macOS下MLPackage格式YOLOv8-seg模型推理慢且GPU未激活求助

我将YOLOv8分割模型（.pt格式）转换为.mlpackage格式，模型能正常检测视频目标，但推理速度极慢，单帧耗时约280ms；原.pt模型在Colab或本地运行仅需数毫秒。我已将model.compute_unit设置为ALL、CPU_and_GPU等选项，但GPU始终未激活（终端输出显示GPU未工作）。

测试代码 #

import coremltools as ct
import cv2
import numpy as np
from PIL import Image
import time
import os

def is_gpu_active():
    # Run the ioreg command and parse the output
    result = os.popen('ioreg -l | grep \"performanceState\"').read()
    return "performanceState\" = 2" in result

def letterbox_image(image, size):
    """Resize image with unchanged aspect ratio using padding."""
    ih, iw = image.shape[:2]
    w, h = size

    # Compute scale
    scale = min(w/iw, h/ih)
    nw = int(iw * scale)
    nh = int(ih * scale)

    # Resize the image using the computed scale
    image_resized = cv2.resize(image, (nw, nh))

    # Compute padding values
    top = (h - nh) // 2
    bottom = h - nh - top
    left = (w - nw) // 2
    right = w - nw - left

    # Add padding to make the image square
    image_padded = cv2.copyMakeBorder(image_resized, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[0, 0, 0])
    return image_padded

# Load the Core ML model
model = ct.models.MLModel('vhssegmentation.mlpackage')

# Set the preferred device
model.compute_units = ct.ComputeUnit.ALL#

# Open the video file
cap = cv2.VideoCapture('VID_20230927_202037.mp4')

while cap.isOpened():
    print("----")
    ret, frame = cap.read()
    
    if not ret:
        break

    # Time the letterboxing operation
    start_time = time.time()
    frame = letterbox_image(frame, (640, 640))
    print(f"Letterboxing Time: {time.time() - start_time:.4f} seconds")
    
    # Time the conversion to PIL Image
    start_time = time.time()
    pil_image = Image.fromarray(frame)
    print(f"Conversion to PIL Image Time: {time.time() - start_time:.4f} seconds")

    # Time the prediction step
    start_time = time.time()
    output = model.predict({'image': pil_image})
    print(f"Prediction Time: {time.time() - start_time:.4f} seconds")

    # Time the post-processing step
    start_time = time.time()
    predictions = output['var_1279']
    mask = np.any(predictions[0, 4:7, :] > 0.5, axis=0)
    filtered_predictions = predictions[0, :, mask]
    for row in filtered_predictions:
        x, y, w, h = row[:4]
        x1 = int(x - w / 2)
        y1 = int(y - h / 2)
        x2 = int(x + w / 2)
        y2 = int(y + h / 2)
        classes = ['class0', 'class1', 'class2']
        detected_class = classes[np.argmax(row[4:7])]
        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.putText(frame, detected_class, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    print(f"Post-Processing Time: {time.time() - start_time:.4f} seconds")

    # Display the processed frame
    cv2.imshow('Frame', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    if is_gpu_active():
        print("GPU is active")
    else:
        print("GPU is not active")    


cap.release()
cv2.destroyAllWindows()

终端耗时输出 #

Letterboxing Time: 0.0009 seconds
Conversion to PIL Image Time: 0.0006 seconds
Prediction Time: 0.2791 seconds
Post-Processing Time: 0.0013 seconds
GPU is not active

Letterboxing Time: 0.0010 seconds
Conversion to PIL Image Time: 0.0006 seconds
Prediction Time: 0.2839 seconds
Post-Processing Time: 0.0015 seconds
GPU is not active


Letterboxing Time: 0.0009 seconds
Conversion to PIL Image Time: 0.0006 seconds
Prediction Time: 0.2821 seconds
Post-Processing Time: 0.0010 seconds
GPU is not active

模型规格 #

shortDescription: "Ultralytics YOLOv8m-seg model trained on /content/dataset.yaml"
versionString: "8.0.153"
author: "Ultralytics"
license: "AGPL-3.0 https://ultralytics.com/license"
userDefined {
  key: "batch"
  value: "1"
}
userDefined {
  key: "com.github.apple.coremltools.source"
  value: "torch==2.0.1+cu118"
}
userDefined {
  key: "com.github.apple.coremltools.version"
  value: "7.0b1"
}
userDefined {
  key: "date"
  value: "2023-08-13T15:19:08.788039"
}
userDefined {
  key: "imgsz"
  value: "[640, 640]"
}
userDefined {
  key: "names"
  value: "{0: 'topvhs', 1: 'frontvhs', 2: 'sidevhs'}"
}
userDefined {
  key: "stride"
  value: "32"
}
userDefined {
  key: "task"
  value: "segment"
}

系统及硬件信息 #

Darwin
('10.16', ('', '', ''), 'x86_64')
posix.uname_result(sysname='Darwin', nodename='perceptundrymbp.home', release='22.6.0', version='Darwin Kernel Version 22.6.0: Fri Sep 15 13:39:52 PDT 2023; root:xnu-8796.141.3.700.8~1/RELEASE_X86_64', machine='x86_64')

GPU信息 #

Graphics/Displays:

    Intel HD Graphics 630:

      Chipset Model: Intel HD Graphics 630
      Type: GPU
      Bus: Built-In
      VRAM (Dynamic, Max): 1536 MB
      Vendor: Intel
      Device ID: 0x591b
      Revision ID: 0x0004
      Automatic Graphics Switching: Supported
      gMux Version: 4.0.29 [3.2.8]
      Metal Support: Metal 3
      Displays:
        Color LCD:
          Display Type: Built-In Retina LCD
          Resolution: 2880 x 1800 Retina
          Framebuffer Depth: 24-Bit Color (ARGB8888)
          Main Display: Yes
          Mirror: Off
          Online: Yes
          Automatically Adjust Brightness: Yes
          Connection Type: Internal

    Radeon Pro 555:

      Chipset Model: Radeon Pro 555
      Type: GPU
      Bus: PCIe
      PCIe Lane Width: x8
      VRAM (Total): 2 GB
      Vendor: AMD (0x1002)
      Device ID: 0x67ef
      Revision ID: 0x00c7
      ROM Revision: 113-C980AJ-927
      VBIOS Version: 113-C9801AP-A02
      EFI Driver Version: 01.A0.927
      Automatic Graphics Switching: Supported
      gMux Version: 4.0.29 [3.2.8]
      Metal Support: Metal 3

运行时截图说明 #

• 设备信息截图：展示系统硬件基础配置
• 活动监视器截图1：显示进程资源占用情况
• 活动监视器截图2：补充进程资源占用细节
• GPU使用率截图：显示GPU运行时使用率为0，未被激活

内容的提问来源于stack exchange，提问作者SoajanII