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