{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import torch\n",
    "from stream_pipeline import stream_pipeline\n",
    "from Qwen_agent import get_best_image, qwen_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pair_path = '/home/ieemoo0337/projects/datasets/dataPair.json'\n",
    "with open(pair_path, 'r') as file:\n",
    "    data = json.load(file)\n",
    "\n",
    "pos_pair = data['positivePair']\n",
    "neg_pair = data['negativePair']\n",
    "mix_pair = data['mixPair']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (60/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (61/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    }
   ],
   "source": [
    "sub_imgs = stream_pipeline(pos_pair[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(198, 977)\n",
      "(291, 1035)\n",
      "(457, 367)\n",
      "(396, 319)\n",
      "(361, 270)\n",
      "(331, 242)\n",
      "(335, 232)\n",
      "(348, 252)\n"
     ]
    },
    {
     "data": {
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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=291x1035>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for img in sub_imgs:\n",
    "    print(img.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=457x367>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_imgs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'这张图片展示了一个包装盒，上面有一个巧克力蛋糕的图片。包装盒上有一些中文文字，可能是产品的名称和描述。包装盒的设计简洁，主要颜色是白色和蓝色。'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = sub_imgs[7]\n",
    "message = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\n",
    "                \"type\": \"image\",\n",
    "                \"image\": img,\n",
    "                \"image_id\": 0,\n",
    "                \"min_pixels\": 224 * 224,\n",
    "                \"max_pixels\": 800 * 800,\n",
    "            },\n",
    "            {\n",
    "                \"type\": \"text\",\n",
    "                \"text\": \"描述这张图片\"\n",
    "            }\n",
    "        ],\n",
    "    }\n",
    "]\n",
    "result = qwen_prompt(img, message)\n",
    "torch.cuda.empty_cache()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "best_img = get_best_image(sub_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=361x270>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'盒'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_pair[0]['单位']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'True'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ast\n",
    "\n",
    "message = [\n",
    "    # {\n",
    "    #     \"role\": \"system\",\n",
    "    #     \"content\": \"现在你是一个在超市中的商品单位识别人员，庆根据图片中商品所提取出来的信息，输出一个python dict. \\\n",
    "    #         \\{ \\\n",
    "    #             \\\"商品名称\\\": 图片中商品的名称, \\\n",
    "    #             \\\"商品类别\\\": 商品的种类, \\\n",
    "    #             \\\"商品单位\\\": 商品的单位有[张，盒，提，个，份，支，杯，桶，条，瓶，袋，箱，罐，组，散装], \\\n",
    "    #         \\} \"\n",
    "    # },\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\n",
    "                \"type\": \"image\",\n",
    "                \"min_pixels\": 224 * 224,\n",
    "                \"max_pixels\": 800 * 800,\n",
    "            },\n",
    "            {\n",
    "                \"type\": \"text\",\n",
    "                \"text\": \"判断图片中的商品是否是\\\"康师傅巧克力妙芙蛋糕\\\", 回答True或False。\"\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "]\n",
    "result = qwen_prompt(best_img, message)\n",
    "result\n",
    "# product_dict = ast.literal_eval(result.strip('```python\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (60/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (61/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/92) /home/ieemoo0337/projects/datasets/constrast_pair/6931744232836/6931744232836_20241016-122508_back_returnGood_5478c9a49bfc_1140_18358205530.mp4: 640x512 9.1ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/67) /home/ieemoo0337/projects/datasets/constrast_pair/6901844706117/6901844706117_20241018-171624_back_returnGood_5478c9a48c02_5_13586533739.mp4: 640x512 7.8ms\n",
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      "video 1/1 (3/67) /home/ieemoo0337/projects/datasets/constrast_pair/6901844706117/6901844706117_20241018-171624_back_returnGood_5478c9a48c02_5_13586533739.mp4: 640x512 5.4ms\n",
      "video 1/1 (4/67) /home/ieemoo0337/projects/datasets/constrast_pair/6901844706117/6901844706117_20241018-171624_back_returnGood_5478c9a48c02_5_13586533739.mp4: 640x512 5.4ms\n",
      "video 1/1 (5/67) /home/ieemoo0337/projects/datasets/constrast_pair/6901844706117/6901844706117_20241018-171624_back_returnGood_5478c9a48c02_5_13586533739.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/120) /home/ieemoo0337/projects/datasets/constrast_pair/4710199085707/4710199085707_20241018-145453_back_addGood_5478c9a48b7e_10_18050251181.mp4: 640x512 6.6ms\n",
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      "video 1/1 (3/120) /home/ieemoo0337/projects/datasets/constrast_pair/4710199085707/4710199085707_20241018-145453_back_addGood_5478c9a48b7e_10_18050251181.mp4: 640x512 5.4ms\n",
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      "video 1/1 (6/120) /home/ieemoo0337/projects/datasets/constrast_pair/4710199085707/4710199085707_20241018-145453_back_addGood_5478c9a48b7e_10_18050251181.mp4: 640x512 5.9ms\n",
      "video 1/1 (7/120) /home/ieemoo0337/projects/datasets/constrast_pair/4710199085707/4710199085707_20241018-145453_back_addGood_5478c9a48b7e_10_18050251181.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (8/120) /home/ieemoo0337/projects/datasets/constrast_pair/4710199085707/4710199085707_20241018-145453_back_addGood_5478c9a48b7e_10_18050251181.mp4: 640x512 5.7ms\n",
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      "video 1/1 (119/120) /home/ieemoo0337/projects/datasets/constrast_pair/4710199085707/4710199085707_20241018-145453_back_addGood_5478c9a48b7e_10_18050251181.mp4: 640x512 5.4ms\n",
      "video 1/1 (120/120) /home/ieemoo0337/projects/datasets/constrast_pair/4710199085707/4710199085707_20241018-145453_back_addGood_5478c9a48b7e_10_18050251181.mp4: 640x512 5.4ms\n",
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (5/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (6/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (62/64) /home/ieemoo0337/projects/datasets/constrast_pair/6921168558032/6921168558032_20241020-141332_back_returnGood_5478c9a53bd4_545_14785332147.mp4: 640x512 5.2ms\n",
      "video 1/1 (63/64) /home/ieemoo0337/projects/datasets/constrast_pair/6921168558032/6921168558032_20241020-141332_back_returnGood_5478c9a53bd4_545_14785332147.mp4: 640x512 5.2ms\n",
      "video 1/1 (64/64) /home/ieemoo0337/projects/datasets/constrast_pair/6921168558032/6921168558032_20241020-141332_back_returnGood_5478c9a53bd4_545_14785332147.mp4: 640x512 5.2ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 6.2ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (116/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 5.1ms\n",
      "video 1/1 (117/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 5.1ms\n",
      "video 1/1 (118/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 5.1ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 6.2ms\n",
      "video 1/1 (2/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 5.1ms\n",
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      "video 1/1 (4/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (7/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (50/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (51/51) /home/ieemoo0337/projects/datasets/constrast_pair/6970870270947/6970870270947_20241019-165040_back_addGood_5478c9a49bfc_325_13773168720.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (2/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.2ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (78/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.1ms\n",
      "video 1/1 (79/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.1ms\n",
      "video 1/1 (80/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.1ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (80/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.1ms\n",
      "video 1/1 (81/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.0ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 6.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 6.5ms\n",
      "video 1/1 (2/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.2ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (43/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (44/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (45/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (46/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 6.2ms\n",
      "video 1/1 (2/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.1ms\n",
      "video 1/1 (5/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.2ms\n",
      "video 1/1 (6/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.2ms\n",
      "video 1/1 (7/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/48) /home/ieemoo0337/projects/datasets/constrast_pair/6924743924185/6924743924185_20241019-162150_back_addGood_5478c9a53bd4_10_13799009402.mp4: 640x512 7.2ms\n",
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      "video 1/1 (7/48) /home/ieemoo0337/projects/datasets/constrast_pair/6924743924185/6924743924185_20241019-162150_back_addGood_5478c9a53bd4_10_13799009402.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 6.0ms\n",
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      "video 1/1 (6/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (71/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.4ms\n",
      "video 1/1 (72/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (73/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
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     "output_type": "stream",
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      "video 1/1 (112/112) /home/ieemoo0337/projects/datasets/constrast_pair/6901209201219/6901209201219_20241016-141635_back_returnGood_5478c9a49bfc_865_18358205530.mp4: 640x512 5.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 6.1ms\n",
      "video 1/1 (2/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (75/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (76/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 6.1ms\n",
      "video 1/1 (77/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 6.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (8/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 6.0ms\n",
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      "video 1/1 (59/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 6.0ms\n",
      "video 1/1 (60/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 6.0ms\n",
      "video 1/1 (61/61) /home/ieemoo0337/projects/datasets/constrast_pair/6940211889602/6940211889602_20241014-184500_back_addGood_5478c9a53bf4_5_13799009402.mp4: 640x512 6.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/78) /home/ieemoo0337/projects/datasets/constrast_pair/8000500032237/8000500032237_20241020-165459_back_addGood_5478c9a53bca_465_18996891184.mp4: 640x512 8.5ms\n",
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      "video 1/1 (3/78) /home/ieemoo0337/projects/datasets/constrast_pair/8000500032237/8000500032237_20241020-165459_back_addGood_5478c9a53bca_465_18996891184.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/78) /home/ieemoo0337/projects/datasets/constrast_pair/8000500032237/8000500032237_20241020-165459_back_addGood_5478c9a53bca_465_18996891184.mp4: 640x512 5.9ms\n",
      "video 1/1 (5/78) /home/ieemoo0337/projects/datasets/constrast_pair/8000500032237/8000500032237_20241020-165459_back_addGood_5478c9a53bca_465_18996891184.mp4: 640x512 5.9ms\n",
      "video 1/1 (6/78) /home/ieemoo0337/projects/datasets/constrast_pair/8000500032237/8000500032237_20241020-165459_back_addGood_5478c9a53bca_465_18996891184.mp4: 640x512 6.4ms\n",
      "video 1/1 (7/78) /home/ieemoo0337/projects/datasets/constrast_pair/8000500032237/8000500032237_20241020-165459_back_addGood_5478c9a53bca_465_18996891184.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 6.3ms\n",
