Comments (8)
Here is what I did: https://github.com/TengdaHan/CoCLR/blob/main/dataset/convert_video_to_lmdb.py
But it's not essential to use lmdb, you can read frames like https://github.com/TengdaHan/DPC/blob/master/dpc/dataset_3d.py,
or read videos online like https://github.com/pytorch/vision/blob/master/examples/python/video_api.ipynb
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Thx for your apply, I am curious which way is the fastest way to load images? Have your compared their time consumption?
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I use lmdb if video dataset is huge, I ready from video frames if the dataset is small.
In terms of speed, lmdb is similar to loading frames, except it saves disk I/O a lot.
I've set up these years ago so I didn't change much since then.
But I heard recently the video reader (something like https://pytorch.org/docs/stable/torchvision/io.html or https://github.com/pytorch/vision/blob/master/examples/python/video_api.ipynb) is getting stable and fast, I suggest you first try this.
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Hi ~! Finally successully run main_nce on k400 with frames only, which tooks ~100 epochs to reach ~80% top5 in training dataset. Is there any code to generate optmial flows ? It seens that I haven't found them neither in this repository nor DPC. Thx ~
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Hi, https://github.com/TengdaHan/MemDPC/blob/master/process_data/src/extract_ff.py here it extracts both frame and flow, if you have frames, you can modify it to only load frames and extract flow. -- In practice, I further split 400 classes into 20 groups, and it could take a week for extracting using about 20 CPU nodes in parallel.
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Sure~ I try the code, which indeed costs lots of time while optical flow generated really slow. Can you share your optical flow ? Or any suggestion to speed up the generation of optical flows ? Thx ~
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Sharing optical flow for Kinetics seems not possible right now, it's too big.
I am trying to host the optical flow for UCF101 somewhere, hope it helps? -- I will update my progress here.
To speed up optical flow computation, there are some GPU TVL1 optical flow extraction examples:
You need to compile OpenCV with GPU support, then compile their C++ function. It would definitely speed up the extraction.
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Optical flow for UCF101 (tar file, 20.5GB) is shared here: http://www.robots.ox.ac.uk/~htd/tar/ucf101_flow_lmdb.tar
Optical flow for Kinetics400 is too big to share. I will write a GPU-TVL1 flow extraction pipeline when I have time.
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Related Issues (20)
- 2-Stream evaluation on UCF101 HOT 1
- Possible to share S3D model trained on UCF101 -RGB only end to end fine tuned (reported accuracy 81.4) HOT 1
- Making Kinetics-400 RGB lmdb dataset. HOT 2
- Command for Linear Probe and Finetuning HOT 1
- How to generate corresponding 'video_source.json'? HOT 1
- AttributeError: 'str' object has no attribute 'decode' HOT 2
- information about requirements and dateset preparations HOT 1
- lmdb_dataset.py txn.get(self.get_video_id[name].encode('ascii'))) HOT 6
- Issue about DistributedDataParallel(DDP) HOT 3
- Reproducing CoCLR RGB Results??? HOT 1
- fusing two-stream predictions HOT 1
- Question about the code in eval/main_classifier.py HOT 2
- question about ten crop
- Can we design a downstream task for action detection? HOT 5
- Two-stream feature
- TypeError: order must be str, not int HOT 2
- main_classifier.py: error: unrecognized arguments: --final_bn
- I can't download the pretrained model HOT 1
- A question about convert_video_to_lmdb.py HOT 2
- Download Link of Checkpoint HOT 2
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