[MO][TF FE] Document freezing as essential step for pruning SM format (openvinotoolkit#17595) (openvinotoolkit#17632)

* [MO][TF FE] Document freezing as essential step for pruning SM format



* Update docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md



---------

Signed-off-by: Kazantsev, Roman <[email protected]>
Co-authored-by: Roman Kazantsev <[email protected]>

Author: tsavina

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -108,12 +108,27 @@ If a model contains operations currently unsupported by OpenVINO™,
 prune these operations by explicit specification of input nodes using the ``--input`` or ``--output``
 options. To determine custom input nodes, visualize a model graph in the TensorBoard.
 
-To generate TensorBoard logs of the graph, use the Model Optimizer ``--tensorboard_logs`` command-line
-option.
-
 TensorFlow 2 SavedModel format has a specific graph structure due to eager execution. In case of
 pruning, find custom input nodes in the ``StatefulPartitionedCall/*`` subgraph.
 
+Since the 2023.0 release, direct pruning of models in SavedModel format is not supported.
+It is essential to freeze the model before pruning. Use the following code snippet for model freezing: 
+
+.. code-block:: python 
+
+   import tensorflow as tf
+   from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+   saved_model_dir = "./saved_model"
+   imported = tf.saved_model.load(saved_model_dir)
+   # retrieve the concrete function and freeze
+   concrete_func = imported.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
+   frozen_func = convert_variables_to_constants_v2(concrete_func,
+                                                   lower_control_flow=False,
+                                                   aggressive_inlining=True)
+   # retrieve GraphDef and save it into .pb format
+   graph_def = frozen_func.graph.as_graph_def(add_shapes=True)
+   tf.io.write_graph(graph_def, '.', 'model.pb', as_text=False)
+
 Keras H5
 ++++++++
 

src/frontends/tensorflow/README.md

@@ -2,7 +2,8 @@
 
 The TensorFlow Frontend (TF FE) is a C++ based OpenVINO Frontend component that is responsible for reading and converting a TensorFlow model to an `ov::Model` object
 that further can be serialized into the Intermediate Representation (IR) format.
-This is an internal API for OpenVINO that is used to implement user facing API such as Model Optimizer, `read_model` function, and OpenVINO Integration with TensorFlow.
+This is an internal API for OpenVINO that is used to implement user-facing API such as MO tool, MO Python API, and OpenVINO Runtime `read_model` function
+for reading TensorFlow models of the original format in run-time. Also, OpenVINO Model Server uses the frontend for serving models.
 Regular users should not use the frontend directly.
 
 ```mermaid
@@ -15,22 +16,22 @@ flowchart BT
     style model3 fill:#427cb0
     ov_model[(ov::Model)]
     style ov_model fill:#427cb0
-    ovtf(OpenVINO Integration with TensorFlow)
-    style ovtf fill:#ffffc2
+    ovms(OpenVINO Model Server)
+    style ovms fill:#ffffc2
     tf_fe(TensorFlow Frontend)
     style tf_fe fill:#ee9a4d
     fem(Frontend Manager)
     mo(Model Optimizer)
     ov_runtime(OpenVINO Runtime)
     model --> mo --> fem --> tf_fe
     model2 --> ov_runtime --> fem
-    model3 --> ovtf --> tf_fe
+    model3 --> ovms --> ov_runtime
     tf_fe --> ov_model
-    click ovtf "https://github.com/openvinotoolkit/openvino_tensorflow"
+    click ovms "https://github.com/openvinotoolkit/model_server"
 ```
 
-Currently, it is only used by [OpenVINO Integration with TensorFlow](https://github.com/openvinotoolkit/openvino_tensorflow).
-Model Optimizer for now relies on the legacy [TensorFlow Frontend](https://docs.openvino.ai/latest/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html) developed in Python.
+The MO tool and MO Python API now use the TensorFlow Frontend as the default path for conversion to IR.
+Known limitations of TF FE are described [here](https://docs.openvino.ai/latest/openvino_docs_MO_DG_TensorFlow_Frontend.html).
 
 ## Key contacts
 
@@ -45,7 +46,7 @@ The structure of OpenVINO TensorFlow Frontend sources includes the following dir
 * [src](./src/) folder contains the sources of the component.
 * [tests](./tests) cover internal transformations.
 
-Additionally, there is a shared [tensorflow common](../tensorflow_common) directory with same structure and purposes.
+Additionally, there is a shared [TensorFlow Common](../tensorflow_common) directory with same structure and purposes.
 Its content depend only on common FrontEnd APIs thus is free to use in other FrontEnds.
 
 ## Architecture
@@ -56,8 +57,8 @@ The whole workflow can be split into two steps: model loading and conversion.
 During loading, the `FrontEnd::load()` method creates `InputModel` that encapsulates the `GraphIterator` object.
 `GraphIterator` is a reader that iterates through the graph nodes in the topological order.
 `GraphIterator::get_decoder()` provides a decoder for the current graph node to read its attributes.
-Each TensorFlow model format has its implementation of `GraphIterator`. Currently, the frontend supports only binary frozen format `.pb`,
-and `GraphIteratorProto` is used for reading and parsing this format. The architecture of the loading step is shown in the picture below:
+Each TensorFlow model format has its implementation of `GraphIterator`. Currently, the frontend supports SavedModel, MetaGraph (`.meta`), and frozen protobuf (`.pb` and `.pbtxt`) formats.
+The base class `GraphIteratorProto` is used for reading and parsing these formats. The architecture of the loading step is shown in the picture below:
 
 ```mermaid
 classDiagram
@@ -70,6 +71,9 @@ classDiagram
     Place --o "1..*" InputModel
     DecoderBase "1" --o "1" Place
     GraphIteratorProto ..|> GraphIterator
+    GraphIteratorProtoTxt ..|> GraphIterator
+    GraphIteratorMeta ..|> GraphIterator
+    GraphIteratorSavedModel ..|> GraphIterator
 ```
 
 After the loading step, `InputModel` includes a container of topologically sorted operation `Place` objects.