# python3.7 """Contains the implementation of generator described in StyleGAN2. Compared to that of StyleGAN, the generator in StyleGAN2 mainly introduces style demodulation, adds skip connections, increases model size, and disables progressive growth. This script ONLY supports config F in the original paper. Paper: https://arxiv.org/pdf/1912.04958.pdf Official TensorFlow implementation: https://github.com/NVlabs/stylegan2 """ import numpy as np import torch import torch.nn as nn from third_party.stylegan2_official_ops import fma from third_party.stylegan2_official_ops import bias_act from third_party.stylegan2_official_ops import upfirdn2d from third_party.stylegan2_official_ops import conv2d_gradfix from .utils.ops import all_gather __all__ = ['StyleGAN2Generator'] # Resolutions allowed. _RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024] # Architectures allowed. _ARCHITECTURES_ALLOWED = ['resnet', 'skip', 'origin'] # pylint: disable=missing-function-docstring class StyleGAN2Generator(nn.Module): """Defines the generator network in StyleGAN2. NOTE: The synthesized images are with `RGB` channel order and pixel range [-1, 1]. Settings for the mapping network: (1) z_dim: Dimension of the input latent space, Z. (default: 512) (2) w_dim: Dimension of the output latent space, W. (default: 512) (3) repeat_w: Repeat w-code for different layers. (default: True) (4) normalize_z: Whether to normalize the z-code. (default: True) (5) mapping_layers: Number of layers of the mapping network. (default: 8) (6) mapping_fmaps: Number of hidden channels of the mapping network. (default: 512) (7) mapping_use_wscale: Whether to use weight scaling for the mapping network. (default: True) (8) mapping_wscale_gain: The factor to control weight scaling for the mapping network (default: 1.0) (9) mapping_lr_mul: Learning rate multiplier for the mapping network. (default: 0.01) Settings for conditional generation: (1) label_dim: Dimension of the additional label for conditional generation. In one-hot conditioning case, it is equal to the number of classes. If set to 0, conditioning training will be disabled. (default: 0) (2) embedding_dim: Dimension of the embedding space, if needed. (default: 512) (3) embedding_bias: Whether to add bias to embedding learning. (default: True) (4) embedding_use_wscale: Whether to use weight scaling for embedding learning. (default: True) (5) embedding_wscale_gain: The factor to control weight scaling for embedding. (default: 1.0) (6) embedding_lr_mul: Learning rate multiplier for the embedding learning. (default: 1.0) (7) normalize_embedding: Whether to normalize the embedding. (default: True) (8) normalize_embedding_latent: Whether to normalize the embedding together with the latent. (default: False) Settings for the synthesis network: (1) resolution: The resolution of the output image. (default: -1) (2) init_res: The initial resolution to start with convolution. (default: 4) (3) image_channels: Number of channels of the output image. (default: 3) (4) final_tanh: Whether to use `tanh` to control the final pixel range. (default: False) (5) const_input: Whether to use a constant in the first convolutional layer. (default: True) (6) architecture: Type of architecture. Support `origin`, `skip`, and `resnet`. (default: `skip`) (7) demodulate: Whether to perform style demodulation. (default: True) (8) use_wscale: Whether to use weight scaling. (default: True) (9) wscale_gain: The factor to control weight scaling. (default: 1.0) (10) lr_mul: Learning rate multiplier for the synthesis network. (default: 1.0) (11) noise_type: Type of noise added to the convolutional results at each layer. (default: `spatial`) (12) fmaps_base: Factor to control number of feature maps for each layer. (default: 32 << 10) (13) fmaps_max: Maximum number of feature maps in each layer. (default: 512) (14) filter_kernel: Kernel used for filtering (e.g., downsampling). (default: (1, 3, 3, 1)) (15) conv_clamp: A threshold to clamp the output of convolution layers to avoid overflow under FP16 training. (default: None) (16) eps: A small value to avoid divide overflow. (default: 1e-8) Runtime settings: (1) w_moving_decay: Decay factor for updating `w_avg`, which is used for training only. Set `None` to disable. (default: None) (2) sync_w_avg: Synchronizing the stats of `w_avg` across replicas. If set as `True`, the stats will be more accurate, yet the speed maybe a little bit slower. (default: False) (3) style_mixing_prob: Probability to perform style mixing as a training regularization. Set `None` to disable. (default: None) (4) trunc_psi: Truncation psi, set `None` to disable. (default: None) (5) trunc_layers: Number of layers to perform truncation. (default: None) (6) noise_mode: Mode of the layer-wise noise. Support `none`, `random`, `const`. (default: `const`) (7) fused_modulate: Whether to fuse `style_modulate` and `conv2d` together. (default: False) (8) fp16_res: Layers at resolution higher than (or equal to) this field will use `float16` precision for computation. This is merely used for acceleration. If set as `None`, all