Loading DenseNet161 model does not work about places365 HOT 3 OPEN

Hey Guys,

I found a solution for this. As I found out, the name of the keys from the downloaded torchvision model and the trained Places365 model just don't match. I'm not the biggest python pro, so I came up with a pretty simple and barbaric solution (feel free to refactor it!).

model = models.__dict__[arch](num_classes=365)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)

state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
state_dict = {str.replace(k,'norm.','norm'): v for k,v in state_dict.items()}
state_dict = {str.replace(k,'conv.','conv'): v for k,v in state_dict.items()}
state_dict = {str.replace(k,'normweight','norm.weight'): v for k,v in state_dict.items()}
state_dict = {str.replace(k,'normrunning','norm.running'): v for k,v in state_dict.items()}
state_dict = {str.replace(k,'normbias','norm.bias'): v for k,v in state_dict.items()}
state_dict = {str.replace(k,'convweight','conv.weight'): v for k,v in state_dict.items()}

I am basically just replacing the key names of the Places365 model with the key names of the model downloaded from the torchvision.models package. I tested the model, and for me it works :)

@RiSaMa strict=False ignores all not matching keys. So the initial model keeps his initial weights when no matching key is found in the new state_dict (in our case i think it's all of them). As you most likely are loading an untrained model from the torchvision.models package this results in random performance.

from places365.

Due to the upgrading of pytorch0.4, the densenet model structure is also updated, thus the old model cannot be loaded properly. I don't have solution for that yet.

from places365.

Same problem here... any solution?

I have been able to load the model as follows:

    model = models.__dict__[arch](num_classes=365)
    checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
    state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
    model.load_state_dict(state_dict,strict=False) #NOTICE THE strict=False
    model.cuda()
    model.eval()

But the performance is almost random...

Any idea?

from places365.