All of the issues below refer to :doc:`legacy functionalities <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer>`.
If your question is not covered by the topics below, use the `OpenVINO Support page <https://software.intel.com/en-us/openvino-toolkit/documentation/get-started>`__, where you can participate on a free forum.
If your question is not covered by the topics below, use the
`OpenVINO Support page <https://community.intel.com/t5/Intel-Distribution-of-OpenVINO/bd-p/distribution-openvino-toolkit>`__,
**A:** Internally, Model Optimizer uses a protobuf library to parse and load Caffe models. This library requires a file grammar and a generated parser. For a Caffe fallback, Model Optimizer uses a Caffe-generated parser for a Caffe-specific ``.proto`` file (which is usually located in the ``src/caffe/proto`` directory). Make sure that you install exactly the same version of Caffe (with Python interface) as that was used to create the model.
However, since your model has custom layers, you must register them as custom. To learn more about it, refer to the :doc:`[Legacy] Custom Layers in Model Optimizer <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer>`.
absent in the :doc:`list of supported operations <openvino_resources_supported_operations_frontend>` for the target framework.
Then, refer to available options in the corresponding section in the :doc:`[Legacy] Custom Layers in Model Optimizer <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer>` page.
**A:** The structure of any Caffe topology is described in the ``caffe.proto`` file of any Caffe version. For example, the following ``.proto`` file in Model Optimizer is used by default: ``mo/front/caffe/proto/my_caffe.proto``, with the structure:
.. code-block:: sh
message NetParameter {
// ... some other parameters
// The layers that make up the net. Each of their configurations, including
// connectivity and behavior, is specified as a LayerParameter.
repeated LayerParameter layer = 100; // ID 100 so layers are printed last.
// DEPRECATED: use 'layer' instead.
repeated V1LayerParameter layers = 2;
}
This means that any topology should contain layers as top-level structures in ``prototxt``. For example, see the `LeNet topology <https://github.com/BVLC/caffe/blob/master/examples/mnist/lenet.prototxt>`__.
.. _question-8:
Q8. What does the message "Old-style inputs (via 'input_dims') are not supported. Please specify inputs via 'input_shape'" mean?
**A:** The structure of any Caffe topology is described in the ``caffe.proto`` file for any Caffe version. For example, the following ``.proto`` file in Model Optimizer is used by default: ``mo/front/caffe/proto/my_caffe.proto``, with the structure:
.. code-block:: sh
message NetParameter {
optional string name = 1; // consider giving the network a name
// DEPRECATED. See InputParameter. The input blobs to the network.
repeated string input = 3;
// DEPRECATED. See InputParameter. The shape of the input blobs.
repeated BlobShape input_shape = 8;
// 4D input dimensions -- deprecated. Use "input_shape" instead.
// If specified, for each input blob there should be four
// values specifying the num, channels, height and width of the input blob.
// Thus, there should be a total of (4 * #input) numbers.
However, if your model contains more than one input, Model Optimizer is able to convert the model with inputs specified in one of the first three forms in the above list. The 4th form is not supported for multi-input topologies.
**A:** Model Optimizer does not support mean file processing for topologies with more than one input. In this case, you need to perform preprocessing of the inputs for a generated Intermediate Representation in OpenVINO Runtime to perform subtraction for every input of your multi-input model. See the :doc:`Overview of Preprocessing <openvino_docs_OV_UG_Preprocessing_Overview>` for details.
**A:** Model Optimizer tried to infer a specified layer via the Caffe framework. However, it cannot construct a net using the Caffe Python interface. Make sure that your ``caffemodel`` and ``prototxt`` files are correct. To ensure that the problem is not in the ``prototxt`` file, see FAQ :ref:`#2<question-2>`.
**A:** Model Optimizer tried to infer a custom layer via the Caffe framework, but the model could not be inferred using Caffe. This might happen if you try to convert the model with some noise weights and biases, which conflict with layers that have dynamic shapes. You should write your own extension for every custom layer your topology might have. For more details, refer to the :doc:`[Legacy] Model Optimizer Extensibility <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer>` page.
