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From "zhangzhaoqi (Jira)" <j...@apache.org>
Subject [jira] [Updated] (SINGA-476) Autograd operators for ONNX
Date Thu, 12 Dec 2019 10:12:00 GMT

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

zhangzhaoqi updated SINGA-476:
------------------------------
    Description: 
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.

  was:
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
 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.


> 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: Critical
>         Attachments: arcface(based resnet100).png, bidaf.png, tiny_yolov2.png
>
>          Time Spent: 1h
>  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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