saliency.py 文件源码

def load_defined_model(path, num_classes,name):
    model = models.__dict__[name](num_classes=num_classes)
    pretrained_state = torch.load(path)
    new_pretrained_state= OrderedDict()

    for k, v in pretrained_state['state_dict'].items():
        layer_name = k.replace("module.", "")
        new_pretrained_state[layer_name] = v

    #Diff
    diff = [s for s in diff_states(model.state_dict(), new_pretrained_state)]
    if(len(diff)!=0):
        print("Mismatch in these layers :", name, ":", [d[0] for d in diff])

    assert len(diff) == 0

    #Merge
    model.load_state_dict(new_pretrained_state)
    return model


#Load the model