Q14. What does the message "Cannot infer shape for node {} because there is no Caffe available. Please register python infer function for op or use Caffe for shape inference" mean?
**A:** Your model contains a custom layer and you have correctly registered it with the ``CustomLayersMapping.xml`` file. These steps are required to offload shape inference of the custom layer with the help of the system Caffe. However, Model Optimizer could not import a Caffe package. Make sure that you have built Caffe with a ``pycaffe`` target and added it to the ``PYTHONPATH`` environment variable. At the same time, it is highly recommended to avoid dependency on Caffe and write your own Model Optimizer extension for your custom layer. For more information, refer to FAQ :ref:`#44<question-44>`.
**A:** You have run Model Optimizer without a flag ``--framework caffe|tf|mxnet``. Model Optimizer tries to deduce the framework by the extension of input model file (``.pb`` for TensorFlow, ``.caffemodel`` for Caffe, ``.params`` for Apache MXNet). Your input model might have a different extension and you need to explicitly set the source framework. For example, use ``--framework caffe``.
**A:** Input shape was not provided. That is mandatory for converting an MXNet model to the OpenVINO Intermediate Representation, because MXNet models do not contain information about input shapes. Use the ``--input_shape`` flag to specify it. For more information about using the ``--input_shape``, refer to FAQ :ref:`#56<question-56>`.
**A:** The ``--scale`` option sets a scaling factor for all channels, while ``--scale_values`` sets a scaling factor per each channel. Using both of them simultaneously produces ambiguity, so you must use only one of them. For more information, refer to the **Using Framework-Agnostic Conversion Parameters** section: for :doc:`Converting a Caffe Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe>`, :doc:`Converting a TensorFlow Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`, :doc:`Converting an MXNet Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet>`.
Q20. What does the message "Cannot find prototxt file: for Caffe please specify --input_proto - a protobuf file that stores topology and --input_model that stores pre-trained weights" mean?
**A:** Model Optimizer cannot find a ``.prototxt`` file for a specified model. By default, it must be located in the same directory as the input model with the same name (except extension). If any of these conditions is not satisfied, use ``--input_proto`` to specify the path to the ``.prototxt`` file.
**A:** Model Optimizer cannot create a directory specified via ``--output_dir``. Make sure that you have enough permissions to create the specified directory.
**A:** One of the layers in the specified topology might not have inputs or values. Make sure that the provided ``caffemodel`` and ``protobuf`` files are correct.
**A:** Some of the operations are not supported by OpenVINO Runtime and cannot be translated to OpenVINO Intermediate Representation. You can extend Model Optimizer by allowing generation of new types of operations and implement these operations in the dedicated OpenVINO plugins. For more information, refer to the :doc:`OpenVINO Extensibility Mechanism <openvino_docs_Extensibility_UG_Intro>` guide.
**A:** You might have specified an output node via the ``--output`` flag that does not exist in a provided model. Make sure that the specified output is correct and this node exists in the current model.
Q27. What does the message "--input parameter was provided. Other inputs are needed for output computation. Provide more inputs or choose another place to cut the net" mean?
**A:** This error occurs when an incorrect input port is specified with the ``--input`` command line argument. When using ``--input``, you may optionally specify an input port in the form: ``X:node_name``, where ``X`` is an integer index of the input port starting from 0 and ``node_name`` is the name of a node in the model. This error occurs when the specified input port ``X`` is not in the range 0..(n-1), where n is the number of input ports for the node. Specify a correct port index, or do not use it if it is not needed.
Q30. What does the message "Node has more than 1 input and input shapes were provided. Try not to provide input shapes or specify input port with PORT:NODE notation, where PORT is an integer" mean?
**A:** This error occurs when an incorrect combination of the ``--input`` and ``--input_shape`` command line options is used. Using both ``--input`` and ``--input_shape`` is valid only if ``--input`` points to the ``Placeholder`` node, a node with one input port or ``--input`` has the form ``PORT:NODE``, where ``PORT`` is an integer port index of input for node ``NODE``. Otherwise, the combination of ``--input`` and ``--input_shape`` is incorrect.
