Auto_drive.ipynb

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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fezzibasma/Auto-drive/blob/main/Auto_drive.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b2qplvr-zeCJ"
      },
      "outputs": [],
      "source": [
        "## Auto-drive using Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBtZl6pfjcqf",
        "outputId": "1813566b-70ee-434f-c6c8-33bf27495457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install pyyaml==5.1\n",
        "import sys, os, distutils.core\n",
        "# Note: This is a faster way to install detectron2 in Colab, but it does not include all functionalities.\n",
        "# See https://detectron2.readthedocs.io/tutorials/install.html for full installation instructions\n",
        "!git clone 'https://github.com/facebookresearch/detectron2'\n",
        "dist = distutils.core.run_setup(\"./detectron2/setup.py\")\n",
        "!python -m pip install {' '.join([f\"'{x}'\" for x in dist.install_requires])}\n",
        "sys.path.insert(0, os.path.abspath('./detectron2'))\n",
        "\n",
        "# Properly install detectron2. (Please do not install twice in both ways)\n",
        "# !python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'"
      ],
      "metadata": {
        "colab": {
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        "id": "MFxswZzY0QGc",
        "outputId": "dbb9048f-485b-496b-def8-f13237b8ece2"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import detectron2\n",
        "import torchvision\n",
        "import cv2"
      ],
      "metadata": {
        "id": "2l5CxM4XGaBR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Register the data to Detectron2 config"
      ],
      "metadata": {
        "id": "7IYe5MYlZZje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import librairies\n",
        "import os\n",
        "import numpy as np\n",
        "import json\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab.patches import cv2_imshow \n",
        "%matplotlib inline\n",
        "\n",
        "# import some common detectron2 utilities\n",
        "from detectron2 import model_zoo\n",
        "from detectron2.engine import DefaultPredictor,DefaultTrainer\n",
        "from detectron2.config import get_cfg\n",
        "from detectron2.utils.visualizer import Visualizer,ColorMode\n",
        "from detectron2.structures import BoxMode\n",
        "from detectron2.data import MetadataCatalog, DatasetCatalog"
      ],
      "metadata": {
        "id": "fZrVIaj1IYqY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare the dataset "
      ],
      "metadata": {
        "id": "jrI3BzzsKBOa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mv /content/drive/MyDrive/RoadSign/Archive.zip /content/Archive.zip"
      ],
      "metadata": {
        "id": "hMCvSHQ8kcji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# download, decompress the data\n",
        "!wget /content/Archive.zip\n",
        "!unzip Archive.zip > /dev/null"
      ],
      "metadata": {
        "id": "Zhcn6rKwIia9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "defc8042-2cb1-45d5-b6f8-b1458328721a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Archive.zip: Scheme missing.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install xmltodict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "274WtQPyxu1W",
        "outputId": "50555cdb-c570-43c4-cffd-340a5ade90e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting xmltodict\n",
            "  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
            "Installing collected packages: xmltodict\n",
            "Successfully installed xmltodict-0.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Convert xml into json file"
      ],
      "metadata": {
        "id": "Yyh-umkDx_jo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import xmltodict\n",
        "\n",
        "path=\"/content/Archive/train\"\n",
        "for filename in os.listdir(path):\n",
        "    if not filename.endswith('.xml'):\n",
        "        continue\n",
        "\n",
        "    fullname = os.path.join(path, filename)\n",
        "\n",
        "    with open(fullname, 'r') as f:\n",
        "        xmlString = f.read()\n",
        "\n",
        "    jsonString = json.dumps(xmltodict.parse(xmlString), indent=4)\n",
        "\n",
        "    with open(fullname[:-4] + \".json\", 'w') as f:\n",
        "        f.write(jsonString)"
      ],
      "metadata": {
        "id": "NnWhRA8iwY_W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import xmltodict\n",
        "\n",
        "path=\"/content/Archive/val\"\n",
        "for filename in os.listdir(path):\n",
        "    if not filename.endswith('.xml'):\n",
        "        continue\n",
        "\n",
        "    fullname = os.path.join(path, filename)\n",
        "\n",
        "    with open(fullname, 'r') as f:\n",
        "        xmlString = f.read()\n",
        "\n",
        "    jsonString = json.dumps(xmltodict.parse(xmlString), indent=4)\n",
        "\n",
        "    with open(fullname[:-4] + \".json\", 'w') as f:\n",
        "        f.write(jsonString)"
      ],
      "metadata": {
        "id": "V6aLgFvyx5nO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Resize image for training"
      ],
      "metadata": {
        "id": "xFKWUygH7Nxi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!/usr/bin/python\n",
        "from PIL import Image\n",
        "import os, sys\n",
        "\n",
        "path = \"/content/Archive/train/\"\n",
        "dirs = os.listdir( path )\n",
        "\n",
        "def resize():\n",
        "  for item in dirs:\n",
        "    if item.endswith(\".png\") :\n",
        "      if os.path.isfile(path+item) and item.endswith(\".png\"):\n",
        "            im = Image.open(path+item)\n",
        "            f, e = os.path.splitext(path+item)\n",
        "            imResize = im.resize((200,200), Image.ANTIALIAS)\n",
        "            imResize.save(path+item)\n",
        "resize()"
      ],
      "metadata": {
        "id": "pOqYYul37MDa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!/usr/bin/python\n",
        "from PIL import Image\n",
        "import os, sys\n",
        "\n",
        "path = \"/content/Archive/val/\"\n",
        "dirs = os.listdir( path )\n",
        "\n",
        "def resize():\n",
        "    for item in dirs:\n",
        "      if item.endswith(\".png\") :\n",
        "        if os.path.isfile(path+item) and item.endswith(\".png\"):\n",
        "            im = Image.open(path+item)\n",
        "            f, e = os.path.splitext(path+item)\n",
        "            imResize = im.resize((200,200), Image.ANTIALIAS)\n",
        "            imResize.save(path+item)\n",
        "\n",
        "resize()"
      ],
      "metadata": {
        "id": "c3CKwvHT7unN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for file in os.listdir(\"/content/Archive/train\") :\n",
        "  if file.endswith(\".png\") :\n",
        "    print(file)"
      ],
      "metadata": {
        "id": "kTex-cvDldq2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_data_dicts(directory, classes):\n",
        "    dataset_dicts = []\n",
        "    for filename in [file for file in os.listdir(directory) if file.endswith('.json')]:\n",
        "      \n",
        "        json_file = os.path.join(directory, filename)\n",
        "        with open(json_file) as f:\n",
        "            img_anns = json.load(f)\n",
        "\n",
        "        record = {}\n",
        "        \n",
        "        filename = os.path.join(directory, img_anns[\"annotation\"][\"filename\"])\n",
        "        #filename = os.listdir(directory) \n",
        "        \n",
        "        record[\"file_name\"] = filename\n",
        "        record[\"height\"] = img_anns[\"annotation\"][\"size\"][\"height\"]\n",
        "        record[\"width\"] = img_anns[\"annotation\"][\"size\"][\"width\"]\n",
        "        record[\"depth\"] = img_anns[\"annotation\"][\"size\"][\"depth\"]\n",
        "\n",
        "        annos = img_anns[\"annotation\"][\"object\"]\n",
        "        objs = []\n",
        "        for anno in annos:\n",
        "        #     px = [a[0] for a in anno['points']] # x coord\n",
        "        #     py = [a[1] for a in anno['points']] # y-coord\n",
        "        #     poly = [(x, y) for x, y in zip(px, py)] # poly for segmentation\n",
        "        #     poly = [p for x in poly for p in x]\n",
        "\n",
        "            obj = {\n",
        "                # \"bbox\": [np.min(px), np.min(py), np.max(px), np.max(py)],\n",
        "                \"bbox\": np.asarray([annos[\"bndbox\"][\"xmin\"], annos[\"bndbox\"][\"ymin\"], annos[\"bndbox\"][\"xmax\"], annos[\"bndbox\"][\"ymax\"]],dtype=int),\n",
        "                \"bbox_mode\": BoxMode.XYXY_ABS,\n",
        "                # \"segmentation\": [poly],\n",
        "                \"segmentation\": [img_anns[\"annotation\"][\"segmented\"]],\n",
        "                \"category_id\": classes.index(annos[\"name\"]),\n",
        "                \"iscrowd\": 0\n",
        "            }\n",
        "            objs.append(obj)\n",
        "        record[\"annotations\"] = objs\n",
        "        dataset_dicts.append(record)\n",
        "    return dataset_dicts"
      ],
      "metadata": {
        "id": "XYRYqZSyKKHi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import json\n",
        "  \n",
        "# Opening JSON file\n",
        "f = open('/content/Archive/train/road0.json')\n",
        "  \n",
        "# returns JSON object as \n",
        "# a dictionary\n",
        "data = json.load(f)"
      ],
      "metadata": {
        "id": "yPkqqmdSwp1V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"annotation\"][\"object\"][\"bndbox\"][\"xmin\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ZXM__Qch69sW",
        "outputId": "58332cc2-5387-4a49-af3e-05e17872a5ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'98'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = ['speedlimit', 'crosswalk','stop','trafficlight']\n",
        "\n",
        "data_path = '/content/Archive/'\n",
        "\n",
        "for d in [\"train\", \"test\"]:\n",
        "    DatasetCatalog.register(\n",
        "        \"cat_\"+d, \n",
        "        lambda d=d: get_data_dicts(data_path+d, classes)\n",
        "    )\n",
        "\n",
        "    MetadataCatalog.get(\"cat_\" + d).set(thing_classes=classes)\n",
        "\n",
        "microcontroller_metadata = MetadataCatalog.get(\"cat_train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "BL_0yzfRRfKi",
