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