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watch

Watch

Bases: TensorOp

Watch one or more tensors for later gradient computation.

Parameters:

Name Type Description Default
inputs Union[None, str, Iterable[str]]

which tensors to watch during future computation.

 required
mode Union[None, str, Iterable[str]]

What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".

 None
ds_id Union[None, str, Iterable[str]]

What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all ds_ids except for a particular one, you can pass an argument like "!ds1".

 None
Source code in fastestimator/fastestimator/op/tensorop/gradient/watch.py 
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@traceable()
class Watch(TensorOp):
    """Watch one or more tensors for later gradient computation.

    Args:
        inputs: which tensors to watch during future computation.
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
    """
    def __init__(self,
                 inputs: Union[None, str, Iterable[str]],
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None) -> None:
        super().__init__(inputs=inputs, outputs=inputs, mode=mode, ds_id=ds_id)
        self.in_list, self.out_list = True, True
        self.retain_graph = True

    def fe_retain_graph(self, retain: Optional[bool] = None) -> Optional[bool]:
        if retain is not None:
            self.retain_graph = retain
        return self.retain_graph

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> List[Tensor]:
        for idx, tensor in enumerate(data):
            data[idx] = watch(tensor=tensor, tape=state['tape'])
        return data