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- """
- Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
- Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
- """
- import torch
- import torch.nn as nn
- class InstanceNorm(nn.Module):
- def __init__(self, epsilon=1e-8):
- """
- @notice: avoid in-place ops.
- https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
- """
- super(InstanceNorm, self).__init__()
- self.epsilon = epsilon
- def forward(self, x):
- x = x - torch.mean(x, (2, 3), True)
- tmp = torch.mul(x, x) # or x ** 2
- tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
- return x * tmp
- class ApplyStyle(nn.Module):
- """
- @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
- """
- def __init__(self, latent_size, channels):
- super(ApplyStyle, self).__init__()
- self.linear = nn.Linear(latent_size, channels * 2)
- def forward(self, x, latent):
- style = self.linear(latent) # style => [batch_size, n_channels*2]
- shape = [-1, 2, x.size(1), 1, 1]
- style = style.view(shape) # [batch_size, 2, n_channels, ...]
- #x = x * (style[:, 0] + 1.) + style[:, 1]
- x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
- return x
- class ResnetBlock_Adain(nn.Module):
- def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
- super(ResnetBlock_Adain, self).__init__()
- p = 0
- conv1 = []
- if padding_type == 'reflect':
- conv1 += [nn.ReflectionPad2d(1)]
- elif padding_type == 'replicate':
- conv1 += [nn.ReplicationPad2d(1)]
- elif padding_type == 'zero':
- p = 1
- else:
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
- conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
- self.conv1 = nn.Sequential(*conv1)
- self.style1 = ApplyStyle(latent_size, dim)
- self.act1 = activation
- p = 0
- conv2 = []
- if padding_type == 'reflect':
- conv2 += [nn.ReflectionPad2d(1)]
- elif padding_type == 'replicate':
- conv2 += [nn.ReplicationPad2d(1)]
- elif padding_type == 'zero':
- p = 1
- else:
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
- conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
- self.conv2 = nn.Sequential(*conv2)
- self.style2 = ApplyStyle(latent_size, dim)
- def forward(self, x, dlatents_in_slice):
- y = self.conv1(x)
- y = self.style1(y, dlatents_in_slice)
- y = self.act1(y)
- y = self.conv2(y)
- y = self.style2(y, dlatents_in_slice)
- out = x + y
- return out
- class Generator_Adain_Upsample(nn.Module):
- def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
- norm_layer=nn.BatchNorm2d,
- padding_type='reflect'):
- assert (n_blocks >= 0)
- super(Generator_Adain_Upsample, self).__init__()
- activation = nn.ReLU(True)
- self.deep = deep
- self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
- norm_layer(64), activation)
- ### downsample
- self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
- norm_layer(128), activation)
- self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
- norm_layer(256), activation)
- self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
- norm_layer(512), activation)
- if self.deep:
- self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
- norm_layer(512), activation)
- ### resnet blocks
- BN = []
- for i in range(n_blocks):
- BN += [
- ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
- self.BottleNeck = nn.Sequential(*BN)
- if self.deep:
- self.up4 = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='bilinear'),
- nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
- nn.BatchNorm2d(512), activation
- )
- self.up3 = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='bilinear'),
- nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
- nn.BatchNorm2d(256), activation
- )
- self.up2 = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='bilinear'),
- nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
- nn.BatchNorm2d(128), activation
- )
- self.up1 = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='bilinear'),
- nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
- nn.BatchNorm2d(64), activation
- )
- self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0),
- nn.Tanh())
- def forward(self, input, dlatents):
- x = input # 3*224*224
- skip1 = self.first_layer(x)
- skip2 = self.down1(skip1)
- skip3 = self.down2(skip2)
- if self.deep:
- skip4 = self.down3(skip3)
- x = self.down4(skip4)
- else:
- x = self.down3(skip3)
- for i in range(len(self.BottleNeck)):
- x = self.BottleNeck[i](x, dlatents)
- if self.deep:
- x = self.up4(x)
- x = self.up3(x)
- x = self.up2(x)
- x = self.up1(x)
- x = self.last_layer(x)
- x = (x + 1) / 2
- return x
- class Discriminator(nn.Module):
- def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
- super(Discriminator, self).__init__()
- kw = 4
- padw = 1
- self.down1 = nn.Sequential(
- nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
- )
- self.down2 = nn.Sequential(
- nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
- norm_layer(128), nn.LeakyReLU(0.2, True)
- )
- self.down3 = nn.Sequential(
- nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
- norm_layer(256), nn.LeakyReLU(0.2, True)
- )
- self.down4 = nn.Sequential(
- nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
- norm_layer(512), nn.LeakyReLU(0.2, True)
- )
- self.conv1 = nn.Sequential(
- nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
- norm_layer(512),
- nn.LeakyReLU(0.2, True)
- )
- if use_sigmoid:
- self.conv2 = nn.Sequential(
- nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
- )
- else:
- self.conv2 = nn.Sequential(
- nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
- )
- def forward(self, input):
- out = []
- x = self.down1(input)
- out.append(x)
- x = self.down2(x)
- out.append(x)
- x = self.down3(x)
- out.append(x)
- x = self.down4(x)
- out.append(x)
- x = self.conv1(x)
- out.append(x)
- x = self.conv2(x)
- out.append(x)
-
- return out
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