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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]
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