[데이터분석캡스톤디자인]DeepSORT.ipynb

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            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "26e09313b7664e7ebd46cba212431cfe": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# MMTracking의 DeepSORT를 이용한 Object Tracking"
      ],
      "metadata": {
        "id": "oz0RHDijn5aU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#코랩의 pytorch 버전이 2.0으로 upgrade됨.\n",
        "import torch\n",
        "print(torch.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUeUlzs5pGuY",
        "outputId": "e5d2f129-a3e5-4c6f-b2da-3b4aebeeed3d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.1.0+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1분 소모\n",
        "\n",
        "#현재 mmdetection이 pytorch 2.x를 지원하지 않음. pytorch 1.13+ CUDA 116 환경으로 코랩 커널 downgrade\n",
        "# downgrade 설치시 ERROR: pip's dependency resolver... 부분은 신경쓰지 않아도 됨.\n",
        "!pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 --extra-index-url https://download.pytorch.org/whl/cu116"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "50zex7o5COAy",
        "outputId": "d78a888b-3ca6-4412-fa47-6a1efb50c234"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cu116\n",
            "Collecting torch==1.13.0+cu116\n",
            "  Downloading https://download.pytorch.org/whl/cu116/torch-1.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl (1983.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 GB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.14.0+cu116\n",
            "  Downloading https://download.pytorch.org/whl/cu116/torchvision-0.14.0%2Bcu116-cp310-cp310-linux_x86_64.whl (24.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.2/24.2 MB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==1.13.0+cu116) (4.5.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision==0.14.0+cu116) (1.23.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision==0.14.0+cu116) (2.31.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision==0.14.0+cu116) (9.4.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.0+cu116) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.0+cu116) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.0+cu116) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.0+cu116) (2023.7.22)\n",
            "Installing collected packages: torch, torchvision\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu118\n",
            "    Uninstalling torch-2.1.0+cu118:\n",
            "      Successfully uninstalled torch-2.1.0+cu118\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.16.0+cu118\n",
            "    Uninstalling torchvision-0.16.0+cu118:\n",
            "      Successfully uninstalled torchvision-0.16.0+cu118\n",
            "\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",
            "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 1.13.0+cu116 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 1.13.0+cu116 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 1.13.0+cu116 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-1.13.0+cu116 torchvision-0.14.0+cu116\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "torch",
                  "torchgen"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mmcv를 위해서 mmcv-full을 먼저 설치해야 함. https://mmcv.readthedocs.io/en/latest/get_started/installation.html 설치 과정 참조.\n",
        "!pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.13/index.html"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSmBxH0FDpI7",
        "outputId": "a2587212-8aff-485a-ac6b-03313a901550"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu116/torch1.13/index.html\n",
            "Collecting mmcv-full\n",
            "  Downloading https://download.openmmlab.com/mmcv/dist/cu116/torch1.13.0/mmcv_full-1.7.1-cp310-cp310-manylinux1_x86_64.whl (46.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 MB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting addict (from mmcv-full)\n",
            "  Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mmcv-full) (1.23.5)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mmcv-full) (23.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from mmcv-full) (9.4.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from mmcv-full) (6.0.1)\n",
            "Collecting yapf (from mmcv-full)\n",
            "  Downloading yapf-0.40.2-py3-none-any.whl (254 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.7/254.7 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.10/dist-packages (from mmcv-full) (4.8.0.76)\n",
            "Requirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full) (6.8.0)\n",
            "Requirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full) (4.0.0)\n",
            "Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full) (2.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->mmcv-full) (3.17.0)\n",
            "Installing collected packages: addict, yapf, mmcv-full\n",
            "Successfully installed addict-2.4.0 mmcv-full-1.7.1 yapf-0.40.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2023년 4월 6일 기준으로 mmdetection이 3.0으로 upgrade됨. 실습 코드는 mmdetection 2.x 기준임.\n",
        "# mmdetection 2.x branch의 소스코드 기반으로 mmdetection 설치 필요.\n",
        "!git clone --branch 2.x https://github.com/open-mmlab/mmdetection.git\n",
        "!cd mmdetection; python setup.py install"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hB7iNoaRoW4L",
        "outputId": "6ebceb2d-388e-4d3e-9623-c1304f057426"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'mmdetection'...\n",
            "remote: Enumerating objects: 37679, done.\u001b[K\n",
            "remote: Counting objects: 100% (280/280), done.\u001b[K\n",
            "remote: Compressing objects: 100% (205/205), done.\u001b[K\n",
            "remote: Total 37679 (delta 114), reused 171 (delta 71), pack-reused 37399\u001b[K\n",
            "Receiving objects: 100% (37679/37679), 63.04 MiB | 8.29 MiB/s, done.\n",
            "Resolving deltas: 100% (25953/25953), done.\n",
            "running install\n",
            "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n",
            "!!\n",
            "\n",
            "        ********************************************************************************\n",
            "        Please avoid running ``setup.py`` directly.\n",
            "        Instead, use pypa/build, pypa/installer, pypa/build or\n",
            "        other standards-based tools.\n",
            "\n",
            "        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n",
            "        ********************************************************************************\n",
            "\n",
            "!!\n",
            "  self.initialize_options()\n",
            "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: EasyInstallDeprecationWarning: easy_install command is deprecated.\n",
            "!!\n",
            "\n",
            "        ********************************************************************************\n",
            "        Please avoid running ``setup.py`` and ``easy_install``.\n",
            "        Instead, use pypa/build, pypa/installer, pypa/build or\n",
            "        other standards-based tools.\n",
            "\n",
            "        See https://github.com/pypa/setuptools/issues/917 for details.\n",
            "        ********************************************************************************\n",
            "\n",
            "!!\n",
            "  self.initialize_options()\n",
            "running bdist_egg\n",
            "running egg_info\n",
            "creating mmdet.egg-info\n",
            "writing mmdet.egg-info/PKG-INFO\n",
            "writing dependency_links to mmdet.egg-info/dependency_links.txt\n",
            "writing requirements to mmdet.egg-info/requires.txt\n",
            "writing top-level names to mmdet.egg-info/top_level.txt\n",
            "writing manifest file 'mmdet.egg-info/SOURCES.txt'\n",
            "reading manifest file 'mmdet.egg-info/SOURCES.txt'\n",
            "reading manifest template 'MANIFEST.in'\n",
            "warning: no files found matching 'mmdet/VERSION'\n",
            "warning: no files found matching 'mmdet/.mim/model-index.yml'\n",
            "warning: no files found matching 'mmdet/.mim/demo/*/*'\n",
            "warning: no files found matching '*.py' under directory 'mmdet/.mim/configs'\n",
            "warning: no files found matching '*.yml' under directory 'mmdet/.mim/configs'\n",
            "warning: no files found matching '*.sh' under directory 'mmdet/.mim/tools'\n",
            "warning: no files found matching '*.py' under directory 'mmdet/.mim/tools'\n",
            "adding license file 'LICENSE'\n",
            "writing manifest file 'mmdet.egg-info/SOURCES.txt'\n",
            "installing library code to build/bdist.linux-x86_64/egg\n",
            "running install_lib\n",
            "running build_py\n",
            "creating build\n",
            "creating build/lib\n",
            "creating build/lib/mmdet\n",
            "copying mmdet/version.py -> build/lib/mmdet\n",
            "copying mmdet/__init__.py -> build/lib/mmdet\n",
            "creating build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/custom.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/cityscapes.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/xml_style.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/dataset_wrappers.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/coco_occluded.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/deepfashion.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/builder.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/voc.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/openimages.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/lvis.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/__init__.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/utils.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/coco_panoptic.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/coco.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/wider_face.py -> build/lib/mmdet/datasets\n",
            "copying mmdet/datasets/objects365.py -> build/lib/mmdet/datasets\n",
            "creating build/lib/mmdet/models\n",
            "copying mmdet/models/builder.py -> build/lib/mmdet/models\n",
            "copying mmdet/models/__init__.py -> build/lib/mmdet/models\n",
            "creating build/lib/mmdet/core\n",
            "copying mmdet/core/__init__.py -> build/lib/mmdet/core\n",
            "creating build/lib/mmdet/apis\n",
            "copying mmdet/apis/__init__.py -> build/lib/mmdet/apis\n",
            "copying mmdet/apis/inference.py -> build/lib/mmdet/apis\n",
            "copying mmdet/apis/test.py -> build/lib/mmdet/apis\n",
            "copying mmdet/apis/train.py -> build/lib/mmdet/apis\n",
            "creating build/lib/mmdet/utils\n",
            "copying mmdet/utils/replace_cfg_vals.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/contextmanagers.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/util_distribution.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/collect_env.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/util_random.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/split_batch.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/compat_config.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/__init__.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/util_mixins.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/logger.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/rfnext.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/ascend_util.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/memory.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/setup_env.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/profiling.py -> build/lib/mmdet/utils\n",
            "copying mmdet/utils/misc.py -> build/lib/mmdet/utils\n",
            "creating build/lib/mmdet/datasets/api_wrappers\n",
            "copying mmdet/datasets/api_wrappers/coco_api.py -> build/lib/mmdet/datasets/api_wrappers\n",
            "copying mmdet/datasets/api_wrappers/__init__.py -> build/lib/mmdet/datasets/api_wrappers\n",
            "copying mmdet/datasets/api_wrappers/panoptic_evaluation.py -> build/lib/mmdet/datasets/api_wrappers\n",
            "creating build/lib/mmdet/datasets/samplers\n",
            "copying mmdet/datasets/samplers/class_aware_sampler.py -> build/lib/mmdet/datasets/samplers\n",
            "copying mmdet/datasets/samplers/__init__.py -> build/lib/mmdet/datasets/samplers\n",
            "copying mmdet/datasets/samplers/distributed_sampler.py -> build/lib/mmdet/datasets/samplers\n",
            "copying mmdet/datasets/samplers/group_sampler.py -> build/lib/mmdet/datasets/samplers\n",
            "copying mmdet/datasets/samplers/infinite_sampler.py -> build/lib/mmdet/datasets/samplers\n",
            "creating build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/instaboost.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/auto_augment.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/transforms.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/formatting.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/__init__.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/formating.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/compose.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/loading.py -> build/lib/mmdet/datasets/pipelines\n",
            "copying mmdet/datasets/pipelines/test_time_aug.py -> build/lib/mmdet/datasets/pipelines\n",
            "creating build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/deformable_detr.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/htc.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/panoptic_two_stage_segmentor.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/base.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/fcos.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/yolof.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/atss.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/maskformer.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/paa.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/fovea.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/scnet.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/mask2former.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/trident_faster_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/fsaf.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/solov2.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/single_stage.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/grid_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/cascade_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/retinanet.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/rpn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/single_stage_instance_seg.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/cornernet.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/nasfcos.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/__init__.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/ddod.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/point_rend.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/fast_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/mask_scoring_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/sparse_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/yolact.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/tood.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/lad.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/gfl.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/two_stage.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/vfnet.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/yolox.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/detr.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/centernet.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/panoptic_fpn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/kd_one_stage.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/queryinst.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/autoassign.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/mask_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/faster_rcnn.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/reppoints_detector.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/solo.py -> build/lib/mmdet/models/detectors\n",
            "copying mmdet/models/detectors/yolo.py -> build/lib/mmdet/models/detectors\n",
            "creating build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/detr_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/yolo_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/anchor_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ddod_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ascend_ssd_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/cascade_rpn_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/guided_anchor_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ga_retina_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ld_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/yolof_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ascend_anchor_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ssd_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/embedding_rpn_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/fsaf_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/fovea_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/atss_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/centripetal_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/rpn_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/yolact_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/pisa_ssd_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/free_anchor_retina_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/corner_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/__init__.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/fcos_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/retina_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/base_mask_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/paa_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/mask2former_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/tood_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/maskformer_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/autoassign_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/centernet_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/nasfcos_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/retina_sepbn_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/yolox_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/vfnet_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/solo_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/reppoints_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/base_dense_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ascend_retina_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/deformable_detr_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/dense_test_mixins.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/solov2_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/sabl_retina_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/anchor_free_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/pisa_retinanet_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/ga_rpn_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/lad_head.py -> build/lib/mmdet/models/dense_heads\n",
            "copying mmdet/models/dense_heads/gfl_head.py -> build/lib/mmdet/models/dense_heads\n",
            "creating build/lib/mmdet/models/plugins\n",
            "copying mmdet/models/plugins/dropblock.py -> build/lib/mmdet/models/plugins\n",
            "copying mmdet/models/plugins/pixel_decoder.py -> build/lib/mmdet/models/plugins\n",
            "copying mmdet/models/plugins/__init__.py -> build/lib/mmdet/models/plugins\n",
            "copying mmdet/models/plugins/msdeformattn_pixel_decoder.py -> build/lib/mmdet/models/plugins\n",
            "creating build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/ct_resnet_neck.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/nas_fpn.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/channel_mapper.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/fpg.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/yolox_pafpn.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/__init__.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/bfp.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/ssd_neck.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/pafpn.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/hrfpn.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/fpn_carafe.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/dyhead.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/rfp.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/dilated_encoder.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/yolo_neck.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/fpn.py -> build/lib/mmdet/models/necks\n",
            "copying mmdet/models/necks/nasfcos_fpn.py -> build/lib/mmdet/models/necks\n",
            "creating build/lib/mmdet/models/seg_heads\n",
            "copying mmdet/models/seg_heads/__init__.py -> build/lib/mmdet/models/seg_heads\n",
            "copying mmdet/models/seg_heads/panoptic_fpn_head.py -> build/lib/mmdet/models/seg_heads\n",
            "copying mmdet/models/seg_heads/base_semantic_head.py -> build/lib/mmdet/models/seg_heads\n",
            "creating build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/make_divisible.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/inverted_residual.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/csp_layer.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/builder.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/res_layer.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/gaussian_target.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/point_sample.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/__init__.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/ckpt_convert.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/se_layer.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/normed_predictor.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/positional_encoding.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/transformer.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/brick_wrappers.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/panoptic_gt_processing.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/conv_upsample.py -> build/lib/mmdet/models/utils\n",
            "copying mmdet/models/utils/misc.py -> build/lib/mmdet/models/utils\n",
            "creating build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/resnest.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/resnet.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/csp_darknet.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/ssd_vgg.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/trident_resnet.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/resnext.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/darknet.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/pvt.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/__init__.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/mobilenet_v2.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/hrnet.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/hourglass.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/swin.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/detectors_resnet.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/efficientnet.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/detectors_resnext.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/res2net.py -> build/lib/mmdet/models/backbones\n",
            "copying mmdet/models/backbones/regnet.py -> build/lib/mmdet/models/backbones\n",
            "creating build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/kd_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/gfocal_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/ghm_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/mse_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/cross_entropy_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/ae_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/accuracy.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/pisa_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/__init__.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/gaussian_focal_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/seesaw_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/focal_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/balanced_l1_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/utils.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/iou_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/dice_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/varifocal_loss.py -> build/lib/mmdet/models/losses\n",
            "copying mmdet/models/losses/smooth_l1_loss.py -> build/lib/mmdet/models/losses\n",
            "creating build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/pisa_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/htc_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/double_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/test_mixins.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/cascade_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/trident_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/__init__.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/mask_scoring_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/base_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/grid_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/standard_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/scnet_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/dynamic_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/point_rend_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "copying mmdet/models/roi_heads/sparse_roi_head.py -> build/lib/mmdet/models/roi_heads\n",
            "creating build/lib/mmdet/models/seg_heads/panoptic_fusion_heads\n",
            "copying mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py -> build/lib/mmdet/models/seg_heads/panoptic_fusion_heads\n",
            "copying mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py -> build/lib/mmdet/models/seg_heads/panoptic_fusion_heads\n",
            "copying mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py -> build/lib/mmdet/models/seg_heads/panoptic_fusion_heads\n",
            "copying mmdet/models/seg_heads/panoptic_fusion_heads/base_panoptic_fusion_head.py -> build/lib/mmdet/models/seg_heads/panoptic_fusion_heads\n",
            "creating build/lib/mmdet/models/roi_heads/roi_extractors\n",
            "copying mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py -> build/lib/mmdet/models/roi_heads/roi_extractors\n",
            "copying mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py -> build/lib/mmdet/models/roi_heads/roi_extractors\n",
            "copying mmdet/models/roi_heads/roi_extractors/__init__.py -> build/lib/mmdet/models/roi_heads/roi_extractors\n",
            "copying mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py -> build/lib/mmdet/models/roi_heads/roi_extractors\n",
            "creating build/lib/mmdet/models/roi_heads/shared_heads\n",
            "copying mmdet/models/roi_heads/shared_heads/res_layer.py -> build/lib/mmdet/models/roi_heads/shared_heads\n",
            "copying mmdet/models/roi_heads/shared_heads/__init__.py -> build/lib/mmdet/models/roi_heads/shared_heads\n",
            "creating build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/fcn_mask_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/scnet_mask_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/fused_semantic_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/htc_mask_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/grid_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/feature_relay_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/__init__.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/coarse_mask_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/global_context_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/maskiou_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/dynamic_mask_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "copying mmdet/models/roi_heads/mask_heads/mask_point_head.py -> build/lib/mmdet/models/roi_heads/mask_heads\n",
            "creating build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "copying mmdet/models/roi_heads/bbox_heads/double_bbox_head.py -> build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "copying mmdet/models/roi_heads/bbox_heads/bbox_head.py -> build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "copying mmdet/models/roi_heads/bbox_heads/sabl_head.py -> build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "copying mmdet/models/roi_heads/bbox_heads/dii_head.py -> build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "copying mmdet/models/roi_heads/bbox_heads/__init__.py -> build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "copying mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py -> build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "copying mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py -> build/lib/mmdet/models/roi_heads/bbox_heads\n",
            "creating build/lib/mmdet/core/evaluation\n",
            "copying mmdet/core/evaluation/panoptic_utils.py -> build/lib/mmdet/core/evaluation\n",
            "copying mmdet/core/evaluation/mean_ap.py -> build/lib/mmdet/core/evaluation\n",
            "copying mmdet/core/evaluation/eval_hooks.py -> build/lib/mmdet/core/evaluation\n",
            "copying mmdet/core/evaluation/__init__.py -> build/lib/mmdet/core/evaluation\n",
            "copying mmdet/core/evaluation/bbox_overlaps.py -> build/lib/mmdet/core/evaluation\n",
            "copying mmdet/core/evaluation/recall.py -> build/lib/mmdet/core/evaluation\n",
            "copying mmdet/core/evaluation/class_names.py -> build/lib/mmdet/core/evaluation\n",
            "creating build/lib/mmdet/core/mask\n",
            "copying mmdet/core/mask/mask_target.py -> build/lib/mmdet/core/mask\n",
            "copying mmdet/core/mask/__init__.py -> build/lib/mmdet/core/mask\n",
            "copying mmdet/core/mask/utils.py -> build/lib/mmdet/core/mask\n",
            "copying mmdet/core/mask/structures.py -> build/lib/mmdet/core/mask\n",
            "creating build/lib/mmdet/core/bbox\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/bbox/iou_calculators/builder.py to builder.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/bbox/iou_calculators/__init__.py to __init__.cpython-310.pyc\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/anchor/utils.py to utils.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/anchor/point_generator.py to point_generator.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/anchor/anchor_generator.py to anchor_generator.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/utils/dist_utils.py to dist_utils.cpython-310.pyc\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/post_processing/matrix_nms.py to matrix_nms.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/post_processing/__init__.py to __init__.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/post_processing/merge_augs.py to merge_augs.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/set_epoch_info_hook.py to set_epoch_info_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/sync_norm_hook.py to sync_norm_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/wandblogger_hook.py to wandblogger_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/memory_profiler_hook.py to memory_profiler_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/__init__.py to __init__.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/ema.py to ema.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/yolox_mode_switch_hook.py to yolox_mode_switch_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/sync_random_size_hook.py to sync_random_size_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/checkloss_hook.py to checkloss_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/hook/yolox_lrupdater_hook.py to yolox_lrupdater_hook.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/visualization/image.py to image.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/visualization/palette.py to palette.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/visualization/__init__.py to __init__.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/export/onnx_helper.py to onnx_helper.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/export/pytorch2onnx.py to pytorch2onnx.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/export/__init__.py to __init__.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/export/model_wrappers.py to model_wrappers.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/optimizers/builder.py to builder.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/optimizers/__init__.py to __init__.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/core/optimizers/layer_decay_optimizer_constructor.py to layer_decay_optimizer_constructor.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/apis/__init__.py to __init__.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/apis/inference.py to inference.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/apis/test.py to test.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/apis/train.py to train.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/replace_cfg_vals.py to replace_cfg_vals.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/contextmanagers.py to contextmanagers.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/util_distribution.py to util_distribution.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/collect_env.py to collect_env.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/util_random.py to util_random.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/split_batch.py to split_batch.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/compat_config.py to compat_config.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/__init__.py to __init__.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/util_mixins.py to util_mixins.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/logger.py to logger.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/rfnext.py to rfnext.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/ascend_util.py to ascend_util.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/memory.py to memory.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/setup_env.py to setup_env.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/profiling.py to profiling.cpython-310.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/mmdet/utils/misc.py to misc.cpython-310.pyc\n",
            "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying mmdet.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying mmdet.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying mmdet.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying mmdet.egg-info/not-zip-safe -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying mmdet.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying mmdet.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "creating dist\n",
            "creating 'dist/mmdet-2.28.2-py3.10.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
            "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
            "Processing mmdet-2.28.2-py3.10.egg\n",
            "creating /usr/local/lib/python3.10/dist-packages/mmdet-2.28.2-py3.10.egg\n",
            "Extracting mmdet-2.28.2-py3.10.egg to /usr/local/lib/python3.10/dist-packages\n",
            "Adding mmdet 2.28.2 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.10/dist-packages/mmdet-2.28.2-py3.10.egg\n",
            "Processing dependencies for mmdet==2.28.2\n",
            "Searching for terminaltables\n",
            "Reading https://pypi.org/simple/terminaltables/\n",
            "Downloading https://files.pythonhosted.org/packages/c4/fb/ea621e0a19733e01fe4005d46087d383693c0f4a8f824b47d8d4122c87e0/terminaltables-3.1.10-py2.py3-none-any.whl#sha256=e4fdc4179c9e4aab5f674d80f09d76fa436b96fdc698a8505e0a36bf0804a874\n",
            "Best match: terminaltables 3.1.10\n",
            "Processing terminaltables-3.1.10-py2.py3-none-any.whl\n",
            "Installing terminaltables-3.1.10-py2.py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
            "Adding terminaltables 3.1.10 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.10/dist-packages/terminaltables-3.1.10-py3.10.egg\n",
            "Searching for six==1.16.0\n",
            "Best match: six 1.16.0\n",
            "Adding six 1.16.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for scipy==1.11.3\n",
            "Best match: scipy 1.11.3\n",
            "Adding scipy 1.11.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for pycocotools==2.0.7\n",
            "Best match: pycocotools 2.0.7\n",
            "Adding pycocotools 2.0.7 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for numpy==1.23.5\n",
            "Best match: numpy 1.23.5\n",
            "Adding numpy 1.23.5 to easy-install.pth file\n",
            "Installing f2py script to /usr/local/bin\n",
            "Installing f2py3 script to /usr/local/bin\n",
            "Installing f2py3.10 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for matplotlib==3.7.1\n",
            "Best match: matplotlib 3.7.1\n",
            "Adding matplotlib 3.7.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for python-dateutil==2.8.2\n",
            "Best match: python-dateutil 2.8.2\n",
            "Adding python-dateutil 2.8.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for pyparsing==3.1.1\n",
            "Best match: pyparsing 3.1.1\n",
            "Adding pyparsing 3.1.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for Pillow==9.4.0\n",
            "Best match: Pillow 9.4.0\n",
            "Adding Pillow 9.4.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for packaging==23.2\n",
            "Best match: packaging 23.2\n",
            "Adding packaging 23.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for kiwisolver==1.4.5\n",
            "Best match: kiwisolver 1.4.5\n",
            "Adding kiwisolver 1.4.5 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for fonttools==4.44.3\n",
            "Best match: fonttools 4.44.3\n",
            "Adding fonttools 4.44.3 to easy-install.pth file\n",
            "Installing fonttools script to /usr/local/bin\n",
            "Installing pyftmerge script to /usr/local/bin\n",
            "Installing pyftsubset script to /usr/local/bin\n",
            "Installing ttx script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for cycler==0.12.1\n",
            "Best match: cycler 0.12.1\n",
            "Adding cycler 0.12.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Searching for contourpy==1.2.0\n",
            "Best match: contourpy 1.2.0\n",
            "Adding contourpy 1.2.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.10/dist-packages\n",
            "Finished processing dependencies for mmdet==2.28.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 아래를 수행하기 전에 kernel을 restart 해야 함(런타임 다시시작)\n",
        "from mmdet.apis import init_detector, inference_detector\n",
        "import mmcv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3shXCGw3oXH_",
        "outputId": "e31f105a-8365-41be-de0d-1669d4b8178f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터가 저장 된 구글 드라이브 마운트\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIzPi3McoXLd",
