Converting a Caffe* Model

A summary of the steps for optimizing and deploying a model that was trained with Caffe*:

1. Configure the Model Optimizer for Caffe*.
2. Convert a Caffe* Model to produce an optimized Intermediate Representation (IR) of the model based on the trained network topology, weights, and biases values
3. Test the model in the Intermediate Representation format using the Inference Engine in the target environment via provided Inference Engine sample applications
4. Integrate the Inference Engine in your application to deploy the model in the target environment

## Supported Topologies

• Classification models:
• AlexNet
• VGG-16, VGG-19
• SqueezeNet v1.0, SqueezeNet v1.1
• ResNet-50, ResNet-101, Res-Net-152
• Inception v1, Inception v2, Inception v3, Inception v4
• CaffeNet
• MobileNet
• Squeeze-and-Excitation Networks: SE-BN-Inception, SE-Resnet-101, SE-ResNet-152, SE-ResNet-50, SE-ResNeXt-101, SE-ResNeXt-50
• ShuffleNet v2
• Object detection models:
• SSD300-VGG16, SSD500-VGG16
• Faster-RCNN
• Face detection models:
• VGG Face
• SSH: Single Stage Headless Face Detector
• Semantic segmentation models:
• FCN8

NOTE: It is necessary to specify mean and scale values for most of the Caffe* models to convert them with the Model Optimizer. The exact values should be determined separately for each model. For example, for Caffe* models trained on ImageNet, the mean values usually are 123.68, 116.779, 103.939 for blue, green and red channels respectively. The scale value is usually 127.5. Refer to Framework-agnostic parameters for the information on how to specify mean and scale values.

## Convert a Caffe* Model

To convert a Caffe* model:

1. Go to the <INSTALL_DIR>/deployment_tools/model_optimizer directory.
2. Use the mo.py script to simply convert a model with the path to the input model .caffemodel file:
python3 mo.py --input_model <INPUT_MODEL>.caffemodel

Two groups of parameters are available to convert your model:

### Using Caffe*-Specific Conversion Parameters

The following list provides the Caffe*-specific parameters.

Caffe*-specific parameters:
--input_proto INPUT_PROTO, -d INPUT_PROTO
Deploy-ready prototxt file that contains a topology
structure and layer attributes
--caffe_parser_path CAFFE_PARSER_PATH
Path to python Caffe parser generated from caffe.proto
-k K Path to CustomLayersMapping.xml to register custom
layers
--mean_file MEAN_FILE, -mf MEAN_FILE
Mean image to be used for the input. Should be a
binaryproto file
--mean_file_offsets MEAN_FILE_OFFSETS, -mo MEAN_FILE_OFFSETS
Mean image offsets to be used for the input
binaryproto file. When the mean image is bigger than
the expected input, it is cropped. By default, centers
of the input image and the mean image are the same and
the mean image is cropped by dimensions of the input
image. The format to pass this option is the
following: "-mo (x,y)". In this case, the mean file is
cropped by dimensions of the input image with offset
(x,y) from the upper left corner of the mean image
--disable_omitting_optional
Disable omitting optional attributes to be used for
custom layers. Use this option if you want to transfer
all attributes of a custom layer to IR. Default
behavior is to transfer the attributes with default
values and the attributes defined by the user to IR.
--enable_flattening_nested_params
Enable flattening optional params to be used for
custom layers. Use this option if you want to transfer
attributes of a custom layer to IR with flattened
nested parameters. Default behavior is to transfer the
attributes without flattening nested parameters.

#### Command-Line Interface (CLI) Examples Using Caffe*-Specific Parameters

• Launching the Model Optimizer for the bvlc_alexnet.caffemodel with a specified prototxt file. This is needed when the name of the Caffe* model and the .prototxt file are different or are placed in different directories. Otherwise, it is enough to provide only the path to the input model.caffemodel file.
python3 mo.py --input_model bvlc_alexnet.caffemodel --input_proto bvlc_alexnet.prototxt
• Launching the Model Optimizer for the bvlc_alexnet.caffemodel with a specified CustomLayersMapping file. This is the legacy method of quickly enabling model conversion if your model has custom layers. This requires system Caffe* on the computer. To read more about this, see Legacy Mode for Caffe* Custom Layers. Optional parameters without default values and not specified by the user in the .prototxt file are removed from the Intermediate Representation, and nested parameters are flattened:
python3 mo.py --input_model bvlc_alexnet.caffemodel -k CustomLayersMapping.xml --disable_omitting_optional --enable_flattening_nested_params
    This example shows a multi-input model with input layers: data, rois

layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape { dim: 1 dim: 3 dim: 224 dim: 224 }
}
}
layer {
name: "rois"
type: "Input"
top: "rois"
input_param {
shape { dim: 1 dim: 5 dim: 1 dim: 1 }
}
}
• Launching the Model Optimizer for a multi-input model with two inputs and providing a new shape for each input in the order they are passed to the Model Optimizer. In particular, for data, set the shape to 1,3,227,227. For rois, set the shape to 1,6,1,1:
python3 mo.py --input_model /path-to/your-model.caffemodel --input data,rois --input_shape (1,3,227,227),[1,6,1,1]

## Custom Layer Definition

Internally, when you run the Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list of known layers. If your topology contains any layers that are not in this list of known layers, the Model Optimizer classifies them as custom.

## Supported Caffe* Layers

Refer to Supported Framework Layers for the list of supported standard layers.