def test_get_expected_feature_map_shapes(self, bifpn_num_iterations):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block3', tf.random_uniform([4, 16, 16, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32))
]
bifpn_generator = bifpn_generators.KerasBiFpnFeatureMaps(
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=128,
fpn_min_level=3,
fpn_max_level=7,
input_max_level=5,
is_training=True,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False)
def graph_fn():
feature_maps = bifpn_generator(image_features)
return feature_maps
expected_feature_map_shapes = {
'{}_dn_lvl_3'.format(bifpn_num_iterations): (4, 16, 16, 128),
'{}_up_lvl_4'.format(bifpn_num_iterations): (4, 8, 8, 128),
'{}_up_lvl_5'.format(bifpn_num_iterations): (4, 4, 4, 128),
'{}_up_lvl_6'.format(bifpn_num_iterations): (4, 2, 2, 128),
'{}_up_lvl_7'.format(bifpn_num_iterations): (4, 1, 1, 128)}
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def build(self, input_shape):
self._bifpn_stage = bifpn_generators.KerasBiFpnFeatureMaps(
bifpn_num_iterations=self._bifpn_num_iterations,
bifpn_num_filters=self._bifpn_num_filters,
fpn_min_level=self._bifpn_min_level,
fpn_max_level=self._bifpn_max_level,
input_max_level=self._backbone_max_level,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
bifpn_node_params=self._bifpn_node_params,
name='bifpn')
self.built = True
def __init__(self,
is_training,
first_stage_features_stride,
conv_hyperparams,
min_depth,
bifpn_min_level,
bifpn_max_level,
bifpn_num_iterations,
bifpn_num_filters,
bifpn_combine_method,
efficientnet_version,
freeze_batchnorm,
pad_to_multiple=32,
weight_decay=0.0,
name=None):
"""Constructor.
Args:
is_training: See base class.
resnet_v1_base_model: base resnet v1 network to use. One of
the resnet_v1.resnet_v1_{50,101,152} models.
resnet_v1_base_model_name: model name under which to construct resnet v1.
first_stage_features_stride: See base class.
conv_hyperparams: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
batch_norm_trainable: See base class.
pad_to_multiple: An integer multiple to pad input image.
weight_decay: See base class.
fpn_min_level: the highest resolution feature map to use in FPN. The valid
values are {2, 3, 4, 5} which map to Resnet v1 layers.
fpn_max_level: the smallest resolution feature map to construct or use in
FPN. FPN constructions uses features maps starting from fpn_min_level
upto the fpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of fpn
levels.
additional_layer_depth: additional feature map layer channel depth.
override_base_feature_extractor_hyperparams: Whether to override
hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
Raises:
ValueError: If `first_stage_features_stride` is not 8 or 16.
"""
if first_stage_features_stride != 8 and first_stage_features_stride != 16:
raise ValueError('`first_stage_features_stride` must be 8 or 16.')
super(ProbabilisticTwoStageEfficientNetBiFPNKerasFeatureExtractor,
self).__init__(
is_training=is_training,
first_stage_features_stride=first_stage_features_stride,
freeze_batchnorm=freeze_batchnorm,
weight_decay=weight_decay)
self._bifpn_min_level = bifpn_min_level
self._bifpn_max_level = bifpn_max_level
self._bifpn_num_iterations = bifpn_num_iterations
self._bifpn_num_filters = max(bifpn_num_filters, min_depth)
self._bifpn_node_params = {'combine_method': bifpn_combine_method}
self._efficientnet_version = efficientnet_version
self._pad_to_multiple = pad_to_multiple
self._conv_hyperparams = conv_hyperparams
self._freeze_batchnorm = freeze_batchnorm
self.classification_backbone = None
logging.info('EfficientDet EfficientNet backbone version: %s',
self._efficientnet_version)
logging.info('EfficientDet BiFPN num filters: %d',
self._bifpn_num_filters)
logging.info('EfficientDet BiFPN num iterations: %d',
self._bifpn_num_iterations)
self._backbone_max_level = min(
max(_EFFICIENTNET_LEVEL_ENDPOINTS.keys()), self._bifpn_max_level)
self._output_layer_names = [
_EFFICIENTNET_LEVEL_ENDPOINTS[i]
for i in range(self._bifpn_min_level, self._backbone_max_level + 1)
]
self._output_layer_alias = [
'level_{}'.format(i)
for i in range(self._bifpn_min_level, self._backbone_max_level + 1)
]
efficientnet_base = efficientnet_model.EfficientNet.from_name(
model_name=self._efficientnet_version,
overrides={'rescale_input': False})
outputs = [
efficientnet_base.get_layer(output_layer_name).output
for output_layer_name in self._output_layer_names
]
self.classification_backbone = tf.keras.Model(
inputs=efficientnet_base.inputs, outputs=outputs)
self._bifpn_stage = bifpn_generators.KerasBiFpnFeatureMaps(
bifpn_num_iterations=self._bifpn_num_iterations,
bifpn_num_filters=self._bifpn_num_filters,
fpn_min_level=self._bifpn_min_level,
fpn_max_level=self._bifpn_max_level,
input_max_level=self._backbone_max_level,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