layers will use `float32` by default. (default: None) (9) impl: Implementation mode of some particular ops, e.g., `filtering`, `bias_act`, etc. `cuda` means using the official CUDA implementation from StyleGAN2, while `ref` means using the native PyTorch ops. (default: `cuda`) """ def __init__(self, # Settings for mapping network. z_dim=512, w_dim=512, repeat_w=True, normalize_z=True, mapping_layers=8, mapping_fmaps=512, mapping_use_wscale=True, mapping_wscale_gain=1.0, mapping_lr_mul=0.01, # Settings for conditional generation. label_dim=0, embedding_dim=512, embedding_bias=True, embedding_use_wscale=True, embedding_wscale_gian=1.0, embedding_lr_mul=1.0, normalize_embedding=True, normalize_embedding_latent=False, # Settings for synthesis network. resolution=-1, init_res=4, image_channels=3, final_tanh=False, const_input=True, architecture='skip', demodulate=True, use_wscale=True, wscale_gain=1.0, lr_mul=1.0, noise_type='spatial', fmaps_base=32 << 10, fmaps_max=512, filter_kernel=(1, 3, 3, 1), conv_clamp=None, eps=1e-8): """Initializes with basic settings. Raises: ValueError: If the `resolution` is not supported, or `architecture` is not supported. """ super().__init__() if resolution not in _RESOLUTIONS_ALLOWED: raise ValueError(f'Invalid resolution: `{resolution}`!\n' f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.') architecture = architecture.lower() if architecture not in _ARCHITECTURES_ALLOWED: raise ValueError(f'Invalid architecture: `{architecture}`!\n' f'Architectures allowed: ' f'{_ARCHITECTURES_ALLOWED}.') self.z_dim = z_dim self.w_dim = w_dim self.repeat_w = repeat_w self.normalize_z = normalize_z self.mapping_layers = mapping_layers self.mapping_fmaps = mapping_fmaps self.mapping_use_wscale = mapping_use_wscale self.mapping_wscale_gain = mapping_wscale_gain self.mapping_lr_mul = mapping_lr_mul self.label_dim = label_dim self.embedding_dim = embedding_dim self.embedding_bias = embedding_bias self.embedding_use_wscale = embedding_use_wscale self.embedding_wscale_gain = embedding_wscale_gian self.embedding_lr_mul = embedding_lr_mul self.normalize_embedding = normalize_embedding self.normalize_embedding_latent = normalize_embedding_latent self.resolution = resolution self.init_res = init_res self.image_channels = image_channels self.final_tanh = final_tanh self.const_input = const_input self.architecture = architecture self.demodulate = demodulate self.use_wscale = use_wscale self.wscale_gain = wscale_gain self.lr_mul = lr_mul self.noise_type = noise_type.lower() self.fmaps_base = fmaps_base self.fmaps_max = fmaps_max self.filter_kernel = filter_kernel self.conv_clamp = conv_clamp self.eps = eps # Dimension of latent space, which is convenient for sampling. self.latent_dim = (z_dim,) # Number of synthesis (convolutional) layers. self.num_layers = int(np.log2(resolution // init_res * 2)) * 2 self.mapping = MappingNetwork( input_dim=z_dim, output_dim=w_dim, num_outputs=self.num_layers, repeat_output=repeat_w, normalize_input=normalize_z, num_layers=mapping_layers, hidden_dim=mapping_fmaps, use_wscale=mapping_use_wscale, wscale_gain=mapping_wscale_gain, lr_mul=mapping_lr_mul, label_dim=label_dim, embedding_dim=embedding_dim, embedding_bias=embedding_bias, embedding_use_wscale=embedding_use_wscale, embedding_wscale_gian=embedding_wscale_gian, embedding_lr_mul=embedding_lr_mul, normalize_embedding=normalize_embedding, normalize_embedding_latent=normalize_embedding_latent, eps=eps) # This is used for truncation trick. if self.repeat_w: self.register_buffer('w_avg', torch.zeros(w_dim)) else: self.register_buffer('w_avg', torch.zeros(self.num_layers * w_dim)) self.synthesis = SynthesisNetwork(resolution=resolution, init_res=init_res, w_dim=w_dim, image_channels=image_channels, final_tanh=final_tanh, const_input=const_input, architecture=architecture, demodulate=demodulate, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type=noise_type, fmaps_base=fmaps_base, filter_kernel=filter_kernel, fmaps_max=fmaps_max, conv_clamp=conv_clamp, eps=eps) self.pth_to_tf_var_mapping = {'w_avg': 'dlatent_avg'} for key, val in self.mapping.pth_to_tf_var_mapping.items(): self.pth_to_tf_var_mapping[f'mapping.{key}'] = val for key, val in self.synthesis.pth_to_tf_var_mapping.items(): self.pth_to_tf_var_mapping[f'synthesis.{key}'] = val def set_space_of_latent(self, space_of_latent): """Sets the space to which the latent code belong. See `SynthesisNetwork` for more details. """ self.synthesis.set_space_of_latent(space_of_latent) def forward(self, z, label=None, w_moving_decay=None, sync_w_avg=False, style_mixing_prob=None, trunc_psi=None, trunc_layers=None, noise_mode='const', fused_modulate=False, fp16_res=None, impl='cuda'): """Connects mapping network and synthesis network. This forward function will also update the average `w_code`, perform style mixing as a training regularizer, and do truncation trick, which is specially designed for inference. Concretely, the truncation trick acts as follows: For layers in range [0, truncation_layers), the truncated w-code is computed as w_new = w_avg + (w - w_avg) * truncation_psi