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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import numpy as np | |
| import torch | |
| def accuracy(output, target, topk=(1,)): | |
| """Computes the accuracy over the k top predictions for the specified values of k""" | |
| with torch.no_grad(): | |
| maxk = max(topk) | |
| batch_size = target.size(0) | |
| _, pred = output.topk(maxk, 1, True, True) | |
| pred = pred.t() | |
| correct = pred.eq(target.reshape(1, -1).expand_as(pred)) | |
| res = [] | |
| for k in topk: | |
| correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) | |
| res.append(correct_k.mul_(100.0 / batch_size)) | |
| return res | |
| def get_mean_accuracy(cm): | |
| list_acc = [] | |
| for i in range(len(cm)): | |
| acc = 0 | |
| if cm[i, :].sum() > 0: | |
| acc = cm[i, i] / cm[i, :].sum() | |
| list_acc.append(acc) | |
| return 100 * np.mean(list_acc), 100 * np.trace(cm) / np.sum(cm) | |