# 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. # ============================================================================== """Base target assigner module. The job of a TargetAssigner is, for a given set of anchors (bounding boxes) and groundtruth detections (bounding boxes), to assign classification and regression targets to each anchor as well as weights to each anchor (specifying, e.g., which anchors should not contribute to training loss). It assigns classification/regression targets by performing the following steps: 1) Computing pairwise similarity between anchors and groundtruth boxes using a provided RegionSimilarity Calculator 2) Computing a matching based on the similarity matrix using a provided Matcher 3) Assigning regression targets based on the matching and a provided BoxCoder 4) Assigning classification targets based on the matching and groundtruth labels Note that TargetAssigners only operate on detections from a single image at a time, so any logic for applying a TargetAssigner to multiple images must be handled externally. """ import tensorflow as tf from object_detection.box_coders import faster_rcnn_box_coder from object_detection.box_coders import mean_stddev_box_coder from object_detection.core import box_coder as bcoder from object_detection.core import box_list from object_detection.core import box_list_ops from object_detection.core import matcher as mat from object_detection.core import region_similarity_calculator as sim_calc from object_detection.matchers import argmax_matcher from object_detection.matchers import bipartite_matcher class TargetAssigner(object): """Target assigner to compute classification and regression targets.""" def __init__(self, similarity_calc, matcher, box_coder, positive_class_weight=1.0, negative_class_weight=1.0, unmatched_cls_target=None): """Construct Multibox Target Assigner. Args: similarity_calc: a RegionSimilarityCalculator matcher: an object_detection.core.Matcher used to match groundtruth to anchors. box_coder: an object_detection.core.BoxCoder used to encode matching groundtruth boxes with respect to anchors. positive_class_weight: classification weight to be associated to positive anchors (default: 1.0) negative_class_weight: classification weight to be associated to negative anchors (default: 1.0) unmatched_cls_target: a float32 tensor with shape [d_1, d_2, ..., d_k] which is consistent with the classification target for each anchor (and can be empty for scalar targets). This shape must thus be compatible with the groundtruth labels that are passed to the "assign" function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]). If set to None, unmatched_cls_target is set to be [0] for each anchor. Raises: ValueError: if similarity_calc is not a RegionSimilarityCalculator or if matcher is not a Matcher or if box_coder is not a BoxCoder """ if not isinstance(similarity_calc, sim_calc.RegionSimilarityCalculator): raise ValueError('similarity_calc must be a RegionSimilarityCalculator') if not isinstance(matcher, mat.Matcher): raise ValueError('matcher must be a Matcher') if not isinstance(box_coder, bcoder.BoxCoder): raise ValueError('box_coder must be a BoxCoder') self._similarity_calc = similarity_calc self._matcher = matcher self._box_coder = box_coder self._positive_class_weight = positive_class_weight self._negative_class_weight = negative_class_weight if unmatched_cls_target is None: self._unmatched_cls_target = tf.constant([0], tf.float32) else: self._unmatched_cls_target = unmatched_cls_target @property def box_coder(self): return self._box_coder def assign(self, anchors, groundtruth_boxes, groundtruth_labels=None, **params): """Assign classification and regression targets to each anchor. For a given set of anchors and groundtruth detections, match anchors to groundtruth_boxes and assign classification and regression targets to each anchor as well as weights based on the resulting match (specifying, e.g., which anchors should not contribute to training loss). Anchors that are not matched to anything are given a classification target of self._unmatched_cls_target which can be specified via the constructor. Args: anchors: a BoxList representing N