.sample(
+ background_labels, max_prompt - len(unique_labels)
+ )
+ _point = []
+ _point_label = []
+ # if use regular sampling
+ if point_sampler is None:
+ Np = min(max_point, int(np.abs(random.gauss(mu=0, sigma=max_point // 2))) + 1)
+ Nn = min(max_point, int(np.abs(random.gauss(mu=0, sigma=max_point // 2))))
+ for id in unique_labels:
+ neg_id, pos_id = get_point_label(id)
+ plabels = labels == int(id)
+ nlabels = ~plabels
+ plabelpoints = torch.nonzero(plabels)
+ nlabelpoints = torch.nonzero(nlabels)
+ _point.append(
+ torch.stack(
+ random.choices(plabelpoints, k=min(len(plabelpoints), Np))
+ + random.choices(nlabelpoints, k=min(len(nlabelpoints), Nn))
+ + [torch.tensor([0, 0, 0], device=device)]
+ * (
+ Np
+ + Nn
+ - min(len(plabelpoints), Np)
+ - min(len(nlabelpoints), Nn)
+ )
+ )
+ )
+ _point_label.append(
+ torch.tensor(
+ [pos_id] * min(len(plabelpoints), Np)
+ + [neg_id] * min(len(nlabelpoints), Nn)
+ + [-1]
+ * (
+ Np
+ + Nn
+ - min(len(plabelpoints), Np)
+ - min(len(nlabelpoints), Nn)
+ )
+ ).to(device)
+ )
+ for id in background_labels:
+ # pad the background labels
+ _point.append(torch.zeros(Np + Nn, 3).to(device)) # all 0
+ _point_label.append(torch.zeros(Np + Nn).to(device) - 1) # -1 not a point
+ else:
+ Np = max_point
+ Nn = 0
+ _point, _point_label = point_sampler(unique_labels, Np=Np, Nn=Nn)
+ for id in background_labels:
+ # pad the background labels
+ _point.append(torch.zeros(len(_point_label[0]), 3).to(device)) # all 0
+ _point_label.append(
+ torch.zeros(len(_point_label[0])).to(device) - 1
+ ) # -1 not a point
+ if len(unique_labels) == 0 and len(background_labels) == 0:
+ # the iteration should be skipped
+ label_prompt, point, point_label, prompt_class = None, None, None, None
+ else:
+ label_prompt = (
+ torch.tensor(unique_labels + background_labels)
+ .unsqueeze(-1)
+ .to(device)
+ .long()
+ )
+ point = torch.stack(_point)
+ point_label = torch.stack(_point_label)
+ prompt_class = copy.deepcopy(label_prompt)
+ if random.uniform(0, 1) < drop_label_prob and len(unique_labels) > 0:
+ label_prompt = None
+ # If label prompt is dropped, there is no need to pad with points with label -1.
+ pad = len(background_labels)
+ point = point[: len(point) - pad]
+ point_label = point_label[: len(point_label) - pad]
+ prompt_class = prompt_class[: len(prompt_class) - pad]
+ else:
+ if random.uniform(0, 1) < drop_point_prob:
+ point = None
+ point_label = None
+ return label_prompt, point, point_label, prompt_class
+
+
+def get_next_points_val(
+ pred,
+ gt,
+ prompt_class,
+ point,
+ point_label,
+ pred_thresh=0.5,
+ mapped=True,
+ include_background=False,
+ use_center=False,
+ erosion2d=False,
+ **kwargs
+):
+ """This function is used to sample points for iterative point evaluation. Each time only 1 point
+ is sampled. background index will be ignored.
+ mapped: If the input prompt_class are mapped to the global index, we will use special index. If not mapped (zero-shot),
+ the special index will not be enabled.
+ """
+ new_points = []
+ new_points_label = []
+ for id in range(len(prompt_class)):
+ if prompt_class[id] == 0 and not include_background:
+ new_points.append(torch.tensor([0, 0, 0], device=pred.device))
+ new_points_label.append(torch.tensor(-1, device=pred.device))
+ continue
+ neg_id, pos_id = get_point_label(-1)
+ _gt = (gt == prompt_class[id])[0, 0]
+ if mapped:
+ # if in the global index, some supported classes need modification.
+ neg_id, pos_id = get_point_label(prompt_class[id])
+ if prompt_class[id].item() in MERGE_LIST.keys():
+ for m in MERGE_LIST[prompt_class[id].item()]:
+ _gt = torch.logical_or(_gt, (gt == m)[0, 0])
+ fn_mask = torch.logical_and(_gt, pred[id][0] < pred_thresh)
+ if erosion2d:
+ fn_mask = erode3d(fn_mask, erosion=(3, 3, 1))
+ else:
+ fn_mask = erode3d(fn_mask, erosion=(3, 3, 3))
+ fn_mask = monai.transforms.utils.get_largest_connected_component_mask(fn_mask)
+ fp_mask = torch.logical_and(torch.logical_not(_gt), pred[id][0] > pred_thresh)
+ if erosion2d:
+ fp_mask = erode3d(fp_mask, erosion=(3, 3, 1))
+ else:
+ fp_mask = erode3d(fp_mask, erosion=(3, 3, 3))
+ fp_mask = monai.transforms.utils.get_largest_connected_component_mask(fp_mask)
+ if fn_mask.sum() >= fp_mask.sum():
+ plabelpoints = torch.nonzero(fn_mask)
+ if len(plabelpoints) > 0:
+ if use_center:
+ pdis = ((plabelpoints - plabelpoints.float().mean(0)) ** 2).sum(-1)
+ _, sorted_indices = torch.sort(pdis)
+ new_points.append(plabelpoints[sorted_indices[0]])
+ new_points_label.append(torch.tensor(pos_id, device=pred.device))
+ else:
+ new_points.append(random.choices(plabelpoints, k=1)[0])
+ new_points_label.append(torch.tensor(pos_id, device=pred.device))
+ print("sampled pos")
+ else:
+ new_points.append(torch.tensor([0, 0, 0], device=pred.device))
+ new_points_label.append(torch.tensor(-1, device=pred.device))
+ else:
+ plabelpoints = torch.nonzero(fp_mask)
+ if len(plabelpoints) > 0:
+ if use_center:
+ pdis = ((plabelpoints - plabelpoints.float().mean(0)) ** 2).sum(-1)
+ _, sorted_indices = torch.sort(pdis)
+ new_points.append(plabelpoints[sorted_indices[0]])
+ new_points_label.append(torch.tensor(neg_id, device=pred.device))
+ else:
+ new_points.append(random.choices(plabelpoints, k=1)[0])
+ new_points_label.append(torch.tensor(neg_id, device=pred.device))
+ print("sampled neg")
+ else:
+ new_points.append(torch.tensor([0, 0, 0], device=pred.device))
+ new_points_label.append(torch.tensor(-1, device=pred.device))
+ new_points = torch.stack(new_points).unsqueeze(1)
+ new_points_label = torch.stack(new_points_label).unsqueeze(1)
+ point = torch.cat([point, new_points], dim=1)
+ point_label = torch.cat([point_label, new_points_label], dim=1)
+ return point, point_label
+
+
+def get_next_points_auto_point(
+ pred,
+ gt,
+ prompt_class,
+ class_vector=None,
+ pred_thresh=0.5,
+ mapped=True,
+ include_background=False,
+ use_fg=False,
+ **kwargs
+):
+ """sample points from foreground or error region. This function is only used during patch based auto + point evaluation. mapped is always true if
+ evaluate dataset with automatic, which requires global index.
+ """
+ new_points = []
+ new_points_label = []
+ for id in range(len(prompt_class)):
+ neg_id, pos_id = get_point_label(-1)
+ _gt = (gt == prompt_class[id])[0, 0]
+ if mapped:
+ # if in the global index, some supported classes need modification. prompt_class is the local index
+ if class_vector is not None:
+ neg_id, pos_id = get_point_label(class_vector[id])
+ if class_vector[id].item() in MERGE_LIST.keys():
+ for m in MERGE_LIST[class_vector[id].item()]:
+ _gt = torch.logical_or(_gt, (gt == m)[0, 0])
+ else:
+ neg_id, pos_id = get_point_label(prompt_class[id])
+ if prompt_class[id].item() in MERGE_LIST.keys():
+ for m in MERGE_LIST[prompt_class[id].item()]:
+ _gt = torch.logical_or(_gt, (gt == m)[0, 0])
+ if (prompt_class[id] == 0 and not include_background) or _gt.sum() == 0:
+ # if background or no foreground and no false positive
+ if _gt.sum() == 0 and (pred[id][0] > pred_thresh).sum() > 0:
+ fp_mask = pred[id][0] > pred_thresh
+ new_points.append(random.choices(torch.nonzero(fp_mask), k=1))
+ new_points_label.append([torch.tensor(neg_id, device=pred.device)])
+ else:
+ new_points.append([torch.tensor([0, 0, 0], device=pred.device)])
+ new_points_label.append([torch.tensor(-1, device=pred.device)])
+ continue
+
+ # DO NOT MERGE HERE. The merge classs evaluation will not perform merge.
+ if use_fg:
+ plabelpoints = torch.nonzero(_gt)
+ new_points.append(random.choices(plabelpoints, k=1))
+ new_points_label.append([torch.tensor(pos_id, device=pred.device)])
+ continue
+ _new_points, _new_points_label = [], []
+ fn_mask = torch.logical_and(_gt, pred[id][0] < pred_thresh)
+ fn_mask = erode3d(fn_mask)
+ fn_mask = monai.transforms.utils.get_largest_connected_component_mask(fn_mask)
+ fp_mask = torch.logical_and(torch.logical_not(_gt), pred[id][0] > pred_thresh)
+ fp_mask = erode3d(fp_mask)
+ fp_mask = monai.transforms.utils.get_largest_connected_component_mask(fp_mask)
+ if fn_mask.sum() <= fp_mask.sum():
+ # if false positive is larger than false negative, we will sample a negative point and one from foreground.
+ # if all of them are 0, will sample a foreground
+ plabelpoints = torch.nonzero(_gt)
+ _new_points.extend(random.choices(plabelpoints, k=1))
+ _new_points_label.extend([torch.tensor(pos_id, device=pred.device)])
+ plabelpoints = torch.nonzero(fp_mask)
+ if len(plabelpoints) > 0:
+ _new_points.extend(random.choices(plabelpoints, k=1))
+ _new_points_label.extend([torch.tensor(neg_id, device=pred.device)])
+ else:
+ _new_points.extend([torch.tensor([0, 0, 0], device=pred.device)])
+ _new_points_label.extend([torch.tensor(-1, device=pred.device)])
+ else:
+ plabelpoints = torch.nonzero(fn_mask)
+ if len(plabelpoints) > 0:
+ _new_points.extend(random.choices(plabelpoints, k=1))
+ _new_points_label.extend([torch.tensor(pos_id, device=pred.device)])
+ else:
+ _new_points.extend([torch.tensor([0, 0, 0], device=pred.device)])
+ _new_points_label.extend([torch.tensor(-1, device=pred.device)])
+ new_points.append(_new_points)
+ new_points_label.append(_new_points_label)
+
+ max_len = max([len(_) for _ in new_points])
+ x = []
+ for _ in new_points:
+ x.append(_ + [torch.tensor([0, 0, 0]).to(pred.device)] * (max_len - len(_)))
+ new_points = torch.stack([torch.stack(_) for _ in x])
+ # new_points = torch.stack([torch.stack(_) for _ in [x + [torch.tensor([0,0,0]).to(pred.device)] * (max_len - len(x)) for x in new_points]])
+ x = []
+ for _ in new_points_label:
+ x.append(_ + [torch.tensor(-1).to(pred.device)] * (max_len - len(_)))
+ new_points_label = torch.vstack([torch.stack(_) for _ in x])
+
+ return new_points, new_points_label
+
+
+def get_next_points(
+ pred,
+ gt,
+ prompt_class,
+ point,
+ point_label,
+ pred_thresh=0.5,
+ mapped=True,
+ include_background=False,
+ **kwargs
+):
+ """Iterative training. Sample points from false positve or false negative. This is used in training.
+ pred [bs, 1, h, w, d]
+ gt [1,1,h,w,d]
+ point [bs, n, 3]
+ Args:
+ mapped: If the input prompt_class are mapped to the global index, we will use special index. If not mapped (zero-shot),
+ the special index will not be enabled.
+ """
+ new_points = []
+ new_points_label = []
+ offset = 10
+ window = torch.ones_like(pred[0, 0], dtype=torch.bool)
+ window[offset:-offset, offset:-offset, :] = False
+ for id in range(len(prompt_class)):
+ if prompt_class[id] == 0 and not include_background:
+ new_points.append(torch.tensor([0, 0, 0], device=pred.device))
+ new_points_label.append(torch.tensor(-1, device=pred.device))
+ continue
+ neg_id, pos_id = get_point_label(-1)
+ _gt = (gt == prompt_class[id])[0, 0]
+ if mapped:
+ # if in the global index, some supported classes need modification.
+ neg_id, pos_id = get_point_label(prompt_class[id])
+ if prompt_class[id].item() in MERGE_LIST.keys():
+ for m in MERGE_LIST[prompt_class[id].item()]:
+ _gt = torch.logical_or(_gt, (gt == m)[0, 0])
+ fn_mask = torch.logical_and(_gt, pred[id][0] < pred_thresh)
+ fn_mask = erode3d(fn_mask)
+ fn_mask[window] = 0
+ fp_mask = torch.logical_and(torch.logical_not(_gt), pred[id][0] > pred_thresh)
+ fp_mask = erode3d(fp_mask)
+ fp_mask[window] = 0
+ # random select a false negative
+ fnlabelpoints = torch.nonzero(fn_mask)
+ fplabelpoints = torch.nonzero(fp_mask)
+ _new_points = []
+ _new_points_label = []
+ if len(fnlabelpoints) > 0:
+ _new_points.append(random.choices(fnlabelpoints, k=1)[0])
+ _new_points_label.append(torch.tensor(pos_id, device=pred.device))
+ else:
+ _new_points.append(torch.tensor([0, 0, 0], device=pred.device))
+ _new_points_label.append(torch.tensor(-1, device=pred.device))
+ if len(fplabelpoints) > 0:
+ _new_points.append(random.choices(fplabelpoints, k=1)[0])
+ _new_points_label.append(torch.tensor(neg_id, device=pred.device))
+ else:
+ _new_points.append(torch.tensor([0, 0, 0], device=pred.device))
+ _new_points_label.append(torch.tensor(-1, device=pred.device))
+ new_points.append(torch.stack(_new_points))
+ new_points_label.append(torch.stack(_new_points_label))
+ if len(new_points) > 0:
+ new_points = torch.stack(new_points)
+ new_points_label = torch.stack(new_points_label)
+ if point is not None:
+ point = torch.cat([point, new_points], dim=1)
+ point_label = torch.cat([point_label, new_points_label], dim=1)
+ else:
+ point = new_points
+ point_label = new_points_label
+
+ return point, point_label
diff --git a/vista3d/scripts/validation/build_vista3d_eval_only.py b/vista3d/scripts/validation/build_vista3d_eval_only.py
new file mode 100644
index 0000000..b5e016a
--- /dev/null
+++ b/vista3d/scripts/validation/build_vista3d_eval_only.py
@@ -0,0 +1,174 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import copy
+
+import numpy as np
+import torch
+from monai.metrics import compute_dice
+from vista3d.modeling import (
+ VISTA3D2,
+ Class_Mapping_Classify,
+ Point_Mapping_SAM,
+ SegResNetDS2,
+)
+
+from ..utils.workflow_utils import get_next_points_auto_point
+
+
+class VISTA3D2_eval_only(VISTA3D2):
+ @torch.no_grad()
+ def point_head_iterative_trial(
+ self,
+ logits,
+ labels,
+ out,
+ point_coords,
+ point_labels,
+ class_vector,
+ prompt_class,
+ n_trials=3,
+ ):
+ """The prompt class is the local label set while class vector is the mapped global label set"""
+ logits_update = logits.detach().clone()
+ for trial_idx in range(n_trials):
+ if trial_idx == 0:
+ point_coords, point_labels = get_next_points_auto_point(
+ logits > 0, labels, prompt_class, class_vector, use_fg=True
+ )
+ else:
+ point_coords, point_labels = get_next_points_auto_point(
+ logits > 0, labels, prompt_class, class_vector, use_fg=False
+ )
+ mapping_index = ((point_labels != -1).sum(1) > 0).to(torch.bool)
+ point_coords = point_coords[mapping_index]
+ point_labels = point_labels[mapping_index]
+ if (torch.sum(mapping_index) == 1 and mapping_index[0]) or torch.sum(
+ mapping_index
+ ) == 0:
+ return logits
+ if trial_idx == 0:
+ best_dice = []
+ for i in range(len(prompt_class)):
+ dice = compute_dice(
+ y_pred=(logits[[i]] > 0).to(labels.device),
+ y=labels == prompt_class[i],
+ ).item()
+ if np.isnan(dice):
+ best_dice.append(-(logits[[i]] > 0).sum())
+ else:
+ best_dice.append(dice)
+
+ point_logits = self.point_head(
+ out,
+ point_coords,
+ point_labels,
+ class_vector=class_vector[mapping_index],
+ )
+
+ target_logits = self.connected_components_combine(
+ logits, point_logits, point_coords, point_labels, mapping_index
+ )
+ combine_dice = []
+ for i in range(len(prompt_class)):
+ if mapping_index[i]:
+ dice = compute_dice(
+ y_pred=(target_logits[[i]] > 0).to(labels.device),
+ y=(labels == prompt_class[i]),
+ ).item()
+ if np.isnan(dice):
+ combine_dice.append(-(target_logits[[i]] > 0).sum())
+ else:
+ combine_dice.append(dice)
+ else:
+ combine_dice.append(-1)
+ # check the dice for each label
+ for i in range(len(prompt_class)):
+ if prompt_class[i] == 0:
+ continue
+ if combine_dice[i] > best_dice[i]:
+ # print(trial_idx, prompt_class[i], combine_dice[i], best_dice[i])
+ logits_update[i] = copy.deepcopy(target_logits[i])
+ best_dice[i] = copy.deepcopy(combine_dice[i])
+
+ labels, target_logits, logits, best_dice, combine_dice = (
+ None,
+ None,
+ None,
+ None,
+ None,
+ )
+ # force releasing memories that set to None
+ torch.cuda.empty_cache()
+ return logits_update
+
+ def forward(
+ self,
+ input_images,
+ point_coords=None,
+ point_labels=None,
+ class_vector=None,
+ prompt_class=None,
+ patch_coords=None,
+ labels=None,
+ label_set=None,
+ prev_mask=None,
+ radius=None,
+ val_point_sampler=None,
+ **kwargs,
+ ):
+ out, out_auto = self.image_encoder(
+ input_images, with_point=True, with_label=True
+ )
+ input_images = None
+ # force releasing memories that set to None
+ torch.cuda.empty_cache()
+ logits, _ = self.class_head(out_auto, class_vector)
+ logits = self.point_head_iterative_trial(
+ logits,
+ labels[patch_coords],
+ out,
+ point_coords,
+ point_labels,
+ class_vector[0],
+ prompt_class[0],
+ n_trials=3,
+ )
+ return logits
+
+
+def build_vista3d_segresnet_decoder(
+ encoder_embed_dim=48, in_channels=1, image_size=(96, 96, 96)
+):
+ segresnet = SegResNetDS2(
+ in_channels=in_channels,
+ blocks_down=(1, 2, 2, 4, 4),
+ norm="instance",
+ out_channels=encoder_embed_dim,
+ init_filters=encoder_embed_dim,
+ dsdepth=1,
+ )
+ point_head = Point_Mapping_SAM(feature_size=encoder_embed_dim, last_supported=132)
+ class_head = Class_Mapping_Classify(
+ n_classes=512, feature_size=encoder_embed_dim, use_mlp=True
+ )
+ vista = VISTA3D2_eval_only(
+ image_encoder=segresnet,
+ class_head=class_head,
+ point_head=point_head,
+ feature_size=encoder_embed_dim,
+ )
+ return vista
+
+
+vista_model_registry = {"vista3d_segresnet_d": build_vista3d_segresnet_decoder}
diff --git a/vista3d/scripts/validation/val_multigpu_autopoint_patch.py b/vista3d/scripts/validation/val_multigpu_autopoint_patch.py
new file mode 100644
index 0000000..61065e3
--- /dev/null
+++ b/vista3d/scripts/validation/val_multigpu_autopoint_patch.py
@@ -0,0 +1,442 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import copy
+import glob
+import json
+import logging
+import os
+import sys
+from datetime import timedelta
+from functools import partial
+from typing import Optional, Sequence, Union
+
+import monai
+import numpy as np
+import torch
+import torch.distributed as dist
+from monai import transforms
+from monai.apps.auto3dseg.auto_runner import logger
+from monai.auto3dseg.utils import datafold_read
+from monai.bundle import ConfigParser
+from monai.bundle.scripts import _pop_args, _update_args
+from monai.data import DataLoader, partition_dataset
+from monai.metrics import compute_dice
+from monai.utils import set_determinism
+from torch.cuda.amp import autocast
+from torch.nn.parallel import DistributedDataParallel
+
+from ..sliding_window import sliding_window_inference
+from ..train import CONFIG, infer_wrapper
+from ..utils.trans_utils import VistaPostTransform
+from .build_vista3d_eval_only import vista_model_registry
+
+
+def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
+ # Initialize distributed and scale parameters based on GPU memory
+ if torch.cuda.device_count() > 1:
+ dist.init_process_group(
+ backend="nccl", init_method="env://", timeout=timedelta(seconds=3600)
+ )
+ world_size = dist.get_world_size()
+ dist.barrier()
+ else:
+ world_size = 1
+
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+ if isinstance(config_file, str) and "," in config_file:
+ config_file = config_file.split(",")
+
+ _args = _update_args(config_file=config_file, **override)
+ config_file_ = _pop_args(_args, "config_file")[0]
+
+ parser = ConfigParser()
+ parser.read_config(config_file_)
+ parser.update(pairs=_args)
+
+ amp = parser.get_parsed_content("amp")
+ data_file_base_dir = parser.get_parsed_content("data_file_base_dir")
+ data_list_file_path = parser.get_parsed_content("data_list_file_path")
+ ckpt = parser.get_parsed_content("ckpt")
+ fold = parser.get_parsed_content("fold")
+ patch_size = parser.get_parsed_content("patch_size")
+ model_registry = parser.get_parsed_content("model")
+ input_channels = parser.get_parsed_content("input_channels")
+ label_set = parser.get_parsed_content("label_set", default=None)
+ val_auto = parser.get_parsed_content("val_auto", default=False)
+ argmax_first = parser.get_parsed_content("argmax_first", default=True)
+ five_fold = parser.get_parsed_content("five_fold", default=True)
+ mapped_label_set = parser.get_parsed_content(
+ "mapped_label_set", default=copy.deepcopy(label_set)
+ )
+ transforms_infer = parser.get_parsed_content("transforms_infer")
+ list_key = parser.get_parsed_content("list_key", default="testing")
+ """ start_prompt and end_prompt are used to save memory. For 117 class prompts, it will go oom. Use these two to limit
+ the prompt number and run the scripts multiple times to cover 117 classes.
+ """
+ start_prompt = parser.get_parsed_content("start_prompt", default=None)
+ end_prompt = parser.get_parsed_content("end_prompt", default=None)
+ dataset_name = parser.get_parsed_content("dataset_name", default=None)
+ if label_set is None:
+ label_mapping = parser.get_parsed_content(
+ "label_mapping", default="./data/jsons/label_mappings.json"
+ )
+
+ with open(label_mapping, "r") as f:
+ label_mapping = json.load(f)
+ label_set = [_xx[0] for _xx in label_mapping[dataset_name]]
+ mapped_label_set = [_xx[1] for _xx in label_mapping[dataset_name]]
+ if start_prompt is not None:
+ # this is used for dataset like totalseg with large number of prompts.
+ label_set = label_set[start_prompt:end_prompt]
+ mapped_label_set = mapped_label_set[start_prompt:end_prompt]
+
+ label_set = [0] + label_set
+ mapped_label_set = [0] + mapped_label_set
+ if dataset_name == "Task07" or dataset_name == "Task03":
+ # disable argmax if there is overlap.
+ argmax_first = False
+ if dataset_name == "Bone-NIH":
+ mapped_label_set = mapped_label_set[:-1]
+ label_set = label_set[:-1]
+
+ random_seed = parser.get_parsed_content("random_seed", default=0)
+ if random_seed is not None and (
+ isinstance(random_seed, int) or isinstance(random_seed, float)
+ ):
+ set_determinism(seed=random_seed)
+
+ CONFIG["handlers"]["file"]["filename"] = parser.get_parsed_content(
+ "log_output_file"
+ )
+ logging.config.dictConfig(CONFIG)
+ logging.getLogger("torch.distributed.distributed_c10d").setLevel(logging.WARNING)
+ logger.debug(f"Number of GPUs: {torch.cuda.device_count()}")
+ logger.debug(f"World_size: {world_size}")
+ logger.debug(f"Validation using auto: {val_auto}")
+ if five_fold:
+ train_files, val_files = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=fold,
+ key="training",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ else:
+ train_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="training",
+ )
+ val_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="validation",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ process_dict = {
+ "training": train_files,
+ "validation": val_files,
+ "testing": test_files,
+ "all": train_files + val_files + test_files,
+ }
+ process_files = process_dict[list_key]
+
+ save_metric = parser.get_parsed_content("save_metric", default=False)
+ if save_metric:
+ output_dirs = os.path.join(
+ os.path.dirname(parser.get_parsed_content("log_output_file")), dataset_name
+ )
+ os.makedirs(output_dirs, exist_ok=True)
+ generated_files = glob.glob(os.path.join(output_dirs, "*.json"))
+ _process_files = []
+ for i in process_files:
+ not_finished = True
+ for j in generated_files:
+ if j.split(".json")[0].split("/")[-1] in i["image"]:
+ not_finished = False
+ break
+ if not_finished:
+ _process_files.append(i)
+ logger.info(f"{len(_process_files)} is remained out from {len(process_files)}")
+ print(f"{len(_process_files)} is remained out from {len(process_files)}")
+ process_files = _process_files
+
+ for i in range(len(process_files)):
+ if (
+ isinstance(process_files[i]["image"], list)
+ and len(process_files[i]["image"]) > 1
+ ):
+ process_files[i]["image"] = process_files[i]["image"][0]
+ if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
+ print(f"Total files {len(process_files)}")
+ print(process_files)
+ overlap = parser.get_parsed_content("overlap", default=0.0)
+ if torch.cuda.device_count() > 1:
+ process_files = partition_dataset(
+ data=process_files,
+ shuffle=False,
+ num_partitions=world_size,
+ even_divisible=False,
+ )[dist.get_rank()]
+ logger.debug(f"Val_files: {len(process_files)}")
+ val_ds = monai.data.Dataset(data=process_files, transform=transforms_infer)
+ val_loader = DataLoader(
+ val_ds,
+ num_workers=parser.get_parsed_content("num_workers_validation", default=2),
+ batch_size=1,
+ shuffle=False,
+ )
+
+ device = (
+ torch.device(f"cuda:{os.environ['LOCAL_RANK']}")
+ if world_size > 1
+ else torch.device("cuda:0")
+ )
+
+ model = vista_model_registry[model_registry](
+ in_channels=input_channels, image_size=patch_size
+ )
+
+ model = model.to(device)
+
+ if torch.cuda.device_count() > 1:
+ model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+ post_transform = transforms.Invertd(
+ keys="pred",
+ transform=transforms_infer,
+ orig_keys="image",
+ meta_keys="pred_meta_dict",
+ orig_meta_keys="image_meta_dict",
+ meta_key_postfix="meta_dict",
+ nearest_interp=True,
+ to_tensor=True,
+ )
+
+ post_pred = transforms.AsDiscrete(threshold=0.0, dtype=torch.uint8)
+
+ if torch.cuda.device_count() > 1:
+ model = DistributedDataParallel(
+ model, device_ids=[device], find_unused_parameters=True
+ )
+
+ if torch.cuda.device_count() > 1:
+ model.module.load_state_dict(
+ torch.load(ckpt, map_location=device), strict=False
+ )
+ else:
+ model.load_state_dict(torch.load(ckpt, map_location=device), strict=False)
+
+ model.eval()
+ metric_dim = len(label_set) - 1
+ model_inferer = partial(infer_wrapper, model=model)
+ with torch.no_grad():
+ metric = torch.zeros(metric_dim * 2, dtype=torch.float, device=device)
+ _index = 0
+ _final_count = 0
+ for val_data in val_loader:
+ if dataset_name == "Bone-NIH":
+ val_data["label_gt"][val_data["label_gt"] == 2] = 1
+ val_data["label"][val_data["label"] == 2] = 1
+ val_filename = val_data["image"].meta["filename_or_obj"][0]
+ _index += 1
+ with autocast(enabled=amp):
+ val_outputs = None
+ torch.cuda.empty_cache()
+ for _device_in, _device_out in zip(
+ [device, device, "cpu"], [device, "cpu", "cpu"]
+ ):
+ try:
+ with autocast(enabled=amp):
+ val_outputs = None
+ torch.cuda.empty_cache()
+ val_outputs = sliding_window_inference(
+ inputs=val_data["image"].to(_device_in),
+ roi_size=patch_size,
+ sw_batch_size=1,
+ predictor=model_inferer,
+ mode="gaussian",
+ overlap=overlap,
+ sw_device=device,
+ device=_device_out,
+ point_coords=None,
+ point_labels=None,
+ class_vector=torch.tensor(mapped_label_set)
+ .to(device)
+ .unsqueeze(0),
+ prompt_class=torch.tensor(label_set)
+ .to(device)
+ .unsqueeze(0),
+ labels=val_data["label"].to(_device_in),
+ label_set=label_set,
+ merge_with_trial=True,
+ )
+ finished = True
+ skipped = False
+ except RuntimeError as e:
+ if not any(
+ x in str(e).lower() for x in ("memory", "cuda", "cudnn")
+ ):
+ raise e
+ logger.warning(e)
+ finished = False
+ skipped = True
+
+ if finished:
+ break
+ if skipped:
+ logger.debug(
+ f"{_index} / {len(val_loader)} / {val_filename}: skipped due to OOM with size {val_data['image'].shape}"
+ )
+ continue
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ if not skipped:
+ if argmax_first:
+ try:
+ try:
+ val_outputs = VistaPostTransform(keys="pred")(
+ {
+ "image": val_data["image"][0],
+ "pred": val_outputs[0],
+ "label_prompt": label_set,
+ }
+ )
+ val_outputs = post_transform(val_outputs)["pred"][None, ...]
