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WorkAround for ValueError: No ground-truth boxes available for one of the images during training
#836
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Following discussion [here](facebookresearch#167), this seemed the best solution
modified: setup.py
update to latest version
update to latest and small fix to empty targets
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May 29, 2019
| @@ -55,6 +55,10 @@ def do_train( | |||
| start_training_time = time.time() | |||
| end = time.time() | |||
| for iteration, (images, targets, _) in enumerate(data_loader, start_iter): | |||
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| if any(len(target) < 1 for target in targets): | |||
| logger.error(f"Iteration={iteration + 1} || Image Ids used for training {_} || targets Length={[len(target) for target in targets]}" ) | |||
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This is python-3 only.
But well, I suppose that with APEX we now have a hard-dependency on Python3 anyway
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Jun 28, 2020
…f the images during training` (facebookresearch#836) * Update setup.py * add a FORCE_CUDA flag Following discussion [here](facebookresearch#167), this seemed the best solution * Update Dockerfile * Update setup.py * add FORCE_CUDA as an ARG * modified: docker/Dockerfile modified: setup.py * small fix to readme of demo * remove test print * keep ARG_CUDA * remove env value and use the one from ARG * keep same formatting as source * change proposed by @miguelvr * Update INSTALL.md * Update trainer.py * Update trainer.py * Update trainer.py * Update trainer.py * Update trainer.py * Update setup.py
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as seen in #805 and #724
In some cases, the dataset is not complete or there are some errors in one of its elements, and it's a shame to just throw an error and stop the training for just one buggy element in a dataset.
this change is to log information about the error (to be able to check the dataset for the buggy element) and then continue the training