Adding quick fixes to YOLOv8 OVEP Demo

openvinotoolkit · Sep 11, 2024 · a6c46e7 · a6c46e7
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diff --git a/...ntime_yolov8_demo/YOLOv8 Object Detection with ONNX and OpenVINO Execution Provider.ipynb b/...ntime_yolov8_demo/YOLOv8 Object Detection with ONNX and OpenVINO Execution Provider.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
     "cellView": "form",
     "id": "5BqlWQzFbhf4"
@@ -24,6 +24,20 @@
     "image_url = \"https://ultralytics.com/images/bus.jpg\" "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "cellView": "form",
+    "id": "5BqlWQzFbhf4"
+   },
+   "outputs": [],
+   "source": [
+    "#Ensure the path to OpenVINO DLLs are in your system PATH\n",
+    "import onnxruntime.tools.add_openvino_win_libs as utils\n",
+    "utils.add_openvino_libs_to_path()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -35,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "id": "XmHT7vWLkKFy"
    },
@@ -56,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "id": "W5B11Gp5aZKz"
    },
@@ -66,7 +80,7 @@
      "output_type": "stream",
      "text": [
       "https://ultralytics.com/images/bus.jpg\n",
-      "Image sucessfully downloaded:  C:\\Users\\riach\\notebooks\\openvino_build_deploy\\demos\\onnxruntime_lcm_demo\n"
+      "Image sucessfully downloaded:  C:\\Users\\riach\\notebooks\\openvino_build_deploy\\demos\\onnxruntime_yolov8_demo\n"
      ]
     }
    ],
@@ -126,9 +140,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
-    "id": "0vAlvihChxnv"
+    "id": "0vAlvihChxnv",
+    "scrolled": true
    },
    "outputs": [
     {
@@ -141,19 +156,19 @@
       "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
       "\n",
       "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.16.0 opset 17...\n",
-      "\u001b[34m\u001b[1mONNX:\u001b[0m export success  2.2s, saved as 'yolov8n.onnx' (12.2 MB)\n",
+      "\u001b[34m\u001b[1mONNX:\u001b[0m export success  2.9s, saved as 'yolov8n.onnx' (12.2 MB)\n",
       "\n",
-      "Export complete (6.2s)\n",
-      "Results saved to \u001b[1mC:\\Users\\riach\\notebooks\\openvino_build_deploy\\demos\\onnxruntime_lcm_demo\u001b[0m\n",
+      "Export complete (6.8s)\n",
+      "Results saved to \u001b[1mC:\\Users\\riach\\notebooks\\openvino_build_deploy\\demos\\onnxruntime_yolov8_demo\u001b[0m\n",
       "Predict:         yolo predict task=detect model=yolov8n.onnx imgsz=640  \n",
       "Validate:        yolo val task=detect model=yolov8n.onnx imgsz=640 data=coco.yaml  \n",
       "Visualize:       https://netron.app\n",
       "WARNING  Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'.\n",
       "Loading yolov8n.onnx for ONNX Runtime inference...\n",
       "\n",
       "Found https://ultralytics.com/images/bus.jpg locally at bus.jpg\n",
-      "image 1/1 C:\\Users\\riach\\notebooks\\openvino_build_deploy\\demos\\onnxruntime_lcm_demo\\bus.jpg: 640x640 4 persons, 1 bus, 64.4ms\n",
-      "Speed: 14.6ms preprocess, 64.4ms inference, 5.7ms postprocess per image at shape (1, 3, 640, 640)\n"
+      "image 1/1 C:\\Users\\riach\\notebooks\\openvino_build_deploy\\demos\\onnxruntime_yolov8_demo\\bus.jpg: 640x640 4 persons, 1 bus, 86.6ms\n",
+      "Speed: 15.6ms preprocess, 86.6ms inference, 10.3ms postprocess per image at shape (1, 3, 640, 640)\n"
      ]
     }
    ],
@@ -184,7 +199,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device_type = \"CPU_FP32\" #GPU, NPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
    "metadata": {
     "id": "WrPPbh0Qgnt-"
    },
@@ -203,11 +227,11 @@
     "            print(\"Inferencing through OVEP\")\n",
     "            ov_model = rt.InferenceSession(quantized_model_path, so,\n",
     "                                       providers=['OpenVINOExecutionProvider'],\n",
-    "                                       provider_options=[{'device_type' : 'CPU_FP32'}])\n",
+    "                                       provider_options=[{'device_type' : \"NPU.0\"}])\n",
     "        else:\n",
     "            ov_model = rt.InferenceSession(original_model_path, so,\n",
     "                                       providers=['OpenVINOExecutionProvider'],\n",
-    "                                        provider_options=[{'device_type' : 'CPU_FP32'}])\n",
+    "                                        provider_options=[{'device_type' : \"NPU.0\"}])\n",
     "    elif device == 'CPUEP':\n",
     "        if quantize == True:\n",
     "            mlas_model = rt.InferenceSession(quantized_model_path, so, providers=['CPUExecutionProvider'])\n",
@@ -221,10 +245,7 @@
     "      input_names = mlas_model.get_inputs()[0].name\n",
     "      outputs = mlas_model.get_outputs()\n",
     "    output_names = list(map(lambda output:output.name, outputs))\n",
-    "    return input_names, output_names, mlas_model, ov_model\n",
-    "\n",
-    "device = 'OVEP'\n",
-    "input_names, output_names, mlas_model, ov_model = initialize(device=device)"
+    "    return input_names, output_names, mlas_model, ov_model"
    ]
   },
   {
@@ -238,22 +259,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = 'OVEP'\n",
+    "input_names, output_names, mlas_model, ov_model = initialize(device=device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {
     "cellView": "form",
     "id": "h1tYkwGQnKtY"
    },
    "outputs": [],
    "source": [
     "#Select number of iterations for inference\n",
-    "\n",
     "no_of_iterations = 20\n",
     "warmup_iterations = 10"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "metadata": {
     "id": "bVFDmjaQoQhP"
    },
@@ -263,7 +293,7 @@
      "output_type": "stream",
      "text": [
       "Performing ONNX Runtime Inference with OpenVINO EP.\n",
-      "Average inference time is for 10 iterations is 0.02383622222222222 sec\n"
+      "Average inference time is for 10 iterations is 0.022818444444444443 sec\n"
      ]
     }
    ],
@@ -317,7 +347,7 @@
     "model = YOLO(\"yolov8n.onnx\")\n",
     "results = model.predict(\"bus.jpg\")\n",
     "results[0].show()  # Show results to screen (in supported environments)\n",
-    "r.save(filename=f\"bus_predictions.jpg\")"
+    "results[0].save(filename=f\"bus_predictions.jpg\")"
    ]
   },
   {
@@ -331,7 +361,6 @@
    "source": [
     "#Inference on webcam\n",
     "from ultralytics import YOLO\n",
-    "from ultralytics.solutions import ai_gym\n",
     "import cv2\n",
     "\n",
     "cap = cv2.VideoCapture(0)\n",
@@ -354,6 +383,13 @@
     "cv2.destroyAllWindows()\n",
     "cap.release() #Release video sources"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/demos/onnxruntime_yolov8_demo/requirements.txt b/demos/onnxruntime_yolov8_demo/requirements.txt
@@ -1,3 +1,4 @@
+openvino==2024.3.0
 ultralytics==8.2.81
 onnxruntime-openvino==1.18.0
 onnx==1.16.2