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Posted to dev@singa.apache.org by "ASF subversion and git services (Jira)" <ji...@apache.org> on 2019/10/18 02:07:00 UTC
[jira] [Commented] (SINGA-476) Autograd operators for ONNX
[ 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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