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253 lines
9.4 KiB
253 lines
9.4 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Box Head.
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Contains Box prediction head classes for different meta architectures.
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All the box prediction heads have a predict function that receives the
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`features` as the first argument and returns `box_encodings`.
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"""
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import functools
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import tensorflow as tf
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from object_detection.predictors.heads import head
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slim = tf.contrib.slim
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class MaskRCNNBoxHead(head.Head):
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"""Box prediction head.
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Please refer to Mask RCNN paper:
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https://arxiv.org/abs/1703.06870
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"""
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def __init__(self,
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is_training,
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num_classes,
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fc_hyperparams_fn,
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use_dropout,
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dropout_keep_prob,
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box_code_size,
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share_box_across_classes=False):
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"""Constructor.
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Args:
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is_training: Indicates whether the BoxPredictor is in training mode.
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num_classes: number of classes. Note that num_classes *does not*
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include the background category, so if groundtruth labels take values
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in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
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assigned classification targets can range from {0,... K}).
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fc_hyperparams_fn: A function to generate tf-slim arg_scope with
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hyperparameters for fully connected ops.
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use_dropout: Option to use dropout or not. Note that a single dropout
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op is applied here prior to both box and class predictions, which stands
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in contrast to the ConvolutionalBoxPredictor below.
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dropout_keep_prob: Keep probability for dropout.
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This is only used if use_dropout is True.
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box_code_size: Size of encoding for each box.
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share_box_across_classes: Whether to share boxes across classes rather
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than use a different box for each class.
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"""
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super(MaskRCNNBoxHead, self).__init__()
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self._is_training = is_training
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self._num_classes = num_classes
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self._fc_hyperparams_fn = fc_hyperparams_fn
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self._use_dropout = use_dropout
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self._dropout_keep_prob = dropout_keep_prob
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self._box_code_size = box_code_size
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self._share_box_across_classes = share_box_across_classes
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def predict(self, features, num_predictions_per_location=1):
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"""Predicts boxes.
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Args:
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features: A float tensor of shape [batch_size, height, width,
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channels] containing features for a batch of images.
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num_predictions_per_location: Int containing number of predictions per
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location.
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Returns:
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box_encodings: A float tensor of shape
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[batch_size, 1, num_classes, code_size] representing the location of the
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objects.
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Raises:
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ValueError: If num_predictions_per_location is not 1.
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"""
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if num_predictions_per_location != 1:
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raise ValueError('Only num_predictions_per_location=1 is supported')
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spatial_averaged_roi_pooled_features = tf.reduce_mean(
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features, [1, 2], keep_dims=True, name='AvgPool')
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flattened_roi_pooled_features = slim.flatten(
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spatial_averaged_roi_pooled_features)
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if self._use_dropout:
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flattened_roi_pooled_features = slim.dropout(
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flattened_roi_pooled_features,
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keep_prob=self._dropout_keep_prob,
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is_training=self._is_training)
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number_of_boxes = 1
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if not self._share_box_across_classes:
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number_of_boxes = self._num_classes
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with slim.arg_scope(self._fc_hyperparams_fn()):
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box_encodings = slim.fully_connected(
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flattened_roi_pooled_features,
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number_of_boxes * self._box_code_size,
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activation_fn=None,
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scope='BoxEncodingPredictor')
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box_encodings = tf.reshape(box_encodings,
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[-1, 1, number_of_boxes, self._box_code_size])
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return box_encodings
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class ConvolutionalBoxHead(head.Head):
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"""Convolutional box prediction head."""
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def __init__(self,
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is_training,
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box_code_size,
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kernel_size,
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use_depthwise=False):
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"""Constructor.
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Args:
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is_training: Indicates whether the BoxPredictor is in training mode.
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box_code_size: Size of encoding for each box.
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kernel_size: Size of final convolution kernel. If the
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spatial resolution of the feature map is smaller than the kernel size,
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then the kernel size is automatically set to be
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min(feature_width, feature_height).
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use_depthwise: Whether to use depthwise convolutions for prediction
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steps. Default is False.
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Raises:
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ValueError: if min_depth > max_depth.
