Convert and Optimize YOLOv11 keypoint detection model with OpenVINO™#

This Jupyter notebook can be launched on-line, opening an interactive environment in a browser window. You can also make a local installation. Choose one of the following options:

Keypoint detection/Pose is a task that involves detecting specific points in an image or video frame. These points are referred to as keypoints and are used to track movement or pose estimation. YOLOv11 can detect keypoints in an image or video frame with high accuracy and speed.

This tutorial demonstrates step-by-step instructions on how to run and optimize PyTorch YOLOv11 Pose model with OpenVINO. We consider the steps required for keypoint detection scenario. You can find more details about model on model page in Ultralytics documentation.

The tutorial consists of the following steps: - Prepare the PyTorch model. - Download and prepare a dataset. - Validate the original model. - Convert the PyTorch model to OpenVINO IR. - Validate the converted model. - Prepare and run optimization pipeline. - Compare performance of the FP32 and quantized models. - Compare accuracy of the FP32 and quantized models. - Live demo

Table of contents:

Get PyTorch model
- Prerequisites
Instantiate model
Optimize model using NNCF Post-training Quantization API
- Validate Quantized model inference
Compare the Original and Quantized Models
- Compare performance of the Original and Quantized Models
Other ways to optimize model
Live demo
- Run Keypoint Detection on video

Installation Instructions#

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.

Get PyTorch model#

Generally, PyTorch models represent an instance of the torch.nn.Module class, initialized by a state dictionary with model weights. We will use the YOLOv11 nano model (also known as yolo11n-pose) pre-trained on a COCO dataset, which is available in this repo. Similar steps are also applicable to other Ultralytics models. Typical steps to obtain a pre-trained model: 1. Create an instance of a model class. 2. Load a checkpoint state dict, which contains the pre-trained model weights. 3. Turn the model to evaluation for switching some operations to inference mode.

In this case, the creators of the model provide an API that enables converting the YOLOv11 model to OpenVINO IR. Therefore, we do not need to do these steps manually.

Prerequisites#

Install necessary packages.

%pip install -q "openvino>=2024.0.0" "nncf>=2.9.0"
%pip install -q "protobuf==3.20.*" "torch>=2.1" "torchvision>=0.16" "ultralytics==8.3.0" tqdm opencv-python --extra-index-url https://download.pytorch.org/whl/cpu

Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.

Import required utility functions. The lower cell will download the notebook_utils Python module from GitHub.

from pathlib import Path

# Fetch `notebook_utils` module
import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)

open("notebook_utils.py", "w").write(r.text)
from notebook_utils import download_file, VideoPlayer, device_widget

# Download a test sample
IMAGE_PATH = Path("./data/intel_rnb.jpg")
download_file(
    url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg",
    filename=IMAGE_PATH.name,
    directory=IMAGE_PATH.parent,
)

data/intel_rnb.jpg:   0%|          | 0.00/288k [00:00<?, ?B/s]

PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/790/archive/.workspace/scm/ov-notebook/notebooks/yolov11-optimization/data/intel_rnb.jpg')

Instantiate model#

For loading the model, required to specify a path to the model checkpoint. It can be some local path or name available on models hub (in this case model checkpoint will be downloaded automatically).

Making prediction, the model accepts a path to input image and returns list with Results class object. Results contains boxes and key points. Also it contains utilities for processing results, for example, plot() method for drawing.

Let us consider the examples:

import ipywidgets as widgets

model_id = [
    "yolo11n-pose",
    "yolo11s-pose",
    "yolo11m-pose",
    "yolo11l-pose",
    "yolo11x-pose",
    "yolov8n-pose",
    "yolov8s-pose",
    "yolov8m-pose",
    "yolov8l-pose",
    "yolov8x-pose",
]

model_name = widgets.Dropdown(options=model_id, value=model_id[0], description="Model")

model_name

Dropdown(description='Model', options=('yolo11n-pose', 'yolo11s-pose', 'yolo11m-pose', 'yolo11l-pose', 'yolo11…

from PIL import Image
from ultralytics import YOLO

POSE_MODEL_NAME = model_name.value

pose_model = YOLO(f"{POSE_MODEL_NAME}.pt")
label_map = pose_model.model.names

res = pose_model(IMAGE_PATH)
Image.fromarray(res[0].plot()[:, :, ::-1])

Downloading ultralytics/assets to 'yolo11n-pose.pt'...

