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research/object_detection/builders/box_predictor_builder.py
992 строки
47 KB
Austin Myers
Allow setting pointwise initializer and regularizer when use_depthwise is true.
31 мар 2021, 00:40
31 мар 2021, 00:40
f55a0eb
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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. # ============================================================================== """Function to build box predictor from configuration.""" import collections import tensorflow.compat.v1 as tf from object_detection.predictors import convolutional_box_predictor from object_detection.predictors import convolutional_keras_box_predictor from object_detection.predictors import mask_rcnn_box_predictor from object_detection.predictors import mask_rcnn_keras_box_predictor from object_detection.predictors import rfcn_box_predictor from object_detection.predictors import rfcn_keras_box_predictor from object_detection.predictors.heads import box_head from object_detection.predictors.heads import class_head from object_detection.predictors.heads import keras_box_head from object_detection.predictors.heads import keras_class_head from object_detection.predictors.heads import keras_mask_head from object_detection.predictors.heads import mask_head from object_detection.protos import box_predictor_pb2 def build_convolutional_box_predictor(is_training, num_classes, conv_hyperparams_fn, min_depth, max_depth, num_layers_before_predictor, use_dropout, dropout_keep_prob, kernel_size, box_code_size, apply_sigmoid_to_scores=False, add_background_class=True, class_prediction_bias_init=0.0, use_depthwise=False, box_encodings_clip_range=None): """Builds the ConvolutionalBoxPredictor from the arguments. Args: is_training: Indicates whether the BoxPredictor is in training mode. num_classes: number of classes. Note that num_classes *does not* include the background category, so if groundtruth labels take values in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the assigned classification targets can range from {0,... K}). conv_hyperparams_fn: A function to generate tf-slim arg_scope with hyperparameters for convolution ops. min_depth: Minimum feature depth prior to predicting box encodings and class predictions. max_depth: Maximum feature depth prior to predicting box encodings and class predictions. If max_depth is set to 0, no additional feature map will be inserted before location and class predictions. num_layers_before_predictor: Number of the additional conv layers before the predictor. use_dropout: Option to use dropout or not. Note that a single dropout op is applied here prior to both box and class predictions, which stands in contrast to the ConvolutionalBoxPredictor below. dropout_keep_prob: Keep probability for dropout. This is only used if use_dropout is True. kernel_size: Size of final convolution kernel. If the spatial resolution of the feature map is smaller than the kernel size, then the kernel size is automatically set to be min(feature_width, feature_height). box_code_size: Size of encoding for each box. apply_sigmoid_to_scores: If True, apply the sigmoid on the output class_predictions. add_background_class: Whether to add an implicit background class. class_prediction_bias_init: Constant value to initialize bias of the last conv2d layer before class prediction. use_depthwise: Whether to use depthwise convolutions for prediction steps. Default is False. box_encodings_clip_range: Min and max values for clipping the box_encodings. Returns: A ConvolutionalBoxPredictor class. """ box_prediction_head = box_head.ConvolutionalBoxHead( is_training=is_training, box_code_size=box_code_size, kernel_size=kernel_size, use_depthwise=use_depthwise, box_encodings_clip_range=box_encodings_clip_range) class_prediction_head = class_head.ConvolutionalClassHead( is_training=is_training, num_class_slots=num_classes + 1 if add_background_class else num_classes, use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob, kernel_size=kernel_size, apply_sigmoid_to_scores=apply_sigmoid_to_scores, class_prediction_bias_init=class_prediction_bias_init, use_depthwise=use_depthwise) other_heads = {} return convolutional_box_predictor.ConvolutionalBoxPredictor( is_training=is_training, num_classes=num_classes, box_prediction_head=box_prediction_head, class_prediction_head=class_prediction_head, other_heads=other_heads, conv_hyperparams_fn=conv_hyperparams_fn, num_layers_before_predictor=num_layers_before_predictor, min_depth=min_depth, max_depth=max_depth) def build_convolutional_keras_box_predictor(is_training, num_classes, conv_hyperparams, freeze_batchnorm, inplace_batchnorm_update, num_predictions_per_location_list, min_depth, max_depth, num_layers_before_predictor, use_dropout, dropout_keep_prob, kernel_size, box_code_size, add_background_class=True, class_prediction_bias_init=0.0, use_depthwise=False, box_encodings_clip_range=None, name='BoxPredictor'): """Builds