def build(self, input_shape):
full_mobilenet_v1 = mobilenet_v1.mobilenet_v1(
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_v1.get_layer(
name='conv_pw_11_relu').output
conv2d_13_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_13_relu').output
self._mobilenet_v1 = tf.keras.Model(
inputs=full_mobilenet_v1.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 build(self, input_shape):
full_mobilenet_v1 = mobilenet_v1.mobilenet_v1(
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_v1.get_layer(
name='conv_pw_11_relu').output
conv2d_13_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_13_relu').output
self._mobilenet_v1 = tf.keras.Model(
inputs=full_mobilenet_v1.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_v1.mobilenet_v1(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='conv_pw_5_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_v1.mobilenet_v1(
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='conv_pw_5_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 MobilenetV1 that extracts intermediate layer outputs."""
if not layer_names:
layer_names = _KERAS_LAYERS_TO_CHECK
full_model = mobilenet_v1.mobilenet_v1(
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 MobilenetV1 that extracts intermediate layer outputs."""
if not layer_names:
layer_names = _KERAS_LAYERS_TO_CHECK
full_model = mobilenet_v1.mobilenet_v1(
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 build(self, input_shape):
full_mobilenet_v1 = mobilenet_v1.mobilenet_v1(
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,
conv_defs=self._conv_defs,
include_top=False)
conv2d_3_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_3_relu').output
conv2d_5_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_5_relu').output
conv2d_11_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_11_relu').output
conv2d_13_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_13_relu').output
self._mobilenet_v1 = tf.keras.Model(inputs=full_mobilenet_v1.inputs,
outputs=[
conv2d_3_pointwise,
conv2d_5_pointwise,
conv2d_11_pointwise,
conv2d_13_pointwise
])
# pylint:disable=g-long-lambda
self._depth_fn = lambda d: max(int(d * self._depth_multiplier), self.
_min_depth)
self._base_fpn_max_level = min(self._fpn_max_level, 5)
self._num_levels = self._base_fpn_max_level + 1 - self._fpn_min_level
self._fpn_features_generator = (
feature_map_generators.KerasFpnTopDownFeatureMaps(
num_levels=self._num_levels,
depth=self._depth_fn(self._additional_layer_depth),
use_depthwise=self._use_depthwise,
use_explicit_padding=self._use_explicit_padding,
use_native_resize_op=self._use_native_resize_op,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
name='FeatureMaps'))
# Construct coarse feature layers
padding = 'VALID' if self._use_explicit_padding else 'SAME'
kernel_size = 3
stride = 2
for i in range(self._base_fpn_max_level + 1, self._fpn_max_level + 1):
coarse_feature_layers = []
if self._use_explicit_padding:
def fixed_padding(features, kernel_size=kernel_size):
return ops.fixed_padding(features, kernel_size)
coarse_feature_layers.append(
tf.keras.layers.Lambda(fixed_padding,
name='fixed_padding'))
layer_name = 'bottom_up_Conv2d_{}'.format(
i - self._base_fpn_max_level + 13)
conv_block = feature_map_generators.create_conv_block(
self._use_depthwise, kernel_size, padding, stride, layer_name,
self._conv_hyperparams, self._is_training,
self._freeze_batchnorm,
self._depth_fn(self._additional_layer_depth))
coarse_feature_layers.extend(conv_block)
self._coarse_feature_layers.append(coarse_feature_layers)
self.built = True
