# End-to-end Command-line Interface example {#pot_configs_examples_README}

This tutorial describes an example of running post-training quantization for **MobileNet v2 model from PyTorch** framework, 
particularly by the DefaultQuantization algorithm.
The example covers the following steps:
- Environment setup
- Model preparation and converting it to the OpenVINO™ Intermediate Representation (IR) format 
- Performance benchmarking of the original full-precision model and the converted one to the IR
- Dataset preparation
- Accuracy validation of the full-precision model in the IR format
- Model quantization by the DefaultQuantization algorithm and accuracy validation of the quantized model
- Performance benchmarking of the quantized model

All the steps are based on the tools and samples of configuration files 
distributed with the Intel&reg; Distribution of OpenVINO&trade; toolkit. 

The example has been verified in Ubuntu 18.04 Operating System with Python 3.6 installed. 

In case of issues while running the example, refer to [POT Frequently Asked Questions](@ref pot_docs_FrequentlyAskedQuestions) for help.

## Environment Setup

1. Install OpenVINO&trade; toolkit and Model Optimizer, Accuracy Checker and Post-training Optimization Tool components following the [Installation Guide](@ref pot_InstallationGuide).
2. Activate the Python* environment and OpenVINO environment as described in the [Installation Guide](@ref pot_InstallationGuide).
3. Create a separate working directory and navigate to it. 

In the instructions below, the Post-Training Optimization Tool directory `<POT_DIR>` is referred to:
 - `<ENV>/lib/python<version>/site-packages/` in the case of PyPI installation, where `<ENV>` is a Python* 
 environment where OpenVINO is installed and `<version>` is a Python* version, e.g. `3.6`.
 - `<INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit` in the case of OpenVINO distribution package. 
`<INSTALL_DIR>` is the directory where Intel&reg; Distribution of OpenVINO&trade; toolkit is installed.

## Model Preparation

1. Navigate to `<EXAMPLE_DIR>`.

2. Download the MobileNet v2 PyTorch model using [Model Downloader](@ref omz_tools_downloader) tool from the Open Model Zoo repository:
   ```sh
   python3 ./downloader.py --name mobilenet-v2-pytorch
   ```
   After that the original full-precision model is located in `<EXAMPLE_DIR>/public/mobilenet-v2-pytorch/`.

3. Convert the model to the OpenVINO™ Intermediate Representation (IR) format using [Model Converter](@ref omz_tools_downloader) tool:
   ```sh
   python3 ./converter.py --name mobilenet-v2-pytorch
   ```
   After that the full-precision model in the IR format is located in `<EXAMPLE_DIR>/public/mobilenet-v2-pytorch/FP32/`.
   
For more information about the Model Optimizer, refer to its [documentation](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide).

## Performance Benchmarking of Full-Precision Models

1. Check the performance of the original model using [Deep Learning Benchmark](@ref openvino_inference_engine_tools_benchmark_tool_README) tool:
   ```sh
   python3 ./benchmark_app.py -m <EXAMPLE_DIR>/public/mobilenet-v2-pytorch/mobilenet-v2.onnx
   ```
   Note that the results might be different dependently on characteristics of your machine. On a machine with Intel&reg; Core&trade; i9-10920X CPU @ 3.50GHz it is like:
   ```sh
   Latency:    4.09 ms
   Throughput: 1456.84 FPS
   ```

2. Check the performance of the full-precision model in the IR format using [Deep Learning Benchmark](@ref openvino_inference_engine_tools_benchmark_tool_README) tool:
   ```sh
   python3 ./benchmark_app.py -m <EXAMPLE_DIR>/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.xml
   ```
   Note that the results might be different dependently on characteristics of your machine. On a machine with Intel&reg; Core&trade; i9-10920X CPU @ 3.50GHz it is like:
   ```sh
   Latency:    4.14 ms
   Throughput: 1436.55 FPS
   ```

## Dataset Preparation

To perform the accuracy validation as well as quantization of a model, the dataset should be prepared. This example uses a real dataset called ImageNet. 

To download images:

1. Go to the [ImageNet](http://www.image-net.org/) homepage.
2. If you do not have an account, click the `Signup` button in the right upper corner, provide your data, and wait for a confirmation email.
3. Log in after receiving the confirmation email or if you already have an account. Go to the `Download` tab.
4. Select `Download Original Images`.
5. You will be redirected to the `Terms of Access` page. If you agree to the Terms, continue by clicking `Agree and Sign`.
6. Click one of the links in the `Download as one tar file` section.
7. Unpack the downloaded archive into `<EXAMPLE_DIR>/ImageNet/`.