+ except BaseException:
+ val_outputs = VistaPostTransform(keys="pred")(
+ {
+ "image": val_data["image"][0].cpu(),
+ "pred": val_outputs[0].cpu(),
+ "label_prompt": label_set,
+ }
+ )
+ val_outputs = post_transform(val_outputs)["pred"][None, ...]
+ for i in range(1, len(label_set)):
+ gt = (
+ val_data["label_gt"].to(val_outputs.device)
+ == label_set[i]
+ )
+ ypred = val_outputs == label_set[i]
+ value[0, i - 1] = compute_dice(
+ y_pred=ypred, y=gt, include_background=False
+ )
+ _final_count += 1
+ except BaseException:
+ logger.debug(
+ f"{_index} / {len(val_loader)} / {val_filename}: Shape mismatch or OOM in postransform"
+ )
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ else:
+ try:
+ val_outputs = post_pred(val_outputs[0])[None, ...]
+ try:
+ val_outputs = post_transform(
+ {"image": val_data["image"][0], "pred": val_outputs[0]}
+ )["pred"][None, ...]
+ except BaseException:
+ val_outputs = post_transform(
+ {
+ "image": val_data["image"][0].cpu(),
+ "pred": val_outputs[0].cpu(),
+ }
+ )["pred"][None, ...]
+ for i in range(1, len(label_set)):
+ gt = (
+ val_data["label_gt"].to(val_outputs.device)
+ == label_set[i]
+ )
+ y_pred = val_outputs[:, [i]]
+ if i == 1 and (
+ dataset_name == "Task07" or dataset_name == "Task03"
+ ):
+ y_pred = torch.logical_and(
+ y_pred > 0.5, val_outputs[:, [i + 1]] < 0.5
+ )
+ value[0, i - 1] = compute_dice(
+ y_pred, y=gt, include_background=False
+ )
+ _final_count += 1
+ if save_metric:
+ output_json_path = os.path.join(
+ output_dirs,
+ os.path.dirname(val_filename).split("/")[-1] + ".json",
+ )
+ with open(output_json_path, "w") as f:
+ json.dump(value[0].cpu().numpy().tolist(), f)
+ except BaseException:
+ logger.debug(
+ f"{_index} / {len(val_loader)} / {val_filename}: Shape mismatch or OOM in postransform"
+ )
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ val_outputs, val_data = None, None
+ torch.cuda.empty_cache()
+ print(f"{_index} / {len(val_loader)} / {val_filename}: {value}")
+ logger.debug(f"{_index} / {len(val_loader)} / {val_filename}: {value}")
+ for _c in range(metric_dim):
+ val0 = torch.nan_to_num(value[0, _c], nan=0.0)
+ val1 = 1.0 - torch.isnan(value[0, _c]).float()
+ metric[2 * _c] += val0
+ metric[2 * _c + 1] += val1
+
+ if torch.cuda.device_count() > 1:
+ dist.all_reduce(metric, op=torch.distributed.ReduceOp.SUM)
+
+ metric = metric.tolist()
+ metric_class = np.zeros(metric_dim)
+ if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
+ avg_metric = 0
+ valid = 0
+ for _c in range(metric_dim):
+ if metric[2 * _c + 1] > 0:
+ v = metric[2 * _c] / metric[2 * _c + 1]
+ avg_metric += v
+ valid += 1
+ else:
+ v = torch.nan
+ metric_class[_c] = v
+ try:
+ logger.debug(f"Evaluation metric - class {_c + 1} : {v:.4f}")
+ except BaseException:
+ logger.debug(f"Evaluation metric - class {_c + 1} : {v:.4f}")
+ avg_metric = avg_metric / valid
+ print(f"Avg_metric: {avg_metric}")
+ logger.debug(f"Avg_metric: {avg_metric}")
+
+ torch.cuda.empty_cache()
+ if torch.cuda.device_count() > 1:
+ dist.barrier()
+ logger.debug(f"Final Evaluated Cases {_final_count}")
+ dist.destroy_process_group()
+
+ return
+
+
+if __name__ == "__main__":
+ from monai.utils import optional_import
+
+ fire, _ = optional_import("fire")
+ fire.Fire()
diff --git a/vista3d/scripts/validation/val_multigpu_point_iterative.py b/vista3d/scripts/validation/val_multigpu_point_iterative.py
new file mode 100644
index 0000000..2ac19f2
--- /dev/null
+++ b/vista3d/scripts/validation/val_multigpu_point_iterative.py
@@ -0,0 +1,392 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import sys
+from datetime import timedelta
+from functools import partial
+from typing import Optional, Sequence, Union
+
+import monai
+import torch
+import torch.distributed as dist
+from monai import transforms
+from monai.apps.auto3dseg.auto_runner import logger
+from monai.auto3dseg.utils import datafold_read
+from monai.bundle import ConfigParser
+from monai.bundle.scripts import _pop_args, _update_args
+from monai.data import DataLoader, partition_dataset
+from monai.metrics import compute_dice
+from monai.utils import set_determinism
+from torch.cuda.amp import autocast
+from torch.nn.parallel import DistributedDataParallel
+
+from vista3d import vista_model_registry
+
+from ..sliding_window import sliding_window_inference
+from ..train import CONFIG, infer_wrapper
+from ..utils.workflow_utils import generate_prompt_pairs_val, get_next_points_val
+
+
+def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
+ # Initialize distributed and scale parameters based on GPU memory
+ if torch.cuda.device_count() > 1:
+ dist.init_process_group(
+ backend="nccl", init_method="env://", timeout=timedelta(seconds=10000)
+ )
+ world_size = dist.get_world_size()
+ dist.barrier()
+ else:
+ world_size = 1
+
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+ if isinstance(config_file, str) and "," in config_file:
+ config_file = config_file.split(",")
+
+ _args = _update_args(config_file=config_file, **override)
+ config_file_ = _pop_args(_args, "config_file")[0]
+
+ parser = ConfigParser()
+ parser.read_config(config_file_)
+ parser.update(pairs=_args)
+
+ amp = parser.get_parsed_content("amp")
+ data_file_base_dir = parser.get_parsed_content("data_file_base_dir")
+ data_list_file_path = parser.get_parsed_content("data_list_file_path")
+ ckpt = parser.get_parsed_content("ckpt")
+ fold = parser.get_parsed_content("fold")
+ patch_size = parser.get_parsed_content("patch_size")
+ model_registry = parser.get_parsed_content("model")
+ input_channels = parser.get_parsed_content("input_channels")
+ label_set = parser.get_parsed_content("label_set", default=None)
+ transforms_infer = parser.get_parsed_content("transforms_infer")
+ list_key = parser.get_parsed_content("list_key", default="testing")
+ five_fold = parser.get_parsed_content("five_fold", default=True)
+ remove_out = parser.get_parsed_content("remove_out", default=True)
+ use_center = parser.get_parsed_content("use_center", default=True)
+ MAX_ITER = parser.get_parsed_content("max_iter", default=10)
+
+ if label_set is None:
+ label_mapping = parser.get_parsed_content(
+ "label_mapping", default="./data/jsons_final_update/label_mappings.json"
+ )
+ dataset_name = parser.get_parsed_content("dataset_name", default=None)
+ with open(label_mapping, "r") as f:
+ label_mapping = json.load(f)
+ label_set = [0] + [_xx[0] for _xx in label_mapping[dataset_name]]
+
+ random_seed = parser.get_parsed_content("random_seed", default=0)
+ if random_seed is not None and (
+ isinstance(random_seed, int) or isinstance(random_seed, float)
+ ):
+ set_determinism(seed=random_seed)
+
+ CONFIG["handlers"]["file"]["filename"] = parser.get_parsed_content(
+ "log_output_file"
+ )
+ logging.config.dictConfig(CONFIG)
+ logging.getLogger("torch.distributed.distributed_c10d").setLevel(logging.WARNING)
+ logger.debug(f"Number of GPUs: {torch.cuda.device_count()}")
+ logger.debug(f"World_size: {world_size}")
+ if five_fold:
+ train_files, val_files = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=fold,
+ key="training",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ else:
+ train_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="training",
+ )
+ val_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="validation",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ process_dict = {
+ "training": train_files,
+ "validation": val_files,
+ "testing": test_files,
+ "all": train_files + val_files + test_files,
+ }
+ process_files = process_dict[list_key]
+ for i in range(len(process_files)):
+ if (
+ isinstance(process_files[i]["image"], list)
+ and len(process_files[i]["image"]) > 1
+ ):
+ process_files[i]["image"] = process_files[i]["image"][0]
+ if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
+ print(f"Total files {len(process_files)}")
+ print(process_files)
+ overlap = parser.get_parsed_content("overlap", default=0.0)
+ if torch.cuda.device_count() > 1:
+ process_files = partition_dataset(
+ data=process_files,
+ shuffle=False,
+ num_partitions=world_size,
+ even_divisible=False,
+ )[dist.get_rank()]
+ logger.debug(f"Val_files: {len(process_files)}")
+ val_ds = monai.data.Dataset(data=process_files, transform=transforms_infer)
+ val_loader = DataLoader(
+ val_ds,
+ num_workers=parser.get_parsed_content("num_workers_validation", default=2),
+ batch_size=1,
+ shuffle=False,
+ )
+
+ device = (
+ torch.device(f"cuda:{os.environ['LOCAL_RANK']}")
+ if world_size > 1
+ else torch.device("cuda:0")
+ )
+
+ model = vista_model_registry[model_registry](
+ in_channels=input_channels, image_size=patch_size
+ )
+
+ model = model.to(device)
+
+ if torch.cuda.device_count() > 1:
+ model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+ post_transform = transforms.Invertd(
+ keys="pred",
+ transform=transforms_infer,
+ orig_keys="image",
+ meta_keys="pred_meta_dict",
+ orig_meta_keys="image_meta_dict",
+ meta_key_postfix="meta_dict",
+ nearest_interp=False,
+ to_tensor=True,
+ )
+
+ post_pred = transforms.AsDiscrete(threshold=0.0, dtype=torch.uint8)
+
+ if torch.cuda.device_count() > 1:
+ model = DistributedDataParallel(
+ model, device_ids=[device], find_unused_parameters=True
+ )
+
+ if torch.cuda.device_count() > 1:
+ model.module.load_state_dict(
+ torch.load(ckpt, map_location=device), strict=False
+ )
+ else:
+ model.load_state_dict(torch.load(ckpt, map_location=device), strict=False)
+
+ model.eval()
+ max_iters = MAX_ITER
+ metric_dim = len(label_set) - 1
+ model_inferer = partial(infer_wrapper, model=model)
+ log_string = []
+ with torch.no_grad():
+ obj_num = len(val_loader)
+ if torch.cuda.device_count() > 1:
+ size_tensor = torch.tensor(obj_num, device=device)
+ output_tensor = [torch.zeros_like(size_tensor) for _ in range(world_size)]
+ dist.barrier()
+ dist.all_gather(output_tensor, size_tensor)
+ # total_size_tensor = sum(output_tensor)
+ obj_num = max(output_tensor)
+ metric = (
+ torch.zeros(
+ obj_num, metric_dim, max_iters, dtype=torch.float, device=device
+ )
+ + torch.nan
+ )
+ point_num = (
+ torch.zeros(obj_num, metric_dim, dtype=torch.float, device=device)
+ + torch.nan
+ )
+ _index = 0
+ for val_data in val_loader:
+ val_filename = val_data["image"].meta["filename_or_obj"][0]
+ _index += 1
+
+ val_outputs = None
+ for idx in range(max_iters):
+ if idx == 0:
+ point, point_label = generate_prompt_pairs_val(
+ val_data["label"].to(device),
+ label_set,
+ max_ppoint=1,
+ use_center=use_center,
+ )
+ point = point.to(device)
+ point_label = point_label.to(device)
+ else:
+ # val_outputs is from model_inferer which moved the batch_dim to 0.
+ _point, _point_label = get_next_points_val(
+ val_outputs.transpose(1, 0),
+ val_data["label"].to(device),
+ torch.tensor(label_set).to(device),
+ point,
+ point_label,
+ use_center=False,
+ )
+ # if labels other than 0 didn't get new points, skip
+ skip_this_iter = torch.all(_point_label[1:, -1] == -1)
+ if skip_this_iter:
+ if idx < 10:
+ _point, _point_label = get_next_points_val(
+ val_outputs.transpose(1, 0),
+ val_data["label"].to(device),
+ torch.tensor(label_set).to(device),
+ point,
+ point_label,
+ use_center=False,
+ erosion2d=True,
+ )
+ skip_this_iter = torch.all(_point_label[1:, -1] == -1)
+ if skip_this_iter:
+ print(f"iteration end at {idx}")
+ break
+ point, point_label = _point, _point_label
+
+ with autocast(enabled=amp):
+ val_outputs = None
+ val_outputs = sliding_window_inference(
+ inputs=val_data["image"].to(device),
+ roi_size=patch_size,
+ sw_batch_size=1,
+ predictor=model_inferer,
+ mode="gaussian",
+ overlap=overlap,
+ sw_device=device,
+ device=device,
+ point_coords=point, # not None
+ point_labels=point_label,
+ class_vector=None,
+ prompt_class=torch.ones(len(label_set), 1).to(device)
+ * 600, # will not be used when val_point_sampler is not None
+ labels=None,
+ label_set=None,
+ use_cfp=True,
+ brush_radius=None,
+ prev_mask=None,
+ val_point_sampler=None,
+ ) # making sure zero-shot
+ # val_outputs = get_largest_connected_component_point(val_outputs, point_coords=point, point_labels=point_label, post_idx=post_idx)
+ val_pred = post_transform(
+ {"image": val_data["image"][0], "pred": val_outputs[0]}
+ )["pred"]
+ val_pred = post_pred(val_pred)[None, ...]
+ val_outputs = post_pred(val_outputs[0, ...])
+ val_outputs = val_outputs[None, ...]
+ if remove_out:
+ # remove false positive in slices with no gt
+ for i in range(1, len(label_set)):
+ gt = val_data["label"].to(val_outputs.device) == label_set[i]
+ remove_slice = gt[0, 0].sum(0).sum(0) == 0
+ val_outputs[:, i, :, :, remove_slice] = 0
+ for i in range(1, len(label_set)):
+ gt = val_data["label_gt"].to(val_outputs.device) == label_set[i]
+ y_pred = val_pred[:, [i]]
+ remove_slice = gt[0, 0].sum(0).sum(0) == 0
+ y_pred[:, :, :, :, remove_slice] = 0
+
+ metric[_index - 1, i - 1, idx] = compute_dice(
+ y_pred=y_pred, y=gt, include_background=False
+ )
+ point_num[_index - 1, i - 1] = idx + 1
+ string = f"Validation Dice score : {idx} / {_index} / {len(val_loader)}/ {val_filename}: {metric[_index-1,:,idx]}"
+ print(string)
+ log_string.append(string)
+ # move all to cpu to avoid potential out memory in invert transform
+ torch.cuda.empty_cache()
+ log_string = sorted(log_string)
+ for _ in log_string:
+ logger.debug(_)
+
+ if torch.cuda.device_count() > 1:
+ dist.barrier()
+ global_combined_tensor = [
+ torch.zeros_like(metric) for _ in range(world_size)
+ ]
+ dist.all_gather(tensor_list=global_combined_tensor, tensor=metric)
+ metric = torch.vstack(global_combined_tensor)
+ dist.barrier()
+ global_combined_tensor = [
+ torch.zeros_like(point_num) for _ in range(world_size)
+ ]
+ dist.all_gather(tensor_list=global_combined_tensor, tensor=point_num)
+ point_num = torch.vstack(global_combined_tensor)
+
+ if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
+ # remove metric that's all NaN
+ keep_index = ~torch.isnan(metric).all(1).all(1)
+ metric = metric[keep_index]
+ point_num = point_num[keep_index]
+ if max_iters > 1:
+ metric_best = torch.nan_to_num(metric, 0).max(2)[0]
+ else:
+ metric_best = metric[:, :, 0]
+ for i in range(metric.shape[0]):
+ logger.debug(f"object {i}: {metric[i].tolist()}")
+ logger.debug(f"object {i}: {metric_best[i].tolist()}")
+ print("point_number", point_num, point_num.nanmean(0))
+ torch.save(
+ {"metric": metric.cpu(), "point": point_num.cpu()},
+ parser.get_parsed_content("log_output_file").replace("log", "pt"),
+ )
+ logger.debug(
+ f"Best metric {metric_best.nanmean(0).tolist()}, best avg {metric_best.nanmean(0).nanmean().tolist()}"
+ )
+ logger.debug(
+ f"point needed, {point_num.tolist()}, mean is {point_num.nanmean(0).tolist()}"
+ )
+ """ Note: the zero-shot plots in the paper is using the saved pt file. For the j-th point, the results might be worse due to random point selection. We chose
+ the best dice from point 1 to j, e.g. point i and treat i as the point click number.
+ data = torch.load(path_to_pt_file)['metric']
+ for j in range(1, max_iters):
+ data_notnan = torch.nan_to_num(data_[:,:j],0)
+ data_notnan = data_notnan.max(1)[0]
+ y.append(data_notnan.mean())
+ x.append((~torch.isnan(data_[:,:j])).sum()/data_.shape[0])
+ plot(x, y)
+ """
+ torch.cuda.empty_cache()
+ if torch.cuda.device_count() > 1:
+ dist.barrier()
+ dist.destroy_process_group()
+
+ return
+
+
+if __name__ == "__main__":
+ from monai.utils import optional_import
+
+ fire, _ = optional_import("fire")
+ fire.Fire()
diff --git a/vista3d/scripts/validation/val_multigpu_point_patch.py b/vista3d/scripts/validation/val_multigpu_point_patch.py
new file mode 100644
index 0000000..b02daf2
--- /dev/null
+++ b/vista3d/scripts/validation/val_multigpu_point_patch.py
@@ -0,0 +1,470 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import copy
+import glob
+import json
+import logging
+import os
+import sys
+from datetime import timedelta
+from functools import partial
+from typing import Optional, Sequence, Union
+
+import monai
+import numpy as np
+import torch
+import torch.distributed as dist
+from monai import transforms
+from monai.apps.auto3dseg.auto_runner import logger
+from monai.auto3dseg.utils import datafold_read
+from monai.bundle import ConfigParser
+from monai.bundle.scripts import _pop_args, _update_args
+from monai.data import DataLoader, partition_dataset
+
+# from .monai_trans_utils import get_largest_connected_component_point
+from monai.metrics import compute_dice
+from monai.utils import set_determinism
+from torch.cuda.amp import autocast
+from torch.nn.parallel import DistributedDataParallel
+
+from vista3d import vista_model_registry
+
+from ..sliding_window import sliding_window_inference
+from ..train import CONFIG, infer_wrapper
+from ..utils.trans_utils import VistaPostTransform
+from ..utils.workflow_utils import sample_points_patch_val
+
+
+def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
+ # Initialize distributed and scale parameters based on GPU memory
+ if torch.cuda.device_count() > 1:
+ dist.init_process_group(
+ backend="nccl", init_method="env://", timeout=timedelta(seconds=3600)
+ )
+ world_size = dist.get_world_size()
+ dist.barrier()
+ else:
+ world_size = 1
+
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+ if isinstance(config_file, str) and "," in config_file:
+ config_file = config_file.split(",")
+
+ _args = _update_args(config_file=config_file, **override)
+ config_file_ = _pop_args(_args, "config_file")[0]
+
+ parser = ConfigParser()
+ parser.read_config(config_file_)
+ parser.update(pairs=_args)
+
+ amp = parser.get_parsed_content("amp")
+ data_file_base_dir = parser.get_parsed_content("data_file_base_dir")
+ data_list_file_path = parser.get_parsed_content("data_list_file_path")
+ ckpt = parser.get_parsed_content("ckpt")
+ fold = parser.get_parsed_content("fold")
+ patch_size = parser.get_parsed_content("patch_size")
+ model_registry = parser.get_parsed_content("model")
+ input_channels = parser.get_parsed_content("input_channels")
+ label_set = parser.get_parsed_content("label_set", default=None)
+ val_auto = parser.get_parsed_content("val_auto", default=False)
+ argmax_first = parser.get_parsed_content("argmax_first", default=True)
+ five_fold = parser.get_parsed_content("five_fold", default=True)
+ mapped_label_set = parser.get_parsed_content(
+ "mapped_label_set", default=copy.deepcopy(label_set)
+ )
+ transforms_infer = parser.get_parsed_content("transforms_infer")
+ list_key = parser.get_parsed_content("list_key", default="testing")
+
+ dataset_name = parser.get_parsed_content("dataset_name", default=None)
+ if label_set is None:
+ label_mapping = parser.get_parsed_content(
+ "label_mapping", default="./data/jsons/label_mappings.json"
+ )
+
+ with open(label_mapping, "r") as f:
+ label_mapping = json.load(f)
+ label_set = [0] + [_xx[0] for _xx in label_mapping[dataset_name]]
+ mapped_label_set = [0] + [_xx[1] for _xx in label_mapping[dataset_name]]
+ if dataset_name == "Task07" or dataset_name == "Task03":
+ # disable argmax if there is overlap.
+ argmax_first = False
+ if dataset_name == "Bone-NIH":
+ mapped_label_set = mapped_label_set[:-1]
+ label_set = label_set[:-1]
+
+ random_seed = parser.get_parsed_content("random_seed", default=0)
+ if random_seed is not None and (
+ isinstance(random_seed, int) or isinstance(random_seed, float)
+ ):
+ set_determinism(seed=random_seed)
+
+ CONFIG["handlers"]["file"]["filename"] = parser.get_parsed_content(
+ "log_output_file"
+ )
+ logging.config.dictConfig(CONFIG)
+ logging.getLogger("torch.distributed.distributed_c10d").setLevel(logging.WARNING)
+ logger.debug(f"Number of GPUs: {torch.cuda.device_count()}")
+ logger.debug(f"World_size: {world_size}")
+ logger.debug(f"Validation using auto: {val_auto}")
+ if five_fold:
+ train_files, val_files = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=fold,
+ key="training",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ else:
+ train_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="training",
+ )
+ val_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="validation",
+ )
+ test_files, _ = datafold_read(
+ datalist=data_list_file_path,
+ basedir=data_file_base_dir,
+ fold=-1,
+ key="testing",
+ )
+ process_dict = {
+ "training": train_files,
+ "validation": val_files,
+ "testing": test_files,
+ "all": train_files + val_files + test_files,
+ }
+ process_files = process_dict[list_key]
+
+ save_metric = parser.get_parsed_content("save_metric", default=False)
+ if save_metric:
+ output_dirs = os.path.join(
+ os.path.dirname(parser.get_parsed_content("log_output_file")), dataset_name
+ )
+ os.makedirs(output_dirs, exist_ok=True)
+ generated_files = glob.glob(os.path.join(output_dirs, "*.json"))
+ _process_files = []
+ for i in process_files:
+ not_finished = True
+ for j in generated_files:
+ if j.split(".json")[0].split("/")[-1] in i["image"]:
+ not_finished = False
+ break
+ if not_finished:
+ _process_files.append(i)
+ logger.info(f"{len(_process_files)} is remained out from {len(process_files)}")
+ print(f"{len(_process_files)} is remained out from {len(process_files)}")
+ process_files = _process_files
+
+ for i in range(len(process_files)):
+ if (
+ isinstance(process_files[i]["image"], list)
+ and len(process_files[i]["image"]) > 1
+ ):
+ process_files[i]["image"] = process_files[i]["image"][0]
+ if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
+ print(f"Total files {len(process_files)}")
+ print(process_files)
+ overlap = parser.get_parsed_content("overlap", default=0.5)
+ if torch.cuda.device_count() > 1:
+ process_files = partition_dataset(
+ data=process_files,
+ shuffle=False,
+ num_partitions=world_size,
+ even_divisible=False,
+ )[dist.get_rank()]
+ logger.debug(f"Val_files: {len(process_files)}")
+ val_ds = monai.data.Dataset(data=process_files, transform=transforms_infer)
+ val_loader = DataLoader(
+ val_ds,
+ num_workers=parser.get_parsed_content("num_workers_validation", default=2),
+ batch_size=1,
+ shuffle=False,
+ )
+
+ device = (
+ torch.device(f"cuda:{os.environ['LOCAL_RANK']}")
+ if world_size > 1
+ else torch.device("cuda:0")
+ )
+
+ model = vista_model_registry[model_registry](
+ in_channels=input_channels, image_size=patch_size
+ )
+
+ model = model.to(device)
+
+ if torch.cuda.device_count() > 1:
+ model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+ post_transform = transforms.Invertd(
+ keys="pred",
+ transform=transforms_infer,
+ orig_keys="image",
+ meta_keys="pred_meta_dict",
+ orig_meta_keys="image_meta_dict",
+ meta_key_postfix="meta_dict",
+ nearest_interp=True,
+ to_tensor=True,
+ )
+
+ post_pred = transforms.AsDiscrete(threshold=0.0, dtype=torch.uint8)
+
+ if torch.cuda.device_count() > 1:
+ model = DistributedDataParallel(
+ model, device_ids=[device], find_unused_parameters=True
+ )
+
+ if torch.cuda.device_count() > 1:
+ model.module.load_state_dict(
+ torch.load(ckpt, map_location=device), strict=False
+ )
+ else:
+ model.load_state_dict(torch.load(ckpt, map_location=device), strict=False)
+
+ model.eval()
+ metric_dim = len(label_set) - 1
+ model_inferer = partial(infer_wrapper, model=model)
+ with torch.no_grad():
+ metric = torch.zeros(metric_dim * 2, dtype=torch.float, device=device)
+ _index = 0
+ _final_count = 0
+ for val_data in val_loader:
+ if dataset_name == "Bone-NIH":
+ val_data["label_gt"][val_data["label_gt"] == 2] = 1
+ val_data["label"][val_data["label"] == 2] = 1
+ val_filename = val_data["image"].meta["filename_or_obj"][0]
+ _index += 1
+ with autocast(enabled=amp):
+ val_outputs = None
+ torch.cuda.empty_cache()
+ if val_auto:
+ try:
+ with autocast(enabled=True):
+ val_outputs = sliding_window_inference(
+ inputs=val_data["image"].to(device),
+ roi_size=patch_size,
+ sw_batch_size=1,
+ predictor=model_inferer,
+ mode="gaussian",
+ overlap=overlap,
+ sw_device=device,
+ device=device,
+ point_coords=None,
+ point_labels=None,
+ class_vector=torch.tensor(mapped_label_set)
+ .to(device)
+ .unsqueeze(0),
+ prompt_class=None,
+ labels=None,
+ label_set=None,
+ use_cfp=True,
+ brush_radius=None,
+ val_point_sampler=None,
+ )
+ skipped = False
+ except BaseException:
+ skipped = True
+ logger.debug(
+ f"{_index} / {len(val_loader)} / {val_filename}: skipped due to OOM"
+ )
+ continue
+ else:
+ for _device_in, _device_out in zip(
+ [device, device, "cpu"], [device, "cpu", "cpu"]
+ ):
+ try:
+ with autocast(enabled=amp):
+ val_outputs = None
+ torch.cuda.empty_cache()
+ val_outputs = sliding_window_inference(
+ inputs=val_data["image"].to(_device_in),
+ roi_size=patch_size,
+ sw_batch_size=1,
+ predictor=model_inferer,
+ mode="gaussian",
+ overlap=overlap,
+ sw_device=device,
+ device=_device_out,
+ point_coords=torch.zeros(len(label_set), 1),
+ point_labels=not None,
+ class_vector=None,
+ prompt_class=None,
+ labels=val_data["label"].to(_device_in),
+ label_set=label_set,
+ use_cfp=True,
+ brush_radius=None,
+ val_point_sampler=partial(
+ sample_points_patch_val,
+ mapped_label_set=mapped_label_set,
+ max_ppoint=1,
+ use_center=True,
+ ),
+ )
+ finished = True
+ skipped = False
+ except RuntimeError as e:
+ if not any(
+ x in str(e).lower() for x in ("memory", "cuda", "cudnn")
+ ):
+ raise e
+ logger.warning(e)
+ finished = False
+ skipped = True
+
+ if finished:
+ break
+ if skipped:
+ logger.debug(
+ f"{_index} / {len(val_loader)} / {val_filename}: skipped due to OOM with size {val_data['image'].shape}"
+ )
+ continue
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ if not skipped:
+ if argmax_first:
+ try:
+ try:
+ val_outputs = VistaPostTransform(keys="pred")(
+ {
+ "image": val_data["image"][0],
+ "pred": val_outputs[0],
+ "label_prompt": label_set,
+ }
+ )
+ val_outputs = post_transform(val_outputs)["pred"][None, ...]
+ except BaseException:
+ val_outputs = VistaPostTransform(keys="pred")(
+ {
+ "image": val_data["image"][0].cpu(),
+ "pred": val_outputs[0].cpu(),
+ "label_prompt": label_set,
+ }
+ )
+ val_outputs = post_transform(val_outputs)["pred"][None, ...]
+ for i in range(1, len(label_set)):
+ gt = (
+ val_data["label_gt"].to(val_outputs.device)
+ == label_set[i]
+ )
+ ypred = val_outputs == label_set[i]
+ value[0, i - 1] = compute_dice(
+ y_pred=ypred, y=gt, include_background=False
+ )
+ _final_count += 1
+ except BaseException:
+ logger.debug(
+ f"{_index} / {len(val_loader)} / {val_filename}: Shape mismatch or OOM in postransform"
+ )
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ else:
+ try:
+ val_outputs = post_pred(val_outputs[0])[None, ...]
+ try:
+ val_outputs = post_transform(
+ {"image": val_data["image"][0], "pred": val_outputs[0]}
+ )["pred"][None, ...]
+ except BaseException:
+ val_outputs = post_transform(
+ {
+ "image": val_data["image"][0].cpu(),
+ "pred": val_outputs[0].cpu(),
+ }
+ )["pred"][None, ...]
