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from torch import nn
from torchvision.models.resnet import ResNet, Bottleneck
class ResNetEncoder(ResNet):
"""
ResNetEncoder inherits from torchvision's official ResNet. It is modified to
use dilation on the last block to maintain output stride 16, and deleted the
global average pooling layer and the fully connected layer that was originally
used for classification. The forward method additionally returns the feature
maps at all resolutions for decoder's use.
"""
layers = {
'resnet50': [3, 4, 6, 3],
'resnet101': [3, 4, 23, 3],
}
def __init__(self, in_channels, variant='resnet101', norm_layer=None):
super().__init__(
block=Bottleneck,
layers=self.layers[variant],
replace_stride_with_dilation=[False, False, True],
norm_layer=norm_layer)
# Replace first conv layer if in_channels doesn't match.
if in_channels != 3:
self.conv1 = nn.Conv2d(in_channels, 64, 7, 2, 3, bias=False)
# Delete fully-connected layer
del self.avgpool
del self.fc
def forward(self, x):
x0 = x # 1/1
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x1 = x # 1/2
x = self.maxpool(x)
x = self.layer1(x)
x2 = x # 1/4
x = self.layer2(x)
x3 = x # 1/8
x = self.layer3(x)
x = self.layer4(x)
x4 = x # 1/16
return x4, x3, x2, x1, x0
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