【G1】实战项目-人流量计数+足迹追踪.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4346739a-98ae-4b8f-859f-7e8b41b9c608",
   "metadata": {},
   "source": [
    "# 人流量计数+足迹追踪"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e1743f-2bb0-4f93-922e-469d48da8c2f",
   "metadata": {},
   "source": [
    "MMtracking 预训练模型库 Model Zoo：https://mmtracking.readthedocs.io/en/latest/model_zoo.html\n",
    "\n",
    "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。\n",
    "\n",
    "作者：同济子豪兄 2022-4-21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d2a9c8-3f07-4f38-8f42-5be8f6bed7b5",
   "metadata": {},
   "source": [
    "## 进入 MMTracking 主目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5882ca9-91c2-4873-8f6d-0375dcca4147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.git',\n",
       " '.circleci',\n",
       " '.dev_scripts',\n",
       " '.github',\n",
       " '.gitignore',\n",
       " '.pre-commit-config.yaml',\n",
       " '.readthedocs.yml',\n",
       " 'CITATION.cff',\n",
       " 'LICENSE',\n",
       " 'MANIFEST.in',\n",
       " 'README.md',\n",
       " 'README_zh-CN.md',\n",
       " 'configs',\n",
       " 'demo',\n",
       " 'docker',\n",
       " 'docs',\n",
       " 'mmtrack',\n",
       " 'model-index.yml',\n",
       " 'requirements.txt',\n",
       " 'requirements',\n",
       " 'resources',\n",
       " 'setup.cfg',\n",
       " 'setup.py',\n",
       " 'tests',\n",
       " 'tools',\n",
       " 'mmtrack.egg-info',\n",
       " 'checkpoints',\n",
       " 'outputs',\n",
       " 'data']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('mmtracking')\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ef7b40-6588-4d47-9121-55760bad7772",
   "metadata": {},
   "source": [
    "## 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cad01651-f3da-4ce6-b420-bb98c02f716a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入 opencv-python\n",
    "import cv2\n",
    "\n",
    "import mmcv\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import random\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "# 导入可视化工具包 matplotlib，并让绘制的图像嵌入在 notebook 中\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import tempfile\n",
    "from mmtrack.apis import inference_mot, init_model\n",
    "    \n",
    "# 定义可视化图像函数，输入图像 array，可视化图像\n",
    "def show_img_from_array(img):\n",
    "    '''输入 array，matplotlib 可视化格式为 RGB，因此需将 BGR 转 RGB，最后可视化出来'''\n",
    "    img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(img_RGB)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989d15da-291f-47b3-80af-4c8e8cafddbc",
   "metadata": {},
   "source": [
    "## 人流量计数 + 足迹绘制 详细实现"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bed7994-66c8-4c77-8ad6-49b0a3ed96de",
   "metadata": {},
   "source": [
    "### 从视频中获取某一帧图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7b9bb37-679a-4aaf-9a17-68887b4c12d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输入输出视频路径\n",
    "input_video = 'data/mot_people_short.mp4'\n",
    "output = 'outputs/output_G1_MOT_people_short.mp4'\n",
    "# 读入视频\n",
    "imgs = mmcv.VideoReader(input_video)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93683f41-d20d-43d0-9155-da9cb95b43cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取视频第i帧画面\n",
    "frame_id = 0\n",
    "img = imgs[frame_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6e4050f-1d10-492b-836a-b3d8157e1512",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1080, 1920, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0dbc3b60-4944-4b3d-be44-32fc9374d3c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_img_from_array(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51bc31e-68eb-4aed-87d6-54a476f0fe9a",
   "metadata": {},
   "source": [
    "### 导入多目标追踪模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68d44c43-e7dd-4424-9436-20a703eb9e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-25 14:04:43,738 - mmdet - INFO - image shape: height=800, width=1440 in YOLOX.__init__\n",
      "2022-04-25 14:04:43,770 - mmtrack - INFO - initialize YOLOX with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth'}\n",
      "2022-04-25 14:04:43,771 - mmcv - INFO - load model from: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth\n",
      "2022-04-25 14:04:43,773 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth\n",
      "2022-04-25 14:04:44,157 - mmcv - WARNING - The model and loaded state dict do not match exactly\n",
      "\n",
      "size mismatch for bbox_head.multi_level_conv_cls.0.weight: copying a param with shape torch.Size([80, 320, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 320, 1, 1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.0.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.1.weight: copying a param with shape torch.Size([80, 320, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 320, 1, 1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.1.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.2.weight: copying a param with shape torch.Size([80, 320, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 320, 1, 1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.2.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/bytetrack/bytetrack_yolox_x/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth\n",
      "The model and loaded state dict do not match exactly\n",
      "\n",
      "unexpected key in source state_dict: ema_detector_backbone_stem_conv_conv_weight, ema_detector_backbone_stem_conv_bn_weight, ema_detector_backbone_stem_conv_bn_bias, ema_detector_backbone_stem_conv_bn_running_mean, ema_detector_backbone_stem_conv_bn_running_var, ema_detector_backbone_stem_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_0_conv_weight, ema_detector_backbone_stage1_0_bn_weight, ema_detector_backbone_stage1_0_bn_bias, ema_detector_backbone_stage1_0_bn_running_mean, ema_detector_backbone_stage1_0_bn_running_var, ema_detector_backbone_stage1_0_bn_num_batches_tracked, ema_detector_backbone_stage1_1_main_conv_conv_weight, ema_detector_backbone_stage1_1_main_conv_bn_weight, ema_detector_backbone_stage1_1_main_conv_bn_bias, ema_detector_backbone_stage1_1_main_conv_bn_running_mean, ema_detector_backbone_stage1_1_main_conv_bn_running_var, ema_detector_backbone_stage1_1_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_1_short_conv_conv_weight, ema_detector_backbone_stage1_1_short_conv_bn_weight, ema_detector_backbone_stage1_1_short_conv_bn_bias, ema_detector_backbone_stage1_1_short_conv_bn_running_mean, ema_detector_backbone_stage1_1_short_conv_bn_running_var, ema_detector_backbone_stage1_1_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_1_final_conv_conv_weight, ema_detector_backbone_stage1_1_final_conv_bn_weight, ema_detector_backbone_stage1_1_final_conv_bn_bias, ema_detector_backbone_stage1_1_final_conv_bn_running_mean, ema_detector_backbone_stage1_1_final_conv_bn_running_var, ema_detector_backbone_stage1_1_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_0_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_0_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_1_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_1_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_2_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_2_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_3_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_3_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_0_conv_weight, ema_detector_backbone_stage2_0_bn_weight, ema_detector_backbone_stage2_0_bn_bias, ema_detector_backbone_stage2_0_bn_running_mean, ema_detector_backbone_stage2_0_bn_running_var, ema_detector_backbone_stage2_0_bn_num_batches_tracked, ema_detector_backbone_stage2_1_main_conv_conv_weight, ema_detector_backbone_stage2_1_main_conv_bn_weight, ema_detector_backbone_stage2_1_main_conv_bn_bias, ema_detector_backbone_stage2_1_main_conv_bn_running_mean, ema_detector_backbone_stage2_1_main_conv_bn_running_var, ema_detector_backbone_stage2_1_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage2_1_short_conv_conv_weight, ema_detector_backbone_stage2_1_short_conv_bn_weight, ema_detector_backbone_stage2_1_short_conv_bn_bias, ema_detector_backbone_stage2_1_short_conv_bn_running_mean, ema_detector_backbone_stage2_1_short_conv_bn_running_var, ema_detector_backbone_stage2_1_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage2_1_final_conv_conv_weight, ema_detector_backbone_stage2_1_final_conv_bn_weight, ema_detector_backbone_stage2_1_final_conv_bn_bias, ema_detector_backbone_stage2_1_final_conv_bn_running_mean, ema_detector_backbone_stage2_1_final_conv_bn_running_var, ema_detector_backbone_stage2_1_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_0_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_0_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_1_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_1_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_2_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_2_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_3_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_3_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_4_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_4_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_5_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_5_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_6_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_6_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_7_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_7_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_8_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_8_