+ for i in range(1, len(label_set)):
+ gt = (
+ val_data["label_gt"].to(val_outputs.device)
+ == label_set[i]
+ )
+ y_pred = val_outputs[:, [i]]
+ if i == 1 and (
+ dataset_name == "Task07" or dataset_name == "Task03"
+ ):
+ y_pred = torch.logical_and(
+ y_pred > 0.5, val_outputs[:, [i + 1]] < 0.5
+ )
+ value[0, i - 1] = compute_dice(
+ y_pred, y=gt, include_background=False
+ )
+ _final_count += 1
+ if save_metric:
+ output_json_path = os.path.join(
+ output_dirs,
+ os.path.dirname(val_filename).split("/")[-1] + ".json",
+ )
+ with open(output_json_path, "w") as f:
+ json.dump(value[0].cpu().numpy().tolist(), f)
+ except BaseException:
+ logger.debug(
+ f"{_index} / {len(val_loader)} / {val_filename}: Shape mismatch or OOM in postransform"
+ )
+ value = torch.full((1, metric_dim), float("nan")).to(device)
+ val_outputs, val_data = None, None
+ torch.cuda.empty_cache()
+ print(f"{_index} / {len(val_loader)} / {val_filename}: {value}")
+ logger.debug(f"{_index} / {len(val_loader)} / {val_filename}: {value}")
+ for _c in range(metric_dim):
+ val0 = torch.nan_to_num(value[0, _c], nan=0.0)
+ val1 = 1.0 - torch.isnan(value[0, _c]).float()
+ metric[2 * _c] += val0
+ metric[2 * _c + 1] += val1
+
+ if torch.cuda.device_count() > 1:
+ dist.all_reduce(metric, op=torch.distributed.ReduceOp.SUM)
+
+ metric = metric.tolist()
+ metric_class = np.zeros(metric_dim)
+ if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
+ avg_metric = 0
+ valid = 0
+ for _c in range(metric_dim):
+ if metric[2 * _c + 1] > 0:
+ v = metric[2 * _c] / metric[2 * _c + 1]
+ avg_metric += v
+ valid += 1
+ else:
+ v = torch.nan
+ metric_class[_c] = v
+ try:
+ logger.debug(f"Evaluation metric - class {_c + 1} : {v:.4f}")
+ except BaseException:
+ logger.debug(f"Evaluation metric - class {_c + 1} : {v:.4f}")
+ avg_metric = avg_metric / valid
+ print(f"Avg_metric: {avg_metric}")
+ logger.debug(f"Avg_metric: {avg_metric}")
+
+ torch.cuda.empty_cache()
+ if torch.cuda.device_count() > 1:
+ dist.barrier()
+ logger.debug(f"Final Evaluated Cases {_final_count}")
+ dist.destroy_process_group()
+
+ return
+
+
+if __name__ == "__main__":
+ from monai.utils import optional_import
+
+ fire, _ = optional_import("fire")
+ fire.Fire()
diff --git a/monailabel/monaivista/lib/configs/__init__.py b/vista3d/tests/__init__.py
similarity index 100%
rename from monailabel/monaivista/lib/configs/__init__.py
rename to vista3d/tests/__init__.py
diff --git a/vista3d/tests/test_config.py b/vista3d/tests/test_config.py
new file mode 100644
index 0000000..0a4fc38
--- /dev/null
+++ b/vista3d/tests/test_config.py
@@ -0,0 +1,39 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import glob
+import os
+import unittest
+
+from monai.apps.utils import get_logger
+from monai.bundle import ConfigParser
+
+
+class TestConfig(unittest.TestCase):
+ def test_vista3d_configs_parsing(self):
+ config_dir = os.path.join(
+ os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "configs"
+ )
+ get_logger("TestConfig").info(config_dir)
+
+ configs = glob.glob(os.path.join(config_dir, "**", "*.yaml"), recursive=True)
+ for x in configs:
+ parser = ConfigParser()
+ parser.read_config(x)
+ keys = sorted(parser.config.keys())
+ # verify parser key fetching
+ get_logger("TestConfig").info(
+ f"{parser[keys[0]]}, {keys[0]}, {parser[keys[-1]]}, {keys[-1]}"
+ )
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/monailabel/plugins/slicer/MONAILabel/Testing/Python/CMakeLists.txt b/vista3d/tests/test_logger.py
similarity index 62%
rename from monailabel/plugins/slicer/MONAILabel/Testing/Python/CMakeLists.txt
rename to vista3d/tests/test_logger.py
index d7649ab..748abd3 100644
--- a/monailabel/plugins/slicer/MONAILabel/Testing/Python/CMakeLists.txt
+++ b/vista3d/tests/test_logger.py
@@ -9,5 +9,19 @@
# See the License for the specific language governing permissions and
# limitations under the License.
+import logging
+import unittest
-#slicer_add_python_unittest(SCRIPT ${MODULE_NAME}ModuleTest.py)
+from monai.apps.auto3dseg.auto_runner import logger
+
+
+class TestLogger(unittest.TestCase):
+ def test_vista3d_logger(self):
+ from scripts.train import CONFIG
+
+ logging.config.dictConfig(CONFIG)
+ logger.warning("check train logging format")
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/vista3d/vista3d/__init__.py b/vista3d/vista3d/__init__.py
new file mode 100644
index 0000000..6bc956f
--- /dev/null
+++ b/vista3d/vista3d/__init__.py
@@ -0,0 +1 @@
+from .build_vista3d import vista_model_registry # noqa: F401
diff --git a/vista3d/vista3d/build_vista3d.py b/vista3d/vista3d/build_vista3d.py
new file mode 100644
index 0000000..b19b239
--- /dev/null
+++ b/vista3d/vista3d/build_vista3d.py
@@ -0,0 +1,40 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .modeling import VISTA3D2, Class_Mapping_Classify, Point_Mapping_SAM, SegResNetDS2
+
+
+def build_vista3d_segresnet_decoder(
+ encoder_embed_dim=48, in_channels=1, image_size=(96, 96, 96)
+):
+ segresnet = SegResNetDS2(
+ in_channels=in_channels,
+ blocks_down=(1, 2, 2, 4, 4),
+ norm="instance",
+ out_channels=encoder_embed_dim,
+ init_filters=encoder_embed_dim,
+ dsdepth=1,
+ )
+ point_head = Point_Mapping_SAM(feature_size=encoder_embed_dim, last_supported=132)
+ class_head = Class_Mapping_Classify(
+ n_classes=512, feature_size=encoder_embed_dim, use_mlp=True
+ )
+ vista = VISTA3D2(
+ image_encoder=segresnet,
+ class_head=class_head,
+ point_head=point_head,
+ feature_size=encoder_embed_dim,
+ )
+ return vista
+
+
+vista_model_registry = {"vista3d_segresnet_d": build_vista3d_segresnet_decoder}
diff --git a/vista3d/vista3d/modeling/__init__.py b/vista3d/vista3d/modeling/__init__.py
new file mode 100644
index 0000000..38ddfb7
--- /dev/null
+++ b/vista3d/vista3d/modeling/__init__.py
@@ -0,0 +1,4 @@
+from .class_head import Class_Mapping_Classify # noqa: F401
+from .point_head import Point_Mapping_SAM # noqa: F401
+from .segresnetds import SegResNetDS2 # noqa: F401
+from .vista3d import VISTA3D2 # noqa: F401
diff --git a/vista3d/vista3d/modeling/class_head.py b/vista3d/vista3d/modeling/class_head.py
new file mode 100644
index 0000000..a55de51
--- /dev/null
+++ b/vista3d/vista3d/modeling/class_head.py
@@ -0,0 +1,53 @@
+import monai
+import torch
+import torch.nn as nn
+
+
+class Class_Mapping_Classify(nn.Module):
+ def __init__(self, n_classes, feature_size, use_mlp=False):
+ super().__init__()
+ self.use_mlp = use_mlp
+ if use_mlp:
+ self.mlp = nn.Sequential(
+ nn.Linear(feature_size, feature_size),
+ nn.InstanceNorm1d(1),
+ nn.GELU(),
+ nn.Linear(feature_size, feature_size),
+ )
+ self.class_embeddings = nn.Embedding(n_classes, feature_size)
+ self.image_post_mapping = nn.Sequential(
+ monai.networks.blocks.UnetrBasicBlock(
+ spatial_dims=3,
+ in_channels=feature_size,
+ out_channels=feature_size,
+ kernel_size=3,
+ stride=1,
+ norm_name="instance",
+ res_block=True,
+ ),
+ monai.networks.blocks.UnetrBasicBlock(
+ spatial_dims=3,
+ in_channels=feature_size,
+ out_channels=feature_size,
+ kernel_size=3,
+ stride=1,
+ norm_name="instance",
+ res_block=True,
+ ),
+ )
+
+ def forward(self, src, class_vector):
+ b, c, h, w, d = src.shape
+ src = self.image_post_mapping(src)
+ class_embedding = self.class_embeddings(class_vector)
+ if self.use_mlp:
+ class_embedding = self.mlp(class_embedding)
+ # [b,1,feat] @ [1,feat,dim], batch dimension become class_embedding batch dimension.
+ masks = []
+ for i in range(b):
+ mask = (class_embedding @ src[[i]].view(1, c, h * w * d)).view(
+ -1, 1, h, w, d
+ )
+ masks.append(mask)
+ masks = torch.cat(masks, 1)
+ return masks, class_embedding
diff --git a/vista3d/vista3d/modeling/point_head.py b/vista3d/vista3d/modeling/point_head.py
new file mode 100644
index 0000000..d8e6463
--- /dev/null
+++ b/vista3d/vista3d/modeling/point_head.py
@@ -0,0 +1,171 @@
+from __future__ import annotations
+
+import numpy as np
+import torch
+import torch.nn as nn
+from monai.utils import optional_import
+
+from .sam_blocks import MLP, PositionEmbeddingRandom, TwoWayTransformer
+
+rearrange, _ = optional_import("einops", name="rearrange")
+
+
+class Point_Mapping_SAM(nn.Module):
+ def __init__(
+ self,
+ feature_size,
+ max_prompt=32,
+ num_add_mask_tokens=2,
+ n_classes=512,
+ last_supported=132,
+ ):
+ super().__init__()
+ transformer_dim = feature_size
+ self.max_prompt = max_prompt
+ self.feat_downsample = nn.Sequential(
+ nn.Conv3d(
+ in_channels=feature_size,
+ out_channels=feature_size,
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ ),
+ nn.InstanceNorm3d(feature_size),
+ nn.GELU(),
+ nn.Conv3d(
+ in_channels=feature_size,
+ out_channels=transformer_dim,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ ),
+ nn.InstanceNorm3d(feature_size),
+ )
+
+ self.mask_downsample = nn.Conv3d(
+ in_channels=2, out_channels=2, kernel_size=3, stride=2, padding=1
+ )
+
+ self.transformer = TwoWayTransformer(
+ depth=2,
+ embedding_dim=transformer_dim,
+ mlp_dim=512,
+ num_heads=4,
+ )
+ self.pe_layer = PositionEmbeddingRandom(transformer_dim // 2)
+ self.point_embeddings = nn.ModuleList(
+ [nn.Embedding(1, transformer_dim), nn.Embedding(1, transformer_dim)]
+ )
+ self.not_a_point_embed = nn.Embedding(1, transformer_dim)
+ self.special_class_embed = nn.Embedding(1, transformer_dim)
+ self.mask_tokens = nn.Embedding(1, transformer_dim)
+
+ self.output_upscaling = nn.Sequential(
+ nn.ConvTranspose3d(
+ transformer_dim,
+ transformer_dim,
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ output_padding=1,
+ ),
+ nn.InstanceNorm3d(transformer_dim),
+ nn.GELU(),
+ nn.Conv3d(
+ transformer_dim, transformer_dim, kernel_size=3, stride=1, padding=1
+ ),
+ )
+
+ self.output_hypernetworks_mlps = MLP(
+ transformer_dim, transformer_dim, transformer_dim, 3
+ )
+
+ ## MultiMask output
+ self.num_add_mask_tokens = num_add_mask_tokens
+ self.output_add_hypernetworks_mlps = nn.ModuleList(
+ [
+ MLP(transformer_dim, transformer_dim, transformer_dim, 3)
+ for i in range(self.num_add_mask_tokens)
+ ]
+ )
+ # class embedding
+ self.n_classes = n_classes
+ self.last_supported = last_supported
+ self.class_embeddings = nn.Embedding(n_classes, feature_size)
+ self.zeroshot_embed = nn.Embedding(1, transformer_dim)
+ self.supported_embed = nn.Embedding(1, transformer_dim)
+
+ def forward(self, out, point_coords, point_labels, class_vector=None):
+ # downsample out
+ out_low = self.feat_downsample(out)
+ out_shape = out.shape[-3:]
+ out = None
+ torch.cuda.empty_cache()
+ # embed points
+ points = point_coords + 0.5 # Shift to center of pixel
+ point_embedding = self.pe_layer.forward_with_coords(points, out_shape)
+ point_embedding[point_labels == -1] = 0.0
+ point_embedding[point_labels == -1] += self.not_a_point_embed.weight
+ point_embedding[point_labels == 0] += self.point_embeddings[0].weight
+ point_embedding[point_labels == 1] += self.point_embeddings[1].weight
+ point_embedding[point_labels == 2] += (
+ self.point_embeddings[0].weight + self.special_class_embed.weight
+ )
+ point_embedding[point_labels == 3] += (
+ self.point_embeddings[1].weight + self.special_class_embed.weight
+ )
+ output_tokens = self.mask_tokens.weight
+
+ output_tokens = output_tokens.unsqueeze(0).expand(
+ point_embedding.size(0), -1, -1
+ )
+ if class_vector is None:
+ tokens_all = torch.cat(
+ (
+ output_tokens,
+ point_embedding,
+ self.supported_embed.weight.unsqueeze(0).expand(
+ point_embedding.size(0), -1, -1
+ ),
+ ),
+ dim=1,
+ )
+ # tokens_all = torch.cat((output_tokens, point_embedding), dim=1)
+ else:
+ class_embeddings = []
+ for i in class_vector:
+ if i > self.last_supported:
+ class_embeddings.append(self.zeroshot_embed.weight)
+ else:
+ class_embeddings.append(self.supported_embed.weight)
+ class_embeddings = torch.stack(class_embeddings)
+ tokens_all = torch.cat(
+ (output_tokens, point_embedding, class_embeddings), dim=1
+ )
+ # cross attention
+ masks = []
+ max_prompt = self.max_prompt
+ for i in range(int(np.ceil(tokens_all.shape[0] / max_prompt))):
+ # remove variables in previous for loops to save peak memory for self.transformer
+ src, upscaled_embedding, hyper_in = None, None, None
+ torch.cuda.empty_cache()
+ idx = (i * max_prompt, min((i + 1) * max_prompt, tokens_all.shape[0]))
+ tokens = tokens_all[idx[0] : idx[1]]
+ src = torch.repeat_interleave(out_low, tokens.shape[0], dim=0)
+ pos_src = torch.repeat_interleave(
+ self.pe_layer(out_low.shape[-3:]).unsqueeze(0), tokens.shape[0], dim=0
+ )
+ b, c, h, w, d = src.shape
+ hs, src = self.transformer(src, pos_src, tokens)
+ mask_tokens_out = hs[:, :1, :]
+ hyper_in = self.output_hypernetworks_mlps(mask_tokens_out)
+ src = src.transpose(1, 2).view(b, c, h, w, d)
+ upscaled_embedding = self.output_upscaling(src)
+ b, c, h, w, d = upscaled_embedding.shape
+ masks.append(
+ (hyper_in @ upscaled_embedding.view(b, c, h * w * d)).view(
+ b, -1, h, w, d
+ )
+ )
+ masks = torch.vstack(masks)
+ return masks
diff --git a/training/segment_anything/modeling/transformer.py b/vista3d/vista3d/modeling/sam_blocks.py
similarity index 61%
rename from training/segment_anything/modeling/transformer.py
rename to vista3d/vista3d/modeling/sam_blocks.py
index 5bdd753..d7bc413 100644
--- a/training/segment_anything/modeling/transformer.py
+++ b/vista3d/vista3d/modeling/sam_blocks.py
@@ -1,3 +1,15 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Adapted from https://github.com/facebookresearch/segment-anything
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
@@ -5,13 +17,13 @@
# LICENSE file in the root directory of this source tree.
import math
-from typing import Tuple, Type
+from typing import Any, Optional, Tuple, Type
+import numpy as np
import torch
+import torch.nn.functional as F
from torch import Tensor, nn
-from .common import MLPBlock
-
class TwoWayTransformer(nn.Module):
def __init__(
@@ -54,7 +66,9 @@ def __init__(
)
)
- self.final_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+ self.final_attn_token_to_image = Attention(
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+ )
self.norm_final_attn = nn.LayerNorm(embedding_dim)
def forward(
@@ -77,7 +91,7 @@ def forward(
torch.Tensor: the processed image_embedding
"""
# BxCxHxW -> BxHWxC == B x N_image_tokens x C
- bs, c, h, w = image_embedding.shape
+ bs, c, h, w, d = image_embedding.shape
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
image_pe = image_pe.flatten(2).permute(0, 2, 1)
@@ -94,7 +108,7 @@ def forward(
key_pe=image_pe,
)
- # Apply the final attenion layer from the points to the image
+ # Apply the final attention layer from the points to the image
q = queries + point_embedding
k = keys + image_pe
attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
@@ -131,18 +145,24 @@ def __init__(
self.self_attn = Attention(embedding_dim, num_heads)
self.norm1 = nn.LayerNorm(embedding_dim)
- self.cross_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+ self.cross_attn_token_to_image = Attention(
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+ )
self.norm2 = nn.LayerNorm(embedding_dim)
self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
self.norm3 = nn.LayerNorm(embedding_dim)
self.norm4 = nn.LayerNorm(embedding_dim)
- self.cross_attn_image_to_token = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+ self.cross_attn_image_to_token = Attention(
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+ )
self.skip_first_layer_pe = skip_first_layer_pe
- def forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor) -> Tuple[Tensor, Tensor]:
+ def forward(
+ self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
+ ) -> Tuple[Tensor, Tensor]:
# Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
@@ -190,7 +210,9 @@ def __init__(
self.embedding_dim = embedding_dim
self.internal_dim = embedding_dim // downsample_rate
self.num_heads = num_heads
- assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
+ assert (
+ self.internal_dim % num_heads == 0
+ ), "num_heads must divide embedding_dim."
self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
@@ -230,3 +252,95 @@ def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
out = self.out_proj(out)
return out
+
+
+class PositionEmbeddingRandom(nn.Module):
+ """
+ Positional encoding using random spatial frequencies.
+ """
+
+ def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+ super().__init__()
+ if scale is None or scale <= 0.0:
+ scale = 1.0
+ self.register_buffer(
+ "positional_encoding_gaussian_matrix",
+ scale * torch.randn((3, num_pos_feats)),
+ )
+
+ def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+ """Positionally encode points that are normalized to [0,1]."""
+ # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+ coords = 2 * coords - 1
+ # [bs=1,N=2,2] @ [2,128]
+ # [bs=1, N=2, 128]
+ coords = coords @ self.positional_encoding_gaussian_matrix
+ coords = 2 * np.pi * coords
+ # outputs d_1 x ... x d_n x C shape
+ # [bs=1, N=2, 128+128=256]
+ return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+ def forward(self, size: Tuple[int, int, int]) -> torch.Tensor:
+ """Generate positional encoding for a grid of the specified size."""
+ h, w, d = size
+ device: Any = self.positional_encoding_gaussian_matrix.device
+ grid = torch.ones((h, w, d), device=device, dtype=torch.float32)
+ x_embed = grid.cumsum(dim=0) - 0.5
+ y_embed = grid.cumsum(dim=1) - 0.5
+ z_embed = grid.cumsum(dim=2) - 0.5
+ x_embed = x_embed / h
+ y_embed = y_embed / w
+ z_embed = z_embed / d
+ pe = self._pe_encoding(torch.stack([x_embed, y_embed, z_embed], dim=-1))
+ return pe.permute(3, 0, 1, 2) # C x H x W
+
+ def forward_with_coords(
+ self, coords_input: torch.Tensor, image_size: Tuple[int, int]
+ ) -> torch.Tensor:
+ """Positionally encode points that are not normalized to [0,1]."""
+ coords = coords_input.clone()
+ coords[:, :, 0] = coords[:, :, 0] / image_size[0]
+ coords[:, :, 1] = coords[:, :, 1] / image_size[1]
+ coords[:, :, 2] = coords[:, :, 2] / image_size[2]
+ return self._pe_encoding(coords.to(torch.float)) # B x N x C
+
+
+class MLPBlock(nn.Module):
+ def __init__(
+ self,
+ embedding_dim: int,
+ mlp_dim: int,
+ act: Type[nn.Module] = nn.GELU,
+ ) -> None:
+ super().__init__()
+ self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+ self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+ self.act = act()
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return self.lin2(self.act(self.lin1(x)))
+
+
+class MLP(nn.Module):
+ def __init__(
+ self,
+ input_dim: int,
+ hidden_dim: int,
+ output_dim: int,
+ num_layers: int,
+ sigmoid_output: bool = False,
+ ) -> None:
+ super().__init__()
+ self.num_layers = num_layers
+ h = [hidden_dim] * (num_layers - 1)
+ self.layers = nn.ModuleList(
+ nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+ )
+ self.sigmoid_output = sigmoid_output
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ if self.sigmoid_output:
+ x = F.sigmoid(x)
+ return x
diff --git a/vista3d/vista3d/modeling/segresnetds.py b/vista3d/vista3d/modeling/segresnetds.py
new file mode 100644
index 0000000..e8c96d5
--- /dev/null
+++ b/vista3d/vista3d/modeling/segresnetds.py
@@ -0,0 +1,536 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from monai.networks.blocks.upsample import UpSample
+from monai.networks.layers.factories import Act, Conv, Norm, split_args
+from monai.networks.layers.utils import get_act_layer, get_norm_layer
+from monai.utils import UpsampleMode, has_option
+
+__all__ = ["SegResNetDS2"]
+
+
+def scales_for_resolution(resolution: tuple | list, n_stages: int | None = None):
+ """
+ A helper function to compute a schedule of scale at different downsampling levels,
+ given the input resolution.
+
+ .. code-block:: python
+
+ scales_for_resolution(resolution=[1,1,5], n_stages=5)
+
+ Args:
+ resolution: input image resolution (in mm)
+ n_stages: optionally the number of stages of the network
+ """
+
+ ndim = len(resolution)
+ res = np.array(resolution)
+ if not all(res > 0):
+ raise ValueError("Resolution must be positive")
+
+ nl = np.floor(np.log2(np.max(res) / res)).astype(np.int32)
+ scales = [tuple(np.where(2**i >= 2**nl, 1, 2)) for i in range(max(nl))]
+ if n_stages and n_stages > max(nl):
+ scales = scales + [(2,) * ndim] * (n_stages - max(nl))
+ else:
+ scales = scales[:n_stages]
+ return scales
+
+
+def aniso_kernel(scale: tuple | list):
+ """
+ A helper function to compute kernel_size, padding and stride for the given scale
+
+ Args:
+ scale: scale from a current scale level
+ """
+ kernel_size = [3 if scale[k] > 1 else 1 for k in range(len(scale))]
+ padding = [k // 2 for k in kernel_size]
+ return kernel_size, padding, scale
+
+
+class SegResBlock(nn.Module):
+ """
+ Residual network block used SegResNet based on `3D MRI brain tumor segmentation using autoencoder regularization
+ `_.
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ norm: tuple | str,
+ kernel_size: tuple | int = 3,
+ act: tuple | str = "relu",
+ ) -> None:
+ """
+ Args:
+ spatial_dims: number of spatial dimensions, could be 1, 2 or 3.
+ in_channels: number of input channels.
+ norm: feature normalization type and arguments.
+ kernel_size: convolution kernel size. Defaults to 3.
+ act: activation type and arguments. Defaults to ``RELU``.
+ """
+ super().__init__()
+
+ if isinstance(kernel_size, (tuple, list)):
+ padding = tuple(k // 2 for k in kernel_size)
+ else:
+ padding = kernel_size // 2 # type: ignore
+
+ self.norm1 = get_norm_layer(
+ name=norm, spatial_dims=spatial_dims, channels=in_channels
+ )
+ self.act1 = get_act_layer(act)
+ self.conv1 = Conv[Conv.CONV, spatial_dims](
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ padding=padding,
+ bias=False,
+ )
+
+ self.norm2 = get_norm_layer(
+ name=norm, spatial_dims=spatial_dims, channels=in_channels
+ )
+ self.act2 = get_act_layer(act)
+ self.conv2 = Conv[Conv.CONV, spatial_dims](
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ padding=padding,
+ bias=False,
+ )
+
+ def forward(self, x):
+ identity = x
+ x = self.conv1(self.act1(self.norm1(x)))
+ x = self.conv2(self.act2(self.norm2(x)))
+ x += identity
+ return x
+
+
+class SegResEncoder(nn.Module):
+ """
+ SegResEncoder based on the econder structure in `3D MRI brain tumor segmentation using autoencoder regularization
+ `_.
+
+ Args:
+ spatial_dims: spatial dimension of the input data. Defaults to 3.
+ init_filters: number of output channels for initial convolution layer. Defaults to 32.
+ in_channels: number of input channels for the network. Defaults to 1.
+ out_channels: number of output channels for the network. Defaults to 2.
+ act: activation type and arguments. Defaults to ``RELU``.
+ norm: feature normalization type and arguments. Defaults to ``BATCH``.
+ blocks_down: number of downsample blocks in each layer. Defaults to ``[1,2,2,4]``.
+ head_module: optional callable module to apply to the final features.
+ anisotropic_scales: optional list of scale for each scale level.
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int = 3,
+ init_filters: int = 32,
+ in_channels: int = 1,
+ act: tuple | str = "relu",
+ norm: tuple | str = "batch",
+ blocks_down: tuple = (1, 2, 2, 4),
+ head_module: nn.Module | None = None,
+ anisotropic_scales: tuple | None = None,
+ ):
+ super().__init__()
+
+ if spatial_dims not in (1, 2, 3):
+ raise ValueError("`spatial_dims` can only be 1, 2 or 3.")
+
+ # ensure normalization has affine trainable parameters (if not specified)
+ norm = split_args(norm)
+ if has_option(Norm[norm[0], spatial_dims], "affine"):
+ norm[1].setdefault("affine", True) # type: ignore
+
+ # ensure activation is inplace (if not specified)
+ act = split_args(act)
+ if has_option(Act[act[0]], "inplace"):
+ act[1].setdefault("inplace", True) # type: ignore
+
+ filters = init_filters # base number of features
+
+ kernel_size, padding, _ = (
+ aniso_kernel(anisotropic_scales[0]) if anisotropic_scales else (3, 1, 1)
+ )
+ self.conv_init = Conv[Conv.CONV, spatial_dims](
+ in_channels=in_channels,
+ out_channels=filters,
+ kernel_size=kernel_size,
+ padding=padding,
+ stride=1,
+ bias=False,
+ )
+ self.layers = nn.ModuleList()
+
+ for i in range(len(blocks_down)):
+ level = nn.ModuleDict()
+
+ kernel_size, padding, stride = (
+ aniso_kernel(anisotropic_scales[i]) if anisotropic_scales else (3, 1, 2)
+ )
+ blocks = [
+ SegResBlock(
+ spatial_dims=spatial_dims,
+ in_channels=filters,
+ kernel_size=kernel_size,
+ norm=norm,
+ act=act,
+ )
+ for _ in range(blocks_down[i])
+ ]
+ level["blocks"] = nn.Sequential(*blocks)
+
+ if i < len(blocks_down) - 1:
+ level["downsample"] = Conv[Conv.CONV, spatial_dims](
+ in_channels=filters,
+ out_channels=2 * filters,
+ bias=False,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ )
+ else:
+ level["downsample"] = nn.Identity()
+
+ self.layers.append(level)
+ filters *= 2
+
+ self.head_module = head_module
+ self.in_channels = in_channels
+ self.blocks_down = blocks_down
+ self.init_filters = init_filters
+ self.norm = norm
+ self.act = act
+ self.spatial_dims = spatial_dims
+
+ def _forward(self, x: torch.Tensor) -> list[torch.Tensor]:
+ outputs = []
+ x = self.conv_init(x)
+
+ for level in self.layers:
+ x = level["blocks"](x)
+ outputs.append(x)
+ x = level["downsample"](x)
+
+ if self.head_module is not None:
+ outputs = self.head_module(outputs)
+
+ return outputs
+
+ def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
+ return self._forward(x)
+
+
+class SegResNetDS2(nn.Module):
+ """
+ SegResNetDS based on `3D MRI brain tumor segmentation using autoencoder regularization
+ `_.
+ It is similar to https://docs.monai.io/en/stable/networks.html#segresnet, with several
+ improvements including deep supervision and non-isotropic kernel support.
+
+ Args:
+ spatial_dims: spatial dimension of the input data. Defaults to 3.
+ init_filters: number of output channels for initial convolution layer. Defaults to 32.
+ in_channels: number of input channels for the network. Defaults to 1.
+ out_channels: number of output channels for the network. Defaults to 2.
+ act: activation type and arguments. Defaults to ``RELU``.
+ norm: feature normalization type and arguments. Defaults to ``BATCH``.
+ blocks_down: number of downsample blocks in each layer. Defaults to ``[1,2,2,4]``.
+ blocks_up: number of upsample blocks (optional).
+ dsdepth: number of levels for deep supervision. This will be the length of the list of outputs at each scale level.
+ At dsdepth==1,only a single output is returned.
+ preprocess: optional callable function to apply before the model's forward pass
+ resolution: optional input image resolution. When provided, the network will first use non-isotropic kernels to bring
+ image spacing into an approximately isotropic space.
+ Otherwise, by default, the kernel size and downsampling is always isotropic.
+
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int = 3,
+ init_filters: int = 32,
+ in_channels: int = 1,
+ out_channels: int = 2,
+ act: tuple | str = "relu",
+ norm: tuple | str = "batch",
+ blocks_down: tuple = (1, 2, 2, 4),
+ blocks_up: tuple | None = None,
+ dsdepth: int = 1,
+ preprocess: nn.Module | Callable | None = None,
+ upsample_mode: UpsampleMode | str = "deconv",
+ resolution: tuple | None = None,
+ ):
+ super().__init__()
+
+ if spatial_dims not in (1, 2, 3):
+ raise ValueError("`spatial_dims` can only be 1, 2 or 3.")