the Keras ConvolutionalBoxPredictor from the arguments. Args: is_training: Indicates whether the BoxPredictor is in training mode. num_classes: number of classes. Note that num_classes *does not* include the background category, so if groundtruth labels take values in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the assigned classification targets can range from {0,... K}). conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object containing hyperparameters for convolution ops. freeze_batchnorm: Whether to freeze batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to freeze batch norm update and use pretrained batch norm params. inplace_batchnorm_update: Whether to update batch norm moving average values inplace. When this is false train op must add a control dependency on tf.graphkeys.UPDATE_OPS collection in order to update batch norm statistics. num_predictions_per_location_list: A list of integers representing the number of box predictions to be made per spatial location for each feature map. min_depth: Minimum feature depth prior to predicting box encodings and class predictions. max_depth: Maximum feature depth prior to predicting box encodings and class predictions. If max_depth is set to 0, no additional feature map will be inserted before location and class predictions. num_layers_before_predictor: Number of the additional conv layers before the predictor. use_dropout: Option to use dropout or not. Note that a single dropout op is applied here prior to both box and class predictions, which stands in contrast to the ConvolutionalBoxPredictor below. dropout_keep_prob: Keep probability for dropout. This is only used if use_dropout is True. kernel_size: Size of final convolution kernel. If the spatial resolution of the feature map is smaller than the kernel size, then the kernel size is automatically set to be min(feature_width, feature_height). box_code_size: Size of encoding for each box. add_background_class: Whether to add an implicit background class. class_prediction_bias_init: constant value to initialize bias of the last conv2d layer before class prediction. use_depthwise: Whether to use depthwise convolutions for prediction steps. Default is False. box_encodings_clip_range: Min and max values for clipping the box_encodings. name: A string name scope to assign to the box predictor. If `None`, Keras will auto-generate one from the class name. Returns: A Keras ConvolutionalBoxPredictor class. """ box_prediction_heads = [] class_prediction_heads = [] other_heads = {} for stack_index, num_predictions_per_location in enumerate( num_predictions_per_location_list): box_prediction_heads.append( keras_box_head.ConvolutionalBoxHead( is_training=is_training, box_code_size=box_code_size, kernel_size=kernel_size, conv_hyperparams=conv_hyperparams, freeze_batchnorm=freeze_batchnorm, num_predictions_per_location=num_predictions_per_location, use_depthwise=use_depthwise, box_encodings_clip_range=box_encodings_clip_range, name='ConvolutionalBoxHead_%d' % stack_index)) class_prediction_heads.append( keras_class_head.ConvolutionalClassHead( is_training=is_training, num_class_slots=( num_classes + 1 if add_background_class else num_classes), use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob, kernel_size=kernel_size, conv_hyperparams=conv_hyperparams, freeze_batchnorm=freeze_batchnorm, num_predictions_per_location=num_predictions_per_location, class_prediction_bias_init=class_prediction_bias_init, use_depthwise=use_depthwise, name='ConvolutionalClassHead_%d' % stack_index)) return convolutional_keras_box_predictor.ConvolutionalBoxPredictor( is_training=is_training, num_classes=num_classes, box_prediction_heads=box_prediction_heads, class_prediction_heads=class_prediction_heads, other_heads=other_heads, conv_hyperparams=conv_hyperparams, num_layers_before_predictor=num_layers_before_predictor, min_depth=min_depth, max_depth=max_depth, freeze_batchnorm=freeze_batchnorm, inplace_batchnorm_update=inplace_batchnorm_update, name=name) def build_weight_shared_convolutional_box_predictor( is_training, num_classes, conv_hyperparams_fn, depth, num_layers_before_predictor, box_code_size, kernel_size=3, add_background_class=True, class_prediction_bias_init=0.0, use_dropout=False, dropout_keep_prob=0.8, share_prediction_tower=False, apply_batch_norm=True, use_depthwise=False, score_converter_fn=tf.identity, box_encodings_clip_range=None, keyword_args=None): """Builds and returns a WeightSharedConvolutionalBoxPredictor class. Args: is_training: Indicates whether the BoxPredictor is in training mode. num_classes: number of classes. Note that num_classes *does not* include the background category, so if groundtruth labels take values in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the assigned classification targets can range from {0,... K}). conv_hyperparams_fn: A function to generate tf-slim arg_scope with hyperparameters for convolution ops. depth: depth