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from typing import Tuple
import torch
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
from ignite.metrics.regression._base import _BaseRegression
class WaveHedgesDistance(_BaseRegression):
r"""Calculates the Wave Hedges Distance.
.. math::
\text{WHD} = \sum_{j=1}^n\frac{|A_j - P_j|}{max(A_j, P_j)}
where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
More details can be found in `Botchkarev 2018`__.
- ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
- `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
__ https://arxiv.org/abs/1809.03006
Parameters are inherited from ``Metric.__init__``.
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.
By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
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.
Examples:
To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
The output of the engine's ``process_function`` needs to be in format of
``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``.
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
metric = WaveHedgesDistance()
metric.attach(default_evaluator, 'whd')
y_true = torch.tensor([0., 1., 2., 3., 4., 5.])
y_pred = y_true * 0.75
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['whd'])
.. testoutput::
1.25...
.. versionchanged:: 0.4.5
- Works with DDP.
"""
_state_dict_all_req_keys = ("_sum_of_errors",)
@reinit__is_reduced
def reset(self) -> None:
self._sum_of_errors = torch.tensor(0.0, device=self._device)
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
y_pred, y = output[0].detach(), output[1].detach()
errors = torch.abs(y.view_as(y_pred) - y_pred) / (torch.max(y_pred, y.view_as(y_pred)) + 1e-30)
self._sum_of_errors += torch.sum(errors).to(self._device)
@sync_all_reduce("_sum_of_errors")
def compute(self) -> float:
return self._sum_of_errors.item()
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