Q31. What does the message "Input port > 0 in --input is not supported if --input_shape is not provided. Node: NAME_OF_THE_NODE. Omit port index and all input ports will be replaced by placeholders. Or provide --input_shape" mean?
**A:** When using the ``PORT:NODE`` notation for the ``--input`` command line argument and ``PORT`` > 0, you should specify ``--input_shape`` for this input. This is a limitation of the current Model Optimizer implementation.
**A:** You might have provided only one shape for the placeholder, while there are none or multiple inputs in the model. Make sure that you have provided the correct data for placeholder nodes.
**A:** This error occurs when the ``SubgraphMatch.single_input_node`` function is used for an input port that supplies more than one node in a sub-graph. The ``single_input_node`` function can be used only for ports that has a single consumer inside the matching sub-graph. When multiple nodes are connected to the port, use the ``input_nodes`` function or ``node_by_pattern`` function instead of ``single_input_node``. For more details, refer to the **Graph Transformation Extensions** section in the :doc:`[Legacy] Model Optimizer Extensibility <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Model_Optimizer_Extensions_Model_Optimizer_Transformation_Extensions>` guide.
**A:** This error occurs when the ``SubgraphMatch._add_output_node`` function is called manually from user's extension code. This is an internal function, and you should not call it directly.
**A:** While using configuration file to implement a TensorFlow front replacement extension, an incorrect match kind was used. Only ``points`` or ``scope`` match kinds are supported. For more details, refer to the :doc:`[Legacy] Model Optimizer Extensibility <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer>` guide.
**A:** Model Optimizer tried to write an event file in the specified directory but failed to do that. That could happen when the specified directory does not exist or you do not have permissions to write in it.
**A** Most likely, you tried to extend Model Optimizer with a new primitive, but you did not specify an infer function. For more information on extensions, see the :doc:`OpenVINO Extensibility Mechanism <openvino_docs_Extensibility_UG_Intro>` guide.
**A:** Model Optimizer cannot infer shapes or values for the specified node. It can happen because of the following reasons: a bug exists in the custom shape infer function, the node inputs have incorrect values/shapes, or the input shapes are incorrect.
**A:** Batch dimension is the first dimension in the shape and it should be equal to 1 or undefined. In your case, it is not either equal to 1 or undefined, which is why the ``-b`` shortcut produces undefined and unspecified behavior. To resolve the issue, specify full shapes for each input with the ``--input_shape`` option. Run Model Optimizer with the ``--help`` option to learn more about the notation for input shapes.
**A:** Most likely, the shape is not defined (partially or fully) for the specified node. You can use ``--input_shape`` with positive integers to override model input shapes.
**A:** This error occurs when the ``--input`` command-line option is used to cut a model and ``--input_shape`` is not used to override shapes for a node, so a shape for the node cannot be inferred by Model Optimizer. You need to help Model Optimizer by specifying shapes with ``--input_shape`` for each node specified with the ``--input`` command-line option.
**A:** To convert TensorFlow models with Model Optimizer, TensorFlow 1.2 or newer must be installed. For more information on prerequisites, see the :doc:`Configuring Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` guide.
**A:** The model file should contain a frozen TensorFlow graph in the text or binary format. Make sure that ``--input_model_is_text`` is provided for a model in the text format. By default, a model is interpreted as binary file.
Q45. What does the message "Found custom layer. Model Optimizer does not support this layer. Please, register it in CustomLayersMapping.xml or implement extension" mean?
**A:** This means that the layer ``{layer_name}`` is not supported in Model Optimizer. You will find a list of all unsupported layers in the corresponding section. You should implement the extensions for this layer. See :doc:`OpenVINO Extensibility Mechanism <openvino_docs_Extensibility_UG_Intro>` for more information.
**A:** A path to the custom replacement configuration file was provided with the ``--transformations_config`` flag, but the file could not be found. Make sure the specified path is correct and the file exists.
**A:** When extending Model Optimizer with new primitives, keep in mind that their names are case-insensitive. Most likely, another operation with the same name is already defined. For more information, see the :doc:`OpenVINO Extensibility Mechanism <openvino_docs_Extensibility_UG_Intro>` guide.
