总结：用strict=False进行加载模型，则“能塞则塞，不能塞则丢”。load一般是依据key来加载的，一旦有key不匹配则出错。如果设置strict=False，则直接忽略不匹配的key，对于匹配的key则进行正常的赋值。

pretrained_dict =… model_dict = model.state_dict()

# 1. filter out unnecessary keys pretrained_dict = {k: v for k, vin pretrained_dict.items() if k inmodel_dict}

# 2. overwrite entries in the existing state dict

model_dict.update(pretrained_dict)

# 3. load the new state dict

model.load_state_dict(model_dict)

checkpoint = torch.load('modelparameters.pth')
model.load_state_dict(checkpoint)

torch.load('modelparameters.pth', map_location=lambda storage, loc: storage.cuda(1))

torch.load('modelparameters.pth', map_location={'cuda:1':'cuda:0'})

torch.load('modelparameters.pth', map_location=lambda storage, loc: storage)

torch.load('modelparameters.pth', map_location='cpu')