From a003f29dd0086e5ec53c15b2841979690bb807a7 Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Mon, 16 Dec 2024 13:24:11 -0800 Subject: [PATCH 1/8] Fix input scaling in centered-instance model --- docs/conf.py | 2 +- sleap/nn/data/pipelines.py | 1 + sleap/nn/inference.py | 41 ++++++++++++++++++++++++++++++++------ 3 files changed, 37 insertions(+), 7 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index a429c7928..796497f6b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -178,7 +178,7 @@ def linkcode_resolve(domain, info): # These paths are either relative to html_static_path # or fully qualified paths (eg. https://...) html_css_files = [ - 'css/tabs.css', + "css/tabs.css", ] # Custom sidebar templates, must be a dictionary that maps document names diff --git a/sleap/nn/data/pipelines.py b/sleap/nn/data/pipelines.py index 2e334456a..60f7130f5 100644 --- a/sleap/nn/data/pipelines.py +++ b/sleap/nn/data/pipelines.py @@ -775,6 +775,7 @@ def make_viz_pipeline(self, data_provider: Provider) -> Pipeline: provider=data_provider, ) pipeline += Normalizer.from_config(self.data_config.preprocessing) + pipeline += Resizer.from_config(self.data_config.preprocessing) pipeline += InstanceCentroidFinder.from_config( self.data_config.instance_cropping, skeletons=self.data_config.labels.skeletons, diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index 14e0d5c6f..d04131c8a 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -727,11 +727,18 @@ class CentroidCropGroundTruth(tf.keras.layers.Layer): Attributes: crop_size: The length of the square box to extract around each centroid. + input_scale: Float indicating if the images should be resized before being + passed to the model. """ - def __init__(self, crop_size: int): + def __init__( + self, + crop_size: int, + input_scale: float = 1.0, + ): super().__init__() self.crop_size = crop_size + self.input_scale = input_scale def call(self, example_gt: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]: """Return the ground truth instance crops. @@ -758,6 +765,8 @@ def call(self, example_gt: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]: """ # Pull out data from example. full_imgs = example_gt["image"] + if self.input_scale != 1.0: + full_imgs = sleap.nn.data.resizing.resize_image(full_imgs, self.input_scale) crop_sample_inds = example_gt["centroids"].value_rowids() # (n_peaks,) n_peaks = tf.shape(crop_sample_inds)[0] # total number of peaks in the batch centroid_points = example_gt["centroids"].flat_values # (n_peaks, 2) @@ -927,11 +936,12 @@ def __init__( self.ensure_grayscale = ensure_grayscale self.ensure_float = ensure_float - def preprocess(self, imgs: tf.Tensor) -> tf.Tensor: + def preprocess(self, imgs: tf.Tensor, resize_img: bool = True) -> tf.Tensor: """Apply all preprocessing operations configured for this layer. Args: imgs: A batch of images as a tensor. + resize_img: Bool to indicate if the images should be resized. Returns: The input tensor after applying preprocessing operations. The tensor will @@ -947,7 +957,7 @@ def preprocess(self, imgs: tf.Tensor) -> tf.Tensor: if self.ensure_float: imgs = sleap.nn.data.normalization.ensure_float(imgs) - if self.input_scale != 1.0: + if resize_img and self.input_scale != 1.0: imgs = sleap.nn.data.resizing.resize_image(imgs, self.input_scale) if self.pad_to_stride > 1: @@ -1954,6 +1964,11 @@ class FindInstancePeaks(InferenceLayer): centered instance confidence maps. input_scale: Float indicating if the images should be resized before being passed to the model. + resize_input_image: Bool indicating if the crops should be resized. If + `CentroidCropGroundTruth` is used along with `FindInstancePeaks`, then the + images are resized in the `CentroidCropGroundTruth` and this is set to `False`. + However, the output keypoints are adjusted to the actual scale with the + `input_scaling` argument. output_stride: Output stride of the model, denoting the scale of the output confidence maps relative to the images (after input scaling). This is used for adjusting the peak coordinates to the image grid. This will be inferred @@ -1984,6 +1999,7 @@ def __init__( self, keras_model: tf.keras.Model, input_scale: float = 1.0, + resize_input_image: bool = True, output_stride: Optional[int] = None, peak_threshold: float = 0.2, refinement: Optional[str] = "local", @@ -1996,6 +2012,7 @@ def __init__( super().