====== strict选项 ======

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。
====== 参数的筛选 ======
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)

====== 载入参数到指定设备 ======

===== 1. cpu -> cpu或者gpu -> gpu =====


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

===== 2. cpu -> gpu 1 =====

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


===== 3. gpu 1 -> gpu 0 =====


''torch.load('modelparameters.pth', map_location={'cuda:1':'cuda:0'})''
===== 4. gpu -> cpu =====


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

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