+
+ self.spatial_dims = spatial_dims
+ self.init_filters = init_filters
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.act = act
+ self.norm = norm
+ self.blocks_down = blocks_down
+ self.dsdepth = max(dsdepth, 1)
+ self.resolution = resolution
+ self.preprocess = preprocess
+
+ if resolution is not None:
+ if not isinstance(resolution, (list, tuple)):
+ raise TypeError("resolution must be a tuple")
+ elif not all(r > 0 for r in resolution):
+ raise ValueError("resolution must be positive")
+
+ # ensure normalization had affine trainable parameters (if not specified)
+ norm = split_args(norm)
+ if has_option(Norm[norm[0], spatial_dims], "affine"):
+ norm[1].setdefault("affine", True) # type: ignore
+
+ # ensure activation is inplace (if not specified)
+ act = split_args(act)
+ if has_option(Act[act[0]], "inplace"):
+ act[1].setdefault("inplace", True) # type: ignore
+
+ anisotropic_scales = None
+ if resolution:
+ anisotropic_scales = scales_for_resolution(
+ resolution, n_stages=len(blocks_down)
+ )
+ self.anisotropic_scales = anisotropic_scales
+
+ self.encoder = SegResEncoder(
+ spatial_dims=spatial_dims,
+ init_filters=init_filters,
+ in_channels=in_channels,
+ act=act,
+ norm=norm,
+ blocks_down=blocks_down,
+ anisotropic_scales=anisotropic_scales,
+ )
+
+ n_up = len(blocks_down) - 1
+ if blocks_up is None:
+ blocks_up = (1,) * n_up # assume 1 upsample block per level
+ self.blocks_up = blocks_up
+
+ filters = init_filters * 2**n_up
+ self.up_layers = nn.ModuleList()
+ self.up_layers_auto = nn.ModuleList()
+
+ for i in range(n_up):
+ filters = filters // 2
+ kernel_size, _, stride = (
+ aniso_kernel(anisotropic_scales[len(blocks_up) - i - 1])
+ if anisotropic_scales
+ else (3, 1, 2)
+ )
+
+ level = nn.ModuleDict()
+ level_auto = nn.ModuleDict()
+ level["upsample"] = UpSample(
+ mode=upsample_mode,
+ spatial_dims=spatial_dims,
+ in_channels=2 * filters,
+ out_channels=filters,
+ kernel_size=kernel_size,
+ scale_factor=stride,
+ bias=False,
+ align_corners=False,
+ )
+ level_auto["upsample"] = UpSample(
+ mode=upsample_mode,
+ spatial_dims=spatial_dims,
+ in_channels=2 * filters,
+ out_channels=filters,
+ kernel_size=kernel_size,
+ scale_factor=stride,
+ bias=False,
+ align_corners=False,
+ )
+ blocks = [
+ SegResBlock(
+ spatial_dims=spatial_dims,
+ in_channels=filters,
+ kernel_size=kernel_size,
+ norm=norm,
+ act=act,
+ )
+ for _ in range(blocks_up[i])
+ ]
+ level["blocks"] = nn.Sequential(*blocks)
+ blocks = [
+ SegResBlock(
+ spatial_dims=spatial_dims,
+ in_channels=filters,
+ kernel_size=kernel_size,
+ norm=norm,
+ act=act,
+ )
+ for _ in range(blocks_up[i])
+ ]
+ level_auto["blocks"] = nn.Sequential(*blocks)
+ if len(blocks_up) - i <= dsdepth: # deep supervision heads
+ level["head"] = Conv[Conv.CONV, spatial_dims](
+ in_channels=filters,
+ out_channels=out_channels,
+ kernel_size=1,
+ bias=True,
+ )
+ level_auto["head"] = Conv[Conv.CONV, spatial_dims](
+ in_channels=filters,
+ out_channels=out_channels,
+ kernel_size=1,
+ bias=True,
+ )
+ else:
+ level["head"] = nn.Identity()
+ level_auto["head"] = nn.Identity()
+
+ self.up_layers.append(level)
+ self.up_layers_auto.append(level_auto)
+
+ if (
+ n_up == 0
+ ): # in a corner case of flat structure (no downsampling), attache a single head
+ level = nn.ModuleDict(
+ {
+ "upsample": nn.Identity(),
+ "blocks": nn.Identity(),
+ "head": Conv[Conv.CONV, spatial_dims](
+ in_channels=filters,
+ out_channels=out_channels,
+ kernel_size=1,
+ bias=True,
+ ),
+ }
+ )
+ level_auto = nn.ModuleDict(
+ {
+ "upsample": nn.Identity(),
+ "blocks": nn.Identity(),
+ "head": Conv[Conv.CONV, spatial_dims](
+ in_channels=filters,
+ out_channels=out_channels,
+ kernel_size=1,
+ bias=True,
+ ),
+ }
+ )
+ self.up_layers.append(level)
+ self.up_layers_auto.append(level_auto)
+
+ def shape_factor(self):
+ """
+ Calculate the factors (divisors) that the input image shape must be divisible by
+ """
+ if self.anisotropic_scales is None:
+ d = [2 ** (len(self.blocks_down) - 1)] * self.spatial_dims
+ else:
+ d = list(np.prod(np.array(self.anisotropic_scales[:-1]), axis=0))
+ return d
+
+ def is_valid_shape(self, x):
+ """
+ Calculate if the input shape is divisible by the minimum factors for the current network configuration
+ """
+ a = [i % j == 0 for i, j in zip(x.shape[2:], self.shape_factor())]
+ return all(a)
+
+ def _forward(
+ self, x: torch.Tensor, with_point, with_label
+ ) -> Union[None, torch.Tensor, list[torch.Tensor]]:
+ if self.preprocess is not None:
+ x = self.preprocess(x)
+
+ if not self.is_valid_shape(x):
+ raise ValueError(
+ f"Input spatial dims {x.shape} must be divisible by {self.shape_factor()}"
+ )
+
+ x_down = self.encoder(x)
+
+ x_down.reverse()
+ x = x_down.pop(0)
+
+ if len(x_down) == 0:
+ x_down = [torch.zeros(1, device=x.device, dtype=x.dtype)]
+
+ outputs: list[torch.Tensor] = []
+ outputs_auto: list[torch.Tensor] = []
+ x_ = x.clone()
+ if with_point:
+ i = 0
+ for level in self.up_layers:
+ x = level["upsample"](x)
+ x = x + x_down[i]
+ x = level["blocks"](x)
+
+ if len(self.up_layers) - i <= self.dsdepth:
+ outputs.append(level["head"](x))
+ i = i + 1
+
+ outputs.reverse()
+ x = x_
+ if with_label:
+ i = 0
+ for level in self.up_layers_auto:
+ x = level["upsample"](x)
+ x = x + x_down[i]
+ x = level["blocks"](x)
+
+ if len(self.up_layers) - i <= self.dsdepth:
+ outputs_auto.append(level["head"](x))
+ i = i + 1
+
+ outputs_auto.reverse()
+
+ # in eval() mode, always return a single final output
+ if not self.training or len(outputs) == 1:
+ outputs = outputs[0] if len(outputs) == 1 else outputs
+
+ if not self.training or len(outputs_auto) == 1:
+ outputs_auto = outputs_auto[0] if len(outputs_auto) == 1 else outputs_auto
+
+ # return a list of DS outputs
+ return outputs, outputs_auto
+
+ def forward(
+ self, x: torch.Tensor, with_point=True, with_label=True, **kwargs
+ ) -> Union[None, torch.Tensor, list[torch.Tensor]]:
+ return self._forward(x, with_point, with_label)
+
+ def set_auto_grad(self, auto_freeze=False, point_freeze=False):
+ for param in self.encoder.parameters():
+ param.requires_grad = (not auto_freeze) and (not point_freeze)
+
+ for param in self.up_layers_auto.parameters():
+ param.requires_grad = not auto_freeze
+
+ for param in self.up_layers.parameters():
+ param.requires_grad = not point_freeze
diff --git a/vista3d/vista3d/modeling/vista3d.py b/vista3d/vista3d/modeling/vista3d.py
new file mode 100644
index 0000000..a67b660
--- /dev/null
+++ b/vista3d/vista3d/modeling/vista3d.py
@@ -0,0 +1,345 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import monai
+import numpy as np
+import torch
+import torch.nn as nn
+from monai.utils import optional_import
+from scripts.utils.trans_utils import convert_points_to_disc
+from scripts.utils.trans_utils import get_largest_connected_component_mask as lcc
+from scripts.utils.workflow_utils import sample_points_patch_val
+
+rearrange, _ = optional_import("einops", name="rearrange")
+NINF_VALUE = -9999
+PINF_VALUE = 9999
+
+
+class VISTA3D2(nn.Module):
+ def __init__(self, image_encoder, class_head, point_head, feature_size):
+ super().__init__()
+ self.image_encoder = image_encoder
+ self.class_head = class_head
+ self.point_head = point_head
+ self.image_embeddings = None
+ self.weight_mapper = nn.Sequential(
+ nn.Linear(feature_size, 4 * feature_size),
+ nn.GELU(),
+ nn.InstanceNorm1d(4 * feature_size),
+ nn.Linear(4 * feature_size, 1),
+ )
+ self.auto_freeze = False
+ self.point_freeze = False
+
+ def precompute_embedding(self, input_images):
+ """precompute image embedding, require sliding window inference"""
+ raise NotImplementedError
+
+ def clear_cache(self):
+ self.image_embeddings = None
+
+ def get_bs(self, class_vector, point_coords):
+ if class_vector is None:
+ assert point_coords is not None, "prompt is required"
+ return point_coords.shape[0]
+ else:
+ return class_vector.shape[0]
+
+ def update_point_to_patch(self, patch_coords, point_coords, point_labels):
+ """Update point_coords with respect to patch coords.
+ If point is outside of the patch, remove the coordinates and set label to -1
+ """
+ patch_ends = [
+ patch_coords[-3].stop,
+ patch_coords[-2].stop,
+ patch_coords[-1].stop,
+ ]
+ patch_starts = [
+ patch_coords[-3].start,
+ patch_coords[-2].start,
+ patch_coords[-1].start,
+ ]
+ # update point coords
+ patch_starts = (
+ torch.tensor(patch_starts, device=point_coords.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+ patch_ends = (
+ torch.tensor(patch_ends, device=point_coords.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+ # [1 N 1]
+ indices = torch.logical_and(
+ ((point_coords - patch_starts) > 0).all(2),
+ ((patch_ends - point_coords) > 0).all(2),
+ )
+ # check if it's within patch coords
+ point_coords = point_coords.clone() - patch_starts
+ point_labels = point_labels.clone()
+ if indices.any():
+ point_labels[~indices] = -1
+ point_coords[~indices] = 0
+ # also remove padded points, mainly used for inference.
+ not_pad_indices = (point_labels != -1).any(0)
+ point_coords = point_coords[:, not_pad_indices]
+ point_labels = point_labels[:, not_pad_indices]
+ else:
+ point_coords = None
+ point_labels = None
+ return point_coords, point_labels
+
+ def connected_components_combine(
+ self, logits, point_logits, point_coords, point_labels, mapping_index, thred=0.5
+ ):
+ """Combine auto results with point click response, or combine previous mask with point click response.
+ For mapping_index with point clicks, NaN values in logits will be replaced with point_logits. Meanwhile, the added/removed
+ region in point clicks must be updated by the lcc function. Notice, if a positive point is within logits/prev_mask, the components containing the positive point
+ will be added.
+ """
+ logits = (
+ logits.as_tensor() if isinstance(logits, monai.data.MetaTensor) else logits
+ )
+ _logits = logits[mapping_index]
+ inside = []
+ for i in range(_logits.shape[0]):
+ inside.append(
+ np.any(
+ [
+ _logits[
+ i,
+ 0,
+ round(p[0].item()),
+ round(p[1].item()),
+ round(p[2].item()),
+ ].item()
+ > 0
+ for p in point_coords[i]
+ ]
+ )
+ )
+ inside = torch.tensor(inside).to(logits.device)
+ nan_mask = torch.isnan(_logits)
+ _logits = torch.nan_to_num(_logits, nan=NINF_VALUE).sigmoid()
+ pos_region = point_logits.sigmoid() > thred
+ diff_pos = torch.logical_and(
+ torch.logical_or(
+ (_logits <= thred),
+ inside.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1),
+ ),
+ pos_region,
+ )
+ diff_neg = torch.logical_and((_logits > thred), ~pos_region)
+ cc = lcc(
+ diff_pos, diff_neg, point_coords=point_coords, point_labels=point_labels
+ )
+ # cc is the region that can be updated by point_logits.
+ cc = cc.to(logits.device)
+ # Need to replace NaN with point_logits. diff_neg will never lie in nan_mask, only remove unconnected positive region.
+ uc_pos_region = torch.logical_and(pos_region, ~cc)
+ fill_mask = torch.logical_and(nan_mask, uc_pos_region)
+ if fill_mask.any():
+ # fill in the mean negative value
+ point_logits[fill_mask] = -1
+ # replace logits nan value and cc with point_logits
+ cc = torch.logical_or(nan_mask, cc).to(logits.dtype)
+ logits[mapping_index] *= 1 - cc
+ logits[mapping_index] += cc * point_logits
+ # debug_ccp(_logits, point_logits.sigmoid(), point_coords, point_labels, diff, cc, logits[mapping_index], np.random.randint(10000))
+ return logits
+
+ def gaussian_combine(
+ self, logits, point_logits, point_coords, point_labels, mapping_index, radius
+ ):
+ """Combine point results with auto results using gaussian."""
+ if radius is None:
+ radius = min(point_logits.shape[-3:]) // 5 # empirical value 5
+ weight = 1 - convert_points_to_disc(
+ point_logits.shape[-3:], point_coords, point_labels, radius=radius
+ ).sum(1, keepdims=True)
+ weight[weight < 0] = 0
+ logits = (
+ logits.as_tensor() if isinstance(logits, monai.data.MetaTensor) else logits
+ )
+ logits[mapping_index] *= weight
+ logits[mapping_index] += (1 - weight) * point_logits
+ return logits
+
+ def set_auto_grad(self, auto_freeze=False, point_freeze=False):
+ """Freeze auto-branch or point-branch"""
+ if auto_freeze != self.auto_freeze:
+ if hasattr(self.image_encoder, "set_auto_grad"):
+ self.image_encoder.set_auto_grad(
+ auto_freeze=auto_freeze, point_freeze=point_freeze
+ )
+ else:
+ for param in self.image_encoder.parameters():
+ param.requires_grad = (not auto_freeze) and (not point_freeze)
+ for param in self.class_head.parameters():
+ param.requires_grad = not auto_freeze
+ self.auto_freeze = auto_freeze
+
+ if point_freeze != self.point_freeze:
+ if hasattr(self.image_encoder, "set_auto_grad"):
+ self.image_encoder.set_auto_grad(
+ auto_freeze=auto_freeze, point_freeze=point_freeze
+ )
+ else:
+ for param in self.image_encoder.parameters():
+ param.requires_grad = (not auto_freeze) and (not point_freeze)
+ for param in self.point_head.parameters():
+ param.requires_grad = not point_freeze
+ self.point_freeze = point_freeze
+
+ def forward(
+ self,
+ input_images,
+ point_coords=None,
+ point_labels=None,
+ class_vector=None,
+ prompt_class=None,
+ patch_coords=None,
+ labels=None,
+ label_set=None,
+ prev_mask=None,
+ radius=None,
+ val_point_sampler=None,
+ **kwargs,
+ ):
+ """
+ The forward function for VISTA3D. We only support single patch in training and inference.
+ One exception is allowing sliding window batch size > 1 for automatic segmentation only case.
+ B represents number of objects, N represents number of points for each objects.
+ Args:
+ input_images: [1, 1, H, W, D]
+ point_coords: [B, N, 3]
+ point_labels: [B, N], -1 represents padding. 0/1 means negative/positive points for regular class.
+ 2/3 means negative/postive ponits for special supported class like tumor.
+ class_vector: [B, 1], the global class index
+ prompt_class: [B, 1], the global class index. This value is associated with point_coords to identify if
+ the points are for zero-shot or supported class. When class_vector and point_coords are both
+ provided, prompt_class is the same as class_vector. For prompt_class[b] > 512, point_coords[b]
+ will be considered novel class.
+ patch_coords: the python slice object representing the patch coordinates during sliding window inference. This value is
+ passed from monai_utils.sliding_window_inferer. This is an indicator for training phase or validation phase.
+ labels: [1, 1, H, W, D], the groundtruth label tensor, only used for point-only evaluation
+ label_set: the label index matching the indexes in labels. If labels are mapped to global index using RelabelID,
+ this label_set should be global mapped index. If labels are not mapped to global index, e.g. in zero-shot
+ evaluation, this label_set should be the original index.
+ prev_mask: [B, N, H_fullsize, W_fullsize, D_fullsize]. This is the transposed raw output from sliding_window_inferer before
+ any postprocessing. When user click points to perform auto-results correction, this can be the auto-results.
+ radius: single float value controling the gaussian blur when combining point and auto results. The gaussian combine is not used
+ in VISTA3D training but might be useful for finetuning purposes.
+ val_point_sampler: function used to sample points from labels. This is only used for point-only evaluation.
+
+ """
+ image_size = input_images.shape[-3:]
+ device = input_images.device
+ if point_coords is None and class_vector is None:
+ return NINF_VALUE + torch.zeros([1, 1, *image_size], device=device)
+
+ bs = self.get_bs(class_vector, point_coords)
+ if patch_coords is not None:
+ # if during validation and perform enable based point-validation.
+ if labels is not None and label_set is not None:
+ # if labels is not None, sample from labels for each patch.
+ if val_point_sampler is None:
+ val_point_sampler = sample_points_patch_val
+ point_coords, point_labels, prompt_class = val_point_sampler(
+ labels, patch_coords, label_set
+ )
+ if prompt_class[0].item() == 0:
+ point_labels[0] = -1
+ labels, prev_mask = None, None
+ elif point_coords is not None:
+ # If not performing patch-based point only validation, use user provided click points for inference.
+ # the point clicks is in original image space, convert it to current patch-coordinate space.
+ point_coords, point_labels = self.update_point_to_patch(
+ patch_coords, point_coords, point_labels
+ )
+
+ if point_coords is not None and point_labels is not None:
+ # remove points that used for padding purposes (point_label = -1)
+ mapping_index = ((point_labels != -1).sum(1) > 0).to(torch.bool)
+ if mapping_index.any():
+ point_coords = point_coords[mapping_index]
+ point_labels = point_labels[mapping_index]
+ if prompt_class is not None:
+ prompt_class = prompt_class[mapping_index]
+ else:
+ if self.auto_freeze or (class_vector is None and patch_coords is None):
+ # if auto_freeze, point prompt must exist to allow loss backward
+ # in training, class_vector and point cannot both be None due to loss.backward()
+ mapping_index.fill_(True)
+ else:
+ point_coords, point_labels = None, None
+
+ if point_coords is None and class_vector is None:
+ return NINF_VALUE + torch.zeros([bs, 1, *image_size], device=device)
+
+ if (
+ self.image_embeddings is not None
+ and kwargs.get("keep_cache", False)
+ and class_vector is None
+ ):
+ out, out_auto = self.image_embeddings, None
+ else:
+ out, out_auto = self.image_encoder(
+ input_images,
+ with_point=point_coords is not None,
+ with_label=class_vector is not None,
+ )
+ input_images = None
+
+ # force releasing memories that set to None
+ torch.cuda.empty_cache()
+ if class_vector is not None:
+ logits, _ = self.class_head(out_auto, class_vector)
+ if point_coords is not None:
+ point_logits = self.point_head(
+ out, point_coords, point_labels, class_vector=prompt_class
+ )
+ if patch_coords is None:
+ logits = self.gaussian_combine(
+ logits,
+ point_logits,
+ point_coords,
+ point_labels,
+ mapping_index,
+ radius,
+ )
+ else:
+ # during validation use largest component
+ logits = self.connected_components_combine(
+ logits, point_logits, point_coords, point_labels, mapping_index
+ )
+ else:
+ logits = NINF_VALUE + torch.zeros(
+ [bs, 1, *image_size], device=device, dtype=out.dtype
+ )
+ logits[mapping_index] = self.point_head(
+ out, point_coords, point_labels, class_vector=prompt_class
+ )
+ if prev_mask is not None and patch_coords is not None:
+ logits = self.connected_components_combine(
+ prev_mask[patch_coords].transpose(1, 0).to(logits.device),
+ logits[mapping_index],
+ point_coords,
+ point_labels,
+ mapping_index,
+ )
+
+ if kwargs.get("keep_cache", False) and class_vector is None:
+ self.image_embeddings = out.detach()
+ return logits
-
-3. Three new buttons appear:
- * 1. Overwrite this version (Warning: "final" segmentation, cannot be overwritten, however)
- * 2. Save as new version (Version_2 --> Version_3, ascending numbering)
- * 3. Delete this version (Warning: "final" segmentation, cannot be deleted)
-
-4. Editing tools appear on the left side of the Hide/Show toolbar.
-
-5. Also, all buttons (like "Easy", "Medium", "Hard", "Previous", "Next") are disabled
-during the editing process; except for the "Flagged" and "Approved" buttons.
-
-
-
-6. After finishing editing the segmentation (a cross is drawn on the image for demonstration purposes)
-
-7. The user has 3 options:
- * 1. Overwrite current version (Caution: "final" segmentation cannot be overwritten because it is the original segmentation)
- * 2. Save as new version (e.g. Version_2 --> Version_3, ascending numbering)
- * 3. Delete technical version (Caution: "final" segmentation cannot be deleted because it is the original segmentation)
-8. When one of the three options has been selected, the user must confirm their choice by clicking on the "Confirm:..." button.
-
-
-
-9. After confirming "Save as new version", the edited segmentation remains in the MONAIServer.
-In addition, the version (e.g. "version_1") now appears in the drop-down list.
-
-10. When you click the "Approve" button, the version tag (of the currently depicted segmentation) is marked as "Version_1 (approved)" in the drop-down list. Consequently, the previously approved segmentation is no longer approved.
-
-
-
-11. When you click the "Approve" button, the version tag (of the currently depicted segmentation) is marked as "Version_1 (approved)" in the drop-down list.
-12. The status "approved" is also displayed in the information field in green.
-
-
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index b5d9429..0000000
--- a/monailabel/plugins/slicer/MONAILabelReviewer/Resources/UI/MONAILabelReviewer.ui
+++ /dev/null
@@ -1,884 +0,0 @@
-
-
-
-### Table of Contents
-- [Supported Applications](#supported-applications)
-- [Installing 3D Slicer](#installing-3d-slicer)
-- [Installing MONAI Label Plugin](#installing-monai-label-plugin)
-- [Plugin in Developer Mode](#plugin-in-developer-mode)
-- [Plugin Settings](#plugin-settings)
-
-### Supported Applications
-Users can find supported applications in the [sample-apps](../../sample-apps/radiology/) folder under the radiology section. They'll find models like DeepEdit, DeepGrow, Segmentation, and more. These applications can be used to create and refine labels for various medical imaging tasks.
-
-### Installing 3D Slicer
-To use MONAI Label with 3D Slicer, you'll need to download and install 3D Slicer. MONAI Label supports stable and preview versions of 3D Slicer, version 5.0 or higher. For more information on installing 3D Slicer, check out the [3D Slicer Documentation](https://slicer.readthedocs.io/en/latest/user_guide/getting_started.html#installing-3d-slicer)
-
-### Installing MONAI Label Plugin
-
-- Go to **View** -> **Extension Manager** -> **Active Learning** -> **MONAI Label**
-- Install MONAI Label plugin
-- _**Restart**_ 3D Slicer
-
-**Note:** To update the plugin to the latest version, you have to uninstall the existing 3D Slicer version and download and install the new preview version of 3D Slicer again
-
-### Plugin in Developer Mode
-
-- `git clone git@github.com:Project-MONAI/MONAILabel.git`
-- Open 3D Slicer: Go to **Edit** -> **Application Settings** -> **Modules** -> **Additional Module Paths**
-- Add New Module Path: _
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index 84f836b..0000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,32 +0,0 @@
-[tool.black]
-line-length = 120
-target-version = ['py38', 'py39', 'py310']
-include = '\.pyi?$'
-exclude = '''
-(
- /(
- # exclude a few common directories in the root of the project
- \.eggs
- | \.git
- | \.hg
- | \.mypy_cache
- | \.tox
- | \.venv
- | venv
- | \.pytype
- | _build
- | buck-out
- | build
- | dist
- )/
-)
-'''
-
-[tool.pycln]
-all = true
-exclude = "monai/bundle/__main__.py"
-
-[tool.ruff]
-line-length = 133
-ignore-init-module-imports = true
-ignore = ["F401", "E741", "F403", "F405"]
diff --git a/training/README.md b/training/README.md
deleted file mode 100644
index d682e67..0000000
--- a/training/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# Model Overview
-
-This repository contains the training code for MONAI VISTA 2.5D model. MONAI VISTA 2.5D is based on SAM [1] but we finetune
-the model (image encoder, prompt encoder, and mask decoder) on 3D medical data. MONAI VISTA introduces
-the class-label prompt and enables the fully automatic inference on known classes. It also shows the potential of
-generalizing to unknown class. In addition, MONAI VISTA takes 2.5D input, so our model can leverage the information
-from multiple slices.
-
-
-# Works in progress
-We are still actively developing this model. Features coming soon:
-1. **MONAI VISTA 3D Model**. It will support 3D volumetric inputs to enable a larger field of view and reduce user’s annotation efforts.
-2. **Text-based class-label prompt**. It will support encoding input text (e.g., “A computerized tomography of {Liver}”) as the class-label prompt.
-3. **Multiple Datasets Training**. We are working on supporting more pre-defined class labels for the fully automatic inference pipeline. Due to the nature of prompt-based segmentation, our model is compatible with the partial label training.
-
-
-# Installing Dependencies
-Dependencies can be installed using:
-``` bash
-pip install -r requirements.txt
-```
-
-# Models
-
-Please download the pre-trained weights from this
- link.
-
-# Data Preparation
-
-Figure source from the TotalSegmentator [2].
-
-The training data is from the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator) [2].
-
-- Target: 104 anatomical structures.
-- Task: Segmentation
-- Modality: CT
-- Size: 1204 3D volumes
-- Spacing: [1.5, 1.5, 1.5]
-
-More details about preprocessing this dataset can be found at
- link.
-
-The json file containing the data list that is used to train our models can be downloaded from
- link.
-
-
-Note that you need to provide the location of your dataset directory and json file by using ```--data_dir``` and ```--json_list```.
-
-# Training
-
-A MONAI VISTA 2.5D model (ViT-B base) with standard hyperparameters is defined as:
-
-```py
-_build_vista2pt5d(
- encoder_in_chans=27,
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=None,
- image_size=1024,
- clip_class_label_prompt=False,
- patch_embed_3d=False,
- )
-```
-
-Or, you may directly call:
-
-```py
-build_vista2pt5d_vit_b()
-```
-
-The above VISTA 2.5D model is used for CT images (9 slices 2.5D) with input spacing size ```(1.5, 1.5, 1.5)``` and for ```104``` class promptable segmentation.
-
-Using the default values for hyperparameters,
-the following command can be used to initiate training using PyTorch native AMP package:
-
-``` bash
-python main_2pt5d.py --max_epochs 100 --val_every 1 --optim_lr 0.000005 \
---num_patch 24 --num_prompt 32 \
---json_list ./totalsegmentator_104organs_folds_v2.json \
---data_dir /data/ \
---roi_z_iter 9 --save_checkpoint \
---sam_base_model vit_b \
---logdir finetune_ckpt_example --point_prompt --label_prompt --distributed --seed 12346 \
---iterative_training_warm_up_epoch 50 --reuse_img_embedding \
---label_prompt_warm_up_epoch 25 \
---checkpoint ./runs/9s_2dembed_model.pt
-```
-
-Above command will start the finetune training for the provided pre-trained weights
-(50 epochs single-step training and 50 epochs iterative training).
-
-# Evaluation
-
-To evaluate the `VISTA 2.5D model` using MONAI Label, please find the detailed instructions from
- here.