To disable truncation, please set (1) truncation_psi = 1.0 (None) OR (2) truncation_layers = 0 (None) """ mapping_results = self.mapping(z, label, impl=impl) w = mapping_results['w'] if self.training and w_moving_decay is not None: if sync_w_avg: batch_w_avg = all_gather(w.detach()).mean(dim=0) else: batch_w_avg = w.detach().mean(dim=0) self.w_avg.copy_(batch_w_avg.lerp(self.w_avg, w_moving_decay)) wp = mapping_results.pop('wp') if self.training and style_mixing_prob is not None: if np.random.uniform() < style_mixing_prob: new_z = torch.randn_like(z) new_wp = self.mapping(new_z, label, impl=impl)['wp'] mixing_cutoff = np.random.randint(1, self.num_layers) wp[:, mixing_cutoff:] = new_wp[:, mixing_cutoff:] if not self.training: trunc_psi = 1.0 if trunc_psi is None else trunc_psi trunc_layers = 0 if trunc_layers is None else trunc_layers if trunc_psi < 1.0 and trunc_layers > 0: w_avg = self.w_avg.reshape(1, -1, self.w_dim)[:, :trunc_layers] wp[:, :trunc_layers] = w_avg.lerp( wp[:, :trunc_layers], trunc_psi) synthesis_results = self.synthesis(wp, noise_mode=noise_mode, fused_modulate=fused_modulate, impl=impl, fp16_res=fp16_res) return {**mapping_results, **synthesis_results} class MappingNetwork(nn.Module): """Implements the latent space mapping network. Basically, this network executes several dense layers in sequence, and the label embedding if needed. """ def __init__(self, input_dim, output_dim, num_outputs, repeat_output, normalize_input, num_layers, hidden_dim, use_wscale, wscale_gain, lr_mul, label_dim, embedding_dim, embedding_bias, embedding_use_wscale, embedding_wscale_gian, embedding_lr_mul, normalize_embedding, normalize_embedding_latent, eps): super().__init__() self.input_dim = input_dim self.output_dim = output_dim self.num_outputs = num_outputs self.repeat_output = repeat_output self.normalize_input = normalize_input self.num_layers = num_layers self.hidden_dim = hidden_dim self.use_wscale = use_wscale self.wscale_gain = wscale_gain self.lr_mul = lr_mul self.label_dim = label_dim self.embedding_dim = embedding_dim self.embedding_bias = embedding_bias self.embedding_use_wscale = embedding_use_wscale self.embedding_wscale_gian = embedding_wscale_gian self.embedding_lr_mul = embedding_lr_mul self.normalize_embedding = normalize_embedding self.normalize_embedding_latent = normalize_embedding_latent self.eps = eps self.pth_to_tf_var_mapping = {} self.norm = PixelNormLayer(dim=1, eps=eps) if self.label_dim > 0: input_dim = input_dim + embedding_dim self.embedding = DenseLayer(in_channels=label_dim, out_channels=embedding_dim, add_bias=embedding_bias, init_bias=0.0, use_wscale=embedding_use_wscale, wscale_gain=embedding_wscale_gian, lr_mul=embedding_lr_mul, activation_type='linear') self.pth_to_tf_var_mapping['embedding.weight'] = 'LabelEmbed/weight' if self.embedding_bias: self.pth_to_tf_var_mapping['embedding.bias'] = 'LabelEmbed/bias' if num_outputs is not None and not repeat_output: output_dim = output_dim * num_outputs for i in range(num_layers): in_channels = (input_dim if i == 0 else hidden_dim) out_channels = (output_dim if i == (num_layers - 1) else hidden_dim) self.add_module(f'dense{i}', DenseLayer(in_channels=in_channels, out_channels=out_channels, add_bias=True, init_bias=0.0, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, activation_type='lrelu')) self.pth_to_tf_var_mapping[f'dense{i}.weight'] = f'Dense{i}/weight' self.pth_to_tf_var_mapping[f'dense{i}.bias'] = f'Dense{i}/bias' def forward(self, z, label=None, impl='cuda'): if z.ndim != 2 or z.shape[1] != self.input_dim: raise ValueError(f'Input latent code should be with shape ' f'[batch_size, input_dim], where ' f'`input_dim` equals to {self.input_dim}!\n' f'But `{z.shape}` is received!') if self.normalize_input: z = self.norm(z) if self.label_dim > 0: if label is None: raise ValueError(f'Model requires an additional label ' f'(with dimension {self.label_dim}) as input, ' f'but no label is received!') if label.ndim != 2 or label.shape != (z.shape[0], self.label_dim): raise ValueError(f'Input label should be with shape ' f'[batch_size, label_dim], where ' f'`batch_size` equals to that of ' f'latent codes ({z.shape[0]}) and ' f'`label_dim` equals to {self.label_dim}!\n' f'But `{label.shape}` is received!') label = label.to(dtype=torch.float32) embedding = self.embedding(label, impl=impl) if self.normalize_embedding: embedding = self.norm(embedding) w = torch.cat((z, embedding), dim=1) else: w = z if self.label_dim > 0 and self.normalize_embedding_latent: w = self.norm(w) for i in range(self.num_layers): w = getattr(self, f'dense{i}')(w, impl=impl) wp = None if self.num_outputs is not None: if self.repeat_output: wp = w.unsqueeze(1).repeat((1, self.num_outputs, 1)) else: wp = w.reshape(-1, self.num_outputs, self.output_dim) results = { 'z': z, 'label': label, 'w': w, 'wp': wp, } if self.label_dim > 0: results['embedding'] = embedding return results class SynthesisNetwork(nn.Module): """Implements the image synthesis network. Basically, this network executes several convolutional layers in sequence. """ def __init__(self, resolution, init_res, w_dim, image_channels, final_tanh, const_input, architecture, demodulate, use_wscale, wscale_gain, lr_mul, noise_type, fmaps_base, fmaps_max, filter_kernel, conv_clamp, eps): super().