# 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 os import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from .sync_op import all_gather from huggingface_hub import PyTorchModelHubMixin, PYTORCH_WEIGHTS_NAME, hf_hub_download __all__ = ['StyleGANGenerator'] # Resolutions allowed. _RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024] # Initial resolution. _INIT_RES = 4 # Fused-scale options allowed. _FUSED_SCALE_ALLOWED = [True, False, 'auto'] # Minimal resolution for `auto` fused-scale strategy. _AUTO_FUSED_SCALE_MIN_RES = 128 # Default gain factor for weight scaling. _WSCALE_GAIN = np.sqrt(2.0) _STYLEMOD_WSCALE_GAIN = 1.0 class StyleGANGenerator(nn.Module, PyTorchModelHubMixin): """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_space_dim: Dimension of the input latent space, Z. (default: 512) (2) w_space_dim: Dimension of the outout latent space, W. (default: 512) (3) label_size: Size of the additional label for conditional generation. (default: 0) (4）mapping_layers: Number of layers of the mapping network. (default: 8) (5) mapping_fmaps: Number of hidden channels of the mapping network. (default: 512) (6) mapping_lr_mul: Learning rate multiplier for the mapping network. (default: 0.01) (7) repeat_w: Repeat w-code for different layers. Settings for the synthesis network: (1) resolution: The resolution of the output image. (2) image_channels: Number of channels of the output image. (default: 3) (3) final_tanh: Whether to use `tanh` to control the final pixel range. (default: False) (4) const_input: Whether to use a constant in the first convolutional layer. (default: True) (5) fused_scale: Whether to fused `upsample` and `conv2d` together, resulting in `conv2d_transpose`. (default: `auto`) (6) use_wscale: Whether to use weight scaling. (default: True) (7) fmaps_base: Factor to control number of feature maps for each layer. (default: 16 << 10) (8) fmaps_max: Maximum number of feature maps in each layer. (default: 512) """ def __init__(self, resolution, z_space_dim=512, w_space_dim=512, label_size=0, mapping_layers=8, mapping_fmaps=512, mapping_lr_mul=0.01, repeat_w=True, image_channels=3, final_tanh=False, const_input=True, fused_scale='auto', use_wscale=True, fmaps_base=16 << 10, fmaps_max=512, **kwargs): """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.init_res = _INIT_RES self.resolution = resolution self.z_space_dim = z_space_dim self.w_space_dim = w_space_dim self.label_size = label_size self.mapping_layers = mapping_layers self.mapping_fmaps = mapping_fmaps self.mapping_lr_mul = mapping_lr_mul self.repeat_w = repeat_w self.image_channels = image_channels self.final_tanh = final_tanh self.const_input = const_input self.fused_scale = fused_scale self.use_wscale = use_wscale self.fmaps_base = fmaps_base self.fmaps_max = fmaps_max self.config = kwargs.pop("config", None) self.num_layers = int(np.log2(self.resolution // self.init_res * 2)) * 2 if self.repeat_w: self.mapping_space_dim = self.w_space_dim else: self.mapping_space_dim = self.w_space_dim * self.num_layers self.mapping = MappingModule(input_space_dim=self.z_space_dim, hidden_space_dim=self.mapping_fmaps, final_space_dim=self.mapping_space_dim, label_size=self.label_size, num_layers=self.mapping_layers, use_wscale=self.use_wscale, lr_mul=self.mapping_lr_mul) self.truncation = TruncationModule(w_space_dim=self.w_space_dim, num_layers=self.num_layers, repeat_w=self.repeat_w) self.synthesis = SynthesisModule(resolution=self.resolution, init_resolution=self.init_res, w_space_dim=self.w_space_dim, image_channels=self.image_channels, final_tanh=self.final_tanh, const_input=self.const_input, fused_scale=self.fused_scale, use_wscale=self.use_wscale, fmaps_base=self.fmaps_base, fmaps_max=self.fmaps_max) self.pth_to_tf_var_mapping = {} 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.truncation.pth_to_tf_var_mapping.items(): self.pth_to_tf_var_mapping[f'truncation.