SAM 2 Update 12/11/2024 -- full model compilation for a major VOS speedup and a new SAM2VideoPredictor to better handle multi-object tracking

.gitignore

@@ -8,3 +8,4 @@ build/*
 _C.*
 outputs/*
 checkpoints/*.pt
+demo/backend/checkpoints/*.pt

INSTALL.md

@@ -2,7 +2,7 @@
 
 ### Requirements
 
-- Linux with Python ≥ 3.10, PyTorch ≥ 2.3.1 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. Install them together at https://pytorch.org to ensure this.
+- Linux with Python ≥ 3.10, PyTorch ≥ 2.5.1 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. Install them together at https://pytorch.org to ensure this.
   * Note older versions of Python or PyTorch may also work. However, the versions above are strongly recommended to provide all features such as `torch.compile`.
 - [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) that match the CUDA version for your PyTorch installation. This should typically be CUDA 12.1 if you follow the default installation command.
 - If you are installing on Windows, it's strongly recommended to use [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) with Ubuntu.
@@ -121,9 +121,9 @@ I got `undefined symbol: _ZN3c1015SmallVectorBaseIjE8grow_podEPKvmm` (or similar
 
 This usually happens because you have multiple versions of dependencies (PyTorch or CUDA) in your environment. During installation, the SAM 2 library is compiled against one version library while at run time it links against another version. This might be due to that you have different versions of PyTorch or CUDA installed separately via `pip` or `conda`. You may delete one of the duplicates to only keep a single PyTorch and CUDA version.
 
-In particular, if you have a lower PyTorch version than 2.3.1, it's recommended to upgrade to PyTorch 2.3.1 or higher first. Otherwise, the installation script will try to upgrade to the latest PyTorch using `pip`, which could sometimes lead to duplicated PyTorch installation if you have previously installed another PyTorch version using `conda`.
+In particular, if you have a lower PyTorch version than 2.5.1, it's recommended to upgrade to PyTorch 2.5.1 or higher first. Otherwise, the installation script will try to upgrade to the latest PyTorch using `pip`, which could sometimes lead to duplicated PyTorch installation if you have previously installed another PyTorch version using `conda`.
 
-We have been building SAM 2 against PyTorch 2.3.1 internally. However, a few user comments (e.g. https://github.com/facebookresearch/sam2/issues/22, https://github.com/facebookresearch/sam2/issues/14) suggested that downgrading to PyTorch 2.1.0 might resolve this problem. In case the error persists, you may try changing the restriction from `torch>=2.3.1` to `torch>=2.1.0` in both [`pyproject.toml`](pyproject.toml) and [`setup.py`](setup.py) to allow PyTorch 2.1.0.
+We have been building SAM 2 against PyTorch 2.5.1 internally. However, a few user comments (e.g. https://github.com/facebookresearch/sam2/issues/22, https://github.com/facebookresearch/sam2/issues/14) suggested that downgrading to PyTorch 2.1.0 might resolve this problem. In case the error persists, you may try changing the restriction from `torch>=2.5.1` to `torch==2.1.0` in both [`pyproject.toml`](pyproject.toml) and [`setup.py`](setup.py) to allow PyTorch 2.1.0.
 </details>
 
 <details>

README.md

@@ -14,6 +14,12 @@
 
 ## Latest updates
 
+**12/11/2024 -- full model compilation for a major VOS speedup and a new `SAM2VideoPredictor` to better handle multi-object tracking**
+
+- We now support `torch.compile` of the entire SAM 2 model on videos, which can be turned on by setting `vos_optimized=True` in `build_sam2_video_predictor`, leading to a major speedup for VOS inference.
+- We update the implementation of `SAM2VideoPredictor` to support independent per-object inference, allowing us to relax the assumption of prompting for multi-object tracking and adding new objects after tracking starts.
+- See [`RELEASE_NOTES.md`](RELEASE_NOTES.md) for full details.
+
 **09/30/2024 -- SAM 2.1 Developer Suite (new checkpoints, training code, web demo) is released**
 
 - A new suite of improved model checkpoints (denoted as **SAM 2.1**) are released. See [Model Description](#model-description) for details.
@@ -23,7 +29,7 @@
 
