yolov7_pothole_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JPB9MAhOSpXo"
   },
   "source": [
    "# Pothole Detection using YOLOv7\n",
    "\n",
    "We carry out the following training experiments using YOLOv7 on the pothole dataset in this notebook:\n",
    "* YOLOv7 Tiny model fixed resolution training.\n",
    "* YOLOv7 Tiny model multi-resolution training.\n",
    "* YOLOv7 normam model fixed resolution training.\n",
    "* YOLOv7 normal model multi-resolution training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Ext1XWfFSpXx"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import glob as glob\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mcmUsjLr4miH"
   },
   "source": [
    "## Download and Extract the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HfpKU3ffSpX3",
    "outputId": "790fd37a-8485-4b3c-fe11-b26b6b6cd597"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'wget' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "# Download the dataset.\n",
    "!wget https://learnopencv.s3.us-west-2.amazonaws.com/pothole_dataset.zip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "4EbeXuwmybIN"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'unzip' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "# Extract the dataset.\n",
    "!unzip -q pothole_dataset.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KrabU8kUSpX5"
   },
   "source": [
    "## Visualize Images from the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "zoa1PLYVSpX6"
   },
   "outputs": [],
   "source": [
    "# Function to convert bounding boxes in YOLO format to xmin, ymin, xmax, ymax.\n",
    "def yolo2bbox(bboxes):\n",
    "    xmin, ymin = bboxes[0]-bboxes[2]/2, bboxes[1]-bboxes[3]/2\n",
    "    xmax, ymax = bboxes[0]+bboxes[2]/2, bboxes[1]+bboxes[3]/2\n",
    "    return xmin, ymin, xmax, ymax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "kw7HlwsVSpX7"
   },
   "outputs": [],
   "source": [
    "def plot_box(image, bboxes, labels):\n",
    "    # Need the image height and width to denormalize\n",
    "    # the bounding box coordinates\n",
    "    h, w, _ = image.shape\n",
    "    for box_num, box in enumerate(bboxes):\n",
    "        x1, y1, x2, y2 = yolo2bbox(box)\n",
    "        # Denormalize the coordinates.\n",
    "        xmin = int(x1*w)\n",
    "        ymin = int(y1*h)\n",
    "        xmax = int(x2*w)\n",
    "        ymax = int(y2*h)\n",
    "\n",
    "        thickness = max(2, int(w/275))\n",
    "                \n",
    "        cv2.rectangle(\n",
    "            image, \n",
    "            (xmin, ymin), (xmax, ymax),\n",
    "            color=(0, 0, 255),\n",
    "            thickness=thickness\n",
    "        )\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "VlVqwFIgSpX-"
   },
   "outputs": [],
   "source": [
    "# Function to plot images with the bounding boxes.\n",
    "def plot(image_paths, label_paths, num_samples):\n",
    "    all_images = []\n",
    "    all_images.extend(glob.glob(image_paths+'/*.jpg'))\n",
    "    all_images.extend(glob.glob(image_paths+'/*.JPG'))\n",
    "    all_labels = glob.glob(label_paths)\n",
    "    \n",
    "    all_images.sort()\n",
    "    all_labels.sort()\n",
    "\n",
    "    num_images = len(all_images)\n",
    "    \n",
    "    plt.figure(figsize=(15, 12))\n",
    "    for i in range(num_samples):\n",
    "        j = random.randint(0,num_images-1)\n",
    "        image = cv2.imread(all_images[j])\n",
    "        with open(all_labels[j], 'r') as f:\n",
    "            bboxes = []\n",
    "            labels = []\n",
    "            label_lines = f.readlines()\n",
    "            for label_line in label_lines:\n",
    "                label = label_line[0]\n",
    "                bbox_string = label_line[2:]\n",
    "                x_c, y_c, w, h = bbox_string.split(' ')\n",
    "                x_c = float(x_c)\n",
    "                y_c = float(y_c)\n",
    "                w = float(w)\n",
    "                h = float(h)\n",
    "                bboxes.append([x_c, y_c, w, h])\n",
    "                labels.append(label)\n",
    "        result_image = plot_box(image, bboxes, labels)\n",
    "        plt.subplot(2, 2, i+1)\n",
    "        plt.imshow(result_image[:, :, ::-1])\n",
    "        plt.axis('off')\n",
    "    plt.subplots_adjust(wspace=1)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 865
    },
    "id": "UrcOAM5OSpYD",
    "outputId": "8afa7a92-ecc1-4fbc-c9c3-147c1b422054"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize a few training images.\n",
    "plot(\n",
    "    image_paths='pothole_dataset/images/train/', \n",
    "    label_paths='pothole_dataset/labels/train/*.txt',\n",
    "    num_samples=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PizCdknl6uSY"
   },
   "source": [
    "## Clone the YOLOv7 GitHub Repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UksrAttJ9IQ5",
    "outputId": "77bf3b4c-5099-4677-dc0e-30fd2257e17d"
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('yolov7'):\n",
    "    !git clone https://github.com/Ryukijano/yolov7.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KJMeuen1_2d4",
    "outputId": "feedbca1-5853-465b-ffb2-97a2375bf443"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g:\\WORK\\YOLOV7\\yolov7\\yolov7\n"
     ]
    }
   ],
   "source": [
    "%cd yolov7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tt_s9eW6BygL",
    "outputId": "b3b499a4-a82e-40d0-d7fd-b50ec28a1120"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib>=3.2.2 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 4)) (3.5.2)\n",
      "Requirement already satisfied: numpy>=1.18.5 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 5)) (1.23.5)\n",
      "Requirement already satisfied: opencv-python>=4.1.1 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 6)) (4.6.0.66)\n",
      "Requirement already satisfied: Pillow>=7.1.2 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 7)) (9.3.0)\n",
      "Requirement already satisfied: PyYAML>=5.3.1 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 8)) (6.0)\n",
      "Requirement already satisfied: requests>=2.23.0 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 9)) (2.28.1)\n",
      "Requirement already satisfied: scipy>=1.4.1 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 10)) (1.9.1)\n",
      "Requirement already satisfied: torch!=1.12.0,>=1.7.0 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 11)) (1.13.0+cu117)\n",
      "Requirement already satisfied: torchvision!=0.13.0,>=0.8.1 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 12)) (0.14.0+cu117)\n",
      "Requirement already satisfied: tqdm>=4.41.0 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 13)) (4.64.1)\n",
      "Requirement already satisfied: protobuf<4.21.3 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 14)) (3.20.3)\n",
      "Requirement already satisfied: tensorboard>=2.4.1 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 17)) (2.11.0)\n",
      "Requirement already satisfied: pandas>=1.1.4 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 21)) (1.4.4)\n",
