Comments (4)
Hi!
This baseline definitely needed some updating, I just added fixes in commit ded24bd and it's running now on 4 gpus.
self.orig_loss was just a legacy parameter that had been set to 1, so it could safely be removed.. it was historically to adjust for the difference between the original softmax loss and the new sigmoid loss.
This baseline includes my experiments with simplifying asynchronous temporal fields, and extending to multi-label sigmoid loss, and i3d base architecture etc. I hope it helps! Let me know if you have any questions.
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That's just due to how I extended Prec@1 and Prec@5 to work with multi-label ground truth. It's easy to add your own metrics under metrics/ and then just include them under --metrics in the config. My extension just counts all the are correct, either in top 1 or top 5. I just use if for analyzing training and over/underfitting, but then I use mAP for all proper evaluations.
This error is due to memory usage of the dataloading threads. The way multithreading works in pytorch/python is that it requires duplicating some of the data across the threads etc, and furthermore the images are queued into memory while they are waiting to be used, and the number of queued images is proportional to the number of workers (2x?). The easiest fix is to reduce the number of --workers. You can also try optimizing the dataloader by using torch.Tensors where possible (they aren't duplicated like lists of strings/numpy arrays/etc I believe).
If this error is happening at the start of the val_video phase you can try changing the number of workers in the val_video phase ( datasets/get.py ) either by just manually setting a number there or creating a new args parameter for it. This is because each dataloader is loading in much larger batch (whole video) in the val_video phase, and thus requires much more memory to store the queue of images.
Hope that helps!
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Thanks for your reply!
This code works very well now, just two small problem, as i use the pertained model, at the begin, the Prec@5 is often bigger than 100, like bellow
Train Epoch: [0][60/2660(2660)] Time 1.629 (2.227) Data 0.032 (0.119) Loss 0.0362 (0.0438) Prec@1 2.051 (47.684) Prec@5 168.718 (135.191)
Another question is
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
may be need to lower the memory_size or video_size?
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fixed, thanks a lot
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Related Issues (20)
- Multi-GPU error HOT 5
- The baseline about the nonlocal resnet50 HOT 2
- about bs of non-local resnet50 on Charades HOT 5
- [Feature Request] Use Videos directly instead of converting to frames HOT 3
- Question about 'process_ava.py' HOT 3
- Question about group_size in non_local_block3d.py HOT 2
- procedure of training AVA dataset HOT 1
- size mismatch error in i3d_ava.py HOT 1
- I3D AVA input full image? HOT 1
- Hyperparameters for ResNet-101-I3D model HOT 2
- Results for the nonlocal net on Charades dataset HOT 1
- an error when I run the TSN experiments on Charades HOT 1
- I3D pretrainded model cannot be loaded
- Hello,
- No exp folder HOT 4
- Frame-wise importance scores while downsampling a video
- Questions about charades_video_tsn.py HOT 2
- Add more baselines! HOT 4
- Add more datasets!
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