# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
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# of this software and associated documentation files (the "Software"), to deal
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# SOFTWARE.
import torch
import torch.nn as nn
from torch import Tensor
from typing import Tuple
[docs]class MaskConv1d(nn.Conv1d):
r"""
1D convolution with masking
Args:
in_channels (int): Number of channels in the input vector
out_channels (int): Number of channels produced by the convolution
kernel_size (int or tuple): Size of the convolving kernel
stride (int): Stride of the convolution. Default: 1
padding (int): Zero-padding added to both sides of the input. Default: 0
dilation (int): Spacing between kernel elements. Default: 1
groups (int): Number of blocked connections from input channels to output channels. Default: 1
bias (bool): If True, adds a learnable bias to the output. Default: True
Inputs: inputs, seq_lengths
- **inputs** (torch.FloatTensor): The input of size (batch, dimension, time)
- **seq_lengths** (torch.IntTensor): The actual length of each sequence in the batch
Returns: output, seq_lengths
- **output**: Masked output from the conv1d
- **seq_lengths**: Sequence length of output from the conv1d
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
groups: int = 1,
bias: bool = False,
) -> None:
super(MaskConv1d, self).__init__(in_channels=in_channels, out_channels=out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, groups=groups, bias=bias)
def _get_sequence_lengths(self, seq_lengths):
return (
(seq_lengths + 2 * self.padding[0] - self.dilation[0] * (self.kernel_size[0] - 1) - 1) // self.stride[0] + 1
)
[docs] def forward(self, inputs: Tensor, input_lengths: Tensor) -> Tuple[Tensor, Tensor]:
r"""
inputs: (batch, dimension, time)
input_lengths: (batch)
"""
max_length = inputs.size(2)
indices = torch.arange(max_length).to(input_lengths.dtype).to(input_lengths.device)
indices = indices.expand(len(input_lengths), max_length)
mask = indices >= input_lengths.unsqueeze(1)
inputs = inputs.masked_fill(mask.unsqueeze(1).to(device=inputs.device), 0)
output_lengths = self._get_sequence_lengths(input_lengths)
output = super(MaskConv1d, self).forward(inputs)
del mask, indices
return output, output_lengths