### Caffe2Fluid
This tool is used to convert a Caffe model to a Fluid model

### 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://github.com/ethereon/caffe-tensorflow/blob/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
        ```
        python alexnet.py alexnet.npy ./fluid #only infer the last layer's result
        python alexnet.py alexnet.npy ./fluid fc8,prob #infer these 2 layer's result
      ```

3. Use the converted model to infer
    - See more details in '*examples/imagenet/run.sh*'

4. Compare the inference results with caffe
    - See more details in '*examples/imagenet/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](https://github.com/ethereon/caffe-tensorflow/blob/master/examples/mnist)

- ResNets:(ResNet-50, ResNet-101, ResNet-152)
[model addr](https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777)

- GoogleNet:
[model addr](https://gist.github.com/jimmie33/7ea9f8ac0da259866b854460f4526034)

- VGG:
[model addr](https://gist.github.com/ksimonyan/211839e770f7b538e2d8)

- AlexNet:
[model addr](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet)

### Notes
Some of this code come from here: [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow)