Minor Logging Correction in `validate` Function

[PellelNitram]

Hi Harald,

First off, let me say this is a fantastic project! I currently reimplement WordDetectorNN for a hobby project of mine (Xournal++ HTR, see corresponding feature branch) and I love this project of yours and your work in general!

While understanding and reimplementing the code, I noticed a small area for improvement in the validate function within src/train.py. The scalar validation metrics (val_loss, val_f1, etc.) are currently logged to TensorBoard on every iteration of the image loop. This causes the same metric values to be written multiple times for a single validation step. I suspect this is an indentation mistake?

Current Code in src/train.py:

def validate(net, loader, writer):
    # ... (evaluation logic) ...
    res = evaluate(net, loader, max_aabbs=1000)

    for i, (img, aabbs) in enumerate(zip(res.batch_imgs, res.batch_aabbs)):
        vis = visualize(img, aabbs)
        writer.add_image(f'img{i}', vis.transpose((2, 0, 1)), global_step)
        # These lines are logged for every image in the batch
        writer.add_scalar('val_loss', res.loss, global_step)
        writer.add_scalar('val_recall', res.metrics.recall(), global_step)
        # ... and so on

Suggested Fix:

Moving the scalar logging outside of the loop would make the TensorBoard logs cleaner.

def validate(net, loader, writer):
    # ... (evaluation logic) ...
    res = evaluate(net, loader, max_aabbs=1000)

    # Log image visualizations
    for i, (img, aabbs) in enumerate(zip(res.batch_imgs, res.batch_aabbs)):
        vis = visualize(img, aabbs)
        writer.add_image(f'img{i}', vis.transpose((2, 0, 1)), global_step)
    
    # Log scalar metrics once per validation run
    writer.add_scalar('val_loss', res.loss, global_step)
    writer.add_scalar('val_recall', res.metrics.recall(), global_step)
    # ... and so on

It's a minor point, but I hope this feedback is helpful. If you agree with the change, I would be happy to create a small pull request to address it.

Thanks again for all your work & cheers! :-)

[githubharald]

good catch, Martin! And yes, a PR would be great 👍