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"""
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super(ConvolutionalBoxHead, self).__init__()
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self._is_training = is_training
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self._box_code_size = box_code_size
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self._kernel_size = kernel_size
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self._use_depthwise = use_depthwise
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def predict(self, features, num_predictions_per_location):
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"""Predicts boxes.
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Args:
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features: A float tensor of shape [batch_size, height, width, channels]
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containing image features.
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num_predictions_per_location: Number of box predictions to be made per
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spatial location. Int specifying number of boxes per location.
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Returns:
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box_encodings: A float tensors of shape
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[batch_size, num_anchors, q, code_size] representing the location of
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the objects, where q is 1 or the number of classes.
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"""
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net = features
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if self._use_depthwise:
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box_encodings = slim.separable_conv2d(
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net, None, [self._kernel_size, self._kernel_size],
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padding='SAME', depth_multiplier=1, stride=1,
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rate=1, scope='BoxEncodingPredictor_depthwise')
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box_encodings = slim.conv2d(
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box_encodings,
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num_predictions_per_location * self._box_code_size, [1, 1],
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activation_fn=None,
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normalizer_fn=None,
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normalizer_params=None,
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scope='BoxEncodingPredictor')
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else:
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box_encodings = slim.conv2d(
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net, num_predictions_per_location * self._box_code_size,
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[self._kernel_size, self._kernel_size],
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activation_fn=None,
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normalizer_fn=None,
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normalizer_params=None,
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scope='BoxEncodingPredictor')
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batch_size = features.get_shape().as_list()[0]
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if batch_size is None:
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batch_size = tf.shape(features)[0]
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box_encodings = tf.reshape(box_encodings,
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[batch_size, -1, 1, self._box_code_size])
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return box_encodings
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# TODO(alirezafathi): See if possible to unify Weight Shared with regular
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# convolutional box head.
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class WeightSharedConvolutionalBoxHead(head.Head):
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"""Weight shared convolutional box prediction head.
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This head allows sharing the same set of parameters (weights) when called more
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then once on different feature maps.
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"""
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def __init__(self,
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box_code_size,
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kernel_size=3,
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use_depthwise=False,
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box_encodings_clip_range=None):
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"""Constructor.
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Args:
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box_code_size: Size of encoding for each box.
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kernel_size: Size of final convolution kernel.
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use_depthwise: Whether to use depthwise convolutions for prediction steps.
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Default is False.
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box_encodings_clip_range: Min and max values for clipping box_encodings.
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"""
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super(WeightSharedConvolutionalBoxHead, self).__init__()
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self._box_code_size = box_code_size
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self._kernel_size = kernel_size
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self._use_depthwise = use_depthwise
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self._box_encodings_clip_range = box_encodings_clip_range
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def predict(self, features, num_predictions_per_location):
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"""Predicts boxes.
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Args:
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features: A float tensor of shape [batch_size, height, width, channels]
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containing image features.
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num_predictions_per_location: Number of box predictions to be made per
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spatial location.
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Returns:
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box_encodings: A float tensor of shape
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[batch_size, num_anchors, code_size] representing the location of
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the objects.
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"""
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box_encodings_net = features
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if self._use_depthwise:
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conv_op = functools.partial(slim.separable_conv2d, depth_multiplier=1)
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else:
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conv_op = slim.conv2d
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box_encodings = conv_op(
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box_encodings_net,
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num_predictions_per_location * self._box_code_size,
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[self._kernel_size, self._kernel_size],
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activation_fn=None, stride=1, padding='SAME',
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normalizer_fn=None,
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scope='BoxPredictor')
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batch_size = features.get_shape().as_list()[0]
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if batch_size is None:
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batch_size = tf.shape(features)[0]
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# Clipping the box encodings to make the inference graph TPU friendly.
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if self._box_encodings_clip_range is not None:
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box_encodings = tf.clip_by_value(
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box_encodings, self._box_encodings_clip_range.min,
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self._box_encodings_clip_range.max)
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box_encodings = tf.reshape(box_encodings,
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[batch_size, -1, self._box_code_size])
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return box_encodings
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