Inference.ipynb

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   "source": [
    "# 配置参数和模型超参数\n",
    "class CFG:\n",
    "    valid_bs = 32 # 验证集batch_size，请根据GPU性能调整\n",
    "    n_worker = 8 # 数据导入线程数，请根据CPU性能调整\n",
    "    multi_gpu = True\n",
    "    tta = True\n",
    "\n",
    "    image_size = 512 # 图片尺寸\n",
    "    model_arch = \"tf_efficientnetv2_l_21k\" # tf_efficientnetv2_l_21k  tf_efficientnet_b4  tf_efficientnetv2_m\n",
    "    model_arch1 = \"tf_efficientnetv2_m\" # tf_efficientnetv2_l_21k  tf_efficientnet_b4  tf_efficientnetv2_m\n",
    "    \n",
    "    model_path = [\"/kaggle/input/study-model-2/stage1_4749_fold0_tf_efficientnetv2_l_21k_512_best_9_5701.pth\",\n",
    "                  \"/kaggle/input/study-model-2/stage1_1852_fold1_tf_efficientnetv2_l_21k_512_best_9_5724.pth\",\n",
    "                  \"/kaggle/input/study-model/stage1_0218_fold2_tf_efficientnetv2_l_21k_512_best_7_6025.pth\",\n",
    "                  \"/kaggle/input/study-model/stage1_0218_fold3_tf_efficientnetv2_l_21k_512_best_9_5855.pth\",\n",
    "                  \"/kaggle/input/study-model/stage1_0218_fold4_tf_efficientnetv2_l_21k_512_best_9_5945.pth\",\n",
    "                 ]\n",
    "    class_weight = [1,1,1,1,1,] ####\n",
    "\n",
    "    class2_path = [\n",
    "        \"/kaggle/input/2class-pth/stage1_4632_fold0_tf_efficientnetv2_l_21k_512_best_9_9685.pth\",\n",
    "        \"/kaggle/input/2class-pth/stage1_5812_fold1_tf_efficientnetv2_l_21k_512_best_5_9688.pth\",\n",
    "        \"/kaggle/input/2class-pth/stage1_5812_fold2_tf_efficientnetv2_l_21k_512_best_7_9729.pth\",\n",
    "        \"/kaggle/input/2class-pth/stage1_5812_fold3_tf_efficientnetv2_l_21k_512_best_8_9691.pth\",\n",
    "        \"/kaggle/input/2class-pth/stage1_5812_fold4_tf_efficientnetv2_l_21k_512_best_9_9682.pth\",\n",
    "    ]"
   ]
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      "\r\n",
      "Downloading and Extracting Packages\r\n",
      "######################################################################## | 100% \r\n",
      "Preparing transaction: - \b\bdone\r\n",
      "Verifying transaction: | \b\bdone\r\n",
      "Executing transaction: - \b\b\\ \b\bdone\r\n",
      "\r\n",
      "Downloading and Extracting Packages\r\n",
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      "\r\n",
      "Downloading and Extracting Packages\r\n",
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      "Preparing transaction: - \b\bdone\r\n",
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      "\r\n",
      "Downloading and Extracting Packages\r\n",
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      "Verifying transaction: | \b\bdone\r\n",
      "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\r\n",
      "\r\n",
      "Downloading and Extracting Packages\r\n",
      "######################################################################## | 100% \r\n",
      "Preparing transaction: - \b\bdone\r\n",
      "Verifying transaction: | \b\bdone\r\n",
      "Executing transaction: - \b\bdone\r\n",
      "\r\n",
      "Downloading and Extracting Packages\r\n",
      "######################################################################## | 100% \r\n",
      "Preparing transaction: - \b\bdone\r\n",
      "Verifying transaction: | \b\bdone\r\n",
      "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\r\n"
     ]
    }
   ],
   "source": [
    "!conda install '/kaggle/input/pydicom-conda-helper/libjpeg-turbo-2.1.0-h7f98852_0.tar.bz2' -c conda-forge -y\n",
    "!conda install '/kaggle/input/pydicom-conda-helper/libgcc-ng-9.3.0-h2828fa1_19.tar.bz2' -c conda-forge -y\n",
    "!conda install '/kaggle/input/pydicom-conda-helper/gdcm-2.8.9-py37h500ead1_1.tar.bz2' -c conda-forge -y\n",
    "!conda install '/kaggle/input/pydicom-conda-helper/conda-4.10.1-py37h89c1867_0.tar.bz2' -c conda-forge -y\n",
    "!conda install '/kaggle/input/pydicom-conda-helper/certifi-2020.12.5-py37h89c1867_1.tar.bz2' -c conda-forge -y\n",
