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| # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| """Network architectures from the paper | |
| "Analyzing and Improving the Image Quality of StyleGAN". | |
| Matches the original implementation of configs E-F by Karras et al. at | |
| https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py""" | |
| import numpy as np | |
| import torch | |
| from torch_utils import misc | |
| from torch_utils import persistence | |
| from torch_utils.ops import conv2d_resample | |
| from torch_utils.ops import upfirdn2d | |
| from torch_utils.ops import bias_act | |
| from torch_utils.ops import fma | |
| #---------------------------------------------------------------------------- | |
| def normalize_2nd_moment(x, dim=1, eps=1e-8): | |
| return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() | |
| #---------------------------------------------------------------------------- | |
| def modulated_conv2d( | |
| x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. | |
| weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. | |
| styles, # Modulation coefficients of shape [batch_size, in_channels]. | |
| noise = None, # Optional noise tensor to add to the output activations. | |
| up = 1, # Integer upsampling factor. | |
| down = 1, # Integer downsampling factor. | |
| padding = 0, # Padding with respect to the upsampled image. | |
| resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). | |
| demodulate = True, # Apply weight demodulation? | |
| flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). | |
| fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? | |
| ): | |
| batch_size = x.shape[0] | |
| out_channels, in_channels, kh, kw = weight.shape | |
| misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] | |
| misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] | |
| misc.assert_shape(styles, [batch_size, in_channels]) # [NI] | |
| # Pre-normalize inputs to avoid FP16 overflow. | |
| if x.dtype == torch.float16 and demodulate: | |
| weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk | |
| styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I | |
| # Calculate per-sample weights and demodulation coefficients. | |
| w = None | |
| dcoefs = None | |
| if demodulate or fused_modconv: | |
| w = weight.unsqueeze(0) # [NOIkk] | |
| w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] | |
| if demodulate: | |
| dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] | |
| if demodulate and fused_modconv: | |
| w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] | |
| # Execute by scaling the activations before and after the convolution. | |
| if not fused_modconv: | |
| x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) | |
| x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) | |
| if demodulate and noise is not None: | |
| x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) | |
| elif demodulate: | |
| x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) | |
| elif noise is not None: | |
| x = x.add_(noise.to(x.dtype)) | |
| return x | |
| # Execute as one fused op using grouped convolution. | |
| with misc.suppress_tracer_warnings(): # this value will be treated as a constant | |
| batch_size = int(batch_size) | |
| misc.assert_shape(x, [batch_size, in_channels, None, None]) | |
| x = x.reshape(1, -1, *x.shape[2:]) | |
| w = w.reshape(-1, in_channels, kh, kw) | |
| x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) | |
| x = x.reshape(batch_size, -1, *x.shape[2:]) | |
| if noise is not None: | |
| x = x.add_(noise) | |
| return x | |
| #---------------------------------------------------------------------------- | |
| class FullyConnectedLayer(torch.nn.Module): | |
| def __init__(self, | |
| in_features, # Number of input features. | |
| out_features, # Number of output features. | |
| bias = True, # Apply additive bias before the activation function? | |
| activation = 'linear', # Activation function: 'relu', 'lrelu', etc. | |
| lr_multiplier = 1, # Learning rate multiplier. | |
| bias_init = 0, # Initial value for the additive bias. | |
| ): | |
| super().__init__() | |
| self.in_features = in_features | |
| self.out_features = out_features | |
| self.activation = activation | |
| self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) | |
| self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None | |
| self.weight_gain = lr_multiplier / np.sqrt(in_features) | |
| self.bias_gain = lr_multiplier | |
| def forward(self, x): | |
| w = self.weight.to(x.dtype) * self.weight_gain | |
| b = self.bias | |
| if b is not None: | |
| b = b.to(x.dtype) | |
| if self.bias_gain != 1: | |
| b = b * self.bias_gain | |
| if self.activation == 'linear' and b is not None: | |
| x = torch.addmm(b.unsqueeze(0), x, w.t()) | |
| else: | |
| x = x.matmul(w.t()) | |
| x = bias_act.bias_act(x, b, act=self.activation) | |
| return x | |
| def extra_repr(self): | |
| return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' | |