      "Requirement already satisfied: seaborn>=0.11.0 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 22)) (0.11.2)\n",
      "Requirement already satisfied: ipython in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 34)) (7.31.1)\n",
      "Requirement already satisfied: psutil in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 35)) (5.9.0)\n",
      "Requirement already satisfied: thop in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from -r requirements.txt (line 36)) (0.1.1.post2209072238)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (3.0.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (1.4.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (21.3)\n",
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      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\gyana\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 17)) (3.2.2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Skipping wandb as it is not installed.\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt\n",
    "!pip uninstall wandb -y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R8WRVYhtyqn1"
   },
   "source": [
    "## Create the Data YAML File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8gCP0ko1_5BA",
    "outputId": "4d50782b-e7df-42e5-9d3f-60bf30d4b338"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting data/pothole.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile data/pothole.yaml\n",
    "train: ../pothole_dataset/images/train \n",
    "val: ../pothole_dataset/images/valid\n",
    "test: ../pothole_dataset/images/test\n",
    "\n",
    "# Classes\n",
    "nc: 1  # number of classes\n",
    "names: ['pothole']  # class names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I8uxae4sCBJL"
   },
   "source": [
    "## YOLOv7 Tiny Model Fixed Resolution Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qi18zK4pSpYN",
    "outputId": "d5777c33-f76a-48a0-9bde-ca3481e0c50c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in c:\\users\\gyana\\anaconda3\\lib\\site-packages (3.2)\n",
      "\n",
      "Saved under yolov7-tiny (1).pt\n"
     ]
    }
   ],
   "source": [
    "!pip install wget\n",
    "# Download the Tiny model weights.\n",
    "!python -m wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AhrOb7rMCGHN",
    "outputId": "33691552-810b-42f1-e0ef-a97a7a3bf8bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting cfg/training/yolov7_pothole-tiny.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile cfg/training/yolov7_pothole-tiny.yaml\n",
    "# parameters\n",
    "nc: 1  # number of classes\n",
    "depth_multiple: 1.0  # model depth multiple\n",
    "width_multiple: 1.0  # layer channel multiple\n",
    "\n",
    "# anchors\n",
    "anchors:\n",
    "  - [10,13, 16,30, 33,23]  # P3/8\n",
    "  - [30,61, 62,45, 59,119]  # P4/16\n",
    "  - [116,90, 156,198, 373,326]  # P5/32\n",
    "\n",
    "# yolov7-tiny backbone\n",
    "backbone:\n",
    "  # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True\n",
    "  [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 0-P1/2  \n",
    "  \n",
    "   [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 1-P2/4    \n",
    "   \n",
    "   [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 7\n",
    "   \n",
    "   [-1, 1, MP, []],  # 8-P3/8\n",
    "   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 14\n",
    "   \n",
    "   [-1, 1, MP, []],  # 15-P4/16\n",
    "   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 21\n",
    "   \n",
    "   [-1, 1, MP, []],  # 22-P5/32\n",
    "   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 28\n",
    "  ]\n",
    "\n",
    "# yolov7-tiny head\n",
    "head:\n",
    "  [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, SP, [5]],\n",
    "   [-2, 1, SP, [9]],\n",
    "   [-3, 1, SP, [13]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -7], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 37\n",
    "  \n",
    "   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
    "   [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4\n",
    "   [[-1, -2], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 47\n",
    "  \n",
    "   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
    "   [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3\n",
    "   [[-1, -2], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 57\n",
    "   \n",
    "   [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, 47], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 65\n",
    "   \n",
    "   [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, 37], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [[-1, -2, -3, -4], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 73\n",
    "      \n",
    "   [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "   [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
    "\n",
    "   [[74,75,76], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ShbcTi4fSpYS",
    "outputId": "fc45664d-10f1-4e09-eda3-99be611f501f"
   },
   "outputs": [],
   "source": [
    "!python train.py --epochs 100 --workers 20 --device 0 --batch-size 50 \\\n",
    "--data data/pothole.yaml --img 640 640 --cfg cfg/training/yolov7_pothole-tiny.yaml \\\n",
    "--weights 'yolov7-tiny.pt' --name yolov7_tiny_pothole_fixed_res --hyp data/hyp.scratch.tiny.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ISV7-kqv2RTe",
    "outputId": "5625170d-72f0-45bb-b1fd-e400fdadd62d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(weights=['runs/train/yolov7_tiny_pothole_fixed_res/weights/best.pt'], data='data/pothole.yaml', batch_size=32, img_size=640, conf_thres=0.001, iou_thres=0.65, task='test', device='', single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project='runs/test', name='exp', exist_ok=False, no_trace=False, v5_metric=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\", line 174, in _new_conn\n",
      "    conn = connection.create_connection(\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\", line 72, in create_connection\n",
      "    for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\socket.py\", line 954, in getaddrinfo\n",
      "    for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\n",
      "socket.gaierror: [Errno 11001] getaddrinfo failed\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\", line 703, in urlopen\n",
      "    httplib_response = self._make_request(\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\", line 386, in _make_request\n",
      "    self._validate_conn(conn)\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\", line 1042, in _validate_conn\n",
      "    conn.connect()\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\", line 358, in connect\n",
      "    self.sock = conn = self._new_conn()\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\", line 186, in _new_conn\n",
      "    raise NewConnectionError(\n",
      "urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x0000028C8F5BD820>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\requests\\adapters.py\", line 489, in send\n",
