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Source code for ignite.metrics.accumulation

import numbers
from typing import Callable, Tuple, Union

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce

__all__ = ["VariableAccumulation", "GeometricAverage", "Average"]


[docs]class VariableAccumulation(Metric): """Single variable accumulator helper to compute (arithmetic, geometric, harmonic) average of a single variable. - ``update`` must receive output of the form `x`. - `x` can be a number or `torch.Tensor`. Note: The class stores input into two public variables: `accumulator` and `num_examples`. Number of samples is updated following the rule: - `+1` if input is a number - `+1` if input is a 1D `torch.Tensor` - `+batch_size` if input is a ND `torch.Tensor`. Batch size is the first dimension (`shape[0]`). Args: op: a callable to update accumulator. Method's signature is `(accumulator, output)`. For example, to compute arithmetic mean value, `op = lambda a, x: a + x`. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. """ required_output_keys = None def __init__( self, op: Callable, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): if not callable(op): raise TypeError(f"Argument op should be a callable, but given {type(op)}") self._op = op super(VariableAccumulation, self).__init__(output_transform=output_transform, device=device)
[docs] @reinit__is_reduced def reset(self) -> None: self.accumulator = torch.tensor(0.0, dtype=torch.float64, device=self._device) self.num_examples = 0
def _check_output_type(self, output: Union[float, torch.Tensor]) -> None: if not isinstance(output, (numbers.Number, torch.Tensor)): raise TypeError(f"Output should be a number or torch.Tensor, but given {type(output)}")
[docs] @reinit__is_reduced def update(self, output: Union[float, torch.Tensor]) -> None: self._check_output_type(output) if isinstance(output, torch.Tensor): output = output.detach() if not (output.device == self._device and output.dtype == self.accumulator.dtype): output = output.to(self.accumulator) self.accumulator = self._op(self.accumulator, output) if isinstance(output, torch.Tensor): self.num_examples += output.shape[0] if len(output.shape) > 1 else 1 else: self.num_examples += 1
[docs] @sync_all_reduce("accumulator", "num_examples") def compute(self) -> Tuple[torch.Tensor, int]: return self.accumulator, self.num_examples
[docs]class Average(VariableAccumulation): """Helper class to compute arithmetic average of a single variable. - ``update`` must receive output of the form `x`. - `x` can be a number or `torch.Tensor`. Note: Number of samples is updated following the rule: - `+1` if input is a number - `+1` if input is a 1D `torch.Tensor` - `+batch_size` if input is an ND `torch.Tensor`. Batch size is the first dimension (`shape[0]`). For input `x` being an ND `torch.Tensor` with N > 1, the first dimension is seen as the number of samples and is summed up and added to the accumulator: `accumulator += x.sum(dim=0)` Examples: .. code-block:: python evaluator = ... custom_var_mean = Average(output_transform=lambda output: output['custom_var']) custom_var_mean.attach(evaluator, 'mean_custom_var') state = evaluator.run(dataset) # state.metrics['mean_custom_var'] -> average of output['custom_var'] Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): def _mean_op(a: Union[float, torch.Tensor], x: Union[float, torch.Tensor]) -> Union[float, torch.Tensor]: if isinstance(x, torch.Tensor) and x.ndim > 1: x = x.sum(dim=0) return a + x super(Average, self).__init__(op=_mean_op, output_transform=output_transform, device=device)
[docs] @sync_all_reduce("accumulator", "num_examples") def compute(self) -> Union[float, torch.Tensor]: if self.num_examples < 1: raise NotComputableError( f"{self.__class__.__name__} must have at least one example before it can be computed." ) return self.accumulator / self.num_examples
[docs]class GeometricAverage(VariableAccumulation): """Helper class to compute geometric average of a single variable. - ``update`` must receive output of the form `x`. - `x` can be a positive number or a positive `torch.Tensor`, such that ``torch.log(x)`` is not `nan`. Note: Number of samples is updated following the rule: - `+1` if input is a number - `+1` if input is a 1D `torch.Tensor` - `+batch_size` if input is a ND `torch.Tensor`. Batch size is the first dimension (`shape[0]`). For input `x` being an ND `torch.Tensor` with N > 1, the first dimension is seen as the number of samples and is aggregated and added to the accumulator: `accumulator *= prod(x, dim=0)` Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): def _geom_op(a: torch.Tensor, x: Union[float, torch.Tensor]) -> torch.Tensor: if not isinstance(x, torch.Tensor): x = torch.tensor(x) x = torch.log(x) if x.ndim > 1: x = x.sum(dim=0) return a + x super(GeometricAverage, self).__init__(op=_geom_op, output_transform=output_transform, device=device)
[docs] @sync_all_reduce("accumulator", "num_examples") def compute(self) -> Union[float, torch.Tensor]: if self.num_examples < 1: raise NotComputableError( f"{self.__class__.__name__} must have at least one example before it can be computed." ) tensor = torch.exp(self.accumulator / self.num_examples) if tensor.numel() == 1: return tensor.item() return tensor

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