 ## Installation
 
-SAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:
+SAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.5.1` and `torchvision>=0.20.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:
 
 ```bash
 git clone https://github.com/facebookresearch/sam2.git && cd sam2
@@ -39,7 +45,7 @@ pip install -e ".[notebooks]"
 ```
 
 Note:
-1. It's recommended to create a new Python environment via [Anaconda](https://www.anaconda.com/) for this installation and install PyTorch 2.3.1 (or higher) via `pip` following https://pytorch.org/. If you have a PyTorch version lower than 2.3.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using `pip`.
+1. It's recommended to create a new Python environment via [Anaconda](https://www.anaconda.com/) for this installation and install PyTorch 2.5.1 (or higher) via `pip` following https://pytorch.org/. If you have a PyTorch version lower than 2.5.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using `pip`.
 2. The step above requires compiling a custom CUDA kernel with the `nvcc` compiler. If it isn't already available on your machine, please install the [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) with a version that matches your PyTorch CUDA version.
 3. If you see a message like `Failed to build the SAM 2 CUDA extension` during installation, you can ignore it and still use SAM 2 (some post-processing functionality may be limited, but it doesn't affect the results in most cases).
 
@@ -158,24 +164,23 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
 The table below shows the improved SAM 2.1 checkpoints released on September 29, 2024.
 |      **Model**       | **Size (M)** |    **Speed (FPS)**     | **SA-V test (J&F)** | **MOSE val (J&F)** | **LVOS v2 (J&F)** |
 | :------------------: | :----------: | :--------------------: | :-----------------: | :----------------: | :---------------: |
-|   sam2.1_hiera_tiny <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_t.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt))    |     38.9     |          47.2          |        76.5         |        71.8        |       77.3        |
-|   sam2.1_hiera_small <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_s.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt))   |      46      | 43.3 (53.0 compiled\*) |        76.6         |        73.5        |       78.3        |
-| sam2.1_hiera_base_plus <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_b+.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt)) |     80.8     | 34.8 (43.8 compiled\*) |        78.2         |        73.7        |       78.2        |
-|   sam2.1_hiera_large <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_l.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt))   |    224.4     | 24.2 (30.2 compiled\*) |        79.5         |        74.6        |       80.6        |
+|   sam2.1_hiera_tiny <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_t.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt))    |     38.9     |          91.2          |        76.5         |        71.8        |       77.3        |
+|   sam2.1_hiera_small <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_s.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt))   |      46      |          84.8          |        76.6         |        73.5        |       78.3        |
+| sam2.1_hiera_base_plus <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_b+.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt)) |     80.8     |        64.1          |        78.2         |        73.7        |       78.2        |
+|   sam2.1_hiera_large <br /> ([config](sam2/configs/sam2.1/sam2.1_hiera_l.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt))   |    224.4     |          39.5          |        79.5         |        74.6        |       80.6        |
 
 ### SAM 2 checkpoints
 
 The previous SAM 2 checkpoints released on July 29, 2024 can be found as follows:
 
 |      **Model**       | **Size (M)** |    **Speed (FPS)**     | **SA-V test (J&F)** | **MOSE val (J&F)** | **LVOS v2 (J&F)** |
 | :------------------: | :----------: | :--------------------: | :-----------------: | :----------------: | :---------------: |
-|   sam2_hiera_tiny <br /> ([config](sam2/configs/sam2/sam2_hiera_t.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt))   |     38.9     |          47.2          |        75.0         |        70.9        |       75.3        |
-|   sam2_hiera_small <br /> ([config](sam2/configs/sam2/sam2_hiera_s.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt))   |      46      | 43.3 (53.0 compiled\*) |        74.9         |        71.5        |       76.4        |
-| sam2_hiera_base_plus <br /> ([config](sam2/configs/sam2/sam2_hiera_b+.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_base_plus.pt)) |     80.8     | 34.8 (43.8 compiled\*) |        74.7         |        72.8        |       75.8        |
-|   sam2_hiera_large <br /> ([config](sam2/configs/sam2/sam2_hiera_l.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt))   |    224.4     | 24.2 (30.2 compiled\*) |        76.0         |        74.6        |       79.8        |
-
-\* Compile the model by setting `compile_image_encoder: True` in the config.
+|   sam2_hiera_tiny <br /> ([config](sam2/configs/sam2/sam2_hiera_t.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt))   |     38.9     |          91.5          |        75.0         |        70.9        |       75.3        |
+|   sam2_hiera_small <br /> ([config](sam2/configs/sam2/sam2_hiera_s.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt))   |      46      |          85.6          |        74.9         |        71.5        |       76.4        |
+| sam2_hiera_base_plus <br /> ([config](sam2/configs/sam2/sam2_hiera_b+.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_base_plus.pt)) |     80.8     |     64.8    |        74.7         |        72.8        |       75.8        |
+|   sam2_hiera_large <br /> ([config](sam2/configs/sam2/sam2_hiera_l.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt))   |    224.4     | 39.7 |        76.0         |        74.6        |       79.8        |
 
