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From "zhangzhaoqi (Jira)" <j...@apache.org>
Subject [jira] [Resolved] (SINGA-476) Autograd operators for ONNX
Date Mon, 04 May 2020 06:56:00 GMT

     [ https://issues.apache.org/jira/browse/SINGA-476?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

zhangzhaoqi resolved SINGA-476.
-------------------------------
    Resolution: Implemented

> Autograd operators for ONNX
> ---------------------------
>
>                 Key: SINGA-476
>                 URL: https://issues.apache.org/jira/browse/SINGA-476
>             Project: Singa
>          Issue Type: New Feature
>            Reporter: zhangzhaoqi
>            Priority: Major
>         Attachments: arcface(based resnet100).png, bidaf.png, tiny_yolov2.png
>
>          Time Spent: 1h 20m
>  Remaining Estimate: 0h
>
> For the demo purpose, we need to implement these three models and their components as
following:
> h2. [Tiny yolov2|https://arxiv.org/pdf/1612.08242.pdf]
> -Add-
>  -BatchNormalization-
>  -Conv-
>  -LeakyRelu-
>  -MaxPool-
>  -Mul-
> h2. [Arcface|https://arxiv.org/pdf/1801.07698.pdf]
> -Acos-
>  -Add-
>  -BatchNormalization-
>  -Conv-
>  -Cos-
>  -Dropout-
>  -Flatten-
>  -Gemm-
>  -Identity-
>  InstanceNormalization
>  LpNormalization
>  -Mul-
>  -PRelu-
>  -Reshape-
>  -Sub-
> h2. [BIDAF|https://arxiv.org/pdf/1611.01603.pdf]
> Abs
>  -Add-
>  ArgMax
>  Cast
>  Ceil
>  -Clip-
>  Compress
>  -Concat-
>  ConstantOfShape
>  -Conv-
>  -Dropout-
>  Gather
>  Hardmax
>  Log
>  LSTM
>  -MatMul-
>  ReduceMax
>  ReduceSum
>  Relu
>  Shape
>  Sigmoid
>  Slice
>  Squeeze
>  Sub
>  Sum
>  Transpose
>  Unsqueeze
>  
> In summary, we already implemented 13 ops, and there're still 27 ops needed to be implemented:
> h2. Already implemented:
> -Acos-
>  -BatchNormalization-
>  -Cos-
>  -Conv-
>  -LeakyRelu-
>  -LSTM-
>  -Abs-
>  -MaxPool-
>  -Flatten-
>  -Add-
>  -MatMul-
>  -Relu-
>  -Sigmoid-
> h2. To be implemented:
> ArgMax
>  Cast
>  Ceil
>  -Clip-
>  Compress
>  -Concat-
>  ConstantOfShape
>  -Dropout-
>  Gather
>  -Gemm-
>  Hardmax
>  -Identity-
>  InstanceNormalization
>  -Log-
>  LpNormalization
>  -Mul-
>  -PRelu-
>  ReduceMax
>  ReduceSum
>  -Reshape-
>  -Shape-
>  Slice
>  Squeeze
>  -Sub-
>  -Sum-
>  -Transpose-
>  Unsqueeze
> Please refer to the [ONNX Operator Schemas|https://github.com/onnx/onnx/blob/master/docs/Operators.md] for
more detailed information.



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