[
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[link title|https://arxiv.org/pdf/1612.08242.pdf]
MaxPooling2D
Conv2D
BatchNormalization
LeakyReLU
Reshape
h2. Arcface[link title|https://arxiv.org/abs/1801.07698]
Conv2D
BatchNormalization
relu
MaxPooling2D
Dropout
Flatten
Dense
Softmax
l2_normalize
acos
cos
h2. BIDAF[link title|https://arxiv.org/pdf/1611.01603]
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.
was:
For the demo purpose, we need to implement these three models, and these are
their components:
h2. Tiny yolov2[link title|https://arxiv.org/pdf/1612.08242.pdf]
MaxPooling2D
Conv2D
BatchNormalization
LeakyReLU
Reshape
h2. Arcface[link title|https://arxiv.org/abs/1801.07698]
Conv2D
BatchNormalization
relu
MaxPooling2D
Dropout
Flatten
Dense
Softmax
l2_normalize
acos
cos
h2. BIDAF[link title|https://arxiv.org/pdf/1611.01603]
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
h2. In summary,
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
>
> For the demo purpose, we need to implement these three models, and these are
> their components:
> h2. Tiny yolov2[link title|https://arxiv.org/pdf/1612.08242.pdf]
> MaxPooling2D
> Conv2D
> BatchNormalization
> LeakyReLU
> Reshape
> h2. Arcface[link title|https://arxiv.org/abs/1801.07698]
> Conv2D
> BatchNormalization
> relu
> MaxPooling2D
> Dropout
> Flatten
> Dense
> Softmax
> l2_normalize
> acos
> cos
> h2. BIDAF[link title|https://arxiv.org/pdf/1611.01603]
> 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.
>
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