From 1c5728743f49e85017cbe4a98af319bf19b1c92b Mon Sep 17 00:00:00 2001 From: Talmo Pereira Date: Thu, 17 Mar 2022 01:50:53 -0700 Subject: [PATCH 01/55] Add support for new maDLC labels format (#678) --- sleap/io/format/deeplabcut.py | 11 +++++++++-- tests/data/dlc/madlc_testdata_v2.csv | 8 ++++++++ tests/io/test_formats.py | 8 ++++++-- 3 files changed, 23 insertions(+), 4 deletions(-) create mode 100644 tests/data/dlc/madlc_testdata_v2.csv diff --git a/sleap/io/format/deeplabcut.py b/sleap/io/format/deeplabcut.py index 3a256b20c..c625e3b56 100644 --- a/sleap/io/format/deeplabcut.py +++ b/sleap/io/format/deeplabcut.py @@ -111,15 +111,17 @@ def read_frames( # Check if this is in the new multi-animal format. is_multianimal = data.columns[0][0] == "individuals" + is_new_format = data.columns[1][1].startswith("Unnamed") if is_multianimal: # Reload with additional header rows if using new format. data = pd.read_csv(filename, header=[1, 2, 3]) # Pull out animal and node names from the columns. + start_col = 3 if is_new_format else 1 animal_names = [] node_names = [] - for animal_name, node_name, _ in data.columns[1:][::2]: + for animal_name, node_name, _ in data.columns[start_col:][::2]: if animal_name not in animal_names: animal_names.append(animal_name) if node_name not in node_names: @@ -136,7 +138,12 @@ def read_frames( skeleton.add_nodes(node_names) # Get list of all images filenames. - img_files = data.iloc[:, 0] + if is_new_format: + # New format has folder name and filename in separate columns. + img_files = [f"{a}/{b}" for a, b in zip(data.iloc[:, 0], data.iloc[:, 2])] + else: + # Old format has filenames in a single column. + img_files = data.iloc[:, 0] if full_video: video = full_video diff --git a/tests/data/dlc/madlc_testdata_v2.csv b/tests/data/dlc/madlc_testdata_v2.csv new file mode 100644 index 000000000..3b332c228 --- /dev/null +++ b/tests/data/dlc/madlc_testdata_v2.csv @@ -0,0 +1,8 @@ +scorer,,,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer +individuals,,,Animal1,Animal1,Animal1,Animal1,Animal1,Animal1,Animal2,Animal2,Animal2,Animal2,Animal2,Animal2 +bodyparts,,,A,A,B,B,C,C,A,A,B,B,C,C +coords,,,x,y,x,y,x,y,x,y,x,y,x,y +labeled-data,species,img000.png,0,1,2,3,4,5,6,7,8,9,10,11 +labeled-data,species,img001.png,12,13,,,15,16,17,18,,,20,21 +labeled-data,species,img002.png,,,,,,,,,,,, +labeled-data,species,img003.png,22,23,24,25,26,27,,,,,, diff --git a/tests/io/test_formats.py b/tests/io/test_formats.py index e9a57597f..61014c992 100644 --- a/tests/io/test_formats.py +++ b/tests/io/test_formats.py @@ -158,9 +158,13 @@ def test_matching_adaptor(): ) -def test_madlc(): +@pytest.mark.parametrize( + "test_data", + ["tests/data/dlc/madlc_testdata.csv", "tests/data/dlc/madlc_testdata_v2.csv"], +) +def test_madlc(test_data): labels = read( - "tests/data/dlc/madlc_testdata.csv", + test_data, for_object="labels", as_format="deeplabcut", ) From a2738ec10ee5dd2c16df678edfddc1d0eb8aadc8 Mon Sep 17 00:00:00 2001 From: roomrys <38435167+roomrys@users.noreply.github.com> Date: Fri, 18 Mar 2022 18:49:27 -0700 Subject: [PATCH 02/55] Supervised Identity Prediction (#460) (#679) * squash merge from roomrys/sleap-1 (#460) * Add test dataset with tracks * Add track indices to labels provider * Add identity class map generator * Update docstring * Add class map model trainer, head and config * Add inference * Docs and tests for identity module - Slightly modified matching to greedy-like behavior * Fix inference - Add imports to evals inference - Move the common Predictor.predict() method to base class - Fix docstrings for new inference classes - Add test model and integration test * Generate tracks from config metadata if not provided * Force typecasting in identity functions * Force boolean masking op * Clean up inference module - Move common Predictor methods to base class - Switch to `model.predict_on_batch()` for massive performance increase with `predictor.predict()`. - Enable prediction directly on arrays (slow) * Enable Qt5Agg backend only when necessary during training * Top-down supervised identity prediction (#476) * Add sizematcher to new training pipelines * Fix topdown ID visualization during training * Add LabeledFrame.tracked_instances property for filtering - Greedy checking in has_* properties * Add Labels.copy() method for creating deep copies - Works by serializing and deserializing to JSON (inefficient, but guaranteed to work since we have lots of coverage on I/O) * Extract labels with tracked instances - Add copy kwarg to extract to return deep copies - Remove user and/or untracked instances in with_user_labels_only(). Previously this functionality was blocked since we couldn't remove the instances from labeled frames without affecting the source labels. - Add remove_untracked_instances() utility for filtering out instances from the labels. * Add track filtering in LabelsReader provider - This is slightly redundant with Labels.with_user_labels_only(..., with_track_only=True) but serves as an extra guarantee that we don't train on instances without tracks accidentally, regardless of how the data is preprocessed. Can still emit "empty" frames if no instances have tracks set, however. * Add track filtering in DataReaders during training - Auto-enabled when training from ID models - Filters out instances without tracks BEFORE train/val splitting - Split is now done on copy of labels - Fix DataReaders arg typing - Tests for DataReaders * Add crop size detection to topdown ID models - Add training integration test for topdown ID * add removal of untracked instances for labeled instances (#460) * Add removal of untracked instances for labeled instances - previously used `LabeledFrame.tracked_instances()` which only returns predicted instances with tracking - created `LabeledFrame.remove_untracked` which returns both user labeled and predicted instances with tracking * Formatting - using black v20.8b1 * add tests for Labels and LabeledFrames (#460) * Add tests - test `Labels.remove_untracked_instances()` for both cases of `remove_empty_frames: bool` -test `LabeledFrames.remove_untracked()` for both user-labeled and predicted frames * formatting (#460) * add newline (no indent) at end of files which had failed Lint test * clean-up comments and unneeded parenthesis (#460) * Last merge fixes * Lint --- sleap/instance.py | 4 + sleap/io/dataset.py | 45 +- sleap/nn/config/__init__.py | 4 + sleap/nn/config/model.py | 152 ++- sleap/nn/data/__init__.py | 1 + sleap/nn/data/identity.py | 202 +++ sleap/nn/data/instance_cropping.py | 8 + sleap/nn/data/pipelines.py | 316 +++++ sleap/nn/data/providers.py | 12 +- sleap/nn/evals.py | 22 +- sleap/nn/heads.py | 279 +++- sleap/nn/identity.py | 250 ++++ sleap/nn/inference.py | 1144 ++++++++++++++++- sleap/nn/model.py | 117 +- sleap/nn/training.py | 368 +++++- .../best_model.h5 | Bin 0 -> 1510468 bytes .../initial_config.json | 154 +++ .../training_config.json | 217 ++++ .../best_model.h5 | Bin 0 -> 1721884 bytes .../initial_config.json | 173 +++ .../training_config.json | 240 ++++ tests/data/tracks/clip.2node.slp | Bin 0 -> 339888 bytes tests/data/tracks/clip.mp4 | Bin 0 -> 306605 bytes tests/data/tracks/clip.slp | Bin 0 -> 935496 bytes tests/fixtures/datasets.py | 16 + tests/fixtures/models.py | 10 + tests/io/test_dataset.py | 33 + tests/nn/data/test_identity.py | 102 ++ tests/nn/test_heads.py | 292 +++++ tests/nn/test_inference.py | 68 + tests/nn/test_inference_identity.py | 75 ++ tests/nn/test_model.py | 50 + tests/nn/test_training.py | 72 +- tests/test_instance.py | 32 + 34 files changed, 4373 insertions(+), 85 deletions(-) create mode 100644 sleap/nn/data/identity.py create mode 100644 sleap/nn/identity.py create mode 100644 tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/best_model.h5 create mode 100644 tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/initial_config.json create mode 100644 tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/training_config.json create mode 100644 tests/data/models/min_tracks_2node.UNet.topdown_multiclass/best_model.h5 create mode 100644 tests/data/models/min_tracks_2node.UNet.topdown_multiclass/initial_config.json create mode 100644 tests/data/models/min_tracks_2node.UNet.topdown_multiclass/training_config.json create mode 100644 tests/data/tracks/clip.2node.slp create mode 100644 tests/data/tracks/clip.mp4 create mode 100644 tests/data/tracks/clip.slp create mode 100644 tests/nn/data/test_identity.py create mode 100644 tests/nn/test_heads.py create mode 100644 tests/nn/test_inference_identity.py create mode 100644 tests/nn/test_model.py diff --git a/sleap/instance.py b/sleap/instance.py index 23c0e45d4..86191dfd6 100644 --- a/sleap/instance.py +++ b/sleap/instance.py @@ -1434,6 +1434,10 @@ def tracked_instances(self) -> List[PredictedInstance]: if type(inst) == PredictedInstance and inst.track is not None ] + def remove_untracked(self): + """Removes any instances without a track assignment.""" + self.instances = [inst for inst in self.instances if inst.track is not None] + @property def has_user_instances(self) -> bool: """Return whether the frame contains any user instances.""" diff --git a/sleap/io/dataset.py b/sleap/io/dataset.py index 81b6a5921..74bc802c4 100644 --- a/sleap/io/dataset.py +++ b/sleap/io/dataset.py @@ -960,9 +960,36 @@ def user_labeled_frame_inds(self) -> List[int]: """Return a list of indices of frames with user labeled instances.""" return [i for i, lf in enumerate(self.labeled_frames) if lf.has_user_instances] - def with_user_labels_only(self) -> "Labels": - """Return a new `Labels` object with only user labels.""" - return self.extract(self.user_labeled_frame_inds) + def with_user_labels_only( + self, + user_instances_only: bool = True, + with_track_only: bool = False, + copy: bool = True, + ) -> "Labels": + """Return a new `Labels` containing only user labels. + + This is useful as a preprocessing step to train on only user-labeled data. + + Args: + user_instances_only: If `True` (the default), predicted instances will be + removed from frames that also have user instances. + with_track_only: If `True`, remove instances without a track. + copy: If `True` (the default), create a new copy of all of the extracted + labeled frames and associated labels. If `False`, a shallow copy with + references to the original labeled frames and other objects will be + returned. Warning: If returning a shallow copy, predicted and untracked + instances will be removed from the original labels as well! + + Returns: + A new `Labels` with only the specified subset of frames and instances. + """ + new_labels = self.extract(self.user_labeled_frame_inds, copy=copy) + if user_instances_only: + new_labels.remove_predictions() + if with_track_only: + new_labels.remove_untracked_instances() + new_labels.remove_empty_frames() + return new_labels def get_labeled_frame_count(self, video: Optional[Video] = None, filter: Text = ""): return self._cache.get_frame_count(video, filter) @@ -1617,6 +1644,18 @@ def remove_predictions(self, new_labels: Optional["Labels"] = None): # Keep only labeled frames with no conflicting predictions. self.labeled_frames = keep_lfs + def remove_untracked_instances(self, remove_empty_frames: bool = True): + """Remove instances that do not have a track assignment. + + Args: + remove_empty_frames: If `True` (the default), removes frames that do not + contain any instances after removing untracked ones. + """ + for lf in self.labeled_frames: + lf.remove_untracked() + if remove_empty_frames: + self.remove_empty_frames() + @classmethod def complex_merge_between( cls, base_labels: "Labels", new_labels: "Labels", unify: bool = True diff --git a/sleap/nn/config/__init__.py b/sleap/nn/config/__init__.py index 0ff2d565d..af262315b 100644 --- a/sleap/nn/config/__init__.py +++ b/sleap/nn/config/__init__.py @@ -11,6 +11,10 @@ MultiInstanceConfmapsHeadConfig, PartAffinityFieldsHeadConfig, MultiInstanceConfig, + ClassMapsHeadConfig, + MultiClassBottomUpConfig, + ClassVectorsHeadConfig, + MultiClassTopDownConfig, HeadsConfig, LEAPConfig, UNetConfig, diff --git a/sleap/nn/config/model.py b/sleap/nn/config/model.py index b442a3ace..ca4573266 100644 --- a/sleap/nn/config/model.py +++ b/sleap/nn/config/model.py @@ -28,6 +28,9 @@ class SingleInstanceConfmapsHeadConfig: results in confidence maps that are 0.5x the size of the input. Increasing this value can considerably speed up model performance and decrease memory requirements, at the cost of decreased spatial resolution. + loss_weight: Scalar float used to weigh the loss term for this head during + training. Increase this to encourage the optimization to focus on improving + this specific output in multi-head models. offset_refinement: If `True`, model will also output an offset refinement map used to achieve subpixel localization of peaks during inference. This can improve the localization accuracy of the model at the cost of additional @@ -40,6 +43,7 @@ class SingleInstanceConfmapsHeadConfig: part_names: Optional[List[Text]] = None sigma: float = 5.0 output_stride: int = 1 + loss_weight: float = 1.0 offset_refinement: bool = False @@ -72,6 +76,9 @@ class CentroidsHeadConfig: results in confidence maps that are 0.5x the size of the input. Increasing this value can considerably speed up model performance and decrease memory requirements, at the cost of decreased spatial resolution. + loss_weight: Scalar float used to weigh the loss term for this head during + training. Increase this to encourage the optimization to focus on improving + this specific output in multi-head models. offset_refinement: If `True`, model will also output an offset refinement map used to achieve subpixel localization of peaks during inference. This can improve the localization accuracy of the model at the cost of additional @@ -84,6 +91,7 @@ class CentroidsHeadConfig: anchor_part: Optional[Text] = None sigma: float = 5.0 output_stride: int = 1 + loss_weight: float = 1.0 offset_refinement: bool = False @@ -129,6 +137,9 @@ class CenteredInstanceConfmapsHeadConfig: results in confidence maps that are 0.5x the size of the input. Increasing this value can considerably speed up model performance and decrease memory requirements, at the cost of decreased spatial resolution. + loss_weight: Scalar float used to weigh the loss term for this head during + training. Increase this to encourage the optimization to focus on improving + this specific output in multi-head models. offset_refinement: If `True`, model will also output an offset refinement map used to achieve subpixel localization of peaks during inference. This can improve the localization accuracy of the model at the cost of additional @@ -142,6 +153,7 @@ class CenteredInstanceConfmapsHeadConfig: part_names: Optional[List[Text]] = None sigma: float = 5.0 output_stride: int = 1 + loss_weight: float = 1.0 offset_refinement: bool = False @@ -274,6 +286,125 @@ class MultiInstanceConfig: pafs: PartAffinityFieldsHeadConfig = attr.ib(factory=PartAffinityFieldsHeadConfig) +@attr.s(auto_attribs=True) +class ClassMapsHeadConfig: + """Configurations for class map heads. + + These heads are used in bottom-up multi-instance models that classify detected + points using a fixed set of learned classes (e.g., animal identities). + + Class maps are an image-space representation of the probability of that each class + occupies a given pixel. This is similar to semantic segmentation, however only the + pixels in the neighborhood of the landmarks have a class assignment. + + Attributes: + classes: List of string names of the classes that this head will predict. + sigma: Spread of the Gaussian distribution that determines the neighborhood + that the class maps will be nonzero around each landmark. + output_stride: The stride of the output class maps relative to the input image. + This is the reciprocal of the