Model Optimizer Extensibility

Model Optimizer extensibility mechanism enables support of new operations and custom transformations to generate the optimized intermediate representation (IR) as described in the Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™. This mechanism is a core part of Model Optimizer, as a huge set of examples showing how to add custom logic to support your model.

There are several cases when the customization is needed:

  • A model contains operation(s) not known for the Model Optimizer, but these operation(s) could be expressed as a combination of supported operations. In this case, a custom transformation should be implemented to replace unsupported operation(s) with supported ones.

  • A model contains a sub-graph of operations that can be replaced with a smaller number of operations to get better performance. This example corresponds to so-called fusing transformations, for example, replacing a sub-graph performing the calculation \(x / (1.0 + e^{-(beta \* x)})\) with a single operation of type Swish.

  • A model contains a custom framework operation (the operation that is not a part of an official operation set of the framework) that was developed using the framework extensibility mechanism. In this case, Model Optimizer should know how to handle the operation and generate a corresponding section in an IR for it.

It is necessary to figure out how Model Optimizer represents a model in a memory and converts it to an IR before going into details of the Model Optimizer extensibility mechanism.

Note

All paths in this article are provided relatively to the Model Optimizer installation directory if not stated otherwise.

Model Representation in Memory

The model can be represented as a directed graph, where nodes are operations and edges correspond to data passing from a producer operation (node) to a consumer operation (node).

Model Optimizer uses Python class mo.graph.graph.Graph instance to represent the computation graph in memory during the model conversion. This class is inherited from the networkx.MultiDiGraph class of the standard networkx Python library. It provides many convenient methods to traverse and modify the graph. For the examples, refer to the mo/graph/graph.py file.

Model Optimizer keeps all necessary information about the operation in node attributes. Model Optimizer uses the mo.graph.graph.Node class defined in the mo/graph/graph.py file, which is a wrapper on top of a networkx node attributes dictionary, and provides many convenient methods to work with the node. For example, the node my_node attribute with a name ‘my_attr can be retrieved from the node with the following code my_node.my_attr<tt>, which is equivalent to obtaining attribute with name’my_attr’ in the graph.node[‘my_node’] dictionary. For the class implementation details, refer to the mo/graph/graph.py` file.

An operation may have several inputs and outputs. For example, operation Split has two inputs: data to split and axis to split along, and variable number of outputs depending on a value of attribute num_splits. Each input data to the operation is passed to a specific operation input port. An operation produces the output data from an output port. Input and output ports are numbered from 0 independently. Model Optimizer uses classes mo.graph.port.Port and mo.graph.connection.Connection, which are useful abstraction to perform graph modifications like nodes connecting/re-connecting and graph traversing. These classes are widely used in the Model Optimizer code so it is easy to find a lot of usage examples.

There is no dedicated class corresponding to an edge, so low-level graph manipulation is needed to get access to edge attributes if needed. Meanwhile, most manipulations with nodes connections should be done with help of the mo.graph.connection.Connection and mo.graph.port.Port classes. Thus, low-level graph manipulation is error prone and is strongly not recommended.

Further details and examples related to a model representation in memory are provided in the sections below, in a context for a better explanation. Also, for more information on how to use ports and connections, refer to the Graph Traversal and Modification Using Ports and Connections section.

Model Conversion Pipeline

A model conversion pipeline can be represented with the following diagram:

Model Conversion pipeline

Each conversion step is reviewed in details below.

Model Loading

Model Optimizer gets a trained model file as an input. The model loader component of Model Optimizer reads a model file using Python bindings provided with the framework and builds an in-memory representation of a computation graph. There is a separate loader for each supported framework. These loaders are implemented in the extensions/load/<FRAMEWORK>/loader.py files of Model Optimizer.

Note

Model Optimizer uses a special parser for Caffe models built on top of the caffe.proto file. In the case of a model loading failure, Model Optimizer throws an error and requests preparation of the parser that can read the model. For more information on how to prepare the custom Caffe parser, refer to the Model Optimizer Frequently Asked Questions #1.

The result of a model loading step is a Graph object, which can be depicted like in the following example:

Graph After Load

Model Optimizer loader saves an operation instance framework description (usually it is a Protobuf message) into a node attribute usually with a name pb for each operation of an input model. It is important that this is a framework-specific description of an operation. This means that an operation, for example, Convolution may be represented differently in, for example, Caffe and TensorFlow frameworks but performs the same calculations from a mathematical point of view.

In the example above, the “Operation 2” has one input and two outputs. The tensor produced from the output “port 0” is consumed with the “Operation 5” (the input “port 0”) and “Operation 3” (the input “port 1”). The tensor produced from the output “port 1” is consumed with the “Operation 4” (the input “port 0”).

Each edge has two attributes in and out containing the input port number of the consumer node and the output port number of the producer node. These attributes describe the fact that nodes are operations consuming some input tensors and producing some output tensors. From the perspective of Model Optimizer, nodes themselves are “black boxes” because they do not contain required information about the operation they perform.

Operations Attributes Extracting

The next step is to parse framework-dependent operation representation saved in a node attribute and update the node attributes with the operation specific attributes. There are three options to do this.

  1. The extractor extension approach. This is a recommended way to extract attributes for an operation and it is explained in details in the Operation Extractor section.

  2. The legacy approach with a built-in extractor. The mo/front/<FRAMEWORK>/extractor.py file (for example, the one for Caffe) defines a dictionary with extractors for specific operation types. A key in the dictionary is a type of an operation to trigger the extracting function for and the value is the function. The function has one parameter – a node to extract attributes from. This is a legacy and non-extensible approach so it should be avoided. This mechanism will be removed in future versions of Model Optimizer.

The extractors execution order is the following:

  • CustomLayersMapping.xml (for Caffe models only).

  • Model Optimizer extension.

  • Built-in Model Optimizer extractor.

The result of operations attributes extracting step can be depicted like in the following example:

Graph After Attributes Extraction

The only difference in the graph from the previous step is that nodes contain dictionary with extracted attributes and operation-specific attributes needed for Model Optimizer. However, starting from this step, Model Optimizer does not need the original representation of the operation/model and uses just Model Optimizer representation (there are some peculiar cases in which Model Optimizer still uses the pb attribute, covered in this article partially). A detailed list of common node attributes and their values is provided below in the Model Optimizer Operation section.

Front Phase

For legacy reasons, you must specify shapes for all not fully-defined inputs of the model. In contrast, other machine learning frameworks, like TensorFlow, let you create a model with undefined or partially defined input shapes. As an example, undefined dimension is marked with an integer value -1 in a TensorFlow model or has some string name in an ONNX model.

During the front phase, Model Optimizer knows shape of the model inputs and constants only and does not know shapes (and even ranks) of the intermediate tensors. But information about shapes may not be needed to implement particular transformation. For example, the transformation extensions/front/TopKNormalize.py removes an attribute k from a TopK node and adds an input constant with the value k. The transformation is needed to convert a TopK operation. It comes from frameworks, where a number of output elements is defined as an attribute of the operation to the OpenVINO TopK operation semantic, which requires this value to be a separate input.

It is important to mention that sometimes it seems like transformation cannot be implemented during the front phase because the actual values of inputs or shapes are needed. In fact, manipulations of shapes or values can be implemented using operations that are added to the graph. Consider the extensions/front/onnx/flattenONNX_to_reshape.py transformation, which replaces an ONNX Flatten operation with a sub-graph of operations performing the following (when axis is not equal to 0 and 1):

  1. Calculate a shape of the Flatten input tensor, using the ShapeOf operation.

  2. Get the first axis elements from the output of Shape operation and calculate their product, using the ReduceProd operation.

