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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 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.
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]
MaxPooling2D
Conv2D
BatchNormalization
LeakyReLU
Reshape
h2. [Arcface|https://arxiv.org/pdf/1801.07698.pdf]
Conv2D
BatchNormalization
relu
MaxPooling2D
Dropout
Flatten
Dense
Softmax
l2_normalize
acos
cos
h2. [BIDAF|https://arxiv.org/pdf/1611.01603.pdf]
K.stack
Softmax
K.expand_dims
K.sum
Constant
Dense
Lambda(lambda x: 1.0 - x, output_shape=(dim,))
Multiply
Add
K.concatenate
K.shape
K.max
K.tile
K.squeeze
linear
TimeDistributed
Bidirectional(LSTM
In summary, we already implemented 12 ops, and there still are 16 ops needed to
be implemented:
h2. Already implemented:
-LSTM-
-Multiply-
-Add-
-linear-
-relu-
-acos-
-cos-
-LeakyReLU-
-Softmax-
-MaxPooling2D-
-Conv2D-
-BatchNormalization-
h2. To be implemented:
Reshape
Flatten
Dropout
max
shape
concatenate
Constant
L2Normalization
Expand
tile
squeeze
Dense*
TimeDistributed*
Bidirectional*
Stack*
Lambda*
*means this op doesn't have a corresponding one at ONNX op sets, therefore, it
needs a converter function by using basic op sets.
> 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 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.
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