Note that the registration process might be quite long.  
Note that the ImageNet size is 50 000 images and takes around 6.5 GB of the disk space.

To download the annotation file:

  1. Download [archive](http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz).
  2. Unpack `val.txt` from the archive into `<EXAMPLE_DIR>/ImageNet/`.

After that the `<EXAMPLE_DIR>/ImageNet/` dataset folder should have a lot of image files like `ILSVRC2012_val_00000001.JPEG` and the `val.txt` annotation file.

## Accuracy Validation of Full-Precision Model in IR Format

1. Create a new file in `<EXAMPLE_DIR>` and name it `mobilenet_v2_pytorch.yaml`. This is the Accuracy Checker configuration file. 

2. Put the following text into `mobilenet_v2_pytorch.yaml`:
```sh
models:
  - name: mobilenet-v2-pytorch

    launchers:
      - framework: dlsdk
        device: CPU
        adapter: classification

    datasets:
      - name: classification_dataset
        data_source: ./ImageNet
        annotation_conversion:
          converter: imagenet
          annotation_file: ./ImageNet/val.txt
        reader: pillow_imread

        preprocessing:
          - type: resize
            size: 256
            aspect_ratio_scale: greater
            use_pillow: True
          - type: crop
            size: 224
            use_pillow: True
          - type: bgr_to_rgb

        metrics:
          - name: accuracy@top1
            type: accuracy
            top_k: 1

          - name: accuracy@top5
            type: accuracy
            top_k: 5

```
where `data_source: ./ImageNet` is the dataset and `annotation_file: ./ImageNet/val.txt` is the annotation file prepared on the previous step. 
For more information about the Accuracy Checker configuration file refer to [Accuracy Checker Tool documentation](@ref omz_tools_accuracy_checker).

3. Evaluate the accuracy of the full-precision model in the IR format by executing the following command in `<EXAMPLE_DIR>`:
   ```sh
   accuracy_check -c mobilenet_v2_pytorch.yaml -m ./public/mobilenet-v2-pytorch/FP32/
   ```
   The actual result should be like **71.81**% of the accuracy top-1 metric on VNNI based CPU.   
   Note that the results might be different on CPUs with different instruction sets.
   
## Model Quantization

1. Create a new file in `<EXAMPLE_DIR>` and name it `mobilenet_v2_pytorch_int8.json`. This is the POT configuration file. 

2. Put the following text into `mobilenet_v2_pytorch_int8.json`:
```sh
{
    "model": {
        "model_name": "mobilenet-v2-pytorch",
        "model": "./public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.xml",
        "weights": "./public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.bin"
    },
    "engine": {
        "config": "./mobilenet_v2_pytorch.yaml"
    },
    "compression": {
        "algorithms": [
            {
                "name": "DefaultQuantization",
                "params": {
                    "preset": "mixed",
                    "stat_subset_size": 300
                }
            }
        ]
    }
}
```
where `"model": "./public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.xml"` and `"weights": "./public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.bin"` 
specify the full-precision model in the IR format, `"config": "./mobilenet_v2_pytorch.yaml"` is the Accuracy Checker configuration file, and 
`"name": "DefaultQuantization"` is the algorithm name.

3. Perform model quantization by executing the following command in `<EXAMPLE_DIR>`: 
   ```sh
   pot -c mobilenet_v2_pytorch_int8.json -e
   ```
   The quantized model is placed into the subfolder with your current date and time in the name under the `./results/mobilenetv2_DefaultQuantization/` directory.
   The accuracy validation of the quantized model is performed right after the quantization. The actual result should be like **71.556**% of the accuracy top-1 metric on VNNI based CPU.   
   Note that the results might be different on CPUs with different instruction sets.
   
## Performance Benchmarking of Quantized Model

Check the performance of the quantized model using [Deep Learning Benchmark](@ref openvino_inference_engine_tools_benchmark_tool_README) tool:
```sh
python3 ./benchmark_app.py -m <INT8_MODEL>
```
where `<INT8_MODEL>` is the path to the quantized model.  
Note that the results might be different dependently on characteristics of your machine. On a machine with Intel&reg; Core&trade; i9-10920X CPU @ 3.50GHz it is like:
```sh
Latency:    1.54 ms
Throughput: 3814.18 FPS
```
