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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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import torch
import torch.nn as nn
from torch import Tensor
from typing import Tuple
[docs]class MaskConv2d(nn.Module):
r"""
Masking Convolutional Neural Network
Adds padding to the output of the module based on the given lengths.
This is to ensure that the results of the model do not change when batch sizes change during inference.
Input needs to be in the shape of (batch_size, channel, hidden_dim, seq_len)
Refer to https://github.com/SeanNaren/deepspeech.pytorch/blob/master/model.py
Copyright (c) 2017 Sean Naren
MIT License
Args:
sequential (torch.nn): sequential list of convolution layer
Inputs: inputs, seq_lengths
- **inputs** (torch.FloatTensor): The input of size BxCxHxT
- **seq_lengths** (torch.IntTensor): The actual length of each sequence in the batch
Returns: output, seq_lengths
- **output**: Masked output from the sequential
- **seq_lengths**: Sequence length of output from the sequential
"""
def __init__(self, sequential: nn.Sequential) -> None:
super(MaskConv2d, self).__init__()
self.sequential = sequential
def forward(self, inputs: Tensor, seq_lengths: Tensor) -> Tuple[Tensor, Tensor]:
output = None
for module in self.sequential:
output = module(inputs)
mask = torch.BoolTensor(output.size()).fill_(0)
if output.is_cuda:
mask = mask.cuda()
seq_lengths = self._get_sequence_lengths(module, seq_lengths)
for idx, length in enumerate(seq_lengths):
length = length.item()
if (mask[idx].size(2) - length) > 0:
mask[idx].narrow(dim=2, start=length, length=mask[idx].size(2) - length).fill_(1)
output = output.masked_fill(mask, 0)
inputs = output
return output, seq_lengths
def _get_sequence_lengths(self, module: nn.Module, seq_lengths: Tensor) -> Tensor:
r"""
Calculate convolutional neural network receptive formula
Args:
module (torch.nn.Module): module of CNN
seq_lengths (torch.IntTensor): The actual length of each sequence in the batch
Returns: seq_lengths
- **seq_lengths**: Sequence length of output from the module
"""
if isinstance(module, nn.Conv2d):
numerator = seq_lengths + 2 * module.padding[1] - module.dilation[1] * (module.kernel_size[1] - 1) - 1
seq_lengths = numerator.float() / float(module.stride[1])
seq_lengths = seq_lengths.int() + 1
elif isinstance(module, nn.MaxPool2d):
seq_lengths >>= 1
return seq_lengths.int()