OpenVINO を使用した YOLOv10 の変換と最適化#

import os 

os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false" 

%pip install -q "nncf>=2.11.0" 
%pip install --pre -Uq openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly 
%pip install -q "git+https://github.com/THU-MIG/yolov10.git" --extra-index-url https://download.pytorch.org/whl/cpu 
%pip install -q "torch>=2.1" "torchvision>=0.16" tqdm opencv-python "gradio>=4.19" --extra-index-url https://download.pytorch.org/whl/cpu

from pathlib import Path 

# `notebook_utils` モジュールを取得 
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

models_dir = Path("./models") 
models_dir.mkdir(exist_ok=True)

model_weights_url = "https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10n.pt" 
file_name = model_weights_url.split("/")[-1] 
model_name = file_name.replace(".pt", "") 

download_file(model_weights_url, directory=models_dir)

import types 
from ultralytics.utils import ops, yaml_load, yaml_save 
from ultralytics import YOLOv10 
import torch 

detection_labels = { 
    0: "person", 
    1: "bicycle", 
    2: "car", 
    3: "motorcycle", 
    4: "airplane", 
    5: "bus", 
    6: "train", 
    7: "truck", 
    8: "boat", 
    9: "traffic light", 
    10: "fire hydrant", 
    11: "stop sign", 
    12: "parking meter", 
    13: "bench", 
    14: "bird", 
    15: "cat", 
    16: "dog", 
    17: "horse", 
    18: "sheep", 
    19: "cow", 
    20: "elephant", 
    21: "bear", 
    22: "zebra", 
    23: "giraffe", 
    24: "backpack", 
    25: "umbrella", 
    26: "handbag", 
    27: "tie", 
    28: "suitcase", 
    29: "frisbee", 
    30: "skis", 
    31: "snowboard", 
    32: "sports ball", 
    33: "kite", 
    34: "baseball bat", 
    35: "baseball glove", 
    36: "skateboard", 
    37: "surfboard", 
    38: "tennis racket", 
    39: "bottle", 
    40: "wine glass", 
    41: "cup", 
    42: "fork", 
    43: "knife", 
    44: "spoon", 
    45: "bowl", 
    46: "banana", 
    47: "apple", 
    48: "sandwich", 
    49: "orange", 
    50: "broccoli", 
    51: "carrot", 
    52: "hot dog", 
    53: "pizza", 
    54: "donut", 
    55: "cake", 
    56: "chair", 
    57: "couch", 
    58: "potted plant", 
    59: "bed", 
    60: "dining table", 
    61: "toilet", 
    62: "tv", 
    63: "laptop", 
    64: "mouse", 
    65: "remote", 
    66: "keyboard", 
    67: "cell phone", 
    68: "microwave", 
    69: "oven", 
    70: "toaster", 
    71: "sink", 
    72: "refrigerator", 
    73: "book", 
    74: "clock", 
    75: "vase", 
    76: "scissors", 
    77: "teddy bear", 
    78: "hair drier", 
    79: "toothbrush", 
} 

def v10_det_head_forward(self, x): 
    one2one = self.forward_feat([xi.detach() for xi in x], self.one2one_cv2, self.one2one_cv3) 
    if not self.export: 
        one2many = super().forward(x) 

    if not self.training: 
        one2one = self.inference(one2one) 
        if not self.export: 
            return {"one2many": one2many, "one2one": one2one} 
        else: 
            assert self.max_det != -1 
            boxes, scores, labels = ops.v10postprocess(one2one.permute(0, 2, 1), self.max_det, self.nc) 
            return torch.cat( 
                [boxes, scores.unsqueeze(-1), labels.unsqueeze(-1).to(boxes.dtype)], 
                dim=-1, 
            ) 
    else: 
        return {"one2many": one2many, "one2one": one2one} 

ov_model_path = models_dir / f"{model_name}_openvino_model/{model_name}.xml" 
if not ov_model_path.exists(): 
    model = YOLOv10(models_dir / file_name) 
    model.model.model[-1].forward = types.MethodType(v10_det_head_forward, model.model.model[-1]) 
    model.export(format="openvino", dynamic=True, half=True) 
    config = yaml_load(ov_model_path.parent / "metadata.yaml") 
    config["names"] = detection_labels 
    yaml_save(ov_model_path.parent / "metadata.yaml", config)

