| """ Conv2d w/ Same Padding | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from typing import Tuple, Optional | |
| from .padding import pad_same, get_padding_value | |
| def conv2d_same( | |
| x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1), | |
| padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1): | |
| x = pad_same(x, weight.shape[-2:], stride, dilation) | |
| return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups) | |
| class Conv2dSame(nn.Conv2d): | |
| """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions | |
| """ | |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1, | |
| padding=0, dilation=1, groups=1, bias=True): | |
| super(Conv2dSame, self).__init__( | |
| in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) | |
| def forward(self, x): | |
| return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) | |
| def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs): | |
| padding = kwargs.pop('padding', '') | |
| kwargs.setdefault('bias', False) | |
| padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs) | |
| if is_dynamic: | |
| return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs) | |
| else: | |
| return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs) | |