resolution, e.g., an output stride of 2 + results in maps that are 0.5x the size of the input. This should be the same + size as the confidence maps they are associated with. + loss_weight: Scalar float used to weigh the loss term for this head during + training. Increase this to encourage the optimization to focus on improving + this specific output in multi-head models. + """ + + classes: Optional[List[Text]] = None + sigma: float = 5.0 + output_stride: int = 1 + loss_weight: float = 1.0 + + +@attr.s(auto_attribs=True) +class MultiClassBottomUpConfig: + """Configuration for multi-instance confidence map and class map models. + + This configuration specifies a multi-head model that outputs both multi-instance + confidence maps and class maps, which together enable multi-instance pose tracking + in a bottom-up fashion, i.e., no instance cropping, centroids or PAFs are required. + The limitation with this approach is that the classes, e.g., animal identities, must + be labeled in the training data and cannot be generalized beyond those classes. This + is still useful for applications in which the animals are uniquely identifiable and + tracking their identities at inference time is critical, e.g., for closed loop + experiments. + + Attributes: + confmaps: Part confidence map configuration (see the description in + `MultiInstanceConfmapsHeadConfig`). + class_maps: Class map configuration (see the description in + `ClassMapsHeadConfig`). + """ + + confmaps: MultiInstanceConfmapsHeadConfig = attr.ib( + factory=MultiInstanceConfmapsHeadConfig + ) + class_maps: ClassMapsHeadConfig = attr.ib(factory=ClassMapsHeadConfig) + + +@attr.s(auto_attribs=True) +class ClassVectorsHeadConfig: + """Configurations for class vectors heads. + + These heads are used in top-down multi-instance models that classify detected + points using a fixed set of learned classes (e.g., animal identities). + + Class vectors represent the probability that the image is associated with each of + the specified classes. This is similar to a standard classification task. + + Attributes: + classes: List of string names of the classes that this head will predict. + num_fc_layers: Number of fully-connected layers before the classification output + layer. These can help in transforming general image features into + classification-specific features. + num_fc_units: Number of units (dimensions) in the fully-connected layers before + classification. Increasing this can improve the representational capacity in + the pre-classification layers. + output_stride: The stride of the output class maps relative to the input image. + This is the reciprocal of the resolution, e.g., an output stride of 2 + results in maps that are 0.5x the size of the input. This should be the same + size as the confidence maps they are associated with. + loss_weight: Scalar float used to weigh the loss term for this head during + training. Increase this to encourage the optimization to focus on improving + this specific output in multi-head models. + """ + + classes: Optional[List[Text]] = None + num_fc_layers: int = 1 + num_fc_units: int = 64 + global_pool: bool = True + output_stride: int = 1 + loss_weight: float = 1.0 + + +@attr.s(auto_attribs=True) +class MultiClassTopDownConfig: + """Configuration for centered-instance confidence map and class map models. + + This configuration specifies a multi-head model that outputs both centered-instance + confidence maps and class vectors, which together enable multi-instance pose + tracking in a top-down fashion, i.e., instance-centered crops followed by pose + estimation and classification. + + The limitation with this approach is that the classes, e.g., animal identities, must + be labeled in the training data and cannot be generalized beyond those classes. This + is still useful for applications in which the animals are uniquely identifiable and + tracking their identities at inference time is critical, e.g., for closed loop + experiments. + + Attributes: + confmaps: Part confidence map configuration (see the description in + `CenteredInstanceConfmapsHeadConfig`). + class_vectors: Class map configuration (see the description in + `ClassVectorsHeadConfig`). + """ + + confmaps: CenteredInstanceConfmapsHeadConfig = attr.ib( + factory=CenteredInstanceConfmapsHeadConfig + ) + class_vectors: ClassVectorsHeadConfig = attr.ib(factory=ClassVectorsHeadConfig) + + @oneof @attr.s(auto_attribs=True) class HeadsConfig: @@ -286,12 +417,16 @@ class HeadsConfig: centroid: An instance of `CentroidsHeadConfig`. centered_instance: An instance of `CenteredInstanceConfmapsHeadConfig`. multi_instance: An instance of `MultiInstanceConfig`. + multi_class_bottomup: An instance of `MultiClassBottomUpConfig`. + multi_class_topdown: An instance of `MultiClassTopDownConfig`. """ single_instance: Optional[SingleInstanceConfmapsHeadConfig] = None centroid: Optional[CentroidsHeadConfig] = None centered_instance: Optional[CenteredInstanceConfmapsHeadConfig] = None multi_instance: Optional[MultiInstanceConfig] = None + multi_class_bottomup: Optional[MultiClassBottomUpConfig] = None + multi_class_topdown: Optional[MultiClassTopDownConfig] = None @attr.s(auto_attribs=True) @@ -459,13 +594,27 @@ class PretrainedEncoderConfig: """Configuration for UNet backbone with pretrained encoder. Attributes: - encoder: Name of the network architecture to use as the encoder. + encoder: Name of the network architecture to use as the encoder. Valid encoder + names are: + - `"vgg16", "vgg19",` + - `"resnet18", "resnet34", "resnet50", "resnet101", "resnet152"` + - `"resnext50", "resnext101"` + - `"inceptionv3", "inceptionresnetv2"` + - `"densenet121", "densenet169", "densenet201"` + - `"seresnet18", "seresnet34", "seresnet50", "seresnet101", "seresnet152",` + `"seresnext50", "seresnext101", "senet154"` + - `"mobilenet", "mobilenetv2"` + - `"efficientnetb0", "efficientnetb1", "efficientnetb2", "efficientnetb3",` + `"efficientnetb4", "efficientnetb5", "efficientnetb6", "efficientnetb7"` + Defaults to `"efficientnetb0"`. pretrained: If `True`, use initialized with weights pretrained on ImageNet. decoder_filters: Base number of filters for the upsampling blocks in the decoder. decoder_filters_rate: Factor to scale the number of filters by at each consecutive upsampling block in the decoder. output_stride: Stride of the final output. + decoder_batchnorm: If `True` (the default), use batch normalization in the + decoder layers. """ encoder: Text = attr.ib(default="efficientnetb0") @@ -473,6 +622,7 @@ class PretrainedEncoderConfig: decoder_filters: int = 256 decoder_filters_rate: float = 1.0 output_stride: int = 2 + decoder_batchnorm: bool = True @oneof diff --git a/sleap/nn/data/__init__.py b/sleap/nn/data/__init__.py index 0ae7edb41..de49adf9f 100644 --- a/sleap/nn/data/__init__.py +++ b/sleap/nn/data/__init__.py @@ -2,6 +2,7 @@ from sleap.nn.data import confidence_maps from sleap.nn.data import instance_centroids from sleap.nn.data import instance_cropping +from sleap.nn.data import identity from sleap.nn.data import normalization from sleap.nn.data import pipelines from sleap.nn.data import providers diff --git a/sleap/nn/data/identity.py b/sleap/nn/data/identity.py new file mode 100644 index 000000000..7d5c57be3 --- /dev/null +++ b/sleap/nn/data/identity.py @@ -0,0 +1,202 @@ +"""Utilities for generating data for track identity models.""" + +import sleap +import tensorflow as tf +import attr +from typing import List, Text + + +def make_class_vectors(class_inds: tf.Tensor, n_classes: int) -> tf.Tensor: + """Make a binary class vectors from class indices. + + Args: + class_inds: Class indices as `tf.Tensor` of dtype `tf.int32` and shape + `(n_instances,)`. Indices of `-1` will be interpreted as having no class. + n_classes: Integer number of maximum classes. + + Returns: + A tensor with binary class vectors of shape `(n_instances, n_classes)` of dtype + `tf.int32`. Instances with no class will have all zeros in their row. + + Notes: A class index can be used to represent a track index. + """ + return tf.one_hot(class_inds, n_classes, dtype=tf.int32) + + +def make_class_maps( + confmaps: tf.Tensor, class_inds: tf.Tensor, n_classes: int, threshold: float = 0.2 +) -> tf.Tensor: + """Generate identity class maps using instance-wise confidence maps. + + This is useful for making class maps defined on local neighborhoods around the + peaks. + + Args: + confmaps: Confidence maps for the same points as the offset maps as a + `tf.Tensor` of shape `(grid_height, grid_width, n_instances)` and dtype + `tf.float32`. This can be generated by + `sleap.nn.data.confidence_maps.make_confmaps`. + class_inds: Class indices as `tf.int32` tensor of shape `(n_instances)`. + n_classes: Integer number of maximum classes. + threshold: Minimum confidence map value below which map values will be replaced + with zeros. + + Returns: + The class maps with shape `(grid_height, grid_width, n_classes)` and dtype + `tf.float32` where each channel will be a binary mask with 1 where the instance + confidence maps were higher than the threshold. + + Notes: + Pixels that have confidence map values from more than one animal will have the + class vectors weighed by the relative contribution of each instance. + + See also: make_class_vectors, sleap.nn.data.confidence_maps.make_confmaps + """ + n_classes = tf.squeeze(n_classes) + n_instances = tf.shape(confmaps)[2] + class_vectors = make_class_vectors(class_inds, n_classes) + class_vectors = tf.reshape( + tf.cast(class_vectors, tf.float32), + [1, 1, n_instances, n_classes], + ) + + # Normalize instance mask. + mask = confmaps / tf.reduce_sum(confmaps, axis=2, keepdims=True) + mask = tf.where(confmaps > threshold, mask, 0.0) # (h, w, n_instances) + mask = tf.expand_dims(mask, axis=3) # (h, w, n_instances, 1) + + # Apply mask to vectors to create class maps. + class_maps = tf.reduce_max(mask * class_vectors, axis=2) + return class_maps + + +@attr.s(auto_attribs=True) +class ClassVectorGenerator: + """Transformer to generate class probability vectors from track indices.""" + + @property + def input_keys(self) -> List[Text]: + """Return the keys that incoming elements are expected to have.""" + return ["track_inds", "n_tracks"] + + @property + def output_keys(self) -> List[Text]: + """Return the keys that outgoing elements will have.""" + return self.input_keys + ["class_vectors"] + + def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset: + """Create a dataset that contains the generated class identity vectors. + + Args: + input_ds: A dataset with elements that contain the keys`"track_inds"` and + `"n_tracks"`. + + Returns: + A `tf.data.Dataset` with the same keys as the input, as well as a `"class"` + key containing the generated class vectors. + """ + + def generate_class_vectors(example): + """Local processing function for dataset mapping.""" + example["class_vectors"] = tf.cast( + make_class_vectors(example["track_inds"], example["n_tracks"]), + tf.float32, + ) + return example + + # Map transformation. + output_ds = input_ds.map( + generate_class_vectors, num_parallel_calls=tf.data.experimental.AUTOTUNE + ) + return output_ds + + +@attr.s(auto_attribs=True) +class ClassMapGenerator: + """Transformer to generate class maps from track indices. + + Attributes: + sigma: Standard deviation of the 2D Gaussian distribution sampled to generate + confidence maps for masking the identity maps. This defines the spread in + units of the input image's grid, i.e., it does not take scaling in previous + steps into account. + output_stride: Relative stride of the generated maps. This is effectively the + reciprocal of the output scale, i.e., increase this to generate maps that + are smaller than the input images. + centroids: If `True`, generate masking confidence maps for centroids rather than + instance points. + class_map_threshold: Minimum confidence map value below which map values will be + replaced with zeros. + """ + + sigma: float = 2.0 + output_stride: int = 1 + centroids: bool = False + class_map_threshold: float = 0.2 + + @property + def input_keys(self) -> List[Text]: + """Return the keys that incoming elements are expected to have.""" + if self.centroids: + return ["centroids", "track_inds", "n_tracks"] + else: + return ["instances", "track_inds", "n_tracks"] + + @property + def output_keys(self) -> List[Text]: + """Return the keys that outgoing elements will have.""" + return self.input_keys + ["class_maps"] + + def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset: + """Create a dataset that contains the generated class identity maps. + + Args: + input_ds: A dataset with elements that contain the keys `"image"`, + `"track_inds"`, `"n_tracks"` and either `"instances"` or `"centroids"` + depending on whether the `centroids` attribute is set to `True`. + + Returns: + A `tf.data.Dataset` with the same keys as the input, as well as a + `"class_maps"` key containing the generated class maps. + """ + # Infer image dimensions to generate the full scale sampling grid. + test_example = next(iter(input_ds)) + image_height = test_example["image"].shape[0] + image_width = test_example["image"].shape[1] + + # Generate sampling grid vectors. + xv, yv = sleap.nn.data.confidence_maps.make_grid_vectors( + image_height=image_height, + image_width=image_width, + output_stride=self.output_stride, + ) + + def generate_class_maps(example): + """Local processing function for dataset mapping.""" + if self.centroids: + points = tf.expand_dims( + example["centroids"], axis=0 + ) # (1, n_instances, 2) + else: + points = tf.transpose( + example["instances"], [1, 0, 2] + ) # (n_nodes, n_instances, 2) + + # Generate confidene maps for masking. + cms = sleap.nn.data.confidence_maps.make_multi_confmaps( + points, xv, yv, self.sigma * self.output_stride + ) # (height, width, n_instances) + + example["class_maps"] = make_class_maps( + cms, + class_inds=example["track_inds"], + n_classes=example["n_tracks"], + threshold=self.class_map_threshold, + ) + return example + + # Map transformation. + output_ds = input_ds.map( + generate_class_maps, num_parallel_calls=tf.data.experimental.AUTOTUNE + ) + return output_ds diff --git a/sleap/nn/data/instance_cropping.py b/sleap/nn/data/instance_cropping.py index fff2e7ea2..db6d93a10 100644 --- a/sleap/nn/data/instance_cropping.py +++ b/sleap/nn/data/instance_cropping.py @@ -289,6 +289,7 @@ def output_keys(self) -> List[Text]: "bbox", "center_instance", "center_instance_ind", + "track_ind", "all_instances", "centroid", "full_image_height", @@ -310,6 +311,7 @@ def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset: (n_instances, n_nodes, 2). "centroids": The computed centroid for each instance in a tf.float32 tensor of shape (n_instances, 2). + "track_inds": The track indices of the indices if available. Any additional keys present will be replicated in each output. Returns: @@ -331,6 +333,7 @@ def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset: "center_instance_ind": Scalar tf.int32 index of the centered instance relative to all the instances in the frame. This can be used to index into additional keys that may contain data from all instances. + "track_ind": Index of the track the instance belongs to if available. "all_instances": The points of all instances in the frame in image coordinates in the "instance_image". This will be a tf.float32 tensor of shape (n_instances, n_nodes, 2). This is useful for multi- @@ -359,6 +362,8 @@ def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset: keys_to_expand = [ key for key in test_example.keys() if key not in self.input_keys ] + if "class_vectors" in keys_to_expand: + keys_to_expand.remove("class_vectors") img_channels = test_example[self.image_key].shape[-1] if self.keep_full_image: keys_to_expand.append(self.image_key) @@ -408,6 +413,7 @@ def