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_9_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_9_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_10_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_10_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_11_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_11_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_0_conv_weight, ema_detector_backbone_stage3_0_bn_weight, ema_detector_backbone_stage3_0_bn_bias, ema_detector_backbone_stage3_0_bn_running_mean, ema_detector_backbone_stage3_0_bn_running_var, ema_detector_backbone_stage3_0_bn_num_batches_tracked, ema_detector_backbone_stage3_1_main_conv_conv_weight, ema_detector_backbone_stage3_1_main_conv_bn_weight, ema_detector_backbone_stage3_1_main_conv_bn_bias, ema_detector_backbone_stage3_1_main_conv_bn_running_mean, ema_detector_backbone_stage3_1_main_conv_bn_running_var, ema_detector_backbone_stage3_1_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage3_1_short_conv_conv_weight, ema_detector_backbone_stage3_1_short_conv_bn_weight, ema_detector_backbone_stage3_1_short_conv_bn_bias, ema_detector_backbone_stage3_1_short_conv_bn_running_mean, ema_detector_backbone_stage3_1_short_conv_bn_running_var, ema_detector_backbone_stage3_1_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage3_1_final_conv_conv_weight, ema_detector_backbone_stage3_1_final_conv_bn_weight, ema_detector_backbone_stage3_1_final_conv_bn_bias, ema_detector_backbone_stage3_1_final_conv_bn_running_mean, ema_detector_backbone_stage3_1_final_conv_bn_running_var, ema_detector_backbone_stage3_1_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_0_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_0_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_1_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_1_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_2_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_2_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_3_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_3_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_4_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_4_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_5_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_5_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_6_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_6_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_7_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_7_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_8_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_8_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_9_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_9_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_10_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_10_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_11_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_11_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_0_conv_weight, ema_detector_backbone_stage4_0_bn_weight, ema_detector_backbone_stage4_0_bn_bias, ema_detector_backbone_stage4_0_bn_running_mean, ema_detector_backbone_stage4_0_bn_running_var, ema_detector_backbone_stage4_0_bn_num_batches_tracked, ema_detector_backbone_stage4_1_conv1_conv_weight, ema_detector_backbone_stage4_1_conv1_bn_weight, ema_detector_backbone_stage4_1_conv1_bn_bias, ema_detector_backbone_stage4_1_conv1_bn_running_mean, ema_detector_backbone_stage4_1_conv1_bn_running_var, ema_detector_backbone_stage4_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_1_conv2_conv_weight, ema_detector_backbone_stage4_1_conv2_bn_weight, ema_detector_backbone_stage4_1_conv2_bn_bias, ema_detector_backbone_stage4_1_conv2_bn_running_mean, ema_detector_backbone_stage4_1_conv2_bn_running_var, ema_detector_backbone_stage4_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_main_conv_conv_weight, ema_detector_backbone_stage4_2_main_conv_bn_weight, ema_detector_backbone_stage4_2_main_conv_bn_bias, ema_detector_backbone_stage4_2_main_conv_bn_running_mean, ema_detector_backbone_stage4_2_main_conv_bn_running_var, ema_detector_backbone_stage4_2_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage4_2_short_conv_conv_weight, ema_detector_backbone_stage4_2_short_conv_bn_weight, ema_detector_backbone_stage4_2_short_conv_bn_bias, ema_detector_backbone_stage4_2_short_conv_bn_running_mean, ema_detector_backbone_stage4_2_short_conv_bn_running_var, ema_detector_backbone_stage4_2_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage4_2_final_conv_conv_weight, ema_detector_backbone_stage4_2_final_conv_bn_weight, ema_detector_backbone_stage4_2_final_conv_bn_bias, ema_detector_backbone_stage4_2_final_conv_bn_running_mean, ema_detector_backbone_stage4_2_final_conv_bn_running_var, ema_detector_backbone_stage4_2_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_0_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_0_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_1_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_1_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_2_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_2_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_3_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_3_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_reduce_layers_0_conv_weight, ema_detector_neck_reduce_layers_0_bn_weight, ema_detector_neck_reduce_layers_0_bn_bias, ema_detector_neck_reduce_layers_0_bn_running_mean, ema_detector_neck_reduce_layers_0_bn_running_var, ema_detector_neck_reduce_layers_0_bn_num_batches_tracked, ema_detector_neck_reduce_layers_1_conv_weight, ema_detector_neck_reduce_layers_1_bn_weight, ema_detector_neck_reduce_layers_1_bn_bias, ema_detector_neck_reduce_layers_1_bn_running_mean, ema_detector_neck_reduce_layers_1_bn_running_var, ema_detector_neck_reduce_layers_1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_main_conv_conv_weight, ema_detector_neck_top_down_blocks_0_main_conv_bn_weight, ema_detector_neck_top_down_blocks_0_main_conv_bn_bias, ema_detector_neck_top_down_blocks_0_main_conv_bn_running_mean, ema_detector_neck_top_down_blocks_0_main_conv_bn_running_var, ema_detector_neck_top_down_blocks_0_main_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_short_conv_conv_weight, ema_detector_neck_top_down_blocks_0_short_conv_bn_weight, ema_detector_neck_top_down_blocks_0_short_conv_bn_bias, ema_detector_neck_top_down_blocks_0_short_conv_bn_running_mean, ema_detector_neck_top_down_blocks_0_short_conv_bn_running_var, ema_detector_neck_top_down_blocks_0_short_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_final_conv_conv_weight, ema_detector_neck_top_down_blocks_0_final_conv_bn_weight, ema_detector_neck_top_down_blocks_0_final_conv_bn_bias, ema_detector_neck_top_down_blocks_0_final_conv_bn_running_mean, ema_detector_neck_top_down_blocks_0_final_conv_bn_running_var, ema_detector_neck_top_down_blocks_0_final_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_main_conv_conv_weight, ema_detector_neck_top_down_blocks_1_main_conv_bn_weight, ema_detector_neck_top_down_blocks_1_main_conv_bn_bias, ema_detector_neck_top_down_blocks_1_main_conv_bn_running_mean, ema_detector_neck_top_down_blocks_1_main_conv_bn_running_var, ema_detector_neck_top_down_blocks_1_main_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_short_conv_conv_weight, ema_detector_neck_top_down_blocks_1_short_conv_bn_weight, ema_detector_neck_top_down_blocks_1_short_conv_bn_bias, ema_detector_neck_top_down_blocks_1_short_conv_bn_running_mean, ema_detector_neck_top_down_blocks_1_short_conv_bn_running_var, ema_detector_neck_top_down_blocks_1_short_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_final_conv_conv_weight, ema_detector_neck_top_down_blocks_1_final_conv_bn_weight, ema_detector_neck_top_down_blocks_1_final_conv_bn_bias, ema_detector_neck_top_down_blocks_1_final_conv_bn_running_mean, ema_detector_neck_top_down_blocks_1_final_conv_bn_running_var, ema_detector_neck_top_down_blocks_1_final_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_downsamples_0_conv_weight, ema_detector_neck_downsamples_0_bn_weight, ema_detector_neck_downsamples_0_bn_bias, ema_detector_neck_downsamples_0_bn_running_mean, ema_detector_neck_downsamples_0_bn_running_var, ema_detector_neck_downsamples_0_bn_num_batches_tracked, ema_detector_neck_downsamples_1_conv_weight, ema_detector_neck_downsamples_1_bn_weight, ema_detector_neck_downsamples_1_bn_bias, ema_detector_neck_downsamples_1_bn_running_mean, ema_detector_neck_downsamples_1_bn_running_var, ema_detector_neck_downsamples_1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_main_conv_conv_weight, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_weight, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_bias, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_short_conv_conv_weight, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_weight, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_bias, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_final_conv_conv_weight, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_weight, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_bias, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_main_conv_conv_weight, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_weight, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_bias, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_short_conv_conv_weight, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_weight, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_bias, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_final_conv_conv_weight, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_weight, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_bias, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_out_convs_0_conv_weight, ema_detector_neck_out_convs_0_bn_weight, ema_detector_neck_out_convs_0_bn_bias, ema_detector_neck_out_convs_0_bn_running_mean, ema_detector_neck_out_convs_0_bn_running_var, ema_detector_neck_out_convs_0_bn_num_batches_tracked, ema_detector_neck_out_convs_1_conv_weight, ema_detector_neck_out_convs_1_bn_weight, ema_detector_neck_out_convs_1_bn_bias, ema_detector_neck_out_convs_1_bn_running_mean, ema_detector_neck_out_convs_1_bn_running_var, ema_detector_neck_out_convs_1_bn_num_batches_tracked, ema_detector_neck_out_convs_2_conv_weight, ema_detector_neck_out_convs_2_bn_weight, ema_detector_neck_out_convs_2_bn_bias, ema_detector_neck_out_convs_2_bn_running_mean, ema_detector_neck_out_convs_2_bn_running_var, ema_detector_neck_out_convs_2_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_0_0_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_0_1_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_1_0_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_1_1_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_2_0_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_2_1_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_0_0_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_0_1_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_1_0_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_1_1_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_2_0_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_2_1_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_conv_cls_0_weight, ema_detector_bbox_head_multi_level_conv_cls_0_bias, ema_detector_bbox_head_multi_level_conv_cls_1_weight, ema_detector_bbox_head_multi_level_conv_cls_1_bias, ema_detector_bbox_head_multi_level_conv_cls_2_weight, ema_detector_bbox_head_multi_level_conv_cls_2_bias, ema_detector_bbox_head_multi_level_conv_reg_0_weight, ema_detector_bbox_head_multi_level_conv_reg_0_bias, ema_detector_bbox_head_multi_level_conv_reg_1_weight, ema_detector_bbox_head_multi_level_conv_reg_1_bias, ema_detector_bbox_head_multi_level_conv_reg_2_weight, ema_detector_bbox_head_multi_level_conv_reg_2_bias, ema_detector_bbox_head_multi_level_conv_obj_0_weight, ema_detector_bbox_head_multi_level_conv_obj_0_bias, ema_detector_bbox_head_multi_level_conv_obj_1_weight, ema_detector_bbox_head_multi_level_conv_obj_1_bias, ema_detector_bbox_head_multi_level_conv_obj_2_weight, ema_detector_bbox_head_multi_level_conv_obj_2_bias\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 指定 config 配置文件 和 模型权重文件，创建模型\n",
    "mot_config = './configs/mot/bytetrack/bytetrack_yolox_x_crowdhuman_mot17-private-half.py'\n",
    "mot_checkpoint = 'https://download.openmmlab.com/mmtracking/mot/bytetrack/bytetrack_yolox_x/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth'\n",
    "# 初始化模型\n",
    "mot_model = init_model(mot_config, mot_checkpoint, device='cuda:0')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02742c6-8377-4430-bb41-d508e7867c2a",
   "metadata": {},
   "source": [
    "### 执行多目标追踪"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fac78f53-3697-45f4-ae37-e65fa8c65803",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/environment/miniconda3/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "/environment/miniconda3/lib/python3.7/site-packages/mmdet/models/dense_heads/yolox_head.py:286: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)\n",
      "  scale_factors).unsqueeze(1)\n"
     ]
    }
   ],
   "source": [
    "result = inference_mot(mot_model, img, frame_id=frame_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807b51a0-edc6-4777-8e64-eb9174fbb8ca",
   "metadata": {},
   "source": [
    "解析多目标追踪 result 结果"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17009b8d-da37-44e1-a847-98fd672ff843",
   "metadata": {},
   "source": [
    "### 解析多目标追踪`result`结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4790e78c-2ba8-4962-9595-17a5ecffdd2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['det_bboxes', 'track_bboxes'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b81ee7ae-9f03-4aa4-b432-6e5a4f8b1808",
   "metadata": {},
   "outputs": [],
   "source": [
    "# result['det_bboxes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "176906fc-c3c8-4a7b-991e-02ed74b8f707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.6019095e+03, 6.0782794e+02, 1.7185277e+03, 8.6616089e+02,\n",
       "        9.4052279e-01],\n",
       "       [1.6459392e+03, 1.1914110e+02, 1.7179167e+03, 2.8813354e+02,\n",
       "        9.2666405e-01],\n",
       "       [1.4493016e+03, 3.2618118e+01, 1.5361381e+03, 1.8836691e+02,\n",
       "        9.1761655e-01],\n",
       "       [7.9675891e+02, 2.4251991e+02, 8.5994458e+02, 4.2668658e+02,\n",
       "        9.1220850e-01],\n",
       "       [1.3466731e+03, 1.4821764e+01, 1.4049393e+03, 1.3409984e+02,\n",
       "        9.0926546e-01],\n",
       "       [8.6739935e+02, 3.8889447e+02, 9.9191016e+02, 6.1932056e+02,\n",
       "        9.0501010e-01],\n",
       "       [1.7965985e+03, 1.7947960e+02, 1.8677836e+03, 3.7402744e+02,\n",
       "        9.0399045e-01],\n",
       "       [7.1240686e+02, 2.2749742e+02, 7.8477185e+02, 4.1062335e+02,\n",
       "        8.9260650e-01],\n",
       "       [8.7765161e+02, 7.5635765e+01, 9.4659149e+02, 2.3703418e+02,\n",
       "        8.8843852e-01],\n",
       "       [2.4237114e+02, 7.8445648e+02, 3.8073230e+02, 1.1103938e+03,\n",
       "        8.8518399e-01]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['det_bboxes'][0][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83296549-55e9-4203-8772-370f1de322cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result['det_bboxes'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0d0fcd63-7680-4a6d-966b-3910c7d25d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result['track_bboxes'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f87fff9-2fd3-4f3b-94fa-270c13486f88",
   "metadata": {},
   "source": [
    "每个目标检测框的左上角xy坐标、右下角xy坐标、置信度\n",
    "\n",
    "[0] 表示只看行人类别"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a662c5b-85a0-47e6-b5e8-9e5eb40ae270",
   "metadata": {},
   "source": [
    "ID、左上角x、左上角y、右下角x、右下角y、置信度\n",
    "\n",
    "[0] 表示只看行人类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f198dec5-ca3c-4bad-afd5-b400730121e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  1.60190955e+03,  6.07827942e+02,\n",
       "         1.71852771e+03,  8.66160889e+02,  9.40522790e-01],\n",
       "       [ 1.00000000e+00,  1.64593921e+03,  1.19141098e+02,\n",
       "         1.71791675e+03,  2.88133545e+02,  9.26664054e-01],\n",
       "       [ 2.00000000e+00,  1.44930164e+03,  3.26181183e+01,\n",
       "         1.53613806e+03,  1.88366913e+02,  9.17616546e-01],\n",
       "       [ 3.00000000e+00,  7.96758911e+02,  2.42519913e+02,\n",
       "         8.59944580e+02,  4.26686584e+02,  9.12208498e-01],\n",
       "       [ 4.00000000e+00,  1.34667310e+03,  1.48217640e+01,\n",
       "         1.40493933e+03,  1.34099838e+02,  9.09265459e-01],\n",
       "       [ 5.00000000e+00,  8.67399353e+02,  3.88894470e+02,\n",
       "         9.91910156e+02,  6.19320557e+02,  9.05010104e-01],\n",
       "       [ 6.00000000e+00,  1.79659851e+03,  1.79479599e+02,\n",
       "         1.86778357e+03,  3.74027435e+02,  9.03990448e-01],\n",
       "       [ 7.00000000e+00,  7.12406860e+02,  2.27497421e+02,\n",
       "         7.84771851e+02,  4.10623352e+02,  8.92606497e-01],\n",
       "       [ 8.00000000e+00,  8.77651611e+02,  7.56357651e+01,\n",
       "         9.46591492e+02,  2.37034180e+02,  8.88438523e-01],\n",
       "       [ 9.00000000e+00,  2.42371140e+02,  7.84456482e+02,\n",
       "         3.80732300e+02,  1.11039380e+03,  8.85183990e-01],\n",
       "       [ 1.00000000e+01,  9.83976379e+02, -1.27987928e+01,\n",
       "         1.03471948e+03,  1.31447433e+02,  8.76524627e-01],\n",
       "       [ 1.10000000e+01,  2.79439545e+02,  2.36933823e+02,\n",
       "         3.78076111e+02,  4.18116821e+02,  8.73696566e-01],\n",
       "       [ 1.20000000e+01,  1.31868176e+03, -5.73302994e+01,\n",
       "         1.36372083e+03,  5.35190697e+01,  8.48536909e-01],\n",
       "       [ 1.30000000e+01,  2.81214996e+02,  2.97591095e+02,\n",
       "         3.66704071e+02,  4.82799744e+02,  8.43403101e-01],\n",
       "       [ 1.40000000e+01,  1.15196277e+03, -3.51361008e+01,\n",
       "         1.20097156e+03,  7.90727158e+01,  8.42435002e-01],\n",
       "       [ 1.50000000e+01,  1.57958789e+03,  9.09903076e+02,\n",
       "         1.72699231e+03,  1.21784924e+03,  7.90326416e-01],\n",
       "       [ 1.60000000e+01,  1.80682068e+03,  2.62655640e+02,\n",
       "         1.90413879e+03,  4.70437317e+02,  7.37273812e-01]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['track_bboxes'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99c37b31-9e16-4912-b528-fc8e8ecf4c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result['track_bboxes'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b5df426-3af6-4cd6-acfa-394c2a72279d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
       "       13., 14., 15., 16.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当前帧各目标的ID\n",
    "result['track_bboxes'][0][:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76bfd60-973f-4070-bdfc-dd4b570bd5b4",
   "metadata": {},
   "source": [