        "outputId": "ee824fb7-638d-460b-b8c4-1d6ca409ebf6"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check nvcc version\n",
        "!nvcc -V\n",
        "# Check GCC version\n",
        "!gcc --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zcOQZuBoXOi",
        "outputId": "ae27e988-f223-4ea2-e306-5b7c22cd2e2d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2022 NVIDIA Corporation\n",
            "Built on Wed_Sep_21_10:33:58_PDT_2022\n",
            "Cuda compilation tools, release 11.8, V11.8.89\n",
            "Build cuda_11.8.r11.8/compiler.31833905_0\n",
            "gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\n",
            "Copyright (C) 2021 Free Software Foundation, Inc.\n",
            "This is free software; see the source for copying conditions.  There is NO\n",
            "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install MMDetection\n",
        "!pip install mmdet==2.28.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiCjAQP9oXR0",
        "outputId": "8332ba0d-0ae7-496a-cf0e-cc0312834e4d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mmdet==2.28.2 in /usr/local/lib/python3.10/dist-packages/mmdet-2.28.2-py3.10.egg (2.28.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mmdet==2.28.2) (3.7.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mmdet==2.28.2) (1.23.5)\n",
            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.10/dist-packages (from mmdet==2.28.2) (2.0.7)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from mmdet==2.28.2) (1.11.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from mmdet==2.28.2) (1.16.0)\n",
            "Requirement already satisfied: terminaltables in /usr/local/lib/python3.10/dist-packages/terminaltables-3.1.10-py3.10.egg (from mmdet==2.28.2) (3.1.10)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (4.44.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmdet==2.28.2) (2.8.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHFwE_nxofYk",
        "outputId": "3e61425b-ddec-4029-f0de-17a2404b6852"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# clone the MMTracking repository\n",
        "!git clone https://github.com/open-mmlab/mmtracking.git\n",
        "\n",
        "%cd mmtracking"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJkX4Hlyof3E",
        "outputId": "e04e5f11-e645-46f0-8991-fa4f7b7f3b03"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'mmtracking'...\n",
            "remote: Enumerating objects: 7927, done.\u001b[K\n",
            "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 7927 (delta 0), reused 5 (delta 0), pack-reused 7915\u001b[K\n",
            "Receiving objects: 100% (7927/7927), 2.91 MiB | 24.85 MiB/s, done.\n",
            "Resolving deltas: 100% (5021/5021), done.\n",
            "/content/mmtracking\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45owGtFDof_D",
        "outputId": "6e88fac4-3a9b-47b3-8594-0fd3d6295b8a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mmtracking\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1분 소모\n",
        "# 설치시 ERROR: pip's dependency resolver... 부분은 신경쓰지 않아도 됨.\n",
        "# install MMTracking and its dependencies\n",
        "!pip install -r requirements/build.txt\n",
        "!pip install -e .\n",
        "# used to MOT evaluation\n",
        "!pip install git+https://github.com/JonathonLuiten/TrackEval.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TY3ik5OpogCG",
        "outputId": "20e12031-b503-416b-897d-a715052d36b2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: cython in /usr/local/lib/python3.10/dist-packages (from -r requirements/build.txt (line 1)) (3.0.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from -r requirements/build.txt (line 2)) (1.23.5)\n",
            "Obtaining file:///content/mmtracking\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting attributee (from mmtrack==0.14.0)\n",
            "  Downloading attributee-0.1.8.tar.gz (11 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting dotty_dict (from mmtrack==0.14.0)\n",
            "  Downloading dotty_dict-1.3.1-py3-none-any.whl (7.0 kB)\n",
            "Collecting einops (from mmtrack==0.14.0)\n",
            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting lap (from mmtrack==0.14.0)\n",
            "  Downloading lap-0.4.0.tar.gz (1.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mmtrack==0.14.0) (3.7.1)\n",
            "Collecting mmcls<1.0.0,>=0.16.0 (from mmtrack==0.14.0)\n",
            "  Downloading mmcls-0.25.0-py2.py3-none-any.whl (648 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m648.8/648.8 kB\u001b[0m \u001b[31m52.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting motmetrics (from mmtrack==0.14.0)\n",
            "  Downloading motmetrics-1.4.0-py3-none-any.whl (161 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m161.5/161.5 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mmtrack==0.14.0) (23.2)\n",
            "Collecting pandas<=1.3.5 (from mmtrack==0.14.0)\n",
            "  Downloading pandas-1.3.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.5/11.5 MB\u001b[0m \u001b[31m104.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pycocotools in /usr/local/lib/python3.10/dist-packages (from mmtrack==0.14.0) (2.0.7)\n",
            "Collecting scipy<=1.7.3 (from mmtrack==0.14.0)\n",
            "  Downloading scipy-1.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m43.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from mmtrack==0.14.0) (0.12.2)\n",
            "Requirement already satisfied: terminaltables in /usr/local/lib/python3.10/dist-packages/terminaltables-3.1.10-py3.10.egg (from mmtrack==0.14.0) (3.1.10)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from mmtrack==0.14.0) (4.66.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mmcls<1.0.0,>=0.16.0->mmtrack==0.14.0) (1.23.5)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmtrack==0.14.0) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmtrack==0.14.0) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmtrack==0.14.0) (4.44.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmtrack==0.14.0) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmtrack==0.14.0) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmtrack==0.14.0) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmtrack==0.14.0) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.10/dist-packages (from pandas<=1.3.5->mmtrack==0.14.0) (2023.3.post1)\n",
            "Collecting numpy (from mmcls<1.0.0,>=0.16.0->mmtrack==0.14.0)\n",
            "  Downloading numpy-1.22.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.8/16.8 MB\u001b[0m \u001b[31m78.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting xmltodict>=0.12.0 (from motmetrics->mmtrack==0.14.0)\n",
            "  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->mmtrack==0.14.0) (1.16.0)\n",
            "Building wheels for collected packages: attributee, lap\n",
            "  Building wheel for attributee (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for attributee: filename=attributee-0.1.8-py3-none-any.whl size=12859 sha256=b3d43737c889a4e8de6e94f56646a142c5e06b4e3497a26df6f1b2e1aff06207\n",
            "  Stored in directory: /root/.cache/pip/wheels/0a/e2/ab/3292c3de25a615d3ef2011d65e68f103c025d82149ac6c2f14\n",
            "  Building wheel for lap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lap: filename=lap-0.4.0-cp310-cp310-linux_x86_64.whl size=1628964 sha256=d02710afac75395a4eb4b77a025f21b1970aa6c7881f45d33e7279a608155889\n",
            "  Stored in directory: /root/.cache/pip/wheels/00/42/2e/9dfe19270eea279d79e84767ff0d7b8082c3bf776cad00e83d\n",
            "Successfully built attributee lap\n",
            "Installing collected packages: lap, xmltodict, numpy, einops, dotty_dict, attributee, scipy, pandas, motmetrics, mmcls, mmtrack\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.23.5\n",
            "    Uninstalling numpy-1.23.5:\n",
            "      Successfully uninstalled numpy-1.23.5\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.11.3\n",
            "    Uninstalling scipy-1.11.3:\n",
            "      Successfully uninstalled scipy-1.11.3\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.5.3\n",
            "    Uninstalling pandas-1.5.3:\n",
            "      Successfully uninstalled pandas-1.5.3\n",
            "  Running setup.py develop for mmtrack\n",
            "\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",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "arviz 0.15.1 requires scipy>=1.8.0, but you have scipy 1.7.3 which is incompatible.\n",
            "bigframes 0.13.0 requires pandas>=1.5.0, but you have pandas 1.3.5 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 1.3.5 which is incompatible.\n",
            "jax 0.4.20 requires scipy>=1.9, but you have scipy 1.7.3 which is incompatible.\n",
            "jaxlib 0.4.20+cuda11.cudnn86 requires scipy>=1.9, but you have scipy 1.7.3 which is incompatible.\n",
            "plotnine 0.12.4 requires numpy>=1.23.0, but you have numpy 1.22.4 which is incompatible.\n",
            "plotnine 0.12.4 requires pandas>=1.5.0, but you have pandas 1.3.5 which is incompatible.\n",
            "tensorflow 2.14.0 requires numpy>=1.23.5, but you have numpy 1.22.4 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 1.13.0+cu116 which is incompatible.\n",
            "xarray 2023.7.0 requires pandas>=1.4, but you have pandas 1.3.5 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed attributee-0.1.8 dotty_dict-1.3.1 einops-0.7.0 lap-0.4.0 mmcls-0.25.0 mmtrack-0.14.0 motmetrics-1.4.0 numpy-1.22.4 pandas-1.3.5 scipy-1.7.3 xmltodict-0.13.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy",
                  "scipy"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/JonathonLuiten/TrackEval.git\n",
            "  Cloning https://github.com/JonathonLuiten/TrackEval.git to /tmp/pip-req-build-ee38h44v\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/JonathonLuiten/TrackEval.git /tmp/pip-req-build-ee38h44v\n",
            "  Resolved https://github.com/JonathonLuiten/TrackEval.git to commit 12c8791b303e0a0b50f753af204249e622d0281a\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from trackeval==1.0.dev1) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from trackeval==1.0.dev1) (1.7.3)\n",
            "Building wheels for collected packages: trackeval\n",
            "  Building wheel for trackeval (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for trackeval: filename=trackeval-1.0.dev1-py3-none-any.whl size=149965 sha256=0cb8c2c389c69b4aeafb520ab30189abc8fa3dc0be6c2a3f5b5ffbbdfa18b4f9\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-wq5wbi7b/wheels/bf/6f/b9/d7f59588070500e34ef9a3377da7dbff1ea5ead3cdbca7c0aa\n",
            "Successfully built trackeval\n",
            "Installing collected packages: trackeval\n",
            "Successfully installed trackeval-1.0.dev1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mmcv import collect_env\n",
        "collect_env() # 설정된 환경 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2XOox8fogFe",
        "outputId": "d7191f16-94ff-40f4-f7ee-64d8fa76ea48"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sys.platform': 'linux',\n",
              " 'Python': '3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]',\n",
              " 'CUDA available': True,\n",
              " 'GPU 0': 'Tesla T4',\n",
              " 'CUDA_HOME': '/usr/local/cuda',\n",
              " 'NVCC': 'Cuda compilation tools, release 11.8, V11.8.89',\n",
              " 'GCC': 'x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0',\n",
              " 'PyTorch': '1.13.0+cu116',\n",
              " 'PyTorch compiling details': 'PyTorch built with:\\n  - GCC 9.3\\n  - C++ Version: 201402\\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\\n  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\\n  - LAPACK is enabled (usually provided by MKL)\\n  - NNPACK is enabled\\n  - CPU capability usage: AVX2\\n  - CUDA Runtime 11.6\\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\\n  - CuDNN 8.3.2  (built against CUDA 11.5)\\n  - Magma 2.6.1\\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \\n',\n",
              " 'TorchVision': '0.14.0+cu116',\n",
              " 'OpenCV': '4.8.0',\n",
              " 'MMCV': '1.7.1',\n",
              " 'MMCV Compiler': 'GCC 9.3',\n",
              " 'MMCV CUDA Compiler': '11.6'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Pytorch installation\n",
        "import torch, torchvision\n",
        "print(torch.__version__, torch.cuda.is_available())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPU_-kkVogIk",
        "outputId": "d3624785-5456-43ec-b140-ead34d78c7ec"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.13.0+cu116 True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check mmcv installation\n",
        "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n",
        "print(get_compiling_cuda_version())\n",
        "print(get_compiler_version())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hs6g-8kXoLtw",
        "outputId": "fdff6924-c25a-4c29-8462-e3675b02246b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11.6\n",
            "GCC 9.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check MMDetection installation\n",
        "import mmdet\n",
        "print(mmdet.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xji1lPvtoLwm",
        "outputId": "41164ef5-5700-4ecb-c44c-23ae6cff1a35"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.28.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check MMTracking installation\n",
        "import mmtrack\n",
        "print(mmtrack.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zT7e57PSoL6v",
        "outputId": "c7fe8498-1d1f-4938-cff4-a3451f49866e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.14.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 49초 소모\n",
        "# unset the proxy for downloading the pretrained models (optional)\n",
        "!unset https_proxy\n",
        "!unset http_proxy\n",
        "\n",
        "# download checkpoints\n",
        "!mkdir checkpoints\n",
        "!wget -c https://download.openmmlab.com/mmtracking/vid/selsa/selsa_faster_rcnn_r50_dc5_1x_imagenetvid/selsa_faster_rcnn_r50_dc5_1x_imagenetvid_20201227_204835-2f5a4952.pth -P ./checkpoints\n",
        "!wget -c https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_lasot/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth -P ./checkpoints\n",
        "!wget -c https://download.openmmlab.com/mmtracking/vis/masktrack_rcnn/masktrack_rcnn_r50_fpn_12e_youtubevis2019/masktrack_rcnn_r50_fpn_12e_youtubevis2019_20211022_194830-6ca6b91e.pth -P ./checkpoints\n",
        "\n",
        "'''\n",
        "Siamese Network 는 (이하 샴 네트워크) 다루어야하는 클래스의 종류가 매우 많고,\n",
        "특정 클래스에 대한 사진을 대량으로 구할 수 없을 때 머신러닝을 활용하여 그\n",
        "클래스를 구분해내기 위하여 고안된 네트워크이다.\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "aP2EVwx2oL90",
        "outputId": "bbd6c5e5-15f2-4528-bf40-2adb5ff82e67"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-12-01 11:34:34--  https://download.openmmlab.com/mmtracking/vid/selsa/selsa_faster_rcnn_r50_dc5_1x_imagenetvid/selsa_faster_rcnn_r50_dc5_1x_imagenetvid_20201227_204835-2f5a4952.pth\n",
            "Resolving download.openmmlab.com (download.openmmlab.com)... 163.181.81.27, 163.181.81.31, 163.181.81.34, ...\n",
            "Connecting to download.openmmlab.com (download.openmmlab.com)|163.181.81.27|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 282801031 (270M) [application/octet-stream]\n",
            "Saving to: ‘./checkpoints/selsa_faster_rcnn_r50_dc5_1x_imagenetvid_20201227_204835-2f5a4952.pth’\n",
            "\n",
            "selsa_faster_rcnn_r 100%[===================>] 269.70M  20.4MB/s    in 15s     \n",
            "\n",
            "2023-12-01 11:34:50 (17.8 MB/s) - ‘./checkpoints/selsa_faster_rcnn_r50_dc5_1x_imagenetvid_20201227_204835-2f5a4952.pth’ saved [282801031/282801031]\n",
            "\n",
            "--2023-12-01 11:34:50--  https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_lasot/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth\n",
            "Resolving download.openmmlab.com (download.openmmlab.com)... 163.181.81.27, 163.181.81.31, 163.181.81.34, ...\n",
            "Connecting to download.openmmlab.com (download.openmmlab.com)|163.181.81.27|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 216134418 (206M) [application/octet-stream]\n",
            "Saving to: ‘./checkpoints/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth’\n",
            "\n",
            "siamese_rpn_r50_1x_ 100%[===================>] 206.12M  12.4MB/s    in 15s     \n",
            "\n",
            "2023-12-01 11:35:05 (13.4 MB/s) - ‘./checkpoints/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth’ saved [216134418/216134418]\n",
            "\n",
            "--2023-12-01 11:35:05--  https://download.openmmlab.com/mmtracking/vis/masktrack_rcnn/masktrack_rcnn_r50_fpn_12e_youtubevis2019/masktrack_rcnn_r50_fpn_12e_youtubevis2019_20211022_194830-6ca6b91e.pth\n",
            "Resolving download.openmmlab.com (download.openmmlab.com)... 163.181.81.34, 163.181.81.29, 163.181.81.27, ...\n",
            "Connecting to download.openmmlab.com (download.openmmlab.com)|163.181.81.34|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 232596799 (222M) [application/octet-stream]\n",
            "Saving to: ‘./checkpoints/masktrack_rcnn_r50_fpn_12e_youtubevis2019_20211022_194830-6ca6b91e.pth’\n",
            "\n",
            "masktrack_rcnn_r50_ 100%[===================>] 221.82M  19.8MB/s    in 13s     \n",
            "\n",
            "2023-12-01 11:35:18 (17.4 MB/s) - ‘./checkpoints/masktrack_rcnn_r50_fpn_12e_youtubevis2019_20211022_194830-6ca6b91e.pth’ saved [232596799/232596799]\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nSiamese Network 는 (이하 샴 네트워크) 다루어야하는 클래스의 종류가 매우 많고,\\n특정 클래스에 대한 사진을 대량으로 구할 수 없을 때 머신러닝을 활용하여 그\\n클래스를 구분해내기 위하여 고안된 네트워크이다.\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tempfile\n",
        "\n",
        "print(tempfile.TemporaryDirectory())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0izB8FUCoMA7",
        "outputId": "5513f54f-3bfd-4cad-c93d-1c1d3b1a41f1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<TemporaryDirectory '/tmp/tmpgv6skobr'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tempfile.TemporaryDirectory().name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldRjmujIq1z0",
        "outputId": "894073ea-b8a4-48ef-afa8-5e198384a1dd"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/tmp/tmp3_4c0w12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tF61vNhXq13O",
        "outputId": "8a17e6d3-50e9-40b2-9c27-625305ed550e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mmtracking\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir data"
      ],
      "metadata": {
        "id": "_8AikGHSq16l"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir data/MOT17_tiny"
      ],
      "metadata": {
        "id": "K9c_e8Z3q19d"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4t2rWlAq2Ak",
        "outputId": "c8d44175-c707-4d1e-afd4-5f0f587fd0c0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-yEvSiPq2DQ",
        "outputId": "5c365b79-e4da-4f09-c186-b8f91b267928"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -qq '/content/gdrive/MyDrive/Colab Notebooks/REAL_MOT17_tiny.zip' -d '/content/mmtracking/data/MOT17_tiny'"
      ],
      "metadata": {
        "id": "lvLabkd-q2G6"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "gt.txt.는 9가지 속성 존재\n",
        "gt.txt => Frame number, ID number, BBox left, BBox top, BBox width, BBox height, Confidence Score, Class, Visibility(가시성)\n",
        "\n",
        "det.txt는 7개의 속성 존재(/det/det.txt 파일은 Re-identification을 위한 Annotation은 제외된채로 Detection만을 위한 Annotation이 되어 있다)\n",
        "det.txt => imageFileNumber(FrameNumber), id(존재하지 않으므로 모두 -1값을 가짐), BBoxTop, BBoxLeft, BBoxWidth, BBoxHeight, Confidence Score\n",
        "'''"
      ],
      "metadata": {
        "id": "60VlS6YKq2Lo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd mmtracking"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQXxSk6Eq2Pa",
        "outputId": "2b5ae360-b933-482d-90c7-76a7e7879ff2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mmtracking\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "이미지 파일 개수는 244장\n",
        "'''\n",
        "import os\n",
        "\n",
        "folder_path = \"/content/mmtracking/data/MOT17_tiny/train/MOT17-02-FRCNN/img1\"\n",
        "file_list = os.listdir(folder_path)\n",
        "file_count = len(file_list)\n",
        "print(file_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDToYAPmoMEQ",
        "outputId": "7b21f9ab-4774-48eb-8580-9d6ce1e48935"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "244\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the dataset to coco format\n",
        "\n",
        "# !rm -rf ./data/MOT17_tiny/annotations\n",
        "# !python ./tools/convert_datasets/mot/mot2coco.py -i ./data/MOT17_tiny/ -o ./data/MOT17_tiny/annotations --split-train --convert-det"
      ],
      "metadata": {
        "id": "cMZW8zefoMHS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Person Re-Identification: 복수의 다양한 각도 시점에서 찍은 사진에서 동일 인물을 찾기\n",
        "'''\n",
        "# crop pedestrian patches from the original dataset for training reid model. It may take a few minutes.\n",
        "\n",
        "# !rm -rf ./data/MOT17_tiny/reid\n",
        "# !python ./tools/convert_datasets/mot/mot2reid.py -i ./data/MOT17_tiny/ -o ./data/MOT17_tiny/reid --val-split 0.9 --vis-threshold 0.8"
      ],
      "metadata": {
        "id": "tZm1_VxnoMJ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir tutorial_exps"
      ],
      "metadata": {
        "id": "0d-GlSBTF4cN"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir tutorial_exps/detector"
      ],
      "metadata": {
        "id": "RV99q3iGF4ec"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "\n",
        "이후\n",
        "/content/mmtracking/configs/det/faster-rcnn_r50_fpn_4e_mot17-half.py에서 아래 복붙\n",
        "\n",
        "\n",
        "USE_MMDET = True\n",
        "_base_ = [\n",
        "    '../_base_/models/faster_rcnn_r50_fpn.py',\n",
        "    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'\n",
        "]\n",
        "model = dict(\n",
        "    detector=dict(\n",
        "        rpn_head=dict(bbox_coder=dict(clip_border=False)),\n",
        "        roi_head=dict(\n",
        "            bbox_head=dict(bbox_coder=dict(clip_border=False), num_classes=1)),\n",
        "        init_cfg=dict(\n",
        "            type='Pretrained',\n",
        "            checkpoint=  # noqa: E251\n",
        "            '/content/gdrive/MyDrive/Colab Notebooks/aircraft_work_dir/epoch_5.pth'  # noqa: E501\n",
        "        )))\n",
        "# learning policy\n",
        "lr_config = dict(\n",
        "    policy='step',\n",
        "    warmup='linear',\n",
        "    warmup_iters=100,\n",
        "    warmup_ratio=1.0 / 100,\n",
        "    step=[3])\n",
        "# runtime settings\n",
        "total_epochs = 10\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "이후\n",
        "/content/mmtracking/configs/_base_/datasets/coco_detection.py'로 들어가서 아래 복붙\n",
        "\n",
        "\n",
        "dataset_type = 'CocoDataset'\n",
        "data_root = 'data/MOT17_tiny/'\n",
        "img_norm_cfg = dict(\n",
        "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
        "train_pipeline = [\n",
        "    dict(type='LoadImageFromFile'),\n",
        "    dict(type='LoadAnnotations', with_bbox=True),\n",
        "    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n",
        "    dict(type='RandomFlip', flip_ratio=0.5),\n",
        "    dict(type='Normalize', **img_norm_cfg),\n",
        "    dict(type='Pad', size_divisor=32),\n",
        "    dict(type='DefaultFormatBundle'),\n",
        "    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),\n",
        "]\n",
        "test_pipeline = [\n",
        "    dict(type='LoadImageFromFile'),\n",
        "    dict(\n",
        "        type='MultiScaleFlipAug',\n",
        "        img_scale=(1333, 800),\n",
        "        flip=False,\n",
        "        transforms=[\n",
        "            dict(type='Resize', keep_ratio=True),\n",
        "            dict(type='RandomFlip'),\n",
        "            dict(type='Normalize', **img_norm_cfg),\n",
        "            dict(type='Pad', size_divisor=32),\n",
        "            dict(type='ImageToTensor', keys=['img']),\n",
        "            dict(type='Collect', keys=['img']),\n",
        "        ])\n",
        "]\n",
        "data = dict(\n",
        "    samples_per_gpu=2,\n",
        "    workers_per_gpu=2,\n",
        "    train=dict(\n",
        "      type=dataset_type,\n",
        "      ann_file=data_root + 'annotations/train_cocoformat.json',\n",
        "      img_prefix=data_root + 'train',\n",
        "      classes=('pedestrian', ),\n",
        "      pipeline=train_pipeline),\n",
        "    val=dict(\n",
        "      type=dataset_type,\n",
        "      ann_file=data_root + 'annotations/half-val_cocoformat.json',\n",
        "      img_prefix=data_root + 'train',\n",
        "      classes=('pedestrian', ),\n",
        "      pipeline=test_pipeline),\n",
        "    test=dict(\n",
        "      type=dataset_type,\n",
        "      ann_file=data_root + 'annotations/half-val_cocoformat.json',\n",
        "      img_prefix=data_root + 'train',\n",
        "      classes=('pedestrian', ),\n",
        "      pipeline=test_pipeline))\n",
        "evaluation = dict(interval=1, metric='bbox')\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "c-Adftq4F4mm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "\n",
        "/content/mmtracking/data/MOT17_tiny/reid/meta/train_9.txt\n",
        "\n",
        "위 경로에 아래 경로의 내용 복붙\n",
        "\n",
        "/content/mmtracking/data/MOT17_tiny/reid/meta/val_90.txt\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "oG0Eq_x8GP4f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi # 할당된 GPU 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Wm2DiEwwoV7",
        "outputId": "7289280a-4696-4a4b-fe89-274c2ec8a56b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Dec  1 11:37:39 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P8     9W /  70W |      3MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mmcv\n",
        "from mmdet.apis import set_random_seed\n",
        "\n",
        "cfg = mmcv.Config.fromfile('./configs/det/faster-rcnn_r50_fpn_4e_mot17-half.py') # 4e는 최대 4번의 epoch를 수행한다는 의미\n",
        "\n",
        "cfg.model.detector.init_cfg.checkpoint= \"/content/gdrive/MyDrive/Colab Notebooks/aircraft_work_dir/epoch_5.pth\"\n",
        "\n",
        "cfg.work_dir = './tutorial_exps/detector'\n",
        "cfg.seed = 0\n",
        "set_random_seed(0, deterministic=False)\n",
        "# step을 10으로 지정\n",
        "cfg.log_config.interval = 10\n",
        "# 1번 마다 한번씩 로그를 작성(학습시 cell에 출력 됨, None.log.json에도 저장됨)\n",
        "cfg.log_config.max_epochs = 1\n",
        "cfg.gpu_ids = range(1)\n",
        "cfg.device='cuda'\n",
        "\n",
        "print(cfg)"
      ],
      "metadata": {
        "id": "-SzkXuo0F4o9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f0e4c53-9303-4257-a95f-f24870850c83"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Config (path: ./configs/det/faster-rcnn_r50_fpn_4e_mot17-half.py): {'model': {'detector': {'type': 'FasterRCNN', 'backbone': {'type': 'ResNet', 'depth': 50, 'num_stages': 4, 'out_indices': (0, 1, 2, 3), 'frozen_stages': 1, 'norm_cfg': {'type': 'BN', 'requires_grad': True}, 'norm_eval': True, 'style': 'pytorch', 'init_cfg': {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}}, 'neck': {'type': 'FPN', 'in_channels': [256, 512, 1024, 2048], 'out_channels': 256, 'num_outs': 5}, 'rpn_head': {'type': 'RPNHead', 'in_channels': 256, 'feat_channels': 256, 'anchor_generator': {'type': 'AnchorGenerator', 'scales': [8], 'ratios': [0.5, 1.0, 2.0], 'strides': [4, 8, 16, 32, 64]}, 'bbox_coder': {'type': 'DeltaXYWHBBoxCoder', 'target_means': [0.0, 0.0, 0.0, 0.0], 'target_stds': [1.0, 1.0, 1.0, 1.0], 'clip_border': False}, 'loss_cls': {'type': 'CrossEntropyLoss', 'use_sigmoid': True, 'loss_weight': 1.0}, 'loss_bbox': {'type': 'SmoothL1Loss', 'beta': 0.1111111111111111, 'loss_weight': 1.0}}, 'roi_head': {'type': 'StandardRoIHead', 'bbox_roi_extractor': {'type': 'SingleRoIExtractor', 'roi_layer': {'type': 'RoIAlign', 'output_size': 7, 'sampling_ratio': 0}, 'out_channels': 256, 'featmap_strides': [4, 8, 16, 32]}, 'bbox_head': {'type': 'Shared2FCBBoxHead', 'in_channels': 256, 'fc_out_channels': 1024, 'roi_feat_size': 7, 'num_classes': 1, 'bbox_coder': {'type': 'DeltaXYWHBBoxCoder', 'target_means': [0.0, 0.0, 0.0, 0.0], 'target_stds': [0.1, 0.1, 0.2, 0.2], 'clip_border': False}, 'reg_class_agnostic': False, 'loss_cls': {'type': 'CrossEntropyLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'loss_bbox': {'type': 'SmoothL1Loss', 'loss_weight': 1.0}}}, 'train_cfg': {'rpn': {'assigner': {'type': 'MaxIoUAssigner', 'pos_iou_thr': 0.7, 'neg_iou_thr': 0.3, 'min_pos_iou': 0.3, 'match_low_quality': True, 'ignore_iof_thr': -1}, 'sampler': {'type': 'RandomSampler', 'num': 256, 'pos_fraction': 0.5, 'neg_pos_ub': -1, 'add_gt_as_proposals': False}, 'allowed_border': -1, 'pos_weight': -1, 'debug': False}, 'rpn_proposal': {'nms_pre': 2000, 'max_per_img': 1000, 'nms': {'type': 'nms', 'iou_threshold': 0.7}, 'min_bbox_size': 0}, 'rcnn': {'assigner': {'type': 'MaxIoUAssigner', 'pos_iou_thr': 0.5, 'neg_iou_thr': 0.5, 'min_pos_iou': 0.5, 'match_low_quality': False, 'ignore_iof_thr': -1}, 'sampler': {'type': 'RandomSampler', 'num': 512, 'pos_fraction': 0.25, 'neg_pos_ub': -1, 'add_gt_as_proposals': True}, 'pos_weight': -1, 'debug': False}}, 'test_cfg': {'rpn': {'nms_pre': 1000, 'max_per_img': 1000, 'nms': {'type': 'nms', 'iou_threshold': 0.7}, 'min_bbox_size': 0}, 'rcnn': {'score_thr': 0.05, 'nms': {'type': 'nms', 'iou_threshold': 0.5}, 'max_per_img': 100}}, 'init_cfg': {'type': 'Pretrained', 'checkpoint': '/content/gdrive/MyDrive/Colab Notebooks/aircraft_work_dir/epoch_5.pth'}}}, 'dataset_type': 'CocoDataset', 'data_root': 'data/MOT17_tiny/', 'img_norm_cfg': {'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, 'train_pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'LoadAnnotations', 'with_bbox': True}, {'type': 'Resize', 'img_scale': (1333, 800), 'keep_ratio': True}, {'type': 'RandomFlip', 'flip_ratio': 0.5}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'DefaultFormatBundle'}, {'type': 'Collect', 'keys': ['img', 'gt_bboxes', 'gt_labels']}], 'test_pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1333, 800), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img']}]}], 'data': {'samples_per_gpu': 2, 'workers_per_gpu': 2, 'train': {'type': 'CocoDataset', 'ann_file': 'data/MOT17_tiny/annotations/train_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'LoadAnnotations', 'with_bbox': True}, {'type': 'Resize', 'img_scale': (1333, 800), 'keep_ratio': True}, {'type': 'RandomFlip', 'flip_ratio': 0.5}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'DefaultFormatBundle'}, {'type': 'Collect', 'keys': ['img', 'gt_bboxes', 'gt_labels']}]}, 'val': {'type': 'CocoDataset', 'ann_file': 'data/MOT17_tiny/annotations/half-val_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1333, 800), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img']}]}]}, 'test': {'type': 'CocoDataset', 'ann_file': 'data/MOT17_tiny/annotations/half-val_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1333, 800), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img']}]}]}}, 'evaluation': {'interval': 1, 'metric': 'bbox'}, 'optimizer': {'type': 'SGD', 'lr': 0.02, 'momentum': 0.9, 'weight_decay': 0.0001}, 'optimizer_config': {'grad_clip': None}, 'checkpoint_config': {'interval': 1}, 'log_config': {'interval': 10, 'hooks': [{'type': 'TextLoggerHook'}], 'max_epochs': 1}, 'dist_params': {'backend': 'nccl'}, 'log_level': 'INFO', 'load_from': None, 'resume_from': None, 'workflow': [('train', 1)], 'opencv_num_threads': 0, 'mp_start_method': 'fork', 'USE_MMDET': True, 'lr_config': {'policy': 'step', 'warmup': 'linear', 'warmup_iters': 100, 'warmup_ratio': 0.01, 'step': [3]}, 'total_epochs': 10, 'work_dir': './tutorial_exps/detector', 'seed': 0, 'gpu_ids': range(0, 1), 'device': 'cuda'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os.path as osp\n",
        "\n",
        "from mmtrack.datasets import build_dataset\n",
        "from mmdet.apis import train_detector as train_model\n",
        "from mmdet.models import build_detector as build_model\n",
        "\n",
        "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
        "\n",
        "model = build_model(cfg.model.detector)\n",
        "# model.init_weights()\n",
        "datasets = [build_dataset(cfg.data.train)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvP2u_lYu72l",
        "outputId": "c44f6dab-62a1-4779-cc53-214a6ef5438d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KakGhpmvAAi",
        "outputId": "c9e1e637-e9e5-436e-9df5-9798cb4dab3c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\n",
              " CocoDataset Train dataset with number of images 215, and instance counts: \n",
              " +----------------+-------+----------+-------+----------+-------+----------+-------+----------+-------+\n",
              " | category       | count | category | count | category | count | category | count | category | count |\n",
              " +----------------+-------+----------+-------+----------+-------+----------+-------+----------+-------+\n",
              " |                |       |          |       |          |       |          |       |          |       |\n",
              " | 0 [pedestrian] | 988   |          |       |          |       |          |       |          |       |\n",
              " +----------------+-------+----------+-------+----------+-------+----------+-------+----------+-------+]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(datasets[0].CLASSES)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDR72r9IvBPz",
        "outputId": "ea1f8fe0-35b3-4443-807d-0b485818cce0"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('pedestrian',)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cfg.data.train.classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2hMTr0Qkq0T",
        "outputId": "a0b54310-b121-4fe1-8aed-a2bce369b42b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('pedestrian',)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "[###### Detection Model 학습 ######]\n",
        "'''\n",
        "# steps: 10\n",
        "# epoch:10회 => 11분 소모\n",
        "# samples_per_gpu는 default 2의 값을 가지므로 batch size는 gpu개수(1) * samples_per_gpu(2) == 2가 된다. 즉 약 200장의 학습 데이터 이미지를 2개의 Batch(각각 약 100장)로 나눠서 학습을 수행\n",
        "\n",
        "train_model(model, datasets, cfg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8CdmmV2vDHB",
        "outputId": "ff309d41-cf2e-475a-988c-135b50c2afee"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/mmdet-2.28.2-py3.10.egg/mmdet/utils/compat_config.py:28: UserWarning: config is now expected to have a `runner` section, please set `runner` in your config.\n",
            "  warnings.warn(\n",
            "2023-12-01 11:39:00,213 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.\n",
            "2023-12-01 11:39:00,216 - mmdet - INFO - Start running, host: root@82979b0ac678, work_dir: /content/mmtracking/tutorial_exps/detector\n",
            "2023-12-01 11:39:00,217 - mmdet - INFO - Hooks will be executed in the following order:\n",
            "before_run:\n",
            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_train_epoch:\n",
            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
            "(LOW         ) IterTimerHook                      \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_train_iter:\n",
            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_train_iter:\n",
            "(ABOVE_NORMAL) OptimizerHook                      \n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(LOW         ) IterTimerHook                      \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "after_train_epoch:\n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_val_epoch:\n",
            "(LOW         ) IterTimerHook                      \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_val_iter:\n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_val_iter:\n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_val_epoch:\n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "after_run:\n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "2023-12-01 11:39:00,218 - mmdet - INFO - workflow: [('train', 1)], max: 10 epochs\n",
            "2023-12-01 11:39:00,220 - mmdet - INFO - Checkpoints will be saved to /content/mmtracking/tutorial_exps/detector by HardDiskBackend.\n",
            "2023-12-01 11:39:09,896 - mmdet - INFO - Epoch [1][10/108]\tlr: 1.982e-03, eta: 0:16:57, time: 0.951, data_time: 0.232, memory: 3663, loss_rpn_cls: 0.6589, loss_rpn_bbox: 0.0252, loss_cls: 0.5936, acc: 96.9922, loss_bbox: 0.0593, loss: 1.3369\n",
            "2023-12-01 11:39:15,309 - mmdet - INFO - Epoch [1][20/108]\tlr: 3.962e-03, eta: 0:13:11, time: 0.541, data_time: 0.023, memory: 3663, loss_rpn_cls: 0.3648, loss_rpn_bbox: 0.0277, loss_cls: 0.2861, acc: 97.8613, loss_bbox: 0.0386, loss: 0.7171\n",
            "2023-12-01 11:39:20,789 - mmdet - INFO - Epoch [1][30/108]\tlr: 5.942e-03, eta: 0:11:54, time: 0.548, data_time: 0.023, memory: 3663, loss_rpn_cls: 0.1157, loss_rpn_bbox: 0.0275, loss_cls: 0.2920, acc: 97.5391, loss_bbox: 0.0376, loss: 0.4729\n",
            "2023-12-01 11:39:26,271 - mmdet - INFO - Epoch [1][40/108]\tlr: 7.922e-03, eta: 0:11:13, time: 0.548, data_time: 0.023, memory: 3663, loss_rpn_cls: 0.1109, loss_rpn_bbox: 0.0221, loss_cls: 0.2614, acc: 95.7910, loss_bbox: 0.0706, loss: 0.4650\n",
            "2023-12-01 11:39:31,742 - mmdet - INFO - Epoch [1][50/108]\tlr: 9.902e-03, eta: 0:10:46, time: 0.547, data_time: 0.023, memory: 3663, loss_rpn_cls: 0.1152, loss_rpn_bbox: 0.0286, loss_cls: 0.2160, acc: 95.4297, loss_bbox: 0.0881, loss: 0.4479\n",
            "2023-12-01 11:39:37,271 - mmdet - INFO - Epoch [1][60/108]\tlr: 1.188e-02, eta: 0:10:27, time: 0.553, data_time: 0.023, memory: 3663, loss_rpn_cls: 0.0889, loss_rpn_bbox: 0.0214, loss_cls: 0.2135, acc: 95.0000, loss_bbox: 0.0878, loss: 0.4116\n",
            "2023-12-01 11:39:42,794 - mmdet - INFO - Epoch [1][70/108]\tlr: 1.386e-02, eta: 0:10:11, time: 0.552, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0895, loss_rpn_bbox: 0.0206, loss_cls: 0.1786, acc: 95.6641, loss_bbox: 0.0796, loss: 0.3683\n",
            "2023-12-01 11:39:48,382 - mmdet - INFO - Epoch [1][80/108]\tlr: 1.584e-02, eta: 0:09:59, time: 0.559, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0992, loss_rpn_bbox: 0.0189, loss_cls: 0.2125, acc: 94.0137, loss_bbox: 0.1192, loss: 0.4496\n",
            "2023-12-01 11:39:54,015 - mmdet - INFO - Epoch [1][90/108]\tlr: 1.782e-02, eta: 0:09:49, time: 0.563, data_time: 0.023, memory: 3663, loss_rpn_cls: 0.0802, loss_rpn_bbox: 0.0251, loss_cls: 0.2030, acc: 93.7402, loss_bbox: 0.1109, loss: 0.4192\n",
            "2023-12-01 11:39:59,649 - mmdet - INFO - Epoch [1][100/108]\tlr: 1.980e-02, eta: 0:09:40, time: 0.563, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0580, loss_rpn_bbox: 0.0176, loss_cls: 0.1438, acc: 95.1465, loss_bbox: 0.0858, loss: 0.3051\n",
            "2023-12-01 11:40:04,059 - mmdet - INFO - Saving checkpoint at 1 epochs\n",
            "2023-12-01 11:40:12,968 - mmdet - INFO - Epoch [2][10/108]\tlr: 2.000e-02, eta: 0:09:06, time: 0.781, data_time: 0.230, memory: 3663, loss_rpn_cls: 0.0333, loss_rpn_bbox: 0.0191, loss_cls: 0.2751, acc: 88.1836, loss_bbox: 0.2341, loss: 0.5617\n",
            "2023-12-01 11:40:18,658 - mmdet - INFO - Epoch [2][20/108]\tlr: 2.000e-02, eta: 0:09:01, time: 0.569, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0223, loss_cls: 0.2625, acc: 90.9375, loss_bbox: 0.2285, loss: 0.5383\n",
