91 lines
4.7 KiB
Python
91 lines
4.7 KiB
Python
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# type: ignore
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from __future__ import annotations
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from openvino._ov_api import Model
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from openvino.tools.ovc.cli_parser import get_all_cli_parser
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from openvino.tools.ovc.convert_impl import _convert
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from openvino.tools.ovc.logger import get_logger_state
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from openvino.tools.ovc.logger import restore_logger_state
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import openvino._ov_api
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import pathlib as pathlib
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__all__ = ['Model', 'convert_model', 'get_all_cli_parser', 'get_logger_state', 'pathlib', 'restore_logger_state']
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def convert_model(input_model: [str, pathlib.Path, typing.Any, list], input: [list, dict, str] = None, output: [str, list] = None, example_input: typing.Any = None, extension: [str, pathlib.Path, list, typing.Any] = None, verbose: bool = False, share_weights: bool = True) -> openvino._ov_api.Model:
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"""
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Converts the model from original framework to OpenVino Model.
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Framework-agnostic parameters:
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:param input_model:
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Model object in original framework (PyTorch, TensorFlow) or path to model file.
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Supported formats of input model:
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PyTorch
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torch.nn.Module
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torch.jit.ScriptModule
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torch.jit.ScriptFunction
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torch.export.ExportedProgram
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TF
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tf.compat.v1.Graph
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tf.compat.v1.GraphDef
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tf.compat.v1.wrap_function
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tf.compat.v1.session
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TF2 / Keras
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tf.keras.Model
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tf.keras.layers.Layer
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tf.function
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tf.Module
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tf.train.checkpoint
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PaddlePaddle
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paddle.hapi.model.Model
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paddle.fluid.dygraph.layers.Layer
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paddle.fluid.executor.Executor
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:param input:
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Information of model input required for model conversion.
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Input can be set by a list of tuples or a dictionary. Each tuple can contain optionally input name (string),
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input type (ov.Type, numpy.dtype) or input shape (ov.Shape, ov.PartialShape, list, tuple).
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Example: input=("op_name", PartialShape([-1, 3, 100, 100]), ov.Type.f32).
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Alternatively input can be set by a dictionary, where key - input name,
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value - tuple with input parameters (shape or type).
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Example 1: input={"op_name_1": ([1, 2, 3], ov.Type.f32), "op_name_2": ov.Type.i32}
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Example 2: input=[("op_name_1", [1, 2, 3], ov.Type.f32), ("op_name_2", ov.Type.i32)]
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Example 3: input=[([1, 2, 3], ov.Type.f32), ov.Type.i32]
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The order of inputs in converted model will match the order of specified inputs.
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If data type is not specified explicitly data type is taken from the original node data type.
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:param output:
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One or more comma-separated model outputs to be preserved in the converted model. Other outputs are removed.
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If `output` parameter is not specified then all outputs from the original model are preserved.
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Do not add :0 to the names for TensorFlow. The order of outputs in the converted model is the same as
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the order of specified names. Example: output="out_1", or output=["out_1", "out_2"].
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For PaddlePaddle model represented as a Python object, you can specify outputs as a PaddlePaddle Python
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objects or a list of such objects.
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:param example_input:
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Sample of model input in original framework.
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For PyTorch it can be torch.Tensor.
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For Tensorflow it can be tf.Tensor or numpy.ndarray.
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For PaddlePaddle it can be Paddle Variable.
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:param extension:
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Paths to libraries (.so or .dll) with extensions, comma-separated
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list of paths, objects derived from BaseExtension class or lists of
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objects.
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:param verbose:
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Print detailed information about conversion. The detailed information is logged via standard logging library.
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The log level can be changed by setting the log level using logging library.
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Example:
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import logging
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logging.getLogger().setLevel(logging.DEBUG)
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:param share_weights:
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Reuse weights allocated in the original model. If input model is in file,
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then mmap is used to allocate weights directly from file. If input model is
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runtime object, then original memory regions allocated in the original model
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are reused for weights in the converted model.
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Returns:
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openvino.Model
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"""
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