Pytorch 计算分类器准确率（总分类及子分类）

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)