demo/MMTracking_Tutorial.ipynb

{
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      "id": "a9d79da1-6001-4f97-8381-b11b5516e640",
      "metadata": {
        "id": "a9d79da1-6001-4f97-8381-b11b5516e640",
        "tags": []
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmtracking/blob/master/demo/MMTracking_Tutorial.ipynb)\n",
        "\n",
        "# **Welcome to MMTracking**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "527a563a-c8b2-44f0-ae20-0d226ab1e547",
      "metadata": {
        "id": "527a563a-c8b2-44f0-ae20-0d226ab1e547"
      },
      "source": [
        "In this tutorial, you will learn to:\n",
        "+ Install MMTracking.\n",
        "+ Perform inference with pretrained weights in MMTracking.\n",
        "+ Train a new MOT model with a toy dataset.\n",
        "Let's start!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab2c382c-a169-46fe-938f-c6687d763e8e",
      "metadata": {
        "id": "ab2c382c-a169-46fe-938f-c6687d763e8e"
      },
      "source": [
        "## **Install MMTracking**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "f8ced8f4-b07b-4216-8953-f7af6928b77c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8ced8f4-b07b-4216-8953-f7af6928b77c",
        "outputId": "b078d371-b978-4c10-fc9b-d4b45e6c5a40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
            "Cuda compilation tools, release 11.1, V11.1.105\n",
            "Build cuda_11.1.TC455_06.29190527_0\n",
            "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "Copyright (C) 2017 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"
          ]
        }
      ],
      "source": [
        "# Check nvcc version\n",
        "!nvcc -V\n",
        "# Check GCC version\n",
        "!gcc --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "6b4f093f-e197-42bd-ba64-dc905e379382",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b4f093f-e197-42bd-ba64-dc905e379382",
        "outputId": "b82ac7ec-2f1c-4d53-95e3-3b4d365a9ab0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "Collecting xmltodict>=0.12.0\n",
            "  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
            "Collecting commonmark<0.10.0,>=0.9.0\n",
            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 8.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich->mmcls>=1.0.0rc0->mmtrack==1.0.0rc0) (2.6.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->mmtrack==1.0.0rc0) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->mmtrack==1.0.0rc0) (3.1.0)\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-0.1.7-py3-none-any.whl size=12696 sha256=73367345c942b1997e2a4c1def6389b0cadb090b21ab6015830b699e7fc69dfd\n",
            "  Stored in directory: /root/.cache/pip/wheels/28/2d/85/6a50232dcc3c9814e3bd623402757e1759e57eb99aed930729\n",
            "  Building wheel for lap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lap: filename=lap-0.4.0-cp37-cp37m-linux_x86_64.whl size=1590201 sha256=cc56f5c066424629f72b58945914bbe5918fe55f9938f7e1a85c43d2c66c6c8e\n",
            "  Stored in directory: /root/.cache/pip/wheels/b1/0b/e3/ef9daf1b5547b56389e42c80c3100f1e6479bf5fd00fd9d6ba\n",
            "Successfully built attributee lap\n",
            "Installing collected packages: commonmark, xmltodict, rich, motmetrics, mmcls, lap, attributee, mmtrack\n",
            "  Running setup.py develop for mmtrack\n",
            "Successfully installed attributee-0.1.7 commonmark-0.9.1 lap-0.4.0 mmcls-1.0.0rc0 mmtrack-1.0.0rc0 motmetrics-1.2.5 rich-12.5.1 xmltodict-0.13.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/JonathonLuiten/TrackEval.git\n",
            "  Cloning https://github.com/JonathonLuiten/TrackEval.git to /tmp/pip-req-build-2qhoo76v\n",
            "  Running command git clone -q https://github.com/JonathonLuiten/TrackEval.git /tmp/pip-req-build-2qhoo76v\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from trackeval==1.0.dev1) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from trackeval==1.0.dev1) (1.7.3)\n",
            "Building wheels for collected packages: trackeval\n",
            "  Building wheel for trackeval (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for trackeval: filename=trackeval-1.0.dev1-py3-none-any.whl size=121499 sha256=2eb58220d115d6d402ec919174b2a665dbcb05063c08547b32d3e4e7523bede6\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-jvadplo0/wheels/f3/ca/38/409a5a8b4faf77d7e99a90462e20a4723c5b0f20fa12364aa7\n",
            "Successfully built trackeval\n",
            "Installing collected packages: trackeval\n",
            "Successfully installed trackeval-1.0.dev1\n"
          ]
        }
      ],
      "source": [
        "# install pytorch\n",
        "!pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "\n",
        "# install MMEngine\n",
        "!pip install mmengine\n",
        "\n",
        "# install MMCV\n",
        "!pip install 'mmcv>=2.0.0rc1' -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.10.0/index.html\n",
        "\n",
        "# install MMDetection\n",
        "!pip install 'mmdet>=3.0.0rc0'\n",
        "\n",
        "# clone the MMTracking repository\n",
        "!git clone -b 1.x https://github.com/open-mmlab/mmtracking.git\n",
        "%cd mmtracking\n",
        "\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "03a4a583-78e7-40a1-a6ef-d80056989546",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03a4a583-78e7-40a1-a6ef-d80056989546",
        "outputId": "01ed3453-9d8b-4d91-e641-bdaf12259194"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('sys.platform', 'linux'),\n",
              "             ('Python', '3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]'),\n",
              "             ('CUDA available', True),\n",
              "             ('numpy_random_seed', 2147483648),\n",
              "             ('GPU 0', 'Tesla T4'),\n",
              "             ('CUDA_HOME', '/usr/local/cuda'),\n",
              "             ('NVCC', 'Cuda compilation tools, release 11.1, V11.1.105'),\n",
              "             ('GCC',\n",
              "              'x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0'),\n",
              "             ('PyTorch', '1.10.0+cu111'),\n",
              "             ('PyTorch compiling details',\n",
              "              'PyTorch built with:\\n  - GCC 7.3\\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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\\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 11.1\\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 8.0.5\\n  - Magma 2.5.2\\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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 -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, 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, \\n'),\n",
              "             ('TorchVision', '0.11.0+cu111'),\n",
              "             ('OpenCV', '4.6.0'),\n",
              "             ('MMEngine', '0.1.0')])"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from mmengine.utils.dl_utils import collect_env\n",
        "collect_env()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "ff6aea79-2ce9-4b1c-b3c4-3f92d1a4e34c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ff6aea79-2ce9-4b1c-b3c4-3f92d1a4e34c",
        "outputId": "b7bced1c-800f-4314-9f2b-ad6c82ee1b47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.10.0+cu111 True\n",
            "11.1\n",
            "GCC 7.3\n",
            "3.0.0rc0\n",
            "1.0.0rc0\n"
          ]
        }
      ],
      "source": [
        "# Check Pytorch installation\n",
        "import torch, torchvision\n",
        "print(torch.__version__, torch.cuda.is_available())\n",
        "\n",
        "# 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())\n",
        "\n",
        "# Check MMDetection installation\n",
        "import mmdet\n",
        "print(mmdet.__version__)\n",
        "\n",
        "# Check MMTracking installation\n",
        "import mmtrack\n",
        "print(mmtrack.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4d43b49c-320a-4b0f-baca-fb4bde0630ff",
      "metadata": {
        "id": "4d43b49c-320a-4b0f-baca-fb4bde0630ff",
        "tags": []
      },
      "source": [
        "## **Perform inference**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "dd7c8466-f057-455f-985a-71e5f22c36e4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dd7c8466-f057-455f-985a-71e5f22c36e4",
        "outputId": "3160be6e-2a84-49c5-f5e0-3dab58a607a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-09-06 08:05:44--  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)... 47.89.140.71\n",
            "Connecting to download.openmmlab.com (download.openmmlab.com)|47.89.140.71|: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  7.75MB/s    in 31s     \n",
            "\n",
            "2022-09-06 08:06:16 (8.70 MB/s) - ‘./checkpoints/selsa_faster_rcnn_r50_dc5_1x_imagenetvid_20201227_204835-2f5a4952.pth’ saved [282801031/282801031]\n",
            "\n",
            "--2022-09-06 08:06:16--  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)... 47.89.140.71\n",
            "Connecting to download.openmmlab.com (download.openmmlab.com)|47.89.140.71|: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  7.60MB/s    in 24s     \n",
            "\n",
            "2022-09-06 08:06:41 (8.60 MB/s) - ‘./checkpoints/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth’ saved [216134418/216134418]\n",
            "\n",
            "--2022-09-06 08:06:41--  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)... 47.89.140.71\n",
            "Connecting to download.openmmlab.com (download.openmmlab.com)|47.89.140.71|: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  9.09MB/s    in 25s     \n",
            "\n",
            "2022-09-06 08:07:07 (8.92 MB/s) - ‘./checkpoints/masktrack_rcnn_r50_fpn_12e_youtubevis2019_20211022_194830-6ca6b91e.pth’ saved [232596799/232596799]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "420dae4b-4426-405e-97fb-7823943b8ee8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "420dae4b-4426-405e-97fb-7823943b8ee8",
        "outputId": "74b49b29-5651-4b25-e866-b755c04c4797"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:08:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth\n",
            "09/06 08:08:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth\n",
            "09/06 08:08:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth\n",
            "09/06 08:08:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth\n",
            "09/06 08:08:27 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - 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",
            "[                                                  ] 0/8, elapsed: 0s, ETA:"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/content/mmtracking/mmtrack/apis/inference.py:90: UserWarning: dataset_meta or class names are missed, use None by default.\n",
            "  warnings.warn('dataset_meta or class names are missed, '\n",
            "/usr/local/lib/python3.7/dist-packages/mmengine/visualization/visualizer.py:170: UserWarning: `Visualizer` backend is not initialized because save_dir is None.\n",
            "  warnings.warn('`Visualizer` backend is not initialized '\n",
            "/usr/local/lib/python3.7/dist-packages/mmengine/visualization/visualizer.py:709: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may not be in the image\n",
            "  ' the drawn bbox may not be in the image', UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/mmengine/visualization/visualizer.py:779: UserWarning: Warning: The polygon is out of bounds, the drawn polygon may not be in the image\n",
            "  ' the drawn polygon may not be in the image', UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/mmengine/visualization/visualizer.py:469: UserWarning: Warning: The text is out of bounds, the drawn text may not be in the image\n",
            "  ' the drawn text may not be in the image', UserWarning)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 1.1 task/s, elapsed: 7s, ETA:     0s\n",
            " making the output video at ./demo/mot.mp4 with a FPS of 3.0\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 10.7 task/s, elapsed: 1s, ETA:     0s\n"
          ]
        }
      ],
      "source": [
        "# run mot demo\n",
        "import mmcv\n",
        "import mmengine\n",
        "import tempfile\n",
        "from mmtrack.apis import inference_mot, init_model\n",
        "from mmtrack.utils import register_all_modules\n",
        "from mmtrack.registry import VISUALIZERS\n",
        "\n",
        "register_all_modules(init_default_scope=True)\n",
        "mot_config = './configs/mot/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py'\n",
        "input_video = './demo/demo.mp4'\n",
        "imgs = mmcv.VideoReader(input_video)\n",
        "# build the model from a config file\n",
        "mot_model = init_model(mot_config, device='cuda:0')\n",
        "\n",
        "# build the visualizer. Different name for creating different visualizer instance\n",
        "mot_model.cfg.visualizer.name = 'mot_visualizer'\n",
        "visualizer = VISUALIZERS.build(mot_model.cfg.visualizer)\n",
        "visualizer.dataset_meta = mot_model.dataset_meta\n",
        "\n",
        "prog_bar = mmengine.ProgressBar(len(imgs))\n",
        "out_dir = tempfile.TemporaryDirectory()\n",
        "out_path = out_dir.name\n",
        "\n",
        "# test and show/save the images\n",
        "for i, img in enumerate(imgs):\n",
        "    result = inference_mot(mot_model, img, frame_id=i)\n",
        "    visualizer.add_datasample(\n",
        "            'mot',\n",
        "            img[..., ::-1],\n",
        "            data_sample=result,\n",
        "            show=False,\n",
        "            out_file=f'{out_path}/{i:06d}.jpg',\n",
        "            wait_time=float(1 / int(imgs.fps)),\n",
        "            step=i)\n",
        "    prog_bar.update()\n",
        "\n",
        "output = './demo/mot.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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "d4a97033-b779-4169-84c9-781c58840ae5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309,
          "referenced_widgets": [
            "a3fc7d320ed3417d833db4e9db0fce5c",
            "4eb851325f4b4cab82b8cbd979237520",
            "9f2fb13e136642728fbc97c8d7390118",
            "33267c31afd64c58b153a357aaa4569b",
            "98f236bb20444b919f136766394978ff",
            "ec62bc9001d349e696b8d82942e5b8c4",
            "b55031500bb345b58d0676c47e3c7843",
            "dd2d50cb741b4a79a13f0bf4a2b2242b",
            "9c5adcbe51ef438db298bff6a09c2065",
            "936d523d65254c10a9c64033283ca0f9",
            "4ea9a77bcea840e686d3525d4ef86cc8"
          ]
        },
        "id": "d4a97033-b779-4169-84c9-781c58840ae5",
        "outputId": "76592f9f-93ff-4ce9-dd03-9839fca4f2f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:09:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth\n",
            "09/06 08:09:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth\" to /root/.cache/torch/hub/checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3fc7d320ed3417d833db4e9db0fce5c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/170M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:09:25 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The model and loaded state dict do not match exactly\n",
            "\n",
            "size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([41, 1024]).\n",
            "size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([41]).\n",
            "size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([160, 1024]).\n",
            "size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([160]).\n",
            "size mismatch for roi_head.mask_head.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([40, 256, 1, 1]).\n",
            "size mismatch for roi_head.mask_head.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([40]).\n",
            "local loads checkpoint from path: ./checkpoints/masktrack_rcnn_r50_fpn_12e_youtubevis2019_20211022_194830-6ca6b91e.pth\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 1.1 task/s, elapsed: 7s, ETA:     0s\n",