{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 forward(self, z, label=None, lod=None, w_moving_decay=0.995, style_mixing_prob=0.9, trunc_psi=None, trunc_layers=None, randomize_noise=False, **_unused_kwargs): mapping_results = self.mapping(z, label) w = mapping_results['w'] if self.training and w_moving_decay < 1: batch_w_avg = all_gather(w).mean(dim=0) self.truncation.w_avg.copy_( self.truncation.w_avg * w_moving_decay + batch_w_avg * (1 - w_moving_decay)) if self.training and style_mixing_prob > 0: new_z = torch.randn_like(z) new_w = self.mapping(new_z, label)['w'] lod = self.synthesis.lod.cpu().tolist() if lod is None else lod current_layers = self.num_layers - int(lod) * 2 if np.random.uniform() < style_mixing_prob: mixing_cutoff = np.random.randint(1, current_layers) w = self.truncation(w) new_w = self.truncation(new_w) w[:, mixing_cutoff:] = new_w[:, mixing_cutoff:] wp = self.truncation(w, trunc_psi, trunc_layers) synthesis_results = self.synthesis(wp, lod, randomize_noise) return {**mapping_results, **synthesis_results} @classmethod def _from_pretrained( cls, model_id, revision, cache_dir, force_download, proxies, resume_download, local_files_only, use_auth_token, map_location="cpu", strict=False, **model_kwargs, ): """ Overwrite this method in case you wish to initialize your model in a different way. """ map_location = torch.device(map_location) if os.path.isdir(model_id): print("Loading weights from local directory") model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME) else: model_file = hf_hub_download( repo_id=model_id, filename=PYTORCH_WEIGHTS_NAME, revision=revision, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, use_auth_token=use_auth_token, local_files_only=local_files_only, ) pretrained = torch.load(model_file, map_location=map_location) return pretrained class MappingModule(nn.Module): """Implements the latent space mapping module. Basically, this module executes several dense layers in sequence. """ def __init__(self, input_space_dim=512, hidden_space_dim=512, final_space_dim=512, label_size=0, num_layers=8, normalize_input=True, use_wscale=True, lr_mul=0.01): super().__init__() self.input_space_dim = input_space_dim self.hidden_space_dim = hidden_space_dim self.final_space_dim = final_space_dim self.label_size = label_size self.num_layers = num_layers self.normalize_input = normalize_input self.use_wscale = use_wscale self.lr_mul = lr_mul self.norm = PixelNormLayer() if self.normalize_input else nn.Identity() self.pth_to_tf_var_mapping = {} for i in range(num_layers): dim_mul = 2 if label_size else 1 in_channels = (input_space_dim * dim_mul if i == 0 else hidden_space_dim) out_channels = (final_space_dim if i == (num_layers - 1) else hidden_space_dim) self.add_module(f'dense{i}', DenseBlock(in_channels=in_channels, out_channels=out_channels, use_wscale=self.use_wscale, lr_mul=self.lr_mul)) 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' if label_size: self.label_weight = nn.Parameter( torch.randn(label_size, input_space_dim)) self.pth_to_tf_var_mapping[f'label_weight'] = f'LabelConcat/weight' def forward(self, z, label=None): if z.ndim != 2 or z.shape[1] != self.input_space_dim: raise ValueError(f'Input latent code should be with shape ' f'[batch_size, input_dim], where ' f'`input_dim` equals to {self.input_space_dim}!\n' f'But `{z.shape}` is received!') if self.label_size: if label is None: raise ValueError(f'Model requires an additional label ' f'(with size {self.label_size}) as input, ' f'but no label is received!') if label.ndim != 2 or label.shape != (z.shape[0], self.label_size): raise ValueError(f'Input label should be with shape ' f'[batch_size, label_size], where ' f'`batch_size` equals to that of ' f'latent codes ({z.shape[0]}) and ' f'`label_size` equals to {self.label_size}!