| |
| """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 os |
|
|
| import numpy as np |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from huggingface_hub import PYTORCH_WEIGHTS_NAME, hf_hub_download, PyTorchModelHubMixin |
|
|
| from .sync_op import all_gather |
|
|
| __all__ = ['StyleGAN2Generator'] |
|
|
| |
| _RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024] |
|
|
| |
| _INIT_RES = 4 |
|
|
| |
| _ARCHITECTURES_ALLOWED = ['resnet', 'skip', 'origin'] |
|
|
| |
| _WSCALE_GAIN = 1.0 |
|
|
|
|
| class StyleGAN2Generator(nn.Module, PyTorchModelHubMixin): |
| """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_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) architecture: Type of architecture. Support `origin`, `skip`, and |
| `resnet`. (default: `resnet`) |
| (6) fused_modulate: Whether to fuse `style_modulate` and `conv2d` together. |
| (default: True) |
| (7) demodulate: Whether to perform style demodulation. (default: True) |
| (8) use_wscale: Whether to use weight scaling. (default: True) |
| (9) fmaps_base: Factor to control number of feature maps for each layer. |
| (default: 16 << 10) |
| (10) 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, |
| architecture='skip', |
| fused_modulate=True, |
| demodulate=True, |
| use_wscale=True, |
| fmaps_base=32 << 10, |
| fmaps_max=512, |
| **kwargs): |
| """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}.') |
| if architecture not in _ARCHITECTURES_ALLOWED: |
| raise ValueError(f'Invalid architecture: `{architecture}`!\n' |
| f'Architectures allowed: ' |
| f'{_ARCHITECTURES_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.architecture = architecture |
| self.fused_modulate = fused_modulate |
| self.demodulate = demodulate |
| self.use_wscale = use_wscale |
| self.fmaps_base = fmaps_base |
| self.fmaps_max = fmaps_max |
|
|
| 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, |
| architecture=self.architecture, |
| fused_modulate=self.fused_modulate, |
| demodulate=self.demodulate, |
| 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, |
| 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'] |
| if np.random.uniform() < style_mixing_prob: |
| mixing_cutoff = np.random.randint(1, self.num_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, 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="stylegan2-ffhq-config-f.pt", |
| 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, |
| architecture='skip', |
| fused_modulate=True, |
| demodulate=True, |
| use_wscale=True, |
| fmaps_base=32 << 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.architecture = architecture |
| self.fused_modulate = fused_modulate |
| self.demodulate = demodulate |
| 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 |
|
|
| self.pth_to_tf_var_mapping = {} |
| 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 |
|
|
| |
| if res == self.init_res: |
| if self.const_input: |
| self.add_module(f'early_layer', |
| InputBlock(init_resolution=self.init_res, |
| channels=self.get_nf(res))) |
| self.pth_to_tf_var_mapping[f'early_layer.const'] = ( |
| f'{res}x{res}/Const/const') |
| else: |
| self.add_module(f'early_layer', |
| DenseBlock(in_channels=self.w_space_dim, |
| out_channels=self.get_nf(res), |
| use_wscale=self.use_wscale)) |
| self.pth_to_tf_var_mapping[f'early_layer.weight'] = ( |
| f'{res}x{res}/Dense/weight') |
| self.pth_to_tf_var_mapping[f'early_layer.bias'] = ( |
| f'{res}x{res}/Dense/bias') |
| else: |
| layer_name = f'layer{2 * block_idx - 1}' |
| self.add_module( |
| layer_name, |
| ModulateConvBlock(in_channels=self.get_nf(res // 2), |
| out_channels=self.get_nf(res), |
| resolution=res, |
| w_space_dim=self.w_space_dim, |
| scale_factor=2, |