| #---------------------------------------------------------------------------- | |
| class Conv2dLayer(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels. | |
| out_channels, # Number of output channels. | |
| kernel_size, # Width and height of the convolution kernel. | |
| bias = True, # Apply additive bias before the activation function? | |
| activation = 'linear', # Activation function: 'relu', 'lrelu', etc. | |
| up = 1, # Integer upsampling factor. | |
| down = 1, # Integer downsampling factor. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = None, # Clamp the output to +-X, None = disable clamping. | |
| channels_last = False, # Expect the input to have memory_format=channels_last? | |
| trainable = True, # Update the weights of this layer during training? | |
| ): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.activation = activation | |
| self.up = up | |
| self.down = down | |
| self.conv_clamp = conv_clamp | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.padding = kernel_size // 2 | |
| self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) | |
| self.act_gain = bias_act.activation_funcs[activation].def_gain | |
| memory_format = torch.channels_last if channels_last else torch.contiguous_format | |
| weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) | |
| bias = torch.zeros([out_channels]) if bias else None | |
| if trainable: | |
| self.weight = torch.nn.Parameter(weight) | |
| self.bias = torch.nn.Parameter(bias) if bias is not None else None | |
| else: | |
| self.register_buffer('weight', weight) | |
| if bias is not None: | |
| self.register_buffer('bias', bias) | |
| else: | |
| self.bias = None | |
| def forward(self, x, gain=1): | |
| w = self.weight * self.weight_gain | |
| b = self.bias.to(x.dtype) if self.bias is not None else None | |
| flip_weight = (self.up == 1) # slightly faster | |
| x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) | |
| act_gain = self.act_gain * gain | |
| act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None | |
| x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) | |
| return x | |
| def extra_repr(self): | |
| return ' '.join([ | |
| f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},', | |
| f'up={self.up}, down={self.down}']) | |
| #---------------------------------------------------------------------------- | |
| class MappingNetwork(torch.nn.Module): | |
| def __init__(self, | |
| z_dim, # Input latent (Z) dimensionality, 0 = no latent. | |
| c_dim, # Conditioning label (C) dimensionality, 0 = no label. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| num_ws, # Number of intermediate latents to output, None = do not broadcast. | |
| num_layers = 8, # Number of mapping layers. | |
| embed_features = None, # Label embedding dimensionality, None = same as w_dim. | |
| layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. | |
| w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track. | |
| ): | |
| super().__init__() | |
| self.z_dim = z_dim | |
| self.c_dim = c_dim | |
| self.w_dim = w_dim | |
| self.num_ws = num_ws | |
| self.num_layers = num_layers | |
| self.w_avg_beta = w_avg_beta | |
| if embed_features is None: | |
| embed_features = w_dim | |
| if c_dim == 0: | |
| embed_features = 0 | |
| if layer_features is None: | |
| layer_features = w_dim | |
| features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] | |
| if c_dim > 0: | |
| self.embed = FullyConnectedLayer(c_dim, embed_features) | |
| for idx in range(num_layers): | |
| in_features = features_list[idx] | |
| out_features = features_list[idx + 1] | |
| layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) | |
| setattr(self, f'fc{idx}', layer) | |
| if num_ws is not None and w_avg_beta is not None: | |
| self.register_buffer('w_avg', torch.zeros([w_dim])) | |
| def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False): | |
| # Embed, normalize, and concat inputs. | |
| x = None | |
| with torch.autograd.profiler.record_function('input'): | |
| if self.z_dim > 0: | |
| misc.assert_shape(z, [None, self.z_dim]) | |
| x = normalize_2nd_moment(z.to(torch.float32)) | |
| if self.c_dim > 0: | |
| misc.assert_shape(c, [None, self.c_dim]) | |
| y = normalize_2nd_moment(self.embed(c.to(torch.float32))) | |
| x = torch.cat([x, y], dim=1) if x is not None else y | |
| # Main layers. | |
| for idx in range(self.num_layers): | |
| layer = getattr(self, f'fc{idx}') | |
| x = layer(x) | |
| # Update moving average of W. | |
| if update_emas and self.w_avg_beta is not None: | |
| with torch.autograd.profiler.record_function('update_w_avg'): | |
| self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) | |
| # Broadcast. | |
| if self.num_ws is not None: | |
| with torch.autograd.profiler.record_function('broadcast'): | |
| x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) | |
| # Apply truncation. | |
| if truncation_psi != 1: | |
| with torch.autograd.profiler.record_function('truncate'): | |