of conv layers. num_layers_before_predictor: Number of the additional conv layers before the predictor. box_code_size: Size of encoding for each box. kernel_size: Size of final convolution kernel. add_background_class: Whether to add an implicit background class. class_prediction_bias_init: constant value to initialize bias of the last conv2d layer before class prediction. use_dropout: Whether to apply dropout to class prediction head. dropout_keep_prob: Probability of keeping activiations. share_prediction_tower: Whether to share the multi-layer tower between box prediction and class prediction heads. apply_batch_norm: Whether to apply batch normalization to conv layers in this predictor. use_depthwise: Whether to use depthwise separable conv2d instead of conv2d. score_converter_fn: Callable score converter to perform elementwise op on class scores. box_encodings_clip_range: Min and max values for clipping the box_encodings. keyword_args: A dictionary with additional args. Returns: A WeightSharedConvolutionalBoxPredictor class. """ box_prediction_head = box_head.WeightSharedConvolutionalBoxHead( box_code_size=box_code_size, kernel_size=kernel_size, use_depthwise=use_depthwise, box_encodings_clip_range=box_encodings_clip_range) class_prediction_head = ( class_head.WeightSharedConvolutionalClassHead( num_class_slots=( num_classes + 1 if add_background_class else num_classes), kernel_size=kernel_size, class_prediction_bias_init=class_prediction_bias_init, use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob, use_depthwise=use_depthwise, score_converter_fn=score_converter_fn)) other_heads = {} return convolutional_box_predictor.WeightSharedConvolutionalBoxPredictor( is_training=is_training, num_classes=num_classes, box_prediction_head=box_prediction_head, class_prediction_head=class_prediction_head, other_heads=other_heads, conv_hyperparams_fn=conv_hyperparams_fn, depth=depth, num_layers_before_predictor=num_layers_before_predictor, kernel_size=kernel_size, apply_batch_norm=apply_batch_norm, share_prediction_tower=share_prediction_tower, use_depthwise=use_depthwise) def build_weight_shared_convolutional_keras_box_predictor( is_training, num_classes, conv_hyperparams, freeze_batchnorm, inplace_batchnorm_update, num_predictions_per_location_list, depth, num_layers_before_predictor, box_code_size, kernel_size=3, add_background_class=True, class_prediction_bias_init=0.0, use_dropout=False, dropout_keep_prob=0.8, share_prediction_tower=False, apply_batch_norm=True, use_depthwise=False, apply_conv_hyperparams_to_heads=False, apply_conv_hyperparams_pointwise=False, score_converter_fn=tf.identity, box_encodings_clip_range=None, name='WeightSharedConvolutionalBoxPredictor', keyword_args=None): """Builds the Keras WeightSharedConvolutionalBoxPredictor from the arguments. Args: is_training: Indicates whether the BoxPredictor is in training mode. num_classes: number of classes. Note that num_classes *does not* include the background category, so if groundtruth labels take values in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the assigned classification targets can range from {0,... K}). conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object containing hyperparameters for convolution ops. freeze_batchnorm: Whether to freeze batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to freeze batch norm update and use pretrained batch norm params. inplace_batchnorm_update: Whether to update batch norm moving average values inplace. When this is false train op must add a control dependency on tf.graphkeys.UPDATE_OPS collection in order to update batch norm statistics. num_predictions_per_location_list: A list of integers representing the number of box predictions to be made per spatial location for each feature map. depth: depth of conv layers. num_layers_before_predictor: Number of the additional conv layers before the predictor. box_code_size: Size of encoding for each box. kernel_size: Size of final convolution kernel. add_background_class: Whether to add an implicit background class. class_prediction_bias_init: constant value to initialize bias of the last conv2d layer before class prediction. use_dropout: Whether to apply dropout to class prediction head. dropout_keep_prob: Probability of keeping activiations. share_prediction_tower: Whether to share the multi-layer tower between box prediction and class prediction heads. apply_batch_norm: Whether to apply batch normalization to conv layers in this predictor. use_depthwise: Whether to use depthwise separable conv2d instead of conv2d. apply_conv_hyperparams_to_heads: Whether to apply conv_hyperparams to depthwise seperable convolution layers in the box and class heads. By default, the conv_hyperparams are only applied to layers in the predictor tower when using depthwise separable convolutions. apply_conv_hyperparams_pointwise: Whether to apply the conv_hyperparams to the pointwise_initializer and pointwise_regularizer when using