\n' f'But `{label.shape}` is received!') embedding = torch.matmul(label, self.label_weight) z = torch.cat((z, embedding), dim=1) z = self.norm(z) w = z for i in range(self.num_layers): w = self.__getattr__(f'dense{i}')(w) results = { 'z': z, 'label': label, 'w': w, } if self.label_size: results['embedding'] = embedding return results class TruncationModule(nn.Module): """Implements the truncation module. Truncation is executed 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) NOTE: The returned tensor is layer-wise style codes. """ def __init__(self, w_space_dim, num_layers, repeat_w=True): super().__init__() self.num_layers = num_layers self.w_space_dim = w_space_dim self.repeat_w = repeat_w if self.repeat_w: self.register_buffer('w_avg', torch.zeros(w_space_dim)) else: self.register_buffer('w_avg', torch.zeros(num_layers * w_space_dim)) self.pth_to_tf_var_mapping = {'w_avg': 'dlatent_avg'} def forward(self, w, trunc_psi=None, trunc_layers=None): if w.ndim == 2: if self.repeat_w and w.shape[1] == self.w_space_dim: w = w.view(-1, 1, self.w_space_dim) wp = w.repeat(1, self.num_layers, 1) else: assert w.shape[1] == self.w_space_dim * self.num_layers wp = w.view(-1, self.num_layers, self.w_space_dim) else: wp = w assert wp.ndim == 3 assert wp.shape[1:] == (self.num_layers, self.w_space_dim) 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: layer_idx = np.arange(self.num_layers).reshape(1, -1, 1) coefs = np.ones_like(layer_idx, dtype=np.float32) coefs[layer_idx < trunc_layers] *= trunc_psi coefs = torch.from_numpy(coefs).to(wp) w_avg = self.w_avg.view(1, -1, self.w_space_dim) wp = w_avg + (wp - w_avg) * coefs return wp class SynthesisModule(nn.Module): """Implements the image synthesis module. Basically, this module executes several convolutional layers in sequence. """ def __init__(self, resolution=1024, init_resolution=4, w_space_dim=512, image_channels=3, final_tanh=False, const_input=True, fused_scale='auto', use_wscale=True, fmaps_base=16 << 10, fmaps_max=512): super().__init__() self.init_res = init_resolution self.init_res_log2 = int(np.log2(self.init_res)) self.resolution = resolution self.final_res_log2 = int(np.log2(self.resolution)) self.w_space_dim = w_space_dim self.image_channels = image_channels self.final_tanh = final_tanh self.const_input = const_input self.fused_scale = fused_scale self.use_wscale = use_wscale self.fmaps_base = fmaps_base self.fmaps_max = fmaps_max self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2 # Level of detail (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 block_idx = res_log2 - self.init_res_log2 # First convolution layer for each resolution. layer_name = f'layer{2 * block_idx}' if res == self.init_res: if self.const_input: self.add_module(layer_name, ConvBlock(in_channels=self.get_nf(res), out_channels=self.get_nf(res), resolution=self.init_res, w_space_dim=self.w_space_dim, position='const_init', use_wscale=self.use_wscale)) 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, ConvBlock(in_channels=self.w_space_dim, out_channels=self.get_nf(res), resolution=self.init_res, w_space_dim=self.w_space_dim, kernel_size=self.init_res, padding=self.init_res - 1, use_wscale=self.use_wscale)) tf_layer_name = 'Dense' self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( f'{res}x{res}/{tf_layer_name}/weight') else: if self.fused_scale == 'auto': fused_scale = (res >= _AUTO_FUSED_SCALE_MIN_RES) else: fused_scale = self.fused_scale self.add_module(layer_name, ConvBlock(in_channels=self.get_nf(res // 2), out_channels=self.get_nf(res), resolution=res, w_space_dim=self.w_space_dim, upsample=True, fused_scale=fused_scale, use_wscale=self.use_wscale)) 