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
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.
Model Preparation¶
Navigate to
<EXAMPLE_DIR>
.Download the MobileNet v2 PyTorch model using Model Downloader tool from the Open Model Zoo repository:
omz_downloader --name mobilenet-v2-pytorch
After that the original full-precision model is located in
<EXAMPLE_DIR>/public/mobilenet-v2-pytorch/
.Convert the model to the OpenVINO™ Intermediate Representation (IR) format using Model Converter tool:
omz_converter --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.
Performance Benchmarking of Full-Precision Models¶
Check the performance of the full-precision model in the IR format using Deep Learning Benchmark tool:
benchmark_app -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.
To download images:
Go to the ImageNet homepage.
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.Log in after receiving the confirmation email or if you already have an account. Go to the
Download
tab.Select
Download Original Images
.You will be redirected to the
Terms of Access
page. If you agree to the Terms, continue by clickingAgree and Sign
.Click one of the links in the
Download as one tar file
section.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:
Download archive.
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¶
Create a new file in
<EXAMPLE_DIR>
and name itmobilenet_v2_pytorch.yaml
. This is the Accuracy Checker configuration file.Put the following text into
mobilenet_v2_pytorch.yaml
: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 andannotation_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.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¶
Create a new file in
<EXAMPLE_DIR>
and name itmobilenet_v2_pytorch_int8.json
. This is the POT configuration file.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.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:
benchmark_app -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