def build_backbone(features, config):
"""Builds backbone model.
Args:
features: input tensor.
config: config for backbone, such as is_training and backbone name.
Returns:
An arrray of features (starting from min_level) from the output of the
backbone model with strides of 8, 16 and 32.
Raises:
ValueError: if backbone_name is not supported.
"""
backbone_name = config.backbone_name
is_training = config.is_training_bn
if 'efficientnet' in backbone_name:
override_params = {
'batch_norm':
utils.batch_norm_class(is_training, config.strategy),
'relu_fn':
functools.partial(utils.activation_fn, act_type=config.act_type),
}
if 'b0' in backbone_name:
override_params['survival_prob'] = 0.0
if config.backbone_config is not None:
override_params['blocks_args'] = (
efficientnet_builder.BlockDecoder().encode(
config.backbone_config.blocks))
override_params['data_format'] = config.data_format
model_builder = backbone_factory.get_model_builder(backbone_name)
_, endpoints = model_builder.build_model_base(
features,
backbone_name,
training=is_training,
override_params=override_params)
all_feats = [
features,
endpoints['reduction_1'],
endpoints['reduction_2'],
endpoints['reduction_3'],
endpoints['reduction_4'],
endpoints['reduction_5'],
]
else:
raise ValueError(
'backbone model {} is not supported.'.format(backbone_name))
# Only return features within the expected levels.
return all_feats[config.min_level:config.max_level + 1]
def build_backbone(features, config):
"""Builds backbone model.
Args:
features: input tensor.
config: config for backbone, such as is_training_bn and backbone name.
Returns:
A dict from levels to the feature maps from the output of the backbone model
with strides of 8, 16 and 32.
Raises:
ValueError: if backbone_name is not supported.
"""
backbone_name = config.backbone_name
is_training_bn = config.is_training_bn
if 'efficientnet' in backbone_name:
override_params = {
'batch_norm':
utils.batch_norm_class(is_training_bn, config.strategy),
'relu_fn':
functools.partial(utils.activation_fn, act_type=config.act_type),
}
if 'b0' in backbone_name:
override_params['survival_prob'] = 0.0
if config.backbone_config is not None:
override_params['blocks_args'] = (
efficientnet_builder.BlockDecoder().encode(
config.backbone_config.blocks))
override_params['data_format'] = config.data_format
model_builder = backbone_factory.get_model_builder(backbone_name)
_, endpoints = model_builder.build_model_base(
features,
backbone_name,
training=is_training_bn,
override_params=override_params)
print(endpoints.keys())
print(backbone_name)
u1 = endpoints['reduction_1']
u2 = endpoints['reduction_2']
u3 = endpoints['reduction_3']
u4 = endpoints['reduction_4']
u5 = endpoints['reduction_5']
else:
raise ValueError(
'backbone model {} is not supported.'.format(backbone_name))
return {0: features, 1: u1, 2: u2, 3: u3, 4: u4, 5: u5}
