File size: 2,214 Bytes
a8f2b95 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import torch.nn as nn
def batchnorm(in_planes):
"batch norm 2d"
return nn.BatchNorm2d(in_planes, affine=True, eps=1e-5, momentum=0.1)
def conv3x3(in_planes, out_planes, stride=1, bias=False):
"3x3 convolution with padding"
return nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=bias
)
def conv1x1(in_planes, out_planes, stride=1, bias=False):
"1x1 convolution"
return nn.Conv2d(
in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=bias
)
def convbnrelu(in_planes, out_planes, kernel_size, stride=1, groups=1, act=True):
"conv-batchnorm-relu"
if act:
return nn.Sequential(
nn.Conv2d(
in_planes,
out_planes,
kernel_size,
stride=stride,
padding=int(kernel_size / 2.0),
groups=groups,
bias=False,
),
batchnorm(out_planes),
nn.ReLU6(inplace=True),
)
else:
return nn.Sequential(
nn.Conv2d(
in_planes,
out_planes,
kernel_size,
stride=stride,
padding=int(kernel_size / 2.0),
groups=groups,
bias=False,
),
batchnorm(out_planes),
)
class CRPBlock(nn.Module):
def __init__(self, in_planes, out_planes, n_stages):
super(CRPBlock, self).__init__()
for i in range(n_stages):
setattr(
self,
"{}_{}".format(i + 1, "outvar_dimred"),
conv1x1(
in_planes if (i == 0) else out_planes,
out_planes,
stride=1,
bias=False,
),
)
self.stride = 1
self.n_stages = n_stages
self.maxpool = nn.MaxPool2d(kernel_size=5, stride=1, padding=2)
def forward(self, x):
top = x
for i in range(self.n_stages):
top = self.maxpool(top)
top = getattr(self, "{}_{}".format(i + 1, "outvar_dimred"))(top)
x = top + x
return x
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