pytorch 预训练模型读取修改相关参数填坑

修改部分层，仍然调用之前的模型参数。

resnet = resnet50(pretrained=False)
resnet.load_state_dict(torch.load(args.predir))

res_conv31 = Bottleneck_dilated(1024, 256,dilated_rate = 2)
print("---------------------",res_conv31)
print("---------------------",resnet.layer3[1])


res_conv31.load_state_dict(resnet.layer3[1].state_dict())

网络预训练模型与之前的模型对应不上，名称差个前缀

model_dict = model.state_dict()
# print(model_dict)
pretrained_dict = torch.load("/yzc/reid_testpcb/se_resnet50-ce0d4300.pth")
keys = []
for k, v in pretrained_dict.items():
       keys.append(k)
i = 0
for k, v in model_dict.items():
    if v.size() == pretrained_dict[keys[i]].size():
         model_dict[k] = pretrained_dict[keys[i]]
         #print(model_dict[k])
         i = i + 1
model.load_state_dict(model_dict)

最后是修改参数名拿来用的，

from collections import OrderedDict
pretrained_dict = torch.load('premodel')

new_state_dict = OrderedDict()

# for k, v in mgn_state_dict.items():
#     name = k[7:]  # remove `module.`
#     new_state_dict[name] = v
# self.model = self.model.load_state_dict(new_state_dict)

for k, v in pretrained_dict.items():
    name = "model.module."+k   # remove `module.`
    # print(name)
    new_state_dict[name] = v
self.model.load_state_dict(new_state_dict)