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| # Copyright 2020 Emiru Tsunoo | |
| # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| """Subsampling layer definition.""" | |
| import math | |
| import torch | |
| class Conv2dSubsamplingWOPosEnc(torch.nn.Module): | |
| """Convolutional 2D subsampling. | |
| Args: | |
| idim (int): Input dimension. | |
| odim (int): Output dimension. | |
| dropout_rate (float): Dropout rate. | |
| kernels (list): kernel sizes | |
| strides (list): stride sizes | |
| """ | |
| def __init__(self, idim, odim, dropout_rate, kernels, strides): | |
| """Construct an Conv2dSubsamplingWOPosEnc object.""" | |
| assert len(kernels) == len(strides) | |
| super().__init__() | |
| conv = [] | |
| olen = idim | |
| for i, (k, s) in enumerate(zip(kernels, strides)): | |
| conv += [ | |
| torch.nn.Conv2d(1 if i == 0 else odim, odim, k, s), | |
| torch.nn.ReLU(), | |
| ] | |
| olen = math.floor((olen - k) / s + 1) | |
| self.conv = torch.nn.Sequential(*conv) | |
| self.out = torch.nn.Linear(odim * olen, odim) | |
| self.strides = strides | |
| self.kernels = kernels | |
| def forward(self, x, x_mask): | |
| """Subsample x. | |
| Args: | |
| x (torch.Tensor): Input tensor (#batch, time, idim). | |
| x_mask (torch.Tensor): Input mask (#batch, 1, time). | |
| Returns: | |
| torch.Tensor: Subsampled tensor (#batch, time', odim), | |
| where time' = time // 4. | |
| torch.Tensor: Subsampled mask (#batch, 1, time'), | |
| where time' = time // 4. | |
| """ | |
| 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 | |
| for k, s in zip(self.kernels, self.strides): | |
| x_mask = x_mask[:, :, : -k + 1 : s] | |
| return x, x_mask | |