| assert self.w_avg_beta is not None | |
| if self.num_ws is None or truncation_cutoff is None: | |
| x = self.w_avg.lerp(x, truncation_psi) | |
| else: | |
| x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) | |
| return x | |
| def extra_repr(self): | |
| return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' | |
| #---------------------------------------------------------------------------- | |
| class SynthesisLayer(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels. | |
| out_channels, # Number of output channels. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| resolution, # Resolution of this layer. | |
| kernel_size = 3, # Convolution kernel size. | |
| up = 1, # Integer upsampling factor. | |
| use_noise = True, # Enable noise input? | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| channels_last = False, # Use channels_last format for the weights? | |
| ): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.w_dim = w_dim | |
| self.resolution = resolution | |
| self.up = up | |
| self.use_noise = use_noise | |
| self.activation = activation | |
| self.conv_clamp = conv_clamp | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.padding = kernel_size // 2 | |
| self.act_gain = bias_act.activation_funcs[activation].def_gain | |
| self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) | |
| memory_format = torch.channels_last if channels_last else torch.contiguous_format | |
| self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) | |
| if use_noise: | |
| self.register_buffer('noise_const', torch.randn([resolution, resolution])) | |
| self.noise_strength = torch.nn.Parameter(torch.zeros([])) | |
| self.bias = torch.nn.Parameter(torch.zeros([out_channels])) | |
| def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1): | |
| assert noise_mode in ['random', 'const', 'none'] | |
| in_resolution = self.resolution // self.up | |
| misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution]) | |
| styles = self.affine(w) | |
| noise = None | |
| if self.use_noise and noise_mode == 'random': | |
| noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength | |
| if self.use_noise and noise_mode == 'const': | |
| noise = self.noise_const * self.noise_strength | |
| flip_weight = (self.up == 1) # slightly faster | |
| x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, | |
| padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) | |
| act_gain = self.act_gain * gain | |
| act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None | |
| x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) | |
| return x | |
| def extra_repr(self): | |
| return ' '.join([ | |
| f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},', | |
| f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}']) | |
| #---------------------------------------------------------------------------- | |
| class ToRGBLayer(torch.nn.Module): | |
| def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.w_dim = w_dim | |
| self.conv_clamp = conv_clamp | |
| self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) | |
| memory_format = torch.channels_last if channels_last else torch.contiguous_format | |
| self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) | |
| self.bias = torch.nn.Parameter(torch.zeros([out_channels])) | |
| self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) | |
| def forward(self, x, w, fused_modconv=True): | |
| styles = self.affine(w) * self.weight_gain | |
| x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) | |
| x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) | |
| return x | |
| def extra_repr(self): | |
| return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}' | |
| #---------------------------------------------------------------------------- | |
| class SynthesisBlock(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels, 0 = first block. | |
| out_channels, # Number of output channels. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| resolution, # Resolution of this block. | |
| img_channels, # Number of output color channels. | |
| is_last, # Is this the last block? | |
| architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| use_fp16 = False, # Use FP16 for this block? | |
| fp16_channels_last = False, # Use channels-last memory format with FP16? | |
| fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training. | |
| **layer_kwargs, # Arguments for SynthesisLayer. | |
| ): | |
| assert architecture in ['orig', 'skip', 'resnet'] | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.w_dim = w_dim | |
| self.resolution = resolution | |
| self.img_channels = img_channels | |
| self.is_last = is_last | |
| self.architecture = architecture | |
| self.use_fp16 = use_fp16 | |
| self.channels_last = (use_fp16 and fp16_channels_last) | |
| self.fused_modconv_default = fused_modconv_default | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.num_conv = 0 | |
| self.num_torgb = 0 | |
| if in_channels == 0: | |