            "2023-12-01 11:40:24,292 - mmdet - INFO - Epoch [2][30/108]\tlr: 2.000e-02, eta: 0:08:55, time: 0.564, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0188, loss_cls: 0.1789, acc: 92.8516, loss_bbox: 0.1490, loss: 0.3740\n",
            "2023-12-01 11:40:29,994 - mmdet - INFO - Epoch [2][40/108]\tlr: 2.000e-02, eta: 0:08:49, time: 0.570, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0253, loss_cls: 0.1915, acc: 91.3672, loss_bbox: 0.1627, loss: 0.3984\n",
            "2023-12-01 11:40:35,713 - mmdet - INFO - Epoch [2][50/108]\tlr: 2.000e-02, eta: 0:08:44, time: 0.572, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0309, loss_cls: 0.1798, acc: 92.0117, loss_bbox: 0.1689, loss: 0.3994\n",
            "2023-12-01 11:40:41,481 - mmdet - INFO - Epoch [2][60/108]\tlr: 2.000e-02, eta: 0:08:38, time: 0.577, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0201, loss_cls: 0.2039, acc: 91.3672, loss_bbox: 0.1567, loss: 0.4028\n",
            "2023-12-01 11:40:47,274 - mmdet - INFO - Epoch [2][70/108]\tlr: 2.000e-02, eta: 0:08:33, time: 0.579, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0253, loss_cls: 0.1831, acc: 91.9434, loss_bbox: 0.1593, loss: 0.3838\n",
            "2023-12-01 11:40:53,106 - mmdet - INFO - Epoch [2][80/108]\tlr: 2.000e-02, eta: 0:08:28, time: 0.583, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0158, loss_cls: 0.1549, acc: 93.3301, loss_bbox: 0.1230, loss: 0.3069\n",
            "2023-12-01 11:40:59,019 - mmdet - INFO - Epoch [2][90/108]\tlr: 2.000e-02, eta: 0:08:23, time: 0.591, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0232, loss_cls: 0.1567, acc: 93.4277, loss_bbox: 0.1237, loss: 0.3155\n",
            "2023-12-01 11:41:04,921 - mmdet - INFO - Epoch [2][100/108]\tlr: 2.000e-02, eta: 0:08:18, time: 0.590, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0175, loss_cls: 0.1513, acc: 93.6719, loss_bbox: 0.1339, loss: 0.3155\n",
            "2023-12-01 11:41:09,541 - mmdet - INFO - Saving checkpoint at 2 epochs\n",
            "2023-12-01 11:41:18,679 - mmdet - INFO - Epoch [3][10/108]\tlr: 2.000e-02, eta: 0:08:00, time: 0.805, data_time: 0.229, memory: 3663, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0131, loss_cls: 0.1523, acc: 93.5547, loss_bbox: 0.1389, loss: 0.3159\n",
            "2023-12-01 11:41:24,618 - mmdet - INFO - Epoch [3][20/108]\tlr: 2.000e-02, eta: 0:07:55, time: 0.594, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0183, loss_cls: 0.1867, acc: 91.8652, loss_bbox: 0.1652, loss: 0.3825\n",
            "2023-12-01 11:41:30,561 - mmdet - INFO - Epoch [3][30/108]\tlr: 2.000e-02, eta: 0:07:51, time: 0.594, data_time: 0.024, memory: 3663, loss_rpn_cls: 0.0104, loss_rpn_bbox: 0.0142, loss_cls: 0.1322, acc: 94.6191, loss_bbox: 0.1216, loss: 0.2784\n",
            "2023-12-01 11:41:36,512 - mmdet - INFO - Epoch [3][40/108]\tlr: 2.000e-02, eta: 0:07:46, time: 0.595, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0095, loss_rpn_bbox: 0.0184, loss_cls: 0.1654, acc: 93.4375, loss_bbox: 0.1557, loss: 0.3490\n",
            "2023-12-01 11:41:42,463 - mmdet - INFO - Epoch [3][50/108]\tlr: 2.000e-02, eta: 0:07:41, time: 0.595, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0099, loss_rpn_bbox: 0.0188, loss_cls: 0.1384, acc: 93.9355, loss_bbox: 0.1360, loss: 0.3031\n",
            "2023-12-01 11:41:48,431 - mmdet - INFO - Epoch [3][60/108]\tlr: 2.000e-02, eta: 0:07:36, time: 0.597, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0182, loss_cls: 0.1370, acc: 94.3066, loss_bbox: 0.1500, loss: 0.3169\n",
            "2023-12-01 11:41:54,439 - mmdet - INFO - Epoch [3][70/108]\tlr: 2.000e-02, eta: 0:07:32, time: 0.601, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0113, loss_cls: 0.1386, acc: 94.0918, loss_bbox: 0.1353, loss: 0.2928\n",
            "2023-12-01 11:42:00,479 - mmdet - INFO - Epoch [3][80/108]\tlr: 2.000e-02, eta: 0:07:27, time: 0.604, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0103, loss_cls: 0.1351, acc: 94.1699, loss_bbox: 0.1314, loss: 0.2850\n",
            "2023-12-01 11:42:06,503 - mmdet - INFO - Epoch [3][90/108]\tlr: 2.000e-02, eta: 0:07:22, time: 0.602, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0099, loss_cls: 0.1271, acc: 94.4629, loss_bbox: 0.1061, loss: 0.2495\n",
            "2023-12-01 11:42:12,563 - mmdet - INFO - Epoch [3][100/108]\tlr: 2.000e-02, eta: 0:07:17, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0067, loss_rpn_bbox: 0.0117, loss_cls: 0.1289, acc: 94.6875, loss_bbox: 0.1117, loss: 0.2590\n",
            "2023-12-01 11:42:17,279 - mmdet - INFO - Saving checkpoint at 3 epochs\n",
            "2023-12-01 11:42:26,560 - mmdet - INFO - Epoch [4][10/108]\tlr: 2.000e-03, eta: 0:07:02, time: 0.817, data_time: 0.230, memory: 3663, loss_rpn_cls: 0.0072, loss_rpn_bbox: 0.0102, loss_cls: 0.1113, acc: 95.5273, loss_bbox: 0.1045, loss: 0.2331\n",
            "2023-12-01 11:42:32,639 - mmdet - INFO - Epoch [4][20/108]\tlr: 2.000e-03, eta: 0:06:57, time: 0.608, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0082, loss_cls: 0.1060, acc: 95.7129, loss_bbox: 0.0980, loss: 0.2185\n",
            "2023-12-01 11:42:38,704 - mmdet - INFO - Epoch [4][30/108]\tlr: 2.000e-03, eta: 0:06:52, time: 0.607, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0075, loss_cls: 0.1093, acc: 95.5469, loss_bbox: 0.0989, loss: 0.2221\n",
            "2023-12-01 11:42:44,760 - mmdet - INFO - Epoch [4][40/108]\tlr: 2.000e-03, eta: 0:06:47, time: 0.605, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0058, loss_rpn_bbox: 0.0081, loss_cls: 0.1136, acc: 95.1270, loss_bbox: 0.1029, loss: 0.2304\n",
            "2023-12-01 11:42:50,817 - mmdet - INFO - Epoch [4][50/108]\tlr: 2.000e-03, eta: 0:06:42, time: 0.606, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0067, loss_rpn_bbox: 0.0068, loss_cls: 0.1111, acc: 95.2148, loss_bbox: 0.0928, loss: 0.2175\n",
            "2023-12-01 11:42:56,858 - mmdet - INFO - Epoch [4][60/108]\tlr: 2.000e-03, eta: 0:06:37, time: 0.604, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0060, loss_rpn_bbox: 0.0082, loss_cls: 0.1147, acc: 95.1758, loss_bbox: 0.1019, loss: 0.2308\n",
            "2023-12-01 11:43:02,916 - mmdet - INFO - Epoch [4][70/108]\tlr: 2.000e-03, eta: 0:06:32, time: 0.606, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0058, loss_cls: 0.1147, acc: 95.0879, loss_bbox: 0.0954, loss: 0.2223\n",
            "2023-12-01 11:43:08,945 - mmdet - INFO - Epoch [4][80/108]\tlr: 2.000e-03, eta: 0:06:27, time: 0.603, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0065, loss_cls: 0.1132, acc: 95.1758, loss_bbox: 0.0928, loss: 0.2191\n",
            "2023-12-01 11:43:14,977 - mmdet - INFO - Epoch [4][90/108]\tlr: 2.000e-03, eta: 0:06:22, time: 0.603, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0067, loss_cls: 0.1187, acc: 94.9805, loss_bbox: 0.0966, loss: 0.2290\n",
            "2023-12-01 11:43:21,037 - mmdet - INFO - Epoch [4][100/108]\tlr: 2.000e-03, eta: 0:06:16, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0059, loss_rpn_bbox: 0.0063, loss_cls: 0.0963, acc: 95.9082, loss_bbox: 0.0902, loss: 0.1987\n",
            "2023-12-01 11:43:25,739 - mmdet - INFO - Saving checkpoint at 4 epochs\n",
            "2023-12-01 11:43:34,937 - mmdet - INFO - Epoch [5][10/108]\tlr: 2.000e-03, eta: 0:06:03, time: 0.809, data_time: 0.229, memory: 3663, loss_rpn_cls: 0.0062, loss_rpn_bbox: 0.0066, loss_cls: 0.0978, acc: 95.7520, loss_bbox: 0.0906, loss: 0.2013\n",
            "2023-12-01 11:43:40,957 - mmdet - INFO - Epoch [5][20/108]\tlr: 2.000e-03, eta: 0:05:58, time: 0.602, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0050, loss_rpn_bbox: 0.0065, loss_cls: 0.1071, acc: 95.5566, loss_bbox: 0.0957, loss: 0.2142\n",
            "2023-12-01 11:43:47,013 - mmdet - INFO - Epoch [5][30/108]\tlr: 2.000e-03, eta: 0:05:52, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0052, loss_rpn_bbox: 0.0058, loss_cls: 0.1031, acc: 95.6836, loss_bbox: 0.0845, loss: 0.1986\n",
            "2023-12-01 11:43:53,075 - mmdet - INFO - Epoch [5][40/108]\tlr: 2.000e-03, eta: 0:05:47, time: 0.606, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0051, loss_rpn_bbox: 0.0064, loss_cls: 0.1173, acc: 95.1172, loss_bbox: 0.0999, loss: 0.2287\n",
            "2023-12-01 11:43:59,172 - mmdet - INFO - Epoch [5][50/108]\tlr: 2.000e-03, eta: 0:05:42, time: 0.610, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0068, loss_cls: 0.1037, acc: 95.6445, loss_bbox: 0.0880, loss: 0.2048\n",
            "2023-12-01 11:44:05,266 - mmdet - INFO - Epoch [5][60/108]\tlr: 2.000e-03, eta: 0:05:37, time: 0.609, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0043, loss_rpn_bbox: 0.0063, loss_cls: 0.1038, acc: 95.4980, loss_bbox: 0.0819, loss: 0.1964\n",
            "2023-12-01 11:44:11,349 - mmdet - INFO - Epoch [5][70/108]\tlr: 2.000e-03, eta: 0:05:31, time: 0.608, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0084, loss_rpn_bbox: 0.0078, loss_cls: 0.1108, acc: 95.6055, loss_bbox: 0.1050, loss: 0.2319\n",
            "2023-12-01 11:44:17,419 - mmdet - INFO - Epoch [5][80/108]\tlr: 2.000e-03, eta: 0:05:26, time: 0.607, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0050, loss_rpn_bbox: 0.0073, loss_cls: 0.1096, acc: 95.3223, loss_bbox: 0.0954, loss: 0.2173\n",
            "2023-12-01 11:44:23,472 - mmdet - INFO - Epoch [5][90/108]\tlr: 2.000e-03, eta: 0:05:20, time: 0.605, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0060, loss_cls: 0.1014, acc: 95.4785, loss_bbox: 0.0858, loss: 0.1999\n",
            "2023-12-01 11:44:29,535 - mmdet - INFO - Epoch [5][100/108]\tlr: 2.000e-03, eta: 0:05:15, time: 0.606, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0067, loss_rpn_bbox: 0.0064, loss_cls: 0.1168, acc: 94.9805, loss_bbox: 0.0978, loss: 0.2276\n",
            "2023-12-01 11:44:34,285 - mmdet - INFO - Saving checkpoint at 5 epochs\n",
            "2023-12-01 11:44:43,499 - mmdet - INFO - Epoch [6][10/108]\tlr: 2.000e-03, eta: 0:05:02, time: 0.811, data_time: 0.227, memory: 3663, loss_rpn_cls: 0.0056, loss_rpn_bbox: 0.0063, loss_cls: 0.1121, acc: 95.3027, loss_bbox: 0.0948, loss: 0.2189\n",
            "2023-12-01 11:44:49,565 - mmdet - INFO - Epoch [6][20/108]\tlr: 2.000e-03, eta: 0:04:57, time: 0.606, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0055, loss_rpn_bbox: 0.0069, loss_cls: 0.0947, acc: 95.9766, loss_bbox: 0.0873, loss: 0.1943\n",
            "2023-12-01 11:44:55,603 - mmdet - INFO - Epoch [6][30/108]\tlr: 2.000e-03, eta: 0:04:52, time: 0.604, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0048, loss_rpn_bbox: 0.0065, loss_cls: 0.1071, acc: 95.5762, loss_bbox: 0.0989, loss: 0.2174\n",
            "2023-12-01 11:45:01,659 - mmdet - INFO - Epoch [6][40/108]\tlr: 2.000e-03, eta: 0:04:46, time: 0.606, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0043, loss_rpn_bbox: 0.0064, loss_cls: 0.0968, acc: 95.8594, loss_bbox: 0.0762, loss: 0.1837\n",
            "2023-12-01 11:45:07,754 - mmdet - INFO - Epoch [6][50/108]\tlr: 2.000e-03, eta: 0:04:41, time: 0.609, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0059, loss_rpn_bbox: 0.0058, loss_cls: 0.1210, acc: 94.6094, loss_bbox: 0.1063, loss: 0.2390\n",
            "2023-12-01 11:45:13,846 - mmdet - INFO - Epoch [6][60/108]\tlr: 2.000e-03, eta: 0:04:35, time: 0.609, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0083, loss_rpn_bbox: 0.0057, loss_cls: 0.0991, acc: 95.7812, loss_bbox: 0.0945, loss: 0.2076\n",
            "2023-12-01 11:45:19,938 - mmdet - INFO - Epoch [6][70/108]\tlr: 2.000e-03, eta: 0:04:30, time: 0.609, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0040, loss_rpn_bbox: 0.0075, loss_cls: 0.1029, acc: 95.7715, loss_bbox: 0.0895, loss: 0.2039\n",
            "2023-12-01 11:45:26,009 - mmdet - INFO - Epoch [6][80/108]\tlr: 2.000e-03, eta: 0:04:24, time: 0.607, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0042, loss_rpn_bbox: 0.0072, loss_cls: 0.1010, acc: 95.9863, loss_bbox: 0.0903, loss: 0.2026\n",
            "2023-12-01 11:45:32,058 - mmdet - INFO - Epoch [6][90/108]\tlr: 2.000e-03, eta: 0:04:19, time: 0.605, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0042, loss_rpn_bbox: 0.0054, loss_cls: 0.1019, acc: 95.5078, loss_bbox: 0.0892, loss: 0.2006\n",
            "2023-12-01 11:45:38,127 - mmdet - INFO - Epoch [6][100/108]\tlr: 2.000e-03, eta: 0:04:13, time: 0.607, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0061, loss_cls: 0.1038, acc: 95.8789, loss_bbox: 0.0954, loss: 0.2119\n",
            "2023-12-01 11:45:42,855 - mmdet - INFO - Saving checkpoint at 6 epochs\n",
            "2023-12-01 11:45:52,062 - mmdet - INFO - Epoch [7][10/108]\tlr: 2.000e-03, eta: 0:04:01, time: 0.811, data_time: 0.230, memory: 3663, loss_rpn_cls: 0.0061, loss_rpn_bbox: 0.0060, loss_cls: 0.1013, acc: 95.5469, loss_bbox: 0.0899, loss: 0.2033\n",
            "2023-12-01 11:45:58,085 - mmdet - INFO - Epoch [7][20/108]\tlr: 2.000e-03, eta: 0:03:56, time: 0.602, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0052, loss_rpn_bbox: 0.0063, loss_cls: 0.1105, acc: 95.3027, loss_bbox: 0.1017, loss: 0.2237\n",
            "2023-12-01 11:46:04,107 - mmdet - INFO - Epoch [7][30/108]\tlr: 2.000e-03, eta: 0:03:50, time: 0.602, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0045, loss_rpn_bbox: 0.0055, loss_cls: 0.0964, acc: 96.0254, loss_bbox: 0.0785, loss: 0.1848\n",
            "2023-12-01 11:46:10,161 - mmdet - INFO - Epoch [7][40/108]\tlr: 2.000e-03, eta: 0:03:45, time: 0.605, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0046, loss_rpn_bbox: 0.0054, loss_cls: 0.1021, acc: 95.7031, loss_bbox: 0.0811, loss: 0.1932\n",
            "2023-12-01 11:46:16,238 - mmdet - INFO - Epoch [7][50/108]\tlr: 2.000e-03, eta: 0:03:39, time: 0.608, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0045, loss_rpn_bbox: 0.0063, loss_cls: 0.1086, acc: 95.5371, loss_bbox: 0.0900, loss: 0.2094\n",
            "2023-12-01 11:46:22,305 - mmdet - INFO - Epoch [7][60/108]\tlr: 2.000e-03, eta: 0:03:34, time: 0.607, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0055, loss_rpn_bbox: 0.0069, loss_cls: 0.0937, acc: 96.2500, loss_bbox: 0.0851, loss: 0.1913\n",
            "2023-12-01 11:46:28,383 - mmdet - INFO - Epoch [7][70/108]\tlr: 2.000e-03, eta: 0:03:28, time: 0.608, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0033, loss_rpn_bbox: 0.0060, loss_cls: 0.1125, acc: 95.0293, loss_bbox: 0.0919, loss: 0.2136\n",
            "2023-12-01 11:46:34,481 - mmdet - INFO - Epoch [7][80/108]\tlr: 2.000e-03, eta: 0:03:22, time: 0.609, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0065, loss_rpn_bbox: 0.0071, loss_cls: 0.1088, acc: 95.4688, loss_bbox: 0.0947, loss: 0.2170\n",
            "2023-12-01 11:46:40,549 - mmdet - INFO - Epoch [7][90/108]\tlr: 2.000e-03, eta: 0:03:17, time: 0.607, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0051, loss_rpn_bbox: 0.0069, loss_cls: 0.0990, acc: 95.7910, loss_bbox: 0.0898, loss: 0.2009\n",
            "2023-12-01 11:46:46,611 - mmdet - INFO - Epoch [7][100/108]\tlr: 2.000e-03, eta: 0:03:11, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0045, loss_rpn_bbox: 0.0074, loss_cls: 0.1090, acc: 95.5078, loss_bbox: 0.0979, loss: 0.2188\n",
            "2023-12-01 11:46:51,313 - mmdet - INFO - Saving checkpoint at 7 epochs\n",
            "2023-12-01 11:47:00,507 - mmdet - INFO - Epoch [8][10/108]\tlr: 2.000e-03, eta: 0:03:00, time: 0.810, data_time: 0.228, memory: 3663, loss_rpn_cls: 0.0041, loss_rpn_bbox: 0.0084, loss_cls: 0.0964, acc: 96.1426, loss_bbox: 0.0922, loss: 0.2010\n",
            "2023-12-01 11:47:06,542 - mmdet - INFO - Epoch [8][20/108]\tlr: 2.000e-03, eta: 0:02:54, time: 0.603, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0067, loss_rpn_bbox: 0.0053, loss_cls: 0.1134, acc: 95.1074, loss_bbox: 0.0910, loss: 0.2164\n",
            "2023-12-01 11:47:12,591 - mmdet - INFO - Epoch [8][30/108]\tlr: 2.000e-03, eta: 0:02:49, time: 0.605, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0050, loss_rpn_bbox: 0.0070, loss_cls: 0.1047, acc: 95.3223, loss_bbox: 0.0935, loss: 0.2101\n",
            "2023-12-01 11:47:18,662 - mmdet - INFO - Epoch [8][40/108]\tlr: 2.000e-03, eta: 0:02:43, time: 0.607, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0039, loss_rpn_bbox: 0.0052, loss_cls: 0.0926, acc: 96.1426, loss_bbox: 0.0824, loss: 0.1842\n",
            "2023-12-01 11:47:24,750 - mmdet - INFO - Epoch [8][50/108]\tlr: 2.000e-03, eta: 0:02:37, time: 0.609, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0050, loss_rpn_bbox: 0.0069, loss_cls: 0.1059, acc: 95.6445, loss_bbox: 0.0932, loss: 0.2110\n",
            "2023-12-01 11:47:30,834 - mmdet - INFO - Epoch [8][60/108]\tlr: 2.000e-03, eta: 0:02:32, time: 0.608, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0041, loss_rpn_bbox: 0.0067, loss_cls: 0.0989, acc: 95.9961, loss_bbox: 0.0908, loss: 0.2005\n",
            "2023-12-01 11:47:36,928 - mmdet - INFO - Epoch [8][70/108]\tlr: 2.000e-03, eta: 0:02:26, time: 0.609, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0051, loss_rpn_bbox: 0.0061, loss_cls: 0.0954, acc: 95.8105, loss_bbox: 0.0887, loss: 0.1954\n",
            "2023-12-01 11:47:43,011 - mmdet - INFO - Epoch [8][80/108]\tlr: 2.000e-03, eta: 0:02:20, time: 0.608, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0044, loss_rpn_bbox: 0.0054, loss_cls: 0.0919, acc: 95.9668, loss_bbox: 0.0743, loss: 0.1760\n",
            "2023-12-01 11:47:49,070 - mmdet - INFO - Epoch [8][90/108]\tlr: 2.000e-03, eta: 0:02:15, time: 0.606, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0048, loss_cls: 0.1113, acc: 95.2344, loss_bbox: 0.0913, loss: 0.2107\n",
            "2023-12-01 11:47:55,136 - mmdet - INFO - Epoch [8][100/108]\tlr: 2.000e-03, eta: 0:02:09, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0053, loss_rpn_bbox: 0.0073, loss_cls: 0.1079, acc: 95.3320, loss_bbox: 0.0922, loss: 0.2126\n",
            "2023-12-01 11:47:59,901 - mmdet - INFO - Saving checkpoint at 8 epochs\n",
            "2023-12-01 11:48:09,135 - mmdet - INFO - Epoch [9][10/108]\tlr: 2.000e-03, eta: 0:01:58, time: 0.815, data_time: 0.229, memory: 3663, loss_rpn_cls: 0.0058, loss_rpn_bbox: 0.0065, loss_cls: 0.1035, acc: 95.7324, loss_bbox: 0.0955, loss: 0.2113\n",
            "2023-12-01 11:48:15,181 - mmdet - INFO - Epoch [9][20/108]\tlr: 2.000e-03, eta: 0:01:52, time: 0.605, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0048, loss_rpn_bbox: 0.0069, loss_cls: 0.0886, acc: 96.3379, loss_bbox: 0.0801, loss: 0.1804\n",
            "2023-12-01 11:48:21,233 - mmdet - INFO - Epoch [9][30/108]\tlr: 2.000e-03, eta: 0:01:47, time: 0.605, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0054, loss_cls: 0.1086, acc: 95.2734, loss_bbox: 0.0876, loss: 0.2050\n",
            "2023-12-01 11:48:27,287 - mmdet - INFO - Epoch [9][40/108]\tlr: 2.000e-03, eta: 0:01:41, time: 0.605, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0054, loss_rpn_bbox: 0.0070, loss_cls: 0.0981, acc: 95.8594, loss_bbox: 0.0872, loss: 0.1976\n",
            "2023-12-01 11:48:33,364 - mmdet - INFO - Epoch [9][50/108]\tlr: 2.000e-03, eta: 0:01:35, time: 0.608, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0042, loss_rpn_bbox: 0.0068, loss_cls: 0.0995, acc: 95.9277, loss_bbox: 0.0817, loss: 0.1923\n",
            "2023-12-01 11:48:39,424 - mmdet - INFO - Epoch [9][60/108]\tlr: 2.000e-03, eta: 0:01:29, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0045, loss_rpn_bbox: 0.0073, loss_cls: 0.1102, acc: 95.7031, loss_bbox: 0.0925, loss: 0.2146\n",
            "2023-12-01 11:48:45,498 - mmdet - INFO - Epoch [9][70/108]\tlr: 2.000e-03, eta: 0:01:24, time: 0.607, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0070, loss_rpn_bbox: 0.0057, loss_cls: 0.0945, acc: 96.2500, loss_bbox: 0.0918, loss: 0.1990\n",
            "2023-12-01 11:48:51,580 - mmdet - INFO - Epoch [9][80/108]\tlr: 2.000e-03, eta: 0:01:18, time: 0.608, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0053, loss_rpn_bbox: 0.0053, loss_cls: 0.1013, acc: 95.6543, loss_bbox: 0.0837, loss: 0.1956\n",
            "2023-12-01 11:48:57,649 - mmdet - INFO - Epoch [9][90/108]\tlr: 2.000e-03, eta: 0:01:12, time: 0.607, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0039, loss_rpn_bbox: 0.0057, loss_cls: 0.1179, acc: 95.0000, loss_bbox: 0.0890, loss: 0.2165\n",
            "2023-12-01 11:49:03,714 - mmdet - INFO - Epoch [9][100/108]\tlr: 2.000e-03, eta: 0:01:07, time: 0.607, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0048, loss_rpn_bbox: 0.0058, loss_cls: 0.1025, acc: 95.6250, loss_bbox: 0.0853, loss: 0.1985\n",
            "2023-12-01 11:49:08,453 - mmdet - INFO - Saving checkpoint at 9 epochs\n",
            "2023-12-01 11:49:17,711 - mmdet - INFO - Epoch [10][10/108]\tlr: 2.000e-03, eta: 0:00:56, time: 0.817, data_time: 0.231, memory: 3663, loss_rpn_cls: 0.0045, loss_rpn_bbox: 0.0050, loss_cls: 0.1021, acc: 95.5176, loss_bbox: 0.0860, loss: 0.1975\n",
            "2023-12-01 11:49:23,747 - mmdet - INFO - Epoch [10][20/108]\tlr: 2.000e-03, eta: 0:00:50, time: 0.604, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0063, loss_rpn_bbox: 0.0058, loss_cls: 0.1000, acc: 95.7715, loss_bbox: 0.0814, loss: 0.1935\n",
            "2023-12-01 11:49:29,840 - mmdet - INFO - Epoch [10][30/108]\tlr: 2.000e-03, eta: 0:00:44, time: 0.609, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0050, loss_rpn_bbox: 0.0061, loss_cls: 0.0939, acc: 96.0449, loss_bbox: 0.0769, loss: 0.1819\n",
            "2023-12-01 11:49:35,905 - mmdet - INFO - Epoch [10][40/108]\tlr: 2.000e-03, eta: 0:00:39, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0046, loss_rpn_bbox: 0.0055, loss_cls: 0.1061, acc: 95.4395, loss_bbox: 0.0842, loss: 0.2005\n",
            "2023-12-01 11:49:41,998 - mmdet - INFO - Epoch [10][50/108]\tlr: 2.000e-03, eta: 0:00:33, time: 0.609, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0055, loss_rpn_bbox: 0.0082, loss_cls: 0.1036, acc: 95.6641, loss_bbox: 0.0910, loss: 0.2083\n",
            "2023-12-01 11:49:48,070 - mmdet - INFO - Epoch [10][60/108]\tlr: 2.000e-03, eta: 0:00:27, time: 0.607, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0045, loss_rpn_bbox: 0.0057, loss_cls: 0.0966, acc: 95.8984, loss_bbox: 0.0806, loss: 0.1875\n",
            "2023-12-01 11:49:54,151 - mmdet - INFO - Epoch [10][70/108]\tlr: 2.000e-03, eta: 0:00:21, time: 0.608, data_time: 0.026, memory: 3663, loss_rpn_cls: 0.0025, loss_rpn_bbox: 0.0067, loss_cls: 0.0994, acc: 95.6641, loss_bbox: 0.0854, loss: 0.1940\n",
            "2023-12-01 11:50:00,219 - mmdet - INFO - Epoch [10][80/108]\tlr: 2.000e-03, eta: 0:00:16, time: 0.607, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0055, loss_rpn_bbox: 0.0072, loss_cls: 0.1018, acc: 95.9473, loss_bbox: 0.0864, loss: 0.2009\n",
            "2023-12-01 11:50:06,283 - mmdet - INFO - Epoch [10][90/108]\tlr: 2.000e-03, eta: 0:00:10, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0065, loss_rpn_bbox: 0.0061, loss_cls: 0.0992, acc: 95.8789, loss_bbox: 0.0845, loss: 0.1963\n",
            "2023-12-01 11:50:12,347 - mmdet - INFO - Epoch [10][100/108]\tlr: 2.000e-03, eta: 0:00:04, time: 0.606, data_time: 0.025, memory: 3663, loss_rpn_cls: 0.0042, loss_rpn_bbox: 0.0062, loss_cls: 0.1088, acc: 95.8203, loss_bbox: 0.0907, loss: 0.2099\n",
            "2023-12-01 11:50:17,101 - mmdet - INFO - Saving checkpoint at 10 epochs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi # 할당된 GPU 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3w4frw447INQ",
        "outputId": "ae966ee3-0cf6-4e08-871c-84e56f5a4d60"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Dec  1 11:50:21 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   73C    P0    31W /  70W |   5613MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "\n",
        "이후\n",
        "/content/mmtracking/configs/_base_/datasets/mot_challenge_reid.py 경로에서 아래 복붙하기\n",
        "\n",
        "dataset_type = 'ReIDDataset'\n",
        "img_norm_cfg = dict(\n",
        "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
        "train_pipeline = [\n",
        "    dict(type='LoadMultiImagesFromFile', to_float32=True),\n",
        "    dict(\n",
        "        type='SeqResize',\n",
        "        img_scale=(128, 256),\n",
        "        share_params=False,\n",
        "        keep_ratio=False,\n",
        "        bbox_clip_border=False,\n",
        "        override=False),\n",
        "    dict(\n",
        "        type='SeqRandomFlip',\n",
        "        share_params=False,\n",
        "        flip_ratio=0.5,\n",
        "        direction='horizontal'),\n",
        "    dict(type='SeqNormalize', **img_norm_cfg),\n",
        "    dict(type='VideoCollect', keys=['img', 'gt_label']),\n",
        "    dict(type='ReIDFormatBundle')\n",
        "]\n",
        "test_pipeline = [\n",
        "    dict(type='LoadImageFromFile'),\n",
        "    dict(type='Resize', img_scale=(128, 256), keep_ratio=False),\n",
        "    dict(type='Normalize', **img_norm_cfg),\n",
        "    dict(type='ImageToTensor', keys=['img']),\n",
        "    dict(type='Collect', keys=['img'], meta_keys=[])\n",
        "]\n",
        "data_root = 'data/MOT17_tiny/'\n",
        "data = dict(\n",
        "    samples_per_gpu=1,\n",
        "    workers_per_gpu=2,\n",
        "    train=dict(\n",
        "        type=dataset_type,\n",
        "        triplet_sampler=dict(num_ids=8, ins_per_id=4),\n",
        "        data_prefix=data_root + 'reid/imgs',\n",
        "        ann_file=data_root + 'reid/meta/train.txt',\n",
        "        pipeline=train_pipeline),\n",
        "    val=dict(\n",
        "        type=dataset_type,\n",
        "        triplet_sampler=None,\n",
        "        data_prefix=data_root + 'reid/imgs',\n",
        "        ann_file=data_root + 'reid/meta/val_90.txt',\n",
        "        pipeline=test_pipeline),\n",
        "    test=dict(\n",
        "        type=dataset_type,\n",
        "        triplet_sampler=None,\n",
        "        data_prefix=data_root + 'reid/imgs',\n",
        "        ann_file=data_root + 'reid/meta/val_90.txt',\n",
        "        pipeline=test_pipeline))\n",
        "evaluation = dict(interval=1, metric='mAP')\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "jQLRoimEvgy5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import mmcv\n",
        "from mmdet.apis import set_random_seed\n",
        "\n",
        "cfg = mmcv.Config.fromfile('./configs/reid/resnet50_b32x8_MOT17.py')\n",
        "\n",
        "# learning policy\n",
        "cfg.lr_config = dict(\n",
        "    policy='step',\n",
        "    warmup='linear',\n",
        "    warmup_iters=200,\n",
        "    warmup_ratio=1.0 / 200,\n",
        "    step=[1])\n",
        "cfg.total_epochs = 15\n",
        "\n",
        "cfg.data.train.triplet_sampler[\"num_ids\"] = 5  # (이 부분이 중요) => 한 프레임(이미지)당 나타나는 고유한 객체의 수를 의미(각 고유한 객체는 고유한 id를 가진다)\n",
        "cfg.data.train.triplet_sampler[\"ins_per_id\"] = 1 # (이 부분이 중요) => 한 id당 이미지의 개수\n",
        "\n",
        "\n",
        "cfg.work_dir = './tutorial_exps/reid'\n",
        "cfg.seed = 0\n",
        "set_random_seed(0, deterministic=False)\n",
        "cfg.gpu_ids = range(1)\n",
        "cfg.device='cuda'\n",
        "print(cfg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0hbhXS0v4cD",
        "outputId": "d05dae4a-cd21-498a-faa8-421812c2f6c3"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Config (path: ./configs/reid/resnet50_b32x8_MOT17.py): {'dataset_type': 'ReIDDataset', 'img_norm_cfg': {'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, 'train_pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqResize', 'img_scale': (128, 256), 'share_params': False, 'keep_ratio': False, 'bbox_clip_border': False, 'override': False}, {'type': 'SeqRandomFlip', 'share_params': False, 'flip_ratio': 0.5, 'direction': 'horizontal'}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_label']}, {'type': 'ReIDFormatBundle'}], 'test_pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'Resize', 'img_scale': (128, 256), 'keep_ratio': False}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img'], 'meta_keys': []}], 'data_root': 'data/MOT17_tiny/', 'data': {'samples_per_gpu': 1, 'workers_per_gpu': 2, 'train': {'type': 'ReIDDataset', 'triplet_sampler': {'num_ids': 5, 'ins_per_id': 1}, 'data_prefix': 'data/MOT17_tiny/reid/imgs', 'ann_file': 'data/MOT17_tiny/reid/meta/train.txt', 'pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqResize', 'img_scale': (128, 256), 'share_params': False, 'keep_ratio': False, 'bbox_clip_border': False, 'override': False}, {'type': 'SeqRandomFlip', 'share_params': False, 'flip_ratio': 0.5, 'direction': 'horizontal'}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_label']}, {'type': 'ReIDFormatBundle'}]}, 'val': {'type': 'ReIDDataset', 'triplet_sampler': None, 'data_prefix': 'data/MOT17_tiny/reid/imgs', 'ann_file': 'data/MOT17_tiny/reid/meta/val_90.txt', 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'Resize', 'img_scale': (128, 256), 'keep_ratio': False}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img'], 'meta_keys': []}]}, 'test': {'type': 'ReIDDataset', 'triplet_sampler': None, 'data_prefix': 'data/MOT17_tiny/reid/imgs', 'ann_file': 'data/MOT17_tiny/reid/meta/val_90.txt', 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'Resize', 'img_scale': (128, 256), 'keep_ratio': False}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img'], 'meta_keys': []}]}}, 'evaluation': {'interval': 1, 'metric': 'mAP'}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'optimizer_config': {'grad_clip': None}, 'checkpoint_config': {'interval': 1}, 'log_config': {'interval': 50, 'hooks': [{'type': 'TextLoggerHook'}]}, 'dist_params': {'backend': 'nccl'}, 'log_level': 'INFO', 'load_from': None, 'resume_from': None, 'workflow': [('train', 1)], 'opencv_num_threads': 0, 'mp_start_method': 'fork', 'TRAIN_REID': True, 'model': {'reid': {'type': 'BaseReID', 'backbone': {'type': 'ResNet', 'depth': 50, 'num_stages': 4, 'out_indices': (3,), 'style': 'pytorch'}, 'neck': {'type': 'GlobalAveragePooling', 'kernel_size': (8, 4), 'stride': 1}, 'head': {'type': 'LinearReIDHead', 'num_fcs': 1, 'in_channels': 2048, 'fc_channels': 1024, 'out_channels': 128, 'num_classes': 380, 'loss': {'type': 'CrossEntropyLoss', 'loss_weight': 1.0}, 'loss_pairwise': {'type': 'TripletLoss', 'margin': 0.3, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN1d'}, 'act_cfg': {'type': 'ReLU'}}, 'init_cfg': {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'}}}, 'lr_config': {'policy': 'step', 'warmup': 'linear', 'warmup_iters': 200, 'warmup_ratio': 0.005, 'step': [1]}, 'total_epochs': 15, 'work_dir': './tutorial_exps/reid', 'seed': 0, 'gpu_ids': range(0, 1), 'device': 'cuda'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mmtrack.datasets import build_dataset\n",
        "from mmdet.apis import train_detector as train_model\n",
        "from mmtrack.models import build_reid as build_model\n",
        "\n",
        "model = build_model(cfg.model.reid)\n",
        "model.init_weights()\n",
        "datasets = [build_dataset(cfg.data.train)]\n",
        "datasets[0].CLASSES = (\"pedestrian\", )\n",
        "model.CLASSES = datasets[0].CLASSES"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XR_yp5lwwxh",
        "outputId": "ee738354-7413-4dd2-bd9f-2b39bee18100"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-01 11:56:17,287 - mmcv - INFO - initialize BaseReID with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'}\n",
            "2023-12-01 11:56:17,289 - mmcv - INFO - load model from: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth\n",
            "2023-12-01 11:56:17,290 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth\n",
            "2023-12-01 11:56:17,380 - mmcv - WARNING - The model and loaded state dict do not match exactly\n",
            "\n",
            "unexpected key in source state_dict: head.fc.weight, head.fc.bias\n",
            "\n",
            "missing keys in source state_dict: head.fcs.0.fc.weight, head.fcs.0.fc.bias, head.fcs.0.bn.weight, head.fcs.0.bn.bias, head.fcs.0.bn.running_mean, head.fcs.0.bn.running_var, head.fc_out.weight, head.fc_out.bias, head.bn.weight, head.bn.bias, head.bn.running_mean, head.bn.running_var, head.classifier.weight, head.classifier.bias\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2orwMy6YpLJ",
        "outputId": "a7fe3c3f-bc31-41b7-950e-35de23b55eb3"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<mmtrack.datasets.reid_dataset.ReIDDataset at 0x79429834a4a0>]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasets[0][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_3Zxv3119yJ",
        "outputId": "4517edd6-ad13-43d3-ff6d-e4c071b6779b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'img': DataContainer(tensor([[[[-0.2342, -0.2342, -0.2342,  ..., -0.2342, -0.2342, -0.2342],\n",
              "           [-0.2342, -0.2342, -0.2342,  ..., -0.2342, -0.2342, -0.2342],\n",
              "           [-0.2342, -0.2342, -0.2342,  ..., -0.2319, -0.2319, -0.2319],\n",
              "           ...,\n",
              "           [-0.1999, -0.1999, -0.1999,  ..., -0.2168, -0.2204, -0.2319],\n",
              "           [-0.1999, -0.1999, -0.1999,  ..., -0.2171, -0.2227, -0.2342],\n",
              "           [-0.1999, -0.1999, -0.1999,  ..., -0.2171, -0.2227, -0.2342]],\n",
              " \n",
              "          [[ 1.0105,  1.0105,  1.0105,  ...,  1.0105,  1.0105,  1.0105],\n",
              "           [ 1.0105,  1.0105,  1.0105,  ...,  1.0105,  1.0105,  1.0105],\n",
              "           [ 1.0105,  1.0105,  1.0105,  ...,  1.0129,  1.0129,  1.0129],\n",
              "           ...,\n",
              "           [ 1.0455,  1.0455,  1.0455,  ...,  1.0282,  1.0246,  1.0129],\n",
              "           [ 1.0455,  1.0455,  1.0455,  ...,  1.0280,  1.0223,  1.0105],\n",
              "           [ 1.0455,  1.0455,  1.0455,  ...,  1.0280,  1.0223,  1.0105]],\n",
              " \n",
              "          [[ 1.7163,  1.7163,  1.7163,  ...,  1.7163,  1.7163,  1.7163],\n",
              "           [ 1.7163,  1.7163,  1.7163,  ...,  1.7163,  1.7163,  1.7163],\n",
              "           [ 1.7163,  1.7163,  1.7163,  ...,  1.7186,  1.7186,  1.7186],\n",
              "           ...,\n",
              "           [ 1.7337,  1.7337,  1.7337,  ...,  1.7339,  1.7303,  1.7186],\n",
              "           [ 1.7337,  1.7337,  1.7337,  ...,  1.7337,  1.7280,  1.7163],\n",
              "           [ 1.7337,  1.7337,  1.7337,  ...,  1.7337,  1.7280,  1.7163]]],\n",
              " \n",
              " \n",
              "         [[[-0.3883, -0.3883, -0.3883,  ..., -0.3883, -0.3883, -0.3883],\n",
              "           [-0.3883, -0.3883, -0.3883,  ..., -0.3883, -0.3883, -0.3883],\n",
              "           [-0.3883, -0.3883, -0.3883,  ..., -0.3883, -0.3883, -0.3883],\n",
              "           ...,\n",
              "           [-0.3712, -0.3712, -0.3712,  ..., -0.3883, -0.3883, -0.3883],\n",
              "           [-0.3712, -0.3712, -0.3712,  ..., -0.3883, -0.3883, -0.3883],\n",
              "           [-0.3712, -0.3712, -0.3712,  ..., -0.3883, -0.3883, -0.3883]],\n",
              " \n",
              "          [[ 0.8354,  0.8354,  0.8354,  ...,  0.8354,  0.8354,  0.8354],\n",
              "           [ 0.8354,  0.8354,  0.8354,  ...,  0.8354,  0.8354,  0.8354],\n",
              "           [ 0.8354,  0.8354,  0.8354,  ...,  0.8354,  0.8354,  0.8354],\n",
              "           ...,\n",
              "           [ 0.8529,  0.8529,  0.8529,  ...,  0.8354,  0.8354,  0.8354],\n",
              "           [ 0.8529,  0.8529,  0.8529,  ...,  0.8354,  0.8354,  0.8354],\n",
              "           [ 0.8529,  0.8529,  0.8529,  ...,  0.8354,  0.8354,  0.8354]],\n",
              " \n",
              "          [[ 1.6291,  1.6291,  1.6291,  ...,  1.5942,  1.5942,  1.5942],\n",
              "           [ 1.6291,  1.6291,  1.6291,  ...,  1.5942,  1.5942,  1.5942],\n",
              "           [ 1.6291,  1.6291,  1.6291,  ...,  1.5942,  1.5942,  1.5942],\n",
              "           ...,\n",
              "           [ 1.6117,  1.6117,  1.6117,  ...,  1.5942,  1.5942,  1.5942],\n",
              "           [ 1.6117,  1.6117,  1.6117,  ...,  1.5942,  1.5942,  1.5942],\n",
              "           [ 1.6117,  1.6117,  1.6117,  ...,  1.5942,  1.5942,  1.5942]]],\n",
              " \n",
              " \n",
              "         [[[-0.3369, -0.3436, -0.3541,  ..., -0.3712, -0.3712, -0.3712],\n",
              "           [-0.3369, -0.3436, -0.3541,  ..., -0.3712, -0.3712, -0.3712],\n",
              "           [-0.3392, -0.3449, -0.3541,  ..., -0.3712, -0.3712, -0.3712],\n",
              "           ...,\n",
              "           [-0.3692, -0.3720, -0.3749,  ..., -0.4226, -0.4226, -0.4226],\n",
              "           [-0.3712, -0.3712, -0.3712,  ..., -0.4226, -0.4226, -0.4226],\n",
              "           [-0.3712, -0.3712, -0.3712,  ..., -0.4226, -0.4226, -0.4226]],\n",
              " \n",
              "          [[ 0.8179,  0.8179,  0.8179,  ...,  0.8179,  0.8179,  0.8179],\n",
              "           [ 0.8179,  0.8179,  0.8179,  ...,  0.8179,  0.8179,  0.8179],\n",
              "           [ 0.8179,  0.8179,  0.8179,  ...,  0.8179,  0.8179,  0.8179],\n",
              "           ...,\n",
              "           [ 0.8024,  0.7995,  0.7967,  ...,  0.7479,  0.7479,  0.7479],\n",
              "           [ 0.8004,  0.8004,  0.8004,  ...,  0.7479,  0.7479,  0.7479],\n",
              "           [ 0.8004,  0.8004,  0.8004,  ...,  0.7479,  0.7479,  0.7479]],\n",
              " \n",
              "          [[ 1.6291,  1.6291,  1.6291,  ...,  1.5644,  1.5420,  1.5723],\n",
              "           [ 1.6291,  1.6291,  1.6291,  ...,  1.5644,  1.5420,  1.5723],\n",
              "           [ 1.6291,  1.6291,  1.6291,  ...,  1.5660,  1.5465,  1.5729],\n",
              "           ...,\n",
              "           [ 1.6137,  1.6108,  1.6079,  ...,  1.5594,  1.5594,  1.5594],\n",
              "           [ 1.6117,  1.6117,  1.6117,  ...,  1.5594,  1.5594,  1.5594],\n",
              "           [ 1.6117,  1.6117,  1.6117,  ...,  1.5594,  1.5594,  1.5594]]],\n",
              " \n",
              " \n",
              "         [[[-0.2513, -0.2680, -0.2684,  ..., -0.3369, -0.3369, -0.3369],\n",
              "           [-0.2513, -0.2680, -0.2684,  ..., -0.3369, -0.3369, -0.3369],\n",
              "           [-0.2447, -0.2679, -0.2652,  ..., -0.3369, -0.3369, -0.3369],\n",
              "           ...,\n",
              "           [-0.2171, -0.2171, -0.2171,  ..., -0.2856, -0.2856, -0.2856],\n",
              "           [-0.2171, -0.2171, -0.2171,  ..., -0.2856, -0.2856, -0.2856],\n",
              "           [-0.2171, -0.2171, -0.2171,  ..., -0.2856, -0.2856, -0.2856]],\n",
              " \n",
              "          [[ 0.9755,  0.9584,  0.9580,  ...,  0.8880,  0.8880,  0.8880],\n",
              "           [ 0.9755,  0.9584,  0.9580,  ...,  0.8880,  0.8880,  0.8880],\n",
              "           [ 0.9755,  0.9584,  0.9515,  ...,  0.8880,  0.8880,  0.8880],\n",
              "           ...,\n",
              "           [ 0.9755,  0.9755,  0.9755,  ...,  0.9405,  0.9405,  0.9405],\n",
              "           [ 0.9755,  0.9755,  0.9755,  ...,  0.9405,  0.9405,  0.9405],\n",
              "           [ 0.9755,  0.9755,  0.9755,  ...,  0.9405,  0.9405,  0.9405]],\n",
              " \n",
              "          [[ 1.7337,  1.7167,  1.7497,  ...,  1.6465,  1.6465,  1.6465],\n",
              "           [ 1.7337,  1.7167,  1.7497,  ...,  1.6465,  1.6465,  1.6465],\n",
              "           [ 1.7337,  1.7167,  1.7465,  ...,  1.6465,  1.6465,  1.6465],\n",
              "           ...,\n",
              "           [ 1.6988,  1.6988,  1.6988,  ...,  1.6988,  1.6988,  1.6988],\n",
              "           [ 1.6988,  1.6988,  1.6988,  ...,  1.6988,  1.6988,  1.6988],\n",
              "           [ 1.6988,  1.6988,  1.6988,  ...,  1.6988,  1.6988,  1.6988]]],\n",
              " \n",
              " \n",
              "         [[[-0.4568, -0.4568, -0.4568,  ..., -0.4226, -0.4226, -0.4226],\n",
              "           [-0.4568, -0.4568, -0.4568,  ..., -0.4226, -0.4226, -0.4226],\n",