  3. Concatenate output of the ReduceProd and constant with the value of -1 (for an explanation of this value refer to the Reshape specification page).

  4. Use the concatenated value as the second input to the Reshape operation.

It is highly recommended to write shape-agnostic transformations to avoid model reshape-ability issues. For more information related to the reshaping of a model, refer to the Using Shape Inference guide.

More information on how to develop front phase transformations and dedicated API description is provided in the Front Phase Transformations section.

Partial Inference

Model Optimizer performs a partial inference of a model during model conversion. This procedure includes output shapes calculation of all operations in a model and constant folding (value calculation for constant sub-graphs). The constant folding is needed for the shape inference because in some cases evaluation of constant sub-graph is needed to calculate output shapes. For example, the output shape for the Reshape operation may be defined as a mathematical expression using the ShapeOf operation output.

Note

Model Optimizer does not fold sub-graphs starting from the ShapeOf operation by default because this leads to a model non-reshape-ability (the command-line parameter --static_shape can override this behavior). For more information related to reshaping of a model, refer to the Using Shape Inference guide.

Model Optimizer calculates output shapes for all operations in a model to write them to Intermediate Representation files.

Note

This is a legacy requirement. Starting with IR version 10, OpenVINO Runtime needs to know shapes of the Const and the Parameter operations only. The OpenVINO Runtime calculates output shapes for all operations in a model, using shapes of Parameter and Const operations defined with respective operation attributes.

Model Optimizer inserts data nodes to the computation graph before starting the partial inference phase. The data node corresponds to the specific tensor produced with the operation. Each data node contains two attributes: shape, containing the shape of the tensor, and value, which may contain the actual value of the tensor. The value for a value attribute is equal to None if this tensor value cannot be calculated. This happens in two cases: when a tensor value depends on a values passed to the Parameter operation of a model or Model Optimizer does not have value propagation implementation for the operation.

Before running partial inference, the graph can be depicted like in the following example:

Graph Before Partial Inference

The difference in a graph structure with a graph during the front phase is not only in the data nodes, but also in the edge attributes. Note that an out attribute is specified for edges from operation nodes only, while an in attribute is specified for edges from data nodes only. This corresponds to the fact that a tensor (data node) is produced from a specific output port of an operation and is consumed with a specific input port of an operation. Also, a unique data node is created for each output port of an operation. The node may be used as an input node for several operation nodes. Similarly to the data node “data2_0”, which is consumed with the input “port 1” of the “Operation 3” and input “port 0” of the “Operation 5”.

Now, consider how Model Optimizer performs shape and value propagation. Model Optimizer performs graph nodes topological sort. An error message is thrown if a graph contains a cycle. Then, shape inference functions are called for each node in the graph, according to the topological order. Each node of the graph must have an attribute called infer with a shape inference function, which is a function with one parameter – an instance of the Node class. The infer attribute is usually set in the operation extractor or when a node is added in some transformation using the Model Optimizer operation class inherited from the mo.pos.Op class. For more information on how to specify a shape inference function, refer to the Model Optimizer Operation and Operation Extractor sections.

A shape inference function should calculate an operation (node) output shape(s) based on input shape(s) and operation (node) attribute(s) and update shape and optionally value attributes of the corresponding data node(s). A simplified example of the shape infer function for the Reshape operation (the full version is available in the mo/ops/reshape.py file):

@staticmethod
def infer(node: Node):
    name = node.soft_get('name', node.id)

    input_shape = node.in_port(0).data.get_shape()  # get the input tensor shape
    new_shape = node.in_port(1).data.get_value()  # get the value defining the output tensor shape. This tensor may
                                                  # have special values like 0 and -1

    output_shape = ... # calculate output shape without special values like 0 and -1

    if node.in_port(0).data.get_value() is not None:  # if the input value is defined then calculate output value;
                                                      # shape will be updated automatically with the value shape
        node.out_port(0).data.set_value(node.in_port(0).data.get_value().reshape(output_shape))
    else:  # in the opposite case calculate the output shape only
        node.out_port(0).data.set_shape(output_shape)

Methods in_port() and output_port() of the Node class are used to get and set data node attributes. For more information on how to use them, refer to the Graph Traversal and Modification Using Ports and Connections section.

Note

A shape inference function should perform output shape calculation in the original model layout. For example, OpenVINO supports Convolution operations in NCHW layout only but TensorFlow supports NHWC layout as well. Model Optimizer shape inference function calculates output shapes for NHWC Convolutions in NHWC layout and only during the layout change phase the shape is converted to NCHW.

Note

There is a legacy approach to read data node attribute, like input_shape = op_node.in_node(0).shape and modify data nodes attributes, like op_node.out_node(0).shape = some_value. This approach is still used in the Model Optimizer code but is not recommended. Instead, use the approach described in the Ports.

Middle Phase

The middle phase starts after partial inference. At this phase, a graph contains data nodes and output shapes of all operations in the graph have been calculated. Any transformation implemented at this stage must update the shape attribute for all newly added operations. It is highly recommended to use API described in the Graph Traversal and Modification Using Ports and Connections because modification of a graph using this API causes automatic re-inference of affected nodes as well as necessary data nodes creation.

More information on how to develop middle transformations and dedicated API description is provided in the Middle Phase Transformations.

NHWC to NCHW Layout Change

There are several middle transformations responsible for changing model layout from NHWC to NCHW. These transformations are triggered by default for TensorFlow models as TensorFlow supports Convolution operations in the NHWC layout.

This layout change is disabled automatically if the model does not have operations that OpenVINO&trade needs to execute in the NCHW layout, for example, Convolutions in NHWC layout.

It is still possible to force Model Optimizer to do a layout change, using --disable_nhwc_to_nchw command-line parameter, although it is not advised.

Layout change is a complex problem and will be addressed here very briefly. For more details on how it works, refer to the source code of the transformations mentioned in the below summary of the process:

  1. Model Optimizer changes output shapes of most of operations producing 4D and 5D (four dimensional and five dimensional) tensors as if they were in NHWC layout to NCHW layout: nchw_shape = np.array(nhwc_shape)[0, 3, 1, 2] for 4D and nchw_shape = np.array(nhwc_shape)[0, 4, 1, 2, 3] for 5D. This permutation does not happen for some operations with specific conditions identified during a model conversion.

  2. Model Optimizer inserts Gather operations to the sub-graph relates to shapes calculation in order to perform shape calculation in a correct layout.

  3. Model Optimizer inserts Transpose operations for some operations with specific conditions, identified during a model conversion, to produce correct inference results.

The list of main transformations responsible for a layout change are: extensions/middle/ApplyPermutations.py, extensions/middle/InsertLayoutPropagationTransposes.py, extensions/middle/MarkSubgraphsWithCorrectLayout.py, extensions/middle/ApplyNHWCtoNCHWpermutation.py and extensions/middle/LayoutChangeForConstantShapePaths.py.

Back Phase

The back phase starts after the layout change to NCHW. This phase contains mostly the following transformations:

  1. Transformations that should work with a graph in the NCHW layout and thus cannot be implemented in the middle phase.

  2. Transformations that replace nodes corresponding to internal Model Optimizer operations with nodes corresponding to the opset operations.

  3. Transformations that normalize operations inputs according to the specification.

  4. Final optimization transformations.

A graph structure during the back phase is the same as during the middle phase. There is no difference in writing middle and back transformations.

More information on how to develop back transformations and dedicated API description is provided in the Back Phase Transformations.