100%|██████████| 5.97M/5.97M [00:00<00:00, 25.1MB/s]

image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/790/archive/.workspace/scm/ov-notebook/notebooks/yolov11-optimization/data/intel_rnb.jpg: 480x640 1 person, 56.4ms
Speed: 1.9ms preprocess, 56.4ms inference, 0.9ms postprocess per image at shape (1, 3, 480, 640)

../_images/yolov11-keypoint-detection-with-output_9_3.png

Convert model to OpenVINO IR#

Ultralytics provides API for convenient model exporting to different formats including OpenVINO IR. model.export is responsible for model conversion. We need to specify the format, and additionally, we can preserve dynamic shapes in the model.

# object detection model
pose_model_path = Path(f"{POSE_MODEL_NAME}_openvino_model/{POSE_MODEL_NAME}.xml")
if not pose_model_path.exists():
    pose_model.export(format="openvino", dynamic=True, half=True)

Ultralytics 8.3.0 🚀 Python-3.8.10 torch-2.3.1+cpu CPU (Intel Core(TM) i9-10920X 3.50GHz)

PyTorch: starting from 'yolo11n-pose.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 56, 8400) (6.0 MB)

OpenVINO: starting export with openvino 2024.5.0-16913-890f2e12c98...
OpenVINO: export success ✅ 1.9s, saved as 'yolo11n-pose_openvino_model/' (6.0 MB)

Export complete (2.2s)
Results saved to /opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/790/archive/.workspace/scm/ov-notebook/notebooks/yolov11-optimization
Predict:         yolo predict task=pose model=yolo11n-pose_openvino_model imgsz=640 half
Validate:        yolo val task=pose model=yolo11n-pose_openvino_model imgsz=640 data=/ultralytics/ultralytics/cfg/datasets/coco-pose.yaml half
Visualize:       https://netron.app

Verify model inference#

We can reuse the base model pipeline for pre- and postprocessing just replacing the inference method where we will use the IR model for inference.

Select inference device#

Select device from dropdown list for running inference using OpenVINO

device = device_widget()

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

Test on single image#

Now, once we have defined preprocessing and postprocessing steps, we are ready to check model prediction.

import openvino as ov

core = ov.Core()
pose_ov_model = core.read_model(pose_model_path)

ov_config = {}
if device.value != "CPU":
    pose_ov_model.reshape({0: [1, 3, 640, 640]})
if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
    ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
pose_compiled_model = core.compile_model(pose_ov_model, device.value, ov_config)

pose_model = YOLO(pose_model_path.parent, task="pose")

if pose_model.predictor is None:
    custom = {"conf": 0.25, "batch": 1, "save": False, "mode": "predict"}  # method defaults
    args = {**pose_model.overrides, **custom}
    pose_model.predictor = pose_model._smart_load("predictor")(overrides=args, _callbacks=pose_model.callbacks)
    pose_model.predictor.setup_model(model=pose_model.model)

pose_model.predictor.model.ov_compiled_model = pose_compiled_model


res = pose_model(IMAGE_PATH)
Image.fromarray(res[0].plot()[:, :, ::-1])

Ultralytics 8.3.0 🚀 Python-3.8.10 torch-2.3.1+cpu CPU (Intel Core(TM) i9-10920X 3.50GHz)
Loading yolo11n-pose_openvino_model for OpenVINO inference...
Using OpenVINO LATENCY mode for batch=1 inference...

image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/790/archive/.workspace/scm/ov-notebook/notebooks/yolov11-optimization/data/intel_rnb.jpg: 640x640 1 person, 21.5ms
Speed: 2.5ms preprocess, 21.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)

../_images/yolov11-keypoint-detection-with-output_16_1.png

Great! The result is the same, as produced by original models.