**A:** There are models where ``Placeholder`` has the UINT8 type and the first operation after it is 'Cast', which casts the input to FP32. Model Optimizer detected that the ``Placeholder`` has the UINT8 type, but the next operation is not 'Cast' to float. Model Optimizer does not support such a case. Make sure you change the model to have ``Placeholder`` for FP32.
**A:** Model Optimizer cannot read the value with the specified data type. Currently, the following types are supported: bool, float16, float32, double, int8, int16, int32, int64, uint8, uint16, uint32, uint64, str.
**A:** Model Optimizer tried to access a node that does not exist. This could happen if you have incorrectly specified placeholder, input or output node name.
**A:** To convert MXNet models with Model Optimizer, Apache MXNet 1.0.0 must be installed. For more information about prerequisites, see the :doc:`Configuring Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` guide.
**A:** Most likely, there is a problem with loading of the MXNet model. Make sure the specified path is correct, the model exists and is not corrupted, and you have sufficient permissions to work with it.
**A:** When extending Model Optimizer with new primitives, keep in mind that their names are case-insensitive. Most likely, another operation with the same name is already defined. For more information, see the :doc:`OpenVINO Extensibility Mechanism <openvino_docs_Extensibility_UG_Intro>` guide.
**A:** Specifying the batch and the input shapes at the same time is not supported. You must specify a desired batch as the first value of the input shape.
**A:** When specifying input shapes for several layers, you must provide names for inputs, whose shapes will be overwritten. For usage examples, see the :doc:`Converting a Caffe Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe>`. Additional information for ``--input_shape`` is in FAQ :ref:`#56<question-56>`.
**A:** Mean values for the given parameter cannot be parsed. It should be a string with a list of mean values. For example, in '(1,2,3)', 1 stands for the RED channel, 2 for the GREEN channel, 3 for the BLUE channel.
**A:** The number of channels and the number of given values for mean values do not match. The shape should be defined as '(R,G,B)' or '[R,G,B]'. The shape should not contain undefined dimensions (? or -1). The order of values is as follows: (value for a RED channel, value for a GREEN channel, value for a BLUE channel).
**A:** Most likely, you didn't specify inputs using ``--mean_values``. Specify inputs with the ``--input`` flag. For usage examples, refer to the FAQ :ref:`#62<question-62>`.
**A:** Most likely, you didn't specify inputs using ``--scale_values``. Specify inputs with the ``--input`` flag. For usage examples, refer to the FAQ :ref:`#63<question-63>`.
**A:** The number of specified mean values and the number of inputs must be equal. For a usage example, refer to the :doc:`Converting a Caffe Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe>` guide.
**A:** The number of specified scale values and the number of inputs must be equal. For a usage example, refer to the :doc:`Converting a Caffe Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe>` guide.
**A:** A replacement defined in the configuration file for sub-graph replacement, using node names patterns or start/end nodes, has the ``match_kind`` attribute. The attribute may have only one of the values: ``scope`` or ``points``. If a different value is provided, this error is displayed.
**A:** A replacement defined in the configuration file for sub-graph replacement, using node names patterns or start/end nodes, has the ``instances`` attribute. This attribute is mandatory. This error will occur if the attribute is missing. For more details, refer to the **Graph Transformation Extensions** section in the :doc:`[Legacy] Model Optimizer Extensibility <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer>` guide.
**A:** A replacement defined in the configuration file for sub-graph replacement, using start/end nodes, has the ``instances`` attribute. For this type of replacement, the instance must be defined with a dictionary with two keys ``start_points`` and ``end_points``. Values for these keys are lists with the start and end node names, respectively. For more details, refer to the **Graph Transformation Extensions** section in the :doc:`[Legacy] Model Optimizer Extensibility <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Model_Optimizer_Extensions_Model_Optimizer_Transformation_Extensions>` guide.
**A:** The path to a custom replacement configuration file was provided with the ``--transformations_config`` flag, but it cannot be found. Make sure the specified path is correct and the file exists.
**A:** The file for custom replacement configuration provided with the ``--transformations_config`` flag cannot be parsed. In particular, it should have a valid JSON structure. For more details, refer to the `JSON Schema Reference <https://spacetelescope.github.io/understanding-json-schema/reference/index.html>`__ page.