depthwise separable convolutions. By default, conv_hyperparams are only applied to the depthwise initializer and regularizer when use_depthwise is true. score_converter_fn: Callable score converter to perform elementwise op on class scores. box_encodings_clip_range: Min and max values for clipping the box_encodings. name: A string name scope to assign to the box predictor. If `None`, Keras will auto-generate one from the class name. keyword_args: A dictionary with additional args. Returns: A Keras WeightSharedConvolutionalBoxPredictor class. """ if len(set(num_predictions_per_location_list)) > 1: raise ValueError('num predictions per location must be same for all' 'feature maps, found: {}'.format( num_predictions_per_location_list)) num_predictions_per_location = num_predictions_per_location_list[0] box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead( box_code_size=box_code_size, kernel_size=kernel_size, conv_hyperparams=conv_hyperparams, num_predictions_per_location=num_predictions_per_location, use_depthwise=use_depthwise, apply_conv_hyperparams_to_heads=apply_conv_hyperparams_to_heads, box_encodings_clip_range=box_encodings_clip_range, name='WeightSharedConvolutionalBoxHead') class_prediction_head = keras_class_head.WeightSharedConvolutionalClassHead( num_class_slots=( num_classes + 1 if add_background_class else num_classes), use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob, kernel_size=kernel_size, conv_hyperparams=conv_hyperparams, num_predictions_per_location=num_predictions_per_location, class_prediction_bias_init=class_prediction_bias_init, use_depthwise=use_depthwise, apply_conv_hyperparams_to_heads=apply_conv_hyperparams_to_heads, score_converter_fn=score_converter_fn, name='WeightSharedConvolutionalClassHead') other_heads = {} return ( convolutional_keras_box_predictor.WeightSharedConvolutionalBoxPredictor( is_training=is_training, num_classes=num_classes, box_prediction_head=box_prediction_head, class_prediction_head=class_prediction_head, other_heads=other_heads, conv_hyperparams=conv_hyperparams, depth=depth, num_layers_before_predictor=num_layers_before_predictor, freeze_batchnorm=freeze_batchnorm, inplace_batchnorm_update=inplace_batchnorm_update, kernel_size=kernel_size, apply_batch_norm=apply_batch_norm, share_prediction_tower=share_prediction_tower, use_depthwise=use_depthwise, apply_conv_hyperparams_pointwise=apply_conv_hyperparams_pointwise, name=name)) def build_mask_rcnn_keras_box_predictor(is_training, num_classes, fc_hyperparams, freeze_batchnorm, use_dropout, dropout_keep_prob, box_code_size, add_background_class=True, share_box_across_classes=False, predict_instance_masks=False, conv_hyperparams=None, mask_height=14, mask_width=14, mask_prediction_num_conv_layers=2, mask_prediction_conv_depth=256, masks_are_class_agnostic=False, convolve_then_upsample_masks=False): """Builds and returns a MaskRCNNKerasBoxPredictor class. Args: is_training: Indicates whether the BoxPredictor is in training mode. num_classes: number of classes. Note that num_classes *does not* include the background category, so if groundtruth labels take values in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the assigned classification targets can range from {0,... K}). fc_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object containing hyperparameters for fully connected dense ops. freeze_batchnorm: Whether to freeze batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to freeze batch norm update and use pretrained batch norm params. use_dropout: Option to use dropout or not. Note that a single dropout op is applied here prior to both box and class predictions, which stands in contrast to the ConvolutionalBoxPredictor below. dropout_keep_prob: Keep probability for dropout. This is only used if use_dropout is True. box_code_size: Size of encoding for each box. add_background_class: Whether to add an implicit background class. share_box_across_classes: Whether to share boxes across classes rather than use a different box for each class. predict_instance_masks: If True, will add a third stage mask prediction to the returned class. conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object containing hyperparameters for convolution ops. mask_height: Desired output mask height. The default value is 14. mask_width: Desired output mask width. The default value is 14. mask_prediction_num_conv_layers: Number of convolution layers applied to the image_features in mask prediction branch. mask_prediction_conv_depth: The depth for the first conv2d_transpose op applied to the image_features in the mask prediction branch. If set to 0, the depth of the convolution layers will be automatically chosen based on the number of object classes and the number of channels in the image features. masks_are_class_agnostic: Boolean determining if the mask-head is class-agnostic or not. convolve_then_upsample_masks: Whether to apply convolutions on mask features before upsampling using