def encoder(self):
""" Encoder Network """
kwargs = dict(kernel_initializer=self.kernel_initializer)
input_ = Input(shape=self.input_shape)
in_conv_filters = self.input_shape[0]
if self.input_shape[0] > 128:
in_conv_filters = 128 + (self.input_shape[0] - 128) // 4
dense_shape = self.input_shape[0] // 16
var_x = Conv2DBlock(in_conv_filters, activation=None, **kwargs)(input_)
tmp_x = var_x
var_x = LeakyReLU(alpha=0.2)(var_x)
res_cycles = 8 if self.config.get("lowmem", False) else 16
for _ in range(res_cycles):
nn_x = ResidualBlock(in_conv_filters, **kwargs)(var_x)
var_x = nn_x
# consider adding scale before this layer to scale the residual chain
tmp_x = LeakyReLU(alpha=0.1)(tmp_x)
var_x = add([var_x, tmp_x])
var_x = Conv2DBlock(128, activation="leakyrelu", **kwargs)(var_x)
var_x = PixelShuffler()(var_x)
var_x = Conv2DBlock(128, activation="leakyrelu", **kwargs)(var_x)
var_x = PixelShuffler()(var_x)
var_x = Conv2DBlock(128, activation="leakyrelu", **kwargs)(var_x)
var_x = SeparableConv2DBlock(256, **kwargs)(var_x)
var_x = Conv2DBlock(512, activation="leakyrelu", **kwargs)(var_x)
if not self.config.get("lowmem", False):
var_x = SeparableConv2DBlock(1024, **kwargs)(var_x)
var_x = Dense(self.encoder_dim, **kwargs)(Flatten()(var_x))
var_x = Dense(dense_shape * dense_shape * 1024, **kwargs)(var_x)
var_x = Reshape((dense_shape, dense_shape, 1024))(var_x)
var_x = UpscaleBlock(512, activation="leakyrelu", **kwargs)(var_x)
return KerasModel(input_, var_x, name="encoder")
def encoder(self):
""" Encoder Network """
kwargs = dict(kernel_initializer=self.kernel_initializer)
input_ = Input(shape=self.input_shape)
in_conv_filters = self.input_shape[0]
if self.input_shape[0] > 128:
in_conv_filters = 128 + (self.input_shape[0] - 128) // 4
dense_shape = self.input_shape[0] // 16
var_x = self.blocks.conv(input_, in_conv_filters, res_block_follows=True, **kwargs)
tmp_x = var_x
res_cycles = 8 if self.config.get("lowmem", False) else 16
for _ in range(res_cycles):
nn_x = self.blocks.res_block(var_x, 128, **kwargs)
var_x = nn_x
# consider adding scale before this layer to scale the residual chain
var_x = add([var_x, tmp_x])
var_x = self.blocks.conv(var_x, 128, **kwargs)
var_x = PixelShuffler()(var_x)
var_x = self.blocks.conv(var_x, 128, **kwargs)
var_x = PixelShuffler()(var_x)
var_x = self.blocks.conv(var_x, 128, **kwargs)
var_x = self.blocks.conv_sep(var_x, 256, **kwargs)
var_x = self.blocks.conv(var_x, 512, **kwargs)
if not self.config.get("lowmem", False):
var_x = self.blocks.conv_sep(var_x, 1024, **kwargs)
var_x = Dense(self.encoder_dim, **kwargs)(Flatten()(var_x))
var_x = Dense(dense_shape * dense_shape * 1024, **kwargs)(var_x)
var_x = Reshape((dense_shape, dense_shape, 1024))(var_x)
var_x = self.blocks.upscale(var_x, 512, **kwargs)
return KerasModel(input_, var_x)