    "!conda install '/kaggle/input/pydicom-conda-helper/openssl-1.1.1k-h7f98852_0.tar.bz2' -c conda-forge -y\n",
    "\n",
    "!pip install /kaggle/input/kerasapplications -q\n",
    "!pip install /kaggle/input/efficientnet-keras-source-code/ -q --no-deps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "convinced-plumbing",
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     "text": [
      "Processing /kaggle/input/effdet-latestvinbigdata-wbf-fused/ensemble_boxes-1.0.4-py3-none-any.whl\r\n",
      "Installing collected packages: ensemble-boxes\r\n",
      "Successfully installed ensemble-boxes-1.0.4\r\n"
     ]
    }
   ],
   "source": [
    "!pip install '/kaggle/input/effdet-latestvinbigdata-wbf-fused/ensemble_boxes-1.0.4-py3-none-any.whl' --no-deps"
   ]
  },
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     "text": [
      "Processing /kaggle/input/effdet-latestvinbigdata-wbf-fused/ensemble_boxes-1.0.4-py3-none-any.whl\r\n",
      "ensemble-boxes is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n",
      "Processing /kaggle/input/mmdetectionv2140/addict-2.4.0-py3-none-any.whl\r\n",
      "Installing collected packages: addict\r\n",
      "Successfully installed addict-2.4.0\r\n",
      "Processing /kaggle/input/mmdetectionv2140/yapf-0.31.0-py2.py3-none-any.whl\r\n",
      "Installing collected packages: yapf\r\n",
      "Successfully installed yapf-0.31.0\r\n",
      "Processing /kaggle/input/mmdetectionv2140/terminal-0.4.0-py3-none-any.whl\r\n",
      "Installing collected packages: terminal\r\n",
      "Successfully installed terminal-0.4.0\r\n",
      "Processing /kaggle/input/mmdetectionv2140/terminaltables-3.1.0-py3-none-any.whl\r\n",
      "terminaltables is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n",
      "Processing /kaggle/input/mmdetectionv2140/mmcv_full-1_3_8-cu110-torch1_7_0/mmcv_full-1.3.8-cp37-cp37m-manylinux1_x86_64.whl\r\n",
      "Installing collected packages: mmcv-full\r\n",
      "Successfully installed mmcv-full-1.3.8\r\n",
      "Processing /kaggle/input/mmdetectionv2140/pycocotools-2.0.2/pycocotools-2.0.2\r\n",
      "Building wheels for collected packages: pycocotools\r\n",
      "  Building wheel for pycocotools (setup.py) ... \u001B[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\r\n",
      "\u001B[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=272446 sha256=f315a2e755cc842b45841ecf628cade62e8ad89ddefe20fd39a39ee3324957f4\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/3b/c9/26/f4ec5e3e2fdc837f5c5539fe16dbe35f82a358c7a4ccca98b4\r\n",
      "Successfully built pycocotools\r\n",
      "Installing collected packages: pycocotools\r\n",
      "Successfully installed pycocotools-2.0.2\r\n",
      "Processing /kaggle/input/mmdetectionv2140/mmpycocotools-12.0.3/mmpycocotools-12.0.3\r\n",
      "Building wheels for collected packages: mmpycocotools\r\n",
      "  Building wheel for mmpycocotools (setup.py) ... \u001B[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\r\n",
      "\u001B[?25h  Created wheel for mmpycocotools: filename=mmpycocotools-12.0.3-cp37-cp37m-linux_x86_64.whl size=272722 sha256=b8b0f08c7fb2b29ba577207eca26f652d0d9f4bc4596e99578e7b74db2811633\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/6b/52/1c/2397d26e2e427dfd5555670bfb92e2882acc8a30aed0c8f890\r\n",
      "Successfully built mmpycocotools\r\n",
      "Installing collected packages: mmpycocotools\r\n",
      "Successfully installed mmpycocotools-12.0.3\r\n",
      "/kaggle/working/mmdetection\n",
      "Obtaining file:///kaggle/working/mmdetection\r\n",
      "Installing collected packages: mmdet\r\n",
      "  Running setup.py develop for mmdet\r\n",
      "Successfully installed mmdet\r\n",
      "/kaggle/working\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "!pip install '/kaggle/input/effdet-latestvinbigdata-wbf-fused/ensemble_boxes-1.0.4-py3-none-any.whl' --no-deps\n",
    "\n",
    "## MMDetection Offline Installation\n",