    "### 整理多目标追踪`result`结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff93e8a5-5e18-472d-a3e0-e078d9f04bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mmtrack.core import imshow_tracks, results2outs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "868c1652-e713-4d08-966d-f59924872a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "outs_track = results2outs(bbox_results=result.get('track_bboxes', None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1a16253f-0ab8-470a-90c8-9e85fcc3d408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'labels': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
       " 'bboxes': array([[ 1.6019095e+03,  6.0782794e+02,  1.7185277e+03,  8.6616089e+02,\n",
       "          9.4052279e-01],\n",
       "        [ 1.6459392e+03,  1.1914110e+02,  1.7179167e+03,  2.8813354e+02,\n",
       "          9.2666405e-01],\n",
       "        [ 1.4493016e+03,  3.2618118e+01,  1.5361381e+03,  1.8836691e+02,\n",
       "          9.1761655e-01],\n",
       "        [ 7.9675891e+02,  2.4251991e+02,  8.5994458e+02,  4.2668658e+02,\n",
       "          9.1220850e-01],\n",
       "        [ 1.3466731e+03,  1.4821764e+01,  1.4049393e+03,  1.3409984e+02,\n",
       "          9.0926546e-01],\n",
       "        [ 8.6739935e+02,  3.8889447e+02,  9.9191016e+02,  6.1932056e+02,\n",
       "          9.0501010e-01],\n",
       "        [ 1.7965985e+03,  1.7947960e+02,  1.8677836e+03,  3.7402744e+02,\n",
       "          9.0399045e-01],\n",
       "        [ 7.1240686e+02,  2.2749742e+02,  7.8477185e+02,  4.1062335e+02,\n",
       "          8.9260650e-01],\n",
       "        [ 8.7765161e+02,  7.5635765e+01,  9.4659149e+02,  2.3703418e+02,\n",
       "          8.8843852e-01],\n",
       "        [ 2.4237114e+02,  7.8445648e+02,  3.8073230e+02,  1.1103938e+03,\n",
       "          8.8518399e-01],\n",
       "        [ 9.8397638e+02, -1.2798793e+01,  1.0347195e+03,  1.3144743e+02,\n",
       "          8.7652463e-01],\n",
       "        [ 2.7943954e+02,  2.3693382e+02,  3.7807611e+02,  4.1811682e+02,\n",
       "          8.7369657e-01],\n",
       "        [ 1.3186818e+03, -5.7330299e+01,  1.3637208e+03,  5.3519070e+01,\n",
       "          8.4853691e-01],\n",
       "        [ 2.8121500e+02,  2.9759109e+02,  3.6670407e+02,  4.8279974e+02,\n",
       "          8.4340310e-01],\n",
       "        [ 1.1519628e+03, -3.5136101e+01,  1.2009716e+03,  7.9072716e+01,\n",
       "          8.4243500e-01],\n",
       "        [ 1.5795879e+03,  9.0990308e+02,  1.7269923e+03,  1.2178492e+03,\n",
       "          7.9032642e-01],\n",
       "        [ 1.8068207e+03,  2.6265564e+02,  1.9041388e+03,  4.7043732e+02,\n",
       "          7.3727381e-01]], dtype=float32),\n",
       " 'ids': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outs_track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e05e320-f14e-4585-b514-5d8786eb0015",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取出结果\n",
    "bboxes = outs_track.get('bboxes', None)\n",
    "labels = outs_track.get('labels', None)\n",
    "ids = outs_track.get('ids', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4f06f0a8-04c1-456d-98c4-5ea43035625a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "952715a3-8dcf-4bdd-af54-88f0e7565b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b19592f-4be0-4698-96cd-11808041341b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 可视化配置-调色板"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1b27485e-bdee-4f4e-92d2-558aa1488581",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成调色板\n",
    "palette = sns.color_palette('hls', 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fe034105-07f2-459c-aa5d-2ebfe98217fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"1375\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db5f57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db7e57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db9e57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dbbe57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d9db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b9db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#99db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#79db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#59db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57db74;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"550\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57db94;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"605\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57dbb4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"660\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57dbd3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"715\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57c3db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"770\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57a4db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"825\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5784db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"880\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5764db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"935\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6957db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"990\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8957db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"1045\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a957db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"1100\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c957db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"1155\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db57ce;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"1210\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db57ae;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"1265\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db578e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"1320\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db576f;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.86, 0.3712, 0.33999999999999997),\n",
       " (0.86, 0.496, 0.33999999999999997),\n",
       " (0.86, 0.6208, 0.33999999999999997),\n",
       " (0.86, 0.7456, 0.33999999999999997),\n",
       " (0.8496, 0.86, 0.33999999999999997),\n",
       " (0.7247999999999999, 0.86, 0.33999999999999997),\n",
       " (0.6000000000000001, 0.86, 0.33999999999999997),\n",
       " (0.47519999999999996, 0.86, 0.33999999999999997),\n",
       " (0.3503999999999999, 0.86, 0.33999999999999997),\n",
       " (0.33999999999999997, 0.86, 0.4544),\n",
       " (0.33999999999999997, 0.86, 0.5792000000000002),\n",
       " (0.33999999999999997, 0.86, 0.7040000000000001),\n",
       " (0.33999999999999997, 0.86, 0.8288),\n",
       " (0.33999999999999997, 0.7663999999999997, 0.86),\n",
       " (0.33999999999999997, 0.6415999999999997, 0.86),\n",
       " (0.33999999999999997, 0.5167999999999999, 0.86),\n",
       " (0.33999999999999997, 0.3919999999999998, 0.86),\n",
       " (0.4128000000000003, 0.33999999999999997, 0.86),\n",
       " (0.5375999999999996, 0.33999999999999997, 0.86),\n",
       " (0.6623999999999999, 0.33999999999999997, 0.86),\n",
       " (0.7871999999999999, 0.33999999999999997, 0.86),\n",
       " (0.86, 0.33999999999999997, 0.808),\n",
       " (0.86, 0.33999999999999997, 0.6831999999999999),\n",
       " (0.86, 0.33999999999999997, 0.5583999999999998),\n",
       " (0.86, 0.33999999999999997, 0.4335999999999997)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "361566e6-96af-4ee7-aabb-e4da454d9087",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从调色板中随机挑选一种颜色\n",
    "random.seed(3)\n",
    "bbox_color = random.choice(palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "89d308eb-a6f0-453a-a137-891c032360eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.47519999999999996, 0.86, 0.33999999999999997)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbox_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f729871-41a6-48d4-aa9d-6f6849fceab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox_color = [int(255 * c) for c in bbox_color][::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "66d339ea-a3df-4208-911f-594504a23416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[86, 219, 121]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbox_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "662f030c-8ee3-488d-becb-eba4d38c159a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_color(seed):\n",
    "    '''\n",
    "    传入追踪ID，生成专属颜色\n",
    "    '''\n",
    "    random.seed(seed)\n",
    "    # 从调色板中随机挑选一种颜色\n",
    "    bbox_color = random.choice(palette)\n",
    "    bbox_color = [int(255 * c) for c in bbox_color][::-1]\n",
    "    return bbox_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "864a429f-f970-41a4-97e0-d8cf6d94a101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[86, 219, 121]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_color(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1821bacf-7687-41ea-bf78-ff76d2da4a13",
   "metadata": {},
   "source": [
    "### 可视化配置-目标检测矩形框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fcaccb90-23e5-4984-8a87-202dcbebbf8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 目标检测框线宽\n",
    "thickness = 2\n",
    "\n",
    "# 轨迹线粗细\n",
    "trace_radius = 10\n",
    "\n",
    "# 目标检测置信度阈值\n",
    "score_thr = 0.0\n",
    "\n",
    "# 字体大小\n",
    "font_scale = 0.4\n",
    "\n",
    "# 置信度和 ID 单个数字的宽高\n",
    "text_width = 9\n",
    "text_height = 13\n",
    "\n",
    "# 类别信息\n",
