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full_models.py
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full_models.py
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from keras.models import Model
from keras.layers import Conv2D, Input, Masking, Concatenate, MaxPooling2D, UpSampling2D, Conv2DTranspose, Flatten, Dense, Reshape
import keras.applications
def unet_full(filters):
input1 = Input((128,128,3))
conv1 = Conv2D(filters, 3, activation = 'relu', padding = 'same')(input1)
input2 = Input((128,128,3))
conv2 = Conv2D(filters,3, activation = 'relu', padding = 'same')(input2)
input3 = Input((128,128,3))
conv3 = Conv2D(filters, 3, activation = 'relu', padding = 'same')(input3)
merged_features = Concatenate()([conv1, conv2, conv3])
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same')(merged_features)
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same')(pool1)
conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same')(pool2)
conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, 3, activation = 'relu', padding = 'same')(pool3)
conv4 = Conv2D(256, 3, activation = 'relu', padding = 'same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same')(pool4)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same')(conv5)
up6 = Conv2D(256, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv5))
merge6 = Concatenate(axis = 3)([conv4,up6])
conv6 = Conv2D(256, 3, activation = 'relu', padding = 'same')(merge6)
conv6 = Conv2D(256, 3, activation = 'relu', padding = 'same')(conv6)
up7 = Conv2D(128, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv6))
merge7 = Concatenate(axis = 3)([conv3,up7])
conv7 = Conv2D(128, 3, activation = 'relu', padding = 'same')(merge7)
conv7 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv7)
up8 = Conv2D(64, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv7))
merge8 = Concatenate(axis = 3)([conv2,up8])
conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same')(merge8)
conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv8)
up9 = Conv2D(32, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv8))
merge9 = Concatenate(axis = 3)([conv1,up9])
conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same')(merge9)
conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same')(conv9)
conv10 = Conv2D(1,1, 1, activation = 'linear')(conv9)
model = Model(inputs = [input1, input2, input3], outputs = conv10)
return model
def segnetlite_full(filters):
input1 = Input((128,128,3))
conv1 = Conv2D(filters, 3, activation = 'relu', padding = 'same')(input1)
input2 = Input((128,128,3))
conv2 = Conv2D(filters,3, activation = 'relu', padding = 'same')(input2)
input3 = Input((128,128,3))
conv3 = Conv2D(filters, 3, activation = 'relu', padding = 'same')(input3)
merged_features = Concatenate()([conv1, conv2, conv3])
conv1 = Conv2D(16, 3, activation = 'relu', padding = 'same')(merged_features)
conv2 = Conv2D(32, 3, activation = 'relu', padding = 'same')(conv1)
conv3 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv2)
conv4 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv3)
conv5 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv4)
conv6 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv5)
conv7 = Conv2DTranspose(128, 3, activation = 'leaky_relu', padding = 'same')(conv6)
merge7 = Concatenate(axis = 3)([conv5,conv7])
conv8 = Conv2DTranspose(64, 3, activation = 'leaky_relu', padding = 'same')(merge7)
merge8 = Concatenate(axis = 3)([conv4,conv8])
conv9 = Conv2DTranspose(64, 3, activation = 'leaky_relu', padding = 'same')(merge8)
merge9 = Concatenate(axis = 3)([conv3,conv9])
conv10 = Conv2DTranspose(32, 3, activation = 'leaky_relu', padding = 'same')(merge9)
merge10 = Concatenate(axis = 3)([conv2,conv10])
conv11 = Conv2DTranspose(16, 3, activation = 'leaky_relu', padding = 'same')(merge10)
merge11 = Concatenate(axis = 3)([conv1,conv11])
conv12 = Conv2D(1,1, 1, activation = 'linear')(merge11)
model = Model(inputs = [input1, input2, input3], outputs = conv12)
return model
def vgg16_full():
input1 = Input((128,128,3))
#input1 = keras.applications.vgg16.preprocess_input(input1)
conv1 = Conv2D(1, 3, activation = 'relu', padding = 'same')(input1)
input2 = Input((128,128,3))
#input2 = keras.applications.vgg16.preprocess_input(input2)
conv2 = Conv2D(1, 3, activation = 'relu', padding = 'same')(input2)
input3 = Input((128,128,3))
#input3 = keras.applications.vgg16.preprocess_input(input3)
conv3 = Conv2D(1, 3, activation = 'relu', padding = 'same')(input3)
merged_features = Concatenate()([conv1, conv2, conv3])
input_shape = (128,128,3)
base = keras.applications.VGG16(include_top=False,
weights='imagenet',
input_shape=input_shape)
for layer in base.layers:
layer.trainable = False
top_layer = base(merged_features)
# conv1 = Conv2D(64, 3, padding="same", activation="relu")(merged_featured)
# conv2 = Conv2D(64, 3, padding="same", activation="relu")(conv1)
# pool1 = MaxPooling2D((2,2), (2,2))(conv2)
# conv3 = Conv2D(128, 3, padding="same", activation="relu")(pool1)
# conv4 = Conv2D(128, 3, padding="same", activation="relu")(conv3)
# pool2 = MaxPooling2D((2,2),(2,2))(conv4)
# conv5 = Conv2D(256, 3, padding="same", activation="relu")(pool2)
# conv6 = Conv2D(256, 3, padding="same", activation="relu")(conv5)
# conv7 = Conv2D(256, 3, padding="same", activation="relu")(conv6)
# pool3 = MaxPooling2D((2,2), (2,2))(conv7)
# conv8 = Conv2D(512, 3, padding="same", activation="relu")(pool3)
# conv9 = Conv2D(512, 3, padding="same", activation="relu")(conv8)
# conv10 = Conv2D(512, 3, padding="same", activation="relu")(conv9)
# pool4 = MaxPooling2D((2,2), (2,2))(conv10)
# conv11 = Conv2D(512, 3, padding="same", activation="relu")(pool4)
# conv12 = Conv2D(512, 3, padding="same", activation="relu")(conv11)
# conv13 = Conv2D(512, 3, padding="same", activation="relu")(conv12)
# pool5 = MaxPooling2D((2,2),(2,2))(conv13)
flatten = Flatten()(top_layer)
dense = Dense(128*128, activation='linear')(flatten)
output = Reshape((128, 128, 1))(dense)
model = Model(inputs = [input1, input2, input3], outputs = output)
return model