-
-
-# Reference
-
-```
-[1]: @article{kirillov2023segany,
- title={Segment Anything},
- author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
- journal={arXiv:2304.02643},
- year={2023}
- }
-
-[2]: @article{wasserthal2022totalsegmentator,
- title={TotalSegmentator: robust segmentation of 104 anatomical structures in CT images},
- author={Wasserthal, Jakob and Meyer, Manfred and Breit, Hanns-Christian and Cyriac, Joshy and Yang, Shan and Segeroth, Martin},
- journal={arXiv preprint arXiv:2208.05868},
- year={2022}
- }
-```
diff --git a/training/example_train_script.sh b/training/example_train_script.sh
deleted file mode 100644
index 4976c56..0000000
--- a/training/example_train_script.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-python main_2pt5d.py --max_epochs 100 --val_every 1 --optim_lr 0.000005 \
---num_patch 24 --num_prompt 32 \
---json_list ./totalsegmentator_104organs_folds_v2.json \
---data_dir /data/ \
---roi_z_iter 9 --save_checkpoint \
---sam_base_model vit_b \
---logdir finetune_ckpt_example --point_prompt --label_prompt --distributed --seed 12346 \
---iterative_training_warm_up_epoch 50 --reuse_img_embedding \
---label_prompt_warm_up_epoch 25 \
---checkpoint ./runs/9s_2dembed_model.pt
diff --git a/training/main_2pt5d.py b/training/main_2pt5d.py
deleted file mode 100644
index 46f3637..0000000
--- a/training/main_2pt5d.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright 2020 - 2022 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import sys
-import warnings
-from subprocess import Popen
-
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-import torch.nn.parallel
-import torch.utils.data.distributed
-from monai.losses import DiceCELoss
-from monai.metrics import DiceMetric
-from monai.transforms import Activations, AsDiscrete, Compose
-from monai.utils import set_determinism
-from monai.utils.enums import MetricReduction
-from optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
-from trainer_2pt5d import run_training
-from utils.data_utils import get_loader
-from vista_2pt5d.model import sam_model_registry
-
-warnings.filterwarnings("ignore", category=UserWarning, module="monai")
-warnings.filterwarnings("ignore", category=UserWarning, module="torch")
-warnings.filterwarnings("ignore", category=UserWarning, module="nibabel")
-parser = argparse.ArgumentParser(description="Swin UNETR segmentation pipeline")
-parser.add_argument("--checkpoint", default=None, help="start training from saved checkpoint")
-parser.add_argument("--logdir", default="vista2pt5d", type=str, help="directory to save the tensorboard logs")
-parser.add_argument("--data_dir", default="/dataset/dataset0/", type=str, help="dataset directory")
-parser.add_argument("--json_list", default="dataset_0.json", type=str, help="dataset json file")
-parser.add_argument("--save_checkpoint", action="store_true", help="save checkpoint during training")
-parser.add_argument("--max_epochs", default=1600, type=int, help="max number of training epochs")
-parser.add_argument("--batch_size", default=1, type=int, help="number of batch size")
-parser.add_argument("--optim_lr", default=1e-4, type=float, help="optimization learning rate")
-parser.add_argument("--optim_name", default="adamw", type=str, help="optimization algorithm")
-parser.add_argument("--reg_weight", default=1e-5, type=float, help="regularization weight")
-parser.add_argument("--momentum", default=0.99, type=float, help="momentum")
-parser.add_argument("--noamp", action="store_true", help="do NOT use amp for training")
-parser.add_argument("--val_every", default=1, type=int, help="validation frequency")
-parser.add_argument("--distributed", action="store_true", help="start distributed training")
-parser.add_argument("--world_size", default=1, type=int, help="number of nodes for distributed training")
-parser.add_argument("--rank", default=0, type=int, help="node rank for distributed training")
-parser.add_argument("--dist-url", default="tcp://127.0.0.1:23456", type=str, help="distributed url")
-parser.add_argument("--dist-backend", default="nccl", type=str, help="distributed backend")
-parser.add_argument("--workers", default=8, type=int, help="number of workers")
-parser.add_argument("--use_normal_dataset", action="store_true", help="use monai Dataset class")
-parser.add_argument("--a_min", default=-1024, type=float, help="a_min in ScaleIntensityRanged")
-parser.add_argument("--a_max", default=1024, type=float, help="a_max in ScaleIntensityRanged")
-parser.add_argument("--b_min", default=0.0, type=float, help="b_min in ScaleIntensityRanged")
-parser.add_argument("--b_max", default=1.0, type=float, help="b_max in ScaleIntensityRanged")
-parser.add_argument("--fold", default=0, type=int, help="fold")
-parser.add_argument(
- "--splitval", default=0, type=float, help="if not zero, split the last portion to validation and validation to test"
-)
-parser.add_argument("--roi_z_iter", default=9, type=int, help="roi size in z direction")
-parser.add_argument("--roi_z_iter_dilation", default=0, type=int, help="dilation size in z direction")
-parser.add_argument("--lrschedule", default="No", type=str, help="type of learning rate scheduler")
-parser.add_argument("--warmup_epochs", default=50, type=int, help="number of warmup epochs")
-parser.add_argument("--resume_ckpt", action="store_true", help="resume training from pretrained checkpoint")
-parser.add_argument("--num_patch", default=4, type=int, help="number of patches in each volume")
-parser.add_argument("--num_patch_val", default=30, type=int, help="number of patches in each volume during validation")
-parser.add_argument("--num_prompt", default=8, type=int, help="number of prompts for each training instance")
-parser.add_argument("--clip", default=None, type=float, help="gradient clip")
-parser.add_argument("--seed", default=-1, type=int, help="seed")
-parser.add_argument("--sam_pretrain_ckpt", type=str, default=None, help="sam_pretrain_ckpt")
-parser.add_argument("--sam_base_model", type=str, default="vit_b", help="sam_pretrain_ckpt")
-parser.add_argument("--sam_image_size", type=int, default=1024, help="sam input res")
-parser.add_argument("--label_prompt", action="store_true", help="using class label prompt in training")
-parser.add_argument("--drop_label_prob", default=0.5, type=float, help="prob for dropping label prompt in training")
-parser.add_argument(
- "--label_prompt_warm_up_epoch",
- default=20,
- type=int,
- help="before this number of epoch, we will drop label prompt with low prob.",
-)
-parser.add_argument("--point_prompt", action="store_true", help="using point prompt in training")
-parser.add_argument("--drop_point_prob", default=0.5, type=float, help="prob for dropping point prompt in training")
-parser.add_argument(
- "--max_points",
- default=8,
- type=int,
- help="max number of point prompts in training for the first ponit prompt generation",
-)
-parser.add_argument("--points_val_pos", default=1, type=int, help="number of positive point prompts in evaluation")
-parser.add_argument("--points_val_neg", default=0, type=int, help="number of negative point prompts in evaluation")
-parser.add_argument("--num_iterative_step", default=5, type=int, help="number of iterative step in training")
-parser.add_argument("--reuse_img_embedding", action="store_true", help="reuse image embedding in iterative training")
-parser.add_argument(
- "--no_more_points_for_cp_only",
- action="store_true",
- help="if no point prompt at the first prompt generation we will not add "
- "more additional pointa during iterative training.",
-)
-parser.add_argument(
- "--iterative_training_warm_up_epoch",
- default=100,
- type=int,
- help="before this number of epoch, we will not start iterative_training_.",
-)
-parser.add_argument("--data_aug", action="store_true", help="using data augmentation in training")
-parser.add_argument("--pop_pos_embed", action="store_true", help="remove pos embedding when load checkpoint")
-parser.add_argument("--pop_point_embed", action="store_true", help="remove point embedding when load checkpoint")
-parser.add_argument("--skip_bk", action="store_true", help="skip background (0) during training")
-parser.add_argument("--patch_embed_3d", action="store_true", help="using 3d patch embedding layer")
-
-
-def start_tb(log_dir):
- cmd = ["tensorboard", "--logdir", log_dir]
- Popen(cmd, stderr=sys.stderr, stdout=sys.stdout, shell=False)
-
-
-def main():
- args = parser.parse_args()
- args.amp = not args.noamp
- args.logdir = "./runs/" + args.logdir
- # start_tb(args.logdir)
- if args.seed > -1:
- set_determinism(seed=args.seed)
- if args.distributed:
- args.ngpus_per_node = torch.cuda.device_count()
- print("Found total gpus", args.ngpus_per_node)
- args.world_size = args.ngpus_per_node * args.world_size
- mp.spawn(main_worker, nprocs=args.ngpus_per_node, args=(args,))
- else:
- main_worker(gpu=0, args=args)
-
-
-def main_worker(gpu, args):
- if args.distributed:
- torch.multiprocessing.set_start_method("fork", force=True)
- np.set_printoptions(formatter={"float": "{: 0.3f}".format}, suppress=True)
- args.gpu = gpu
- if args.distributed:
- args.rank = args.rank * args.ngpus_per_node + gpu
- dist.init_process_group(
- backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
- )
- torch.cuda.set_device(args.gpu)
- torch.backends.cudnn.benchmark = True
- args.test_mode = False
- loader = get_loader(args)
- print(args.rank, " gpu", args.gpu)
- if args.rank == 0:
- print("Batch size is:", args.batch_size, "epochs", args.max_epochs)
-
- model = sam_model_registry[args.sam_base_model](
- args.sam_pretrain_ckpt,
- image_size=args.sam_image_size,
- encoder_in_chans=args.roi_z_iter * 3,
- patch_embed_3d=args.patch_embed_3d,
- )
-
- dice_loss = DiceCELoss(sigmoid=True)
-
- post_label = AsDiscrete(to_onehot=105)
- post_pred = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
- dice_acc = DiceMetric(include_background=False, reduction=MetricReduction.MEAN, get_not_nans=True)
-
- pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
- print("Total trainable parameters count", pytorch_total_params * 1.0e-6, "M")
-
- best_acc = 0
- start_epoch = 0
- optimizer_state = None
-
- if args.checkpoint is not None:
- checkpoint = torch.load(args.checkpoint, map_location="cpu")
- from collections import OrderedDict
-
- new_state_dict = OrderedDict()
- for k, v in checkpoint["state_dict"].items():
- new_state_dict[k] = v
- if args.pop_pos_embed:
- print("pop_pos_embed")
- new_state_dict.pop("image_encoder.patch_embed.proj.weight")
- new_state_dict.pop("image_encoder.patch_embed.proj.bias")
- model.load_state_dict(new_state_dict, strict=False)
- elif args.pop_point_embed:
- print("pop_point_embed")
- new_state_dict.pop("prompt_encoder.point_embeddings.0.weight")
- new_state_dict.pop("prompt_encoder.point_embeddings.1.weight")
- new_state_dict.pop("prompt_encoder.point_embeddings.2.weight")
- new_state_dict.pop("prompt_encoder.point_embeddings.3.weight")
- model.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=True)
- if args.resume_ckpt:
- if "epoch" in checkpoint:
- start_epoch = checkpoint["epoch"]
- if "best_acc" in checkpoint:
- best_acc = checkpoint["best_acc"]
- if "optimizer" in checkpoint:
- optimizer_state = checkpoint["optimizer"]
- print("=> loaded checkpoint '{}' (epoch {}) (bestacc {})".format(args.checkpoint, start_epoch, best_acc))
-
- model.cuda(args.gpu)
-
- if args.distributed:
- torch.cuda.set_device(args.gpu)
- model.cuda(args.gpu)
- model = torch.nn.parallel.DistributedDataParallel(
- model, device_ids=[args.gpu], output_device=args.gpu, find_unused_parameters=True
- )
- if args.optim_name == "adam":
- optimizer = torch.optim.Adam(model.parameters(), lr=args.optim_lr, weight_decay=args.reg_weight)
- elif args.optim_name == "adamw":
- optimizer = torch.optim.AdamW(model.parameters(), lr=args.optim_lr, weight_decay=args.reg_weight)
- elif args.optim_name == "sgd":
- optimizer = torch.optim.SGD(
- model.parameters(), lr=args.optim_lr, momentum=args.momentum, nesterov=True, weight_decay=args.reg_weight
- )
- else:
- raise ValueError("Unsupported Optimization Procedure: " + str(args.optim_name))
-
- if optimizer_state is not None:
- optimizer.load_state_dict(optimizer_state)
- # override lr by the given value
- for param_group in optimizer.param_groups:
- param_group["lr"] = args.optim_lr
-
- if args.lrschedule == "warmup_cosine":
- scheduler = LinearWarmupCosineAnnealingLR(
- optimizer, warmup_epochs=args.warmup_epochs, max_epochs=args.max_epochs
- )
- elif args.lrschedule == "cosine_anneal":
- scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.max_epochs)
- if args.checkpoint is not None:
- scheduler.step(epoch=start_epoch)
- else:
- scheduler = None
- accuracy = run_training(
- model=model,
- train_loader=loader[0],
- val_loader=loader[1],
- optimizer=optimizer,
- loss_func=dice_loss,
- acc_func=dice_acc,
- args=args,
- scheduler=scheduler,
- start_epoch=start_epoch,
- post_label=post_label,
- post_pred=post_pred,
- )
- return accuracy
-
-
-if __name__ == "__main__":
- main()
diff --git a/training/optimizers/__init__.py b/training/optimizers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/training/optimizers/lr_scheduler.py b/training/optimizers/lr_scheduler.py
deleted file mode 100644
index 0c35292..0000000
--- a/training/optimizers/lr_scheduler.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright 2020 - 2021 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-import warnings
-from typing import List
-
-from torch import nn as nn
-from torch.optim import Adam, Optimizer
-from torch.optim.lr_scheduler import LambdaLR, _LRScheduler
-
-__all__ = ["LinearLR", "ExponentialLR"]
-
-
-class _LRSchedulerMONAI(_LRScheduler):
- """Base class for increasing the learning rate between two boundaries over a number
- of iterations"""
-
- def __init__(self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1) -> None:
- """
- Args:
- optimizer: wrapped optimizer.
- end_lr: the final learning rate.
- num_iter: the number of iterations over which the test occurs.
- last_epoch: the index of last epoch.
- Returns:
- None
- """
- self.end_lr = end_lr
- self.num_iter = num_iter
- super(_LRSchedulerMONAI, self).__init__(optimizer, last_epoch)
-
-
-class LinearLR(_LRSchedulerMONAI):
- """Linearly increases the learning rate between two boundaries over a number of
- iterations.
- """
-
- def get_lr(self):
- r = self.last_epoch / (self.num_iter - 1)
- return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
-
-
-class ExponentialLR(_LRSchedulerMONAI):
- """Exponentially increases the learning rate between two boundaries over a number of
- iterations.
- """
-
- def get_lr(self):
- r = self.last_epoch / (self.num_iter - 1)
- return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
-
-
-class WarmupCosineSchedule(LambdaLR):
- """Linear warmup and then cosine decay.
- Based on https://huggingface.co/ implementation.
- """
-
- def __init__(
- self, optimizer: Optimizer, warmup_steps: int, t_total: int, cycles: float = 0.5, last_epoch: int = -1
- ) -> None:
- """
- Args:
- optimizer: wrapped optimizer.
- warmup_steps: number of warmup iterations.
- t_total: total number of training iterations.
- cycles: cosine cycles parameter.
- last_epoch: the index of last epoch.
- Returns:
- None
- """
- self.warmup_steps = warmup_steps
- self.t_total = t_total
- self.cycles = cycles
- super(WarmupCosineSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch)
-
- def lr_lambda(self, step):
- if step < self.warmup_steps:
- return float(step) / float(max(1.0, self.warmup_steps))
- progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps))
- return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.cycles) * 2.0 * progress)))
-
-
-class LinearWarmupCosineAnnealingLR(_LRScheduler):
- def __init__(
- self,
- optimizer: Optimizer,
- warmup_epochs: int,
- max_epochs: int,
- warmup_start_lr: float = 0.0,
- eta_min: float = 0.0,
- last_epoch: int = -1,
- ) -> None:
- """
- Args:
- optimizer (Optimizer): Wrapped optimizer.
- warmup_epochs (int): Maximum number of iterations for linear warmup
- max_epochs (int): Maximum number of iterations
- warmup_start_lr (float): Learning rate to start the linear warmup. Default: 0.
- eta_min (float): Minimum learning rate. Default: 0.
- last_epoch (int): The index of last epoch. Default: -1.
- """
- self.warmup_epochs = warmup_epochs
- self.max_epochs = max_epochs
- self.warmup_start_lr = warmup_start_lr
- self.eta_min = eta_min
-
- super(LinearWarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch)
-
- def get_lr(self) -> List[float]:
- """
- Compute learning rate using chainable form of the scheduler
- """
- if not self._get_lr_called_within_step:
- warnings.warn(
- "To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", UserWarning
- )
-
- if self.last_epoch == 0:
- return [self.warmup_start_lr] * len(self.base_lrs)
- elif self.last_epoch < self.warmup_epochs:
- return [
- group["lr"] + (base_lr - self.warmup_start_lr) / (self.warmup_epochs - 1)
- for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
- ]
- elif self.last_epoch == self.warmup_epochs:
- return self.base_lrs
- elif (self.last_epoch - 1 - self.max_epochs) % (2 * (self.max_epochs - self.warmup_epochs)) == 0:
- return [
- group["lr"]
- + (base_lr - self.eta_min) * (1 - math.cos(math.pi / (self.max_epochs - self.warmup_epochs))) / 2
- for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
- ]
-
- return [
- (1 + math.cos(math.pi * (self.last_epoch - self.warmup_epochs) / (self.max_epochs - self.warmup_epochs)))
- / (
- 1
- + math.cos(
- math.pi * (self.last_epoch - self.warmup_epochs - 1) / (self.max_epochs - self.warmup_epochs)
- )
- )
- * (group["lr"] - self.eta_min)
- + self.eta_min
- for group in self.optimizer.param_groups
- ]
-
- def _get_closed_form_lr(self) -> List[float]:
- """
- Called when epoch is passed as a param to the `step` function of the scheduler.
- """
- if self.last_epoch < self.warmup_epochs:
- return [
- self.warmup_start_lr + self.last_epoch * (base_lr - self.warmup_start_lr) / (self.warmup_epochs - 1)
- for base_lr in self.base_lrs
- ]
-
- return [
- self.eta_min
- + 0.5
- * (base_lr - self.eta_min)
- * (1 + math.cos(math.pi * (self.last_epoch - self.warmup_epochs) / (self.max_epochs - self.warmup_epochs)))
- for base_lr in self.base_lrs
- ]
diff --git a/training/requirements.txt b/training/requirements.txt
deleted file mode 100644
index 4666771..0000000
--- a/training/requirements.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-monai>=1.2.0
-nibabel
-fvcore
-tqdm
-einops
-tensorboardX
-scipy
-positional-encodings[pytorch]
-SimpleITK
-tensorboard
-scipy
-scikit-learn
-scikit-image
-ptflops
-pillow
-timm
-nnunet
-h5py
-torch
-torchvision
diff --git a/training/segment_anything/__init__.py b/training/segment_anything/__init__.py
deleted file mode 100644
index 718fdc3..0000000
--- a/training/segment_anything/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .automatic_mask_generator import SamAutomaticMaskGenerator
-from .build_sam import (
- build_sam,
- build_sam_vit_b,
- build_sam_vit_h,
- build_sam_vit_l,
- sam_model_registry,
-)
-from .predictor import SamPredictor
diff --git a/training/segment_anything/automatic_mask_generator.py b/training/segment_anything/automatic_mask_generator.py
deleted file mode 100644
index 2cd252d..0000000
--- a/training/segment_anything/automatic_mask_generator.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-from torchvision.ops.boxes import batched_nms, box_area # type: ignore
-
-from .modeling import Sam
-from .predictor import SamPredictor
-from .utils.amg import (
- MaskData,
- area_from_rle,
- batch_iterator,
- batched_mask_to_box,
- box_xyxy_to_xywh,
- build_all_layer_point_grids,
- calculate_stability_score,
- coco_encode_rle,
- generate_crop_boxes,
- is_box_near_crop_edge,
- mask_to_rle_pytorch,
- remove_small_regions,
- rle_to_mask,
- uncrop_boxes_xyxy,
- uncrop_masks,
- uncrop_points,
-)
-
-
-class SamAutomaticMaskGenerator:
- def __init__(
- self,
- model: Sam,
- points_per_side: Optional[int] = 32,
- points_per_batch: int = 64,
- pred_iou_thresh: float = 0.88,
- stability_score_thresh: float = 0.95,
- stability_score_offset: float = 1.0,
- box_nms_thresh: float = 0.7,
- crop_n_layers: int = 0,
- crop_nms_thresh: float = 0.7,
- crop_overlap_ratio: float = 512 / 1500,
- crop_n_points_downscale_factor: int = 1,
- point_grids: Optional[List[np.ndarray]] = None,
- min_mask_region_area: int = 0,
- output_mode: str = "binary_mask",
- ) -> None:
- """
- Using a SAM model, generates masks for the entire image.
- Generates a grid of point prompts over the image, then filters
- low quality and duplicate masks. The default settings are chosen
- for SAM with a ViT-H backbone.
-
- Arguments:
- model (Sam): The SAM model to use for mask prediction.
- points_per_side (int or None): The number of points to be sampled
- along one side of the image. The total number of points is
- points_per_side**2. If None, 'point_grids' must provide explicit
- point sampling.
- points_per_batch (int): Sets the number of points run simultaneously
- by the model. Higher numbers may be faster but use more GPU memory.
- pred_iou_thresh (float): A filtering threshold in [0,1], using the
- model's predicted mask quality.
- stability_score_thresh (float): A filtering threshold in [0,1], using
- the stability of the mask under changes to the cutoff used to binarize
- the model's mask predictions.
- stability_score_offset (float): The amount to shift the cutoff when
- calculated the stability score.
- box_nms_thresh (float): The box IoU cutoff used by non-maximal
- suppression to filter duplicate masks.
- crops_n_layers (int): If >0, mask prediction will be run again on
- crops of the image. Sets the number of layers to run, where each
- layer has 2**i_layer number of image crops.
- crops_nms_thresh (float): The box IoU cutoff used by non-maximal
- suppression to filter duplicate masks between different crops.
- crop_overlap_ratio (float): Sets the degree to which crops overlap.
- In the first crop layer, crops will overlap by this fraction of
- the image length. Later layers with more crops scale down this overlap.
- crop_n_points_downscale_factor (int): The number of points-per-side
- sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
- point_grids (list(np.ndarray) or None): A list over explicit grids
- of points used for sampling, normalized to [0,1]. The nth grid in the
- list is used in the nth crop layer. Exclusive with points_per_side.
- min_mask_region_area (int): If >0, postprocessing will be applied
- to remove disconnected regions and holes in masks with area smaller
- than min_mask_region_area. Requires opencv.
- output_mode (str): The form masks are returned in. Can be 'binary_mask',
- 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
- For large resolutions, 'binary_mask' may consume large amounts of
- memory.
- """
-
- assert (points_per_side is None) != (
- point_grids is None
- ), "Exactly one of points_per_side or point_grid must be provided."
- if points_per_side is not None:
- self.point_grids = build_all_layer_point_grids(
- points_per_side,
- crop_n_layers,
- crop_n_points_downscale_factor,
- )
- elif point_grids is not None:
- self.point_grids = point_grids
- else:
- raise ValueError("Can't have both points_per_side and point_grid be None.")
-
- assert output_mode in [
- "binary_mask",
- "uncompressed_rle",
- "coco_rle",
- ], f"Unknown output_mode {output_mode}."
- if output_mode == "coco_rle":
- from pycocotools import mask as mask_utils # type: ignore # noqa: F401
-
- if min_mask_region_area > 0:
- import cv2 # type: ignore # noqa: F401
-
- self.predictor = SamPredictor(model)
- self.points_per_batch = points_per_batch
- self.pred_iou_thresh = pred_iou_thresh
- self.stability_score_thresh = stability_score_thresh
- self.stability_score_offset = stability_score_offset
- self.box_nms_thresh = box_nms_thresh
- self.crop_n_layers = crop_n_layers
- self.crop_nms_thresh = crop_nms_thresh
- self.crop_overlap_ratio = crop_overlap_ratio
- self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
- self.min_mask_region_area = min_mask_region_area
- self.output_mode = output_mode
-
- @torch.no_grad()
- def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
- """
- Generates masks for the given image.
-
- Arguments:
- image (np.ndarray): The image to generate masks for, in HWC uint8 format.
-
- Returns:
- list(dict(str, any)): A list over records for masks. Each record is
- a dict containing the following keys:
- segmentation (dict(str, any) or np.ndarray): The mask. If
- output_mode='binary_mask', is an array of shape HW. Otherwise,
- is a dictionary containing the RLE.
- bbox (list(float)): The box around the mask, in XYWH format.
- area (int): The area in pixels of the mask.
- predicted_iou (float): The model's own prediction of the mask's
- quality. This is filtered by the pred_iou_thresh parameter.
- point_coords (list(list(float))): The point coordinates input
- to the model to generate this mask.
- stability_score (float): A measure of the mask's quality. This
- is filtered on using the stability_score_thresh parameter.
- crop_box (list(float)): The crop of the image used to generate
- the mask, given in XYWH format.
- """
-
- # Generate masks
- mask_data = self._generate_masks(image)
-
- # Filter small disconnected regions and holes in masks
- if self.min_mask_region_area > 0:
- mask_data = self.postprocess_small_regions(
- mask_data,
- self.min_mask_region_area,
- max(self.box_nms_thresh, self.crop_nms_thresh),
- )
-
- # Encode masks
- if self.output_mode == "coco_rle":
- mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
- elif self.output_mode == "binary_mask":
- mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
- else:
- mask_data["segmentations"] = mask_data["rles"]
-
- # Write mask records
- curr_anns = []
- for idx in range(len(mask_data["segmentations"])):
- ann = {
- "segmentation": mask_data["segmentations"][idx],
- "area": area_from_rle(mask_data["rles"][idx]),
- "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
- "predicted_iou": mask_data["iou_preds"][idx].item(),
- "point_coords": [mask_data["points"][idx].tolist()],
- "stability_score": mask_data["stability_score"][idx].item(),
- "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
- }
- curr_anns.append(ann)
-
- return curr_anns
-
- def _generate_masks(self, image: np.ndarray) -> MaskData:
- orig_size = image.shape[:2]
- crop_boxes, layer_idxs = generate_crop_boxes(orig_size, self.crop_n_layers, self.crop_overlap_ratio)
-
- # Iterate over image crops
- data = MaskData()
- for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
- crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
- data.cat(crop_data)
-
- # Remove duplicate masks between crops
- if len(crop_boxes) > 1:
- # Prefer masks from smaller crops
- scores = 1 / box_area(data["crop_boxes"])
- scores = scores.to(data["boxes"].device)
- keep_by_nms = batched_nms(
- data["boxes"].float(),
- scores,
- torch.zeros(len(data["boxes"])), # categories
- iou_threshold=self.crop_nms_thresh,
- )
- data.filter(keep_by_nms)
-
- data.to_numpy()
- return data
-
- def _process_crop(
- self,
- image: np.ndarray,
- crop_box: List[int],
- crop_layer_idx: int,
- orig_size: Tuple[int, ...],
- ) -> MaskData:
- # Crop the image and calculate embeddings
- x0, y0, x1, y1 = crop_box
- cropped_im = image[y0:y1, x0:x1, :]
- cropped_im_size = cropped_im.shape[:2]
- self.predictor.set_image(cropped_im)
-
- # Get points for this crop
- points_scale = np.array(cropped_im_size)[None, ::-1]
- points_for_image = self.point_grids[crop_layer_idx] * points_scale
-
- # Generate masks for this crop in batches
- data = MaskData()
- for (points,) in batch_iterator(self.points_per_batch, points_for_image):
- batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
- data.cat(batch_data)
- del batch_data
- self.predictor.reset_image()
-
- # Remove duplicates within this crop.
- keep_by_nms = batched_nms(
- data["boxes"].float(),
- data["iou_preds"],
- torch.zeros(len(data["boxes"])), # categories
- iou_threshold=self.box_nms_thresh,
- )
- data.filter(keep_by_nms)
-
- # Return to the original image frame
- data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
- data["points"] = uncrop_points(data["points"], crop_box)
- data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
-
- return data
-
- def _process_batch(
- self,
- points: np.ndarray,
- im_size: Tuple[int, ...],
- crop_box: List[int],
- orig_size: Tuple[int, ...],
- ) -> MaskData:
- orig_h, orig_w = orig_size
-
- # Run model on this batch
- transformed_points = self.predictor.transform.apply_coords(points, im_size)
- in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
- in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
- masks, iou_preds, _ = self.predictor.predict_torch(
- in_points[:, None, :],
- in_labels[:, None],
- multimask_output=True,
- return_logits=True,
- )
-
- # Serialize predictions and store in MaskData
- data = MaskData(
- masks=masks.flatten(0, 1),
- iou_preds=iou_preds.flatten(0, 1),
- points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
- )
- del masks
-
- # Filter by predicted IoU
- if self.pred_iou_thresh > 0.0:
- keep_mask = data["iou_preds"] > self.pred_iou_thresh
- data.filter(keep_mask)
-
- # Calculate stability score
- data["stability_score"] = calculate_stability_score(
- data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
- )
- if self.stability_score_thresh > 0.0:
- keep_mask = data["stability_score"] >= self.stability_score_thresh
- data.filter(keep_mask)
-
- # Threshold masks and calculate boxes
- data["masks"] = data["masks"] > self.predictor.model.mask_threshold
- data["boxes"] = batched_mask_to_box(data["masks"])
-
- # Filter boxes that touch crop boundaries
- keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
- if not torch.all(keep_mask):
- data.filter(keep_mask)
-
- # Compress to RLE
- data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
- data["rles"] = mask_to_rle_pytorch(data["masks"])
- del data["masks"]
-
- return data
-
- @staticmethod
- def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData:
- """
- Removes small disconnected regions and holes in masks, then reruns
- box NMS to remove any new duplicates.
-
- Edits mask_data in place.
-
- Requires open-cv as a dependency.
- """
- if len(mask_data["rles"]) == 0:
- return mask_data
-
- # Filter small disconnected regions and holes
- new_masks = []
- scores = []
- for rle in mask_data["rles"]:
- mask = rle_to_mask(rle)
-
- mask, changed = remove_small_regions(mask, min_area, mode="holes")
- unchanged = not changed
- mask, changed = remove_small_regions(mask, min_area, mode="islands")
- unchanged = unchanged and not changed
-
- new_masks.append(torch.as_tensor(mask).unsqueeze(0))
- # Give score=0 to changed masks and score=1 to unchanged masks
- # so NMS will prefer ones that didn't need postprocessing
- scores.append(float(unchanged))
-
- # Recalculate boxes and remove any new duplicates
- masks = torch.cat(new_masks, dim=0)
- boxes = batched_mask_to_box(masks)
- keep_by_nms = batched_nms(
- boxes.float(),
- torch.as_tensor(scores),
- torch.zeros(len(boxes)), # categories
- iou_threshold=nms_thresh,
- )
-
- # Only recalculate RLEs for masks that have changed
- for i_mask in keep_by_nms:
- if scores[i_mask] == 0.0:
- mask_torch = masks[i_mask].unsqueeze(0)
- mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
- mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
- mask_data.filter(keep_by_nms)
-
- return mask_data
diff --git a/training/segment_anything/build_sam.py b/training/segment_anything/build_sam.py
deleted file mode 100644
index fef9980..0000000
--- a/training/segment_anything/build_sam.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-
-import torch
-
-from .modeling import (
- ImageEncoderViT,
- MaskDecoder,
- PromptEncoder,
- Sam,
- TwoWayTransformer,
-)
-
-
-def build_sam_vit_h(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=1280,
- encoder_depth=32,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[7, 15, 23, 31],
- checkpoint=checkpoint,
- )
-
-
-build_sam = build_sam_vit_h
-
-
-def build_sam_vit_l(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=1024,
- encoder_depth=24,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[5, 11, 17, 23],
- checkpoint=checkpoint,
- )
-
-
-def build_sam_vit_b(checkpoint=None):
- return _build_sam(
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=checkpoint,
- )
-
-
-sam_model_registry = {
- "default": build_sam,
- "vit_h": build_sam,
- "vit_l": build_sam_vit_l,
- "vit_b": build_sam_vit_b,
-}
-
-
-def _build_sam(
- encoder_embed_dim,
- encoder_depth,
- encoder_num_heads,
- encoder_global_attn_indexes,
- checkpoint=None,
-):
- prompt_embed_dim = 256
- image_size = 1024
- vit_patch_size = 16
- image_embedding_size = image_size // vit_patch_size
- sam = Sam(
- image_encoder=ImageEncoderViT(
- depth=encoder_depth,
- embed_dim=encoder_embed_dim,
- img_size=image_size,
- mlp_ratio=4,
- norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
- num_heads=encoder_num_heads,
- patch_size=vit_patch_size,
- qkv_bias=True,
- use_rel_pos=True,
- global_attn_indexes=encoder_global_attn_indexes,
- window_size=14,
- out_chans=prompt_embed_dim,
- ),
- prompt_encoder=PromptEncoder(
- embed_dim=prompt_embed_dim,
- image_embedding_size=(image_embedding_size, image_embedding_size),
- input_image_size=(image_size, image_size),
- mask_in_chans=16,
- ),
- mask_decoder=MaskDecoder(
- num_multimask_outputs=3,
- transformer=TwoWayTransformer(
- depth=2,
- embedding_dim=prompt_embed_dim,
- mlp_dim=2048,
- num_heads=8,
- ),
- transformer_dim=prompt_embed_dim,
- iou_head_depth=3,
- iou_head_hidden_dim=256,
- ),
- pixel_mean=[123.675, 116.28, 103.53],
- pixel_std=[58.395, 57.12, 57.375],
- )
- sam.eval()
- if checkpoint is not None:
- with open(checkpoint, "rb") as f:
- state_dict = torch.load(f)
- sam.load_state_dict(state_dict)
- return sam
diff --git a/training/segment_anything/modeling/__init__.py b/training/segment_anything/modeling/__init__.py
deleted file mode 100644
index 088af38..0000000
--- a/training/segment_anything/modeling/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-from .sam import Sam
-from .transformer import TwoWayTransformer
diff --git a/training/segment_anything/modeling/common.py b/training/segment_anything/modeling/common.py
deleted file mode 100644
index e872781..0000000
--- a/training/segment_anything/modeling/common.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Type
-
-import torch
-import torch.nn as nn
-
-
-class MLPBlock(nn.Module):
- def __init__(
- self,
- embedding_dim: int,
- mlp_dim: int,
- act: Type[nn.Module] = nn.GELU,
- ) -> None:
- super().__init__()
- self.lin1 = nn.Linear(embedding_dim, mlp_dim)
- self.lin2 = nn.Linear(mlp_dim, embedding_dim)
- self.act = act()
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.lin2(self.act(self.lin1(x)))
-
-
-# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
-# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
-class LayerNorm2d(nn.Module):
- def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
- super().__init__()
- self.weight = nn.Parameter(torch.ones(num_channels))
- self.bias = nn.Parameter(torch.zeros(num_channels))
- self.eps = eps
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- u = x.mean(1, keepdim=True)
- s = (x - u).pow(2).mean(1, keepdim=True)
- x = (x - u) / torch.sqrt(s + self.eps)
- x = self.weight[:, None, None] * x + self.bias[:, None, None]
- return x
diff --git a/training/segment_anything/modeling/image_encoder.py b/training/segment_anything/modeling/image_encoder.py
deleted file mode 100644
index 31360d3..0000000
--- a/training/segment_anything/modeling/image_encoder.py
+++ /dev/null
@@ -1,389 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional, Tuple, Type
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .common import LayerNorm2d, MLPBlock
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class ImageEncoderViT(nn.Module):
- def __init__(
- self,
- img_size: int = 1024,
- patch_size: int = 16,
- in_chans: int = 3,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- out_chans: int = 256,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_abs_pos: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- global_attn_indexes: Tuple[int, ...] = (),
- ) -> None:
- """
- Args:
- img_size (int): Input image size.
