[
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 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
>
>
> 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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