DOCS: NNCF documentation - port to master (#13183)

* Updating NNCF documentation

* nncf-doc-update-ms

* Merge branch 'nncf-documentation-for-22.2' of https://github.com/sgolebiewski-intel/openvino into nncf-documentation-for-22.2

* Adding python files

* Changing ID of Range Supervision

* Minor fixes

Fixing formatting and renaming ID

* Proofreading

Minor corrections and removal of Neural Network Compression Framework article

Co-authored-by: msmykx <101244365+msmykx-intel@users.noreply.github.com>
Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>

docs/optimization_guide/nncf/code/pruning_tf.py

@@ -0,0 +1,82 @@
+# Copyright (C) 2018-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+#! [imports]
+import tensorflow as tf
+
+from nncf import NNCFConfig
+from nncf.tensorflow import create_compressed_model, create_compression_callbacks, \
+                            register_default_init_args
+#! [imports]
+
+#! [nncf_congig]
+nncf_config_dict = {
+    "input_info": {"sample_size": [1, 3, 224, 224]}, # input shape required for model tracing
+    "compression": [
+        {
+            "algorithm": "filter_pruning",
+            "pruning_init": 0.1,
+            "params": {
+                "pruning_target": 0.4,
+                "pruning_steps": 15
+            }
+        },
+        {
+            "algorithm": "quantization",  # 8-bit quantization with default settings
+        },
+    ]
+}
+nncf_config = NNCFConfig.from_dict(nncf_config_dict)
+nncf_config = register_default_init_args(nncf_config, train_dataset, batch_size=1) # train_dataset is an instance of tf.data.Dataset
+#! [nncf_congig]
+
+#! [wrap_model]
+model = KerasModel() # instance of the tensorflow.keras.Model
+compression_ctrl, model = create_compressed_model(model, nncf_config)
+#! [wrap_model]
+
+#! [distributed]
+compression_ctrl.distributed() # call it before the training
+#! [distributed]
+
+#! [tune_model]
+... # fine-tuning preparations, e.g. dataset, loss, optimizer setup, etc.
+
+# create compression callbacks to control pruning parameters and dump compression statistics
+# all the setting are being taked from compression_ctrl, i.e. from NNCF config 
+compression_callbacks = create_compression_callbacks(compression_ctrl, log_dir="./compression_log")
+
+# tune quantized model for 50 epochs as the baseline
+model.fit(train_dataset, epochs=50, callbacks=compression_callbacks) 
+#! [tune_model]
+
+#! [export]
+compression_ctrl.export_model("compressed_model.pb") #export to Frozen Graph
+#! [export] 
+
+#! [save_checkpoint]
+from nncf.tensorflow.utils.state import TFCompressionState 
+from nncf.tensorflow.callbacks.checkpoint_callback import CheckpointManagerCallback
+
+checkpoint = tf.train.Checkpoint(model=model,
+                                 compression_state=TFCompressionState(compression_ctrl),
+                                 ... # the rest of the user-defined objects to save
+                                 )
+callbacks = []
+callbacks.append(CheckpointManagerCallback(checkpoint, path_to_checkpoint))
+...
+model.fit(..., callbacks=callbacks)
+#! [save_checkpoint]
+
+#! [load_checkpoint]
+from nncf.tensorflow.utils.state import TFCompressionStateLoader
+
+checkpoint = tf.train.Checkpoint(compression_state=TFCompressionStateLoader())
+checkpoint.restore(path_to_checkpoint)
+compression_state = checkpoint.compression_state.state
+
+compression_ctrl, model = create_compressed_model(model, nncf_config, compression_state)
+checkpoint = tf.train.Checkpoint(model=model,
+                                 ...)
+checkpoint.restore(path_to_checkpoint)
+#! [load_checkpoint]