- patch_size (int): Patch size.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- depth (int): Depth of ViT.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_abs_pos (bool): If True, use absolute positional embeddings.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks.
- global_attn_indexes (list): Indexes for blocks using global attention.
- """
- super().__init__()
- self.img_size = img_size
-
- self.patch_embed = PatchEmbed(
- kernel_size=(patch_size, patch_size),
- stride=(patch_size, patch_size),
- in_chans=in_chans,
- embed_dim=embed_dim,
- )
-
- self.pos_embed: Optional[nn.Parameter] = None
- if use_abs_pos:
- # Initialize absolute positional embedding with pretrain image size.
- self.pos_embed = nn.Parameter(torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))
-
- self.blocks = nn.ModuleList()
- for i in range(depth):
- block = Block(
- dim=embed_dim,
- num_heads=num_heads,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- norm_layer=norm_layer,
- act_layer=act_layer,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- window_size=window_size if i not in global_attn_indexes else 0,
- input_size=(img_size // patch_size, img_size // patch_size),
- )
- self.blocks.append(block)
-
- self.neck = nn.Sequential(
- nn.Conv2d(
- embed_dim,
- out_chans,
- kernel_size=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- nn.Conv2d(
- out_chans,
- out_chans,
- kernel_size=3,
- padding=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.patch_embed(x)
- if self.pos_embed is not None:
- x = x + self.pos_embed
-
- for blk in self.blocks:
- x = blk(x)
-
- x = self.neck(x.permute(0, 3, 1, 2))
-
- return x
-
-
-class Block(nn.Module):
- """Transformer blocks with support of window attention and residual propagation blocks"""
-
- def __init__(
- self,
- dim: int,
- num_heads: int,
- mlp_ratio: float = 4.0,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks. If it equals 0, then
- use global attention.
- input_size (tuple(int, int) or None): Input resolution for calculating the relative
- positional parameter size.
- """
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads,
- qkv_bias=qkv_bias,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- input_size=input_size if window_size == 0 else (window_size, window_size),
- )
-
- self.norm2 = norm_layer(dim)
- self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
-
- self.window_size = window_size
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- shortcut = x
- x = self.norm1(x)
- # Window partition
- if self.window_size > 0:
- H, W = x.shape[1], x.shape[2]
- x, pad_hw = window_partition(x, self.window_size)
-
- x = self.attn(x)
- # Reverse window partition
- if self.window_size > 0:
- x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-
- x = shortcut + x
- x = x + self.mlp(self.norm2(x))
-
- return x
-
-
-class Attention(nn.Module):
- """Multi-head Attention block with relative position embeddings."""
-
- def __init__(
- self,
- dim: int,
- num_heads: int = 8,
- qkv_bias: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- input_size (tuple(int, int) or None): Input resolution for calculating the relative
- positional parameter size.
- """
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = head_dim**-0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.proj = nn.Linear(dim, dim)
-
- self.use_rel_pos = use_rel_pos
- if self.use_rel_pos:
- assert input_size is not None, "Input size must be provided if using relative positional encoding."
- # initialize relative positional embeddings
- self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
- self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, H, W, _ = x.shape
- # qkv with shape (3, B, nHead, H * W, C)
- qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- # q, k, v with shape (B * nHead, H * W, C)
- q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
-
- attn = (q * self.scale) @ k.transpose(-2, -1)
-
- if self.use_rel_pos:
- attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
-
- attn = attn.softmax(dim=-1)
- x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
- x = self.proj(x)
-
- return x
-
-
-def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
- """
- Partition into non-overlapping windows with padding if needed.
- Args:
- x (tensor): input tokens with [B, H, W, C].
- window_size (int): window size.
-
- Returns:
- windows: windows after partition with [B * num_windows, window_size, window_size, C].
- (Hp, Wp): padded height and width before partition
- """
- B, H, W, C = x.shape
-
- pad_h = (window_size - H % window_size) % window_size
- pad_w = (window_size - W % window_size) % window_size
- if pad_h > 0 or pad_w > 0:
- x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
- Hp, Wp = H + pad_h, W + pad_w
-
- x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows, (Hp, Wp)
-
-
-def window_unpartition(
- windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
-) -> torch.Tensor:
- """
- Window unpartition into original sequences and removing padding.
- Args:
- windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
- window_size (int): window size.
- pad_hw (Tuple): padded height and width (Hp, Wp).
- hw (Tuple): original height and width (H, W) before padding.
-
- Returns:
- x: unpartitioned sequences with [B, H, W, C].
- """
- Hp, Wp = pad_hw
- H, W = hw
- B = windows.shape[0] // (Hp * Wp // window_size // window_size)
- x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-
- if Hp > H or Wp > W:
- x = x[:, :H, :W, :].contiguous()
- return x
-
-
-def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
- """
- Get relative positional embeddings according to the relative positions of
- query and key sizes.
- Args:
- q_size (int): size of query q.
- k_size (int): size of key k.
- rel_pos (Tensor): relative position embeddings (L, C).
-
- Returns:
- Extracted positional embeddings according to relative positions.
- """
- max_rel_dist = int(2 * max(q_size, k_size) - 1)
- # Interpolate rel pos if needed.
- if rel_pos.shape[0] != max_rel_dist:
- # Interpolate rel pos.
- rel_pos_resized = F.interpolate(
- rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
- size=max_rel_dist,
- mode="linear",
- )
- rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
- else:
- rel_pos_resized = rel_pos
-
- # Scale the coords with short length if shapes for q and k are different.
- q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
- k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
- relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
-
- return rel_pos_resized[relative_coords.long()]
-
-
-def add_decomposed_rel_pos(
- attn: torch.Tensor,
- q: torch.Tensor,
- rel_pos_h: torch.Tensor,
- rel_pos_w: torch.Tensor,
- q_size: Tuple[int, int],
- k_size: Tuple[int, int],
-) -> torch.Tensor:
- """
- Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
- https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
- Args:
- attn (Tensor): attention map.
- q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
- rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
- rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
- q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
- k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
-
- Returns:
- attn (Tensor): attention map with added relative positional embeddings.
- """
- q_h, q_w = q_size
- k_h, k_w = k_size
- Rh = get_rel_pos(q_h, k_h, rel_pos_h)
- Rw = get_rel_pos(q_w, k_w, rel_pos_w)
-
- B, _, dim = q.shape
- r_q = q.reshape(B, q_h, q_w, dim)
- rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
- rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-
- attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
- B, q_h * q_w, k_h * k_w
- )
-
- return attn
-
-
-class PatchEmbed(nn.Module):
- """
- Image to Patch Embedding.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int] = (16, 16),
- stride: Tuple[int, int] = (16, 16),
- padding: Tuple[int, int] = (0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.proj(x)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/training/segment_anything/modeling/mask_decoder.py b/training/segment_anything/modeling/mask_decoder.py
deleted file mode 100644
index dfacfe5..0000000
--- a/training/segment_anything/modeling/mask_decoder.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import List, Tuple, Type
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .common import LayerNorm2d
-
-
-class MaskDecoder(nn.Module):
- def __init__(
- self,
- *,
- transformer_dim: int,
- transformer: nn.Module,
- num_multimask_outputs: int = 3,
- activation: Type[nn.Module] = nn.GELU,
- iou_head_depth: int = 3,
- iou_head_hidden_dim: int = 256,
- ) -> None:
- """
- Predicts masks given an image and prompt embeddings, using a
- tranformer architecture.
-
- Arguments:
- transformer_dim (int): the channel dimension of the transformer
- transformer (nn.Module): the transformer used to predict masks
- num_multimask_outputs (int): the number of masks to predict
- when disambiguating masks
- activation (nn.Module): the type of activation to use when
- upscaling masks
- iou_head_depth (int): the depth of the MLP used to predict
- mask quality
- iou_head_hidden_dim (int): the hidden dimension of the MLP
- used to predict mask quality
- """
- super().__init__()
- self.transformer_dim = transformer_dim
- self.transformer = transformer
-
- self.num_multimask_outputs = num_multimask_outputs
-
- self.iou_token = nn.Embedding(1, transformer_dim)
- self.num_mask_tokens = num_multimask_outputs + 1
- self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
- self.output_upscaling = nn.Sequential(
- nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
- LayerNorm2d(transformer_dim // 4),
- activation(),
- nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
- activation(),
- )
- self.output_hypernetworks_mlps = nn.ModuleList(
- [MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for i in range(self.num_mask_tokens)]
- )
-
- self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)
-
- def forward(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- multimask_output: bool,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Predict masks given image and prompt embeddings.
-
- Arguments:
- image_embeddings (torch.Tensor): the embeddings from the image encoder
- image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
- sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
- dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
- multimask_output (bool): Whether to return multiple masks or a single
- mask.
-
- Returns:
- torch.Tensor: batched predicted masks
- torch.Tensor: batched predictions of mask quality
- """
- masks, iou_pred = self.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=image_pe,
- sparse_prompt_embeddings=sparse_prompt_embeddings,
- dense_prompt_embeddings=dense_prompt_embeddings,
- )
-
- # Select the correct mask or masks for output
- if multimask_output:
- mask_slice = slice(1, None)
- else:
- mask_slice = slice(0, 1)
- masks = masks[:, mask_slice, :, :]
- iou_pred = iou_pred[:, mask_slice]
-
- # Prepare output
- return masks, iou_pred
-
- def predict_masks(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Predicts masks. See 'forward' for more details."""
- # Concatenate output tokens
- output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
- output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
- tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
-
- # Expand per-image data in batch direction to be per-mask
- src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
- src = src + dense_prompt_embeddings
- pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
- b, c, h, w = src.shape
-
- # Run the transformer
- hs, src = self.transformer(src, pos_src, tokens)
- iou_token_out = hs[:, 0, :]
- mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
-
- # Upscale mask embeddings and predict masks using the mask tokens
- src = src.transpose(1, 2).view(b, c, h, w)
- upscaled_embedding = self.output_upscaling(src)
- hyper_in_list: List[torch.Tensor] = []
- for i in range(self.num_mask_tokens):
- hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
- hyper_in = torch.stack(hyper_in_list, dim=1)
- b, c, h, w = upscaled_embedding.shape
-
- masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
-
- # Generate mask quality predictions
- iou_pred = self.iou_prediction_head(iou_token_out)
-
- return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
- def __init__(
- self,
- input_dim: int,
- hidden_dim: int,
- output_dim: int,
- num_layers: int,
- sigmoid_output: bool = False,
- ) -> None:
- super().__init__()
- self.num_layers = num_layers
- h = [hidden_dim] * (num_layers - 1)
- self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
- self.sigmoid_output = sigmoid_output
-
- def forward(self, x):
- for i, layer in enumerate(self.layers):
- x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
- if self.sigmoid_output:
- x = F.sigmoid(x)
- return x
diff --git a/training/segment_anything/modeling/prompt_encoder.py b/training/segment_anything/modeling/prompt_encoder.py
deleted file mode 100644
index 1887c8d..0000000
--- a/training/segment_anything/modeling/prompt_encoder.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from torch import nn
-
-from .common import LayerNorm2d
-
-
-class PromptEncoder(nn.Module):
- def __init__(
- self,
- embed_dim: int,
- image_embedding_size: Tuple[int, int],
- input_image_size: Tuple[int, int],
- mask_in_chans: int,
- activation: Type[nn.Module] = nn.GELU,
- ) -> None:
- """
- Encodes prompts for input to SAM's mask decoder.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- """
- super().__init__()
- self.embed_dim = embed_dim
- self.input_image_size = input_image_size
- self.image_embedding_size = image_embedding_size
- self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
-
- self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
- point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
- self.point_embeddings = nn.ModuleList(point_embeddings)
- self.not_a_point_embed = nn.Embedding(1, embed_dim)
-
- self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
- self.mask_downscaling = nn.Sequential(
- nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans // 4),
- activation(),
- nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans),
- activation(),
- nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
- )
- self.no_mask_embed = nn.Embedding(1, embed_dim)
-
- def get_dense_pe(self) -> torch.Tensor:
- """
- Returns the positional encoding used to encode point prompts,
- applied to a dense set of points the shape of the image encoding.
-
- Returns:
- torch.Tensor: Positional encoding with shape
- 1x(embed_dim)x(embedding_h)x(embedding_w)
- """
- return self.pe_layer(self.image_embedding_size).unsqueeze(0)
-
- def _embed_points(
- self,
- points: torch.Tensor,
- labels: torch.Tensor,
- pad: bool,
- ) -> torch.Tensor:
- """Embeds point prompts."""
- points = points + 0.5 # Shift to center of pixel
- if pad:
- padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
- padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
- points = torch.cat([points, padding_point], dim=1)
- labels = torch.cat([labels, padding_label], dim=1)
- point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
- point_embedding[labels == -1] = 0.0
- point_embedding[labels == -1] += self.not_a_point_embed.weight
- point_embedding[labels == 0] += self.point_embeddings[0].weight
- point_embedding[labels == 1] += self.point_embeddings[1].weight
- return point_embedding
-
- def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
- """Embeds box prompts."""
- boxes = boxes + 0.5 # Shift to center of pixel
- coords = boxes.reshape(-1, 2, 2)
- corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
- corner_embedding[:, 0, :] += self.point_embeddings[2].weight
- corner_embedding[:, 1, :] += self.point_embeddings[3].weight
- return corner_embedding
-
- def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
- """Embeds mask inputs."""
- mask_embedding = self.mask_downscaling(masks)
- return mask_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- else:
- return 1
-
- def _get_device(self) -> torch.device:
- return self.point_embeddings[0].weight.device
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks)
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
-
-
-class PositionEmbeddingRandom(nn.Module):
- """
- Positional encoding using random spatial frequencies.
- """
-
- def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
- super().__init__()
- if scale is None or scale <= 0.0:
- scale = 1.0
- self.register_buffer(
- "positional_encoding_gaussian_matrix",
- scale * torch.randn((2, num_pos_feats)),
- )
-
- def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
- """Positionally encode points that are normalized to [0,1]."""
- # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
- coords = 2 * coords - 1
- coords = coords @ self.positional_encoding_gaussian_matrix
- coords = 2 * np.pi * coords
- # outputs d_1 x ... x d_n x C shape
- return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
-
- def forward(self, size: Tuple[int, int]) -> torch.Tensor:
- """Generate positional encoding for a grid of the specified size."""
- h, w = size
- device: Any = self.positional_encoding_gaussian_matrix.device
- grid = torch.ones((h, w), device=device, dtype=torch.float32)
- y_embed = grid.cumsum(dim=0) - 0.5
- x_embed = grid.cumsum(dim=1) - 0.5
- y_embed = y_embed / h
- x_embed = x_embed / w
-
- pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
- return pe.permute(2, 0, 1) # C x H x W
-
- def forward_with_coords(self, coords_input: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
- """Positionally encode points that are not normalized to [0,1]."""
- coords = coords_input.clone()
- coords[:, :, 0] = coords[:, :, 0] / image_size[1]
- coords[:, :, 1] = coords[:, :, 1] / image_size[0]
- return self._pe_encoding(coords.to(torch.float)) # B x N x C
diff --git a/training/segment_anything/modeling/sam.py b/training/segment_anything/modeling/sam.py
deleted file mode 100644
index c1ce4ec..0000000
--- a/training/segment_anything/modeling/sam.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Dict, List, Tuple
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-
-
-class Sam(nn.Module):
- mask_threshold: float = 0.0
- image_format: str = "RGB"
-
- def __init__(
- self,
- image_encoder: ImageEncoderViT,
- prompt_encoder: PromptEncoder,
- mask_decoder: MaskDecoder,
- pixel_mean: List[float] = [123.675, 116.28, 103.53],
- pixel_std: List[float] = [58.395, 57.12, 57.375],
- ) -> None:
- """
- SAM predicts object masks from an image and input prompts.
-
- Arguments:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
- prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
- pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
- pixel_std (list(float)): Std values for normalizing pixels in the input image.
- """
- super().__init__()
- self.image_encoder = image_encoder
- self.prompt_encoder = prompt_encoder
- self.mask_decoder = mask_decoder
- self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
- @property
- def device(self) -> Any:
- return self.pixel_mean.device
-
- @torch.no_grad()
- def forward(
- self,
- batched_input: List[Dict[str, Any]],
- multimask_output: bool,
- ) -> List[Dict[str, torch.Tensor]]:
- """
- Predicts masks end-to-end from provided images and prompts.
- If prompts are not known in advance, using SamPredictor is
- recommended over calling the model directly.
-
- Arguments:
- batched_input (list(dict)): A list over input images, each a
- dictionary with the following keys. A prompt key can be
- excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
- multimask_output (bool): Whether the model should predict multiple
- disambiguating masks, or return a single mask.
-
- Returns:
- (list(dict)): A list over input images, where each element is
- as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
- """
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
-
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
- masks = self.postprocess_masks(
- low_res_masks,
- input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- }
- )
- return outputs
-
- def postprocess_masks(
- self,
- masks: torch.Tensor,
- input_size: Tuple[int, ...],
- original_size: Tuple[int, ...],
- ) -> torch.Tensor:
- """
- Remove padding and upscale masks to the original image size.
-
- Arguments:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
-
- Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
- """
- masks = F.interpolate(
- masks,
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- )
- masks = masks[..., : input_size[0], : input_size[1]]
- masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
- return masks
-
- def preprocess(self, x: torch.Tensor) -> torch.Tensor:
- """Normalize pixel values and pad to a square input."""
- # Normalize colors
- x = (x - self.pixel_mean) / self.pixel_std
-
- # Pad
- h, w = x.shape[-2:]
- padh = self.image_encoder.img_size - h
- padw = self.image_encoder.img_size - w
- x = F.pad(x, (0, padw, 0, padh))
- return x
diff --git a/training/segment_anything/predictor.py b/training/segment_anything/predictor.py
deleted file mode 100644
index fb252a4..0000000
--- a/training/segment_anything/predictor.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional, Tuple
-
-import numpy as np
-import torch
-from segment_anything.modeling import Sam
-
-from .utils.transforms import ResizeLongestSide
-
-
-class SamPredictor:
- def __init__(
- self,
- sam_model: Sam,
- ) -> None:
- """
- Uses SAM to calculate the image embedding for an image, and then
- allow repeated, efficient mask prediction given prompts.
-
- Arguments:
- sam_model (Sam): The model to use for mask prediction.
- """
- super().__init__()
- self.model = sam_model
- self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
- self.reset_image()
-
- def set_image(
- self,
- image: np.ndarray,
- image_format: str = "RGB",
- ) -> None:
- """
- Calculates the image embeddings for the provided image, allowing
- masks to be predicted with the 'predict' method.
-
- Arguments:
- image (np.ndarray): The image for calculating masks. Expects an
- image in HWC uint8 format, with pixel values in [0, 255].
- image_format (str): The color format of the image, in ['RGB', 'BGR'].
- """
- assert image_format in [
- "RGB",
- "BGR",
- ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
- if image_format != self.model.image_format:
- image = image[..., ::-1]
-
- # Transform the image to the form expected by the model
- input_image = self.transform.apply_image(image)
- input_image_torch = torch.as_tensor(input_image, device=self.device)
- input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
-
- self.set_torch_image(input_image_torch, image.shape[:2])
-
- @torch.no_grad()
- def set_torch_image(
- self,
- transformed_image: torch.Tensor,
- original_image_size: Tuple[int, ...],
- ) -> None:
- """
- Calculates the image embeddings for the provided image, allowing
- masks to be predicted with the 'predict' method. Expects the input
- image to be already transformed to the format expected by the model.
-
- Arguments:
- transformed_image (torch.Tensor): The input image, with shape
- 1x3xHxW, which has been transformed with ResizeLongestSide.
- original_image_size (tuple(int, int)): The size of the image
- before transformation, in (H, W) format.
- """
- assert (
- len(transformed_image.shape) == 4
- and transformed_image.shape[1] == 3
- and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
- ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
- self.reset_image()
-
- self.original_size = original_image_size
- self.input_size = tuple(transformed_image.shape[-2:])
- input_image = self.model.preprocess(transformed_image)
- self.features = self.model.image_encoder(input_image)
- self.is_image_set = True
-
- def predict(
- self,
- point_coords: Optional[np.ndarray] = None,
- point_labels: Optional[np.ndarray] = None,
- box: Optional[np.ndarray] = None,
- mask_input: Optional[np.ndarray] = None,
- multimask_output: bool = True,
- return_logits: bool = False,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Predict masks for the given input prompts, using the currently set image.
-
- Arguments:
- point_coords (np.ndarray or None): A Nx2 array of point prompts to the
- model. Each point is in (X,Y) in pixels.
- point_labels (np.ndarray or None): A length N array of labels for the
- point prompts. 1 indicates a foreground point and 0 indicates a
- background point.
- box (np.ndarray or None): A length 4 array given a box prompt to the
- model, in XYXY format.
- mask_input (np.ndarray): A low resolution mask input to the model, typically
- coming from a previous prediction iteration. Has form 1xHxW, where
- for SAM, H=W=256.
- multimask_output (bool): If true, the model will return three masks.
- For ambiguous input prompts (such as a single click), this will often
- produce better masks than a single prediction. If only a single
- mask is needed, the model's predicted quality score can be used
- to select the best mask. For non-ambiguous prompts, such as multiple
- input prompts, multimask_output=False can give better results.
- return_logits (bool): If true, returns un-thresholded masks logits
- instead of a binary mask.
-
- Returns:
- (np.ndarray): The output masks in CxHxW format, where C is the
- number of masks, and (H, W) is the original image size.
- (np.ndarray): An array of length C containing the model's
- predictions for the quality of each mask.
- (np.ndarray): An array of shape CxHxW, where C is the number
- of masks and H=W=256. These low resolution logits can be passed to
- a subsequent iteration as mask input.
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
- # Transform input prompts
- coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
- if point_coords is not None:
- assert point_labels is not None, "point_labels must be supplied if point_coords is supplied."
- point_coords = self.transform.apply_coords(point_coords, self.original_size)
- coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
- labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
- coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
- if box is not None:
- box = self.transform.apply_boxes(box, self.original_size)
- box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
- box_torch = box_torch[None, :]
- if mask_input is not None:
- mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
- mask_input_torch = mask_input_torch[None, :, :, :]
-
- masks, iou_predictions, low_res_masks = self.predict_torch(
- coords_torch,
- labels_torch,
- box_torch,
- mask_input_torch,
- multimask_output,
- return_logits=return_logits,
- )
-
- masks = masks[0].detach().cpu().numpy()
- iou_predictions = iou_predictions[0].detach().cpu().numpy()
- low_res_masks = low_res_masks[0].detach().cpu().numpy()
- return masks, iou_predictions, low_res_masks
-
- @torch.no_grad()
- def predict_torch(
- self,
- point_coords: Optional[torch.Tensor],
- point_labels: Optional[torch.Tensor],
- boxes: Optional[torch.Tensor] = None,
- mask_input: Optional[torch.Tensor] = None,
- multimask_output: bool = True,
- return_logits: bool = False,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Predict masks for the given input prompts, using the currently set image.
- Input prompts are batched torch tensors and are expected to already be
- transformed to the input frame using ResizeLongestSide.
-
- Arguments:
- point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
- model. Each point is in (X,Y) in pixels.
- point_labels (torch.Tensor or None): A BxN array of labels for the
- point prompts. 1 indicates a foreground point and 0 indicates a
- background point.
- box (np.ndarray or None): A Bx4 array given a box prompt to the
- model, in XYXY format.
- mask_input (np.ndarray): A low resolution mask input to the model, typically
- coming from a previous prediction iteration. Has form Bx1xHxW, where
- for SAM, H=W=256. Masks returned by a previous iteration of the
- predict method do not need further transformation.
- multimask_output (bool): If true, the model will return three masks.
- For ambiguous input prompts (such as a single click), this will often
- produce better masks than a single prediction. If only a single
- mask is needed, the model's predicted quality score can be used
- to select the best mask. For non-ambiguous prompts, such as multiple
- input prompts, multimask_output=False can give better results.
- return_logits (bool): If true, returns un-thresholded masks logits
- instead of a binary mask.
-
- Returns:
- (torch.Tensor): The output masks in BxCxHxW format, where C is the
- number of masks, and (H, W) is the original image size.
- (torch.Tensor): An array of shape BxC containing the model's
- predictions for the quality of each mask.
- (torch.Tensor): An array of shape BxCxHxW, where C is the number
- of masks and H=W=256. These low res logits can be passed to
- a subsequent iteration as mask input.
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
- if point_coords is not None:
- points = (point_coords, point_labels)
- else:
- points = None
-
- # Embed prompts
- sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
- points=points,
- boxes=boxes,
- masks=mask_input,
- )
-
- # Predict masks
- low_res_masks, iou_predictions = self.model.mask_decoder(
- image_embeddings=self.features,
- image_pe=self.model.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
-
- # Upscale the masks to the original image resolution
- masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
-
- if not return_logits:
- masks = masks > self.model.mask_threshold
-
- return masks, iou_predictions, low_res_masks
-
- def get_image_embedding(self) -> torch.Tensor:
- """
- Returns the image embeddings for the currently set image, with
- shape 1xCxHxW, where C is the embedding dimension and (H,W) are
- the embedding spatial dimension of SAM (typically C=256, H=W=64).
- """
- if not self.is_image_set:
- raise RuntimeError("An image must be set with .set_image(...) to generate an embedding.")
- assert self.features is not None, "Features must exist if an image has been set."
- return self.features
-
- @property
- def device(self) -> torch.device:
- return self.model.device
-
- def reset_image(self) -> None:
- """Resets the currently set image."""
- self.is_image_set = False
- self.features = None
- self.orig_h = None
- self.orig_w = None
- self.input_h = None
- self.input_w = None
diff --git a/training/segment_anything/utils/__init__.py b/training/segment_anything/utils/__init__.py
deleted file mode 100644
index 5277f46..0000000
--- a/training/segment_anything/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/training/segment_anything/utils/amg.py b/training/segment_anything/utils/amg.py
deleted file mode 100644
index 1c9c491..0000000
--- a/training/segment_anything/utils/amg.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from copy import deepcopy
-from itertools import product
-from typing import Any, Dict, Generator, ItemsView, List, Tuple
-
-import numpy as np
-import torch
-
-
-class MaskData:
- """
- A structure for storing masks and their related data in batched format.
- Implements basic filtering and concatenation.
- """
-
- def __init__(self, **kwargs) -> None:
- for v in kwargs.values():
- assert isinstance(
- v, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats = dict(**kwargs)
-
- def __setitem__(self, key: str, item: Any) -> None:
- assert isinstance(
- item, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats[key] = item
-
- def __delitem__(self, key: str) -> None:
- del self._stats[key]
-
- def __getitem__(self, key: str) -> Any:
- return self._stats[key]
-
- def items(self) -> ItemsView[str, Any]:
- return self._stats.items()
-
- def filter(self, keep: torch.Tensor) -> None:
- for k, v in self._stats.items():
- if v is None:
- self._stats[k] = None
- elif isinstance(v, torch.Tensor):
- self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
- elif isinstance(v, np.ndarray):
- self._stats[k] = v[keep.detach().cpu().numpy()]
- elif isinstance(v, list) and keep.dtype == torch.bool:
- self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
- elif isinstance(v, list):
- self._stats[k] = [v[i] for i in keep]
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def cat(self, new_stats: "MaskData") -> None:
- for k, v in new_stats.items():
- if k not in self._stats or self._stats[k] is None:
- self._stats[k] = deepcopy(v)
- elif isinstance(v, torch.Tensor):
- self._stats[k] = torch.cat([self._stats[k], v], dim=0)
- elif isinstance(v, np.ndarray):
- self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
- elif isinstance(v, list):
- self._stats[k] = self._stats[k] + deepcopy(v)
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def to_numpy(self) -> None:
- for k, v in self._stats.items():
- if isinstance(v, torch.Tensor):
- self._stats[k] = v.detach().cpu().numpy()
-
-
-def is_box_near_crop_edge(
- boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
-) -> torch.Tensor:
- """Filter masks at the edge of a crop, but not at the edge of the original image."""
- crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
- orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
- boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
- near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
- near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
- near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
- return torch.any(near_crop_edge, dim=1)
-
-
-def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
- box_xywh = deepcopy(box_xyxy)
- box_xywh[2] = box_xywh[2] - box_xywh[0]
- box_xywh[3] = box_xywh[3] - box_xywh[1]
- return box_xywh
-
-
-def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
- assert len(args) > 0 and all(
- len(a) == len(args[0]) for a in args
- ), "Batched iteration must have inputs of all the same size."