**A:** Every custom replacement should declare a set of mandatory attributes and their values. For more details, refer to FAQ :ref:`#71 <question-71>`.
**A:** The file for custom replacement configuration provided with the ``--transformations_config`` flag cannot pass validation. Make sure you have specified ``id``, ``instances``, and ``match_kind`` for all the patterns.
**A:** The custom replacement configuration file provided with the ``--tensorflow_custom_operations_config_update`` cannot be parsed. Make sure that the file is correct and refer to FAQ :ref:`#68<question-68>`, :ref:`#69<question-69>`, :ref:`#70<question-70>`, and :ref:`#71<question-71>`.
**A:** This error occurs when you try to make a sub-graph match. It is detected that between the start and end nodes that were specified as inputs/outputs for the subgraph to find, there are nodes marked as outputs but there is no path from them to the input nodes. Make sure the subgraph you want to match does actually contain all the specified output nodes.
**A:** The start or end node for the sub-graph replacement using start/end nodes is specified incorrectly. Model Optimizer finds internal nodes of the sub-graph strictly "between" the start and end nodes, and then adds all input nodes to the sub-graph (and the inputs of their inputs, etc.) for these "internal" nodes. This error reports that Model Optimizer reached input node during this phase. This means that the start/end points are specified incorrectly in the configuration file. For more details, refer to the **Graph Transformation Extensions** section in the :doc:`[Legacy] Model Optimizer Extensibility <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Model_Optimizer_Extensions_Model_Optimizer_Transformation_Extensions>` guide.
**A:** This message may appear when the ``--compress_to_fp16`` command-line option is used. This option implies compression of all the model weights, biases, and other constant values to FP16. If a value of a constant is out of the range of valid FP16 values, the value is converted to positive or negative infinity. It may lead to incorrect results of inference or may not be a problem, depending on the model. The number of such elements and the total number of elements in the constant value is printed out together with the name of the node, where this value is used.
**A:** This message may appear when the ``--compress_to_fp16`` command-line option is used. This option implies conversion of all blobs in the mode to FP16. If a value in the blob is so close to zero that it cannot be represented as a valid FP16 value, it is converted to a true zero FP16 value. Depending on the model, it may lead to incorrect results of inference or may not be a problem. The number of such elements and the total number of elements in the blob are printed out together with a name of the node, where this blob is used.
**A:** This error occurs when the ``SubgraphMatch.node_by_pattern`` function is used with a pattern that does not uniquely identify a single node in a sub-graph. Try to extend the pattern string to make unambiguous match to a single sub-graph node. For more details, refer to the **Graph Transformation Extensions** section in the :doc:`[Legacy] Model Optimizer Extensibility <openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Model_Optimizer_Extensions_Model_Optimizer_Transformation_Extensions>` guide.
**A:** Your Caffe topology ``.prototxt`` file is intended for training. Model Optimizer expects a deployment-ready ``.prototxt`` file. To fix the problem, prepare a deployment-ready ``.prototxt`` file. Preparation of a deploy-ready topology usually results in removing ``data`` layer(s), adding ``input`` layer(s), and removing loss layer(s).
**A:** You are using an unsupported Python version. Use only versions 3.4 - 3.6 for the C++ ``protobuf`` implementation that is supplied with OpenVINO toolkit. You can still boost the conversion speed by building the protobuf library from sources. For complete instructions about building ``protobuf`` from sources, see the appropriate section in the :doc:`Converting a Model to Intermediate Representation <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` guide.
Q81. What does the message "Arguments --nd_prefix_name, --pretrained_model_name and --input_symbol should be provided. Please provide all or do not use any." mean?
**A:** When the model has multiple inputs and you want to provide mean/scale values, you need to pass those values for each input. More specifically, the number of passed values should be the same as the number of inputs of the model.
For more information, refer to the :doc:`Converting a Model to Intermediate Representation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>` guide.
**A:** When you passed the mean/scale values and specify names of input layers of the model, you might have used the name that does not correspond to any input layer. Make sure that you list only names of the input layers of your model when passing values with the ``--input`` option.
