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Running
on
T4
| import math | |
| import random | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import numpy as np | |
| from models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d | |
| class PixelNorm(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def forward(self, input): | |
| return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) | |
| def make_kernel(k): | |
| k = torch.tensor(k, dtype=torch.float32) | |
| if k.ndim == 1: | |
| k = k[None, :] * k[:, None] | |
| k /= k.sum() | |
| return k | |
| class Upsample(nn.Module): | |
| def __init__(self, kernel, factor=2): | |
| super().__init__() | |
| self.factor = factor | |
| kernel = make_kernel(kernel) * (factor ** 2) | |
| self.register_buffer('kernel', kernel) | |
| p = kernel.shape[0] - factor | |
| pad0 = (p + 1) // 2 + factor - 1 | |
| pad1 = p // 2 | |
| self.pad = (pad0, pad1) | |
| def forward(self, input): | |
| out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) | |
| return out | |
| class Downsample(nn.Module): | |
| def __init__(self, kernel, factor=2): | |
| super().__init__() | |
| self.factor = factor | |
| kernel = make_kernel(kernel) | |
| self.register_buffer('kernel', kernel) | |
| p = kernel.shape[0] - factor | |
| pad0 = (p + 1) // 2 | |
| pad1 = p // 2 | |
| self.pad = (pad0, pad1) | |
| def forward(self, input): | |
| out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) | |
| return out | |
| class Blur(nn.Module): | |
| def __init__(self, kernel, pad, upsample_factor=1): | |
| super().__init__() | |
| kernel = make_kernel(kernel) | |
| if upsample_factor > 1: | |
| kernel = kernel * (upsample_factor ** 2) | |
| self.register_buffer('kernel', kernel) | |
| self.pad = pad | |
| def forward(self, input): | |
| out = upfirdn2d(input, self.kernel, pad=self.pad) | |
| return out | |
| class EqualConv2d(nn.Module): | |
| def __init__( | |
| self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dilation=1 ## modified | |
| ): | |
| super().__init__() | |
| self.weight = nn.Parameter( | |
| torch.randn(out_channel, in_channel, kernel_size, kernel_size) | |
| ) | |
| self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) | |
| self.stride = stride | |
| self.padding = padding | |
| self.dilation = dilation ## modified | |
| if bias: | |
| self.bias = nn.Parameter(torch.zeros(out_channel)) | |
| else: | |
| self.bias = None | |
| def forward(self, input): | |
| out = F.conv2d( | |
| input, | |
| self.weight * self.scale, | |
| bias=self.bias, | |
| stride=self.stride, | |
| padding=self.padding, | |
| dilation=self.dilation, ## modified | |
| ) | |
| return out | |
| def __repr__(self): | |
| return ( | |
| f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]}," | |
| f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding}, dilation={self.dilation})" ## modified | |
| ) | |
| class EqualLinear(nn.Module): | |
| def __init__( | |
| self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None | |
| ): | |
| super().__init__() | |
| self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) | |
| if bias: | |
| self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) | |
| else: | |
| self.bias = None | |
| self.activation = activation | |
| self.scale = (1 / math.sqrt(in_dim)) * lr_mul | |
| self.lr_mul = lr_mul | |
| def forward(self, input): | |
| if self.activation: | |
| out = F.linear(input, self.weight * self.scale) | |
| out = fused_leaky_relu(out, self.bias * self.lr_mul) | |
| else: | |
| out = F.linear( | |
| input, self.weight * self.scale, bias=self.bias * self.lr_mul | |
| ) | |
| return out | |
| def __repr__(self): | |
| return ( | |
| f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' | |
| ) | |
| class ScaledLeakyReLU(nn.Module): | |
| def __init__(self, negative_slope=0.2): | |
| super().__init__() | |
| self.negative_slope = negative_slope | |
| def forward(self, input): | |
| out = F.leaky_relu(input, negative_slope=self.negative_slope) | |
| return out * math.sqrt(2) | |
| class ModulatedConv2d(nn.Module): | |
| def __init__( | |
| self, | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| style_dim, | |
| demodulate=True, | |
| upsample=False, | |
