49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
|
# Improving convolutional networks with self-calibrated convolutions (CVPR 2020)
|
||
|
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
|
||
|
|
class SCConv(nn.Module):
|
||
|
|
def __init__(self, inplanes, planes, stride, padding, dilation, groups, pooling_r, norm_layer):
|
||
|
|
super(SCConv, self).__init__()
|
||
|
|
self.k2 = nn.Sequential(
|
||
|
|
nn.AvgPool2d(kernel_size=pooling_r, stride=pooling_r),
|
||
|
|
nn.Conv2d(inplanes, planes, kernel_size=3, stride=1,
|
||
|
|
padding=padding, dilation=dilation,
|
||
|
|
groups=groups, bias=False),
|
||
|
|
norm_layer(planes),
|
||
|
|
)
|
||
|
|
self.k3 = nn.Sequential(
|
||
|
|
nn.Conv2d(inplanes, planes, kernel_size=3, stride=1,
|
||
|
|
padding=padding, dilation=dilation,
|
||
|
|
groups=groups, bias=False),
|
||
|
|
norm_layer(planes),
|
||
|
|
)
|
||
|
|
self.k4 = nn.Sequential(
|
||
|
|
nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
|
||
|
|
padding=padding, dilation=dilation,
|
||
|
|
groups=groups, bias=False),
|
||
|
|
norm_layer(planes),
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
identity = x
|
||
|
|
|
||
|
|
out = torch.sigmoid(torch.add(identity, F.interpolate(
|
||
|
|
self.k2(x), identity.size()[2:]))) # sigmoid(identity + k2)
|
||
|
|
out = torch.multiply(self.k3(x), out) # k3 * sigmoid(identity + k2)
|
||
|
|
out = self.k4(out) # k4
|
||
|
|
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
# 输入 N C H W, 输出 N C H W
|
||
|
|
if __name__ == '__main__':
|
||
|
|
scconv = SCConv(64, 64, stride=1,
|
||
|
|
padding=2, dilation=2, groups=1, pooling_r=4, norm_layer=nn.BatchNorm2d)
|
||
|
|
|
||
|
|
input = torch.rand(1, 64, 64, 64)
|
||
|
|
output = scconv(input)
|
||
|
|
print(input.size(), output.size())
|