Intermediate Representation Emitting

The last phase of a model conversion is the Intermediate Representation emitting. Model Optimizer performs the following steps:

  1. Iterates over all operation nodes in the graph and checks that all nodes have the type attribute set. This attribute defines the operation type and is used in the OpenVINO to instantiate proper operation from the opset specified in the version attribute of the node. If a node does not have attribute type or its value is equal to None, Model Optimizer exits with an error.

  2. Performs type inference of graph operations similar to the shape inference. Inferred data types are saved to a port attributes in the IR.

  3. Performs topological sort of the graph and changes id attribute of all operation nodes to be sequential integer values starting from 0.

  4. Saves all Constants values to the .bin file. Constants with the same value are shared among different operations.

  5. Generates an .xml file defining a graph structure. The information about operation inputs and outputs are prepared uniformly for all operations regardless of their type. A list of attributes to be saved to the .xml file is defined with the backend_attrs() or supported_attrs() of the Op class used for a graph node instantiation. For more information on how the operation attributes are saved to XML, refer to the function prepare_emit_ir() in the mo/pipeline/common.py file and Model Optimizer Operation section.

Graph Traversal and Modification Using Ports and Connections

There are three APIs for a graph traversal and transformation used in the Model Optimizer:

  1. The API provided with the networkx Python library for the networkx.MultiDiGraph class, which is the base class for the mo.graph.graph.Graph object. For more details, refer to the Model Representation in Memory section. For example, the following methods belong to this API level: graph.add_edges_from([list]), graph.add_node(x, attrs), graph.out_edges(node_id) etc where graph is a an instance of the networkx.MultiDiGraph class. This is the lowest-level API. Avoid using it in the Model Optimizer transformations.

  2. The API built around the mo.graph.graph.Node class. The Node class is the primary class to work with graph nodes and their attributes. There are some Node class methods not recommended for use and some functions defined in the mo.graph.graph have been deprecated. Examples of such methods and functions are: node.in_node(y), node.out_node(x), node.get_outputs(), node.insert_node_after(n1, y), create_edge(n1, n2) etc. For more details, refer to the mo/graph/graph.py file.

  3. The high-level API called Model Optimizer Graph API, which uses mo.graph.graph.Graph, mo.graph.port.Port and mo.graph.connection.Connection classes. For example, the following methods belong to this API level: node.in_port(x), node.out_port(y), port.get_connection(), connection.get_source(), connection.set_destination(dest_port) etc. This is the recommended API for the Model Optimizer transformations and operations implementation.

The main benefit of using the Model Optimizer Graph API is that it hides some internal implementation details (the fact that the graph contains data nodes), provides API to perform safe and predictable graph manipulations, and adds operation semantic to the graph. This is achieved with introduction of concepts of ports and connections. This article is dedicated to the Model Optimizer Graph API only and does not cover other two non-recommended APIs.

Ports

An operation semantic describes how many inputs and outputs the operation has. For example, Parameter and Const operations have no inputs and have one output, ReLU operation has one input and one output, Split operation has 2 inputs and a variable number of outputs depending on the value of the attribute num_splits.

Each operation node in the graph (an instance of the Node class) has 0 or more input and output ports (instances of the mo.graph.port.Port class). The Port object has several attributes:

  • node - the instance of the Node object the port belongs to.

  • idx - the port number. Input and output ports are numbered independently, starting from 0. Thus, ReLU operation has one input port (with index 0) and one output port (with index 0).

  • type - the type of the port. Could be equal to either "in" or "out".

  • data - the object that should be used to get attributes of the corresponding data node. This object has methods get_shape() / set_shape() and get_value() / set_value() to get/set shape/value of the corresponding data node. For example, in_port.data.get_shape() returns an input shape of a tensor connected to input port in_port (in_port.type == ‘in ), out_port.data.get_value() returns a value of a tensor produced from output port out_port ( out_port.type == ‘out’`).

Note

Functions get_shape() and get_value() return None until the partial inference phase. For more information about model conversion phases, refer to the Model Conversion Pipeline section. For information about partial inference phase, see the Partial Inference section.

There are several methods of the Node class to get the instance of a corresponding port:

  • in_port(x) and out_port(x) to get the input/output port with number x.

  • in_ports() and out_ports() to get a dictionary, where key is a port number and the value is the corresponding input/output port.

Attributes in_ports_count and out_ports_count of the Op class instance define default number of input and output ports to be created for the Node. However, additional input/output ports can be added using methods add_input_port() and add_output_port(). Port also can be removed, using the delete_input_port() and delete_output_port() methods.

The Port class is just an abstraction that works with edges incoming/outgoing to/from a specific Node instance. For example, output port with idx = 1 corresponds to the outgoing edge of a node with an attribute out = 1, the input port with idx = 2 corresponds to the incoming edge of a node with an attribute in = 2.

Consider the example of a graph part with 4 operation nodes “Op1”, “Op2”, “Op3”, and “Op4” and a number of data nodes depicted with light green boxes.

Ports example 1

Operation nodes have input ports (yellow squares) and output ports (light purple squares). Input port may not be connected. For example, the input “port 2” of node “Op1” does not have incoming edge, while output port always has an associated data node (after the partial inference when the data nodes are added to the graph), which may have no consumers.

Ports can be used to traverse a graph. The method get_source() of an input port returns an output port producing the tensor consumed by the input port. It is important that the method works the same during front, middle and back phases of a model conversion even though the graph structure changes (there are no data nodes in the graph during the front phase).

Let’s assume that there are 4 instances of Node object op1, op2, op3, and op4 * corresponding to nodes “Op1”, “Op2”, “Op3”, and “Op4”, respectively. The result of op2.in_port(0).get_source() and op4.in_port(1).get_source() is the same object op1.out_port(1) of type Port.

The method get_destination() of an output port returns the input port of the node consuming this tensor. If there are multiple consumers of this tensor, the error is raised. The method get_destinations() of an output port returns a list of input ports consuming the tensor.

The method disconnect() removes a node incoming edge corresponding to the specific input port. The method removes several edges if it is applied during the front phase for a node output port connected with multiple nodes.

The method port.connect(another_port) connects output port port and input port another_port. The method handles situations when the graph contains data nodes (middle and back phases) and does not create an edge between two nodes but also automatically creates data node or reuses existing data node. If the method is used during the front phase and data nodes do not exist, the method creates edge and properly sets in and out edge attributes.

For example, applying the following two methods to the graph above will result in the graph depicted below:

op4.in_port(1).disconnect()
op3.out_port(0).connect(op4.in_port(1))
Ports example 2

Note

For a full list of available methods, refer to the Node class implementation in the mo/graph/graph.py and Port class implementation in the mo/graph/port.py files.

Connections

Connection is a concept introduced to easily and reliably perform graph modifications. Connection corresponds to a link between a source output port with one or more destination input ports or a link between a destination input port and source output port producing data. So each port is connected with one or more ports with help of a connection. Model Optimizer uses the mo.graph.connection.Connection class to represent a connection.

There is only one get_connection() method of the Port class to get the instance of the corresponding Connection object. If the port is not connected, the returned value is None.

For example, the op3.out_port(0).get_connection() method returns a Connection object encapsulating edges from node “Op3” to data node “data_3_0” and two edges from data node “data_3_0” to two ports of the node “Op4”.

The Connection class provides methods to get source and destination(s) ports the connection corresponds to:

  • connection.get_source() - returns an output Port object producing the tensor.

  • connection.get_destinations() * - returns a list of input Port consuming the data.

  • connection.get_destination() * - returns a single input Port consuming the data. If there are multiple consumers, the exception is raised.

