def effnet_retinanet(num_classes,
backbone='EfficientNetB0',
inputs=None,
modifier=None,
**kwargs):
""" Constructs a retinanet model using a resnet backbone.
Args
num_classes: Number of classes to predict.
backbone: Which backbone to use (one of ('resnet50', 'resnet101', 'resnet152')).
inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)).
modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example).
Returns
RetinaNet model with a ResNet backbone.
"""
# choose default input
if inputs is None:
if keras.backend.image_data_format() == 'channels_first':
inputs = keras.layers.Input(shape=(3, None, None))
else:
# inputs = keras.layers.Input(shape=(224, 224, 3))
inputs = keras.layers.Input(shape=(None, None, 3))
# get last conv layer from the end of each block [28x28, 14x14, 7x7]
if backbone == 'EfficientNetB0':
model = efn.EfficientNetB0(input_tensor=inputs,
include_top=False,
weights=None)
elif backbone == 'EfficientNetB1':
model = efn.EfficientNetB1(input_tensor=inputs,
include_top=False,
weights=None)
elif backbone == 'EfficientNetB2':
model = efn.EfficientNetB2(input_tensor=inputs,
include_top=False,
weights=None)
elif backbone == 'EfficientNetB3':
model = efn.EfficientNetB3(input_tensor=inputs,
include_top=False,
weights=None)
elif backbone == 'EfficientNetB4':
model = efn.EfficientNetB4(input_tensor=inputs,
include_top=False,
weights=None)
elif backbone == 'EfficientNetB5':
model = efn.EfficientNetB5(input_tensor=inputs,
include_top=False,
weights=None)
elif backbone == 'EfficientNetB6':
model = efn.EfficientNetB6(input_tensor=inputs,
include_top=False,
weights=None)
elif backbone == 'EfficientNetB7':
model = efn.EfficientNetB7(input_tensor=inputs,
include_top=False,
weights=None)
else:
raise ValueError('Backbone (\'{}\') is invalid.'.format(backbone))
layer_outputs = [
'block4a_expand_activation', 'block6a_expand_activation',
'top_activation'
]
layer_outputs = [
model.get_layer(name=layer_outputs[0]).output, # 28x28
model.get_layer(name=layer_outputs[1]).output, # 14x14
model.get_layer(name=layer_outputs[2]).output, # 7x7
]
# create the densenet backbone
model = keras.models.Model(inputs=inputs,
outputs=layer_outputs,
name=model.name)
# invoke modifier if given
if modifier:
model = modifier(model)
# create the full model
return retinanet.retinanet(inputs=inputs,
num_classes=num_classes,
backbone_layers=model.outputs,
**kwargs)
model = efn.EfficientNetB0(weights=weights)
if b_name == "1":
model = efn.EfficientNetB1(weights=weights)
if b_name == "2":
model = efn.EfficientNetB2(weights=weights)
if b_name == "3":
model = efn.EfficientNetB3(weights=weights)
if b_name == "4":
model = efn.EfficientNetB4(weights=weights)
if b_name == "5":
model = efn.EfficientNetB5(weights=weights)
if b_name == "6":
model = efn.EfficientNetB6(weights=weights)
if b_name == "7":
model = efn.EfficientNetB7(weights=weights)
image_size = model.input_shape[1]
def read_image(path):
try:
return preprocess_input(
center_crop_and_resize(imread(path)[:, :, :3],
image_size=image_size))