research/object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py
# 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
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# ==============================================================================
"""SSDFeatureExtractor for MobilenetV2 features."""
import tensorflow.compat.v1 as tf
from object_detection.meta_architectures import ssd_meta_arch
from object_detection.models import feature_map_generators
from object_detection.models.keras_models import mobilenet_v2
from object_detection.utils import ops
from object_detection.utils import shape_utils
class SSDMobileNetV2KerasFeatureExtractor(
ssd_meta_arch.SSDKerasFeatureExtractor):
"""SSD Feature Extractor using MobilenetV2 features."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
use_explicit_padding=False,
use_depthwise=False,
num_layers=6,
override_base_feature_extractor_hyperparams=False,
name=None):
"""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 (Functions
as a width multiplier for the mobilenet_v2 network itself).
min_depth: minimum feature extractor depth.
pad_to_multiple: the nearest multiple to zero pad the input height and
width dimensions to.
conv_hyperparams: `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
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.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
use_depthwise: Whether to use depthwise convolutions. Default is False.
num_layers: Number of SSD layers.
override_base_feature_extractor_hyperparams: Whether to override
hyperparameters of the base feature extractor with the one from
`conv_hyperparams_fn`.
name: A string name scope to assign to the model. If 'None', Keras
will auto-generate one from the class name.
"""
super(SSDMobileNetV2KerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
num_layers=num_layers,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
self._feature_map_layout = {
'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''
][:self._num_layers],
'layer_depth': [-1, -1, 512, 256, 256, 128][:self._num_layers],
'use_depthwise': self._use_depthwise,
'use_explicit_padding': self._use_explicit_padding,
}
self.classification_backbone = None
self.feature_map_generator = None
def build(self, input_shape):
full_mobilenet_v2 = mobilenet_v2.mobilenet_v2(
batchnorm_training=(self._is_training and not self._freeze_batchnorm),
conv_hyperparams=(self._conv_hyperparams
if self._override_base_feature_extractor_hyperparams
else None),
weights=None,
use_explicit_padding=self._use_explicit_padding,
alpha=self._depth_multiplier,
min_depth=self._min_depth,
include_top=False)
conv2d_11_pointwise = full_mobilenet_v2.get_layer(
name='block_13_expand_relu').output
conv2d_13_pointwise = full_mobilenet_v2.get_layer(name='out_relu').output
self.classification_backbone = tf.keras.Model(
inputs=full_mobilenet_v2.inputs,
outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
self.feature_map_generator = (
feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
name='FeatureMaps'))
self.built = True
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)
image_features = self.classification_backbone(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))
feature_maps = self.feature_map_generator({
'layer_15/expansion_output': image_features[0],
'layer_19': image_features[1]})
return list(feature_maps.values())