def create_sr_model(self, ip):
x = Convolution2D(64,
3,
3,
activation='relu',
border_mode='same',
name='sr_res_conv1')(ip)
nb_residual = 5 if self.small_model else 15
for i in range(nb_residual):
x = self._residual_block(x, i + 1)
x = Deconvolution2D(64,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2),
name='sr_res_deconv1',
output_shape=(self.batch_size, 64,
self.img_width * 2,
self.img_height * 2))(x)
x = Deconvolution2D(64,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2),
name='sr_res_deconv2',
output_shape=(self.batch_size, 64,
self.img_width * 4,
self.img_height * 4))(x)
tv_regularizer = TVRegularizer(img_width=self.img_width * 4,
img_height=self.img_height * 4,
weight=self.tv_weight)
x = Convolution2D(3,
3,
3,
activation="linear",
border_mode='same',
activity_regularizer=tv_regularizer,
name='sr_res_conv_final')(x)
#x = Denormalize(name='sr_res_output')(x)
return x
def create_sr_model(self, ip):
x = Convolution2D(self.filters,
5,
5,
activation='linear',
border_mode='same',
name='sr_res_conv1')(ip)
x = BatchNormalization(axis=channel_axis,
mode=self.mode,
name='sr_res_bn_1')(x)
x = LeakyReLU(alpha=0.25, name='sr_res_lr1')(x)
x = Convolution2D(self.filters,
5,
5,
activation='linear',
border_mode='same',
name='sr_res_conv2')(x)
x = BatchNormalization(axis=channel_axis,
mode=self.mode,
name='sr_res_bn_2')(x)
x = LeakyReLU(alpha=0.25, name='sr_res_lr2')(x)
nb_residual = 5 if self.small_model else 15
for i in range(nb_residual):
x = self._residual_block(x, i + 1)
for scale in range(self.nb_scales):
x = self._upscale_block(x, scale + 1)
scale = 2**self.nb_scales
tv_regularizer = TVRegularizer(img_width=self.img_width * scale,
img_height=self.img_height * scale,
weight=self.tv_weight)
x = Convolution2D(3,
5,
5,
activation='tanh',
border_mode='same',
activity_regularizer=tv_regularizer,
name='sr_res_conv_final')(x)
x = Denormalize()(x)
return x
def add_total_variation_loss(transform_output_layer, weight):
# Total Variation Regularization
layer = transform_output_layer # Output layer
tv_regularizer = TVRegularizer(weight)(layer)
layer.add_loss(tv_regularizer)