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      "video 1/1 (3/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (61/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (62/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (63/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.9ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (127/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.9ms\n",
      "video 1/1 (128/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.9ms\n",
      "video 1/1 (129/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (130/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 6.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 7.1ms\n",
      "video 1/1 (2/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.6ms\n",
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      "video 1/1 (9/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (10/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.7ms\n",
      "video 1/1 (11/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.7ms\n",
      "video 1/1 (12/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.7ms\n",
      "video 1/1 (13/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.7ms\n",
      "video 1/1 (14/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (15/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 7.1ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 6.1ms\n",
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      "video 1/1 (4/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 6.4ms\n",
      "video 1/1 (5/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (6/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (111/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (58/60) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480003/6975682480003_20241015-165404_back_addGood_5478c9a48c3e_1315_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (59/60) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480003/6975682480003_20241015-165404_back_addGood_5478c9a48c3e_1315_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (60/60) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480003/6975682480003_20241015-165404_back_addGood_5478c9a48c3e_1315_18133699824.mp4: 640x512 5.5ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 6.6ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (52/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/68) /home/ieemoo0337/projects/datasets/constrast_pair/6921168500970/6921168500970_20241018-143649_back_returnGood_5478c9a4eb2c_490_18969520488.mp4: 640x512 7.2ms\n",
      "video 1/1 (2/68) /home/ieemoo0337/projects/datasets/constrast_pair/6921168500970/6921168500970_20241018-143649_back_returnGood_5478c9a4eb2c_490_18969520488.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/68) /home/ieemoo0337/projects/datasets/constrast_pair/6921168500970/6921168500970_20241018-143649_back_returnGood_5478c9a4eb2c_490_18969520488.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/68) /home/ieemoo0337/projects/datasets/constrast_pair/6921168500970/6921168500970_20241018-143649_back_returnGood_5478c9a4eb2c_490_18969520488.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/68) /home/ieemoo0337/projects/datasets/constrast_pair/6921168500970/6921168500970_20241018-143649_back_returnGood_5478c9a4eb2c_490_18969520488.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (68/68) /home/ieemoo0337/projects/datasets/constrast_pair/6921168500970/6921168500970_20241018-143649_back_returnGood_5478c9a4eb2c_490_18969520488.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.3ms\n",
      "video 1/1 (2/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.0ms\n",
      "video 1/1 (4/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (5/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.4ms\n",
      "video 1/1 (6/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.1ms\n",
      "video 1/1 (7/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.3ms\n",
      "video 1/1 (8/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.0ms\n",
      "video 1/1 (9/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.2ms\n",
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      "video 1/1 (47/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.2ms\n",
      "video 1/1 (48/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.9ms\n",
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      "video 1/1 (50/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.3ms\n",
      "video 1/1 (51/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.9ms\n",
      "video 1/1 (52/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.8ms\n",
      "video 1/1 (6/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (7/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 6.1ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.0ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (49/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (50/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.1ms\n",
      "video 1/1 (51/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (52/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.9ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 7.3ms\n",
      "video 1/1 (2/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (5/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 6.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 7.2ms\n",
      "video 1/1 (2/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.9ms\n",
      "video 1/1 (5/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (3/73) /home/ieemoo0337/projects/datasets/constrast_pair/6901010117440/6901010117440_20241018-150145_back_addGood_5478c9a4eb2c_605_18969520488.mp4: 640x512 5.9ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 7.3ms\n",
      "video 1/1 (2/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (42/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.2ms\n",
      "video 1/1 (43/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (44/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (45/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (46/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (47/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (48/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/85) /home/ieemoo0337/projects/datasets/constrast_pair/6906303004469/6906303004469_20241018-173731_back_returnGood_5478c9a48c02_25_13586533739.mp4: 640x512 6.8ms\n",
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      "video 1/1 (3/85) /home/ieemoo0337/projects/datasets/constrast_pair/6906303004469/6906303004469_20241018-173731_back_returnGood_5478c9a48c02_25_13586533739.mp4: 640x512 5.8ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 108.2ms\n",
      "video 1/1 (2/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (80/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 7.3ms\n",
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      "video 1/1 (3/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (6/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (70/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.8ms\n",
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      "video 1/1 (72/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 110.9ms\n",
      "video 1/1 (2/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (5/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (7/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.7ms\n",
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      "video 1/1 (70/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (71/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 7.7ms\n",
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      "video 1/1 (3/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.9ms\n",
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      "video 1/1 (5/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 6.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (43/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.7ms\n",
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      "video 1/1 (45/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (46/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (47/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (48/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (49/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (50/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (51/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (52/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (53/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 6\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 6.9ms\n",
      "video 1/1 (2/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (3/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (64/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 6.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 96.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (37/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (38/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (39/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 6.1ms\n",
      "video 1/1 (40/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.9ms\n",
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 7.7ms\n",
      "video 1/1 (2/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.8ms\n",
      "video 1/1 (5/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/76) /home/ieemoo0337/projects/datasets/constrast_pair/6970690474501/6970690474501_20241019-142915_back_returnGood_5478c9a48c3e_595_15027299265.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/76) /home/ieemoo0337/projects/datasets/constrast_pair/6970690474501/6970690474501_20241019-142915_back_returnGood_5478c9a48c3e_595_15027299265.mp4: 640x512 5.4ms\n",
      "video 1/1 (3/76) /home/ieemoo0337/projects/datasets/constrast_pair/6970690474501/6970690474501_20241019-142915_back_returnGood_5478c9a48c3e_595_15027299265.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/76) /home/ieemoo0337/projects/datasets/constrast_pair/6970690474501/6970690474501_20241019-142915_back_returnGood_5478c9a48c3e_595_15027299265.mp4: 640x512 5.4ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (2/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.2ms\n",
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      "video 1/1 (4/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (2/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.3ms\n",
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      "video 1/1 (4/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.2ms\n",
      "video 1/1 (6/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.5ms\n",
      "video 1/1 (7/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (217/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (218/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (219/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.6ms\n",
      "video 1/1 (220/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.3ms\n",
      "video 1/1 (221/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.3ms\n",
      "video 1/1 (222/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (72/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.2ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (4/85) /home/ieemoo0337/projects/datasets/constrast_pair/6925303796716/6925303796716_20241015-151222_back_addGood_5478c9a4db2c_720_13586533739.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/58) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631326/6973095631326_20241018-144229_back_addGood_5478c9a51ba6_270_18358205530.mp4: 640x512 6.5ms\n",
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      "video 1/1 (4/58) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631326/6973095631326_20241018-144229_back_addGood_5478c9a51ba6_270_18358205530.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (56/62) /home/ieemoo0337/projects/datasets/constrast_pair/8711000254769/8711000254769_20241017-163132_back_addGood_5478c9a48c3e_380_13586533739.mp4: 640x512 5.2ms\n",
      "video 1/1 (57/62) /home/ieemoo0337/projects/datasets/constrast_pair/8711000254769/8711000254769_20241017-163132_back_addGood_5478c9a48c3e_380_13586533739.mp4: 640x512 5.2ms\n",
