def build_backbone(features, config):
backbone_name = config.backbone_name
is_training_bn = config.is_training_bn
if 'efficientnet' in backbone_name:
override_params = {
'relu_fn': tf.nn.swish,
'batch_norm': utils.batch_norm_class(is_training_bn),
}
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))
_, endpoints = efficientnet_builder.build_model_base(
features,
backbone_name,
training=is_training_bn,
override_params=override_params)
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 {2: u2, 3: u3, 4: u4, 5: u5}
def build_backbone(features, config):
"""Builds backbone model.
Args:
features: input tensor.
config: config for backbone, such as is_training 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 = 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}
def get_model_params(model_name, override_params):
"""Get the block args and global params for a given model."""
if model_name.startswith('efficientnet-lite'):
width_coefficient, depth_coefficient, _, dropout_rate = (
efficientnet_lite_params(model_name))
global_params = efficientnet_lite(
width_coefficient, depth_coefficient, dropout_rate)
else:
raise NotImplementedError('model name is not pre-defined: %s' % model_name)
if override_params:
# ValueError will be raised here if override_params has fields not included
# in global_params.
global_params = global_params._replace(**override_params)
decoder = efficientnet_builder.BlockDecoder()
blocks_args = decoder.decode(global_params.blocks_args)
logging.info('global_params= %s', global_params)
return blocks_args, global_params
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 = {
'relu_fn': utils.backbone_relu_fn,
'batch_norm': utils.batch_norm_class(is_training_bn),
}
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))
_, endpoints = efficientnet_builder.build_model_base(
features,
backbone_name,
training=is_training_bn,
override_params=override_params)
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 {2: u2, 3: u3, 4: u4, 5: u5}
def __init__(self, model_name=None, config=None, name=''):
"""Initialize model."""
super().__init__(name=name)
config = config or hparams_config.get_efficientdet_config(model_name)
self.config = config
# Backbone.
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
self.backbone = backbone_factory.get_model(
backbone_name, override_params=override_params)
# Feature network.
self.resample_layers = [] # additional resampling layers.
for level in range(6, config.max_level + 1):
# Adds a coarser level by downsampling the last feature map.
self.resample_layers.append(
ResampleFeatureMap(
feat_level=(level - config.min_level),
target_num_channels=config.fpn_num_filters,
apply_bn=config.apply_bn_for_resampling,
is_training_bn=config.is_training_bn,
conv_after_downsample=config.conv_after_downsample,
strategy=config.strategy,
data_format=config.data_format,
name='resample_p%d' % level,
))
self.fpn_cells = FPNCells(config)
# class/box output prediction network.
num_anchors = len(config.aspect_ratios) * config.num_scales
num_filters = config.fpn_num_filters
for head in config.heads:
if head == 'object_detection':
self.class_net = ClassNet(
num_classes=config.num_classes,
num_anchors=num_anchors,
num_filters=num_filters,
min_level=config.min_level,
max_level=config.max_level,
is_training_bn=config.is_training_bn,
act_type=config.act_type,
repeats=config.box_class_repeats,
separable_conv=config.separable_conv,
survival_prob=config.survival_prob,
strategy=config.strategy,
data_format=config.data_format)
self.box_net = BoxNet(
num_anchors=num_anchors,
num_filters=num_filters,
min_level=config.min_level,
max_level=config.max_level,
is_training_bn=config.is_training_bn,
act_type=config.act_type,
repeats=config.box_class_repeats,
separable_conv=config.separable_conv,
survival_prob=config.survival_prob,
strategy=config.strategy,
data_format=config.data_format)
if head == 'segmentation':
self.seg_head = SegmentationHead(
num_classes=config.seg_num_classes,
num_filters=num_filters,
min_level=config.min_level,
max_level=config.max_level,
is_training_bn=config.is_training_bn,
act_type=config.act_type,
strategy=config.strategy,
data_format=config.data_format)