- n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
- for b in range(n_batches):
- yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
-
-
-def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
- """
- Encodes masks to an uncompressed RLE, in the format expected by
- pycoco tools.
- """
- # Put in fortran order and flatten h,w
- b, h, w = tensor.shape
- tensor = tensor.permute(0, 2, 1).flatten(1)
-
- # Compute change indices
- diff = tensor[:, 1:] ^ tensor[:, :-1]
- change_indices = diff.nonzero()
-
- # Encode run length
- out = []
- for i in range(b):
- cur_idxs = change_indices[change_indices[:, 0] == i, 1]
- cur_idxs = torch.cat(
- [
- torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
- cur_idxs + 1,
- torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
- ]
- )
- btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
- counts = [] if tensor[i, 0] == 0 else [0]
- counts.extend(btw_idxs.detach().cpu().tolist())
- out.append({"size": [h, w], "counts": counts})
- return out
-
-
-def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
- """Compute a binary mask from an uncompressed RLE."""
- h, w = rle["size"]
- mask = np.empty(h * w, dtype=bool)
- idx = 0
- parity = False
- for count in rle["counts"]:
- mask[idx : idx + count] = parity
- idx += count
- parity ^= True
- mask = mask.reshape(w, h)
- return mask.transpose() # Put in C order
-
-
-def area_from_rle(rle: Dict[str, Any]) -> int:
- return sum(rle["counts"][1::2])
-
-
-def calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor:
- """
- Computes the stability score for a batch of masks. The stability
- score is the IoU between the binary masks obtained by thresholding
- the predicted mask logits at high and low values.
- """
- # One mask is always contained inside the other.
- # Save memory by preventing unnecesary cast to torch.int64
- intersections = (masks > (mask_threshold + threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
- unions = (masks > (mask_threshold - threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
- return intersections / unions
-
-
-def build_point_grid(n_per_side: int) -> np.ndarray:
- """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
- offset = 1 / (2 * n_per_side)
- points_one_side = np.linspace(offset, 1 - offset, n_per_side)
- points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
- points_y = np.tile(points_one_side[:, None], (1, n_per_side))
- points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
- return points
-
-
-def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> List[np.ndarray]:
- """Generates point grids for all crop layers."""
- points_by_layer = []
- for i in range(n_layers + 1):
- n_points = int(n_per_side / (scale_per_layer**i))
- points_by_layer.append(build_point_grid(n_points))
- return points_by_layer
-
-
-def generate_crop_boxes(
- im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
-) -> Tuple[List[List[int]], List[int]]:
- """
- Generates a list of crop boxes of different sizes. Each layer
- has (2**i)**2 boxes for the ith layer.
- """
- crop_boxes, layer_idxs = [], []
- im_h, im_w = im_size
- short_side = min(im_h, im_w)
-
- # Original image
- crop_boxes.append([0, 0, im_w, im_h])
- layer_idxs.append(0)
-
- def crop_len(orig_len, n_crops, overlap):
- return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
-
- for i_layer in range(n_layers):
- n_crops_per_side = 2 ** (i_layer + 1)
- overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
-
- crop_w = crop_len(im_w, n_crops_per_side, overlap)
- crop_h = crop_len(im_h, n_crops_per_side, overlap)
-
- crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
- crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
-
- # Crops in XYWH format
- for x0, y0 in product(crop_box_x0, crop_box_y0):
- box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
- crop_boxes.append(box)
- layer_idxs.append(i_layer + 1)
-
- return crop_boxes, layer_idxs
-
-
-def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
- # Check if boxes has a channel dimension
- if len(boxes.shape) == 3:
- offset = offset.unsqueeze(1)
- return boxes + offset
-
-
-def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0]], device=points.device)
- # Check if points has a channel dimension
- if len(points.shape) == 3:
- offset = offset.unsqueeze(1)
- return points + offset
-
-
-def uncrop_masks(masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int) -> torch.Tensor:
- x0, y0, x1, y1 = crop_box
- if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
- return masks
- # Coordinate transform masks
- pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
- pad = (x0, pad_x - x0, y0, pad_y - y0)
- return torch.nn.functional.pad(masks, pad, value=0)
-
-
-def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tuple[np.ndarray, bool]:
- """
- Removes small disconnected regions and holes in a mask. Returns the
- mask and an indicator of if the mask has been modified.
- """
- import cv2 # type: ignore
-
- assert mode in ["holes", "islands"]
- correct_holes = mode == "holes"
- working_mask = (correct_holes ^ mask).astype(np.uint8)
- n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
- sizes = stats[:, -1][1:] # Row 0 is background label
- small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
- if len(small_regions) == 0:
- return mask, False
- fill_labels = [0] + small_regions
- if not correct_holes:
- fill_labels = [i for i in range(n_labels) if i not in fill_labels]
- # If every region is below threshold, keep largest
- if len(fill_labels) == 0:
- fill_labels = [int(np.argmax(sizes)) + 1]
- mask = np.isin(regions, fill_labels)
- return mask, True
-
-
-def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
- from pycocotools import mask as mask_utils # type: ignore
-
- h, w = uncompressed_rle["size"]
- rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
- rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json
- return rle
-
-
-def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
- """
- Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
- an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
- """
- # torch.max below raises an error on empty inputs, just skip in this case
- if torch.numel(masks) == 0:
- return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
-
- # Normalize shape to CxHxW
- shape = masks.shape
- h, w = shape[-2:]
- if len(shape) > 2:
- masks = masks.flatten(0, -3)
- else:
- masks = masks.unsqueeze(0)
-
- # Get top and bottom edges
- in_height, _ = torch.max(masks, dim=-1)
- in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
- bottom_edges, _ = torch.max(in_height_coords, dim=-1)
- in_height_coords = in_height_coords + h * (~in_height)
- top_edges, _ = torch.min(in_height_coords, dim=-1)
-
- # Get left and right edges
- in_width, _ = torch.max(masks, dim=-2)
- in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
- right_edges, _ = torch.max(in_width_coords, dim=-1)
- in_width_coords = in_width_coords + w * (~in_width)
- left_edges, _ = torch.min(in_width_coords, dim=-1)
-
- # If the mask is empty the right edge will be to the left of the left edge.
- # Replace these boxes with [0, 0, 0, 0]
- empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
- out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
- out = out * (~empty_filter).unsqueeze(-1)
-
- # Return to original shape
- if len(shape) > 2:
- out = out.reshape(*shape[:-2], 4)
- else:
- out = out[0]
-
- return out
diff --git a/training/segment_anything/utils/onnx.py b/training/segment_anything/utils/onnx.py
deleted file mode 100644
index 9cd17c7..0000000
--- a/training/segment_anything/utils/onnx.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Tuple
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from ..modeling import Sam
-from .amg import calculate_stability_score
-
-
-class SamOnnxModel(nn.Module):
- """
- This model should not be called directly, but is used in ONNX export.
- It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
- with some functions modified to enable model tracing. Also supports extra
- options controlling what information. See the ONNX export script for details.
- """
-
- def __init__(
- self,
- model: Sam,
- return_single_mask: bool,
- use_stability_score: bool = False,
- return_extra_metrics: bool = False,
- ) -> None:
- super().__init__()
- self.mask_decoder = model.mask_decoder
- self.model = model
- self.img_size = model.image_encoder.img_size
- self.return_single_mask = return_single_mask
- self.use_stability_score = use_stability_score
- self.stability_score_offset = 1.0
- self.return_extra_metrics = return_extra_metrics
-
- @staticmethod
- def resize_longest_image_size(input_image_size: torch.Tensor, longest_side: int) -> torch.Tensor:
- input_image_size = input_image_size.to(torch.float32)
- scale = longest_side / torch.max(input_image_size)
- transformed_size = scale * input_image_size
- transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
- return transformed_size
-
- def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
- point_coords = point_coords + 0.5
- point_coords = point_coords / self.img_size
- point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
- point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
-
- point_embedding = point_embedding * (point_labels != -1)
- point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1)
-
- for i in range(self.model.prompt_encoder.num_point_embeddings):
- point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[i].weight * (
- point_labels == i
- )
-
- return point_embedding
-
- def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
- mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
- mask_embedding = mask_embedding + (1 - has_mask_input) * self.model.prompt_encoder.no_mask_embed.weight.reshape(
- 1, -1, 1, 1
- )
- return mask_embedding
-
- def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
- masks = F.interpolate(
- masks,
- size=(self.img_size, self.img_size),
- mode="bilinear",
- align_corners=False,
- )
-
- prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size)
- masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])]
-
- orig_im_size = orig_im_size.to(torch.int64)
- h, w = orig_im_size[0], orig_im_size[1]
- masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
- return masks
-
- def select_masks(
- self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- # Determine if we should return the multiclick mask or not from the number of points.
- # The reweighting is used to avoid control flow.
- score_reweight = torch.tensor([[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]).to(
- iou_preds.device
- )
- score = iou_preds + (num_points - 2.5) * score_reweight
- best_idx = torch.argmax(score, dim=1)
- masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
- iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
-
- return masks, iou_preds
-
- @torch.no_grad()
- def forward(
- self,
- image_embeddings: torch.Tensor,
- point_coords: torch.Tensor,
- point_labels: torch.Tensor,
- mask_input: torch.Tensor,
- has_mask_input: torch.Tensor,
- orig_im_size: torch.Tensor,
- ):
- sparse_embedding = self._embed_points(point_coords, point_labels)
- dense_embedding = self._embed_masks(mask_input, has_mask_input)
-
- masks, scores = self.model.mask_decoder.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=self.model.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embedding,
- dense_prompt_embeddings=dense_embedding,
- )
-
- if self.use_stability_score:
- scores = calculate_stability_score(masks, self.model.mask_threshold, self.stability_score_offset)
-
- if self.return_single_mask:
- masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
-
- upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
-
- if self.return_extra_metrics:
- stability_scores = calculate_stability_score(
- upscaled_masks, self.model.mask_threshold, self.stability_score_offset
- )
- areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
- return upscaled_masks, scores, stability_scores, areas, masks
-
- return upscaled_masks, scores, masks
diff --git a/training/segment_anything/utils/transforms.py b/training/segment_anything/utils/transforms.py
deleted file mode 100644
index 96a4ed6..0000000
--- a/training/segment_anything/utils/transforms.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from copy import deepcopy
-from typing import Tuple
-
-import numpy as np
-import torch
-from torch.nn import functional as F
-from torchvision.transforms.functional import resize, to_pil_image # type: ignore
-
-
-class ResizeLongestSide:
- """
- Resizes images to longest side 'target_length', as well as provides
- methods for resizing coordinates and boxes. Provides methods for
- transforming both numpy array and batched torch tensors.
- """
-
- def __init__(self, target_length: int) -> None:
- self.target_length = target_length
-
- def apply_image(self, image: np.ndarray) -> np.ndarray:
- """
- Expects a numpy array with shape HxWxC in uint8 format.
- """
- target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
- return np.array(resize(to_pil_image(image), target_size))
-
- def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array of length 2 in the final dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
- coords = deepcopy(coords).astype(float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array shape Bx4. Requires the original image size
- in (H, W) format.
- """
- boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
- """
- Expects batched images with shape BxCxHxW and float format. This
- transformation may not exactly match apply_image. apply_image is
- the transformation expected by the model.
- """
- # Expects an image in BCHW format. May not exactly match apply_image.
- target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
- return F.interpolate(image, target_size, mode="bilinear", align_corners=False, antialias=True)
-
- def apply_coords_torch(self, coords: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
- """
- Expects a torch tensor with length 2 in the last dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
- coords = deepcopy(coords).to(torch.float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes_torch(self, boxes: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
- """
- Expects a torch tensor with shape Bx4. Requires the original image
- size in (H, W) format.
- """
- boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- @staticmethod
- def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
- """
- Compute the output size given input size and target long side length.
- """
- scale = long_side_length * 1.0 / max(oldh, oldw)
- newh, neww = oldh * scale, oldw * scale
- neww = int(neww + 0.5)
- newh = int(newh + 0.5)
- return (newh, neww)
diff --git a/training/trainer_2pt5d.py b/training/trainer_2pt5d.py
deleted file mode 100644
index 1d1e906..0000000
--- a/training/trainer_2pt5d.py
+++ /dev/null
@@ -1,654 +0,0 @@
-# Copyright 2020 - 2023 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-import random
-import time
-from copy import deepcopy
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.nn.parallel
-import torch.utils.data.distributed
-from monai.data import decollate_batch
-from monai.metrics import compute_dice
-from tensorboardX import SummaryWriter
-from torch.cuda.amp import GradScaler, autocast
-from utils.utils import AverageMeter, distributed_all_gather
-
-
-def apply_coords_torch(coords, original_size, sam_image_size) -> np.ndarray:
- """
- Expects a numpy array of length 2 in the final dimension. Requires the
- original image size in (H, W) format.
- """
- old = original_size
- new = sam_image_size
- coords = deepcopy(coords).float()
- # Here, we can apply a same scale factor to h and w, because we first pad the input to a square image along the
- # longest side then resize it to sam_image_size. In other words, the scale factor is determined by the longest side.
- coords[..., 0] = coords[..., 0] * (new / old)
- coords[..., 1] = coords[..., 1] * (new / old)
- return coords
-
-
-def sample_points(labelpoints, n_points):
- idx = torch.randperm(len(labelpoints), dtype=torch.long, device=labelpoints.device)[:n_points]
- return [labelpoints[idx]]
-
-
-def generate_point_prompt(batch_labels_, args, points_pos=None, points_neg=None, previous_pred=None):
- max_point = args.max_points
- Np = (
- points_pos
- if points_pos is not None
- else min(max_point, int(np.abs(random.gauss(mu=0, sigma=max_point // 2))) + 1)
- )
- Nn = points_neg if points_neg is not None else min(max_point, int(np.abs(random.gauss(mu=0, sigma=max_point // 2))))
- # To follow original SAM, with equal probability either a foreground point
- # is selected randomly for the target mask
- _point = []
- _point_label = []
- b, h, w = batch_labels_.shape
- device = batch_labels_.device
- for i in range(b):
- plabels = batch_labels_[i, ...]
- nlabels = (plabels == 0.0).float()
- if previous_pred is not None:
- ppred = previous_pred[i, 0, ...]
- npred = (previous_pred[i, 0, ...] == 0.0).float()
-
- # False positive mask (pixels that are predicted as positive but are actually negative)
- fp_mask = torch.logical_and(nlabels, ppred)
- # False negative mask (pixels that are predicted as negative but are actually positive)
- fn_mask = torch.logical_and(plabels, npred)
- # we sample positive points from false negative pred.
- # we sample negative points from false positive pred.
- plabelpoints = torch.nonzero(fn_mask)
- nlabelpoints = torch.nonzero(fp_mask)
-
- else:
- plabelpoints = torch.nonzero(plabels)
- nlabelpoints = torch.nonzero(nlabels)
- # 1 indicates a foreground point and 0 indicates a background point.
- # -1 indicates a dummy non-point as the placeholder.
- n_placeholder = Np + Nn - min(len(plabelpoints), Np) - min(len(nlabelpoints), Nn)
-
- # Use torch.randperm to generate indices on a GPU tensor
- _point.append(
- torch.cat(
- sample_points(plabelpoints, min(len(plabelpoints), Np))
- + sample_points(nlabelpoints, min(len(nlabelpoints), Nn))
- + [torch.zeros((1, 2), device=device)] * n_placeholder,
- dim=0,
- )
- )
- _point_label.append(
- torch.tensor([1] * min(len(plabelpoints), Np) + [0] * min(len(nlabelpoints), Nn) + [-1] * n_placeholder).to(
- device
- )
- )
-
- point = torch.stack(_point)
- point_label = torch.stack(_point_label)
- point_coords = apply_coords_torch(point, max(h, w), args.sam_image_size)
-
- return point_coords, point_label
-
-
-def prepare_sam_training_input(inputs, labels, args, model):
- unique_labels = torch.unique(labels).as_tensor().long()
-
- if args.skip_bk:
- unique_labels = unique_labels[1:]
-
- if len(unique_labels) == 0:
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
- batch_labels = torch.zeros(1, 1, args.sam_image_size // 4, args.sam_image_size // 4).cuda(args.rank)
- skip = True
- return prepared_input, batch_labels, None, skip
-
- # random sample args.num_prompt prompts, this will help to manage the GPU memory upper bound.
- if len(unique_labels) > args.num_prompt:
- idxs = random.sample(range(len(unique_labels)), args.num_prompt)
- idxs = torch.tensor(idxs)
- unique_labels = unique_labels[idxs]
- if len(unique_labels) < args.num_prompt:
- while len(unique_labels) < args.num_prompt:
- unique_labels = torch.cat([unique_labels, unique_labels], 0)
- unique_labels = unique_labels[: args.num_prompt]
-
- # add 4 background labels to every batch
- background_labels = list(set([i for i in range(1, 105)]) - set(unique_labels.cpu().numpy()))
- random.shuffle(background_labels)
- unique_labels = torch.cat([unique_labels, torch.tensor(background_labels[:4]).cuda(args.rank)])
-
- # preprocess make the size of label same as low_res_logit
- batch_labels_ = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
-
- if args.distributed:
- batch_labels = model.module.preprocess(batch_labels_, is_input=False)
- else:
- batch_labels = model.preprocess(batch_labels_, is_input=False)
-
- # TODO: we currently only use class-label and points prompt.
-
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
- if args.label_prompt:
- labels_prompt = unique_labels.unsqueeze(-1)
- prepared_input[0].update({"labels": labels_prompt})
-
- if args.point_prompt:
- point_coords, point_labels = generate_point_prompt(batch_labels_, args)
- prepared_input[0].update({"point_coords": point_coords, "point_labels": point_labels})
-
- if args.label_prompt and args.point_prompt:
- # if we use both two kinds of prompts, then we randomly drop one kind.
- if random.uniform(0, 1) < args.drop_label_prob:
- prepared_input[0].pop("labels")
- else:
- if random.uniform(0, 1) < args.drop_point_prob:
- prepared_input[0].pop("point_coords")
- prepared_input[0].pop("point_labels")
-
- return prepared_input, batch_labels.unsqueeze(1).cuda(args.rank), batch_labels_, False
-
-
-def train_epoch(model, loader, optimizer, scaler, epoch, loss_func, args):
- model.train()
- start_time = time.time()
- run_loss = AverageMeter()
- # we need to make sure the number of 2.5D input is an odd number.
- assert args.roi_z_iter % 2 == 1
- for idx, batch_data in enumerate(loader):
- # only take 1 batch
- inputs_l = batch_data["image"]
- labels_l = batch_data["label"]
- # TODO: we only support batch_size = 1 for data loader.
- inputs_l = inputs_l.squeeze()
- labels_l = labels_l.squeeze()
- n_z_before_pad = labels_l.shape[-1]
-
- n_slice = args.roi_z_iter
- # pad the z direction, so we can easily extract 2.5D input and predict labels for the center slice
- pd = (n_slice // 2, n_slice // 2)
- inputs_l = F.pad(inputs_l, pd, "constant", 0)
- labels_l = F.pad(labels_l, pd, "constant", 0)
- _loss = torch.tensor(0.0).cuda(args.rank)
-
- for _k in range(args.num_patch):
- # Return random integers from `low` (inclusive) to `high` (exclusive).
- start_idx = int(np.random.randint(low=n_slice // 2, high=(n_slice // 2 + n_z_before_pad)))
-
- inputs = inputs_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1].permute(2, 0, 1)
-
- # we only need the label for the center slice
- labels = labels_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1][..., n_slice // 2]
-
- data, target, target_original, skip = prepare_sam_training_input(
- inputs.cuda(args.rank), labels.cuda(args.rank), args, model
- )
-
- for param in model.parameters():
- param.grad = None
-
- with autocast(enabled=args.amp):
- outputs = model(data, is_train=True)
- loss = loss_func(outputs[0]["low_res_logits"], target)
-
- if skip:
- loss = loss * 0.0
-
- if args.amp:
- scaler.scale(loss).backward()
- if args.clip is not None:
- scaler.unscale_(optimizer)
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- scaler.step(optimizer)
- scaler.update()
- else:
- loss.backward()
- if args.clip is not None:
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- optimizer.step()
-
- _loss += loss.detach()
- _loss /= min(args.num_patch, n_z_before_pad)
- if args.distributed:
- loss_list = distributed_all_gather(
- [_loss],
- out_numpy=True,
- )
- run_loss.update(
- np.mean(np.mean(np.stack(loss_list, axis=0), axis=0), axis=0), n=args.batch_size * args.world_size
- )
- else:
- run_loss.update(_loss.item(), n=args.num_patch)
- if args.rank == 0:
- print(
- "Epoch {}/{} {}/{}".format(epoch, args.max_epochs, idx, len(loader)),
- "loss: {:.4f}".format(run_loss.avg),
- "time {:.2f}s".format(time.time() - start_time),
- )
- start_time = time.time()
- for param in model.parameters():
- param.grad = None
- return run_loss.avg
-
-
-def train_epoch_iterative(model, loader, optimizer, scaler, epoch, loss_func, args):
- model.train()
- start_time = time.time()
- run_loss = AverageMeter()
- # we need to make sure the number of 2.5D input is an odd number.
- assert args.roi_z_iter % 2 == 1
- for idx, batch_data in enumerate(loader):
- # only take 1 batch
- inputs_l = batch_data["image"]
- labels_l = batch_data["label"]
- # TODO: we only support batch_size = 1 for data loader.
- inputs_l = inputs_l.squeeze()
- labels_l = labels_l.squeeze()
- n_z_before_pad = labels_l.shape[-1]
-
- n_slice = args.roi_z_iter
- # pad the z direction, so we can easily extract 2.5D input and predict labels for the center slice
- pd = (n_slice // 2, n_slice // 2)
- inputs_l = F.pad(inputs_l, pd, "constant", 0)
- labels_l = F.pad(labels_l, pd, "constant", 0)
- _loss = torch.tensor(0.0).cuda(args.rank)
- for _k in range(min(args.num_patch, n_z_before_pad)):
- # Return random integers from `low` (inclusive) to `high` (exclusive).
- start_idx = int(np.random.randint(low=n_slice // 2, high=(n_slice // 2 + n_z_before_pad)))
-
- inputs = inputs_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1].permute(2, 0, 1)
-
- # we only need the label for the center slice
- labels = labels_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1][..., n_slice // 2]
-
- data, target, target_original, skip = prepare_sam_training_input(
- inputs.cuda(args.rank), labels.cuda(args.rank), args, model
- )
- for param in model.parameters():
- param.grad = None
-
- with autocast(enabled=args.amp):
- if args.distributed:
- image_embeddings = model.module.get_image_embeddings(data)
- else:
- image_embeddings = model.get_image_embeddings(data)
-
- if skip:
- with autocast(enabled=args.amp):
- if args.distributed:
- outputs = model.module.get_mask_prediction(data, image_embeddings)
- else:
- outputs = model.get_mask_prediction(data, image_embeddings)
- loss = loss_func(outputs[0]["low_res_logits"], target) * 0.0
- else:
- # iterative training
- loss = 0
- drop_iter = random.randint(0, args.num_iterative_step - 2)
- for i in range(args.num_iterative_step):
- with autocast(enabled=args.amp):
- if args.distributed:
- outputs = model.module.get_mask_prediction(data, image_embeddings)
- else:
- outputs = model.get_mask_prediction(data, image_embeddings)
- loss += loss_func(outputs[0]["low_res_logits"], target)
- if i == args.num_iterative_step - 1:
- # no need to perform the following operations after the last step
- continue
- # we also supply the mask prediction from the previous iteration
- # as an additional prompt to our model (follow original SAM).
- data[0]["mask_inputs"] = outputs[0]["low_res_logits"].detach()
- if i == drop_iter:
- # for drop iter, no additional points are sampled (follow original SAM).
- continue
-
- previous_point_coords = data[0].get("point_coords", None)
- previous_point_labels = data[0].get("point_labels", None)
-
- if previous_point_coords is None and args.no_more_points_for_cp_only:
- # if no point prompt at the first prompt generation,
- # we will not add more additional pointa during iterative training.
- continue
-
- # sample one pos and on neg point based on previous prediction
- previous_pred = (F.sigmoid(outputs[0]["high_res_logits"].detach()) > 0.5).float()
- point_coords, point_labels = generate_point_prompt(
- target_original, args=args, points_pos=1, points_neg=1, previous_pred=previous_pred
- )
-
- if previous_point_coords is not None:
- data[0]["point_coords"] = torch.cat([previous_point_coords, point_coords], dim=1)
- data[0]["point_labels"] = torch.cat([previous_point_labels, point_labels], dim=1)
- else:
- data[0]["point_coords"] = point_coords
- data[0]["point_labels"] = point_labels
-
- if args.amp:
- scaler.scale(loss).backward()
- if args.clip is not None:
- scaler.unscale_(optimizer)
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- scaler.step(optimizer)
- scaler.update()
- else:
- loss.backward()
- if args.clip is not None:
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
- optimizer.step()
-
- _loss += loss.detach() / args.num_iterative_step
- _loss /= min(args.num_patch, n_z_before_pad)
- if args.distributed:
- loss_list = distributed_all_gather(
- [_loss],
- out_numpy=True,
- )
- run_loss.update(
- np.mean(np.mean(np.stack(loss_list, axis=0), axis=0), axis=0), n=args.batch_size * args.world_size
- )
- else:
- run_loss.update(_loss.item(), n=args.num_patch)
- if args.rank == 0:
- print(
- "Epoch {}/{} {}/{}".format(epoch, args.max_epochs, idx, len(loader)),
- "loss: {:.4f}".format(run_loss.avg),
- "time {:.2f}s".format(time.time() - start_time),
- )
- start_time = time.time()
- for param in model.parameters():
- param.grad = None
- return run_loss.avg
-
-
-def prepare_sam_test_input(inputs, labels, args, previous_pred=None):
- unique_labels = torch.tensor([i for i in range(1, 105)]).cuda(args.rank)
-
- # preprocess make the size of lable same as high_res_logit
- batch_labels = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
-
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
- if args.label_prompt:
- labels_prompt = unique_labels.unsqueeze(-1)
- prepared_input[0].update({"labels": labels_prompt})
-
- if args.point_prompt:
- point_coords, point_labels = generate_point_prompt(
- batch_labels,
- args,
- points_pos=args.points_val_pos,
- points_neg=args.points_val_neg,
- previous_pred=previous_pred,
- )
- prepared_input[0].update({"point_coords": point_coords, "point_labels": point_labels})
-
- return prepared_input, batch_labels.unsqueeze(1).cuda(args.rank), unique_labels
-
-
-def prepare_sam_val_input_cp_only(inputs, labels, args):
- # Don't exclude background in val but will ignore it in metric calculation
- unique_labels = torch.tensor([i for i in range(1, 105)]).cuda(args.rank)
-
- # preprocess make the size of lable same as high_res_logit
- batch_labels = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
-
- prepared_input = [{"image": inputs, "original_size": tuple(labels.shape)}]
-
- labels_prompt = unique_labels.unsqueeze(-1)
- prepared_input[0].update({"labels": labels_prompt})
-
- return prepared_input, batch_labels.unsqueeze(1).cuda(args.rank), unique_labels
-
-
-def val_epoch(model, loader, epoch, acc_func, args, iterative=False, post_label=None, post_pred=None):
- model.eval()
- run_acc = AverageMeter()
- start_time = time.time()
- with torch.no_grad():
- for idx, batch_data in enumerate(loader):
- # only take 1 batch
- inputs_l = batch_data["image"]
- labels_l = batch_data["label"]
- labels_l.shape[-1]
- # assert n_z_before_pad >= args.num_patch_val + args.roi_z_iter
-
- # TODO: we only support batch_size = 1 for data loader.
- inputs_l = inputs_l.squeeze()
- labels_l = labels_l.squeeze()
-
- n_slice = args.roi_z_iter
- # pad the z direction, so we can easily extract 2.5D input and predict labels for the center slice
- pd = (n_slice // 2, n_slice // 2)
-
- inputs_l = F.pad(inputs_l, pd, "constant", 0)
- labels_l = F.pad(labels_l, pd, "constant", 0)
- n_z_after_pad = labels_l.shape[-1]
-
- acc_sum_total = 0.0
- not_nans_total = 0.0
- # We only loop the center args.num_patch_val slices to save val time
- for start_idx in range(
- n_z_after_pad // 2 - args.num_patch_val // 2, n_z_after_pad // 2 + args.num_patch_val // 2
- ):
- inputs = inputs_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1].permute(2, 0, 1)
-
- # we only need the label for the center slice
- labels = labels_l[..., start_idx - n_slice // 2 : start_idx + n_slice // 2 + 1][..., n_slice // 2]
-
- data, target, _ = prepare_sam_val_input_cp_only(inputs.cuda(args.rank), labels.cuda(args.rank), args)
-
- with autocast(enabled=args.amp):
- outputs = model(data)
- logit = outputs[0]["high_res_logits"]
-
- y_pred = torch.stack(post_pred(decollate_batch(logit)), 0)
-
- # TODO: we compute metric for each prompt for simplicity in validation.