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      "video 1/1 (59/62) /home/ieemoo0337/projects/datasets/constrast_pair/8711000254769/8711000254769_20241017-163132_back_addGood_5478c9a48c3e_380_13586533739.mp4: 640x512 5.2ms\n",
      "video 1/1 (60/62) /home/ieemoo0337/projects/datasets/constrast_pair/8711000254769/8711000254769_20241017-163132_back_addGood_5478c9a48c3e_380_13586533739.mp4: 640x512 5.2ms\n",
      "video 1/1 (61/62) /home/ieemoo0337/projects/datasets/constrast_pair/8711000254769/8711000254769_20241017-163132_back_addGood_5478c9a48c3e_380_13586533739.mp4: 640x512 5.1ms\n",
      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/8711000254769/8711000254769_20241017-163132_back_addGood_5478c9a48c3e_380_13586533739.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (41/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (42/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (43/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (44/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (45/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (46/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (47/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (48/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (6/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (63/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.5ms\n",
      "video 1/1 (64/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.5ms\n",
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      "video 1/1 (71/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.6ms\n",
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      "video 1/1 (75/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.9ms\n",
      "video 1/1 (76/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.6ms\n",
      "video 1/1 (77/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.6ms\n",
      "video 1/1 (78/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.6ms\n",
      "video 1/1 (79/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.6ms\n",
      "video 1/1 (80/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.6ms\n",
      "video 1/1 (81/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (3/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (5/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (6/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (49/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (50/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (51/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 6.1ms\n",
      "video 1/1 (2/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.3ms\n",
      "video 1/1 (5/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (2/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 5.3ms\n",
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      "video 1/1 (4/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 5.4ms\n",
      "video 1/1 (5/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 5.3ms\n",
      "video 1/1 (7/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (69/69) /home/ieemoo0337/projects/datasets/constrast_pair/3073781189872/3073781189872_20241017-163505_back_returnGood_5478c9a49bfc_95_13773168720.mp4: 640x512 5.2ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/60) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016567/6928804016567_20241016-184458_back_returnGood_5478c9a48b7e_535_13586533739.mp4: 640x512 86.9ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  {'index': 1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
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      "video 1/1 (120/120) /home/ieemoo0337/projects/datasets/constrast_pair/6976558260187/6976558260187_20241016-121605_back_addGood_5478c9a49bfc_455_18358205530.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.2ms\n",
      "video 1/1 (2/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (7/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (48/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (49/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (50/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (2/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (5/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (6/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (7/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.3ms\n",
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      "video 1/1 (59/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (60/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (61/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/56) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570049/6970618570049_20241014-183211_back_returnGood_5478c9a53bf4_630_13799009402.mp4: 640x512 6.5ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 6.5ms\n",
      "video 1/1 (2/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.3ms\n",
      "video 1/1 (3/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.3ms\n",
      "video 1/1 (5/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.2ms\n",
      "video 1/1 (7/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (70/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.3ms\n",
      "video 1/1 (71/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.3ms\n",
      "video 1/1 (72/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.2ms\n",
      "video 1/1 (73/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.2ms\n",
      "video 1/1 (74/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.2ms\n",
      "video 1/1 (75/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.2ms\n",
      "video 1/1 (76/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.1ms\n",
      "video 1/1 (3/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.1ms\n",
      "video 1/1 (4/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (7/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (8/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (9/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (10/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (11/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.1ms\n",
      "video 1/1 (12/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.2ms\n",
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      "video 1/1 (14/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.2ms\n",
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      "video 1/1 (16/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.2ms\n",
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      "video 1/1 (23/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.3ms\n",
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      "video 1/1 (25/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (26/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (27/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (28/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (29/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.4ms\n",
      "video 1/1 (3/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.5ms\n",
      "video 1/1 (4/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (7/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (5/76) /home/ieemoo0337/projects/datasets/constrast_pair/6920999701730/6920999701730_20241019-185328_back_returnGood_5478c9a48c02_130_18996891184.mp4: 640x512 5.2ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 5.2ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (69/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.2ms\n",
      "video 1/1 (70/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.2ms\n",
      "video 1/1 (71/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (72/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.2ms\n",
      "video 1/1 (73/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (74/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (75/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 6.2ms\n",
      "video 1/1 (2/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (7/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (8/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (9/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (10/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (11/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (12/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (13/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (14/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (15/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (16/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (17/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
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      "video 1/1 (21/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.2ms\n",
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      "video 1/1 (24/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.1ms\n",
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      "video 1/1 (27/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
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      "video 1/1 (29/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (30/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (31/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (32/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (33/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (34/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (35/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (36/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 6.9ms\n",
      "video 1/1 (2/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.1ms\n",
      "video 1/1 (4/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.1ms\n",
      "video 1/1 (5/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (6/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (7/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (8/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (9/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (10/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (11/56) /home/ieemoo0337/projects/datasets/constrast_pair/6925508233047/6925508233047_20241020-164232_back_returnGood_5478c9a49bfc_10_13799009402.mp4: 640x512 5.2ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (5/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  {'index': 6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
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     "output_type": "stream",
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      "video 1/1 (69/70) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938572/6901668938572_20241016-170712_back_addGood_5478c9a48b8c_220_18133699824.mp4: 640x512 5.2ms\n",
      "video 1/1 (70/70) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938572/6901668938572_20241016-170712_back_addGood_5478c9a48b8c_220_18133699824.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907868581389/6907868581389_20241020-111448_back_returnGood_5478c9a48b7e_105_15027299265.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907868581389/6907868581389_20241020-111448_back_returnGood_5478c9a48b7e_105_15027299265.mp4: 640x512 5.3ms\n",
      "video 1/1 (3/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907868581389/6907868581389_20241020-111448_back_returnGood_5478c9a48b7e_105_15027299265.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907868581389/6907868581389_20241020-111448_back_returnGood_5478c9a48b7e_105_15027299265.mp4: 640x512 5.4ms\n",
      "video 1/1 (5/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907868581389/6907868581389_20241020-111448_back_returnGood_5478c9a48b7e_105_15027299265.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907868581389/6907868581389_20241020-111448_back_returnGood_5478c9a48b7e_105_15027299265.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  {'index': 0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (3/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.5ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (89/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (90/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (91/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.4ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (4/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (260/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 5.3ms\n",
