caffe2fluid

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

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
   • Register these two functions register(kind='MyType', shape=shape_func, layer=layer_func)
   • Notes: more examples can be found in kaffe/custom_layers

2. Add import mylayer to kaffe/custom_layers/\_\_init__.py

3. Prepare your pycaffe as your customized version(same as previous env prepare)

   • (option1) replace proto/caffe.proto with your own caffe.proto and compile it
   • (option2) change your pycaffe to the customized version

4. Convert the Caffe model to Fluid model

5. Set env $CAFFE2FLUID_CUSTOM_LAYERS to the parent directory of 'custom_layers'

   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

• Lenet: model addr

• ResNets:(ResNet-50, ResNet-101, ResNet-152) model addr

• GoogleNet: model addr

• VGG: model addr

• AlexNet: model addr

Notes

Some of this code come from here: caffe-tensorflow