| self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) | |
| if in_channels != 0: | |
| self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, | |
| resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) | |
| self.num_conv += 1 | |
| self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, | |
| conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) | |
| self.num_conv += 1 | |
| if is_last or architecture == 'skip': | |
| self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, | |
| conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| self.num_torgb += 1 | |
| if in_channels != 0 and architecture == 'resnet': | |
| self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, | |
| resample_filter=resample_filter, channels_last=self.channels_last) | |
| def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs): | |
| _ = update_emas # unused | |
| misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) | |
| w_iter = iter(ws.unbind(dim=1)) | |
| if ws.device.type != 'cuda': | |
| force_fp32 = True | |
| dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 | |
| memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format | |
| if fused_modconv is None: | |
| fused_modconv = self.fused_modconv_default | |
| if fused_modconv == 'inference_only': | |
| fused_modconv = (not self.training) | |
| # Input. | |
| if self.in_channels == 0: | |
| x = self.const.to(dtype=dtype, memory_format=memory_format) | |
| x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) | |
| else: | |
| misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) | |
| x = x.to(dtype=dtype, memory_format=memory_format) | |
| # Main layers. | |
| if self.in_channels == 0: | |
| x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| elif self.architecture == 'resnet': | |
| y = self.skip(x, gain=np.sqrt(0.5)) | |
| x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) | |
| x = y.add_(x) | |
| else: | |
| x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| # ToRGB. | |
| if img is not None: | |
| misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) | |
| img = upfirdn2d.upsample2d(img, self.resample_filter) | |
| if self.is_last or self.architecture == 'skip': | |
| y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) | |
| y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) | |
| img = img.add_(y) if img is not None else y | |
| assert x.dtype == dtype | |
| assert img is None or img.dtype == torch.float32 | |
| return x, img | |
| def extra_repr(self): | |
| return f'resolution={self.resolution:d}, architecture={self.architecture:s}' | |
| #---------------------------------------------------------------------------- | |
| class SynthesisNetwork(torch.nn.Module): | |
| def __init__(self, | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| img_resolution, # Output image resolution. | |
| img_channels, # Number of color channels. | |
| channel_base = 32768, # Overall multiplier for the number of channels. | |
| channel_max = 512, # Maximum number of channels in any layer. | |
| num_fp16_res = 4, # Use FP16 for the N highest resolutions. | |
| **block_kwargs, # Arguments for SynthesisBlock. | |
| ): | |
| assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 | |
| super().__init__() | |
| self.w_dim = w_dim | |
| self.img_resolution = img_resolution | |
| self.img_resolution_log2 = int(np.log2(img_resolution)) | |
| self.img_channels = img_channels | |
| self.num_fp16_res = num_fp16_res | |
| self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] | |
| channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} | |
| fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) | |
| self.num_ws = 0 | |
| for res in self.block_resolutions: | |
| in_channels = channels_dict[res // 2] if res > 4 else 0 | |
| out_channels = channels_dict[res] | |
| use_fp16 = (res >= fp16_resolution) | |
| is_last = (res == self.img_resolution) | |
| block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, | |
| img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) | |
| self.num_ws += block.num_conv | |
| if is_last: | |
| self.num_ws += block.num_torgb | |
| setattr(self, f'b{res}', block) | |
| def forward(self, ws, **block_kwargs): | |
| block_ws = [] | |
| with torch.autograd.profiler.record_function('split_ws'): | |
| misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) | |
| ws = ws.to(torch.float32) | |
| w_idx = 0 | |
| for res in self.block_resolutions: | |
| block = getattr(self, f'b{res}') | |
| block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) | |
| w_idx += block.num_conv | |
| x = img = None | |
| for res, cur_ws in zip(self.block_resolutions, block_ws): | |
| block = getattr(self, f'b{res}') | |
| x, img = block(x, img, cur_ws, **block_kwargs) | |
| return img | |
| def extra_repr(self): | |
| return ' '.join([ | |
| f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},', | |
| f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', | |
| f'num_fp16_res={self.num_fp16_res:d}']) | |
