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From "zhangzhaoqi (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SINGA-476) Autograd operators for ONNX
Date Wed, 31 Jul 2019 08:00:00 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
 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 these are their components:
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 they'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
>
>
> 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.



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