    "!pip install '/kaggle/input/mmdetectionv2140/addict-2.4.0-py3-none-any.whl' --no-deps\n",
    "!pip install '/kaggle/input/mmdetectionv2140/yapf-0.31.0-py2.py3-none-any.whl' --no-deps\n",
    "!pip install '/kaggle/input/mmdetectionv2140/terminal-0.4.0-py3-none-any.whl' --no-deps\n",
    "!pip install '/kaggle/input/mmdetectionv2140/terminaltables-3.1.0-py3-none-any.whl' --no-deps\n",
    "!pip install '/kaggle/input/mmdetectionv2140/mmcv_full-1_3_8-cu110-torch1_7_0/mmcv_full-1.3.8-cp37-cp37m-manylinux1_x86_64.whl' --no-deps\n",
    "!pip install '/kaggle/input/mmdetectionv2140/pycocotools-2.0.2/pycocotools-2.0.2' --no-deps\n",
    "!pip install '/kaggle/input/mmdetectionv2140/mmpycocotools-12.0.3/mmpycocotools-12.0.3' --no-deps\n",
    "\n",
    "!cp -r /kaggle/input/mmdetectionv2140/mmdetection-2.14.0 /kaggle/working/\n",
    "!mv /kaggle/working/mmdetection-2.14.0 /kaggle/working/mmdetection\n",
    "%cd /kaggle/working/mmdetection\n",
    "!pip install -e . --no-deps\n",
    "%cd /kaggle/working/\n",
    "\n",
    "import sys\n",
    "sys.path.append('/kaggle/working/mmdetection')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acoustic-england",
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   "source": [
    "import os\n",
    "\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm\n",
    "import numpy as np\n",
    "import pydicom\n",
    "from pydicom.pixel_data_handlers.util import apply_voi_lut\n",
    "from numba import cuda\n",
    "import gc\n",
    "import torch\n",
    "import sys\n",
    "sys.path.append(\"/kaggle/input/timmeffnetv2\")\n",
    "\n",
    "import time\n",
    "import cv2\n",
    "import PIL.Image\n",
    "import random\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm.notebook import tqdm\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch.nn as nn\n",
    "from torch.nn import Parameter\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.optim as optim\n",
    "import albumentations\n",
    "import timm\n",
    "import gc\n",
    "from collections import OrderedDict\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")\n",
    "device = torch.device('cuda')\n",
    "\n",
    "\n",
    "from glob import glob\n",
    "import shutil, os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import GroupKFold\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "radical-rochester",
   "metadata": {
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    },
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   },
   "outputs": [],
   "source": [
    "# 设置随机种子，以便实验复现\n",
    "def seed_everything(seed):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False # set True to be faster\n",
    "seed_everything(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "vocational-steel",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [],
   "source": [
    "if pd.read_csv('/kaggle/input/siim-covid19-detection/sample_submission.csv').shape[0] == 2477:\n",
    "    fast_sub = True\n",
    "    fast_df = pd.DataFrame(([['00086460a852_study', 'negative 1 0 0 1 1'], \n",
    "                         ['000c9c05fd14_study', 'negative 1 0 0 1 1'], \n",
    "                         ['65761e66de9f_image', 'none 1 0 0 1 1'], \n",
    "                         ['51759b5579bc_image', 'none 1 0 0 1 1']]), \n",
    "                       columns=['id', 'PredictionString'])\n",
    "else:\n",
    "    fast_sub = False\n",
    "    \n",
    "# fast_sub = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "limited-animation",
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     "start_time": "2021-08-08T15:20:01.032944",
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    },
    "tags": []
   },
   "source": [
    "# .dcm to .png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "smaller-bicycle",
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   "outputs": [],
   "source": [
    "def read_xray(path, voi_lut = True, fix_monochrome = True):\n",