crop_instances(frame_data): "bbox": bboxes, "center_instance": center_instances, "center_instance_ind": tf.range(n_instances, dtype=tf.int32), + "track_ind": frame_data["track_inds"], "all_instances": all_instances, "centroid": frame_data[self.centroids_key], "full_image_height": tf.repeat( @@ -417,6 +423,8 @@ def crop_instances(frame_data): tf.shape(frame_data[self.image_key])[1], n_instances ), } + if "class_vectors" in frame_data: + instances_data["class_vectors"] = frame_data["class_vectors"] if self.mock_centroid_confidence: instances_data["centroid_confidence"] = tf.ones( [n_instances], dtype=tf.float32 diff --git a/sleap/nn/data/pipelines.py b/sleap/nn/data/pipelines.py index faf340189..b0892f8a1 100644 --- a/sleap/nn/data/pipelines.py +++ b/sleap/nn/data/pipelines.py @@ -32,6 +32,7 @@ SingleInstanceConfidenceMapGenerator, ) from sleap.nn.data.edge_maps import PartAffinityFieldsGenerator +from sleap.nn.data.identity import ClassVectorGenerator, ClassMapGenerator from sleap.nn.data.dataset_ops import ( Shuffler, Batcher, @@ -58,6 +59,8 @@ PartAffinityFieldsHead, CentroidConfmapsHead, CenteredInstanceConfmapsHead, + ClassMapsHead, + ClassVectorsHead, SingleInstanceConfmapsHead, OffsetRefinementHead, ) @@ -76,6 +79,8 @@ InstanceConfidenceMapGenerator, PartAffinityFieldsGenerator, SingleInstanceConfidenceMapGenerator, + ClassVectorGenerator, + ClassMapGenerator, Shuffler, Batcher, Unbatcher, @@ -942,3 +947,314 @@ def make_viz_pipeline( ) # TODO: PAF grouping inference return pipeline + + +@attr.s(auto_attribs=True) +class BottomUpMultiClassPipeline: + """Pipeline builder for confidence maps and class maps models. + + Attributes: + data_config: Data-related configuration. + optimization_config: Optimization-related configuration. + confmaps_head: Instantiated head describing the output confidence maps tensor. + class_maps_head: Instantiated head describing the output class maps tensor. + offsets_head: Optional head describing the offset refinement maps. + """ + + data_config: DataConfig + optimization_config: OptimizationConfig + confmaps_head: MultiInstanceConfmapsHead + class_maps_head: ClassMapsHead + offsets_head: Optional[OffsetRefinementHead] = None + + def make_base_pipeline(self, data_provider: Provider) -> Pipeline: + """Create base pipeline with input data only. + + Args: + data_provider: A `Provider` that generates data examples, typically a + `LabelsReader` instance. + + Returns: + A `Pipeline` instance configured to produce input examples. + """ + pipeline = Pipeline(providers=data_provider) + if self.data_config.preprocessing.resize_and_pad_to_target: + pipeline += SizeMatcher.from_config( + config=self.data_config.preprocessing, + provider=data_provider, + ) + pipeline += Normalizer.from_config(self.data_config.preprocessing) + pipeline += Resizer.from_config(self.data_config.preprocessing) + if self.optimization_config.augmentation_config.random_crop: + pipeline += RandomCropper( + crop_height=self.optimization_config.augmentation_config.random_crop_height, + crop_width=self.optimization_config.augmentation_config.random_crop_width, + ) + return pipeline + + def make_training_pipeline(self, data_provider: Provider) -> Pipeline: + """Create full training pipeline. + + Args: + data_provider: A `Provider` that generates data examples, typically a + `LabelsReader` instance. + + Returns: + A `Pipeline` instance configured to produce all data keys required for + training. + + Notes: + This does not remap keys to model outputs. Use `KeyMapper` to pull out keys + with the appropriate format for the instantiated `tf.keras.Model`. + """ + pipeline = Pipeline(providers=data_provider) + + if self.optimization_config.preload_data: + pipeline += Preloader() + + if self.optimization_config.online_shuffling: + pipeline += Shuffler( + shuffle=True, buffer_size=self.optimization_config.shuffle_buffer_size + ) + if self.data_config.preprocessing.resize_and_pad_to_target: + pipeline += SizeMatcher.from_config( + config=self.data_config.preprocessing, + provider=data_provider, + ) + aug_config = self.optimization_config.augmentation_config + if aug_config.random_flip: + pipeline += RandomFlipper.from_skeleton( + self.data_config.labels.skeletons[0], + horizontal=aug_config.flip_horizontal, + ) + + pipeline += ImgaugAugmenter.from_config(aug_config) + if aug_config.random_crop: + pipeline += RandomCropper( + crop_height=aug_config.random_crop_height, + crop_width=aug_config.random_crop_width, + ) + pipeline += Normalizer.from_config(self.data_config.preprocessing) + pipeline += Resizer.from_config(self.data_config.preprocessing) + + pipeline += MultiConfidenceMapGenerator( + sigma=self.confmaps_head.sigma, + output_stride=self.confmaps_head.output_stride, + centroids=False, + with_offsets=self.offsets_head is not None, + offsets_threshold=self.offsets_head.sigma_threshold + if self.offsets_head is not None + else 1.0, + ) + pipeline += ClassMapGenerator( + sigma=self.class_maps_head.sigma, + output_stride=self.class_maps_head.output_stride, + centroids=False, + ) + + if len(data_provider) >= self.optimization_config.batch_size: + # Batching before repeating is preferred since it preserves epoch boundaries + # such that no sample is repeated within the epoch. But this breaks if there + # are fewer samples than the batch size. + pipeline += Batcher( + batch_size=self.optimization_config.batch_size, + drop_remainder=True, + unrag=True, + ) + pipeline += Repeater() + + else: + pipeline += Repeater() + pipeline += Batcher( + batch_size=self.optimization_config.batch_size, + drop_remainder=True, + unrag=True, + ) + + if self.optimization_config.prefetch: + pipeline += Prefetcher() + + return pipeline + + def make_viz_pipeline( + self, data_provider: Provider, keras_model: tf.keras.Model + ) -> Pipeline: + """Create visualization pipeline. + + Args: + data_provider: A `Provider` that generates data examples, typically a + `LabelsReader` instance. + keras_model: A `tf.keras.Model` that can be used for inference. + + Returns: + A `Pipeline` instance configured to fetch data and run inference to generate + predictions useful for visualization during training. + """ + pipeline = self.make_base_pipeline(data_provider=data_provider) + pipeline += Prefetcher() + pipeline += Repeater() + pipeline += KerasModelPredictor( + keras_model=keras_model, + model_input_keys="image", + model_output_keys=[ + "predicted_confidence_maps", + "predicted_class_maps", + ], + ) + pipeline += LocalPeakFinder( + confmaps_stride=self.confmaps_head.output_stride, + peak_threshold=0.2, + confmaps_key="predicted_confidence_maps", + peaks_key="predicted_peaks", + peak_vals_key="predicted_peak_confidences", + peak_sample_inds_key="predicted_peak_sample_inds", + peak_channel_inds_key="predicted_peak_channel_inds", + ) + return pipeline + + +@attr.s(auto_attribs=True) +class TopDownMultiClassPipeline: + """Pipeline builder for confidence maps and class maps models. + + Attributes: + data_config: Data-related configuration. + optimization_config: Optimization-related configuration. + confmaps_head: Instantiated head describing the output confidence maps tensor. + class_vectors_head: Instantiated head describing the output class vectors + tensor. + offsets_head: Optional head describing the offset refinement maps. + """ + + data_config: DataConfig + optimization_config: OptimizationConfig + instance_confmap_head: CenteredInstanceConfmapsHead + class_vectors_head: ClassVectorsHead + offsets_head: Optional[OffsetRefinementHead] = None + + def make_base_pipeline(self, data_provider: Provider) -> Pipeline: + """Create base pipeline with input data only. + + Args: + data_provider: A `Provider` that generates data examples, typically a + `LabelsReader` instance. + + Returns: + A `Pipeline` instance configured to produce input examples. + """ + pipeline = Pipeline(providers=data_provider) + if self.data_config.preprocessing.resize_and_pad_to_target: + pipeline += SizeMatcher.from_config( + config=self.data_config.preprocessing, + 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, + ) + pipeline += InstanceCropper.from_config(self.data_config.instance_cropping) + return pipeline + + def make_training_pipeline(self, data_provider: Provider) -> Pipeline: + """Create full training pipeline. + + Args: + data_provider: A `Provider` that generates data examples, typically a + `LabelsReader` instance. + + Returns: + A `Pipeline` instance configured to produce all data keys required for + training. + + Notes: + This does not remap keys to model outputs. Use `KeyMapper` to pull out keys + with the appropriate format for the instantiated `tf.keras.Model`. + """ + pipeline = Pipeline(providers=data_provider) + + if self.optimization_config.preload_data: + pipeline += Preloader() + + if self.optimization_config.online_shuffling: + pipeline += Shuffler( + shuffle=True, buffer_size=self.optimization_config.shuffle_buffer_size + ) + if self.data_config.preprocessing.resize_and_pad_to_target: + pipeline += SizeMatcher.from_config( + config=self.data_config.preprocessing, + provider=data_provider, + ) + pipeline += ImgaugAugmenter.from_config( + self.optimization_config.augmentation_config + ) + pipeline += Normalizer.from_config(self.data_config.preprocessing) + pipeline += Resizer.from_config(self.data_config.preprocessing) + + pipeline += ClassVectorGenerator() + pipeline += InstanceCentroidFinder.from_config( + self.data_config.instance_cropping, + skeletons=self.data_config.labels.skeletons, + ) + pipeline += InstanceCropper.from_config(self.data_config.instance_cropping) + pipeline += InstanceConfidenceMapGenerator( + sigma=self.instance_confmap_head.sigma, + output_stride=self.instance_confmap_head.output_stride, + all_instances=False, + with_offsets=self.offsets_head is not None, + offsets_threshold=self.offsets_head.sigma_threshold + if self.offsets_head is not None + else 1.0, + ) + + if len(data_provider) >= self.optimization_config.batch_size: + # Batching before repeating is preferred since it preserves epoch boundaries + # such that no sample is repeated within the epoch. But this breaks if there + # are fewer samples than the batch size. + pipeline += Batcher( + batch_size=self.optimization_config.batch_size, + drop_remainder=True, + unrag=True, + ) + pipeline += Repeater() + + else: + pipeline += Repeater() + pipeline += Batcher( + batch_size=self.optimization_config.batch_size, + drop_remainder=True, + unrag=True, + ) + + if self.optimization_config.prefetch: + pipeline += Prefetcher() + + return pipeline + + def make_viz_pipeline(self, data_provider: Provider) -> Pipeline: + """Create visualization pipeline. + + Args: + data_provider: A `Provider` that generates data examples, typically a + `LabelsReader` instance. + + Returns: + A `Pipeline` instance configured to fetch data and run inference to generate + predictions useful for visualization during training. + """ + pipeline = Pipeline(data_provider) + if self.data_config.preprocessing.resize_and_pad_to_target: + pipeline += SizeMatcher.from_config( + config=self.data_config.preprocessing, + provider=data_provider, + ) + pipeline += Normalizer.from_config(self.data_config.preprocessing) + pipeline += InstanceCentroidFinder.from_config( + self.data_config.instance_cropping, + skeletons=self.data_config.labels.skeletons, + ) + pipeline += InstanceCropper.from_config(self.data_config.instance_cropping) + pipeline += Repeater() + pipeline += Prefetcher() + return pipeline diff --git a/sleap/nn/data/providers.py b/sleap/nn/data/providers.py index e8decb9e3..16f439d10 100644 --- a/sleap/nn/data/providers.py +++ b/sleap/nn/data/providers.py @@ -25,17 +25,24 @@ class LabelsReader: or filtered. user_instances_only: If `True`, load only user labeled instances. If `False`, all instances will be loaded. + with_track_only: If `True`, load only instances that have a track assigned. + Useful when training supervised ID models. """ labels: sleap.Labels example_indices: Optional[Union[Sequence[int], np.ndarray]] = None user_instances_only: bool = False + with_track_only: bool = False @classmethod - def from_user_instances(cls, labels: sleap.Labels) -> "LabelsReader": + def from_user_instances( + cls, labels: sleap.Labels, with_track_only: bool = False + ) -> "LabelsReader": """Create a `LabelsReader` using the user instances in a `Labels` set. Args: labels: A `sleap.Labels` instance containing user instances. + with_track_only: If `True`, load only instances that have a track assigned. + Useful when training supervised ID models. Returns: A `LabelsReader` instance that can create a dataset for pipelining. @@ -48,6 +55,7 @@ def from_user_instances(cls, labels: sleap.Labels) -> "LabelsReader": labels.remove_empty_instances(keep_empty_frames=False) obj = cls.from_user_labeled_frames(labels) obj.user_instances_only = True + obj.with_track_only = with_track_only return obj @classmethod @@ -195,6 +203,8 @@ def py_fetch_lf(ind): else: insts = lf.instances insts = [inst for inst in insts if len(inst) > 0] + if self.with_track_only: + insts = [inst for inst in insts if inst.track is not None] n_instances = len(insts) n_nodes = len(insts[0].skeleton) if n_instances > 0 else 0 diff --git a/sleap/nn/evals.py b/sleap/nn/evals.py index 8c57fd6b4..670c339c7 100644 --- a/sleap/nn/evals.py +++ b/sleap/nn/evals.py @@ -32,6 +32,8 @@ CentroidsHeadConfig, CenteredInstanceConfmapsHeadConfig, MultiInstanceConfig, + MultiClassBottomUpConfig, + MultiClassTopDownConfig, SingleInstanceConfmapsHeadConfig, ) from sleap.nn.model import Model @@ -39,6 +41,8 @@ from sleap.nn.inference import ( TopDownPredictor, BottomUpPredictor, + BottomUpMultiClassPredictor, + TopDownMultiClassPredictor, SingleInstancePredictor, ) @@ -685,12 +689,20 @@ def evaluate_model( confmap_model=model, ) elif isinstance(head_config, MultiInstanceConfig): - predictor = sleap.nn.inference.BottomUpPredictor( - bottomup_config=cfg, bottomup_model=model - ) + predictor = BottomUpPredictor(bottomup_config=cfg, bottomup_model=model) elif isinstance(head_config, SingleInstanceConfmapsHeadConfig): - predictor = sleap.nn.inference.SingleInstancePredictor( - confmap_config=cfg, confmap_model=model + predictor = SingleInstancePredictor(confmap_config=cfg, confmap_model=model) + elif isinstance(head_config, MultiClassBottomUpConfig): + predictor = BottomUpMultiClassPredictor( + config=cfg, + model=model, + ) + elif isinstance(head_config, MultiClassTopDownConfig): + predictor = TopDownMultiClassPredictor( + centroid_config=None, + centroid_model=None, + confmap_config=cfg, + confmap_model=model, ) else: raise ValueError("Unrecognized model type:", head_config) diff --git a/sleap/nn/heads.py b/sleap/nn/heads.py index 79e3756b4..52928ca4e 100644 --- a/sleap/nn/heads.py +++ b/sleap/nn/heads.py @@ -1,7 +1,9 @@ """Model head definitions for defining model output types.""" +import tensorflow as tf import attr from typing import Optional, Text, List, Sequence, Tuple, Union +from abc import ABC, abstractmethod from sleap.nn.config import ( CentroidsHeadConfig, @@ -9,12 +11,68 @@ CenteredInstanceConfmapsHeadConfig, MultiInstanceConfmapsHeadConfig, PartAffinityFieldsHeadConfig, + ClassMapsHeadConfig, + ClassVectorsHeadConfig, ) @attr.s(auto_attribs=True) -class SingleInstanceConfmapsHead: - """Head for specifying single instance confidence maps.""" +class Head(ABC): + """Base class for model output heads.""" + + output_stride: int = 1 + loss_weight: float = 1.0 + + @property + @abstractmethod + def channels(self) -> int: + """Return the number of channels in the tensor output by this head.""" + pass + + @property + def activation(self) -> str: + """Return the activation function of the head output layer.""" + return "linear" + + @property + def loss_function(self) -> str: + """Return the name of the loss function to use for this head.""" + return "mse" + + def make_head(self, x_in: tf.Tensor, name: Optional[Text] = None) -> tf.Tensor: + """Make head output tensor from input feature tensor. + + Args: + x_in: An input `tf.Tensor`. + name: If provided, specifies the name of the output layer. If not (the + default), uses the name of the head as the layer name. + + Returns: + A `tf.Tensor` with the correct shape for the head. + """ + if name is None: + name = f"{type(self).