Legacy Mode for Caffe* Custom Layers

NOTE: This functionality is deprecated and will be removed in the future releases.

Model Optimizer can register custom layers in a way that the output shape is calculated by the Caffe* framework installed on your system. This approach has several limitations:

• If your layer output shape depends on dynamic parameters, input data or previous layers parameters, calculation of output shape of the layer via Caffe can be incorrect. For example, SimplerNMS is filtering out bounding boxes that do not satisfy the condition. Internally, Caffe fallback forwards the whole net without any meaningful data - just some noise. It is natural to get only one bounding box (0,0,0,0) instead of expected number (for example, 15). There is an option to patch Caffe accordingly, however, it makes success of Intermediate Representation generation on the patched Caffe on the particular machine. To keep the solution independent from Caffe, we recommend to use extensions mechanism for such layers described in the Model Optimizer Extensibility.
• It is not possible to produce Intermediate Representation on a machine that does not have Caffe installed.

NOTE: Caffe Python* API has an issue when layer name does not correspond to the name of its top. The fix was implemented on BVLC Caffe*. The Caffe framework on your computer must contain this fix. Otherwise, Caffe framework can unexpectedly fail during the fallback procedure.

NOTE: The Caffe fallback feature was validated against this GitHub revision. You may have issues with forks or later Caffe framework versions.

1. Create a file CustomLayersMapping.xml:

   mv extensions/front/caffe/CustomLayersMapping.xml.example extensions/front/caffe/CustomLayersMapping.xml

2. Add (register) custom layers to CustomLayersMapping.xml:

   <CustomLayer NativeType="${Type}" hasParam="${has_params}" protoParamName="${layer_param}"/>

Where:

• ${Type} is a type of the layer in the Caffe
• ${has_params} is "true" if the layer has parameters, and is "false" otherwise
• ${layer_param} is a name of the layer parameters in caffe.proto if the layer has it

Example:

1. Proposal layer has parameters, and they appear in the Intermediate Representation. The parameters are stored in the proposal_param property of the layer:

   <CustomLayer NativeType="Proposal" hasParam ="true" protoParamName = "proposal_param"/> 

2. CustomLayer layer has no parameters:

   <CustomLayer NativeType="CustomLayer" hasParam ="false"/>

Building Caffe*

1. Build Caffe* with Python* 3.5:

   export CAFFE_HOME=PATH_TO_CAFFE
   cd $CAFFE_HOME
   rm -rf  ./build
   mkdir ./build
   cd ./build
   cmake -DCPU_ONLY=ON -DOpenCV_DIR=<your opencv install dir> -DPYTHON_EXECUTABLE=/usr/bin/python3.5 ..
   make all # also builds pycaffe
   make install
   make runtest # optional

2. Add Caffe Python directory to PYTHONPATH to let it be imported from the Python program:

   export PYTHONPATH=$CAFFE_HOME/python;$PYTHONPATH

3. Check the Caffe installation:

   python3
   import caffe

If Caffe was installed correctly, the caffe module is imported without errors.