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Zero
| # Copyright (c) 2022 Ximalaya Inc. (authors: Yuguang Yang) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """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) | |