__name__}" + return tf.keras.layers.Conv2D( + filters=self.channels, + kernel_size=1, + strides=1, + padding="same", + activation=self.activation, + name=name, + )(x_in) + + +@attr.s(auto_attribs=True) +class SingleInstanceConfmapsHead(Head): + """Head for specifying single instance confidence maps. + + Attributes: + part_names: List of strings specifying the part names associated with channels. + sigma: Spread of the confidence maps. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ part_names: List[Text] sigma: float = 5.0 @@ -51,12 +109,22 @@ def from_config( part_names=part_names, sigma=config.sigma, output_stride=config.output_stride, + loss_weight=config.loss_weight, ) @attr.s(auto_attribs=True) -class CentroidConfmapsHead: - """Head for specifying instance centroid confidence maps.""" +class CentroidConfmapsHead(Head): + """Head for specifying instance centroid confidence maps. + + Attributes: + anchor_part: Name of the part to use as an anchor node. If not specified, the + bounding box centroid will be used. + sigma: Spread of the confidence maps. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ anchor_part: Optional[Text] = None sigma: float = 5.0 @@ -82,12 +150,23 @@ def from_config(cls, config: CentroidsHeadConfig) -> "CentroidConfmapsHead": anchor_part=config.anchor_part, sigma=config.sigma, output_stride=config.output_stride, + loss_weight=config.loss_weight, ) @attr.s(auto_attribs=True) -class CenteredInstanceConfmapsHead: - """Head for specifying centered instance confidence maps.""" +class CenteredInstanceConfmapsHead(Head): + """Head for specifying centered instance confidence maps. + + Attributes: + part_names: List of strings specifying the part names associated with channels. + anchor_part: Name of the part to use as an anchor node. If not specified, the + bounding box centroid will be used. + sigma: Spread of the confidence maps. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ part_names: List[Text] anchor_part: Optional[Text] = None @@ -126,12 +205,21 @@ def from_config( anchor_part=config.anchor_part, sigma=config.sigma, output_stride=config.output_stride, + loss_weight=config.loss_weight, ) @attr.s(auto_attribs=True) -class MultiInstanceConfmapsHead: - """Head for specifying multi-instance confidence maps.""" +class MultiInstanceConfmapsHead(Head): + """Head for specifying multi-instance confidence maps. + + Attributes: + part_names: List of strings specifying the part names associated with channels. + sigma: Spread of the confidence maps. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ part_names: List[Text] sigma: float = 5.0 @@ -173,8 +261,16 @@ def from_config( @attr.s(auto_attribs=True) -class PartAffinityFieldsHead: - """Head for specifying multi-instance part affinity fields.""" +class PartAffinityFieldsHead(Head): + """Head for specifying multi-instance part affinity fields. + + Attributes: + edges: List of tuples of `(source, destination)` node names. + sigma: Spread of the part affinity fields. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ edges: Sequence[Tuple[Text, Text]] sigma: float = 15.0 @@ -214,6 +310,156 @@ def from_config( ) +@attr.s(auto_attribs=True) +class ClassMapsHead(Head): + """Head for specifying class identity maps. + + Attributes: + classes: List of string names of the classes. + sigma: Spread of the class maps around each node. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ + + classes: List[Text] + sigma: float = 5.0 + output_stride: int = 1 + loss_weight: float = 1.0 + + @property + def channels(self) -> int: + """Return the number of channels in the tensor output by this head.""" + return len(self.classes) + + @property + def activation(self) -> str: + """Return the activation function of the head output layer.""" + return "sigmoid" + + @classmethod + def from_config( + cls, + config: ClassMapsHeadConfig, + classes: Optional[List[Text]] = None, + ) -> "ClassMapsHead": + """Create this head from a set of configurations. + + Attributes: + config: A `ClassMapsHeadConfig` instance specifying the head parameters. + classes: List of string names of the classes that this head will predict. + This must be set if the `classes` attribute of the configuration is not + set. + + Returns: + The instantiated head with the specified configuration options. + """ + if config.classes is not None: + classes = config.classes + return cls( + classes=classes, + sigma=config.sigma, + output_stride=config.output_stride, + loss_weight=config.loss_weight, + ) + + +@attr.s(auto_attribs=True) +class ClassVectorsHead(Head): + """Head for specifying classification heads. + + Attributes: + classes: List of string names of the classes. + num_fc_layers: Number of fully connected layers after flattening input features. + num_fc_units: Number of units (dimensions) in fully connected layers prior to + classification output. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ + + classes: List[Text] + num_fc_layers: int = 1 + num_fc_units: int = 64 + global_pool: bool = True + output_stride: int = 1 + loss_weight: float = 1.0 + + @property + def channels(self) -> int: + """Return the number of channels in the tensor output by this head.""" + return len(self.classes) + + @property + def activation(self) -> str: + """Return the activation function of the head output layer.""" + return "softmax" + + @property + def loss_function(self) -> str: + """Return the name of the loss function to use for this head.""" + return "categorical_crossentropy" + + @classmethod + def from_config( + cls, + config: ClassVectorsHeadConfig, + classes: Optional[List[Text]] = None, + ) -> "ClassVectorsHead": + """Create this head from a set of configurations. + + Attributes: + config: A `ClassVectorsHeadConfig` instance specifying the head parameters. + classes: List of string names of the classes that this head will predict. + This must be set if the `classes` attribute of the configuration is not + set. + + Returns: + The instantiated head with the specified configuration options. + """ + if config.classes is not None: + classes = config.classes + return cls( + classes=classes, + num_fc_layers=config.num_fc_layers, + num_fc_units=config.num_fc_units, + global_pool=config.global_pool, + output_stride=config.output_stride, + loss_weight=config.loss_weight, + ) + + def make_head(self, x_in: tf.Tensor, name: Optional[Text] = None) -> tf.Tensor: + """Make head output tensor from input feature tensor. + + Args: + x_in: An input `tf.Tensor`. + name: If provided, specifies the name of the output layer. If not (the + default), uses the name of the head as the layer name. + + Returns: + A `tf.Tensor` with the correct shape for the head. + """ + if name is None: + name = f"{type(self).__name__}" + x = x_in + if self.global_pool: + x = tf.keras.layers.GlobalMaxPool2D(name="pre_classification_global_pool")( + x + ) + x = tf.keras.layers.Flatten(name="pre_classification_flatten")(x) + for i in range(self.num_fc_layers): + x = tf.keras.layers.Dense( + self.num_fc_units, name=f"pre_classification{i}_fc" + )(x) + x = tf.keras.layers.Activation("relu", name=f"pre_classification{i}_relu")( + x + ) + x = tf.keras.layers.Dense(self.channels, activation=self.activation, name=name)( + x + ) + return x + + ConfmapConfig = Union[ CentroidsHeadConfig, SingleInstanceConfmapsHeadConfig, @@ -223,8 +469,17 @@ def from_config( @attr.s(auto_attribs=True) -class OffsetRefinementHead: - """Head for specifying offset refinement maps.""" +class OffsetRefinementHead(Head): + """Head for specifying offset refinement maps. + + Attributes: + part_names: List of strings specifying the part names associated with channels. + sigma_threshold: Threshold of confidence map values to use for defining the + boundary of the offset maps. + output_stride: Stride of the output head tensor. The input tensor is expected to + be at the same stride. + loss_weight: Weight of the loss term for this head during optimization. + """ part_names: List[Text] output_stride: int = 1 diff --git a/sleap/nn/identity.py b/sleap/nn/identity.py new file mode 100644 index 000000000..61ebcef21 --- /dev/null +++ b/sleap/nn/identity.py @@ -0,0 +1,250 @@ +"""Utilities for models that learn identity. + +These functions implement the inference logic for classifying peaks using class maps or +classification vectors. +""" + +import tensorflow as tf +import numpy as np +from typing import Tuple +from sleap.nn.utils import tf_linear_sum_assignment + + +def group_class_peaks( + peak_class_probs: tf.Tensor, + peak_sample_inds: tf.Tensor, + peak_channel_inds: tf.Tensor, + n_samples: int, + n_channels: int, +) -> Tuple[tf.Tensor, tf.Tensor]: + """Group local peaks using class probabilities. + + This is useful for matching peaks that span multiple samples and channels into + classes (e.g., instance identities) by their class probability. + + Args: + peak_class_probs: Class probabilities for each peak as `tf.Tensor` of dtype + `tf.float32` and shape `(n_peaks, n_classes)`. + peak_sample_inds: Sample index for each peak as `tf.Tensor` of dtype `tf.int32` + and shape `(n_peaks,)`. + peak_channel_inds: Channel index for each peak as `tf.Tensor` of dtype + `tf.int32` and shape `(n_peaks,)`. + n_samples: Integer number of samples in the batch. + n_channels: Integer number of channels (nodes) the instances should have. + + Returns: + A tuple of `(peak_inds, class_inds)`. + + `peak_inds`: Indices of class-grouped peaks within `[0, n_peaks)`. Will be at + most `n_classes` long. + + `class_inds`: Indices of the corresponding class for each peak within + `[0, n_peaks)`. Will be at most `n_classes` long. + + Notes: + Peaks will be assigned to classes by their probability using the Hungarian + algorithm. Peaks that are assigned to classes that are not the highest + probability for each class are removed from the matches. + + See also: classify_peaks_from_maps, classify_peaks_from_vectors + """ + peak_sample_inds = tf.cast(peak_sample_inds, tf.int32) + peak_channel_inds = tf.cast(peak_channel_inds, tf.int32) + peak_inds = tf.TensorArray( + tf.int32, size=n_samples * n_channels, infer_shape=False, element_shape=[None] + ) + class_inds = tf.TensorArray( + tf.int32, size=n_samples * n_channels, infer_shape=False, element_shape=[None] + ) + + # Match peaks samples and channels-wise. + for sample in range(n_samples): + for channel in range(n_channels): + # Get relevant peaks. + is_sample_channel = (peak_sample_inds == sample) & ( + peak_channel_inds == channel + ) + probs = tf.boolean_mask(peak_class_probs, is_sample_channel) + + # Assign group peaks by probability. + peak_inds_sc, class_inds_sc = tf_linear_sum_assignment(-probs) + + # Adjust indices and save. + peak_inds_sc = tf.gather(tf.where(is_sample_channel), peak_inds_sc) + i = sample * n_channels + channel + peak_inds = peak_inds.write( + i, tf.cast(tf.reshape(peak_inds_sc, [-1]), tf.int32) + ) + class_inds = class_inds.write( + i, tf.cast(tf.reshape(class_inds_sc, [-1]), tf.int32) + ) + + peak_inds = peak_inds.concat() + class_inds = class_inds.concat() + + # Keep only the matches that are the most likely class for each peak. + matched_probs = tf.gather_nd( + peak_class_probs, tf.stack([peak_inds, class_inds], axis=1) + ) + best_probs = tf.reduce_max(tf.gather(peak_class_probs, peak_inds), axis=1) + is_best_match = matched_probs == best_probs + peak_inds = peak_inds[is_best_match] + class_inds = class_inds[is_best_match] + + return peak_inds, class_inds + + +def classify_peaks_from_maps( + class_maps: tf.Tensor, + peak_points: tf.Tensor, + peak_vals: tf.Tensor, + peak_sample_inds: tf.Tensor, + peak_channel_inds: tf.Tensor, + n_channels: int, +) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: + """Classify and group local peaks by their class map probability. + + Args: + class_maps: Class maps for a batch as a `tf.Tensor` of dtype `tf.float32` and + shape `(n_samples, height, width, n_classes)`. + peak_points: Local peak coordinates as a `tf.Tensor` of dtype `tf.float32` and + shape `(n_peaks,)`. These should be in the same scale as the class maps. + peak_vals: Confidence map value each peak as a `tf.Tensor` of dtype `tf.float32` + and shape `(n_peaks,)`. + peak_sample_inds: Sample index for each peak as a `tf.Tensor` of dtype `tf.int32` + and shape `(n_peaks,)`. + peak_channel_inds: Channel index for each peak as a `tf.Tensor` of dtype + `tf.int32` and shape `(n_peaks,)`. + n_channels: Integer number of channels (nodes) the instances should have. + + Returns: + A tuple of `(points, point_vals, class_probs)` containing the grouped peaks. + + `points`: Class-grouped peaks as a `tf.Tensor` of dtype `tf.float32` and shape + `(n_samples, n_classes, n_channels, 2)`. Missing points will be denoted by + NaNs. + + `point_vals`: The confidence map values for each point as a `tf.Tensor` of dtype + `tf.float32` and shape `(n_samples, n_classes, n_channels)`. + + `class_probs`: Classification probabilities for matched points as a `tf.Tensor` + of dtype `tf.float32` and shape `(n_samples, n_classes, n_channels)`. + + See also: group_class_peaks + """ + # Build subscripts and pull out class probabilities for each peak from class maps. + n_samples = tf.shape(class_maps)[0] + n_instances = tf.shape(class_maps)[3] + peak_sample_inds = tf.cast(peak_sample_inds, tf.int32) + peak_channel_inds = tf.cast(peak_channel_inds, tf.int32) + subs = tf.concat( + [ + tf.reshape(peak_sample_inds, [-1, 1]), + tf.cast(tf.round(tf.reverse(peak_points, [1])), tf.int32), + ], + axis=1, + ) + peak_class_probs = tf.gather_nd(class_maps, subs) + + # Classify the peaks. + peak_inds, class_inds = group_class_peaks( + peak_class_probs, peak_sample_inds, peak_channel_inds, n_samples, n_channels + ) + + # Assign the results to fixed size tensors. + subs = tf.stack( + [ + tf.gather(peak_sample_inds, peak_inds), + class_inds, + tf.gather(peak_channel_inds, peak_inds), + ], + axis=1, + ) + points = tf.tensor_scatter_nd_update( + tf.fill([n_samples, n_instances, n_channels, 2], np.nan), + subs, + tf.gather(peak_points, peak_inds), + ) + point_vals = tf.tensor_scatter_nd_update( + tf.fill([n_samples, n_instances, n_channels], np.nan), + subs, + tf.gather(peak_vals, peak_inds), + ) + class_probs = tf.tensor_scatter_nd_update( + tf.fill([n_samples, n_instances, n_channels], np.nan), + subs, + tf.gather_nd(peak_class_probs, tf.stack([peak_inds, class_inds], axis=1)), + ) + + return points, point_vals, class_probs + + +def classify_peaks_from_vectors( + peak_points: tf.Tensor, + peak_vals: tf.Tensor, + peak_class_probs: tf.Tensor, + crop_sample_inds: tf.Tensor, + n_samples: int, +) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: + """Group peaks by classification probabilities. + + This is used in top-down classification models. + + Args: + peak_points: + peak_vals: + peak_class_probs: + crop_sample_inds: + n_samples: Number of samples in the batch. + + Returns: + A tuple of `(points, point_vals, class_probs)`. + + `points`: Class-grouped peaks as a `tf.Tensor` of dtype `tf.float32` and shape + `(n_samples, n_classes, n_channels, 2)`. Missing points will be denoted by + NaNs. + + `point_vals`: The confidence map values for each point as a `tf.Tensor` of dtype + `tf.float32` and shape `(n_samples, n_classes, n_channels)`. + + `class_probs`: Classification probabilities for matched points as a `tf.Tensor` + of dtype `tf.float32` and shape `(n_samples, n_classes, n_channels)`. + """ + crop_sample_inds = tf.cast(crop_sample_inds, tf.int32) + n_samples = tf.cast(n_samples, tf.int32) + n_channels = tf.shape(peak_points)[1] + n_instances = tf.shape(peak_class_probs)[1] + + peak_inds, class_inds = group_class_peaks( + peak_class_probs, + crop_sample_inds, + tf.zeros_like(crop_sample_inds), + n_samples, + 1, + ) + + # Assign the results to fixed size tensors. + subs = tf.stack( + [ + tf.gather(crop_sample_inds, peak_inds), + class_inds, + ], + axis=1, + ) + points = tf.tensor_scatter_nd_update( + tf.fill([n_samples, n_instances, n_channels, 2], np.nan), + subs, + tf.gather(peak_points, peak_inds), + ) + point_vals = tf.tensor_scatter_nd_update( + tf.fill([n_samples, n_instances, n_channels], np.nan), + subs, + tf.gather(peak_vals, peak_inds), + ) + class_probs = tf.tensor_scatter_nd_update( + tf.fill([n_samples, n_instances], np.nan), + subs, + tf.gather_nd(peak_class_probs, tf.stack([peak_inds, class_inds], axis=1)), + ) + + return points, point_vals, class_probs diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index d72d5e8ac..20d222291 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -159,7 +159,11 @@ def from_model_paths( batch_size=batch_size, ) - elif "centroid" in model_types or "centered_instance" in model_types: + elif ( + "centroid" in model_types + or "centered_instance" in model_types + or "multi_class_topdown" in model_types + ): centroid_model_path = None if "centroid" in model_types: centroid_model_path = model_paths[model_types.index("centroid")] @@ -168,18 +172,43 @@ def from_model_paths( if "centered_instance" in model_types: confmap_model_path = model_paths[model_types.index("centered_instance")] - predictor = TopDownPredictor.from_trained_models( - centroid_model_path=centroid_model_path, - confmap_model_path=confmap_model_path, - batch_size=batch_size, + td_multiclass_model_path = None + if "multi_class_topdown" in model_types: + td_multiclass_model_path = model_paths[ + model_types.index("multi_class_topdown") + ] + + if td_multiclass_model_path is not None: + predictor = TopDownMultiClassPredictor.from_trained_models( + centroid_model_path=centroid_model_path, + confmap_model_path=td_multiclass_model_path, + batch_size=batch_size, + peak_threshold=peak_threshold, + integral_refinement=integral_refinement, + integral_patch_size=integral_patch_size, + ) + else: + predictor = TopDownPredictor.from_trained_models( + centroid_model_path=centroid_model_path, + confmap_model_path=confmap_model_path, + batch_size=batch_size, + peak_threshold=peak_threshold, + integral_refinement=integral_refinement, + integral_patch_size=integral_patch_size, + ) + + elif "multi_instance" in model_types: + predictor = BottomUpPredictor.from_trained_models( + model_path=model_paths[model_types.index("multi_instance")], peak_threshold=peak_threshold, integral_refinement=integral_refinement, integral_patch_size=integral_patch_size, + batch_size=batch_size, ) - elif "multi_instance" in model_types: - predictor = BottomUpPredictor.from_trained_models( - model_path=model_paths[model_types.index("multi_instance")], + elif "multi_class_bottomup" in model_types: + predictor = BottomUpMultiClassPredictor.from_trained_models( + model_path=model_paths[model_types.index("multi_class_bottomup")], peak_threshold=peak_threshold, integral_refinement=integral_refinement, integral_patch_size=integral_patch_size, @@ -2642,6 +2671,1105 @@ def _make_labeled_frames_from_generator( return predicted_frames +class BottomUpMultiClassInferenceLayer(InferenceLayer): + """Keras layer that predicts instances from images using a trained model. + + This layer encapsulates all of the inference operations required for generating + predictions from a centered instance confidence map model. This includes + preprocessing, model forward pass, peak finding and coordinate adjustment. + + Attributes: + keras_model: A `tf.keras.Model` that accepts rank-4 images as input and predicts + rank-4 confidence maps and class maps as output. + input_scale: Float indicating if the images should be resized before being + passed to the model. + cm_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 + from the model shapes if not provided. + class_maps_output_stride: Output stride of the model, denoting the scale of the + output class maps relative to the images (after input scaling). This is + used for adjusting the peak coordinates to the class maps grid. This will be + inferred from the model shapes if not provided. + peak_threshold: Minimum confidence map value to consider a global peak as valid. + refinement: If `None`, returns the grid-aligned peaks with no refinement. If + `"integral"`, peaks will be refined with integral regression. If `"local"`, + peaks will be refined with quarter pixel local gradient offset. This has no + effect if the model has an offset regression head. + integral_patch_size: Size of patches to crop around each rough peak for integral + refinement as an integer scalar. + return_confmaps: If `True`, the confidence maps will be returned together with + the predicted instances. This will result in slower inference times since + the data must be copied off of the GPU, but is useful for visualizing the + raw output of the model. + return_class_maps: If `True`, the class maps will be returned together with + the predicted instances. This will result in slower inference times since + the data must be copied off of the GPU, but is useful for visualizing the + raw output of the model. + confmaps_ind: Index of the output tensor of the model corresponding to + confidence maps. If `None` (the default), this will be detected + automatically by searching for the first tensor that contains + `"MultiInstanceConfmapsHead"` in its name. + class_maps_ind: Index of the output tensor of the model corresponding to class + maps. If `None` (the default), this will be detected automatically by + searching for the first tensor that contains `"ClassMapsHead"` in its name. + offsets_ind: Index of the output tensor of the model corresponding to + offset regression maps. If `None` (the default), this will be detected + automatically by searching for the first tensor that contains + `"OffsetRefinementHead"` in its name. If the head is not present, the method + specified in the `refinement` attribute will be used. + """ + + def __init__( + self, + keras_model: tf.keras.Model, + input_scale: float = 1.0, + pad_to_stride: int = 1, + cm_output_stride: Optional[int] = None, + class_maps_output_stride: Optional[int] = None, + peak_threshold: float = 0.2, + refinement: Optional[str] = "local", + integral_patch_size: int = 5, + return_confmaps: bool = False, + return_class_maps: bool = False, + confmaps_ind: Optional[int] = None, + class_maps_ind: Optional[int] = None, + offsets_ind: Optional[int] = None, + **kwargs, + ): + super().__init__( + keras_model=keras_model, + input_scale=input_scale, + pad_to_stride=pad_to_stride, + **kwargs, + ) + self.confmaps_ind = confmaps_ind + self.class_maps_ind = class_maps_ind + self.offsets_ind = offsets_ind + + if self.confmaps_ind is None: + self.confmaps_ind = find_head(self.keras_model, "MultiInstanceConfmapsHead") + + if self.confmaps_ind is None: + raise ValueError( + "Index of the confidence maps output tensor must be specified if not " + "named 'MultiInstanceConfmapsHead'." + ) + + if self.class_maps_ind is None: + self.class_maps_ind = find_head(self.keras_model, "ClassMapsHead") + + if self.class_maps_ind is None: + raise ValueError( + "Index of the part affinity fields output tensor must be specified if " + "not named 'ClassMapsHead'." + ) + + if self.offsets_ind is None: + self.offsets_ind = find_head(self.keras_model, "OffsetRefinementHead") + + if cm_output_stride is None: + # Attempt to automatically infer the output stride. + cm_output_stride = get_model_output_stride( + self.keras_model, output_ind=self.confmaps_ind + ) + self.cm_output_stride = cm_output_stride + if class_maps_output_stride is None: + # Attempt to automatically infer the output stride. + class_maps_output_stride = get_model_output_stride( + self.keras_model, output_ind=self.class_maps_ind + ) + self.class_maps_output_stride = class_maps_output_stride + + self.peak_threshold = peak_threshold + self.refinement = refinement + self.integral_patch_size = integral_patch_size + self.return_confmaps = return_confmaps + self.return_class_maps = return_class_maps + + def forward_pass(self, data): + """Run preprocessing and model inference on a batch.""" + if isinstance(data, dict): + imgs = data["image"] + else: + imgs = data + + # Preprocess full images. + imgs = self.preprocess(imgs) + + # Model forward pass. + preds = self.keras_model(imgs) + cms = preds[self.confmaps_ind] + class_maps = preds[self.class_maps_ind] + if self.offsets_ind is not None: + offsets = preds[self.offsets_ind] + else: + offsets = None + + if isinstance(cms, list): + cms = cms[-1] + if isinstance(class_maps, list): + class_maps = class_maps[-1] + + return cms, class_maps, offsets + + def find_peaks(self, cms, offsets): + """Run peak finding on predicted confidence maps.""" + # Find local peaks. + if self.offsets_ind is None: + # Use deterministic refinement. + ( + peaks, + peak_vals, + peak_sample_inds, + peak_channel_inds, + ) = sleap.nn.peak_finding.find_local_peaks( + cms, + threshold=self.peak_threshold, + refinement=self.refinement, + integral_patch_size=self.integral_patch_size, + ) + else: + # Use learned offsets. + ( + peaks, + peak_vals, + peak_sample_inds, + peak_channel_inds, + ) = sleap.nn.peak_finding.find_local_peaks_with_offsets( + cms, offsets, threshold=self.peak_threshold + ) + + # Adjust for confidence map output stride. + peaks = peaks * tf.cast(self.cm_output_stride, tf.float32) + + return peaks, peak_vals, peak_sample_inds, peak_channel_inds + + def call(self, data): + """Predict instances for one batch of images. + + Args: + data: This may be either a single batch of images as a 4-D tensor of shape + `(batch_size, height, width, channels)`, or a dictionary containing the + image batch in the `"images"` key. + + Returns: + The predicted instances as a dictionary of tensors with keys: + + `"instance_peaks": (batch_size, n_instances, n_nodes, 2)`: Instance skeleton + points. + + `"instance_peak_vals": (batch_size, n_instances, n_nodes)`: Confidence + values for the instance skeleton points. + + `"instance_scores": (batch_size, n_instances)`: PAF matching score for each + instance. + + If `inference_layer.return_confmaps` is `True`, the predicted confidence + maps will be returned in the `"confmaps"` key. + + If `inference_layer.return_class_maps` is `True`, the predicted class maps + will be returned in the `"class_maps"` key. + """ + cms, class_maps, offsets = self.forward_pass(data) + peaks, peak_vals, peak_sample_inds, peak_channel_inds = self.find_peaks( + cms, offsets + ) + peaks = peaks / tf.cast(self.class_maps_output_stride, tf.float32) + ( + predicted_instances, + predicted_peak_scores, + predicted_instance_scores, + ) = sleap.nn.identity.classify_peaks_from_maps( + class_maps, + peaks, + peak_vals, + peak_sample_inds, + peak_channel_inds, + n_channels=tf.shape(cms)[3], + ) + predicted_instances = predicted_instances * tf.cast( + self.class_maps_output_stride, tf.float32 + ) + + # Adjust for input scaling. + if self.input_scale != 1.0: + # Note: We add 0.5 here to offset TensorFlow's weird image resizing. This + # may not always(?) be the most correct approach. + # See: https://github.com/tensorflow/tensorflow/issues/6720 + predicted_instances = (predicted_instances / self.input_scale) + 0.5 + + # Build outputs and return. + out = { + "instance_peaks": predicted_instances, + "instance_peak_vals": predicted_peak_scores, + "instance_scores": predicted_instance_scores, + } + if self.return_confmaps: + out["confmaps"] = cms + if self.return_class_maps: + out["class_maps"] = class_maps + return out + + +class BottomUpMultiClassInferenceModel(InferenceModel): + """Bottom-up multi-class instance prediction model. + + This model encapsulates the bottom-up multi-class approach where points are first + detected by local peak finding and then grouped into instances by their identity + classifications. + + Attributes: + inference_layer: A `BottomUpMultiClassInferenceLayer`. This layer takes as input + a full image and outputs the predicted instances. + """ + + def __init__(self, inference_layer, **kwargs): + super().__init__(**kwargs) + self.inference_layer = inference_layer + + def call(self, example): + """Predict instances for one batch of images. + + Args: + example: This may be either a single batch of images as a 4-D tensor of + shape `(batch_size, height, width, channels)`, or a dictionary + containing the image batch in the `"images"` key. + + Returns: + The predicted instances as a dictionary of tensors with keys: + + `"instance_peaks": (batch_size, n_instances, n_nodes, 2)`: Instance skeleton + points. + `"instance_peak_vals": (batch_size, n_instances, n_nodes)`: Confidence + values for the instance skeleton points. + `"instance_scores": (batch_size, n_instances)`: PAF matching score for each + instance. + + If `inference_layer.return_confmaps` is `True`, the predicted confidence + maps will be returned in the `"confmaps"` key. + + If `inference_layer.return_class_maps` is `True`, the predicted class maps + will be returned in the `"class_maps"` key. + """ + if isinstance(example, tf.Tensor): + example = dict(image=example) + return self.inference_layer(example) + + +@attr.s(auto_attribs=True) +class BottomUpMultiClassPredictor(Predictor): + """Bottom-up multi-instance predictor. + + This high-level class handles initialization, preprocessing and tracking using a + trained bottom-up multi-instance SLEAP model. + + This should be initialized using the `from_trained_models()` constructor or the + high-level API (`sleap.load_model`). + + Attributes: + config: The `sleap.nn.config.TrainingJobConfig` containing the metadata for the + trained model. + model: A `sleap.nn.model.Model` instance created from the trained model. If + `None`, ground truth centroids will be used if available from the data + source. + inference_model: A `sleap.nn.inference.BottomUpMultiClassInferenceModel` that + wraps a trained `tf.keras.Model` to implement preprocessing, peak finding + and classification. + pipeline: A `sleap.nn.data.Pipeline` that loads the data and batches input data. + This will be updated dynamically if new data sources are used. + batch_size: The default batch size to use when loading data for inference. + Higher values increase inference speed at the cost of higher memory usage. + peak_threshold: Minimum confidence map value to consider a local peak as valid. + integral_refinement: If `True`, peaks will be refined with integral regression. + If `False`, `"local"`, peaks will be refined with quarter pixel local + gradient offset. This has no effect if the model has an offset regression + head. + integral_patch_size: Size of patches to crop around each rough peak for integral + refinement as an integer scalar. + tracks: If provided, instances will be created using these track instances. If + not, instances will be assigned tracks from the provider if possible. + """ + + config: TrainingJobConfig + model: Model + inference_model: Optional[BottomUpMultiClassInferenceModel] = attr.ib(default=None) + pipeline: Optional[Pipeline] = attr.ib(default=None, init=False) + peak_threshold: float = 0.2 + batch_size: int = 4 + integral_refinement: bool = True + integral_patch_size: int = 5 + tracks: Optional[List[sleap.Track]] = None + + def _initialize_inference_model(self): + """Initialize the inference model from the trained model and configuration.""" + self.inference_model = BottomUpMultiClassInferenceModel( + BottomUpMultiClassInferenceLayer( + keras_model=self.model.keras_model, + input_scale=self.config.data.preprocessing.input_scaling, + pad_to_stride=self.model.maximum_stride, + peak_threshold=self.peak_threshold, + refinement="integral" if