ov_yolo_model = YOLOv10(ov_model_path.parent, task="detect")

from PIL import Image 

IMAGE_PATH = Path("./data/coco_bike.jpg") 
download_file( 
    url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg", 
    filename=IMAGE_PATH.name, 
    directory=IMAGE_PATH.parent, 
)

res = ov_yolo_model(IMAGE_PATH, iou=0.45, conf=0.2) 
Image.fromarray(res[0].plot()[:, :, ::-1])

import openvino as ov 

import ipywidgets as widgets 

core = ov.Core() 

device = widgets.Dropdown( 
    options=core.available_devices + ["AUTO"], 
    value="CPU", 
    description="Device:", 
    disabled=False, 
) 

device

ov_model = core.read_model(ov_model_path) 

# 選択したデバイスにモデルをロード 
if "GPU" in device.value or "NPU" in device.value: 
    ov_model.reshape({0: [1, 3, 640, 640]}) 
ov_config = {} 
if "GPU" in device.value: 
    ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"} 
det_compiled_model = core.compile_model(ov_model, device.value, ov_config)

ov_yolo_model.predictor.model.ov_compiled_model = det_compiled_model

res = ov_yolo_model(IMAGE_PATH, iou=0.45, conf=0.2)

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

import ipywidgets as widgets 

int8_model_det_path = models_dir / "int8" / f"{model_name}_openvino_model/{model_name}.xml" ov_yolo_int8_model = None 

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

to_quantize

# skip_kernel_extension モジュールを取得 
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

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

# 物体検出を実行するメイン処理関数 
def run_object_detection( 
    source=0, 
    flip=False, 
    use_popup=False, 
    skip_first_frames=0, 
    det_model=ov_yolo_int8_model, 
    device=device.value, 
): 
    player = None 
    try:
        # ターゲット fps で再生するビデオプレーヤーを作成 
        player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames) 
        # キャプチャー開始 
        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:
            # フレームをグラブ 
            frame = player.next() 
            if frame is None: 
                print("Source ended") 
                break 
            # フレームがフル HD より大きい場合は、サイズを縮小してパフォーマンスを向上させます 
            scale = 1280 / max(frame.shape) 
            if scale < 1: 
                frame = cv2.resize( 
                    src=frame, 
                    dsize=None, 
                    fx=scale, 
                    fy=scale, 
                    interpolation=cv2.INTER_AREA, 
                ) 
            # 結果を取得 
            input_image = np.array(frame) 

            start_time = time.time() 
            detections = det_model(input_image, iou=0.45, conf=0.2, verbose=False) 
            stop_time = time.time() 
            frame = detections[0].plot() 

            processing_times.append(stop_time - start_time) 
            # 最後の 200 フレームの処理時間を使用 
            if len(processing_times) > 200: 
                processing_times.popleft() 

            _, f_width = frame.shape[:2] 
            # 平均処理時間 [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, 
            )
 
            # ちらつきがある場合はこの回避策を使用 
            if use_popup: 
                cv2.imshow(winname=title, mat=frame) 
                key = cv2.waitKey(1) 
                # escape = 27 
                if key == 27: 
                    break 
                else: 
                    # numpy 配列を jpg にエンコード
                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]) 
                    # IPython イメージを作成 
                    i = display.Image(data=encoded_img) 
                    # このノートブックに画像を表示 
                    display.clear_output(wait=True) 
                    display.display(i) 

# ctrl-c 
except KeyboardInterrupt: 
    print("Interrupted") 
# 異なるエラー 
except RuntimeError as e: 
    print(e) 
finally: 
    if player is not None: 
        # キャプチャーを停止 
        player.stop().stop() 
    if use_popup: 
        cv2.destroyAllWindows()

use_int8 = widgets.Checkbox( 
    value=ov_yolo_int8_model is not None, 
    description="Use int8 model", 
    disabled=ov_yolo_int8_model is None, 
) 

use_int8

WEBCAM_INFERENCE = False 

if WEBCAM_INFERENCE:     VIDEO_SOURCE = 0 # ウェブカメラ 
else: 
    download_file( 
        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4", 
        directory="data", 
    ) 
    VIDEO_SOURCE = "data/people.mp4"

Run_object_detection(( 
    det_model=ov_yolo_model if not use_int8.value else ov_yolo_int8_model, 
    source=VIDEO_SOURCE, 
    flip=True, 
    use_popup=False, 
)