            " making the output video at ./demo/vis.mp4 with a FPS of 3.0\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 10.8 task/s, elapsed: 1s, ETA:     0s\n"
          ]
        }
      ],
      "source": [
        "# run vis demo\n",
        "from mmtrack.apis import inference_mot\n",
        "vis_config = './configs/vis/masktrack_rcnn/masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2019.py'\n",
        "vis_checkpoint = './checkpoints/masktrack_rcnn_r50_fpn_12e_youtubevis2019_20211022_194830-6ca6b91e.pth'\n",
        "# build the model from a config file and a checkpoint file\n",
        "vis_model = init_model(vis_config, vis_checkpoint, device='cuda:0')\n",
        "\n",
        "# build the visualizer. Different name for creating different visualizer instance\n",
        "vis_model.cfg.visualizer.name = 'vis_visualizer'\n",
        "visualizer = VISUALIZERS.build(vis_model.cfg.visualizer)\n",
        "visualizer.dataset_meta = vis_model.dataset_meta\n",
        "\n",
        "imgs = mmcv.VideoReader(input_video)\n",
        "prog_bar = mmengine.ProgressBar(len(imgs))\n",
        "out_dir = tempfile.TemporaryDirectory()\n",
        "out_path = out_dir.name\n",
        "for i, img in enumerate(imgs):\n",
        "    result = inference_mot(vis_model, img, frame_id=i)\n",
        "    visualizer.add_datasample(\n",
        "            'vis',\n",
        "            img[..., ::-1],\n",
        "            data_sample=result,\n",
        "            show=False,\n",
        "            out_file=f'{out_path}/{i:06d}.jpg',\n",
        "            wait_time=float(1 / int(imgs.fps)),\n",
        "            step=i)\n",
        "    prog_bar.update()\n",
        "output = './demo/vis.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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "abd0863b-933c-42d1-8442-70565d1b4b55",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
            "d8911837396446c19d76bc22c5be63e2",
            "4679ff15b0db45fb9931757e0328d296",
            "64e629d4b1c74d2ab6c9ddc52ff4a2a9",
            "e00476c38c4d473191eeed5d6c42ecce",
            "a5c4355973c342acbae02ff5c32a677f",
            "5d7f321bb1fe4f32b3c1733a846ef8f5",
            "dc309139c6764cefa17ef542ebb36d3e",
            "3a09540c2a4247debd7c35b384e7ebed",
            "20b5b5e82f5548c98037ee236732ccfe",
            "4ebcc9b78ac64aedb146685a2437612c",
            "3d25971bbb3444b797b0faac0a934ffb"
          ]
        },
        "id": "abd0863b-933c-42d1-8442-70565d1b4b55",
        "outputId": "ba9acbe5-1488-4992-9916-36aff7bd4622"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:09:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: torchvision://resnet50\n",
            "09/06 08:09:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - torchvision loads checkpoint from path: torchvision://resnet50\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8911837396446c19d76bc22c5be63e2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:09:34 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The model and loaded state dict do not match exactly\n",
            "\n",
            "unexpected key in source state_dict: fc.weight, fc.bias\n",
            "\n",
            "local loads checkpoint from path: ./checkpoints/selsa_faster_rcnn_r50_dc5_1x_imagenetvid_20201227_204835-2f5a4952.pth\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 1.1 task/s, elapsed: 7s, ETA:     0s\n",
            " making the output video at ./demo/vid.mp4 with a FPS of 3.0\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 11.1 task/s, elapsed: 1s, ETA:     0s\n"
          ]
        }
      ],
      "source": [
        "# run vid demo\n",
        "from mmtrack.apis import inference_vid\n",
        "vid_config = './configs/vid/selsa/selsa_faster-rcnn_r50-dc5_8xb1-7e_imagenetvid.py'\n",
        "vid_checkpoint = './checkpoints/selsa_faster_rcnn_r50_dc5_1x_imagenetvid_20201227_204835-2f5a4952.pth'\n",
        "# build the model from a config file and a checkpoint file\n",
        "vid_model = init_model(vid_config, vid_checkpoint, device='cuda:0')\n",
        "\n",
        "# build the visualizer. Different name for creating different visualizer instance\n",
        "vid_model.cfg.visualizer.name = 'vid_visualizer'\n",
        "visualizer = VISUALIZERS.build(vid_model.cfg.visualizer)\n",
        "visualizer.dataset_meta = vid_model.dataset_meta\n",
        "\n",
        "imgs = mmcv.VideoReader(input_video)\n",
        "prog_bar = mmengine.ProgressBar(len(imgs))\n",
        "out_dir = tempfile.TemporaryDirectory()\n",
        "out_path = out_dir.name\n",
        "for i, img in enumerate(imgs):\n",
        "    result = inference_vid(vid_model, img, frame_id=i)\n",
        "    visualizer.add_datasample(\n",
        "            'vid',\n",
        "            img[..., ::-1],\n",
        "            data_sample=result,\n",
        "            show=False,\n",
        "            out_file=f'{out_path}/{i:06d}.jpg',\n",
        "            wait_time=float(1 / int(imgs.fps)),\n",
        "            step=i)\n",
        "    prog_bar.update()\n",
        "output = './demo/vid.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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "0189f86a-b216-4f63-a58a-97e40e326869",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
            "2db8224b7bb849c0a3c8a281bb57eb22",
            "b811cd2dd72040fe86cdc5238b522d06",
            "378b791183a24c558b2ce4afc1fe8a57",
            "9f46a04413cf4cce9f56a710aebe1424",
            "fb5e2db40e004e21a852c9f28740615c",
            "40b5616f3b3741d68210206a1d852418",
            "482683c5f93f41108ef0added3850519",
            "3942fdab3efa47e98ef1398b27eade57",
            "357be7f73b2848f89efe394758edc839",
            "b704c75bb577455dbac176c728b5540b",
            "e34931d882f44879aee48bfd46d62dce"
          ]
        },
        "id": "0189f86a-b216-4f63-a58a-97e40e326869",
        "outputId": "3160aaa9-20cf-45d6-e78d-ff4ffdb08bff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:09:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: https://download.openmmlab.com/mmtracking/pretrained_weights/sot_resnet50.model\n",
            "09/06 08:09:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: https://download.openmmlab.com/mmtracking/pretrained_weights/sot_resnet50.model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.openmmlab.com/mmtracking/pretrained_weights/sot_resnet50.model\" to /root/.cache/torch/hub/checkpoints/sot_resnet50.model\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2db8224b7bb849c0a3c8a281bb57eb22",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/174M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "local loads checkpoint from path: ./checkpoints/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth\n",
            "[                                                  ] 0/8, elapsed: 0s, ETA:"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/content/mmtracking/mmtrack/apis/inference.py:90: UserWarning: dataset_meta or class names are missed, use None by default.\n",
            "  warnings.warn('dataset_meta or class names are missed, '\n",
            "/usr/local/lib/python3.7/dist-packages/mmengine/visualization/visualizer.py:170: UserWarning: `Visualizer` backend is not initialized because save_dir is None.\n",
            "  warnings.warn('`Visualizer` backend is not initialized '\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 1.6 task/s, elapsed: 5s, ETA:     0s\n",
            " making the output video at ./demo/sot.mp4 with a FPS of 3.0\n",
            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 8/8, 11.0 task/s, elapsed: 1s, ETA:     0s\n"
          ]
        }
      ],
      "source": [
        "# run sot demo\n",
        "from mmtrack.apis import inference_sot\n",
        "sot_config = './configs/sot/siamese_rpn/siamese-rpn_r50_8xb28-20e_imagenetvid-imagenetdet-coco_test-lasot.py'\n",
        "sot_checkpoint = './checkpoints/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth'\n",
        "# build the model from a config file and a checkpoint file\n",
        "sot_model = init_model(sot_config, sot_checkpoint, device='cuda:0')\n",
        "\n",
        "# build the visualizer. Different name for creating different visualizer instance\n",
        "sot_model.cfg.visualizer.name = 'sot_visualizer'\n",
        "visualizer = VISUALIZERS.build(sot_model.cfg.visualizer)\n",
        "visualizer.dataset_meta = sot_model.dataset_meta\n",
        "\n",
        "init_bbox = [371, 411, 450, 646]\n",
        "imgs = mmcv.VideoReader(input_video)\n",
        "prog_bar = mmengine.ProgressBar(len(imgs))\n",
        "out_dir = tempfile.TemporaryDirectory()\n",
        "out_path = out_dir.name\n",
        "for i, img in enumerate(imgs):\n",
        "    result = inference_sot(sot_model, img, init_bbox, frame_id=i)\n",
        "    visualizer.add_datasample(\n",
        "            'vid',\n",
        "            img[..., ::-1],\n",
        "            data_sample=result,\n",
        "            show=False,\n",
        "            out_file=f'{out_path}/{i:06d}.jpg',\n",
        "            wait_time=float(1 / int(imgs.fps)),\n",
        "            step=i)\n",
        "    prog_bar.update()\n",
        "output = './demo/sot.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()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "500ff07b-9664-4429-9e3f-e97dd4fa1c29",
      "metadata": {
        "id": "500ff07b-9664-4429-9e3f-e97dd4fa1c29",
        "tags": []
      },
      "source": [
        "## **Train a MOT model with a toy dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7bd4f44-447a-49a5-8c9c-cf160691bda5",
      "metadata": {
        "id": "e7bd4f44-447a-49a5-8c9c-cf160691bda5",
        "tags": []
      },
      "source": [
        "### **Prepare dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "a91a55bd-14be-46bf-aa18-5e30d9abe5b7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a91a55bd-14be-46bf-aa18-5e30d9abe5b7",
        "outputId": "837e0474-91fd-4090-f2e1-9017d2f6a389"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-09-06 08:10:13--  https://download.openmmlab.com/mmtracking/data/MOT17_tiny.zip\n",
            "Resolving download.openmmlab.com (download.openmmlab.com)... 47.89.140.71\n",
            "Connecting to download.openmmlab.com (download.openmmlab.com)|47.89.140.71|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 344566302 (329M) [application/zip]\n",
            "Saving to: ‘./data/MOT17_tiny.zip’\n",
            "\n",
            "MOT17_tiny.zip      100%[===================>] 328.60M  8.43MB/s    in 38s     \n",
            "\n",
            "2022-09-06 08:10:52 (8.63 MB/s) - ‘./data/MOT17_tiny.zip’ saved [344566302/344566302]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!mkdir data\n",
        "!wget https://download.openmmlab.com/mmtracking/data/MOT17_tiny.zip -P ./data\n",
        "!unzip -q ./data/MOT17_tiny.zip -d ./data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "d0db0d5f-b192-48ee-b145-149f33ad3685",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0db0d5f-b192-48ee-b145-149f33ad3685",
        "outputId": "8cad1091-09cf-4c97-9992-74af7d480dc3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Converting train set to COCO format\n",
            "100% 2/2 [00:00<00:00,  2.09it/s]\n",
            "train has 224 instances.\n",
            "Done! Saved as ./data/MOT17_tiny/annotations/train_cocoformat.json and ./data/MOT17_tiny/annotations/train_detections.pkl\n",
            "Converting test set to COCO format\n",
            "0it [00:00, ?it/s]\n",
            "test has 0 instances.\n",
            "Done! Saved as ./data/MOT17_tiny/annotations/test_cocoformat.json and ./data/MOT17_tiny/annotations/test_detections.pkl\n",
            "Converting half-train set to COCO format\n",
            "100% 2/2 [00:01<00:00,  1.01it/s]\n",
            "half-train has 182 instances.\n",
            "Done! Saved as ./data/MOT17_tiny/annotations/half-train_cocoformat.json and ./data/MOT17_tiny/annotations/half-train_detections.pkl\n",
            "Converting half-val set to COCO format\n",
            "100% 2/2 [00:02<00:00,  1.01s/it]\n",
            "half-val has 201 instances.\n",
            "Done! Saved as ./data/MOT17_tiny/annotations/half-val_cocoformat.json and ./data/MOT17_tiny/annotations/half-val_detections.pkl\n",
            "100% 2/2 [09:35<00:00, 287.68s/it]\n"
          ]
        }
      ],
      "source": [
        "# convert the dataset to coco format\n",
        "!python ./tools/dataset_converters/mot/mot2coco.py -i ./data/MOT17_tiny/ -o ./data/MOT17_tiny/annotations --split-train --convert-det\n",
        "# crop pedestrian patches from the original dataset for training reid model. It may take a few minutes.\n",
        "!rm -rf ./data/MOT17_tiny/reid\n",
        "!python ./tools/dataset_converters/mot/mot2reid.py -i ./data/MOT17_tiny/ -o ./data/MOT17_tiny/reid --val-split 0.9 --vis-threshold 0.8"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae887970-7382-4bd0-a739-6b07e6dded6f",
      "metadata": {
        "id": "ae887970-7382-4bd0-a739-6b07e6dded6f",
        "tags": []
      },
      "source": [
        "### **Train a detector for MOT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "bce04095-8586-45c5-a556-40c51d08b2cb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bce04095-8586-45c5-a556-40c51d08b2cb",
        "outputId": "407e272f-b1fe-4fb2-9265-55af95525335"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Config:\n",
            "dataset_type = 'mmdet.CocoDataset'\n",
            "data_root = 'data/MOT17_tiny/'\n",
            "train_pipeline = [\n",
            "    dict(type='LoadImageFromFile', to_float32=True),\n",
            "    dict(type='LoadAnnotations', with_bbox=True),\n",
            "    dict(\n",
            "        type='RandomResize',\n",
            "        scale=(1088, 1088),\n",
            "        ratio_range=(0.8, 1.2),\n",
            "        keep_ratio=True,\n",
            "        clip_object_border=False),\n",
            "    dict(type='PhotoMetricDistortion'),\n",
            "    dict(type='RandomCrop', crop_size=(1088, 1088), bbox_clip_border=False),\n",
            "    dict(type='RandomFlip', prob=0.5),\n",
            "    dict(type='PackDetInputs')\n",
            "]\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile'),\n",
            "    dict(type='Resize', scale=(1088, 1088), keep_ratio=True),\n",
            "    dict(\n",
            "        type='PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                   'scale_factor'))\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=2,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    batch_sampler=dict(type='mmdet.AspectRatioBatchSampler'),\n",
            "    dataset=dict(\n",
            "        type='mmdet.CocoDataset',\n",
            "        data_root='data/MOT17_tiny/',\n",
            "        _scope_='mmdet',\n",
            "        ann_file='annotations/half-train_cocoformat.json',\n",
            "        data_prefix=dict(img='train/'),\n",
            "        metainfo=dict(CLASSES=('pedestrian', )),\n",
            "        filter_cfg=dict(filter_empty_gt=True, min_size=32),\n",
            "        pipeline=[\n",
            "            dict(type='LoadImageFromFile', to_float32=True),\n",
            "            dict(type='LoadAnnotations', with_bbox=True),\n",
            "            dict(\n",
            "                type='RandomResize',\n",
            "                scale=(1088, 1088),\n",
            "                ratio_range=(0.8, 1.2),\n",
            "                keep_ratio=True,\n",
            "                clip_object_border=False),\n",
            "            dict(type='PhotoMetricDistortion'),\n",
            "            dict(\n",
            "                type='RandomCrop',\n",
            "                crop_size=(1088, 1088),\n",
            "                bbox_clip_border=False),\n",
            "            dict(type='RandomFlip', prob=0.5),\n",
            "            dict(type='PackDetInputs')\n",
            "        ]))\n",
            "val_dataloader = None\n",
            "test_dataloader = None\n",
            "val_evaluator = None\n",
            "test_evaluator = None\n",
            "default_scope = 'mmtrack'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(type='ParamSchedulerHook'),\n",