      "    resp = conn.urlopen(\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\", line 787, in urlopen\n",
      "    retries = retries.increment(\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\urllib3\\util\\retry.py\", line 592, in increment\n",
      "    raise MaxRetryError(_pool, url, error or ResponseError(cause))\n",
      "urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='api.github.com', port=443): Max retries exceeded with url: /repos/WongKinYiu/yolov7/releases/latest (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x0000028C8F5BD820>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 25, in attempt_download\n",
      "    response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\requests\\api.py\", line 73, in get\n",
      "    return request(\"get\", url, params=params, **kwargs)\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\requests\\api.py\", line 59, in request\n",
      "    return session.request(method=method, url=url, **kwargs)\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\requests\\sessions.py\", line 587, in request\n",
      "    resp = self.send(prep, **send_kwargs)\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\requests\\sessions.py\", line 701, in send\n",
      "    r = adapter.send(request, **kwargs)\n",
      "  File \"c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\requests\\adapters.py\", line 565, in send\n",
      "    raise ConnectionError(e, request=request)\n",
      "requests.exceptions.ConnectionError: HTTPSConnectionPool(host='api.github.com', port=443): Max retries exceeded with url: /repos/WongKinYiu/yolov7/releases/latest (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x0000028C8F5BD820>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 319, in <module>\n",
      "    test(opt.data,\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 58, in test\n",
      "    model = attempt_load(weights, map_location=device)  # load FP32 model\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\models\\experimental.py\", line 251, in attempt_load\n",
      "    attempt_download(w)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 31, in attempt_download\n",
      "    tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]\n",
      "IndexError: list index out of range\n"
     ]
    }
   ],
   "source": [
    "!python test.py --weights runs/train/yolov7_tiny_pothole_fixed_res/weights/best.pt --task test --data data/pothole.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(weights=['runs/train/yolov7_tiny_pothole_fixed_res/weights/best.pt'], source='../pothole_dataset/images/test/G0010124.JPG', img_size=640, conf_thres=0.25, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 26, in attempt_download\n",
      "    assets = [x['name'] for x in response['assets']]  # release assets\n",
      "KeyError: 'assets'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\detect.py\", line 196, in <module>\n",
      "    detect()\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\detect.py\", line 34, in detect\n",
      "    model = attempt_load(weights, map_location=device)  # load FP32 model\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\models\\experimental.py\", line 251, in attempt_load\n",
      "    attempt_download(w)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 31, in attempt_download\n",
      "    tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]\n",
      "IndexError: list index out of range\n"
     ]
    }
   ],
   "source": [
    "!python detect.py --weights runs/train/yolov7_tiny_pothole_fixed_res/weights/best.pt --conf 0.25 --img-size 640 --source ../pothole_dataset/images/test/G0010124.JPG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LYBcrxIPrXRP"
   },
   "source": [
    "## YOLOv7 Tiny Model Multi-Resolution Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "BkNC0DvRrc9n"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n",
      "\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 2.49, Best Possible Recall (BPR) = 0.8156. Attempting to improve anchors, please wait...\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mWARNING: Extremely small objects found. 538 of 3497 labels are < 3 pixels in size.\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mRunning kmeans for 9 anchors on 3497 points...\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 1.0000 best possible recall, 3.29 anchors past thr\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mn=9, img_size=640, metric_all=0.239/0.713-mean/best, past_thr=0.507-mean: 13,4,  29,9,  59,20,  93,48,  187,60,  161,114,  335,113,  243,202,  501,245\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 1.0000 best possible recall, 3.75 anchors past thr\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mn=9, img_size=640, metric_all=0.268/0.749-mean/best, past_thr=0.516-mean: 9,3,  17,5,  28,9,  56,17,  87,46,  162,56,  159,118,  293,120,  382,215\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mNew anchors saved to model. Update model *.yaml to use these anchors in the future.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Namespace(weights=\"'yolov7-tiny.pt'\", cfg='cfg/training/yolov7_pothole-tiny.yaml', data='data/pothole.yaml', hyp='data/hyp.scratch.tiny.yaml', epochs=100, batch_size=32, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=True, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=4, project='runs/train', entity=None, name='yolov7_tiny_pothole_multi_res', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs\\\\train\\\\yolov7_tiny_pothole_multi_res', total_batch_size=32)\n",
      "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.05, copy_paste=0.0, paste_in=0.05, loss_ota=1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1       928  models.common.Conv                      [3, 32, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      "  2                -1  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      "  3                -2  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      "  4                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      "  5                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      "  6  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      "  7                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      "  8                -1  1         0  models.common.MP                        []                            \n",
      "  9                -1  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 10                -2  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 11                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 12                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 13  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 15                -1  1         0  models.common.MP                        []                            \n",
      " 16                -1  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 17                -2  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 20  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 21                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 22                -1  1         0  models.common.MP                        []                            \n",
