From 47668644812f5826af1324b8961512420183e163 Mon Sep 17 00:00:00 2001 From: Varun Arora Date: Mon, 23 Apr 2018 13:43:43 -0700 Subject: [PATCH] Minor fixes based on @kuke's feedback --- doc/fluid/design/onnx/onnx_convertor.md | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/doc/fluid/design/onnx/onnx_convertor.md b/doc/fluid/design/onnx/onnx_convertor.md index 275d79753..bc1665d7c 100644 --- a/doc/fluid/design/onnx/onnx_convertor.md +++ b/doc/fluid/design/onnx/onnx_convertor.md @@ -4,8 +4,6 @@ Therefore, it is necessary to enable the conversion between PaddlePaddle and ONNX. This design doc is aimed at implementing a convertor, mainly for converting between **Fluid** models and ONNX (it is very likely that we may support older v2 models in the future). A complete convertor should be bidirectional - with a frontend AND a backend, but considering the importance, the we will start with the frontend i.e. Fluid models to ONNX models. -One thing that makes it doable in Fluid's case is the use of a static IR - the `ProgramDesc` - as opposed to a dynamic graph, as created in the cases of frameworks like PyTorch. - # How it works @@ -24,6 +22,8 @@ Here are a few major considerations when it comes to converting models: 2. Checking to see if the generated nodes on the graph are validated by the internal ONNX checkers. - **Versioning**: ONNX versions its op listing over versions. In fact, it has versioning on 3 different levels: ops, graphs, and ONNX models. This requires that we are conscious about versioning the convertor and updating tests and op convertor logic for each release. It also implies that we release pre-trained ONNX models upon each version release. +One thing that makes this conversion more feasible in Fluid's case is the use of a static IR - the `ProgramDesc` - as opposed to a dynamic graph, as created in the cases of frameworks like PyTorch. + # Project structure @@ -45,11 +45,17 @@ The project contains four important parts: # Usage The converter should be designed to very easy-to-use. Bidirectional conversion between a Fluid inference model and an ONNX binary model will be supported. Model validation will also provided to verify the correctness of converted model. -* Fluid inference model to ONNX binary model +* Convert Fluid inference model to ONNX binary model + + ``` + python convert.py --fluid_model --onnx_model validate True + ``` + +* Validate the converted model -``` -python convert.py --fluid_model --onnx_model validate True -``` + ``` + python validate.py --fluid_model --onnx_model + ``` The conversion and model validation will be completed consecutively, finally output a readable model structure description. And for the converse conversion, users only need to exchange the input and output. -- GitLab