+Speed measured on an A100 with `torch 2.5.1, cuda 12.4`. See `benchmark.py` for an example on benchmarking (compiling all the model components). Compiling only the image encoder can be more flexible and also provide (a smaller) speed-up (set `compile_image_encoder: True` in the config).
 ## Segment Anything Video Dataset
 
 See [sav_dataset/README.md](sav_dataset/README.md) for details.

RELEASE_NOTES.md

@@ -0,0 +1,27 @@
+## SAM 2 release notes
+
+### 12/11/2024 -- full model compilation for a major VOS speedup and a new `SAM2VideoPredictor` to better handle multi-object tracking
+
+- We now support `torch.compile` of the entire SAM 2 model on videos, which can be turned on by setting `vos_optimized=True` in `build_sam2_video_predictor` (it uses the new `SAM2VideoPredictorVOS` predictor class in `sam2/sam2_video_predictor.py`).
+  * Compared to the previous setting (which only compiles the image encoder backbone), the new full model compilation gives a major speedup in inference FPS.
+  * In the VOS prediction script `tools/vos_inference.py`, you can specify this option in `tools/vos_inference.py` via the `--use_vos_optimized_video_predictor` flag.
+  * Note that turning on this flag might introduce a small variance in the predictions due to numerical differences caused by `torch.compile` of the full model.
+  * **PyTorch 2.5.1 is the minimum version for full support of this feature**. (Earlier PyTorch versions might run into compilation errors in some cases.) Therefore, we have updated the minimum PyTorch version to 2.5.1 accordingly in the installation scripts.
+- We also update the implementation of the `SAM2VideoPredictor` class for the SAM 2 video prediction in `sam2/sam2_video_predictor.py`, which allows for independent per-object inference. Specifically, in the new `SAM2VideoPredictor`:
+  * Now **we handle the inference of each object independently** (as if we are opening a separate session for each object) while sharing their backbone features.
+  * This change allows us to relax the assumption of prompting for multi-object tracking. Previously (due to the batching behavior in inference), if a video frame receives clicks for only a subset of objects, the rest of the (non-prompted) objects are assumed to be non-existent in this frame (i.e., in such frames, the user is telling SAM 2 that the rest of the objects don't appear). Now, if a frame receives clicks for only a subset of objects, we do not make any assumptions about the remaining (non-prompted) objects (i.e., now each object is handled independently and is not affected by how other objects are prompted). As a result, **we allow adding new objects after tracking starts** after this change (which was previously a restriction on usage).
+  * We believe that the new version is a more natural inference behavior and therefore switched to it as the default behavior. The previous implementation of `SAM2VideoPredictor` is backed up to in `sam2/sam2_video_predictor_legacy.py`. All the VOS inference results using `tools/vos_inference.py` should remain the same after this change to the `SAM2VideoPredictor` class.
+
+### 09/30/2024 -- SAM 2.1 Developer Suite (new checkpoints, training code, web demo) is released
+
+- A new suite of improved model checkpoints (denoted as **SAM 2.1**) are released. See [Model Description](#model-description) for details.
+  * To use the new SAM 2.1 checkpoints, you need the latest model code from this repo. If you have installed an earlier version of this repo, please first uninstall the previous version via `pip uninstall SAM-2`, pull the latest code from this repo (with `git pull`), and then reinstall the repo following [Installation](#installation) below.
+- The training (and fine-tuning) code has been released. See [`training/README.md`](training/README.md) on how to get started.
+- The frontend + backend code for the SAM 2 web demo has been released. See [`demo/README.md`](demo/README.md) for details.
+
+### 07/29/2024 -- SAM 2 is released
+
+- We release Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos.
+  * SAM 2 code: https://github.com/facebookresearch/sam2
+  * SAM 2 demo: https://sam2.metademolab.com/
+  * SAM 2 paper: https://arxiv.org/abs/2408.00714

backend.Dockerfile

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime
+ARG BASE_IMAGE=pytorch/pytorch:2.5.1-cuda12.1-cudnn9-runtime
 ARG MODEL_SIZE=base_plus
 
