# 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. # ============================================================================== """SSD Meta-architecture definition. General tensorflow implementation of convolutional Multibox/SSD detection models. """ from abc import abstractmethod import re import tensorflow as tf from object_detection.core import box_list from object_detection.core import box_predictor as bpredictor from object_detection.core import model from object_detection.core import standard_fields as fields from object_detection.core import target_assigner from object_detection.utils import shape_utils slim = tf.contrib.slim class SSDFeatureExtractor(object): """SSD Feature Extractor definition.""" def __init__(self, depth_multiplier, min_depth, conv_hyperparams, reuse_weights=None): self._depth_multiplier = depth_multiplier self._min_depth = min_depth self._conv_hyperparams = conv_hyperparams self._reuse_weights = reuse_weights @abstractmethod def preprocess(self, resized_inputs): """Preprocesses images for feature extraction (minus image resizing). Args: resized_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. """ pass @abstractmethod def extract_features(self, preprocessed_inputs): """Extracts features from preprocessed inputs. This function is responsible for extracting feature maps from preprocessed images. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ pass class SSDMetaArch(model.DetectionModel): """SSD Meta-architecture definition.""" def __init__(self, is_training, anchor_generator, box_predictor, box_coder, feature_extractor, matcher, region_similarity_calculator, image_resizer_fn, non_max_suppression_fn, score_conversion_fn, classification_loss, localization_loss, classification_loss_weight, localization_loss_weight, normalize_loss_by_num_matches, hard_example_miner, add_summaries=True): """SSDMetaArch Constructor. TODO: group NMS parameters + score converter into a class and loss parameters into a class and write config protos for postprocessing and losses. Args: is_training: A boolean indicating whether the training version of the computation graph should be constructed. anchor_generator: an anchor_generator.AnchorGenerator object. box_predictor: a box_predictor.BoxPredictor object. box_coder: a box_coder.BoxCoder object. feature_extractor: a SSDFeatureExtractor object. matcher: a matcher.Matcher object. region_similarity_calculator: a region_similarity_calculator.RegionSimilarityCalculator object. image_resizer_fn: a callable for image resizing. This callable always takes a rank-3 image tensor (corresponding to a single image) and returns a rank-3 image tensor, possibly with new spatial dimensions. See builders/image_resizer_builder.py. non_max_suppression_fn: batch_multiclass_non_max_suppression callable that takes `boxes`, `scores` and optional `clip_window` inputs (with all other inputs already set) and returns a dictionary hold tensors with keys: `detection_boxes`, `detection_scores`, `detection_classes` and `num_detections`. See `post_processing. batch_multiclass_non_max_suppression` for the type and shape of these tensors. score_conversion_fn: callable elementwise nonlinearity (that takes tensors as inputs and returns tensors). This is usually used to convert logits to probabilities. classification_loss: an object_detection.core.losses.Loss object. localization_loss: a object_detection.core.losses.Loss object. classification_loss_weight: float localization_loss_weight: float normalize_loss_by_num_matches: boolean hard_example_miner: a losses.HardExampleMiner object (can be None) add_summaries: boolean (default: True) controlling whether summary ops should be added to tensorflow graph. """ super(SSDMetaArch, self).__init__(num_classes=box_predictor.num_classes) self._is_training = is_training # Needed for fine-tuning from classification checkpoints whose # variables do not have the feature extractor scope. self._extract_features_scope = 'FeatureExtractor' self._anchor_generator = anchor_generator self._box_predictor = box_predictor self._box_coder = box_coder self._feature_extractor = feature_extractor self._matcher = matcher self._region_similarity_calculator = region_similarity_calculator # TODO: handle agnostic mode and positive/negative class weights unmatched_cls_target = None unmatched_cls_target = tf.constant([1] + self.num_classes * [0], tf.float32) self._target_assigner = target_assigner.TargetAssigner( self._region_similarity_calculator, self._matcher, self._box_coder, positive_class_weight=1.0, negative_class_weight=1.0, unmatched_cls_target=unmatched_cls_target) self._classification_loss = classification_loss self._localization_loss = localization_loss self._classification_loss_weight = classification_loss_weight self._localization_loss_weight = localization_loss_weight self._normalize_loss_by_num_matches = normalize_loss_by_num_matches self._hard_example_miner = hard_example_miner self._image_resizer_fn = image_resizer_fn self._non_max_suppression_fn = non_max_suppression_fn self._score_conversion_fn = score_conversion_fn self._anchors = None self._add_summaries = add_summaries @property def anchors(self): if not self._anchors: raise RuntimeError('anchors have not been constructed yet!') if not isinstance(self._anchors, box_list.BoxList): raise RuntimeError('anchors should be a BoxList object, but is not.') return self._anchors def preprocess(self, inputs): """Feature-extractor specific preprocessing. See base class. Args: inputs: a [batch, height_in, width_in, channels] float tensor representing a batch of images with values between 0 and 255.0. Returns: preprocessed_inputs: a [batch, height_out, width_out, channels] float tensor representing a batch of images. Raises: ValueError: if inputs tensor does not have type tf.float32 """ if inputs.dtype is not tf.float32: raise ValueError('`preprocess` expects a tf.float32 tensor') with tf.name_scope('Preprocessor'): # TODO: revisit whether to always use batch size as the number of # parallel iterations vs allow for dynamic batching. resized_inputs = tf.map_fn(self._image_resizer_fn, elems=inputs, dtype=tf.float32) return self._feature_extractor.preprocess(resized_inputs) def predict(self, preprocessed_inputs): """Predicts unpostprocessed tensors from input tensor. This function takes an input batch of images and runs it through the forward pass of the network to yield unpostprocessesed predictions. A side effect of calling the predict method is that self._anchors is populated with a box_list.BoxList of anchors. These anchors must be constructed before the postprocess or loss functions can be called. Args: preprocessed_inputs: a [batch, height, width, channels] image tensor. Returns: prediction_dict: a dictionary holding "raw" prediction tensors: 1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors, box_code_dimension] containing predicted boxes. 2) class_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, num_classes+1] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions (at class index 0). 3) feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i]. """ with tf.variable_scope(None, self._extract_features_scope, [preprocessed_inputs]): feature_maps = self._feature_extractor.extract_features( preprocessed_inputs) feature_map_spatial_dims = self._get_feature_map_spatial_dims(feature_maps) self._anchors = self._anchor_generator.generate(feature_map_spatial_dims) (box_encodings, class_predictions_with_background ) = self._add_box_predictions_to_feature_maps(feature_maps) predictions_dict = { 'box_encodings': box_encodings, 'class_predictions_with_background': class_predictions_with_background, 'feature_maps': feature_maps } return predictions_dict def _add_box_predictions_to_feature_maps(self, feature_maps): """Adds box predictors to each feature map and returns concatenated results. Args: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] Returns: box_encodings: 3-D float tensor of shape [batch_size, num_anchors, box_code_dimension] containing predicted boxes. class_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, num_classes+1] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions (at class index 0). Raises: RuntimeError: if the number of feature maps extracted via the extract_features method does not match the length of the num_anchors_per_locations list that was passed to the constructor. RuntimeError: if box_encodings from the box_predictor does not have shape of the form [batch_size, num_anchors, 1, code_size]. """ num_anchors_per_location_list = ( self._anchor_generator.num_anchors_per_location()) if len(feature_maps) != len(num_anchors_per_location_list): raise RuntimeError('the number of feature maps must match the ' 'length of self.anchors.NumAnchorsPerLocation().') box_encodings_list = [] cls_predictions_with_background_list = [] for idx, (feature_map, num_anchors_per_location ) in enumerate(zip(feature_maps, num_anchors_per_location_list)): box_predictor_scope = 'BoxPredictor_{}'.format(idx) box_predictions = self._box_predictor.predict(feature_map, num_anchors_per_location, box_predictor_scope) box_encodings = box_predictions[bpredictor.BOX_ENCODINGS] cls_predictions_with_background = box_predictions[ bpredictor.CLASS_PREDICTIONS_WITH_BACKGROUND] box_encodings_shape = box_encodings.get_shape().as_list() if len(box_encodings_shape) != 4 or box_encodings_shape[2] != 1: raise RuntimeError('box_encodings from the box_predictor must be of ' 'shape `[batch_size, num_anchors, 1, code_size]`; ' 'actual shape', box_encodings_shape) box_encodings = tf.squeeze(box_encodings, axis=2) box_encodings_list.append(box_encodings) cls_predictions_with_background_list.append( cls_predictions_with_background) num_predictions = sum( [tf.shape(box_encodings)[1] for box_encodings in box_encodings_list]) num_anchors = self.anchors.num_boxes() anchors_assert = tf.assert_equal(num_anchors, num_predictions, [ 'Mismatch: number of anchors vs number of predictions', num_anchors, num_predictions ]) with tf.control_dependencies([anchors_assert]): box_encodings = tf.concat(box_encodings_list, 1) class_predictions_with_background = tf.concat( cls_predictions_with_background_list, 1) return box_encodings, class_predictions_with_background