def main(image_path):
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
predict_dataloader = my_dataset.get_predict_data_loader(image_path)
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, captcha_setting.ALL_CHAR_SET_LEN:2 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 2 * captcha_setting.ALL_CHAR_SET_LEN:3 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c3 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c = '%s%s%s%s' % (c0, c1, c2, c3)
print(c)
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
print("load cnn net.")
predict_dataloader = my_dataset.get_predict_data_loader()
# vis = Visdom()
results = []
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, captcha_setting.ALL_CHAR_SET_LEN:2 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 2 * captcha_setting.ALL_CHAR_SET_LEN:3 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c3 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c = '%s%s%s%s' % (c0, c1, c2, c3)
print(c)
results.append(c)
# vis.images(image, opts=dict(caption=c))
codes = get_file_codes()
hits = get_result(codes, results)
print float(len(hits)) / float(len(codes)) * 100
def main():
cnn = CNN()
if use_cuda:
cnn = CNN().cuda()
cnn.eval()
cnn.load_state_dict(torch.load('model_splic_cos_300_512_0.001.pkl'))
print("load cnn net.")
predict_dataloader = my_dataset.get_predict_data_loader()
#vis = Visdom()
correct = 0
total = 0
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image).cuda()
predict_label = cnn(vimage).cpu()
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, captcha_setting.ALL_CHAR_SET_LEN:2 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 2 * captcha_setting.ALL_CHAR_SET_LEN:3 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c3 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
# c = '%s%s%s%s' % (c0, c1, c2, c3)
# print(c)
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
# predict = '%s%s%s%s' % (c0, c1, c2, c3)
# predict_label = one_hot_encoding.decode(predict.numpy()[0])
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
print(predict_label, true_label)
if (predict_label == true_label):
correct += 1
if (total % 200 == 0):
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))