# Copyright 2020 Emiru Tsunoo
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Subsampling layer definition."""
import math
import torch
[docs]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
[docs] 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