self.integral_refinement else "local", + integral_patch_size=self.integral_patch_size, + ) + ) + + @classmethod + def from_trained_models( + cls, + model_path: Text, + batch_size: int = 4, + peak_threshold: float = 0.2, + integral_refinement: bool = True, + integral_patch_size: int = 5, + ) -> "BottomUpMultiClassPredictor": + """Create predictor from a saved model. + + Args: + model_path: Path to a model folder or training job JSON file inside a model + folder. This folder should contain `training_config.json` and + `best_model.h5` files for a trained model. + batch_size: The default batch size to use when loading data for inference. + Higher values increase inference speed at the cost of higher memory + usage. + peak_threshold: Minimum confidence map value to consider a local peak as + valid. + integral_refinement: If `True`, peaks will be refined with integral + regression. If `False`, `"local"`, peaks will be refined with quarter + pixel local gradient offset. This has no effect if the model has an + offset regression head. + integral_patch_size: Size of patches to crop around each rough peak for + integral refinement as an integer scalar. + + Returns: + An instance of `BottomUpPredictor` with the loaded model. + """ + # Load bottomup model. + config = TrainingJobConfig.load_json(model_path) + keras_model_path = get_keras_model_path(model_path) + model = Model.from_config(config.model) + model.keras_model = tf.keras.models.load_model(keras_model_path, compile=False) + obj = cls( + config=config, + model=model, + peak_threshold=peak_threshold, + integral_refinement=integral_refinement, + integral_patch_size=integral_patch_size, + batch_size=batch_size, + ) + obj._initialize_inference_model() + return obj + + @property + def data_config(self) -> DataConfig: + return self.config.data + + @property + def is_grayscale(self) -> bool: + """Return whether the model expects grayscale inputs.""" + return self.model.keras_model.input.shape[-1] == 1 + + def _make_labeled_frames_from_generator( + self, generator: Iterator[Dict[str, np.ndarray]], data_provider: Provider + ) -> List[sleap.LabeledFrame]: + """Create labeled frames from a generator that yields inference results. + + This method converts pure arrays into SLEAP-specific data structures and runs + them through the tracker if it is specified. + + Args: + generator: A generator that returns dictionaries with inference results. + This should return dictionaries with keys `"image"`, `"video_ind"`, + `"frame_ind"`, `"instance_peaks"`, `"instance_peak_vals"`, and + `"instance_scores"`. This can be created using the + `_predict_generator()` method. + data_provider: The `sleap.pipelines.Provider` that the predictions are being + created from. This is used to retrieve the `sleap.Video` instance + associated with each inference result. + + Returns: + A list of `sleap.LabeledFrame`s with `sleap.PredictedInstance`s created from + arrays returned from the inference result generator. + """ + skeleton = self.config.data.labels.skeletons[0] + tracks = self.tracks + if tracks is None: + if hasattr(data_provider, "tracks"): + tracks = data_provider.tracks + elif ( + self.config.model.heads.multi_class_bottomup.class_maps.classes + is not None + ): + names = self.config.model.heads.multi_class_bottomup.class_maps.classes + tracks = [sleap.Track(name=n, spawned_on=0) for n in names] + + # Loop over batches. + predicted_frames = [] + for ex in generator: + + # Loop over frames. + for image, video_ind, frame_ind, points, confidences, scores in zip( + ex["image"], + ex["video_ind"], + ex["frame_ind"], + ex["instance_peaks"], + ex["instance_peak_vals"], + ex["instance_scores"], + ): + + # Loop over instances. + predicted_instances = [] + for i, (pts, confs, score) in enumerate( + zip(points, confidences, scores) + ): + if np.isnan(pts).all(): + continue + track = None + if tracks is not None and len(tracks) >= (i - 1): + track = tracks[i] + predicted_instances.append( + sleap.instance.PredictedInstance.from_arrays( + points=pts, + point_confidences=confs, + instance_score=np.nanmean(score), + skeleton=skeleton, + track=track, + ) + ) + + predicted_frames.append( + sleap.LabeledFrame( + video=data_provider.videos[video_ind], + frame_idx=frame_ind, + instances=predicted_instances, + ) + ) + + return predicted_frames + + +class TopDownMultiClassFindPeaks(InferenceLayer): + """Keras layer that predicts and classifies peaks from images using a trained model. + + This layer encapsulates all of the inference operations required for generating + predictions from a centered instance confidence map and multi-class model. This + includes preprocessing, model forward pass, peak finding, coordinate adjustment, and + classification. + + Attributes: + keras_model: A `tf.keras.Model` that accepts rank-4 images as input and predicts + rank-4 confidence maps as output. This should be a model that is trained on + centered instance confidence maps and classification. + input_scale: Float indicating if the images should be resized before being + passed to the model. + 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 + from the model shapes if not provided. + peak_threshold: Minimum confidence map value to consider a global peak as valid. + refinement: If `None`, returns the grid-aligned peaks with no refinement. If + `"integral"`, peaks will be refined with integral regression. If `"local"`, + peaks will be refined with quarter pixel local gradient offset. This has no + effect if the model has an offset regression head. + integral_patch_size: Size of patches to crop around each rough peak for integral + refinement as an integer scalar. + return_confmaps: If `True`, the confidence maps will be returned together with + the predicted peaks. This will result in slower inference times since the + data must be copied off of the GPU, but is useful for visualizing the raw + output of the model. + return_class_vectors: If `True`, the classification probabilities will be + returned together with the predicted peaks. This will not line up with the + grouped instances, for which the associtated class probabilities will always + be returned in `"instance_scores"`. + confmaps_ind: Index of the output tensor of the model corresponding to + confidence maps. If `None` (the default), this will be detected + automatically by searching for the first tensor that contains + `"CenteredInstanceConfmapsHead"` in its name. + offsets_ind: Index of the output tensor of the model corresponding to + offset regression maps. If `None` (the default), this will be detected + automatically by searching for the first tensor that contains + `"OffsetRefinementHead"` in its name. If the head is not present, the method + specified in the `refinement` attribute will be used. + class_vectors_ind: Index of the output tensor of the model corresponding to the + classification vectors. If `None` (the default), this will be detected + automatically by searching for the first tensor that contains + `"ClassVectorsHead"` in its name. + """ + + def __init__( + self, + keras_model: tf.keras.Model, + input_scale: float = 1.0, + output_stride: Optional[int] = None, + peak_threshold: float = 0.2, + refinement: Optional[str] = "local", + integral_patch_size: int = 5, + return_confmaps: bool = False, + return_class_vectors: bool = False, + confmaps_ind: Optional[int] = None, + offsets_ind: Optional[int] = None, + class_vectors_ind: Optional[int] = None, + **kwargs, + ): + super().__init__( + keras_model=keras_model, input_scale=input_scale, pad_to_stride=1, **kwargs + ) + self.peak_threshold = peak_threshold + self.refinement = refinement + self.integral_patch_size = integral_patch_size + self.return_confmaps = return_confmaps + self.return_class_vectors = return_class_vectors + self.confmaps_ind = confmaps_ind + self.class_vectors_ind = class_vectors_ind + self.offsets_ind = offsets_ind + + if self.confmaps_ind is None: + self.confmaps_ind = find_head( + self.keras_model, "CenteredInstanceConfmapsHead" + ) + if self.confmaps_ind is None: + raise ValueError( + "Index of the confidence maps output tensor must be specified if not " + "named 'CenteredInstanceConfmapsHead'." + ) + + if self.class_vectors_ind is None: + self.class_vectors_ind = find_head(self.keras_model, "ClassVectorsHead") + if self.class_vectors_ind is None: + raise ValueError( + "Index of the classifications output tensor must be specified if not " + "named 'ClassVectorsHead'." + ) + + if self.offsets_ind is None: + self.offsets_ind = find_head(self.keras_model, "OffsetRefinementHead") + + if output_stride is None: + # Attempt to automatically infer the output stride. + output_stride = get_model_output_stride( + self.keras_model, 0, self.confmaps_ind + ) + self.output_stride = output_stride + + def call( + self, inputs: Union[Dict[str, tf.Tensor], tf.Tensor] + ) -> Dict[str, tf.Tensor]: + """Predict confidence maps and infer peak coordinates. + + This layer can be chained with a `CentroidCrop` layer to create a top-down + inference function from full images. + + Args: + inputs: Instance-centered images as a `tf.Tensor` of shape + `(samples, height, width, channels)` or `tf.RaggedTensor` of shape + `(samples, ?, height, width, channels)` where images are grouped by + sample and may contain a variable number of crops, or a dictionary with + keys: + `"crops"`: Cropped images in either format above. + `"crop_offsets"`: (Optional) Coordinates of the top-left of the crops as + `(x, y)` offsets of shape `(samples, ?, 2)` for adjusting the + predicted peak coordinates. No adjustment is performed if not + provided. + `"centroids"`: (Optional) If provided, will be passed through to the + output. + `"centroid_vals"`: (Optional) If provided, will be passed through to the + output. + + Returns: + A dictionary of outputs with keys: + + `"instance_peaks"`: The predicted peaks for each instance in the batch as a + `tf.Tensor` of shape `(samples, n_classes, nodes, 2)`. Instances will + be ordered by class and will be filled with `NaN` where not found. + `"instance_peak_vals"`: The value of the confidence maps at the predicted + peaks for each instance in the batch as a `tf.Tensor` of shape + `(samples, n_classes, nodes)`. + + If provided (e.g., from an input `CentroidCrop` layer), the centroids that + generated the crops will also be included in the keys `"centroids"` and + `"centroid_vals"`. + + If the `return_confmaps` attribute is set to `True`, the output will also + contain a key named `"instance_confmaps"` containing a `tf.RaggedTensor` of + shape `(samples, ?, output_height, output_width, nodes)` containing the + confidence maps predicted by the model. + + If the `return_class_vectors` attribe is set to `True`, the output will also + contain a key named `"class_vectors"` containing the full classification + probabilities for all crops. + """ + if isinstance(inputs, dict): + crops = inputs["crops"] + else: + # Tensor input provided. We'll infer the extra fields in the expected input + # dictionary. + crops = inputs + inputs = {} + + if isinstance(crops, tf.RaggedTensor): + crops = inputs["crops"] # (samples, ?, height, width, channels) + + # Flatten crops into (n_peaks, height, width, channels) + crop_sample_inds = crops.value_rowids() # (n_peaks,) + samples = crops.nrows() + crops = crops.merge_dims(0, 1) + + else: + if "crop_sample_inds" in inputs: + # Crops provided as a regular tensor, use the metadata are in the input. + samples = inputs["samples"] + crop_sample_inds = inputs["crop_sample_inds"] + else: + # Assuming crops is (samples, height, width, channels). + samples = tf.shape(crops)[0] + crop_sample_inds = tf.range(samples, dtype=tf.int32) + + # Preprocess inputs (scaling, padding, colorspace, int to float). + crops = self.preprocess(crops) + + # Network forward pass. + out = self.keras_model(crops) + + # Sort outputs. + cms = out[self.confmaps_ind] + peak_class_probs = out[self.class_vectors_ind] + offsets = None + if self.offsets_ind is not None: + offsets = out[self.offsets_ind] + + # Find peaks. + if self.offsets_ind is None: + # Use deterministic refinement. + peak_points, peak_vals = sleap.nn.peak_finding.find_global_peaks( + cms, + threshold=self.peak_threshold, + refinement=self.refinement, + integral_patch_size=self.integral_patch_size, + ) + else: + # Use learned offsets. + ( + peak_points, + peak_vals, + ) = sleap.nn.peak_finding.find_global_peaks_with_offsets( + cms, offsets, threshold=self.peak_threshold + ) + + # Adjust for stride and scale. + peak_points = peak_points * self.output_stride + if self.input_scale != 1.0: + # Note: We add 0.5 here to offset TensorFlow's weird image resizing. This + # may not always(?) be the most correct approach. + # See: https://github.com/tensorflow/tensorflow/issues/6720 + peak_points = (peak_points / self.input_scale) + 0.5 + + # Adjust for crop offsets if provided. + 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) + + # Group peaks from classification probabilities. + points, point_vals, class_probs = sleap.nn.identity.classify_peaks_from_vectors( + peak_points, peak_vals, peak_class_probs, crop_sample_inds, samples + ) + + # Build outputs. + outputs = { + "instance_peaks": points, + "instance_peak_vals": point_vals, + "instance_scores": class_probs, + } + if "centroids" in inputs: + outputs["centroids"] = inputs["centroids"] + if "centroids" in inputs: + outputs["centroid_vals"] = inputs["centroid_vals"] + if self.return_confmaps: + cms = tf.RaggedTensor.from_value_rowids( + cms, crop_sample_inds, nrows=samples + ) + outputs["instance_confmaps"] = cms + if self.return_class_vectors: + outputs["class_vectors"] = peak_class_probs + return outputs + + +class TopDownMultiClassInferenceModel(InferenceModel): + """Top-down instance prediction model. + + This model encapsulates the top-down approach where instances are first detected by + local peak detection of an anchor point and then cropped. These instance-centered + crops are then passed to an instance peak detector which is trained to detect all + remaining body parts for the instance that is centered within the crop. + + Attributes: + centroid_crop: A centroid cropping layer. This can be either `CentroidCrop` or + `CentroidCropGroundTruth`. This layer takes the full image as input and + outputs a set of centroids and cropped boxes. + instance_peaks: A instance peak detection and classification layer, an instance + of `TopDownMultiClassFindPeaks`. This layer takes as input the output of the + centroid cropper and outputs the detected peaks and classes for the + instances within each crop. + """ + + def __init__( + self, + centroid_crop: Union[CentroidCrop, CentroidCropGroundTruth], + instance_peaks: TopDownMultiClassFindPeaks, + ): + super().