__init__() self.init_res = init_res self.init_res_log2 = int(np.log2(init_res)) self.resolution = resolution self.final_res_log2 = int(np.log2(resolution)) self.w_dim = w_dim self.image_channels = image_channels self.final_tanh = final_tanh self.const_input = const_input self.architecture = architecture.lower() self.demodulate = demodulate self.use_wscale = use_wscale self.wscale_gain = wscale_gain self.lr_mul = lr_mul self.noise_type = noise_type.lower() self.fmaps_base = fmaps_base self.fmaps_max = fmaps_max self.filter_kernel = filter_kernel self.conv_clamp = conv_clamp self.eps = eps self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2 self.pth_to_tf_var_mapping = {} for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1): res = 2 ** res_log2 in_channels = self.get_nf(res // 2) out_channels = self.get_nf(res) block_idx = res_log2 - self.init_res_log2 # Early layer. if res == init_res: if self.const_input: self.add_module('early_layer', InputLayer(init_res=res, channels=out_channels)) self.pth_to_tf_var_mapping['early_layer.const'] = ( f'{res}x{res}/Const/const') else: channels = out_channels * res * res self.add_module('early_layer', DenseLayer(in_channels=w_dim, out_channels=channels, add_bias=True, init_bias=0.0, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, activation_type='lrelu')) self.pth_to_tf_var_mapping['early_layer.weight'] = ( f'{res}x{res}/Dense/weight') self.pth_to_tf_var_mapping['early_layer.bias'] = ( f'{res}x{res}/Dense/bias') else: # Residual branch (kernel 1x1) with upsampling, without bias, # with linear activation. if self.architecture == 'resnet': layer_name = f'residual{block_idx}' self.add_module(layer_name, ConvLayer(in_channels=in_channels, out_channels=out_channels, kernel_size=1, add_bias=False, scale_factor=2, filter_kernel=filter_kernel, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, activation_type='linear', conv_clamp=None)) self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( f'{res}x{res}/Skip/weight') # First layer (kernel 3x3) with upsampling. layer_name = f'layer{2 * block_idx - 1}' self.add_module(layer_name, ModulateConvLayer(in_channels=in_channels, out_channels=out_channels, resolution=res, w_dim=w_dim, kernel_size=3, add_bias=True, scale_factor=2, filter_kernel=filter_kernel, demodulate=demodulate, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type=noise_type, activation_type='lrelu', conv_clamp=conv_clamp, eps=eps)) self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( f'{res}x{res}/Conv0_up/weight') self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = ( f'{res}x{res}/Conv0_up/bias') self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = ( f'{res}x{res}/Conv0_up/mod_weight') self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( f'{res}x{res}/Conv0_up/mod_bias') self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = ( f'{res}x{res}/Conv0_up/noise_strength') self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = ( f'noise{2 * block_idx - 1}') # Second layer (kernel 3x3) without upsampling. layer_name = f'layer{2 * block_idx}' self.add_module(layer_name, ModulateConvLayer(in_channels=out_channels, out_channels=out_channels, resolution=res, w_dim=w_dim, kernel_size=3, add_bias=True, scale_factor=1, filter_kernel=None, demodulate=demodulate, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type=noise_type, activation_type='lrelu', conv_clamp=conv_clamp, eps=eps)) tf_layer_name = 'Conv' if res == self.init_res else 'Conv1' self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( f'{res}x{res}/{tf_layer_name}/weight') self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = ( f'{res}x{res}/{tf_layer_name}/bias') self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = ( f'{res}x{res}/{tf_layer_name}/mod_weight') self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( f'{res}x{res}/{tf_layer_name}/mod_bias') self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = ( f'{res}x{res}/{tf_layer_name}/noise_strength') self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = ( f'noise{2 * block_idx}') # Output convolution layer for each resolution (if needed). if res_log2 == self.final_res_log2 or self.architecture == 'skip': layer_name = f'output{block_idx}' self.add_module(layer_name, ModulateConvLayer(in_channels=out_channels, out_channels=image_channels, resolution=res, w_dim=w_dim, kernel_size=1, add_bias=True, scale_factor=1, filter_kernel=None, demodulate=False, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type='none', activation_type='linear', conv_clamp=conv_clamp, eps=eps)) self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( f'{res}x{res}/ToRGB/weight') self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = ( f'{res}x{res}/ToRGB/bias') self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = ( f'{res}x{res}/ToRGB/mod_weight') self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( f'{res}x{res}/ToRGB/mod_bias') # Used