| #---------------------------------------------------------------------------- | |
| class Generator(torch.nn.Module): | |
| def __init__(self, | |
| z_dim, # Input latent (Z) dimensionality. | |
| c_dim, # Conditioning label (C) dimensionality. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| img_resolution, # Output resolution. | |
| img_channels, # Number of output color channels. | |
| mapping_kwargs = {}, # Arguments for MappingNetwork. | |
| **synthesis_kwargs, # Arguments for SynthesisNetwork. | |
| ): | |
| super().__init__() | |
| self.z_dim = z_dim | |
| self.c_dim = c_dim | |
| self.w_dim = w_dim | |
| self.img_resolution = img_resolution | |
| self.img_channels = img_channels | |
| self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) | |
| self.num_ws = self.synthesis.num_ws | |
| self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) | |
| def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): | |
| ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) | |
| img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs) | |
| return img | |
| #---------------------------------------------------------------------------- | |
| class DiscriminatorBlock(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels, 0 = first block. | |
| tmp_channels, # Number of intermediate channels. | |
| out_channels, # Number of output channels. | |
| resolution, # Resolution of this block. | |
| img_channels, # Number of input color channels. | |
| first_layer_idx, # Index of the first layer. | |
| architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| use_fp16 = False, # Use FP16 for this block? | |
| fp16_channels_last = False, # Use channels-last memory format with FP16? | |
| freeze_layers = 0, # Freeze-D: Number of layers to freeze. | |
| ): | |
| assert in_channels in [0, tmp_channels] | |
| assert architecture in ['orig', 'skip', 'resnet'] | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.resolution = resolution | |
| self.img_channels = img_channels | |
| self.first_layer_idx = first_layer_idx | |
| self.architecture = architecture | |
| self.use_fp16 = use_fp16 | |
| self.channels_last = (use_fp16 and fp16_channels_last) | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.num_layers = 0 | |
| def trainable_gen(): | |
| while True: | |
| layer_idx = self.first_layer_idx + self.num_layers | |
| trainable = (layer_idx >= freeze_layers) | |
| self.num_layers += 1 | |
| yield trainable | |
| trainable_iter = trainable_gen() | |
| if in_channels == 0 or architecture == 'skip': | |
| self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation, | |
| trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation, | |
| trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, | |
| trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| if architecture == 'resnet': | |
| self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2, | |
| trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) | |
| def forward(self, x, img, force_fp32=False): | |
| if (x if x is not None else img).device.type != 'cuda': | |
| force_fp32 = True | |
| dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 | |
| memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format | |
| # Input. | |
| if x is not None: | |
| misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) | |
| x = x.to(dtype=dtype, memory_format=memory_format) | |
| # FromRGB. | |
| if self.in_channels == 0 or self.architecture == 'skip': | |
| misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) | |
| img = img.to(dtype=dtype, memory_format=memory_format) | |
| y = self.fromrgb(img) | |
| x = x + y if x is not None else y | |
| img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None | |
| # Main layers. | |
| if self.architecture == 'resnet': | |
| y = self.skip(x, gain=np.sqrt(0.5)) | |
| x = self.conv0(x) | |
| x = self.conv1(x, gain=np.sqrt(0.5)) | |
| x = y.add_(x) | |
| else: | |
| x = self.conv0(x) | |
| x = self.conv1(x) | |
| assert x.dtype == dtype | |
| return x, img | |
| def extra_repr(self): | |
| return f'resolution={self.resolution:d}, architecture={self.architecture:s}' | |
| #---------------------------------------------------------------------------- | |
| class MinibatchStdLayer(torch.nn.Module): | |
| def __init__(self, group_size, num_channels=1): | |
| super().__init__() | |
| self.group_size = group_size | |
| self.num_channels = num_channels | |
| def forward(self, x): | |
| N, C, H, W = x.shape | |
| with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants | |
| G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N | |
| F = self.num_channels | |
| c = C // F | |
| y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. | |
| y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. | |
| y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. | |
| y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. | |
| y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels. | |
| y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. | |
