def resnet152(pretrained=False, **kwargs):
if pretrained:
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
pretrained_state_dict = torch.load(
'./Authority/resnet152-b121ed2d.pth'
) # load_url函数根据model_urls字典下载或导入相应的预训练模型
now_state_dict = model.state_dict() # 返回model模块的字典
pretrained_state_dict.pop('fc.weight') # 排除全连接层的参数(全连接层返回分类个数)
pretrained_state_dict.pop('fc.bias')
now_state_dict.update(pretrained_state_dict)
model.load_state_dict(now_state_dict)
# 最后通过调用model的load_state_dict方法用预训练的模型参数来初始化你构建的网络结构，
# 这个方法就是PyTorch中通用的用一个模型的参数初始化另一个模型的层的操作。load_state_dict方法还有一个重要的参数是strict，
# 该参数默认是True，表示预训练模型的层和你的网络结构层严格对应相等（比如层名和维度）
return model
return ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
def se_resnet34(num_classes=1000, pretrained="/home/ibian/.torch/models/resnet34-333f7ec4.pth"):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
# use pretrained model
if pretrained is not None:
print("=> loading pretrained model '{}'".format(pretrained))
model_dict = model.state_dict()
checkpoint = torch.load(pretrained)
checkpoint = {k: v for k, v in checkpoint.items() if k in model_dict}
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
return model
