Update to support TensorFlow 1.2.0

[emedvedev]

TensorFlow API has been changed again, updating the code to reflect the recent changes.

[arpitkh96]

Old weights do not work anymore due to version change. Can you upload weights of new model if you have trained it. I don't have such powerful machine

[emedvedev]

I'm using another dataset to train the model, so can't help here, unfortunately, but my suggestion would be to use Google's ML Engine: https://cloud.google.com/ml-engine/. They give $300 in credits for trial run, which is more than enough for training (mine took around $20 with the BASIC_GPU instance, and the dataset was way bigger than the one in the example here).

[emedvedev]

Another suggestion, if you decide to go the Cloud ML route, would be converting your dataset into one large TFRecords file instead of thousands of individual small images. Otherwise I/O will become a very critical bottleneck for you.

Here's a gist on how to generate the TFRecords file: https://gist.github.com/emedvedev/dd056666337b54c13176da93d5b987b7
You'll also have to modify src/data_util/data_gen.py to read from this file though, so it might be too much work (I did have to make quite a lot of changes to the tooling around this model in my fork), but it does make training significantly faster.

[arpitkh96]

That can be done. Do you have any idea how much time will it require approx?(with BASIC_GPU)

[emedvedev]

A couple hours to a couple days depending on how comfortable you are with tensorflow. :) I'll update my fork today and document all the changes, so maybe you will be able to just use it without changing the code too much.

[arpitkh96]

i was talking about training time. Well if your work is going to save me time of data conversion coding, i am waiting desperately ;-)

[emedvedev]

@arpitkh96 I've moved my fork to https://github.com/emedvedev/attention-ocr and changed the interface quite a bit. It's also bundled into a package now.

Didn't have the time to update the README and clean up, but here's the brief instructions:

1. If you want CLI, clone the repo and run pip install . inside. Then you'll be able to use the aocr ... command. Otherwise you can do python -m aocr inside the repo dir instead.

2. For dataset generation, you'll need a .txt file with annotations in the format of image/path.png yourimagetext as described in README. To merge the files and the annotations into a .tfrecords file you can use aocr dataset:

aocr dataset datasets/annotations-training.txt datasets/training.tfrecords
aocr dataset datasets/annotations-training.txt datasets/testing.tfrecords

3. For training, use aocr train:

aocr train datasets/training.tfrecords

4. For testing, use aocr test:

aocr test datasets/testing.tfrecords

The fork is still a little screwed up (the logs don't make too much sense during the training stage, for instance), but the basics are working, and converting to tfrecords makes it much faster.