    "    # Original from: https://www.kaggle.com/raddar/convert-dicom-to-np-array-the-correct-way\n",
    "    dicom = pydicom.read_file(path)\n",
    "\n",
    "    if voi_lut:\n",
    "        data = apply_voi_lut(dicom.pixel_array, dicom)\n",
    "    else:\n",
    "        data = dicom.pixel_array\n",
    "               \n",
    "    # depending on this value, X-ray may look inverted - fix that:\n",
    "    if fix_monochrome and dicom.PhotometricInterpretation == \"MONOCHROME1\":\n",
    "        data = np.amax(data) - data\n",
    "        \n",
    "    data = data - np.min(data)\n",
    "    data = data / np.max(data)\n",
    "    data = (data * 255).astype(np.uint8)\n",
    "        \n",
    "    return data\n",
    "\n",
    "def resize(array, size, keep_ratio=False, resample=Image.LANCZOS):\n",
    "    # Original from: https://www.kaggle.com/xhlulu/vinbigdata-process-and-resize-to-image\n",
    "    im = Image.fromarray(array)\n",
    "    \n",
    "    if keep_ratio:\n",
    "        im.thumbnail((size, size), resample)\n",
    "    else:\n",
    "        im = im.resize((size, size), resample)\n",
    "    \n",
    "    return im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fresh-cargo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:01.398292Z",
     "iopub.status.busy": "2021-08-08T15:20:01.397549Z",
     "iopub.status.idle": "2021-08-08T15:20:02.880991Z",
     "shell.execute_reply": "2021-08-08T15:20:02.880031Z"
    },
    "papermill": {
     "duration": 1.553083,
     "end_time": "2021-08-08T15:20:02.881144",
     "exception": false,
     "start_time": "2021-08-08T15:20:01.328061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "split = 'test'\n",
    "save_dir = f'/kaggle/tmp/{split}/'\n",
    "\n",
    "os.makedirs(save_dir, exist_ok=True)\n",
    "\n",
    "save_study_dir = f'/kaggle/tmp/{split}/study/'\n",
    "os.makedirs(save_study_dir, exist_ok=True)\n",
    "if fast_sub:\n",
    "    xray = read_xray('/kaggle/input/siim-covid19-detection/train/00086460a852/9e8302230c91/65761e66de9f.dcm')\n",
    "    im = resize(xray, size=512)  \n",
    "    study = '00086460a852' + '_study.png'\n",
    "    im.save(os.path.join(save_study_dir, study))\n",
    "    xray = read_xray('/kaggle/input/siim-covid19-detection/train/000c9c05fd14/e555410bd2cd/51759b5579bc.dcm')\n",
    "    im = resize(xray, size=512)  \n",
    "    study = '000c9c05fd14' + '_study.png'\n",
    "    im.save(os.path.join(save_study_dir, study))\n",
    "else:   \n",
    "    for dirname, _, filenames in tqdm(os.walk(f'/kaggle/input/siim-covid19-detection/{split}')):\n",
    "        for file in filenames:\n",
    "            # set keep_ratio=True to have original aspect ratio\n",
    "            xray = read_xray(os.path.join(dirname, file))\n",
    "            im = resize(xray, size=512)  \n",
    "            study = dirname.split('/')[-2] + '_study.png'\n",
    "            im.save(os.path.join(save_study_dir, study))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "annual-training",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:03.027630Z",
     "iopub.status.busy": "2021-08-08T15:20:03.026836Z",
     "iopub.status.idle": "2021-08-08T15:20:03.489730Z",
     "shell.execute_reply": "2021-08-08T15:20:03.489285Z"
    },
    "papermill": {
     "duration": 0.541227,
     "end_time": "2021-08-08T15:20:03.489850",
     "exception": false,
     "start_time": "2021-08-08T15:20:02.948623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dim0</th>\n",
       "      <th>dim1</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>2330</td>\n",
       "      <td>2783</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>3093</td>\n",
       "      <td>2850</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  dim0  dim1 split\n",
       "0  65761e66de9f_image  2330  2783  test\n",
       "1  51759b5579bc_image  3093  2850  test"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_id = []\n",
    "dim0 = []\n",
    "dim1 = []\n",
    "splits = []\n",