| fused_modulate=self.fused_modulate, |
| demodulate=self.demodulate, |
| use_wscale=self.use_wscale)) |
| 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}') |
|
|
| if self.architecture == 'resnet': |
| layer_name = f'layer{2 * block_idx - 1}' |
| self.add_module( |
| layer_name, |
| ConvBlock(in_channels=self.get_nf(res // 2), |
| out_channels=self.get_nf(res), |
| kernel_size=1, |
| add_bias=False, |
| scale_factor=2, |
| use_wscale=self.use_wscale, |
| activation_type='linear')) |
| self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( |
| f'{res}x{res}/Skip/weight') |
|
|
| |
| layer_name = f'layer{2 * block_idx}' |
| self.add_module( |
| layer_name, |
| ModulateConvBlock(in_channels=self.get_nf(res), |
| out_channels=self.get_nf(res), |
| resolution=res, |
| w_space_dim=self.w_space_dim, |
| fused_modulate=self.fused_modulate, |
| demodulate=self.demodulate, |
| 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}/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}') |
|
|
| |
| if res_log2 == self.final_res_log2 or self.architecture == 'skip': |
| layer_name = f'output{block_idx}' |
| self.add_module( |
| layer_name, |
| ModulateConvBlock(in_channels=self.get_nf(res), |
| out_channels=image_channels, |
| resolution=res, |
| w_space_dim=self.w_space_dim, |
| kernel_size=1, |
| fused_modulate=self.fused_modulate, |
| demodulate=False, |
| use_wscale=self.use_wscale, |
| add_noise=False, |
| activation_type='linear')) |
| 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') |
|
|
| if self.architecture == 'skip': |
| 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, 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!') |
|
|
| results = {'wp': wp} |
| x = self.early_layer(wp[:, 0]) |
| if self.architecture == 'origin': |
| for layer_idx in range(self.num_layers - 1): |
| x, style = self.__getattr__(f'layer{layer_idx}')( |
| x, wp[:, layer_idx], randomize_noise) |
| results[f'style{layer_idx:02d}'] = style |
| image, style = self.__getattr__(f'output{layer_idx // 2}')( |
| x, wp[:, layer_idx + 1]) |
| results[f'output_style{layer_idx // 2}'] = style |
| elif self.architecture == 'skip': |
| for layer_idx in range(self.num_layers - 1): |
| x, style = self.__getattr__(f'layer{layer_idx}')( |
| x, wp[:, layer_idx], randomize_noise) |
| results[f'style{layer_idx:02d}'] = style |
| if layer_idx % 2 == 0: |
| temp, style = self.__getattr__(f'output{layer_idx // 2}')( |
| x, wp[:, layer_idx + 1]) |
| results[f'output_style{layer_idx // 2}'] = style |
| if layer_idx == 0: |
| image = temp |
| else: |
| image = temp + self.upsample(image) |
| elif self.architecture == 'resnet': |
| x, style = self.layer0(x) |
| results[f'style00'] = style |
| for layer_idx in range(1, self.num_layers - 1, 2): |
| residual = self.__getattr__(f'skip_layer{layer_idx // 2}')(x) |
| x, style = self.__getattr__(f'layer{layer_idx}')( |
| x, wp[:, layer_idx], randomize_noise) |
| results[f'style{layer_idx:02d}'] = style |
| x, style = self.__getattr__(f'layer{layer_idx + 1}')( |
| x, wp[:, layer_idx + 1], randomize_noise) |
| results[f'style{layer_idx + 1:02d}'] = style |
| x = (x + residual) / np.sqrt(2.0) |
| image, style = self.__getattr__(f'output{layer_idx // 2 + 1}')( |
| x, wp[:, layer_idx + 2]) |
| results[f'output_style{layer_idx // 2}'] = style |
| results['image'] = self.final_activate(image) |
| return results |
|
|
|
|
| class PixelNormLayer(nn.Module): |
| """Implements pixel-wise feature vector normalization layer.""" |
|
|
| def __init__(self, dim=1, epsilon=1e-8): |
| super().__init__() |
| self.dim = dim |
| self.eps = epsilon |
|
|
| def forward(self, x): |
| norm = torch.sqrt( |
| torch.mean(x ** 2, dim=self.dim, keepdim=True) + self.eps) |