| downsample=False, | |
| blur_kernel=[1, 3, 3, 1], | |
| dilation=1, ##### modified | |
| ): | |
| super().__init__() | |
| self.eps = 1e-8 | |
| self.kernel_size = kernel_size | |
| self.in_channel = in_channel | |
| self.out_channel = out_channel | |
| self.upsample = upsample | |
| self.downsample = downsample | |
| self.dilation = dilation ##### modified | |
| if upsample: | |
| factor = 2 | |
| p = (len(blur_kernel) - factor) - (kernel_size - 1) | |
| pad0 = (p + 1) // 2 + factor - 1 | |
| pad1 = p // 2 + 1 | |
| self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) | |
| # to simulate transconv + blur | |
| # we use dilated transposed conv with blur kernel as weight + dilated transconv | |
| if dilation > 1: ##### modified | |
| blur_weight = torch.randn(1, 1, 3, 3) * 0 + 1 | |
| blur_weight[:,:,0,1] = 2 | |
| blur_weight[:,:,1,0] = 2 | |
| blur_weight[:,:,1,2] = 2 | |
| blur_weight[:,:,2,1] = 2 | |
| blur_weight[:,:,1,1] = 4 | |
| blur_weight = blur_weight / 16.0 | |
| self.register_buffer("blur_weight", blur_weight) | |
| if downsample: | |
| factor = 2 | |
| p = (len(blur_kernel) - factor) + (kernel_size - 1) | |
| pad0 = (p + 1) // 2 | |
| pad1 = p // 2 | |
| self.blur = Blur(blur_kernel, pad=(pad0, pad1)) | |
| fan_in = in_channel * kernel_size ** 2 | |
| self.scale = 1 / math.sqrt(fan_in) | |
| self.padding = kernel_size // 2 + dilation - 1 ##### modified | |
| self.weight = nn.Parameter( | |
| torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) | |
| ) | |
| self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) | |
| self.demodulate = demodulate | |
| def __repr__(self): | |
| return ( | |
| f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' | |
| f'upsample={self.upsample}, downsample={self.downsample})' | |
| ) | |
| def forward(self, input, style): | |
| batch, in_channel, height, width = input.shape | |
| style = self.modulation(style).view(batch, 1, in_channel, 1, 1) | |
| weight = self.scale * self.weight * style | |
| if self.demodulate: | |
| demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) | |
| weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) | |
| weight = weight.view( | |
| batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size | |
| ) | |
| if self.upsample: | |
| input = input.view(1, batch * in_channel, height, width) | |
| weight = weight.view( | |
| batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size | |
| ) | |
| weight = weight.transpose(1, 2).reshape( | |
| batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size | |
| ) | |
| if self.dilation > 1: ##### modified | |
| # to simulate out = self.blur(out) | |
| out = F.conv_transpose2d( | |
| input, self.blur_weight.repeat(batch*in_channel,1,1,1), padding=0, groups=batch*in_channel, dilation=self.dilation//2) | |
| # to simulate the next line | |
| out = F.conv_transpose2d( | |
| out, weight, padding=self.dilation, groups=batch, dilation=self.dilation//2) | |
| _, _, height, width = out.shape | |
| out = out.view(batch, self.out_channel, height, width) | |
| return out | |
| out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) | |
| _, _, height, width = out.shape | |
| out = out.view(batch, self.out_channel, height, width) | |
| out = self.blur(out) | |
| elif self.downsample: | |
| input = self.blur(input) | |
| _, _, height, width = input.shape | |
| input = input.view(1, batch * in_channel, height, width) | |
| out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) | |
| _, _, height, width = out.shape | |
| out = out.view(batch, self.out_channel, height, width) | |
| else: | |
| input = input.view(1, batch * in_channel, height, width) | |
| out = F.conv2d(input, weight, padding=self.padding, groups=batch, dilation=self.dilation) ##### modified | |
| _, _, height, width = out.shape | |
| out = out.view(batch, self.out_channel, height, width) | |
| return out | |
| class NoiseInjection(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.weight = nn.Parameter(torch.zeros(1)) | |
| def forward(self, image, noise=None): | |
| if noise is None: | |
| batch, _, height, width = image.shape | |
| noise = image.new_empty(batch, 1, height, width).normal_() | |
| else: ##### modified, to make the resolution matches | |
