from typing import Callable, Optional, Sequence, Tuple, 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: Union[str, torch.device] = torch.device("cpu"),
):
self._is_multilabel = is_multilabel
self._type: Optional[str] = None
self._num_classes: Optional[int] = 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, ...), "
f"but given {y.shape} vs {y_pred.shape}."
)
y_shape = y.shape
y_pred_shape: Tuple[int, ...] = 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(
f"Invalid shapes of y (shape={y.shape}) and y_pred (shape={y_pred.shape}), check documentation."
" for expected shapes of y and y_pred."
)
if self._type is None:
self._type = update_type
self._num_classes = num_classes
else:
if self._type != update_type:
raise RuntimeError(f"Input data type has changed from {self._type} to {update_type}.")
if self._num_classes != num_classes:
raise ValueError(f"Input data number of classes has changed from {self._num_classes} to {num_classes}")
[docs]class Accuracy(_BaseClassification):
r"""Calculates the accuracy for binary, multiclass and multilabel data.
.. math:: \text{Accuracy} = \frac{ TP + TN }{ TP + TN + FP + FN }
where :math:`\text{TP}` is true positives, :math:`\text{TN}` is true negatives,
:math:`\text{FP}` is false positives and :math:`\text{FN}` is false negatives.
- ``update`` must receive output of the form ``(y_pred, 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.
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.
is_multilabel: flag to use in multilabel case. By default, False.
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:
For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
.. include:: defaults.rst
:start-after: :orphan:
Binary case
.. testcode:: 1
metric = Accuracy()
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
y_pred = torch.tensor([1, 0, 1, 0, 1, 1])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])
.. testoutput:: 1
0.6666...
Multiclass case
.. testcode:: 2
metric = Accuracy()
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([2, 0, 2, 1, 0, 1])
y_pred = torch.tensor([
[0.0266, 0.1719, 0.3055],
[0.6886, 0.3978, 0.8176],
[0.9230, 0.0197, 0.8395],
[0.1785, 0.2670, 0.6084],
[0.8448, 0.7177, 0.7288],
[0.7748, 0.9542, 0.8573],
])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])
.. testoutput:: 2
0.5
Multilabel case
.. testcode:: 3
metric = Accuracy(is_multilabel=True)
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([
[0, 0, 1, 0, 1],
[1, 0, 1, 0, 0],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[0, 1, 1, 0, 1],
])
y_pred = torch.tensor([
[1, 1, 0, 0, 0],
[1, 0, 1, 0, 0],
[1, 0, 0, 0, 0],
[1, 0, 1, 1, 1],
[1, 1, 0, 0, 1],
])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])
.. testoutput:: 3
0.2
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:
.. testcode:: 4
def thresholded_output_transform(output):
y_pred, y = output
y_pred = torch.round(y_pred)
return y_pred, y
metric = Accuracy(output_transform=thresholded_output_transform)
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])
.. testoutput:: 4
0.6666...
"""
_state_dict_all_req_keys = ("_num_correct", "_num_examples")
def __init__(
self,
output_transform: Callable = lambda x: x,
is_multilabel: bool = False,
device: Union[str, torch.device] = torch.device("cpu"),
):
super(Accuracy, self).__init__(output_transform=output_transform, is_multilabel=is_multilabel, device=device)
[docs] @reinit__is_reduced
def reset(self) -> None:
self._num_correct = torch.tensor(0, device=self._device)
self._num_examples = 0
super(Accuracy, self).reset()
[docs] @reinit__is_reduced
def update(self, output: Sequence[torch.Tensor]) -> None:
self._check_shape(output)
self._check_type(output)
y_pred, y = output[0].detach(), output[1].detach()
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).to(self._device)
self._num_examples += correct.shape[0]
[docs] @sync_all_reduce("_num_examples", "_num_correct")
def compute(self) -> float:
if self._num_examples == 0:
raise NotComputableError("Accuracy must have at least one example before it can be computed.")
return self._num_correct.item() / self._num_examples