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# Copyright 2018 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.
# ==============================================================================
"""SSDFeatureExtractor for MobilenetV2 features."""
import tensorflow as tf
from object_detection.meta_architectures import ssd_meta_arch
from object_detection.models import feature_map_generators
from object_detection.utils import context_manager
from object_detection.utils import ops
from object_detection.utils import shape_utils
from nets.mobilenet import mobilenet
from nets.mobilenet import mobilenet_v2
slim = tf.contrib.slim
class SSDMobileNetV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
"""SSD Feature Extractor using MobilenetV2 features."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams_fn,
reuse_weights=None,
use_explicit_padding=False,
use_depthwise=False,
override_base_feature_extractor_hyperparams=False):
"""MobileNetV2 Feature Extractor for SSD Models.
Mobilenet v2 (experimental), designed by sandler@. More details can be found
in //knowledge/cerebra/brain/compression/mobilenet/mobilenet_experimental.py
Args:
is_training: whether the network is in training mode.
depth_multiplier: float depth multiplier for feature extractor.
min_depth: minimum feature extractor depth.
pad_to_multiple: the nearest multiple to zero pad the input height and
width dimensions to.
conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
and separable_conv2d ops in the layers that are added on top of the
base feature extractor.
reuse_weights: Whether to reuse variables. Default is None.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
use_depthwise: Whether to use depthwise convolutions. Default is False.
override_base_feature_extractor_hyperparams: Whether to override
hyperparameters of the base feature extractor with the one from
`conv_hyperparams_fn`.
"""
super(SSDMobileNetV2FeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams_fn=conv_hyperparams_fn,
reuse_weights=reuse_weights,
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams)
def preprocess(self, resized_inputs):
"""SSD preprocessing.
Maps pixel values to the range [-1, 1].
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.
"""
return (2.0 / 255.0) * resized_inputs - 1.0
def extract_features(self, preprocessed_inputs):
"""Extract features from preprocessed inputs.
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_depthwise': self._use_depthwise,
'use_explicit_padding': self._use_explicit_padding,
}
with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
slim.arg_scope(
[mobilenet.depth_multiplier], min_depth=self._min_depth):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = mobilenet_v2.mobilenet_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='layer_19',
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
image_features=image_features)
return feature_maps.values()
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