| return x / norm |
|
|
|
|
| class UpsamplingLayer(nn.Module): |
| """Implements the upsampling layer. |
| |
| This layer can also be used as filtering by setting `scale_factor` as 1. |
| """ |
|
|
| def __init__(self, |
| scale_factor=2, |
| kernel=(1, 3, 3, 1), |
| extra_padding=0, |
| kernel_gain=None): |
| super().__init__() |
| assert scale_factor >= 1 |
| self.scale_factor = scale_factor |
|
|
| if extra_padding != 0: |
| assert scale_factor == 1 |
|
|
| if kernel is None: |
| kernel = np.ones((scale_factor), dtype=np.float32) |
| else: |
| kernel = np.array(kernel, dtype=np.float32) |
| assert kernel.ndim == 1 |
| kernel = np.outer(kernel, kernel) |
| kernel = kernel / np.sum(kernel) |
| if kernel_gain is None: |
| kernel = kernel * (scale_factor ** 2) |
| else: |
| assert kernel_gain > 0 |
| kernel = kernel * (kernel_gain ** 2) |
| assert kernel.ndim == 2 |
| assert kernel.shape[0] == kernel.shape[1] |
| kernel = kernel[np.newaxis, np.newaxis] |
| self.register_buffer('kernel', torch.from_numpy(kernel)) |
| self.kernel = self.kernel.flip(0, 1) |
|
|
| self.upsample_padding = (0, scale_factor - 1, |
| 0, 0, |
| 0, scale_factor - 1, |
| 0, 0, |
| 0, 0, |
| 0, 0) |
|
|
| padding = kernel.shape[2] - scale_factor + extra_padding |
| self.padding = ((padding + 1) // 2 + scale_factor - 1, padding // 2, |
| (padding + 1) // 2 + scale_factor - 1, padding // 2) |
|
|
| def forward(self, x): |
| assert x.ndim == 4 |
| channels = x.shape[1] |
| if self.scale_factor > 1: |
| x = x.view(-1, channels, x.shape[2], 1, x.shape[3], 1) |
| x = F.pad(x, self.upsample_padding, mode='constant', value=0) |
| x = x.view(-1, channels, x.shape[2] * self.scale_factor, |
| x.shape[4] * self.scale_factor) |
| x = x.view(-1, 1, x.shape[2], x.shape[3]) |
| x = F.pad(x, self.padding, mode='constant', value=0) |
| x = F.conv2d(x, self.kernel, stride=1) |
| x = x.view(-1, channels, x.shape[2], x.shape[3]) |
| return x |
|
|
|
|
| class InputBlock(nn.Module): |
| """Implements the input block. |
| |
| Basically, this block starts from a const input, which is with shape |
| `(channels, init_resolution, init_resolution)`. |
| """ |
|
|
| def __init__(self, init_resolution, channels): |
| super().__init__() |
| self.const = nn.Parameter( |
| torch.randn(1, channels, init_resolution, init_resolution)) |
|
|
| def forward(self, w): |
| x = self.const.repeat(w.shape[0], 1, 1, 1) |
| return x |
|
|
|
|
| class ConvBlock(nn.Module): |
| """Implements the convolutional block (no style modulation). |
| |
| Basically, this block executes, convolutional layer, filtering layer (if |
| needed), and activation layer in sequence. |
| |
| NOTE: This block is particularly used for skip-connection branch in the |
| `resnet` structure. |
| """ |
|
|
| def __init__(self, |
| in_channels, |
| out_channels, |
| kernel_size=3, |
| add_bias=True, |
| scale_factor=1, |
| filtering_kernel=(1, 3, 3, 1), |
| 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. |
| kernel_size: Size of the convolutional kernels. (default: 3) |
| add_bias: Whether to add bias onto the convolutional result. |
| (default: True) |
| scale_factor: Scale factor for upsampling. `1` means skip |
| upsampling. (default: 1) |
| filtering_kernel: Kernel used for filtering after upsampling. |
| (default: (1, 3, 3, 1)) |
| 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__() |
|
|
| if scale_factor > 1: |
| self.use_conv2d_transpose = True |
| extra_padding = scale_factor - kernel_size |
| self.filter = UpsamplingLayer(scale_factor=1, |
| kernel=filtering_kernel, |
| extra_padding=extra_padding, |
| kernel_gain=scale_factor) |
| self.stride = scale_factor |
| self.padding = 0 |
| else: |