__init__( keras_model=keras_model, input_scale=input_scale, pad_to_stride=1, **kwargs ) + self.resize_input_image = resize_input_image self.peak_threshold = peak_threshold self.refinement = refinement self.integral_patch_size = integral_patch_size @@ -2093,7 +2110,7 @@ def call( crop_sample_inds = tf.range(samples, dtype=tf.int32) # Preprocess inputs (scaling, padding, colorspace, int to float). - crops = self.preprocess(crops) + crops = self.preprocess(crops, resize_img=self.resize_input_image) # Network forward pass. out = self.keras_model(crops) @@ -2343,7 +2360,7 @@ def _initialize_inference_model(self): if use_gt_centroid: centroid_crop_layer = CentroidCropGroundTruth( - crop_size=self.confmap_config.data.instance_cropping.crop_size + crop_size=self.confmap_config.data.instance_cropping.crop_size, ) else: if use_gt_confmap: @@ -2375,7 +2392,10 @@ def _initialize_inference_model(self): refinement="integral" if self.integral_refinement else "local", integral_patch_size=self.integral_patch_size, return_confmaps=False, + resize_input_image=False, ) + if use_gt_centroid: + centroid_crop_layer.input_scale = cfg.data.preprocessing.input_scaling self.inference_model = TopDownInferenceModel( centroid_crop=centroid_crop_layer, instance_peaks=instance_peaks_layer @@ -3831,6 +3851,11 @@ class TopDownMultiClassFindPeaks(InferenceLayer): centered instance confidence maps and classification. input_scale: Float indicating if the images should be resized before being passed to the model. + resize_input_image: Bool indicating if the crops should be resized. If + `CentroidCropGroundTruth` is used along with `FindInstancePeaks`, then the + images are resized in the `CentroidCropGroundTruth` and this is set to `False`. + However, the output keypoints are adjusted to the actual scale with the + `input_scaling` argument. output_stride: Output stride of the model, denoting the scale of the output confidence maps relative to the images (after input scaling). This is used for adjusting the peak coordinates to the image grid. This will be inferred @@ -3872,6 +3897,7 @@ def __init__( self, keras_model: tf.keras.Model, input_scale: float = 1.0, + resize_input_image: bool = True, output_stride: Optional[int] = None, peak_threshold: float = 0.2, refinement: Optional[str] = "local", @@ -3887,6 +3913,7 @@ def __init__( super().__init__( keras_model=keras_model, input_scale=input_scale, pad_to_stride=1, **kwargs ) + self.resize_input_image = resize_input_image self.peak_threshold = peak_threshold self.refinement = refinement self.integral_patch_size = integral_patch_size @@ -4004,7 +4031,7 @@ def call( crop_sample_inds = tf.range(samples, dtype=tf.int32) # Preprocess inputs (scaling, padding, colorspace, int to float). - crops = self.preprocess(crops) + crops = self.preprocess(crops, resize_img=self.resize_input_image) # Network forward pass. out = self.keras_model(crops) @@ -4254,6 +4281,8 @@ def _initialize_inference_model(self): integral_patch_size=self.integral_patch_size, return_confmaps=False, ) + if use_gt_centroid: + centroid_crop_layer.input_scale = cfg.data.preprocessing.input_scaling self.inference_model = TopDownMultiClassInferenceModel( centroid_crop=centroid_crop_layer, instance_peaks=instance_peaks_layer From 058a86150a525bac9638b935c6cafa054ee69610 Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Mon, 16 Dec 2024 14:03:08 -0800 Subject: [PATCH 2/8] Fix topdown multi-class model --- sleap/nn/data/pipelines.py | 1 + sleap/nn/inference.py | 1 + sleap/nn/training.py | 2 ++ 3 files changed, 4 insertions(+) diff --git a/sleap/nn/data/pipelines.py b/sleap/nn/data/pipelines.py index 60f7130f5..d15c4491f 100644 --- a/sleap/nn/data/pipelines.py +++ b/sleap/nn/data/pipelines.py @@ -1251,6 +1251,7 @@ def make_viz_pipeline(self, data_provider: Provider) -> Pipeline: provider=data_provider, ) pipeline += Normalizer.from_config(self.data_config.preprocessing) + pipeline += Resizer.from_config(self.data_config.preprocessing) pipeline += InstanceCentroidFinder.from_config( self.data_config.instance_cropping, skeletons=self.data_config.labels.skeletons, diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index d04131c8a..426c28b36 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -4280,6 +4280,7 @@ def _initialize_inference_model(self): refinement="integral" if self.integral_refinement else "local", integral_patch_size=self.integral_patch_size, return_confmaps=False, + resize_input_image=False, ) if use_gt_centroid: centroid_crop_layer.input_scale = cfg.data.preprocessing.input_scaling diff --git a/sleap/nn/training.py b/sleap/nn/training.py index c3692637c..f56d8cf46 100644 --- a/sleap/nn/training.py +++ b/sleap/nn/training.py @@ -1319,6 +1319,7 @@ def _setup_visualization(self): peak_threshold=0.2, refinement="local", return_confmaps=True, + resize_input_image=False, ) def visualize_example(example): @@ -1759,6 +1760,7 @@ def _setup_visualization(self): peak_threshold=0.2, refinement="local", return_confmaps=True, + resize_input_image=False, ) def visualize_example(example): From ad03905d5310fbaec7a9f4f9c4fa57ba21219493 Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Mon, 16 Dec 2024 14:12:51 -0800 Subject: [PATCH 3/8] Adjust centroids --- sleap/nn/inference.py | 1 + 1 file changed, 1 insertion(+) diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index 426c28b36..5b65c825d 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -767,6 +767,7 @@ def call(self, example_gt: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]: full_imgs = example_gt["image"] if self.input_scale != 1.0: full_imgs = sleap.nn.data.resizing.resize_image(full_imgs, self.input_scale) + example_gt["centroids"] *= self.input_scale crop_sample_inds = example_gt["centroids"].value_rowids() # (n_peaks,) n_peaks = tf.shape(crop_sample_inds)[0] # total number of peaks in the batch centroid_points = example_gt["centroids"].flat_values # (n_peaks, 2) From d47c98a0c1e22dcd72fd6daeaf1662df922f4bd6 Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Tue, 17 Dec 2024 15:49:03 -0800 Subject: [PATCH 4/8] Fix scaling in centroidcrop --- sleap/nn/inference.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index 5b65c825d..4f00419a5 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -1647,6 +1647,9 @@ class CentroidCrop(InferenceLayer): crop_size: Integer scalar specifying the height/width of the centered crops. input_scale: Float indicating if the images should be resized before being passed to the model. + precrop_resize: Float indicating the factor by which the original images + (not images resized for centroid model) should be resized before cropping. + Note: this resize only after getting the predictions for centroid model. pad_to_stride: If not 1, input image will be paded to ensure that it is divisible by this value (after scaling). This should be set to the max stride of the model. @@ -1687,6 +1690,7 @@ def __init__( keras_model: tf.keras.Model, crop_size: int, input_scale: float = 1.0, + precrop_resize: Optional[float] = 1.0, pad_to_stride: int = 1, output_stride: Optional[int] = None, peak_threshold: float = 0.2, @@ -1707,6 +1711,7 @@ def __init__( ) self.crop_size = crop_size + self.precrop_resize = precrop_resize self.confmaps_ind = confmaps_ind self.offsets_ind = offsets_ind @@ -1901,6 +1906,13 @@ def call(self, inputs): crop_offsets = centroid_points - (self.crop_size / 2) + # resize full images + if self.precrop_resize: + full_imgs = sleap.nn.data.resizing.resize_image( + full_imgs, self.precrop_resize + ) + centroid_points *= self.precrop_resize + # Crop instances around centroids. bboxes = sleap.nn.data.instance_cropping.make_centered_bboxes( centroid_points, self.crop_size, self.crop_size @@ -2372,6 +2384,7 @@ def _initialize_inference_model(self): keras_model=self.centroid_model.keras_model, crop_size=crop_size, input_scale=self.centroid_config.data.preprocessing.input_scaling, + precrop_resize=None, pad_to_stride=self.centroid_config.data.preprocessing.pad_to_stride, output_stride=self.centroid_config.model.heads.centroid.output_stride, peak_threshold=self.peak_threshold, @@ -2397,6 +2410,10 @@ def _initialize_inference_model(self): ) if use_gt_centroid: centroid_crop_layer.input_scale = cfg.data.preprocessing.input_scaling + else: + centroid_crop_layer.precrop_resize = ( + cfg.data.preprocessing.input_scaling + ) self.inference_model = TopDownInferenceModel( centroid_crop=centroid_crop_layer, instance_peaks=instance_peaks_layer From 28ccb70747b040a3b7a302f02b4d97f00a3b5802 Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Wed, 18 Dec 2024 11:20:48 -0800 Subject: [PATCH 5/8] Fix scaling for visualizer --- sleap/nn/inference.py | 30 ++++++++++++++++-------------- sleap/nn/training.py | 4 ++-- 2 files changed, 18 insertions(+), 16 deletions(-) diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index 4f00419a5..df061a289 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -1690,7 +1690,7 @@ def __init__( keras_model: tf.keras.Model, crop_size: int, input_scale: float = 1.0, - precrop_resize: Optional[float] = 1.0, + precrop_resize: float = 1.0, pad_to_stride: int = 1, output_stride: Optional[int] = None, peak_threshold: float = 0.2, @@ -1830,6 +1830,13 @@ def call(self, inputs): # See: https://github.com/tensorflow/tensorflow/issues/6720 centroid_points = (centroid_points / self.input_scale) + 0.5 + # resize full images + if self.precrop_resize != 1.0: + full_imgs = sleap.nn.data.resizing.resize_image( + full_imgs, self.precrop_resize + ) + centroid_points *= self.precrop_resize + # Store crop offsets. crop_offsets = centroid_points - (self.crop_size / 2) @@ -1906,13 +1913,6 @@ def call(self, inputs): crop_offsets = centroid_points - (self.crop_size / 2) - # resize full images - if self.precrop_resize: - full_imgs = sleap.nn.data.resizing.resize_image( - full_imgs, self.precrop_resize - ) - centroid_points *= self.precrop_resize - # Crop instances around centroids. bboxes = sleap.nn.data.instance_cropping.make_centered_bboxes( centroid_points, self.crop_size, self.crop_size @@ -1978,10 +1978,10 @@ class FindInstancePeaks(InferenceLayer): input_scale: Float indicating if the images should be resized before being passed to the model. resize_input_image: Bool indicating if the crops should be resized. If - `CentroidCropGroundTruth` is used along with `FindInstancePeaks`, then the - images are resized in the `CentroidCropGroundTruth` and this is set to `False`. - However, the output keypoints are adjusted to the actual scale with the - `input_scaling` argument. + `CentroidCropGroundTruth` or `CentroidCrop` is used along with `FindInstancePeaks`, + then the images are resized in the `CentroidCropGroundTruth` or `CentroidCrop` + before cropping and this is set to `False`. However, the output keypoints + are adjusted to the actual scale with the `input_scaling` argument. output_stride: Output stride of the model, denoting the scale of the output confidence maps relative to the images (after input scaling). This is used for adjusting the peak coordinates to the image grid. This will be inferred @@ -2170,7 +2170,9 @@ def call( if "crop_offsets" in inputs: # Flatten (samples, ?, 2) -> (n_peaks, 2). crop_offsets = inputs["crop_offsets"].merge_dims(0, 1) - peak_points = peak_points + tf.expand_dims(crop_offsets, axis=1) + peak_points = peak_points + ( + tf.expand_dims(crop_offsets, axis=1) / self.input_scale + ) # Group peaks by sample (samples, ?, nodes, 2). peaks = tf.RaggedTensor.from_value_rowids( @@ -2384,7 +2386,7 @@ def _initialize_inference_model(self): keras_model=self.centroid_model.keras_model, crop_size=crop_size, input_scale=self.centroid_config.data.preprocessing.input_scaling, - precrop_resize=None, + precrop_resize=1.0, pad_to_stride=self.centroid_config.data.preprocessing.pad_to_stride, output_stride=self.centroid_config.model.heads.centroid.output_stride, peak_threshold=self.peak_threshold, diff --git a/sleap/nn/training.py b/sleap/nn/training.py index f56d8cf46..7d32dd797 100644 --- a/sleap/nn/training.py +++ b/sleap/nn/training.py @@ -1315,7 +1315,7 @@ def _setup_visualization(self): # Create an instance peak finding layer. find_peaks = FindInstancePeaks( keras_model=self.keras_model, - input_scale=self.config.data.preprocessing.input_scaling, + input_scale=1.0, peak_threshold=0.2, refinement="local", return_confmaps=True, @@ -1756,7 +1756,7 @@ def _setup_visualization(self): # Create an instance peak finding layer. find_peaks = FindInstancePeaks( keras_model=self.keras_model, - input_scale=self.config.data.preprocessing.input_scaling, + input_scale=1.0, peak_threshold=0.2, refinement="local", return_confmaps=True, From 53d84f7cc87bd447c93dfe92726ecdf6fdb7c47d Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Fri, 20 Dec 2024 08:24:49 -0800 Subject: [PATCH 6/8] Add tests for centered instance with scaling --- sleap/nn/inference.py | 12 +- .../best_model.h5 | Bin 0 -> 2594328 bytes .../initial_config.json | 164 +++++++++++++ .../labels_gt.train.slp | Bin 0 -> 15832 bytes .../labels_gt.val.slp | Bin 0 -> 15832 bytes .../labels_pr.train.slp | Bin 0 -> 15832 bytes .../labels_pr.val.slp | Bin 0 -> 15832 bytes .../metrics.train.npz | Bin 0 -> 2054 bytes .../metrics.val.npz | Bin 0 -> 2054 bytes .../training_config.json | 226 ++++++++++++++++++ .../training_log.csv | 11 + tests/fixtures/models.py | 5 + tests/nn/test_inference.py | 49 ++++ 13 files changed, 461 insertions(+), 6 deletions(-) create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/best_model.h5 create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/initial_config.json create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/labels_gt.train.slp create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/labels_gt.val.slp create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/labels_pr.train.slp create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/labels_pr.val.slp create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/metrics.train.npz create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/metrics.val.npz create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/training_config.json create mode 100644 tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/training_log.csv diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index f33af8e73..7e0de5aa2 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -1832,12 +1832,12 @@ def call(self, inputs): # See: https://github.com/tensorflow/tensorflow/issues/6720 centroid_points = (centroid_points / self.input_scale) + 0.5 - # resize full images - if self.precrop_resize != 1.0: - full_imgs = sleap.nn.data.resizing.resize_image( - full_imgs, self.precrop_resize - ) - centroid_points *= self.precrop_resize + # resize full images + if self.precrop_resize != 1.0: + full_imgs = sleap.nn.data.resizing.resize_image( + full_imgs, self.precrop_resize + ) + centroid_points *= self.precrop_resize # Store crop offsets. crop_offsets = centroid_points - (self.crop_size / 2) diff --git a/tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/best_model.h5 b/tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/best_model.h5 new file mode 100644 index 0000000000000000000000000000000000000000..c379b0860ddba54a16f01e96a3892346eaa5dabf GIT binary patch literal 2594328 zcmeFa1wd5K_Bg(TiVC(USb&Kl5`w^;vx=x-f?|M(fP|owfG8FvC@lyoih`n|*e&dw zwY$5!ySx5(ckjgo0nzt)-{1Sc$9+%k&dljKXL{}@&Zb8%wHlf=1d>NZMW8IGBmK$$ z`N2Jn=d_h(@oB!?mHVN1k;v(bR!c*MNH;0X?4hd#60|G;Q z{Cp;P2Fc|~V0nLS$0L^UuFLsrNxxPV*hn!H1hoVW1S*2Do9?~ zf)rB{O)_389K7@;s3LjEvZUpzf^O0R1vXPcZ;a`Yr2`jcKADZP*&Hy?i=|IscS$=}fnmMG8BQ)YiE zu$oJ<_&Da`uv#wAcx#6Bc0l~$|NKrXif;>l$_jL>6WRba%jBKeTQeAyQ 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a/tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/training_config.json b/tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/training_config.json index 5dda15721..af563c1bf 100644 --- a/tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/training_config.json +++ b/tests/data/models/minimal_instance.UNet.centered_instance_with_scaling/training_config.json @@ -1,8 +1,8 @@ { "data": { "labels": { - "training_labels": "C:\\Users\\TalmoLab\\Desktop\\Divya\\SLEAP\\sleap\\tests\\data\\slp_hdf5\\minimal_instance.slp", - "validation_labels": "C:\\Users\\TalmoLab\\Desktop\\Divya\\SLEAP\\sleap\\tests\\data\\slp_hdf5\\minimal_instance.slp", + "training_labels": null, + "validation_labels": null, "validation_fraction": 0.1, "test_labels": null, "split_by_inds": false, From e0f7547f54d2dfd077fc94aa4c0bd13ee173ef12 Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Fri, 20 Dec 2024 09:06:11 -0800 Subject: [PATCH 8/8] Remove unused files --- 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