- acc_batch = compute_dice(y_pred=y_pred, y=target)
- acc_sum, not_nans = (
- torch.nansum(acc_batch).item(),
- 104 - torch.sum(torch.isnan(acc_batch).float()).item(),
- )
- acc_sum_total += acc_sum
- not_nans_total += not_nans
-
- acc, not_nans = acc_sum_total / not_nans_total, not_nans_total
- f_name = batch_data["image"].meta["filename_or_obj"]
- print(f"Rank: {args.rank}, Case: {f_name}, Acc: {acc:.4f}, N_prompts: {int(not_nans)} ")
-
- acc = torch.tensor(acc).cuda(args.rank)
- not_nans = torch.tensor(not_nans).cuda(args.rank)
-
- if args.distributed:
- acc_list, not_nans_list = distributed_all_gather([acc, not_nans], out_numpy=True)
- for al, nl in zip(acc_list, not_nans_list):
- run_acc.update(al, n=nl)
-
- else:
- run_acc.update(acc.cpu().numpy(), n=not_nans.cpu().numpy())
-
- if args.rank == 0:
- avg_acc = np.mean(run_acc.avg)
- print(
- "Val {}/{} {}/{}".format(epoch, args.max_epochs, idx + 1, len(loader)),
- "acc",
- avg_acc,
- "time {:.2f}s".format(time.time() - start_time),
- )
- start_time = time.time()
- return run_acc.avg
-
-
-def save_checkpoint(model, epoch, args, filename="model.pt", best_acc=0, optimizer=None, scheduler=None):
- state_dict = model.state_dict() if not args.distributed else model.module.state_dict()
- save_dict = {"epoch": epoch, "best_acc": best_acc, "state_dict": state_dict}
- if optimizer is not None:
- save_dict["optimizer"] = optimizer.state_dict()
- if scheduler is not None:
- save_dict["scheduler"] = scheduler.state_dict()
- filename = os.path.join(args.logdir, filename)
- torch.save(save_dict, filename)
- print("Saving checkpoint", filename)
-
-
-def run_training(
- model,
- train_loader,
- val_loader,
- optimizer,
- loss_func,
- acc_func,
- args,
- scheduler=None,
- start_epoch=0,
- post_label=None,
- post_pred=None,
-):
- writer = None
- if args.logdir is not None and args.rank == 0:
- writer = SummaryWriter(log_dir=args.logdir)
- if args.rank == 0:
- print("Writing Tensorboard logs to ", args.logdir)
- scaler = None
- if args.amp:
- scaler = GradScaler()
- val_acc_max = 0.0
- best_epoch = -1
- val_MA = None
- best_log = {}
- for epoch in range(start_epoch, args.max_epochs):
- if args.distributed:
- torch.distributed.barrier()
- print(args.rank, time.ctime(), "Epoch:", epoch)
- epoch_time = time.time()
- if args.rank == 0:
- if scheduler is not None:
- print("Current lr:", scheduler.get_last_lr())
- else:
- print("Current lr:", optimizer.param_groups[0]["lr"])
-
- if args.label_prompt and args.point_prompt:
- if epoch < args.label_prompt_warm_up_epoch:
- # during warm up, we drop class label prompt embedding with less prob,
- # since class label prompt embedding layer is trained from scratch.
- args.drop_label_prob = 0.2
- args.drop_point_prob = 0.5
- else:
- # after warmp up, we evenly drop two kinds of prompts
- args.drop_label_prob = 0.5
- args.drop_point_prob = 0.5
- print(
- "rank:",
- args.rank,
- "label_prompt (train):",
- args.label_prompt,
- ", label_drop_prob:",
- args.drop_label_prob,
- "| point_prompt (train):",
- args.point_prompt,
- ", point_drop_prob:",
- args.drop_point_prob,
- )
-
- # we don't perform iterative training for the first args.iterative_training_warm_up_epoch epochs
- if epoch > args.iterative_training_warm_up_epoch:
- if args.reuse_img_embedding:
- if args.rank == 0:
- print("Iterative Training: Reuse image embedding!")
- train_loss = train_epoch_iterative(
- model, train_loader, optimizer, scaler=scaler, epoch=epoch, loss_func=loss_func, args=args
- )
- else:
- if args.rank == 0:
- print("Iterative Training: Don't reuse image embedding!")
- raise NotImplementedError
- else:
- print(f" Rank: {args.rank} Single-step Training")
- train_loss = train_epoch(
- model, train_loader, optimizer, scaler=scaler, epoch=epoch, loss_func=loss_func, args=args
- )
-
- if args.rank == 0:
- print(
- "Final training {}/{}".format(epoch, args.max_epochs - 1),
- "loss: {:.4f}".format(train_loss),
- "time {:.2f}s".format(time.time() - epoch_time),
- )
- if args.rank == 0 and writer is not None:
- writer.add_scalar("train_loss", train_loss, epoch)
-
- if (epoch + 1) % args.val_every == 0:
- if args.distributed:
- torch.distributed.barrier()
- if args.rank == 0:
- print("Start validation")
- print("label_prompt (val):", args.label_prompt, "point_prompt (val):", args.point_prompt)
- epoch_time = time.time()
- val_avg_acc = val_epoch(
- model,
- val_loader,
- iterative=False,
- epoch=epoch,
- acc_func=acc_func,
- args=args,
- post_label=post_label,
- post_pred=post_pred,
- )
-
- val_avg_acc = np.mean(val_avg_acc)
- if val_MA is None:
- val_MA = val_avg_acc
- else:
- val_MA = 0.9 * val_MA + 0.1 * val_avg_acc
- if args.rank == 0:
- print(
- "Final validation {}/{},".format(epoch, args.max_epochs - 1),
- f"Acc {val_avg_acc:.4f},",
- f"mv Acc {val_MA:.4f},",
- "Previous Best validation at epoch {} is {:.4f},".format(best_epoch, val_acc_max),
- "time {:.2f}s".format(time.time() - epoch_time),
- )
- if writer is not None:
- writer.add_scalar("val_acc", val_avg_acc, epoch)
- if val_avg_acc > val_acc_max:
- print("new best ({:.6f} --> {:.6f}). ".format(val_acc_max, val_avg_acc))
- val_acc_max = val_avg_acc
- best_log[epoch] = float(val_acc_max)
- best_epoch = epoch
- if args.rank == 0 and args.logdir is not None and args.save_checkpoint:
- save_checkpoint(
- model,
- epoch,
- args,
- best_acc=val_acc_max,
- filename="model_best.pt",
- optimizer=optimizer,
- scheduler=scheduler,
- )
- with open(os.path.join(args.logdir, "train.log"), "w") as f:
- json.dump(best_log, f)
- if args.rank == 0 and args.logdir is not None and args.save_checkpoint:
- save_checkpoint(model, epoch, args, best_acc=val_acc_max, filename="model_final.pt")
-
- if scheduler is not None:
- scheduler.step()
-
- if args.rank == 0 and writer is not None:
- writer.close()
-
- print("Training Finished !, Best Accuracy: ", val_acc_max, "at epoch", best_epoch)
-
- return val_acc_max
diff --git a/training/utils/__init__.py b/training/utils/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/training/utils/data_utils.py b/training/utils/data_utils.py
deleted file mode 100644
index 5d4064b..0000000
--- a/training/utils/data_utils.py
+++ /dev/null
@@ -1,251 +0,0 @@
-# Copyright 2020 - 2023 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import copy
-import math
-import os
-
-import numpy as np
-import torch
-from monai import data, transforms
-from monai.transforms import (
- EnsureChannelFirstd,
- LoadImaged,
- Orientationd,
- RandRotate90d,
- RandShiftIntensityd,
- ScaleIntensityRanged,
- Spacingd,
-)
-
-
-class Sampler(torch.utils.data.Sampler):
- def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, make_even=True):
- if num_replicas is None:
- if not torch.distributed.is_available():
- raise RuntimeError("Requires distributed package to be available")
- num_replicas = torch.distributed.get_world_size()
- if rank is None:
- if not torch.distributed.is_available():
- raise RuntimeError("Requires distributed package to be available")
- rank = torch.distributed.get_rank()
- self.shuffle = shuffle
- self.make_even = make_even
- self.dataset = dataset
- self.num_replicas = num_replicas
- self.rank = rank
- self.epoch = 0
- self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
- self.total_size = self.num_samples * self.num_replicas
- indices = list(range(len(self.dataset)))
- self.valid_length = len(indices[self.rank : self.total_size : self.num_replicas])
-
- def __iter__(self):
- if self.shuffle:
- g = torch.Generator()
- g.manual_seed(self.epoch)
- indices = torch.randperm(len(self.dataset), generator=g).tolist()
- else:
- indices = list(range(len(self.dataset)))
- if self.make_even:
- if len(indices) < self.total_size:
- if self.total_size - len(indices) < len(indices):
- indices += indices[: (self.total_size - len(indices))]
- else:
- extra_ids = np.random.randint(low=0, high=len(indices), size=self.total_size - len(indices))
- indices += [indices[ids] for ids in extra_ids]
- assert len(indices) == self.total_size
- indices = indices[self.rank : self.total_size : self.num_replicas]
- self.num_samples = len(indices)
- return iter(indices)
-
- def __len__(self):
- return self.num_samples
-
- def set_epoch(self, epoch):
- self.epoch = epoch
-
-
-def get_loader(args):
- train_files, val_files, test_files = split_data(args)
-
- random_transforms = (
- [
- RandRotate90d(
- keys=["image", "label"],
- prob=0.10,
- max_k=3,
- ),
- RandShiftIntensityd(
- keys=["image"],
- offsets=0.10,
- prob=0.10,
- ),
- ]
- if args.data_aug
- else []
- )
-
- if args.data_aug:
- print("using data augmentation")
- else:
- print("No data augmentation")
-
- train_transform = transforms.Compose(
- [
- LoadImaged(keys=["image", "label"], image_only=True),
- EnsureChannelFirstd(keys=["image", "label"]),
- Orientationd(keys=["image", "label"], axcodes="RAS"),
- Spacingd(keys=["image", "label"], pixdim=(1.5, 1.5, 1.5), mode=("bilinear", "nearest")),
- ScaleIntensityRanged(
- keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True
- ),
- ]
- + random_transforms
- )
-
- val_transform = transforms.Compose(
- [
- LoadImaged(keys=["image", "label"], image_only=True),
- EnsureChannelFirstd(keys=["image", "label"]),
- Orientationd(keys=["image", "label"], axcodes="RAS"),
- Spacingd(keys=["image", "label"], pixdim=(1.5, 1.5, 1.5), mode=("bilinear", "nearest")),
- ScaleIntensityRanged(
- keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True
- ),
- ]
- )
-
- if args.test_mode:
- pass
- else:
- datalist = train_files
- if args.use_normal_dataset:
- train_ds = data.Dataset(data=datalist[:1], transform=train_transform)
- else:
- if args.distributed:
- datalist = data.partition_dataset(
- data=datalist,
- shuffle=True,
- num_partitions=args.world_size,
- even_divisible=True,
- )[args.rank]
-
- train_ds = data.CacheDataset(
- data=datalist,
- transform=train_transform,
- cache_rate=1.0,
- num_workers=args.workers,
- )
- train_sampler = None
-
- train_loader = data.DataLoader(
- train_ds,
- batch_size=args.batch_size,
- shuffle=(train_sampler is None),
- num_workers=args.workers,
- sampler=train_sampler,
- pin_memory=True,
- )
- val_files = val_files
- if args.distributed:
- val_files = data.partition_dataset(
- data=val_files,
- shuffle=False,
- num_partitions=args.world_size,
- even_divisible=False,
- )[args.rank]
- val_ds = data.CacheDataset(
- data=val_files,
- transform=val_transform,
- cache_rate=1.0,
- num_workers=args.workers,
- )
- val_sampler = None
- val_loader = data.DataLoader(
- val_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=val_sampler, pin_memory=True
- )
- loader = [train_loader, val_loader]
-
- return loader
-
-
-def split_data(args):
- data_dir = args.data_dir
- import json
-
- with open(args.json_list, "r") as f:
- json_data = json.load(f)
-
- list_train = []
- list_valid = []
- if "validation" in json_data.keys():
- list_train = json_data["training"]
- list_valid = json_data["validation"]
- list_test = json_data["testing"]
- else:
- for item in json_data["training"]:
- if item["fold"] == args.fold:
- item.pop("fold", None)
- list_valid.append(item)
- else:
- item.pop("fold", None)
- list_train.append(item)
- if "testing" in json_data.keys() and "label" in json_data["testing"][0]:
- list_test = json_data["testing"]
- else:
- list_test = copy.deepcopy(list_valid)
- if args.splitval > 0:
- list_train = sorted(list_train, key=lambda x: x["image"])
- l = int((len(list_train) + len(list_valid)) * args.splitval)
- list_valid = list_train[-l:]
- list_train = list_train[:-l]
-
- if hasattr(args, "rank") and args.rank == 0:
- print("train files", len(list_train), [os.path.basename(_["image"]).split(".")[0] for _ in list_train])
- print("val files", len(list_valid), [os.path.basename(_["image"]).split(".")[0] for _ in list_valid])
- print("test files", len(list_test), [os.path.basename(_["image"]).split(".")[0] for _ in list_test])
-
- # training data
- files = []
- for _i in range(len(list_train)):
- str_img = os.path.join(data_dir, list_train[_i]["image"])
- str_seg = os.path.join(data_dir, list_train[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
-
- train_files = copy.deepcopy(files)
-
- files = []
- for _i in range(len(list_valid)):
- str_img = os.path.join(data_dir, list_valid[_i]["image"])
- str_seg = os.path.join(data_dir, list_valid[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
- val_files = copy.deepcopy(files)
-
- files = []
- for _i in range(len(list_test)):
- str_img = os.path.join(data_dir, list_test[_i]["image"])
- str_seg = os.path.join(data_dir, list_test[_i]["label"])
-
- if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
- continue
-
- files.append({"image": str_img, "label": str_seg})
- test_files = copy.deepcopy(files)
- return train_files, val_files, test_files
diff --git a/training/utils/utils.py b/training/utils/utils.py
deleted file mode 100644
index 8ac31c6..0000000
--- a/training/utils/utils.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright 2020 - 2022 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import scipy.ndimage as ndimage
-import torch
-
-
-def resample_3d(img, target_size):
- imx, imy, imz = img.shape
- tx, ty, tz = target_size
- zoom_ratio = (float(tx) / float(imx), float(ty) / float(imy), float(tz) / float(imz))
- img_resampled = ndimage.zoom(img, zoom_ratio, order=0, prefilter=False)
- return img_resampled
-
-
-def dice(x, y):
- intersect = np.sum(np.sum(np.sum(x * y)))
- y_sum = np.sum(np.sum(np.sum(y)))
- if y_sum == 0:
- return 0.0
- x_sum = np.sum(np.sum(np.sum(x)))
- return 2 * intersect / (x_sum + y_sum)
-
-
-class AverageMeter(object):
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
-
-
-def distributed_all_gather(
- tensor_list, valid_batch_size=None, out_numpy=False, world_size=None, no_barrier=False, is_valid=None
-):
- if world_size is None:
- world_size = torch.distributed.get_world_size()
- if valid_batch_size is not None:
- valid_batch_size = min(valid_batch_size, world_size)
- elif is_valid is not None:
- is_valid = torch.tensor(bool(is_valid), dtype=torch.bool, device=tensor_list[0].device)
- if not no_barrier:
- torch.distributed.barrier()
- tensor_list_out = []
- with torch.no_grad():
- if is_valid is not None:
- is_valid_list = [torch.zeros_like(is_valid) for _ in range(world_size)]
- torch.distributed.all_gather(is_valid_list, is_valid)
- is_valid = [x.item() for x in is_valid_list]
- for tensor in tensor_list:
- gather_list = [torch.zeros_like(tensor) for _ in range(world_size)]
- torch.distributed.all_gather(gather_list, tensor)
- if valid_batch_size is not None:
- gather_list = gather_list[:valid_batch_size]
- elif is_valid is not None:
- gather_list = [g for g, v in zip(gather_list, is_valid_list) if v]
- if out_numpy:
- gather_list = [t.cpu().numpy() for t in gather_list]
- tensor_list_out.append(gather_list)
- return tensor_list_out
diff --git a/training/vista_2pt5d/__init__.py b/training/vista_2pt5d/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/training/vista_2pt5d/model.py b/training/vista_2pt5d/model.py
deleted file mode 100644
index 1df8097..0000000
--- a/training/vista_2pt5d/model.py
+++ /dev/null
@@ -1,441 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-from typing import Any, Dict, List, Tuple
-
-import monai
-import torch
-from segment_anything.modeling import TwoWayTransformer
-from segment_anything.modeling.mask_decoder import MaskDecoder
-from torch import nn
-from torch.nn import functional as F
-from vista_2pt5d.vista_image_encoder import VistaImageEncoderViT
-from vista_2pt5d.vista_prompt_encoder import VistaPromptEncoder
-
-
-class Vista2pt5D(nn.Module):
- mask_threshold: float = 0.5
- image_format: str = "RGB"
-
- def __init__(
- self,
- image_encoder: VistaImageEncoderViT,
- prompt_encoder: VistaPromptEncoder,
- mask_decoder: MaskDecoder,
- pixel_mean: List[float] = [123.675, 116.28, 103.53],
- pixel_std: List[float] = [58.395, 57.12, 57.375],
- ) -> None:
- """
- SAM predicts object masks from an image and input prompts.
-
- Arguments:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
- prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
- pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
- pixel_std (list(float)): Std values for normalizing pixels in the input image.
- """
- super().__init__()
- self.image_encoder = image_encoder
- self.prompt_encoder = prompt_encoder
- self.mask_decoder = mask_decoder
- self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
- @property
- def device(self) -> Any:
- return self.pixel_mean.device
-
- def get_image_embeddings(
- self,
- batched_input: List[Dict[str, Any]],
- ):
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
- return image_embeddings
-
- def get_mask_prediction(
- self, batched_input: List[Dict[str, Any]], image_embeddings, multimask_output: bool = False
- ):
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- # raise NotImplementedError
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- class_labels=image_record.get("labels", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
-
- high_res_masks = self.postprocess_masks(
- low_res_masks,
- # input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = high_res_masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- "high_res_logits": high_res_masks,
- }
- )
- return outputs
-
- def forward(
- self,
- batched_input: List[Dict[str, Any]],
- multimask_output: bool = False,
- is_train: bool = False,
- ) -> List[Dict[str, torch.Tensor]]:
- """
- Predicts masks end-to-end from provided images and prompts.
- If prompts are not known in advance, using SamPredictor is
- recommended over calling the model directly.
-
- Arguments:
- batched_input (list(dict)): A list over input images, each a
- dictionary with the following keys. A prompt key can be
- excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'labels': (torch.Tensor) Batched labels for class-label prompt,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
- multimask_output (bool): Whether the model should predict multiple
- disambiguating masks, or return a single mask.
-
- Returns:
- (list(dict)): A list over input images, where each element is
- as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
- """
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
-
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- # raise NotImplementedError
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- class_labels=image_record.get("labels", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
- if is_train:
- outputs.append(
- {
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- }
- )
- else:
- high_res_masks = self.postprocess_masks(
- low_res_masks,
- # input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = high_res_masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- "high_res_logits": high_res_masks,
- }
- )
- return outputs
-
- def postprocess_masks(
- self,
- masks: torch.Tensor,
- # input_size: Tuple[int, ...],
- original_size: Tuple[int, ...],
- ) -> torch.Tensor:
- """
- Remove padding and upscale masks to the original image size.
-
- Arguments:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
-
- Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
- """
- # make it high resolution
- masks = F.interpolate(
- masks,
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- )
- # resize it back to the longest dim (square image)
- masks = F.interpolate(masks, max(original_size), mode="bilinear", align_corners=False)
- # remove padding
- masks = masks[..., : original_size[0], : original_size[1]]
- return masks
-
- def preprocess(self, x: torch.Tensor, is_input=True) -> torch.Tensor:
- """Normalize pixel values and pad to a square input."""
- if is_input:
- if x.shape[0] == 1:
- # Normalize colors map the values in [0,1] to [0,255] for input images and then using
- # original pixel_mean and pixel_std to do normalization
- x = (x * 255.0 - self.pixel_mean) / self.pixel_std
- else:
- # for other 2.5d data, we normalize each input slice
- x = torch.cat(
- [(x[i].unsqueeze(0) * 255.0 - self.pixel_mean) / self.pixel_std for i in range(x.shape[0])], dim=0
- )
-
- # Pad image and make it a square image
- h, w = x.shape[-2:]
- # find the longest dim
- target_length = max(h, w)
- padh = target_length - h
- padw = target_length - w
- x = F.pad(x, (0, padw, 0, padh))
- if is_input:
- # Resize it to self.image_encoder.img_size
- x = F.interpolate(
- x.unsqueeze(0),
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- ).squeeze(0)
- else:
- # Resize it to self.image_encoder.img_size // 4 (for labels). the size is same as low-res logit
- x = F.interpolate(
- x.unsqueeze(0), (self.image_encoder.img_size // 4, self.image_encoder.img_size // 4), mode="nearest"
- ).squeeze(0)
- return x
-
-
-def _build_vista2pt5d(
- encoder_in_chans,
- encoder_embed_dim,
- encoder_depth,
- encoder_num_heads,
- encoder_global_attn_indexes,
- checkpoint=None,
- image_size=1024,
- clip_class_label_prompt=False,
- patch_embed_3d=False,
-):
- prompt_embed_dim = 256
- image_size = image_size # TODO: Shall we try to adapt model to 512x512 ?
- vit_patch_size = 16
- image_embedding_size = image_size // vit_patch_size
- sam = Vista2pt5D(
- image_encoder=VistaImageEncoderViT(
- in_chans=encoder_in_chans,
- depth=encoder_depth,
- embed_dim=encoder_embed_dim,
- img_size=image_size,
- mlp_ratio=4,
- norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
- num_heads=encoder_num_heads,
- patch_size=vit_patch_size,
- qkv_bias=True,
- use_rel_pos=True,
- global_attn_indexes=encoder_global_attn_indexes,
- window_size=14,
- out_chans=prompt_embed_dim,
- patch_embed_3d=patch_embed_3d,
- ),
- prompt_encoder=VistaPromptEncoder(
- embed_dim=prompt_embed_dim,
- image_embedding_size=(image_embedding_size, image_embedding_size),
- input_image_size=(image_size, image_size),
- mask_in_chans=16,
- clip_class_label_prompt=clip_class_label_prompt,
- ),
- mask_decoder=MaskDecoder(
- num_multimask_outputs=3, # TODO: only predict one binary mask
- transformer=TwoWayTransformer(
- depth=2,
- embedding_dim=prompt_embed_dim,
- mlp_dim=2048,
- num_heads=8,
- ),
- transformer_dim=prompt_embed_dim,
- iou_head_depth=3,
- iou_head_hidden_dim=256,
- ),
- pixel_mean=[123.675, 116.28, 103.53],
- pixel_std=[58.395, 57.12, 57.375],
- )
-
- if checkpoint is not None:
- with open(checkpoint, "rb") as f:
- state_dict = torch.load(f)
-
- if image_size == 1024:
- # we try to use all pretrained weights
- new_dict = state_dict
- else:
- new_dict = {}
- for k, v in state_dict.items():
- # skip weights in position embedding and learned relative positional embeddings
- # due to the change of input size
- if ("pos_embed" in k and k.startswith("image_encoder")) or (
- "attn.rel_pos" in k and k.startswith("image_encoder")
- ):
- continue
- else:
- new_dict[k] = v
-
- if encoder_in_chans != 3:
- new_dict.pop("image_encoder.patch_embed.proj.weight")
- new_dict.pop("image_encoder.patch_embed.proj.bias")
-
- sam.load_state_dict(new_dict, strict=False)
- print(f"Load {len(new_dict)} keys from checkpoint {checkpoint}, current model has {len(sam.state_dict())} keys")
-
- total_params = []
- image_encoder_params = []
- prompt_encoder_params = []
- mask_decoder_params = []
- for name, param in sam.named_parameters():
- n_param = param.numel()
- total_params.append(n_param)
- if name.startswith("image_encoder"):
- image_encoder_params.append(n_param)
- elif name.startswith("prompt_encoder"):
- prompt_encoder_params.append(n_param)
- elif name.startswith("mask_decoder"):
- mask_decoder_params.append(n_param)
-
- print(
- f"{sam.__class__.__name__} has {sum(total_params) * 1.e-6:.2f} M params, "
- f"{sum(image_encoder_params) * 1.e-6:.2f} M params in image encoder,"
- f"{sum(prompt_encoder_params) * 1.e-6:.2f} M params in prompt encoder,"
- f"{sum(mask_decoder_params) * 1.e-6:.2f} M params in mask decoder."
- )
-
- total_trainable_params = sum(p.numel() if p.requires_grad else 0 for p in sam.parameters())
- print(f"{sam.__class__.__name__} has {total_trainable_params * 1.e-6:.2f} M trainable params.")
- return sam
-
-
-def build_vista2pt5d_vit_h(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=1280,
- encoder_depth=32,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[7, 15, 23, 31],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-def build_vista2pt5d_vit_l(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=1024,
- encoder_depth=24,
- encoder_num_heads=16,
- encoder_global_attn_indexes=[5, 11, 17, 23],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-def build_vista2pt5d_vit_b(
- checkpoint=None, image_size=1024, encoder_in_chans=3, clip_class_label_prompt=False, patch_embed_3d=False
-):
- return _build_vista2pt5d(
- encoder_in_chans=encoder_in_chans,
- encoder_embed_dim=768,
- encoder_depth=12,
- encoder_num_heads=12,
- encoder_global_attn_indexes=[2, 5, 8, 11],
- checkpoint=checkpoint,
- image_size=image_size,
- clip_class_label_prompt=clip_class_label_prompt,
- patch_embed_3d=patch_embed_3d,
- )
-
-
-sam_model_registry = {
- "default": build_vista2pt5d_vit_h,
- "vit_h": build_vista2pt5d_vit_h,
- "vit_l": build_vista2pt5d_vit_l,
- "vit_b": build_vista2pt5d_vit_b,
-}
-
-if __name__ == "__main__":
- model = build_vista2pt5d_vit_b()
- model.cuda()
diff --git a/training/vista_2pt5d/vista_image_encoder.py b/training/vista_2pt5d/vista_image_encoder.py
deleted file mode 100644
index 4382425..0000000
--- a/training/vista_2pt5d/vista_image_encoder.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Tuple, Type
-
-import torch
-import torch.nn as nn
-from segment_anything.modeling.image_encoder import ImageEncoderViT, PatchEmbed
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class VistaImageEncoderViT(ImageEncoderViT):
- def __init__(
- self,
- img_size: int = 1024,
- patch_size: int = 16,
- in_chans: int = 3,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- out_chans: int = 256,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_abs_pos: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- global_attn_indexes: Tuple[int, ...] = (),
- patch_embed_3d: bool = False,
- ) -> None:
- """
- Args:
- img_size (int): Input image size.
- patch_size (int): Patch size.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- depth (int): Depth of ViT.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_abs_pos (bool): If True, use absolute positional embeddings.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks.
- global_attn_indexes (list): Indexes for blocks using global attention.
- patch_embed_3d (bool): If True, use 3D Patch Embedding.
- """
- super().__init__(
- img_size,
- patch_size,
- in_chans,
- embed_dim,
- depth,
- num_heads,
- mlp_ratio,
- out_chans,
- qkv_bias,
- norm_layer,
- act_layer,
- use_abs_pos,
- use_rel_pos,
- rel_pos_zero_init,
- window_size,
- global_attn_indexes,
- )
-
- self.img_size = img_size
-
- if in_chans > 3 and patch_embed_3d:
- print("ImageEncoderViT: Using 3D PatchEmbed")
- self.patch_embed = PatchEmbed2pt5D(
- kernel_size=(patch_size, patch_size, in_chans // 3),
- stride=(patch_size, patch_size, in_chans // 3),
- in_chans=3,
- embed_dim=embed_dim,
- )
- else:
- self.patch_embed = PatchEmbed(
- kernel_size=(patch_size, patch_size),
- stride=(patch_size, patch_size),
- in_chans=in_chans,
- embed_dim=embed_dim,
- )
-
-
-class PatchEmbed2pt5D(nn.Module):
- """
- Image to Patch Embedding by 3D Conv.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int, int] = (16, 16, 1),
- stride: Tuple[int, int, int] = (16, 16, 1),
- padding: Tuple[int, int, int] = (0, 0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- # got restore RGB channel dim and the depth dim
- c = x.shape[1]
- x = torch.stack(x.chunk(c // 3, dim=1), dim=-1)
- x = self.proj(x)
- # remove dummy depth dim to make it 2d
- x = x.squeeze(-1)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/training/vista_2pt5d/vista_prompt_encoder.py b/training/vista_2pt5d/vista_prompt_encoder.py
deleted file mode 100644
index 42568f8..0000000
--- a/training/vista_2pt5d/vista_prompt_encoder.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Any, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from segment_anything.modeling.common import LayerNorm2d
-from segment_anything.modeling.prompt_encoder import PromptEncoder
-from torch import nn
-
-
-class VistaPromptEncoder(PromptEncoder):
- def __init__(
- self,
- embed_dim: int,
- image_embedding_size: Tuple[int, int],
- input_image_size: Tuple[int, int],
- mask_in_chans: int,
- activation: Type[nn.Module] = nn.GELU,
- n_classes: int = 512,
- clip_class_label_prompt: bool = False,
- ) -> None:
- """
- Encodes prompts for input to SAM's mask decoder.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- n_classes (int): The number of pre-defined classes.
- clip_class_label_prompt (bool): Using clip txt features
- as class label prompt.
- """
- super().__init__(embed_dim, image_embedding_size, input_image_size, mask_in_chans, activation)
-
- self.clip_class_label_prompt = clip_class_label_prompt
- # Add support for onehot vector embedding for pre-defined classes
- if self.clip_class_label_prompt:
- raise NotImplementedError
- else:
- self.label_embeddings = nn.Embedding(n_classes, embed_dim)
- self.no_label_embed = nn.Embedding(1, embed_dim)
-
- def _embed_labels(self, labels: torch.Tensor) -> torch.Tensor:
- """Embeds onehot vector inputs."""
- if self.clip_class_label_prompt:
- raise NotImplementedError
- else:
- # Add support for onehot vector embedding for pre-defined classes
- label_embedding = self.label_embeddings(labels)
- return label_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- labels: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- elif labels is not None:
- return labels.shape[0]
- else:
- return 1
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- class_labels: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
- class_labels (torch.Tensor or none): labels to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks, class_labels)
-
- # Add support for onehot vector embedding for pre-defined classes
- if class_labels is not None:
- label_embeddings = self._embed_labels(class_labels)
- else:
- label_embeddings = self.no_label_embed.weight.reshape(1, 1, -1).expand(bs, -1, -1)
-
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
-
- # Add support for onehot vector embedding for pre-defined classes
- sparse_embeddings = torch.cat([sparse_embeddings, label_embeddings], dim=1)
-
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
diff --git a/LICENSE b/vista3d/LICENSE
similarity index 100%
rename from LICENSE
rename to vista3d/LICENSE
diff --git a/vista3d/README.md b/vista3d/README.md
new file mode 100644
index 0000000..1ac5bc9
--- /dev/null
+++ b/vista3d/README.md
@@ -0,0 +1,146 @@
+
+
+# MONAI **V**ersatile **I**maging **S**egmen**T**ation and **A**nnotation
+[[`Paper`](https://arxiv.org/pdf/2406.05285)] [[`Demo`](https://build.nvidia.com/nvidia/vista-3d)] [[`Container`](https://docs.nvidia.com/ai-enterprise/nim-medical-imaging/latest/vista-3d.html)]
+## Overview
+
+The **VISTA3D** is a foundation model trained systematically on 11,454 volumes encompassing 127 types of human anatomical structures and various lesions. It provides accurate out-of-the-box segmentation that matches state-of-the-art supervised models which are trained on each dataset. The model also achieves state-of-the-art zero-shot interactive segmentation in 3D, representing a promising step toward developing a versatile medical image foundation model.
+
+ 
+ 
+ 
+ 