| y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. | |
| x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. | |
| return x | |
| def extra_repr(self): | |
| return f'group_size={self.group_size}, num_channels={self.num_channels:d}' | |
| #---------------------------------------------------------------------------- | |
| class DiscriminatorEpilogue(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels. | |
| cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. | |
| resolution, # Resolution of this block. | |
| img_channels, # Number of input color channels. | |
| architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. | |
| mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch. | |
| mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable. | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| ): | |
| assert architecture in ['orig', 'skip', 'resnet'] | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.cmap_dim = cmap_dim | |
| self.resolution = resolution | |
| self.img_channels = img_channels | |
| self.architecture = architecture | |
| if architecture == 'skip': | |
| self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation) | |
| self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None | |
| self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp) | |
| self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) | |
| self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) | |
| def forward(self, x, img, cmap, force_fp32=False): | |
| misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW] | |
| _ = force_fp32 # unused | |
| dtype = torch.float32 | |
| memory_format = torch.contiguous_format | |
| # FromRGB. | |
| x = x.to(dtype=dtype, memory_format=memory_format) | |
| if self.architecture == 'skip': | |
| misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) | |
| img = img.to(dtype=dtype, memory_format=memory_format) | |
| x = x + self.fromrgb(img) | |
| # Main layers. | |
| if self.mbstd is not None: | |
| x = self.mbstd(x) | |
| x = self.conv(x) | |
| x = self.fc(x.flatten(1)) | |
| x = self.out(x) | |
| # Conditioning. | |
| if self.cmap_dim > 0: | |
| misc.assert_shape(cmap, [None, self.cmap_dim]) | |
| x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) | |
| assert x.dtype == dtype | |
| return x | |
| def extra_repr(self): | |
| return f'resolution={self.resolution:d}, architecture={self.architecture:s}' | |
| #---------------------------------------------------------------------------- | |
| class Discriminator(torch.nn.Module): | |
| def __init__(self, | |
| c_dim, # Conditioning label (C) dimensionality. | |
| img_resolution, # Input resolution. | |
| img_channels, # Number of input color channels. | |
| architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. | |
| channel_base = 32768, # Overall multiplier for the number of channels. | |
| channel_max = 512, # Maximum number of channels in any layer. | |
| num_fp16_res = 4, # Use FP16 for the N highest resolutions. | |
| conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. | |
| block_kwargs = {}, # Arguments for DiscriminatorBlock. | |
| mapping_kwargs = {}, # Arguments for MappingNetwork. | |
| epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue. | |
| ): | |
| super().__init__() | |
| self.c_dim = c_dim | |
| self.img_resolution = img_resolution | |
| self.img_resolution_log2 = int(np.log2(img_resolution)) | |
| self.img_channels = img_channels | |
| self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)] | |
| channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]} | |
| fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) | |
| if cmap_dim is None: | |
| cmap_dim = channels_dict[4] | |
| if c_dim == 0: | |
| cmap_dim = 0 | |
| common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp) | |
| cur_layer_idx = 0 | |
| for res in self.block_resolutions: | |
| in_channels = channels_dict[res] if res < img_resolution else 0 | |
| tmp_channels = channels_dict[res] | |
| out_channels = channels_dict[res // 2] | |
| use_fp16 = (res >= fp16_resolution) | |
| block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res, | |
| first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs) | |
| setattr(self, f'b{res}', block) | |
| cur_layer_idx += block.num_layers | |
| if c_dim > 0: | |
| self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) | |
| self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs) | |
| def forward(self, img, c, update_emas=False, **block_kwargs): | |
| _ = update_emas # unused | |
| x = None | |
| for res in self.block_resolutions: | |
| block = getattr(self, f'b{res}') | |
| x, img = block(x, img, **block_kwargs) | |
| cmap = None | |
| if self.c_dim > 0: | |
| cmap = self.mapping(None, c) | |
| x = self.b4(x, img, cmap) | |
| return x | |
| def extra_repr(self): | |
| return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}' | |
| #---------------------------------------------------------------------------- | |