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      "video 1/1 (262/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 5.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (4/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (6/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (60/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (61/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.4ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (4/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.2ms\n",
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      "video 1/1 (7/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (62/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (63/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.3ms\n",
      "video 1/1 (64/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.2ms\n",
      "video 1/1 (65/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (66/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 5.2ms\n",
      "video 1/1 (3/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (4/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 6.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (51/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (52/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (53/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (54/54) /home/ieemoo0337/projects/datasets/constrast_pair/4901306076354/4901306076354_20241019-153812_back_addGood_5478c9a49bfc_220_13773168720.mp4: 640x512 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 6.7ms\n",
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      "video 1/1 (3/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.4ms\n",
      "video 1/1 (5/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (104/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.3ms\n",
      "video 1/1 (105/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.2ms\n",
      "video 1/1 (106/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.4ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (3/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (5/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (6/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.2ms\n",
      "video 1/1 (7/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (70/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (71/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (72/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.3ms\n",
      "video 1/1 (73/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (74/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (75/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.5ms\n",
      "video 1/1 (76/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (77/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (78/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.4ms\n",
      "video 1/1 (79/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (80/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.2ms\n",
      "video 1/1 (81/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    'index': 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 103.3ms\n",
      "video 1/1 (2/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (3/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (4/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (5/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.2ms\n",
      "video 1/1 (6/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (7/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (8/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (9/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.5ms\n",
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      "video 1/1 (48/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.4ms\n",
      "video 1/1 (49/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (50/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (51/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.5ms\n",
      "video 1/1 (52/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.4ms\n",
      "video 1/1 (53/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.3ms\n",
      "video 1/1 (54/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/154) /home/ieemoo0337/projects/datasets/constrast_pair/6972373580813/6972373580813_20241015-103853_back_addGood_5478c9a48c3e_100_18133699824.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/154) /home/ieemoo0337/projects/datasets/constrast_pair/6972373580813/6972373580813_20241015-103853_back_addGood_5478c9a48c3e_100_18133699824.mp4: 640x512 5.2ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 6.0ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (3/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (5/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (6/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (66/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (67/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (68/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "true_list = []\n",
    "false_list = []\n",
    "for ele in pos_pair:\n",
    "    sub_imgs = stream_pipeline(ele)\n",
    "    torch.cuda.empty_cache()\n",
    "    if not sub_imgs:\n",
    "        continue\n",
    "    bestimg = get_best_image(sub_imgs)\n",
    "    product_name = ele['商品名称']\n",
    "    message = [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\n",
    "                    \"type\": \"image\",\n",
    "                    \"min_pixels\": 224 * 224,\n",
    "                    \"max_pixels\": 800 * 800,\n",
    "                },\n",
    "                {\n",
    "                    \"type\": \"text\",\n",
    "                    \"text\": f\"判断图片中的商品是否是'{product_name}', 回答True或False。\"\n",
    "                }\n",
    "            ]\n",
    "        }\n",
    "    ]\n",
    "    result = qwen_prompt(bestimg, message)\n",
    "    if result == 'True':\n",
    "        true_list.append(ele)\n",
    "    else:\n",
    "        false_list.append(ele)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 63)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(true_list), len(false_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "true2_list = []\n",
    "false2_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 9.2ms\n",
      "video 1/1 (2/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 6.9ms\n",
      "video 1/1 (3/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (60/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (61/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920731700205/6920731700205_20241020-142002_back_addGood_5478c9a48b8c_270_18358205530.mp4: 640x512 5.7ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    'index': 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (2/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924743915770/6924743915770_20241017-184242_back_addGood_5478c9a51ba6_150_18358205530.mp4: 640x512 5.6ms\n",
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      "video 1/1 (4/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924743915770/6924743915770_20241017-184242_back_addGood_5478c9a51ba6_150_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924743915770/6924743915770_20241017-184242_back_addGood_5478c9a51ba6_150_18358205530.mp4: 640x512 5.8ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (69/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924743915770/6924743915770_20241017-184242_back_addGood_5478c9a51ba6_150_18358205530.mp4: 640x512 5.5ms\n",
      "video 1/1 (70/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924743915770/6924743915770_20241017-184242_back_addGood_5478c9a51ba6_150_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (71/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924743915770/6924743915770_20241017-184242_back_addGood_5478c9a51ba6_150_18358205530.mp4: 640x512 5.7ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 6.5ms\n",
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      "video 1/1 (4/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (6/59) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806616/6970768806616_20241019-173235_back_returnGood_5478c9a49bfc_5_13773168720.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 5.6ms\n",
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      "video 1/1 (4/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/118) /home/ieemoo0337/projects/datasets/constrast_pair/6926603300061/6926603300061_20241018-135800_back_returnGood_5478c9a49bfc_390_13867273486.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 11\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (64/64) /home/ieemoo0337/projects/datasets/constrast_pair/6921168558032/6921168558032_20241020-141332_back_returnGood_5478c9a53bd4_545_14785332147.mp4: 640x512 5.7ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (4/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 6.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (78/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (79/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (80/80) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040168/6932571040168_20241016-112710_back_returnGood_5478c9a49bfc_340_18358205530.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 6.1ms\n",
      "video 1/1 (2/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (78/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (79/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (80/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (81/81) /home/ieemoo0337/projects/datasets/constrast_pair/6973961430909/6973961430909_20241014-140503_back_returnGood_5478c9a53bf4_50_13799009402.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/61) /home/ieemoo0337/projects/datasets/constrast_pair/6928804010220/6928804010220_20241018-175459_back_addGood_5478c9a51ba6_530_18358205530.mp4: 640x512 6.9ms\n",
      "video 1/1 (2/61) /home/ieemoo0337/projects/datasets/constrast_pair/6928804010220/6928804010220_20241018-175459_back_addGood_5478c9a51ba6_530_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/61) /home/ieemoo0337/projects/datasets/constrast_pair/6928804010220/6928804010220_20241018-175459_back_addGood_5478c9a51ba6_530_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/61) /home/ieemoo0337/projects/datasets/constrast_pair/6928804010220/6928804010220_20241018-175459_back_addGood_5478c9a51ba6_530_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/61) /home/ieemoo0337/projects/datasets/constrast_pair/6928804010220/6928804010220_20241018-175459_back_addGood_5478c9a51ba6_530_18358205530.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (61/61) /home/ieemoo0337/projects/datasets/constrast_pair/6928804010220/6928804010220_20241018-175459_back_addGood_5478c9a51ba6_530_18358205530.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 6.0ms\n",
      "video 1/1 (5/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.8ms\n",
      "video 1/1 (6/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (62/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.9ms\n",
      "video 1/1 (63/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (64/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 7.3ms\n",
      "video 1/1 (2/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.9ms\n",
      "video 1/1 (4/68) /home/ieemoo0337/projects/datasets/constrast_pair/6934660516783/6934660516783_20241019-152608_back_addGood_5478c9a48c3e_265_15027299265.mp4: 640x512 5.6ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 6.6ms\n",
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      "video 1/1 (3/46) /home/ieemoo0337/projects/datasets/constrast_pair/6914973105379/6914973105379_20241019-181333_back_returnGood_5478c9a53bd4_60_13799009402.mp4: 640x512 5.6ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 6.9ms\n",
