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40 lines
1.5 KiB
40 lines
1.5 KiB
6 years ago
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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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import math
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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,
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groups, bias)
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def forward(self, x):
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ih, iw = x.size()[-2:]
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kh, kw = self.weight.size()[-2:]
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oh = math.ceil(ih / self.stride[0])
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ow = math.ceil(iw / self.stride[1])
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pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
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pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, [pad_w//2, pad_w - pad_w//2, pad_h//2, pad_h - pad_h//2])
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return F.conv2d(x, self.weight, self.bias, self.stride,
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self.padding, self.dilation, self.groups)
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# helper method
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def sconv2d(in_chs, out_chs, kernel_size, **kwargs):
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padding = kwargs.pop('padding', 0)
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if isinstance(padding, str):
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if padding.lower() == 'same':
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return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
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else:
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# 'valid'
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return nn.Conv2d(in_chs, out_chs, kernel_size, padding=0, **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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