    "classes = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "67d4131d-6127-4c15-9c38-e4cef82f918a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 目标检测框的 x坐标 不超过图像宽度\n",
    "bboxes[:, [0,2]] = np.clip(bboxes[:, [0,2]], 0, img.shape[1])\n",
    "# 目标检测框的 y坐标 不超过图像高度\n",
    "bboxes[:, [1,3]] = np.clip(bboxes[:, [1,3]], 0, img.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aed44e58-c819-4ea7-8e4e-5080547ff623",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 筛选出目标检测置信度大于指定阈值的目标，并取出其索引、目标检测、类别、ID信息\n",
    "inds = np.where(bboxes[:, -1] > score_thr)[0]\n",
    "bboxes = bboxes[inds]\n",
    "labels = labels[inds]\n",
    "ids = ids[inds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b56850b4-09ba-4a36-9418-85aae75ebe15",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 遍历当前帧中的每个目标，可视化\n",
    "for i, (bbox, label, id) in enumerate(zip(bboxes, labels, ids)):\n",
    "    \n",
    "    # 获取目标检测框的左上角、右下角坐标，及置信度\n",
    "    x1, y1, x2, y2 = bbox[:4].astype(np.int32)\n",
    "    score = float(bbox[-1])\n",
    "\n",
    "    # 绘制目标检测矩形框\n",
    "    bbox_color = get_color(id)\n",
    "    cv2.rectangle(img, (x1, y1), (x2, y2), bbox_color, thickness=thickness)\n",
    "\n",
    "    # 置信度文字\n",
    "    text = '{:.02f}'.format(score)\n",
    "    if classes is not None: # 类别信息\n",
    "        text += f'|{classes[label]}'\n",
    "    # 绘制置信度文字的背景\n",
    "    width = len(text) * text_width\n",
    "    img[y1:y1 + text_height, x1:x1 + width, :] = bbox_color\n",
    "    # 写置信度文字：图像，文字，左上角坐标，字体，字体大小，颜色，字体粗细\n",
    "    cv2.putText(img, text, (x1, y1 + text_height - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, color=(0, 0, 0))\n",
    "\n",
    "    # 目标 ID 文字\n",
    "    text = str(id)\n",
    "    # 绘制 ID 文字的背景\n",
    "    width = len(text) * text_width\n",
    "    img[y1 + text_height:y1 + 2 * text_height, x1:x1 + width, :] = bbox_color\n",
    "    # 写 ID 文字：图像，文字，左上角坐标，字体，字体大小，颜色，字体粗细\n",
    "    cv2.putText(img, str(id), (x1, y1 + 2 * text_height - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, color=(0, 0, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f76f273e-a860-4cd5-98bd-2640d79ea9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_img_from_array(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d16e8443-64e0-4880-920d-56d46b97839e",
   "metadata": {},
   "source": [
    "## 封装函数-人流量计数+足迹绘制"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30938e7-12d4-4c7c-b942-dc7900703642",
   "metadata": {},
   "source": [
    "重启`kernel`，运行下面代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fae97111-01dc-479e-84a0-88a285debbea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 进入 mmtracking 主目录\n",
    "import os\n",
    "os.chdir('mmtracking')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89dad216-4724-404a-a373-40d921e73319",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入 opencv-python\n",
    "import cv2\n",
    "\n",
    "import mmcv\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import random\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "# 导入可视化工具包 matplotlib，并让绘制的图像嵌入在 notebook 中\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import mmcv\n",
    "import tempfile\n",
    "from mmtrack.apis import inference_mot, init_model\n",
    "from mmtrack.core import imshow_tracks, results2outs\n",
    "    \n",
    "# 定义可视化图像函数，输入图像 array，可视化图像\n",
    "def show_img_from_array(img):\n",
    "    '''输入 array，matplotlib 可视化格式为 RGB，因此需将 BGR 转 RGB，最后可视化出来'''\n",
    "    img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(img_RGB)\n",
    "    plt.show()\n",
    "\n",
    "def get_color(seed):\n",
    "    '''\n",
    "    传入追踪ID，生成专属颜色\n",
    "    '''\n",
    "    random.seed(seed)\n",
    "    # 从调色板中随机挑选一种颜色\n",
    "    bbox_color = random.choice(palette)\n",
    "    bbox_color = [int(255 * c) for c in bbox_color][::-1]\n",
    "    return bbox_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9112a08d-cbb0-470f-b17c-468888169900",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mmtrack.core import imshow_tracks, results2outs\n",
    "\n",
    "# 目标检测框线宽\n",
    "thickness = 2\n",
    "\n",
    "# 轨迹线粗细\n",
    "trace_radius = 10\n",
    "\n",
    "# 目标检测置信度阈值\n",
    "score_thr = 0.0\n",
    "\n",
    "# 字体大小\n",
    "font_scale = 0.4\n",
    " \n",
    "# 置信度和 ID 单个数字的宽高\n",
    "text_width = 9\n",
    "text_height = 13\n",
    "\n",
    "# 类别信息\n",
    "classes = None\n",
    "\n",
    "# 生成调色板\n",
    "palette = sns.color_palette('hls',30)\n",
    "\n",
    "# 目标检测框线宽\n",
    "thickness = 2\n",
    "\n",
    "# 轨迹线粗细\n",
    "trace_radius = 10\n",
    "\n",
    "# 目标检测置信度阈值\n",
    "score_thr=0.0\n",
    "\n",
    "# 字体大小\n",
    "font_scale=0.4\n",
    "\n",
    "# 置信度和 ID 单个数字的宽高\n",
    "text_width = 9\n",
    "text_height = 13\n",
    "\n",
    "# 类别信息\n",
    "classes = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7271e13c-230e-4264-9a5d-2e697664f605",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_color(seed):\n",
    "    random.seed(seed)\n",
    "    # 从调色板中随机挑选一种颜色\n",
    "    bbox_color = random.choice(palette)\n",
    "    bbox_color = [int(255 * c) for c in bbox_color][::-1]\n",
    "    return bbox_color\n",
    "\n",
    "def process_result(result):\n",
    "    '''处理单帧函数，输入MOT多目标追踪结果，输出 MOT多目标追踪可视化图像、所有目标轨迹中心点坐标'''\n",
    "\n",
    "    #######################\n",
    "    # 整理多目标追踪结果\n",
    "    #######################\n",
    "    # 进一步整理result结果\n",
    "    outs_track = results2outs(bbox_results=result.get('track_bboxes', None))\n",
    "\n",
    "    # 取出结果\n",
    "    bboxes = outs_track.get('bboxes', None)\n",
    "    labels = outs_track.get('labels', None)\n",
    "    ids = outs_track.get('ids', None)\n",
    "\n",
    "    # 目标检测框的 x坐标 不超过图像宽度\n",
    "    bboxes[:, [0,2]] = np.clip(bboxes[:, [0,2]], 0, img.shape[1])\n",
    "    # 目标检测框的 y坐标 不超过图像高度\n",
    "    bboxes[:, [1,3]] = np.clip(bboxes[:, [1,3]], 0, img.shape[0])\n",
    "\n",
    "    # 筛选出目标检测置信度大于指定阈值的目标，并取出其索引、目标检测、类别、ID信息\n",
    "    inds = np.where(bboxes[:, -1] > score_thr)[0]\n",
    "    bboxes = bboxes[inds]\n",
    "    labels = labels[inds]\n",
    "    ids = ids[inds]\n",
    "\n",
    "    #######################\n",
    "    # 遍历每个目标，可视化\n",
    "    #######################\n",
    "    trace_coords_frame = [] # 存放当前帧画面中 所有目标轨迹中心点坐标\n",
    "    for i, (bbox, label, id) in enumerate(zip(bboxes, labels, ids)):\n",
    "        # 获取目标检测框的左上角、右下角坐标，及置信度\n",
    "        x1, y1, x2, y2 = bbox[:4].astype(np.int32)\n",
    "        score = float(bbox[-1])\n",
    "\n",
    "        # 绘制目标检测矩形框\n",
    "        bbox_color = get_color(id)\n",
    "        cv2.rectangle(img, (x1, y1), (x2, y2), bbox_color, thickness=thickness)\n",
    "\n",
    "        # 绘制矩形框中心点轨迹点，指定圆心坐标和半径，红色，最后一个参数为线宽，-1表示填充\n",
    "        # trace_coord = {id:[[(x1+x2)//2, (y1+y2)//2], bbox_color]}\n",
    "        # trace_coord_obj = [id, [(x1+x2)//2, (y1+y2)//2, bbox_color]] # 目标检测框中心点作为轨迹点\n",
    "        trace_coord_obj = [id, [(x1+x2)//2, y2, bbox_color]]           # 目标检测框底边中点作为轨迹点\n",
    "        trace_coords_frame.append(trace_coord_obj)\n",
    "        \n",
    "        # 置信度文字\n",
    "        text = '{:.02f}'.format(score)\n",
    "        if classes is not None: # 类别信息\n",
    "            text += f'|{classes[label]}'\n",
    "        # 绘制置信度文字的背景\n",
    "        width = len(text) * text_width\n",
    "        img[y1:y1 + text_height, x1:x1 + width, :] = bbox_color\n",
    "        # 写置信度文字：图像，文字，左上角坐标，字体，字体大小，颜色，字体粗细\n",
    "        cv2.putText(img, text, (x1, y1 + text_height - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, color=(0, 0, 0))\n",
    "\n",
    "        # ID\n",
    "        text = str(id)\n",
    "        # 绘制ID文字的背景\n",
    "        width = len(text) * text_width\n",
    "        img[y1 + text_height:y1 + 2 * text_height, x1:x1 + width, :] = bbox_color\n",
    "        # 写 ID文字：图像，文字，左上角坐标，字体，字体大小，颜色，字体粗细\n",
    "        cv2.putText(img, str(id), (x1, y1 + 2 * text_height - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, color=(0, 0, 0))\n",
    "\n",
    "    return img, trace_coords_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05acdbb0-a53f-4cf7-88d9-6acea2192390",
   "metadata": {},
   "source": [
    "## 人流量计数+足迹绘制，视频结果生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a6779a4-f02d-4565-a94f-8b45348e6c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-25 14:13:24,942 - mmdet - INFO - image shape: height=800, width=1440 in YOLOX.__init__\n",
      "2022-04-25 14:13:24,975 - mmtrack - INFO - initialize YOLOX with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth'}\n",
      "2022-04-25 14:13:24,976 - mmcv - INFO - load model from: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth\n",
      "2022-04-25 14:13:24,977 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth\n",
      "2022-04-25 14:13:25,354 - mmcv - WARNING - The model and loaded state dict do not match exactly\n",
      "\n",
      "size mismatch for bbox_head.multi_level_conv_cls.0.weight: copying a param with shape torch.Size([80, 320, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 320, 1, 1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.0.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.1.weight: copying a param with shape torch.Size([80, 320, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 320, 1, 1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.1.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.2.weight: copying a param with shape torch.Size([80, 320, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 320, 1, 1]).\n",