anchors groundtruth_boxes: a BoxList representing M groundtruth boxes groundtruth_labels: a tensor of shape [num_gt_boxes, d_1, ... d_k] with labels for each of the ground_truth boxes. The subshape [d_1, ... d_k] can be empty (corresponding to scalar inputs). When set to None, groundtruth_labels assumes a binary problem where all ground_truth boxes get a positive label (of 1). **params: Additional keyword arguments for specific implementations of the Matcher. Returns: cls_targets: a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has shape [num_gt_boxes, d_1, d_2, ... d_k]. cls_weights: a float32 tensor with shape [num_anchors] reg_targets: a float32 tensor with shape [num_anchors, box_code_dimension] reg_weights: a float32 tensor with shape [num_anchors] match: a matcher.Match object encoding the match between anchors and groundtruth boxes, with rows corresponding to groundtruth boxes and columns corresponding to anchors. Raises: ValueError: if anchors or groundtruth_boxes are not of type box_list.BoxList """ if not isinstance(anchors, box_list.BoxList): raise ValueError('anchors must be an BoxList') if not isinstance(groundtruth_boxes, box_list.BoxList): raise ValueError('groundtruth_boxes must be an BoxList') if groundtruth_labels is None: groundtruth_labels = tf.ones(tf.expand_dims(groundtruth_boxes.num_boxes(), 0)) groundtruth_labels = tf.expand_dims(groundtruth_labels, -1) shape_assert = tf.assert_equal(tf.shape(groundtruth_labels)[1:], tf.shape(self._unmatched_cls_target)) with tf.control_dependencies([shape_assert]): match_quality_matrix = self._similarity_calc.compare(groundtruth_boxes, anchors) match = self._matcher.match(match_quality_matrix, **params) reg_targets = self._create_regression_targets(anchors, groundtruth_boxes, match) cls_targets = self._create_classification_targets(groundtruth_labels, match) reg_weights = self._create_regression_weights(match) cls_weights = self._create_classification_weights( match, self._positive_class_weight, self._negative_class_weight) num_anchors = anchors.num_boxes_static() if num_anchors is not None: reg_targets = self._reset_target_shape(reg_targets, num_anchors) cls_targets = self._reset_target_shape(cls_targets, num_anchors) reg_weights = self._reset_target_shape(reg_weights, num_anchors) cls_weights = self._reset_target_shape(cls_weights, num_anchors) return cls_targets, cls_weights, reg_targets, reg_weights, match def _reset_target_shape(self, target, num_anchors): """Sets the static shape of the target. Args: target: the target tensor. Its first dimension will be overwritten. num_anchors: the number of anchors, which is used to override the target's first dimension. Returns: A tensor with the shape info filled in. """ target_shape = target.get_shape().as_list() target_shape[0] = num_anchors target.set_shape(target_shape) return target def _create_regression_targets(self, anchors, groundtruth_boxes, match): """Returns a regression target for each anchor. Args: anchors: a BoxList representing N anchors groundtruth_boxes: a BoxList representing M groundtruth_boxes match: a matcher.Match object Returns: reg_targets: a float32 tensor with shape [N, box_code_dimension] """ matched_anchor_indices = match.matched_column_indices() unmatched_ignored_anchor_indices = (match. unmatched_or_ignored_column_indices()) matched_gt_indices = match.matched_row_indices() matched_anchors = box_list_ops.gather(anchors, matched_anchor_indices) matched_gt_boxes = box_list_ops.gather(groundtruth_boxes, matched_gt_indices) matched_reg_targets = self._box_coder.encode(matched_gt_boxes, matched_anchors) unmatched_ignored_reg_targets = tf.tile( self._default_regression_target(), tf.stack([tf.size(unmatched_ignored_anchor_indices), 1])) reg_targets = tf.dynamic_stitch( [matched_anchor_indices, unmatched_ignored_anchor_indices], [matched_reg_targets, unmatched_ignored_reg_targets]) # TODO: summarize the number of matches on average. return reg_targets def _default_regression_target(self): """Returns the default target for anchors to regress to. Default regression targets are set to zero (though in this implementation what these targets are set to should not matter as the regression weight of any box set