for upsampling output images for each resolution block for sum. if self.architecture == 'skip': self.register_buffer( 'filter', upfirdn2d.setup_filter(filter_kernel)) def get_nf(self, res): """Gets number of feature maps according to the given resolution.""" return min(self.fmaps_base // res, self.fmaps_max) def set_space_of_latent(self, space_of_latent): """Sets the space to which the latent code belong. This function is particularly used for choosing how to inject the latent code into the convolutional layers. The original generator will take a W-Space code and apply it for style modulation after an affine transformation. But, sometimes, it may need to directly feed an already affine-transformed code into the convolutional layer, e.g., when training an encoder for GAN inversion. We term the transformed space as Style Space (or Y-Space). This function is designed to tell the convolutional layers how to use the input code. Args: space_of_latent: The space to which the latent code belong. Case insensitive. Support `W` and `Y`. """ space_of_latent = space_of_latent.upper() for module in self.modules(): if isinstance(module, ModulateConvLayer): setattr(module, 'space_of_latent', space_of_latent) def forward(self, wp, noise_mode='const', fused_modulate=False, fp16_res=None, impl='cuda'): results = {'wp': wp} if self.const_input: x = self.early_layer(wp[:, 0]) else: x = self.early_layer(wp[:, 0], impl=impl) # Cast to `torch.float16` if needed. if fp16_res is not None and self.init_res >= fp16_res: x = x.to(torch.float16) if self.architecture == 'origin': for layer_idx in range(self.num_layers - 1): layer = getattr(self, f'layer{layer_idx}') x, style = layer(x, wp[:, layer_idx], noise_mode=noise_mode, fused_modulate=fused_modulate, impl=impl) results[f'style{layer_idx}'] = style # Cast to `torch.float16` if needed. if layer_idx % 2 == 0 and layer_idx != self.num_layers - 2: res = self.init_res * (2 ** (layer_idx // 2)) if fp16_res is not None and res * 2 >= fp16_res: x = x.to(torch.float16) else: x = x.to(torch.float32) output_layer = getattr(self, f'output{layer_idx // 2}') image, style = output_layer(x, wp[:, layer_idx + 1], fused_modulate=fused_modulate, impl=impl) image = image.to(torch.float32) results[f'output_style{layer_idx // 2}'] = style elif self.architecture == 'skip': for layer_idx in range(self.num_layers - 1): layer = getattr(self, f'layer{layer_idx}') x, style = layer(x, wp[:, layer_idx], noise_mode=noise_mode, fused_modulate=fused_modulate, impl=impl) results[f'style{layer_idx}'] = style if layer_idx % 2 == 0: output_layer = getattr(self, f'output{layer_idx // 2}') y, style = output_layer(x, wp[:, layer_idx + 1], fused_modulate=fused_modulate, impl=impl) results[f'output_style{layer_idx // 2}'] = style if layer_idx == 0: image = y.to(torch.float32) else: image = y.to(torch.float32) + upfirdn2d.upsample2d( image, self.filter, impl=impl) # Cast to `torch.float16` if needed. if layer_idx != self.num_layers - 2: res = self.init_res * (2 ** (layer_idx // 2)) if fp16_res is not None and res * 2 >= fp16_res: x = x.to(torch.float16) else: x = x.to(torch.float32) elif self.architecture == 'resnet': x, style = self.layer0(x, wp[:, 0], noise_mode=noise_mode, fused_modulate=fused_modulate, impl=impl) results['style0'] = style for layer_idx in range(1, self.num_layers - 1, 2): # Cast to `torch.float16` if needed. if layer_idx % 2 == 1: res = self.init_res * (2 ** (layer_idx // 2)) if fp16_res is not None and res * 2 >= fp16_res: x = x.to(torch.float16) else: x = x.to(torch.float32) skip_layer = getattr(self, f'residual{layer_idx // 2 + 1}') residual = skip_layer(x, runtime_gain=np.sqrt(0.5), impl=impl) layer = getattr(self, f'layer{layer_idx}') x, style = layer(x, wp[:, layer_idx], noise_mode=noise_mode, fused_modulate=fused_modulate, impl=impl) results[f'style{layer_idx}'] = style layer = getattr(self, f'layer{layer_idx + 1}') x, style = layer(x, wp[:, layer_idx + 1], runtime_gain=np.sqrt(0.5), noise_mode=noise_mode, fused_modulate=fused_modulate, impl=impl) results[f'style{layer_idx + 1}'] = style x = x + residual output_layer = getattr(self, f'output{layer_idx // 2 + 1}') image, style = output_layer(x, wp[:, layer_idx + 2], fused_modulate=fused_modulate, impl=impl) image = image.to(torch.float32) results[f'output_style{layer_idx // 2}'] = style if self.final_tanh: image = torch.tanh(image) results['image'] = image return results class PixelNormLayer(nn.Module): """Implements pixel-wise feature vector normalization layer.""" def __init__(self, dim, eps): super().__init__() self.dim = dim self.eps = eps def extra_repr(self): return f'dim={self.dim}, epsilon={self.eps}' def forward(self, x): scale = (x.square().mean(dim=self.dim, keepdim=True) + self.eps).rsqrt() return x * scale class InputLayer(nn.Module): """Implements the input layer to start convolution with. Basically, this block starts from a const input, which is with shape `(channels, init_res, init_res)`. """ def __init__(self, init_res, channels): super().