              "           [-0.4568, -0.4568, -0.4568,  ..., -0.4226, -0.4226, -0.4226],\n",
              "           ...,\n",
              "           [-0.4911, -0.4911, -0.4911,  ..., -0.4739, -0.4739, -0.4739],\n",
              "           [-0.4911, -0.4911, -0.4911,  ..., -0.4739, -0.4739, -0.4739],\n",
              "           [-0.4911, -0.4911, -0.4911,  ..., -0.4739, -0.4739, -0.4739]],\n",
              " \n",
              "          [[ 0.7129,  0.7129,  0.7129,  ...,  0.7479,  0.7479,  0.7479],\n",
              "           [ 0.7129,  0.7129,  0.7129,  ...,  0.7479,  0.7479,  0.7479],\n",
              "           [ 0.7129,  0.7129,  0.7129,  ...,  0.7479,  0.7479,  0.7479],\n",
              "           ...,\n",
              "           [ 0.7304,  0.7304,  0.7304,  ...,  0.7479,  0.7479,  0.7479],\n",
              "           [ 0.7304,  0.7304,  0.7304,  ...,  0.7479,  0.7479,  0.7479],\n",
              "           [ 0.7304,  0.7304,  0.7304,  ...,  0.7479,  0.7479,  0.7479]],\n",
              " \n",
              "          [[ 1.5245,  1.5245,  1.5245,  ...,  1.5594,  1.5594,  1.5594],\n",
              "           [ 1.5245,  1.5245,  1.5245,  ...,  1.5594,  1.5594,  1.5594],\n",
              "           [ 1.5245,  1.5245,  1.5245,  ...,  1.5594,  1.5594,  1.5594],\n",
              "           ...,\n",
              "           [ 1.4897,  1.4897,  1.4897,  ...,  1.5071,  1.5071,  1.5071],\n",
              "           [ 1.4897,  1.4897,  1.4897,  ...,  1.5071,  1.5071,  1.5071],\n",
              "           [ 1.4897,  1.4897,  1.4897,  ...,  1.5071,  1.5071,  1.5071]]]])),\n",
              " 'gt_label': DataContainer(tensor([0, 3, 1, 2, 4]))}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(cfg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnmLr3BY1isO",
        "outputId": "6d91d515-3387-4ae0-81c3-23a2d3f2e9ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Config (path: ./configs/reid/resnet50_b32x8_MOT17.py): {'dataset_type': 'ReIDDataset', 'img_norm_cfg': {'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, 'train_pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqResize', 'img_scale': (128, 256), 'share_params': False, 'keep_ratio': False, 'bbox_clip_border': False, 'override': False}, {'type': 'SeqRandomFlip', 'share_params': False, 'flip_ratio': 0.5, 'direction': 'horizontal'}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_label']}, {'type': 'ReIDFormatBundle'}], 'test_pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'Resize', 'img_scale': (128, 256), 'keep_ratio': False}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img'], 'meta_keys': []}], 'data_root': 'data/MOT17_tiny/', 'data': {'samples_per_gpu': 1, 'workers_per_gpu': 2, 'train': {'type': 'ReIDDataset', 'triplet_sampler': {'num_ids': 5, 'ins_per_id': 1}, 'data_prefix': 'data/MOT17_tiny/reid/imgs', 'ann_file': 'data/MOT17_tiny/reid/meta/train.txt', 'pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqResize', 'img_scale': (128, 256), 'share_params': False, 'keep_ratio': False, 'bbox_clip_border': False, 'override': False}, {'type': 'SeqRandomFlip', 'share_params': False, 'flip_ratio': 0.5, 'direction': 'horizontal'}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_label']}, {'type': 'ReIDFormatBundle'}]}, 'val': {'type': 'ReIDDataset', 'triplet_sampler': None, 'data_prefix': 'data/MOT17_tiny/reid/imgs', 'ann_file': 'data/MOT17_tiny/reid/meta/val_90.txt', 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'Resize', 'img_scale': (128, 256), 'keep_ratio': False}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img'], 'meta_keys': []}]}, 'test': {'type': 'ReIDDataset', 'triplet_sampler': None, 'data_prefix': 'data/MOT17_tiny/reid/imgs', 'ann_file': 'data/MOT17_tiny/reid/meta/val_90.txt', 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'Resize', 'img_scale': (128, 256), 'keep_ratio': False}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'Collect', 'keys': ['img'], 'meta_keys': []}]}}, 'evaluation': {'interval': 1, 'metric': 'mAP'}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'optimizer_config': {'grad_clip': None}, 'checkpoint_config': {'interval': 1}, 'log_config': {'interval': 50, 'hooks': [{'type': 'TextLoggerHook'}]}, 'dist_params': {'backend': 'nccl'}, 'log_level': 'INFO', 'load_from': None, 'resume_from': None, 'workflow': [('train', 1)], 'opencv_num_threads': 0, 'mp_start_method': 'fork', 'TRAIN_REID': True, 'model': {'reid': {'type': 'BaseReID', 'backbone': {'type': 'ResNet', 'depth': 50, 'num_stages': 4, 'out_indices': (3,), 'style': 'pytorch'}, 'neck': {'type': 'GlobalAveragePooling', 'kernel_size': (8, 4), 'stride': 1}, 'head': {'type': 'LinearReIDHead', 'num_fcs': 1, 'in_channels': 2048, 'fc_channels': 1024, 'out_channels': 128, 'num_classes': 380, 'loss': {'type': 'CrossEntropyLoss', 'loss_weight': 1.0}, 'loss_pairwise': {'type': 'TripletLoss', 'margin': 0.3, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN1d'}, 'act_cfg': {'type': 'ReLU'}}, 'init_cfg': {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'}}}, 'lr_config': {'policy': 'step', 'warmup': 'linear', 'warmup_iters': 200, 'warmup_ratio': 0.005, 'step': [1]}, 'total_epochs': 2, 'work_dir': './tutorial_exps/reid', 'seed': 0, 'gpu_ids': range(0, 1), 'device': 'cuda'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.state_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAREqLkZpdaQ",
        "outputId": "84158e4f-9516-46c4-b2b5-57ea352e34c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Module.state_dict of BaseReID(\n",
              "  (backbone): ResNet(\n",
              "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (relu): ReLU(inplace=True)\n",
              "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "    (layer1): ResLayer(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "    )\n",
              "    (layer2): ResLayer(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (3): Bottleneck(\n",
              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "    )\n",
              "    (layer3): ResLayer(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (3): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (4): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (5): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "    )\n",
              "    (layer4): ResLayer(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (drop_path): Identity()\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n",
              "  (neck): GlobalAveragePooling(\n",
              "    (gap): AvgPool2d(kernel_size=(8, 4), stride=1, padding=0)\n",
              "  )\n",
              "  (head): LinearReIDHead(\n",
              "    (loss_cls): CrossEntropyLoss(avg_non_ignore=False)\n",
              "    (loss_triplet): TripletLoss(\n",
              "      (ranking_loss): MarginRankingLoss()\n",
              "    )\n",
              "    (accuracy): Accuracy()\n",
              "    (fcs): ModuleList(\n",
              "      (0): FcModule(\n",
              "        (fc): Linear(in_features=2048, out_features=1024, bias=True)\n",
              "        (bn): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (activate): ReLU(inplace=True)\n",
              "      )\n",
              "      init_cfg={'type': 'Kaiming', 'layer': 'Linear'}\n",
              "    )\n",
              "    (fc_out): Linear(in_features=1024, out_features=128, bias=True)\n",
              "    (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (classifier): Linear(in_features=128, out_features=1, bias=True)\n",
              "  )\n",
              "  init_cfg={'type': 'Normal', 'layer': 'Linear', 'mean': 0, 'std': 0.01, 'bias': 0}\n",
              ")\n",
              "init_cfg={'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'}>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cfg.data.train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDI7fGacw2RA",
        "outputId": "b59417d9-9081-4cef-e948-64baf31ac29a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': 'ReIDDataset',\n",
              " 'triplet_sampler': {'num_ids': 5, 'ins_per_id': 4},\n",
              " 'data_prefix': 'data/MOT17_tiny/reid/imgs',\n",
              " 'ann_file': 'data/MOT17_tiny/reid/meta/train.txt',\n",
              " 'pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True},\n",
              "  {'type': 'SeqResize',\n",
              "   'img_scale': (128, 256),\n",
              "   'share_params': False,\n",
              "   'keep_ratio': False,\n",
              "   'bbox_clip_border': False,\n",
              "   'override': False},\n",
              "  {'type': 'SeqRandomFlip',\n",
              "   'share_params': False,\n",
              "   'flip_ratio': 0.5,\n",
              "   'direction': 'horizontal'},\n",
              "  {'type': 'SeqNormalize',\n",
              "   'mean': [123.675, 116.28, 103.53],\n",
              "   'std': [58.395, 57.12, 57.375],\n",
              "   'to_rgb': True},\n",
              "  {'type': 'VideoCollect', 'keys': ['img', 'gt_label']},\n",
              "  {'type': 'ReIDFormatBundle'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(datasets[0].CLASSES)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFgApnDpw6h4",
        "outputId": "b537b99f-4864-469d-b994-1b91deb4f2a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('pedestrian',)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.CLASSES)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "whPAUyI2w60y",
        "outputId": "4897c7c9-5c97-47be-936a-afd284134a3e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('pedestrian',)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi # 할당된 GPU 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAhj7pm69490",
        "outputId": "6909fc57-33a7-4320-a4a0-751e23d4164e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Dec  1 11:51:45 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   71C    P0    31W /  70W |   5613MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "###### ReID Model 학습 ######\n",
        "'''\n",
        "# epoch:15회 => 분 소모\n",
        "\n",
        "train_model(model, datasets, cfg)\n",
        "# train_model(model, datasets, cfg, validate=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2E-TgVSlv4eq",
        "outputId": "a81db995-8535-489e-e1a0-e8ddff2343a9"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-01 11:56:57,056 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.\n",
            "2023-12-01 11:56:57,060 - mmdet - INFO - Start running, host: root@82979b0ac678, work_dir: /content/mmtracking/tutorial_exps/reid\n",
            "2023-12-01 11:56:57,061 - mmdet - INFO - Hooks will be executed in the following order:\n",
            "before_run:\n",
            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_train_epoch:\n",
            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
            "(LOW         ) IterTimerHook                      \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_train_iter:\n",
            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_train_iter:\n",
            "(ABOVE_NORMAL) OptimizerHook                      \n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(LOW         ) IterTimerHook                      \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "after_train_epoch:\n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_val_epoch:\n",
            "(LOW         ) IterTimerHook                      \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "before_val_iter:\n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_val_iter:\n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_val_epoch:\n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "after_run:\n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            " -------------------- \n",
            "2023-12-01 11:56:57,062 - mmdet - INFO - workflow: [('train', 1)], max: 15 epochs\n",
            "2023-12-01 11:56:57,063 - mmdet - INFO - Checkpoints will be saved to /content/mmtracking/tutorial_exps/reid by HardDiskBackend.\n",
            "2023-12-01 11:57:01,740 - mmdet - INFO - Epoch [1][50/988]\tlr: 2.488e-02, eta: 0:22:57, time: 0.093, data_time: 0.045, memory: 3663, triplet_loss: 0.0018, ce_loss: 5.2802, top-1: 26.4000, loss: 5.2821\n",
            "2023-12-01 11:57:04,241 - mmdet - INFO - Epoch [1][100/988]\tlr: 4.975e-02, eta: 0:17:35, time: 0.050, data_time: 0.003, memory: 3663, triplet_loss: 0.0092, ce_loss: 3.9723, top-1: 19.2000, loss: 3.9816\n",
            "2023-12-01 11:57:06,695 - mmdet - INFO - Epoch [1][150/988]\tlr: 7.463e-02, eta: 0:15:40, time: 0.049, data_time: 0.002, memory: 3663, triplet_loss: 0.0089, ce_loss: 2.2692, top-1: 20.8000, loss: 2.2780\n",
            "2023-12-01 11:57:09,197 - mmdet - INFO - Epoch [1][200/988]\tlr: 9.950e-02, eta: 0:14:45, time: 0.050, data_time: 0.003, memory: 3663, triplet_loss: 0.0053, ce_loss: 1.6904, top-1: 19.6000, loss: 1.6957\n",
            "2023-12-01 11:57:11,637 - mmdet - INFO - Epoch [1][250/988]\tlr: 1.000e-01, eta: 0:14:08, time: 0.049, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.6515, top-1: 18.8000, loss: 1.6515\n",
            "2023-12-01 11:57:14,083 - mmdet - INFO - Epoch [1][300/988]\tlr: 1.000e-01, eta: 0:13:43, time: 0.049, data_time: 0.002, memory: 3663, triplet_loss: 0.0010, ce_loss: 1.6177, top-1: 20.8000, loss: 1.6187\n",
            "2023-12-01 11:57:16,548 - mmdet - INFO - Epoch [1][350/988]\tlr: 1.000e-01, eta: 0:13:24, time: 0.049, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.6176, top-1: 22.4000, loss: 1.6176\n",
            "2023-12-01 11:57:19,075 - mmdet - INFO - Epoch [1][400/988]\tlr: 1.000e-01, eta: 0:13:13, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0010, ce_loss: 1.6272, top-1: 17.6000, loss: 1.6282\n",
            "2023-12-01 11:57:21,565 - mmdet - INFO - Epoch [1][450/988]\tlr: 1.000e-01, eta: 0:13:01, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0006, ce_loss: 1.6196, top-1: 20.4000, loss: 1.6202\n",
            "2023-12-01 11:57:24,024 - mmdet - INFO - Epoch [1][500/988]\tlr: 1.000e-01, eta: 0:12:51, time: 0.049, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.6002, top-1: 23.2000, loss: 1.6002\n",
            "2023-12-01 11:57:26,479 - mmdet - INFO - Epoch [1][550/988]\tlr: 1.000e-01, eta: 0:12:42, time: 0.049, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.6216, top-1: 21.6000, loss: 1.6216\n",
            "2023-12-01 11:57:28,955 - mmdet - INFO - Epoch [1][600/988]\tlr: 1.000e-01, eta: 0:12:35, time: 0.050, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.5900, top-1: 28.8000, loss: 1.5900\n",
            "2023-12-01 11:57:31,451 - mmdet - INFO - Epoch [1][650/988]\tlr: 1.000e-01, eta: 0:12:29, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.5724, top-1: 29.2000, loss: 1.5724\n",
            "2023-12-01 11:57:33,967 - mmdet - INFO - Epoch [1][700/988]\tlr: 1.000e-01, eta: 0:12:24, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.3913, top-1: 38.0000, loss: 1.3913\n",
            "2023-12-01 11:57:36,451 - mmdet - INFO - Epoch [1][750/988]\tlr: 1.000e-01, eta: 0:12:18, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.3825, top-1: 37.2000, loss: 1.3825\n",
            "2023-12-01 11:57:38,926 - mmdet - INFO - Epoch [1][800/988]\tlr: 1.000e-01, eta: 0:12:13, time: 0.049, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.2873, top-1: 40.8000, loss: 1.2873\n",
            "2023-12-01 11:57:41,410 - mmdet - INFO - Epoch [1][850/988]\tlr: 1.000e-01, eta: 0:12:08, time: 0.050, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.3400, top-1: 37.2000, loss: 1.3400\n",
            "2023-12-01 11:57:43,952 - mmdet - INFO - Epoch [1][900/988]\tlr: 1.000e-01, eta: 0:12:04, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.2348, top-1: 41.2000, loss: 1.2348\n",
            "2023-12-01 11:57:46,459 - mmdet - INFO - Epoch [1][950/988]\tlr: 1.000e-01, eta: 0:12:00, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.2688, top-1: 41.6000, loss: 1.2688\n",
            "2023-12-01 11:57:48,430 - mmdet - INFO - Saving checkpoint at 1 epochs\n",
            "2023-12-01 11:57:53,647 - mmdet - INFO - Epoch [2][50/988]\tlr: 1.000e-02, eta: 0:11:58, time: 0.094, data_time: 0.045, memory: 3663, triplet_loss: 0.0001, ce_loss: 1.0717, top-1: 53.2000, loss: 1.0718\n",
            "2023-12-01 11:57:56,138 - mmdet - INFO - Epoch [2][100/988]\tlr: 1.000e-02, eta: 0:11:54, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.0409, top-1: 51.6000, loss: 1.0409\n",
            "2023-12-01 11:57:58,641 - mmdet - INFO - Epoch [2][150/988]\tlr: 1.000e-02, eta: 0:11:50, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.0110, top-1: 53.6000, loss: 1.0110\n",
            "2023-12-01 11:58:01,187 - mmdet - INFO - Epoch [2][200/988]\tlr: 1.000e-02, eta: 0:11:47, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.0490, top-1: 51.2000, loss: 1.0490\n",
            "2023-12-01 11:58:03,711 - mmdet - INFO - Epoch [2][250/988]\tlr: 1.000e-02, eta: 0:11:43, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.0528, top-1: 53.2000, loss: 1.0528\n",
            "2023-12-01 11:58:06,481 - mmdet - INFO - Epoch [2][300/988]\tlr: 1.000e-02, eta: 0:11:43, time: 0.055, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.0142, top-1: 52.0000, loss: 1.0142\n",
            "2023-12-01 11:58:08,983 - mmdet - INFO - Epoch [2][350/988]\tlr: 1.000e-02, eta: 0:11:39, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.0082, top-1: 52.4000, loss: 1.0082\n",
            "2023-12-01 11:58:11,493 - mmdet - INFO - Epoch [2][400/988]\tlr: 1.000e-02, eta: 0:11:36, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9900, top-1: 55.2000, loss: 0.9900\n",
            "2023-12-01 11:58:14,011 - mmdet - INFO - Epoch [2][450/988]\tlr: 1.000e-02, eta: 0:11:32, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 1.0062, top-1: 58.0000, loss: 1.0062\n",
            "2023-12-01 11:58:16,528 - mmdet - INFO - Epoch [2][500/988]\tlr: 1.000e-02, eta: 0:11:29, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9875, top-1: 55.6000, loss: 0.9875\n",
            "2023-12-01 11:58:19,058 - mmdet - INFO - Epoch [2][550/988]\tlr: 1.000e-02, eta: 0:11:26, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9279, top-1: 58.0000, loss: 0.9279\n",
            "2023-12-01 11:58:21,612 - mmdet - INFO - Epoch [2][600/988]\tlr: 1.000e-02, eta: 0:11:23, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9772, top-1: 51.6000, loss: 0.9772\n",
            "2023-12-01 11:58:24,126 - mmdet - INFO - Epoch [2][650/988]\tlr: 1.000e-02, eta: 0:11:20, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9789, top-1: 56.0000, loss: 0.9789\n",
            "2023-12-01 11:58:26,658 - mmdet - INFO - Epoch [2][700/988]\tlr: 1.000e-02, eta: 0:11:17, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9265, top-1: 58.8000, loss: 0.9265\n",
            "2023-12-01 11:58:29,189 - mmdet - INFO - Epoch [2][750/988]\tlr: 1.000e-02, eta: 0:11:14, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9635, top-1: 57.6000, loss: 0.9635\n",
            "2023-12-01 11:58:31,730 - mmdet - INFO - Epoch [2][800/988]\tlr: 1.000e-02, eta: 0:11:11, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9431, top-1: 55.2000, loss: 0.9431\n",
            "2023-12-01 11:58:34,259 - mmdet - INFO - Epoch [2][850/988]\tlr: 1.000e-02, eta: 0:11:08, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9300, top-1: 58.0000, loss: 0.9300\n",
            "2023-12-01 11:58:36,830 - mmdet - INFO - Epoch [2][900/988]\tlr: 1.000e-02, eta: 0:11:06, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.8066, top-1: 63.2000, loss: 0.8066\n",
            "2023-12-01 11:58:39,364 - mmdet - INFO - Epoch [2][950/988]\tlr: 1.000e-02, eta: 0:11:03, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.8793, top-1: 64.0000, loss: 0.8793\n",
            "2023-12-01 11:58:41,351 - mmdet - INFO - Saving checkpoint at 2 epochs\n",
            "2023-12-01 11:58:46,652 - mmdet - INFO - Epoch [3][50/988]\tlr: 1.000e-02, eta: 0:11:00, time: 0.096, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.8884, top-1: 59.6000, loss: 0.8884\n",
            "2023-12-01 11:58:49,275 - mmdet - INFO - Epoch [3][100/988]\tlr: 1.000e-02, eta: 0:10:58, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.8407, top-1: 62.4000, loss: 0.8407\n",
            "2023-12-01 11:58:51,818 - mmdet - INFO - Epoch [3][150/988]\tlr: 1.000e-02, eta: 0:10:55, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7835, top-1: 63.2000, loss: 0.7835\n",
            "2023-12-01 11:58:54,347 - mmdet - INFO - Epoch [3][200/988]\tlr: 1.000e-02, eta: 0:10:52, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7841, top-1: 68.4000, loss: 0.7841\n",
            "2023-12-01 11:58:56,886 - mmdet - INFO - Epoch [3][250/988]\tlr: 1.000e-02, eta: 0:10:49, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9133, top-1: 64.4000, loss: 0.9133\n",
            "2023-12-01 11:58:59,432 - mmdet - INFO - Epoch [3][300/988]\tlr: 1.000e-02, eta: 0:10:46, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7622, top-1: 69.2000, loss: 0.7622\n",
            "2023-12-01 11:59:01,986 - mmdet - INFO - Epoch [3][350/988]\tlr: 1.000e-02, eta: 0:10:44, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7700, top-1: 63.6000, loss: 0.7700\n",
            "2023-12-01 11:59:04,605 - mmdet - INFO - Epoch [3][400/988]\tlr: 1.000e-02, eta: 0:10:41, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9025, top-1: 59.2000, loss: 0.9025\n",
            "2023-12-01 11:59:07,156 - mmdet - INFO - Epoch [3][450/988]\tlr: 1.000e-02, eta: 0:10:39, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7713, top-1: 66.8000, loss: 0.7713\n",
            "2023-12-01 11:59:09,690 - mmdet - INFO - Epoch [3][500/988]\tlr: 1.000e-02, eta: 0:10:36, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.6906, top-1: 73.2000, loss: 0.6906\n",
            "2023-12-01 11:59:12,222 - mmdet - INFO - Epoch [3][550/988]\tlr: 1.000e-02, eta: 0:10:33, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.6525, top-1: 71.6000, loss: 0.6525\n",
            "2023-12-01 11:59:14,760 - mmdet - INFO - Epoch [3][600/988]\tlr: 1.000e-02, eta: 0:10:30, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7451, top-1: 68.8000, loss: 0.7451\n",
            "2023-12-01 11:59:17,345 - mmdet - INFO - Epoch [3][650/988]\tlr: 1.000e-02, eta: 0:10:28, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7068, top-1: 73.2000, loss: 0.7068\n",
            "2023-12-01 11:59:19,910 - mmdet - INFO - Epoch [3][700/988]\tlr: 1.000e-02, eta: 0:10:25, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.9862, top-1: 58.4000, loss: 0.9862\n",
            "2023-12-01 11:59:22,454 - mmdet - INFO - Epoch [3][750/988]\tlr: 1.000e-02, eta: 0:10:22, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7609, top-1: 69.6000, loss: 0.7609\n",
            "2023-12-01 11:59:24,990 - mmdet - INFO - Epoch [3][800/988]\tlr: 1.000e-02, eta: 0:10:20, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7275, top-1: 70.0000, loss: 0.7275\n",
            "2023-12-01 11:59:27,518 - mmdet - INFO - Epoch [3][850/988]\tlr: 1.000e-02, eta: 0:10:17, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5358, top-1: 79.6000, loss: 0.5358\n",
            "2023-12-01 11:59:30,058 - mmdet - INFO - Epoch [3][900/988]\tlr: 1.000e-02, eta: 0:10:14, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7041, top-1: 70.0000, loss: 0.7041\n",
            "2023-12-01 11:59:32,659 - mmdet - INFO - Epoch [3][950/988]\tlr: 1.000e-02, eta: 0:10:12, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7958, top-1: 69.2000, loss: 0.7958\n",
            "2023-12-01 11:59:34,634 - mmdet - INFO - Saving checkpoint at 3 epochs\n",
            "2023-12-01 11:59:39,891 - mmdet - INFO - Epoch [4][50/988]\tlr: 1.000e-02, eta: 0:10:08, time: 0.095, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7758, top-1: 66.8000, loss: 0.7758\n",
            "2023-12-01 11:59:42,425 - mmdet - INFO - Epoch [4][100/988]\tlr: 1.000e-02, eta: 0:10:05, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5577, top-1: 76.8000, loss: 0.5577\n",
            "2023-12-01 11:59:44,977 - mmdet - INFO - Epoch [4][150/988]\tlr: 1.000e-02, eta: 0:10:03, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.6611, top-1: 73.6000, loss: 0.6611\n",
            "2023-12-01 11:59:47,536 - mmdet - INFO - Epoch [4][200/988]\tlr: 1.000e-02, eta: 0:10:00, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5889, top-1: 76.0000, loss: 0.5889\n",
            "2023-12-01 11:59:50,137 - mmdet - INFO - Epoch [4][250/988]\tlr: 1.000e-02, eta: 0:09:57, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5726, top-1: 76.8000, loss: 0.5726\n",
            "2023-12-01 11:59:52,653 - mmdet - INFO - Epoch [4][300/988]\tlr: 1.000e-02, eta: 0:09:55, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.4205, top-1: 86.4000, loss: 0.4205\n",
            "2023-12-01 11:59:55,192 - mmdet - INFO - Epoch [4][350/988]\tlr: 1.000e-02, eta: 0:09:52, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.6570, top-1: 76.0000, loss: 0.6570\n",
            "2023-12-01 11:59:57,747 - mmdet - INFO - Epoch [4][400/988]\tlr: 1.000e-02, eta: 0:09:49, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5945, top-1: 75.2000, loss: 0.5945\n",
            "2023-12-01 12:00:00,289 - mmdet - INFO - Epoch [4][450/988]\tlr: 1.000e-02, eta: 0:09:47, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.4799, top-1: 82.0000, loss: 0.4799\n",
            "2023-12-01 12:00:02,838 - mmdet - INFO - Epoch [4][500/988]\tlr: 1.000e-02, eta: 0:09:44, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2793, top-1: 89.6000, loss: 0.2793\n",
            "2023-12-01 12:00:05,375 - mmdet - INFO - Epoch [4][550/988]\tlr: 1.000e-02, eta: 0:09:41, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.6857, top-1: 77.6000, loss: 0.6857\n",
            "2023-12-01 12:00:07,937 - mmdet - INFO - Epoch [4][600/988]\tlr: 1.000e-02, eta: 0:09:39, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.4458, top-1: 84.8000, loss: 0.4458\n",
            "2023-12-01 12:00:10,460 - mmdet - INFO - Epoch [4][650/988]\tlr: 1.000e-02, eta: 0:09:36, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5162, top-1: 83.6000, loss: 0.5162\n",
            "2023-12-01 12:00:13,034 - mmdet - INFO - Epoch [4][700/988]\tlr: 1.000e-02, eta: 0:09:33, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.3809, top-1: 86.8000, loss: 0.3809\n",
            "2023-12-01 12:00:15,573 - mmdet - INFO - Epoch [4][750/988]\tlr: 1.000e-02, eta: 0:09:31, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.8052, top-1: 75.6000, loss: 0.8052\n",
            "2023-12-01 12:00:18,143 - mmdet - INFO - Epoch [4][800/988]\tlr: 1.000e-02, eta: 0:09:28, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.7359, top-1: 74.4000, loss: 0.7359\n",
            "2023-12-01 12:00:20,713 - mmdet - INFO - Epoch [4][850/988]\tlr: 1.000e-02, eta: 0:09:26, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5302, top-1: 82.0000, loss: 0.5302\n",
            "2023-12-01 12:00:23,245 - mmdet - INFO - Epoch [4][900/988]\tlr: 1.000e-02, eta: 0:09:23, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.3686, top-1: 88.4000, loss: 0.3686\n",
            "2023-12-01 12:00:25,808 - mmdet - INFO - Epoch [4][950/988]\tlr: 1.000e-02, eta: 0:09:20, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.5973, top-1: 76.4000, loss: 0.5973\n",
            "2023-12-01 12:00:27,783 - mmdet - INFO - Saving checkpoint at 4 epochs\n",
            "2023-12-01 12:00:33,075 - mmdet - INFO - Epoch [5][50/988]\tlr: 1.000e-02, eta: 0:09:16, time: 0.096, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.3724, top-1: 85.6000, loss: 0.3724\n",
            "2023-12-01 12:00:35,607 - mmdet - INFO - Epoch [5][100/988]\tlr: 1.000e-02, eta: 0:09:14, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2403, top-1: 91.6000, loss: 0.2403\n",
            "2023-12-01 12:00:38,130 - mmdet - INFO - Epoch [5][150/988]\tlr: 1.000e-02, eta: 0:09:11, time: 0.050, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2502, top-1: 89.6000, loss: 0.2502\n",
            "2023-12-01 12:00:40,659 - mmdet - INFO - Epoch [5][200/988]\tlr: 1.000e-02, eta: 0:09:08, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2652, top-1: 88.8000, loss: 0.2652\n",
            "2023-12-01 12:00:43,200 - mmdet - INFO - Epoch [5][250/988]\tlr: 1.000e-02, eta: 0:09:06, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1863, top-1: 93.2000, loss: 0.1863\n",
            "2023-12-01 12:00:45,727 - mmdet - INFO - Epoch [5][300/988]\tlr: 1.000e-02, eta: 0:09:03, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.3329, top-1: 89.2000, loss: 0.3329\n",
            "2023-12-01 12:00:48,287 - mmdet - INFO - Epoch [5][350/988]\tlr: 1.000e-02, eta: 0:09:00, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2309, top-1: 92.0000, loss: 0.2309\n",
            "2023-12-01 12:00:50,820 - mmdet - INFO - Epoch [5][400/988]\tlr: 1.000e-02, eta: 0:08:58, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1354, top-1: 94.0000, loss: 0.1354\n",
            "2023-12-01 12:00:53,393 - mmdet - INFO - Epoch [5][450/988]\tlr: 1.000e-02, eta: 0:08:55, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2399, top-1: 89.2000, loss: 0.2399\n",
            "2023-12-01 12:00:55,939 - mmdet - INFO - Epoch [5][500/988]\tlr: 1.000e-02, eta: 0:08:53, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1687, top-1: 94.0000, loss: 0.1687\n",
            "2023-12-01 12:00:58,465 - mmdet - INFO - Epoch [5][550/988]\tlr: 1.000e-02, eta: 0:08:50, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2962, top-1: 89.2000, loss: 0.2962\n",
            "2023-12-01 12:01:01,016 - mmdet - INFO - Epoch [5][600/988]\tlr: 1.000e-02, eta: 0:08:47, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2293, top-1: 89.2000, loss: 0.2293\n",
            "2023-12-01 12:01:03,665 - mmdet - INFO - Epoch [5][650/988]\tlr: 1.000e-02, eta: 0:08:45, time: 0.053, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2985, top-1: 89.2000, loss: 0.2985\n",
            "2023-12-01 12:01:06,198 - mmdet - INFO - Epoch [5][700/988]\tlr: 1.000e-02, eta: 0:08:42, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2574, top-1: 92.0000, loss: 0.2574\n",
            "2023-12-01 12:01:08,776 - mmdet - INFO - Epoch [5][750/988]\tlr: 1.000e-02, eta: 0:08:40, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2111, top-1: 91.2000, loss: 0.2111\n",
            "2023-12-01 12:01:11,333 - mmdet - INFO - Epoch [5][800/988]\tlr: 1.000e-02, eta: 0:08:37, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2013, top-1: 93.6000, loss: 0.2013\n",
            "2023-12-01 12:01:13,877 - mmdet - INFO - Epoch [5][850/988]\tlr: 1.000e-02, eta: 0:08:34, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2152, top-1: 92.4000, loss: 0.2152\n",
            "2023-12-01 12:01:16,428 - mmdet - INFO - Epoch [5][900/988]\tlr: 1.000e-02, eta: 0:08:32, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1899, top-1: 91.6000, loss: 0.1899\n",
            "2023-12-01 12:01:19,007 - mmdet - INFO - Epoch [5][950/988]\tlr: 1.000e-02, eta: 0:08:29, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1062, top-1: 96.8000, loss: 0.1062\n",
            "2023-12-01 12:01:20,990 - mmdet - INFO - Saving checkpoint at 5 epochs\n",
            "2023-12-01 12:01:26,246 - mmdet - INFO - Epoch [6][50/988]\tlr: 1.000e-02, eta: 0:08:25, time: 0.095, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1693, top-1: 93.2000, loss: 0.1693\n",
            "2023-12-01 12:01:28,775 - mmdet - INFO - Epoch [6][100/988]\tlr: 1.000e-02, eta: 0:08:23, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0770, top-1: 97.2000, loss: 0.0770\n",
            "2023-12-01 12:01:31,319 - mmdet - INFO - Epoch [6][150/988]\tlr: 1.000e-02, eta: 0:08:20, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0930, top-1: 96.4000, loss: 0.0930\n",
            "2023-12-01 12:01:33,885 - mmdet - INFO - Epoch [6][200/988]\tlr: 1.000e-02, eta: 0:08:17, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2401, top-1: 92.8000, loss: 0.2401\n",
            "2023-12-01 12:01:36,416 - mmdet - INFO - Epoch [6][250/988]\tlr: 1.000e-02, eta: 0:08:15, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1315, top-1: 96.4000, loss: 0.1315\n",
            "2023-12-01 12:01:38,942 - mmdet - INFO - Epoch [6][300/988]\tlr: 1.000e-02, eta: 0:08:12, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0564, top-1: 99.2000, loss: 0.0564\n",
            "2023-12-01 12:01:41,464 - mmdet - INFO - Epoch [6][350/988]\tlr: 1.000e-02, eta: 0:08:09, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0609, top-1: 97.6000, loss: 0.0609\n",
            "2023-12-01 12:01:44,000 - mmdet - INFO - Epoch [6][400/988]\tlr: 1.000e-02, eta: 0:08:07, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1519, top-1: 93.2000, loss: 0.1519\n",
            "2023-12-01 12:01:46,548 - mmdet - INFO - Epoch [6][450/988]\tlr: 1.000e-02, eta: 0:08:04, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0915, top-1: 96.4000, loss: 0.0915\n",
            "2023-12-01 12:01:49,150 - mmdet - INFO - Epoch [6][500/988]\tlr: 1.000e-02, eta: 0:08:02, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2004, top-1: 93.6000, loss: 0.2004\n",
            "2023-12-01 12:01:51,717 - mmdet - INFO - Epoch [6][550/988]\tlr: 1.000e-02, eta: 0:07:59, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2586, top-1: 92.8000, loss: 0.2586\n",
            "2023-12-01 12:01:54,245 - mmdet - INFO - Epoch [6][600/988]\tlr: 1.000e-02, eta: 0:07:56, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2659, top-1: 90.8000, loss: 0.2659\n",
            "2023-12-01 12:01:56,785 - mmdet - INFO - Epoch [6][650/988]\tlr: 1.000e-02, eta: 0:07:54, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1870, top-1: 94.4000, loss: 0.1870\n",
            "2023-12-01 12:01:59,317 - mmdet - INFO - Epoch [6][700/988]\tlr: 1.000e-02, eta: 0:07:51, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0372, top-1: 99.2000, loss: 0.0372\n",
            "2023-12-01 12:02:01,885 - mmdet - INFO - Epoch [6][750/988]\tlr: 1.000e-02, eta: 0:07:49, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0599, top-1: 97.6000, loss: 0.0599\n",
            "2023-12-01 12:02:04,429 - mmdet - INFO - Epoch [6][800/988]\tlr: 1.000e-02, eta: 0:07:46, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0289, top-1: 99.2000, loss: 0.0289\n",
            "2023-12-01 12:02:06,983 - mmdet - INFO - Epoch [6][850/988]\tlr: 1.000e-02, eta: 0:07:43, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0778, top-1: 97.2000, loss: 0.0778\n",
            "2023-12-01 12:02:09,528 - mmdet - INFO - Epoch [6][900/988]\tlr: 1.000e-02, eta: 0:07:41, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1283, top-1: 96.0000, loss: 0.1283\n",
            "2023-12-01 12:02:12,057 - mmdet - INFO - Epoch [6][950/988]\tlr: 1.000e-02, eta: 0:07:38, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1281, top-1: 95.2000, loss: 0.1281\n",
            "2023-12-01 12:02:14,018 - mmdet - INFO - Saving checkpoint at 6 epochs\n",
            "2023-12-01 12:02:19,338 - mmdet - INFO - Epoch [7][50/988]\tlr: 1.000e-02, eta: 0:07:34, time: 0.096, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1452, top-1: 96.0000, loss: 0.1452\n",
            "2023-12-01 12:02:21,889 - mmdet - INFO - Epoch [7][100/988]\tlr: 1.000e-02, eta: 0:07:31, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0816, top-1: 98.0000, loss: 0.0816\n",
            "2023-12-01 12:02:24,416 - mmdet - INFO - Epoch [7][150/988]\tlr: 1.000e-02, eta: 0:07:29, time: 0.050, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0473, top-1: 99.2000, loss: 0.0473\n",
            "2023-12-01 12:02:26,949 - mmdet - INFO - Epoch [7][200/988]\tlr: 1.000e-02, eta: 0:07:26, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0097, top-1: 100.0000, loss: 0.0097\n",
            "2023-12-01 12:02:29,500 - mmdet - INFO - Epoch [7][250/988]\tlr: 1.000e-02, eta: 0:07:24, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0310, top-1: 99.2000, loss: 0.0310\n",
            "2023-12-01 12:02:32,055 - mmdet - INFO - Epoch [7][300/988]\tlr: 1.000e-02, eta: 0:07:21, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0104, top-1: 100.0000, loss: 0.0104\n",
            "2023-12-01 12:02:34,635 - mmdet - INFO - Epoch [7][350/988]\tlr: 1.000e-02, eta: 0:07:18, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0144, top-1: 99.6000, loss: 0.0144\n",
            "2023-12-01 12:02:37,185 - mmdet - INFO - Epoch [7][400/988]\tlr: 1.000e-02, eta: 0:07:16, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0420, top-1: 98.4000, loss: 0.0420\n",
            "2023-12-01 12:02:39,718 - mmdet - INFO - Epoch [7][450/988]\tlr: 1.000e-02, eta: 0:07:13, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0358, top-1: 98.4000, loss: 0.0358\n",
            "2023-12-01 12:02:42,259 - mmdet - INFO - Epoch [7][500/988]\tlr: 1.000e-02, eta: 0:07:11, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0547, top-1: 98.0000, loss: 0.0547\n",
            "2023-12-01 12:02:44,789 - mmdet - INFO - Epoch [7][550/988]\tlr: 1.000e-02, eta: 0:07:08, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0461, top-1: 98.4000, loss: 0.0461\n",
            "2023-12-01 12:02:47,366 - mmdet - INFO - Epoch [7][600/988]\tlr: 1.000e-02, eta: 0:07:05, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1245, top-1: 95.6000, loss: 0.1245\n",
            "2023-12-01 12:02:49,942 - mmdet - INFO - Epoch [7][650/988]\tlr: 1.000e-02, eta: 0:07:03, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2023, top-1: 94.4000, loss: 0.2023\n",
            "2023-12-01 12:02:52,483 - mmdet - INFO - Epoch [7][700/988]\tlr: 1.000e-02, eta: 0:07:00, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2103, top-1: 94.0000, loss: 0.2103\n",
            "2023-12-01 12:02:55,046 - mmdet - INFO - Epoch [7][750/988]\tlr: 1.000e-02, eta: 0:06:58, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0838, top-1: 97.6000, loss: 0.0838\n",
            "2023-12-01 12:02:57,677 - mmdet - INFO - Epoch [7][800/988]\tlr: 1.000e-02, eta: 0:06:55, time: 0.053, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0974, top-1: 97.2000, loss: 0.0974\n",
            "2023-12-01 12:03:00,220 - mmdet - INFO - Epoch [7][850/988]\tlr: 1.000e-02, eta: 0:06:53, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0722, top-1: 96.8000, loss: 0.0722\n",
            "2023-12-01 12:03:02,760 - mmdet - INFO - Epoch [7][900/988]\tlr: 1.000e-02, eta: 0:06:50, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0608, top-1: 98.0000, loss: 0.0608\n",
            "2023-12-01 12:03:05,336 - mmdet - INFO - Epoch [7][950/988]\tlr: 1.000e-02, eta: 0:06:47, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1346, top-1: 94.4000, loss: 0.1346\n",
            "2023-12-01 12:03:07,347 - mmdet - INFO - Saving checkpoint at 7 epochs\n",
            "2023-12-01 12:03:12,608 - mmdet - INFO - Epoch [8][50/988]\tlr: 1.000e-02, eta: 0:06:43, time: 0.095, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0937, top-1: 97.2000, loss: 0.0937\n",
            "2023-12-01 12:03:15,146 - mmdet - INFO - Epoch [8][100/988]\tlr: 1.000e-02, eta: 0:06:41, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.3240, top-1: 91.6000, loss: 0.3240\n",
            "2023-12-01 12:03:17,704 - mmdet - INFO - Epoch [8][150/988]\tlr: 1.000e-02, eta: 0:06:38, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.6362, top-1: 81.2000, loss: 0.6362\n",
            "2023-12-01 12:03:20,280 - mmdet - INFO - Epoch [8][200/988]\tlr: 1.000e-02, eta: 0:06:35, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2046, top-1: 94.0000, loss: 0.2046\n",
            "2023-12-01 12:03:22,868 - mmdet - INFO - Epoch [8][250/988]\tlr: 1.000e-02, eta: 0:06:33, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1333, top-1: 95.6000, loss: 0.1333\n",
            "2023-12-01 12:03:25,395 - mmdet - INFO - Epoch [8][300/988]\tlr: 1.000e-02, eta: 0:06:30, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1459, top-1: 94.8000, loss: 0.1459\n",
            "2023-12-01 12:03:28,041 - mmdet - INFO - Epoch [8][350/988]\tlr: 1.000e-02, eta: 0:06:28, time: 0.053, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1224, top-1: 95.2000, loss: 0.1224\n",
            "2023-12-01 12:03:30,610 - mmdet - INFO - Epoch [8][400/988]\tlr: 1.000e-02, eta: 0:06:25, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0285, top-1: 99.2000, loss: 0.0285\n",
