There is a problem with openvino's yolov8 tutorial. about openvino_notebooks HOT 5 CLOSED

from PIL import Image
from ultralytics import YOLO

DET_MODEL_NAME = "yolov8n"

det_model = YOLO(models_dir / f"{DET_MODEL_NAME}.pt")
label_map = det_model.model.names

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

det_model.predictor.inference = infer
AttributeError: 'NoneType' object has no attribute 'inference'

import cv2
import torch
import numpy as np
import openvino as ov
from PIL import Image
from ultralytics.models.yolo.detect import DetectionPredictor
from ultralytics.utils import ASSETS


class RunIntelModel:
    def __init__(self) -> None:
        # openvino official tutorial and the official issue of yolov8 provided guidelines and the original code base can not correspond to, here according to the library source code logic to make changes
        # Set up the cfg file first (call it according to the comment part of /ultralytics/models/yolo/detect/predict.py).
        # Use the setup_model method in ultralytics/engine/predictor.py to initialise the model, otherwise the model property in __init__ is None
        # None value can not change model.pt property, not change the property will lead to letterbox output and openvino fixed input size discrepancy, resulting in openvino can not reason about the issue

        self.args = dict(model='hand.pt', source=ASSETS, imgsz=(640,640), save=True)
        self.predictor = DetectionPredictor(overrides=self.args)
        self.predictor.setup_model("hand.pt")
        self.predictor.model.pt = False
        self.det_model_path = "hand_openvino_model/hand.xml"
        self.openvino_model = self.setup_model()
        self.predictor.inference = self.infer

    def setup_model(self):
        core = ov.Core() 
        det_ov_model = core.read_model(self.det_model_path)
        ov_config = {}
        det_ov_model.reshape({0: [1, 3, 640, 640]})
        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
        det_compiled_model = core.compile_model(det_ov_model, "GPU.1", ov_config) 
        return det_compiled_model

    def infer(self, *args):
        print(args[0].shape)
        result = self.openvino_model(args)

        return torch.from_numpy(result[0])

    def predict(self, raw_img):
        res = self.predictor(raw_img)
        if res[0].boxes.shape[0] > 0:
            return res[0].boxes.conf[0], res[0].boxes.xywh
        else:
            return 0, [0, 0, 0, 0]

    def __call__(self, img):
        return self.predict(raw_img=img)

class RunIntelModel:
    def __init__(self) -> None:
        # openvino official tutorial and the official issue of yolov8 provided guidelines and the original code base can not correspond to, here according to the library source code logic to make changes
        # Set up the cfg file first (call it according to the comment part of /ultralytics/models/yolo/detect/predict.py).
        # Use the setup_model method in ultralytics/engine/predictor.py to initialise the model, otherwise the model property in __init__ is None
        # None value can not change model.pt property, not change the property will lead to letterbox output and openvino fixed input size discrepancy, resulting in openvino can not reason about the issue

        self.args = dict(model="models/yolov8n_openvino_model/yolov8n.bin", source=ASSETS, imgsz=(640,640), save=True)
        self.predictor = DetectionPredictor(overrides=self.args)
        self.predictor.setup_model("models/yolov8n_openvino_model/yolov8n.bin")
        self.predictor.model.pt = False
        self.det_model_path = "models/yolov8n_openvino_model/yolov8n.xml"
        self.openvino_model = self.setup_model()
        self.predictor.inference = self.infer

    def setup_model(self):
        core = ov.Core() 
        det_ov_model = core.read_model(self.det_model_path)
        ov_config = {}
        det_ov_model.reshape({0: [1, 3, 640, 640]})
        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
        det_compiled_model = core.compile_model(det_ov_model, "GPU", ov_config) 
        return det_compiled_model

    def infer(self, *args):
        print(args[0].shape)
        result = self.openvino_model(args)

        return torch.from_numpy(result[0])

    def predict(self, raw_img):
        res = self.predictor(raw_img)
        if res[0].boxes.shape[0] > 0:
            return res[0].boxes.conf[0], res[0].boxes.xywh
        else:
            return 0, [0, 0, 0, 0]

    def __call__(self, img):
        return self.predict(raw_img=img)

r=RunIntelModel()
r.setup_model()
IMAGE_PATH=cv2.imread('coco_bike.jpg')

r.predict(IMAGE_PATH)

fp16 &= pt or jit or onnx or xml or engine or nn_module or triton  # FP16
TypeError: unsupported operand type(s) for &=: 'bool' and 'str'