nearest neighbor resizing. Otherwise, mask features are resized to [`mask_height`, `mask_width`] using bilinear resizing before applying convolutions. Returns: A MaskRCNNKerasBoxPredictor class. """ box_prediction_head = keras_box_head.MaskRCNNBoxHead( is_training=is_training, num_classes=num_classes, fc_hyperparams=fc_hyperparams, freeze_batchnorm=freeze_batchnorm, use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob, box_code_size=box_code_size, share_box_across_classes=share_box_across_classes) class_prediction_head = keras_class_head.MaskRCNNClassHead( is_training=is_training, num_class_slots=num_classes + 1 if add_background_class else num_classes, fc_hyperparams=fc_hyperparams, freeze_batchnorm=freeze_batchnorm, use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob) third_stage_heads = {} if predict_instance_masks: third_stage_heads[ mask_rcnn_box_predictor. MASK_PREDICTIONS] = keras_mask_head.MaskRCNNMaskHead( is_training=is_training, num_classes=num_classes, conv_hyperparams=conv_hyperparams, freeze_batchnorm=freeze_batchnorm, mask_height=mask_height, mask_width=mask_width, mask_prediction_num_conv_layers=mask_prediction_num_conv_layers, mask_prediction_conv_depth=mask_prediction_conv_depth, masks_are_class_agnostic=masks_are_class_agnostic, convolve_then_upsample=convolve_then_upsample_masks) return mask_rcnn_keras_box_predictor.MaskRCNNKerasBoxPredictor( is_training=is_training, num_classes=num_classes, freeze_batchnorm=freeze_batchnorm, box_prediction_head=box_prediction_head, class_prediction_head=class_prediction_head, third_stage_heads=third_stage_heads) def build_mask_rcnn_box_predictor(is_training, num_classes, fc_hyperparams_fn, use_dropout, dropout_keep_prob, box_code_size, add_background_class=True, share_box_across_classes=False, predict_instance_masks=False, conv_hyperparams_fn=None, mask_height=14, mask_width=14, mask_prediction_num_conv_layers=2, mask_prediction_conv_depth=256, masks_are_class_agnostic=False, convolve_then_upsample_masks=False): """Builds and returns a MaskRCNNBoxPredictor class. Args: is_training: Indicates whether the BoxPredictor is in training mode. num_classes: number of classes. Note that num_classes *does not* include the background category, so if groundtruth labels take values in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the assigned classification targets can range from {0,... K}). fc_hyperparams_fn: A function to generate tf-slim arg_scope with hyperparameters for fully connected ops. use_dropout: Option to use dropout or not. Note that a single dropout op is applied here prior to both box and class predictions, which stands in contrast to the ConvolutionalBoxPredictor below. dropout_keep_prob: Keep probability for dropout. This is only used if use_dropout is True. box_code_size: Size of encoding for each box. add_background_class: Whether to add an implicit background class. share_box_across_classes: Whether to share boxes across classes rather than use a different box for each class. predict_instance_masks: If True, will add a third stage mask prediction to the returned class. conv_hyperparams_fn: A function to generate tf-slim arg_scope with hyperparameters for convolution ops. mask_height: Desired output mask height. The default value is 14. mask_width: Desired output mask width. The default value is 14. mask_prediction_num_conv_layers: Number of convolution layers applied to the image_features in mask prediction branch. mask_prediction_conv_depth: The depth for the first conv2d_transpose op applied to the image_features in the mask prediction branch. If set to 0, the depth of the convolution layers will be automatically chosen based on the number of object classes and the number of channels in the image features. masks_are_class_agnostic: Boolean determining if the mask-head is class-agnostic or not. convolve_then_upsample_masks: Whether to apply convolutions on mask features before upsampling using nearest neighbor resizing. Otherwise, mask features are resized to [`mask_height`, `mask_width`] using bilinear resizing before applying convolutions. Returns: A MaskRCNNBoxPredictor class. """ box_prediction_head = box_head.MaskRCNNBoxHead( is_training=is_training, num_classes=num_classes, fc_hyperparams_fn=fc_hyperparams_fn, use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob, box_code_size=box_code_size, share_box_across_classes=share_box_across_classes) class_prediction_head = class_head.MaskRCNNClassHead( is_training=is_training, num_class_slots=num_classes + 1 if add_background_class else num_classes, fc_hyperparams_fn=fc_hyperparams_fn, use_dropout=use_dropout, dropout_keep_prob=dropout_keep_prob) third_stage_heads = {} if predict_instance_masks: third_stage_heads[ mask_rcnn_box_predictor. MASK_PREDICTIONS] = mask_head.MaskRCNNMaskHead( num_classes=num_classes, conv_hyperparams_fn=conv_hyperparams_fn, mask_height=mask_height, mask_width=mask_width, mask_prediction_num_conv_layers=mask_prediction_num_conv_layers, mask_prediction_conv_depth=mask_prediction_conv_depth, masks_are_class_agnostic=masks_are_class_agnostic, convolve_then_upsample=convolve_then_upsample_masks) return