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from typing import Callable, Sequence, Union
import torch
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
__all__ = ["TopKCategoricalAccuracy"]
class TopKCategoricalAccuracy(Metric):
"""
Calculates the top-k categorical accuracy.
- ``update`` must receive output of the form ``(y_pred, y)``.
Args:
k: the k in “top-k”.
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.
skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be
true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)``
Alternatively, ``output_transform`` can be used to handle this.
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 the format of
``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added
to the metric to transform the output into the form expected by the metric.
For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
def process_function(engine, batch):
y_pred, y = batch
return y_pred, y
def one_hot_to_binary_output_transform(output):
y_pred, y = output
y = torch.argmax(y, dim=1) # one-hot vector to label index vector
return y_pred, y
engine = Engine(process_function)
metric = TopKCategoricalAccuracy(
k=2, output_transform=one_hot_to_binary_output_transform)
metric.attach(engine, 'top_k_accuracy')
preds = torch.tensor([
[0.7, 0.2, 0.05, 0.05], # 1 is in the top 2
[0.2, 0.3, 0.4, 0.1], # 0 is not in the top 2
[0.4, 0.4, 0.1, 0.1], # 0 is in the top 2
[0.7, 0.05, 0.2, 0.05] # 2 is in the top 2
])
target = torch.tensor([ # targets as one-hot vectors
[0, 1, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
[0, 0, 1, 0]
])
state = engine.run([[preds, target]])
print(state.metrics['top_k_accuracy'])
.. testoutput::
0.75
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
_state_dict_all_req_keys = ("_num_correct", "_num_examples")
def __init__(
self,
k: int = 5,
output_transform: Callable = lambda x: x,
device: Union[str, torch.device] = torch.device("cpu"),
skip_unrolling: bool = False,
) -> None:
super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device, skip_unrolling=skip_unrolling)
self._k = k
@reinit__is_reduced
def reset(self) -> None:
self._num_correct = torch.tensor(0, device=self._device)
self._num_examples = 0
@reinit__is_reduced
def update(self, output: Sequence[torch.Tensor]) -> None:
y_pred, y = output[0].detach(), output[1].detach()
sorted_indices = torch.topk(y_pred, self._k, dim=1)[1]
expanded_y = y.view(-1, 1).expand(-1, self._k)
correct = torch.sum(torch.eq(sorted_indices, expanded_y), dim=1)
self._num_correct += torch.sum(correct).to(self._device)
self._num_examples += correct.shape[0]
@sync_all_reduce("_num_correct", "_num_examples")
def compute(self) -> Union[float, torch.Tensor]:
if self._num_examples == 0:
raise NotComputableError(
"TopKCategoricalAccuracy must have at least one example before it can be computed."
)
return self._num_correct.item() / self._num_examples
|