def _get_feature_map_spatial_dims(self, feature_maps): """Return list of spatial dimensions for each feature map in a list. Args: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i]. Returns: a list of pairs (height, width) for each feature map in feature_maps """ feature_map_shapes = [ shape_utils.combined_static_and_dynamic_shape( feature_map) for feature_map in feature_maps ] return [(shape[1], shape[2]) for shape in feature_map_shapes] def postprocess(self, prediction_dict): """Converts prediction tensors to final detections. This function converts raw predictions tensors to final detection results by slicing off the background class, decoding box predictions and applying non max suppression and clipping to the image window. See base class for output format conventions. Note also that by default, scores are to be interpreted as logits, but if a score_conversion_fn is used, then scores are remapped (and may thus have a different interpretation). Args: prediction_dict: a dictionary holding prediction tensors with 1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors, box_code_dimension] containing predicted boxes. 2) class_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, num_classes+1] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions. Returns: detections: a dictionary containing the following fields detection_boxes: [batch, max_detection, 4] detection_scores: [batch, max_detections] detection_classes: [batch, max_detections] num_detections: [batch] Raises: ValueError: if prediction_dict does not contain `box_encodings` or `class_predictions_with_background` fields. """ if ('box_encodings' not in prediction_dict or 'class_predictions_with_background' not in prediction_dict): raise ValueError('prediction_dict does not contain expected entries.') with tf.name_scope('Postprocessor'): box_encodings = prediction_dict['box_encodings'] class_predictions = prediction_dict['class_predictions_with_background'] detection_boxes = self._batch_decode(box_encodings) detection_boxes = tf.expand_dims(detection_boxes, axis=2) class_predictions_without_background = tf.slice(class_predictions, [0, 0, 1], [-1, -1, -1]) detection_scores = self._score_conversion_fn( class_predictions_without_background) clip_window = tf.constant([0, 0, 1, 1], tf.float32) (nmsed_boxes, nmsed_scores, nmsed_classes, _, num_detections) = self._non_max_suppression_fn(detection_boxes, detection_scores, clip_window=clip_window) return {'detection_boxes': nmsed_boxes, 'detection_scores': nmsed_scores, 'detection_classes': nmsed_classes, 'num_detections': tf.to_float(num_detections)} def loss(self, prediction_dict, scope=None): """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 prediction tensors with 1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors, box_code_dimension] containing predicted boxes. 2) class_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, num_classes+1] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions. scope: Optional scope name. Returns: a dictionary mapping loss keys (`localization_loss` and `classification_loss`) to scalar tensors representing corresponding loss values. """ with tf.name_scope(scope, 'Loss', prediction_dict.values()): (batch_cls_targets, batch_cls_weights, batch_reg_targets, batch_reg_weights, match_list) = self._assign_targets( self.groundtruth_lists(fields.BoxListFields.boxes), self.groundtruth_lists(fields.BoxListFields.classes)) if self._add_summaries: self._summarize_input( self.groundtruth_lists(fields.BoxListFields.boxes), match_list) num_matches = tf.stack( [match.num_matched_columns() for match in match_list]) location_losses = self._localization_loss( prediction_dict['box_encodings'], batch_reg_targets, weights=batch_reg_weights) cls_losses = self._classification_loss( prediction_dict['class_predictions_with_background'], batch_cls_targets, weights=batch_cls_weights) # Optionally apply hard mining on top of loss values localization_loss = tf.reduce_sum(location_losses) classification_loss = tf.reduce_sum(cls_losses) if self._hard_example_miner: (localization_loss, classification_loss) = self._apply_hard_mining( location_losses, cls_losses, prediction_dict, match_list) if self._add_summaries: self._hard_example_miner.summarize() # Optionally normalize by number of positive matches normalizer = tf.constant(1.0, dtype=tf.float32) if self._normalize_loss_by_num_matches: normalizer = tf.maximum(tf.to_float(tf.reduce_sum(num_matches)), 1.0) loss_dict = { 'localization_loss': (self._localization_loss_weight / normalizer) * localization_loss, 'classification_loss': (self._classification_loss_weight / normalizer) * classification_loss } return loss_dict def _assign_targets(self, groundtruth_boxes_list, groundtruth_classes_list): """Assign groundtruth targets. Adds a background class to each one-hot encoding of groundtruth classes and uses target assigner to obtain regression and classification targets. Args: groundtruth_boxes_list: a list of 2-D 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 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. 