def se_resnet152(num_classes=1_000):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 8, 36, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
if __name__ == "__main__":
import torch
model = se_resnet50()
# 替换网络层
for name, module in model.named_modules():
print("layer name:{}, layer instance:{}".format(name, module))
in_feat_num = model.fc.in_features
model.fc = nn.Linear(in_feat_num, 102)
# forward
fake_img = torch.randn((1, 3, 224, 224)) # batchsize * channel * height * width
output = model(fake_img)
print(output.shape)
def se_resnet50(num_classes=1_000, pretrained=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 6, 3])
model.avgpool = nn.AdaptiveAvgPool2d(1)
if pretrained:
# state_dict = load_state_dict_from_url(model_urls['resnet50'],
# progress=progress)
# model.load_state_dict(state_dict)
model.load_state_dict(torch.load(PATH))
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, num_classes)
return model
def se_resnet101(num_classes=1_000):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet152(num_classes=1_000):
def cbamresnet10(n_classes: int) -> nn.Module:
model = ResNet(PreActCbamBlock, [1, 1, 1, 1])
model.fc = nn.Linear(model.fc.in_features, n_classes)
return model
def prepare_resnet(resnet: ResNet, num_classes: int):
resnet.fc = nn.Linear(resnet.fc.in_features, num_classes)