For more information, refer to the :doc:`Converting a Model to Intermediate Representation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>` guide.
**A:** Most likely, ``.json`` file does not exist or has a name that does not match the notation of Apache MXNet. Make sure the file exists and has a correct name.
For more information, refer to the :doc:`Converting an MXNet Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet>` guide.
**A:** Model Optimizer for Apache MXNet supports only ``.params`` and ``.nd`` files formats. Most likely, you specified an unsupported file format in ``--input_model``.
For more information, refer to :doc:`Converting an MXNet Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet>`.
**A:** This error appears if the class of implementation of ``Op`` for Python Caffe layer could not be used by Model Optimizer. Python layers should be handled differently comparing to ordinary Caffe layers.
Note that the first call ``register_caffe_python_extractor(ProposalPythonExampleOp, 'rpn.proposal_layer.ProposalLayer')`` registers an extension of the layer in Model Optimizer, which will be found by the specific name (mandatory to join module name and layer name): ``rpn.proposal_layer.ProposalLayer``.
Q89. What do the messages "File ... does not appear to be a Kaldi file (magic number does not match)", "Kaldi model should start with <Nnet> tag" mean?
Q93. What does the message "For legacy MXNet models Model Optimizer does not support conversion of old MXNet models (trained with 1.0.0 version of MXNet and lower) with custom layers." mean?
**A:** This error messages mean that Model Optimizer does not support your Kaldi model, because the Net contains ``ParallelComponent`` that does not end with the ``</ParallelComponent>`` tag.
Make sure that you provide a path to a true Kaldi model and try again.
.. _question-95:
.. _question-96:
.. _question-97:
Q97. What does the message "Graph contains a cycle. Can not proceed .." mean?
* :doc:`Convert models, created with TensorFlow Object Detection API <openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models>`
Q100. What does the message "Interp layer shape inference function may be wrong, please, try to update layer shape inference function in the file (extensions/ops/interp.op at the line ...)." mean?
**A:** There are many flavors of Caffe framework, and most layers in them are implemented identically.
However, there are exceptions. For example, the output value of layer Interp is calculated differently in Deeplab-Caffe and classic Caffe. Therefore, if your model contains layer Interp and the conversion of your model has failed, modify the ``interp_infer`` function in the ``extensions/ops/interp.op`` file according to the comments in the file.
**A:** It means that your mean/scale values have a wrong format. Specify mean/scale values in the form of ``layer_name(val1,val2,val3)``.
You need to specify values for each input of the model. For more information, refer to the :doc:`Converting a Model to Intermediate Representation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>` guide.
**A:** It means that you are trying to convert a topology contains the ``_contrib_box_nms`` operation which is not supported directly. However, the sub-graph of operations including ``_contrib_box_nms`` could be replaced with the DetectionOutput layer if your topology is one of the ``gluoncv`` topologies. Specify the ``--enable_ssd_gluoncv`` command-line parameter for Model Optimizer to enable this transformation.
**A:** If a ``*.caffemodel`` file exists and is correct, the error occurred possibly because of the use of Python protobuf implementation. In some cases, error messages may appear during model parsing, for example: "``utf-8`` codec can't decode byte 0xe0 in position 4: invalid continuation byte in field: mo_caffe.SpatialTransformerParameter.transform_type". You can either use a newer Python version (3.7 - 3.11) or build the ``cpp`` implementation of ``protobuf`` yourself for your version of Python. For the complete instructions about building ``protobuf`` from sources, see the appropriate section in the :doc:`Converting Models with Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` guide.
Certain features, such as ``--extensions`` and ``--transformations_config``, are not yet fully supported on the new frontends.
The new frontends support only paths to shared libraries (.dll and .so) for ``--extensions``. They support JSON configurations with defined library fields for ``--transformations_config``.
Inputs freezing (enabled by ``--freeze_placeholder_with_value`` or ``--input`` arguments) is not supported by the new frontends.
The IR conversion falls back to the old path if a user does not select any expected path of conversion explicitly (with ``--use_new_frontend`` or ``--use_legacy_frontend`` MO arguments) and unsupported pre-defined scenario is detected on the new frontend path.
