pytorch 查全率 recall 查准率 precision F1调和平均准确率 accuracy

╰半夏微凉° 2021-06-24 15:59 647阅读 0赞

def eval():
        net.eval()
        test_loss = 0
        correct = 0
        total = 0
    
        classnum = 9
        target_num = torch.zeros((1,classnum))
        predict_num = torch.zeros((1,classnum))
        acc_num = torch.zeros((1,classnum))
    
        for batch_idx, (inputs, targets) in enumerate(testloader):
            if use_cuda:
                inputs, targets = inputs.cuda(), targets.cuda()
            inputs, targets = Variable(inputs, volatile=True), Variable(targets)
            outputs = net(inputs)
            loss = criterion(outputs, targets)
            # loss is variable , if add it(+=loss) directly, there will be a bigger ang bigger graph.
            test_loss += loss.data[0]
            _, predicted = torch.max(outputs.data, 1)
            total += targets.size(0)
            correct += predicted.eq(targets.data).cpu().sum()
    
            pre_mask = torch.zeros(outputs.size()).scatter_(1, predicted.cpu().view(-1, 1), 1.)
            predict_num += pre_mask.sum(0)
            tar_mask = torch.zeros(outputs.size()).scatter_(1, targets.data.cpu().view(-1, 1), 1.)
            target_num += tar_mask.sum(0)
            acc_mask = pre_mask*tar_mask
            acc_num += acc_mask.sum(0)
    
        recall = acc_num/target_num
        precision = acc_num/predict_num
        F1 = 2*recall*precision/(recall+precision)
        accuracy = acc_num.sum(1)/target_num.sum(1)
    
    #精度调整
        recall = (recall.numpy()[0]*100).round(3)
        precision = (precision.numpy()[0]*100).round(3)
        F1 = (F1.numpy()[0]*100).round(3)
        accuracy = (accuracy.numpy()[0]*100).round(3)
    
    # 打印格式方便复制
        print('recall'," ".join('%s' % id for id in recall))
        print('precision'," ".join('%s' % id for id in precision))
        print('F1'," ".join('%s' % id for id in F1))
        print('accuracy',accuracy)