            "    checkpoint=dict(type='CheckpointHook', interval=1),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='mmdet.DetVisualizationHook', draw=False))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=False,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='mmdet.DetLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='mot_visualizer')\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "model = dict(\n",
            "    data_preprocessor=dict(\n",
            "        type='DetDataPreprocessor',\n",
            "        mean=[123.675, 116.28, 103.53],\n",
            "        std=[58.395, 57.12, 57.375],\n",
            "        bgr_to_rgb=True,\n",
            "        pad_size_divisor=32),\n",
            "    type='FasterRCNN',\n",
            "    _scope_='mmdet',\n",
            "    backbone=dict(\n",
            "        type='ResNet',\n",
            "        depth=50,\n",
            "        num_stages=4,\n",
            "        out_indices=(0, 1, 2, 3),\n",
            "        frozen_stages=1,\n",
            "        norm_cfg=dict(type='BN', requires_grad=True),\n",
            "        norm_eval=True,\n",
            "        style='pytorch',\n",
            "        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n",
            "    neck=dict(\n",
            "        type='FPN',\n",
            "        in_channels=[256, 512, 1024, 2048],\n",
            "        out_channels=256,\n",
            "        num_outs=5),\n",
            "    rpn_head=dict(\n",
            "        type='RPNHead',\n",
            "        in_channels=256,\n",
            "        feat_channels=256,\n",
            "        anchor_generator=dict(\n",
            "            type='AnchorGenerator',\n",
            "            scales=[8],\n",
            "            ratios=[0.5, 1.0, 2.0],\n",
            "            strides=[4, 8, 16, 32, 64]),\n",
            "        bbox_coder=dict(\n",
            "            type='DeltaXYWHBBoxCoder',\n",
            "            target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "            target_stds=[1.0, 1.0, 1.0, 1.0],\n",
            "            clip_border=False),\n",
            "        loss_cls=dict(\n",
            "            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
            "        loss_bbox=dict(\n",
            "            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n",
            "    roi_head=dict(\n",
            "        type='StandardRoIHead',\n",
            "        bbox_roi_extractor=dict(\n",
            "            type='SingleRoIExtractor',\n",
            "            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n",
            "            out_channels=256,\n",
            "            featmap_strides=[4, 8, 16, 32]),\n",
            "        bbox_head=dict(\n",
            "            type='Shared2FCBBoxHead',\n",
            "            in_channels=256,\n",
            "            fc_out_channels=1024,\n",
            "            roi_feat_size=7,\n",
            "            num_classes=1,\n",
            "            bbox_coder=dict(\n",
            "                type='DeltaXYWHBBoxCoder',\n",
            "                target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "                target_stds=[0.1, 0.1, 0.2, 0.2],\n",
            "                clip_border=False),\n",
            "            reg_class_agnostic=False,\n",
            "            loss_cls=dict(\n",
            "                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n",
            "            loss_bbox=dict(type='SmoothL1Loss', loss_weight=1.0))),\n",
            "    train_cfg=dict(\n",
            "        rpn=dict(\n",
            "            assigner=dict(\n",
            "                type='MaxIoUAssigner',\n",
            "                pos_iou_thr=0.7,\n",
            "                neg_iou_thr=0.3,\n",
            "                min_pos_iou=0.3,\n",
            "                match_low_quality=True,\n",
            "                ignore_iof_thr=-1),\n",
            "            sampler=dict(\n",
            "                type='RandomSampler',\n",
            "                num=256,\n",
            "                pos_fraction=0.5,\n",
            "                neg_pos_ub=-1,\n",
            "                add_gt_as_proposals=False),\n",
            "            allowed_border=-1,\n",
            "            pos_weight=-1,\n",
            "            debug=False),\n",
            "        rpn_proposal=dict(\n",
            "            nms_pre=2000,\n",
            "            max_per_img=1000,\n",
            "            nms=dict(type='nms', iou_threshold=0.7),\n",
            "            min_bbox_size=0),\n",
            "        rcnn=dict(\n",
            "            assigner=dict(\n",
            "                type='MaxIoUAssigner',\n",
            "                pos_iou_thr=0.5,\n",
            "                neg_iou_thr=0.5,\n",
            "                min_pos_iou=0.5,\n",
            "                match_low_quality=False,\n",
            "                ignore_iof_thr=-1),\n",
            "            sampler=dict(\n",
            "                type='RandomSampler',\n",
            "                num=512,\n",
            "                pos_fraction=0.25,\n",
            "                neg_pos_ub=-1,\n",
            "                add_gt_as_proposals=True),\n",
            "            pos_weight=-1,\n",
            "            debug=False)),\n",
            "    test_cfg=dict(\n",
            "        rpn=dict(\n",
            "            nms_pre=1000,\n",
            "            max_per_img=1000,\n",
            "            nms=dict(type='nms', iou_threshold=0.7),\n",
            "            min_bbox_size=0),\n",
            "        rcnn=dict(\n",
            "            score_thr=0.05,\n",
            "            nms=dict(type='nms', iou_threshold=0.5),\n",
            "            max_per_img=100)),\n",
            "    init_cfg=dict(\n",
            "        type='Pretrained',\n",
            "        checkpoint=\n",
            "        'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth'\n",
            "    ))\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=4, val_interval=1)\n",
            "val_cfg = None\n",
            "test_cfg = None\n",
            "param_scheduler = [\n",
            "    dict(type='LinearLR', start_factor=0.01, by_epoch=False, begin=0, end=100),\n",
            "    dict(\n",
            "        type='MultiStepLR',\n",
            "        begin=0,\n",
            "        end=4,\n",
            "        by_epoch=True,\n",
            "        milestones=[3],\n",
            "        gamma=0.1)\n",
            "]\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))\n",
            "work_dir = './tutorial_exps/detector'\n",
            "randomness = dict(seed=0, deterministic=False)\n",
            "gpu_ids = range(0, 1)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import mmengine\n",
        "from mmengine.runner import set_random_seed\n",
        "cfg = mmengine.Config.fromfile('./configs/det/faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py')\n",
        "cfg.data_root = 'data/MOT17_tiny/'\n",
        "cfg.train_dataloader.dataset.data_root = 'data/MOT17_tiny/'\n",
        "cfg.test_dataloader = cfg.test_cfg = cfg.test_evaluator = None\n",
        "cfg.val_dataloader = cfg.val_cfg = cfg.val_evaluator = None\n",
        "# different name for creating different visualizer instance\n",
        "cfg.visualizer.name = 'mot_visualizer'\n",
        "\n",
        "\n",
        "cfg.work_dir = './tutorial_exps/detector'\n",
        "cfg.randomness = dict(seed=0, deterministic=False)\n",
        "cfg.gpu_ids = range(1)\n",
        "print(f'Config:\\n{cfg.pretty_text}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "889b4255-50be-4da3-85c4-dcd19c8111ac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "4757d999925e44748b3b4b8476ff7069",
            "64133dab9fa94eb9ad7819da4740a90f",
            "34b3be1af1704bd4a1a25992ff3a2c33",
            "1a722030c2344bf6a5ba1941c9e056de",
            "e0b78a16c9d8488eb6f2b78788bc1987",
            "36f603cf7dab445ab6120b805799c1d0",
            "f572376e2dbb437c95e1f62820d6f0d3",
            "9c5e87a7ba0f4b8395a3fd0027508930",
            "a83c0a2e5ee8432d9ecb05468194f3ed",
            "c1a6f0f21a864752bb77a435e5633c5f",
            "f4510cc71cf54df785fbe760b7dfc32c"
          ]
        },
        "id": "889b4255-50be-4da3-85c4-dcd19c8111ac",
        "outputId": "b9fb7a8b-ef72-4735-99a8-48e721f64130"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:48:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 0\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.1, V11.1.105\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.10.0+cu111\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 7.3\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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\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 11.1\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 8.0.5\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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 -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, 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, \n",
            "\n",
            "    TorchVision: 0.11.0+cu111\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.1.0\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: False\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: 0\n",
            "    deterministic: False\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "09/06 08:48:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "dataset_type = 'mmdet.CocoDataset'\n",
            "data_root = 'data/MOT17_tiny/'\n",
            "train_pipeline = [\n",
            "    dict(type='LoadImageFromFile', to_float32=True),\n",
            "    dict(type='LoadAnnotations', with_bbox=True),\n",
            "    dict(\n",
            "        type='RandomResize',\n",
            "        scale=(1088, 1088),\n",
            "        ratio_range=(0.8, 1.2),\n",
            "        keep_ratio=True,\n",
            "        clip_object_border=False),\n",
            "    dict(type='PhotoMetricDistortion'),\n",
            "    dict(type='RandomCrop', crop_size=(1088, 1088), bbox_clip_border=False),\n",
            "    dict(type='RandomFlip', prob=0.5),\n",
            "    dict(type='PackDetInputs')\n",
            "]\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile'),\n",
            "    dict(type='Resize', scale=(1088, 1088), keep_ratio=True),\n",
            "    dict(\n",
            "        type='PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                   'scale_factor'))\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=2,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    batch_sampler=dict(type='mmdet.AspectRatioBatchSampler'),\n",
            "    dataset=dict(\n",
            "        type='mmdet.CocoDataset',\n",
            "        data_root='data/MOT17_tiny/',\n",
            "        _scope_='mmdet',\n",
            "        ann_file='annotations/half-train_cocoformat.json',\n",
            "        data_prefix=dict(img='train/'),\n",
            "        metainfo=dict(CLASSES=('pedestrian', )),\n",
            "        filter_cfg=dict(filter_empty_gt=True, min_size=32),\n",
            "        pipeline=[\n",
            "            dict(type='LoadImageFromFile', to_float32=True),\n",
            "            dict(type='LoadAnnotations', with_bbox=True),\n",
            "            dict(\n",
            "                type='RandomResize',\n",
            "                scale=(1088, 1088),\n",
            "                ratio_range=(0.8, 1.2),\n",
            "                keep_ratio=True,\n",
            "                clip_object_border=False),\n",
            "            dict(type='PhotoMetricDistortion'),\n",
            "            dict(\n",
            "                type='RandomCrop',\n",
            "                crop_size=(1088, 1088),\n",
            "                bbox_clip_border=False),\n",
            "            dict(type='RandomFlip', prob=0.5),\n",
            "            dict(type='PackDetInputs')\n",
            "        ]))\n",
            "val_dataloader = None\n",
            "test_dataloader = None\n",
            "val_evaluator = None\n",
            "test_evaluator = None\n",
            "default_scope = 'mmtrack'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(type='ParamSchedulerHook'),\n",
            "    checkpoint=dict(type='CheckpointHook', interval=1),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='mmdet.DetVisualizationHook', draw=False))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=False,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='mmdet.DetLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='mot_visualizer')\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "model = dict(\n",
            "    data_preprocessor=dict(\n",
            "        type='DetDataPreprocessor',\n",
            "        mean=[123.675, 116.28, 103.53],\n",
            "        std=[58.395, 57.12, 57.375],\n",
            "        bgr_to_rgb=True,\n",
            "        pad_size_divisor=32),\n",
            "    type='FasterRCNN',\n",
            "    _scope_='mmdet',\n",
            "    backbone=dict(\n",
            "        type='ResNet',\n",
            "        depth=50,\n",
            "        num_stages=4,\n",
            "        out_indices=(0, 1, 2, 3),\n",
            "        frozen_stages=1,\n",
            "        norm_cfg=dict(type='BN', requires_grad=True),\n",
            "        norm_eval=True,\n",
            "        style='pytorch',\n",
            "        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n",
            "    neck=dict(\n",
            "        type='FPN',\n",
            "        in_channels=[256, 512, 1024, 2048],\n",
            "        out_channels=256,\n",
            "        num_outs=5),\n",
            "    rpn_head=dict(\n",
            "        type='RPNHead',\n",
            "        in_channels=256,\n",
            "        feat_channels=256,\n",
            "        anchor_generator=dict(\n",
            "            type='AnchorGenerator',\n",
            "            scales=[8],\n",
            "            ratios=[0.5, 1.0, 2.0],\n",
            "            strides=[4, 8, 16, 32, 64]),\n",
            "        bbox_coder=dict(\n",
            "            type='DeltaXYWHBBoxCoder',\n",
            "            target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "            target_stds=[1.0, 1.0, 1.0, 1.0],\n",
            "            clip_border=False),\n",
            "        loss_cls=dict(\n",
            "            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
            "        loss_bbox=dict(\n",
            "            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n",
            "    roi_head=dict(\n",
            "        type='StandardRoIHead',\n",
            "        bbox_roi_extractor=dict(\n",
            "            type='SingleRoIExtractor',\n",
            "            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n",
            "            out_channels=256,\n",
            "            featmap_strides=[4, 8, 16, 32]),\n",
            "        bbox_head=dict(\n",
            "            type='Shared2FCBBoxHead',\n",
            "            in_channels=256,\n",
            "            fc_out_channels=1024,\n",
            "            roi_feat_size=7,\n",
            "            num_classes=1,\n",
            "            bbox_coder=dict(\n",
            "                type='DeltaXYWHBBoxCoder',\n",
            "                target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "                target_stds=[0.1, 0.1, 0.2, 0.2],\n",
            "                clip_border=False),\n",
            "            reg_class_agnostic=False,\n",
            "            loss_cls=dict(\n",
            "                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n",
            "            loss_bbox=dict(type='SmoothL1Loss', loss_weight=1.0))),\n",
