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# mmcls.core.evaluation.mean_ap 源代码

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

[文档]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

[文档]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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