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. """ groundtruth_boxlists = [ box_list.BoxList(boxes) for boxes in groundtruth_boxes_list ] groundtruth_classes_with_background_list = [ tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT') for one_hot_encoding in groundtruth_classes_list ] return target_assigner.batch_assign_targets( self._target_assigner, self.anchors, groundtruth_boxlists, groundtruth_classes_with_background_list) def _summarize_input(self, groundtruth_boxes_list, match_list): """Creates tensorflow summaries for the input boxes and anchors. This function creates four summaries corresponding to the average number (over images in a batch) of (1) groundtruth boxes, (2) anchors marked as positive, (3) anchors marked as negative, and (4) anchors marked as ignored. Args: groundtruth_boxes_list: a list of 2-D tensors of shape [num_boxes, 4] containing corners of the groundtruth boxes. 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. """ num_boxes_per_image = tf.stack( [tf.shape(x)[0] for x in groundtruth_boxes_list]) pos_anchors_per_image = tf.stack( [match.num_matched_columns() for match in match_list]) neg_anchors_per_image = tf.stack( [match.num_unmatched_columns() for match in match_list]) ignored_anchors_per_image = tf.stack( [match.num_ignored_columns() for match in match_list]) tf.summary.scalar('Input/AvgNumGroundtruthBoxesPerImage', tf.reduce_mean(tf.to_float(num_boxes_per_image))) tf.summary.scalar('Input/AvgNumPositiveAnchorsPerImage', tf.reduce_mean(tf.to_float(pos_anchors_per_image))) tf.summary.scalar('Input/AvgNumNegativeAnchorsPerImage', tf.reduce_mean(tf.to_float(neg_anchors_per_image))) tf.summary.scalar('Input/AvgNumIgnoredAnchorsPerImage', tf.reduce_mean(tf.to_float(ignored_anchors_per_image))) def _apply_hard_mining(self, location_losses, cls_losses, prediction_dict, match_list): """Applies hard mining to anchorwise losses. Args: location_losses: Float tensor of shape [batch_size, num_anchors] representing anchorwise location losses. cls_losses: Float tensor of shape [batch_size, num_anchors] representing anchorwise classification losses. prediction_dict: p a dictionary holding prediction tensors with 1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors, box_code_dimension] containing predicted boxes. 2) class_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, num_classes+1] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions. 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. Returns: mined_location_loss: a float scalar with sum of localization losses from selected hard examples. mined_cls_loss: a float scalar with sum of classification losses from selected hard examples. """ class_pred_shape = [-1, self.anchors.num_boxes_static(), self.num_classes] class_predictions = tf.reshape( tf.slice(prediction_dict['class_predictions_with_background'], [0, 0, 1], class_pred_shape), class_pred_shape) decoded_boxes = self._batch_decode(prediction_dict['box_encodings']) decoded_box_tensors_list = tf.unstack(decoded_boxes) class_prediction_list = tf.unstack(class_predictions) decoded_boxlist_list = [] for box_location, box_score in zip(decoded_box_tensors_list, class_prediction_list): decoded_boxlist = box_list.BoxList(box_location) decoded_boxlist.add_field('scores', box_score) decoded_boxlist_list.append(decoded_boxlist) return self._hard_example_miner( location_losses=location_losses, cls_losses=cls_losses, decoded_boxlist_list=decoded_boxlist_list, match_list=match_list) def _batch_decode(self, box_encodings): """Decodes a batch of box encodings with respect to the anchors. Args: box_encodings: A float32 tensor of shape [batch_size, num_anchors, box_code_size] containing box encodings. Returns: decoded_boxes: A float32 tensor of shape [batch_size, num_anchors, 4] containing the decoded boxes. """ combined_shape = shape_utils.combined_static_and_dynamic_shape( box_encodings) batch_size = combined_shape[0] tiled_anchor_boxes = tf.tile( tf.expand_dims(self.anchors.get(), 0), [batch_size, 1, 1]) tiled_anchors_boxlist = box_list.BoxList( tf.reshape(tiled_anchor_boxes, [-1, self._box_coder.code_size])) decoded_boxes = self._box_coder.decode( tf.reshape(box_encodings, [-1, self._box_coder.code_size]), tiled_anchors_boxlist) return tf.reshape(decoded_boxes.get(), tf.stack([combined_shape[0], combined_shape[1], 4])) def restore_map(self, from_detection_checkpoint=True): """Returns a map of variables to load from a foreign checkpoint. See parent class for details. Args: from_detection_checkpoint: whether to restore from a full detection checkpoint (with compatible variable names) or to restore from a classification checkpoint for initialization prior to training. Returns: A dict mapping variable names (to load from a checkpoint) to variables in the model graph. """ variables_to_restore = {} for variable in tf.all_variables(): if variable.op.name.startswith(self._extract_features_scope): var_name = variable.op.name if not from_detection_checkpoint: var_name = (re.split('^' + self._extract_features_scope + '/', var_name)[-1]) variables_to_restore[var_name] = variable return variables_to_restore