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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.
# ==============================================================================
"""Convolutional Box Predictors with and without weight sharing."""
import collections
import tensorflow as tf
from object_detection.core import box_predictor
from object_detection.utils import static_shape
keras = tf.keras.layers
BOX_ENCODINGS = box_predictor.BOX_ENCODINGS
CLASS_PREDICTIONS_WITH_BACKGROUND = (
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND)
MASK_PREDICTIONS = box_predictor.MASK_PREDICTIONS
class _NoopVariableScope(object):
"""A dummy class that does not push any scope."""
def __enter__(self):
return None
def __exit__(self, exc_type, exc_value, traceback):
return False
class ConvolutionalBoxPredictor(box_predictor.KerasBoxPredictor):
"""Convolutional Keras Box Predictor.
Optionally add an intermediate 1x1 convolutional layer after features and
predict in parallel branches box_encodings and
class_predictions_with_background.
Currently this box predictor assumes that predictions are "shared" across
classes --- that is each anchor makes box predictions which do not depend
on class.
"""
def __init__(self,
is_training,
num_classes,
box_prediction_heads,
class_prediction_heads,
other_heads,
conv_hyperparams,
num_layers_before_predictor,
min_depth,
max_depth,
freeze_batchnorm,
inplace_batchnorm_update,
name=None):
"""Constructor.
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}).
box_prediction_heads: A list of heads that predict the boxes.
class_prediction_heads: A list of heads that predict the classes.
other_heads: A dictionary mapping head names to lists of convolutional
heads.
conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
containing hyperparameters for convolution ops.
num_layers_before_predictor: Number of the additional conv layers before
the predictor.
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.
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.
name: A string name scope to assign to the model. If `None`, Keras
will auto-generate one from the class name.
Raises:
ValueError: if min_depth > max_depth.
"""
super(ConvolutionalBoxPredictor, self).__init__(
is_training, num_classes, freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
name=name)
if min_depth > max_depth:
raise ValueError('min_depth should be less than or equal to max_depth')
if len(box_prediction_heads) != len(class_prediction_heads):
raise ValueError('All lists of heads must be the same length.')
for other_head_list in other_heads.values():
if len(box_prediction_heads) != len(other_head_list):
raise ValueError('All lists of heads must be the same length.')
self._prediction_heads = {
BOX_ENCODINGS: box_prediction_heads,
CLASS_PREDICTIONS_WITH_BACKGROUND: class_prediction_heads,
}
if other_heads:
self._prediction_heads.update(other_heads)
# We generate a consistent ordering for the prediction head names,
# So that all workers build the model in the exact same order
self._sorted_head_names = sorted(self._prediction_heads.keys())
self._conv_hyperparams = conv_hyperparams
self._min_depth = min_depth
self._max_depth = max_depth
self._num_layers_before_predictor = num_layers_before_predictor
self._shared_nets = []
def build(self, input_shapes):
"""Creates the variables of the layer."""
if len(input_shapes) != len(self._prediction_heads[BOX_ENCODINGS]):
raise ValueError('This box predictor was constructed with %d heads,'
'but there are %d inputs.' %
(len(self._prediction_heads[BOX_ENCODINGS]),
len(input_shapes)))
for stack_index, input_shape in enumerate(input_shapes):
net = []
# Add additional conv layers before the class predictor.
features_depth = static_shape.get_depth(input_shape)
depth = max(min(features_depth, self._max_depth), self._min_depth)
tf.logging.info(
'depth of additional conv before box predictor: {}'.format(depth))
if depth > 0 and self._num_layers_before_predictor > 0:
for i in range(self._num_layers_before_predictor):
net.append(keras.Conv2D(depth, [1, 1],
name='SharedConvolutions_%d/Conv2d_%d_1x1_%d'
% (stack_index, i, depth),
padding='SAME',
**self._conv_hyperparams.params()))
net.append(self._conv_hyperparams.build_batch_norm(
training=(self._is_training and not self._freeze_batchnorm),
name='SharedConvolutions_%d/Conv2d_%d_1x1_%d_norm'
% (stack_index, i, depth)))
net.append(self._conv_hyperparams.build_activation_layer(
name='SharedConvolutions_%d/Conv2d_%d_1x1_%d_activation'
% (stack_index, i, depth),
))
# Until certain bugs are fixed in checkpointable lists,
# this net must be appended only once it's been filled with layers
self._shared_nets.append(net)
self.built = True
def _predict(self, image_features):
"""Computes encoded object locations and corresponding confidences.
Args:
image_features: A list of float tensors of shape [batch_size, height_i,
width_i, channels_i] containing features for a batch of images.
Returns:
box_encodings: A list of float tensors of shape
[batch_size, num_anchors_i, q, code_size] representing the location of
the objects, where q is 1 or the number of classes. Each entry in the
list corresponds to a feature map in the input `image_features` list.
class_predictions_with_background: A list of float tensors of shape
[batch_size, num_anchors_i, num_classes + 1] representing the class
predictions for the proposals. Each entry in the list corresponds to a
feature map in the input `image_features` list.
"""
predictions = collections.defaultdict(list)
for (index, net) in enumerate(image_features):
# Apply shared conv layers before the head predictors.
for layer in self._shared_nets[index]:
net = layer(net)
for head_name in self._sorted_head_names:
head_obj = self._prediction_heads[head_name][index]
prediction = head_obj(net)
predictions[head_name].append(prediction)
return predictions
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