Pytorch之SAME padding
Implement "same" padding for convolution operations
mimics TensorFlow SAME padding (I'm writing it down into the functional interface, so that nn.Conv2d can just call into F.conv2d_same_padding):
1 def conv2d_same_padding(input, weight, bias=None, stride=1, dilation=1, groups=1): 2 input_rows = input.size(2) 3 filter_rows = weight.size(2) 4 effective_filter_size_rows = (filter_rows - 1) * dilation[0] + 1 5 out_rows = (input_rows + stride[0] - 1) // stride[0] 6 padding_needed = 7 max(0, (out_rows - 1) * stride[0] + effective_filter_size_rows - 8 input_rows) 9 padding_rows = max(0, (out_rows - 1) * stride[0] + 10 (filter_rows - 1) * dilation[0] + 1 - input_rows) 11 rows_odd = (padding_rows % 2 != 0) 12 # same for padding_cols 13 14 if rows_odd or cols_odd: 15 input = F.pad(input, [0, int(cols_odd), 0, int(rows_odd)]) 16 17 return F.conv2d(input, weight, bias, stride, 18 padding=(padding_rows // 2, padding_cols // 2), 19 dilation=dilation, groups=groups) 20
It was mostly copy-pasted from TensorFlow code in here and here.
“As you can see, there is a lot of hidden things going on there, and that's why it might not be worth it adding a padding='same'. And I think not replicating the SAME behavior in TensorFlow is not ideal either.
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本文来自于：
Francisco Massa ： Implement "same" padding for convolution operations?
谢谢！！！
