# End-to-end Command-line Interface example¶

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 Distribution of OpenVINO 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 for help.

## Environment Setup¶

1. Install OpenVINO toolkit and Model Optimizer, Accuracy Checker and Post-training Optimization Tool components following the Installation Guide.

2. Activate the Python* environment and OpenVINO environment as described in the Installation Guide.

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 Distribution of OpenVINO toolkit is installed.

## Model Preparation¶

1. Navigate to <EXAMPLE_DIR>.

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 tool:

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/.

## Performance Benchmarking of Full-Precision Models¶

1. Check the performance of the original model using Deep Learning Benchmark tool:

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 Core i9-10920X CPU @ 3.50GHz it is like:

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 tool:

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 Core i9-10920X CPU @ 3.50GHz it is like:

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.

1. Go to the ImageNet 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.

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 :

models:
- name: mobilenet-v2-pytorch

launchers:
- framework: dlsdk
device: CPU

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

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.

3. Evaluate the accuracy of the full-precision model in the IR format by executing the following command in <EXAMPLE_DIR> :

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 :

{
"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> :

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 tool:

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 Core i9-10920X CPU @ 3.50GHz it is like:

Latency:    1.54 ms
Throughput: 3814.18 FPS