Comments (7)
Another question, refer to the paper for recommended input shapes, it's [b, t, h, w, c], but i have checked your input shapes when using HMDB51 datasets, it's [b, c, t, h, w]. why didn't you use the same input shapes?
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Dear @erwangccc i think u reapeated a basic mistake as i did it too. Never ever save torch model like save(model,/.pth) or like that
do torch.save(model.state_dict(),/.pth)
then see it in netron again .I wasted one month literally llike this. If u dont see difference let me know
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Hi, @papasanimohansrinivas Thanks for your reply.
I've tried this way you mentioned before and just weights are displayed. But it's not an interconnected model. I think we can look through the model via tensorborad.
I see you've used movites in your case, well done. I have just add training phase to this repo and want to do inference frame-by-frame via model trained by this repo, do u have some tips and inference code to share, i'll appreciate it!
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@erwangccc sorry ! being late to reply ,caught up with my project .
Ok I have combined pytorchvideo framework ucf101 dataloader and the evaluate and training functions used in this repos jupyter notebook to train my own model , inferences are good for my very small dataset
I could have shared the code but my partner insists code repo of my project to be private etc
And write your own custom video sampler to suit your needs , just that
Wish u good luck
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Hi, @papasanimohansrinivas thanks for your reply.
And don't misunderstand, i just want to know how to do inference correctly.
Did you inference frame-by- frame based on evaluation code?
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Hi @erwangccc no I do use movinet a5 base model and I accumulate all frames from a video and use uniformtemporalsubsample function from pytorchvideo to pass it to ucf101 class arguments for choosing nframes of video
I do get where u are coming from ,No issues
Below these are the functions to transform videos
ApplyTransformToKey, ShortSideScale, UniformTemporalSubsample
Just go through ucf101 class and see what are the requirements of it and I would fill in the gaps for anyone , I myself had faced same issues
Besides I am trying to write code for movinets a3,a4,a5 streams models by extending this repo .
Any tips from anyone is welcome , as I am super new to this
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OK, i see.
Besides I am trying to write code for movinets a3,a4,a5 streams models by extending this repo .
This means you want to train your data based on a3-5? If so, i think u can refer to implementation details from paper.
Hope it can help u.
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Related Issues (20)
- Using MoViNet in a dataset with variable-length videos HOT 1
- why don't you use 'T.Normalize' when you train HMDB51? HOT 1
- Tips for Implementing a3 ,a4,a5 movinets streaming version HOT 3
- Test model based on 'evaluate_stream' is ok, but do inference frame by frame is very different? HOT 2
- Validation Loss did not decrease in the HMDB51 notebook? HOT 9
- Modifying for binary classification HOT 2
- Kinetics 400 models HOT 1
- Very low validation accuracy with pretrained models! HOT 1
- F.ToFloatTensorInZeroOne not exist HOT 2
- 。
- There seems no implementation of positional_encoding HOT 4
- How can we access the stream buffer? HOT 1
- need to process HMDB51 dataset?
- got wrong results during test
- weight
- The parameters that trained on Charades.
- Kinetics400/600
- Training
- Training on the custom dataset HOT 1
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