The Connection class provides methods to modify a graph by changing a source or destination(s) of a connection. For example, the function call op3.out_port(0).get_connection().set_source(op1.out_port(0)) changes source port of edges consuming data from port op3.out_port(0) to op1.out_port(0). The transformed graph from the sample above is depicted below:

Connection example 1

Another example is the connection.set_destination(dest_port) method. It disconnects dest_port and all input ports to which the connection is currently connected and connects the connection source port to dest_port.

Note that connection works seamlessly during front, middle, and back phases and hides the fact that the graph structure is different.

Note

For a full list of available methods, refer to the Connection class implementation in the mo/graph/connection.py file.

Model Optimizer Extensions

Model Optimizer extensions enable you to inject some logic to the model conversion pipeline without changing the Model Optimizer core code. There are three types of the Model Optimizer extensions:

  1. Model Optimizer operation.

  2. A framework operation extractor.

  3. A model transformation, which can be executed during front, middle or back phase of the model conversion.

An extension is just a plain text file with a Python code. The file should contain a class (or classes) inherited from one of extension base classes. Extension files should be saved to a directory with the following structure:

./<MY_EXT>/
           ops/                  - custom operations
           front/                - framework independent front transformations
                 <FRAMEWORK_1>/  - front transformations for <FRAMEWORK_1> models only and extractors for <FRAMEWORK_1> operations
                 <FRAMEWORK_2>/  - front transformations for <FRAMEWORK_2> models only and extractors for <FRAMEWORK_2> operations
                 ...
           middle/               - middle transformations
           back/                 - back transformations

Model Optimizer uses the same layout internally to keep built-in extensions. The only exception is that the mo/ops/ directory is also used as a source of the Model Optimizer operations due to historical reasons.

Note

The name of a root directory with extensions should not be equal to “extensions” because it will result in a name conflict with the built-in Model Optimizer extensions.

Note

Model Optimizer itself is built by using these extensions, so there is a huge number of examples of their usage in the Model Optimizer code.

Model Optimizer Operation

Model Optimizer defines a mo.ops.Op class (Op will be used later in the document to be short), which is a base class for an operation used in the Model Optimizer. The instance of the Op class serves several purposes:

  1. Stores the operation attributes.

  2. Stores the operation shape/value and type inference functions.

  3. Defines operation attributes to be saved to the corresponding IR section.

  4. Contains convenient methods to create a graph node from an Op object instance and connect it with the existing graph.

  5. Used in the extractors to store parsed attributes and operation specific attributes in the dedicated graph node.

It is important to mention that there is no connection between the instance of the Op class and the Node object created from it. The Op class is just a container for attributes describing the operation. Model Optimizer uses the Op class during a model conversion to create a node of the graph with attributes copied from the Op class instance. Graph manipulations are performed with graph Node s and their attributes and does not involve Op s.

There are a number of common attributes used in the operations. Below is the list of these attributes with description.

  • id — unique identifier of a node in a graph. Generated automatically, equal to the number of nodes in the graph plus 1 if not specified. Mandatory.

  • name — name of the operation. Generated automatically, equal to the id if not specified. Mandatory.

  • type — type of the operation according to the opset specification. For the internal Model Optimizer operations, this attribute should be set to None. The model conversion fails if an operation with type equal to None comes to the IR emitting phase. Mandatory.

  • version — the operation set (opset) name the operation belongs to. If not specified, Model Optimizer sets it equal to experimental. For more information about operation sets, refer to OpenVINO Model Representation section. Mandatory.

  • op — Model Optimizer type of the operation. In many cases, the value of type is equal to the value of op. However, when Model Optimizer cannot instantiate the opset operation during model loading, it creates an instance of an internal operation. Thus, the attribute op is used as a type of this internal operation. Later in the pipeline, the node created from an internal operation will be replaced during front, middle or back phase with node(s) created from the opset.

  • infer — the attribute defines a function calculating output tensor(s) shape and optional value(s). The attribute may be set to None for the internal Model Optimizer operations used during the front phase only. For more information about the shape inference function, refer to the Partial Inference section.

  • type_infer — the attribute defines a function calculating output tensor(s) data type. If the attribute is not defined, the default function is used. The function checks if the data_type node attribute is set and then propagates this type to the output tensor from the “port 0”. Otherwise, it propagates the data type of the tensor coming into the input “port 0” to the output tensor from the “port 0”.

  • in_ports_count — default number of input ports to be created for the operation. Additional ports can be created or redundant ports can be removed using dedicated Node class API methods.

  • out_ports_count — default number of output ports to be created for the operation. Additional ports can be created or redundant ports can be removed using dedicated Node class API methods.

Below is an example of the Model Optimizer class for the SoftMax operation from the mo/ops/softmax.py file with the comments in code.

class Softmax(Op):
    # The class attribute defines a name of the operation so the operation class can be obtained using the
    # "Op.get_op_class_by_name()" static method
    op = 'SoftMax'

    # The operation works as an extractor by default. This is a legacy behavior, currently not recommended for use,
    # thus "enabled" class attribute is set to False. The recommended approach is to use dedicated extractor extension.
    enabled = False

    def __init__(self, graph: Graph, attrs: dict):
        super().__init__(graph, {  # The constructor of the base class Op is called with additional default attributes.
            'type': __class__.op,  # The operation is from the opset so the type is set to 'SoftMax'.
            'op': __class__.op,  # Internal Model Optimizer operation has the same type.
            'version': 'opset1',  # The operation corresponds to opset1.
            'infer': Softmax.infer,  # Shape inference function is defined below.
            'axis': 1,  # Default value for the "axis" attribute of the operation SoftMax.
            'in_ports_count': 1,  # The operation has one input.
            'out_ports_count': 1,  # The operation produces one output.
        }, attrs)

    # The method returns operation specific attributes list. This method is important when implementing
    # extractor inherited from CaffePythonFrontExtractorOp class to extract attribute for Caffe Python operation.
    # However, it is currently used interchangeably with the "backend_attrs()" method. If the "backend_attrs()" is not used,
    # then the "supported_attrs()" is used instead. In this particular case, the operation has just one attribute "axis".
    def supported_attrs(self):
        return ['axis']

    @staticmethod
    def infer(node: Node):
        "some code calculating output shape and values"

There is a dedicated method called backend_attrs() defining a list of attributes to be saved to the IR. Consider an example from the mo/ops/pooling.py file:

def backend_attrs(self):
     return [
         ('strides', lambda node: ','.join(map(str, node['stride'][node.spatial_dims]))),
         ('kernel', lambda node: ','.join(map(str, node['window'][node.spatial_dims]))),

         ('pads_begin', lambda node: ','.join(map(str, get_backend_pad(node.pad, node.spatial_dims, 0)))),
         ('pads_end', lambda node: ','.join(map(str, get_backend_pad(node.pad, node.spatial_dims, 1)))),

         ('pool-method', 'pool_method'),
         ('exclude-pad', 'exclude_pad'),

         'rounding_type',
         'auto_pad',
     ]

The backend_attrs() function returns a list of records. A record can be of one of the following formats:

  1. A string defining the attribute to be saved to the IR. If the value of the attribute is None, the attribute is not saved. Examples of this case are rounding_type and auto_pad.

  2. A tuple, where the first element is a string defining the name of the attribute as it will appear in the IR and the second element is a function to produce the value for this attribute. The function gets an instance of the Node as the only parameter and returns a string with the value to be saved to the IR. Examples of this case are strides, kernel, pads_begin and pads_end.

  3. A tuple, where the first element is a string defining the name of the attribute as it will appear in the IR and the second element is the name of the Node attribute to get the value from. Examples of this case are pool-method and exclude-pad.

Operation Extractor

Model Optimizer runs specific extractor for each operation in the model during the model loading. For more information about this process, refer to the operations-attributes-extracting section.

