Datasets:
jblitzar
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github-python-corpus

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model.train()
# training loss and validation loss
loss_log = f'[{iteration + 1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss: 0.5f}, Elapsed_time: {elapsed_time:0.5f}'
loss_avg.reset()
current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}'
# keep best accuracy model (on valid dataset)
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/best_accuracy.pth')
if current_norm_ED > best_norm_ED:
best_norm_ED = current_norm_ED
torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/best_norm_ED.pth')
best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}'
loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}'
print(loss_model_log)
log.write(loss_model_log + '\n')
# show some predicted results
dashed_line = '-' * 80
head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F'
predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n'
for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]):
if 'Attn' in opt.Prediction:
gt = gt[:gt.find('[s]')]
pred = pred[:pred.find('[s]')]
predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n'
predicted_result_log += f'{dashed_line}'
print(predicted_result_log)
log.write(predicted_result_log + '\n')
# save model per 1e+5 iter.
if (iteration + 1) % 5e+4 == 0:
torch.save(
model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration + 1}.pth')
if (iteration + 1) == opt.num_iter:
print('end the training')
sys.exit()
iteration += 1
if __name__ == '__main__':
train_dir = "/home/chengxf/cqc/text-recognition/data_lmdb_release/training/" # 可修改
valid_dir = "/home/chengxf/cqc/text-recognition/data_lmdb_release/validation/" # 可修改
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default='exp', help='Where to store logs and models') # 可修改
parser.add_argument('--train_data', type=str, default=train_dir, help='path to training dataset')
parser.add_argument('--valid_data', type=str, default=valid_dir, help='path to validation dataset')
parser.add_argument('--manualSeed', type=int, default=1111, help='for random seed setting')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=8)
parser.add_argument('--batch_size', type=int, default=512, help='input batch size') # 192 # 可修改
parser.add_argument('--num_iter', type=int, default=600000, help='number of iterations to train for') # 可修改
parser.add_argument('--valInterval', type=int, default=500, help='Interval between each validation') # 可修改
parser.add_argument('--saved_model', default='', # ./saved_models/eng_benchmark19/best_accuracy.pth',
help="path to model to continue training") # ./saved_models/exp_230719_yzaug/best_accuracy.pth
parser.add_argument('--FT', action='store_true', help='whether to do fine-tuning')
parser.add_argument('--adam', action='store_true', help='Whether to use adam (default is Adadelta)')
parser.add_argument('--lr', type=float, default=1, help='learning rate, default=1.0 for Adadelta')
parser.add_argument('--beta1', type=float, default=0.9, help='beta1 for adam. default=0.9')
parser.add_argument('--rho', type=float, default=0.95, help='decay rate rho for Adadelta. default=0.95')
parser.add_argument('--eps', type=float, default=1e-8, help='eps for Adadelta. default=1e-8')
parser.add_argument('--lr_scheduler', action='store_true', help='whether to set lr_scheduler')
parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping value. default=5')
parser.add_argument('--baiduCTC', action='store_true', help='for data_filtering_off mode')
""" Data processing """
parser.add_argument('--select_data', type=str, default='MJ-ST', # 'valid_random_lmdb', # 可修改
help='select training data (default is MJ-ST, which means MJ and ST used as training data)')
parser.add_argument('--batch_ratio', type=str, default='0.5-0.5',
help='assign ratio for each selected data in the batch')
parser.add_argument('--total_data_usage_ratio', type=str, default='1.0',
help='total data usage ratio, this ratio is multiplied to total number of data.')
parser.add_argument('--batch_max_length', type=int, default=25, help='maximum-label-length')
parser.add_argument('--imgH', type=int, default=32, help='the height of the input image')
parser.add_argument('--imgW', type=int, default=100, help='the width of the input image')
parser.add_argument('--rgb', action='store_true', help='use rgb input')
parser.add_argument('--character', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz', help='character label')
parser.add_argument('--sensitive', action='store_true', help='for sensitive character mode')
parser.add_argument('--PAD', action='store_true', help='whether to keep ratio then pad for image resize')
parser.add_argument('--data_filtering_off', action='store_true', help='for data_filtering_off mode')
""" Model Architecture """
parser.add_argument('--Transformation', type=str, default='TPS', help='Transformation stage. None|TPS')
parser.add_argument('--FeatureExtraction', type=str, default='ResNet18',
help='FeatureExtraction stage. VGG|RCNN|ResNet')
parser.add_argument('--SequenceModeling', type=str, default='BiLSTM', help='SequenceModeling stage. None|BiLSTM')
parser.add_argument('--Prediction', type=str, default='CTC', help='Prediction stage. CTC|Attn')
parser.add_argument('--num_fiducial', type=int, default=20, help='number of fiducial points of TPS-STN')
parser.add_argument('--input_channel', type=int, default=3,
help='the number of input channel of Feature extractor')
parser.add_argument('--output_channel', type=int, default=256,
help='the number of output channel of Feature extractor')
parser.add_argument('--hidden_size', type=int, default=256, help='the size of the LSTM hidden state')
opt = parser.parse_args()
opt.rgb = True