            "    train_cfg=dict(\n",
            "        rpn=dict(\n",
            "            assigner=dict(\n",
            "                type='MaxIoUAssigner',\n",
            "                pos_iou_thr=0.7,\n",
            "                neg_iou_thr=0.3,\n",
            "                min_pos_iou=0.3,\n",
            "                match_low_quality=True,\n",
            "                ignore_iof_thr=-1),\n",
            "            sampler=dict(\n",
            "                type='RandomSampler',\n",
            "                num=256,\n",
            "                pos_fraction=0.5,\n",
            "                neg_pos_ub=-1,\n",
            "                add_gt_as_proposals=False),\n",
            "            allowed_border=-1,\n",
            "            pos_weight=-1,\n",
            "            debug=False),\n",
            "        rpn_proposal=dict(\n",
            "            nms_pre=2000,\n",
            "            max_per_img=1000,\n",
            "            nms=dict(type='nms', iou_threshold=0.7),\n",
            "            min_bbox_size=0),\n",
            "        rcnn=dict(\n",
            "            assigner=dict(\n",
            "                type='MaxIoUAssigner',\n",
            "                pos_iou_thr=0.5,\n",
            "                neg_iou_thr=0.5,\n",
            "                min_pos_iou=0.5,\n",
            "                match_low_quality=False,\n",
            "                ignore_iof_thr=-1),\n",
            "            sampler=dict(\n",
            "                type='RandomSampler',\n",
            "                num=512,\n",
            "                pos_fraction=0.25,\n",
            "                neg_pos_ub=-1,\n",
            "                add_gt_as_proposals=True),\n",
            "            pos_weight=-1,\n",
            "            debug=False)),\n",
            "    test_cfg=dict(\n",
            "        rpn=dict(\n",
            "            nms_pre=1000,\n",
            "            max_per_img=1000,\n",
            "            nms=dict(type='nms', iou_threshold=0.7),\n",
            "            min_bbox_size=0),\n",
            "        rcnn=dict(\n",
            "            score_thr=0.05,\n",
            "            nms=dict(type='nms', iou_threshold=0.5),\n",
            "            max_per_img=100)),\n",
            "    init_cfg=dict(\n",
            "        type='Pretrained',\n",
            "        checkpoint=\n",
            "        'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth'\n",
            "    ))\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=4, val_interval=1)\n",
            "val_cfg = None\n",
            "test_cfg = None\n",
            "param_scheduler = [\n",
            "    dict(type='LinearLR', start_factor=0.01, by_epoch=False, begin=0, end=100),\n",
            "    dict(\n",
            "        type='MultiStepLR',\n",
            "        begin=0,\n",
            "        end=4,\n",
            "        by_epoch=True,\n",
            "        milestones=[3],\n",
            "        gamma=0.1)\n",
            "]\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))\n",
            "work_dir = './tutorial_exps/detector'\n",
            "randomness = dict(seed=0, deterministic=False)\n",
            "gpu_ids = range(0, 1)\n",
            "\n",
            "Result has been saved to /content/mmtracking/tutorial_exps/detector/modules_statistic_results.json\n",
            "09/06 08:48:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "loading annotations into memory...\n",
            "Done (t=0.54s)\n",
            "creating index...\n",
            "index created!\n",
            "09/06 08:48:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth\n",
            "09/06 08:48:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth\" to /root/.cache/torch/hub/checkpoints/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4757d999925e44748b3b4b8476ff7069",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/160M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 08:48:53 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The model and loaded state dict do not match exactly\n",
            "\n",
            "size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([2, 1024]).\n",
            "size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([2]).\n",
            "size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).\n",
            "size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([4]).\n",
            "09/06 08:48:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmtracking/tutorial_exps/detector by HardDiskBackend.\n",
            "09/06 08:49:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/414]  lr: 1.0000e-02  eta: 0:12:31  time: 0.4476  data_time: 0.0086  memory: 4097  loss_rpn_cls: 0.0432  loss_rpn_bbox: 0.1128  loss_cls: 0.3478  acc: 87.7930  loss_bbox: 0.2661  loss: 0.7698\n",
            "09/06 08:49:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][100/414]  lr: 2.0000e-02  eta: 0:12:03  time: 0.4664  data_time: 0.0077  memory: 4096  loss_rpn_cls: 0.0310  loss_rpn_bbox: 0.1023  loss_cls: 0.3020  acc: 85.7422  loss_bbox: 0.2093  loss: 0.6447\n",
            "09/06 08:50:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][150/414]  lr: 2.0000e-02  eta: 0:11:41  time: 0.4669  data_time: 0.0085  memory: 4097  loss_rpn_cls: 0.0210  loss_rpn_bbox: 0.0953  loss_cls: 0.3098  acc: 87.9883  loss_bbox: 0.2082  loss: 0.6343\n",
            "09/06 08:50:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][200/414]  lr: 2.0000e-02  eta: 0:11:20  time: 0.4539  data_time: 0.0089  memory: 4097  loss_rpn_cls: 0.0263  loss_rpn_bbox: 0.0771  loss_cls: 0.2932  acc: 84.3750  loss_bbox: 0.2172  loss: 0.6138\n",
            "09/06 08:50:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][250/414]  lr: 2.0000e-02  eta: 0:10:54  time: 0.4517  data_time: 0.0098  memory: 4097  loss_rpn_cls: 0.0162  loss_rpn_bbox: 0.0778  loss_cls: 0.2588  acc: 86.9141  loss_bbox: 0.1758  loss: 0.5286\n",
            "09/06 08:51:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][300/414]  lr: 2.0000e-02  eta: 0:10:33  time: 0.4759  data_time: 0.0090  memory: 4097  loss_rpn_cls: 0.0133  loss_rpn_bbox: 0.0772  loss_cls: 0.2558  acc: 87.4023  loss_bbox: 0.1703  loss: 0.5165\n",
            "09/06 08:51:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][350/414]  lr: 2.0000e-02  eta: 0:10:07  time: 0.4605  data_time: 0.0085  memory: 4096  loss_rpn_cls: 0.0155  loss_rpn_bbox: 0.0722  loss_cls: 0.2608  acc: 90.4297  loss_bbox: 0.1860  loss: 0.5345\n",
            "09/06 08:51:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][400/414]  lr: 2.0000e-02  eta: 0:09:43  time: 0.4660  data_time: 0.0085  memory: 4097  loss_rpn_cls: 0.0159  loss_rpn_bbox: 0.0762  loss_cls: 0.2045  acc: 92.2852  loss_bbox: 0.1252  loss: 0.4218\n",
            "09/06 08:52:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval_20220906_084830\n",
            "09/06 08:52:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n",
            "09/06 08:52:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][50/414]  lr: 2.0000e-02  eta: 0:09:08  time: 0.4438  data_time: 0.0085  memory: 4097  loss_rpn_cls: 0.0117  loss_rpn_bbox: 0.0568  loss_cls: 0.2145  acc: 92.7734  loss_bbox: 0.1412  loss: 0.4241\n",
            "09/06 08:52:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][100/414]  lr: 2.0000e-02  eta: 0:08:47  time: 0.4764  data_time: 0.0090  memory: 4097  loss_rpn_cls: 0.0128  loss_rpn_bbox: 0.0551  loss_cls: 0.2117  acc: 92.8711  loss_bbox: 0.1495  loss: 0.4291\n",
            "09/06 08:53:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][150/414]  lr: 2.0000e-02  eta: 0:08:24  time: 0.4226  data_time: 0.0090  memory: 4097  loss_rpn_cls: 0.0185  loss_rpn_bbox: 0.0557  loss_cls: 0.2072  acc: 89.3555  loss_bbox: 0.1273  loss: 0.4087\n",
            "09/06 08:53:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][200/414]  lr: 2.0000e-02  eta: 0:08:00  time: 0.4544  data_time: 0.0081  memory: 4097  loss_rpn_cls: 0.0110  loss_rpn_bbox: 0.0498  loss_cls: 0.1904  acc: 90.1367  loss_bbox: 0.1130  loss: 0.3641\n",
            "09/06 08:54:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][250/414]  lr: 2.0000e-02  eta: 0:07:36  time: 0.4227  data_time: 0.0082  memory: 4097  loss_rpn_cls: 0.0146  loss_rpn_bbox: 0.0475  loss_cls: 0.2017  acc: 90.4297  loss_bbox: 0.1279  loss: 0.3917\n",
            "09/06 08:54:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][300/414]  lr: 2.0000e-02  eta: 0:07:13  time: 0.4572  data_time: 0.0083  memory: 4097  loss_rpn_cls: 0.0100  loss_rpn_bbox: 0.0637  loss_cls: 0.1965  acc: 93.2617  loss_bbox: 0.1399  loss: 0.4102\n",
            "09/06 08:54:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][350/414]  lr: 2.0000e-02  eta: 0:06:50  time: 0.4661  data_time: 0.0093  memory: 4097  loss_rpn_cls: 0.0096  loss_rpn_bbox: 0.0440  loss_cls: 0.1747  acc: 91.6016  loss_bbox: 0.1094  loss: 0.3377\n",
            "09/06 08:55:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][400/414]  lr: 2.0000e-02  eta: 0:06:27  time: 0.4538  data_time: 0.0085  memory: 4097  loss_rpn_cls: 0.0083  loss_rpn_bbox: 0.0413  loss_cls: 0.1579  acc: 94.7266  loss_bbox: 0.1079  loss: 0.3153\n",
            "09/06 08:55:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval_20220906_084830\n",
            "09/06 08:55:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 2 epochs\n",
            "09/06 08:55:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][50/414]  lr: 2.0000e-02  eta: 0:05:56  time: 0.4519  data_time: 0.0089  memory: 4097  loss_rpn_cls: 0.0062  loss_rpn_bbox: 0.0405  loss_cls: 0.1676  acc: 91.6992  loss_bbox: 0.1083  loss: 0.3226\n",
            "09/06 08:56:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][100/414]  lr: 2.0000e-02  eta: 0:05:33  time: 0.4734  data_time: 0.0091  memory: 4097  loss_rpn_cls: 0.0049  loss_rpn_bbox: 0.0337  loss_cls: 0.1571  acc: 94.1406  loss_bbox: 0.0945  loss: 0.2903\n",
            "09/06 08:56:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][150/414]  lr: 2.0000e-02  eta: 0:05:10  time: 0.4527  data_time: 0.0087  memory: 4097  loss_rpn_cls: 0.0112  loss_rpn_bbox: 0.0406  loss_cls: 0.1630  acc: 93.9453  loss_bbox: 0.1075  loss: 0.3223\n",
            "09/06 08:56:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval_20220906_084830\n",
            "09/06 08:56:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][200/414]  lr: 2.0000e-02  eta: 0:04:48  time: 0.4468  data_time: 0.0086  memory: 4097  loss_rpn_cls: 0.0069  loss_rpn_bbox: 0.0554  loss_cls: 0.1763  acc: 90.3320  loss_bbox: 0.1082  loss: 0.3468\n",
            "09/06 08:57:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][250/414]  lr: 2.0000e-02  eta: 0:04:25  time: 0.4645  data_time: 0.0084  memory: 4097  loss_rpn_cls: 0.0079  loss_rpn_bbox: 0.0441  loss_cls: 0.1617  acc: 94.3359  loss_bbox: 0.1107  loss: 0.3244\n",
            "09/06 08:57:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][300/414]  lr: 2.0000e-02  eta: 0:04:02  time: 0.4733  data_time: 0.0089  memory: 4097  loss_rpn_cls: 0.0078  loss_rpn_bbox: 0.0374  loss_cls: 0.1390  acc: 95.5078  loss_bbox: 0.1000  loss: 0.2842\n",
            "09/06 08:58:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][350/414]  lr: 2.0000e-02  eta: 0:03:39  time: 0.4550  data_time: 0.0088  memory: 4097  loss_rpn_cls: 0.0071  loss_rpn_bbox: 0.0365  loss_cls: 0.1668  acc: 93.8477  loss_bbox: 0.1056  loss: 0.3161\n",
            "09/06 08:58:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][400/414]  lr: 2.0000e-02  eta: 0:03:16  time: 0.4494  data_time: 0.0080  memory: 4097  loss_rpn_cls: 0.0048  loss_rpn_bbox: 0.0341  loss_cls: 0.1349  acc: 95.3125  loss_bbox: 0.0942  loss: 0.2680\n",
            "09/06 08:58:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval_20220906_084830\n",
            "09/06 08:58:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 3 epochs\n",
            "09/06 08:58:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][50/414]  lr: 2.0000e-03  eta: 0:02:46  time: 0.4481  data_time: 0.0088  memory: 4097  loss_rpn_cls: 0.0031  loss_rpn_bbox: 0.0288  loss_cls: 0.1293  acc: 96.3867  loss_bbox: 0.0897  loss: 0.2510\n",
            "09/06 08:59:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][100/414]  lr: 2.0000e-03  eta: 0:02:23  time: 0.4767  data_time: 0.0085  memory: 4097  loss_rpn_cls: 0.0048  loss_rpn_bbox: 0.0314  loss_cls: 0.1376  acc: 94.2383  loss_bbox: 0.1000  loss: 0.2737\n",
            "09/06 08:59:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][150/414]  lr: 2.0000e-03  eta: 0:02:01  time: 0.4850  data_time: 0.0098  memory: 4096  loss_rpn_cls: 0.0037  loss_rpn_bbox: 0.0232  loss_cls: 0.1152  acc: 92.9688  loss_bbox: 0.0722  loss: 0.2142\n",
            "09/06 09:00:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][200/414]  lr: 2.0000e-03  eta: 0:01:38  time: 0.4694  data_time: 0.0089  memory: 4097  loss_rpn_cls: 0.0054  loss_rpn_bbox: 0.0279  loss_cls: 0.1285  acc: 93.4570  loss_bbox: 0.0880  loss: 0.2497\n",
            "09/06 09:00:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][250/414]  lr: 2.0000e-03  eta: 0:01:15  time: 0.4595  data_time: 0.0084  memory: 4097  loss_rpn_cls: 0.0030  loss_rpn_bbox: 0.0255  loss_cls: 0.1147  acc: 95.4102  loss_bbox: 0.0822  loss: 0.2254\n",
            "09/06 09:00:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][300/414]  lr: 2.0000e-03  eta: 0:00:52  time: 0.4778  data_time: 0.0090  memory: 4097  loss_rpn_cls: 0.0063  loss_rpn_bbox: 0.0279  loss_cls: 0.1356  acc: 93.7500  loss_bbox: 0.0900  loss: 0.2598\n",
            "09/06 09:01:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][350/414]  lr: 2.0000e-03  eta: 0:00:29  time: 0.4581  data_time: 0.0090  memory: 4097  loss_rpn_cls: 0.0064  loss_rpn_bbox: 0.0233  loss_cls: 0.1073  acc: 96.5820  loss_bbox: 0.0767  loss: 0.2138\n",
            "09/06 09:01:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][400/414]  lr: 2.0000e-03  eta: 0:00:06  time: 0.4541  data_time: 0.0090  memory: 4097  loss_rpn_cls: 0.0037  loss_rpn_bbox: 0.0238  loss_cls: 0.1253  acc: 96.5820  loss_bbox: 0.0766  loss: 0.2293\n",
            "09/06 09:01:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval_20220906_084830\n",
            "09/06 09:01:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 4 epochs\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "FasterRCNN(\n",
              "  (data_preprocessor): DetDataPreprocessor()\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\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",
              "      )\n",
              "    )\n",
              "  )\n",
              "  init_cfg={'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}\n",
              "  (neck): FPN(\n",
              "    (lateral_convs): ModuleList(\n",
              "      (0): ConvModule(\n",
              "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (1): ConvModule(\n",
              "        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (2): ConvModule(\n",
              "        (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (3): ConvModule(\n",
              "        (conv): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "    )\n",
              "    (fpn_convs): ModuleList(\n",
              "      (0): ConvModule(\n",
              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      )\n",
              "      (1): ConvModule(\n",
              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      )\n",
              "      (2): ConvModule(\n",
              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      )\n",
              "      (3): ConvModule(\n",
              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}\n",
              "  (rpn_head): RPNHead(\n",
              "    (loss_cls): CrossEntropyLoss(avg_non_ignore=False)\n",
              "    (loss_bbox): SmoothL1Loss()\n",
              "    (rpn_conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (rpn_cls): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (rpn_reg): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              "  init_cfg={'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}\n",
              "  (roi_head): StandardRoIHead(\n",
