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| # This module is from [WeNet](https://github.com/wenet-e2e/wenet). | |
| # ## Citations | |
| # ```bibtex | |
| # @inproceedings{yao2021wenet, | |
| # title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, | |
| # author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, | |
| # booktitle={Proc. Interspeech}, | |
| # year={2021}, | |
| # address={Brno, Czech Republic }, | |
| # organization={IEEE} | |
| # } | |
| # @article{zhang2022wenet, | |
| # title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, | |
| # author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, | |
| # journal={arXiv preprint arXiv:2203.15455}, | |
| # year={2022} | |
| # } | |
| # | |
| """Conv2d Module with Valid Padding""" | |
| import torch.nn.functional as F | |
| from torch.nn.modules.conv import _ConvNd, _size_2_t, Union, _pair, Tensor, Optional | |
| class Conv2dValid(_ConvNd): | |
| """ | |
| Conv2d operator for VALID mode padding. | |
| """ | |
| def __init__( | |
| self, | |
| in_channels: int, | |
| out_channels: int, | |
| kernel_size: _size_2_t, | |
| stride: _size_2_t = 1, | |
| padding: Union[str, _size_2_t] = 0, | |
| dilation: _size_2_t = 1, | |
| groups: int = 1, | |
| bias: bool = True, | |
| padding_mode: str = "zeros", # TODO: refine this type | |
| device=None, | |
| dtype=None, | |
| valid_trigx: bool = False, | |
| valid_trigy: bool = False, | |
| ) -> None: | |
| factory_kwargs = {"device": device, "dtype": dtype} | |
| kernel_size_ = _pair(kernel_size) | |
| stride_ = _pair(stride) | |
| padding_ = padding if isinstance(padding, str) else _pair(padding) | |
| dilation_ = _pair(dilation) | |
| super(Conv2dValid, self).__init__( | |
| in_channels, | |
| out_channels, | |
| kernel_size_, | |
| stride_, | |
| padding_, | |
| dilation_, | |
| False, | |
| _pair(0), | |
| groups, | |
| bias, | |
| padding_mode, | |
| **factory_kwargs, | |
| ) | |
| self.valid_trigx = valid_trigx | |
| self.valid_trigy = valid_trigy | |
| def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]): | |
| validx, validy = 0, 0 | |
| if self.valid_trigx: | |
| validx = ( | |
| input.size(-2) * (self.stride[-2] - 1) - 1 + self.kernel_size[-2] | |
| ) // 2 | |
| if self.valid_trigy: | |
| validy = ( | |
| input.size(-1) * (self.stride[-1] - 1) - 1 + self.kernel_size[-1] | |
| ) // 2 | |
| return F.conv2d( | |
| input, | |
| weight, | |
| bias, | |
| self.stride, | |
| (validx, validy), | |
| self.dilation, | |
| self.groups, | |
| ) | |
| def forward(self, input: Tensor) -> Tensor: | |
| return self._conv_forward(input, self.weight, self.bias) | |