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}.apply_noise.weight'] = ( f'{res}x{res}/{tf_layer_name}/Noise/weight') self.pth_to_tf_var_mapping[f'{layer_name}.apply_noise.noise'] = ( f'noise{2 * block_idx}') # Second convolution layer for each resolution. layer_name = f'layer{2 * block_idx + 1}' self.add_module(layer_name, ConvBlock(in_channels=self.get_nf(res), out_channels=self.get_nf(res), resolution=res, w_space_dim=self.w_space_dim, use_wscale=self.use_wscale)) 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}.apply_noise.weight'] = ( f'{res}x{res}/{tf_layer_name}/Noise/weight') self.pth_to_tf_var_mapping[f'{layer_name}.apply_noise.noise'] = ( f'noise{2 * block_idx + 1}') # Output convolution layer for each resolution. self.add_module(f'output{block_idx}', ConvBlock(in_channels=self.get_nf(res), out_channels=self.image_channels, resolution=res, w_space_dim=self.w_space_dim, position='last', kernel_size=1, padding=0, use_wscale=self.use_wscale, wscale_gain=1.0, activation_type='linear')) 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') self.upsample = UpsamplingLayer() self.final_activate = nn.Tanh() if final_tanh else nn.Identity() def get_nf(self, res): """Gets number of feature maps according to current resolution.""" return min(self.fmaps_base // res, self.fmaps_max) def forward(self, wp, lod=None, randomize_noise=False): if wp.ndim != 3 or wp.shape[1:] != (self.num_layers, self.w_space_dim): raise ValueError(f'Input tensor should be with shape ' f'[batch_size, num_layers, w_space_dim], where ' f'`num_layers` equals to {self.num_layers}, and ' f'`w_space_dim` equals to {self.w_space_dim}!\n' f'But `{wp.shape}` is received!') lod = self.lod.cpu().tolist() if lod is None else lod if lod + self.init_res_log2 > self.final_res_log2: raise ValueError(f'Maximum level-of-detail (lod) is ' f'{self.final_res_log2 - self.init_res_log2}, ' f'but `{lod}` is received!') results = {'wp': wp} for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1): current_lod = self.final_res_log2 - res_log2 if lod < current_lod + 1: block_idx = res_log2 - self.init_res_log2 if block_idx == 0: if self.const_input: x, style = self.layer0(None, wp[:, 0], randomize_noise) else: x = wp[:, 0].view(-1, self.w_space_dim, 1, 1) x, style = self.layer0(x, wp[:, 0], randomize_noise) else: x, style = self.__getattr__(f'layer{2 * block_idx}')( x, wp[:, 2 * block_idx]) results[f'style{2 * block_idx:02d}'] = style x, style = self.__getattr__(f'layer{2 * block_idx + 1}')( x, wp[:, 2 * block_idx + 1]) results[f'style{2 * block_idx + 1:02d}'] = style if current_lod - 1 < lod <= current_lod: image = self.__getattr__(f'output{block_idx}')(x, None) elif current_lod < lod < current_lod + 1: alpha = np.ceil(lod) - lod image = (self.__getattr__(f'output{block_idx}')(x, None) * alpha + self.upsample(image) * (1 - alpha)) elif lod >= current_lod + 1: image = self.upsample(image) results['image'] = self.final_activate(image) return results class PixelNormLayer(nn.Module): """Implements pixel-wise feature vector normalization layer.""" def __init__(self, epsilon=1e-8): super().__init__() self.eps = epsilon def forward(self, x): norm = torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + self.eps) return x / norm class InstanceNormLayer(nn.Module): """Implements instance normalization layer.""" def __init__(self, epsilon=1e-8): super().__init__() self.eps = epsilon def forward(self, x): if x.ndim != 4: raise ValueError(f'The input tensor should be with shape ' f'[batch_size, channel, height, width], ' f'but `{x.shape}` is received!') x = x - torch.mean(x, dim=[2, 3], keepdim=True) norm = torch.sqrt( torch.mean(x ** 2, dim=[2, 3], keepdim=True) + self.eps) return x / norm class UpsamplingLayer(nn.Module): """Implements the upsampling layer. Basically, this layer can be used to upsample feature maps with nearest neighbor interpolation. """ def __init__(self, scale_factor=2): super().