              "    (bbox_roi_extractor): SingleRoIExtractor(\n",
              "      (roi_layers): ModuleList(\n",
              "        (0): RoIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
              "        (1): RoIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
              "        (2): RoIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
              "        (3): RoIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
              "      )\n",
              "    )\n",
              "    (bbox_head): Shared2FCBBoxHead(\n",
              "      (loss_cls): CrossEntropyLoss(avg_non_ignore=False)\n",
              "      (loss_bbox): SmoothL1Loss()\n",
              "      (fc_cls): Linear(in_features=1024, out_features=2, bias=True)\n",
              "      (fc_reg): Linear(in_features=1024, out_features=4, bias=True)\n",
              "      (shared_convs): ModuleList()\n",
              "      (shared_fcs): ModuleList(\n",
              "        (0): Linear(in_features=12544, out_features=1024, bias=True)\n",
              "        (1): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "      )\n",
              "      (cls_convs): ModuleList()\n",
              "      (cls_fcs): ModuleList()\n",
              "      (reg_convs): ModuleList()\n",
              "      (reg_fcs): ModuleList()\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    init_cfg=[{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]\n",
              "  )\n",
              ")\n",
              "init_cfg={'type': 'Pretrained', 'checkpoint': 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth'}"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os.path as osp\n",
        "\n",
        "from mmengine.utils import mkdir_or_exist\n",
        "from mmengine.runner import Runner\n",
        "\n",
        "mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
        "runner = Runner.from_cfg(cfg)\n",
        "runner.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7f747c1f-7944-49f2-b9fc-a51dc3f85857",
      "metadata": {
        "id": "7f747c1f-7944-49f2-b9fc-a51dc3f85857",
        "tags": []
      },
      "source": [
        "### **Train a ReID model for MOT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "6705deeb-a9d7-42e2-9d52-b51b7b588d1f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6705deeb-a9d7-42e2-9d52-b51b7b588d1f",
        "outputId": "a4f24af8-fc91-4459-f4c0-3f0ced62e0f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Config:\n",
            "dataset_type = 'ReIDDataset'\n",
            "data_root = 'data/MOT17/'\n",
            "train_pipeline = [\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=False,\n",
            "        transforms=[\n",
            "            dict(type='LoadImageFromFile', to_float32=True),\n",
            "            dict(\n",
            "                type='mmdet.Resize',\n",
            "                scale=(128, 256),\n",
            "                keep_ratio=False,\n",
            "                clip_object_border=False),\n",
            "            dict(type='RandomFlip', prob=0.5, direction='horizontal')\n",
            "        ]),\n",
            "    dict(type='PackReIDInputs')\n",
            "]\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile', to_float32=True),\n",
            "    dict(type='mmdet.Resize', scale=(128, 256), keep_ratio=False),\n",
            "    dict(type='PackReIDInputs')\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    dataset=dict(\n",
            "        type='ReIDDataset',\n",
            "        data_root='data/MOT17_tiny/',\n",
            "        triplet_sampler=dict(num_ids=8, ins_per_id=4),\n",
            "        data_prefix=dict(img_path='reid/imgs'),\n",
            "        ann_file='reid/meta/train_9.txt',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='TransformBroadcaster',\n",
            "                share_random_params=False,\n",
            "                transforms=[\n",
            "                    dict(type='LoadImageFromFile', to_float32=True),\n",
            "                    dict(\n",
            "                        type='mmdet.Resize',\n",
            "                        scale=(128, 256),\n",
            "                        keep_ratio=False,\n",
            "                        clip_object_border=False),\n",
            "                    dict(type='RandomFlip', prob=0.5, direction='horizontal')\n",
            "                ]),\n",
            "            dict(type='PackReIDInputs')\n",
            "        ]))\n",
            "val_dataloader = None\n",
            "test_dataloader = None\n",
            "val_evaluator = None\n",
            "test_evaluator = None\n",
            "default_scope = 'mmtrack'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(type='ParamSchedulerHook'),\n",
            "    checkpoint=dict(type='CheckpointHook', interval=1),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='TrackVisualizationHook', draw=False))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=False,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='TrackLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='mot_reid_visualizer')\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "model = dict(\n",
            "    type='BaseReID',\n",
            "    data_preprocessor=dict(\n",
            "        type='mmcls.ClsDataPreprocessor',\n",
            "        mean=[123.675, 116.28, 103.53],\n",
            "        std=[58.395, 57.12, 57.375],\n",
            "        to_rgb=True),\n",
            "    backbone=dict(\n",
            "        type='mmcls.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_cls=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),\n",
            "        loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),\n",
            "        norm_cfg=dict(type='BN1d'),\n",
            "        act_cfg=dict(type='ReLU')),\n",
            "    init_cfg=dict(\n",
            "        type='Pretrained',\n",
            "        checkpoint=\n",
            "        'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'\n",
            "    ))\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    clip_grad=None,\n",
            "    optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))\n",
            "param_scheduler = [\n",
            "    dict(\n",
            "        type='LinearLR', start_factor=0.005, by_epoch=False, begin=0, end=200),\n",
            "    dict(\n",
            "        type='MultiStepLR',\n",
            "        begin=0,\n",
            "        end=2,\n",
            "        by_epoch=True,\n",
            "        milestones=[1],\n",
            "        gamma=0.1)\n",
            "]\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2, val_begin=3)\n",
            "val_cfg = None\n",
            "test_cfg = None\n",
            "work_dir = './tutorial_exps/reid'\n",
            "randomness = dict(seed=0, deterministic=False)\n",
            "gpu_ids = range(0, 1)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import mmengine\n",
        "\n",
        "cfg = mmengine.Config.fromfile('./configs/reid/reid_r50_8xb32-6e_mot17train80_test-mot17val20.py')\n",
        "cfg.train_dataloader.dataset.data_root = 'data/MOT17_tiny/'\n",
        "cfg.train_dataloader.dataset.ann_file = 'reid/meta/train_9.txt'\n",
        "cfg.test_dataloader = cfg.test_cfg = cfg.test_evaluator = None\n",
        "cfg.val_dataloader = cfg.val_cfg = cfg.val_evaluator = None\n",
        "cfg.visualizer.name = 'mot_reid_visualizer'\n",
        "\n",
        "# learning policy\n",
        "cfg.param_scheduler = [\n",
        "    dict(\n",
        "        type='LinearLR',\n",
        "        start_factor=1.0 / 200,\n",
        "        by_epoch=False,\n",
        "        begin=0,\n",
        "        end=200),\n",
        "    dict(\n",
        "        type='MultiStepLR',\n",
        "        begin=0,\n",
        "        end=2,\n",
        "        by_epoch=True,\n",
        "        milestones=[1],\n",
        "        gamma=0.1)\n",
        "]\n",
        "cfg.train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2, val_begin=3)\n",
        "\n",
        "cfg.work_dir = './tutorial_exps/reid'\n",
        "cfg.randomness = dict(seed=0, deterministic=False)\n",
        "cfg.gpu_ids = range(1)\n",
        "print(f'Config:\\n{cfg.pretty_text}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "12f2b54e-7e5f-4d95-9e27-528115717e03",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "34594c7fd26c4f61af5e4ec5b34b6b02",
            "8727613cf45f4cd4a79557afd1973632",
            "28ef474b2c7c466baa81c1196f8e7505",
            "7b268f5a3b334d2c89ae8d43fda6eb53",
            "9d467daaa30e44288dd49152948db460",
            "9793eaee82db45beaddf9e80cab6d0a1",
            "e75e2e078b4246a9984fa0841de27a15",
            "504570ec6de24f40b824fb8eacc65447",
            "b9470c130a69411b8be481f4aa045d26",
            "0b4dd8ace9b5467e9e7c0214df6df1a9",
            "e495e81fecf844a4ae4a15ca9cb0329c"
          ]
        },
        "id": "12f2b54e-7e5f-4d95-9e27-528115717e03",
        "outputId": "c9952c03-33c9-450b-d325-a526821a93ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 09:16:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 0\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.1, V11.1.105\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.10.0+cu111\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 7.3\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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\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 11.1\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 8.0.5\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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 -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, 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, \n",
            "\n",
            "    TorchVision: 0.11.0+cu111\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.1.0\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: False\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: 0\n",
            "    deterministic: False\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "09/06 09:16:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "dataset_type = 'ReIDDataset'\n",
            "data_root = 'data/MOT17/'\n",
            "train_pipeline = [\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=False,\n",
            "        transforms=[\n",
            "            dict(type='LoadImageFromFile', to_float32=True),\n",
            "            dict(\n",
            "                type='mmdet.Resize',\n",
            "                scale=(128, 256),\n",
            "                keep_ratio=False,\n",
            "                clip_object_border=False),\n",
            "            dict(type='RandomFlip', prob=0.5, direction='horizontal')\n",
            "        ]),\n",
            "    dict(type='PackReIDInputs')\n",
            "]\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile', to_float32=True),\n",
            "    dict(type='mmdet.Resize', scale=(128, 256), keep_ratio=False),\n",
            "    dict(type='PackReIDInputs')\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    dataset=dict(\n",
            "        type='ReIDDataset',\n",
            "        data_root='data/MOT17_tiny/',\n",
            "        triplet_sampler=dict(num_ids=8, ins_per_id=4),\n",
            "        data_prefix=dict(img_path='reid/imgs'),\n",
            "        ann_file='reid/meta/train_9.txt',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='TransformBroadcaster',\n",
            "                share_random_params=False,\n",
            "                transforms=[\n",
            "                    dict(type='LoadImageFromFile', to_float32=True),\n",
            "                    dict(\n",
            "                        type='mmdet.Resize',\n",
            "                        scale=(128, 256),\n",
            "                        keep_ratio=False,\n",
            "                        clip_object_border=False),\n",
            "                    dict(type='RandomFlip', prob=0.5, direction='horizontal')\n",
            "                ]),\n",
            "            dict(type='PackReIDInputs')\n",
            "        ]))\n",
            "val_dataloader = None\n",
            "test_dataloader = None\n",
            "val_evaluator = None\n",
            "test_evaluator = None\n",
            "default_scope = 'mmtrack'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(type='ParamSchedulerHook'),\n",
            "    checkpoint=dict(type='CheckpointHook', interval=1),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='TrackVisualizationHook', draw=False))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=False,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='TrackLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='mot_reid_visualizer')\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "model = dict(\n",
            "    type='BaseReID',\n",
            "    data_preprocessor=dict(\n",
            "        type='mmcls.ClsDataPreprocessor',\n",
            "        mean=[123.675, 116.28, 103.53],\n",
            "        std=[58.395, 57.12, 57.375],\n",
            "        to_rgb=True),\n",
            "    backbone=dict(\n",
            "        type='mmcls.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_cls=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),\n",
            "        loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),\n",
            "        norm_cfg=dict(type='BN1d'),\n",
            "        act_cfg=dict(type='ReLU')),\n",
            "    init_cfg=dict(\n",
            "        type='Pretrained',\n",
            "        checkpoint=\n",
            "        'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'\n",
            "    ))\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    clip_grad=None,\n",
            "    optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))\n",
            "param_scheduler = [\n",
            "    dict(\n",
            "        type='LinearLR', start_factor=0.005, by_epoch=False, begin=0, end=200),\n",
            "    dict(\n",
            "        type='MultiStepLR',\n",
            "        begin=0,\n",
            "        end=2,\n",
            "        by_epoch=True,\n",
            "        milestones=[1],\n",
            "        gamma=0.1)\n",
            "]\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2, val_begin=3)\n",
            "val_cfg = None\n",
            "test_cfg = None\n",
            "work_dir = './tutorial_exps/reid'\n",
            "randomness = dict(seed=0, deterministic=False)\n",
            "gpu_ids = range(0, 1)\n",
            "\n",
            "Result has been saved to /content/mmtracking/tutorial_exps/reid/modules_statistic_results.json\n",
            "09/06 09:16:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "09/06 09:16:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth\n",
            "09/06 09:16:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth\" to /root/.cache/torch/hub/checkpoints/resnet50_batch256_imagenet_20200708-cfb998bf.pth\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34594c7fd26c4f61af5e4ec5b34b6b02",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/97.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 09:16:40 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - 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",
            "09/06 09:16:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmtracking/tutorial_exps/reid by HardDiskBackend.\n",
            "09/06 09:16:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/1576]  lr: 2.5000e-02  eta: 0:16:34  time: 0.3139  data_time: 0.0053  memory: 4097  triplet_loss: 0.0000  ce_loss: 0.0004  accuracy_top-1: 100.0000  loss: 0.0006\n",