            "2023-12-01 12:03:33,188 - mmdet - INFO - Epoch [8][450/988]\tlr: 1.000e-02, eta: 0:06:23, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1538, top-1: 96.0000, loss: 0.1538\n",
            "2023-12-01 12:03:35,736 - mmdet - INFO - Epoch [8][500/988]\tlr: 1.000e-02, eta: 0:06:20, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0935, top-1: 96.8000, loss: 0.0935\n",
            "2023-12-01 12:03:38,265 - mmdet - INFO - Epoch [8][550/988]\tlr: 1.000e-02, eta: 0:06:17, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0685, top-1: 98.0000, loss: 0.0685\n",
            "2023-12-01 12:03:40,806 - mmdet - INFO - Epoch [8][600/988]\tlr: 1.000e-02, eta: 0:06:15, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0317, top-1: 98.8000, loss: 0.0317\n",
            "2023-12-01 12:03:43,335 - mmdet - INFO - Epoch [8][650/988]\tlr: 1.000e-02, eta: 0:06:12, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1542, top-1: 94.4000, loss: 0.1542\n",
            "2023-12-01 12:03:45,917 - mmdet - INFO - Epoch [8][700/988]\tlr: 1.000e-02, eta: 0:06:10, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0978, top-1: 97.2000, loss: 0.0978\n",
            "2023-12-01 12:03:48,479 - mmdet - INFO - Epoch [8][750/988]\tlr: 1.000e-02, eta: 0:06:07, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1078, top-1: 97.2000, loss: 0.1078\n",
            "2023-12-01 12:03:51,012 - mmdet - INFO - Epoch [8][800/988]\tlr: 1.000e-02, eta: 0:06:05, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0698, top-1: 96.8000, loss: 0.0698\n",
            "2023-12-01 12:03:53,596 - mmdet - INFO - Epoch [8][850/988]\tlr: 1.000e-02, eta: 0:06:02, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1998, top-1: 94.0000, loss: 0.1998\n",
            "2023-12-01 12:03:56,127 - mmdet - INFO - Epoch [8][900/988]\tlr: 1.000e-02, eta: 0:05:59, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1472, top-1: 97.2000, loss: 0.1472\n",
            "2023-12-01 12:03:58,706 - mmdet - INFO - Epoch [8][950/988]\tlr: 1.000e-02, eta: 0:05:57, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0268, top-1: 99.2000, loss: 0.0268\n",
            "2023-12-01 12:04:00,687 - mmdet - INFO - Saving checkpoint at 8 epochs\n",
            "2023-12-01 12:04:05,968 - mmdet - INFO - Epoch [9][50/988]\tlr: 1.000e-02, eta: 0:05:52, time: 0.095, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0146, top-1: 99.6000, loss: 0.0146\n",
            "2023-12-01 12:04:08,523 - mmdet - INFO - Epoch [9][100/988]\tlr: 1.000e-02, eta: 0:05:50, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0392, top-1: 98.4000, loss: 0.0392\n",
            "2023-12-01 12:04:11,073 - mmdet - INFO - Epoch [9][150/988]\tlr: 1.000e-02, eta: 0:05:47, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0146, top-1: 99.6000, loss: 0.0146\n",
            "2023-12-01 12:04:13,611 - mmdet - INFO - Epoch [9][200/988]\tlr: 1.000e-02, eta: 0:05:45, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0148, top-1: 100.0000, loss: 0.0148\n",
            "2023-12-01 12:04:16,145 - mmdet - INFO - Epoch [9][250/988]\tlr: 1.000e-02, eta: 0:05:42, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0170, top-1: 99.2000, loss: 0.0170\n",
            "2023-12-01 12:04:18,696 - mmdet - INFO - Epoch [9][300/988]\tlr: 1.000e-02, eta: 0:05:40, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0209, top-1: 98.8000, loss: 0.0209\n",
            "2023-12-01 12:04:21,242 - mmdet - INFO - Epoch [9][350/988]\tlr: 1.000e-02, eta: 0:05:37, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0098, top-1: 100.0000, loss: 0.0098\n",
            "2023-12-01 12:04:23,778 - mmdet - INFO - Epoch [9][400/988]\tlr: 1.000e-02, eta: 0:05:34, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0637, top-1: 97.6000, loss: 0.0637\n",
            "2023-12-01 12:04:26,315 - mmdet - INFO - Epoch [9][450/988]\tlr: 1.000e-02, eta: 0:05:32, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1186, top-1: 96.8000, loss: 0.1186\n",
            "2023-12-01 12:04:28,860 - mmdet - INFO - Epoch [9][500/988]\tlr: 1.000e-02, eta: 0:05:29, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1246, top-1: 96.8000, loss: 0.1246\n",
            "2023-12-01 12:04:31,417 - mmdet - INFO - Epoch [9][550/988]\tlr: 1.000e-02, eta: 0:05:27, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0385, top-1: 99.2000, loss: 0.0385\n",
            "2023-12-01 12:04:33,997 - mmdet - INFO - Epoch [9][600/988]\tlr: 1.000e-02, eta: 0:05:24, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0056, top-1: 100.0000, loss: 0.0056\n",
            "2023-12-01 12:04:36,534 - mmdet - INFO - Epoch [9][650/988]\tlr: 1.000e-02, eta: 0:05:21, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0614, top-1: 98.0000, loss: 0.0614\n",
            "2023-12-01 12:04:39,049 - mmdet - INFO - Epoch [9][700/988]\tlr: 1.000e-02, eta: 0:05:19, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0067, top-1: 100.0000, loss: 0.0067\n",
            "2023-12-01 12:04:41,592 - mmdet - INFO - Epoch [9][750/988]\tlr: 1.000e-02, eta: 0:05:16, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0113, top-1: 100.0000, loss: 0.0113\n",
            "2023-12-01 12:04:44,119 - mmdet - INFO - Epoch [9][800/988]\tlr: 1.000e-02, eta: 0:05:14, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0275, top-1: 99.2000, loss: 0.0275\n",
            "2023-12-01 12:04:46,648 - mmdet - INFO - Epoch [9][850/988]\tlr: 1.000e-02, eta: 0:05:11, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0323, top-1: 98.4000, loss: 0.0323\n",
            "2023-12-01 12:04:49,206 - mmdet - INFO - Epoch [9][900/988]\tlr: 1.000e-02, eta: 0:05:08, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0393, top-1: 98.4000, loss: 0.0393\n",
            "2023-12-01 12:04:51,777 - mmdet - INFO - Epoch [9][950/988]\tlr: 1.000e-02, eta: 0:05:06, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0952, top-1: 96.8000, loss: 0.0952\n",
            "2023-12-01 12:04:53,791 - mmdet - INFO - Saving checkpoint at 9 epochs\n",
            "2023-12-01 12:04:59,001 - mmdet - INFO - Epoch [10][50/988]\tlr: 1.000e-02, eta: 0:05:02, time: 0.094, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0201, top-1: 99.6000, loss: 0.0201\n",
            "2023-12-01 12:05:01,563 - mmdet - INFO - Epoch [10][100/988]\tlr: 1.000e-02, eta: 0:04:59, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0072, top-1: 100.0000, loss: 0.0072\n",
            "2023-12-01 12:05:04,086 - mmdet - INFO - Epoch [10][150/988]\tlr: 1.000e-02, eta: 0:04:56, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0071, top-1: 100.0000, loss: 0.0071\n",
            "2023-12-01 12:05:06,624 - mmdet - INFO - Epoch [10][200/988]\tlr: 1.000e-02, eta: 0:04:54, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1115, top-1: 96.4000, loss: 0.1115\n",
            "2023-12-01 12:05:09,147 - mmdet - INFO - Epoch [10][250/988]\tlr: 1.000e-02, eta: 0:04:51, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1592, top-1: 96.0000, loss: 0.1592\n",
            "2023-12-01 12:05:11,744 - mmdet - INFO - Epoch [10][300/988]\tlr: 1.000e-02, eta: 0:04:49, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0202, top-1: 99.6000, loss: 0.0202\n",
            "2023-12-01 12:05:14,268 - mmdet - INFO - Epoch [10][350/988]\tlr: 1.000e-02, eta: 0:04:46, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0211, top-1: 99.2000, loss: 0.0211\n",
            "2023-12-01 12:05:16,813 - mmdet - INFO - Epoch [10][400/988]\tlr: 1.000e-02, eta: 0:04:43, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0188, top-1: 100.0000, loss: 0.0188\n",
            "2023-12-01 12:05:19,367 - mmdet - INFO - Epoch [10][450/988]\tlr: 1.000e-02, eta: 0:04:41, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0365, top-1: 98.4000, loss: 0.0365\n",
            "2023-12-01 12:05:21,896 - mmdet - INFO - Epoch [10][500/988]\tlr: 1.000e-02, eta: 0:04:38, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0182, top-1: 100.0000, loss: 0.0182\n",
            "2023-12-01 12:05:24,433 - mmdet - INFO - Epoch [10][550/988]\tlr: 1.000e-02, eta: 0:04:36, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0128, top-1: 99.6000, loss: 0.0128\n",
            "2023-12-01 12:05:27,019 - mmdet - INFO - Epoch [10][600/988]\tlr: 1.000e-02, eta: 0:04:33, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0327, top-1: 98.8000, loss: 0.0327\n",
            "2023-12-01 12:05:29,573 - mmdet - INFO - Epoch [10][650/988]\tlr: 1.000e-02, eta: 0:04:31, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0803, top-1: 97.6000, loss: 0.0803\n",
            "2023-12-01 12:05:32,150 - mmdet - INFO - Epoch [10][700/988]\tlr: 1.000e-02, eta: 0:04:28, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1114, top-1: 95.6000, loss: 0.1114\n",
            "2023-12-01 12:05:34,740 - mmdet - INFO - Epoch [10][750/988]\tlr: 1.000e-02, eta: 0:04:25, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1095, top-1: 97.2000, loss: 0.1095\n",
            "2023-12-01 12:05:37,264 - mmdet - INFO - Epoch [10][800/988]\tlr: 1.000e-02, eta: 0:04:23, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0690, top-1: 97.6000, loss: 0.0690\n",
            "2023-12-01 12:05:39,802 - mmdet - INFO - Epoch [10][850/988]\tlr: 1.000e-02, eta: 0:04:20, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0573, top-1: 98.8000, loss: 0.0573\n",
            "2023-12-01 12:05:42,324 - mmdet - INFO - Epoch [10][900/988]\tlr: 1.000e-02, eta: 0:04:18, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0308, top-1: 99.2000, loss: 0.0308\n",
            "2023-12-01 12:05:44,904 - mmdet - INFO - Epoch [10][950/988]\tlr: 1.000e-02, eta: 0:04:15, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0481, top-1: 97.2000, loss: 0.0481\n",
            "2023-12-01 12:05:46,919 - mmdet - INFO - Saving checkpoint at 10 epochs\n",
            "2023-12-01 12:05:52,167 - mmdet - INFO - Epoch [11][50/988]\tlr: 1.000e-02, eta: 0:04:11, time: 0.095, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0151, top-1: 100.0000, loss: 0.0151\n",
            "2023-12-01 12:05:54,707 - mmdet - INFO - Epoch [11][100/988]\tlr: 1.000e-02, eta: 0:04:08, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0845, top-1: 96.8000, loss: 0.0845\n",
            "2023-12-01 12:05:57,264 - mmdet - INFO - Epoch [11][150/988]\tlr: 1.000e-02, eta: 0:04:06, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0618, top-1: 98.0000, loss: 0.0618\n",
            "2023-12-01 12:05:59,805 - mmdet - INFO - Epoch [11][200/988]\tlr: 1.000e-02, eta: 0:04:03, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0227, top-1: 99.6000, loss: 0.0227\n",
            "2023-12-01 12:06:02,318 - mmdet - INFO - Epoch [11][250/988]\tlr: 1.000e-02, eta: 0:04:00, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0648, top-1: 98.0000, loss: 0.0648\n",
            "2023-12-01 12:06:04,875 - mmdet - INFO - Epoch [11][300/988]\tlr: 1.000e-02, eta: 0:03:58, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1641, top-1: 94.8000, loss: 0.1641\n",
            "2023-12-01 12:06:07,389 - mmdet - INFO - Epoch [11][350/988]\tlr: 1.000e-02, eta: 0:03:55, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0272, top-1: 99.2000, loss: 0.0272\n",
            "2023-12-01 12:06:09,957 - mmdet - INFO - Epoch [11][400/988]\tlr: 1.000e-02, eta: 0:03:53, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0153, top-1: 100.0000, loss: 0.0153\n",
            "2023-12-01 12:06:12,511 - mmdet - INFO - Epoch [11][450/988]\tlr: 1.000e-02, eta: 0:03:50, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0621, top-1: 96.4000, loss: 0.0621\n",
            "2023-12-01 12:06:15,056 - mmdet - INFO - Epoch [11][500/988]\tlr: 1.000e-02, eta: 0:03:47, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0455, top-1: 98.4000, loss: 0.0455\n",
            "2023-12-01 12:06:17,590 - mmdet - INFO - Epoch [11][550/988]\tlr: 1.000e-02, eta: 0:03:45, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0333, top-1: 99.2000, loss: 0.0333\n",
            "2023-12-01 12:06:20,194 - mmdet - INFO - Epoch [11][600/988]\tlr: 1.000e-02, eta: 0:03:42, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0197, top-1: 99.6000, loss: 0.0197\n",
            "2023-12-01 12:06:22,747 - mmdet - INFO - Epoch [11][650/988]\tlr: 1.000e-02, eta: 0:03:40, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0257, top-1: 99.2000, loss: 0.0257\n",
            "2023-12-01 12:06:25,276 - mmdet - INFO - Epoch [11][700/988]\tlr: 1.000e-02, eta: 0:03:37, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0799, top-1: 98.0000, loss: 0.0799\n",
            "2023-12-01 12:06:27,818 - mmdet - INFO - Epoch [11][750/988]\tlr: 1.000e-02, eta: 0:03:35, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0255, top-1: 99.2000, loss: 0.0255\n",
            "2023-12-01 12:06:30,389 - mmdet - INFO - Epoch [11][800/988]\tlr: 1.000e-02, eta: 0:03:32, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0112, top-1: 99.6000, loss: 0.0112\n",
            "2023-12-01 12:06:32,978 - mmdet - INFO - Epoch [11][850/988]\tlr: 1.000e-02, eta: 0:03:29, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0120, top-1: 99.6000, loss: 0.0120\n",
            "2023-12-01 12:06:35,519 - mmdet - INFO - Epoch [11][900/988]\tlr: 1.000e-02, eta: 0:03:27, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1125, top-1: 96.0000, loss: 0.1125\n",
            "2023-12-01 12:06:38,045 - mmdet - INFO - Epoch [11][950/988]\tlr: 1.000e-02, eta: 0:03:24, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0767, top-1: 97.2000, loss: 0.0767\n",
            "2023-12-01 12:06:40,037 - mmdet - INFO - Saving checkpoint at 11 epochs\n",
            "2023-12-01 12:06:45,277 - mmdet - INFO - Epoch [12][50/988]\tlr: 1.000e-02, eta: 0:03:20, time: 0.095, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0115, top-1: 99.6000, loss: 0.0115\n",
            "2023-12-01 12:06:47,806 - mmdet - INFO - Epoch [12][100/988]\tlr: 1.000e-02, eta: 0:03:17, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0148, top-1: 99.6000, loss: 0.0148\n",
            "2023-12-01 12:06:50,353 - mmdet - INFO - Epoch [12][150/988]\tlr: 1.000e-02, eta: 0:03:15, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0091, top-1: 100.0000, loss: 0.0091\n",
            "2023-12-01 12:06:52,929 - mmdet - INFO - Epoch [12][200/988]\tlr: 1.000e-02, eta: 0:03:12, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0040, top-1: 100.0000, loss: 0.0040\n",
            "2023-12-01 12:06:55,458 - mmdet - INFO - Epoch [12][250/988]\tlr: 1.000e-02, eta: 0:03:10, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0087, top-1: 99.6000, loss: 0.0087\n",
            "2023-12-01 12:06:57,988 - mmdet - INFO - Epoch [12][300/988]\tlr: 1.000e-02, eta: 0:03:07, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1340, top-1: 95.6000, loss: 0.1340\n",
            "2023-12-01 12:07:00,534 - mmdet - INFO - Epoch [12][350/988]\tlr: 1.000e-02, eta: 0:03:04, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0863, top-1: 96.4000, loss: 0.0863\n",
            "2023-12-01 12:07:03,098 - mmdet - INFO - Epoch [12][400/988]\tlr: 1.000e-02, eta: 0:03:02, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1113, top-1: 96.4000, loss: 0.1113\n",
            "2023-12-01 12:07:05,659 - mmdet - INFO - Epoch [12][450/988]\tlr: 1.000e-02, eta: 0:02:59, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0732, top-1: 98.0000, loss: 0.0732\n",
            "2023-12-01 12:07:08,190 - mmdet - INFO - Epoch [12][500/988]\tlr: 1.000e-02, eta: 0:02:57, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0068, top-1: 100.0000, loss: 0.0068\n",
            "2023-12-01 12:07:10,722 - mmdet - INFO - Epoch [12][550/988]\tlr: 1.000e-02, eta: 0:02:54, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0234, top-1: 99.2000, loss: 0.0234\n",
            "2023-12-01 12:07:13,271 - mmdet - INFO - Epoch [12][600/988]\tlr: 1.000e-02, eta: 0:02:52, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0134, top-1: 99.6000, loss: 0.0134\n",
            "2023-12-01 12:07:15,862 - mmdet - INFO - Epoch [12][650/988]\tlr: 1.000e-02, eta: 0:02:49, time: 0.052, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0051, top-1: 100.0000, loss: 0.0051\n",
            "2023-12-01 12:07:18,394 - mmdet - INFO - Epoch [12][700/988]\tlr: 1.000e-02, eta: 0:02:46, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0064, top-1: 100.0000, loss: 0.0064\n",
            "2023-12-01 12:07:20,927 - mmdet - INFO - Epoch [12][750/988]\tlr: 1.000e-02, eta: 0:02:44, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0882, top-1: 97.2000, loss: 0.0882\n",
            "2023-12-01 12:07:23,456 - mmdet - INFO - Epoch [12][800/988]\tlr: 1.000e-02, eta: 0:02:41, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0558, top-1: 98.4000, loss: 0.0558\n",
            "2023-12-01 12:07:25,986 - mmdet - INFO - Epoch [12][850/988]\tlr: 1.000e-02, eta: 0:02:39, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0265, top-1: 99.2000, loss: 0.0265\n",
            "2023-12-01 12:07:28,564 - mmdet - INFO - Epoch [12][900/988]\tlr: 1.000e-02, eta: 0:02:36, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0507, top-1: 98.0000, loss: 0.0507\n",
            "2023-12-01 12:07:31,101 - mmdet - INFO - Epoch [12][950/988]\tlr: 1.000e-02, eta: 0:02:34, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0139, top-1: 100.0000, loss: 0.0139\n",
            "2023-12-01 12:07:33,128 - mmdet - INFO - Saving checkpoint at 12 epochs\n",
            "2023-12-01 12:07:38,418 - mmdet - INFO - Epoch [13][50/988]\tlr: 1.000e-02, eta: 0:02:29, time: 0.096, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0181, top-1: 99.6000, loss: 0.0181\n",
            "2023-12-01 12:07:40,950 - mmdet - INFO - Epoch [13][100/988]\tlr: 1.000e-02, eta: 0:02:27, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0543, top-1: 98.0000, loss: 0.0543\n",
            "2023-12-01 12:07:43,469 - mmdet - INFO - Epoch [13][150/988]\tlr: 1.000e-02, eta: 0:02:24, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0111, top-1: 100.0000, loss: 0.0111\n",
            "2023-12-01 12:07:46,012 - mmdet - INFO - Epoch [13][200/988]\tlr: 1.000e-02, eta: 0:02:21, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1576, top-1: 96.0000, loss: 0.1576\n",
            "2023-12-01 12:07:48,551 - mmdet - INFO - Epoch [13][250/988]\tlr: 1.000e-02, eta: 0:02:19, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0572, top-1: 98.0000, loss: 0.0572\n",
            "2023-12-01 12:07:51,079 - mmdet - INFO - Epoch [13][300/988]\tlr: 1.000e-02, eta: 0:02:16, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0386, top-1: 98.4000, loss: 0.0386\n",
            "2023-12-01 12:07:53,622 - mmdet - INFO - Epoch [13][350/988]\tlr: 1.000e-02, eta: 0:02:14, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0209, top-1: 99.2000, loss: 0.0209\n",
            "2023-12-01 12:07:56,150 - mmdet - INFO - Epoch [13][400/988]\tlr: 1.000e-02, eta: 0:02:11, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0109, top-1: 100.0000, loss: 0.0109\n",
            "2023-12-01 12:07:58,710 - mmdet - INFO - Epoch [13][450/988]\tlr: 1.000e-02, eta: 0:02:09, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0108, top-1: 99.6000, loss: 0.0108\n",
            "2023-12-01 12:08:01,287 - mmdet - INFO - Epoch [13][500/988]\tlr: 1.000e-02, eta: 0:02:06, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0059, top-1: 100.0000, loss: 0.0059\n",
            "2023-12-01 12:08:03,834 - mmdet - INFO - Epoch [13][550/988]\tlr: 1.000e-02, eta: 0:02:03, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0176, top-1: 99.2000, loss: 0.0176\n",
            "2023-12-01 12:08:06,374 - mmdet - INFO - Epoch [13][600/988]\tlr: 1.000e-02, eta: 0:02:01, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0286, top-1: 98.8000, loss: 0.0286\n",
            "2023-12-01 12:08:08,917 - mmdet - INFO - Epoch [13][650/988]\tlr: 1.000e-02, eta: 0:01:58, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0618, top-1: 98.0000, loss: 0.0618\n",
            "2023-12-01 12:08:11,441 - mmdet - INFO - Epoch [13][700/988]\tlr: 1.000e-02, eta: 0:01:56, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1899, top-1: 94.0000, loss: 0.1899\n",
            "2023-12-01 12:08:14,005 - mmdet - INFO - Epoch [13][750/988]\tlr: 1.000e-02, eta: 0:01:53, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1225, top-1: 97.2000, loss: 0.1225\n",
            "2023-12-01 12:08:16,569 - mmdet - INFO - Epoch [13][800/988]\tlr: 1.000e-02, eta: 0:01:51, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2620, top-1: 94.0000, loss: 0.2620\n",
            "2023-12-01 12:08:19,114 - mmdet - INFO - Epoch [13][850/988]\tlr: 1.000e-02, eta: 0:01:48, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0342, top-1: 98.8000, loss: 0.0342\n",
            "2023-12-01 12:08:21,654 - mmdet - INFO - Epoch [13][900/988]\tlr: 1.000e-02, eta: 0:01:45, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0452, top-1: 98.8000, loss: 0.0452\n",
            "2023-12-01 12:08:24,207 - mmdet - INFO - Epoch [13][950/988]\tlr: 1.000e-02, eta: 0:01:43, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0379, top-1: 99.2000, loss: 0.0379\n",
            "2023-12-01 12:08:26,187 - mmdet - INFO - Saving checkpoint at 13 epochs\n",
            "2023-12-01 12:08:31,470 - mmdet - INFO - Epoch [14][50/988]\tlr: 1.000e-02, eta: 0:01:38, time: 0.096, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0135, top-1: 99.6000, loss: 0.0135\n",
            "2023-12-01 12:08:34,004 - mmdet - INFO - Epoch [14][100/988]\tlr: 1.000e-02, eta: 0:01:36, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0139, top-1: 99.6000, loss: 0.0139\n",
            "2023-12-01 12:08:36,529 - mmdet - INFO - Epoch [14][150/988]\tlr: 1.000e-02, eta: 0:01:33, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1081, top-1: 95.6000, loss: 0.1081\n",
            "2023-12-01 12:08:39,079 - mmdet - INFO - Epoch [14][200/988]\tlr: 1.000e-02, eta: 0:01:31, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.2645, top-1: 91.2000, loss: 0.2645\n",
            "2023-12-01 12:08:41,596 - mmdet - INFO - Epoch [14][250/988]\tlr: 1.000e-02, eta: 0:01:28, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.1796, top-1: 94.4000, loss: 0.1796\n",
            "2023-12-01 12:08:44,162 - mmdet - INFO - Epoch [14][300/988]\tlr: 1.000e-02, eta: 0:01:26, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0443, top-1: 98.8000, loss: 0.0443\n",
            "2023-12-01 12:08:46,688 - mmdet - INFO - Epoch [14][350/988]\tlr: 1.000e-02, eta: 0:01:23, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0175, top-1: 100.0000, loss: 0.0175\n",
            "2023-12-01 12:08:49,267 - mmdet - INFO - Epoch [14][400/988]\tlr: 1.000e-02, eta: 0:01:20, time: 0.052, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0036, top-1: 100.0000, loss: 0.0036\n",
            "2023-12-01 12:08:51,805 - mmdet - INFO - Epoch [14][450/988]\tlr: 1.000e-02, eta: 0:01:18, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0213, top-1: 99.2000, loss: 0.0213\n",
            "2023-12-01 12:08:54,323 - mmdet - INFO - Epoch [14][500/988]\tlr: 1.000e-02, eta: 0:01:15, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0172, top-1: 99.6000, loss: 0.0172\n",
            "2023-12-01 12:08:56,862 - mmdet - INFO - Epoch [14][550/988]\tlr: 1.000e-02, eta: 0:01:13, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0123, top-1: 100.0000, loss: 0.0123\n",
            "2023-12-01 12:08:59,381 - mmdet - INFO - Epoch [14][600/988]\tlr: 1.000e-02, eta: 0:01:10, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0029, top-1: 100.0000, loss: 0.0029\n",
            "2023-12-01 12:09:01,916 - mmdet - INFO - Epoch [14][650/988]\tlr: 1.000e-02, eta: 0:01:08, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0041, top-1: 100.0000, loss: 0.0041\n",
            "2023-12-01 12:09:04,455 - mmdet - INFO - Epoch [14][700/988]\tlr: 1.000e-02, eta: 0:01:05, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0122, top-1: 99.6000, loss: 0.0122\n",
            "2023-12-01 12:09:06,988 - mmdet - INFO - Epoch [14][750/988]\tlr: 1.000e-02, eta: 0:01:02, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0056, top-1: 100.0000, loss: 0.0056\n",
            "2023-12-01 12:09:09,541 - mmdet - INFO - Epoch [14][800/988]\tlr: 1.000e-02, eta: 0:01:00, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0050, top-1: 100.0000, loss: 0.0050\n",
            "2023-12-01 12:09:12,097 - mmdet - INFO - Epoch [14][850/988]\tlr: 1.000e-02, eta: 0:00:57, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0026, top-1: 100.0000, loss: 0.0026\n",
            "2023-12-01 12:09:14,669 - mmdet - INFO - Epoch [14][900/988]\tlr: 1.000e-02, eta: 0:00:55, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0066, top-1: 100.0000, loss: 0.0066\n",
            "2023-12-01 12:09:17,208 - mmdet - INFO - Epoch [14][950/988]\tlr: 1.000e-02, eta: 0:00:52, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0012, top-1: 100.0000, loss: 0.0012\n",
            "2023-12-01 12:09:19,196 - mmdet - INFO - Saving checkpoint at 14 epochs\n",
            "2023-12-01 12:09:24,469 - mmdet - INFO - Epoch [15][50/988]\tlr: 1.000e-02, eta: 0:00:48, time: 0.095, data_time: 0.045, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0186, top-1: 99.6000, loss: 0.0186\n",
            "2023-12-01 12:09:27,020 - mmdet - INFO - Epoch [15][100/988]\tlr: 1.000e-02, eta: 0:00:45, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0107, top-1: 99.6000, loss: 0.0107\n",
            "2023-12-01 12:09:29,542 - mmdet - INFO - Epoch [15][150/988]\tlr: 1.000e-02, eta: 0:00:42, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0062, top-1: 100.0000, loss: 0.0062\n",
            "2023-12-01 12:09:32,177 - mmdet - INFO - Epoch [15][200/988]\tlr: 1.000e-02, eta: 0:00:40, time: 0.053, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0042, top-1: 100.0000, loss: 0.0042\n",
            "2023-12-01 12:09:34,722 - mmdet - INFO - Epoch [15][250/988]\tlr: 1.000e-02, eta: 0:00:37, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0016, top-1: 100.0000, loss: 0.0016\n",
            "2023-12-01 12:09:37,296 - mmdet - INFO - Epoch [15][300/988]\tlr: 1.000e-02, eta: 0:00:35, time: 0.051, data_time: 0.003, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0009, top-1: 100.0000, loss: 0.0009\n",
            "2023-12-01 12:09:39,849 - mmdet - INFO - Epoch [15][350/988]\tlr: 1.000e-02, eta: 0:00:32, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0003, top-1: 100.0000, loss: 0.0003\n",
            "2023-12-01 12:09:42,376 - mmdet - INFO - Epoch [15][400/988]\tlr: 1.000e-02, eta: 0:00:30, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0011, top-1: 100.0000, loss: 0.0011\n",
            "2023-12-01 12:09:44,917 - mmdet - INFO - Epoch [15][450/988]\tlr: 1.000e-02, eta: 0:00:27, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0019, top-1: 100.0000, loss: 0.0019\n",
            "2023-12-01 12:09:47,459 - mmdet - INFO - Epoch [15][500/988]\tlr: 1.000e-02, eta: 0:00:25, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0007, top-1: 100.0000, loss: 0.0007\n",
            "2023-12-01 12:09:49,999 - mmdet - INFO - Epoch [15][550/988]\tlr: 1.000e-02, eta: 0:00:22, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0004, top-1: 100.0000, loss: 0.0004\n",
            "2023-12-01 12:09:52,559 - mmdet - INFO - Epoch [15][600/988]\tlr: 1.000e-02, eta: 0:00:19, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0005, top-1: 100.0000, loss: 0.0005\n",
            "2023-12-01 12:09:55,108 - mmdet - INFO - Epoch [15][650/988]\tlr: 1.000e-02, eta: 0:00:17, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0012, top-1: 100.0000, loss: 0.0012\n",
            "2023-12-01 12:09:57,651 - mmdet - INFO - Epoch [15][700/988]\tlr: 1.000e-02, eta: 0:00:14, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0018, top-1: 100.0000, loss: 0.0018\n",
            "2023-12-01 12:10:00,172 - mmdet - INFO - Epoch [15][750/988]\tlr: 1.000e-02, eta: 0:00:12, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0007, top-1: 100.0000, loss: 0.0007\n",
            "2023-12-01 12:10:02,730 - mmdet - INFO - Epoch [15][800/988]\tlr: 1.000e-02, eta: 0:00:09, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0005, top-1: 100.0000, loss: 0.0005\n",
            "2023-12-01 12:10:05,265 - mmdet - INFO - Epoch [15][850/988]\tlr: 1.000e-02, eta: 0:00:07, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0263, top-1: 98.8000, loss: 0.0263\n",
            "2023-12-01 12:10:07,802 - mmdet - INFO - Epoch [15][900/988]\tlr: 1.000e-02, eta: 0:00:04, time: 0.051, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0048, top-1: 99.6000, loss: 0.0048\n",
            "2023-12-01 12:10:10,327 - mmdet - INFO - Epoch [15][950/988]\tlr: 1.000e-02, eta: 0:00:01, time: 0.050, data_time: 0.002, memory: 3663, triplet_loss: 0.0000, ce_loss: 0.0282, top-1: 99.2000, loss: 0.0282\n",
            "2023-12-01 12:10:12,294 - mmdet - INFO - Saving checkpoint at 15 epochs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "\n",
        "/content/mmtracking/configs/_base_/datasets/mot_challenge.py\n",
        "\n",
        "\n",
        "위 경로에서 아래 내용 복붙\n",
        "\n",
        "\n",
        "# dataset settings\n",
        "dataset_type = 'MOTChallengeDataset'\n",
        "img_norm_cfg = dict(\n",
        "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
        "train_pipeline = [\n",
        "    dict(type='LoadMultiImagesFromFile', to_float32=True),\n",
        "    dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),\n",
        "    dict(\n",
        "        type='SeqResize',\n",
        "        img_scale=(1088, 1088),\n",
        "        share_params=True,\n",
        "        ratio_range=(0.8, 1.2),\n",
        "        keep_ratio=True,\n",
        "        bbox_clip_border=False),\n",
        "    dict(type='SeqPhotoMetricDistortion', share_params=True),\n",
        "    dict(\n",
        "        type='SeqRandomCrop',\n",
        "        share_params=False,\n",
        "        crop_size=(1088, 1088),\n",
        "        bbox_clip_border=False),\n",
        "    dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),\n",
        "    dict(type='SeqNormalize', **img_norm_cfg),\n",
        "    dict(type='SeqPad', size_divisor=32),\n",
        "    dict(type='MatchInstances', skip_nomatch=True),\n",
        "    dict(\n",
        "        type='VideoCollect',\n",
        "        keys=[\n",
        "            'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices',\n",
        "            'gt_instance_ids'\n",
        "        ]),\n",
        "    dict(type='SeqDefaultFormatBundle', ref_prefix='ref')\n",
        "]\n",
        "test_pipeline = [\n",
        "    dict(type='LoadImageFromFile'),\n",
        "    dict(\n",
        "        type='MultiScaleFlipAug',\n",
        "        img_scale=(1088, 1088),\n",
        "        flip=False,\n",
        "        transforms=[\n",
        "            dict(type='Resize', keep_ratio=True),\n",
        "            dict(type='RandomFlip'),\n",
        "            dict(type='Normalize', **img_norm_cfg),\n",
        "            dict(type='Pad', size_divisor=32),\n",
        "            dict(type='ImageToTensor', keys=['img']),\n",
        "            dict(type='VideoCollect', keys=['img'])\n",
        "        ])\n",
        "]\n",
        "data_root = 'data/MOT17_tiny/'\n",
        "data = dict(\n",
        "    samples_per_gpu=2,\n",
        "    workers_per_gpu=2,\n",
        "    train=dict(\n",
        "        type=dataset_type,\n",
        "        visibility_thr=-1,\n",
        "        ann_file=data_root + 'annotations/train_cocoformat.json',\n",
        "        img_prefix=data_root + 'train',\n",
        "        ref_img_sampler=dict(\n",
        "            num_ref_imgs=1,\n",
        "            frame_range=10,\n",
        "            filter_key_img=True,\n",
        "            method='uniform'),\n",
        "        classes=('pedestrian', ),\n",
        "        pipeline=train_pipeline),\n",
        "    val=dict(\n",
        "        type=dataset_type,\n",
        "        ann_file=data_root + 'annotations/half-val_cocoformat.json',\n",
        "        img_prefix=data_root + 'train',\n",
        "        ref_img_sampler=None,\n",
        "        classes=('pedestrian', ),\n",
        "        pipeline=test_pipeline),\n",
        "    test=dict(\n",
        "        type=dataset_type,\n",
        "        ann_file=data_root + 'annotations/half-val_cocoformat.json',\n",
        "        img_prefix=data_root + 'train',\n",
        "        ref_img_sampler=None,\n",
        "        classes=('pedestrian', ),\n",
        "        pipeline=test_pipeline))\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "yodOwktNv4hU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import mmcv\n",
        "from mmdet.apis import set_random_seed\n",
        "\n",
        "cfg = mmcv.Config.fromfile('./configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py')\n",
        "\n",
        "cfg.model.detector.init_cfg.checkpoint = './tutorial_exps/detector/epoch_10.pth'\n",
        "cfg.model.reid.init_cfg.checkpoint = './tutorial_exps/reid/epoch_15.pth'\n",
        "\n",
        "cfg.work_dir = './tutorial_exps'\n",
        "cfg.seed = 0\n",
        "set_random_seed(0, deterministic=False)\n",
        "cfg.gpu_ids = range(1)\n",
        "cfg.data.test.test_mode = True\n",
        "cfg.device='cuda'\n",
        "\n",
        "print(cfg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBMV69niv4kg",
        "outputId": "26f63ec1-28b2-49ff-e94b-8031fd8bd19b"
      },
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Config (path: ./configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py): {'model': {'detector': {'type': 'FasterRCNN', 'backbone': {'type': 'ResNet', 'depth': 50, 'num_stages': 4, 'out_indices': (0, 1, 2, 3), 'frozen_stages': 1, 'norm_cfg': {'type': 'BN', 'requires_grad': True}, 'norm_eval': True, 'style': 'pytorch', 'init_cfg': {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}}, 'neck': {'type': 'FPN', 'in_channels': [256, 512, 1024, 2048], 'out_channels': 256, 'num_outs': 5}, 'rpn_head': {'type': 'RPNHead', 'in_channels': 256, 'feat_channels': 256, 'anchor_generator': {'type': 'AnchorGenerator', 'scales': [8], 'ratios': [0.5, 1.0, 2.0], 'strides': [4, 8, 16, 32, 64]}, 'bbox_coder': {'type': 'DeltaXYWHBBoxCoder', 'target_means': [0.0, 0.0, 0.0, 0.0], 'target_stds': [1.0, 1.0, 1.0, 1.0], 'clip_border': False}, 'loss_cls': {'type': 'CrossEntropyLoss', 'use_sigmoid': True, 'loss_weight': 1.0}, 'loss_bbox': {'type': 'SmoothL1Loss', 'beta': 0.1111111111111111, 'loss_weight': 1.0}}, 'roi_head': {'type': 'StandardRoIHead', 'bbox_roi_extractor': {'type': 'SingleRoIExtractor', 'roi_layer': {'type': 'RoIAlign', 'output_size': 7, 'sampling_ratio': 0}, 'out_channels': 256, 'featmap_strides': [4, 8, 16, 32]}, 'bbox_head': {'type': 'Shared2FCBBoxHead', 'in_channels': 256, 'fc_out_channels': 1024, 'roi_feat_size': 7, 'num_classes': 1, 'bbox_coder': {'type': 'DeltaXYWHBBoxCoder', 'target_means': [0.0, 0.0, 0.0, 0.0], 'target_stds': [0.1, 0.1, 0.2, 0.2], 'clip_border': False}, 'reg_class_agnostic': False, 'loss_cls': {'type': 'CrossEntropyLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'loss_bbox': {'type': 'SmoothL1Loss', 'loss_weight': 1.0}}}, 'train_cfg': {'rpn': {'assigner': {'type': 'MaxIoUAssigner', 'pos_iou_thr': 0.7, 'neg_iou_thr': 0.3, 'min_pos_iou': 0.3, 'match_low_quality': True, 'ignore_iof_thr': -1}, 'sampler': {'type': 'RandomSampler', 'num': 256, 'pos_fraction': 0.5, 'neg_pos_ub': -1, 'add_gt_as_proposals': False}, 'allowed_border': -1, 'pos_weight': -1, 'debug': False}, 'rpn_proposal': {'nms_pre': 2000, 'max_per_img': 1000, 'nms': {'type': 'nms', 'iou_threshold': 0.7}, 'min_bbox_size': 0}, 'rcnn': {'assigner': {'type': 'MaxIoUAssigner', 'pos_iou_thr': 0.5, 'neg_iou_thr': 0.5, 'min_pos_iou': 0.5, 'match_low_quality': False, 'ignore_iof_thr': -1}, 'sampler': {'type': 'RandomSampler', 'num': 512, 'pos_fraction': 0.25, 'neg_pos_ub': -1, 'add_gt_as_proposals': True}, 'pos_weight': -1, 'debug': False}}, 'test_cfg': {'rpn': {'nms_pre': 1000, 'max_per_img': 1000, 'nms': {'type': 'nms', 'iou_threshold': 0.7}, 'min_bbox_size': 0}, 'rcnn': {'score_thr': 0.05, 'nms': {'type': 'nms', 'iou_threshold': 0.5}, 'max_per_img': 100}}, 'init_cfg': {'type': 'Pretrained', 'checkpoint': './tutorial_exps/detector/epoch_10.pth'}}, 'type': 'DeepSORT', 'motion': {'type': 'KalmanFilter', 'center_only': False}, 'reid': {'type': 'BaseReID', 'backbone': {'type': 'ResNet', 'depth': 50, 'num_stages': 4, 'out_indices': (3,), 'style': 'pytorch'}, 'neck': {'type': 'GlobalAveragePooling', 'kernel_size': (8, 4), 'stride': 1}, 'head': {'type': 'LinearReIDHead', 'num_fcs': 1, 'in_channels': 2048, 'fc_channels': 1024, 'out_channels': 128, 'num_classes': 380, 'loss': {'type': 'CrossEntropyLoss', 'loss_weight': 1.0}, 'loss_pairwise': {'type': 'TripletLoss', 'margin': 0.3, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN1d'}, 'act_cfg': {'type': 'ReLU'}}, 'init_cfg': {'type': 'Pretrained', 'checkpoint': './tutorial_exps/reid/epoch_15.pth'}}, 'tracker': {'type': 'SortTracker', 'obj_score_thr': 0.5, 'reid': {'num_samples': 10, 'img_scale': (256, 128), 'img_norm_cfg': None, 'match_score_thr': 2.0}, 'match_iou_thr': 0.5, 'momentums': None, 'num_tentatives': 2, 'num_frames_retain': 100}}, 'dataset_type': 'MOTChallengeDataset', 'img_norm_cfg': {'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, 'train_pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqLoadAnnotations', 'with_bbox': True, 'with_track': True}, {'type': 'SeqResize', 'img_scale': (1088, 1088), 'share_params': True, 'ratio_range': (0.8, 1.2), 'keep_ratio': True, 'bbox_clip_border': False}, {'type': 'SeqPhotoMetricDistortion', 'share_params': True}, {'type': 'SeqRandomCrop', 'share_params': False, 'crop_size': (1088, 1088), 'bbox_clip_border': False}, {'type': 'SeqRandomFlip', 'share_params': True, 'flip_ratio': 0.5}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'SeqPad', 'size_divisor': 32}, {'type': 'MatchInstances', 'skip_nomatch': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices', 'gt_instance_ids']}, {'type': 'SeqDefaultFormatBundle', 'ref_prefix': 'ref'}], 'test_pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1088, 1088), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'VideoCollect', 'keys': ['img']}]}], 'data_root': 'data/MOT17_tiny/', 'data': {'samples_per_gpu': 2, 'workers_per_gpu': 2, 'train': {'type': 'MOTChallengeDataset', 'visibility_thr': -1, 'ann_file': 'data/MOT17_tiny/annotations/train_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'ref_img_sampler': {'num_ref_imgs': 1, 'frame_range': 10, 'filter_key_img': True, 'method': 'uniform'}, 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqLoadAnnotations', 'with_bbox': True, 'with_track': True}, {'type': 'SeqResize', 'img_scale': (1088, 1088), 'share_params': True, 'ratio_range': (0.8, 1.2), 'keep_ratio': True, 'bbox_clip_border': False}, {'type': 'SeqPhotoMetricDistortion', 'share_params': True}, {'type': 'SeqRandomCrop', 'share_params': False, 'crop_size': (1088, 1088), 'bbox_clip_border': False}, {'type': 'SeqRandomFlip', 'share_params': True, 'flip_ratio': 0.5}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'SeqPad', 'size_divisor': 32}, {'type': 'MatchInstances', 'skip_nomatch': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices', 'gt_instance_ids']}, {'type': 'SeqDefaultFormatBundle', 'ref_prefix': 'ref'}]}, 'val': {'type': 'MOTChallengeDataset', 'ann_file': 'data/MOT17_tiny/annotations/half-val_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'ref_img_sampler': None, 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1088, 1088), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'VideoCollect', 'keys': ['img']}]}]}, 'test': {'type': 'MOTChallengeDataset', 'ann_file': 'data/MOT17_tiny/annotations/half-val_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'ref_img_sampler': None, 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1088, 1088), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'VideoCollect', 'keys': ['img']}]}], 'test_mode': True}}, 'optimizer': {'type': 'SGD', 'lr': 0.02, 'momentum': 0.9, 'weight_decay': 0.0001}, 'optimizer_config': {'grad_clip': None}, 'checkpoint_config': {'interval': 1}, 'log_config': {'interval': 50, 'hooks': [{'type': 'TextLoggerHook'}]}, 'dist_params': {'backend': 'nccl'}, 'log_level': 'INFO', 'load_from': None, 'resume_from': None, 'workflow': [('train', 1)], 'opencv_num_threads': 0, 'mp_start_method': 'fork', 'lr_config': {'policy': 'step', 'warmup': 'linear', 'warmup_iters': 100, 'warmup_ratio': 0.01, 'step': [3]}, 'total_epochs': 4, 'evaluation': {'metric': ['bbox', 'track'], 'interval': 1}, 'search_metrics': ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML'], 'work_dir': './tutorial_exps', 'seed': 0, 'gpu_ids': range(0, 1), 'device': 'cuda'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cfg.data.test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpzkfYnlFybC",
