Convolutional Recurrent Neural Network + CTCLoss

I think i have fixed the ctcloss nan problem!

Now!

Please pull the latest code from master.

Please update the pytorch to >= v1.2.0

Enjoy it!

PS: Once there is ctclossnan, please

1. Change the batchSize to smaller (eg: 8, 16, 32)
2. Change the lr to smaller (eg: 0.00001, 0.0001)

Dependence

• CentOS7
• Python3.6.5
• torch==1.2.0
• torchvision==0.4.0
• Tesla P40 - Nvidia

Run demo

• Run demo

  python demo.py -m path/to/model -i data/demo.jpg

Feature

• Variable length

  It support variable length.

• Change CTCLoss from warp-ctc to torch.nn.CTCLoss

  As we know, warp-ctc need to compile and it seems that it only support PyTorch 0.4. But PyTorch support CTCLoss itself, so i change the loss function to torch.nn.CTCLoss .

• Solved PyTorch CTCLoss become nan after several epoch

  Just don't know why, but when i train the net, the loss always become nan after several epoch.

  I add a param dealwith_lossnan to params.py . If set it to True , the net will autocheck and replace all nan/inf in gradients to zero.

• DataParallel

  I add a param multi_gpu to params.py . If you want to use multi gpu to train your net, please set it to True and set the param ngpu to a proper number.

Train your data

Prepare data

The data-loader expects the IAM dataset (or any other dataset that is compatible with it) in the data/ directory. Follow these instructions to get the dataset:

1. Register for free at this website.

2. Download words/words.tgz.

3. Download ascii/words.txt.

4. Put words.txt into the data/ directory.

5. Create the directory data/words/.

6. Put the content (directories a01, a02, ...) of words.tgz into data/words/.

7. Go to data/ and run python checkDirs.py for a rough check if everything is ok.

Change parameters and alphabets

Parameters and alphabets can't always be the same in different situation.

• Change parameters

  Your can see the params.py in detail.

• Change alphabets

  Please put all the alphabets appeared in your labels to alphabets.py , or the program will throw error during training process.

Train

Run train.py by

python train.py --trainroot path/to/train/dataset --valroot path/to/val/dataset

Reference

meijieru/crnn.pytorch

Sierkinhane/crnn_chinese_characters_rec