      " 23                -1  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 24                -2  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 25                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 26                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 27  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 28                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 29                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 30                -2  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 31                -1  1         0  models.common.SP                        [5]                           \n",
      " 32                -2  1         0  models.common.SP                        [9]                           \n",
      " 33                -3  1         0  models.common.SP                        [13]                          \n",
      " 34  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 35                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 36          [-1, -7]  1         0  models.common.Concat                    [1]                           \n",
      " 37                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 38                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 39                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 40                21  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 41          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
      " 42                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 43                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 44                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 45                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 46  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 47                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 48                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 49                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 50                14  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 51          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
      " 52                -1  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 53                -2  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 54                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 55                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 56  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 57                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 58                -1  1     73984  models.common.Conv                      [64, 128, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 59          [-1, 47]  1         0  models.common.Concat                    [1]                           \n",
      " 60                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 61                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 62                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 63                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 64  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 65                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 66                -1  1    295424  models.common.Conv                      [128, 256, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 67          [-1, 37]  1         0  models.common.Concat                    [1]                           \n",
      " 68                -1  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 69                -2  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 70                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 71                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 72  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
      " 73                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 74                57  1     73984  models.common.Conv                      [64, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 75                65  1    295424  models.common.Conv                      [128, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 76                73  1   1180672  models.common.Conv                      [256, 512, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
      " 77      [74, 75, 76]  1     17132  models.yolo.IDetect                     [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\torch\\functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\TensorShape.cpp:3191.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "Model Summary: 263 layers, 6014988 parameters, 6014988 gradients, 13.2 GFLOPS\n",
      "\n",
      "Scaled weight_decay = 0.0005\n",
      "Optimizer groups: 58 .bias, 58 conv.weight, 61 other\n",
      "\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '..\\pothole_dataset\\labels\\train.cache' images and labels... 1265 found, 0 missing, 0 empty, 1 corrupted: 100%|██████████| 1265/1265 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '..\\pothole_dataset\\labels\\train.cache' images and labels... 1265 found, 0 missing, 0 empty, 1 corrupted: 100%|██████████| 1265/1265 [00:00<?, ?it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '..\\pothole_dataset\\labels\\valid.cache' images and labels... 401 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 401/401 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '..\\pothole_dataset\\labels\\valid.cache' images and labels... 401 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 401/401 [00:00<?, ?it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm::   0%|          | 0/1000 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7284:  11%|█▏        | 113/1000 [00:00<00:00, 1123.38it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7359:  23%|██▎       | 226/1000 [00:00<00:00, 1094.84it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7374:  34%|███▍      | 338/1000 [00:00<00:00, 1102.93it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7402:  46%|████▋     | 463/1000 [00:00<00:00, 1159.04it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7406:  58%|█████▊    | 579/1000 [00:00<00:00, 1113.59it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7425:  69%|██████▉   | 691/1000 [00:00<00:00, 1112.71it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7434:  82%|████████▏ | 817/1000 [00:00<00:00, 1159.24it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7469:  93%|█████████▎| 934/1000 [00:00<00:00, 1160.08it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7486: 100%|██████████| 1000/1000 [00:00<00:00, 1141.14it/s]\n",
      "Image sizes 640 train, 640 test\n",
      "Using 4 dataloader workers\n",
      "Logging results to runs\\train\\yolov7_tiny_pothole_multi_res\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
      "\n",
