From 5c143edc0d18a1f641ccb3c59ebc7a362feb7f0d Mon Sep 17 00:00:00 2001 From: SunAhong1993 <48579383+SunAhong1993@users.noreply.github.com> Date: Thu, 11 Apr 2019 15:14:00 +0800 Subject: [PATCH] Update README_en.md --- caffe2fluid/README_en.md | 116 ++++++++++++++++++++++----------------- 1 file changed, 66 insertions(+), 50 deletions(-) diff --git a/caffe2fluid/README_en.md b/caffe2fluid/README_en.md index 8520342..8e648a2 100644 --- a/caffe2fluid/README_en.md +++ b/caffe2fluid/README_en.md @@ -1,51 +1,68 @@ -### Caffe2Fluid -This tool is used to convert a Caffe model to a Fluid model - -### Key Features -1. Convert caffe model to fluid model with codes of defining a network(useful for re-training) - -2. Pycaffe is not necessary when just want convert model without do caffe-inference - -3. Caffe's customized layers convertion also be supported by extending this tool - -4. A bunch of tools in `examples/imagenet/tools` are provided to compare the difference - -### HowTo -1. Prepare `caffepb.py` in `./proto` if your python has no `pycaffe` module, two options provided here: - - Generate pycaffe from caffe.proto - ``` - bash ./proto/compile.sh - ``` - - - Download one from github directly - ``` - cd proto/ && wget https://raw.githubusercontent.com/ethereon/caffe-tensorflow/master/kaffe/caffe/caffepb.py - ``` - -2. Convert the Caffe model to Fluid model - - Generate fluid code and weight file - ``` - python convert.py alexnet.prototxt \ - --caffemodel alexnet.caffemodel \ - --data-output-path alexnet.npy \ - --code-output-path alexnet.py - ``` - - - Save weights as fluid model file - ``` - # only infer the last layer's result - python alexnet.py alexnet.npy ./fluid - # infer these 2 layer's result - python alexnet.py alexnet.npy ./fluid fc8,prob - ``` - -3. Use the converted model to infer - - See more details in `examples/imagenet/tools/run.sh` - -4. Compare the inference results with caffe - - See more details in `examples/imagenet/tools/diff.sh` - -### How to convert custom layer +# caffe2fluid +[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) + +This tool is used to convert a Caffe model to a Fluid model. In the [[doc](doc/ReadMe.md)] directory, the common APIs of Caffe-PaddlePaddle are compared and analyzed. + +## Prerequisites + +> python >= 2.7 +> numpy +> protobuf >= 3.6.0 +> future + +**The running process of caffe2fluid only relies on above conditions.** +It is recommended to install the Caffe and PaddlePaddle in the environment for testing after converting the model. For environmental installation, please refer to [Installation Documentation](prepare.md) + +## HowTo + +### Model Conversion +1. Convert the Caffe's model to the PaddlePaddle's model code and parameter file (The parameters are saved as the form of numpy). + +``` +# --def_path : The path of Caffe's configuration file +# --caffemodel : The save path of Caffe's model file +# --data-output-path : The save path of the model after converting +# --code-output-path : The save path of the model code after converting +python convert.py --def_path alexnet.prototxt \ + --caffemodel alexnet.caffemodel \ + --data-output-path alexnet.npy \ + --code-output-path alexnet.py +``` + +2. The model network structure and parameters can be serialized as the model format supported by the PaddlePaddle framework. +``` +# --model-param-path ： The save path of PaddlePaddle's serialized model +python alexnet.py --npy_path alexnet.npy --model-param-path ./fluid_model +``` +Or you can specify the output of the saved model when saving. +``` +# The output of model is the fc8 layer and prob layer. +python alexnet.py --npy_path alexnet.npy --model-param-path ./fluid --need-layers-name fc8,prob +``` +Model loading and prediction can refer to the [official PaddlePaddle document](http://www.paddlepaddle.org/documentation/docs/en/1.3/api_guides/low_level/inference_en.html). + +### Comparison of differences before and after model conversion +After the model is converted, the difference between the converted model and the original model can be compared layer by layer (**the running environment depends on caffe and paddlepaddle**) +``` +# alexnet : The value of "name" in the Caffe's configuration file (.prototxt) +# ../../alexnet.prototxt : The path of Caffe's configuration file +# ../../alexnet.caffemodel : The save path of Caffe's model file +# ../../alexnet.py : The save path of the model after converting +# ../../alexnet.npy : The save path of the model code after converting +# ./data/65.jpeg : The path of image which is need to reference +cd examples/imagenet +bash tools/diff.sh alexnet ../../alexnet.prototxt \ + ../../alexnet.caffemodel \ + ../../alexnet.py \ + ../../alexnet.npy \ + ./data/65.jpeg +``` + + + +## How to convert custom layer +In the model conversion, when encounter an unsupported custom layer, users can add code to achieve a custom layer according to their needs. thus supporting the complete conversion of the model. The implementation is the following process. + 1. Implement your custom layer in a file under `kaffe/custom_layers`, eg: mylayer.py - Implement ```shape_func(input_shape, [other_caffe_params])``` to calculate the output shape - Implement ```layer_func(inputs, name, [other_caffe_params])``` to construct a fluid layer @@ -65,9 +82,8 @@ This tool is used to convert a Caffe model to a Fluid model export CAFFE2FLUID_CUSTOM_LAYERS=/path/to/caffe2fluid/kaffe ``` -6. Use the converted model when loading model in `xxxnet.py` and `xxxnet.npy`(no need if model is already in `fluid/model` and `fluid/params`) - ### Tested models +The caffe2fluid passed the test on the following model: - Lenet: [model addr](https://github.com/ethereon/caffe-tensorflow/blob/master/examples/mnist) -- GitLab