| batch, _, height, width = image.shape | |
| _, _, height1, width1 = noise.shape | |
| if height != height1 or width != width1: | |
| noise = F.adaptive_avg_pool2d(noise, (height, width)) | |
| return image + self.weight * noise | |
| class ConstantInput(nn.Module): | |
| def __init__(self, channel, size=4): | |
| super().__init__() | |
| self.input = nn.Parameter(torch.randn(1, channel, size, size)) | |
| def forward(self, input): | |
| batch = input.shape[0] | |
| out = self.input.repeat(batch, 1, 1, 1) | |
| return out | |
| class StyledConv(nn.Module): | |
| def __init__( | |
| self, | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| style_dim, | |
| upsample=False, | |
| blur_kernel=[1, 3, 3, 1], | |
| demodulate=True, | |
| dilation=1, ##### modified | |
| ): | |
| super().__init__() | |
| self.conv = ModulatedConv2d( | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| style_dim, | |
| upsample=upsample, | |
| blur_kernel=blur_kernel, | |
| demodulate=demodulate, | |
| dilation=dilation, ##### modified | |
| ) | |
| self.noise = NoiseInjection() | |
| self.activate = FusedLeakyReLU(out_channel) | |
| def forward(self, input, style, noise=None): | |
| out = self.conv(input, style) | |
| out = self.noise(out, noise=noise) | |
| out = self.activate(out) | |
| return out | |
| class ToRGB(nn.Module): | |
| def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1], dilation=1): ##### modified | |
| super().__init__() | |
| if upsample: | |
| self.upsample = Upsample(blur_kernel) | |
| self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) | |
| self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) | |
| self.dilation = dilation ##### modified | |
| if dilation > 1: ##### modified | |
| blur_weight = torch.randn(1, 1, 3, 3) * 0 + 1 | |
| blur_weight[:,:,0,1] = 2 | |
| blur_weight[:,:,1,0] = 2 | |
| blur_weight[:,:,1,2] = 2 | |
| blur_weight[:,:,2,1] = 2 | |
| blur_weight[:,:,1,1] = 4 | |
| blur_weight = blur_weight / 16.0 | |
| self.register_buffer("blur_weight", blur_weight) | |
| def forward(self, input, style, skip=None): | |
| out = self.conv(input, style) | |
| out = out + self.bias | |
| if skip is not None: | |
| if self.dilation == 1: | |
| skip = self.upsample(skip) | |
| else: ##### modified, to simulate skip = self.upsample(skip) | |
| batch, in_channel, _, _ = skip.shape | |
| skip = F.conv2d(skip, self.blur_weight.repeat(in_channel,1,1,1), | |
| padding=self.dilation//2, groups=in_channel, dilation=self.dilation//2) | |
| out = out + skip | |
| return out | |
| class Generator(nn.Module): | |
| def __init__( | |
| self, | |
| size, | |
| style_dim, | |
| n_mlp, | |
| channel_multiplier=2, | |
| blur_kernel=[1, 3, 3, 1], | |
| lr_mlp=0.01, | |
| ): | |
| super().__init__() | |
| self.size = size | |
| self.style_dim = style_dim | |
| layers = [PixelNorm()] | |
| for i in range(n_mlp): | |
| layers.append( | |
| EqualLinear( | |
| style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu' | |
| ) | |
| ) | |
| self.style = nn.Sequential(*layers) | |
| self.channels = { | |
| 4: 512, | |
| 8: 512, | |
| 16: 512, | |
| 32: 512, | |
| 64: 256 * channel_multiplier, | |
| 128: 128 * channel_multiplier, | |
| 256: 64 * channel_multiplier, | |
| 512: 32 * channel_multiplier, | |
| 1024: 16 * channel_multiplier, | |
| } | |
| self.input = ConstantInput(self.channels[4]) | |
| self.conv1 = StyledConv( | |
| self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel, dilation=8 ##### modified | |
| ) | |
| self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) | |
| self.log_size = int(math.log(size, 2)) | |
| self.num_layers = (self.log_size - 2) * 2 + 1 | |
| self.convs = nn.ModuleList() | |
| self.upsamples = nn.ModuleList() | |
| self.to_rgbs = nn.ModuleList() | |
| self.noises = nn.Module() | |
| in_channel = self.channels[4] | |
| for layer_idx in range(self.num_layers): | |
| res = (layer_idx + 5) // 2 | |
| shape = [1, 1, 2 ** res, 2 ** res] | |
| self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) | |
| for i in range(3, self.log_size + 1): | |
| out_channel = self.channels[2 ** i] | |
| self.convs.append( | |
| StyledConv( | |
| in_channel, | |
| out_channel, | |
| 3, | |
| style_dim, | |