to regress to the default target is zero). Returns: default_target: a float32 tensor with shape [1, box_code_dimension] """ return tf.constant([self._box_coder.code_size*[0]], tf.float32) def _create_classification_targets(self, groundtruth_labels, match): """Create classification targets for each anchor. Assign a classification target of for each anchor to the matching groundtruth label that is provided by match. Anchors that are not matched to anything are given the target self._unmatched_cls_target Args: groundtruth_labels: a tensor of shape [num_gt_boxes, d_1, ... d_k] with labels for each of the ground_truth boxes. The subshape [d_1, ... d_k] can be empty (corresponding to scalar labels). match: a matcher.Match object that provides a matching between anchors and groundtruth boxes. Returns: cls_targets: a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has shape [num_gt_boxes, d_1, d_2, ... d_k]. """ matched_anchor_indices = match.matched_column_indices() unmatched_ignored_anchor_indices = (match. unmatched_or_ignored_column_indices()) matched_gt_indices = match.matched_row_indices() matched_cls_targets = tf.gather(groundtruth_labels, matched_gt_indices) ones = self._unmatched_cls_target.shape.ndims * [1] unmatched_ignored_cls_targets = tf.tile( tf.expand_dims(self._unmatched_cls_target, 0), tf.stack([tf.size(unmatched_ignored_anchor_indices)] + ones)) cls_targets = tf.dynamic_stitch( [matched_anchor_indices, unmatched_ignored_anchor_indices], [matched_cls_targets, unmatched_ignored_cls_targets]) return cls_targets def _create_regression_weights(self, match): """Set regression weight for each anchor. Only positive anchors are set to contribute to the regression loss, so this method returns a weight of 1 for every positive anchor and 0 for every negative anchor. Args: match: a matcher.Match object that provides a matching between anchors and groundtruth boxes. Returns: reg_weights: a float32 tensor with shape [num_anchors] representing regression weights """ reg_weights = tf.cast(match.matched_column_indicator(), tf.float32) return reg_weights def _create_classification_weights(self, match, positive_class_weight=1.0, negative_class_weight=1.0): """Create classification weights for each anchor. Positive (matched) anchors are associated with a weight of positive_class_weight and negative (unmatched) anchors are associated with a weight of negative_class_weight. When anchors are ignored, weights are set to zero. By default, both positive/negative weights are set to 1.0, but they can be adjusted to handle class imbalance (which is almost always the case in object detection). Args: match: a matcher.Match object that provides a matching between anchors and groundtruth boxes. positive_class_weight: weight to be associated to positive anchors negative_class_weight: weight to be associated to negative anchors Returns: cls_weights: a float32 tensor with shape [num_anchors] representing classification weights. """ matched_indicator = tf.cast(match.matched_column_indicator(), tf.float32) ignore_indicator = tf.cast(match.ignored_column_indicator(), tf.float32) unmatched_indicator = 1.0 - matched_indicator - ignore_indicator cls_weights = (positive_class_weight * matched_indicator + negative_class_weight * unmatched_indicator) return cls_weights def get_box_coder(self): """Get BoxCoder of this TargetAssigner. Returns: BoxCoder: BoxCoder object. """ return self._box_coder # TODO: This method pulls in all the implementation dependencies into core. # Therefore its best to have this factory method outside of core. def create_target_assigner(reference, stage=None, positive_class_weight=1.0, negative_class_weight=1.0, unmatched_cls_target=None): """Factory function for creating standard target assigners. Args: reference: string referencing the type of TargetAssigner. stage: string denoting stage: {proposal, detection}. positive_class_weight: classification weight to be associated to positive anchors (default: 1.0) negative_class_weight: classification weight to be associated to negative anchors (default: 1.0) unmatched_cls_target: a float32 tensor with shape [d_1, d_2, ..., d_k] which is consistent with the classification target for each anchor (and can be empty for scalar targets). This shape must thus be compatible with the groundtruth labels that are passed to the Assign function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]). If set to None, unmatched_cls_target is set to be 0 for each anchor. Returns: TargetAssigner: desired target assigner. Raises: ValueError: if combination reference+stage is invalid. """ if reference == 'Multibox' and stage == 'proposal': similarity_calc = sim_calc.NegSqDistSimilarity() matcher = bipartite_matcher.GreedyBipartiteMatcher() box_coder = mean_stddev_box_coder.MeanStddevBoxCoder() elif reference == 'FasterRCNN' and stage == 'proposal': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.7, unmatched_threshold=0.3, force_match_for_each_row=True) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FasterRCNN' and stage == 'detection': similarity_calc = sim_calc.IouSimilarity() # Uses all proposals with IOU < 0.5 as candidate negatives. matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, negatives_lower_than_unmatched=True) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FastRCNN': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.1, force_match_for_each_row=False, negatives_lower_than_unmatched=False) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder() else: raise ValueError('No valid combination of reference and stage.') return TargetAssigner(similarity_calc, matcher, box_coder, positive_class_weight=positive_class_weight, negative_class_weight=negative_class_weight, unmatched_cls_target=unmatched_cls_target) def batch_assign_targets(target_assigner, anchors_batch, gt_box_batch, gt_class_targets_batch): """Batched assignment of classification and regression targets. Args: target_assigner: a target assigner. anchors_batch: BoxList representing N box anchors or list of BoxList objects with length batch_size representing anchor sets. gt_box_batch: a list of BoxList objects with length batch_size representing groundtruth boxes for each image in the batch gt_class_targets_batch: a list of tensors with length batch_size, where each tensor has shape [num_gt_boxes_i, classification_target_size] and num_gt_boxes_i is the number of boxes in the ith boxlist of gt_box_batch. Returns: batch_cls_targets: a tensor with shape [batch_size, num_anchors, num_classes], batch_cls_weights: a tensor with shape [batch_size, num_anchors], batch_reg_targets: a tensor with shape [batch_size, num_anchors, box_code_dimension] batch_reg_weights: a tensor with shape [batch_size, num_anchors], match_list: a list of matcher.Match objects encoding the match between anchors and groundtruth boxes for each image of the batch, with rows of the Match objects corresponding to groundtruth boxes and columns corresponding to anchors. Raises: ValueError: if input list lengths are inconsistent, i.e., batch_size == len(gt_box_batch) == len(gt_class_targets_batch) and batch_size == len(anchors_batch) unless anchors_batch is a single BoxList. """ if not isinstance(anchors_batch, list): anchors_batch = len(gt_box_batch) * [anchors_batch] if not all( isinstance(anchors, box_list.BoxList) for anchors in anchors_batch): raise ValueError('anchors_batch must be a BoxList or list of BoxLists.') if not (len(anchors_batch) == len(gt_box_batch) == len(gt_class_targets_batch)): raise ValueError('batch size incompatible with lengths of anchors_batch, ' 'gt_box_batch and gt_class_targets_batch.') cls_targets_list = [] cls_weights_list = [] reg_targets_list = [] reg_weights_list = [] match_list = [] for anchors, gt_boxes, gt_class_targets in zip( anchors_batch, gt_box_batch, gt_class_targets_batch): (cls_targets, cls_weights, reg_targets, reg_weights, match) = target_assigner.assign( anchors, gt_boxes, gt_class_targets) cls_targets_list.append(cls_targets) cls_weights_list.append(cls_weights) reg_targets_list.append(reg_targets) reg_weights_list.append(reg_weights) match_list.append(match) batch_cls_targets = tf.stack(cls_targets_list) batch_cls_weights = tf.stack(cls_weights_list) batch_reg_targets = tf.stack(reg_targets_list) batch_reg_weights = tf.stack(reg_weights_list) return (batch_cls_targets, batch_cls_weights, batch_reg_targets, batch_reg_weights, match_list)