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research/object_detection/core/model.py
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A. Unique TensorFlower
Image-level labels are not propagated to the open images challenge metric.
20 дек 2021, 17:32
20 дек 2021, 17:32
00f71bf
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Abstract detection model. This file defines a generic base class for detection models. Programs that are designed to work with arbitrary detection models should only depend on this class. We intend for the functions in this class to follow tensor-in/tensor-out design, thus all functions have tensors or lists/dictionaries holding tensors as inputs and outputs. Abstractly, detection models predict output tensors given input images which can be passed to a loss function at training time or passed to a postprocessing function at eval time. The computation graphs at a high level consequently look as follows: Training time: inputs (images tensor) -> preprocess -> predict -> loss -> outputs (loss tensor) Evaluation time: inputs (images tensor) -> preprocess -> predict -> postprocess -> outputs (boxes tensor, scores tensor, classes tensor, num_detections tensor) DetectionModels must thus implement four functions (1) preprocess, (2) predict, (3) postprocess and (4) loss. DetectionModels should make no assumptions about the input size or aspect ratio --- they are responsible for doing any resize/reshaping necessary (see docstring for the preprocess function). Output classes are always integers in the range [0, num_classes). Any mapping of these integers to semantic labels is to be handled outside of this class. Images are resized in the `preprocess` method. All of `preprocess`, `predict`, and `postprocess` should be reentrant. The `preprocess` method runs `image_resizer_fn` that returns resized_images and `true_image_shapes`. Since `image_resizer_fn` can pad the images with zeros, true_image_shapes indicate the slices that contain the image without padding. This is useful for padding images to be a fixed size for batching. The `postprocess` method uses the true image shapes to clip predictions that lie outside of images. By default, DetectionModels produce bounding box detections; However, we support a handful of auxiliary annotations associated with each bounding box, namely, instance masks and keypoints. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import six import tensorflow.compat.v1 as tf from object_detection.core import standard_fields as fields # If using a new enough version of TensorFlow, detection models should be a # tf module or keras model for tracking. try: _BaseClass = tf.keras.layers.Layer except AttributeError: _BaseClass = object class DetectionModel(six.with_metaclass(abc.ABCMeta, _BaseClass)): """Abstract base class for detection models. Extends tf.Module to guarantee variable tracking. """ def __init__(self, num_classes): """Constructor. Args: num_classes: number of classes. Note that num_classes *does not* include background categories that might be implicitly predicted in various implementations. """ self._num_classes = num_classes self._groundtruth_lists = {} self._training_step = None super(DetectionModel, self).__init__() @property def num_classes(self): return self._num_classes def groundtruth_lists(self, field): """Access list of groundtruth tensors. Args: field: a string key, options are fields.BoxListFields.{boxes,classes,masks,mask_weights,keypoints, keypoint_visibilities, densepose_*, track_ids, temporal_offsets, track_match_flags} fields.InputDataFields.is_annotated. Returns: a list of tensors holding groundtruth information (see also provide_groundtruth function below), with one entry for each image in the batch. Raises: RuntimeError: if the field has not been provided via provide_groundtruth. """ if field not in self._groundtruth_lists: raise RuntimeError('Groundtruth tensor {} has not been provided'.format( field)) return self._groundtruth_lists[field] def groundtruth_has_field(self, field): """Determines whether the groundtruth includes the given field. Args: field: a string key, options are fields.BoxListFields.