# -*- encoding:utf-8 -*-

'''
@Author : dingjiawen
@Date : 2023/12/12 15:03
@Usage : 
@Desc :
'''

# -*- coding: UTF-8 -*-
import numpy as np
import torch
from torch.autograd import Variable
# from visdom import Visdom # pip install Visdom
import setting
import dataset
from model import CNN
import encoding as encode


def main():
    cnn = CNN()
    cnn.eval()
    cnn.load_state_dict(torch.load('./output/best_model.pkl', map_location=torch.device('cpu')))
    print("load cnn net.")

    predict_dataloader = dataset.get_predict_data_loader()

    # vis = Visdom()
    correct = 0
    for i, (images, labels) in enumerate(predict_dataloader):
        image = images
        vimage = Variable(image)
        predict_label = cnn(vimage)
        actual_label = encode.decode(labels[0].numpy())

        c0 = setting.ALL_CHAR_SET[np.argmax(predict_label[0, 0:setting.ALL_CHAR_SET_LEN].data.numpy())]
        c1 = setting.ALL_CHAR_SET[np.argmax(
            predict_label[0, setting.ALL_CHAR_SET_LEN:2 * setting.ALL_CHAR_SET_LEN].data.numpy())]
        c2 = setting.ALL_CHAR_SET[np.argmax(
            predict_label[0, 2 * setting.ALL_CHAR_SET_LEN:3 * setting.ALL_CHAR_SET_LEN].data.numpy())]
        c3 = setting.ALL_CHAR_SET[np.argmax(
            predict_label[0, 3 * setting.ALL_CHAR_SET_LEN:4 * setting.ALL_CHAR_SET_LEN].data.numpy())]
        correct += 1 if "".join([c0, c1, c2, c3]) == actual_label else 0
        c = '%s%s%s%s' % (c0, c1, c2, c3)
        print(f"predict:{c},actual:{actual_label}")
        # vis.images(image, opts=dict(caption=c))
    print(f"total:{len(predict_dataloader)},correct:{correct}")
    # 经过测试，最终正确率约74%，total:3000,correct:2233


if __name__ == '__main__':
    main()