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      "video 1/1 (3/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (97/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    'index': 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (4/48) /home/ieemoo0337/projects/datasets/constrast_pair/6924743924185/6924743924185_20241019-162150_back_addGood_5478c9a53bd4_10_13799009402.mp4: 640x512 5.6ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (2/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (6/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (70/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 6.0ms\n",
      "video 1/1 (71/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (72/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (73/73) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801661/6924810801661_20241016-142714_back_addGood_5478c9a49bfc_380_18358205530.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 11\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/78) /home/ieemoo0337/projects/datasets/constrast_pair/6924810822376/6924810822376_20241016-143059_back_addGood_5478c9a49bfc_1060_18358205530.mp4: 640x512 7.7ms\n",
      "video 1/1 (2/78) /home/ieemoo0337/projects/datasets/constrast_pair/6924810822376/6924810822376_20241016-143059_back_addGood_5478c9a49bfc_1060_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/78) /home/ieemoo0337/projects/datasets/constrast_pair/6924810822376/6924810822376_20241016-143059_back_addGood_5478c9a49bfc_1060_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/78) /home/ieemoo0337/projects/datasets/constrast_pair/6924810822376/6924810822376_20241016-143059_back_addGood_5478c9a49bfc_1060_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/78) /home/ieemoo0337/projects/datasets/constrast_pair/6924810822376/6924810822376_20241016-143059_back_addGood_5478c9a49bfc_1060_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/78) /home/ieemoo0337/projects/datasets/constrast_pair/6924810822376/6924810822376_20241016-143059_back_addGood_5478c9a49bfc_1060_18358205530.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (78/78) /home/ieemoo0337/projects/datasets/constrast_pair/6924810822376/6924810822376_20241016-143059_back_addGood_5478c9a49bfc_1060_18358205530.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (77/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/112) /home/ieemoo0337/projects/datasets/constrast_pair/6901209201219/6901209201219_20241016-141635_back_returnGood_5478c9a49bfc_865_18358205530.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/112) /home/ieemoo0337/projects/datasets/constrast_pair/6901209201219/6901209201219_20241016-141635_back_returnGood_5478c9a49bfc_865_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/112) /home/ieemoo0337/projects/datasets/constrast_pair/6901209201219/6901209201219_20241016-141635_back_returnGood_5478c9a49bfc_865_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/112) /home/ieemoo0337/projects/datasets/constrast_pair/6901209201219/6901209201219_20241016-141635_back_returnGood_5478c9a49bfc_865_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/112) /home/ieemoo0337/projects/datasets/constrast_pair/6901209201219/6901209201219_20241016-141635_back_returnGood_5478c9a49bfc_865_18358205530.mp4: 640x512 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 10\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 6.2ms\n",
      "video 1/1 (2/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (130/130) /home/ieemoo0337/projects/datasets/constrast_pair/6902083881085/6902083881085_20241014-133631_back_returnGood_5478c9a49bfc_950_18133699824.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 6.6ms\n",
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      "video 1/1 (4/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (62/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (63/63) /home/ieemoo0337/projects/datasets/constrast_pair/3073781202823/3073781202823_20241017-163642_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.7ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 7.4ms\n",
      "video 1/1 (2/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (3/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (4/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.9ms\n",
      "video 1/1 (5/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.9ms\n",
      "video 1/1 (6/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (7/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (8/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (9/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.9ms\n",
      "video 1/1 (10/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (11/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n",
      "video 1/1 (12/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 6.1ms\n",
      "video 1/1 (13/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.9ms\n",
      "video 1/1 (14/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (15/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 (no detections), 6.1ms\n",
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      "video 1/1 (112/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.6ms\n",
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      "video 1/1 (114/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (115/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (116/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (117/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (118/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (119/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.6ms\n",
      "video 1/1 (120/120) /home/ieemoo0337/projects/datasets/constrast_pair/6922477400333/6922477400333_20241020-100655_back_addGood_5478c9a48c3e_135_18058629568.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.5ms\n",
      "video 1/1 (6/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.5ms\n",
      "video 1/1 (7/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (56/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (57/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (58/58) /home/ieemoo0337/projects/datasets/constrast_pair/6977295239993/6977295239993_20241017-160638_back_addGood_5478c9a48c3e_5_13586533739.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 10\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/91) /home/ieemoo0337/projects/datasets/constrast_pair/6901668935946/6901668935946_20241019-160504_back_returnGood_5478c9a4db2c_225_13867273486.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/91) /home/ieemoo0337/projects/datasets/constrast_pair/6901668935946/6901668935946_20241019-160504_back_returnGood_5478c9a4db2c_225_13867273486.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/91) /home/ieemoo0337/projects/datasets/constrast_pair/6901668935946/6901668935946_20241019-160504_back_returnGood_5478c9a4db2c_225_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/91) /home/ieemoo0337/projects/datasets/constrast_pair/6901668935946/6901668935946_20241019-160504_back_returnGood_5478c9a4db2c_225_13867273486.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/91) /home/ieemoo0337/projects/datasets/constrast_pair/6901668935946/6901668935946_20241019-160504_back_returnGood_5478c9a4db2c_225_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/91) /home/ieemoo0337/projects/datasets/constrast_pair/6901668935946/6901668935946_20241019-160504_back_returnGood_5478c9a4db2c_225_13867273486.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 7.2ms\n",
      "video 1/1 (2/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 6.2ms\n",
      "video 1/1 (4/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (5/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 6.1ms\n",
      "video 1/1 (6/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (111/111) /home/ieemoo0337/projects/datasets/constrast_pair/6970356165774/6970356165774_20241015-102226_back_returnGood_5478c9a48c3e_85_18133699824.mp4: 640x512 5.9ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 13\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 6.6ms\n",
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      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 6.0ms\n",
      "video 1/1 (5/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (43/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (44/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (45/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (46/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.9ms\n",
      "video 1/1 (47/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (48/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (49/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 6.0ms\n",
      "video 1/1 (50/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (51/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (52/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 8.5ms\n",
      "video 1/1 (2/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.9ms\n",
      "video 1/1 (4/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.9ms\n",
      "video 1/1 (5/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (7/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 6.2ms\n",
      "video 1/1 (8/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (9/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.7ms\n",
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      "video 1/1 (46/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (47/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 5.9ms\n",
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      "video 1/1 (49/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 6.2ms\n",
      "video 1/1 (50/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 6.1ms\n",
      "video 1/1 (51/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (52/52) /home/ieemoo0337/projects/datasets/constrast_pair/6975682483066/6975682483066_20241015-165611_back_returnGood_5478c9a48c3e_5_18133699824.mp4: 640x512 6.2ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    'index': 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 7.3ms\n",
      "video 1/1 (2/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 6.2ms\n",
      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 6.3ms\n",
      "video 1/1 (4/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/52) /home/ieemoo0337/projects/datasets/constrast_pair/8859468700319/8859468700319_20241017-140211_back_returnGood_5478c9a48c3e_650_13586533739.mp4: 640x512 5.8ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 6.2ms\n",
      "video 1/1 (2/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (61/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (62/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (63/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (64/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.6ms\n",
      "video 1/1 (65/65) /home/ieemoo0337/projects/datasets/constrast_pair/6938737204030/6938737204030_20241015-140944_back_addGood_5478c9a4db2c_550_13586533739.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 6\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 7.2ms\n",
      "video 1/1 (2/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (5/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.1ms\n",
      "video 1/1 (6/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (7/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 6.1ms\n",
      "video 1/1 (8/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (9/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.8ms\n",
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      "video 1/1 (47/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.8ms\n",
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      "video 1/1 (49/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (50/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (51/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (52/52) /home/ieemoo0337/projects/datasets/constrast_pair/6901668006042/6901668006042_20241020-133755_back_returnGood_5478c9a48b8c_220_18358205530.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 6.9ms\n",
      "video 1/1 (2/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 6.0ms\n",
      "video 1/1 (5/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (6/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (7/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (8/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 6.3ms\n",
