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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Copyright 2019 Shigeki Karita | |
| # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| """Subsampling layer definition.""" | |
| import logging | |
| import torch | |
| from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding | |
| class Conv2dSubsampling(torch.nn.Module): | |
| """Convolutional 2D subsampling (to 1/4 length or 1/2 length). | |
| :param int idim: input dim | |
| :param int odim: output dim | |
| :param flaot dropout_rate: dropout rate | |
| :param torch.nn.Module pos_enc: custom position encoding layer | |
| """ | |
| def __init__(self, idim, odim, dropout_rate, pos_enc=None, | |
| subsample_by_2=False, | |
| ): | |
| """Construct an Conv2dSubsampling object.""" | |
| super(Conv2dSubsampling, self).__init__() | |
| self.subsample_by_2 = subsample_by_2 | |
| if subsample_by_2: | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, kernel_size=5, stride=1, padding=2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, kernel_size=4, stride=2, padding=1), | |
| torch.nn.ReLU(), | |
| ) | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * (idim // 2), odim), | |
| pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), | |
| ) | |
| else: | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, kernel_size=4, stride=2, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, kernel_size=4, stride=2, padding=1), | |
| torch.nn.ReLU(), | |
| ) | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * (idim // 4), odim), | |
| pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """Subsample x. | |
| :param torch.Tensor x: input tensor | |
| :param torch.Tensor x_mask: input mask | |
| :return: subsampled x and mask | |
| :rtype Tuple[torch.Tensor, torch.Tensor] | |
| """ | |
| x = x.unsqueeze(1) # (b, c, t, f) | |
| x = self.conv(x) | |
| b, c, t, f = x.size() | |
| x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| if x_mask is None: | |
| return x, None | |
| if self.subsample_by_2: | |
| return x, x_mask[:, :, ::2] | |
| else: | |
| return x, x_mask[:, :, ::2][:, :, ::2] | |
| def __getitem__(self, key): | |
| """Subsample x. | |
| When reset_parameters() is called, if use_scaled_pos_enc is used, | |
| return the positioning encoding. | |
| """ | |
| if key != -1: | |
| raise NotImplementedError("Support only `-1` (for `reset_parameters`).") | |
| return self.out[key] | |
| class Conv2dNoSubsampling(torch.nn.Module): | |
| """Convolutional 2D without subsampling. | |
| :param int idim: input dim | |
| :param int odim: output dim | |
| :param flaot dropout_rate: dropout rate | |
| :param torch.nn.Module pos_enc: custom position encoding layer | |
| """ | |
| def __init__(self, idim, odim, dropout_rate, pos_enc=None): | |
| """Construct an Conv2dSubsampling object.""" | |
| super().__init__() | |
| logging.info("Encoder does not do down-sample on mel-spectrogram.") | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, kernel_size=5, stride=1, padding=2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, kernel_size=5, stride=1, padding=2), | |
| torch.nn.ReLU(), | |
| ) | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * idim, odim), | |
| pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """Subsample x. | |
| :param torch.Tensor x: input tensor | |
| :param torch.Tensor x_mask: input mask | |
| :return: subsampled x and mask | |
| :rtype Tuple[torch.Tensor, torch.Tensor] | |
| """ | |
| x = x.unsqueeze(1) # (b, c, t, f) | |
| x = self.conv(x) | |
| b, c, t, f = x.size() | |
| x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| if x_mask is None: | |
| return x, None | |
| return x, x_mask | |
| def __getitem__(self, key): | |
| """Subsample x. | |
| When reset_parameters() is called, if use_scaled_pos_enc is used, | |
| return the positioning encoding. | |
| """ | |
| if key != -1: | |
| raise NotImplementedError("Support only `-1` (for `reset_parameters`).") | |
| return self.out[key] | |
| class Conv2dSubsampling6(torch.nn.Module): | |
| """Convolutional 2D subsampling (to 1/6 length). | |
| :param int idim: input dim | |
| :param int odim: output dim | |
| :param flaot dropout_rate: dropout rate | |
| """ | |
| def __init__(self, idim, odim, dropout_rate): | |
| """Construct an Conv2dSubsampling object.""" | |
| super(Conv2dSubsampling6, self).__init__() | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 5, 3), | |
| torch.nn.ReLU(), | |
| ) | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim), | |
| PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """Subsample x. | |
| :param torch.Tensor x: input tensor | |
| :param torch.Tensor x_mask: input mask | |
| :return: subsampled x and mask | |
| :rtype Tuple[torch.Tensor, torch.Tensor] | |
| """ | |
| x = x.unsqueeze(1) # (b, c, t, f) | |
| x = self.conv(x) | |
| b, c, t, f = x.size() | |
| x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| if x_mask is None: | |
| return x, None | |
| return x, x_mask[:, :, :-2:2][:, :, :-4:3] | |
| class Conv2dSubsampling8(torch.nn.Module): | |
| """Convolutional 2D subsampling (to 1/8 length). | |
| :param int idim: input dim | |
| :param int odim: output dim | |
| :param flaot dropout_rate: dropout rate | |
| """ | |
| def __init__(self, idim, odim, dropout_rate): | |
| """Construct an Conv2dSubsampling object.""" | |
| super(Conv2dSubsampling8, self).__init__() | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| ) | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim), | |
| PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """Subsample x. | |
| :param torch.Tensor x: input tensor | |
| :param torch.Tensor x_mask: input mask | |
| :return: subsampled x and mask | |
| :rtype Tuple[torch.Tensor, torch.Tensor] | |
| """ | |
| x = x.unsqueeze(1) # (b, c, t, f) | |
| x = self.conv(x) | |
| b, c, t, f = x.size() | |
| x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| if x_mask is None: | |
| return x, None | |
| return x, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2] | |