# python3.7 """Contains the implementation of generator described in StyleGAN. Paper: https://arxiv.org/pdf/1812.04948.pdf Official TensorFlow implementation: https://github.com/NVlabs/stylegan """ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.cuda.amp import autocast from .utils.ops import all_gather __all__ = ['StyleGANGenerator'] # Resolutions allowed. _RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024] # Fused-scale options allowed. _FUSED_SCALE_ALLOWED = [True, False, 'auto'] # pylint: disable=missing-function-docstring class StyleGANGenerator(nn.Module): """Defines the generator network in StyleGAN. 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: sqrt(2.0)) (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) 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) fused_scale: The strategy of fusing `upsample` and `conv2d` as one operator. `True` means blocks from all resolutions will fuse. `False` means blocks from all resolutions will not fuse. `auto` means blocks from resolutions higher than (or equal to) `fused_scale_res` will fuse. (default: `auto`) (6) fused_scale_res: Minimum resolution to fuse `conv2d` and `downsample` as one operator. This field only takes effect if `fused_scale` is set as `auto`. (default: 128) (7) use_wscale: Whether to use weight scaling. (default: True) (8) wscale_gain: The factor to control weight scaling. (default: sqrt(2.0)) (9) lr_mul: Learning rate multiplier for the synthesis network. (default: 1.0) (10) noise_type: Type of noise added to the convolutional results at each layer. (default: `spatial`) (11) fmaps_base: Factor to control number of feature maps for each layer. (default: 16 << 10) (12) fmaps_max: Maximum number of feature maps in each layer. (default: 512) (13) filter_kernel: Kernel used for filtering (e.g., downsampling). (default: (1, 2, 1)) (14) 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) enable_amp: Whether to enable automatic mixed precision training. (default: False) """ 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=np.sqrt(2.0), mapping_lr_mul=0.01, # Settings for conditional generation. label_dim=0, embedding_dim=512, # Settings for synthesis network. resolution=-1, init_res=4, image_channels=3, final_tanh=False, fused_scale='auto', fused_scale_res=128, use_wscale=True, wscale_gain=np.sqrt(2.0), lr_mul=1.0, noise_type='spatial', fmaps_base=16 << 10, fmaps_max=512, filter_kernel=(1, 2, 1), eps=1e-8): """Initializes with basic settings. Raises: ValueError: If the `resolution` is not supported, or `fused_scale` is not supported. """ super().__init__() if resolution not in _RESOLUTIONS_ALLOWED: raise ValueError(f'Invalid resolution: `{resolution}`!\n' f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.') if fused_scale not in _FUSED_SCALE_ALLOWED: raise ValueError(f'Invalid fused-scale option: `{fused_scale}`!\n' f'Options allowed: {_FUSED_SCALE_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.resolution = resolution self.init_res = init_res self.image_channels = image_channels self.final_tanh = final_tanh self.fused_scale = fused_scale self.fused_scale_res = fused_scale_res 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.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, 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, fused_scale=fused_scale, fused_scale_res=fused_scale_res, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type=noise_type, fmaps_base=fmaps_base, fmaps_max=fmaps_max, filter_kernel=filter_kernel, 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, lod=None, w_moving_decay=None, sync_w_avg=False, style_mixing_prob=None, trunc_psi=None, trunc_layers=None, noise_mode='const', enable_amp=False): mapping_results = self.mapping(z, label) 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)['wp'] lod = self.synthesis.lod.item() if lod is None else lod current_layers = self.num_layers - int(lod) * 2 mixing_cutoff = np.random.randint(1, current_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) with autocast(enabled=enable_amp): synthesis_results = self.synthesis(wp, lod=lod, noise_mode=noise_mode) return {**mapping_results, **synthesis_results} class