      "size mismatch for bbox_head.multi_level_conv_cls.2.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/bytetrack/bytetrack_yolox_x/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth\n",
      "The model and loaded state dict do not match exactly\n",
      "\n",
      "unexpected key in source state_dict: ema_detector_backbone_stem_conv_conv_weight, ema_detector_backbone_stem_conv_bn_weight, ema_detector_backbone_stem_conv_bn_bias, ema_detector_backbone_stem_conv_bn_running_mean, ema_detector_backbone_stem_conv_bn_running_var, ema_detector_backbone_stem_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_0_conv_weight, ema_detector_backbone_stage1_0_bn_weight, ema_detector_backbone_stage1_0_bn_bias, ema_detector_backbone_stage1_0_bn_running_mean, ema_detector_backbone_stage1_0_bn_running_var, ema_detector_backbone_stage1_0_bn_num_batches_tracked, ema_detector_backbone_stage1_1_main_conv_conv_weight, ema_detector_backbone_stage1_1_main_conv_bn_weight, ema_detector_backbone_stage1_1_main_conv_bn_bias, ema_detector_backbone_stage1_1_main_conv_bn_running_mean, ema_detector_backbone_stage1_1_main_conv_bn_running_var, ema_detector_backbone_stage1_1_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_1_short_conv_conv_weight, ema_detector_backbone_stage1_1_short_conv_bn_weight, ema_detector_backbone_stage1_1_short_conv_bn_bias, ema_detector_backbone_stage1_1_short_conv_bn_running_mean, ema_detector_backbone_stage1_1_short_conv_bn_running_var, ema_detector_backbone_stage1_1_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_1_final_conv_conv_weight, ema_detector_backbone_stage1_1_final_conv_bn_weight, ema_detector_backbone_stage1_1_final_conv_bn_bias, ema_detector_backbone_stage1_1_final_conv_bn_running_mean, ema_detector_backbone_stage1_1_final_conv_bn_running_var, ema_detector_backbone_stage1_1_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_0_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_0_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_1_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_1_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_2_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_2_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_3_conv1_conv_weight, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_weight, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_bias, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage1_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage1_1_blocks_3_conv2_conv_weight, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_weight, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_bias, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage1_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_0_conv_weight, ema_detector_backbone_stage2_0_bn_weight, ema_detector_backbone_stage2_0_bn_bias, ema_detector_backbone_stage2_0_bn_running_mean, ema_detector_backbone_stage2_0_bn_running_var, ema_detector_backbone_stage2_0_bn_num_batches_tracked, ema_detector_backbone_stage2_1_main_conv_conv_weight, ema_detector_backbone_stage2_1_main_conv_bn_weight, ema_detector_backbone_stage2_1_main_conv_bn_bias, ema_detector_backbone_stage2_1_main_conv_bn_running_mean, ema_detector_backbone_stage2_1_main_conv_bn_running_var, ema_detector_backbone_stage2_1_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage2_1_short_conv_conv_weight, ema_detector_backbone_stage2_1_short_conv_bn_weight, ema_detector_backbone_stage2_1_short_conv_bn_bias, ema_detector_backbone_stage2_1_short_conv_bn_running_mean, ema_detector_backbone_stage2_1_short_conv_bn_running_var, ema_detector_backbone_stage2_1_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage2_1_final_conv_conv_weight, ema_detector_backbone_stage2_1_final_conv_bn_weight, ema_detector_backbone_stage2_1_final_conv_bn_bias, ema_detector_backbone_stage2_1_final_conv_bn_running_mean, ema_detector_backbone_stage2_1_final_conv_bn_running_var, ema_detector_backbone_stage2_1_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_0_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_0_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_1_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_1_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_2_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_2_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_3_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_3_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_4_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_4_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_4_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_4_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_5_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_5_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_5_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_5_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_6_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_6_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_6_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_6_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_7_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_7_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_7_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_7_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_8_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_8_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_8_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_8_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_9_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_9_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_9_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_9_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_10_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_10_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_10_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_10_conv2_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_11_conv1_conv_weight, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_weight, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_bias, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_running_mean, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_running_var, ema_detector_backbone_stage2_1_blocks_11_conv1_bn_num_batches_tracked, ema_detector_backbone_stage2_1_blocks_11_conv2_conv_weight, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_weight, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_bias, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_running_mean, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_running_var, ema_detector_backbone_stage2_1_blocks_11_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_0_conv_weight, ema_detector_backbone_stage3_0_bn_weight, ema_detector_backbone_stage3_0_bn_bias, ema_detector_backbone_stage3_0_bn_running_mean, ema_detector_backbone_stage3_0_bn_running_var, ema_detector_backbone_stage3_0_bn_num_batches_tracked, ema_detector_backbone_stage3_1_main_conv_conv_weight, ema_detector_backbone_stage3_1_main_conv_bn_weight, ema_detector_backbone_stage3_1_main_conv_bn_bias, ema_detector_backbone_stage3_1_main_conv_bn_running_mean, ema_detector_backbone_stage3_1_main_conv_bn_running_var, ema_detector_backbone_stage3_1_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage3_1_short_conv_conv_weight, ema_detector_backbone_stage3_1_short_conv_bn_weight, ema_detector_backbone_stage3_1_short_conv_bn_bias, ema_detector_backbone_stage3_1_short_conv_bn_running_mean, ema_detector_backbone_stage3_1_short_conv_bn_running_var, ema_detector_backbone_stage3_1_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage3_1_final_conv_conv_weight, ema_detector_backbone_stage3_1_final_conv_bn_weight, ema_detector_backbone_stage3_1_final_conv_bn_bias, ema_detector_backbone_stage3_1_final_conv_bn_running_mean, ema_detector_backbone_stage3_1_final_conv_bn_running_var, ema_detector_backbone_stage3_1_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_0_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_0_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_1_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_1_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_2_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_2_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_3_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_3_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_4_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_4_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_4_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_4_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_5_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_5_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_5_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_5_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_6_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_6_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_6_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_6_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_7_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_7_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_7_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_7_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_8_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_8_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_8_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_8_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_9_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_9_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