accuracy.py

import numpy as np
import torch
import torch.nn as nn


def accuracy_numpy(pred, target, topk=1, thrs=None):
    if thrs is None:
        thrs = 0.0
    if isinstance(thrs, float):
        thrs = (thrs, )
        res_single = True
    elif isinstance(thrs, tuple):
        res_single = False
    else:
        raise TypeError(
            f'thrs should be float or tuple, but got {type(thrs)}.')

    res = []
    maxk = max(topk)
    num = pred.shape[0]
    pred_label = pred.argsort(axis=1)[:, -maxk:][:, ::-1]
    pred_score = np.sort(pred, axis=1)[:, -maxk:][:, ::-1]

    for k in topk:
        correct_k = pred_label[:, :k] == target.reshape(-1, 1)
        res_thr = []
        for thr in thrs:
            # Only prediction values larger than thr are counted as correct
            _correct_k = correct_k & (pred_score[:, :k] > thr)
            _correct_k = np.logical_or.reduce(_correct_k, axis=1)
            res_thr.append(_correct_k.sum() * 100. / num)
        if res_single:
            res.append(res_thr[0])
        else:
            res.append(res_thr)
    return res


def accuracy_torch(pred, target, topk=1, thrs=None):
    if thrs is None:
        thrs = 0.0
    if isinstance(thrs, float):
        thrs = (thrs, )
        res_single = True
    elif isinstance(thrs, tuple):
        res_single = False
    else:
        raise TypeError(
            f'thrs should be float or tuple, but got {type(thrs)}.')

    res = []
    maxk = max(topk)
    num = pred.size(0)
    pred_score, pred_label = pred.topk(maxk, dim=1)
    pred_label = pred_label.t()
    correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
    for k in topk:
        res_thr = []
        for thr in thrs:
            # Only prediction values larger than thr are counted as correct
            _correct = correct & (pred_score.t() > thr)
            correct_k = _correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res_thr.append(correct_k.mul_(100. / num))
        if res_single:
            res.append(res_thr[0])
        else:
            res.append(res_thr)
    return res


def accuracy(pred, target, topk=1, thrs=None):
    """Calculate accuracy according to the prediction and target.

    Args:
        pred (torch.Tensor | np.array): The model prediction.
        target (torch.Tensor | np.array): The target of each prediction
        topk (int | tuple[int]): If the predictions in ``topk``
            matches the target, the predictions will be regarded as
            correct ones. Defaults to 1.
        thrs (float, optional): thrs (float | tuple[float], optional):
            Predictions with scores under the thresholds are considered
            negative. Default to None.

    Returns:
        float | list[float] | list[list[float]]: If the input ``topk`` is a
            single integer, the function will return a single float or a list
            depending on whether ``thrs`` is a single float. If the input
            ``topk`` is a tuple, the function will return a list of results
            of accuracies of each ``topk`` number. That is to say, as long as
            ``topk`` is a tuple, the returned list shall be of the same length
            as topk.
    """
    assert isinstance(topk, (int, tuple))
    if isinstance(topk, int):
        topk = (topk, )
        return_single = True
    else:
        return_single = False

    if isinstance(pred, torch.Tensor) and isinstance(target, torch.Tensor):
        res = accuracy_torch(pred, target, topk, thrs)
    elif isinstance(pred, np.ndarray) and isinstance(target, np.ndarray):
        res = accuracy_numpy(pred, target, topk, thrs)
    else:
        raise TypeError(
            f'pred and target should both be torch.Tensor or np.ndarray, '
            f'but got {type(pred)} and {type(target)}.')

    return res[0] if return_single else res


class Accuracy(nn.Module):

    def __init__(self, topk=(1, )):
        """Module to calculate the accuracy.

        Args:
            topk (tuple): The criterion used to calculate the
                accuracy. Defaults to (1,).
        """
        super().__init__()
        self.topk = topk

    def forward(self, pred, target):
        """Forward function to calculate accuracy.

        Args:
            pred (torch.Tensor): Prediction of models.
            target (torch.Tensor): Target for each prediction.

        Returns:
            list[float]: The accuracies under different topk criterions.
        """
        return accuracy(pred, target, self.topk)