__init__() + self.centroid_crop = centroid_crop + self.instance_peaks = instance_peaks + + def call( + self, example: Union[Dict[str, tf.Tensor], tf.Tensor] + ) -> Dict[str, tf.Tensor]: + """Predict instances for one batch of images. + + Args: + example: This may be either a single batch of images as a 4-D tensor of + shape `(batch_size, height, width, channels)`, or a dictionary + containing the image batch in the `"images"` key. If using a ground + truth model for either centroid cropping or instance peaks, the full + example from a `Pipeline` is required for providing the metadata. + + Returns: + The predicted instances as a dictionary of tensors with keys: + + `"centroids": (batch_size, n_instances, 2)`: Instance centroids. + `"centroid_vals": (batch_size, n_instances)`: Instance centroid confidence + values. + `"instance_peaks": (batch_size, n_instances, n_nodes, 2)`: Instance skeleton + points. + `"instance_peak_vals": (batch_size, n_instances, n_nodes)`: Confidence + values for the instance skeleton points. + """ + if isinstance(example, tf.Tensor): + example = dict(image=example) + + crop_output = self.centroid_crop(example) + peaks_output = self.instance_peaks(crop_output) + return peaks_output + + +@attr.s(auto_attribs=True) +class TopDownMultiClassPredictor(Predictor): + """Top-down multi-instance predictor with classification. + + This high-level class handles initialization, preprocessing and tracking using a + trained top-down multi-instance classification SLEAP model. + + This should be initialized using the `from_trained_models()` constructor or the + high-level API (`sleap.load_model`). + + Attributes: + centroid_config: The `sleap.nn.config.TrainingJobConfig` containing the metadata + for the trained centroid model. If `None`, ground truth centroids will be + used if available from the data source. + centroid_model: A `sleap.nn.model.Model` instance created from the trained + centroid model. If `None`, ground truth centroids will be used if available + from the data source. + confmap_config: The `sleap.nn.config.TrainingJobConfig` containing the metadata + for the trained centered instance model. If `None`, ground truth instances + will be used if available from the data source. + confmap_model: A `sleap.nn.model.Model` instance created from the trained + centered-instance model. If `None`, ground truth instances will be used if + available from the data source. + inference_model: A `TopDownMultiClassInferenceModel` that wraps a trained + `tf.keras.Model` to implement preprocessing, centroid detection, cropping, + peak finding and classification. + pipeline: A `sleap.nn.data.Pipeline` that loads the data and batches input data. + This will be updated dynamically if new data sources are used. + tracker: A `sleap.nn.tracking.Tracker` that will be called to associate + detections over time. Predicted instances will not be assigned to tracks if + if this is `None`. + batch_size: The default batch size to use when loading data for inference. + Higher values increase inference speed at the cost of higher memory usage. + peak_threshold: Minimum confidence map value to consider a local peak as valid. + integral_refinement: If `True`, peaks will be refined with integral regression. + If `False`, `"local"`, peaks will be refined with quarter pixel local + gradient offset. This has no effect if the model has an offset regression + head. + integral_patch_size: Size of patches to crop around each rough peak for integral + refinement as an integer scalar. + tracks: If provided, instances will be created using these track instances. If + not, instances will be assigned tracks from the provider if possible. + """ + + centroid_config: Optional[TrainingJobConfig] = attr.ib(default=None) + centroid_model: Optional[Model] = attr.ib(default=None) + confmap_config: Optional[TrainingJobConfig] = attr.ib(default=None) + confmap_model: Optional[Model] = attr.ib(default=None) + inference_model: Optional[TopDownMultiClassInferenceModel] = attr.ib(default=None) + pipeline: Optional[Pipeline] = attr.ib(default=None, init=False) + tracker: Optional[Tracker] = attr.ib(default=None, init=False) + batch_size: int = 4 + peak_threshold: float = 0.2 + integral_refinement: bool = True + integral_patch_size: int = 5 + tracks: Optional[List[sleap.Track]] = None + + def _initialize_inference_model(self): + """Initialize the inference model from the trained models and configuration.""" + use_gt_centroid = self.centroid_config is None + use_gt_confmap = self.confmap_config is None + if use_gt_confmap: + raise ValueError( + "Both a centroid and a confidence map model must be provided to " + "initialize a TopDownMultiClassPredictor." + ) + + if use_gt_centroid: + centroid_crop_layer = CentroidCropGroundTruth( + crop_size=self.confmap_config.data.instance_cropping.crop_size + ) + else: + crop_size = self.confmap_config.data.instance_cropping.crop_size + centroid_crop_layer = CentroidCrop( + keras_model=self.centroid_model.keras_model, + crop_size=crop_size, + input_scale=self.centroid_config.data.preprocessing.input_scaling, + 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, + refinement="integral" if self.integral_refinement else "local", + integral_patch_size=self.integral_patch_size, + return_confmaps=False, + ) + + cfg = self.confmap_config + instance_peaks_layer = TopDownMultiClassFindPeaks( + keras_model=self.confmap_model.keras_model, + input_scale=cfg.data.preprocessing.input_scaling, + peak_threshold=self.peak_threshold, + output_stride=cfg.model.heads.multi_class_topdown.confmaps.output_stride, + refinement="integral" if self.integral_refinement else "local", + integral_patch_size=self.integral_patch_size, + return_confmaps=False, + ) + + self.inference_model = TopDownMultiClassInferenceModel( + centroid_crop=centroid_crop_layer, instance_peaks=instance_peaks_layer + ) + + @classmethod + def from_trained_models( + cls, + centroid_model_path: Optional[Text] = None, + confmap_model_path: Optional[Text] = None, + batch_size: int = 4, + peak_threshold: float = 0.2, + integral_refinement: bool = True, + integral_patch_size: int = 5, + ) -> "TopDownMultiClassPredictor": + """Create predictor from saved models. + + Args: + centroid_model_path: Path to a centroid model folder or training job JSON + file inside a model folder. This folder should contain + `training_config.json` and `best_model.h5` files for a trained model. + confmap_model_path: Path to a centered instance model folder or training job + JSON file inside a model folder. This folder should contain + `training_config.json` and `best_model.h5` files for a trained model. + batch_size: The default batch size to use when loading data for inference. + Higher values increase inference speed at the cost of higher memory + usage. + peak_threshold: Minimum confidence map value to consider a local peak as + valid. + integral_refinement: If `True`, peaks will be refined with integral + regression. If `False`, `"local"`, peaks will be refined with quarter + pixel local gradient offset. This has no effect if the model has an + offset regression head. + integral_patch_size: Size of patches to crop around each rough peak for + integral refinement as an integer scalar. + + Returns: + An instance of `TopDownPredictor` with the loaded models. + + One of the two models can be left as `None` to perform inference with ground + truth data. This will only work with `LabelsReader` as the provider. + """ + if centroid_model_path is None and confmap_model_path is None: + raise ValueError( + "Either the centroid or topdown confidence map model must be provided." + ) + + if centroid_model_path is not None: + # Load centroid model. + centroid_config = TrainingJobConfig.load_json(centroid_model_path) + centroid_keras_model_path = get_keras_model_path(centroid_model_path) + centroid_model = Model.from_config(centroid_config.model) + centroid_model.keras_model = tf.keras.models.load_model( + centroid_keras_model_path, compile=False + ) + else: + centroid_config = None + centroid_model = None + + if confmap_model_path is not None: + # Load confmap model. + confmap_config = TrainingJobConfig.load_json(confmap_model_path) + confmap_keras_model_path = get_keras_model_path(confmap_model_path) + confmap_model = Model.from_config(confmap_config.model) + confmap_model.keras_model = tf.keras.models.load_model( + confmap_keras_model_path, compile=False + ) + else: + confmap_config = None + confmap_model = None + + obj = cls( + centroid_config=centroid_config, + centroid_model=centroid_model, + confmap_config=confmap_config, + confmap_model=confmap_model, + batch_size=batch_size, + peak_threshold=peak_threshold, + integral_refinement=integral_refinement, + integral_patch_size=integral_patch_size, + ) + obj._initialize_inference_model() + return obj + + @property + def data_config(self) -> DataConfig: + return ( + self.centroid_config.data + if self.centroid_config + else self.confmap_config.data + ) + + @property + def is_grayscale(self) -> bool: + """Return whether the model expects grayscale inputs.""" + is_gray = False + if self.centroid_model is not None: + is_gray = self.centroid_model.keras_model.input.shape[-1] == 1 + else: + is_gray = self.confmap_model.keras_model.input.shape[-1] == 1 + return is_gray + + def make_pipeline(self, data_provider: Optional[Provider] = None) -> Pipeline: + """Make a data loading pipeline. + + Args: + data_provider: If not `None`, the pipeline will be created with an instance + of a `sleap.pipelines.Provider`. + + Returns: + The created `sleap.pipelines.Pipeline` with batching and prefetching. + + Notes: + This method also updates the class attribute for the pipeline and will be + called automatically when predicting on data from a new source. + """ + pipeline = Pipeline() + if data_provider is not None: + pipeline.providers = [data_provider] + + if self.centroid_model is None: + anchor_part = self.confmap_config.data.instance_cropping.center_on_part + pipeline += sleap.nn.data.pipelines.InstanceCentroidFinder( + center_on_anchor_part=anchor_part is not None, + anchor_part_names=anchor_part, + skeletons=self.confmap_config.data.labels.skeletons, + ) + + pipeline += sleap.nn.data.pipelines.Batcher( + batch_size=self.batch_size, drop_remainder=False, unrag=False + ) + + pipeline += Prefetcher() + + self.pipeline = pipeline + + return pipeline + + def _make_labeled_frames_from_generator( + self, generator: Iterator[Dict[str, np.ndarray]], data_provider: Provider + ) -> List[sleap.LabeledFrame]: + """Create labeled frames from a generator that yields inference results. + + This method converts pure arrays into SLEAP-specific data structures and runs + them through the tracker if it is specified. + + Args: + generator: A generator that returns dictionaries with inference results. + This should return dictionaries with keys `"image"`, `"video_ind"`, + `"frame_ind"`, `"instance_peaks"`, `"instance_peak_vals"`, and + `"centroid_vals"`. This can be created using the `_predict_generator()` + method. + data_provider: The `sleap.pipelines.Provider` that the predictions are being + created from. This is used to retrieve the `sleap.Video` instance + associated with each inference result. + + Returns: + A list of `sleap.LabeledFrame`s with `sleap.PredictedInstance`s created from + arrays returned from the inference result generator. + """ + if self.confmap_config is not None: + skeleton = self.confmap_config.data.labels.skeletons[0] + else: + skeleton = self.centroid_config.data.labels.skeletons[0] + + tracks = self.tracks + if tracks is None: + if hasattr(data_provider, "tracks"): + tracks = data_provider.tracks + elif ( + self.confmap_config.model.heads.multi_class_topdown.class_vectors.classes + is not None + ): + names = ( + self.confmap_config.model.heads.multi_class_topdown.class_vectors.classes + ) + tracks = [sleap.Track(name=n, spawned_on=0) for n in names] + + # Loop over batches. + predicted_frames = [] + for ex in generator: + + # Loop over frames. + for image, video_ind, frame_ind, points, confidences, scores in zip( + ex["image"], + ex["video_ind"], + ex["frame_ind"], + ex["instance_peaks"], + ex["instance_peak_vals"], + ex["instance_scores"], + ): + + # Loop over instances. + predicted_instances = [] + for i, (pts, confs, score) in enumerate( + zip(points, confidences, scores) + ): + if np.isnan(pts).all(): + continue + track = None + if tracks is not None and len(tracks) >= (i - 1): + track = tracks[i] + predicted_instances.append( + sleap.instance.PredictedInstance.from_arrays( + points=pts, + point_confidences=confs, + instance_score=np.nanmean(score), + skeleton=skeleton, + track=track, + ) + ) + + predicted_frames.append( + sleap.LabeledFrame( + video=data_provider.videos[video_ind], + frame_idx=frame_ind, + instances=predicted_instances, + ) + ) + + return predicted_frames + + def load_model( model_path: Union[str, List[str]], batch_size: int = 4, diff --git a/sleap/nn/model.py b/sleap/nn/model.py index ff39eb311..08faa4aaa 100644 --- a/sleap/nn/model.py +++ b/sleap/nn/model.py @@ -22,11 +22,14 @@ IntermediateFeature, ) from sleap.nn.heads import ( + Head, CentroidConfmapsHead, SingleInstanceConfmapsHead, CenteredInstanceConfmapsHead, MultiInstanceConfmapsHead, PartAffinityFieldsHead, + ClassMapsHead, + ClassVectorsHead, OffsetRefinementHead, ) from sleap.nn.config import ( @@ -39,6 +42,8 @@ CentroidsHeadConfig, CenteredInstanceConfmapsHeadConfig, MultiInstanceConfig, + MultiClassBottomUpConfig, + MultiClassTopDownConfig, BackboneConfig, HeadsConfig, ModelConfig, @@ -68,11 +73,14 @@ } HEADS = [ + Head, CentroidConfmapsHead, SingleInstanceConfmapsHead, CenteredInstanceConfmapsHead, MultiInstanceConfmapsHead, PartAffinityFieldsHead, + ClassMapsHead, + ClassVectorsHead, OffsetRefinementHead, ] Head = TypeVar("Head", *HEADS) @@ -98,6 +106,7 @@ def from_config( cls, config: ModelConfig, skeleton: Optional[sleap.Skeleton] = None, + tracks: Optional[List[sleap.Track]] = None, update_config: bool = False, ) -> "Model": """Create a SLEAP model from configurations. @@ -113,7 +122,11 @@ def from_config( """ # Figure out which backbone class to use. backbone_config = config.backbone.which_oneof() - backbone_cls = BACKBONE_CONFIG_TO_CLS[type(backbone_config)] + backbone_cls = BACKBONE_CONFIG_TO_CLS.get(type(backbone_config), None) + if backbone_cls is None: + raise ValueError( + "Backbone architecture (config.model.backbone) was not specified." + ) # Figure out which head class to use. head_config = config.heads.which_oneof() @@ -205,6 +218,88 @@ def from_config( ) ) + elif isinstance(head_config, MultiClassBottomUpConfig): + part_names = head_config.confmaps.part_names + if part_names is None: + if skeleton is None: + raise ValueError( + "Skeleton must be provided when the head configuration is " + "incomplete." + ) + part_names = skeleton.node_names + if update_config: + head_config.confmaps.part_names = part_names + + classes = head_config.class_maps.classes + if classes is None: + if tracks is None: + raise ValueError( + "Classes must be provided when the head configuration is " + "incomplete." + ) + classes = [t.name for t in tracks] + if update_config: + head_config.class_maps.classes = classes + + heads = [ + MultiInstanceConfmapsHead.from_config( + head_config.confmaps, part_names=part_names + ), + ClassMapsHead.from_config(head_config.class_maps, classes=classes), + ] + output_stride = min(heads[0].output_stride, heads[1].output_stride) + output_stride = heads[0].output_stride + if head_config.confmaps.offset_refinement: + heads.append( + OffsetRefinementHead.from_config( + head_config.confmaps, part_names=part_names + ) + ) + + elif isinstance(head_config, MultiClassTopDownConfig): + part_names = head_config.confmaps.part_names + if part_names is None: + if skeleton is None: + raise ValueError( + "Skeleton must be provided when the head configuration is " + "incomplete." + ) + part_names = skeleton.node_names + if update_config: + head_config.confmaps.part_names = part_names + + classes = head_config.class_vectors.classes + if classes is None: + if tracks is None: + raise ValueError( + "Classes must be provided when the head configuration is " + "incomplete." + ) + classes = [t.name for t in tracks] + if update_config: + head_config.class_vectors.classes = classes + + heads = [ + CenteredInstanceConfmapsHead.from_config( + head_config.confmaps, part_names=part_names + ), + ClassVectorsHead.from_config( + head_config.class_vectors, classes=classes + ), + ] + output_stride = min(heads[0].output_stride, heads[1].output_stride) + output_stride = heads[0].output_stride + if head_config.confmaps.offset_refinement: + heads.append( + OffsetRefinementHead.from_config( + head_config.confmaps, part_names=part_names + ) + ) + else: + raise ValueError( + "Head configuration (config.model.heads) was not specified." + ) + backbone_config.output_stride = output_stride return cls(backbone=backbone_cls.from_config(backbone_config), heads=heads) @@ -244,15 +339,7 @@ def make_model(self, input_shape: Tuple[int, int, int]) -> tf.keras.Model: # The main output has the same stride as the head, so build output layer # from that tensor. for i, x in enumerate(x_main): - x_head.append( - tf.keras.layers.Conv2D( - filters=output.channels, - kernel_size=1, - strides=1, - padding="same", - name=f"{type(output).