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custom_models.py
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171 lines (126 loc) · 8.53 KB
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import keras
from keras import losses
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from keras.layers import Input, MaxPooling2D, AveragePooling2D, average
from keras.layers import concatenate, Conv2D, Conv2DTranspose, Dropout
from keras.models import Model
from keras.optimizers import Adadelta
from keras.models import Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization, UpSampling2D
from keras.layers import Convolution2D, ZeroPadding2D, Embedding, LSTM, merge, Lambda, Deconvolution2D, Cropping2D
from keras.layers import ELU, ReLU
act = ReLU
def get_unet(do=0, activation=act):
inputs = Input((None, None, 3))
conv1 = Dropout(do)(activation()(Conv2D(32, (4, 4), padding='same')(inputs)))
conv1 = Dropout(do)(activation()(Conv2D(32, (4, 4), padding='same')(conv1)))
pool1 = MaxPooling2D(pool_size=(4, 4))(conv1)
conv2 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(pool1)))
conv2 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(conv2)))
pool2 = MaxPooling2D(pool_size=(4, 4))(conv2)
conv3 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(pool2)))
conv3 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(conv3)))
pool3 = MaxPooling2D(pool_size=(4, 4))(conv3)
conv4 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(pool3)))
conv4 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(conv4)))
pool4 = MaxPooling2D(pool_size=(4, 4))(conv4)
conv5 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(pool4)))
conv5 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(conv5)))
up6 = concatenate([UpSampling2D(size=(4, 4))(conv5), conv4], axis=3)
conv6 = Dropout(do)(activation()(Conv2D(128, (3, 3), padding='same')(up6)))
conv6 = Dropout(do)(activation()(Conv2D(128, (3, 3), padding='same')(conv6)))
up7 = concatenate([UpSampling2D(size=(4, 4))(conv6), conv3], axis=3)
conv7 = Dropout(do)(activation()(Conv2D(64, (3, 3), padding='same')(up7)))
conv7 = Dropout(do)(activation()(Conv2D(64, (3, 3), padding='same')(conv7)))
up8 = concatenate([UpSampling2D(size=(4, 4))(conv7), conv2], axis=3)
conv8 = Dropout(do)(activation()(Conv2D(64, (3, 3), padding='same')(up8)))
conv8 = Dropout(do)(activation()(Conv2D(64, (3, 3), padding='same')(conv8)))
up9 = concatenate([UpSampling2D(size=(4, 4))(conv8), conv1], axis=3)
conv9 = Dropout(do)(activation()(Conv2D(32, (3, 3), padding='same')(up9)))
conv9 = Dropout(do)(activation()(Conv2D(32, (3, 3), padding='same')(conv9)))
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=[inputs], outputs=[conv10])
return model
def DeepModel(size_set=256):
img_input = Input(shape=(size_set, size_set, 3))
scale_img_2 = AveragePooling2D(pool_size=(2, 2))(img_input)
scale_img_3 = AveragePooling2D(pool_size=(2, 2))(scale_img_2)
scale_img_4 = AveragePooling2D(pool_size=(2, 2))(scale_img_3)
conv1 = Conv2D(32, (3, 3), padding='same', activation='relu', name='block1_conv1')(img_input)
conv1 = Conv2D(32, (3, 3), padding='same', activation='relu', name='block1_conv2')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
input2 = Conv2D(64, (3, 3), padding='same', activation='relu', name='block2_input1')(scale_img_2)
input2 = concatenate([input2, pool1], axis=3)
conv2 = Conv2D(64, (3, 3), padding='same', activation='relu', name='block2_conv1')(input2)
conv2 = Conv2D(64, (3, 3), padding='same', activation='relu', name='block2_conv2')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
input3 = Conv2D(128, (3, 3), padding='same', activation='relu', name='block3_input1')(scale_img_3)
input3 = concatenate([input3, pool2], axis=3)
conv3 = Conv2D(128, (3, 3), padding='same', activation='relu', name='block3_conv1')(input3)
conv3 = Conv2D(128, (3, 3), padding='same', activation='relu', name='block3_conv2')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
input4 = Conv2D(256, (3, 3), padding='same', activation='relu', name='block4_input1')(scale_img_4)