| upsample=True, | |
| blur_kernel=blur_kernel, | |
| dilation=max(1, 32 // (2**(i-1))) ##### modified | |
| ) | |
| ) | |
| self.convs.append( | |
| StyledConv( | |
| out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel, dilation=max(1, 32 // (2**i)) ##### modified | |
| ) | |
| ) | |
| self.to_rgbs.append(ToRGB(out_channel, style_dim, dilation=max(1, 32 // (2**(i-1))))) ##### modified | |
| in_channel = out_channel | |
| self.n_latent = self.log_size * 2 - 2 | |
| def make_noise(self): | |
| device = self.input.input.device | |
| noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] | |
| for i in range(3, self.log_size + 1): | |
| for _ in range(2): | |
| noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) | |
| return noises | |
| def mean_latent(self, n_latent): | |
| latent_in = torch.randn( | |
| n_latent, self.style_dim, device=self.input.input.device | |
| ) | |
| latent = self.style(latent_in).mean(0, keepdim=True) | |
| return latent | |
| def get_latent(self, input): | |
| return self.style(input) | |
| # styles is the latent code w+ | |
| # first_layer_feature is the first-layer input feature f | |
| # first_layer_feature_ind indicate which layer of G accepts f (should always=0, the first layer) | |
| # skip_layer_feature is the encoder features sent by skip connection | |
| # fusion_block is the network to fuse the encoder feature and decoder feature | |
| # zero_noise is to force the noise to be zero (to avoid flickers for videos) | |
| # editing_w is the editing vector v used in video face editing | |
| def forward( | |
| self, | |
| styles, | |
| return_latents=False, | |
| return_features=False, | |
| inject_index=None, | |
| truncation=1, | |
| truncation_latent=None, | |
| input_is_latent=False, | |
| noise=None, | |
| randomize_noise=True, | |
| first_layer_feature = None, ##### modified | |
| first_layer_feature_ind = 0, ##### modified | |
| skip_layer_feature = None, ##### modified | |
| fusion_block = None, ##### modified | |
| zero_noise = False, ##### modified | |
| editing_w = None, ##### modified | |
| ): | |
| if not input_is_latent: | |
| styles = [self.style(s) for s in styles] | |
| if zero_noise: | |
| noise = [ | |
| getattr(self.noises, f'noise_{i}') * 0.0 for i in range(self.num_layers) | |
| ] | |
| elif noise is None: | |
| if randomize_noise: | |
| noise = [None] * self.num_layers | |
| else: | |
| noise = [ | |
| getattr(self.noises, f'noise_{i}') for i in range(self.num_layers) | |
| ] | |
| if truncation < 1: | |
| style_t = [] | |
| for style in styles: | |
| style_t.append( | |
| truncation_latent + truncation * (style - truncation_latent) | |
| ) | |
| styles = style_t | |
| if len(styles) < 2: | |
| inject_index = self.n_latent | |
| if styles[0].ndim < 3: | |
| latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) | |
| else: | |
| latent = styles[0] | |
| else: | |
| if inject_index is None: | |
| inject_index = random.randint(1, self.n_latent - 1) | |
| latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) | |
| latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) | |
| latent = torch.cat([latent, latent2], 1) | |
| # w+ + v for video face editing | |
| if editing_w is not None: ##### modified | |
| latent = latent + editing_w | |
| # the original StyleGAN | |
| if first_layer_feature is None: ##### modified | |
| out = self.input(latent) | |
| out = F.adaptive_avg_pool2d(out, 32) ##### modified | |
| out = self.conv1(out, latent[:, 0], noise=noise[0]) | |
| skip = self.to_rgb1(out, latent[:, 1]) | |
| # the default StyleGANEX, replacing the first layer of G | |
| elif first_layer_feature_ind == 0: ##### modified | |
| out = first_layer_feature[0] ##### modified | |
| out = self.conv1(out, latent[:, 0], noise=noise[0]) | |
| skip = self.to_rgb1(out, latent[:, 1]) | |
| # maybe we can also use the second layer of G to accept f? | |
| else: ##### modified | |
| out = first_layer_feature[0] ##### modified | |
| skip = first_layer_feature[1] ##### modified | |
| i = 1 | |
| for conv1, conv2, noise1, noise2, to_rgb in zip( | |
| self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs | |
| ): | |
| # these layers accepts skipped encoder layer, use fusion block to fuse the encoder feature and decoder feature | |