MappingNetwork(nn.Module): """Implements the latent space mapping module. Basically, this module 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, 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.eps = eps self.pth_to_tf_var_mapping = {} if normalize_input: self.norm = PixelNormLayer(dim=1, eps=eps) if self.label_dim > 0: input_dim = input_dim + embedding_dim self.embedding = nn.Parameter( torch.randn(label_dim, embedding_dim)) self.pth_to_tf_var_mapping['embedding'] = 'LabelConcat/weight' 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, 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): 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.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 = torch.matmul(label, self.embedding) z = torch.cat((z, embedding), dim=1) if self.normalize_input: w = self.norm(z) else: w = z for i in range(self.num_layers): w = getattr(self, f'dense{i}')(w) 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 module. Basically, this module executes several convolutional layers in sequence. """ def __init__(self, resolution, init_res, w_dim, image_channels, final_tanh, fused_scale, fused_scale_res, use_wscale, wscale_gain, lr_mul, noise_type, fmaps_base, fmaps_max, filter_kernel, 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.fused_scale = fused_scale self.fused_scale_res = fused_scale_res 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.eps = eps self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2 # Level-of-details (used for progressive training). self.register_buffer('lod', torch.zeros(())) self.pth_to_tf_var_mapping = {'lod': 'lod'} 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 # First layer (kernel 3x3) with upsampling layer_name = f'layer{2 * block_idx}' if res == self.init_res: self.add_module(layer_name, ModulateConvLayer(in_channels=0, out_channels=out_channels, resolution=res, w_dim=w_dim, kernel_size=None, add_bias=True, scale_factor=None, fused_scale=None, filter_kernel=None, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type=noise_type, activation_type='lrelu', use_style=True, eps=eps)) tf_layer_name = 'Const' self.pth_to_tf_var_mapping[f'{layer_name}.const'] = ( f'{res}x{res}/{tf_layer_name}/const') else: 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, fused_scale=(res >= fused_scale_res if fused_scale == 'auto' else fused_scale), filter_kernel=filter_kernel, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type=noise_type, activation_type='lrelu', use_style=True, eps=eps)) tf_layer_name = 'Conv0_up' 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}/StyleMod/weight') self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( f'{res}x{res}/{tf_layer_name}/StyleMod/bias') self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = ( f'{res}x{res}/{tf_layer_name}/Noise/weight') self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = ( f'noise{2 * block_idx}') # Second layer (kernel 3x3) without upsampling. layer_name = f'layer{2 * block_idx + 1}' 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, fused_scale=False, filter_kernel=None, use_wscale=use_wscale, wscale_gain=wscale_gain, lr_mul=lr_mul, noise_type=noise_type, activation_type='lrelu', use_style=True, 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}/StyleMod/weight') self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( f'{res}x{res}/{tf_layer_name}/StyleMod/bias') self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = ( f'{res}x{res}/{tf_layer_name}/Noise/weight') self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = ( f'noise{2 * block_idx + 1}') # Output convolution layer for each resolution. self.add_module(f'output{block_idx}', ModulateConvLayer(in_channels=out_channels, out_channels=image_channels, resolution=res, w_dim=w_dim, kernel_size=1, add_bias=True, scale_factor=1, fused_scale=False, filter_kernel=None, use_wscale=use_wscale, wscale_gain=1.0, lr_mul=lr_mul, noise_type='none', activation_type='linear', use_style=False, eps=eps)) self.pth_to_tf_var_mapping[f'output{block_idx}.weight'] = ( f'ToRGB_lod{self.final_res_log2 - res_log2}/weight') self.pth_to_tf_var_mapping[f'output{block_idx}.bias'] = ( f'ToRGB_lod{self.final_res_log2 - res_log2}/bias') 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) and module.use_style: setattr(module, 'space_of_latent', space_of_latent) def forward(self, wp, lod=None, noise_mode='const'): lod = self.lod.item() if lod is None else lod if lod + self.init_res_log2 > self.final_res_log2: raise ValueError(f'Maximum level-of-details (lod) is ' f'{self.final_res_log2 - self.init_res_log2}, ' f'but `{lod}` is received!') results = {'wp': wp} x = None for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1): current_lod = self.final_res_log2 - res_log2 block_idx = res_log2 - self.init_res_log2 if lod < current_lod + 1: layer = getattr(self, f'layer{2 * block_idx}') x, style = layer(x, wp[:, 2 * block_idx], noise_mode) results[f'style{2 * block_idx}'] = style layer = getattr(self, f'layer{2 * block_idx + 1}') x, style = layer(x, wp[:, 2 * block_idx + 1], noise_mode) results[f'style{2 * block_idx + 1}'] = style if current_lod - 1 < lod <= current_lod: image = getattr(self, f'output{block_idx}')(x) elif current_lod < lod < current_lod + 1: alpha = np.ceil(lod) - lod temp = getattr(self, f'output{block_idx}')(x) image = F.interpolate(image, scale_factor=2, mode='nearest') image = temp * alpha + image * (1 - alpha) elif lod >= current_lod + 1: image = F.interpolate(image, scale_factor=2, mode='nearest') 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 Blur(torch.autograd.Function): """Defines blur operation with customized gradient computation.""" @staticmethod def forward(ctx, x, kernel): assert kernel.shape[2] == 3 and kernel.shape[3] == 3 ctx.save_for_backward(kernel) y = F.conv2d(input=x, weight=kernel, bias=None, stride=1, padding=1, groups=x.shape[1]) return y @staticmethod def backward(ctx, dy): kernel, = ctx.saved_tensors dx = F.conv2d(input=dy, weight=kernel.flip((2, 3)), bias=None, stride=1, padding=1, groups=dy.shape[1]) return dx, None, None 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, fused_scale, filter_kernel, use_wscale, wscale_gain, lr_mul, noise_type, activation_type, use_style, 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. fused_scale: Whether to fuse `upsample` and `conv2d` as one operator, using transpose convolution. 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. 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. use_style: Whether to apply style modulation. 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.fused_scale = fused_scale self.filter_kernel = filter_kernel 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.use_style = use_style self.eps = eps # Set up noise. if self.noise_type == 'none': pass elif self.noise_type == 'spatial': self.register_buffer( 'noise', torch.randn(1, 1, resolution, resolution)) self.noise_strength = nn.Parameter( torch.zeros(1, out_channels, 1, 1)) elif self.noise_type == 'channel': self.register_buffer( 'noise', torch.randn(1, out_channels, 1, 1)) self.noise_strength = nn.Parameter( torch.zeros(1, 1, resolution, resolution)) else: raise NotImplementedError(f'Not implemented noise type: ' f'`{noise_type}`!') # Set up bias. if add_bias: self.bias = nn.Parameter(torch.zeros(out_channels)) self.bscale = lr_mul else: self.bias = None # Set up activation. assert activation_type in ['linear', 'relu', 'lrelu'] # Set up style. if use_style: self.space_of_latent = 'W' self.style = DenseLayer(in_channels=w_dim, out_channels=out_channels * 2, add_bias=True, use_wscale=use_wscale, wscale_gain=1.0, lr_mul=1.0, activation_type='linear') if in_channels == 0: # First layer. self.const = nn.Parameter( torch.ones(1, out_channels, resolution, resolution)) return # 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 upsampling filter (if needed). if scale_factor > 1: assert filter_kernel is not None kernel = np.array(filter_kernel, dtype=np.float32).reshape(1, -1) kernel = kernel.T.dot(kernel) kernel = kernel / np.sum(kernel) kernel = kernel[np.newaxis, np.newaxis] self.register_buffer('filter', torch.from_numpy(kernel)) if scale_factor > 1 and fused_scale: # use transpose convolution. self.stride = scale_factor else: self.stride = 1 self.padding = kernel_size // 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'fused_scale={self.fused_scale}, ' f'upsample_filter={self.filter_kernel}, ' f'noise_type={self.noise_type}, ' f'act={self.activation_type}, ' f'use_style={self.use_style}') def forward_style(self, w): """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) elif space_of_latent == 'Y': if w.ndim != 2 or w.shape[1] < self.out_channels * 2: raise ValueError(f'The input tensor should be with shape ' f'[batch_size, y_dim], where ' f'`y_dim` equals to {self.out_channels * 2}!