__init__() self.scale_factor = scale_factor def forward(self, x): if self.scale_factor <= 1: return x return F.interpolate(x, scale_factor=self.scale_factor, mode='nearest') class Blur(torch.autograd.Function): """Defines blur operation with customized gradient computation.""" @staticmethod def forward(ctx, x, kernel): 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 BlurLayer(nn.Module): """Implements the blur layer.""" def __init__(self, channels, kernel=(1, 2, 1), normalize=True): super().__init__() kernel = np.array(kernel, dtype=np.float32).reshape(1, -1) kernel = kernel.T.dot(kernel) if normalize: kernel /= np.sum(kernel) kernel = kernel[np.newaxis, np.newaxis] kernel = np.tile(kernel, [channels, 1, 1, 1]) self.register_buffer('kernel', torch.from_numpy(kernel)) def forward(self, x): return Blur.apply(x, self.kernel) class NoiseApplyingLayer(nn.Module): """Implements the noise applying layer.""" def __init__(self, resolution, channels): super().__init__() self.res = resolution self.register_buffer('noise', torch.randn(1, 1, self.res, self.res)) self.weight = nn.Parameter(torch.zeros(channels)) def forward(self, x, randomize_noise=False): if x.ndim != 4: raise ValueError(f'The input tensor should be with shape ' f'[batch_size, channel, height, width], ' f'but `{x.shape}` is received!') if randomize_noise: noise = torch.randn(x.shape[0], 1, self.res, self.res).to(x) else: noise = self.noise return x + noise * self.weight.view(1, -1, 1, 1) class StyleModLayer(nn.Module): """Implements the style modulation layer.""" def __init__(self, w_space_dim, out_channels, use_wscale=True): super().__init__() self.w_space_dim = w_space_dim self.out_channels = out_channels weight_shape = (self.out_channels * 2, self.w_space_dim) wscale = _STYLEMOD_WSCALE_GAIN / np.sqrt(self.w_space_dim) if use_wscale: self.weight = nn.Parameter(torch.randn(*weight_shape)) self.wscale = wscale else: self.weight = nn.Parameter(torch.randn(*weight_shape) * wscale) self.wscale = 1.0 self.bias = nn.Parameter(torch.zeros(self.out_channels * 2)) def forward(self, x, w): if w.ndim != 2 or w.shape[1] != self.w_space_dim: raise ValueError(f'The input tensor should be with shape ' f'[batch_size, w_space_dim], where ' f'`w_space_dim` equals to {self.w_space_dim}!\n' f'But `{w.shape}` is received!') style = F.linear(w, weight=self.weight * self.wscale, bias=self.bias) style_split = style.view(-1, 2, self.out_channels, 1, 1) x = x * (style_split[:, 0] + 1) + style_split[:, 1] return x, style class ConvBlock(nn.Module): """Implements the normal convolutional block. Basically, this block executes upsampling layer (if needed), convolutional layer, blurring layer, noise applying layer, activation layer, instance normalization layer, and style modulation layer in sequence. """ def __init__(self, in_channels, out_channels, resolution, w_space_dim, position=None, kernel_size=3, stride=1, padding=1, add_bias=True, upsample=False, fused_scale=False, use_wscale=True, wscale_gain=_WSCALE_GAIN, lr_mul=1.0, activation_type='lrelu'): """Initializes with block 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_space_dim: Dimension of W space for style modulation. position: Position of the layer. `const_init`, `last` would lead to different behavior. (default: None) kernel_size: Size of the convolutional kernels. (default: 3) stride: Stride parameter for convolution operation. (default: 1) padding: Padding parameter for convolution operation. (default: 1) add_bias: Whether to add bias onto the convolutional result. (default: True) upsample: Whether to upsample the input tensor before convolution. (default: False) fused_scale: Whether to fused `upsample` and `conv2d` together, resulting in `conv2d_transpose`. (default: False) use_wscale: Whether to use weight scaling. (default: True) wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN) lr_mul: Learning multiplier for both weight and bias. (default: 1.0) activation_type: Type of activation. Support `linear` and `lrelu`. (default: `lrelu`) Raises: NotImplementedError: If the `activation_type` is not supported. """ super().