How to use pretrained model such as densnet161?

[Allen-wg]

[NothingToSay99]

RuntimeError: Error(s) in loading state_dict for DenseASPP: Unexpected key(s) in state_dict: "features.denseblock1.denselayer1.norm.1.weight", "features.denseblock1.denselayer1.norm.1.bias", "features.denseblock1.denselayer1.norm.1.running_mean", "features.denseblock1.denselayer1.norm.1.running_var", "features.denseblock1.denselayer1.conv.1.weight", "features.denseblock1.denselayer1.norm.2.weight", "features.denseblock1.denselayer1.norm.2.bias", "features.denseblock1.denselayer1.norm.2.running_mean", "features.denseblock1.denselayer1.norm.2.running_var", "features.denseblock1.denselayer1.conv.2.weight", "features.denseblock1.denselayer2.norm.1.weight", "features.denseblock1.denselayer2.norm.1.bias", "features.denseblock1.denselayer2.norm.1.running_mean", "features.denseblock1.denselayer2.norm.1.running_var", "features.denseblock1.denselayer2.conv.1.weight", "features.denseblock1.denselayer2.norm.2.weight", "features.denseblock1.denselayer2.norm.2.bias", "features.denseblock1.denselayer2.norm.2.running_mean", "features.denseblock1.denselayer2.norm.2.running_var", "features.denseblock1.denselayer2.conv.2.weight", "features.denseblock1.denselayer3.norm.1.weight", "features.denseblock1.denselayer3.norm.1.bias", "features.denseblock1.denselayer3.norm.1.running_mean", "features.denseblock1.denselayer3.norm.1.running_var", "features.denseblock1.denselayer3.conv.1.weight", "features.denseblock1.denselayer3.norm.2.weight", "features.denseblock1.denselayer3.norm.2.bias", "features.denseblock1.denselayer3.norm.2.running_mean", "features.denseblock1.denselayer3.norm.2.running_var", "features.denseblock1.denselayer3.conv.2.weight", "features.denseblock1.denselayer4.norm.1.weight", "features.denseblock1.denselayer4.norm.1.bias", "features.denseblock1.denselayer4.norm.1.running_mean", "features.denseblock1.denselayer4.norm.1.running_var", "features.denseblock1.denselayer4.conv.1.weight", "features.denseblock1.denselayer4.norm.2.weight", "features.denseblock1.denselayer4.norm.2.bias", "features.denseblock1.denselayer4.norm.2.running_mean", "features.denseblock1.denselayer4.norm.2.running_var", "features.denseblock1.denselayer4.conv.2.weight", "features.denseblock1.denselayer5.norm.1.weight", "features.denseblock1.denselayer5.norm.1.bias", "features.denseblock1.denselayer5.norm.1.running_mean", "features.denseblock1.denselayer5.norm.1.running_var", "features.denseblock1.denselayer5.conv.1.weight", "features.denseblock1.denselayer5.norm.2.weight", "features.denseblock1.denselayer5.norm.2.bias", "features.denseblock1.denselayer5.norm.2.running_mean", "features.denseblock1.denselayer5.norm.2.running_var", "features.denseblock1.denselayer5.conv.2.weight", "features.denseblock1.denselayer6.norm.1.weight", "features.denseblock1.denselayer6.norm.1.bias", "features.denseblock1.denselayer6.norm.1.running_mean", "features.denseblock1.denselayer6.norm.1.running_var", "features.denseblock1.denselayer6.conv.1.weight", 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"features.denseblock2.denselayer2.conv.1.weight", "features.denseblock2.denselayer2.norm.2.weight", "features.denseblock2.denselayer2.norm.2.bias", "features.denseblock2.denselayer2.norm.2.running_mean", "features.denseblock2.denselayer2.norm.2.running_var", "features.denseblock2.denselayer2.conv.2.weight", "features.denseblock2.denselayer3.norm.1.weight", "features.denseblock2.denselayer3.norm.1.bias", "features.denseblock2.denselayer3.norm.1.running_mean", "features.denseblock2.denselayer3.norm.1.running_var", "features.denseblock2.denselayer3.conv.1.weight", "features.denseblock2.denselayer3.norm.2.weight", "features.denseblock2.denselayer3.norm.2.bias", "features.denseblock2.denselayer3.norm.2.running_mean", "features.denseblock2.denselayer3.norm.2.running_var", "features.denseblock2.denselayer3.conv.2.weight", "features.denseblock2.denselayer4.norm.1.weight", "features.denseblock2.denselayer4.norm.1.bias", "features.denseblock2.denselayer4.norm.1.running_mean", 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why all weights r unexpected？

[hali1122]

i meet the same error as you ,did you solve this problem? thx

[DomainFlag]

For anyone that is stuck, use the modified version that may help much:

Inference.py

with the (basically a few remapping is needed for the weight state), and don't forget to import re at the top of the file:

@staticmethod
def __load_weight(seg_model, model_path):
    print("LOADING WEIGHTS.")
    weight = torch.load(model_path, map_location=lambda storage, loc: storage)

    new_state_dict = OrderedDict()
    for k, v in weight.items():
        # remove "module." from keys
        name = k[7:]
        if "classification" not in name:
            name = re.sub(r'\.(\d)', r'\1', name)

        new_state_dict[name] = v

    seg_model.load_state_dict(new_state_dict)