      "video 1/1 (9/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (10/66) /home/ieemoo0337/projects/datasets/constrast_pair/6901754180311/6901754180311_20241017-141917_back_returnGood_5478c9a49bfc_315_13773168720.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 8.1ms\n",
      "video 1/1 (2/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.1ms\n",
      "video 1/1 (3/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.0ms\n",
      "video 1/1 (4/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (50/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.5ms\n",
      "video 1/1 (51/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (52/52) /home/ieemoo0337/projects/datasets/constrast_pair/6928804016703/6928804016703_20241018-180841_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.5ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (4/73) /home/ieemoo0337/projects/datasets/constrast_pair/6901010117440/6901010117440_20241018-150145_back_addGood_5478c9a4eb2c_605_18969520488.mp4: 640x512 5.7ms\n",
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      "video 1/1 (6/73) /home/ieemoo0337/projects/datasets/constrast_pair/6901010117440/6901010117440_20241018-150145_back_addGood_5478c9a4eb2c_605_18969520488.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 7.1ms\n",
      "video 1/1 (2/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 6.0ms\n",
      "video 1/1 (4/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (5/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (6/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (97/97) /home/ieemoo0337/projects/datasets/constrast_pair/6930506817083/6930506817083_20241019-182020_back_returnGood_5478c9a48b7e_320_18058629568.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (4/85) /home/ieemoo0337/projects/datasets/constrast_pair/6906303004469/6906303004469_20241018-173731_back_returnGood_5478c9a48c02_25_13586533739.mp4: 640x512 5.3ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    'index': 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.3ms\n",
      "video 1/1 (4/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (45/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (46/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (47/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (48/48) /home/ieemoo0337/projects/datasets/constrast_pair/6921168594993/6921168594993_20241015-175604_back_returnGood_5478c9a48c3e_455_18133699824.mp4: 640x512 6.1ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 7.2ms\n",
      "video 1/1 (2/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (3/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 6.3ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (69/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (70/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.6ms\n",
      "video 1/1 (71/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (72/72) /home/ieemoo0337/projects/datasets/constrast_pair/6924187835627/6924187835627_20241017-153439_back_returnGood_5478c9a4db2c_200_18133699824.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 7.5ms\n",
      "video 1/1 (2/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (77/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (78/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (79/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 6.2ms\n",
      "video 1/1 (80/80) /home/ieemoo0337/projects/datasets/constrast_pair/6924810801838/6924810801838_20241016-143129_back_returnGood_5478c9a49bfc_475_18358205530.mp4: 640x512 5.7ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 6.4ms\n",
      "video 1/1 (5/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 6.1ms\n",
      "video 1/1 (6/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (70/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (71/71) /home/ieemoo0337/projects/datasets/constrast_pair/6924810824646/6924810824646_20241017-174409_back_returnGood_5478c9a49bfc_90_13773168720.mp4: 640x512 5.9ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (5/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (6/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.9ms\n",
      "video 1/1 (7/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (8/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (9/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (10/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n",
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      "video 1/1 (12/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.9ms\n",
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      "video 1/1 (14/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.9ms\n",
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      "video 1/1 (41/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (42/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (43/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (44/44) /home/ieemoo0337/projects/datasets/constrast_pair/6972303255897/6972303255897_20241018-153331_back_returnGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (6/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (7/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (8/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.5ms\n",
      "video 1/1 (9/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.9ms\n",
      "video 1/1 (10/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.6ms\n",
      "video 1/1 (11/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.7ms\n",
      "video 1/1 (12/63) /home/ieemoo0337/projects/datasets/constrast_pair/6973095631180/6973095631180_20241020-104639_back_returnGood_5478c9a53be2_380_13867273486.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 11\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.5ms\n",
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      "video 1/1 (5/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (51/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (52/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (53/53) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938459/6901668938459_20241020-164947_back_returnGood_5478c9a4db2c_230_13773168720.mp4: 640x512 5.5ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 6.9ms\n",
      "video 1/1 (2/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (3/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (4/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (5/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (71/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (72/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (73/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (74/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (75/75) /home/ieemoo0337/projects/datasets/constrast_pair/6924810800572/6924810800572_20241015-101609_back_returnGood_5478c9a48b7e_90_13799009402.mp4: 640x512 5.4ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 6.5ms\n",
      "video 1/1 (2/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.4ms\n",
      "video 1/1 (4/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.4ms\n",
      "video 1/1 (5/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.4ms\n",
      "video 1/1 (6/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (62/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 6.1ms\n",
      "video 1/1 (63/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 6.0ms\n",
      "video 1/1 (64/64) /home/ieemoo0337/projects/datasets/constrast_pair/80177609/80177609_20241016-155031_back_addGood_5478c9a48b8c_60_18133699824.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 7.2ms\n",
      "video 1/1 (2/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.9ms\n",
      "video 1/1 (4/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.8ms\n",
      "video 1/1 (5/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 6.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (87/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.8ms\n",
      "video 1/1 (88/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.8ms\n",
      "video 1/1 (89/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.6ms\n",
      "video 1/1 (90/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.7ms\n",
      "video 1/1 (91/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.6ms\n",
      "video 1/1 (92/92) /home/ieemoo0337/projects/datasets/constrast_pair/6970302922932/6970302922932_20241018-114547_back_addGood_5478c9a49bfc_470_18969520488.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (6/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 6.2ms\n",
      "video 1/1 (7/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (8/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (9/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (10/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (11/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (12/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 6.1ms\n",
      "video 1/1 (13/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 6.4ms\n",
      "video 1/1 (14/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (15/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 6.2ms\n",
      "video 1/1 (16/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (17/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.6ms\n",
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      "video 1/1 (38/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (39/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.9ms\n",
      "video 1/1 (40/40) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040144/6932571040144_20241020-142323_back_returnGood_5478c9a49bfc_5_13799009402.mp4: 640x512 5.6ms\n",
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.5ms\n",
      "video 1/1 (6/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.5ms\n",
      "video 1/1 (7/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (8/63) /home/ieemoo0337/projects/datasets/constrast_pair/6971564334723/6971564334723_20241019-145314_back_addGood_5478c9a48c3e_100_15027299265.mp4: 640x512 5.6ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (3/76) /home/ieemoo0337/projects/datasets/constrast_pair/6970690474501/6970690474501_20241019-142915_back_returnGood_5478c9a48c3e_595_15027299265.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 10\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (53/53) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801498/6933313801498_20241020-185139_back_addGood_5478c9a53bca_230_18996891184.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 7.3ms\n",
      "video 1/1 (2/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (218/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.9ms\n",
      "video 1/1 (219/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.9ms\n",
      "video 1/1 (220/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.7ms\n",
      "video 1/1 (221/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.7ms\n",
      "video 1/1 (222/222) /home/ieemoo0337/projects/datasets/constrast_pair/6902083892104/6902083892104_20241019-111451_back_returnGood_5478c9a48b8c_7700_18996891184.mp4: 640x512 (no detections), 5.7ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.9ms\n",
      "video 1/1 (3/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (71/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (72/72) /home/ieemoo0337/projects/datasets/constrast_pair/6901209322105/6901209322105_20241020-110326_back_returnGood_5478c9a48c3e_1020_18058629568.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/81) /home/ieemoo0337/projects/datasets/FP/9315090211498/9315090211498_20241017-172454_back_addGood_5478c9a49bfc_1080_13773168720.mp4: 640x512 8.8ms\n",
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      "video 1/1 (4/81) /home/ieemoo0337/projects/datasets/FP/9315090211498/9315090211498_20241017-172454_back_addGood_5478c9a49bfc_1080_13773168720.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/87) /home/ieemoo0337/projects/datasets/constrast_pair/6903624800213/6903624800213_20241016-105326_back_addGood_5478c9a53bf4_25_13773168720.mp4: 640x512 6.8ms\n",