| if skip_layer_feature and fusion_block and i//2 < len(skip_layer_feature) and i//2 < len(fusion_block): | |
| if editing_w is None: | |
| out, skip = fusion_block[i//2](skip_layer_feature[i//2], out, skip) | |
| else: | |
| out, skip = fusion_block[i//2](skip_layer_feature[i//2], out, skip, editing_w[:,i]) | |
| out = conv1(out, latent[:, i], noise=noise1) | |
| out = conv2(out, latent[:, i + 1], noise=noise2) | |
| skip = to_rgb(out, latent[:, i + 2], skip) | |
| i += 2 | |
| image = skip | |
| if return_latents: | |
| return image, latent | |
| elif return_features: | |
| return image, out | |
| else: | |
| return image, None | |
| class ConvLayer(nn.Sequential): | |
| def __init__( | |
| self, | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| downsample=False, | |
| blur_kernel=[1, 3, 3, 1], | |
| bias=True, | |
| activate=True, | |
| dilation=1, ## modified | |
| ): | |
| layers = [] | |
| if downsample: | |
| factor = 2 | |
| p = (len(blur_kernel) - factor) + (kernel_size - 1) | |
| pad0 = (p + 1) // 2 | |
| pad1 = p // 2 | |
| layers.append(Blur(blur_kernel, pad=(pad0, pad1))) | |
| stride = 2 | |
| self.padding = 0 | |
| else: | |
| stride = 1 | |
| self.padding = kernel_size // 2 + dilation-1 ## modified | |
| layers.append( | |
| EqualConv2d( | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| padding=self.padding, | |
| stride=stride, | |
| bias=bias and not activate, | |
| dilation=dilation, ## modified | |
| ) | |
| ) | |
| if activate: | |
| if bias: | |
| layers.append(FusedLeakyReLU(out_channel)) | |
| else: | |
| layers.append(ScaledLeakyReLU(0.2)) | |
| super().__init__(*layers) | |
| class ResBlock(nn.Module): | |
| def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): | |
| super().__init__() | |
| self.conv1 = ConvLayer(in_channel, in_channel, 3) | |
| self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) | |
| self.skip = ConvLayer( | |
| in_channel, out_channel, 1, downsample=True, activate=False, bias=False | |
| ) | |
| def forward(self, input): | |
| out = self.conv1(input) | |
| out = self.conv2(out) | |
| skip = self.skip(input) | |
| out = (out + skip) / math.sqrt(2) | |
| return out | |
| class Discriminator(nn.Module): | |
| def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], img_channel=3): | |
| super().__init__() | |
| channels = { | |
| 4: 512, | |
| 8: 512, | |
| 16: 512, | |
| 32: 512, | |
| 64: 256 * channel_multiplier, | |
| 128: 128 * channel_multiplier, | |
| 256: 64 * channel_multiplier, | |
| 512: 32 * channel_multiplier, | |
| 1024: 16 * channel_multiplier, | |
| } | |
| convs = [ConvLayer(img_channel, channels[size], 1)] | |
| log_size = int(math.log(size, 2)) | |
| in_channel = channels[size] | |
| for i in range(log_size, 2, -1): | |
| out_channel = channels[2 ** (i - 1)] | |
| convs.append(ResBlock(in_channel, out_channel, blur_kernel)) | |
| in_channel = out_channel | |
| self.convs = nn.Sequential(*convs) | |
| self.stddev_group = 4 | |
| self.stddev_feat = 1 | |
| self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) | |
| self.final_linear = nn.Sequential( | |
| EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'), | |
| EqualLinear(channels[4], 1), | |
| ) | |
| self.size = size ##### modified | |
| def forward(self, input): | |
| # for input that not satisfies the target size, we crop it to extract a small image of the target size. | |
| _, _, h, w = input.shape ##### modified | |
| i, j = torch.randint(0, h+1-self.size, size=(1,)).item(), torch.randint(0, w+1-self.size, size=(1,)).item() ##### modified | |
| out = self.convs(input[:,:,i:i+self.size,j:j+self.size]) ##### modified | |
| batch, channel, height, width = out.shape | |
| group = min(batch, self.stddev_group) | |
| stddev = out.view( | |
| group, -1, self.stddev_feat, channel // self.stddev_feat, height, width | |
| ) | |
| stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) | |
| stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) | |
| stddev = stddev.repeat(group, 1, height, width) | |
| out = torch.cat([out, stddev], 1) | |
| out = self.final_conv(out) | |
| out = out.view(batch, -1) | |
| out = self.final_linear(out) | |
| return out |