Check model accuracy on the dataset#

For comparing the optimized model result with the original, it is good to know some measurable results in terms of model accuracy on the validation dataset.

Download the validation dataset#

YOLOv8 is pre-trained on the COCO dataset, so to evaluate the model accuracy we need to download it. According to the instructions provided in the YOLOv8 repo, we also need to download annotations in the format used by the author of the model, for use with the original model evaluation function.

Note: The initial dataset download may take a few minutes to complete. The download speed will vary depending on the quality of your internet connection.

Optimize model using NNCF Post-training Quantization API#

NNCF provides a suite of advanced algorithms for Neural Networks inference optimization in OpenVINO with minimal accuracy drop. We will use 8-bit quantization in post-training mode (without the fine-tuning pipeline) to optimize YOLOv8.

The optimization process contains the following steps:

Create a Dataset for quantization.
Run nncf.quantize for getting an optimized model.
Serialize OpenVINO IR model, using the openvino.runtime.serialize function.

Please select below whether you would like to run quantization to improve model inference speed.

import ipywidgets as widgets

int8_model_pose_path = Path(f"{POSE_MODEL_NAME}_openvino_int8_model/{POSE_MODEL_NAME}.xml")
quantized_pose_model = None

to_quantize = widgets.Checkbox(
    value=True,
    description="Quantization",
    disabled=False,
)

to_quantize

Checkbox(value=True, description='Quantization')

Let’s load skip magic extension to skip quantization if to_quantize is not selected

# Fetch skip_kernel_extension module
r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
)
open("skip_kernel_extension.py", "w").write(r.text)

%load_ext skip_kernel_extension

Reuse validation dataloader in accuracy testing for quantization. For that, it should be wrapped into the nncf.Dataset object and define a transformation function for getting only input tensors.

%%skip not $to_quantize.value

import nncf
from typing import Dict

from zipfile import ZipFile

from ultralytics.data.utils import DATASETS_DIR
from ultralytics.utils import DEFAULT_CFG
from ultralytics.cfg import get_cfg
from ultralytics.data.utils import check_det_dataset
from ultralytics.models.yolo.pose import PoseValidator
from ultralytics.utils.metrics import OKS_SIGMA

if not int8_model_pose_path.exists():

    DATA_URL = "https://ultralytics.com/assets/coco8-pose.zip"
    CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco8-pose.yaml"

    OUT_DIR = DATASETS_DIR

    DATA_PATH = OUT_DIR / "val2017.zip"
    CFG_PATH = OUT_DIR / "coco8-pose.yaml"

    download_file(DATA_URL, DATA_PATH.name, DATA_PATH.parent)
    download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)

    if not (OUT_DIR / "coco8-pose/labels").exists():
        with ZipFile(DATA_PATH, "r") as zip_ref:
            zip_ref.extractall(OUT_DIR)

    args = get_cfg(cfg=DEFAULT_CFG)
    args.data = "coco8-pose.yaml"

    pose_validator = PoseValidator(args=args)
    pose_validator.data = check_det_dataset(args.data)
    pose_validator.stride = 32
    pose_data_loader = pose_validator.get_dataloader(OUT_DIR / "coco8-pose", 1)

    pose_validator.is_coco = True
    pose_validator.names = label_map
    pose_validator.metrics.names = pose_validator.names
    pose_validator.nc = 1
    pose_validator.sigma = OKS_SIGMA


    def transform_fn(data_item:Dict):
        """
        Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
        Parameters:
           data_item: Dict with data item produced by DataLoader during iteration
        Returns:
            input_tensor: Input data for quantization
        """
        input_tensor = pose_validator.preprocess(data_item)['img'].numpy()
        return input_tensor


    quantization_dataset = nncf.Dataset(pose_data_loader, transform_fn)

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino

/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-785/.workspace/scm/datasets/val2017.zip:   0%|   …