        "outputId": "6d8b8c25-597a-4032-fc9b-d3bc5ead89c7"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': 'MOTChallengeDataset',\n",
              " 'ann_file': 'data/MOT17_tiny/annotations/half-val_cocoformat.json',\n",
              " 'img_prefix': 'data/MOT17_tiny/train',\n",
              " 'ref_img_sampler': None,\n",
              " 'classes': ('pedestrian',),\n",
              " 'pipeline': [{'type': 'LoadImageFromFile'},\n",
              "  {'type': 'MultiScaleFlipAug',\n",
              "   'img_scale': (1088, 1088),\n",
              "   'flip': False,\n",
              "   'transforms': [{'type': 'Resize', 'keep_ratio': True},\n",
              "    {'type': 'RandomFlip'},\n",
              "    {'type': 'Normalize',\n",
              "     'mean': [123.675, 116.28, 103.53],\n",
              "     'std': [58.395, 57.12, 57.375],\n",
              "     'to_rgb': True},\n",
              "    {'type': 'Pad', 'size_divisor': 32},\n",
              "    {'type': 'ImageToTensor', 'keys': ['img']},\n",
              "    {'type': 'VideoCollect', 'keys': ['img']}]}],\n",
              " 'test_mode': True}"
            ]
          },
          "metadata": {},
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mmtrack.datasets import build_dataloader\n",
        "from mmtrack.apis import init_model\n",
        "from mmcv.parallel import MMDataParallel\n",
        "from mmtrack.apis import single_gpu_test\n",
        "from mmtrack.datasets import build_dataset\n",
        "\n",
        "dataset = build_dataset(cfg.data.test)\n",
        "data_loader = build_dataloader(\n",
        "    dataset,\n",
        "    samples_per_gpu=1,\n",
        "    workers_per_gpu=cfg.data.workers_per_gpu,\n",
        "    dist=False,\n",
        "    shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AtKZ2PKv4wI",
        "outputId": "ad0822ca-c918-4c3c-9809-13deec634ee8"
      },
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGEw83XtIq0q",
        "outputId": "6db375cd-150f-477a-c450-944da8d1ea58"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "MOTChallengeDataset Test dataset with number of images 121, and instance counts: \n",
              "+----------------+-------+---------------+-------+----------+-------+----------+-------+----------+-------+\n",
              "| category       | count | category      | count | category | count | category | count | category | count |\n",
              "+----------------+-------+---------------+-------+----------+-------+----------+-------+----------+-------+\n",
              "|                |       |               |       |          |       |          |       |          |       |\n",
              "| 0 [pedestrian] | 373   | -1 background | 29    |          |       |          |       |          |       |\n",
              "+----------------+-------+---------------+-------+----------+-------+----------+-------+----------+-------+"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.CLASSES"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlkwPESSmcUO",
        "outputId": "87d1388f-f762-432d-96e6-246fd310a1a7"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('pedestrian',)"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# build the model and load checkpoint\n",
        "model = init_model(cfg)\n",
        "\n",
        "model = MMDataParallel(model, device_ids=cfg.gpu_ids)\n",
        "model.CLASSES = dataset.CLASSES\n",
        "outputs = single_gpu_test(model, data_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CS-IOto5JOwe",
        "outputId": "5cb46724-3158-41f7-b752-6944bd71977a"
      },
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-01 14:20:06,736 - mmcv - INFO - initialize FasterRCNN with init_cfg {'type': 'Pretrained', 'checkpoint': './tutorial_exps/detector/epoch_10.pth'}\n",
            "2023-12-01 14:20:06,738 - mmcv - INFO - load model from: ./tutorial_exps/detector/epoch_10.pth\n",
            "2023-12-01 14:20:06,739 - mmcv - INFO - load checkpoint from local path: ./tutorial_exps/detector/epoch_10.pth\n",
            "2023-12-01 14:20:06,989 - mmcv - INFO - initialize BaseReID with init_cfg {'type': 'Pretrained', 'checkpoint': './tutorial_exps/reid/epoch_15.pth'}\n",
            "2023-12-01 14:20:06,991 - mmcv - INFO - load model from: ./tutorial_exps/reid/epoch_15.pth\n",
            "2023-12-01 14:20:06,992 - mmcv - INFO - load checkpoint from local path: ./tutorial_exps/reid/epoch_15.pth\n",
            "2023-12-01 14:20:07,139 - mmcv - INFO - \n",
            "detector.backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,140 - mmcv - INFO - \n",
            "detector.backbone.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,141 - mmcv - INFO - \n",
            "detector.backbone.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,143 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,144 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,148 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,149 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,150 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,151 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,154 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,155 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn3.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,156 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,157 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,159 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,160 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,161 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,163 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,165 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,173 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,175 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,178 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,179 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,181 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn3.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,182 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,183 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,184 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,185 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,186 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,187 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,189 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,189 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,195 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn3.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,196 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 14:20:07,197 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,199 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,200 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,201 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,202 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,206 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,207 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,208 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,209 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,210 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,211 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,212 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,215 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,217 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,218 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,220 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,221 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,223 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,224 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,225 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,226 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,229 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,230 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,231 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,232 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,233 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,234 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,235 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,239 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,240 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,241 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,242 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,243 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,244 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,245 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,246 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,251 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,252 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,253 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,254 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,255 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,256 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,257 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,258 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,259 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,260 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,266 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,268 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,269 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,271 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,273 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,274 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,275 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,276 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,279 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,280 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,281 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,282 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,285 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,287 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,289 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,290 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,291 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,293 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,294 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,295 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,297 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,298 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,299 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,301 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,302 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,303 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,305 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,306 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,307 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,309 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,310 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,311 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,313 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,314 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,315 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,317 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,318 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,319 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,321 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,322 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,323 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,329 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,330 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,331 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,332 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,334 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,335 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,336 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,337 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,339 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,340 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,341 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,343 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,344 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,346 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,347 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,348 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,350 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,351 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,353 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,354 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,356 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,357 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,358 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,360 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,361 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,362 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,363 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,364 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,365 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,366 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,367 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,390 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,392 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,393 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,394 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,395 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,396 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,400 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,401 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,403 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,404 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.0.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,405 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,406 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.1.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,407 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,409 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.2.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,410 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,411 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.3.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,412 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,413 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.0.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,415 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,416 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.1.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,416 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,418 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.2.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,419 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,420 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.3.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,421 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,422 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,423 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,424 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_cls.bias - torch.Size([3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,425 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,426 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_reg.bias - torch.Size([12]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,431 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_cls.weight - torch.Size([2, 1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,437 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_cls.bias - torch.Size([2]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,438 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_reg.weight - torch.Size([4, 1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,440 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_reg.bias - torch.Size([4]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,441 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,442 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,443 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,444 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_10.pth \n",
            " \n",
            "2023-12-01 14:20:07,445 - mmcv - INFO - \n",
            "reid.backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,446 - mmcv - INFO - \n",
            "reid.backbone.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,447 - mmcv - INFO - \n",
            "reid.backbone.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,455 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,456 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,457 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,458 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,461 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,462 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,463 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,464 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,465 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,469 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,470 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,471 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,472 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,473 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,474 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,478 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,481 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,482 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,483 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,485 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,486 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,488 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,489 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,490 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,492 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,493 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,495 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,496 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,497 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,498 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,500 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,501 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,502 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,504 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,505 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,507 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,518 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,519 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,521 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,523 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,524 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,525 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,528 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,529 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,530 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,531 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,532 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,533 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,534 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,536 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,537 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,538 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,539 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,540 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,541 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,550 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,551 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,553 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,554 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,555 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,558 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,560 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,561 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,562 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,565 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,566 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,567 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,568 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,569 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,572 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,574 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,575 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,576 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,577 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,578 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,581 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,582 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,583 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,586 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,588 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,589 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,590 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,592 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,593 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,594 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,595 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,596 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,598 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,599 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,600 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,602 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,603 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,604 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,605 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,607 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,616 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,617 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,618 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,619 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,620 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,621 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,622 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,624 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,625 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,626 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,627 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,629 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,630 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,631 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,639 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,640 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,641 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,642 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,643 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,645 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,646 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,647 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,651 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,652 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,654 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,654 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,655 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,659 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,660 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,661 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,662 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,664 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,667 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,669 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,670 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,671 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,672 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,676 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,677 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,678 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,679 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,682 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,683 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,685 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,686 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,687 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,688 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,691 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,692 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,693 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,695 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,696 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,699 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,700 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,701 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,702 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,705 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,707 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,708 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,709 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,710 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,711 - mmcv - INFO - \n",
            "reid.head.fcs.0.fc.weight - torch.Size([1024, 2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,712 - mmcv - INFO - \n",
            "reid.head.fcs.0.fc.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,713 - mmcv - INFO - \n",
            "reid.head.fcs.0.bn.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,717 - mmcv - INFO - \n",
            "reid.head.fcs.0.bn.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,718 - mmcv - INFO - \n",
            "reid.head.fc_out.weight - torch.Size([128, 1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,719 - mmcv - INFO - \n",
            "reid.head.fc_out.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,721 - mmcv - INFO - \n",
            "reid.head.bn.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,722 - mmcv - INFO - \n",
            "reid.head.bn.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,726 - mmcv - INFO - \n",
            "reid.head.classifier.weight - torch.Size([380, 128]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n",
            "2023-12-01 14:20:07,727 - mmcv - INFO - \n",
            "reid.head.classifier.bias - torch.Size([380]): \n",
            "PretrainedInit: load from ./tutorial_exps/reid/epoch_15.pth \n",
            " \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: The model doesn't have classes\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 121/121, 9.0 task/s, elapsed: 13s, ETA:     0s"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_kwargs = cfg.get('evaluation', {}).copy()\n",
        "# hard-code way to remove EvalHook args\n",
        "eval_hook_args = [\n",
        "    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',\n",
        "    'rule', 'by_epoch'\n",
        "]\n",
        "for key in eval_hook_args:\n",
        "    eval_kwargs.pop(key, None)\n",
        "eval_kwargs.update(dict(metric=['track']))\n",
        "metric = dataset.evaluate(outputs, **eval_kwargs)\n",
        "\n",
        "print(metric)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cxbcmDeFF-mY",
        "outputId": "2d46b862-836f-4292-9011-7293cc17bebd"
      },
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate CLEAR MOT results.\n",
            "\n",
            "Eval Config:\n",
            "USE_PARALLEL         : False                         \n",
            "NUM_PARALLEL_CORES   : 8                             \n",
            "BREAK_ON_ERROR       : True                          \n",
            "RETURN_ON_ERROR      : False                         \n",
            "LOG_ON_ERROR         : /usr/local/lib/python3.10/dist-packages/error_log.txt\n",
            "PRINT_RESULTS        : True                          \n",
            "PRINT_ONLY_COMBINED  : False                         \n",
            "PRINT_CONFIG         : True                          \n",
            "TIME_PROGRESS        : True                          \n",
            "DISPLAY_LESS_PROGRESS : True                          \n",
            "OUTPUT_SUMMARY       : True                          \n",
            "OUTPUT_EMPTY_CLASSES : True                          \n",
            "OUTPUT_DETAILED      : True                          \n",
            "PLOT_CURVES          : True                          \n",
            "\n",
            "MotChallenge2DBox Config:\n",
            "GT_FOLDER            : /tmp/tmp7uq0kf9w              \n",
            "TRACKERS_FOLDER      : /tmp/tmp05q98_c7              \n",
            "OUTPUT_FOLDER        : None                          \n",
            "TRACKERS_TO_EVAL     : ['track']                     \n",
            "CLASSES_TO_EVAL      : ['pedestrian']                \n",
            "BENCHMARK            : MOT17                         \n",
            "SPLIT_TO_EVAL        : train                         \n",
            "INPUT_AS_ZIP         : False                         \n",
            "PRINT_CONFIG         : True                          \n",
            "DO_PREPROC           : True                          \n",
            "TRACKER_SUB_FOLDER   :                               \n",
            "OUTPUT_SUB_FOLDER    :                               \n",
            "TRACKER_DISPLAY_NAMES : None                          \n",
            "SEQMAP_FOLDER        : None                          \n",
            "SEQMAP_FILE          : /tmp/tmp05q98_c7/videoseq.txt \n",
            "SEQ_INFO             : None                          \n",
            "GT_LOC_FORMAT        : {gt_folder}/{seq}/gt/gt_half-val.txt\n",
            "SKIP_SPLIT_FOL       : True                          \n",
            "\n",
            "Evaluating 1 tracker(s) on 1 sequence(s) for 1 class(es) on MotChallenge2DBox dataset using the following metrics: HOTA, Count\n",
            "\n",
            "\n",
            "Evaluating track\n",
            "\n",
            "36 eval_sequence(MOT17-02-FRCNN, track)                                   0.0979 sec\n",
            "\n",
            "All sequences for track finished in 0.10 seconds\n",
            "\n",
            "HOTA: track-pedestrian             HOTA      DetA      AssA      DetRe     DetPr     AssRe     AssPr     LocA      OWTA      HOTA(0)   LocA(0)   HOTALocA(0)\n",
            "MOT17-02-FRCNN                     75.114    73.513    76.895    78.99     78.152    80.954    81.175    81.698    77.926    98.548    77.938    76.806    \n",
            "COMBINED                           75.114    73.513    76.895    78.99     78.152    80.954    81.175    81.698    77.926    98.548    77.938    76.806    \n",
            "\n",
            "Count: track-pedestrian            Dets      GT_Dets   IDs       GT_IDs    \n",
            "MOT17-02-FRCNN                     377       373       8         5         \n",
            "COMBINED                           377       373       8         5         \n",
            "                IDF1   IDP   IDR  Rcll  Prcn GT MT PT ML FP FN IDs  FM  MOTA  MOTP IDt IDa IDm      HOTA\n",
            "MOT17-02-FRCNN 96.5% 96.0% 97.1% 97.1% 96.0%  5  5  0  0 15 11   0   1 93.0% 0.212   0   0   0  0.751145\n",
            "OVERALL        96.5% 96.0% 97.1% 97.1% 96.0%  5  5  0  0 15 11   0   1 93.0% 0.212   0   0   0  0.751145\n",
            "{'IDF1': 0.965, 'IDP': 0.96, 'IDR': 0.971, 'Rcll': 0.971, 'Prcn': 0.96, 'GT': 5, 'MT': 5, 'PT': 0, 'ML': 0, 'FP': 15, 'FN': 11, 'IDs': 0, 'FM': 1, 'MOTA': 0.93, 'MOTP': 0.212, 'IDt': 0, 'IDa': 0, 'IDm': 0, 'HOTA': 0.751}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Video Inference 수행\n",
        "# 3분 소모\n",
        "\n",
        "from mmtrack.apis import inference_mot, init_model\n",
        "from mmdet.apis import init_detector, inference_detector\n",
        "\n",
        "mot_config = './configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'\n",
        "input_video = '/content/gdrive/MyDrive/Colab Notebooks/DeepSORT_inference_video/pexels-kindel-media-8824697 (Original).mp4'\n",
        "imgs = mmcv.VideoReader(input_video)\n",
        "\n",
        "# build the model from a config file\n",
        "mot_model = init_model(mot_config, device='cuda:0')\n",
        "prog_bar = mmcv.ProgressBar(len(imgs))\n",
        "out_dir = tempfile.TemporaryDirectory()\n",
        "out_path = out_dir.name\n",
        "\n",
        "# test and show/save the images(inference)\n",
        "for i, img in enumerate(imgs):\n",
        "    result = inference_mot(mot_model, img, frame_id=i)\n",
        "    mot_model.show_result(\n",
        "            img,\n",
        "            result,\n",
        "            show=False,\n",
        "            wait_time=int(1000. / imgs.fps),\n",
        "            out_file=f'{out_path}/{i:06d}.jpg')\n",
        "    prog_bar.update()\n",
        "\n",
        "output = '/content/mmtracking/tutorial_exps/Tracking_result_video.mp4'\n",
        "print(f'\\n making the output video at {output} with a FPS of {imgs.fps}')\n",
        "mmcv.frames2video(out_path, output, fps=imgs.fps, fourcc='mp4v')\n",
        "out_dir.cleanup()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "361a3dbb0fb149c3816fc3720fc15dc1",
            "04c2972c987f447384ab340056c7eb73",
            "299288c432be423baabad7cffea3a3c7",
            "732122d5b9a84f8998ee81a90472691d",
            "8298676e9e8049d08cc08b7ad9053ec5",
            "bfe88756f5d0412aa9915adaea781cc3",
            "840d6fb086b54d5ea3e8e8cf86ff6c8c",
            "d1fbf841a2c94917ab83a2504b60ba2f",
            "251f4b6f0c134f45ae6242401a3e8713",
            "f8a6e4bb1ca9450e8a7c81c563fd37ba",
            "701a12458009418bb34c0f7a5a33f198",
            "b5428b166e424bbf961089311c53bafa",
            "6eaf9be128a94277bec2e80bd53a3c71",
            "b7e54bcd2d86478c8920e5ae9366edb3",
            "e3e6abea090f4d61a39876c7eb36a43c",
            "51d82e81d6c144c581d5ce6d43025f2d",
            "34849f07c5b74c10b358e1254924a740",
            "827e7241e2d94c5ea71fcaef9cb26e4f",
            "7d162328ce71451682990ce47b13088c",
            "723a36385ccc4809a04dfe52321773c5",
            "646eb6b0525f49a3b82ae7197a122dac",
            "26e09313b7664e7ebd46cba212431cfe"
          ]
        },
        "id": "iyRLDEr3xNxV",
        "outputId": "a02e14aa-ea10-4fae-8223-357273465af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-11-30 10:23:05,727 - mmcv - INFO - initialize FasterRCNN with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth'}\n",
            "2023-11-30 10:23:05,729 - mmcv - INFO - load model from: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth\n",
            "2023-11-30 10:23:05,731 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth\n",
            "Downloading: \"https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth\" to /root/.cache/torch/hub/checkpoints/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "361a3dbb0fb149c3816fc3720fc15dc1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-11-30 10:23:11,715 - mmcv - INFO - initialize BaseReID with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth'}\n",
            "2023-11-30 10:23:11,716 - mmcv - INFO - load model from: https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth\n",
            "2023-11-30 10:23:11,718 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth\n",
            "Downloading: \"https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth\" to /root/.cache/torch/hub/checkpoints/tracktor_reid_r50_iter25245-a452f51f.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5428b166e424bbf961089311c53bafa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-11-30 10:23:19,851 - mmcv - WARNING - The model and loaded state dict do not match exactly\n",
            "\n",
            "missing keys in source state_dict: head.bn.weight, head.bn.bias, head.bn.running_mean, head.bn.running_var, head.classifier.weight, head.classifier.bias\n",
            "\n",
            "2023-11-30 10:23:19,875 - mmcv - INFO - \n",
            "detector.backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,876 - mmcv - INFO - \n",
            "detector.backbone.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,877 - mmcv - INFO - \n",