        "outputId": "42ce0b2e-e4b7-4555-99c0-37e4720b7a2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AssertionError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-99-be63393a26ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     DatasetCatalog.register(\n\u001b[1;32m      7\u001b[0m         \u001b[0;34m\"cat_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0;32mlambda\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_data_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_path\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     )\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/data/catalog.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(self, name, func)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \"\"\"\n\u001b[1;32m     36\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"You must register a function with `DatasetCatalog.register`!\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Dataset '{}' is already registered!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAssertionError\u001b[0m: Dataset 'cat_train' is already registered!"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training model with Instance Segmentation "
      ],
      "metadata": {
        "id": "ubnS7ZQuvTA2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cfg = get_cfg()\n",
        "cfg.merge_from_file(model_zoo.get_config_file(\"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\"))\n",
        "cfg.DATASETS.TRAIN = (\"cat_train\",)\n",
        "cfg.DATASETS.TEST = ()\n",
        "cfg.DATALOADER.NUM_WORKERS = 4\n",
        "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\")\n",
        "cfg.SOLVER.IMS_PER_BATCH = 4\n",
        "cfg.SOLVER.BASE_LR = 0.00000\n",
        "cfg.SOLVER.MAX_ITER = 1000\n",
        "cfg.MODEL.ROI_HEADS.NUM_CLASSES = 4"
      ],
      "metadata": {
        "id": "0b1QSoF6R4VG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\n",
        "trainer = DefaultTrainer(cfg)\n",
        "# trainer.resume_or_load(resume=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ybeq3ga8vsQ4",
        "outputId": "a64351eb-1844-42c1-b4b5-06204258c02c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[11/24 23:05:40 d2.engine.defaults]: Model:\n",
            "GeneralizedRCNN(\n",
            "  (backbone): FPN(\n",
            "    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (top_block): LastLevelMaxPool()\n",
            "    (bottom_up): ResNet(\n",
            "      (stem): BasicStem(\n",
            "        (conv1): Conv2d(\n",
            "          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
            "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "        )\n",
            "      )\n",
            "      (res2): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res3): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res4): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (4): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (5): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res5): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (proposal_generator): RPN(\n",
            "    (rpn_head): StandardRPNHead(\n",
            "      (conv): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (anchor_generator): DefaultAnchorGenerator(\n",
            "      (cell_anchors): BufferList()\n",
            "    )\n",
            "  )\n",
            "  (roi_heads): StandardROIHeads(\n",
            "    (box_pooler): ROIPooler(\n",
            "      (level_poolers): ModuleList(\n",
            "        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
            "        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
            "        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
            "        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
            "      )\n",
            "    )\n",
            "    (box_head): FastRCNNConvFCHead(\n",
            "      (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "      (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n",
            "      (fc_relu1): ReLU()\n",
            "      (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "      (fc_relu2): ReLU()\n",
            "    )\n",
            "    (box_predictor): FastRCNNOutputLayers(\n",
            "      (cls_score): Linear(in_features=1024, out_features=5, bias=True)\n",
            "      (bbox_pred): Linear(in_features=1024, out_features=16, bias=True)\n",
            "    )\n",
            "    (mask_pooler): ROIPooler(\n",
            "      (level_poolers): ModuleList(\n",
            "        (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
            "        (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
            "        (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
            "        (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
            "      )\n",
            "    )\n",
            "    (mask_head): MaskRCNNConvUpsampleHead(\n",
            "      (mask_fcn1): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn2): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn3): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn4): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n",
            "      (deconv_relu): ReLU()\n",