/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-785/.workspace/scm/datasets/coco8-pose.yaml:   0%…

val: Scanning /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-785/.workspace/scm/datasets/coco8-pose/labels/train.cache... 8 images, 0 backgrounds, 0 corrupt: 100%|██████████| 8/8 [00:00<?, ?it/s]

The nncf.quantize function provides an interface for model quantization. It requires an instance of the OpenVINO Model and quantization dataset. Optionally, some additional parameters for the configuration quantization process (number of samples for quantization, preset, ignored scope, etc.) can be provided. YOLOv8 model contains non-ReLU activation functions, which require asymmetric quantization of activations. To achieve a better result, we will use a mixed quantization preset. It provides symmetric quantization of weights and asymmetric quantization of activations. For more accurate results, we should keep the operation in the postprocessing subgraph in floating point precision, using the ignored_scope parameter.

Note: Model post-training quantization is time-consuming process. Be patient, it can take several minutes depending on your hardware.

%%skip not $to_quantize.value

if not int8_model_pose_path.exists():

    ignored_scope = nncf.IgnoredScope(  # post-processing
        subgraphs=[
            nncf.Subgraph(inputs=[f"__module.model.{22 if 'v8' in POSE_MODEL_NAME else 23}/aten::cat/Concat",
                                  f"__module.model.{22 if 'v8' in POSE_MODEL_NAME else 23}/aten::cat/Concat_1",
                                  f"__module.model.{22 if 'v8' in POSE_MODEL_NAME else 23}/aten::cat/Concat_2",
                                 f"__module.model.{22 if 'v8' in POSE_MODEL_NAME else 23}/aten::cat/Concat_7"],
                          outputs=[f"__module.model.{22 if 'v8' in POSE_MODEL_NAME else 23}/aten::cat/Concat_9"])
        ]
    )

    # Detection model
    quantized_pose_model = nncf.quantize(
        pose_ov_model,
        quantization_dataset,
        preset=nncf.QuantizationPreset.MIXED,
        ignored_scope=ignored_scope
    )
    print(f"Quantized keypoint detection model will be saved to {int8_model_pose_path}")
    ov.save_model(quantized_pose_model, str(int8_model_pose_path))

INFO:nncf:116 ignored nodes were found by subgraphs in the NNCFGraph
INFO:nncf:Not adding activation input quantizer for operation: 140 __module.model.23/aten::cat/Concat
INFO:nncf:Not adding activation input quantizer for operation: 149 __module.model.23/aten::view/Reshape_3
INFO:nncf:Not adding activation input quantizer for operation: 297 __module.model.23/aten::cat/Concat_1
INFO:nncf:Not adding activation input quantizer for operation: 311 __module.model.23/aten::view/Reshape_4
INFO:nncf:Not adding activation input quantizer for operation: 418 __module.model.23/aten::cat/Concat_2
INFO:nncf:Not adding activation input quantizer for operation: 422 __module.model.23/aten::view/Reshape_5
INFO:nncf:Not adding activation input quantizer for operation: 151 __module.model.23/aten::cat/Concat_7
INFO:nncf:Not adding activation input quantizer for operation: 163 __module.model.23/aten::view/Reshape_9
INFO:nncf:Not adding activation input quantizer for operation: 177 __module.model.23/aten::slice/Slice_2
INFO:nncf:Not adding activation input quantizer for operation: 178 __module.model.23/aten::slice/Slice_5
INFO:nncf:Not adding activation input quantizer for operation: 195 __module.model.23/aten::mul/Multiply_4
215 __module.model.23/aten::add/Add_8