__init__() self.const = nn.Parameter(torch.randn(1, channels, init_res, init_res)) def forward(self, w): x = self.const.repeat(w.shape[0], 1, 1, 1) return x class ConvLayer(nn.Module): """Implements the convolutional layer. If upsampling is needed (i.e., `scale_factor = 2`), the feature map will be filtered with `filter_kernel` after convolution. This layer will only be used for skip connection in `resnet` architecture. """ def __init__(self, in_channels, out_channels, kernel_size, add_bias, scale_factor, filter_kernel, use_wscale, wscale_gain, lr_mul, activation_type, conv_clamp): """Initializes with layer settings. Args: in_channels: Number of channels of the input tensor. out_channels: Number of channels of the output tensor. kernel_size: Size of the convolutional kernels. add_bias: Whether to add bias onto the convolutional result. scale_factor: Scale factor for upsampling. filter_kernel: Kernel used for filtering. use_wscale: Whether to use weight scaling. wscale_gain: Gain factor for weight scaling. lr_mul: Learning multiplier for both weight and bias. activation_type: Type of activation. conv_clamp: A threshold to clamp the output of convolution layers to avoid overflow under FP16 training. """ super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.add_bias = add_bias self.scale_factor = scale_factor self.filter_kernel = filter_kernel self.use_wscale = use_wscale self.wscale_gain = wscale_gain self.lr_mul = lr_mul self.activation_type = activation_type self.conv_clamp = conv_clamp weight_shape = (out_channels, in_channels, kernel_size, kernel_size) fan_in = kernel_size * kernel_size * in_channels wscale = wscale_gain / np.sqrt(fan_in) if use_wscale: self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul) self.wscale = wscale * lr_mul else: self.weight = nn.Parameter( torch.randn(*weight_shape) * wscale / lr_mul) self.wscale = lr_mul if add_bias: self.bias = nn.Parameter(torch.zeros(out_channels)) self.bscale = lr_mul else: self.bias = None self.act_gain = bias_act.activation_funcs[activation_type].def_gain if scale_factor > 1: assert filter_kernel is not None self.register_buffer( 'filter', upfirdn2d.setup_filter(filter_kernel)) fh, fw = self.filter.shape self.filter_padding = ( kernel_size // 2 + (fw + scale_factor - 1) // 2, kernel_size // 2 + (fw - scale_factor) // 2, kernel_size // 2 + (fh + scale_factor - 1) // 2, kernel_size // 2 + (fh - scale_factor) // 2) def extra_repr(self): return (f'in_ch={self.in_channels}, ' f'out_ch={self.out_channels}, ' f'ksize={self.kernel_size}, ' f'wscale_gain={self.wscale_gain:.3f}, ' f'bias={self.add_bias}, ' f'lr_mul={self.lr_mul:.3f}, ' f'upsample={self.scale_factor}, ' f'upsample_filter={self.filter_kernel}, ' f'act={self.activation_type}, ' f'clamp={self.conv_clamp}') def forward(self, x, runtime_gain=1.0, impl='cuda'): dtype = x.dtype weight = self.weight if self.wscale != 1.0: weight = weight * self.wscale bias = None if self.bias is not None: bias = self.bias.to(dtype) if self.bscale != 1.0: bias = bias * self.bscale if self.scale_factor == 1: # Native convolution without upsampling. padding = self.kernel_size // 2 x = conv2d_gradfix.conv2d( x, weight.to(dtype), stride=1, padding=padding, impl=impl) else: # Convolution with upsampling. up = self.scale_factor f = self.filter # When kernel size = 1, use filtering function for upsampling. if self.kernel_size == 1: padding = self.filter_padding x = conv2d_gradfix.conv2d( x, weight.to(dtype), stride=1, padding=0, impl=impl) x = upfirdn2d.upfirdn2d( x, f, up=up, padding=padding, gain=up ** 2, impl=impl) # When kernel size != 1, use transpose convolution for upsampling. else: # Following codes are borrowed from # https://github.com/NVlabs/stylegan2-ada-pytorch px0, px1, py0, py1 = self.filter_padding kh, kw = weight.shape[2:] px0 = px0 - (kw - 1) px1 = px1 - (kw - up) py0 = py0 - (kh - 1) py1 = py1 - (kh - up) pxt = max(min(-px0, -px1), 0) pyt = max(min(-py0, -py1), 0) weight = weight.transpose(0, 1) padding = (pyt, pxt) x = conv2d_gradfix.conv_transpose2d( x, weight.to(dtype), stride=up, padding=padding, impl=impl) padding = (px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt) x = upfirdn2d.upfirdn2d( x, f, up=1, padding=padding, gain=up ** 2, impl=impl) act_gain = self.act_gain * runtime_gain act_clamp = None if self.conv_clamp is not None: act_clamp = self.conv_clamp * runtime_gain x = bias_act.bias_act(x, bias, act=self.activation_type, gain=act_gain, clamp=act_clamp, impl=impl) assert x.dtype == dtype return x class ModulateConvLayer(nn.Module): """Implements the convolutional layer with style modulation.""" def __init__(self, in_channels, out_channels, resolution, w_dim, kernel_size, add_bias, scale_factor, filter_kernel, demodulate, use_wscale, wscale_gain, lr_mul, noise_type, activation_type, conv_clamp, eps): """Initializes with layer settings. Args: in_channels: Number of channels of the input tensor. out_channels: Number of channels of the output tensor. resolution: Resolution of the output tensor. w_dim: Dimension of W space for style modulation. kernel_size: Size of the convolutional kernels. add_bias: Whether to add bias onto