[LEGACY] Convert Models Represented as Python Objects


The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Model Preparation article.

Model conversion API is represented by convert_model() method in openvino.tools.mo namespace. convert_model() is compatible with types from openvino.runtime, like PartialShape, Layout, Type, etc.

convert_model() has the ability available from the command-line tool, plus the ability to pass Python model objects, such as a PyTorch model or TensorFlow Keras model directly, without saving them into files and without leaving the training environment (Jupyter Notebook or training scripts). In addition to input models consumed directly from Python, convert_model can take OpenVINO extension objects constructed directly in Python for easier conversion of operations that are not supported in OpenVINO.


Model conversion can be performed by the convert_model() method and MO command line tool. The functionality from this article is applicable for convert_model() only and it is not present in command line tool.

convert_model() returns an openvino.runtime.Model object which can be compiled and inferred or serialized to IR.

Example of converting a PyTorch model directly from memory:

import torchvision

model = torchvision.models.resnet50(weights='DEFAULT')
ov_model = convert_model(model)

The following types are supported as an input model for convert_model():

  • PyTorch - torch.nn.Module, torch.jit.ScriptModule, torch.jit.ScriptFunction. Refer to the Converting a PyTorch Model article for more details.

  • TensorFlow / TensorFlow 2 / Keras - tf.keras.Model, tf.keras.layers.Layer, tf.compat.v1.Graph, tf.compat.v1.GraphDef, tf.Module, tf.function, tf.compat.v1.session, tf.train.checkpoint. Refer to the Converting a TensorFlow Model article for more details.

convert_model() accepts all parameters available in the MO command-line tool. Parameters can be specified by Python classes or string analogs, similar to the command-line tool.

Example of using native Python classes to set input_shape, mean_values and layout:

from openvino.runtime import PartialShape, Layout

ov_model = convert_model(model, input_shape=PartialShape([1,3,100,100]), mean_values=[127, 127, 127], layout=Layout("NCHW"))

Example of using strings for setting input_shape, mean_values and layout:

ov_model = convert_model(model, input_shape="[1,3,100,100]", mean_values="[127,127,127]", layout="NCHW")

The input parameter can be set by a tuple with a name, shape, and type. The input name of the type string is required in the tuple. The shape and type are optional. The shape can be a list or tuple of dimensions (int or openvino.runtime.Dimension), or openvino.runtime.PartialShape, or openvino.runtime.Shape. The type can be of numpy type or openvino.runtime.Type.

Example of using a tuple in the input parameter to cut a model:

ov_model = convert_model(model, input=("input_name", [3], np.float32))

For complex cases, when a value needs to be set in the input parameter, the InputCutInfo class can be used. InputCutInfo accepts four parameters: name, shape, type, and value.

InputCutInfo("input_name", [3], np.float32, [0.5, 2.1, 3.4]) is equivalent of InputCutInfo(name="input_name", shape=[3], type=np.float32, value=[0.5, 2.1, 3.4]).

Supported types for InputCutInfo:

  • name: string.

  • shape: list or tuple of dimensions (int or openvino.runtime.Dimension), openvino.runtime.PartialShape, openvino.runtime.Shape.

  • type: numpy type, openvino.runtime.Type.

  • value: numpy.ndarray, list of numeric values, bool.

Example of using InputCutInfo to freeze an input with value:

from openvino.tools.mo import convert_model, InputCutInfo

ov_model = convert_model(model, input=InputCutInfo("input_name", [3], np.float32, [0.5, 2.1, 3.4]))

To set parameters for models with multiple inputs, use list of parameters. Parameters supporting list:

  • input

  • input_shape

  • layout

  • source_layout

  • dest_layout

  • mean_values

  • scale_values

Example of using lists to set shapes, types and layout for multiple inputs:

ov_model = convert_model(model, input=[("input1", [1,3,100,100], np.float32), ("input2", [1,3,100,100], np.float32)], layout=[Layout("NCHW"), LayoutMap("NCHW", "NHWC")])

layout, source_layout and dest_layout accept an openvino.runtime.Layout object or string.

Example of using the Layout class to set the layout of a model input:

from openvino.runtime import Layout
from openvino.tools.mo import convert_model

ov_model = convert_model(model, source_layout=Layout("NCHW"))

To set both source and destination layouts in the layout parameter, use the LayoutMap class. LayoutMap accepts two parameters: source_layout and target_layout.

LayoutMap("NCHW", "NHWC") is equivalent to LayoutMap(source_layout="NCHW", target_layout="NHWC").

Example of using the LayoutMap class to change the layout of a model input:

from openvino.tools.mo import convert_model, LayoutMap

ov_model = convert_model(model, layout=LayoutMap("NCHW", "NHWC"))