      "  0%|          | 0/40 [00:00<?, ?it/s]\n",
      "  0%|          | 0/40 [00:04<?, ?it/s]\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\train.py\", line 616, in <module>\n",
      "    train(hyp, opt, device, tb_writer)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\train.py\", line 363, in train\n",
      "    loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs)  # loss scaled by batch_size\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\loss.py\", line 585, in __call__\n",
      "    bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\loss.py\", line 759, in build_targets\n",
      "    from_which_layer = from_which_layer[fg_mask_inboxes]\n",
      "RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)\n"
     ]
    }
   ],
   "source": [
    "!python train.py --epochs 100 --workers 4 --device 0 --batch-size 32 \\\n",
    "--data data/pothole.yaml --img 640 640 --cfg cfg/training/yolov7_pothole-tiny.yaml \\\n",
    "--weights 'yolov7-tiny.pt' --name yolov7_tiny_pothole_multi_res --hyp data/hyp.scratch.tiny.yaml \\\n",
    "--multi-scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7NGkBpbX1-Hp",
    "outputId": "810506eb-8486-4a67-e724-a59ab35fbd92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(weights=['runs/train/yolov7_tiny_pothole_multi_res/weights/best.pt'], data='data/pothole.yaml', batch_size=32, img_size=640, conf_thres=0.001, iou_thres=0.65, task='test', device='', single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project='runs/test', name='exp', exist_ok=False, no_trace=False, v5_metric=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 26, in attempt_download\n",
      "    assets = [x['name'] for x in response['assets']]  # release assets\n",
      "KeyError: 'assets'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 319, in <module>\n",
      "    test(opt.data,\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 58, in test\n",
      "    model = attempt_load(weights, map_location=device)  # load FP32 model\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\models\\experimental.py\", line 251, in attempt_load\n",
      "    attempt_download(w)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 31, in attempt_download\n",
      "    tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]\n",
      "IndexError: list index out of range\n"
     ]
    }
   ],
   "source": [
    "!python test.py --weights runs/train/yolov7_tiny_pothole_multi_res/weights/best.pt --task test --data data/pothole.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cK6UXlGaCPr9"
   },
   "source": [
    "## YOLOv7 Normal Model Fixed Resolution Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Moqxh1NCU2i",
    "outputId": "e9562f47-971b-4c29-feb8-bd92ebd6dc1d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing cfg/training/yolov7_pothole.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile cfg/training/yolov7_pothole.yaml\n",
    "# parameters\n",
    "nc: 1  # number of classes\n",
    "depth_multiple: 1.0  # model depth multiple\n",
    "width_multiple: 1.0  # layer channel multiple\n",
    "\n",
    "# anchors\n",
    "anchors:\n",
    "  - [12,16, 19,36, 40,28]  # P3/8\n",
    "  - [36,75, 76,55, 72,146]  # P4/16\n",
    "  - [142,110, 192,243, 459,401]  # P5/32\n",
    "\n",
    "# yolov7 backbone\n",
    "backbone:\n",
    "  # [from, number, module, args]\n",
    "  [[-1, 1, Conv, [32, 3, 1]],  # 0\n",
    "  \n",
    "   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      \n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   \n",
    "   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  \n",
    "   [-1, 1, Conv, [64, 1, 1]],\n",
    "   [-2, 1, Conv, [64, 1, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [[-1, -3, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [256, 1, 1]],  # 11\n",
    "         \n",
    "   [-1, 1, MP, []],\n",
    "   [-1, 1, Conv, [128, 1, 1]],\n",
    "   [-3, 1, Conv, [128, 1, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 2]],\n",
    "   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  \n",
    "   [-1, 1, Conv, [128, 1, 1]],\n",
    "   [-2, 1, Conv, [128, 1, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [[-1, -3, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [512, 1, 1]],  # 24\n",
    "         \n",
    "   [-1, 1, MP, []],\n",
    "   [-1, 1, Conv, [256, 1, 1]],\n",
    "   [-3, 1, Conv, [256, 1, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 2]],\n",
    "   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  \n",
    "   [-1, 1, Conv, [256, 1, 1]],\n",
    "   [-2, 1, Conv, [256, 1, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [[-1, -3, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [1024, 1, 1]],  # 37\n",
    "         \n",
    "   [-1, 1, MP, []],\n",
    "   [-1, 1, Conv, [512, 1, 1]],\n",
    "   [-3, 1, Conv, [512, 1, 1]],\n",
    "   [-1, 1, Conv, [512, 3, 2]],\n",
    "   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  \n",
    "   [-1, 1, Conv, [256, 1, 1]],\n",
    "   [-2, 1, Conv, [256, 1, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [[-1, -3, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [1024, 1, 1]],  # 50\n",
    "  ]\n",
    "\n",
    "# yolov7 head\n",
    "head:\n",
    "  [[-1, 1, SPPCSPC, [512]], # 51\n",
    "  \n",
    "   [-1, 1, Conv, [256, 1, 1]],\n",
    "   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
    "   [37, 1, Conv, [256, 1, 1]], # route backbone P4\n",
    "   [[-1, -2], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [256, 1, 1]],\n",
    "   [-2, 1, Conv, [256, 1, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [256, 1, 1]], # 63\n",
    "   \n",
    "   [-1, 1, Conv, [128, 1, 1]],\n",
    "   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
    "   [24, 1, Conv, [128, 1, 1]], # route backbone P3\n",
    "   [[-1, -2], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [128, 1, 1]],\n",
    "   [-2, 1, Conv, [128, 1, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [-1, 1, Conv, [64, 3, 1]],\n",
    "   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [128, 1, 1]], # 75\n",
    "      \n",
    "   [-1, 1, MP, []],\n",
    "   [-1, 1, Conv, [128, 1, 1]],\n",
    "   [-3, 1, Conv, [128, 1, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 2]],\n",
    "   [[-1, -3, 63], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [256, 1, 1]],\n",
    "   [-2, 1, Conv, [256, 1, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [-1, 1, Conv, [128, 3, 1]],\n",
    "   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [256, 1, 1]], # 88\n",
    "      \n",
    "   [-1, 1, MP, []],\n",
    "   [-1, 1, Conv, [256, 1, 1]],\n",
    "   [-3, 1, Conv, [256, 1, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 2]],\n",
    "   [[-1, -3, 51], 1, Concat, [1]],\n",
    "   \n",
    "   [-1, 1, Conv, [512, 1, 1]],\n",
    "   [-2, 1, Conv, [512, 1, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [-1, 1, Conv, [256, 3, 1]],\n",
    "   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n",
    "   [-1, 1, Conv, [512, 1, 1]], # 101\n",
    "   \n",
    "   [75, 1, RepConv, [256, 3, 1]],\n",