the convolutional result. scale_factor: Scale factor for upsampling. filter_kernel: Kernel used for filtering. demodulate: Whether to perform style demodulation. use_wscale: Whether to use weight scaling. wscale_gain: Gain factor for weight scaling. lr_mul: Learning multiplier for both weight and bias. noise_type: Type of noise added to the feature map after the convolution (if needed). Support `none`, `spatial` and `channel`. activation_type: Type of activation. conv_clamp: A threshold to clamp the output of convolution layers to avoid overflow under FP16 training. eps: A small value to avoid divide overflow. """ super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.resolution = resolution self.w_dim = w_dim self.kernel_size = kernel_size self.add_bias = add_bias self.scale_factor = scale_factor self.filter_kernel = filter_kernel self.demodulate = demodulate self.use_wscale = use_wscale self.wscale_gain = wscale_gain self.lr_mul = lr_mul self.noise_type = noise_type.lower() self.activation_type = activation_type self.conv_clamp = conv_clamp self.eps = eps self.space_of_latent = 'W' # Set up weight. weight_shape = (out_channels, in_channels, kernel_size, kernel_size) fan_in = kernel_size * kernel_size * in_channels wscale = wscale_gain / np.sqrt(fan_in) if use_wscale: self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul) self.wscale = wscale * lr_mul else: self.weight = nn.Parameter( torch.randn(*weight_shape) * wscale / lr_mul) self.wscale = lr_mul # Set up bias. if add_bias: self.bias = nn.Parameter(torch.zeros(out_channels)) self.bscale = lr_mul else: self.bias = None self.act_gain = bias_act.activation_funcs[activation_type].def_gain # Set up style. self.style = DenseLayer(in_channels=w_dim, out_channels=in_channels, add_bias=True, init_bias=1.0, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, activation_type='linear') # Set up noise. if self.noise_type != 'none': self.noise_strength = nn.Parameter(torch.zeros(())) if self.noise_type == 'spatial': self.register_buffer( 'noise', torch.randn(1, 1, resolution, resolution)) elif self.noise_type == 'channel': self.register_buffer( 'noise', torch.randn(1, out_channels, 1, 1)) else: raise NotImplementedError(f'Not implemented noise type: ' f'`{self.noise_type}`!') if scale_factor > 1: assert filter_kernel is not None self.register_buffer( 'filter', upfirdn2d.setup_filter(filter_kernel)) fh, fw = self.filter.shape self.filter_padding = ( kernel_size // 2 + (fw + scale_factor - 1) // 2, kernel_size // 2 + (fw - scale_factor) // 2, kernel_size // 2 + (fh + scale_factor - 1) // 2, kernel_size // 2 + (fh - scale_factor) // 2) def extra_repr(self): return (f'in_ch={self.in_channels}, ' f'out_ch={self.out_channels}, ' f'ksize={self.kernel_size}, ' f'wscale_gain={self.wscale_gain:.3f}, ' f'bias={self.add_bias}, ' f'lr_mul={self.lr_mul:.3f}, ' f'upsample={self.scale_factor}, ' f'upsample_filter={self.filter_kernel}, ' f'demodulate={self.demodulate}, ' f'noise_type={self.noise_type}, ' f'act={self.activation_type}, ' f'clamp={self.conv_clamp}') def forward_style(self, w, impl='cuda'): """Gets style code from the given input. More specifically, if the input is from W-Space, it will be projected by an affine transformation. If it is from the Style Space (Y-Space), no operation is required. NOTE: For codes from Y-Space, we use slicing to make sure the dimension is correct, in case that the code is padded before fed into this layer. """ space_of_latent = self.space_of_latent.upper() if space_of_latent == 'W': if w.ndim != 2 or w.shape[1] != self.w_dim: raise ValueError(f'The input tensor should be with shape ' f'[batch_size, w_dim], where ' f'`w_dim` equals to {self.w_dim}!\n' f'But `{w.shape}` is received!') style = self.style(w, impl=impl) elif space_of_latent == 'Y': if w.ndim != 2 or w.shape[1] < self.in_channels: raise ValueError(f'The input tensor should be with shape ' f'[batch_size, y_dim], where ' f'`y_dim` equals to {self.in_channels}!\n' f'But `{w.shape}` is received!') style = w[:, :self.in_channels] else: raise NotImplementedError(f'Not implemented `space_of_latent`: ' f'`{space_of_latent}`!') return style def forward(self, x, w, runtime_gain=1.0, noise_mode='const', fused_modulate=False, impl='cuda'): dtype = x.dtype N, C, H, W = x.shape fused_modulate = (fused_modulate and not self.training and (dtype == torch.float32 or N == 1)) weight = self.weight out_ch, in_ch, kh, kw = weight.shape assert in_ch == C # Affine on `w`. style = self.forward_style(w, impl=impl) if not self.demodulate: _style = style * self.wscale # Equivalent to scaling weight. else: _style = style # Prepare noise. noise = None noise_mode = noise_mode.lower() if self.noise_type != 'none' and noise_mode != 'none': if noise_mode == 'random': noise = torch.randn((N, *self.noise.shape[1:]), device=x.device) elif noise_mode == 'const': noise = self.noise else: raise ValueError(f'Unknown noise mode `{noise_mode}`!') noise = (noise * self.noise_strength).to(dtype) # Pre-normalize inputs to avoid FP16 overflow. if dtype == torch.float16 and self.demodulate: weight_max = weight.norm(float('inf'), dim=(1, 2, 3), keepdim=True) weight = weight * (self.wscale / weight_max) style_max = _style.norm(float('inf'), dim=1, keepdim=True) _style = _style / style_max if self.demodulate or fused_modulate: _weight = weight.unsqueeze(0) _weight = _weight * _style.reshape(N, 1, in_ch, 1, 1) if self.demodulate: decoef = (_weight.square().sum(dim=(2, 3, 4)) + self.eps).rsqrt() if self.demodulate and fused_modulate: _weight = _weight * decoef.reshape(N, out_ch, 1, 1, 1) if not fused_modulate: x = x * _style.to(dtype).reshape(N, in_ch, 1, 1) w = weight.to(dtype) groups = 1 else: # Use group convolution to fuse style modulation and convolution. x = x.reshape(1, N * in_ch, H, W) w = _weight.reshape(N * out_ch, in_ch, kh, kw).to(dtype) groups = N if self.scale_factor == 1: # Native convolution without upsampling. up = 1 padding = self.kernel_size // 2 x = conv2d_gradfix.conv2d( x, w, stride=1, padding=padding, groups=groups, impl=impl) else: # Convolution with upsampling. up = self.scale_factor f = self.filter # When kernel size = 1, use filtering function for upsampling. if self.kernel_size == 1: padding = self.filter_padding x = conv2d_gradfix.conv2d( x, w, stride=1, padding=0, groups=groups, impl=impl) x = upfirdn2d.upfirdn2d( x, f, up=up, padding=padding, gain=up ** 2, impl=impl) # When kernel size != 1, use stride convolution for upsampling. else: # Following codes are borrowed from # https://github.com/NVlabs/stylegan2-ada-pytorch px0, px1, py0, py1 = self.filter_padding px0 = px0 - (kw - 1) px1 = px1 - (kw - up) py0 = py0 - (kh - 1) py1 = py1 - (kh - up) pxt = max(min(-px0, -px1), 0) pyt = max(min(-py0, -py1), 0) if groups == 1: w = w.transpose(0, 1) else: w = w.reshape(N, out_ch, in_ch, kh, kw) w = w.transpose(1, 2) w = w.reshape(N * in_ch, out_ch, kh, kw) padding = (pyt, pxt) x = conv2d_gradfix.conv_transpose2d( x, w, stride=up, padding=padding, groups=groups, impl=impl) padding = (px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt) x = upfirdn2d.upfirdn2d( x, f, up=1, padding=padding, gain=up ** 2, impl=impl) if not fused_modulate: if self.demodulate: decoef = decoef.to(dtype).reshape(N, out_ch, 1, 1) if self.demodulate and noise is not None: x = fma.fma(x, decoef, noise, impl=impl) else: if self.demodulate: x = x * decoef if noise is not None: x = x + noise else: x = x.reshape(N, out_ch, H * up, W * up) if noise is not None: x = x + noise bias = None if self.bias is not None: bias = self.bias.to(dtype) if self.bscale != 1.0: bias = bias * self.bscale if self.activation_type == 'linear': # Shortcut for output layer. x = bias_act.bias_act( x, bias, act='linear', clamp=self.conv_clamp, impl=impl) else: act_gain = self.act_gain * runtime_gain act_clamp = None if self.conv_clamp is not None: act_clamp = self.conv_clamp * runtime_gain x = bias_act.bias_act(x, bias, act=self.activation_type, gain=act_gain, clamp=act_clamp, impl=impl) assert x.dtype == dtype assert style.dtype == torch.float32 return x, style class DenseLayer(nn.Module): """Implements the dense layer.""" def __init__(self, in_channels, out_channels, add_bias, init_bias, use_wscale, wscale_gain, lr_mul, activation_type): """Initializes with layer settings. Args: in_channels: Number of channels of the input tensor. out_channels: Number of channels of the output tensor. add_bias: Whether to add bias onto the fully-connected result. init_bias: The initial bias value before training. use_wscale: Whether to use weight scaling. wscale_gain: Gain factor for weight scaling. lr_mul: Learning multiplier for both weight and bias. activation_type: Type of activation. """ super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.add_bias = add_bias self.init_bias = init_bias self.use_wscale = use_wscale self.wscale_gain = wscale_gain self.lr_mul = lr_mul self.activation_type = activation_type weight_shape = (out_channels, in_channels) wscale = wscale_gain / np.sqrt(in_channels) if use_wscale: self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul) self.wscale = wscale * lr_mul else: self.weight = nn.Parameter( torch.randn(*weight_shape) * wscale / lr_mul) self.wscale = lr_mul if add_bias: init_bias = np.float32(init_bias) / lr_mul self.bias = nn.Parameter(torch.full([out_channels], init_bias)) self.bscale = lr_mul else: self.bias = None def extra_repr(self): return (f'in_ch={self.in_channels}, ' f'out_ch={self.out_channels}, ' f'wscale_gain={self.wscale_gain:.3f}, ' f'bias={self.add_bias}, ' f'init_bias={self.init_bias}, ' f'lr_mul={self.lr_mul:.3f}, ' f'act={self.activation_type}') def forward(self, x, impl='cuda'): dtype = x.dtype if x.ndim != 2: x = x.flatten(start_dim=1) weight = self.weight.to(dtype) * self.wscale bias = None if self.bias is not None: bias = self.bias.to(dtype) if self.bscale != 1.0: bias = bias * self.bscale # Fast pass for linear activation. if self.activation_type == 'linear' and bias is not None: x = torch.addmm(bias.unsqueeze(0), x, weight.t()) else: x = x.matmul(weight.t()) x = bias_act.bias_act(x, bias, act=self.activation_type, impl=impl) assert x.dtype == dtype return x # pylint: enable=missing-function-docstring