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Source code for ignite.metrics.accumulation

import numbers
from typing import Any, Callable, Optional, 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 (callable): 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 (callable, optional): 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 (str of torch.device, optional): optional device specification for internal storage. """ _required_output_keys = None def __init__( self, op: Callable, output_transform: Callable = lambda x: x, device: Optional[Union[str, torch.device]] = None ): if not callable(op): raise TypeError("Argument op should be a callable, but given {}".format(type(op))) self.accumulator = None self.num_examples = None self._op = op super(VariableAccumulation, self).__init__(output_transform=output_transform, device=device) @reinit__is_reduced def reset(self) -> None: self.accumulator = torch.tensor(0.0, dtype=torch.float64, device=self._device) self.num_examples = torch.tensor(0, dtype=torch.long, device=self._device) def _check_output_type(self, output: Union[Any, torch.Tensor, numbers.Number]) -> None: if not (isinstance(output, numbers.Number) or isinstance(output, torch.Tensor)): raise TypeError("Output should be a number or torch.Tensor, but given {}".format(type(output))) @reinit__is_reduced def update(self, output: Union[Any, torch.Tensor, numbers.Number]) -> None: self._check_output_type(output) if self._device is not None: # Put output to the metric's device if isinstance(output, torch.Tensor) and (output.device != self._device): output = output.to(self._device) self.accumulator = self._op(self.accumulator, output) if hasattr(output, "shape"): self.num_examples += output.shape[0] if len(output.shape) > 1 else 1 else: self.num_examples += 1 @sync_all_reduce("accumulator", "num_examples") def compute(self) -> list: 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 (callable, optional): 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 (str of torch.device, optional): optional device specification for internal storage. """ def __init__(self, output_transform: Callable = lambda x: x, device: Optional[Union[str, torch.device]] = None): def _mean_op(a, x): 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) @sync_all_reduce("accumulator", "num_examples") def compute(self) -> Union[Any, torch.Tensor, numbers.Number]: if self.num_examples < 1: raise NotComputableError( "{} must have at least one example before" " it can be computed.".format(self.__class__.__name__) ) 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 (callable, optional): 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 (str of torch.device, optional): optional device specification for internal storage. """ def __init__(self, output_transform: Callable = lambda x: x, device: Optional[Union[str, torch.device]] = None): def _geom_op(a: torch.Tensor, x: Union[Any, numbers.Number, 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) @sync_all_reduce("accumulator", "num_examples") def compute(self) -> torch.Tensor: if self.num_examples < 1: raise NotComputableError( "{} must have at least one example before" " it can be computed.".format(self.__class__.__name__) ) return torch.exp(self.accumulator / self.num_examples)

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