Function validate

Code/utils/utils.py:73–109

对一批数据进行预测，返回混淆矩阵以及Accuracy :param net: :param data_loader: :param set_name: eg: 'valid' 'train' 'tesst :param classes_name: :return:

(net, data_loader, set_name, classes_name)

def validate(net, data_loader, set_name, classes_name):
    """
    对一批数据进行预测，返回混淆矩阵以及Accuracy
    :param net:
    :param data_loader:
    :param set_name:  eg: 'valid' 'train' 'tesst
    :param classes_name:
    :return:
    """
    net.eval()
    cls_num = len(classes_name)
    conf_mat = np.zeros([cls_num, cls_num])

    for data in data_loader:
        images, labels = data
        images = Variable(images)
        labels = Variable(labels)

        outputs = net(images)
        outputs.detach_()

        _, predicted = torch.max(outputs.data, 1)

        # 统计混淆矩阵
        for i in range(len(labels)):
            cate_i = labels[i].numpy()
            pre_i = predicted[i].numpy()
            conf_mat[cate_i, pre_i] += 1.0

    for i in range(cls_num):
        print('class:{:<10}, total num:{:<6}, correct num:{:<5}  Recall: {:.2%} Precision: {:.2%}'.format(
            classes_name[i], np.sum(conf_mat[i, :]), conf_mat[i, i], conf_mat[i, i] / (1 + np.sum(conf_mat[i, :])),
                                                                conf_mat[i, i] / (1 + np.sum(conf_mat[:, i]))))

    print('{} set Accuracy:{:.2%}'.format(set_name, np.trace(conf_mat) / np.sum(conf_mat)))

    return conf_mat, '{:.2}'.format(np.trace(conf_mat) / np.sum(conf_mat))


def show_confMat(confusion_mat, classes, set_name, out_dir):