      "video 1/1 (2/87) /home/ieemoo0337/projects/datasets/constrast_pair/6903624800213/6903624800213_20241016-105326_back_addGood_5478c9a53bf4_25_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/87) /home/ieemoo0337/projects/datasets/constrast_pair/6903624800213/6903624800213_20241016-105326_back_addGood_5478c9a53bf4_25_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/87) /home/ieemoo0337/projects/datasets/constrast_pair/6903624800213/6903624800213_20241016-105326_back_addGood_5478c9a53bf4_25_13773168720.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (7/87) /home/ieemoo0337/projects/datasets/constrast_pair/6903624800213/6903624800213_20241016-105326_back_addGood_5478c9a53bf4_25_13773168720.mp4: 640x512 5.5ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/62) /home/ieemoo0337/projects/datasets/constrast_pair/8711000254769/8711000254769_20241017-163132_back_addGood_5478c9a48c3e_380_13586533739.mp4: 640x512 6.8ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  {'index': 5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (3/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 6.1ms\n",
      "video 1/1 (4/77) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570599/6970618570599_20241020-102542_back_addGood_5478c9a4db2c_110_13773168720.mp4: 640x512 5.9ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (4/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.8ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (44/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (45/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (46/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (47/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (48/48) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040748/6932571040748_20241017-165856_back_returnGood_5478c9a49bfc_340_13773168720.mp4: 640x512 5.5ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (4/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (6/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (7/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (8/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (9/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (10/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.6ms\n",
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      "video 1/1 (45/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (46/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.6ms\n",
      "video 1/1 (47/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (48/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (49/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (50/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (51/51) /home/ieemoo0337/projects/datasets/constrast_pair/6922441400031/6922441400031_20241019-165855_back_returnGood_5478c9a49bfc_310_13773168720.mp4: 640x512 5.4ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  {'index': 1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 6.1ms\n",
      "video 1/1 (3/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 5.9ms\n",
      "video 1/1 (4/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 6.4ms\n",
      "video 1/1 (5/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 6.0ms\n",
      "video 1/1 (6/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 6.1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (7/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 5.8ms\n",
      "video 1/1 (8/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 6.3ms\n",
      "video 1/1 (9/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 6.2ms\n",
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      "video 1/1 (81/81) /home/ieemoo0337/projects/datasets/constrast_pair/4711561462942/4711561462942_20241020-102305_back_returnGood_5478c9a53bca_370_18996891184.mp4: 640x512 (no detections), 6.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 7.9ms\n",
      "video 1/1 (2/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 6.2ms\n",
      "video 1/1 (3/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 6.0ms\n",
      "video 1/1 (4/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 6.0ms\n",
      "video 1/1 (5/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 5.9ms\n",
      "video 1/1 (6/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (7/49) /home/ieemoo0337/projects/datasets/constrast_pair/6940188803618/6940188803618_20241020-114914_back_addGood_5478c9a53be2_80_13867273486.mp4: 640x512 6.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/64) /home/ieemoo0337/projects/datasets/constrast_pair/6977264870103/6977264870103_20241016-103126_back_returnGood_5478c9a48b7e_145_13586533739.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (3/69) /home/ieemoo0337/projects/datasets/constrast_pair/3073781189872/3073781189872_20241017-163505_back_returnGood_5478c9a49bfc_95_13773168720.mp4: 640x512 6.0ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (67/69) /home/ieemoo0337/projects/datasets/constrast_pair/3073781189872/3073781189872_20241017-163505_back_returnGood_5478c9a49bfc_95_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (68/69) /home/ieemoo0337/projects/datasets/constrast_pair/3073781189872/3073781189872_20241017-163505_back_returnGood_5478c9a49bfc_95_13773168720.mp4: 640x512 6.0ms\n",
      "video 1/1 (69/69) /home/ieemoo0337/projects/datasets/constrast_pair/3073781189872/3073781189872_20241017-163505_back_returnGood_5478c9a49bfc_95_13773168720.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 5\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/74) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040434/6932571040434_20241020-142346_back_returnGood_5478c9a49bfc_1800_13799009402.mp4: 640x512 108.8ms\n",
      "video 1/1 (2/74) /home/ieemoo0337/projects/datasets/constrast_pair/6932571040434/6932571040434_20241020-142346_back_returnGood_5478c9a49bfc_1800_13799009402.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 12\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 6\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/120) /home/ieemoo0337/projects/datasets/constrast_pair/6976558260187/6976558260187_20241016-121605_back_addGood_5478c9a49bfc_455_18358205530.mp4: 640x512 7.1ms\n",
      "video 1/1 (2/120) /home/ieemoo0337/projects/datasets/constrast_pair/6976558260187/6976558260187_20241016-121605_back_addGood_5478c9a49bfc_455_18358205530.mp4: 640x512 6.4ms\n",
      "video 1/1 (3/120) /home/ieemoo0337/projects/datasets/constrast_pair/6976558260187/6976558260187_20241016-121605_back_addGood_5478c9a49bfc_455_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/120) /home/ieemoo0337/projects/datasets/constrast_pair/6976558260187/6976558260187_20241016-121605_back_addGood_5478c9a49bfc_455_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (5/120) /home/ieemoo0337/projects/datasets/constrast_pair/6976558260187/6976558260187_20241016-121605_back_addGood_5478c9a49bfc_455_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/120) /home/ieemoo0337/projects/datasets/constrast_pair/6976558260187/6976558260187_20241016-121605_back_addGood_5478c9a49bfc_455_18358205530.mp4: 640x512 5.9ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 2\n",
      "output_text:  {'index': 1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
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     "output_type": "stream",
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      "video 1/1 (49/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 6.0ms\n",
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      "video 1/1 (55/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (56/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 6.2ms\n",
      "video 1/1 (57/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (58/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (59/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (60/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 6.2ms\n",
      "video 1/1 (61/61) /home/ieemoo0337/projects/datasets/constrast_pair/6927321719951/6927321719951_20241017-185505_back_addGood_5478c9a49bfc_265_13773168720.mp4: 640x512 5.9ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 6.2ms\n",
      "video 1/1 (2/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (48/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (49/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.8ms\n",
      "video 1/1 (50/50) /home/ieemoo0337/projects/datasets/constrast_pair/6975682480638/6975682480638_20241018-160748_back_addGood_5478c9a51ba6_5_18358205530.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (3/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/76) /home/ieemoo0337/projects/datasets/constrast_pair/6907992102764/6907992102764_20241020-111816_back_returnGood_5478c9a48c3e_280_18058629568.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/56) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570049/6970618570049_20241014-183211_back_returnGood_5478c9a53bf4_630_13799009402.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/56) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570049/6970618570049_20241014-183211_back_returnGood_5478c9a53bf4_630_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (3/56) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570049/6970618570049_20241014-183211_back_returnGood_5478c9a53bf4_630_13799009402.mp4: 640x512 5.9ms\n",
      "video 1/1 (4/56) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570049/6970618570049_20241014-183211_back_returnGood_5478c9a53bf4_630_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/56) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570049/6970618570049_20241014-183211_back_returnGood_5478c9a53bf4_630_13799009402.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (6/56) /home/ieemoo0337/projects/datasets/constrast_pair/6970618570049/6970618570049_20241014-183211_back_returnGood_5478c9a53bf4_630_13799009402.mp4: 640x512 5.6ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (3/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (63/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.6ms\n",
      "video 1/1 (64/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (65/65) /home/ieemoo0337/projects/datasets/constrast_pair/6902227014119/6902227014119_20241017-162648_back_addGood_5478c9a51ba6_220_18358205530.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 7.7ms\n",
      "video 1/1 (2/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (4/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (5/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (6/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (7/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (8/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (9/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (10/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (11/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
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      "video 1/1 (14/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
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      "video 1/1 (16/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.6ms\n",
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      "video 1/1 (18/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (19/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.9ms\n",
      "video 1/1 (20/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (21/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (22/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (23/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.6ms\n",
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      "video 1/1 (25/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (26/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (27/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (28/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (29/29) /home/ieemoo0337/projects/datasets/constrast_pair/6922577739784/6922577739784_20241020-162557_back_addGood_5478c9a49bfc_1050_13799009402.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  {'index': 5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 6.3ms\n",
      "video 1/1 (2/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (3/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (5/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 5.4ms\n",