    "   [88, 1, RepConv, [512, 3, 1]],\n",
    "   [101, 1, RepConv, [1024, 3, 1]],\n",
    "\n",
    "   [[102,103,104], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J7pjSy--CU0M",
    "outputId": "bfca6240-0641-4516-f7a1-f310471c6477"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'wget' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ze7PjjxFCUxV",
    "outputId": "c8f56e74-7bb4-48a6-8fda-29890735bf76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n",
      "\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 2.30, Best Possible Recall (BPR) = 0.7486. Attempting to improve anchors, please wait...\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mWARNING: Extremely small objects found. 538 of 3497 labels are < 3 pixels in size.\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mRunning kmeans for 9 anchors on 3497 points...\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 1.0000 best possible recall, 3.29 anchors past thr\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mn=9, img_size=640, metric_all=0.239/0.713-mean/best, past_thr=0.507-mean: 13,4,  29,9,  59,20,  93,48,  187,60,  161,114,  335,113,  243,202,  501,245\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 1.0000 best possible recall, 3.75 anchors past thr\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mn=9, img_size=640, metric_all=0.268/0.749-mean/best, past_thr=0.516-mean: 9,3,  17,5,  28,9,  56,17,  87,46,  162,56,  159,118,  293,120,  382,215\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mNew anchors saved to model. Update model *.yaml to use these anchors in the future.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Namespace(weights=\"'yolov7_training.pt'\", cfg='cfg/training/yolov7_pothole.yaml', data='data/pothole.yaml', hyp='data/hyp.scratch.custom.yaml', epochs=100, batch_size=16, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=4, project='runs/train', entity=None, name='yolov7_pothole_fixed_res', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs\\\\train\\\\yolov7_pothole_fixed_res', total_batch_size=16)\n",
      "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, paste_in=0.0, loss_ota=1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
      "  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
      "  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
      " 12                -1  1         0  models.common.MP                        []                            \n",
      " 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 16          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              \n",
      " 25                -1  1         0  models.common.MP                        []                            \n",
      " 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 29          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
      " 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
      " 38                -1  1         0  models.common.MP                        []                            \n",
      " 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
      " 42          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
      " 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
      " 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                \n",
      " 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 55          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
      " 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
      " 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
      " 67          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
      " 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               \n",
      " 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
      " 76                -1  1         0  models.common.MP                        []                            \n",
      " 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
      " 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
      " 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           \n",
      " 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
      " 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 89                -1  1         0  models.common.MP                        []                            \n",
      " 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
      " 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
      " 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           \n",
      " 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              \n",
      " 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      "100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      "101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             \n",
      "102                75  1    328704  models.common.RepConv                   [128, 256, 3, 1]              \n",
      "103                88  1   1312768  models.common.RepConv                   [256, 512, 3, 1]              \n",
      "104               101  1   5246976  models.common.RepConv                   [512, 1024, 3, 1]             \n",
      "105   [102, 103, 104]  1     34156  models.yolo.IDetect                     [1, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n",
      "c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\torch\\functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\TensorShape.cpp:3191.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "Model Summary: 415 layers, 37196556 parameters, 37196556 gradients, 105.1 GFLOPS\n",
      "\n",
      "Scaled weight_decay = 0.0005\n",
      "Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n",
      "\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '..\\pothole_dataset\\labels\\train.cache' images and labels... 1265 found, 0 missing, 0 empty, 1 corrupted: 100%|██████████| 1265/1265 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '..\\pothole_dataset\\labels\\train.cache' images and labels... 1265 found, 0 missing, 0 empty, 1 corrupted: 100%|██████████| 1265/1265 [00:00<?, ?it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '..\\pothole_dataset\\labels\\valid.cache' images and labels... 401 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 401/401 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '..\\pothole_dataset\\labels\\valid.cache' images and labels... 401 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 401/401 [00:00<?, ?it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm::   0%|          | 0/1000 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7271:  10%|▉         | 98/1000 [00:00<00:00, 967.13it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7349:  20%|█▉        | 197/1000 [00:00<00:00, 978.47it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7371:  30%|██▉       | 298/1000 [00:00<00:00, 989.70it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7386:  40%|███▉      | 397/1000 [00:00<00:00, 983.15it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7405:  50%|████▉     | 498/1000 [00:00<00:00, 990.35it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7406:  60%|██████    | 604/1000 [00:00<00:00, 1011.61it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7429:  71%|███████   | 709/1000 [00:00<00:00, 1022.31it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7434:  81%|████████  | 812/1000 [00:00<00:00, 1003.28it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7469:  92%|█████████▏| 924/1000 [00:00<00:00, 1038.08it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7486: 100%|██████████| 1000/1000 [00:00<00:00, 1021.39it/s]\n",
