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[TF FE] Update TensorFlow Frontend developer guide (#17521)

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* [TF FE] Update TensorFlow Frontend developer guide

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Apply suggestions from code review

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

---------

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
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src/frontends/tensorflow/README.md
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@ -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 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. 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. Regular users should not use the frontend directly.
```mermaid ```mermaid
@ -15,8 +16,8 @@ flowchart BT
style model3 fill:#427cb0 style model3 fill:#427cb0
ov_model[(ov::Model)] ov_model[(ov::Model)]
style ov_model fill:#427cb0 style ov_model fill:#427cb0
ovtf(OpenVINO Integration with TensorFlow) ovms(OpenVINO Model Server)
style ovtf fill:#ffffc2 style ovms fill:#ffffc2
tf_fe(TensorFlow Frontend) tf_fe(TensorFlow Frontend)
style tf_fe fill:#ee9a4d style tf_fe fill:#ee9a4d
fem(Frontend Manager) fem(Frontend Manager)
@ -24,13 +25,13 @@ flowchart BT
ov_runtime(OpenVINO Runtime) ov_runtime(OpenVINO Runtime)
model --> mo --> fem --> tf_fe model --> mo --> fem --> tf_fe
model2 --> ov_runtime --> fem model2 --> ov_runtime --> fem
model3 --> ovtf --> tf_fe model3 --> ovms --> ov_runtime
tf_fe --> ov_model 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). The MO tool and MO Python API now use the TensorFlow Frontend as the default path for conversion to IR.
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. Known limitations of TF FE are described [here](https://docs.openvino.ai/latest/openvino_docs_MO_DG_TensorFlow_Frontend.html).
## Key contacts ## 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. * [src](./src/) folder contains the sources of the component.
* [tests](./tests) cover internal transformations. * [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. Its content depend only on common FrontEnd APIs thus is free to use in other FrontEnds.
## Architecture ## 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. 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` 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. `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`, Each TensorFlow model format has its implementation of `GraphIterator`. Currently, the frontend supports SavedModel, MetaGraph (`.meta`), and frozen protobuf (`.pb` and `.pbtxt`) formats.
and `GraphIteratorProto` is used for reading and parsing this format. The architecture of the loading step is shown in the picture below: 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 ```mermaid
classDiagram classDiagram
@ -70,6 +71,9 @@ classDiagram
Place --o "1..*" InputModel Place --o "1..*" InputModel
DecoderBase "1" --o "1" Place DecoderBase "1" --o "1" Place
GraphIteratorProto ..|> GraphIterator GraphIteratorProto ..|> GraphIterator
GraphIteratorProtoTxt ..|> GraphIterator
GraphIteratorMeta ..|> GraphIterator
GraphIteratorSavedModel ..|> GraphIterator
``` ```
After the loading step, `InputModel` includes a container of topologically sorted operation `Place` objects. After the loading step, `InputModel` includes a container of topologically sorted operation `Place` objects.