 FROM ${BASE_IMAGE}

demo/README.md

@@ -105,7 +105,7 @@ cd demo/backend/server/
 ```bash
 PYTORCH_ENABLE_MPS_FALLBACK=1 \
 APP_ROOT="$(pwd)/../../../" \
-APP_URL=http://localhost:7263 \
+API_URL=http://localhost:7263 \
 MODEL_SIZE=base_plus \
 DATA_PATH="$(pwd)/../../data" \
 DEFAULT_VIDEO_PATH=gallery/05_default_juggle.mp4 \

pyproject.toml

@@ -1,6 +1,6 @@
 [build-system]
 requires = [
     "setuptools>=61.0",  
-    "torch>=2.3.1",
+    "torch>=2.5.1",
     ]
 build-backend = "setuptools.build_meta"

sam2/benchmark.py

@@ -0,0 +1,92 @@
+# 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 os
+import time
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from sam2.build_sam import build_sam2_video_predictor
+
+# Only cuda supported
+assert torch.cuda.is_available()
+device = torch.device("cuda")
+
+torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+if torch.cuda.get_device_properties(0).major >= 8:
+    # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+# Config and checkpoint
+sam2_checkpoint = "checkpoints/sam2.1_hiera_base_plus.pt"
+model_cfg = "configs/sam2.1/sam2.1_hiera_b+.yaml"
+
+# Build video predictor with vos_optimized=True setting
+predictor = build_sam2_video_predictor(
+    model_cfg, sam2_checkpoint, device=device, vos_optimized=True
+)
+
+
+# Initialize with video
+video_dir = "notebooks/videos/bedroom"
+# scan all the JPEG frame names in this directory
+frame_names = [
+    p
+    for p in os.listdir(video_dir)
+    if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
+]
+frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
+inference_state = predictor.init_state(video_path=video_dir)
+
+
+# Number of runs, warmup etc
+warm_up, runs = 5, 25
+verbose = True
+num_frames = len(frame_names)
+total, count = 0, 0
+torch.cuda.empty_cache()
+
+# We will select an object with a click.
+# See video_predictor_example.ipynb for more detailed explanation
+ann_frame_idx, ann_obj_id = 0, 1
+# Add a positive click at (x, y) = (210, 350)
+# For labels, `1` means positive click
+points = np.array([[210, 350]], dtype=np.float32)
+labels = np.array([1], np.int32)
+
+_, out_obj_ids, out_mask_logits = predictor.add_new_points_or_box(
+    inference_state=inference_state,
+    frame_idx=ann_frame_idx,
+    obj_id=ann_obj_id,
+    points=points,
+    labels=labels,
+)
+
+# Warmup and then average FPS over several runs
+with torch.autocast("cuda", torch.bfloat16):
+    with torch.inference_mode():
+        for i in tqdm(range(runs), disable=not verbose, desc="Benchmarking"):
+            start = time.time()
+            # Start tracking
+            for (
+                out_frame_idx,
+                out_obj_ids,
+                out_mask_logits,
+            ) in predictor.propagate_in_video(inference_state):
+                pass
+
+            end = time.time()
+            total += end - start
+            count += 1
+            if i == warm_up - 1:
+                print("Warmup FPS: ", count * num_frames / total)
+                total = 0
+                count = 0
+
+print("FPS: ", count * num_frames / total)

sam2/build_sam.py

@@ -104,11 +104,18 @@ def build_sam2_video_predictor(
     mode="eval",
     hydra_overrides_extra=[],
     apply_postprocessing=True,
+    vos_optimized=False,
     **kwargs,
 ):
     hydra_overrides = [
         "++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictor",
     ]
+    if vos_optimized:
+        hydra_overrides = [
+            "++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictorVOS",
+            "++model.compile_image_encoder=True",  # Let sam2_base handle this
+        ]
+
     if apply_postprocessing:
         hydra_overrides_extra = hydra_overrides_extra.copy()
         hydra_overrides_extra += [