[
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-
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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