__init__() self.position = position if add_bias: self.bias = nn.Parameter(torch.zeros(out_channels)) self.bscale = lr_mul else: self.bias = None if activation_type == 'linear': self.activate = nn.Identity() elif activation_type == 'lrelu': self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) else: raise NotImplementedError(f'Not implemented activation function: ' f'`{activation_type}`!') if self.position != 'last': self.apply_noise = NoiseApplyingLayer(resolution, out_channels) self.normalize = InstanceNormLayer() self.style = StyleModLayer(w_space_dim, out_channels, use_wscale) if self.position == 'const_init': self.const = nn.Parameter( torch.ones(1, in_channels, resolution, resolution)) return self.blur = BlurLayer(out_channels) if upsample else nn.Identity() if upsample and not fused_scale: self.upsample = UpsamplingLayer() else: self.upsample = nn.Identity() if upsample and fused_scale: self.use_conv2d_transpose = True self.stride = 2 self.padding = 1 else: self.use_conv2d_transpose = False self.stride = stride self.padding = padding 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 def forward(self, x, w, randomize_noise=False): if self.position != 'const_init': x = self.upsample(x) weight = self.weight * self.wscale if self.use_conv2d_transpose: 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]) weight = weight.permute(1, 0, 2, 3) x = F.conv_transpose2d(x, weight=weight, bias=None, stride=self.stride, padding=self.padding) else: x = F.conv2d(x, weight=weight, bias=None, stride=self.stride, padding=self.padding) x = self.blur(x) else: x = self.const.repeat(w.shape[0], 1, 1, 1) bias = self.bias * self.bscale if self.bias is not None else None if self.position == 'last': if bias is not None: x = x + bias.view(1, -1, 1, 1) return x x = self.apply_noise(x, randomize_noise) if bias is not None: x = x + bias.view(1, -1, 1, 1) x = self.activate(x) x = self.normalize(x) x, style = self.style(x, w) return x, style class DenseBlock(nn.Module): """Implements the dense block. Basically, this block executes fully-connected layer and activation layer. """ def __init__(self, in_channels, out_channels, add_bias=True, use_wscale=True, wscale_gain=_WSCALE_GAIN, lr_mul=1.0, activation_type='lrelu'): """Initializes with block 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. (default: True) use_wscale: Whether to use weight scaling. (default: True) wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN) lr_mul: Learning multiplier for both weight and bias. (default: 1.0) activation_type: Type of activation. Support `linear` and `lrelu`. (default: `lrelu`) Raises: NotImplementedError: If the `activation_type` is not supported. """ super().__init__() 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 if activation_type == 'linear': self.activate = nn.Identity() elif activation_type == 'lrelu': self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) else: raise NotImplementedError(f'Not implemented activation function: ' f'`{activation_type}`!') def forward(self, x): if x.ndim != 2: x = x.view(x.shape[0], -1) bias = self.bias * self.bscale if self.bias is not None else None x = F.linear(x, weight=self.weight * self.wscale, bias=bias) x = self.activate(x) return x