There are several types of Model Optimizer extractor extensions:

  1. The generic one, which is described in this section.

  2. The special extractor for Caffe models with Python layers. This kind of extractor is described in the Extending Model Optimizer with Caffe Python Layers guide.

This section is focused on the option #1, which provides a generic mechanism for the operation extractor applicable for all frameworks. Model Optimizer provides the mo.front.extractor.FrontExtractorOp class as a base class to implement the extractor. It has the extract class method, which gets the only parameter Node, which corresponds to the graph node to extract data from. The operation description in the original framework format is stored in the attribute pb of the node. The extractor goal is to parse this attribute and save necessary attributes to the corresponding node of the graph. Consider the extractor for the Const TensorFlow operation (refer to the extensions/front/tf/const_ext.py file):

from openvino.tools.mo.front.extractor import FrontExtractorOp
from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor, tf_tensor_shape, tf_tensor_content
from openvino.tools.mo.ops.const import Const


class ConstExtractor(FrontExtractorOp):
    # The "op" class attribute defines a type of the operation in the framework (in this case it is a TensorFlow),
    # for which the extractor should be triggered.
    op = 'Const'
    enabled = True  # The flag that indicates that this extractor is enabled.

    @classmethod
    def extract(cls, node):  # The entry point of the extractor.
        # The `node.pb` attribute stores the TensorFlow representation of the operation, which is a Protobuf message of the
        # specific format. In particular, the message contains the attribute called "value" containing the description of
        # the constant. The string "pb.attr["value"].tensor" is just a Python binding for Protobuf message parsing.
        pb_tensor = node.pb.attr["value"].tensor
        # Get the shape of the tensor from the protobuf message, using the helper function "tf_tensor_shape".
        shape = tf_tensor_shape(pb_tensor.tensor_shape)
        # Create a dictionary with necessary attributes.
        attrs = {
            'shape': shape,
            # Get the tensor value, using "tf_tensor_content" helper function.
            'value': tf_tensor_content(pb_tensor.dtype, shape, pb_tensor),
            # Get the tensor data type, using "tf_dtype_extractor" helper function.
            'data_type': tf_dtype_extractor(pb_tensor.dtype),
        }
        # Update the node attributes, using default attributes from the "Const" operation and attributes saved to the
        # "attrs" dictionary.
        Const.update_node_stat(node, attrs)
        return cls.enabled

Consider another example with an extractor of the Constant ONNX operation (refer to the extensions/front/onnx/const_ext.py file):

from onnx import numpy_helper
from onnx.numpy_helper import to_array

from openvino.tools.mo.front.extractor import FrontExtractorOp
from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
from openvino.tools.mo.ops.const import Const


class ConstantExtractor(FrontExtractorOp):
    op = 'Constant'
    enabled = True

    @classmethod
    def extract(cls, node):
        # Use "onnx_attr" helper method, which parses the Protobuf representation of the operation saved in the "node".
        # Gets the value of the attribute with name "value" as "TensorProto" type (specified with a keyword "t").
        pb_value = onnx_attr(node, 'value', 't')
        # Use "numpy_helper.to_array()" ONNX helper method to convert "TensorProto" object to a numpy array.
        value = numpy_helper.to_array(pb_value)

        attrs = {
            'data_type': value.dtype,
            'value': value,
        }
        # Update the node attributes, using default attributes from the "Const" operation and attributes saved to the
        # "attrs" dictionary.
        Const.update_node_stat(node, attrs)
        return cls.enabled

The extractors for operations from different frameworks work similarly. The only difference is in the helper methods used to parse operation attributes encoded with a framework-specific representation.

A common practice is to use update_node_stat() method of the dedicated Op class to update the node attributes. This method does the following:

  1. Sets values for common attributes like op, type, infer, in_ports_count, out_ports_count, version to values specific to the dedicated operation (Const operation in this case).

  2. Uses supported_attrs() and backend_attrs() methods, defined in the Op class to update specific node attribute IE. The IR emitter uses the value stored in the IE attribute to pre-process attribute values and save them to IR.

  3. Optionally sets additional attributes provided to the update_node_stat() function as a second parameter. Usually these attributes are parsed from the particular instance of the operation.

Note

Model Optimizer uses numpy arrays to store values and numpy arrays of np.int64 type to store shapes in the graph.

Graph Transformation Extensions

Model Optimizer provides various base classes to implement Front Phase Transformations, Middle Phase Transformations, and Back Phase Transformations. All classes have the following common class attributes and methods:

  1. The enabled attribute specifies whether the transformation is enabled or not. The value can be changed during runtime to enable or disable execution of the transformation during a model conversion. Default value is True.

  2. The id attribute specifies a unique transformation string identifier. This transformation identifier can be used to enable (disable) the transformation by setting environment variable MO_ENABLED_TRANSFORMS (MO_DISABLED_TRANSFORMS) with a comma separated list of id s. The environment variables override the value of the enabled attribute of the transformation. Instead of using id attribute value you can add fully defined class name to MO_ENABLED_TRANSFORMS (MO_DISABLED_TRANSFORMS) variable, extensions.back.NonmalizeToNormalizeL2.NormalizeToNormalizeL2 for example. It is an optional attribute.

  3. The run_not_recursively attribute specifies whether the transformation should be executed in the sub-graphs, for example, body of the TensorIterator and the Loop. Default value is True.

  4. The force_clean_up attribute specifies whether the graph clean up should be executed after the transformation. The graph cleanup removes nodes of the graph not reachable from the model inputs. Default value is False.

  5. The force_shape_inference attribute specifies whether the nodes marked with need_shape_inference attribute equal to True should be re-inferred after the transformation. Model Optimizer sets this attribute automatically for nodes, input(s) of which were changed during the transformation, or you can set this attribute manually in the transformation for the specific nodes. Default value is False.

  6. Attribute graph_condition specifies a list of functions with one parameter Graph object. The transformation is executed if and only if all functions return True. If the attribute is not set, no check is performed.

  7. Method run_before() returns a list of transformation classes which this transformation should be executed before.

  8. Method run_after() returns a list of transformation classes which this transformation should be executed after.

Note

Some of the transformation types have specific class attributes and methods, which are explained in the corresponding sections of this document.

Model Optimizer builds a graph of dependencies between registered transformations and executes them in the topological order. To execute the transformation during a proper model conversion phase, Model Optimizer defines several anchor transformations that do nothing. All transformations are ordered with respect to these anchor transformations. The diagram below shows anchor transformations, some of built-in transformations and dependencies between them:

Transformations Graph

User-defined transformations are executed after the corresponding Start and before the corresponding Finish anchor transformations by default (if run_before() and run_after() methods have not been overridden).

Note

The PreMiddleStart and PostMiddleStart anchors were introduced due to historical reasons to refactor the Model Optimizer pipeline, which initially had a hardcoded order of transformations.

Front Phase Transformations

There are several types of a front phase transformation:

  1. Pattern-Defined Front Phase Transformations triggered for each sub-graph of the original graph isomorphic to the specified pattern.

  2. Specific Operation Front Phase Transformations triggered for the node with a specific op attribute value.

  3. Generic Front Phase Transformations.

  4. Manually enabled transformation, defined with a JSON configuration file (for TensorFlow, ONNX, Apache MXNet, and PaddlePaddle models), specified using the --transformations_config command-line parameter:

    1. Node Name Pattern Front Phase Transformations.

    2. Front Phase Transformations Using Start and End Points.

    3. Generic Front Phase Transformations Enabled with Transformations Configuration File.

Pattern-Defined Front Phase Transformations

This type of transformation is implemented using mo.front.common.replacement.FrontReplacementSubgraph and mo.front.common.replacement.FrontReplacementPattern as base classes and works as follows:

  1. Define a sub-graph to be matched, using a list of nodes with attributes and edges connecting them (edges may also have attributes).