      "Image sizes 640 train, 640 test\n",
      "Using 4 dataloader workers\n",
      "Logging results to runs\\train\\yolov7_pothole_fixed_res\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
      "\n",
      "  0%|          | 0/79 [00:00<?, ?it/s]\n",
      "  0%|          | 0/79 [00:04<?, ?it/s]\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\train.py\", line 616, in <module>\n",
      "    train(hyp, opt, device, tb_writer)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\train.py\", line 363, in train\n",
      "    loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs)  # loss scaled by batch_size\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\loss.py\", line 585, in __call__\n",
      "    bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\loss.py\", line 759, in build_targets\n",
      "    from_which_layer = from_which_layer[fg_mask_inboxes]\n",
      "RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)\n"
     ]
    }
   ],
   "source": [
    "!python train.py --epochs 100 --workers 4 --device 0 --batch-size 16 --data data/pothole.yaml \\\n",
    "--img 640 640 --cfg cfg/training/yolov7_pothole.yaml --weights 'yolov7_training.pt' \\\n",
    "--name yolov7_pothole_fixed_res --hyp data/hyp.scratch.custom.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jr6uqi3snnk5",
    "outputId": "70784d93-5216-46e4-baf5-9223cd7a0b35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(weights=['runs/train/yolov7_pothole_fixed_res/weights/best.pt'], data='data/pothole.yaml', batch_size=32, img_size=640, conf_thres=0.001, iou_thres=0.65, task='test', device='', single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project='runs/test', name='exp', exist_ok=False, no_trace=False, v5_metric=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 26, in attempt_download\n",
      "    assets = [x['name'] for x in response['assets']]  # release assets\n",
      "KeyError: 'assets'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 319, in <module>\n",
      "    test(opt.data,\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 58, in test\n",
      "    model = attempt_load(weights, map_location=device)  # load FP32 model\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\models\\experimental.py\", line 251, in attempt_load\n",
      "    attempt_download(w)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 31, in attempt_download\n",
      "    tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]\n",
      "IndexError: list index out of range\n"
     ]
    }
   ],
   "source": [
    "!python test.py --weights runs/train/yolov7_pothole_fixed_res/weights/best.pt --task test --data data/pothole.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nb4KOJXtCWR9"
   },
   "source": [
    "## YOLOv7 Normal Model Multi-Resolution Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SlGvAv3lM9LO",
    "outputId": "dcfe0661-650f-4fdc-ec8c-6067e45f98eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n",
      "\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 2.30, Best Possible Recall (BPR) = 0.7486. Attempting to improve anchors, please wait...\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mWARNING: Extremely small objects found. 538 of 3497 labels are < 3 pixels in size.\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mRunning kmeans for 9 anchors on 3497 points...\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 1.0000 best possible recall, 3.29 anchors past thr\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mn=9, img_size=640, metric_all=0.239/0.713-mean/best, past_thr=0.507-mean: 13,4,  29,9,  59,20,  93,48,  187,60,  161,114,  335,113,  243,202,  501,245\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 1.0000 best possible recall, 3.75 anchors past thr\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mn=9, img_size=640, metric_all=0.268/0.749-mean/best, past_thr=0.516-mean: 9,3,  17,5,  28,9,  56,17,  87,46,  162,56,  159,118,  293,120,  382,215\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mNew anchors saved to model. Update model *.yaml to use these anchors in the future.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Namespace(weights=\"'yolov7_training.pt'\", cfg='cfg/training/yolov7_pothole.yaml', data='data/pothole.yaml', hyp='data/hyp.scratch.custom.yaml', epochs=100, batch_size=8, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=True, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=4, project='runs/train', entity=None, name='yolov7_pothole_multi_res', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs\\\\train\\\\yolov7_pothole_multi_res', total_batch_size=8)\n",
      "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, paste_in=0.0, loss_ota=1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
      "  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
      "  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      "  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
      " 12                -1  1         0  models.common.MP                        []                            \n",
      " 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 16          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              \n",
      " 25                -1  1         0  models.common.MP                        []                            \n",
      " 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 29          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
      " 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
      " 38                -1  1         0  models.common.MP                        []                            \n",
      " 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
      " 42          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
      " 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
      " 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                \n",
      " 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 55          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
      " 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
      " 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
      " 67          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
      " 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               \n",
      " 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
      " 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
      " 76                -1  1         0  models.common.MP                        []                            \n",
