Pytorch 实现计算分类器准确率(总分类及子分类)
作者:疯狂的小猪oO
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分类器平均准确率计算:
correct = torch.zeros(1).squeeze().cuda() total = torch.zeros(1).squeeze().cuda() for i, (images, labels) in enumerate(train_loader): images = Variable(images.cuda()) labels = Variable(labels.cuda()) output = model(images) prediction = torch.argmax(output, 1) correct += (prediction == labels).sum().float() total += len(labels) acc_str = 'Accuracy: %f'%((correct/total).cpu().detach().data.numpy())
分类器各个子类准确率计算:
correct = list(0. for i in range(args.class_num)) total = list(0. for i in range(args.class_num)) for i, (images, labels) in enumerate(train_loader): images = Variable(images.cuda()) labels = Variable(labels.cuda()) output = model(images) prediction = torch.argmax(output, 1) res = prediction == labels for label_idx in range(len(labels)): label_single = label[label_idx] correct[label_single] += res[label_idx].item() total[label_single] += 1 acc_str = 'Accuracy: %f'%(sum(correct)/sum(total)) for acc_idx in range(len(train_class_correct)): try: acc = correct[acc_idx]/total[acc_idx] except: acc = 0 finally: acc_str += '\tclassID:%d\tacc:%f\t'%(acc_idx+1, acc)
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