  2. Model Optimizer searches for all sub-graphs of the original graph, isomorphic to the specified sub-graph (pattern).

  3. Model Optimizer executes the defined function performing graph transformation for each instance of a matched sub-graph. You can override different functions in the base transformation class so the Model Optimizer works differently:

    1. The replace_sub_graph(self, graph, match) override the method. In this case Model Optimizer only executes the overridden function, pass the graph object and a dictionary describing the matched sub-graph. You are required to write the transformation and connect the newly created nodes to the rest of the graph.

    2. The generate_sub_graph(self, graph, match) override the method. This case is not recommended for use because it is the most complicated approach. It can be effectively replaced with one of two previous approaches. The explanation of this function is provided in the Node Name Defined Sub-Graph Transformations section.

The sub-graph pattern is defined in the pattern() function. This function should return a dictionary with two keys: nodes and edges :

  • The value for the nodes key is a list of tuples with two elements.

    • The first element is an alias name for a node that will be used to define edges between nodes and in the transformation function.

    • The second element is a dictionary with attributes. The key is a name of an attribute that should exist in the node. The value for the attribute can be some specific value to match or a function that gets a single parameter - the attribute value from the node. The function should return the result of attribute comparison with a dedicated value.

  • The value for the edges key is a list of tuples with two or three elements.

    • The first element is the alias name of the node producing a tensor.

    • The second element is the alias name of the node consuming the tensor.

    • The third element (optional) is the dictionary with expected edge attributes. This dictionary usually contains attributes like in and out, defining input and output ports.

Consider the example of a front transformation implemented in the extensions/front/Mish_fusion.py file performing fusing of the sub-graph defining the Mish activation function into a single operation:

from openvino.tools.mo.front.Softplus_fusion import SoftplusFusion
from openvino.tools.mo.ops.activation_ops import Mish
from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
from openvino.tools.mo.front.subgraph_matcher import SubgraphMatch
from openvino.tools.mo.graph.graph import Graph, rename_nodes


class MishFusion(FrontReplacementSubgraph):
    """
    The transformation looks for the pattern with Softplus defining the Mish function: Mish(x) = x \* tanh(SoftPlus(x)).
    """
    enabled = True  # Transformation is enabled.

    def run_after(self):  # Run this transformation after "SoftplusFusion" transformation.
        return [SoftplusFusion]

    def pattern(self):  # Define pattern according to formulae x \* tanh(SoftPlus(x)).
        return dict(
            nodes=[
                ('mul', dict(op='Mul')),
                ('tanh', dict(op='Tanh')),
                ('softplus', dict(op='SoftPlus')),
            ],
            edges=[
                ('softplus', 'tanh'),
                ('tanh', 'mul'),
            ])

    def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):  # Entry point for the transformation.
        mul = match['mul']  # Get the Node corresponding to matched "mul" node.
        mul_name = mul.soft_get('name', mul.id)
        softplus = match['softplus']  # Get the Node corresponding to the matched "softplus" node.

        # Determine the input port of Mul which gets the 'input' node output.
        input_port_idx = int(mul.in_port(0).get_connection().get_source().node.soft_get('op') == 'Tanh')

        # Check that the same tensor is provided as input to Mul and SoftPlus.
        if mul.in_port(input_port_idx).get_source() != softplus.in_port(0).get_source():
            return

        mish = Mish(graph, {}).create_node()  # Create Mish operation.
        mish.in_port(0).connect(mul.in_port(input_port_idx).get_source())  # Connect input to the Mish.
        mul.out_port(0).get_connection().set_source(mish.out_port(0))  # Reconnect outgoing edge from "mul" to Mish.

        # Rename the created Mish operation to have the name of the "mul" node, which produced the value equal to the
        # Mish output.
        rename_nodes([(mul, mul_name + '/TBR'), (mish, mul_name)])
Specific Operation Front Phase Transformations

This type of transformation is implemented using mo.front.common.replacement.FrontReplacementOp as base class and works as follows:

  1. Define an operation type to trigger the transformation.

  2. Model Optimizer searches for all nodes in the graph with the attribute op equal to the specified value.

  3. Model Optimizer executes the defined function performing graph transformation for each instance of a matched node. You can override different functions in the base transformation class and Model Optimizer works differently:

    1. The replace_sub_graph(self, graph, match) override method. In this case, Model Optimizer only executes the overridden function. Pass the graph object and a dictionary with a single key op with the matched node as value. You are required to write the transformation and connect the newly created nodes to the rest of the graph.

    2. The replace_op(self, graph, node) override method. In this case, Model Optimizer executes the overridden function. Pass the graph object and the matched node as node parameter. If the function returns an id of some node, then the Node with this id is connected to the consumers of the matched node. After applying the transformation, the matched node is removed from the graph.

The FrontReplacementOp class provides a simpler mechanism to match a single operation with specific value of the op (write the op attribute in the class instead of defining a pattern() function) attribute and perform the transformation.

Consider an example transformation from the extensions/front/Pack.py file, which replaces Pack operation from the TensorFlow:

from openvino.tools.mo.front.common.partial_infer.utils import int64_array
from openvino.tools.mo.front.common.replacement import FrontReplacementOp
from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
from openvino.tools.mo.graph.graph import Node, Graph, rename_nodes
from openvino.tools.mo.ops.concat import Concat
from openvino.tools.mo.ops.unsqueeze import Unsqueeze


class Pack(FrontReplacementOp):
    op = "Pack"  # Trigger transformation for all nodes in the graph with the op = "Pack" attribute
    enabled = True  # Transformation is enabled.

    def replace_op(self, graph: Graph, node: Node):  # Entry point for the transformation.
        # Create a Concat operation with a number of inputs equal to a number of inputs to Pack.
        out_node = Concat(graph, {'axis': node.axis, 'in_ports_count': len(node.in_ports())}).create_node()
        pack_name = node.soft_get('name', node.id)

        for ind in node.in_ports():
            # Add dimension of size 1 to all inputs of the Pack operation and add them as Concat inputs.
            unsqueeze_node = create_op_with_const_inputs(graph, Unsqueeze, {1: int64_array([node.axis])},
                                                         {'name': node.soft_get('name', node.id) + '/Unsqueeze'})
            node.in_port(ind).get_connection().set_destination(unsqueeze_node.in_port(0))
            unsqueeze_node.out_port(0).connect(out_node.in_port(ind))

        # Rename the created Concat operation to have the name of the "pack" node, which produced the value equal to the
        # Concat output.
        rename_nodes([(node, pack_name + '/TBR'), (out_node, pack_name)])
        return [out_node.id]  # Reconnect the Pack operation consumers to get input from Concat instead.
Generic Front Phase Transformations

Model Optimizer provides a mechanism to implement generic front phase transformation. This type of transformation is implemented using mo.front.common.replacement.FrontReplacementSubgraph or mo.front.common.replacement.FrontReplacementPattern as base classes. Make sure the transformation is enabled before trying to execute it. Then, Model Optimizer executes the find_and_replace_pattern(self, graph) method and provides a Graph object as an input.

