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import functools
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import warnings
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import torch
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class OutputHook:
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"""Output feature map of some layers.
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Args:
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module (nn.Module): The whole module to get layers.
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outputs (tuple[str] | list[str]): Layer name to output. Default: None.
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as_tensor (bool): Determine to return a tensor or a numpy array.
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Default: False.
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"""
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def __init__(self, module, outputs=None, as_tensor=False):
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self.outputs = outputs
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self.as_tensor = as_tensor
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self.layer_outputs = {}
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self.handles = []
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self.register(module)
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def register(self, module):
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def hook_wrapper(name):
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def hook(model, input, output):
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if not isinstance(output, torch.Tensor):
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warnings.warn(f'Directly return the output from {name}, '
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f'since it is not a tensor')
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self.layer_outputs[name] = output
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elif self.as_tensor:
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self.layer_outputs[name] = output
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else:
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self.layer_outputs[name] = output.detach().cpu().numpy()
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return hook
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if isinstance(self.outputs, (list, tuple)):
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for name in self.outputs:
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try:
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layer = rgetattr(module, name)
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h = layer.register_forward_hook(hook_wrapper(name))
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except AttributeError:
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raise AttributeError(f'Module {name} not found')
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self.handles.append(h)
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def remove(self):
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for h in self.handles:
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h.remove()
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.remove()
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def rgetattr(obj, attr, *args):
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def _getattr(obj, attr):
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return getattr(obj, attr, *args)
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return functools.reduce(_getattr, [obj] + attr.split('.'))
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