object_detection/cutler.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/softmurata/colab_notebooks/blob/main/object_detection/cutler.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "W6xm1Gpmo0E_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c30997e4-6d95-47ac-a77a-7eb9a4a93f27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'CutLER'...\n",
            "remote: Enumerating objects: 180, done.\u001b[K\n",
            "remote: Counting objects: 100% (43/43), done.\u001b[K\n",
            "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
            "remote: Total 180 (delta 18), reused 15 (delta 7), pack-reused 137\u001b[K\n",
            "Receiving objects: 100% (180/180), 21.37 MiB | 29.94 MiB/s, done.\n",
            "Resolving deltas: 100% (46/46), done.\n",
            "Submodule 'third_party/TokenCut' (https://github.com/YangtaoWANG95/TokenCut.git) registered for path 'third_party/TokenCut'\n",
            "Cloning into '/content/CutLER/third_party/TokenCut'...\n",
            "remote: Enumerating objects: 209, done.        \n",
            "remote: Counting objects: 100% (59/59), done.        \n",
            "remote: Compressing objects: 100% (35/35), done.        \n",
            "remote: Total 209 (delta 40), reused 32 (delta 23), pack-reused 150        \n",
            "Receiving objects: 100% (209/209), 7.29 MiB | 9.70 MiB/s, done.\n",
            "Resolving deltas: 100% (79/79), done.\n",
            "Submodule path 'third_party/TokenCut': checked out '5eba77f56f0fc92ffc79589d0cbb119aab5f9b2d'\n"
          ]
        }
      ],
      "source": [
        "!git clone --recursive https://github.com/facebookresearch/CutLER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ugDH5UwIo0FA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad9531c7-40ef-4fdc-f226-35e0de135659"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/CutLER\n"
          ]
        }
      ],
      "source": [
        "%cd CutLER/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ZT0zKYpco0FB",
        "outputId": "754f6243-3891-4f6c-d1b2-e482979a5c02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: detectron2, fvcore, antlr4-python3-runtime\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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          ]
        }
      ],
      "source": [
        "!python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'\n",
        "!pip install git+https://github.com/cocodataset/panopticapi.git\n",
        "!pip install git+https://github.com/mcordts/cityscapesScripts.git\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "QLuhywKmo0FB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44c9a7fc-abcb-4504-c044-0ea73980fd33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/CutLER/cutler/demo\n"
          ]
        }
      ],
      "source": [
        "%cd cutler/demo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "DNdQki4co0FC"
      },
      "outputs": [],
      "source": [
        "import argparse\n",
        "import multiprocessing as mp\n",
        "import numpy as np\n",
        "import os\n",
        "import tempfile\n",
        "import time\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from detectron2.config import get_cfg\n",
        "from detectron2.data.detection_utils import read_image\n",
        "from detectron2.utils.logger import setup_logger\n",
        "import sys\n",
        "sys.path.append('./')\n",
        "sys.path.append('../')\n",
        "from config import add_cutler_config\n",
        "from predictor import VisualizationDemo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "28jjJmR4o0FC"
      },
      "outputs": [],
      "source": [
        "def setup_cfg(args):\n",
        "    # load config from file and command-line arguments\n",
        "    cfg = get_cfg()\n",
        "    add_cutler_config(cfg)\n",
        "    # To use demo for Panoptic-DeepLab, please uncomment the following two lines.\n",
        "    # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config  # noqa\n",
        "    # add_panoptic_deeplab_config(cfg)\n",
        "    cfg.merge_from_file(args.config_file)\n",
        "    cfg.merge_from_list(args.opts)\n",
        "    # Disable the use of SyncBN normalization when running on a CPU\n",
        "    # SyncBN is not supported on CPU and can cause errors, so we switch to BN instead\n",
        "    if cfg.MODEL.DEVICE == 'cpu' and cfg.MODEL.RESNETS.NORM == 'SyncBN':\n",
        "        cfg.MODEL.RESNETS.NORM = \"BN\"\n",
        "        cfg.MODEL.FPN.NORM = \"BN\"\n",
        "    # Set score_threshold for builtin models\n",
        "    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold\n",
        "    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold\n",
        "    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold\n",
        "    cfg.freeze()\n",
        "    return cfg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "dGs4L6lko0FD"
      },
      "outputs": [],
      "source": [
        "def get_parser(inputs):\n",
        "    parser = argparse.ArgumentParser(description=\"Detectron2 demo for builtin configs\")\n",
        "    parser.add_argument(\n",
        "        \"--config-file\",\n",
        "        default=\"configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml\",\n",
        "        metavar=\"FILE\",\n",
        "        help=\"path to config file\",\n",
        "    )\n",
        "    parser.add_argument(\"--webcam\", action=\"store_true\", help=\"Take inputs from webcam.\")\n",