{boxes,classes,masks,mask_weights,keypoints, keypoint_visibilities, densepose_*, track_ids} or fields.InputDataFields.is_annotated. Returns: True if the groundtruth includes the given field, False otherwise. """ return field in self._groundtruth_lists @property def training_step(self): if self._training_step is None: raise ValueError('Training step was not provided to the model.') return self._training_step @staticmethod def get_side_inputs(features): """Get side inputs from input features. This placeholder method provides a way for a meta-architecture to specify how to grab additional side inputs from input features (in addition to the image itself) and allows models to depend on contextual information. By default, detection models do not use side information (and thus this method returns an empty dictionary by default. However it can be overridden if side inputs are necessary." Args: features: A dictionary of tensors. Returns: An empty dictionary by default. """ return {} @abc.abstractmethod def preprocess(self, inputs): """Input preprocessing. To be overridden by implementations. This function is responsible for any scaling/shifting of input values that is necessary prior to running the detector on an input image. It is also responsible for any resizing, padding that might be necessary as images are assumed to arrive in arbitrary sizes. While this function could conceivably be part of the predict method (below), it is often convenient to keep these separate --- for example, we may want to preprocess on one device, place onto a queue, and let another device (e.g., the GPU) handle prediction. A few important notes about the preprocess function: + We assume that this operation does not have any trainable variables nor does it affect the groundtruth annotations in any way (thus data augmentation operations such as random cropping should be performed externally). + There is no assumption that the batchsize in this function is the same as the batch size in the predict function. In fact, we recommend calling the preprocess function prior to calling any batching operations (which should happen outside of the model) and thus assuming that batch sizes are equal to 1 in the preprocess function. + There is also no explicit assumption that the output resolutions must be fixed across inputs --- this is to support "fully convolutional" settings in which input images can have different shapes/resolutions. Args: inputs: a [batch, height_in, width_in, channels] float32 tensor representing a batch of images with values between 0 and 255.0. Returns: preprocessed_inputs: a [batch, height_out, width_out, channels] float32 tensor representing a batch of images. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. """ pass @abc.abstractmethod def predict(self, preprocessed_inputs, true_image_shapes, **side_inputs): """Predict prediction tensors from inputs tensor. Outputs of this function can be passed to loss or postprocess functions. Args: preprocessed_inputs: a [batch, height, width, channels] float32 tensor representing a batch of images. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. **side_inputs: additional tensors that are required by the network. Returns: prediction_dict: a dictionary holding prediction tensors to be passed to the Loss or Postprocess functions. """ pass @abc.abstractmethod def postprocess(self, prediction_dict, true_image_shapes, **params): """Convert predicted output tensors to final detections. This stage typically performs a few things such as * Non-Max Suppression to remove overlapping detection boxes. * Score conversion and background class removal. Outputs adhere to the following conventions: * Classes are integers in [0, num_classes); background classes are removed and the first non-background class is mapped to 0. If the model produces class-agnostic detections, then no output is produced for classes. * Boxes are to be interpreted as being in [y_min, x_min, y_max, x_max] format and normalized relative to the image window. * `num_detections` is provided for settings where detections are padded to a fixed number of boxes. * We do not specifically assume any kind of probabilistic interpretation of the scores --- the only important thing is their relative ordering. Thus implementations of the postprocess function are free to output logits, probabilities, calibrated probabilities, or anything else. Args: prediction_dict: a dictionary holding prediction tensors. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. **params: Additional keyword arguments for specific implementations of DetectionModel. Returns: detections: a dictionary containing the following fields detection_boxes: [batch, max_detections, 4] detection_scores: [batch, max_detections] detection_classes: [batch, max_detections] (If a model is producing class-agnostic detections, this field may be missing) detection_masks: [batch, max_detections, mask_height, mask_width] (optional) detection_keypoints: [batch, max_detections, num_keypoints, 2] (optional) detection_keypoint_scores: [batch, max_detections, num_keypoints] (optional) detection_surface_coords: [batch, max_detections, mask_height, mask_width, 2] (optional) num_detections: [batch] In addition to the above fields this stage also outputs the following raw tensors: raw_detection_boxes: [batch, total_detections, 4] tensor containing all detection boxes from `prediction_dict` in the format [ymin, xmin, ymax, xmax] and normalized co-ordinates. raw_detection_scores: [batch, total_detections, num_classes_with_background] tensor of class score logits for raw detection boxes. """ pass @abc.abstractmethod def loss(self, prediction_dict, true_image_shapes): """Compute scalar loss tensors with respect to provided groundtruth. Calling this function requires that groundtruth tensors have been provided via the provide_groundtruth function. Args: prediction_dict: a dictionary holding predicted tensors true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Returns: a dictionary mapping strings (loss names) to scalar tensors representing loss values. """ pass def provide_groundtruth( self, groundtruth_boxes_list, groundtruth_classes_list, groundtruth_masks_list=None, groundtruth_mask_weights_list=None, groundtruth_keypoints_list=None, groundtruth_keypoint_visibilities_list=None, groundtruth_dp_num_points_list=None, groundtruth_dp_part_ids_list=None, groundtruth_dp_surface_coords_list=None, groundtruth_track_ids_list=None, groundtruth_temporal_offsets_list=None, groundtruth_track_match_flags_list=None, groundtruth_weights_list=None, groundtruth_confidences_list=None, groundtruth_is_crowd_list=None, groundtruth_group_of_list=None, groundtruth_area_list=None, is_annotated_list=None, groundtruth_labeled_classes=None, groundtruth_verified_neg_classes=None, groundtruth_not_exhaustive_classes=None, groundtruth_keypoint_depths_list=None, groundtruth_keypoint_depth_weights_list=None, groundtruth_image_classes=None, training_step=None): """Provide groundtruth tensors. Args: groundtruth_boxes_list: a list of 2-D tf.float32 tensors of shape [num_boxes, 4] containing coordinates of the groundtruth boxes. Groundtruth boxes are provided in [y_min, x_min, y_max, x_max] format and assumed to be normalized and clipped relative to the image window with y_min <= y_max and x_min <= x_max. groundtruth_classes_list: a list of 2-D tf.float32 one-hot (or k-hot) tensors of shape [num_boxes, num_classes] containing the class targets with the 0th index assumed to map to the first non-background class. groundtruth_masks_list: a list of 3-D tf.float32 tensors of shape [num_boxes, height_in, width_in] containing instance masks with values in {0, 1}. If None, no masks are provided. Mask resolution `height_in`x`width_in` must agree with the resolution of the input image tensor provided to the `preprocess` function. groundtruth_mask_weights_list: a list of 1-D tf.float32 tensors of shape [num_boxes] with weights for each instance mask. groundtruth_keypoints_list: a list of 3-D tf.float32 tensors of shape [num_boxes, num_keypoints, 2] containing keypoints. Keypoints are assumed to be provided in normalized coordinates and missing keypoints should be encoded as NaN (but it is recommended to use `groundtruth_keypoint_visibilities_list`). groundtruth_keypoint_visibilities_list: a list of 3-D tf.bool tensors of shape [num_boxes, num_keypoints] containing keypoint visibilities. groundtruth_dp_num_points_list: a list of 1-D tf.int32 tensors of shape [num_boxes] containing the number of DensePose sampled points. groundtruth_dp_part_ids_list: a list of 2-D tf.int32 tensors of shape [num_boxes, max_sampled_points] containing the DensePose part ids (0-indexed) for each sampled point. Note that there may be padding. groundtruth_dp_surface_coords_list: a list of 3-D tf.float32 tensors of shape [num_boxes, max_sampled_points, 4] containing the DensePose surface coordinates for each sampled point. Note that there may be padding. groundtruth_track_ids_list: a list of 1-D tf.int32 tensors of shape [num_boxes] containing the track IDs of groundtruth objects. groundtruth_temporal_offsets_list: a list of 2-D tf.float32 tensors of shape [num_boxes, 2] containing the spatial