[DOCS] Port optimization doc changes to release docs (#21366)

* [DOCS] Update reference in quantization_w_accuracy_control.md (#21337)

* Update quantization_w_accuracy_control.md

avoid 404

* Update quantization_w_accuracy_control.md

* migrate to new convert API (#21323)

---------

Co-authored-by: Maksim Proshin <maksim.proshin@intel.com>
Co-authored-by: Alexander Suslov <alexander.suslov@intel.com>

docs/articles_en/openvino_workflow/model_optimization_guide/ptq_introduction/quantization_w_accuracy_control.md

@@ -91,7 +91,7 @@ If the accuracy or performance of the quantized model is not satisfactory, you c
 Examples of NNCF post-training quantization with control of accuracy metric:
 #############################################################################
 
-* `Post-Training Quantization of Anomaly Classification OpenVINO model with control of accuracy metric <https://github.com/openvinotoolkit/nncf/blob/develop/examples/post_training_quantization/openvino/quantize_with_accuracy_control>`__
+* `Post-Training Quantization of Anomaly Classification OpenVINO model with control of accuracy metric <https://github.com/openvinotoolkit/nncf/blob/develop/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control>`__
 * `Post-Training Quantization of YOLOv8 OpenVINO Model with control of accuracy metric <https://github.com/openvinotoolkit/nncf/blob/develop/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control>`__
 
 See also

docs/optimization_guide/nncf/ptq/code/ptq_aa_openvino.py

@@ -18,9 +18,11 @@ validation_dataset = nncf.Dataset(calibration_loader, transform_fn)
 #! [validation]
 import numpy as np
 import torch
-import openvino
 from sklearn.metrics import accuracy_score
 
+import openvino
+
+
 def validate(model: openvino.CompiledModel, 
              validation_loader: torch.utils.data.DataLoader) -> float:
     predictions = []
@@ -34,19 +36,21 @@ def validate(model: openvino.CompiledModel,
         references.append(target)
 
     predictions = np.concatenate(predictions, axis=0)
-    references = np.concatenate(references, axis=0)  
+    references = np.concatenate(references, axis=0)
     return accuracy_score(predictions, references)
 #! [validation]
 
 #! [quantization]
 model = ... # openvino.Model object
 
-quantized_model = nncf.quantize_with_accuracy_control(model,
-                        calibration_dataset=calibration_dataset,
-                        validation_dataset=validation_dataset,
-                        validation_fn=validate,
-                        max_drop=0.01,
-                        drop_type=nncf.DropType.ABSOLUTE)
+quantized_model = nncf.quantize_with_accuracy_control(
+    model,
+    calibration_dataset=calibration_dataset,
+    validation_dataset=validation_dataset,
+    validation_fn=validate,
+    max_drop=0.01,
+    drop_type=nncf.DropType.ABSOLUTE,
+)
 #! [quantization]
 
 #! [inference]

docs/optimization_guide/nncf/ptq/code/ptq_onnx.py

@@ -24,10 +24,9 @@ quantized_model = nncf.quantize(model, calibration_dataset)
 
 #! [inference]
 import openvino as ov
-from openvino.tools.mo import convert_model
 
 # convert ONNX model to OpenVINO model
-ov_quantized_model = convert_model(quantized_model)
+ov_quantized_model = ov.convert_model(quantized_model)
 
 # compile the model to transform quantized operations to int8
 model_int8 = ov.compile_model(ov_quantized_model)

docs/optimization_guide/nncf/ptq/code/ptq_tensorflow.py

@@ -23,10 +23,9 @@ quantized_model = nncf.quantize(model, calibration_dataset)
 
 #! [inference]
 import openvino as ov
-from openvino.tools.mo import convert_model
 
 # convert TensorFlow model to OpenVINO model
-ov_quantized_model = convert_model(quantized_model)
+ov_quantized_model = ov.convert_model(quantized_model)
 
 # compile the model to transform quantized operations to int8
 model_int8 = ov.compile_model(ov_quantized_model)

docs/optimization_guide/nncf/ptq/code/ptq_torch.py

@@ -23,16 +23,11 @@ quantized_model = nncf.quantize(model, calibration_dataset)
 
 #! [inference]
 import openvino as ov
-from openvino.tools.mo import convert_model
 
 input_fp32 = ... # FP32 model input
 
-# export PyTorch model to ONNX model
-onnx_model_path = "model.onnx"
-torch.onnx.export(quantized_model, input_fp32, onnx_model_path)
-
-# convert ONNX model to OpenVINO model
-ov_quantized_model = convert_model(onnx_model_path)
+# convert PyTorch model to OpenVINO model
+ov_quantized_model = ov.convert_model(quantized_model, example_input=input_fp32)
 
 # compile the model to transform quantized operations to int8
 model_int8 = ov.compile_model(ov_quantized_model)