\n' f'But `{w.shape}` is received!') style = w[:, :self.out_channels * 2] else: raise NotImplementedError(f'Not implemented `space_of_latent`: ' f'`{space_of_latent}`!') return style def forward(self, x, w=None, noise_mode='const'): if self.in_channels == 0: assert x is None x = self.const.repeat(w.shape[0], 1, 1, 1) else: weight = self.weight if self.wscale != 1.0: weight = weight * self.wscale if self.scale_factor > 1 and self.fused_scale: weight = F.pad(weight, (1, 1, 1, 1, 0, 0, 0, 0), 'constant', 0) weight = (weight[:, :, 1:, 1:] + weight[:, :, :-1, 1:] + weight[:, :, 1:, :-1] + weight[:, :, :-1, :-1]) x = F.conv_transpose2d(x, weight=weight.transpose(0, 1), bias=None, stride=self.stride, padding=self.padding) else: if self.scale_factor > 1: up = self.scale_factor x = F.interpolate(x, scale_factor=up, mode='nearest') x = F.conv2d(x, weight=weight, bias=None, stride=self.stride, padding=self.padding) if self.scale_factor > 1: # Disable `autocast` for customized autograd function. # Please check reference: # https://pytorch.org/docs/stable/notes/amp_examples.html#autocast-and-custom-autograd-functions with autocast(enabled=False): f = self.filter.repeat(self.out_channels, 1, 1, 1) x = Blur.apply(x.float(), f) # Always use FP32. # Prepare noise. noise_mode = noise_mode.lower() if self.noise_type != 'none' and noise_mode != 'none': if noise_mode == 'random': noise = torch.randn( (x.shape[0], *self.noise.shape[1:]), device=x.device) elif noise_mode == 'const': noise = self.noise else: raise ValueError(f'Unknown noise mode `{noise_mode}`!') x = x + noise * self.noise_strength if self.bias is not None: bias = self.bias if self.bscale != 1.0: bias = bias * self.bscale x = x + bias.reshape(1, self.out_channels, 1, 1) if self.activation_type == 'linear': pass elif self.activation_type == 'relu': x = F.relu(x, inplace=True) elif self.activation_type == 'lrelu': x = F.leaky_relu(x, negative_slope=0.2, inplace=True) else: raise NotImplementedError(f'Not implemented activation type ' f'`{self.activation_type}`!') if not self.use_style: return x # Instance normalization. x = x - x.mean(dim=(2, 3), keepdim=True) scale = (x.square().mean(dim=(2, 3), keepdim=True) + self.eps).rsqrt() x = x * scale # Style modulation. style = self.forward_style(w) style_split = style.unsqueeze(2).unsqueeze(3).chunk(2, dim=1) x = x * (style_split[0] + 1) + style_split[1] return x, style class DenseLayer(nn.Module): """Implements the dense layer.""" def __init__(self, in_channels, out_channels, add_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. 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.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: self.bias = nn.Parameter(torch.zeros(out_channels)) self.bscale = lr_mul else: self.bias = None assert activation_type in ['linear', 'relu', 'lrelu'] 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'lr_mul={self.lr_mul:.3f}, ' f'act={self.activation_type}') def forward(self, x): if x.ndim != 2: x = x.flatten(start_dim=1) weight = self.weight if self.wscale != 1.0: weight = weight * self.wscale bias = None if self.bias is not None: bias = self.bias if self.bscale != 1.0: bias = bias * self.bscale x = F.linear(x, weight=weight, bias=bias) if self.activation_type == 'linear': pass elif self.activation_type == 'relu': x = F.relu(x, inplace=True) elif self.activation_type == 'lrelu': x = F.leaky_relu(x, negative_slope=0.2, inplace=True) else: raise NotImplementedError(f'Not implemented activation type ' f'`{self.activation_type}`!') return x # pylint: enable=missing-function-docstring