1. Convert caffe model to fluid model with codes of defining a network(useful for re-training)
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.
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
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
```
## HowTo
- Download one from github directly
```
cd proto/ && wget https://raw.githubusercontent.com/ethereon/caffe-tensorflow/master/kaffe/caffe/caffepb.py
```
### 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).
2. Convert the Caffe model to Fluid model
- Generate fluid code and weight file
```
python convert.py alexnet.prototxt \
```
# --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
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
- 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
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@@ -65,9 +82,8 @@ This tool is used to convert a Caffe model to a Fluid model
