Source code for ignite.metrics.accuracy

from __future__ import division

from ignite.metrics import Metric
from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced
from ignite.exceptions import NotComputableError

import torch

class _BaseClassification(Metric):

    def __init__(self, output_transform=lambda x: x, is_multilabel=False, 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):
        self._type = None
        self._num_classes = None

    def _check_shape(self, output):
        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, ...).")

    def _check_binary_multilabel_cases(self, output):
        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):
        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))
                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]
                update_type = "binary"
                num_classes = 1
            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
            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, ...) 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`'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): device specification in case of distributed computation usage. In most of the cases, it can be defined as "cuda:local_rank" or "cuda" if already set `torch.cuda.set_device(local_rank)`. By default, if a distributed process group is initialized and available, device is set to `cuda`. """ def __init__(self, output_transform=lambda x: x, is_multilabel=False, 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): self._num_correct = 0 self._num_examples = 0 super(Accuracy, self).reset() @reinit__is_reduced def update(self, output): 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): 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

© Copyright 2023, PyTorch-Ignite Contributors. Last updated on 12/04/2023, 3:00:00 PM.

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