62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
# Copyright 2020 Emiru Tsunoo
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Subsampling layer definition."""
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import math
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import torch
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class Conv2dSubsamplingWOPosEnc(torch.nn.Module):
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"""Convolutional 2D subsampling.
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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kernels (list): kernel sizes
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strides (list): stride sizes
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"""
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def __init__(self, idim, odim, dropout_rate, kernels, strides):
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"""Construct an Conv2dSubsamplingWOPosEnc object."""
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assert len(kernels) == len(strides)
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super().__init__()
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conv = []
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olen = idim
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for i, (k, s) in enumerate(zip(kernels, strides)):
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conv += [
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torch.nn.Conv2d(1 if i == 0 else odim, odim, k, s),
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torch.nn.ReLU(),
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]
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olen = math.floor((olen - k) / s + 1)
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self.conv = torch.nn.Sequential(*conv)
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self.out = torch.nn.Linear(odim * olen, odim)
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self.strides = strides
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self.kernels = kernels
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def forward(self, x, x_mask):
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"""Subsample x.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, idim).
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x_mask (torch.Tensor): Input mask (#batch, 1, time).
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Returns:
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torch.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 4.
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torch.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 4.
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"""
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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if x_mask is None:
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return x, None
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for k, s in zip(self.kernels, self.strides):
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x_mask = x_mask[:, :, : -k + 1 : s]
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return x, x_mask
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