pytorch预训练模型读取修改相关参数填坑

修改部分层&＃xff0c;仍然调用之前的模型参数。

resnet &＃61; resnet50(pretrained&＃61;False)
resnet.load_state_dict(torch.load(args.predir))res_conv31 &＃61; Bottleneck_dilated(1024, 256,dilated_rate &＃61; 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&＃xff0c;名称差个前缀

model_dict &＃61; model.state_dict()
# print(model_dict)
pretrained_dict &＃61; torch.load("/yzc/reid_testpcb/se_resnet50-ce0d4300.pth")
keys &＃61; []
for k, v in pretrained_dict.items():keys.append(k)
i &＃61; 0
for k, v in model_dict.items():if v.size() &＃61;&＃61; pretrained_dict[keys[i]].size():model_dict[k] &＃61; pretrained_dict[keys[i]]#print(model_dict[k])i &＃61; i &＃43; 1
model.load_state_dict(model_dict)

最后是修改参数名拿来用的&＃xff0c;

from collections import OrderedDict
pretrained_dict &＃61; torch.load(&＃39;premodel&＃39;)new_state_dict &＃61; OrderedDict()# for k, v in mgn_state_dict.items():
# name &＃61; k[7:] # remove &＃96;module.&＃96;
# new_state_dict[name] &＃61; v
# self.model &＃61; self.model.load_state_dict(new_state_dict)for k, v in pretrained_dict.items():name &＃61; "model.module."&＃43;k # remove &＃96;module.&＃96;# print(name)new_state_dict[name] &＃61; v
self.model.load_state_dict(new_state_dict)