    "save_dir = f'/kaggle/tmp/{split}/image/'\n",
    "os.makedirs(save_dir, exist_ok=True)\n",
    "if fast_sub:\n",
    "    xray = read_xray('/kaggle/input/siim-covid19-detection/train/00086460a852/9e8302230c91/65761e66de9f.dcm')\n",
    "    im = resize(xray, size=512)  \n",
    "    im.save(os.path.join(save_dir,'65761e66de9f_image.png'))\n",
    "    image_id.append('65761e66de9f.dcm'.replace('.dcm', ''))\n",
    "    dim0.append(xray.shape[0])\n",
    "    dim1.append(xray.shape[1])\n",
    "    splits.append(split)\n",
    "    \n",
    "    xray = read_xray('/kaggle/input/siim-covid19-detection/train/000c9c05fd14/e555410bd2cd/51759b5579bc.dcm')\n",
    "    im = resize(xray, size=512)  \n",
    "    im.save(os.path.join(save_dir, '51759b5579bc_image.png'))\n",
    "    image_id.append('51759b5579bc.dcm'.replace('.dcm', ''))\n",
    "    dim0.append(xray.shape[0])\n",
    "    dim1.append(xray.shape[1])\n",
    "    splits.append(split)\n",
    "else:\n",
    "    for dirname, _, filenames in tqdm(os.walk(f'/kaggle/input/siim-covid19-detection/{split}')):\n",
    "        for file in filenames:\n",
    "            # set keep_ratio=True to have original aspect ratio\n",
    "            xray = read_xray(os.path.join(dirname, file))\n",
    "            im = resize(xray, size=512)  \n",
    "            im.save(os.path.join(save_dir, file.replace('.dcm', '_image.png')))\n",
    "            image_id.append(file.replace('.dcm', ''))\n",
    "            dim0.append(xray.shape[0])\n",
    "            dim1.append(xray.shape[1])\n",
    "            splits.append(split)\n",
    "            \n",
    "meta = pd.DataFrame.from_dict({'image_id': image_id, 'dim0': dim0, 'dim1': dim1, 'split': splits})\n",
    "meta = meta[meta['split'] == 'test']\n",
    "meta['image_id'] = meta['image_id'] + '_image'\n",
    "meta.columns = ['id', 'dim0', 'dim1', 'split']\n",
    "meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "textile-oasis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:03.621621Z",
     "iopub.status.busy": "2021-08-08T15:20:03.620817Z",
     "iopub.status.idle": "2021-08-08T15:20:03.798848Z",
     "shell.execute_reply": "2021-08-08T15:20:03.797640Z"
    },
    "papermill": {
     "duration": 0.246197,
     "end_time": "2021-08-08T15:20:03.799001",
     "exception": false,
     "start_time": "2021-08-08T15:20:03.552804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "meta.to_csv(\"/kaggle/working/meta.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dying-barbados",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:03.931629Z",
     "iopub.status.busy": "2021-08-08T15:20:03.930815Z",
     "iopub.status.idle": "2021-08-08T15:20:03.977525Z",
     "shell.execute_reply": "2021-08-08T15:20:03.977115Z"
    },
    "papermill": {
     "duration": 0.114605,
     "end_time": "2021-08-08T15:20:03.977634",
     "exception": false,
     "start_time": "2021-08-08T15:20:03.863029",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if fast_sub:\n",
    "    all_sub_df = fast_df.copy()\n",
    "else:\n",
    "    all_sub_df = pd.read_csv('/kaggle/input/siim-covid19-detection/sample_submission.csv')\n",
    "    \n",
    "all_sub_df['id_last_str'] = all_sub_df[\"id\"].str[-1]\n",
    "\n",
    "study_len = all_sub_df[all_sub_df['id_last_str'] == 'y'].shape[0]\n",
    "study_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "indie-dodge",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:04.108884Z",
     "iopub.status.busy": "2021-08-08T15:20:04.108035Z",
     "iopub.status.idle": "2021-08-08T15:20:04.154373Z",
     "shell.execute_reply": "2021-08-08T15:20:04.154761Z"
    },
    "papermill": {
     "duration": 0.114155,
     "end_time": "2021-08-08T15:20:04.154886",
     "exception": false,
     "start_time": "2021-08-08T15:20:04.040731",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "      <th>id_last_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00086460a852_study</td>\n",
