def get_copy_of_layer(layer):
from keras.applications.mobilenet import relu6
from keras.layers.core import Activation
from keras import layers
config = layer.get_config()
# Non-standard relu6 layer (from MobileNet)
if layer.__class__.__name__ == 'Activation':
if config['activation'] == 'relu6':
layer_copy = Activation(relu6, name=layer.name)
return layer_copy
# DeepLabV3+ non-standard layer
if layer.__class__.__name__ == 'BilinearUpsampling':
from neural_nets.deeplab_v3_plus_model import BilinearUpsampling
layer_copy = BilinearUpsampling(upsampling=config['upsampling'],
output_size=config['output_size'],
name=layer.name)
return layer_copy
layer_copy = layers.deserialize({
'class_name': layer.__class__.__name__,
'config': config
})
layer_copy.name = layer.name
return layer_copy
def get_copy_of_layer(layer, verbose=False):
from keras.layers.core import Activation
from keras import layers
config = layer.get_config()
# Non-standard relu6 layer (from MobileNet)
if layer.__class__.__name__ == 'Activation':
if config['activation'] == 'relu6':
if get_keras_sub_version() == 1:
from keras.applications.mobilenet import relu6
else:
from keras_applications.mobilenet import relu6
layer_copy = Activation(relu6, name=layer.name)
return layer_copy
# DeepLabV3+ non-standard layer
if layer.__class__.__name__ == 'BilinearUpsampling':
from neural_nets.deeplab_v3_plus_model import BilinearUpsampling
layer_copy = BilinearUpsampling(upsampling=config['upsampling'],
output_size=config['output_size'],
name=layer.name)
return layer_copy
# RetinaNet non-standard layer
if layer.__class__.__name__ == 'UpsampleLike':
from keras_retinanet.layers import UpsampleLike
layer_copy = UpsampleLike(name=layer.name)
return layer_copy
# RetinaNet non-standard layer
if layer.__class__.__name__ == 'Anchors':
from keras_retinanet.layers import Anchors
layer_copy = Anchors(name=layer.name,
size=config['size'],
stride=config['stride'],
ratios=config['ratios'],
scales=config['scales'])
return layer_copy
# RetinaNet non-standard layer
if layer.__class__.__name__ == 'RegressBoxes':
from keras_retinanet.layers import RegressBoxes
layer_copy = RegressBoxes(name=layer.name,
mean=config['mean'],
std=config['std'])
return layer_copy
# RetinaNet non-standard layer
if layer.__class__.__name__ == 'PriorProbability':
from keras_retinanet.layers import PriorProbability
layer_copy = PriorProbability(name=layer.name,
mean=config['mean'],
std=config['std'])
return layer_copy
# RetinaNet non-standard layer
if layer.__class__.__name__ == 'ClipBoxes':
from keras_retinanet.layers import ClipBoxes
layer_copy = ClipBoxes(name=layer.name)
return layer_copy
# RetinaNet non-standard layer
if layer.__class__.__name__ == 'FilterDetections':
from keras_retinanet.layers import FilterDetections
layer_copy = FilterDetections(
name=layer.name,
max_detections=config['max_detections'],
nms_threshold=config['nms_threshold'],
score_threshold=config['score_threshold'],
nms=config['nms'],
class_specific_filter=config['class_specific_filter'],
trainable=config['trainable'],
parallel_iterations=config['parallel_iterations'])
return layer_copy
layer_copy = layers.deserialize({
'class_name': layer.__class__.__name__,
'config': config
})
layer_copy.name = layer.name
return layer_copy
