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on
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Running
on
Zero
| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import numpy as np | |
| from torch import nn | |
| from torch.nn import functional as F | |
| class Stretch2d(nn.Module): | |
| def __init__(self, x_scale, y_scale, mode="nearest"): | |
| super(Stretch2d, self).__init__() | |
| self.x_scale = x_scale | |
| self.y_scale = y_scale | |
| self.mode = mode | |
| def forward(self, x): | |
| return F.interpolate( | |
| x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode | |
| ) | |
| def _get_activation(upsample_activation): | |
| nonlinear = getattr(nn, upsample_activation) | |
| return nonlinear | |
| class UpsampleNetwork(nn.Module): | |
| def __init__( | |
| self, | |
| upsample_scales, | |
| upsample_activation="none", | |
| upsample_activation_params={}, | |
| mode="nearest", | |
| freq_axis_kernel_size=1, | |
| cin_pad=0, | |
| cin_channels=128, | |
| ): | |
| super(UpsampleNetwork, self).__init__() | |
| self.up_layers = nn.ModuleList() | |
| total_scale = np.prod(upsample_scales) | |
| self.indent = cin_pad * total_scale | |
| for scale in upsample_scales: | |
| freq_axis_padding = (freq_axis_kernel_size - 1) // 2 | |
| k_size = (freq_axis_kernel_size, scale * 2 + 1) | |
| padding = (freq_axis_padding, scale) | |
| stretch = Stretch2d(scale, 1, mode) | |
| conv = nn.Conv2d(1, 1, kernel_size=k_size, padding=padding, bias=False) | |
| conv.weight.data.fill_(1.0 / np.prod(k_size)) | |
| conv = nn.utils.weight_norm(conv) | |
| self.up_layers.append(stretch) | |
| self.up_layers.append(conv) | |
| if upsample_activation != "none": | |
| nonlinear = _get_activation(upsample_activation) | |
| self.up_layers.append(nonlinear(**upsample_activation_params)) | |
| def forward(self, c): | |
| """ | |
| Args: | |
| c : B x C x T | |
| """ | |
| # B x 1 x C x T | |
| c = c.unsqueeze(1) | |
| for f in self.up_layers: | |
| c = f(c) | |
| # B x C x T | |
| c = c.squeeze(1) | |
| if self.indent > 0: | |
| c = c[:, :, self.indent : -self.indent] | |
| return c | |
| class ConvInUpsampleNetwork(nn.Module): | |
| def __init__( | |
| self, | |
| upsample_scales, | |
| upsample_activation="none", | |
| upsample_activation_params={}, | |
| mode="nearest", | |
| freq_axis_kernel_size=1, | |
| cin_pad=0, | |
| cin_channels=128, | |
| ): | |
| super(ConvInUpsampleNetwork, self).__init__() | |
| # To capture wide-context information in conditional features | |
| # meaningless if cin_pad == 0 | |
| ks = 2 * cin_pad + 1 | |
| self.conv_in = nn.Conv1d( | |
| cin_channels, cin_channels, kernel_size=ks, padding=cin_pad, bias=False | |
| ) | |
| self.upsample = UpsampleNetwork( | |
| upsample_scales, | |
| upsample_activation, | |
| upsample_activation_params, | |
| mode, | |
| freq_axis_kernel_size, | |
| cin_pad=cin_pad, | |
| cin_channels=cin_channels, | |
| ) | |
| def forward(self, c): | |
| c_up = self.upsample(self.conv_in(c)) | |
| return c_up | |