mask_rcnn_box_predictor.MaskRCNNBoxPredictor( is_training=is_training, num_classes=num_classes, box_prediction_head=box_prediction_head, class_prediction_head=class_prediction_head, third_stage_heads=third_stage_heads) def build_score_converter(score_converter_config, is_training): """Builds score converter based on the config. Builds one of [tf.identity, tf.sigmoid] score converters based on the config and whether the BoxPredictor is for training or inference. Args: score_converter_config: box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.score_converter. is_training: Indicates whether the BoxPredictor is in training mode. Returns: Callable score converter op. Raises: ValueError: On unknown score converter. """ if score_converter_config == ( box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.IDENTITY): return tf.identity if score_converter_config == ( box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.SIGMOID): return tf.identity if is_training else tf.sigmoid raise ValueError('Unknown score converter.') BoxEncodingsClipRange = collections.namedtuple('BoxEncodingsClipRange', ['min', 'max']) def build(argscope_fn, box_predictor_config, is_training, num_classes, add_background_class=True): """Builds box predictor based on the configuration. Builds box predictor based on the configuration. See box_predictor.proto for configurable options. Also, see box_predictor.py for more details. Args: argscope_fn: A function that takes the following inputs: * hyperparams_pb2.Hyperparams proto * a boolean indicating if the model is in training mode. and returns a tf slim argscope for Conv and FC hyperparameters. box_predictor_config: box_predictor_pb2.BoxPredictor proto containing configuration. is_training: Whether the models is in training mode. num_classes: Number of classes to predict. add_background_class: Whether to add an implicit background class. Returns: box_predictor: box_predictor.BoxPredictor object. Raises: ValueError: On unknown box predictor. """ if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor): raise ValueError('box_predictor_config not of type ' 'box_predictor_pb2.BoxPredictor.') box_predictor_oneof = box_predictor_config.WhichOneof('box_predictor_oneof') if box_predictor_oneof == 'convolutional_box_predictor': config_box_predictor = box_predictor_config.convolutional_box_predictor conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams, is_training) # Optionally apply clipping to box encodings, when box_encodings_clip_range # is set. box_encodings_clip_range = None if config_box_predictor.HasField('box_encodings_clip_range'): box_encodings_clip_range = BoxEncodingsClipRange( min=config_box_predictor.box_encodings_clip_range.min, max=config_box_predictor.box_encodings_clip_range.max) return build_convolutional_box_predictor( is_training=is_training, num_classes=num_classes, add_background_class=add_background_class, conv_hyperparams_fn=conv_hyperparams_fn, use_dropout=config_box_predictor.use_dropout, dropout_keep_prob=config_box_predictor.dropout_keep_probability, box_code_size=config_box_predictor.box_code_size, kernel_size=config_box_predictor.kernel_size, num_layers_before_predictor=( config_box_predictor.num_layers_before_predictor), min_depth=config_box_predictor.min_depth, max_depth=config_box_predictor.max_depth, apply_sigmoid_to_scores=config_box_predictor.apply_sigmoid_to_scores, class_prediction_bias_init=( config_box_predictor.class_prediction_bias_init), use_depthwise=config_box_predictor.use_depthwise, box_encodings_clip_range=box_encodings_clip_range) if box_predictor_oneof == 'weight_shared_convolutional_box_predictor': config_box_predictor = ( box_predictor_config.weight_shared_convolutional_box_predictor) conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams, is_training) apply_batch_norm = config_box_predictor.conv_hyperparams.HasField( 'batch_norm') # During training phase, logits are used to compute the loss. Only apply # sigmoid at inference to make the inference graph TPU friendly. score_converter_fn = build_score_converter( config_box_predictor.score_converter, is_training) # Optionally apply clipping to box encodings, when box_encodings_clip_range # is set. box_encodings_clip_range = None if config_box_predictor.HasField('box_encodings_clip_range'): box_encodings_clip_range = BoxEncodingsClipRange( min=config_box_predictor.box_encodings_clip_range.min, max=config_box_predictor.box_encodings_clip_range.max) keyword_args = None return