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mit-han-lab / temporal-shift-module

[ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
https://arxiv.org/abs/1811.08383
MIT License
2.07k stars 417 forks source link

for online version, onnx seems not support some OP in forward( ): #189

Open chongyangwang-song opened 3 years ago

chongyangwang-song commented 3 years ago

When transform torch model to onnx model: for this line of code:x1, x2 = x[:,:,c//8],x[:,c//8:] TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! I think we can't ignore this warning

Junan007 commented 3 years ago

I have the same problem, but when i run online_demo/main.py to test, the result is correct.so it can ignore? I am doubtful about that.

chongyangwang-song commented 3 years ago

@Junan007 note the warning:This means that the trace might not generalize to other inputs! Maybe we should replace this line of code,using other operation to implement it.

chongyangwang-song commented 3 years ago

@Junan007 you can check this issue https://github.com/mit-han-lab/temporal-shift-module/issues/181#issuecomment-818571464,similar to our problem. If you have any idea, please tell me, thanks

chongyangwang-song commented 3 years ago

@Junan007 The result is correct?! Maybe onnx-simplifier make it works?

chongyangwang-song commented 3 years ago

@Junan007 I reproduce the online work, but it shows 1.7vid/s, Not only me show this slow speed. Can you accelerate using tvm?

Junan007 commented 3 years ago

I try another implementation of shift module from here but it has the same warning too.

chongyangwang-song commented 3 years ago

@the online version is not the same as offline

chongyangwang-song commented 3 years ago

@Junan007 the online version is not the same as offline

Junan007 commented 3 years ago

@Junan007 I reproduce the online work, but it shows 1.7vid/s, Not only me show this slow speed. Can you accelerate using tvm?

Yes, i test on the cpu(2.8G Quad-Core Intel i7), only change target to "llvm", it shows 45~75vid/s when running.

chongyangwang-song commented 3 years ago

@Junan007 I test it on NVIDIA TX2. I follow the official steps, but it shows 1.7vid/s.Do you know the reason?

chongyangwang-song commented 3 years ago

@Junan007 I remove onnx and tvm ,then use torch to inference directly,it show 20 vid/s. This problem make me doubtful several months

Junan007 commented 3 years ago

@Junan007 I test it on NVIDIA TX2. I follow the official steps, but it shows 1.7vid/s.Do you know the reason?

sorry, I don't have NVIDIA TX2, do you compile tvm with cuda?

chongyangwang-song commented 3 years ago

@Junan007 I just follow the official steps to install tvm:

sudo apt install llvm # install llvm which is required by tvm git clone -b v0.6 https://github.com/apache/incubator-tvm.git cd incubator-tvm git submodule update --init mkdir build cp cmake/config.cmake build/ cd build

[

edit config.cmake to change

32 line: USE_CUDA OFF -> USE_CUDA ON

104 line: USE_LLVM OFF -> USE_LLVM ON

]

cmake .. make -j4 cd .. cd python; sudo python3 setup.py install; cd .. cd topi/python; sudo python3 setup.py install; cd ../..

Have it is compiled with cuda?

Junan007 commented 3 years ago

you can use tvm.runtime.enabled('cuda') to check it.

chongyangwang-song commented 3 years ago

you can use tvm.runtime.enabled('cuda') to check it.

thank you for your reply

chongyangwang-song commented 3 years ago

@Junan007 I test the onnx model with several inputs, the outputs are equals to torch outputs, So, We can ignore this warning?

Junan007 commented 3 years ago

yes, maybe it can ignored, and i test on a jetson nano use tvm, only 0.7 vid/s, did you solve your problem?

Junan007 commented 3 years ago

I'm sure that it has used gpu resources. it can 17.5 Vid/s when use llvm only, I don't know why it's so slow when use cuda.

Junan007 commented 3 years ago

after fixed tophub error, it can reach 27.2 Vid/s when use cuda. i think i solve my problem.

chongyangwang-song commented 3 years ago

after fixed tophub error, it can reach 27.2 Vid/s when use cuda. i think i solve my problem.

Cool, But what's the tophub error?

Junan007 commented 3 years ago

tophub is a part of tvm, it will be auto download when compile module, and save to ~/.tvm/tophub, but it failed in my environment.

chongyangwang-song commented 3 years ago

tophub is a part of tvm, it will be auto download when compile module, and save to ~/.tvm/tophub, but it failed in my environment.

You are excellent！It will be great if I know about you early。Now I am trying to use tensorRT to accelerate it。 I have another question about this online model， The train for online version is same to offline version？ I trained online model using offline version code（since they only provide this code），and I test it using online version， The result seems to be right（I don't test it with a large of inputs , just test it with several sample）, I want to know, It is right in theory?

Junan007 commented 3 years ago

Yes, the train for online is same to offline.but it is different on test, online version need caching the last features to shift.

chongyangwang-song commented 3 years ago

tophub is a part of tvm, it will be auto download when compile module, and save to ~/.tvm/tophub, but it failed in my environment.

@Junan007 Hi，tophub will be auto download, but I'am a Chinese student, there are so many websites and urls which I can't visit, everytime I compile the model, tophub package can't be downloaded, Is there any way to solve this problem? thanks

Junan007 commented 3 years ago

you can get it from here: https://github.com/tlc-pack/tophub/tree/main/tophub, and then copy to ~/.tvm/tophub

chongyangwang-song commented 3 years ago

many thanks

------------------ 原始邮件 ------------------ 发件人: @.>; 发送时间: 2021年7月20日(星期二) 下午4:49 收件人: @.>; 抄送: @.>; @.>; 主题: Re: [mit-han-lab/temporal-shift-module] for online version, onnx seems not support some OP in forward( ): (#189)

you can get it from here: https://github.com/tlc-pack/tophub/tree/main/tophub, and then copy to ~/.tvm/tophub

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