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Source code for mmcls.core.evaluation.mean_ap

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch


[docs]def average_precision(pred, target): r"""Calculate the average precision for a single class. AP summarizes a precision-recall curve as the weighted mean of maximum precisions obtained for any r'>r, where r is the recall: .. math:: \text{AP} = \sum_n (R_n - R_{n-1}) P_n Note that no approximation is involved since the curve is piecewise constant. Args: pred (np.ndarray): The model prediction with shape (N, ). target (np.ndarray): The target of each prediction with shape (N, ). Returns: float: a single float as average precision value. """ eps = np.finfo(np.float32).eps # sort examples sort_inds = np.argsort(-pred) sort_target = target[sort_inds] # count true positive examples pos_inds = sort_target == 1 tp = np.cumsum(pos_inds) total_pos = tp[-1] # count not difficult examples pn_inds = sort_target != -1 pn = np.cumsum(pn_inds) tp[np.logical_not(pos_inds)] = 0 precision = tp / np.maximum(pn, eps) ap = np.sum(precision) / np.maximum(total_pos, eps) return ap
[docs]def mAP(pred, target): """Calculate the mean average precision with respect of classes. Args: pred (torch.Tensor | np.ndarray): The model prediction with shape (N, C), where C is the number of classes. target (torch.Tensor | np.ndarray): The target of each prediction with shape (N, C), where C is the number of classes. 1 stands for positive examples, 0 stands for negative examples and -1 stands for difficult examples. Returns: float: A single float as mAP value. """ if isinstance(pred, torch.Tensor) and isinstance(target, torch.Tensor): pred = pred.detach().cpu().numpy() target = target.detach().cpu().numpy() elif not (isinstance(pred, np.ndarray) and isinstance(target, np.ndarray)): raise TypeError('pred and target should both be torch.Tensor or' 'np.ndarray') assert pred.shape == \ target.shape, 'pred and target should be in the same shape.' num_classes = pred.shape[1] ap = np.zeros(num_classes) for k in range(num_classes): ap[k] = average_precision(pred[:, k], target[:, k]) mean_ap = ap.mean() * 100.0 return mean_ap
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