Pytorch模型推理注意事項

with torch.no_grad

• Disabling gradient calculation is useful for inference, when you are sure that you will not call Tensor.backward().
• It will reduce memory consumption for computations that would otherwise have requires_grad=True.
• In this mode, the result of every computation will have requires_grad=False, even when the inputs have requires_grad=True.

nn.Module.eval()

• This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc

Remark : 如果只需要模型進行推理，with torch.no_grad 和 model.eval()都需要顯示地寫出來！

model = ModelArch()
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint)
model.eval()   # CORE-1
with torch.no_grad():  # CORE-2
    ...
    output = model(input)
    ...