      " 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
      " 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
      " 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           \n",
      " 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
      " 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
      " 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      " 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
      " 89                -1  1         0  models.common.MP                        []                            \n",
      " 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
      " 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
      " 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           \n",
      " 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
      " 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              \n",
      " 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      " 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
      "100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
      "101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             \n",
      "102                75  1    328704  models.common.RepConv                   [128, 256, 3, 1]              \n",
      "103                88  1   1312768  models.common.RepConv                   [256, 512, 3, 1]              \n",
      "104               101  1   5246976  models.common.RepConv                   [512, 1024, 3, 1]             \n",
      "105   [102, 103, 104]  1     34156  models.yolo.IDetect                     [1, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n",
      "c:\\Users\\Gyana\\anaconda3\\lib\\site-packages\\torch\\functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\TensorShape.cpp:3191.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "Model Summary: 415 layers, 37196556 parameters, 37196556 gradients, 105.1 GFLOPS\n",
      "\n",
      "Scaled weight_decay = 0.0005\n",
      "Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n",
      "\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '..\\pothole_dataset\\labels\\train.cache' images and labels... 1265 found, 0 missing, 0 empty, 1 corrupted: 100%|██████████| 1265/1265 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '..\\pothole_dataset\\labels\\train.cache' images and labels... 1265 found, 0 missing, 0 empty, 1 corrupted: 100%|██████████| 1265/1265 [00:00<?, ?it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '..\\pothole_dataset\\labels\\valid.cache' images and labels... 401 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 401/401 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '..\\pothole_dataset\\labels\\valid.cache' images and labels... 401 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 401/401 [00:00<?, ?it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm::   0%|          | 0/1000 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7273:  11%|█         | 106/1000 [00:00<00:00, 1058.40it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7353:  21%|██        | 212/1000 [00:00<00:00, 1040.06it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7372:  32%|███▏      | 317/1000 [00:00<00:00, 1027.56it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7400:  44%|████▎     | 436/1000 [00:00<00:00, 1090.07it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7405:  56%|█████▌    | 562/1000 [00:00<00:00, 1148.71it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7425:  69%|██████▉   | 689/1000 [00:00<00:00, 1189.09it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7434:  81%|████████  | 812/1000 [00:00<00:00, 1201.62it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7469:  93%|█████████▎| 934/1000 [00:00<00:00, 1206.92it/s]\n",
      "\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7486: 100%|██████████| 1000/1000 [00:00<00:00, 1168.78it/s]\n",
      "Image sizes 640 train, 640 test\n",
      "Using 4 dataloader workers\n",
      "Logging results to runs\\train\\yolov7_pothole_multi_res\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
      "\n",
      "  0%|          | 0/158 [00:00<?, ?it/s]\n",
      "  0%|          | 0/158 [00:04<?, ?it/s]\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\train.py\", line 616, in <module>\n",
      "    train(hyp, opt, device, tb_writer)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\train.py\", line 363, in train\n",
      "    loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs)  # loss scaled by batch_size\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\loss.py\", line 585, in __call__\n",
      "    bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\loss.py\", line 759, in build_targets\n",
      "    from_which_layer = from_which_layer[fg_mask_inboxes]\n",
      "RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)\n"
     ]
    }
   ],
   "source": [
    "!python train.py --epochs 100 --workers 4 --device 0 --batch-size 8 --data data/pothole.yaml \\\n",
    "--img 640 640 --cfg cfg/training/yolov7_pothole.yaml --weights 'yolov7_training.pt' \\\n",
    "--name yolov7_pothole_multi_res --hyp data/hyp.scratch.custom.yaml \\\n",
    "--multi-scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rN39mOYvAHHQ",
    "outputId": "45b83ffa-d3a7-494a-ca38-565d0b4ed240"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(weights=['runs/train/yolov7_pothole_multi_res/weights/best.pt'], data='data/pothole.yaml', batch_size=32, img_size=640, conf_thres=0.001, iou_thres=0.65, task='test', device='', single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project='runs/test', name='exp', exist_ok=False, no_trace=False, v5_metric=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOR  072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11263.8125MB)\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 26, in attempt_download\n",
      "    assets = [x['name'] for x in response['assets']]  # release assets\n",
      "KeyError: 'assets'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 319, in <module>\n",
      "    test(opt.data,\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\test.py\", line 58, in test\n",
      "    model = attempt_load(weights, map_location=device)  # load FP32 model\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\models\\experimental.py\", line 251, in attempt_load\n",
      "    attempt_download(w)\n",
      "  File \"g:\\WORK\\YOLOV7\\yolov7\\yolov7\\utils\\google_utils.py\", line 31, in attempt_download\n",
      "    tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]\n",
      "IndexError: list index out of range\n"
     ]
    }
   ],
   "source": [
    "!python test.py --weights runs/train/yolov7_pothole_multi_res/weights/best.pt --task test --data data/pothole.yaml"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
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   "collapsed_sections": [],
   "name": "yolov7_pothole_detection.ipynb",
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  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "vscode": {
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