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From "ASF subversion and git services (Jira)" <j...@apache.org>
Subject [jira] [Commented] (SINGA-476) Autograd operators for ONNX
Date Fri, 18 Oct 2019 02:07:00 GMT

    [ https://issues.apache.org/jira/browse/SINGA-476?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16954192#comment-16954192
] 

ASF subversion and git services commented on SINGA-476:
-------------------------------------------------------

Commit 0d1eaaac549e574d75a496eee3037ba91fc8f6b9 in incubator-singa's branch refs/heads/master
from Wei Wang
[ https://gitbox.apache.org/repos/asf?p=incubator-singa.git;h=0d1eaaa ]

Merge pull request #540 from dcslin/SoftMaxOnAxis

SINGA-476 added softmax with axis

> 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: 10m
>  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
>  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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