__name__}_{i}", - )(x) - ) + x_head.append(output.make_head(x)) else: # Look for an intermediate activation that has the correct stride. @@ -261,15 +348,7 @@ def make_model(self, input_shape: Tuple[int, int, int]) -> tf.keras.Model: assert all([feat.stride == feats[0].stride for feat in feats]) if feats[0].stride == output.output_stride: for i, feat in enumerate(feats): - x_head.append( - tf.keras.layers.Conv2D( - filters=output.channels, - kernel_size=1, - strides=1, - padding="same", - name=f"{type(output).__name__}_{i}", - )(feat.tensor) - ) + x_head.append(output.make_head(feat.tensor)) break if len(x_head) == 0: diff --git a/sleap/nn/training.py b/sleap/nn/training.py index 9aac9993c..06b86a841 100644 --- a/sleap/nn/training.py +++ b/sleap/nn/training.py @@ -28,6 +28,8 @@ CentroidsHeadConfig, CenteredInstanceConfmapsHeadConfig, MultiInstanceConfig, + MultiClassBottomUpConfig, + MultiClassTopDownConfig, ) # Model @@ -42,6 +44,8 @@ CentroidConfmapsPipeline, TopdownConfmapsPipeline, BottomUpPipeline, + BottomUpMultiClassPipeline, + TopDownMultiClassPipeline, KeyMapper, ) from sleap.nn.data.training import split_labels_train_val @@ -104,6 +108,7 @@ def from_config( test: Optional[Union[Text, sleap.Labels]] = None, video_search_paths: Optional[List[Text]] = None, update_config: bool = False, + with_track_only: bool = False, ) -> "DataReaders": """Create data readers from a (possibly incomplete) configuration.""" # Use config values if not provided in the arguments. @@ -142,6 +147,7 @@ def from_config( video_search_paths=video_search_paths, labels_config=labels_config, update_config=update_config, + with_track_only=with_track_only, ) @classmethod @@ -153,6 +159,7 @@ def from_labels( video_search_paths: Optional[List[Text]] = None, labels_config: Optional[LabelsConfig] = None, update_config: bool = False, + with_track_only: bool = False, ) -> "DataReaders": """Create data readers from sleap.Labels datasets as data providers.""" if isinstance(training, str): @@ -205,7 +212,12 @@ def from_labels( training_inds, validation, validation_inds, - ) = split_labels_train_val(training.with_user_labels_only(), validation) + ) = split_labels_train_val( + training.with_user_labels_only( + with_track_only=with_track_only, copy=True + ), + validation, + ) logger.info( f" Splits: Training = {len(training_inds)} /" f" Validation = {len(validation_inds)}." @@ -221,11 +233,17 @@ def from_labels( test_reader = None if test is not None: - test_reader = LabelsReader.from_user_instances(test) + test_reader = LabelsReader.from_user_instances( + test, with_track_only=with_track_only + ) return cls( - training_labels_reader=LabelsReader.from_user_instances(training), - validation_labels_reader=LabelsReader.from_user_instances(validation), + training_labels_reader=LabelsReader.from_user_instances( + training, with_track_only=with_track_only + ), + validation_labels_reader=LabelsReader.from_user_instances( + validation, with_track_only=with_track_only + ), test_labels_reader=test_reader, ) @@ -530,6 +548,8 @@ def sanitize_scope_name(name: Text) -> Text: CentroidConfmapsPipeline, TopdownConfmapsPipeline, BottomUpPipeline, + TopDownMultiClassPipeline, + BottomUpMultiClassPipeline, SingleInstanceConfmapsPipeline, ) @@ -617,6 +637,29 @@ def from_config( # Store SLEAP version on the training process. config.sleap_version = sleap.__version__ + # Determine output type to create type-specific model trainer. + head_config = config.model.heads.which_oneof() + is_id_model = False + if isinstance(head_config, SingleInstanceConfmapsHeadConfig): + trainer_cls = SingleInstanceModelTrainer + elif isinstance(head_config, CentroidsHeadConfig): + trainer_cls = CentroidConfmapsModelTrainer + elif isinstance(head_config, CenteredInstanceConfmapsHeadConfig): + trainer_cls = TopdownConfmapsModelTrainer + elif isinstance(head_config, MultiInstanceConfig): + trainer_cls = BottomUpModelTrainer + elif isinstance(head_config, MultiClassBottomUpConfig): + trainer_cls = BottomUpMultiClassModelTrainer + is_id_model = True + elif isinstance(head_config, MultiClassTopDownConfig): + trainer_cls = TopDownMultiClassModelTrainer + is_id_model = True + else: + raise ValueError( + "Model head not specified or configured. Check the config.model.heads" + " setting." + ) + # Create data readers and store loaded skeleton. data_readers = DataReaders.from_config( config.data.labels, @@ -625,30 +668,18 @@ def from_config( test=test_labels, video_search_paths=video_search_paths, update_config=True, + with_track_only=is_id_model, ) config.data.labels.skeletons = data_readers.training_labels.skeletons # Create model. model = Model.from_config( - config.model, skeleton=config.data.labels.skeletons[0], update_config=True + config.model, + skeleton=config.data.labels.skeletons[0], + tracks=data_readers.training_labels_reader.tracks, + update_config=True, ) - # Determine output type to create type-specific model trainer. - head_config = config.model.heads.which_oneof() - if isinstance(head_config, CentroidsHeadConfig): - trainer_cls = CentroidConfmapsModelTrainer - elif isinstance(head_config, CenteredInstanceConfmapsHeadConfig): - trainer_cls = TopdownConfmapsModelTrainer - elif isinstance(head_config, MultiInstanceConfig): - trainer_cls = BottomUpModelTrainer - elif isinstance(head_config, SingleInstanceConfmapsHeadConfig): - trainer_cls = SingleInstanceModelTrainer - else: - raise ValueError( - "Model head not specified or configured. Check the config.model.heads" - " setting." - ) - return trainer_cls( config=config, initial_config=initial_config, @@ -1445,6 +1476,301 @@ def visualize_pafs_example(example): ) +@attr.s(auto_attribs=True) +class BottomUpMultiClassModelTrainer(Trainer): + """Trainer for models that output multi-instance confidence maps and class maps.""" + + pipeline_builder: BottomUpMultiClassPipeline = attr.ib(init=False) + + @property + def has_offsets(self) -> bool: + """Whether model is configured to output refinement offsets.""" + return self.config.model.heads.multi_class_bottomup.confmaps.offset_refinement + + def _update_config(self): + """Update the configuration with inferred values.""" + if self.config.data.preprocessing.pad_to_stride is None: + self.config.data.preprocessing.pad_to_stride = self.model.maximum_stride + + if self.config.optimization.batches_per_epoch is None: + n_training_examples = len(self.data_readers.training_labels) + n_training_batches = ( + n_training_examples // self.config.optimization.batch_size + ) + self.config.optimization.batches_per_epoch = max( + self.config.optimization.min_batches_per_epoch, n_training_batches + ) + + if self.config.optimization.val_batches_per_epoch is None: + n_validation_examples = len(self.data_readers.validation_labels) + n_validation_batches = ( + n_validation_examples // self.config.optimization.batch_size + ) + self.config.optimization.val_batches_per_epoch = max( + self.config.optimization.min_val_batches_per_epoch, n_validation_batches + ) + + def _setup_pipeline_builder(self): + """Initialize pipeline builder.""" + self.pipeline_builder = BottomUpMultiClassPipeline( + data_config=self.config.data, + optimization_config=self.config.optimization, + confmaps_head=self.model.heads[0], + class_maps_head=self.model.heads[1], + offsets_head=self.model.heads[2] if self.has_offsets else None, + ) + + @property + def input_keys(self) -> List[Text]: + """Return example keys to be mapped to model inputs.""" + return ["image"] + + @property + def output_keys(self) -> List[Text]: + """Return example keys to be mapped to model outputs.""" + output_keys = ["confidence_maps", "class_maps"] + if self.has_offsets: + output_keys.append("offsets") + return output_keys + + def _setup_visualization(self): + """Set up visualization pipelines and callbacks.""" + # Create visualization/inference pipelines. + self.training_viz_pipeline = self.pipeline_builder.make_viz_pipeline( + self.data_readers.training_labels_reader, self.keras_model + ) + self.validation_viz_pipeline = self.pipeline_builder.make_viz_pipeline( + self.data_readers.validation_labels_reader, self.keras_model + ) + + # Create static iterators. + training_viz_ds_iter = iter(self.training_viz_pipeline.make_dataset()) + validation_viz_ds_iter = iter(self.validation_viz_pipeline.make_dataset()) + + def visualize_confmaps_example(example): + img = example["image"].numpy() + cms = example["predicted_confidence_maps"].numpy() + pts_gt = example["instances"].numpy() + pts_pr = example["predicted_peaks"].numpy() + + scale = 1.0 + if img.shape[0] < 512: + scale = 2.0 + if img.shape[0] < 256: + scale = 4.0 + fig = plot_img(img, dpi=72 * scale, scale=scale) + plot_confmaps(cms, output_scale=cms.shape[0] / img.shape[0]) + plt.xlim(plt.xlim()) + plt.ylim(plt.ylim()) + plot_peaks(pts_gt, pts_pr, paired=False) + return fig + + def visualize_class_maps_example(example): + img = example["image"].numpy() + class_maps = example["predicted_class_maps"].numpy() + + scale = 1.0 + if img.shape[0] < 512: + scale = 2.0 + if img.shape[0] < 256: + scale = 4.0 + fig = plot_img(img, dpi=72 * scale, scale=scale) + plot_confmaps(class_maps, output_scale=class_maps.shape[0] / img.shape[0]) + return fig + + self.visualization_callbacks.extend( + setup_visualization( + self.config.outputs, + run_path=self.run_path, + viz_fn=lambda: visualize_confmaps_example(next(training_viz_ds_iter)), + name=f"train", + ) + ) + self.visualization_callbacks.extend( + setup_visualization( + self.config.outputs, + run_path=self.run_path, + viz_fn=lambda: visualize_confmaps_example(next(validation_viz_ds_iter)), + name=f"validation", + ) + ) + + self.visualization_callbacks.extend( + setup_visualization( + self.config.outputs, + run_path=self.run_path, + viz_fn=lambda: visualize_class_maps_example(next(training_viz_ds_iter)), + name=f"train_class_maps", + ) + ) + self.visualization_callbacks.extend( + setup_visualization( + self.config.outputs, + run_path=self.run_path, + viz_fn=lambda: visualize_class_maps_example( + next(validation_viz_ds_iter) + ), + name=f"validation_class_maps", + ) + ) + + +@attr.s(auto_attribs=True) +class TopDownMultiClassModelTrainer(Trainer): + """Trainer for models that output multi-instance confidence maps and class maps.""" + + pipeline_builder: TopDownMultiClassPipeline = attr.ib(init=False) + + @property + def has_offsets(self) -> bool: + """Whether model is configured to output refinement offsets.""" + return self.config.model.heads.multi_class_topdown.confmaps.offset_refinement + + def _update_config(self): + """Update the configuration with inferred values.""" + if self.config.data.preprocessing.pad_to_stride is None: + self.config.data.preprocessing.pad_to_stride = self.model.maximum_stride + + if self.config.data.instance_cropping.crop_size is None: + self.config.data.instance_cropping.crop_size = sleap.nn.data.instance_cropping.find_instance_crop_size( + self.data_readers.training_labels, + padding=self.config.data.instance_cropping.crop_size_detection_padding, + maximum_stride=self.model.maximum_stride, + input_scaling=self.config.data.preprocessing.input_scaling, + ) + + if self.config.optimization.batches_per_epoch is None: + n_training_examples = len(self.data_readers.training_labels.user_instances) + n_training_batches = ( + n_training_examples // self.config.optimization.batch_size + ) + self.config.optimization.batches_per_epoch = max( + self.config.optimization.min_batches_per_epoch, n_training_batches + ) + + if self.config.optimization.val_batches_per_epoch is None: + n_validation_examples = len( + self.data_readers.validation_labels.user_instances + ) + n_validation_batches = ( + n_validation_examples // self.config.optimization.batch_size + ) + self.config.optimization.val_batches_per_epoch = max( + self.config.optimization.min_val_batches_per_epoch, n_validation_batches + ) + + def _setup_pipeline_builder(self): + """Initialize pipeline builder.""" + self.pipeline_builder = TopDownMultiClassPipeline( + data_config=self.config.data, + optimization_config=self.config.optimization, + instance_confmap_head=self.model.heads[0], + class_vectors_head=self.model.heads[1], + offsets_head=self.model.heads[2] if self.has_offsets else None, + ) + + @property + def input_keys(self) -> List[Text]: + """Return example keys to be mapped to model inputs.""" + return ["instance_image"] + + @property + def output_keys(self) -> List[Text]: + """Return example keys to be mapped to model outputs.""" + output_keys = ["instance_confidence_maps", "class_vectors"] + if self.has_offsets: + output_keys.append("offsets") + return output_keys + + def _setup_optimization(self): + """Set up optimizer, loss functions and compile the model.""" + optimizer = setup_optimizer(self.config.optimization) + # loss_fn = setup_losses(self.config.optimization) + + # part_names = None + # metrics = setup_metrics(self.config.optimization, part_names=None) + metrics = {"ClassVectorsHead": "accuracy"} + + self.optimization_callbacks = setup_optimization_callbacks( + self.config.optimization + ) + + self.keras_model.compile( + optimizer=optimizer, + loss={ + output_name: head.loss_function + for output_name, head in zip( + self.keras_model.output_names, self.model.heads + ) + }, + metrics=metrics, + loss_weights={ + output_name: head.loss_weight + for output_name, head in zip( + self.keras_model.output_names, self.model.heads + ) + }, + ) + + def _setup_visualization(self): + """Set up visualization pipelines and callbacks.""" + # Create visualization/inference pipelines. + self.training_viz_pipeline = self.pipeline_builder.make_viz_pipeline( + self.data_readers.training_labels_reader + ) + self.validation_viz_pipeline = self.pipeline_builder.make_viz_pipeline( + self.data_readers.validation_labels_reader + ) + + # Create static iterators. + training_viz_ds_iter = iter(self.training_viz_pipeline.make_dataset()) + validation_viz_ds_iter = iter(self.validation_viz_pipeline.make_dataset()) + + # Create an instance peak finding layer. + find_peaks = FindInstancePeaks( + keras_model=self.keras_model, + input_scale=self.config.data.preprocessing.input_scaling, + peak_threshold=0.2, + refinement="local", + return_confmaps=True, + ) + + def visualize_example(example): + # Find peaks by evaluating model. + preds = find_peaks(tf.expand_dims(example["instance_image"], axis=0)) + img = example["instance_image"].numpy() + cms = preds["instance_confmaps"][0][0].numpy() + pts_gt = example["center_instance"].numpy() + pts_pr = preds["instance_peaks"][0][0].numpy() + + scale = 1.0 + if img.shape[0] < 512: + scale = 2.0 + if img.shape[0] < 256: + scale = 4.0 + fig = plot_img(img, dpi=72 * scale, scale=scale) + plot_confmaps(cms, output_scale=cms.shape[0] / img.shape[0]) + plot_peaks(pts_gt, pts_pr, paired=True) + return fig + + self.visualization_callbacks.extend( + setup_visualization( + self.config.outputs, + run_path=self.run_path, + viz_fn=lambda: visualize_example(next(training_viz_ds_iter)), + name="train", + ) + ) + self.visualization_callbacks.extend( + setup_visualization( + self.config.outputs, + run_path=self.run_path, + viz_fn=lambda: visualize_example(next(validation_viz_ds_iter)), + name="validation", + ) + ) + + def main(): """Create CLI for training and run.""" import argparse diff --git a/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/best_model.h5 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