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.
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
## Prerequisites
3. Caffe's customized layers convertion also be supported by extending this tool
> python >= 2.7
> numpy
> protobuf >= 3.6.0
> future
4. A bunch of tools in `examples/imagenet/tools` are provided to compare the difference
**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
## 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
### 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).
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
# --def_path : The path of Caffe's configuration file
```
# --caffemodel : The save path of Caffe's model file
python convert.py alexnet.prototxt \
# --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 \
--caffemodel alexnet.caffemodel \
--data-output-path alexnet.npy \
--data-output-path alexnet.npy \
--code-output-path alexnet.py
--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
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
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.
- 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
1. Implement your custom layer in a file under `kaffe/custom_layers`, eg: mylayer.py
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 ```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
- Implement ```layer_func(inputs, name, [other_caffe_params])``` to construct a fluid layer
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