        "    parser.add_argument(\"--video-input\", help=\"Path to video file.\")\n",
        "    parser.add_argument(\n",
        "        \"--input\", help=\"path to the input image\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--output\",\n",
        "        help=\"A file or directory to save output visualizations. \"\n",
        "        \"If not given, will show output in an OpenCV window.\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--confidence-threshold\",\n",
        "        type=float,\n",
        "        default=0.35,\n",
        "        help=\"Minimum score for instance predictions to be shown\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--opts\",\n",
        "        help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n",
        "        default=[],\n",
        "        nargs=argparse.REMAINDER,\n",
        "    )\n",
        "    args = parser.parse_args(inputs)\n",
        "    return args"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "5dklS0uEo0FF",
        "outputId": "7a517c3b-9308-4dc9-b8da-d5478299a08a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-02-06 02:38:32--  http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 172.67.9.4, 104.22.74.142, 104.22.75.142, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|172.67.9.4|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 574672112 (548M) [binary/octet-stream]\n",
            "Saving to: ‘cutler_cascade_final.pth’\n",
            "\n",
            "cutler_cascade_fina 100%[===================>] 548.05M  30.7MB/s    in 15s     \n",
            "\n",
            "2023-02-06 02:38:47 (35.9 MB/s) - ‘cutler_cascade_final.pth’ saved [574672112/574672112]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ZnEEaorqo0FF"
      },
      "outputs": [],
      "source": [
        "# provide arguments for running CutLER demo.\n",
        "\n",
        "# Note: to run on cpu, add \"MODEL.DEVICE\", \"cpu\" after --opts. to run on GPU, remove \"MODEL.DEVICE\", \"cpu\".\n",
        "# Note: please specify a path with \"--input\" if you want to try your own images.\n",
        "# Note: you can use a lower --confidence-threshold to get a higher recall.\n",
        "mp.set_start_method(\"spawn\", force=True)\n",
        "inputs = ['--config-file', \"../model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml\", '--input', \"imgs/demo1.jpg\", '--confidence-threshold', '0.5', \"--opts\", \"MODEL.WEIGHTS\", \"cutler_cascade_final.pth\", \"MODEL.DEVICE\", \"cpu\"]\n",
        "args = get_parser(inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "_-Z0jSyXo0FG",
        "outputId": "4c3f5d3d-2fb4-4e5f-eb0e-296c5e0ae1e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[02/06 02:38:53 detectron2]: Arguments: Namespace(confidence_threshold=0.5, config_file='../model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml', input='imgs/demo1.jpg', opts=['MODEL.WEIGHTS', 'cutler_cascade_final.pth', 'MODEL.DEVICE', 'cpu'], output=None, video_input=None, webcam=False)\n",
            "[02/06 02:38:55 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from cutler_cascade_final.pth ...\n",
            "[02/06 02:38:55 fvcore.common.checkpoint]: [Checkpointer] Loading from cutler_cascade_final.pth ...\n"
          ]
        }
      ],
      "source": [
        "setup_logger(name=\"fvcore\")\n",
        "logger = setup_logger()\n",
        "logger.info(\"Arguments: \" + str(args))\n",
        "cfg = setup_cfg(args)\n",
        "demo = VisualizationDemo(cfg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "BdhUe5mVo0FG",
        "outputId": "a10c9d3a-b704-486b-a145-954ae628529b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[02/06 02:39:13 detectron2]: imgs/demo1.jpg: detected 39 instances in 12.49s\n"
          ]
        }
      ],
      "source": [
        "# use PIL, to be consistent with evaluation\n",
        "img = read_image(args.input, format=\"BGR\")\n",
        "start_time = time.time()\n",
        "predictions, visualized_output = demo.run_on_image(img)\n",
        "logger.info(\n",
        "    \"{}: {} in {:.2f}s\".format(\n",
        "        args.input,\n",
        "        \"detected {} instances\".format(len(predictions[\"instances\"]))\n",
        "        if \"instances\" in predictions\n",
        "        else \"finished\",\n",
        "        time.time() - start_time,\n",
        "    )\n",
        ")\n",
        "\n",
        "# save image to your local directory\n",
        "if args.output:\n",
        "    if os.path.isdir(args.output):\n",
        "        assert os.path.isdir(args.output), args.output\n",
        "        out_filename = os.path.join(args.output, os.path.basename(args.input))\n",
        "    else:\n",
        "        assert len(args.input) == 1, \"Please specify a directory with args.output\"\n",
        "        out_filename = args.output\n",
        "    visualized_output.save(out_filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Qq45RaGSo0FH",
        "outputId": "a809e72e-355c-46d0-9154-7a745045546d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f106a9df100>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.imshow(visualized_output.get_image())"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.8.15 ('demo')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.15"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "ca70d3f7931001324cb5fcee38e997f2b85843bd55d9cd6092e0d7a04569d52c"
      }
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}