            "09/06 09:17:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][100/1576]  lr: 5.0000e-02  eta: 0:16:09  time: 0.3246  data_time: 0.0059  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:17:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][150/1576]  lr: 7.5000e-02  eta: 0:15:49  time: 0.3163  data_time: 0.0052  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:17:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][200/1576]  lr: 1.0000e-01  eta: 0:15:37  time: 0.3179  data_time: 0.0054  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:18:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][250/1576]  lr: 1.0000e-01  eta: 0:15:23  time: 0.3169  data_time: 0.0057  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:18:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][300/1576]  lr: 1.0000e-01  eta: 0:15:06  time: 0.3144  data_time: 0.0054  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:18:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][350/1576]  lr: 1.0000e-01  eta: 0:14:49  time: 0.3130  data_time: 0.0055  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:18:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][400/1576]  lr: 1.0000e-01  eta: 0:14:33  time: 0.3278  data_time: 0.0051  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:19:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][450/1576]  lr: 1.0000e-01  eta: 0:14:17  time: 0.3153  data_time: 0.0066  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:19:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][500/1576]  lr: 1.0000e-01  eta: 0:14:02  time: 0.3160  data_time: 0.0062  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:19:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][550/1576]  lr: 1.0000e-01  eta: 0:13:48  time: 0.3330  data_time: 0.0112  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:19:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][600/1576]  lr: 1.0000e-01  eta: 0:13:32  time: 0.3242  data_time: 0.0061  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:20:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][650/1576]  lr: 1.0000e-01  eta: 0:13:16  time: 0.3155  data_time: 0.0070  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:20:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][700/1576]  lr: 1.0000e-01  eta: 0:13:00  time: 0.3125  data_time: 0.0067  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:20:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][750/1576]  lr: 1.0000e-01  eta: 0:12:43  time: 0.3165  data_time: 0.0063  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:20:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][800/1576]  lr: 1.0000e-01  eta: 0:12:27  time: 0.3162  data_time: 0.0056  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:21:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][850/1576]  lr: 1.0000e-01  eta: 0:12:11  time: 0.3189  data_time: 0.0053  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:21:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][900/1576]  lr: 1.0000e-01  eta: 0:11:56  time: 0.3293  data_time: 0.0081  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:21:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][950/1576]  lr: 1.0000e-01  eta: 0:11:40  time: 0.3159  data_time: 0.0067  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:21:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: reid_r50_8xb32-6e_mot17train80_test-mot17val20_20220906_091626\n",
            "09/06 09:21:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1000/1576]  lr: 1.0000e-01  eta: 0:11:24  time: 0.3142  data_time: 0.0055  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:22:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1050/1576]  lr: 1.0000e-01  eta: 0:11:08  time: 0.3153  data_time: 0.0052  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0001\n",
            "09/06 09:22:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1100/1576]  lr: 1.0000e-01  eta: 0:10:52  time: 0.3137  data_time: 0.0059  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0001  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:22:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1150/1576]  lr: 1.0000e-01  eta: 0:10:36  time: 0.3151  data_time: 0.0068  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:23:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1200/1576]  lr: 1.0000e-01  eta: 0:10:20  time: 0.3174  data_time: 0.0059  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:23:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1250/1576]  lr: 1.0000e-01  eta: 0:10:04  time: 0.3185  data_time: 0.0065  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:23:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1300/1576]  lr: 1.0000e-01  eta: 0:09:48  time: 0.3165  data_time: 0.0061  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:23:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1350/1576]  lr: 1.0000e-01  eta: 0:09:33  time: 0.3163  data_time: 0.0066  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:24:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1400/1576]  lr: 1.0000e-01  eta: 0:09:17  time: 0.3148  data_time: 0.0064  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:24:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1450/1576]  lr: 1.0000e-01  eta: 0:09:01  time: 0.3157  data_time: 0.0061  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:24:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1500/1576]  lr: 1.0000e-01  eta: 0:08:45  time: 0.3160  data_time: 0.0067  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:24:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1550/1576]  lr: 1.0000e-01  eta: 0:08:29  time: 0.3164  data_time: 0.0055  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:25:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: reid_r50_8xb32-6e_mot17train80_test-mot17val20_20220906_091626\n",
            "09/06 09:25:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n",
            "09/06 09:25:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][50/1576]  lr: 1.0000e-02  eta: 0:08:03  time: 0.3158  data_time: 0.0053  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:25:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][100/1576]  lr: 1.0000e-02  eta: 0:07:47  time: 0.3160  data_time: 0.0053  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:25:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][150/1576]  lr: 1.0000e-02  eta: 0:07:31  time: 0.3164  data_time: 0.0062  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:26:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][200/1576]  lr: 1.0000e-02  eta: 0:07:15  time: 0.3159  data_time: 0.0062  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:26:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][250/1576]  lr: 1.0000e-02  eta: 0:07:00  time: 0.3163  data_time: 0.0065  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:26:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][300/1576]  lr: 1.0000e-02  eta: 0:06:44  time: 0.3149  data_time: 0.0056  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:26:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][350/1576]  lr: 1.0000e-02  eta: 0:06:28  time: 0.3167  data_time: 0.0063  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:27:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][400/1576]  lr: 1.0000e-02  eta: 0:06:12  time: 0.3148  data_time: 0.0057  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:27:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: reid_r50_8xb32-6e_mot17train80_test-mot17val20_20220906_091626\n",
            "09/06 09:27:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][450/1576]  lr: 1.0000e-02  eta: 0:05:56  time: 0.3127  data_time: 0.0057  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:27:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][500/1576]  lr: 1.0000e-02  eta: 0:05:40  time: 0.3186  data_time: 0.0064  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:27:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][550/1576]  lr: 1.0000e-02  eta: 0:05:24  time: 0.3151  data_time: 0.0053  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:28:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][600/1576]  lr: 1.0000e-02  eta: 0:05:09  time: 0.3158  data_time: 0.0058  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:28:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][650/1576]  lr: 1.0000e-02  eta: 0:04:53  time: 0.3162  data_time: 0.0063  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:28:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][700/1576]  lr: 1.0000e-02  eta: 0:04:37  time: 0.3156  data_time: 0.0066  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:29:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][750/1576]  lr: 1.0000e-02  eta: 0:04:21  time: 0.3164  data_time: 0.0060  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:29:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][800/1576]  lr: 1.0000e-02  eta: 0:04:05  time: 0.3172  data_time: 0.0067  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:29:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][850/1576]  lr: 1.0000e-02  eta: 0:03:49  time: 0.3134  data_time: 0.0054  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:29:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][900/1576]  lr: 1.0000e-02  eta: 0:03:34  time: 0.3172  data_time: 0.0066  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:30:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][950/1576]  lr: 1.0000e-02  eta: 0:03:18  time: 0.3129  data_time: 0.0059  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:30:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1000/1576]  lr: 1.0000e-02  eta: 0:03:02  time: 0.3145  data_time: 0.0059  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:30:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1050/1576]  lr: 1.0000e-02  eta: 0:02:46  time: 0.3142  data_time: 0.0066  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:30:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1100/1576]  lr: 1.0000e-02  eta: 0:02:30  time: 0.3169  data_time: 0.0063  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:31:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1150/1576]  lr: 1.0000e-02  eta: 0:02:14  time: 0.3158  data_time: 0.0064  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:31:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1200/1576]  lr: 1.0000e-02  eta: 0:01:59  time: 0.3150  data_time: 0.0067  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:31:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1250/1576]  lr: 1.0000e-02  eta: 0:01:43  time: 0.3169  data_time: 0.0056  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:31:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1300/1576]  lr: 1.0000e-02  eta: 0:01:27  time: 0.3153  data_time: 0.0061  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:32:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1350/1576]  lr: 1.0000e-02  eta: 0:01:11  time: 0.3148  data_time: 0.0053  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:32:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1400/1576]  lr: 1.0000e-02  eta: 0:00:55  time: 0.3171  data_time: 0.0057  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:32:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: reid_r50_8xb32-6e_mot17train80_test-mot17val20_20220906_091626\n",
            "09/06 09:32:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1450/1576]  lr: 1.0000e-02  eta: 0:00:39  time: 0.3148  data_time: 0.0051  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:32:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1500/1576]  lr: 1.0000e-02  eta: 0:00:24  time: 0.3157  data_time: 0.0053  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:33:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1550/1576]  lr: 1.0000e-02  eta: 0:00:08  time: 0.3156  data_time: 0.0068  memory: 3519  triplet_loss: 0.0000  ce_loss: 0.0002  accuracy_top-1: 100.0000  loss: 0.0002\n",
            "09/06 09:33:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: reid_r50_8xb32-6e_mot17train80_test-mot17val20_20220906_091626\n",
            "09/06 09:33:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 2 epochs\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "BaseReID(\n",
              "  (data_preprocessor): ClsDataPreprocessor()\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()\n",
              "    (loss_triplet): TripletLoss(\n",
              "      (ranking_loss): MarginRankingLoss()\n",
              "    )\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=380, 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'}"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os.path as osp\n",
        "\n",
        "from mmengine.utils import mkdir_or_exist\n",
        "from mmengine.runner import Runner\n",
        "\n",
        "mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
        "runner = Runner.from_cfg(cfg)\n",
        "runner.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5ac4b40-7023-498a-9ddf-bc06ff2100af",
      "metadata": {
        "id": "e5ac4b40-7023-498a-9ddf-bc06ff2100af",
        "tags": []
      },
      "source": [
        "### **Test the DeepSORT model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "c837c657-cc81-426d-8060-ad19f5494461",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c837c657-cc81-426d-8060-ad19f5494461",
        "outputId": "9950328d-6ef9-4add-f79d-ceb576eef936"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Config:\n",
            "model = dict(\n",
            "    data_preprocessor=dict(\n",
            "        type='TrackDataPreprocessor',\n",
            "        mean=[123.675, 116.28, 103.53],\n",
            "        std=[58.395, 57.12, 57.375],\n",
            "        bgr_to_rgb=True,\n",
            "        rgb_to_bgr=False,\n",
            "        pad_size_divisor=32),\n",
            "    detector=dict(\n",
            "        type='FasterRCNN',\n",
            "        _scope_='mmdet',\n",
            "        backbone=dict(\n",
            "            type='ResNet',\n",
            "            depth=50,\n",
            "            num_stages=4,\n",
            "            out_indices=(0, 1, 2, 3),\n",
            "            frozen_stages=1,\n",
            "            norm_cfg=dict(type='BN', requires_grad=True),\n",
            "            norm_eval=True,\n",
            "            style='pytorch',\n",
            "            init_cfg=dict(\n",
            "                type='Pretrained', checkpoint='torchvision://resnet50')),\n",
            "        neck=dict(\n",
            "            type='FPN',\n",
            "            in_channels=[256, 512, 1024, 2048],\n",
            "            out_channels=256,\n",
            "            num_outs=5),\n",
            "        rpn_head=dict(\n",
            "            type='RPNHead',\n",
            "            in_channels=256,\n",
            "            feat_channels=256,\n",
            "            anchor_generator=dict(\n",
            "                type='AnchorGenerator',\n",
            "                scales=[8],\n",
            "                ratios=[0.5, 1.0, 2.0],\n",
            "                strides=[4, 8, 16, 32, 64]),\n",
            "            bbox_coder=dict(\n",
            "                type='DeltaXYWHBBoxCoder',\n",
            "                target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "                target_stds=[1.0, 1.0, 1.0, 1.0],\n",
            "                clip_border=False),\n",
            "            loss_cls=dict(\n",
            "                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
            "            loss_bbox=dict(\n",
            "                type='SmoothL1Loss', beta=0.1111111111111111,\n",
            "                loss_weight=1.0)),\n",
            "        roi_head=dict(\n",
            "            type='StandardRoIHead',\n",
            "            bbox_roi_extractor=dict(\n",
            "                type='SingleRoIExtractor',\n",
            "                roi_layer=dict(\n",
            "                    type='RoIAlign', output_size=7, sampling_ratio=0),\n",
            "                out_channels=256,\n",
            "                featmap_strides=[4, 8, 16, 32]),\n",
            "            bbox_head=dict(\n",
            "                type='Shared2FCBBoxHead',\n",
            "                in_channels=256,\n",
            "                fc_out_channels=1024,\n",
            "                roi_feat_size=7,\n",
            "                num_classes=1,\n",