| self.use_conv2d_transpose = False |
| assert kernel_size % 2 == 1 |
| self.stride = 1 |
| self.padding = kernel_size // 2 |
|
|
| 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)) |
| else: |
| self.bias = None |
| self.bscale = lr_mul |
|
|
| if activation_type == 'linear': |
| self.activate = nn.Identity() |
| self.activate_scale = 1.0 |
| elif activation_type == 'lrelu': |
| self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| self.activate_scale = np.sqrt(2.0) |
| else: |
| raise NotImplementedError(f'Not implemented activation function: ' |
| f'`{activation_type}`!') |
|
|
| def forward(self, x): |
| weight = self.weight * self.wscale |
| bias = self.bias * self.bscale if self.bias is not None else None |
| if self.use_conv2d_transpose: |
| weight = weight.permute(1, 0, 2, 3).flip(2, 3) |
| x = F.conv_transpose2d(x, |
| weight=weight, |
| bias=bias, |
| stride=self.scale_factor, |
| padding=self.padding) |
| x = self.filter(x) |
| else: |
| x = F.conv2d(x, |
| weight=weight, |
| bias=bias, |
| stride=self.stride, |
| padding=self.padding) |
| x = self.activate(x) * self.activate_scale |
| return x |
|
|
|
|
| class ModulateConvBlock(nn.Module): |
| """Implements the convolutional block with style modulation.""" |
|
|
| def __init__(self, |
| in_channels, |
| out_channels, |
| resolution, |
| w_space_dim, |
| kernel_size=3, |
| add_bias=True, |
| scale_factor=1, |
| filtering_kernel=(1, 3, 3, 1), |
| fused_modulate=True, |
| demodulate=True, |
| use_wscale=True, |
| wscale_gain=_WSCALE_GAIN, |
| lr_mul=1.0, |
| add_noise=True, |
| activation_type='lrelu', |
| epsilon=1e-8): |
| """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. |
| kernel_size: Size of the convolutional kernels. (default: 3) |
| add_bias: Whether to add bias onto the convolutional result. |
| (default: True) |
| scale_factor: Scale factor for upsampling. `1` means skip |
| upsampling. (default: 1) |
| filtering_kernel: Kernel used for filtering after upsampling. |
| (default: (1, 3, 3, 1)) |
| fused_modulate: Whether to fuse `style_modulate` and `conv2d` |
| together. (default: True) |
| demodulate: Whether to perform style demodulation. (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) |
| add_noise: Whether to add noise onto the output tensor. (default: |
| True) |
| activation_type: Type of activation. Support `linear` and `lrelu`. |
| (default: `lrelu`) |
| epsilon: Small number to avoid `divide by zero`. (default: 1e-8) |
| |
| Raises: |
| NotImplementedError: If the `activation_type` is not supported. |
| """ |
| super().__init__() |
|
|
| self.res = resolution |
| self.in_c = in_channels |
| self.out_c = out_channels |
| self.ksize = kernel_size |
| self.eps = epsilon |
|
|
| if scale_factor > 1: |
| self.use_conv2d_transpose = True |
| extra_padding = scale_factor - kernel_size |
| self.filter = UpsamplingLayer(scale_factor=1, |
| kernel=filtering_kernel, |
| extra_padding=extra_padding, |
| kernel_gain=scale_factor) |
| self.stride = scale_factor |
| self.padding = 0 |
| else: |
| self.use_conv2d_transpose = False |
| assert kernel_size % 2 == 1 |
| self.stride = 1 |
| self.padding = kernel_size // 2 |
|
|
| 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 |
|
|
| self.style = DenseBlock(in_channels=w_space_dim, |
| out_channels=in_channels, |
| additional_bias=1.0, |
| use_wscale=use_wscale, |
| activation_type='linear') |
|
|
| self.fused_modulate = fused_modulate |
| self.demodulate = demodulate |
|
|
| if add_bias: |
| self.bias = nn.Parameter(torch.zeros(out_channels)) |
| else: |
| self.bias = None |
| self.bscale = lr_mul |
|
|
| if activation_type == 'linear': |
| self.activate = nn.Identity() |
| self.activate_scale = 1.0 |
