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segnet.py
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segnet.py
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from keras.models import *
from keras.layers import *
from .config import IMAGE_ORDERING
from .model_utils import get_segmentation_model
from .vgg16 import get_vgg_encoder
from .mobilenet import get_mobilenet_encoder
from .basic_models import vanilla_encoder
from .resnet50 import get_resnet50_encoder
def segnet_decoder(f, n_classes, n_up=3):
assert n_up >= 2
o = f
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
for _ in range(n_up-2):
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(128, (3, 3), padding='valid',
data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, name="seg_feats"))(o)
o = (BatchNormalization())(o)
o = Conv2D(n_classes, (3, 3), padding='same',
data_format=IMAGE_ORDERING)(o)
return o
def _segnet(n_classes, encoder, input_height=416, input_width=608,
encoder_level=3, channels=3):
img_input, levels = encoder(
input_height=input_height, input_width=input_width, channels=channels)
feat = levels[encoder_level]
o = segnet_decoder(feat, n_classes, n_up=3)
model = get_segmentation_model(img_input, o)
return model
def segnet(n_classes, input_height=416, input_width=608, encoder_level=3, channels=3):
model = _segnet(n_classes, vanilla_encoder, input_height=input_height,
input_width=input_width, encoder_level=encoder_level, channels=channels)
model.model_name = "segnet"
return model
def vgg_segnet(n_classes, input_height=416, input_width=608, encoder_level=3, channels=3):
model = _segnet(n_classes, get_vgg_encoder, input_height=input_height,
input_width=input_width, encoder_level=encoder_level, channels=channels)
model.model_name = "vgg_segnet"
return model
def resnet50_segnet(n_classes, input_height=416, input_width=608,
encoder_level=3, channels=3):
model = _segnet(n_classes, get_resnet50_encoder, input_height=input_height,
input_width=input_width, encoder_level=encoder_level, channels=channels)
model.model_name = "resnet50_segnet"
return model
def mobilenet_segnet(n_classes, input_height=224, input_width=224,
encoder_level=3, channels=3):
model = _segnet(n_classes, get_mobilenet_encoder,
input_height=input_height,
input_width=input_width, encoder_level=encoder_level, channels=channels)
model.model_name = "mobilenet_segnet"
return model
if __name__ == '__main__':
m = vgg_segnet(101)
m = segnet(101)
# m = mobilenet_segnet( 101 )
# from keras.utils import plot_model
# plot_model( m , show_shapes=True , to_file='model.png')