pytorch加载模型时使用map_location来在CPU、GPU间辗转腾挪

假设我们只保存了模型的参数(model.state_dict())到文件名为modelparameters.pth, model = Net()

cpu -> cpu或者gpu -> gpu:

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

cpu -> gpu 1

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

gpu 1 -> gpu 0

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

gpu -> cpu

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