bifpn_node_params=self._bifpn_node_params,
name='bifpn')
self.proposal_feature_extractor_model = _EfficientNetBiFPN(
efficientnet_backbone=self.classification_backbone,
bifpn_generator=self._bifpn_stage,
pad_to_multiple=self._pad_to_multiple,
output_layer_alias=self._output_layer_alias)
self.box_classifier_model_conv = tf.keras.models.Sequential([
tf.keras.layers.SeparableConv2D(filters=128,
kernel_size=[3, 3],
strides=2,
activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=1024, activation='relu'),
tf.keras.layers.Dense(units=512, activation='relu'),
tf.keras.layers.Dense(units=256, activation='relu'),
tf.keras.layers.Reshape((1, 1, 256))
])
def test_get_expected_variable_names(self, bifpn_num_iterations):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block3', tf.random_uniform([4, 16, 16, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32))
]
bifpn_generator = bifpn_generators.KerasBiFpnFeatureMaps(
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=128,
fpn_min_level=3,
fpn_max_level=7,
input_max_level=5,
is_training=True,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
name='bifpn')
def graph_fn():
return bifpn_generator(image_features)
self.execute(graph_fn, [], g)
expected_variables = [
'bifpn/node_00/0_up_lvl_6/input_0_up_lvl_5/1x1_pre_sample/conv/bias',
'bifpn/node_00/0_up_lvl_6/input_0_up_lvl_5/1x1_pre_sample/conv/kernel',
'bifpn/node_03/1_dn_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/bias',
'bifpn/node_03/1_dn_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/kernel',
'bifpn/node_04/1_dn_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/bias',
'bifpn/node_04/1_dn_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/kernel',
'bifpn/node_05/1_dn_lvl_3/input_0_up_lvl_3/1x1_pre_sample/conv/bias',
'bifpn/node_05/1_dn_lvl_3/input_0_up_lvl_3/1x1_pre_sample/conv/kernel',
'bifpn/node_06/1_up_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/bias',
'bifpn/node_06/1_up_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/kernel',
'bifpn/node_07/1_up_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/bias',
'bifpn/node_07/1_up_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/kernel']
expected_node_variable_patterns = [
['bifpn/node_{:02}/{}_dn_lvl_6/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_dn_lvl_6/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_dn_lvl_6/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_dn_lvl_6/post_combine/separable_conv/pointwise_kernel'],
['bifpn/node_{:02}/{}_dn_lvl_5/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_dn_lvl_5/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_dn_lvl_5/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_dn_lvl_5/post_combine/separable_conv/pointwise_kernel'],
['bifpn/node_{:02}/{}_dn_lvl_4/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_dn_lvl_4/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_dn_lvl_4/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_dn_lvl_4/post_combine/separable_conv/pointwise_kernel'],
['bifpn/node_{:02}/{}_dn_lvl_3/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_dn_lvl_3/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_dn_lvl_3/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_dn_lvl_3/post_combine/separable_conv/pointwise_kernel'],
['bifpn/node_{:02}/{}_up_lvl_4/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_up_lvl_4/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_up_lvl_4/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_up_lvl_4/post_combine/separable_conv/pointwise_kernel'],
['bifpn/node_{:02}/{}_up_lvl_5/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_up_lvl_5/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_up_lvl_5/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_up_lvl_5/post_combine/separable_conv/pointwise_kernel'],
['bifpn/node_{:02}/{}_up_lvl_6/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_up_lvl_6/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_up_lvl_6/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_up_lvl_6/post_combine/separable_conv/pointwise_kernel'],
['bifpn/node_{:02}/{}_up_lvl_7/combine/bifpn_combine_weights',
'bifpn/node_{:02}/{}_up_lvl_7/post_combine/separable_conv/bias',
'bifpn/node_{:02}/{}_up_lvl_7/post_combine/separable_conv/depthwise_kernel',
'bifpn/node_{:02}/{}_up_lvl_7/post_combine/separable_conv/pointwise_kernel']]
node_i = 2
for iter_i in range(1, bifpn_num_iterations+1):
for node_variable_patterns in expected_node_variable_patterns:
for pattern in node_variable_patterns:
expected_variables.append(pattern.format(node_i, iter_i))
node_i += 1
expected_variables = set(expected_variables)
actual_variable_set = set(
[var.name.split(':')[0] for var in bifpn_generator.variables])
self.assertSetEqual(expected_variables, actual_variable_set)