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