            "detector.backbone.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,878 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,880 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,881 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,881 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,883 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,886 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,887 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,888 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,889 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,890 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,891 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,894 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,895 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,896 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,898 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,899 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,900 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,901 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,903 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,904 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,905 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,905 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,907 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,907 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,908 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,912 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,913 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,914 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,914 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,915 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,916 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,917 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,918 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,919 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,923 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,923 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,925 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,927 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,928 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,930 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,931 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,933 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,934 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,935 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,936 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,937 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,939 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,940 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,941 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,942 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,943 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,944 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,945 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,950 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,951 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,952 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,953 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,954 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,955 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,956 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,960 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,961 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,962 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,963 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,964 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,965 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,968 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,969 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,971 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,972 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,973 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,975 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,976 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,977 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,978 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,980 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,981 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,982 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,983 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,985 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,986 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,987 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,990 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,991 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,992 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,998 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,999 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:19,999 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,002 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,003 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,004 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,005 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,006 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,008 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,009 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,010 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,011 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,012 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,013 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,014 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,015 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,016 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,017 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,018 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,019 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,020 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,027 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,029 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,030 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,031 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,033 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,034 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,035 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,036 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,037 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,038 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,040 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,041 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,042 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,043 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,044 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,049 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,050 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,052 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,053 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,055 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,056 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,057 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,058 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,060 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,062 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,063 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,064 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,065 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,066 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,068 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,069 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,071 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,072 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,073 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,074 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,076 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,077 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,078 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,079 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,081 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,082 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,083 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,084 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,085 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,086 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,089 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,090 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,091 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,092 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,093 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,095 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,105 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.0.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,106 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,108 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.1.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,109 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,112 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.2.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,114 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,115 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.3.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,116 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,119 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.0.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,121 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,123 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.1.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,125 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,125 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.2.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,127 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,131 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.3.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,131 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,132 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,135 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,136 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_cls.bias - torch.Size([3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,137 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,138 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_reg.bias - torch.Size([12]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,139 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_cls.weight - torch.Size([2, 1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,140 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_cls.bias - torch.Size([2]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,141 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_reg.weight - torch.Size([4, 1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,143 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_reg.bias - torch.Size([4]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,144 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,149 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,150 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,152 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth \n",
            " \n",
            "2023-11-30 10:23:20,153 - mmcv - INFO - \n",
            "reid.backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,154 - mmcv - INFO - \n",
            "reid.backbone.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,155 - mmcv - INFO - \n",
            "reid.backbone.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,157 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,158 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,159 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,160 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,162 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,163 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,165 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,166 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,172 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,173 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,176 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,177 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,180 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,181 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,182 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,184 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,185 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,188 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,189 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,190 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,191 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,194 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,195 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn1.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,196 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn1.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,197 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,198 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn2.weight - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,204 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn2.bias - torch.Size([64]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,204 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,206 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn3.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,207 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn3.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,208 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,210 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,211 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,212 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,213 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,214 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,215 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,216 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,221 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,222 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,222 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,223 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,224 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,227 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,228 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,229 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,230 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,233 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,234 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,235 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,237 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,239 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,240 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,241 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,243 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,244 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,245 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,247 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,248 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,249 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,250 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,251 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,254 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,255 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,256 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,257 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,260 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,261 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,263 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,264 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,265 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,266 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,267 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,268 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,272 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,273 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,276 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,276 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,278 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,279 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,280 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,283 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,284 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,285 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,286 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,287 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,290 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,291 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,293 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,294 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,295 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,297 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,298 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,299 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,300 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,301 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,302 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,303 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,304 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,305 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,306 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,312 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,313 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,313 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,314 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,315 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,316 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,317 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,320 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,324 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,325 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,327 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,328 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,329 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,332 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,333 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,334 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,337 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,338 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,339 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,340 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,340 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,342 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,343 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,344 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,345 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,346 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,347 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,348 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,349 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,356 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,356 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,357 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,358 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,361 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,363 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,364 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,365 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,366 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,367 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,369 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,370 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,373 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,373 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,375 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,375 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,376 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,379 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,380 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,381 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,382 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,383 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,384 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,385 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,386 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,387 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,391 - mmcv - INFO - \n",
            "reid.head.fcs.0.fc.weight - torch.Size([1024, 2048]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,392 - mmcv - INFO - \n",
            "reid.head.fcs.0.fc.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,394 - mmcv - INFO - \n",
            "reid.head.fcs.0.bn.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,396 - mmcv - INFO - \n",
            "reid.head.fcs.0.bn.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,397 - mmcv - INFO - \n",
            "reid.head.fc_out.weight - torch.Size([128, 1024]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,398 - mmcv - INFO - \n",
            "reid.head.fc_out.bias - torch.Size([128]): \n",
            "PretrainedInit: load from https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth \n",
            " \n",
            "2023-11-30 10:23:20,398 - mmcv - INFO - \n",
            "reid.head.bn.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-11-30 10:23:20,399 - mmcv - INFO - \n",
            "reid.head.bn.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-11-30 10:23:20,400 - mmcv - INFO - \n",
            "reid.head.classifier.weight - torch.Size([380, 128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-11-30 10:23:20,401 - mmcv - INFO - \n",
            "reid.head.classifier.bias - torch.Size([380]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: The model doesn't have classes\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 304/304, 3.8 task/s, elapsed: 81s, ETA:     0s\n",
            " making the output video at /content/mmtracking/tutorial_exps/Tracking_result_video.mp4 with a FPS of 30.0\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 304/304, 6.5 task/s, elapsed: 47s, ETA:     0s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ReID Model을 이용하지 않고 DeepSORT를 이용한 Object Tracking을 수행하는 경우"
      ],
      "metadata": {
        "id": "HTL-MD4cNRg1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "\n",
        "/content/mmtracking/configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py\n",
        "\n",
        "위 경로에서 아래내용 복붙\n",
        "\n",
        "_base_ = [\n",
        "    '../../_base_/models/faster_rcnn_r50_fpn.py',\n",
        "    '../../_base_/datasets/mot_challenge.py', '../../_base_/default_runtime.py'\n",
        "]\n",
        "model = dict(\n",
        "    type='DeepSORT',\n",
        "    detector=dict(\n",
        "        rpn_head=dict(bbox_coder=dict(clip_border=False)),\n",
        "        roi_head=dict(\n",
        "            bbox_head=dict(bbox_coder=dict(clip_border=False), num_classes=1)),\n",
        "        init_cfg=dict(\n",
        "            type='Pretrained',\n",
        "            checkpoint=  # noqa: E251\n",
        "            'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth'  # noqa: E501\n",
        "        )),\n",
        "    motion=dict(type='KalmanFilter', center_only=False),\n",
        "    reid=dict(\n",
        "        type='BaseReID',\n",
        "        backbone=dict(\n",
        "            type='ResNet',\n",
        "            depth=50,\n",
        "            num_stages=4,\n",
        "            out_indices=(3, ),\n",
        "            style='pytorch'),\n",
        "        neck=dict(type='GlobalAveragePooling', kernel_size=(8, 4), stride=1),\n",
        "        head=dict(\n",
        "            type='LinearReIDHead',\n",
        "            num_fcs=1,\n",
        "            in_channels=2048,\n",
        "            fc_channels=1024,\n",
        "            out_channels=128,\n",
        "            num_classes=380,\n",
        "            loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n",
        "            loss_pairwise=dict(\n",
        "                type='TripletLoss', margin=0.3, loss_weight=1.0),\n",
        "            norm_cfg=dict(type='BN1d'),\n",
        "            act_cfg=dict(type='ReLU')),\n",
        "        ),\n",
        "    tracker=dict(\n",
        "        type='SortTracker',\n",
        "        obj_score_thr=0.5,\n",
        "        reid=dict(\n",
        "            num_samples=10,\n",
        "            img_scale=(256, 128),\n",
        "            img_norm_cfg=None,\n",
        "            match_score_thr=2.0),\n",
        "        match_iou_thr=0.5,\n",
        "        momentums=None,\n",
        "        num_tentatives=2,\n",
        "        num_frames_retain=100))\n",
        "# learning policy\n",
        "lr_config = dict(\n",
        "    policy='step',\n",
        "    warmup='linear',\n",
        "    warmup_iters=100,\n",
        "    warmup_ratio=1.0 / 100,\n",
        "    step=[3])\n",
        "# runtime settings\n",
        "total_epochs = 4\n",
        "evaluation = dict(metric=['bbox', 'track'], interval=1)\n",
        "search_metrics = ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML']\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "/content/mmtracking/configs/_base_/datasets/mot_challenge.py\n",
        "\n",
        "\n",
        "위 경로에서 아래 내용 복붙\n",
        "\n",
        "\n",
        "# dataset settings\n",
        "dataset_type = 'MOTChallengeDataset'\n",
        "img_norm_cfg = dict(\n",
        "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
        "train_pipeline = [\n",
        "    dict(type='LoadMultiImagesFromFile', to_float32=True),\n",
        "    dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),\n",
        "    dict(\n",
        "        type='SeqResize',\n",
        "        img_scale=(1088, 1088),\n",
        "        share_params=True,\n",
        "        ratio_range=(0.8, 1.2),\n",
        "        keep_ratio=True,\n",
        "        bbox_clip_border=False),\n",
        "    dict(type='SeqPhotoMetricDistortion', share_params=True),\n",
        "    dict(\n",
        "        type='SeqRandomCrop',\n",
        "        share_params=False,\n",
        "        crop_size=(1088, 1088),\n",
        "        bbox_clip_border=False),\n",
        "    dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),\n",
        "    dict(type='SeqNormalize', **img_norm_cfg),\n",
        "    dict(type='SeqPad', size_divisor=32),\n",
        "    dict(type='MatchInstances', skip_nomatch=True),\n",
        "    dict(\n",
        "        type='VideoCollect',\n",
        "        keys=[\n",
        "            'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices',\n",
        "            'gt_instance_ids'\n",
        "        ]),\n",
        "    dict(type='SeqDefaultFormatBundle', ref_prefix='ref')\n",
        "]\n",
        "test_pipeline = [\n",
        "    dict(type='LoadImageFromFile'),\n",
        "    dict(\n",
        "        type='MultiScaleFlipAug',\n",
        "        img_scale=(1088, 1088),\n",
        "        flip=False,\n",
        "        transforms=[\n",
        "            dict(type='Resize', keep_ratio=True),\n",
        "            dict(type='RandomFlip'),\n",
        "            dict(type='Normalize', **img_norm_cfg),\n",
        "            dict(type='Pad', size_divisor=32),\n",
        "            dict(type='ImageToTensor', keys=['img']),\n",
        "            dict(type='VideoCollect', keys=['img'])\n",
        "        ])\n",
        "]\n",
        "data_root = 'data/MOT17_tiny/'\n",
        "data = dict(\n",
        "    samples_per_gpu=2,\n",
        "    workers_per_gpu=2,\n",
        "    train=dict(\n",
        "        type=dataset_type,\n",
        "        visibility_thr=-1,\n",
        "        ann_file=data_root + 'annotations/train_cocoformat.json',\n",
        "        img_prefix=data_root + 'train',\n",
        "        ref_img_sampler=dict(\n",
        "            num_ref_imgs=1,\n",
        "            frame_range=10,\n",
        "            filter_key_img=True,\n",
        "            method='uniform'),\n",
        "        classes=('pedestrian', ),\n",
        "        pipeline=train_pipeline),\n",
        "    val=dict(\n",
        "        type=dataset_type,\n",
        "        ann_file=data_root + 'annotations/half-val_cocoformat.json',\n",
        "        img_prefix=data_root + 'train',\n",
        "        ref_img_sampler=None,\n",
        "        classes=('pedestrian', ),\n",
        "        pipeline=test_pipeline),\n",
        "    test=dict(\n",
        "        type=dataset_type,\n",
        "        ann_file=data_root + 'annotations/half-val_cocoformat.json',\n",
        "        img_prefix=data_root + 'train',\n",
        "        ref_img_sampler=None,\n",
        "        classes=('pedestrian', ),\n",
        "        pipeline=test_pipeline))\n",
        "\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "W-2l__uoOROI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import mmcv\n",
        "from mmdet.apis import set_random_seed\n",
        "\n",
        "cfg = mmcv.Config.fromfile('./configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py')\n",
        "\n",
        "cfg.model.detector.init_cfg.checkpoint = './tutorial_exps/detector/epoch_4.pth'\n",
        "\n",
        "cfg.work_dir = './tutorial_exps'\n",
        "cfg.seed = 0\n",
        "set_random_seed(0, deterministic=False)\n",
        "cfg.gpu_ids = range(1)\n",
        "cfg.data.test.test_mode = True\n",
        "cfg.device='cuda'\n",
        "\n",
        "print(cfg)"
      ],
      "metadata": {
        "id": "RvKIL-HQxN0A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "908a4dbc-db1a-4328-bad8-3cfdc34c1769"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Config (path: ./configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py): {'model': {'detector': {'type': 'FasterRCNN', 'backbone': {'type': 'ResNet', 'depth': 50, 'num_stages': 4, 'out_indices': (0, 1, 2, 3), 'frozen_stages': 1, 'norm_cfg': {'type': 'BN', 'requires_grad': True}, 'norm_eval': True, 'style': 'pytorch', 'init_cfg': {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}}, 'neck': {'type': 'FPN', 'in_channels': [256, 512, 1024, 2048], 'out_channels': 256, 'num_outs': 5}, 'rpn_head': {'type': 'RPNHead', 'in_channels': 256, 'feat_channels': 256, 'anchor_generator': {'type': 'AnchorGenerator', 'scales': [8], 'ratios': [0.5, 1.0, 2.0], 'strides': [4, 8, 16, 32, 64]}, 'bbox_coder': {'type': 'DeltaXYWHBBoxCoder', 'target_means': [0.0, 0.0, 0.0, 0.0], 'target_stds': [1.0, 1.0, 1.0, 1.0], 'clip_border': False}, 'loss_cls': {'type': 'CrossEntropyLoss', 'use_sigmoid': True, 'loss_weight': 1.0}, 'loss_bbox': {'type': 'SmoothL1Loss', 'beta': 0.1111111111111111, 'loss_weight': 1.0}}, 'roi_head': {'type': 'StandardRoIHead', 'bbox_roi_extractor': {'type': 'SingleRoIExtractor', 'roi_layer': {'type': 'RoIAlign', 'output_size': 7, 'sampling_ratio': 0}, 'out_channels': 256, 'featmap_strides': [4, 8, 16, 32]}, 'bbox_head': {'type': 'Shared2FCBBoxHead', 'in_channels': 256, 'fc_out_channels': 1024, 'roi_feat_size': 7, 'num_classes': 1, 'bbox_coder': {'type': 'DeltaXYWHBBoxCoder', 'target_means': [0.0, 0.0, 0.0, 0.0], 'target_stds': [0.1, 0.1, 0.2, 0.2], 'clip_border': False}, 'reg_class_agnostic': False, 'loss_cls': {'type': 'CrossEntropyLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'loss_bbox': {'type': 'SmoothL1Loss', 'loss_weight': 1.0}}}, 'train_cfg': {'rpn': {'assigner': {'type': 'MaxIoUAssigner', 'pos_iou_thr': 0.7, 'neg_iou_thr': 0.3, 'min_pos_iou': 0.3, 'match_low_quality': True, 'ignore_iof_thr': -1}, 'sampler': {'type': 'RandomSampler', 'num': 256, 'pos_fraction': 0.5, 'neg_pos_ub': -1, 'add_gt_as_proposals': False}, 'allowed_border': -1, 'pos_weight': -1, 'debug': False}, 'rpn_proposal': {'nms_pre': 2000, 'max_per_img': 1000, 'nms': {'type': 'nms', 'iou_threshold': 0.7}, 'min_bbox_size': 0}, 'rcnn': {'assigner': {'type': 'MaxIoUAssigner', 'pos_iou_thr': 0.5, 'neg_iou_thr': 0.5, 'min_pos_iou': 0.5, 'match_low_quality': False, 'ignore_iof_thr': -1}, 'sampler': {'type': 'RandomSampler', 'num': 512, 'pos_fraction': 0.25, 'neg_pos_ub': -1, 'add_gt_as_proposals': True}, 'pos_weight': -1, 'debug': False}}, 'test_cfg': {'rpn': {'nms_pre': 1000, 'max_per_img': 1000, 'nms': {'type': 'nms', 'iou_threshold': 0.7}, 'min_bbox_size': 0}, 'rcnn': {'score_thr': 0.05, 'nms': {'type': 'nms', 'iou_threshold': 0.5}, 'max_per_img': 100}}, 'init_cfg': {'type': 'Pretrained', 'checkpoint': './tutorial_exps/detector/epoch_4.pth'}}, 'type': 'DeepSORT', 'motion': {'type': 'KalmanFilter', 'center_only': False}, 'reid': {'type': 'BaseReID', 'backbone': {'type': 'ResNet', 'depth': 50, 'num_stages': 4, 'out_indices': (3,), 'style': 'pytorch'}, 'neck': {'type': 'GlobalAveragePooling', 'kernel_size': (8, 4), 'stride': 1}, 'head': {'type': 'LinearReIDHead', 'num_fcs': 1, 'in_channels': 2048, 'fc_channels': 1024, 'out_channels': 128, 'num_classes': 380, 'loss': {'type': 'CrossEntropyLoss', 'loss_weight': 1.0}, 'loss_pairwise': {'type': 'TripletLoss', 'margin': 0.3, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN1d'}, 'act_cfg': {'type': 'ReLU'}}}, 'tracker': {'type': 'SortTracker', 'obj_score_thr': 0.5, 'reid': {'num_samples': 10, 'img_scale': (256, 128), 'img_norm_cfg': None, 'match_score_thr': 2.0}, 'match_iou_thr': 0.5, 'momentums': None, 'num_tentatives': 2, 'num_frames_retain': 100}}, 'dataset_type': 'MOTChallengeDataset', 'img_norm_cfg': {'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, 'train_pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqLoadAnnotations', 'with_bbox': True, 'with_track': True}, {'type': 'SeqResize', 'img_scale': (1088, 1088), 'share_params': True, 'ratio_range': (0.8, 1.2), 'keep_ratio': True, 'bbox_clip_border': False}, {'type': 'SeqPhotoMetricDistortion', 'share_params': True}, {'type': 'SeqRandomCrop', 'share_params': False, 'crop_size': (1088, 1088), 'bbox_clip_border': False}, {'type': 'SeqRandomFlip', 'share_params': True, 'flip_ratio': 0.5}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'SeqPad', 'size_divisor': 32}, {'type': 'MatchInstances', 'skip_nomatch': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices', 'gt_instance_ids']}, {'type': 'SeqDefaultFormatBundle', 'ref_prefix': 'ref'}], 'test_pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1088, 1088), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'VideoCollect', 'keys': ['img']}]}], 'data_root': 'data/MOT17_tiny/', 'data': {'samples_per_gpu': 2, 'workers_per_gpu': 2, 'train': {'type': 'MOTChallengeDataset', 'visibility_thr': -1, 'ann_file': 'data/MOT17_tiny/annotations/train_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'ref_img_sampler': {'num_ref_imgs': 1, 'frame_range': 10, 'filter_key_img': True, 'method': 'uniform'}, 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadMultiImagesFromFile', 'to_float32': True}, {'type': 'SeqLoadAnnotations', 'with_bbox': True, 'with_track': True}, {'type': 'SeqResize', 'img_scale': (1088, 1088), 'share_params': True, 'ratio_range': (0.8, 1.2), 'keep_ratio': True, 'bbox_clip_border': False}, {'type': 'SeqPhotoMetricDistortion', 'share_params': True}, {'type': 'SeqRandomCrop', 'share_params': False, 'crop_size': (1088, 1088), 'bbox_clip_border': False}, {'type': 'SeqRandomFlip', 'share_params': True, 'flip_ratio': 0.5}, {'type': 'SeqNormalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'SeqPad', 'size_divisor': 32}, {'type': 'MatchInstances', 'skip_nomatch': True}, {'type': 'VideoCollect', 'keys': ['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices', 'gt_instance_ids']}, {'type': 'SeqDefaultFormatBundle', 'ref_prefix': 'ref'}]}, 'val': {'type': 'MOTChallengeDataset', 'ann_file': 'data/MOT17_tiny/annotations/half-val_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'ref_img_sampler': None, 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1088, 1088), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'VideoCollect', 'keys': ['img']}]}]}, 'test': {'type': 'MOTChallengeDataset', 'ann_file': 'data/MOT17_tiny/annotations/half-val_cocoformat.json', 'img_prefix': 'data/MOT17_tiny/train', 'ref_img_sampler': None, 'classes': ('pedestrian',), 'pipeline': [{'type': 'LoadImageFromFile'}, {'type': 'MultiScaleFlipAug', 'img_scale': (1088, 1088), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True}, {'type': 'Pad', 'size_divisor': 32}, {'type': 'ImageToTensor', 'keys': ['img']}, {'type': 'VideoCollect', 'keys': ['img']}]}], 'test_mode': True}}, 'optimizer': {'type': 'SGD', 'lr': 0.02, 'momentum': 0.9, 'weight_decay': 0.0001}, 'optimizer_config': {'grad_clip': None}, 'checkpoint_config': {'interval': 1}, 'log_config': {'interval': 50, 'hooks': [{'type': 'TextLoggerHook'}]}, 'dist_params': {'backend': 'nccl'}, 'log_level': 'INFO', 'load_from': None, 'resume_from': None, 'workflow': [('train', 1)], 'opencv_num_threads': 0, 'mp_start_method': 'fork', 'lr_config': {'policy': 'step', 'warmup': 'linear', 'warmup_iters': 100, 'warmup_ratio': 0.01, 'step': [3]}, 'total_epochs': 4, 'evaluation': {'metric': ['bbox', 'track'], 'interval': 1}, 'search_metrics': ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML'], 'work_dir': './tutorial_exps', 'seed': 0, 'gpu_ids': range(0, 1), 'device': 'cuda'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mmtrack.datasets import build_dataloader\n",
        "from mmtrack.apis import init_model\n",
        "from mmcv.parallel import MMDataParallel\n",
        "from mmtrack.apis import single_gpu_test\n",
        "from mmtrack.datasets import build_dataset\n",
        "\n",
        "dataset = build_dataset(cfg.data.test)\n",
        "data_loader = build_dataloader(\n",
        "    dataset,\n",
        "    samples_per_gpu=1,\n",
        "    workers_per_gpu=cfg.data.workers_per_gpu,\n",
        "    dist=False,\n",
        "    shuffle=False)"
      ],
      "metadata": {
        "id": "85RF5aHpxOKD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8f9e133-a8a8-4ecf-e422-6523bfab83e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "exbUyr-JxONA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c728a38d-fb50-440f-8e5a-643b946b8a8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "MOTChallengeDataset Test dataset with number of images 121, and instance counts: \n",
              "+----------------+-------+---------------+-------+----------+-------+----------+-------+----------+-------+\n",
              "| category       | count | category      | count | category | count | category | count | category | count |\n",
              "+----------------+-------+---------------+-------+----------+-------+----------+-------+----------+-------+\n",
              "|                |       |               |       |          |       |          |       |          |       |\n",
              "| 0 [pedestrian] | 373   | -1 background | 29    |          |       |          |       |          |       |\n",
              "+----------------+-------+---------------+-------+----------+-------+----------+-------+----------+-------+"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# build the model and load checkpoint\n",
        "model = init_model(cfg)\n",
        "\n",
        "model = MMDataParallel(model, device_ids=cfg.gpu_ids)\n",
        "outputs = single_gpu_test(model, data_loader)"
      ],
      "metadata": {
        "id": "UNpozoSMxOP5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8dd6278-29c6-4cd4-e70d-50da17628843"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-01 02:38:02,095 - mmcv - INFO - initialize FasterRCNN with init_cfg {'type': 'Pretrained', 'checkpoint': './tutorial_exps/detector/epoch_4.pth'}\n",
            "2023-12-01 02:38:02,097 - mmcv - INFO - load model from: ./tutorial_exps/detector/epoch_4.pth\n",
            "2023-12-01 02:38:02,101 - mmcv - INFO - load checkpoint from local path: ./tutorial_exps/detector/epoch_4.pth\n",
            "2023-12-01 02:38:02,392 - mmcv - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n",
            "2023-12-01 02:38:02,567 - mmcv - INFO - initialize LinearReIDHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'mean': 0, 'std': 0.01, 'bias': 0}\n",
            "2023-12-01 02:38:02,592 - mmcv - INFO - \n",
            "detector.backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,593 - mmcv - INFO - \n",
            "detector.backbone.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,594 - mmcv - INFO - \n",
            "detector.backbone.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,595 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,596 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,599 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,600 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,600 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,601 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,603 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,604 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn3.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,605 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,606 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,607 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,608 - mmcv - INFO - \n",