Consider the example of a generic front transformation from the extensions/front/SqueezeNormalize.py file performing normalization of the Squeeze operation. Older version of the operation had a list of axes to squeeze as an attribute, but now it is a separate input. For backward compatibility, the Model Optimizer operation supports both semantics. Before IR generation, however, the operation should be normalized according to the specification.

import logging as log

from openvino.tools.mo.front.common.partial_infer.utils import int64_array
from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
from openvino.tools.mo.graph.graph import Graph
from openvino.tools.mo.ops.const import Const
from openvino.tools.mo.utils.error import Error


class SqueezeNormalize(FrontReplacementPattern):
    """
    Normalizes inputs of the Squeeze layers. The layers should have two inputs: the input with data and input with the
    dimensions to squeeze. If the second input is omitted then all dimensions of size 1 should be removed.
    """
    enabled = True  # The transformation is enabled.

    def find_and_replace_pattern(self, graph: Graph):  # The function is called unconditionally.
        for squeeze_node in graph.get_op_nodes(op='Squeeze'):  # Iterate over all nodes with op='Squeeze'.
            # If the operation has only 1 input node and no 'squeeze_dims' Node attribute, then convert the attribute to
            # the operation input.
            if len(squeeze_node.in_nodes()) == 1 and squeeze_node.has_valid('squeeze_dims'):
                dims_node = Const(graph, {'name': squeeze_node.id + '/Dims',
                                          'value': int64_array(squeeze_node.squeeze_dims)}).create_node()
                squeeze_node.in_port(1).connect(dims_node.out_port(0))
                del squeeze_node['squeeze_dims']
            # If two inputs already exist, that means the operation is already normalized.
            elif len(squeeze_node.in_nodes()) == 2:
                log.debug('The Squeeze node "{}" is already normalized'.format(squeeze_node.name))
            # In all other cases, raise an error.
            else:
                raise Error('The Squeeze layer "{}" should either have 2 inputs or one input and an "squeeze_dims" '
                            'attribute'.format(squeeze_node.soft_get('name')))

For the details on implementation and how these front phase transformations work, refer to the mo/front/common/replacement.py file.

Node Name Pattern Front Phase Transformations

TensorFlow uses a mechanism of scope to group related operation nodes. It is a good practice to put nodes performing particular task into the same scope. This approach divides a graph into logical blocks that are easier to review in the TensorBoard. The scope, in fact, just defines a common name prefix for the nodes belonging to it.

For example, Inception topologies contain several types of so-called “Inception blocks”. Some of them are equal to each other, but located in different places of the network. For example, Inception V4 from the TensorFlow-Slim image classification model library has Mixed_5b, Mixed_5c and Mixed_5d inception blocks with exactly the same nodes, with the same set of attributes.

Consider a situation when these Inception blocks are implemented extremely efficiently using a single Inference Engine operation called InceptionBlock and these blocks in the model need to be replaced with instances of this operation. Model Optimizer provides mechanism to trigger the transformation for a sub-graph of operations defined by the node name regular expressions (scope). In this particular case, some of the patterns are: .\*InceptionV4/Mixed_5b, .\*InceptionV4/Mixed_5c * and .\*InceptionV4/Mixed_5d. Each pattern starts with .\*, because the InceptionV4 prefix is added to all nodes names during a model freeze.

This type of transformation is implemented using mo.front.tf.replacement.FrontReplacementFromConfigFileSubGraph as a base class and works as follows:

  1. Prepare a JSON configuration file template defining node names patterns.

  2. Run Model Optimizer with the --tensorflow_custom_operations_config_update command-line parameter, and Model Optimizer adds information about input and output nodes of the specified sub-graphs.

  3. Model Optimizer executes the defined transformation only when you specify the path to the configuration file updated in step 2 using the --transformations_config command-line parameter .

Consider the following possible configuration file template for the Inception Block transformation:

[
    {
        "custom_attributes": {
            "attr1_key": "attr1_value",
            "attr2_key": 123456
        },
        "id": "InceptionBlockTransformation",
        "instances": [
            ".\*InceptionV4/Mixed_5b",
            ".\*InceptionV4/Mixed_5c",
            ".\*InceptionV4/Mixed_5d"
        ],
        "match_kind": "scope"
    }
]

The configuration file contains a list of dictionaries. Each dictionary defines one transformation. Each transformation is defined with several parameters:

  • id (mandatory) - is a unique identifier of the transformation. It is used in the Python code that implements the transformation to link the class and the transformation description from the configuration file.

  • match_kind (mandatory) - is a string that specifies the matching algorithm. For the node name pattern case, the value should be equal to scope. Another possible values are described in the dedicated sections below.

  • instances (mandatory) - specifies instances of the sub-graph to be matched. It contains a list of node names prefixes patterns for the match kind of the scope type.

  • custom_attributes (optional) - is a dictionary with attributes that can be used in the transformation code.

After running Model Optimizer with additional --tensorflow_custom_operations_config_update parameter pointing to the template configuration file, the content of the file should be updated with two new sections inputs and outputs. The file content after the update is as follows:

[
    {
        "id": "InceptionBlockTransformation",
        "custom_attributes": {
            "attr1_key": "attr1_value",
            "attr2_key": 123456
        },
        "instances": [
            ".\*InceptionV4/Mixed_5b",
            ".\*InceptionV4/Mixed_5c",
            ".\*InceptionV4/Mixed_5d"
        ],
        "match_kind": "scope",
        "inputs": [
            [
                {
                    "node": "Branch_2/Conv2d_0a_1x1/Conv2D$",
                    "port": 0
                },
                {
                    "node": "Branch_3/AvgPool_0a_3x3/AvgPool$",
                    "port": 0
                },
                {
                    "node": "Branch_1/Conv2d_0a_1x1/Conv2D$",
                    "port": 0
                },
                {
                    "node": "Branch_0/Conv2d_0a_1x1/Conv2D$",
                    "port": 0
                }
            ]
        ],
        "outputs": [
            {
                "node": "concat$",
                "port": 0
            }
        ]
    }
]

The value for inputs key is a list of lists describing input tensors of the sub-graph. Each element of the top-level list corresponds to one unique input tensor of the sub-graph. Each internal list describes a list of nodes consuming this tensor and port numbers, where the tensor is consumed. Model Optimizer generates regular expressions for the input nodes names to uniquely identify them in each instance of the sub-graph, defined by the instances. Denote these nodes as input nodes of the sub-graph.

In the InceptionV4 topology, the InceptionV4/Mixed_5b block has four input tensors from outside of the sub-graph, but all of them are produced by the InceptionV4/Mixed_5a/concat node. Therefore, the top-level list of the inputs contains one list corresponding to this tensor. Four input nodes of the sub-graph consume the tensor produced by InceptionV4/Mixed_5a/concat node. In this case, all four input nodes consume input tensor into “port 0”.

The order of items in the internal list describing nodes does not matter, but the order of elements in the top-level list is important. This order defines how Model Optimizer attaches input tensors to a new generated node if the sub-graph is replaced with a single node. The i -th input node of the sub-graph is obtained using match.single_input_node(i) call in the sub-graph transformation code. More information about API is given below. If it is necessary to change the order of input tensors, the configuration file can be edited in the text editor.

The value for the outputs key is a list describing nodes of the sub-graph producing tensor, that goes outside of the sub-graph or does not have child nodes. Denote these nodes as output nodes of the sub-graph. The order of elements in the list is important. The i -th element of the list describes the i -th output tensor of the sub-graph, which could be obtained using match.output_node(i) call. The order of elements can be manually changed in the configuration file. Model Optimizer uses this order to connect output edges if the sub-graph is replaced with a single node.

For more examples of this type of transformation, refer to the Converting TensorFlow Object Detection API Models guide.

Front Phase Transformations Using Start and End Points

This type of transformation is implemented using mo.front.tf.replacement.FrontReplacementFromConfigFileSubGraph as a base class and works as follows:

  1. Prepare a JSON configuration file that defines the sub-graph to match, using two lists of node names: “start” and “end” nodes.

