[GitHub] [singa] joddiy edited a comment on issue #700: Expand the model zoo (example model set)

[GitBox <gi...@apache.org>]

joddiy edited a comment on issue #700:
URL: https://github.com/apache/singa/issues/700#issuecomment-642418693


   Thanks @agnesnatasya , I'll test the InceptionV2 again to check the error.
   
   And thanks you all for your contribution! @Shashankwer @agnesnatasya @Alvinnyk 
   
   For the next plan, I list some necessary operators we have to implement:
   
   ## Type 1
   Equal 1h
   Abs 1h
   Floor 1h
   
   ## Type 2
   Exp 1h
   Round 1h
   Pad 3h
   Expand 6h
   Upsample 6h
   
   ## Type 3
   Hardmax 6h
   RoiAlign 	6h
   ConvTranspose 6h
   NonMaxSuppression 12h
   Resize 12h
   
   ## Type 4
   InstanceNormalization c++
   ReduceMin c++
   
   Type 1 means the operators have been implemented in autograd.py, we only need to add it into the sonnx.py.
   Type 2 and Type 3 mean the operators don't exist in autograd.py, we need to implement them first. Type 2 is easier but Type 3 is harder.
   Type 4 need to modify the c++ code, we'll consider it later.
   Xh means the workload hours.
   
   So the plan is:
   1. You should read the code of soonx.py under [dev branch](https://github.com/apache/singa/tree/dev). Because we have updated the soonx to support the re-training, so it's a little different from the old version.
   2. You try to add an operator at the `Type 1`, you may add a key in the SingaBackend._rename_operators, and if you need a function to modify the operator, add a key in the SingaBackend._special_operators to indicate such a function.
   3. Try to add the test cases to test your new operator, the test cases list at [here](https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node), and you should check the code in test_onnx_backend.py. There are two patterns, the include pattern means the operators which do not in it will be excluded, and the exclude patterns will exclude the pattern in it.
   4. When you finish the Type 1, I guess you must have been familiar with the sonnx, so you can try the Type 2 and Type 3. These need you to add operators in `autograd.py`, you can read the code in it and add test cases in `test_operation.py`. In the `autograd.py`, you need to add a class with forward and backward functions. and you need to add a function to call the class's forward function. Don't forget to add test cases in `test_operation.py`. After you add the operator in the autograd, you can add it to the sonnx following the point 2 and 3.
   
   I know it's a little difficult at first. So I hope you don't be urgent to do, just read the code carefully first and if you have any questions, feel free to ask me.
   
   By the way, please add your code to dev branch. And please skip the frontend code in sonnx.py, only read the backend one. I'm working on the frontend to upgrade it.
   


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