build_weight_shared_convolutional_box_predictor( is_training=is_training, num_classes=num_classes, add_background_class=add_background_class, conv_hyperparams_fn=conv_hyperparams_fn, depth=config_box_predictor.depth, num_layers_before_predictor=( config_box_predictor.num_layers_before_predictor), box_code_size=config_box_predictor.box_code_size, kernel_size=config_box_predictor.kernel_size, class_prediction_bias_init=( config_box_predictor.class_prediction_bias_init), use_dropout=config_box_predictor.use_dropout, dropout_keep_prob=config_box_predictor.dropout_keep_probability, share_prediction_tower=config_box_predictor.share_prediction_tower, apply_batch_norm=apply_batch_norm, use_depthwise=config_box_predictor.use_depthwise, score_converter_fn=score_converter_fn, box_encodings_clip_range=box_encodings_clip_range, keyword_args=keyword_args) if box_predictor_oneof == 'mask_rcnn_box_predictor': config_box_predictor = box_predictor_config.mask_rcnn_box_predictor fc_hyperparams_fn = argscope_fn(config_box_predictor.fc_hyperparams, is_training) conv_hyperparams_fn = None if config_box_predictor.HasField('conv_hyperparams'): conv_hyperparams_fn = argscope_fn( config_box_predictor.conv_hyperparams, is_training) return build_mask_rcnn_box_predictor( is_training=is_training, num_classes=num_classes, add_background_class=add_background_class, fc_hyperparams_fn=fc_hyperparams_fn, use_dropout=config_box_predictor.use_dropout, dropout_keep_prob=config_box_predictor.dropout_keep_probability, box_code_size=config_box_predictor.box_code_size, share_box_across_classes=( config_box_predictor.share_box_across_classes), predict_instance_masks=config_box_predictor.predict_instance_masks, conv_hyperparams_fn=conv_hyperparams_fn, mask_height=config_box_predictor.mask_height, mask_width=config_box_predictor.mask_width, mask_prediction_num_conv_layers=( config_box_predictor.mask_prediction_num_conv_layers), mask_prediction_conv_depth=( config_box_predictor.mask_prediction_conv_depth), masks_are_class_agnostic=( config_box_predictor.masks_are_class_agnostic), convolve_then_upsample_masks=( config_box_predictor.convolve_then_upsample_masks)) if box_predictor_oneof == 'rfcn_box_predictor': config_box_predictor = box_predictor_config.rfcn_box_predictor conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams, is_training) box_predictor_object = rfcn_box_predictor.RfcnBoxPredictor( is_training=is_training, num_classes=num_classes, conv_hyperparams_fn=conv_hyperparams_fn, crop_size=[config_box_predictor.crop_height, config_box_predictor.crop_width], num_spatial_bins=[config_box_predictor.num_spatial_bins_height, config_box_predictor.num_spatial_bins_width], depth=config_box_predictor.depth, box_code_size=config_box_predictor.box_code_size) return box_predictor_object raise ValueError('Unknown box predictor: {}'.format(box_predictor_oneof)) def build_keras(hyperparams_fn, freeze_batchnorm, inplace_batchnorm_update, num_predictions_per_location_list, box_predictor_config, is_training, num_classes, add_background_class=True): """Builds a Keras-based box predictor based on the configuration. Builds Keras-based box predictor based on the configuration. See box_predictor.proto for configurable options. Also, see box_predictor.py for more details. Args: hyperparams_fn: A function that takes a hyperparams_pb2.Hyperparams proto and returns a `hyperparams_builder.KerasLayerHyperparams` for Conv or FC hyperparameters. freeze_batchnorm: Whether to freeze batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to freeze batch norm update and use pretrained batch norm params. inplace_batchnorm_update: Whether to update batch norm moving average values inplace. When this is false train op must add a control dependency on tf.graphkeys.UPDATE_OPS collection in order to update batch norm statistics. num_predictions_per_location_list: A list of integers representing the number of box predictions to be made per spatial location for each feature map. box_predictor_config: box_predictor_pb2.BoxPredictor proto containing configuration. is_training: Whether the models is in training mode. num_classes: Number of classes to predict. add_background_class: Whether to add an implicit background class. Returns: box_predictor: box_predictor.KerasBoxPredictor object. Raises: ValueError: On unknown box predictor, or one with no Keras box predictor. """ if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor): raise ValueError('box_predictor_config not of type ' 'box_predictor_pb2.BoxPredictor.') box_predictor_oneof = box_predictor_config.WhichOneof('box_predictor_oneof') if box_predictor_oneof == 'convolutional_box_predictor': config_box_predictor = box_predictor_config.convolutional_box_predictor conv_hyperparams = hyperparams_fn( config_box_predictor.conv_hyperparams) # Optionally apply clipping to box encodings, when box_encodings_clip_range # is set. box_encodings_clip_range = None if config_box_predictor.HasField('box_encodings_clip_range'): box_encodings_clip_range = BoxEncodingsClipRange( min=config_box_predictor.box_encodings_clip_range.min, max=config_box_predictor.box_encodings_clip_range.max) return