How to train with multiple videos in my custom dataset?

Hello, I’ve tried to train with my own dataset whose folder is as below: /dataset_folder/HR/video_num/*.png /dataset_folder/LR/X4/video_num/*.png

And I’ve organized them following the instructions in Data/datasets.yaml: Root: /home/user

Path: CUSTOM-TRAINHR[video]: dataset_folder/HR CUSTOM-TRAINLR[video]: dataset_folder/LR/X4

Dataset: CUSTOM[video]: train: hr: CUSTOM_TRAINHR lr: CUSTOM_TRAINLR my valset is the same format as organized above.

However, Traceback was thown when I tried the command: python train.py sofvsr --dataset custom --epochs 100 --cuda which is:

Traceback (most recent call last): File "train.py", line 99, in <module> main() File "train.py", line 93, in main t.fit([lt, lv], config) File "/home/zp/VideoSuperResolution-master/VSR/Backend/Torch/Framework/Trainer.py", line 110, in fit
memory_limit=mem) File "/home/zp/VideoSuperResolution-master/VSR/DataLoader/Loader.py", line 322, in make_one_shot_iterator raise fs.exception() File "/home/user/.conda/envs/zp/lib/python3.6/concurrent/futures/thread.py", line 56, in run result = self.fn(*self.args, **self.kwargs) File "/home/zp/VideoSuperResolution-master/VSR/DataLoader/Loader.py", line 393, in _prefecth_chunk self.cache['hr'].append(img.read_frame(img.frames)) File "/home/zp/VideoSuperResolution-master/VSR/DataLoader/VirtualFile.py", line 362, in read_frame image_bytes = [BytesIO(self.read()) for _ in range(frames)] File "/home/zp/VideoSuperResolution-master/VSR/DataLoader/VirtualFile.py", line 362, in <listcomp> image_bytes = [BytesIO(self.read()) for _ in range(frames)] File "/home/zp/VideoSuperResolution-master/VSR/DataLoader/VirtualFile.py", line 129, in read raise EOFError(f'End of File! {self.name}') EOFError: End of File! 068

It usually occurs after one epoch, but sometimes the epoch_num can also be 2 or 3. Besides, totally my trainset has 240 videos or 240*100=24000 frames. However, it can just read 200 batchs when my batch_size is set to 4.

I just wonder how to train with multiple videos as mentioned above? Cause when I just use one video folder containing 100 frames, everything is ok.

Thx.

[LoSealL]

@iPrayerr Affirmative. It’s a bug. To work around it, use --threads=1. It takes me some time to fix this 😦

[LoSealL]

Usually 2 ways to debug:

• Record the training image patches (through SummaryWriter/tensorboard, or just save on the disk). Check the training pair to see if they matched as desired.
• Check the hyper-parameters, especially the learning rate and batch size. The convergence may be very sensitive to them.
• Training on the pre-trained weights, it will be easier to converge.