INFO:nncf:Not adding activation input quantizer for operation: 196 __module.model.23/aten::sigmoid/Sigmoid_1
INFO:nncf:Not adding activation input quantizer for operation: 232 __module.model.23/aten::mul/Multiply_5
INFO:nncf:Not adding activation input quantizer for operation: 216 __module.model.23/aten::cat/Concat_8
INFO:nncf:Not adding activation input quantizer for operation: 233 __module.model.23/aten::view/Reshape_10
INFO:nncf:Not adding activation input quantizer for operation: 161 __module.model.23/aten::cat/Concat_4
INFO:nncf:Not adding activation input quantizer for operation: 175 __module.model.23/prim::ListUnpack
INFO:nncf:Not adding activation input quantizer for operation: 192 __module.model.23.dfl/aten::view/Reshape
INFO:nncf:Not adding activation input quantizer for operation: 193 __module.model.23/aten::sigmoid/Sigmoid
INFO:nncf:Not adding activation input quantizer for operation: 212 __module.model.23.dfl/aten::transpose/Transpose
INFO:nncf:Not adding activation input quantizer for operation: 230 __module.model.23.dfl/aten::softmax/Softmax
INFO:nncf:Not adding activation input quantizer for operation: 245 __module.model.23.dfl.conv/aten::_convolution/Convolution
INFO:nncf:Not adding activation input quantizer for operation: 256 __module.model.23.dfl/aten::view/Reshape_1
INFO:nncf:Not adding activation input quantizer for operation: 268 __module.model.23/prim::ListUnpack/VariadicSplit
INFO:nncf:Not adding activation input quantizer for operation: 279 __module.model.23/aten::sub/Subtract
INFO:nncf:Not adding activation input quantizer for operation: 280 __module.model.23/aten::add/Add_6
INFO:nncf:Not adding activation input quantizer for operation: 291 __module.model.23/aten::add/Add_7
304 __module.model.23/aten::div/Divide

INFO:nncf:Not adding activation input quantizer for operation: 292 __module.model.23/aten::sub/Subtract_1
INFO:nncf:Not adding activation input quantizer for operation: 305 __module.model.23/aten::cat/Concat_5
INFO:nncf:Not adding activation input quantizer for operation: 265 __module.model.23/aten::mul/Multiply_3
INFO:nncf:Not adding activation input quantizer for operation: 213 __module.model.23/aten::cat/Concat_9

Output()

Output()

Quantized keypoint detection model will be saved to yolo11n-pose_openvino_int8_model/yolo11n-pose.xml

Validate Quantized model inference#

nncf.quantize returns the OpenVINO Model class instance, which is suitable for loading on a device for making predictions. INT8 model input data and output result formats have no difference from the floating point model representation. Therefore, we can reuse the same detect function defined above for getting the INT8 model result on the image.

%%skip not $to_quantize.value

device

%%skip not $to_quantize.value

if quantized_pose_model is None:
    quantized_pose_model = core.read_model()

ov_config = {}
if device.value != "CPU":
    quantized_pose_model.reshape({0: [1, 3, 640, 640]})
if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
    ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
quantized_pose_compiled_model = core.compile_model(quantized_pose_model, device.value, ov_config)

pose_model = YOLO(pose_model_path.parent, task="pose")

if pose_model.predictor is None:
    custom = {"conf": 0.25, "batch": 1, "save": False, "mode": "predict"}  # method defaults
    args = {**pose_model.overrides, **custom}
    pose_model.predictor = pose_model._smart_load("predictor")(overrides=args, _callbacks=pose_model.callbacks)
    pose_model.predictor.setup_model(model=pose_model.model)

pose_model.predictor.model.ov_compiled_model = pose_compiled_model

res = pose_model(IMAGE_PATH)
display(Image.fromarray(res[0].plot()[:, :, ::-1]))

Ultralytics 8.3.0 🚀 Python-3.8.10 torch-2.3.1+cpu CPU (Intel Core(TM) i9-10920X 3.50GHz)
Loading yolo11n-pose_openvino_model for OpenVINO inference...
Using OpenVINO LATENCY mode for batch=1 inference...

image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/790/archive/.workspace/scm/ov-notebook/notebooks/yolov11-optimization/data/intel_rnb.jpg: 640x640 1 person, 31.0ms
Speed: 2.1ms preprocess, 31.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)

../_images/yolov11-keypoint-detection-with-output_30_1.png

Compare the Original and Quantized Models#

Compare performance of the Original and Quantized Models#

Finally, use the OpenVINO Benchmark Tool to measure the inference performance of the FP32 and INT8 models.