build_convolutional_keras_box_predictor( is_training=is_training, num_classes=num_classes, add_background_class=add_background_class, conv_hyperparams=conv_hyperparams, freeze_batchnorm=freeze_batchnorm, inplace_batchnorm_update=inplace_batchnorm_update, num_predictions_per_location_list=num_predictions_per_location_list, use_dropout=config_box_predictor.use_dropout, dropout_keep_prob=config_box_predictor.dropout_keep_probability, box_code_size=config_box_predictor.box_code_size, kernel_size=config_box_predictor.kernel_size, num_layers_before_predictor=( config_box_predictor.num_layers_before_predictor), min_depth=config_box_predictor.min_depth, max_depth=config_box_predictor.max_depth, class_prediction_bias_init=( config_box_predictor.class_prediction_bias_init), use_depthwise=config_box_predictor.use_depthwise, box_encodings_clip_range=box_encodings_clip_range) if box_predictor_oneof == 'weight_shared_convolutional_box_predictor': config_box_predictor = ( box_predictor_config.weight_shared_convolutional_box_predictor) conv_hyperparams = hyperparams_fn(config_box_predictor.conv_hyperparams) apply_batch_norm = config_box_predictor.conv_hyperparams.HasField( 'batch_norm') # During training phase, logits are used to compute the loss. Only apply # sigmoid at inference to make the inference graph TPU friendly. This is # required because during TPU inference, model.postprocess is not called. score_converter_fn = build_score_converter( config_box_predictor.score_converter, is_training) # Optionally apply clipping to box encodings, when box_encodings_clip_range # is set. box_encodings_clip_range = None if config_box_predictor.HasField('box_encodings_clip_range'): box_encodings_clip_range = BoxEncodingsClipRange( min=config_box_predictor.box_encodings_clip_range.min, max=config_box_predictor.box_encodings_clip_range.max) keyword_args = None return build_weight_shared_convolutional_keras_box_predictor( is_training=is_training, num_classes=num_classes, conv_hyperparams=conv_hyperparams, freeze_batchnorm=freeze_batchnorm, inplace_batchnorm_update=inplace_batchnorm_update, num_predictions_per_location_list=num_predictions_per_location_list, depth=config_box_predictor.depth, num_layers_before_predictor=( config_box_predictor.num_layers_before_predictor), box_code_size=config_box_predictor.box_code_size, kernel_size=config_box_predictor.kernel_size, add_background_class=add_background_class, class_prediction_bias_init=( config_box_predictor.class_prediction_bias_init), use_dropout=config_box_predictor.use_dropout, dropout_keep_prob=config_box_predictor.dropout_keep_probability, share_prediction_tower=config_box_predictor.share_prediction_tower, apply_batch_norm=apply_batch_norm, use_depthwise=config_box_predictor.use_depthwise, apply_conv_hyperparams_to_heads=( config_box_predictor.apply_conv_hyperparams_to_heads), apply_conv_hyperparams_pointwise=( config_box_predictor.apply_conv_hyperparams_pointwise), score_converter_fn=score_converter_fn, box_encodings_clip_range=box_encodings_clip_range, keyword_args=keyword_args) if box_predictor_oneof == 'mask_rcnn_box_predictor': config_box_predictor = box_predictor_config.mask_rcnn_box_predictor fc_hyperparams = hyperparams_fn(config_box_predictor.fc_hyperparams) conv_hyperparams = None if config_box_predictor.HasField('conv_hyperparams'): conv_hyperparams = hyperparams_fn( config_box_predictor.conv_hyperparams) return build_mask_rcnn_keras_box_predictor( is_training=is_training, num_classes=num_classes, add_background_class=add_background_class, fc_hyperparams=fc_hyperparams, freeze_batchnorm=freeze_batchnorm, use_dropout=config_box_predictor.use_dropout, dropout_keep_prob=config_box_predictor.dropout_keep_probability, box_code_size=config_box_predictor.box_code_size, share_box_across_classes=( config_box_predictor.share_box_across_classes), predict_instance_masks=config_box_predictor.predict_instance_masks, conv_hyperparams=conv_hyperparams, mask_height=config_box_predictor.mask_height, mask_width=config_box_predictor.mask_width, mask_prediction_num_conv_layers=( config_box_predictor.mask_prediction_num_conv_layers), mask_prediction_conv_depth=( config_box_predictor.mask_prediction_conv_depth), masks_are_class_agnostic=( config_box_predictor.masks_are_class_agnostic), convolve_then_upsample_masks=( config_box_predictor.convolve_then_upsample_masks)) if box_predictor_oneof == 'rfcn_box_predictor': config_box_predictor = box_predictor_config.rfcn_box_predictor conv_hyperparams = hyperparams_fn(config_box_predictor.conv_hyperparams) box_predictor_object = rfcn_keras_box_predictor.RfcnKerasBoxPredictor( is_training=is_training, num_classes=num_classes, conv_hyperparams=conv_hyperparams, freeze_batchnorm=freeze_batchnorm, crop_size=[config_box_predictor.crop_height, config_box_predictor.crop_width], num_spatial_bins=[config_box_predictor.num_spatial_bins_height, config_box_predictor.num_spatial_bins_width], depth=config_box_predictor.depth, box_code_size=config_box_predictor.box_code_size) return box_predictor_object raise ValueError( 'Unknown box predictor for Keras: {}'.format(box_predictor_oneof))