def _get_upscale_layer(method, filters, activation=None):
""" Obtain an instance of the requested upscale method.
Parameters
----------
method: str
The user selected upscale method to use
filters: int
The number of filters to use in the upscale layer
activation: str, optional
The activation function to use in the upscale layer. ``None`` to use no activation.
Default: ``None``
Returns
-------
:class:`keras.layers.Layer`
The selected configured upscale layer
"""
if method == "upsample2d":
return UpSampling2D()
if method == "subpixel":
return UpscaleBlock(filters, activation=activation)
if method == "upscale_fast":
return Upscale2xBlock(filters, activation=activation, fast=True)
if method == "upscale_hybrid":
return Upscale2xBlock(filters, activation=activation, fast=False)
return UpscaleResizeImagesBlock(filters, activation=activation)
def decoder_b_fast(self):
""" DeLight Fast Decoder B(new face) Network """
input_ = Input(shape=(4, 4, 1024))
decoder_b_complexity = 512
mask_complexity = 128
var_xy = input_
var_xy = UpscaleBlock(512, scale_factor=self.upscale_ratio)(var_xy)
var_x = var_xy
var_x = Upscale2xBlock(decoder_b_complexity, fast=True)(var_x)
var_x = Upscale2xBlock(decoder_b_complexity // 2, fast=True)(var_x)
var_x = Upscale2xBlock(decoder_b_complexity // 4, fast=True)(var_x)
var_x = Upscale2xBlock(decoder_b_complexity // 8, fast=True)(var_x)
var_x = Conv2DOutput(3, 5, name="face_out")(var_x)
outputs = [var_x]
if self.config.get("learn_mask", False):
var_y = var_xy # mask decoder
var_y = Upscale2xBlock(mask_complexity, fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 2, fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 4, fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 8, fast=False)(var_y)
var_y = Conv2DOutput(1, 5, name="mask_out")(var_y)
outputs.append(var_y)
return KerasModel([input_], outputs=outputs, name="decoder_b_fast")
def decoder_a(self):
""" DeLight Decoder A(old face) Network """
input_ = Input(shape=(4, 4, 1024))
decoder_a_complexity = 256
mask_complexity = 128
var_xy = input_
var_xy = UpSampling2D(self.upscale_ratio,
interpolation='bilinear')(var_xy)
var_x = var_xy
var_x = Upscale2xBlock(decoder_a_complexity, fast=False)(var_x)
var_x = Upscale2xBlock(decoder_a_complexity // 2, fast=False)(var_x)
var_x = Upscale2xBlock(decoder_a_complexity // 4, fast=False)(var_x)
var_x = Upscale2xBlock(decoder_a_complexity // 8, fast=False)(var_x)
var_x = Conv2DOutput(3, 5, name="face_out")(var_x)
outputs = [var_x]
if self.config.get("learn_mask", False):
var_y = var_xy # mask decoder
var_y = Upscale2xBlock(mask_complexity, fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 2, fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 4, fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 8, fast=False)(var_y)
var_y = Conv2DOutput(1, 5, name="mask_out")(var_y)
outputs.append(var_y)
return KerasModel([input_], outputs=outputs, name="decoder_a")
def decoder_b(self):
""" DeLight Decoder B(new face) Network """
input_ = Input(shape=(4, 4, 1024))
dec_b_complexity = 512
mask_complexity = 128
var_xy = input_
var_xy = Upscale2xBlock(512,
scale_factor=self.upscale_ratio,
activation=None,
fast=False)(var_xy)
var_x = var_xy
var_x = LeakyReLU(alpha=0.2)(var_x)
var_x = ResidualBlock(512, use_bias=True)(var_x)
var_x = ResidualBlock(512, use_bias=False)(var_x)
var_x = ResidualBlock(512, use_bias=False)(var_x)
var_x = Upscale2xBlock(dec_b_complexity, activation=None,
fast=False)(var_x)
var_x = LeakyReLU(alpha=0.2)(var_x)
var_x = ResidualBlock(dec_b_complexity, use_bias=True)(var_x)
var_x = ResidualBlock(dec_b_complexity, use_bias=False)(var_x)
var_x = BatchNormalization()(var_x)
var_x = Upscale2xBlock(dec_b_complexity // 2,
activation=None,
fast=False)(var_x)
var_x = LeakyReLU(alpha=0.2)(var_x)
var_x = ResidualBlock(dec_b_complexity // 2, use_bias=True)(var_x)
var_x = Upscale2xBlock(dec_b_complexity // 4,
activation=None,
fast=False)(var_x)
var_x = LeakyReLU(alpha=0.2)(var_x)
var_x = ResidualBlock(dec_b_complexity // 4, use_bias=False)(var_x)
var_x = BatchNormalization()(var_x)
var_x = Upscale2xBlock(dec_b_complexity // 8,
activation="leakyrelu",
fast=False)(var_x)
var_x = Conv2DOutput(3, 5, name="face_out")(var_x)
outputs = [var_x]
if self.config.get("learn_mask", False):
var_y = var_xy # mask decoder
var_y = LeakyReLU(alpha=0.1)(var_y)
var_y = Upscale2xBlock(mask_complexity,
activation="leakyrelu",
fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 2,
activation="leakyrelu",
fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 4,
activation="leakyrelu",
fast=False)(var_y)
var_y = Upscale2xBlock(mask_complexity // 8,
activation="leakyrelu",
fast=False)(var_y)
var_y = Conv2DOutput(1, 5, name="mask_out")(var_y)
outputs.append(var_y)
return KerasModel([input_], outputs=outputs, name="decoder_b")