这篇文章主要讲解了“register_backward_hook(hook)和register_forward_hook(hook)怎么使用”,文中的讲解内容简单清晰,易于学习与理解,下面请大家跟着小编的思路慢慢深入,一起来研究和学习“register_backward_hook(hook)和register_forward_hook(hook)怎么使用”吧!
register_backward_hook(hook) Registers a backward hook on the module. 将一个反向传播的钩子函数登记注册到一个模块上. The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature: 每次计算模型输入的梯度时都会调用这个钩子函数.该钩子函数应该具有 如下签名形式: hook(module, grad_input, grad_output) -> Tensor or None The grad_input and grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of grad_input in subsequent computations. 如果模块的输入数据和输出数据有多个的话,那么grad_input和 grad_output可能是一个元组.该钩子函数,不应该修改它的参数,但是 它可以可选地返回一个新的相对于输入input的梯度,该梯度可以被用来 在随后的计算中代替grad_input. Returns 返回 a handle that can be used to remove the added hook by calling handle.remove()返回一个句柄,该句柄通过调用handle.remove()可以移除已添加 的钩子函数. Return type 返回类型 torch.utils.hooks.RemovableHandle Warning 警告 The current implementation will not have the presented behavior for complex Module that perform many operations. In some failure cases, grad_input and grad_output will only contain the gradientsfor a subset of the inputs and outputs. For such Module, you should use torch.Tensor.register_hook() directly on a specific input or output to get the required gradients.当前的实现没有展现执行许多操作的复杂模块的行为.在某些错误的例子中,grad_input和grad_output只能包含输入数据和输出数据子集的梯度.对于 这样的模块,你应该在特定的输入和输出数据上直接使用 torch.Tensor.register_hook()来获得所需的梯度.
register_forward_hook(hook) Registers a forward hook on the module. The hook will be called every time after forward() has computed an output. It should have the following signature: hook(module, input, output) -> None or modified output The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called. Returns a handle that can be used to remove the added hook by calling handle.remove() Return type torch.utils.hooks.RemovableHandle
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