Note: For more accurate performance, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications. Run benchmark_app -m <model_path> -d CPU -shape "<input_shape>" to benchmark async inference on CPU on specific input data shape for one minute. Change CPU to GPU to benchmark on GPU. Run benchmark_app --help to see an overview of all command-line options.

%%skip not $to_quantize.value

device

if int8_model_pose_path.exists():
    # Inference FP32 model (OpenVINO IR)
    !benchmark_app -m $pose_model_path -d $device.value -api async -shape "[1,3,640,640]"

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ WARNING ] Default duration 120 seconds is used for unknown device AUTO
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.5.0-16913-890f2e12c98
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.5.0-16913-890f2e12c98
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 19.80 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.model.23/aten::cat/Concat_9) : f32 / [...] / [?,56,21..]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[ INFO ] Reshaping model: 'x': [1,3,640,640]
[ INFO ] Reshape model took 5.17 ms
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.model.23/aten::cat/Concat_9) : f32 / [...] / [1,56,8400]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 338.05 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model0
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
[ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
[ INFO ]   CPU:
[ INFO ]     AFFINITY: Affinity.CORE
[ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
[ INFO ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: True
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 24
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
[ INFO ]     LOG_LEVEL: Level.NO
[ INFO ]     MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]     NETWORK_NAME: Model0
[ INFO ]     NUM_STREAMS: 6
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
[ INFO ]     PERFORMANCE_HINT: THROUGHPUT
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: NO
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: False
[ INFO ]   PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
[ INFO ] Fill input 'x' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 120000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 33.00 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            17292 iterations
[ INFO ] Duration:         120045.86 ms
[ INFO ] Latency:
[ INFO ]    Median:        40.71 ms
[ INFO ]    Average:       41.52 ms
[ INFO ]    Min:           29.33 ms
[ INFO ]    Max:           102.50 ms
[ INFO ] Throughput:   144.04 FPS

if int8_model_pose_path.exists():
    # Inference INT8 model (OpenVINO IR)
    !benchmark_app -m $int8_model_pose_path -d $device.value -api async -shape "[1,3,640,640]" -t 15

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.5.0-16913-890f2e12c98
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.5.0-16913-890f2e12c98
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 29.03 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     x (node: x) : f32 / [...] / [1,3,640,640]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.model.23/aten::cat/Concat_9) : f32 / [...] / [1,56,8400]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[ INFO ] Reshaping model: 'x': [1,3,640,640]
[ INFO ] Reshape model took 0.04 ms
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.model.23/aten::cat/Concat_9) : f32 / [...] / [1,56,8400]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 558.98 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model0
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
[ INFO ]   CPU:
[ INFO ]     AFFINITY: Affinity.CORE
[ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
[ INFO ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: True
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 24
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
[ INFO ]     LOG_LEVEL: Level.NO
[ INFO ]     MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]     NETWORK_NAME: Model0
[ INFO ]     NUM_STREAMS: 12
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]     PERFORMANCE_HINT: THROUGHPUT
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: NO
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: False
[ INFO ]   PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
[ INFO ] Fill input 'x' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 31.29 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            5184 iterations
[ INFO ] Duration:         15060.72 ms
[ INFO ] Latency:
[ INFO ]    Median:        34.51 ms
[ INFO ]    Average:       34.67 ms
[ INFO ]    Min:           21.44 ms
[ INFO ]    Max:           51.10 ms
[ INFO ] Throughput:   344.21 FPS

Compare accuracy of the Original and Quantized Models#

As we can see, there is no significant difference between INT8 and float model result in a single image test. To understand how quantization influences model prediction precision, we can compare model accuracy on a dataset.

Other ways to optimize model#

The performance could be also improved by another OpenVINO method such as async inference pipeline or preprocessing API.