      "video 1/1 (6/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/6922577766124/6922577766124_20241014-180727_back_returnGood_5478c9a53bf4_495_13799009402.mp4: 640x512 6.3ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 5\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.6ms\n",
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      "video 1/1 (4/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (7/36) /home/ieemoo0337/projects/datasets/constrast_pair/6936462327079/6936462327079_20241019-144701_back_addGood_5478c9a53bd4_215_13799009402.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 6\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 6.5ms\n",
      "video 1/1 (2/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.4ms\n",
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      "video 1/1 (4/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (6/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (7/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (73/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.4ms\n",
      "video 1/1 (74/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.4ms\n",
      "video 1/1 (75/75) /home/ieemoo0337/projects/datasets/constrast_pair/6970768806883/6970768806883_20241015-153825_back_addGood_5478c9a48c3e_1065_18133699824.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 10\n",
      "output_text:  ```python\n",
      "{\n",
      "    'index': 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 6.7ms\n",
      "video 1/1 (2/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (4/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (6/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 6.0ms\n",
      "video 1/1 (7/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "video 1/1 (60/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.8ms\n",
      "video 1/1 (61/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/6920907808537/6920907808537_20241018-164305_back_addGood_5478c9a48b8c_80_13799009402.mp4: 640x512 5.7ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/91) /home/ieemoo0337/projects/datasets/constrast_pair/6932571080959/6932571080959_20241020-113634_back_addGood_5478c9a48c3e_820_18058629568.mp4: 640x512 6.2ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
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     "output_type": "stream",
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      "video 1/1 (70/70) /home/ieemoo0337/projects/datasets/constrast_pair/6901668938572/6901668938572_20241016-170712_back_addGood_5478c9a48b8c_220_18133699824.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 13\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 1\n",
      "output_text:  {'index': 0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 5.5ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (262/262) /home/ieemoo0337/projects/datasets/constrast_pair/6959479300347/6959479300347_20241020-150701_back_addGood_5478c9a48b8c_1085_18358205530.mp4: 640x512 5.8ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 20\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (2/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.5ms\n",
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      "video 1/1 (4/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 6.0ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (91/91) /home/ieemoo0337/projects/datasets/constrast_pair/6975394410053/6975394410053_20241016-144523_back_returnGood_5478c9a49bfc_270_18358205530.mp4: 640x512 5.6ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
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     "output_type": "stream",
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      "video 1/1 (66/66) /home/ieemoo0337/projects/datasets/constrast_pair/6932571042322/6932571042322_20241014-173747_back_returnGood_5478c9a53bf4_1015_13799009402.mp4: 640x512 5.5ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (60/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (61/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.5ms\n",
      "video 1/1 (62/62) /home/ieemoo0337/projects/datasets/constrast_pair/4901525010986/4901525010986_20241015-164837_back_returnGood_5478c9a53bf4_860_13773168720.mp4: 640x512 5.5ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 4\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.4ms\n",
      "video 1/1 (3/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.5ms\n",
      "video 1/1 (4/106) /home/ieemoo0337/projects/datasets/constrast_pair/4891913367252/4891913367252_20241020-135343_back_returnGood_5478c9a48b7e_810_15027299265.mp4: 640x512 5.9ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 11\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 0\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 3\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (54/54) /home/ieemoo0337/projects/datasets/constrast_pair/6907376311461/6907376311461_20241020-102713_back_returnGood_5478c9a48c02_60_18358205530.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 7.0ms\n",
      "video 1/1 (2/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.4ms\n",
      "video 1/1 (3/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (5/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.4ms\n",
      "video 1/1 (6/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.5ms\n",
      "video 1/1 (7/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (81/81) /home/ieemoo0337/projects/datasets/constrast_pair/6904682123207/6904682123207_20241016-183655_back_returnGood_5478c9a48b8c_90_18133699824.mp4: 640x512 (no detections), 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 9\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907992105772/6907992105772_20241020-115027_back_addGood_5478c9a48c3e_265_18058629568.mp4: 640x512 6.6ms\n",
      "video 1/1 (2/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907992105772/6907992105772_20241020-115027_back_addGood_5478c9a48c3e_265_18058629568.mp4: 640x512 5.6ms\n",
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      "video 1/1 (5/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907992105772/6907992105772_20241020-115027_back_addGood_5478c9a48c3e_265_18058629568.mp4: 640x512 5.7ms\n",
      "video 1/1 (6/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907992105772/6907992105772_20241020-115027_back_addGood_5478c9a48c3e_265_18058629568.mp4: 640x512 5.6ms\n",
      "video 1/1 (7/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907992105772/6907992105772_20241020-115027_back_addGood_5478c9a48c3e_265_18058629568.mp4: 640x512 5.6ms\n",
      "video 1/1 (8/93) /home/ieemoo0337/projects/datasets/constrast_pair/6907992105772/6907992105772_20241020-115027_back_addGood_5478c9a48c3e_265_18058629568.mp4: 640x512 5.6ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 7\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 4\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
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      "video 1/1 (5/154) /home/ieemoo0337/projects/datasets/constrast_pair/6972373580813/6972373580813_20241015-103853_back_addGood_5478c9a48c3e_100_18133699824.mp4: 640x512 5.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (152/154) /home/ieemoo0337/projects/datasets/constrast_pair/6972373580813/6972373580813_20241015-103853_back_addGood_5478c9a48c3e_100_18133699824.mp4: 640x512 5.8ms\n",
      "video 1/1 (153/154) /home/ieemoo0337/projects/datasets/constrast_pair/6972373580813/6972373580813_20241015-103853_back_addGood_5478c9a48c3e_100_18133699824.mp4: 640x512 5.6ms\n",
      "video 1/1 (154/154) /home/ieemoo0337/projects/datasets/constrast_pair/6972373580813/6972373580813_20241015-103853_back_addGood_5478c9a48c3e_100_18133699824.mp4: 640x512 5.5ms\n",
      "/home/ieemoo0337/projects/detecttracking/tracking/traclus.py:163: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  tan_theta0 = (shorter_slope - longer_slope) / (1 + shorter_slope * longer_slope)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 10\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 2\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (2/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (3/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.7ms\n",
      "video 1/1 (4/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.8ms\n",
      "video 1/1 (5/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "video 1/1 (64/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (65/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.4ms\n",
      "video 1/1 (66/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.9ms\n",
      "video 1/1 (67/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.3ms\n",
      "video 1/1 (68/68) /home/ieemoo0337/projects/datasets/constrast_pair/6922300667636/6922300667636_20241016-145703_back_returnGood_5478c9a53bf4_345_13773168720.mp4: 640x512 5.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 2\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 1\n",
      "}\n",
      "```\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ad85022 Python-3.10.16 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
      "\n",
      "/home/ieemoo0337/projects/detecttracking/models/experimental.py:79: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(attempt_download(w), map_location=device)  # load\n",
      "Fusing layers... \n",
      "Model summary: 213 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
      "/home/ieemoo0337/projects/detecttracking/contrast/feat_extract/inference.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  self.model.load_state_dict(torch.load(modpath, map_location=conf.device))\n",
      "video 1/1 (1/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 6.4ms\n",
      "video 1/1 (2/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (3/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (4/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.6ms\n",
      "video 1/1 (5/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.7ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load model resnet18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "video 1/1 (6/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (7/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (8/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.8ms\n",
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      "video 1/1 (33/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (34/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.5ms\n",
      "video 1/1 (35/35) /home/ieemoo0337/projects/datasets/constrast_pair/6933313801481/6933313801481_20241018-175016_back_returnGood_5478c9a48b8c_250_13799009402.mp4: 640x512 5.5ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_imgs len: 8\n",
      "output_text:  ```python\n",
      "{\n",
      "    \"index\": 3\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "for ele in neg_pair:\n",
    "    sub_imgs = stream_pipeline(ele)\n",
    "    print(\"sub_imgs len:\", len(sub_imgs))\n",
    "    if not sub_imgs:\n",
    "        continue\n",
    "    bestimg = get_best_image(sub_imgs)\n",
    "    product_name = ele['商品名称']\n",
    "    message = [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\n",
    "                    \"type\": \"image\",\n",
    "                    \"min_pixels\": 224 * 224,\n",
    "                    \"max_pixels\": 800 * 800,\n",
    "                },\n",
    "                {\n",
    "                    \"type\": \"text\",\n",
    "                    \"text\": f\"判断图片中的商品是否是'{product_name}', 回答True或False。\"\n",
    "                }\n",
    "            ]\n",
    "        }\n",
    "    ]\n",
    "    result = qwen_prompt(bestimg, message)\n",
    "    if result == 'True':\n",
    "        true2_list.append(ele)\n",
    "    else:\n",
    "        false2_list.append(ele)\n",
    "    torch.cuda.empty_cache()\n",
    "    del sub_imgs\n",
    "    del bestimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 96)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(true2_list), len(false2_list)"
   ]
  }
 ],
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  "kernelspec": {
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