name='Up3')(conv7_1))
merge8 = keras.layers.Concatenate(name='Concat3')([conv2, up8])
conv8 = Residual15(96, 32, merge8)
conv8_1 = Residual16(32, 16, conv8)
up9 = Conv2D(16,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal',
name='UpConv4')(UpSampling2D(size=(2, 2),
name='Up4')(conv8_1))
merge9 = keras.layers.Concatenate(name='Concat4')([conv1, up9])
conv9 = Residual17(48, 16, merge9)
conv10 = Residual18(16, 2, conv9)
conv10 = Residual19(2, 1, conv10)
conv11 = Conv2D(1, 1, activation='sigmoid', name='Output')(conv10)
with tf.device('/device:GPU:3'):
init = initial_conv_block(input, weight_decay=5e-4)
#x1 = ResidualR(32, 64, init) #192x192x64
#x1 = ResidualR(64, 64, x1)
#x1 = ResidualR(64, 64, x1) #192x192x64
x1 = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(init)
x1 = BatchNormalization()(x1)
x1 = layers.LeakyReLU()(x1)
x1concat = keras.layers.Concatenate()([x1, conv9]) #192x192x80
x1se = squeeze_excite_block(x1concat)
x1conv1 = SeparableConv2D(80, (1, 1),
kernel_initializer='he_normal',
name='UpConv3')(UpSampling2D(size=(2, 2), name='Up3')(conv7))
merge8 = keras.layers.Concatenate(name='Concat3')([conv2, up8])
conv8 = Residual15(256, 128, merge8)
#conv8 = Residual16(128, 128, conv8)
up9 = Conv2D(64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal',
name='UpConv4')(UpSampling2D(size=(2, 2), name='Up4')(conv8))
merge9 = keras.layers.Concatenate(name='Concat4')([conv1, up9])
conv9 = Residual17(128, 64, merge9)
conv10 = Residual18(64, 16, conv9)
conv10 = Residual19(16, 1, conv10)
conv11 = Conv2D(1, 1, activation='sigmoid', name='Output')(conv10)
conv1r = _conv_bn_relu(filters=64, kernel_size=(7, 7), strides=(1, 1))(input)
block1 = _residual_block(basic_block,
filters=64,
repetitions=1,
is_first_layer=True)(conv1r)
block1concat = keras.layers.Concatenate()([block1, conv9])
block1se = squeeze_excite_block(block1concat)
block1conv1 = Conv2D(64, (1, 1),
padding='same',
kernel_initializer='he_normal')(block1se)
block1conv1 = BatchNormalization(axis=CHANNEL_AXIS)(block1conv1)
block1conv1 = layers.LeakyReLU()(block1conv1)