| elif activation_type == 'lrelu': |
| self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| self.activate_scale = np.sqrt(2.0) |
| else: |
| raise NotImplementedError(f'Not implemented activation function: ' |
| f'`{activation_type}`!') |
|
|
| self.add_noise = add_noise |
| if self.add_noise: |
| self.register_buffer('noise', torch.randn(1, 1, self.res, self.res)) |
| self.noise_strength = nn.Parameter(torch.zeros(())) |
|
|
| def forward(self, x, w, randomize_noise=False): |
| batch = x.shape[0] |
|
|
| weight = self.weight * self.wscale |
| weight = weight.permute(2, 3, 1, 0) |
|
|
| |
| style = self.style(w) |
| _weight = weight.view(1, self.ksize, self.ksize, self.in_c, self.out_c) |
| _weight = _weight * style.view(batch, 1, 1, self.in_c, 1) |
|
|
| |
| if self.demodulate: |
| _weight_norm = torch.sqrt( |
| torch.sum(_weight ** 2, dim=[1, 2, 3]) + self.eps) |
| _weight = _weight / _weight_norm.view(batch, 1, 1, 1, self.out_c) |
|
|
| if self.fused_modulate: |
| x = x.view(1, batch * self.in_c, x.shape[2], x.shape[3]) |
| weight = _weight.permute(1, 2, 3, 0, 4).reshape( |
| self.ksize, self.ksize, self.in_c, batch * self.out_c) |
| else: |
| x = x * style.view(batch, self.in_c, 1, 1) |
|
|
| if self.use_conv2d_transpose: |
| weight = weight.flip(0, 1) |
| if self.fused_modulate: |
| weight = weight.view( |
| self.ksize, self.ksize, self.in_c, batch, self.out_c) |
| weight = weight.permute(0, 1, 4, 3, 2) |
| weight = weight.reshape( |
| self.ksize, self.ksize, self.out_c, batch * self.in_c) |
| weight = weight.permute(3, 2, 0, 1) |
| else: |
| weight = weight.permute(2, 3, 0, 1) |
| x = F.conv_transpose2d(x, |
| weight=weight, |
| bias=None, |
| stride=self.stride, |
| padding=self.padding, |
| groups=(batch if self.fused_modulate else 1)) |
| x = self.filter(x) |
| else: |
| weight = weight.permute(3, 2, 0, 1) |
| x = F.conv2d(x, |
| weight=weight, |
| bias=None, |
| stride=self.stride, |
| padding=self.padding, |
| groups=(batch if self.fused_modulate else 1)) |
|
|
| if self.fused_modulate: |
| x = x.view(batch, self.out_c, self.res, self.res) |
| elif self.demodulate: |
| x = x / _weight_norm.view(batch, self.out_c, 1, 1) |
|
|
| if self.add_noise: |
| if randomize_noise: |
| noise = torch.randn(x.shape[0], 1, self.res, self.res).to(x) |
| else: |
| noise = self.noise |
| x = x + noise * self.noise_strength.view(1, 1, 1, 1) |
|
|
| bias = self.bias * self.bscale if self.bias is not None else None |
| if bias is not None: |
| x = x + bias.view(1, -1, 1, 1) |
| x = self.activate(x) * self.activate_scale |
| return x, style |
|
|
|
|
| class DenseBlock(nn.Module): |
| """Implements the dense block. |
| |
| Basically, this block executes fully-connected layer and activation layer. |
| |
| NOTE: This layer supports adding an additional bias beyond the trainable |
| bias parameter. This is specially used for the mapping from the w code to |
| the style code. |
| """ |
|
|
| def __init__(self, |
| in_channels, |
| out_channels, |
| add_bias=True, |
| additional_bias=0, |
| 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) |
| additional_bias: The additional bias, which is independent from the |
| bias parameter. (default: 0.0) |
| 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)) |
| else: |
| self.bias = None |
| self.bscale = lr_mul |
| self.additional_bias = additional_bias |
|
|
| if activation_type == 'linear': |
| self.activate = nn.Identity() |
| self.activate_scale = 1.0 |
| elif activation_type == 'lrelu': |
| self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| self.activate_scale = np.sqrt(2.0) |
| 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 + self.additional_bias) * self.activate_scale |
| return x |
|
|
