openvino.convert_model(input_model: [<class 'str'>, <class 'pathlib.Path'>, typing.Any, <class 'list'>], input: [<class 'list'>, <class 'dict'>, <class 'str'>] = None, output: [<class 'str'>, <class 'list'>] = None, example_input: ~typing.Any = None, extension: [<class 'str'>, <class 'pathlib.Path'>, <class 'list'>, typing.Any] = None, verbose: bool = False, share_weights: bool = True) Model#

Converts the model from original framework to OpenVino Model.

Framework-agnostic parameters:
param input_model:

Model object in original framework (PyTorch, Tensorflow) or path to model file.

Supported formats of input model:

PyTorch torch.nn.Module torch.jit.ScriptModule torch.jit.ScriptFunction

TF tf.compat.v1.Graph tf.compat.v1.GraphDef tf.compat.v1.wrap_function tf.compat.v1.session

TF2 / Keras tf.keras.Model tf.keras.layers.Layer tf.function tf.Module tf.train.checkpoint

PaddlePaddle paddle.hapi.model.Model paddle.fluid.dygraph.layers.Layer paddle.fluid.executor.Executor

param input:

Information of model input required for model conversion. Input can be set by a list of tuples or a dictionary. Each tuple can contain optionally input name (string), input type (ov.Type, numpy.dtype) or input shape (ov.Shape, ov.PartialShape, list, tuple). Example: input=(“op_name”, PartialShape([-1, 3, 100, 100]), ov.Type.f32). Alternatively input can be set by a dictionary, where key - input name, value - tuple with input parameters (shape or type). Example 1: input={“op_name_1”: ([1, 2, 3], ov.Type.f32), “op_name_2”: ov.Type.i32} Example 2: input=[(“op_name_1”, [1, 2, 3], ov.Type.f32), (“op_name_2”, ov.Type.i32)] Example 3: input=[([1, 2, 3], ov.Type.f32), ov.Type.i32] The order of inputs in converted model will match the order of specified inputs. If data type is not specified explicitly data type is taken from the original node data type.

param output:

One or more comma-separated model outputs to be preserved in the converted model. Other outputs are removed. If output parameter is not specified then all outputs from the original model are preserved. Do not add :0 to the names for TensorFlow. The order of outputs in the converted model is the same as the order of specified names. Example: output=”out_1”, or output=[“out_1”, “out_2”]. For PaddlePaddle model represented as a Python object, you can specify outputs as a PaddlePaddle Python objects or a list of such objects.

param example_input:

Sample of model input in original framework. For PyTorch it can be torch.Tensor. For Tensorflow it can be tf.Tensor or numpy.ndarray. For PaddlePaddle it can be Paddle Variable.

param extension:

Paths to libraries (.so or .dll) with extensions, comma-separated list of paths, objects derived from BaseExtension class or lists of objects.

param verbose:

Print detailed information about conversion.

param share_weights:

Reuse weights allocated in the original model. If input model is in file, then mmap is used to allocate weights directly from file. If input model is runtime object, then original memory regions allocated in the original model are reused for weights in the converted model.