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pytorch-image-models/models/conv2d_same.py

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1.5 KiB

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