UnboundLocalError: local variable 'l1_loss' referenced before assignment in Doctr's compute_loss function

[ep0p]

Bug description

While training a Doctr model with my own dataset, I encountered an UnboundLocalError in the compute_loss function of the differentiable_binarization module.

Code snippet to reproduce the bug

python references/detection/train_pytorch.py ../dataset_detection/train ../dataset_detection/val db_resnet50 --epochs 5 --device 0

Error traceback

Train set loaded in 63.19s (11871 samples in 5935 batches)
Training loss: 1.21584:   2%|█▊                                                                                  | 131/5935 [01:24<1:02:43,  1.54it/s]
Traceback (most recent call last):                                                                               | 131/5935 [01:24<1:02:04,  1.56it/s]
  File "/home/epop/dataset/doctr/references/detection/train_pytorch.py", line 481, in <module>
    main(args)
  File "/home/epop/dataset/doctr/references/detection/train_pytorch.py", line 388, in main
    fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, amp=args.amp)
  File "/home/epop/dataset/doctr/references/detection/train_pytorch.py", line 126, in fit_one_epoch
    train_loss = model(images, targets)["loss"]
  File "/home/epop/anaconda3/envs/ds3.9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/epop/anaconda3/envs/ds3.9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/epop/dataset/doctr/doctr/models/detection/differentiable_binarization/pytorch.py", line 216, in forward
    loss = self.compute_loss(logits, thresh_map, target)
  File "/home/epop/dataset/doctr/doctr/models/detection/differentiable_binarization/pytorch.py", line 286, in compute_loss
    return l1_loss + focal_scale * focal_loss + dice_loss
UnboundLocalError: local variable 'l1_loss' referenced before assignment

Environment

Collecting environment information...

DocTR version: 0.9.0a0
TensorFlow version: N/A
PyTorch version: 2.3.1+cu121 (torchvision 0.18.1+cu121)
OpenCV version: 4.10.0
OS: Ubuntu 22.04.4 LTS
Python version: 3.9.19
Is CUDA available (TensorFlow): N/A
Is CUDA available (PyTorch): Yes
CUDA runtime version: 11.5.119
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 555.42.02
cuDNN version: Could not collect

Deep Learning backend

>>> from doctr.file_utils import is_tf_available, is_torch_available
>>> print(f"is_tf_available: {is_tf_available()}")
is_tf_available: False
>>> print(f"is_torch_available: {is_torch_available()}")
is_torch_available: True

[felixdittrich92]

Hey @ep0p 👋,

This will only happen if we couldn't build the target mask/s
reasons for this:

• annotations (coordinates) are wrong
• a sample in the batch has no annotations
• all annotations of an sample was filtered because the are to small (less than 3x3 pixels)

[ep0p]

Hi @felixdittrich92,

I am in the process of checking my dataset but it will take a while, i'll come back to you as soon as i find the issue.