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| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from math import sqrt | |
| from utils.hparams import hparams | |
| from modules.commons.common_layers import Mish | |
| Linear = nn.Linear | |
| ConvTranspose2d = nn.ConvTranspose2d | |
| class AttrDict(dict): | |
| def __init__(self, *args, **kwargs): | |
| super(AttrDict, self).__init__(*args, **kwargs) | |
| self.__dict__ = self | |
| def override(self, attrs): | |
| if isinstance(attrs, dict): | |
| self.__dict__.update(**attrs) | |
| elif isinstance(attrs, (list, tuple, set)): | |
| for attr in attrs: | |
| self.override(attr) | |
| elif attrs is not None: | |
| raise NotImplementedError | |
| return self | |
| class SinusoidalPosEmb(nn.Module): | |
| def __init__(self, dim): | |
| super().__init__() | |
| self.dim = dim | |
| def forward(self, x): | |
| device = x.device | |
| half_dim = self.dim // 2 | |
| emb = math.log(10000) / (half_dim - 1) | |
| emb = torch.exp(torch.arange(half_dim, device=device) * -emb) | |
| emb = x[:, None] * emb[None, :] | |
| emb = torch.cat((emb.sin(), emb.cos()), dim=-1) | |
| return emb | |
| def Conv1d(*args, **kwargs): | |
| layer = nn.Conv1d(*args, **kwargs) | |
| nn.init.kaiming_normal_(layer.weight) | |
| return layer | |
| def silu(x): | |
| return x * torch.sigmoid(x) | |
| class ResidualBlock(nn.Module): | |
| def __init__(self, encoder_hidden, residual_channels, dilation): | |
| super().__init__() | |
| self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation) | |
| self.diffusion_projection = Linear(residual_channels, residual_channels) | |
| self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1) | |
| self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) | |
| def forward(self, x, conditioner, diffusion_step): | |
| diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) | |
| conditioner = self.conditioner_projection(conditioner) | |
| y = x + diffusion_step | |
| y = self.dilated_conv(y) + conditioner | |
| gate, filter = torch.chunk(y, 2, dim=1) | |
| # Using torch.split instead of torch.chunk to avoid using onnx::Slice | |
| # gate, filter = torch.split(y, torch.div(y.shape[1], 2), dim=1) | |
| y = torch.sigmoid(gate) * torch.tanh(filter) | |
| y = self.output_projection(y) | |
| residual, skip = torch.chunk(y, 2, dim=1) | |
| # Using torch.split instead of torch.chunk to avoid using onnx::Slice | |
| # residual, skip = torch.split(y, torch.div(y.shape[1], 2), dim=1) | |
| return (x + residual) / sqrt(2.0), skip | |
| class DiffNet(nn.Module): | |
| def __init__(self, in_dims=80): | |
| super().__init__() | |
| self.params = params = AttrDict( | |
| # Model params | |
| encoder_hidden=hparams['hidden_size'], | |
| residual_layers=hparams['residual_layers'], | |
| residual_channels=hparams['residual_channels'], | |
| dilation_cycle_length=hparams['dilation_cycle_length'], | |
| ) | |
| self.input_projection = Conv1d(in_dims, params.residual_channels, 1) | |
| self.diffusion_embedding = SinusoidalPosEmb(params.residual_channels) | |
| dim = params.residual_channels | |
| self.mlp = nn.Sequential( | |
| nn.Linear(dim, dim * 4), | |
| Mish(), | |
| nn.Linear(dim * 4, dim) | |
| ) | |
| self.residual_layers = nn.ModuleList([ | |
| ResidualBlock(params.encoder_hidden, params.residual_channels, 2 ** (i % params.dilation_cycle_length)) | |
| for i in range(params.residual_layers) | |
| ]) | |
| self.skip_projection = Conv1d(params.residual_channels, params.residual_channels, 1) | |
| self.output_projection = Conv1d(params.residual_channels, in_dims, 1) | |
| nn.init.zeros_(self.output_projection.weight) | |
| def forward(self, spec, diffusion_step, cond): | |
| """ | |
| :param spec: [B, 1, M, T] | |
| :param diffusion_step: [B, 1] | |
| :param cond: [B, M, T] | |
| :return: | |
| """ | |
| x = spec[:, 0] | |
| x = self.input_projection(x) # x [B, residual_channel, T] | |
| x = F.relu(x) | |
| diffusion_step = self.diffusion_embedding(diffusion_step) | |
| diffusion_step = self.mlp(diffusion_step) | |
| skip = [] | |
| for layer_id, layer in enumerate(self.residual_layers): | |
| x, skip_connection = layer(x, cond, diffusion_step) | |
| skip.append(skip_connection) | |
| x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers)) | |
| x = self.skip_projection(x) | |
| x = F.relu(x) | |
| x = self.output_projection(x) # [B, 80, T] | |
| return x[:, None, :, :] | |