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import torch.nn as nn
def conv1x1(in_planes, out_planes, stride=1, bias=False):
"""1x1 convolution without padding"""
return nn.Conv2d(
in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=bias
)
def conv3x3(in_planes, out_planes, stride=1, groups=1, bias=False):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
groups=groups,
bias=bias,
)
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1):
super().__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.conv2 = conv3x3(planes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
if stride == 1:
self.downsample = None
else:
self.downsample = nn.Sequential(
conv1x1(in_planes, planes, stride=stride), nn.BatchNorm2d(planes)
)
def forward(self, x):
y = x
y = self.relu(self.bn1(self.conv1(y)))
y = self.bn2(self.conv2(y))
if self.downsample is not None:
x = self.downsample(x)
return self.relu(x + y)
class ResNet18(nn.Module):
"""
Fewer channels
"""
def __init__(self, config=None):
super().__init__()
# Config
block_dims = config["backbone"]["block_dims"]
# Networks
self.conv1 = nn.Conv2d(
1, block_dims[0], kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = nn.BatchNorm2d(block_dims[0])
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(
BasicBlock, block_dims[0], block_dims[0], stride=1
) # 1/2
self.layer2 = self._make_layer(
BasicBlock, block_dims[0], block_dims[1], stride=2
) # 1/4
self.layer3 = self._make_layer(
BasicBlock, block_dims[1], block_dims[2], stride=2
) # 1/8
self.layer4 = self._make_layer(
BasicBlock, block_dims[2], block_dims[3], stride=2
) # 1/16
self.layer5 = self._make_layer(
BasicBlock, block_dims[3], block_dims[4], stride=2
) # 1/32
# For fine matching
self.fine_conv = nn.Sequential(
self._make_layer(
BasicBlock, block_dims[2], block_dims[2], stride=1),
conv1x1(block_dims[2], block_dims[4]),
nn.BatchNorm2d(block_dims[4]),
)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, in_dim, out_dim, stride=1):
layer1 = block(in_dim, out_dim, stride=stride)
layer2 = block(out_dim, out_dim, stride=1)
layers = (layer1, layer2)
return nn.Sequential(*layers)
def forward(self, x):
x0 = self.relu(self.bn1(self.conv1(x)))
x1 = self.layer1(x0) # 1/2
x2 = self.layer2(x1) # 1/4
x3 = self.layer3(x2) # 1/8
x4 = self.layer4(x3) # 1/16
x5 = self.layer5(x4) # 1/32
xf = self.fine_conv(x3) # 1/8
return [x3, x4, x5, xf]
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