| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215 | """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 torchimport torch.nn as nnclass 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 * tmpclass 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 xclass 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 outclass 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 xclass 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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