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one_of

OneOf

Bases: TensorOp

Perform one of several possible TensorOps.

Parameters:

Name Type Description Default
*tensor_ops TensorOp

Ops to choose between with a specified (or uniform) probability.

 ()
probs Optional[List[float]]

List of probabilities, must sum to 1. When None, the probabilities will be equally distributed.

 None
Source code in fastestimator/fastestimator/op/tensorop/meta/one_of.py 
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@traceable()
class OneOf(TensorOp):
    """Perform one of several possible TensorOps.

    Args:
        *tensor_ops: Ops to choose between with a specified (or uniform) probability.
        probs: List of probabilities, must sum to 1. When None, the probabilities will be equally distributed.
    """
    def __init__(self, *tensor_ops: TensorOp, probs: Optional[List[float]] = None) -> None:
        inputs = tensor_ops[0].inputs
        outputs = tensor_ops[0].outputs
        mode = tensor_ops[0].mode
        ds_id = tensor_ops[0].ds_id
        super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
        self.in_list = tensor_ops[0].in_list
        self.out_list = tensor_ops[0].out_list
        for op in tensor_ops[1:]:
            assert inputs == op.inputs, "All ops within a OneOf must share the same inputs"
            assert self.in_list == op.in_list, "All ops within OneOf must share the same input configuration"
            assert outputs == op.outputs, "All ops within a OneOf must share the same outputs"
            assert self.out_list == op.out_list, "All ops within OneOf must share the same output configuration"
            assert mode == op.mode, "All ops within a OneOf must share the same mode"
            assert ds_id == op.ds_id, "All ops within a OneOf must share the same ds_id"
        if probs:
            assert len(tensor_ops) == len(probs), "The number of probabilities do not match with number of Operators"
            assert abs(sum(probs) - 1) < 1e-8, "Probabilities must sum to 1"
        else:
            probs = [1 / len(tensor_ops) for _ in tensor_ops]
        self.ops = tensor_ops
        self.probs = probs
        self.framework = None

    def build(self, framework: str, device: Optional[torch.device] = None) -> None:
        assert framework in {"tf", "torch"}, "unrecognized framework: {}".format(framework)
        self.framework = framework
        for op in self.ops:
            op.build(framework, device)

    def get_fe_loss_keys(self) -> Set[str]:
        return set.union(*[op.get_fe_loss_keys() for op in self.ops])

    def get_fe_models(self) -> Set[Model]:
        return set.union(*[op.get_fe_models() for op in self.ops])

    def fe_retain_graph(self, retain: Optional[bool] = None) -> Optional[bool]:
        resp = None
        for op in self.ops:
            resp = resp or op.fe_retain_graph(retain)
        return resp

    def __getstate__(self) -> Dict[str, List[Dict[Any, Any]]]:
        return {'ops': [elem.__getstate__() if hasattr(elem, '__getstate__') else {} for elem in self.ops]}

    def forward(self, data: Union[Tensor, List[Tensor]], state: Dict[str, Any]) -> Union[Tensor, List[Tensor]]:
        """Execute a randomly selected op from the list of `numpy_ops`.

        Args:
            data: The information to be passed to one of the wrapped operators.
            state: Information about the current execution context, for example {"mode": "train"}.

        Returns:
            The `data` after application of one of the available numpyOps.
        """
        if self.framework == 'tf':
            idx = cast(tf.random.categorical(tf.math.log([self.probs]), 1), dtype='int32')[0, 0]
            results = tf.switch_case(idx, [lambda op=op: op.forward(data, state) for op in self.ops])
        else:
            results = np.random.choice(self.ops, p=self.probs).forward(data, state)
        return results

forward

Execute a randomly selected op from the list of numpy_ops.

Parameters:

Name Type Description Default
data Union[Tensor, List[Tensor]]

The information to be passed to one of the wrapped operators.

 required
state Dict[str, Any]

Information about the current execution context, for example {"mode": "train"}.

 required

Returns:

Type Description
Union[Tensor, List[Tensor]]

The data after application of one of the available numpyOps.
Source code in fastestimator/fastestimator/op/tensorop/meta/one_of.py 
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def forward(self, data: Union[Tensor, List[Tensor]], state: Dict[str, Any]) -> Union[Tensor, List[Tensor]]:
    """Execute a randomly selected op from the list of `numpy_ops`.

    Args:
        data: The information to be passed to one of the wrapped operators.
        state: Information about the current execution context, for example {"mode": "train"}.

    Returns:
        The `data` after application of one of the available numpyOps.
    """
    if self.framework == 'tf':
        idx = cast(tf.random.categorical(tf.math.log([self.probs]), 1), dtype='int32')[0, 0]
        results = tf.switch_case(idx, [lambda op=op: op.forward(data, state) for op in self.ops])
    else:
        results = np.random.choice(self.ops, p=self.probs).forward(data, state)
    return results