_9_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_9_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_10_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_10_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_10_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_10_conv2_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_11_conv1_conv_weight, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_weight, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_bias, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_running_mean, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_running_var, ema_detector_backbone_stage3_1_blocks_11_conv1_bn_num_batches_tracked, ema_detector_backbone_stage3_1_blocks_11_conv2_conv_weight, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_weight, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_bias, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_running_mean, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_running_var, ema_detector_backbone_stage3_1_blocks_11_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_0_conv_weight, ema_detector_backbone_stage4_0_bn_weight, ema_detector_backbone_stage4_0_bn_bias, ema_detector_backbone_stage4_0_bn_running_mean, ema_detector_backbone_stage4_0_bn_running_var, ema_detector_backbone_stage4_0_bn_num_batches_tracked, ema_detector_backbone_stage4_1_conv1_conv_weight, ema_detector_backbone_stage4_1_conv1_bn_weight, ema_detector_backbone_stage4_1_conv1_bn_bias, ema_detector_backbone_stage4_1_conv1_bn_running_mean, ema_detector_backbone_stage4_1_conv1_bn_running_var, ema_detector_backbone_stage4_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_1_conv2_conv_weight, ema_detector_backbone_stage4_1_conv2_bn_weight, ema_detector_backbone_stage4_1_conv2_bn_bias, ema_detector_backbone_stage4_1_conv2_bn_running_mean, ema_detector_backbone_stage4_1_conv2_bn_running_var, ema_detector_backbone_stage4_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_main_conv_conv_weight, ema_detector_backbone_stage4_2_main_conv_bn_weight, ema_detector_backbone_stage4_2_main_conv_bn_bias, ema_detector_backbone_stage4_2_main_conv_bn_running_mean, ema_detector_backbone_stage4_2_main_conv_bn_running_var, ema_detector_backbone_stage4_2_main_conv_bn_num_batches_tracked, ema_detector_backbone_stage4_2_short_conv_conv_weight, ema_detector_backbone_stage4_2_short_conv_bn_weight, ema_detector_backbone_stage4_2_short_conv_bn_bias, ema_detector_backbone_stage4_2_short_conv_bn_running_mean, ema_detector_backbone_stage4_2_short_conv_bn_running_var, ema_detector_backbone_stage4_2_short_conv_bn_num_batches_tracked, ema_detector_backbone_stage4_2_final_conv_conv_weight, ema_detector_backbone_stage4_2_final_conv_bn_weight, ema_detector_backbone_stage4_2_final_conv_bn_bias, ema_detector_backbone_stage4_2_final_conv_bn_running_mean, ema_detector_backbone_stage4_2_final_conv_bn_running_var, ema_detector_backbone_stage4_2_final_conv_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_0_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_0_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_0_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_0_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_1_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_1_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_1_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_1_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_2_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_2_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_2_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_2_conv2_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_3_conv1_conv_weight, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_weight, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_bias, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_running_mean, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_running_var, ema_detector_backbone_stage4_2_blocks_3_conv1_bn_num_batches_tracked, ema_detector_backbone_stage4_2_blocks_3_conv2_conv_weight, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_weight, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_bias, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_running_mean, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_running_var, ema_detector_backbone_stage4_2_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_reduce_layers_0_conv_weight, ema_detector_neck_reduce_layers_0_bn_weight, ema_detector_neck_reduce_layers_0_bn_bias, ema_detector_neck_reduce_layers_0_bn_running_mean, ema_detector_neck_reduce_layers_0_bn_running_var, ema_detector_neck_reduce_layers_0_bn_num_batches_tracked, ema_detector_neck_reduce_layers_1_conv_weight, ema_detector_neck_reduce_layers_1_bn_weight, ema_detector_neck_reduce_layers_1_bn_bias, ema_detector_neck_reduce_layers_1_bn_running_mean, ema_detector_neck_reduce_layers_1_bn_running_var, ema_detector_neck_reduce_layers_1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_main_conv_conv_weight, ema_detector_neck_top_down_blocks_0_main_conv_bn_weight, ema_detector_neck_top_down_blocks_0_main_conv_bn_bias, ema_detector_neck_top_down_blocks_0_main_conv_bn_running_mean, ema_detector_neck_top_down_blocks_0_main_conv_bn_running_var, ema_detector_neck_top_down_blocks_0_main_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_short_conv_conv_weight, ema_detector_neck_top_down_blocks_0_short_conv_bn_weight, ema_detector_neck_top_down_blocks_0_short_conv_bn_bias, ema_detector_neck_top_down_blocks_0_short_conv_bn_running_mean, ema_detector_neck_top_down_blocks_0_short_conv_bn_running_var, ema_detector_neck_top_down_blocks_0_short_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_final_conv_conv_weight, ema_detector_neck_top_down_blocks_0_final_conv_bn_weight, ema_detector_neck_top_down_blocks_0_final_conv_bn_bias, ema_detector_neck_top_down_blocks_0_final_conv_bn_running_mean, ema_detector_neck_top_down_blocks_0_final_conv_bn_running_var, ema_detector_neck_top_down_blocks_0_final_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_conv_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_weight, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_bias, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_running_var, ema_detector_neck_top_down_blocks_0_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_main_conv_conv_weight, ema_detector_neck_top_down_blocks_1_main_conv_bn_weight, ema_detector_neck_top_down_blocks_1_main_conv_bn_bias, ema_detector_neck_top_down_blocks_1_main_conv_bn_running_mean, ema_detector_neck_top_down_blocks_1_main_conv_bn_running_var, ema_detector_neck_top_down_blocks_1_main_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_short_conv_conv_weight, ema_detector_neck_top_down_blocks_1_short_conv_bn_weight, ema_detector_neck_top_down_blocks_1_short_conv_bn_bias, ema_detector_neck_top_down_blocks_1_short_conv_bn_running_mean, ema_detector_neck_top_down_blocks_1_short_conv_bn_running_var, ema_detector_neck_top_down_blocks_1_short_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_final_conv_conv_weight, ema_detector_neck_top_down_blocks_1_final_conv_bn_weight, ema_detector_neck_top_down_blocks_1_final_conv_bn_bias, ema_detector_neck_top_down_blocks_1_final_conv_bn_running_mean, ema_detector_neck_top_down_blocks_1_final_conv_bn_running_var, ema_detector_neck_top_down_blocks_1_final_conv_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_conv_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_weight, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_bias, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_running_mean, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_running_var, ema_detector_neck_top_down_blocks_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_downsamples_0_conv_weight, ema_detector_neck_downsamples_0_bn_weight, ema_detector_neck_downsamples_0_bn_bias, ema_detector_neck_downsamples_0_bn_running_mean, ema_detector_neck_downsamples_0_bn_running_var, ema_detector_neck_downsamples_0_bn_num_batches_tracked, ema_detector_neck_downsamples_1_conv_weight, ema_detector_neck_downsamples_1_bn_weight, ema_detector_neck_downsamples_1_bn_bias, ema_detector_neck_downsamples_1_bn_running_mean, ema_detector_neck_downsamples_1_bn_running_var, ema_detector_neck_downsamples_1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_main_conv_conv_weight, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_weight, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_bias, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_0_main_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_short_conv_conv_weight, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_weight, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_bias, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_0_short_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_final_conv_conv_weight, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_weight, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_bias, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_0_final_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_0_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_main_conv_conv_weight, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_weight, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_bias, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_1_main_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_short_conv_conv_weight, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_weight, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_bias, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_1_short_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_final_conv_conv_weight, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_weight, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_bias, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_running_var, ema_detector_neck_bottom_up_blocks_1_final_conv_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_0_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_1_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_2_conv2_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv1_bn_num_batches_tracked, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_conv_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_weight, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_bias, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_running_mean, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_running_var, ema_detector_neck_bottom_up_blocks_1_blocks_3_conv2_bn_num_batches_tracked, ema_detector_neck_out_convs_0_conv_weight, ema_detector_neck_out_convs_0_bn_weight, ema_detector_neck_out_convs_0_bn_bias, ema_detector_neck_out_convs_0_bn_running_mean, ema_detector_neck_out_convs_0_bn_running_var, ema_detector_neck_out_convs_0_bn_num_batches_tracked, ema_detector_neck_out_convs_1_conv_weight, ema_detector_neck_out_convs_1_bn_weight, ema_detector_neck_out_convs_1_bn_bias, ema_detector_neck_out_convs_1_bn_running_mean, ema_detector_neck_out_convs_1_bn_running_var, ema_detector_neck_out_convs_1_bn_num_batches_tracked, ema_detector_neck_out_convs_2_conv_weight, ema_detector_neck_out_convs_2_bn_weight, ema_detector_neck_out_convs_2_bn_bias, ema_detector_neck_out_convs_2_bn_running_mean, ema_detector_neck_out_convs_2_bn_running_var, ema_detector_neck_out_convs_2_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_0_0_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_0_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_0_1_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_0_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_1_0_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_1_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_1_1_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_1_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_2_0_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_2_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_cls_convs_2_1_conv_weight, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_weight, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_bias, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_running_mean, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_running_var, ema_detector_bbox_head_multi_level_cls_convs_2_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_0_0_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_0_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_0_1_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_0_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_1_0_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_1_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_1_1_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_1_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_2_0_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_2_0_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_reg_convs_2_1_conv_weight, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_weight, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_bias, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_running_mean, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_running_var, ema_detector_bbox_head_multi_level_reg_convs_2_1_bn_num_batches_tracked, ema_detector_bbox_head_multi_level_conv_cls_0_weight, ema_detector_bbox_head_multi_level_conv_cls_0_bias, ema_detector_bbox_head_multi_level_conv_cls_1_weight, ema_detector_bbox_head_multi_level_conv_cls_1_bias, ema_detector_bbox_head_multi_level_conv_cls_2_weight, ema_detector_bbox_head_multi_level_conv_cls_2_bias, ema_detector_bbox_head_multi_level_conv_reg_0_weight, ema_detector_bbox_head_multi_level_conv_reg_0_bias, ema_detector_bbox_head_multi_level_conv_reg_1_weight, ema_detector_bbox_head_multi_level_conv_reg_1_bias, ema_detector_bbox_head_multi_level_conv_reg_2_weight, ema_detector_bbox_head_multi_level_conv_reg_2_bias, ema_detector_bbox_head_multi_level_conv_obj_0_weight, ema_detector_bbox_head_multi_level_conv_obj_0_bias, ema_detector_bbox_head_multi_level_conv_obj_1_weight, ema_detector_bbox_head_multi_level_conv_obj_1_bias, ema_detector_bbox_head_multi_level_conv_obj_2_weight, ema_detector_bbox_head_multi_level_conv_obj_2_bias\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 输入输出视频路径\n",
    "input_video = 'data/mot_people_short.mp4'\n",
    "output = 'outputs/G1_MOT_people_short.mp4'\n",
    "\n",
    "## ByteTrack算法\n",
    "# 指定 config 配置文件 和 模型权重文件，创建模型\n",
    "mot_config = './configs/mot/bytetrack/bytetrack_yolox_x_crowdhuman_mot17-private-half.py'\n",
    "mot_checkpoint = 'https://download.openmmlab.com/mmtracking/mot/bytetrack/bytetrack_yolox_x/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth'\n",
    "# 初始化模型\n",
    "mot_model = init_model(mot_config, mot_checkpoint, device='cuda:0')\n",
    "\n",
    "## Deepsort算法\n",
    "# # 指定 config 配置文件 和 模型权重文件，创建模型\n",
    "# mot_config = './configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'\n",
    "# # 初始化模型\n",
    "# mot_model = init_model(mot_config, device='cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10ecdd9f-0bdf-4acf-a40a-cac3a1c35600",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                                  ] 0/99, elapsed: 0s, ETA:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/environment/miniconda3/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "/environment/miniconda3/lib/python3.7/site-packages/mmdet/models/dense_heads/yolox_head.py:286: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)\n",
      "  scale_factors).unsqueeze(1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 24.9 task/s, elapsed: 4s, ETA:     0s[                                                  ] 0/99, elapsed: 0s, ETA:\n",
      "帧图像保存路径 /tmp/tmp212bi4jw\n",
      "总人数 24\n"
     ]
    }
   ],
   "source": [
    "# 读入待预测视频\n",
    "imgs = mmcv.VideoReader(input_video)\n",
    "\n",
    "prog_bar = mmcv.ProgressBar(len(imgs))\n",
    "out_dir = tempfile.TemporaryDirectory()\n",
    "out_path = out_dir.name\n",
    "\n",
    "# 人数计数\n",
    "num = 0\n",
    "\n",
    "# 记录每一帧的各目标轨迹点坐标\n",
    "trace_coord_video = []\n",
    "\n",
    "# 对视频逐帧处理\n",
    "for frame_id, img in enumerate(imgs):\n",
    "    \n",
    "    #######################\n",
    "    # 运行多目标追踪\n",
    "    #######################\n",
    "    result = inference_mot(mot_model, img, frame_id=frame_id)\n",
    "    \n",
    "    # 获取当前帧目标检测可视化效果，及各目标轨迹点坐标\n",
    "    img, trace_coord_frame = process_result(result)\n",
    "    trace_coord_video.append(trace_coord_frame)\n",
    "    \n",
    "    #######################\n",
    "    # 绘制轨迹点：从第一帧到当前帧 所有目标的轨迹点\n",
    "    #######################\n",
    "    for trace_coord_frame in trace_coord_video: # 遍历每一帧\n",
    "        for trace_coord_obj in trace_coord_frame: # 遍历每一个目标\n",
    "            # 绘制圆，指定圆心坐标和半径，颜色，最后一个参数为线宽，-1表示填充\n",
    "            cv2.circle(img, (trace_coord_obj[1][0], trace_coord_obj[1][1]),5, trace_coord_obj[1][2], -1)\n",
    "    \n",
    "    #######################\n",
    "    #  获取人数\n",
    "    #######################\n",
    "    # 用当前最大的 ID 号作为人数值\n",
    "    if result['track_bboxes'][0][:,0].any(): # 检测出行人\n",
    "        num = max(num, 1+max(result['track_bboxes'][0][:,0]))\n",
    "    else: # 没有检测出行人\n",
    "        num = num\n",
    "    \n",
    "    # 可视化总人数：图片，添加的文字，左上角坐标，字体，字体大小，颜色，字体粗细\n",
    "    img = cv2.putText(img, str(int(num)), (25, 150), cv2.FONT_HERSHEY_SIMPLEX, 5, (0, 0, 255), 3)\n",
    "    # 可视化当前人数\n",
    "    img = cv2.putText(img, str(len(trace_coord_frame)), (25, 350), cv2.FONT_HERSHEY_SIMPLEX, 5, (255, 0, 0), 3)\n",
    "\n",
    "    # 将处理后的该帧画面图像文件，保存至 /tmp 目录下\n",
    "    cv2.imwrite(f'{out_path}/{frame_id:06d}.jpg', img)\n",
    "\n",
    "    # 更新进度条\n",
    "    prog_bar.update()\n",
    "\n",
    "# 由每一帧的图像文件，生成视频\n",
    "mmcv.frames2video(out_path, output, fps=imgs.fps, fourcc='mp4v')\n",
    "out_dir.cleanup()\n",
    "\n",
    "print('帧图像保存路径',out_path)\n",
    "print('总人数', int(num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4eaa105-bcf5-4457-951b-c94ef076768b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trace_coord_video)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ade2c8b8-e90e-47b9-8d6d-0ac3100e404e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5, [641, 857, [219, 105, 86]]],\n",
       " [1, [1685, 208, [86, 200, 219]]],\n",
       " [3, [1016, 316, [86, 219, 158]]],\n",
       " [7, [955, 307, [94, 219, 86]]],\n",
       " [11, [548, 331, [200, 219, 86]]],\n",
       " [20, [957, 960, [131, 86, 219]]],\n",
       " [2, [1555, 123, [158, 86, 219]]],\n",
       " [8, [1056, 182, [86, 219, 158]]],\n",
       " [18, [1679, 1080, [86, 219, 211]]],\n",
       " [16, [1814, 646, [121, 219, 86]]],\n",
       " [19, [1681, 39, [219, 86, 121]]],\n",
       " [14, [1076, 123, [86, 174, 219]]],\n",
       " [10, [1094, 82, [219, 131, 86]]],\n",
       " [12, [1359, 18, [219, 211, 86]]]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 第一帧的目标ID、轨迹坐标、颜色\n",
    "trace_coord_video[70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b04bf0-d821-4024-9f26-ed8db8249e1b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}