  2. Model Optimizer executes the defined transformation only when you specify the path to the configuration file using the --transformations_config command-line parameter . Model Optimizer performs the following steps to match the sub-graph:

    1. Starts a graph traversal from every start node following the direction of the graph edges. The search stops in an end node or in the case of a node without consumers. All visited nodes are added to the matched sub-graph.

    2. Starts another graph traversal from each non-start node of the sub-graph, i.e. every node except nodes from the “start” list. In this step, the edges are traversed in the opposite edge direction. All newly visited nodes are added to the matched sub-graph. This step is needed to add nodes required for calculation values of internal nodes of the matched sub-graph.

    3. Checks that all “end” nodes were reached from “start” nodes. If not, it exits with an error.

    4. Checks that there are no Parameter operations among added nodes. If they exist, the sub-graph depends on the inputs of the model. Such configuration is considered incorrect so Model Optimizer exits with an error.

This algorithm finds all nodes “between” start and end nodes and nodes needed for calculation of non-input nodes of the matched sub-graph.

The example of a JSON configuration file for a transformation with start and end points is extensions/front/tf/ssd_support_api_v1.15.json :

[
    {
        "custom_attributes": {
            "code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
            "pad_mode": "caffe.ResizeParameter.CONSTANT",
            "resize_mode": "caffe.ResizeParameter.WARP",
            "clip_before_nms": false,
            "clip_after_nms": true
        },
        "id": "ObjectDetectionAPISSDPostprocessorReplacement",
        "include_inputs_to_sub_graph": true,
        "include_outputs_to_sub_graph": true,
        "instances": {
            "end_points": [
                "detection_boxes",
                "detection_scores",
                "num_detections"
            ],
            "start_points": [
                "Postprocessor/Shape",
                "Postprocessor/scale_logits",
                "Postprocessor/Tile",
                "Postprocessor/Reshape_1",
                "Postprocessor/Cast_1"
            ]
        },
        "match_kind": "points"
    }
]

The format of the file is similar to the one provided as an example in the Node Name Pattern Front Phase Transformations section. The difference is in the value of the match_kind parameter, which should be equal to the points and the format of the instances parameter, which should be a dictionary with two keys start_points and end_points, defining start and end node names respectively.

Note

The include_inputs_to_sub_graph and include_outputs_to_sub_graph parameters are redundant and should be always equal to true.

Note

This sub-graph match algorithm has a limitation that each start node must have only one input. Therefore, it is not possible to specify, for example, the Convolution node as input because it has two inputs: data tensor and tensor with weights.

For other examples of transformations with points, refer to the Converting TensorFlow Object Detection API Models guide.

Generic Front Phase Transformations Enabled with Transformations Configuration File

This type of transformation works similarly to the Generic Front Phase Transformations but require a JSON configuration file to enable it similarly to Node Name Pattern Front Phase Transformations and Front Phase Transformations Using Start and End Points.

The base class for this type of transformation is mo.front.common.replacement.FrontReplacementFromConfigFileGeneral. Model Optimizer executes the transform_graph(self, graph, replacement_descriptions) method and provides the Graph object and dictionary with values parsed from the custom_attributes attribute of the provided JSON configuration file.

The example of the configuration file for this type of transformation is extensions/front/tf/yolo_v1_tiny.json :

[
  {
    "id": "TFYOLO",
    "match_kind": "general",
    "custom_attributes": {
      "classes": 20,
      "coords": 4,
      "num": 2,
      "do_softmax": 0
    }
  }
]

and the corresponding transformation file is ./extensions/front/YOLO.py :

from openvino.tools.mo.front.no_op_eraser import NoOpEraser
from openvino.tools.mo.front.standalone_const_eraser import StandaloneConstEraser
from openvino.tools.mo.ops.regionyolo import RegionYoloOp
from openvino.tools.mo.front.tf.replacement import FrontReplacementFromConfigFileGeneral
from openvino.tools.mo.graph.graph import Node, Graph
from openvino.tools.mo.ops.result import Result
from openvino.tools.mo.utils.error import Error


class YoloRegionAddon(FrontReplacementFromConfigFileGeneral):
    """
    Replaces all Result nodes in graph with YoloRegion->Result nodes chain.
    YoloRegion node attributes are taken from configuration file
    """
    replacement_id = 'TFYOLO'  # The identifier matching the "id" attribute in the JSON file.

    def run_after(self):
        return [NoOpEraser, StandaloneConstEraser]

    def transform_graph(self, graph: Graph, replacement_descriptions):
        op_outputs = [n for n, d in graph.nodes(data=True) if 'op' in d and d['op'] == 'Result']
        for op_output in op_outputs:
            last_node = Node(graph, op_output).in_node(0)
            op_params = dict(name=last_node.id + '/YoloRegion', axis=1, end_axis=-1)
            op_params.update(replacement_descriptions)
            region_layer = RegionYoloOp(graph, op_params)
            region_layer_node = region_layer.create_node([last_node])
            # In here, 'axis' from 'dim_attrs' can be removed to avoid permutation from axis = 1 to axis = 2.
            region_layer_node.dim_attrs.remove('axis')
            Result(graph).create_node([region_layer_node])
            graph.remove_node(op_output)

The configuration file has only 3 parameters: id identifier of the transformation , match_kind (which should be equal to general) and the custom_attributes dictionary with custom attributes accessible in the transformation.

Middle Phase Transformations

There are two types of middle phase transformations:

  1. Pattern-Defined Middle Phase Transformations triggered for each sub-graph of the original graph, isomorphic to the specified pattern.

  2. Generic Middle Phase Transformations.

Pattern-Defined Middle Phase Transformations

This type of transformation is implemented using mo.middle.replacement.MiddleReplacementPattern as a base class and works similarly to the Pattern-Defined Front Phase Transformations. The are two differences:

  1. The transformation entry function name is replace_pattern(self, graph, match).

  2. The pattern defining the graph should contain data nodes because the structure of the graph is different between front and middle phases. For more information about the graph structure changes, refer to the Partial Inference section.

For the example of a pattern-defined middle transformation, refer to the extensions/middle/L2NormToNorm.py file.

Generic Middle Phase Transformations

Model Optimizer provides a mechanism to implement generic middle phase transformations. This type of transformation is implemented using mo.middle.replacement.MiddleReplacementPattern as a base class and works similarly to the Generic Front Phase Transformations. The only difference is that the transformation entry function name is find_and_replace_pattern(self, graph: Graph).

For the example of this transformation, refer to the extensions/middle/CheckForCycle.py file.

Back Phase Transformations

There are two types of back phase transformations:

  1. Pattern-Defined Back Phase Transformations triggered for each sub-graph of the original graph, isomorphic to the specified pattern.

  2. Generic Back Phase Transformations.

Note

The graph layout during the back phase is always NCHW. However, during the front and middle phases it could be NHWC if the original model was using it. For more details, refer to Model Conversion Pipeline section.

Pattern-Defined Back Phase Transformations

This type of transformation is implemented using mo.back.replacement.MiddleReplacementPattern as a base class and works the same way as Pattern-Defined Front Phase Transformations.

For the example of a pattern-defined back transformation, refer to the extensions/back/ShufflenetReLUReorder.py file.

Generic Back Phase Transformations

Model Optimizer provides mechanism to implement generic back phase transformations. This type of transformation is implemented using mo.back.replacement.BackReplacementPattern as a base class and works the same way as Generic Middle Phase Transformations.

For the example of this transformation, refer to the extensions/back/GatherNormalizer.py file.