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6a0b93e | 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 | import torch
from torchvision import models
class resnet18(torch.nn.Module):
def __init__(self, pretrained=True):
super().__init__()
self.features = models.resnet18(pretrained=pretrained)
self.conv1 = self.features.conv1
self.bn1 = self.features.bn1
self.relu = self.features.relu
self.maxpool1 = self.features.maxpool
self.layer1 = self.features.layer1
self.layer2 = self.features.layer2
self.layer3 = self.features.layer3
self.layer4 = self.features.layer4
def forward(self, input):
x = self.conv1(input)
x = self.relu(self.bn1(x))
x = self.maxpool1(x)
feature1 = self.layer1(x) # 1 / 4
feature2 = self.layer2(feature1) # 1 / 8
feature3 = self.layer3(feature2) # 1 / 16
feature4 = self.layer4(feature3) # 1 / 32
# global average pooling to build tail
tail = torch.mean(feature4, 3, keepdim=True)
tail = torch.mean(tail, 2, keepdim=True)
return feature3, feature4, tail
class resnet101(torch.nn.Module):
def __init__(self, pretrained=True):
super().__init__()
self.features = models.resnet101(pretrained=pretrained)
self.conv1 = self.features.conv1
self.bn1 = self.features.bn1
self.relu = self.features.relu
self.maxpool1 = self.features.maxpool
self.layer1 = self.features.layer1
self.layer2 = self.features.layer2
self.layer3 = self.features.layer3
self.layer4 = self.features.layer4
def forward(self, input):
x = self.conv1(input)
x = self.relu(self.bn1(x))
x = self.maxpool1(x)
feature1 = self.layer1(x) # 1 / 4
feature2 = self.layer2(feature1) # 1 / 8
feature3 = self.layer3(feature2) # 1 / 16
feature4 = self.layer4(feature3) # 1 / 32
# global average pooling to build tail
tail = torch.mean(feature4, 3, keepdim=True)
tail = torch.mean(tail, 2, keepdim=True)
return feature3, feature4, tail
def build_contextpath(name):
model = {
'resnet18': resnet18(pretrained=True),
'resnet101': resnet101(pretrained=True)
}
return model[name]
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