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.mobilenet_v2 = 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 test_hyperparam_override(self):
hyperparams = self._build_conv_hyperparams()
model = mobilenet_v2.mobilenet_v2(batchnorm_training=True,
conv_hyperparams=hyperparams,
weights=None,
use_explicit_padding=False,
alpha=1.0,
min_depth=32,
include_top=False)
hyperparams.params()
bn_layer = model.get_layer(name='block_5_project_BN')
self.assertAllClose(bn_layer.momentum, 0.2)
self.assertAllClose(bn_layer.epsilon, 0.1)
def test_hyperparam_override(self):
hyperparams = self._build_conv_hyperparams()
model = mobilenet_v2.mobilenet_v2(
batchnorm_training=True,
conv_hyperparams=hyperparams,
weights=None,
use_explicit_padding=False,
alpha=1.0,
min_depth=32,
include_top=False)
hyperparams.params()
bn_layer = model.get_layer(name='block_5_project_BN')
self.assertAllClose(bn_layer.momentum, 0.2)
self.assertAllClose(bn_layer.epsilon, 0.1)
def _create_application_with_layer_outputs(self,
layer_names,
batchnorm_training,
conv_hyperparams=None,
use_explicit_padding=False,
alpha=1.0,
min_depth=None):
"""Constructs Keras mobilenetv2 that extracts intermediate layer outputs."""
if not layer_names:
layer_names = _layers_to_check
full_model = mobilenet_v2.mobilenet_v2(
batchnorm_training=batchnorm_training,
conv_hyperparams=conv_hyperparams,
weights=None,
use_explicit_padding=use_explicit_padding,
alpha=alpha,
min_depth=min_depth,
include_top=False)
layer_outputs = [
full_model.get_layer(name=layer).output for layer in layer_names
]
return tf.keras.Model(inputs=full_model.inputs, outputs=layer_outputs)
def _create_application_with_layer_outputs(
self, layer_names, batchnorm_training,
conv_hyperparams=None,
use_explicit_padding=False,
alpha=1.0,
min_depth=None):
"""Constructs Keras mobilenetv2 that extracts intermediate layer outputs."""
if not layer_names:
layer_names = _layers_to_check
full_model = mobilenet_v2.mobilenet_v2(
batchnorm_training=batchnorm_training,
conv_hyperparams=conv_hyperparams,
weights=None,
use_explicit_padding=use_explicit_padding,
alpha=alpha,
min_depth=min_depth,
include_top=False)
layer_outputs = [full_model.get_layer(name=layer).output
for layer in layer_names]
return tf.keras.Model(
inputs=full_model.inputs,
outputs=layer_outputs)
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,
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.
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,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
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.name_scope('MobilenetV2'):
full_mobilenet_v2 = mobilenet_v2.mobilenet_v2(
batchnorm_training=(is_training and not freeze_batchnorm),
conv_hyperparams=(conv_hyperparams
if self._override_base_feature_extractor_hyperparams
else None),
weights=None,
use_explicit_padding=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.mobilenet_v2 = 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=feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
is_training=is_training,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
name='FeatureMaps'))