Are there any methods to check whether parameters loaded from .pth file fits the model?

1

I saved my pre-trained model and load it when need re-training, but sometimes I modify the net’s structure and I want the program to automatically check whether the parameters still fit, if not, train the net from scratch. Are there any methods can do this?

2

i did it this way:

# https://discuss.pytorch.org/t/how-to-load-part-of-pre-trained-model/1113/3 ###### 
def load_valid(model, pretrained_file, skip_list=None):

    pretrained_dict = torch.load(pretrained_file)
    model_dict = model.state_dict()

    # 1. filter out unnecessary keys
    pretrained_dict1 = {k: v for k, v in pretrained_dict.items() if k in model_dict and k not in skip_list }

    # 2. overwrite entries in the existing state dict
    model_dict.update(pretrained_dict1)
    model.load_state_dict(model_dict)


##-------------------------
#example on usage
pretrained_file = '/root/share/project/pytorch/data/pretrain/inception/inception_v3_google-1a9a5a14.pth'

net=Inception3(num_classes=10)
if pretrained_file is not None:  #pretrain
    skip_list = ['fc.weight', 'fc.bias'] 
    load_valid(net, pretrained_file, skip_list=skip_list)