            "                bbox_coder=dict(\n",
            "                    type='DeltaXYWHBBoxCoder',\n",
            "                    target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "                    target_stds=[0.1, 0.1, 0.2, 0.2],\n",
            "                    clip_border=False),\n",
            "                reg_class_agnostic=False,\n",
            "                loss_cls=dict(\n",
            "                    type='CrossEntropyLoss',\n",
            "                    use_sigmoid=False,\n",
            "                    loss_weight=1.0),\n",
            "                loss_bbox=dict(type='SmoothL1Loss', loss_weight=1.0))),\n",
            "        train_cfg=dict(\n",
            "            rpn=dict(\n",
            "                assigner=dict(\n",
            "                    type='MaxIoUAssigner',\n",
            "                    pos_iou_thr=0.7,\n",
            "                    neg_iou_thr=0.3,\n",
            "                    min_pos_iou=0.3,\n",
            "                    match_low_quality=True,\n",
            "                    ignore_iof_thr=-1),\n",
            "                sampler=dict(\n",
            "                    type='RandomSampler',\n",
            "                    num=256,\n",
            "                    pos_fraction=0.5,\n",
            "                    neg_pos_ub=-1,\n",
            "                    add_gt_as_proposals=False),\n",
            "                allowed_border=-1,\n",
            "                pos_weight=-1,\n",
            "                debug=False),\n",
            "            rpn_proposal=dict(\n",
            "                nms_pre=2000,\n",
            "                max_per_img=1000,\n",
            "                nms=dict(type='nms', iou_threshold=0.7),\n",
            "                min_bbox_size=0),\n",
            "            rcnn=dict(\n",
            "                assigner=dict(\n",
            "                    type='MaxIoUAssigner',\n",
            "                    pos_iou_thr=0.5,\n",
            "                    neg_iou_thr=0.5,\n",
            "                    min_pos_iou=0.5,\n",
            "                    match_low_quality=False,\n",
            "                    ignore_iof_thr=-1),\n",
            "                sampler=dict(\n",
            "                    type='RandomSampler',\n",
            "                    num=512,\n",
            "                    pos_fraction=0.25,\n",
            "                    neg_pos_ub=-1,\n",
            "                    add_gt_as_proposals=True),\n",
            "                pos_weight=-1,\n",
            "                debug=False)),\n",
            "        test_cfg=dict(\n",
            "            rpn=dict(\n",
            "                nms_pre=1000,\n",
            "                max_per_img=1000,\n",
            "                nms=dict(type='nms', iou_threshold=0.7),\n",
            "                min_bbox_size=0),\n",
            "            rcnn=dict(\n",
            "                score_thr=0.05,\n",
            "                nms=dict(type='nms', iou_threshold=0.5),\n",
            "                max_per_img=100)),\n",
            "        init_cfg=dict(\n",
            "            type='Pretrained',\n",
            "            checkpoint='./tutorial_exps/detector/epoch_4.pth')),\n",
            "    type='DeepSORT',\n",
            "    motion=dict(type='KalmanFilter', center_only=False),\n",
            "    reid=dict(\n",
            "        type='BaseReID',\n",
            "        data_preprocessor=None,\n",
            "        backbone=dict(\n",
            "            type='mmcls.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_cls=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),\n",
            "            loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),\n",
            "            norm_cfg=dict(type='BN1d'),\n",
            "            act_cfg=dict(type='ReLU')),\n",
            "        init_cfg=dict(\n",
            "            type='Pretrained', checkpoint='./tutorial_exps/reid/epoch_2.pth')),\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",
            "dataset_type = 'MOTChallengeDataset'\n",
            "data_root = 'data/MOT17/'\n",
            "train_pipeline = [\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=True,\n",
            "        transforms=[\n",
            "            dict(type='LoadImageFromFile'),\n",
            "            dict(type='LoadTrackAnnotations', with_instance_id=True),\n",
            "            dict(\n",
            "                type='mmdet.RandomResize',\n",
            "                scale=(1088, 1088),\n",
            "                ratio_range=(0.8, 1.2),\n",
            "                keep_ratio=True,\n",
            "                clip_object_border=False),\n",
            "            dict(type='mmdet.PhotoMetricDistortion')\n",
            "        ]),\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=False,\n",
            "        transforms=[\n",
            "            dict(\n",
            "                type='mmdet.RandomCrop',\n",
            "                crop_size=(1088, 1088),\n",
            "                bbox_clip_border=False)\n",
            "        ]),\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=True,\n",
            "        transforms=[dict(type='mmdet.RandomFlip', prob=0.5)]),\n",
            "    dict(type='PackTrackInputs', ref_prefix='ref', num_key_frames=1)\n",
            "]\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile'),\n",
            "    dict(type='LoadTrackAnnotations', with_instance_id=True),\n",
            "    dict(type='mmdet.Resize', scale=(1088, 1088), keep_ratio=True),\n",
            "    dict(type='PackTrackInputs', pack_single_img=True)\n",
            "]\n",
            "train_dataloader = None\n",
            "val_dataloader = None\n",
            "test_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='VideoSampler'),\n",
            "    dataset=dict(\n",
            "        type='MOTChallengeDataset',\n",
            "        data_root='data/MOT17_tiny/',\n",
            "        ann_file='annotations/half-val_cocoformat.json',\n",
            "        data_prefix=dict(img_path='train'),\n",
            "        ref_img_sampler=None,\n",
            "        load_as_video=True,\n",
            "        test_mode=True,\n",
            "        pipeline=[\n",
            "            dict(type='LoadImageFromFile'),\n",
            "            dict(type='LoadTrackAnnotations', with_instance_id=True),\n",
            "            dict(type='mmdet.Resize', scale=(1088, 1088), keep_ratio=True),\n",
            "            dict(type='PackTrackInputs', pack_single_img=True)\n",
            "        ]))\n",
            "val_evaluator = None\n",
            "test_evaluator = dict(\n",
            "    type='MOTChallengeMetrics', metric=['HOTA', 'CLEAR', 'Identity'])\n",
            "default_scope = 'mmtrack'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(type='ParamSchedulerHook'),\n",
            "    checkpoint=dict(type='CheckpointHook', interval=1),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='TrackVisualizationHook', draw=False))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=False,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='TrackLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='deepsort_visualizer')\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "train_cfg = None\n",
            "val_cfg = None\n",
            "test_cfg = dict(type='TestLoop')\n",
            "work_dir = './tutorial_exps'\n",
            "randomness = dict(seed=0, deterministic=False)\n",
            "gpu_ids = range(0, 1)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import mmengine\n",
        "\n",
        "cfg = mmengine.Config.fromfile('./configs/mot/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py')\n",
        "cfg.test_dataloader.dataset.data_root = 'data/MOT17_tiny/'\n",
        "cfg.test_dataloader.dataset.test_mode = True\n",
        "cfg.train_dataloader = cfg.train_cfg = None\n",
        "cfg.val_dataloader = cfg.val_cfg = cfg.val_evaluator = None\n",
        "cfg.visualizer.name = 'deepsort_visualizer'\n",
        "cfg.model.detector.init_cfg.checkpoint = './tutorial_exps/detector/epoch_4.pth'\n",
        "cfg.model.reid.init_cfg.checkpoint = './tutorial_exps/reid/epoch_2.pth'\n",
        "\n",
        "cfg.work_dir = './tutorial_exps'\n",
        "cfg.randomness = dict(seed=0, deterministic=False)\n",
        "cfg.gpu_ids = range(1)\n",
        "print(f'Config:\\n{cfg.pretty_text}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "29c9f531-3ea9-42d3-9fb3-bcdd13594406",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "29c9f531-3ea9-42d3-9fb3-bcdd13594406",
        "outputId": "6d133c91-8323-45c0-e031-5bc8be89485d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "09/06 09:37:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 0\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.1, V11.1.105\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.10.0+cu111\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 7.3\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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\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 11.1\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 8.0.5\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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 -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, 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, \n",
            "\n",
            "    TorchVision: 0.11.0+cu111\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.1.0\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: False\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: 0\n",
            "    deterministic: False\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "09/06 09:37:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "model = dict(\n",
            "    data_preprocessor=dict(\n",
            "        type='TrackDataPreprocessor',\n",
            "        mean=[123.675, 116.28, 103.53],\n",
            "        std=[58.395, 57.12, 57.375],\n",
            "        bgr_to_rgb=True,\n",
            "        rgb_to_bgr=False,\n",
            "        pad_size_divisor=32),\n",
            "    detector=dict(\n",
            "        type='FasterRCNN',\n",
            "        _scope_='mmdet',\n",
            "        backbone=dict(\n",
            "            type='ResNet',\n",
            "            depth=50,\n",
            "            num_stages=4,\n",
            "            out_indices=(0, 1, 2, 3),\n",
            "            frozen_stages=1,\n",
            "            norm_cfg=dict(type='BN', requires_grad=True),\n",
            "            norm_eval=True,\n",
            "            style='pytorch',\n",
            "            init_cfg=dict(\n",
            "                type='Pretrained', checkpoint='torchvision://resnet50')),\n",
            "        neck=dict(\n",
            "            type='FPN',\n",
            "            in_channels=[256, 512, 1024, 2048],\n",
            "            out_channels=256,\n",
            "            num_outs=5),\n",
            "        rpn_head=dict(\n",
            "            type='RPNHead',\n",
            "            in_channels=256,\n",
            "            feat_channels=256,\n",
            "            anchor_generator=dict(\n",
            "                type='AnchorGenerator',\n",
            "                scales=[8],\n",
            "                ratios=[0.5, 1.0, 2.0],\n",
            "                strides=[4, 8, 16, 32, 64]),\n",
            "            bbox_coder=dict(\n",
            "                type='DeltaXYWHBBoxCoder',\n",
            "                target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "                target_stds=[1.0, 1.0, 1.0, 1.0],\n",
            "                clip_border=False),\n",
            "            loss_cls=dict(\n",
            "                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
            "            loss_bbox=dict(\n",
            "                type='SmoothL1Loss', beta=0.1111111111111111,\n",
            "                loss_weight=1.0)),\n",
            "        roi_head=dict(\n",
            "            type='StandardRoIHead',\n",
            "            bbox_roi_extractor=dict(\n",
            "                type='SingleRoIExtractor',\n",
            "                roi_layer=dict(\n",
            "                    type='RoIAlign', output_size=7, sampling_ratio=0),\n",
            "                out_channels=256,\n",
            "                featmap_strides=[4, 8, 16, 32]),\n",
            "            bbox_head=dict(\n",
            "                type='Shared2FCBBoxHead',\n",
            "                in_channels=256,\n",
            "                fc_out_channels=1024,\n",
            "                roi_feat_size=7,\n",
            "                num_classes=1,\n",
            "                bbox_coder=dict(\n",
            "                    type='DeltaXYWHBBoxCoder',\n",
            "                    target_means=[0.0, 0.0, 0.0, 0.0],\n",
            "                    target_stds=[0.1, 0.1, 0.2, 0.2],\n",
            "                    clip_border=False),\n",
            "                reg_class_agnostic=False,\n",
            "                loss_cls=dict(\n",
            "                    type='CrossEntropyLoss',\n",
            "                    use_sigmoid=False,\n",
            "                    loss_weight=1.0),\n",
            "                loss_bbox=dict(type='SmoothL1Loss', loss_weight=1.0))),\n",
            "        train_cfg=dict(\n",
            "            rpn=dict(\n",
            "                assigner=dict(\n",
            "                    type='MaxIoUAssigner',\n",
            "                    pos_iou_thr=0.7,\n",
            "                    neg_iou_thr=0.3,\n",
            "                    min_pos_iou=0.3,\n",
            "                    match_low_quality=True,\n",
            "                    ignore_iof_thr=-1),\n",
            "                sampler=dict(\n",
            "                    type='RandomSampler',\n",
            "                    num=256,\n",
            "                    pos_fraction=0.5,\n",
            "                    neg_pos_ub=-1,\n",
            "                    add_gt_as_proposals=False),\n",
            "                allowed_border=-1,\n",
            "                pos_weight=-1,\n",
            "                debug=False),\n",
            "            rpn_proposal=dict(\n",
            "                nms_pre=2000,\n",
            "                max_per_img=1000,\n",
            "                nms=dict(type='nms', iou_threshold=0.7),\n",
            "                min_bbox_size=0),\n",
            "            rcnn=dict(\n",
            "                assigner=dict(\n",
            "                    type='MaxIoUAssigner',\n",
            "                    pos_iou_thr=0.5,\n",
            "                    neg_iou_thr=0.5,\n",
            "                    min_pos_iou=0.5,\n",
            "                    match_low_quality=False,\n",
            "                    ignore_iof_thr=-1),\n",
            "                sampler=dict(\n",
            "                    type='RandomSampler',\n",
            "                    num=512,\n",
            "                    pos_fraction=0.25,\n",
            "                    neg_pos_ub=-1,\n",
            "                    add_gt_as_proposals=True),\n",
            "                pos_weight=-1,\n",
            "                debug=False)),\n",
            "        test_cfg=dict(\n",
            "            rpn=dict(\n",
            "                nms_pre=1000,\n",
            "                max_per_img=1000,\n",
            "                nms=dict(type='nms', iou_threshold=0.7),\n",
            "                min_bbox_size=0),\n",
            "            rcnn=dict(\n",
            "                score_thr=0.05,\n",
            "                nms=dict(type='nms', iou_threshold=0.5),\n",
