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evaluate.py
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evaluate.py
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# -*- coding: UTF-8 -*-
import numpy as np
import torch
from torch.autograd import Variable
import setting
import dataset
from model import CNN
import encoding
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
print("load cnn net.")
eval_dataloader = dataset.get_eval_data_loader()
correct = 0
total = 0
for i, (images, labels) in enumerate(eval_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
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())]
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = encoding.decode(labels.numpy()[0])
total += labels.size(0)
if (predict_label == true_label):
correct += 1
if (total % 200 == 0):
print('Test Accuracy of the model on the %d eval images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d eval images: %f %%' %
(total, 100 * correct / total))
return correct / total
if __name__ == '__main__':
main()