offsets of objects' centers compared with the previous frame. groundtruth_track_match_flags_list: a list of 1-D tf.float32 tensors of shape [num_boxes] containing 0-1 flags that indicate if an object has existed in the previous frame. groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape [num_boxes] containing weights for groundtruth boxes. groundtruth_confidences_list: A list of 2-D tf.float32 tensors of shape [num_boxes, num_classes] containing class confidences for groundtruth boxes. groundtruth_is_crowd_list: A list of 1-D tf.bool tensors of shape [num_boxes] containing is_crowd annotations. groundtruth_group_of_list: A list of 1-D tf.bool tensors of shape [num_boxes] containing group_of annotations. groundtruth_area_list: A list of 1-D tf.float32 tensors of shape [num_boxes] containing the area (in the original absolute coordinates) of the annotations. is_annotated_list: A list of scalar tf.bool tensors indicating whether images have been labeled or not. groundtruth_labeled_classes: A list of 1-D tf.float32 tensors of shape [num_classes], containing label indices encoded as k-hot of the classes that are exhaustively annotated. groundtruth_verified_neg_classes: A list of 1-D tf.float32 tensors of shape [num_classes], containing a K-hot representation of classes which were verified as not present in the image. groundtruth_not_exhaustive_classes: A list of 1-D tf.float32 tensors of shape [num_classes], containing a K-hot representation of classes which don't have all of their instances marked exhaustively. groundtruth_keypoint_depths_list: a list of 2-D tf.float32 tensors of shape [num_boxes, num_keypoints] containing keypoint relative depths. groundtruth_keypoint_depth_weights_list: a list of 2-D tf.float32 tensors of shape [num_boxes, num_keypoints] containing the weights of the relative depths. groundtruth_image_classes: A list of 1-D tf.float32 tensors of shape [num_classes], containing label indices encoded as k-hot of the classes that are present or not present in the image. training_step: An integer denoting the current training step. This is useful when models want to anneal loss terms. """ self._groundtruth_lists[fields.BoxListFields.boxes] = groundtruth_boxes_list self._groundtruth_lists[ fields.BoxListFields.classes] = groundtruth_classes_list if groundtruth_weights_list: self._groundtruth_lists[fields.BoxListFields. weights] = groundtruth_weights_list if groundtruth_confidences_list: self._groundtruth_lists[fields.BoxListFields. confidences] = groundtruth_confidences_list if groundtruth_masks_list: self._groundtruth_lists[ fields.BoxListFields.masks] = groundtruth_masks_list if groundtruth_mask_weights_list: self._groundtruth_lists[ fields.BoxListFields.mask_weights] = groundtruth_mask_weights_list if groundtruth_keypoints_list: self._groundtruth_lists[ fields.BoxListFields.keypoints] = groundtruth_keypoints_list if groundtruth_keypoint_visibilities_list: self._groundtruth_lists[ fields.BoxListFields.keypoint_visibilities] = ( groundtruth_keypoint_visibilities_list) if groundtruth_keypoint_depths_list: self._groundtruth_lists[ fields.BoxListFields.keypoint_depths] = ( groundtruth_keypoint_depths_list) if groundtruth_keypoint_depth_weights_list: self._groundtruth_lists[ fields.BoxListFields.keypoint_depth_weights] = ( groundtruth_keypoint_depth_weights_list) if groundtruth_dp_num_points_list: self._groundtruth_lists[ fields.BoxListFields.densepose_num_points] = ( groundtruth_dp_num_points_list) if groundtruth_dp_part_ids_list: self._groundtruth_lists[ fields.BoxListFields.densepose_part_ids] = ( groundtruth_dp_part_ids_list) if groundtruth_dp_surface_coords_list: self._groundtruth_lists[ fields.BoxListFields.densepose_surface_coords] = ( groundtruth_dp_surface_coords_list) if groundtruth_track_ids_list: self._groundtruth_lists[ fields.BoxListFields.track_ids] = groundtruth_track_ids_list if groundtruth_temporal_offsets_list: self._groundtruth_lists[ fields.BoxListFields.temporal_offsets] = ( groundtruth_temporal_offsets_list) if groundtruth_track_match_flags_list: self._groundtruth_lists[ fields.BoxListFields.track_match_flags] = ( groundtruth_track_match_flags_list) if groundtruth_is_crowd_list: self._groundtruth_lists[ fields.BoxListFields.is_crowd] = groundtruth_is_crowd_list if groundtruth_group_of_list: self._groundtruth_lists[ fields.BoxListFields.group_of] = groundtruth_group_of_list if groundtruth_area_list: self._groundtruth_lists[ fields.InputDataFields.groundtruth_area] = groundtruth_area_list if is_annotated_list: self._groundtruth_lists[ fields.InputDataFields.is_annotated] = is_annotated_list if groundtruth_labeled_classes: self._groundtruth_lists[ fields.InputDataFields .groundtruth_labeled_classes] = groundtruth_labeled_classes if groundtruth_verified_neg_classes: self._groundtruth_lists[ fields.InputDataFields .groundtruth_verified_neg_classes] = groundtruth_verified_neg_classes if groundtruth_image_classes: self._groundtruth_lists[ fields.InputDataFields .groundtruth_image_classes] = groundtruth_image_classes if groundtruth_not_exhaustive_classes: self._groundtruth_lists[ fields.InputDataFields .groundtruth_not_exhaustive_classes] = ( groundtruth_not_exhaustive_classes) if training_step is not None: self._training_step = training_step @abc.abstractmethod def regularization_losses(self): """Returns a list of regularization losses for this model. Returns a list of regularization losses for this model that the estimator needs to use during training/optimization. Returns: A list of regularization loss tensors. """ pass @abc.abstractmethod def restore_map(self, fine_tune_checkpoint_type='detection', load_all_detection_checkpoint_vars=False): """Returns a map of variables to load from a foreign checkpoint. Returns a map of variable names to load from a checkpoint to variables in the model graph. This enables the model to initialize based on weights from another task. For example, the feature extractor variables from a classification model can be used to bootstrap training of an object detector. When loading from an object detection model, the checkpoint model should have the same parameters as this detection model with exception of the num_classes parameter. Args: fine_tune_checkpoint_type: whether to restore from a full detection checkpoint (with compatible variable names) or to restore from a classification checkpoint for initialization prior to training. Valid values: `detection`, `classification`. Default 'detection'. load_all_detection_checkpoint_vars: whether to load all variables (when `fine_tune_checkpoint_type` is `detection`). If False, only variables within the feature extractor scope are included. Default False. Returns: A dict mapping variable names (to load from a checkpoint) to variables in the model graph. """ pass @abc.abstractmethod def restore_from_objects(self, fine_tune_checkpoint_type='detection'): """Returns a map of variables to load from a foreign checkpoint. Returns a dictionary of Tensorflow 2 Trackable objects (e.g. tf.Module or Checkpoint). This enables the model to initialize based on weights from another task. For example, the feature extractor variables from a classification model can be used to bootstrap training of an object detector. When loading from an object detection model, the checkpoint model should have the same parameters as this detection model with exception of the num_classes parameter. Note that this function is intended to be used to restore Keras-based models when running Tensorflow 2, whereas restore_map (above) is intended to be used to restore Slim-based models when running Tensorflow 1.x. TODO(jonathanhuang,rathodv): Check tf_version and raise unimplemented error for both restore_map and restore_from_objects depending on version. Args: fine_tune_checkpoint_type: whether to restore from a full detection checkpoint (with compatible variable names) or to restore from a classification checkpoint for initialization prior to training. Valid values: `detection`, `classification`. Default 'detection'. Returns: A dict mapping keys to Trackable objects (tf.Module or Checkpoint). """ pass @abc.abstractmethod def updates(self): """Returns a list of update operators for this model. Returns a list of update operators for this model that must be executed at each training step. The estimator's train op needs to have a control dependency on these updates. Returns: A list of update operators. """ pass def call(self, images): """Returns detections from a batch of images. This method calls the preprocess, predict and postprocess function sequentially and returns the output. Args: images: a [batch_size, height, width, channels] float tensor. Returns: detetcions: The dict of tensors returned by the postprocess function. """ preprocessed_images, shapes = self.preprocess(images) prediction_dict = self.predict(preprocessed_images, shapes) return self.postprocess(prediction_dict, shapes)