Source code for ignite.metrics.accuracy
from typing import Callable, Optional, Sequence, Union
import torch
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
__all__ = ["Accuracy"]
class _BaseClassification(Metric):
def __init__(
self,
output_transform: Callable = lambda x: x,
is_multilabel: bool = False,
device: Optional[Union[str, torch.device]] = None,
):
self._is_multilabel = is_multilabel
self._type = None
self._num_classes = None
super(_BaseClassification, self).__init__(output_transform=output_transform, device=device)
def reset(self) -> None:
self._type = None
self._num_classes = None
def _check_shape(self, output: Sequence[torch.Tensor]) -> None:
y_pred, y = output
if not (y.ndimension() == y_pred.ndimension() or y.ndimension() + 1 == y_pred.ndimension()):
raise ValueError(
"y must have shape of (batch_size, ...) and y_pred must have "
"shape of (batch_size, num_categories, ...) or (batch_size, ...), "
"but given {} vs {}.".format(y.shape, y_pred.shape)
)
y_shape = y.shape
y_pred_shape = y_pred.shape
if y.ndimension() + 1 == y_pred.ndimension():
y_pred_shape = (y_pred_shape[0],) + y_pred_shape[2:]
if not (y_shape == y_pred_shape):
raise ValueError("y and y_pred must have compatible shapes.")
if self._is_multilabel and not (y.shape == y_pred.shape and y.ndimension() > 1 and y.shape[1] > 1):
raise ValueError(
"y and y_pred must have same shape of (batch_size, num_categories, ...) and num_categories > 1."
)
def _check_binary_multilabel_cases(self, output: Sequence[torch.Tensor]) -> None:
y_pred, y = output
if not torch.equal(y, y ** 2):
raise ValueError("For binary cases, y must be comprised of 0's and 1's.")
if not torch.equal(y_pred, y_pred ** 2):
raise ValueError("For binary cases, y_pred must be comprised of 0's and 1's.")
def _check_type(self, output: Sequence[torch.Tensor]) -> None:
y_pred, y = output
if y.ndimension() + 1 == y_pred.ndimension():
num_classes = y_pred.shape[1]
if num_classes == 1:
update_type = "binary"
self._check_binary_multilabel_cases((y_pred, y))
else:
update_type = "multiclass"
elif y.ndimension() == y_pred.ndimension():
self._check_binary_multilabel_cases((y_pred, y))
if self._is_multilabel:
update_type = "multilabel"
num_classes = y_pred.shape[1]
else:
update_type = "binary"
num_classes = 1
else:
raise RuntimeError(
"Invalid shapes of y (shape={}) and y_pred (shape={}), check documentation."
" for expected shapes of y and y_pred.".format(y.shape, y_pred.shape)
)
if self._type is None:
self._type = update_type
self._num_classes = num_classes
else:
if self._type != update_type:
raise RuntimeError("Input data type has changed from {} to {}.".format(self._type, update_type))
if self._num_classes != num_classes:
raise ValueError(
"Input data number of classes has changed from {} to {}".format(self._num_classes, num_classes)
)
[docs]class Accuracy(_BaseClassification):
"""
Calculates the accuracy for binary, multiclass and multilabel data.
- ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
- `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...).
- `y` must be in the following shape (batch_size, ...).
- `y` and `y_pred` must be in the following shape of (batch_size, num_categories, ...) and
num_categories must be greater than 1 for multilabel cases.
In binary and multilabel cases, the elements of `y` and `y_pred` should have 0 or 1 values. Thresholding of
predictions can be done as below:
.. code-block:: python
def thresholded_output_transform(output):
y_pred, y = output
y_pred = torch.round(y_pred)
return y_pred, y
binary_accuracy = Accuracy(thresholded_output_transform)
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.
is_multilabel (bool, optional): flag to use in multilabel case. By default, False.
device (str of torch.device, optional): unused argument.
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
is_multilabel: bool = False,
device: Optional[Union[str, torch.device]] = None,
):
self._num_correct = None
self._num_examples = None
super(Accuracy, self).__init__(output_transform=output_transform, is_multilabel=is_multilabel, device=device)
@reinit__is_reduced
def reset(self) -> None:
self._num_correct = 0
self._num_examples = 0
super(Accuracy, self).reset()
@reinit__is_reduced
def update(self, output: Sequence[torch.Tensor]) -> None:
y_pred, y = output
self._check_shape((y_pred, y))
self._check_type((y_pred, y))
if self._type == "binary":
correct = torch.eq(y_pred.view(-1).to(y), y.view(-1))
elif self._type == "multiclass":
indices = torch.argmax(y_pred, dim=1)
correct = torch.eq(indices, y).view(-1)
elif self._type == "multilabel":
# if y, y_pred shape is (N, C, ...) -> (N x ..., C)
num_classes = y_pred.size(1)
last_dim = y_pred.ndimension()
y_pred = torch.transpose(y_pred, 1, last_dim - 1).reshape(-1, num_classes)
y = torch.transpose(y, 1, last_dim - 1).reshape(-1, num_classes)
correct = torch.all(y == y_pred.type_as(y), dim=-1)
self._num_correct += torch.sum(correct).item()
self._num_examples += correct.shape[0]
@sync_all_reduce("_num_examples", "_num_correct")
def compute(self) -> torch.Tensor:
if self._num_examples == 0:
raise NotComputableError("Accuracy must have at least one example before it can be computed.")
return self._num_correct / self._num_examples