Add POT how-to (#5092)

docs/how_tos/POT_how_to_example.md

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+# Post-Training Optimization Tool - A real example
+
+This tutorial describes the example from the following YouTube* video:
+
+http://XXXXX
+
+Watch this video to learn the basics of Post-training Optimization Tool (POT): 
+   https://www.youtube.com/watch?v=SvkI25Ca_SQ   
+
+The example has been tested on OpenVINO™ 2021 on Ubuntu 18.04 Operating System.
+
+
+## 1. Installation
+
+Install OpenVINO™ toolkit and Model Optimizer, Accuracy Checker, and Post-training Optimization Tool components.
+
+1. Define the OpenVINO™ install directory:
+```
+export OV=/opt/intel/openvino_2021/
+```
+2. Install the Model Optimizer prerequisites:
+```
+cd $OV/deployment_tools/model_optimizer/install_prerequisites
+sudo ./install_prerequisites.sh
+```
+3. Install the Accuracy Checker requirements:
+```
+cd $OV/deployment_tools/open_model_zoo/tools/accuracy_checker
+sudo python3 setup.py install
+```
+4. Install the Post-training Optimization Tool:
+```
+cd $OV/deployment_tools/tools/post_training_optimization_toolkit
+sudo python3 setup.py install
+```
+
+## 2. Download Model
+
+This tutorial describes MobileNet v2 model from PyTorch* framework. You can choose any other model. 
+
+Download the MobileNet v2 PyTorch* model using the commands below:
+```
+mkdir ~/POT
+```
+```
+cd ~/POT
+```
+```
+python3 $OV/deployment_tools/tools/model_downloader/downloader.py --name mobilenet-v2-pytorch -o .
+```
+
+## 3. Prepare Model for Inference
+
+Install requirements for PyTorch using the commands below:
+```
+cd $OV/deployment_tools/open_model_zoo/tools/downloader
+```
+```
+python3 -mpip install --user -r ./requirements-pytorch.in
+```
+
+You can find the parameters for Mobilnet v2 conversion here:
+```
+vi /opt/intel/openvino_2021/deployment_tools/open_model_zoo/models/public/mobilenet-v2-pytorch/model.yml
+```
+
+Convert the model from PyTorch to ONNX*:
+```
+cd ~/POT/public/mobilenet-v2-pytorch
+python3 /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/pytorch_to_onnx.py  \
+    --model-name=MobileNetV2 \
+    --model-path=.  \
+    --weights=mobilenet-v2.pth \
+    --import-module=MobileNetV2  \
+    --input-shape=1,3,224,224 /
+    --output-file=mobilenet-v2.onnx  \
+    --input-names=data  \
+    --output-names=prob
+
+```
+Convert the model from ONNX to the OpenVINO™ Intermediate Representation (IR):
+```
+mo_onnx.py  \
+    -m mobilenet-v2.onnx \  
+    --input=data  \
+    --mean_values=data[123.675,116.28,103.53]  \
+    --scale_values=data[58.624,57.12,57.375]  \
+    --reverse_input_channels        \
+    --output=prob  
+```
+
+Move the IR files to my directory:
+
+```
+mv mobilenet-v2.xml ~/POT/model.xml
+mv mobilenet-v2.bin ~/POT/model.bin
+```
+
+## 4. Edit Configurations 
+
+Edit the configuration files:
+```
+sudo vi $OV/deployment_tools/open_model_zoo/tools/accuracy_checker/dataset_definitions.yml
+(edit imagenet_1000_classes)
+```
+```
+export DEFINITIONS_FILE=/opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/accuracy_checker/dataset_definitions.yml
+```
+
+Copy the JSON file to my directory and edit:
+
+```
+cp $OV/deployment_tools/tools/post_training_optimization_toolkit/configs/examples/quantization/classification/mobilenetV2_pytorch_int8.json ~/POT
+```
+```
+vi mobilenetV2_pytorch_int8.json
+```
+
+Copy the YML file to my directory and edit:
+
+```
+cp /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/accuracy_checker/configs/mobilenet-v2.yml ~/POT
+```
+```
+vi mobilenet-v2.yml
+```
+
+## 5. Run Baseline 
+
+Run Accuracy Checker on the original model:
+
+```
+accuracy_check -c mobilenet-v2.yml
+```
+
+Install the Benchmark Tool first. To learn more about Benchmark Tool refer to [Benchmark C++ Tool](https://docs.openvinotoolkit.org/latest/openvino_inference_engine_samples_benchmark_app_README.html)
+ or [Benchmark Python* Tool](https://docs.openvinotoolkit.org/latest/openvino_inference_engine_tools_benchmark_tool_README.html).
+
+Run performance benchmark:
+```
+~/inference_engine_cpp_samples_build/intel64/Release/benchmark_app -m ~/POT/model.xml
+```
+
+## 6. Run Integer Calibration
+
+You can edit the JSON file to switch between two modes of calibration:
+
+ -  AccuracyAwareQuantization
+ -  DefaultQuantization
+
+
+```
+pot --config      /home/~/POT/mobilenetV2_pytorch_int8.json   \
+        --output-dir /home/~/POT/        \ 
+        --evaluate                            \
+        --log-level INFO 
+```
+
+Run the Benchmark Tool for the calibrated model. Make sure the name contains `DafultQuantization/.../optimized/...`
+
+```
+~/inference_engine_cpp_samples_build/intel64/Release/benchmark_app -m mobilenetv2_DefaultQuantization/2021-03-07/optimized/mobilenetv2.xml
+```