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Don't use plain forward in python #3

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@justusschock

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@justusschock

In opposite to what you say in the section

A nn.module can be used on input data in two ways whereas the latter one is commonly used for better readability. self.net(input) simply uses the call() method of the object to feed the input through the module.

output = self.net.forward(input)
# or
output = self.net(input)

it is not recommended, to call the plain forward in python.

If this would, be the same, the __call__ method (which is called if you call a class) would be something like this:

def __call__(self, *args, **kwargs):
    return self.forward(*args, **kwargs)

But instead it is slightly more complex :

 def __call__(self, *input, **kwargs):
        for hook in self._forward_pre_hooks.values():
            hook(self, input)
        if torch._C._get_tracing_state():
            result = self._slow_forward(*input, **kwargs)
        else:
            result = self.forward(*input, **kwargs)
        for hook in self._forward_hooks.values():
            hook_result = hook(self, input, result)
            if hook_result is not None:
                raise RuntimeError(
                    "forward hooks should never return any values, but '{}'"
                    "didn't return None".format(hook))
        if len(self._backward_hooks) > 0:
            var = result
            while not isinstance(var, torch.Tensor):
                if isinstance(var, dict):
                    var = next((v for v in var.values() if isinstance(v, torch.Tensor)))
                else:
                    var = var[0]
            grad_fn = var.grad_fn
            if grad_fn is not None:
                for hook in self._backward_hooks.values():
                    wrapper = functools.partial(hook, self)
                    functools.update_wrapper(wrapper, hook)
                    grad_fn.register_hook(wrapper)
        return result

because it also deals with all the registered hooks (which wouldn't be considered when calling the plain forward).

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