Converting a Caffe* Model¶

Convert a Caffe* Model¶

To convert a Caffe* model, run Model Optimizer with the path to the input model .caffemodel file:

mo --input_model <INPUT_MODEL>.caffemodel

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
[DEPRECATED] Mean image to be used for the input. Should be a
binaryproto file
--mean_file_offsets MEAN_FILE_OFFSETS, -mo MEAN_FILE_OFFSETS
[DEPRECATED] 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.

mo --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 the Caffe* system on the computer. 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:

mo --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 :

mo --input_model /path-to/your-model.caffemodel --input data,rois --input_shape (1,3,227,227),[1,6,1,1]

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.

The Model Optimizer provides explanatory messages if it is unable to run to completion due to issues like typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the Model Optimizer FAQ. The FAQ has instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.

Summary¶

In this document, you learned:

• Basic information about how the Model Optimizer works with Caffe* models

• Which Caffe* models are supported

• How to convert a trained Caffe* model using the Model Optimizer with both framework-agnostic and Caffe-specific command-line options