Async Inference pipeline help to utilize the device more optimal. The key advantage of the Async API is that when a device is busy with inference, the application can perform other tasks in parallel (for example, populating inputs or scheduling other requests) rather than wait for the current inference to complete first. To understand how to perform async inference using openvino, refer to Async API tutorial

Preprocessing API enables making preprocessing a part of the model reducing application code and dependency on additional image processing libraries. The main advantage of Preprocessing API is that preprocessing steps will be integrated into the execution graph and will be performed on a selected device (CPU/GPU etc.) rather than always being executed on CPU as part of an application. This will also improve selected device utilization. For more information, refer to the overview of Preprocessing API tutorial. To see, how it could be used with YOLOV8 object detection model , please, see Convert and Optimize YOLOv8 real-time object detection with OpenVINO tutorial

Live demo#

The following code runs model inference on a video:

import collections
import time
from IPython import display
import cv2
import numpy as np


def run_keypoint_detection(
    source=0,
    flip=False,
    use_popup=False,
    skip_first_frames=0,
    model=pose_model,
    device=device.value,
):
    player = None

    ov_config = {}
    if device != "CPU":
        model.reshape({0: [1, 3, 640, 640]})
    if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
    compiled_model = core.compile_model(model, device, ov_config)

    if pose_model.predictor is None:
        custom = {"conf": 0.25, "batch": 1, "save": False, "mode": "predict"}  # method defaults
        args = {**seg_model.overrides, **custom}
        pose_model.predictor = pose_model._smart_load("predictor")(overrides=args, _callbacks=pose_model.callbacks)
        pose_model.predictor.setup_model(model=pose_model.model)

    pose_model.predictor.model.ov_compiled_model = compiled_model

    try:
        # Create a video player to play with target fps.
        player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
        # Start capturing.
        player.start()
        if use_popup:
            title = "Press ESC to Exit"
            cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)

        processing_times = collections.deque()
        while True:
            # Grab the frame.
            frame = player.next()
            if frame is None:
                print("Source ended")
                break
            # If the frame is larger than full HD, reduce size to improve the performance.
            scale = 1280 / max(frame.shape)
            if scale < 1:
                frame = cv2.resize(
                    src=frame,
                    dsize=None,
                    fx=scale,
                    fy=scale,
                    interpolation=cv2.INTER_AREA,
                )
            # Get the results
            input_image = np.array(frame)

            start_time = time.time()

            detections = pose_model(input_image)
            stop_time = time.time()
            frame = detections[0].plot()

            processing_times.append(stop_time - start_time)
            # Use processing times from last 200 frames.
            if len(processing_times) > 200:
                processing_times.popleft()

            _, f_width = frame.shape[:2]
            # Mean processing time [ms].
            processing_time = np.mean(processing_times) * 1000
            fps = 1000 / processing_time
            cv2.putText(
                img=frame,
                text=f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)",
                org=(20, 40),
                fontFace=cv2.FONT_HERSHEY_COMPLEX,
                fontScale=f_width / 1000,
                color=(0, 0, 255),
                thickness=1,
                lineType=cv2.LINE_AA,
            )
            # Use this workaround if there is flickering.
            if use_popup:
                cv2.imshow(winname=title, mat=frame)
                key = cv2.waitKey(1)
                # escape = 27
                if key == 27:
                    break
            else:
                # Encode numpy array to jpg.
                _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                # Create an IPython image.
                i = display.Image(data=encoded_img)
                # Display the image in this notebook.
                display.clear_output(wait=True)
                display.display(i)
    # ctrl-c
    except KeyboardInterrupt:
        print("Interrupted")
    # any different error
    except RuntimeError as e:
        print(e)
    finally:
        if player is not None:
            # Stop capturing.
            player.stop()
        if use_popup:
            cv2.destroyAllWindows()

Run Keypoint Detection on video#

VIDEO_SOURCE = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4"

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

run_keypoint_detection(
    source=VIDEO_SOURCE,
    flip=True,
    use_popup=False,
    model=pose_ov_model,
    device=device.value,
)

../_images/yolov11-keypoint-detection-with-output_43_0.png

Source ended