            "      (predictor): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-101-7a62313d1e5c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDefaultTrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# trainer.resume_or_load(resume=False)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/engine/defaults.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, cfg)\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_optimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m         \u001b[0mdata_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_train_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_ddp_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbroadcast_buffers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/engine/defaults.py\u001b[0m in \u001b[0;36mbuild_train_loader\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0mOverwrite\u001b[0m \u001b[0mit\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0myou\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0md\u001b[0m \u001b[0mlike\u001b[0m \u001b[0ma\u001b[0m \u001b[0mdifferent\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \"\"\"\n\u001b[0;32m--> 547\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbuild_detection_train_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/config/config.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0m_called_with_cfg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m                     \u001b[0mexplicit_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_args_from_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0morig_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mexplicit_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/config/config.py\u001b[0m in \u001b[0;36m_get_args_from_config\u001b[0;34m(from_config_func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    243\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msupported_arg_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m                 \u001b[0mextra_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrom_config_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m         \u001b[0;31m# forward the other arguments to __init__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m         \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/data/build.py\u001b[0m in \u001b[0;36m_train_loader_from_config\u001b[0;34m(cfg, mapper, dataset, sampler)\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMODEL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKEYPOINT_ON\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m             \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m             \u001b[0mproposal_files\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDATASETS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPROPOSAL_FILES_TRAIN\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMODEL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLOAD_PROPOSALS\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m         )\n\u001b[1;32m    352\u001b[0m         \u001b[0m_log_api_usage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dataset.\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDATASETS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/data/build.py\u001b[0m in \u001b[0;36mget_detection_dataset_dicts\u001b[0;34m(names, filter_empty, min_keypoints, proposal_files, check_consistency)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m     \u001b[0mdataset_dicts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mDatasetCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdataset_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_dicts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorchdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/data/build.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m     \u001b[0mdataset_dicts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mDatasetCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdataset_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_dicts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorchdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/detectron2/detectron2/data/catalog.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m     56\u001b[0m                 )\n\u001b[1;32m     57\u001b[0m             ) from e\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-28-be63393a26ae>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m      6\u001b[0m     DatasetCatalog.register(\n\u001b[1;32m      7\u001b[0m         \u001b[0;34m\"cat_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0;32mlambda\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_data_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_path\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     )\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-91-b340b1d41224>\u001b[0m in \u001b[0;36mget_data_dicts\u001b[0;34m(directory, classes)\u001b[0m\n\u001b[1;32m     27\u001b[0m             obj = {\n\u001b[1;32m     28\u001b[0m                 \u001b[0;31m# \"bbox\": [np.min(px), np.min(py), np.max(px), np.max(py)],\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m                 \u001b[0;34m\"bbox\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mannos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"bndbox\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"xmin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"bndbox\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ymin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"bndbox\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"xmax\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"bndbox\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ymax\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m                 \u001b[0;34m\"bbox_mode\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mBoxMode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXYXY_ABS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m                 \u001b[0;31m# \"segmentation\": [poly],\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QEwv5CEcvvOr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}