            "                max_per_img=100)),\n",
            "        init_cfg=dict(\n",
            "            type='Pretrained',\n",
            "            checkpoint='./tutorial_exps/detector/epoch_4.pth')),\n",
            "    type='DeepSORT',\n",
            "    motion=dict(type='KalmanFilter', center_only=False),\n",
            "    reid=dict(\n",
            "        type='BaseReID',\n",
            "        data_preprocessor=None,\n",
            "        backbone=dict(\n",
            "            type='mmcls.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_cls=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),\n",
            "            loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),\n",
            "            norm_cfg=dict(type='BN1d'),\n",
            "            act_cfg=dict(type='ReLU')),\n",
            "        init_cfg=dict(\n",
            "            type='Pretrained', checkpoint='./tutorial_exps/reid/epoch_2.pth')),\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",
            "dataset_type = 'MOTChallengeDataset'\n",
            "data_root = 'data/MOT17/'\n",
            "train_pipeline = [\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=True,\n",
            "        transforms=[\n",
            "            dict(type='LoadImageFromFile'),\n",
            "            dict(type='LoadTrackAnnotations', with_instance_id=True),\n",
            "            dict(\n",
            "                type='mmdet.RandomResize',\n",
            "                scale=(1088, 1088),\n",
            "                ratio_range=(0.8, 1.2),\n",
            "                keep_ratio=True,\n",
            "                clip_object_border=False),\n",
            "            dict(type='mmdet.PhotoMetricDistortion')\n",
            "        ]),\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=False,\n",
            "        transforms=[\n",
            "            dict(\n",
            "                type='mmdet.RandomCrop',\n",
            "                crop_size=(1088, 1088),\n",
            "                bbox_clip_border=False)\n",
            "        ]),\n",
            "    dict(\n",
            "        type='TransformBroadcaster',\n",
            "        share_random_params=True,\n",
            "        transforms=[dict(type='mmdet.RandomFlip', prob=0.5)]),\n",
            "    dict(type='PackTrackInputs', ref_prefix='ref', num_key_frames=1)\n",
            "]\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile'),\n",
            "    dict(type='LoadTrackAnnotations', with_instance_id=True),\n",
            "    dict(type='mmdet.Resize', scale=(1088, 1088), keep_ratio=True),\n",
            "    dict(type='PackTrackInputs', pack_single_img=True)\n",
            "]\n",
            "train_dataloader = None\n",
            "val_dataloader = None\n",
            "test_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='VideoSampler'),\n",
            "    dataset=dict(\n",
            "        type='MOTChallengeDataset',\n",
            "        data_root='data/MOT17_tiny/',\n",
            "        ann_file='annotations/half-val_cocoformat.json',\n",
            "        data_prefix=dict(img_path='train'),\n",
            "        ref_img_sampler=None,\n",
            "        load_as_video=True,\n",
            "        test_mode=True,\n",
            "        pipeline=[\n",
            "            dict(type='LoadImageFromFile'),\n",
            "            dict(type='LoadTrackAnnotations', with_instance_id=True),\n",
            "            dict(type='mmdet.Resize', scale=(1088, 1088), keep_ratio=True),\n",
            "            dict(type='PackTrackInputs', pack_single_img=True)\n",
            "        ]))\n",
            "val_evaluator = None\n",
            "test_evaluator = dict(\n",
            "    type='MOTChallengeMetrics', metric=['HOTA', 'CLEAR', 'Identity'])\n",
            "default_scope = 'mmtrack'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(type='ParamSchedulerHook'),\n",
            "    checkpoint=dict(type='CheckpointHook', interval=1),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='TrackVisualizationHook', draw=False))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=False,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='TrackLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='deepsort_visualizer')\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "train_cfg = None\n",
            "val_cfg = None\n",
            "test_cfg = dict(type='TestLoop')\n",
            "work_dir = './tutorial_exps'\n",
            "randomness = dict(seed=0, deterministic=False)\n",
            "gpu_ids = range(0, 1)\n",
            "\n",
            "Result has been saved to /content/mmtracking/tutorial_exps/modules_statistic_results.json\n",
            "09/06 09:37:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "09/06 09:37:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: ./tutorial_exps/detector/epoch_4.pth\n",
            "09/06 09:37:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - local loads checkpoint from path: ./tutorial_exps/detector/epoch_4.pth\n",
            "09/06 09:37:06 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The model and loaded state dict do not match exactly\n",
            "\n",
            "unexpected key in source state_dict: data_preprocessor.mean, data_preprocessor.std\n",
            "\n",
            "09/06 09:37:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: ./tutorial_exps/reid/epoch_2.pth\n",
            "09/06 09:37:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - local loads checkpoint from path: ./tutorial_exps/reid/epoch_2.pth\n",
            "09/06 09:37:06 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The model and loaded state dict do not match exactly\n",
            "\n",
            "unexpected key in source state_dict: data_preprocessor.mean, data_preprocessor.std\n",
            "\n",
            "loading annotations into memory...\n",
            "Done (t=10.59s)\n",
            "creating index...\n",
            "index created!\n",
            "09/06 09:37:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [50/823]    eta: 0:02:33  time: 0.1980  data_time: 0.0045  memory: 3519  \n",
            "09/06 09:37:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [100/823]    eta: 0:02:15  time: 0.1873  data_time: 0.0042  memory: 2148  \n",
            "09/06 09:37:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [150/823]    eta: 0:02:50  time: 0.2529  data_time: 0.0073  memory: 2148  \n",
            "09/06 09:38:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [200/823]    eta: 0:02:47  time: 0.2690  data_time: 0.0107  memory: 2190  \n",
            "09/06 09:38:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [250/823]    eta: 0:02:24  time: 0.2518  data_time: 0.0070  memory: 2190  \n",
            "09/06 09:38:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [300/823]    eta: 0:02:05  time: 0.2391  data_time: 0.0063  memory: 2236  \n",
            "09/06 09:38:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [350/823]    eta: 0:01:42  time: 0.2171  data_time: 0.0048  memory: 2237  \n",
            "09/06 09:38:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [400/823]    eta: 0:01:35  time: 0.2247  data_time: 0.0056  memory: 2224  \n",
            "09/06 09:39:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [450/823]    eta: 0:01:27  time: 0.2335  data_time: 0.0058  memory: 2218  \n",
            "09/06 09:39:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [500/823]    eta: 0:01:10  time: 0.2188  data_time: 0.0106  memory: 2212  \n",
            "09/06 09:39:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [550/823]    eta: 0:01:01  time: 0.2267  data_time: 0.0098  memory: 2206  \n",
            "09/06 09:39:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [600/823]    eta: 0:00:49  time: 0.2198  data_time: 0.0072  memory: 2200  \n",
            "09/06 09:39:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [650/823]    eta: 0:00:41  time: 0.2403  data_time: 0.0043  memory: 2264  \n",
            "09/06 09:40:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [700/823]    eta: 0:00:29  time: 0.2418  data_time: 0.0047  memory: 2372  \n",
            "09/06 09:40:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [750/823]    eta: 0:00:17  time: 0.2427  data_time: 0.0041  memory: 2246  \n",
            "09/06 09:40:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [800/823]    eta: 0:00:05  time: 0.2434  data_time: 0.0041  memory: 2317  \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/python3.7/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/tmpw97bk3y0/gt           \n",
            "TRACKERS_FOLDER      : /tmp/tmpw97bk3y0              \n",
            "OUTPUT_FOLDER        : None                          \n",
            "TRACKERS_TO_EVAL     : ['default-tracker']           \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/tmpw97bk3y0/default-tracker/videoseq.txt\n",
            "SEQ_INFO             : {'MOT17-02-FRCNN': 299, 'MOT17-04-FRCNN': 524}\n",
            "GT_LOC_FORMAT        : {gt_folder}/{seq}.txt         \n",
            "SKIP_SPLIT_FOL       : True                          \n",
            "\n",
            "CLEAR Config:\n",
            "METRICS              : ['CLEAR']                     \n",
            "THRESHOLD            : 0.5                           \n",
            "PRINT_CONFIG         : True                          \n",
            "\n",
            "Identity Config:\n",
            "METRICS              : ['Identity']                  \n",
            "THRESHOLD            : 0.5                           \n",
            "PRINT_CONFIG         : True                          \n",
            "\n",
            "Evaluating 1 tracker(s) on 2 sequence(s) for 1 class(es) on MotChallenge2DBox dataset using the following metrics: HOTA, CLEAR, Identity, Count\n",
            "\n",
            "\n",
            "Evaluating default-tracker\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/trackeval/datasets/mot_challenge_2d_box.py:228: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  time_data = np.asarray(read_data[time_key], dtype=np.float)\n",
            "/usr/local/lib/python3.7/dist-packages/trackeval/datasets/mot_challenge_2d_box.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  to_remove_tracker = np.array([], np.int)\n",
            "/usr/local/lib/python3.7/dist-packages/trackeval/metrics/hota.py:31: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  res[field] = np.zeros((len(self.array_labels)), dtype=np.float)\n",
            "/usr/local/lib/python3.7/dist-packages/trackeval/metrics/identity.py:83: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  res['IDFN'] = fn_mat[match_rows, match_cols].sum().astype(np.int)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 eval_sequence(MOT17-02-FRCNN, default-tracker)                         0.4670 sec\n",
            "2 eval_sequence(MOT17-04-FRCNN, default-tracker)                         1.3160 sec\n",
            "\n",
            "All sequences for default-tracker finished in 1.78 seconds\n",
            "\n",
            "HOTA: default-tracker-pedestrian   HOTA      DetA      AssA      DetRe     DetPr     AssRe     AssPr     LocA      RHOTA     HOTA(0)   LocA(0)   HOTALocA(0)\n",
            "MOT17-02-FRCNN                     26.07     39.469    17.888    46.443    63.395    20.298    59.499    79.439    28.497    34.31     68.423    23.476    \n",
            "MOT17-04-FRCNN                     54.514    66.438    45.771    70.914    82.049    51.423    62.818    84.856    56.637    64.719    81.721    52.889    \n",
            "COMBINED                           47.481    57.937    40.091    63.815    77.25     45.144    62.333    83.649    50.152    57.312    78.488    44.983    \n",
            "\n",
            "CLEAR: default-tracker-pedestrian  MOTA      MOTP      MODA      CLR_Re    CLR_Pr    MTR       PTR       MLR       sMOTA     CLR_TP    CLR_FN    CLR_FP    IDSW      MT        PT        ML        Frag      \n",
            "MOT17-02-FRCNN                     28.279    77.975    37.287    55.273    75.449    20.755    60.377    18.868    16.105    5461      4419      1777      890       11        32        10        346       \n",
            "MOT17-04-FRCNN                     77.19     82.679    80.937    83.684    96.823    65.217    31.884    2.8986    62.695    20233     3945      664       906       45        22        2         285       \n",
            "COMBINED                           63.001    81.679    68.275    75.442    91.324    45.902    44.262    9.8361    49.18     25694     8364      2441      1796      56        54        12        631       \n",
            "\n",
            "Identity: default-tracker-pedestrianIDF1      IDR       IDP       IDTP      IDFN      IDFP      \n",
            "MOT17-02-FRCNN                     29.186    25.283    34.512    2498      7382      4740      \n",
            "MOT17-04-FRCNN                     59.448    55.414    64.114    13398     10780     7499      \n",
            "COMBINED                           51.118    46.673    56.499    15896     18162     12239     \n",
            "\n",
            "Count: default-tracker-pedestrian  Dets      GT_Dets   IDs       GT_IDs    \n",
            "MOT17-02-FRCNN                     7238      9880      434       53        \n",
            "MOT17-04-FRCNN                     20897     24178     188       69        \n",
            "COMBINED                           28135     34058     622       122       \n",
            "09/06 09:40:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating HOTA Metrics...\n",
            "09/06 09:40:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating CLEAR Metrics...\n",
            "09/06 09:40:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating Identity Metrics...\n",
            "09/06 09:40:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(test) [823/823]  motchallenge-metric/HOTA: 0.4748  motchallenge-metric/AssA: 0.4009  motchallenge-metric/DetA: 0.5794  motchallenge-metric/MOTA: 0.6300  motchallenge-metric/MOTP: 0.8168  motchallenge-metric/IDSW: 1796.0000  motchallenge-metric/TP: 25694.0000  motchallenge-metric/FP: 2441.0000  motchallenge-metric/FN: 8364.0000  motchallenge-metric/Frag: 631.0000  motchallenge-metric/MT: 56.0000  motchallenge-metric/ML: 12.0000  motchallenge-metric/IDF1: 0.5112  motchallenge-metric/IDTP: 15896.0000  motchallenge-metric/IDFN: 18162.0000  motchallenge-metric/IDFP: 12239.0000  motchallenge-metric/IDP: 0.5650  motchallenge-metric/IDR: 0.4667\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'motchallenge-metric/HOTA': 0.47480527965525604,\n",
              " 'motchallenge-metric/AssA': 0.40090624838232497,\n",
              " 'motchallenge-metric/DetA': 0.5793687250260392,\n",
              " 'motchallenge-metric/MOTA': 0.6300135063714839,\n",
              " 'motchallenge-metric/MOTP': 0.8167920002689131,\n",
              " 'motchallenge-metric/IDSW': 1796.0,\n",
              " 'motchallenge-metric/TP': 25694.0,\n",
              " 'motchallenge-metric/FP': 2441.0,\n",
              " 'motchallenge-metric/FN': 8364.0,\n",
              " 'motchallenge-metric/Frag': 631.0,\n",
              " 'motchallenge-metric/MT': 56.0,\n",
              " 'motchallenge-metric/ML': 12.0,\n",
              " 'motchallenge-metric/IDF1': 0.5111829305548856,\n",
              " 'motchallenge-metric/IDTP': 15896.0,\n",
              " 'motchallenge-metric/IDFN': 18162.0,\n",
              " 'motchallenge-metric/IDFP': 12239.0,\n",
              " 'motchallenge-metric/IDP': 0.5649902256975298,\n",
              " 'motchallenge-metric/IDR': 0.46673321980151505}"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from mmengine.model import is_model_wrapper\n",
        "from mmengine.runner import Runner\n",
        "\n",
        "runner = Runner.from_cfg(cfg)\n",
        "\n",
        "if is_model_wrapper(runner.model):\n",
        "    runner.model.module.init_weights()\n",
        "else:\n",
        "    runner.model.init_weights()\n",
        "\n",
        "runner.test()"
      ]
    }
  ],
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