            "detector.backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,609 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,610 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,611 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,618 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,618 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,620 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,621 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,622 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn3.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,623 - mmcv - INFO - \n",
            "detector.backbone.layer1.1.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,624 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,625 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,628 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,629 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,630 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,631 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,632 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,633 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn3.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,634 - mmcv - INFO - \n",
            "detector.backbone.layer1.2.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,635 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,636 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,637 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,638 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,639 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,640 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,641 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,642 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,643 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,644 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,651 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,652 - mmcv - INFO - \n",
            "detector.backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,653 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,655 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,656 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,657 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,659 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,660 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,661 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,662 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,663 - mmcv - INFO - \n",
            "detector.backbone.layer2.1.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,664 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,665 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,669 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,670 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,671 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,672 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,673 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,674 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,674 - mmcv - INFO - \n",
            "detector.backbone.layer2.2.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,675 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,676 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn1.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,678 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn1.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,679 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,680 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn2.weight - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,681 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn2.bias - torch.Size([128]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,682 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,683 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn3.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,689 - mmcv - INFO - \n",
            "detector.backbone.layer2.3.bn3.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,690 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,691 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,693 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,694 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,696 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,697 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,699 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,700 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,701 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,701 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,703 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,704 - mmcv - INFO - \n",
            "detector.backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,705 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,709 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,710 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,712 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,714 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,715 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,717 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,719 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,721 - mmcv - INFO - \n",
            "detector.backbone.layer3.1.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,723 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,724 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,726 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,727 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,730 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,731 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,732 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,733 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,734 - mmcv - INFO - \n",
            "detector.backbone.layer3.2.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,735 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,738 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,740 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,741 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,742 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,743 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,744 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,747 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,748 - mmcv - INFO - \n",
            "detector.backbone.layer3.3.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,748 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,750 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,751 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,752 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,756 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,757 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,757 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,759 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,760 - mmcv - INFO - \n",
            "detector.backbone.layer3.4.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,761 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,762 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn1.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,765 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn1.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,766 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,767 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn2.weight - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,770 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn2.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,771 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,772 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn3.weight - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,773 - mmcv - INFO - \n",
            "detector.backbone.layer3.5.bn3.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,774 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,777 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,778 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,778 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,780 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,781 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,782 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,783 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,783 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,784 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,785 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,786 - mmcv - INFO - \n",
            "detector.backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,787 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,792 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,793 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,794 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,795 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,796 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,797 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,798 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,799 - mmcv - INFO - \n",
            "detector.backbone.layer4.1.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,800 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,801 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn1.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,802 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn1.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,803 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,804 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn2.weight - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,805 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn2.bias - torch.Size([512]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,806 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,807 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn3.weight - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,808 - mmcv - INFO - \n",
            "detector.backbone.layer4.2.bn3.bias - torch.Size([2048]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,808 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,809 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.0.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,810 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,811 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.1.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,818 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,819 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.2.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,820 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,821 - mmcv - INFO - \n",
            "detector.neck.lateral_convs.3.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,822 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,825 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.0.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,826 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,827 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.1.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,828 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,829 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.2.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,832 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,833 - mmcv - INFO - \n",
            "detector.neck.fpn_convs.3.conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,834 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,836 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_conv.bias - torch.Size([256]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,837 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,838 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_cls.bias - torch.Size([3]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,839 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,840 - mmcv - INFO - \n",
            "detector.rpn_head.rpn_reg.bias - torch.Size([12]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,844 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_cls.weight - torch.Size([2, 1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,845 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_cls.bias - torch.Size([2]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,846 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_reg.weight - torch.Size([4, 1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,847 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.fc_reg.bias - torch.Size([4]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,848 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,850 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,852 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,854 - mmcv - INFO - \n",
            "detector.roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]): \n",
            "PretrainedInit: load from ./tutorial_exps/detector/epoch_4.pth \n",
            " \n",
            "2023-12-01 02:38:02,855 - mmcv - INFO - \n",
            "reid.backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,856 - mmcv - INFO - \n",
            "reid.backbone.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,858 - mmcv - INFO - \n",
            "reid.backbone.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,859 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,860 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,861 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,862 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,866 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,866 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,867 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,868 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn3.weight - torch.Size([256]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,869 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,872 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,873 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,874 - mmcv - INFO - \n",
            "reid.backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,875 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,876 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,877 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,878 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,879 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,880 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,881 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,882 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn3.weight - torch.Size([256]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,883 - mmcv - INFO - \n",
            "reid.backbone.layer1.1.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,884 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,885 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn1.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,886 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn1.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,887 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,888 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn2.weight - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,889 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn2.bias - torch.Size([64]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,890 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,897 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn3.weight - torch.Size([256]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,898 - mmcv - INFO - \n",
            "reid.backbone.layer1.2.bn3.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,899 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,900 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn1.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,900 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn1.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,901 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,902 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn2.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,903 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn2.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,908 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,908 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn3.weight - torch.Size([512]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,909 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.bn3.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,911 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,912 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,913 - mmcv - INFO - \n",
            "reid.backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,914 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,915 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn1.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,916 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn1.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,917 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,921 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn2.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,922 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn2.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,923 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,924 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn3.weight - torch.Size([512]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,925 - mmcv - INFO - \n",
            "reid.backbone.layer2.1.bn3.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,926 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,927 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn1.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,928 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn1.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,929 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,930 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn2.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,931 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn2.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,932 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,938 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn3.weight - torch.Size([512]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,938 - mmcv - INFO - \n",
            "reid.backbone.layer2.2.bn3.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,939 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,940 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn1.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,941 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn1.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,944 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,945 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn2.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,946 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn2.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,947 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,948 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn3.weight - torch.Size([512]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,949 - mmcv - INFO - \n",
            "reid.backbone.layer2.3.bn3.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,953 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,954 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,955 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,955 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,958 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn2.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,960 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn2.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,961 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,963 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn3.weight - torch.Size([1024]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,964 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.bn3.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,965 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,966 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,970 - mmcv - INFO - \n",
            "reid.backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,971 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,972 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,973 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,974 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,976 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn2.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,977 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn2.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,978 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,979 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn3.weight - torch.Size([1024]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,980 - mmcv - INFO - \n",
            "reid.backbone.layer3.1.bn3.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,981 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,983 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,984 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,985 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,986 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn2.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,989 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn2.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,990 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,992 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn3.weight - torch.Size([1024]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:02,993 - mmcv - INFO - \n",
            "reid.backbone.layer3.2.bn3.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:02,994 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:02,995 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,001 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,002 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,003 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn2.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,004 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn2.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,005 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,006 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn3.weight - torch.Size([1024]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:03,007 - mmcv - INFO - \n",
            "reid.backbone.layer3.3.bn3.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,008 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,009 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,011 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,015 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,016 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn2.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,017 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn2.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,018 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,020 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn3.weight - torch.Size([1024]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:03,021 - mmcv - INFO - \n",
            "reid.backbone.layer3.4.bn3.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,022 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,023 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn1.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,024 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn1.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,026 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,027 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn2.weight - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,029 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn2.bias - torch.Size([256]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,033 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,033 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn3.weight - torch.Size([1024]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:03,034 - mmcv - INFO - \n",
            "reid.backbone.layer3.5.bn3.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,035 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,036 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn1.weight - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,037 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn1.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,038 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,039 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn2.weight - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,040 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn2.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,044 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,044 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn3.weight - torch.Size([2048]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:03,045 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.bn3.bias - torch.Size([2048]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,046 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,047 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,048 - mmcv - INFO - \n",
            "reid.backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,049 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,050 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn1.weight - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,050 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn1.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,051 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,052 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn2.weight - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,053 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn2.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,054 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,055 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn3.weight - torch.Size([2048]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:03,056 - mmcv - INFO - \n",
            "reid.backbone.layer4.1.bn3.bias - torch.Size([2048]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,057 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,058 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn1.weight - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,059 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn1.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,060 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,060 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn2.weight - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,061 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn2.bias - torch.Size([512]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,062 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n",
            "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,063 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn3.weight - torch.Size([2048]): \n",
            "Initialized by user-defined `init_weights` in ResNet  \n",
            " \n",
            "2023-12-01 02:38:03,064 - mmcv - INFO - \n",
            "reid.backbone.layer4.2.bn3.bias - torch.Size([2048]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,065 - mmcv - INFO - \n",
            "reid.head.fcs.0.fc.weight - torch.Size([1024, 2048]): \n",
            "NormalInit: mean=0, std=0.01, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,065 - mmcv - INFO - \n",
            "reid.head.fcs.0.fc.bias - torch.Size([1024]): \n",
            "NormalInit: mean=0, std=0.01, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,068 - mmcv - INFO - \n",
            "reid.head.fcs.0.bn.weight - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,069 - mmcv - INFO - \n",
            "reid.head.fcs.0.bn.bias - torch.Size([1024]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,070 - mmcv - INFO - \n",
            "reid.head.fc_out.weight - torch.Size([128, 1024]): \n",
            "NormalInit: mean=0, std=0.01, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,081 - mmcv - INFO - \n",
            "reid.head.fc_out.bias - torch.Size([128]): \n",
            "NormalInit: mean=0, std=0.01, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,082 - mmcv - INFO - \n",
            "reid.head.bn.weight - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,083 - mmcv - INFO - \n",
            "reid.head.bn.bias - torch.Size([128]): \n",
            "The value is the same before and after calling `init_weights` of DeepSORT  \n",
            " \n",
            "2023-12-01 02:38:03,084 - mmcv - INFO - \n",
            "reid.head.classifier.weight - torch.Size([380, 128]): \n",
            "NormalInit: mean=0, std=0.01, bias=0 \n",
            " \n",
            "2023-12-01 02:38:03,085 - mmcv - INFO - \n",
            "reid.head.classifier.bias - torch.Size([380]): \n",
            "NormalInit: mean=0, std=0.01, bias=0 \n",
            " \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: The model doesn't have classes\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 121/121, 9.2 task/s, elapsed: 13s, ETA:     0s"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_kwargs = cfg.get('evaluation', {}).copy()\n",
        "# hard-code way to remove EvalHook args\n",
        "eval_hook_args = [\n",
        "    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',\n",
        "    'rule', 'by_epoch'\n",
        "]\n",
        "for key in eval_hook_args:\n",
        "    eval_kwargs.pop(key, None)\n",
        "eval_kwargs.update(dict(metric=['track']))\n",
        "metric = dataset.evaluate(outputs, **eval_kwargs)\n",
        "\n",
        "print(metric)"
      ],
      "metadata": {
        "id": "-2pq7JsjxOSr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "977edb01-d036-4bc1-aeb3-4f0e621f49df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate CLEAR MOT results.\n",
            "\n",
            "Eval Config:\n",
            "USE_PARALLEL         : False                         \n",
            "NUM_PARALLEL_CORES   : 8                             \n",
            "BREAK_ON_ERROR       : True                          \n",
            "RETURN_ON_ERROR      : False                         \n",
            "LOG_ON_ERROR         : /usr/local/lib/python3.10/dist-packages/error_log.txt\n",
            "PRINT_RESULTS        : True                          \n",
            "PRINT_ONLY_COMBINED  : False                         \n",
            "PRINT_CONFIG         : True                          \n",
            "TIME_PROGRESS        : True                          \n",
            "DISPLAY_LESS_PROGRESS : True                          \n",
            "OUTPUT_SUMMARY       : True                          \n",
            "OUTPUT_EMPTY_CLASSES : True                          \n",
            "OUTPUT_DETAILED      : True                          \n",
            "PLOT_CURVES          : True                          \n",
            "\n",
            "MotChallenge2DBox Config:\n",
            "GT_FOLDER            : /tmp/tmp3rwt8xln              \n",
            "TRACKERS_FOLDER      : /tmp/tmpg1tsgq6h              \n",
            "OUTPUT_FOLDER        : None                          \n",
            "TRACKERS_TO_EVAL     : ['track']                     \n",
            "CLASSES_TO_EVAL      : ['pedestrian']                \n",
            "BENCHMARK            : MOT17                         \n",
            "SPLIT_TO_EVAL        : train                         \n",
            "INPUT_AS_ZIP         : False                         \n",
            "PRINT_CONFIG         : True                          \n",
            "DO_PREPROC           : True                          \n",
            "TRACKER_SUB_FOLDER   :                               \n",
            "OUTPUT_SUB_FOLDER    :                               \n",
            "TRACKER_DISPLAY_NAMES : None                          \n",
            "SEQMAP_FOLDER        : None                          \n",
            "SEQMAP_FILE          : /tmp/tmpg1tsgq6h/videoseq.txt \n",
            "SEQ_INFO             : None                          \n",
            "GT_LOC_FORMAT        : {gt_folder}/{seq}/gt/gt_half-val.txt\n",
            "SKIP_SPLIT_FOL       : True                          \n",
            "\n",
            "Evaluating 1 tracker(s) on 1 sequence(s) for 1 class(es) on MotChallenge2DBox dataset using the following metrics: HOTA, Count\n",
            "\n",
            "\n",
            "Evaluating track\n",
            "\n",
            "1 eval_sequence(MOT17-02-FRCNN, track)                                   0.0962 sec\n",
            "\n",
            "All sequences for track finished in 0.10 seconds\n",
            "\n",
            "HOTA: track-pedestrian             HOTA      DetA      AssA      DetRe     DetPr     AssRe     AssPr     LocA      OWTA      HOTA(0)   LocA(0)   HOTALocA(0)\n",
            "MOT17-02-FRCNN                     62.921    68.17     58.451    76.661    72.392    62.829    73.5      80.054    66.912    83.175    75.539    62.829    \n",
            "COMBINED                           62.921    68.17     58.451    76.661    72.392    62.829    73.5      80.054    66.912    83.175    75.539    62.829    \n",
            "\n",
            "Count: track-pedestrian            Dets      GT_Dets   IDs       GT_IDs    \n",
            "MOT17-02-FRCNN                     395       373       11        5         \n",
            "COMBINED                           395       373       11        5         \n",
            "                IDF1   IDP   IDR  Rcll  Prcn GT MT PT ML FP FN IDs  FM  MOTA  MOTP IDt IDa IDm      HOTA\n",
            "MOT17-02-FRCNN 76.6% 74.4% 78.8% 97.1% 91.6%  5  5  0  0 33 11  35   1 78.8% 0.230   4   3   0  0.629212\n",
            "OVERALL        76.6% 74.4% 78.8% 97.1% 91.6%  5  5  0  0 33 11  35   1 78.8% 0.230   4   3   0  0.629212\n",
            "{'IDF1': 0.766, 'IDP': 0.744, 'IDR': 0.788, 'Rcll': 0.971, 'Prcn': 0.916, 'GT': 5, 'MT': 5, 'PT': 0, 'ML': 0, 'FP': 33, 'FN': 11, 'IDs': 35, 'FM': 1, 'MOTA': 0.788, 'MOTP': 0.23, 'IDt': 4, 'IDa': 3, 'IDm': 0, 'HOTA': 0.629}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7UNgS0HyPPBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UpzmNEQLPPHL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V4j7GvwvPPLF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4dZK-sNiPPOY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rFsjd4ggPPRK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b8J6JsW_PPUH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xpas_gk9PPW6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nCcJUZujPPZ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qsOfROMZPPcn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dVypkTAoPPfw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "50VyiJ-YPPiv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "###### 아래 Cell 들은 무시해도 된다 ######"
      ],
      "metadata": {
        "id": "bj1GSm8VxOVR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# gt.txt 파일 수정 (Class를 1에서 0으로 수정)\n",
        "\n",
        "edited_lines = []\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/train/MOT17-02-FRCNN/gt/gt.txt\") as f:\n",
        "  lines = f.readlines()\n",
        "  for line in lines:\n",
        "    tmp = list(line)\n",
        "    tmp[-6:-5] = str(0)\n",
        "    line = \"\".join(tmp)\n",
        "\n",
        "    edited_lines.append(line)\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/train/MOT17-02-FRCNN/gt/gt.txt\", 'w') as f:\n",
        "  f.writelines(edited_lines)"
      ],
      "metadata": {
        "id": "VsFbaVnvpNTz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs\n",
        "\n",
        "위 경로에서\n",
        "\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000001\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000002\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000003\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000004\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000005\n",
        "\n",
        "위 폴더명을\n",
        "\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000000\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000001\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000002\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000003\n",
        "/content/mmtracking/data/MOT17_tiny/reid/imgs/MOT17-02-FRCNN_000004\n",
        "\n",
        "로 바꾸기\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "8jKJEfjJxOYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "edited_lines = []\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/reid/meta/train.txt\") as f:\n",
        "    lines = f.readlines()\n",
        "    for line in lines:\n",
        "        # 조건에 따라 원하는 대로 line을 수정\n",
        "        if (line[15:21] == \"000001\"):\n",
        "          line = line.replace(\"000001\", \"000000\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000002\"):\n",
        "          line = line.replace(\"000002\", \"000001\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000003\"):\n",
        "          line = line.replace(\"000003\", \"000002\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000004\"):\n",
        "          line = line.replace(\"000004\", \"000003\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000005\"):\n",
        "          line = line.replace(\"000005\", \"000004\")\n",
        "          edited_lines.append(line)\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/reid/meta/train.txt\", 'w') as f:\n",
        "    f.writelines(edited_lines)\n",
        "\n",
        "edited_lines = []\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/reid/meta/train_9.txt\") as f:\n",
        "    lines = f.readlines()\n",
        "    for line in lines:\n",
        "        # 조건에 따라 원하는 대로 line을 수정\n",
        "        if (line[15:21] == \"000001\"):\n",
        "          line = line.replace(\"000001\", \"000000\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000002\"):\n",
        "          line = line.replace(\"000002\", \"000001\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000003\"):\n",
        "          line = line.replace(\"000003\", \"000002\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000004\"):\n",
        "          line = line.replace(\"000004\", \"000003\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000005\"):\n",
        "          line = line.replace(\"000005\", \"000004\")\n",
        "          edited_lines.append(line)\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/reid/meta/train_9.txt\", 'w') as f:\n",
        "    f.writelines(edited_lines)\n",
        "\n",
        "edited_lines = []\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/reid/meta/val_90.txt\") as f:\n",
        "    lines = f.readlines()\n",
        "    for line in lines:\n",
        "        # 조건에 따라 원하는 대로 line을 수정\n",
        "        if (line[15:21] == \"000001\"):\n",
        "          line = line.replace(\"000001\", \"000000\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000002\"):\n",
        "          line = line.replace(\"000002\", \"000001\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000003\"):\n",
        "          line = line.replace(\"000003\", \"000002\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000004\"):\n",
        "          line = line.replace(\"000004\", \"000003\")\n",
        "          edited_lines.append(line)\n",
        "        elif (line[15:21] == \"000005\"):\n",
        "          line = line.replace(\"000005\", \"000004\")\n",
        "          edited_lines.append(line)\n",
        "\n",
        "with open(\"/content/mmtracking/data/MOT17_tiny/reid/meta/val_90.txt\", 'w') as f:\n",
        "    f.writelines(edited_lines)"
      ],
      "metadata": {
        "id": "EOqyQg1gxSik"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}