input4 = concatenate([input4, pool3], axis=3)
conv4 = Conv2D(256, (3, 3), padding='same', activation='relu', name='block4_conv1')(input4)
conv4 = Conv2D(256, (3, 3), padding='same', activation='relu', name='block4_conv2')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), padding='same', activation='relu', name='block5_conv1')(pool4)
conv5 = Conv2D(512, (3, 3), padding='same', activation='relu', name='block5_conv2')(conv5)
up6 = concatenate(
[Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same', name='block6_dconv')(conv5), conv4],
axis=3)
conv6 = Conv2D(256, (3, 3), padding='same', activation='relu', name='block6_conv1')(up6)
conv6 = Conv2D(256, (3, 3), padding='same', activation='relu', name='block6_conv2')(conv6)
up7 = concatenate(
[Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same', name='block7_dconv')(conv6), conv3],
axis=3)
conv7 = Conv2D(128, (3, 3), padding='same', activation='relu', name='block7_conv1')(up7)
conv7 = Conv2D(128, (3, 3), padding='same', activation='relu', name='block7_conv2')(conv7)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same', name='block8_dconv')(conv7), conv2],
axis=3)
conv8 = Conv2D(64, (3, 3), padding='same', activation='relu', name='block8_conv1')(up8)
conv8 = Conv2D(64, (3, 3), padding='same', activation='relu', name='block8_conv2')(conv8)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same', name='block9_dconv')(conv8), conv1],
axis=3)
conv9 = Conv2D(32, (3, 3), padding='same', activation='relu', name='block9_conv1')(up9)
conv9 = Conv2D(32, (3, 3), padding='same', activation='relu', name='block9_conv2')(conv9)
side6 = UpSampling2D(size=(8, 8))(conv6)
side7 = UpSampling2D(size=(4, 4))(conv7)
side8 = UpSampling2D(size=(2, 2))(conv8)
out6 = Conv2D(1, (1, 1), activation='sigmoid', name='side_63')(side6)
out7 = Conv2D(1, (1, 1), activation='sigmoid', name='side_73')(side7)
out8 = Conv2D(1, (1, 1), activation='sigmoid', name='side_83')(side8)
out9 = Conv2D(1, (1, 1), activation='sigmoid', name='side_93')(conv9)
out10 = average([out6, out7, out8, out9])
return Model(inputs=[img_input], outputs=[out10])
def get_unet1(do=0, activation=act):
inputs = Input((None, None, 3))
conv1 = Dropout(do)(activation()(Conv2D(32, (4, 4), padding='same')(inputs)))
conv1 = Dropout(do)(activation()(Conv2D(32, (4, 4), padding='same')(conv1)))
pool1 = MaxPooling2D(pool_size=(4, 4))(conv1)
conv2 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(pool1)))
conv2 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(conv2)))
pool2 = MaxPooling2D(pool_size=(4, 4))(conv2)
conv3 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(pool2)))
conv3 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(conv3)))
pool3 = MaxPooling2D(pool_size=(4, 4))(conv3)
conv4 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(pool3)))
conv4 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(conv4)))
pool4 = MaxPooling2D(pool_size=(4, 4))(conv4)
conv5 = Dropout(do)(activation()(Conv2D(256, (4, 4), padding='same')(pool4)))
conv5 = Dropout(do)(activation()(Conv2D(256, (4, 4), padding='same')(conv5)))
up6 = concatenate([UpSampling2D(size=(4, 4))(conv5), conv4], axis=3)
conv6 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(up6)))
conv6 = Dropout(do)(activation()(Conv2D(128, (4, 4), padding='same')(conv6)))
up7 = concatenate([UpSampling2D(size=(4, 4))(conv6), conv3], axis=3)
conv7 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(up7)))
conv7 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(conv7)))
up8 = concatenate([UpSampling2D(size=(4, 4))(conv7), conv2], axis=3)
conv8 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(up8)))
conv8 = Dropout(do)(activation()(Conv2D(64, (4, 4), padding='same')(conv8)))
up9 = concatenate([UpSampling2D(size=(4, 4))(conv8), conv1], axis=3)
conv9 = Dropout(do)(activation()(Conv2D(32, (4, 4), padding='same')(up9)))
conv9 = Dropout(do)(activation()(Conv2D(32, (4, 4), padding='same')(conv9)))
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=[inputs], outputs=[conv10])
return model