# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
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# SOFTWARE.
import torch
import torch.nn as nn
from typing import Tuple
from openspeech.modules import Conv2dExtractor
[docs]class Conv2dSubsampling(Conv2dExtractor):
r"""
Convolutional 2D subsampling (to 1/4 length)
Args:
input_dim (int): Dimension of input vector
in_channels (int): Number of channels in the input vector
out_channels (int): Number of channels produced by the convolution
activation (str): Activation function
Inputs: inputs
- **inputs** (batch, time, dim): Tensor containing sequence of inputs
- **input_lengths** (batch): list of sequence input lengths
Returns: outputs, output_lengths
- **outputs** (batch, time, dim): Tensor produced by the convolution
- **output_lengths** (batch): list of sequence output lengths
"""
def __init__(
self,
input_dim: int,
in_channels: int,
out_channels: int,
activation: str = 'relu',
) -> None:
super(Conv2dSubsampling, self).__init__(input_dim, activation)
self.in_channels = in_channels
self.out_channels = out_channels
from openspeech.modules import MaskConv2d
self.conv = MaskConv2d(
nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2),
self.activation,
nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=2),
self.activation,
)
)
[docs] def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
outputs, input_lengths = super().forward(inputs, input_lengths)
output_lengths = input_lengths >> 2
output_lengths -= 1
return outputs, output_lengths