def test_revnet_creation(self, model_id):
"""Test creation of RevNet models."""
network = backbones.RevNet(model_id=model_id,
norm_momentum=0.99,
norm_epsilon=1e-5)
backbone_config = backbones_cfg.Backbone(
type='revnet', revnet=backbones_cfg.RevNet(model_id=model_id))
norm_activation_config = common_cfg.NormActivation(norm_momentum=0.99,
norm_epsilon=1e-5,
use_sync_bn=False)
factory_network = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(shape=[None, None, None, 3]),
backbone_config=backbone_config,
norm_activation_config=norm_activation_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def test_revnet_network_creation(self):
"""Test for creation of a RevNet-56 classifier."""
revnet_model_id = 56
inputs = np.random.rand(2, 224, 224, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.RevNet(model_id=revnet_model_id)
self.assertEqual(backbone.count_params(), 19473792)
num_classes = 1000
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
add_head_batch_norm=True,
)
self.assertEqual(model.count_params(), 22816104)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
def build_backbone(input_specs: tf.keras.layers.InputSpec,
model_config,
l2_regularizer: tf.keras.regularizers.Regularizer = None):
"""Builds backbone from a config.
Args:
input_specs: tf.keras.layers.InputSpec.
model_config: a OneOfConfig. Model config.
l2_regularizer: tf.keras.regularizers.Regularizer instance. Default to None.
Returns:
tf.keras.Model instance of the backbone.
"""
backbone_type = model_config.backbone.type
backbone_cfg = model_config.backbone.get()
norm_activation_config = model_config.norm_activation
if backbone_type == 'resnet':
backbone = backbones.ResNet(
model_id=backbone_cfg.model_id,
input_specs=input_specs,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
kernel_regularizer=l2_regularizer)
elif backbone_type == 'efficientnet':
backbone = backbones.EfficientNet(
model_id=backbone_cfg.model_id,
input_specs=input_specs,
stochastic_depth_drop_rate=backbone_cfg.stochastic_depth_drop_rate,
se_ratio=backbone_cfg.se_ratio,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
kernel_regularizer=l2_regularizer)
elif backbone_type == 'spinenet':
model_id = backbone_cfg.model_id
if model_id not in spinenet.SCALING_MAP:
raise ValueError(
'SpineNet-{} is not a valid architecture.'.format(model_id))
scaling_params = spinenet.SCALING_MAP[model_id]
backbone = backbones.SpineNet(
input_specs=input_specs,
min_level=model_config.min_level,
max_level=model_config.max_level,
endpoints_num_filters=scaling_params['endpoints_num_filters'],
resample_alpha=scaling_params['resample_alpha'],
block_repeats=scaling_params['block_repeats'],
filter_size_scale=scaling_params['filter_size_scale'],
kernel_regularizer=l2_regularizer,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon)
elif backbone_type == 'revnet':
backbone = backbones.RevNet(
model_id=backbone_cfg.model_id,
input_specs=input_specs,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
kernel_regularizer=l2_regularizer)
else:
raise ValueError('Backbone {!r} not implement'.format(backbone_type))
return backbone