       "      <td>negative 1 0 0 1 1</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000c9c05fd14_study</td>\n",
       "      <td>negative 1 0 0 1 1</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id    PredictionString id_last_str\n",
       "0  00086460a852_study  negative 1 0 0 1 1           y\n",
       "1  000c9c05fd14_study  negative 1 0 0 1 1           y\n",
       "2  65761e66de9f_image      none 1 0 0 1 1           e\n",
       "3  51759b5579bc_image      none 1 0 0 1 1           e"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sub_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polish-lightweight",
   "metadata": {
    "papermill": {
     "duration": 0.063072,
     "end_time": "2021-08-08T15:20:04.281516",
     "exception": false,
     "start_time": "2021-08-08T15:20:04.218444",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2 Class Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "unlimited-russia",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:04.413043Z",
     "iopub.status.busy": "2021-08-08T15:20:04.412284Z",
     "iopub.status.idle": "2021-08-08T15:20:04.460830Z",
     "shell.execute_reply": "2021-08-08T15:20:04.460436Z"
    },
    "papermill": {
     "duration": 0.116051,
     "end_time": "2021-08-08T15:20:04.460946",
     "exception": false,
     "start_time": "2021-08-08T15:20:04.344895",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "      <th>file_path</th>\n",
       "      <th>pred_2class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>/kaggle/tmp/test/image/65761e66de9f_image.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>/kaggle/tmp/test/image/51759b5579bc_image.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id PredictionString  \\\n",
       "2  65761e66de9f_image   none 1 0 0 1 1   \n",
       "3  51759b5579bc_image   none 1 0 0 1 1   \n",
       "\n",
       "                                       file_path  pred_2class  \n",
       "2  /kaggle/tmp/test/image/65761e66de9f_image.png            0  \n",
       "3  /kaggle/tmp/test/image/51759b5579bc_image.png            0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if fast_sub:\n",
    "    class2_sub_df = fast_df.copy()\n",
    "else:\n",
    "    class2_sub_df = pd.read_csv('/kaggle/input/siim-covid19-detection/sample_submission.csv')\n",
    "    \n",
    "    \n",
    "class2_sub_df = class2_sub_df[study_len:]\n",
    "\n",
    "class2_sub_df[\"file_path\"] = save_dir + class2_sub_df[\"id\"] + \".png\"\n",
    "label_cols = 'pred_2class'\n",
    "class2_sub_df[label_cols] = 0\n",
    "class2_sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "lasting-concern",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:04.601771Z",
     "iopub.status.busy": "2021-08-08T15:20:04.601033Z",
     "iopub.status.idle": "2021-08-08T15:20:04.644872Z",
     "shell.execute_reply": "2021-08-08T15:20:04.644471Z"
    },
    "papermill": {
     "duration": 0.1168,
     "end_time": "2021-08-08T15:20:04.645006",
     "exception": false,
     "start_time": "2021-08-08T15:20:04.528206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 对 train set 和 valid set 做数据增强\n",
    "from albumentations import (\n",
    "    HorizontalFlip, VerticalFlip, IAAPerspective, CLAHE, RandomRotate90,\n",
    "    Transpose, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,\n",
    "    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,\n",
    "    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose, Normalize, Cutout, CoarseDropout, ShiftScaleRotate, \n",
    "    CenterCrop, Resize, RandomCrop, GaussianBlur, JpegCompression, Downscale, ElasticTransform, ImageCompression\n",
    ")\n",
    "import albumentations\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "\n",
    "transforms_test = Compose([\n",
    "    Resize(CFG.image_size, CFG.image_size),\n",
    "    Normalize()\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "requested-thesis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:04.781095Z",
     "iopub.status.busy": "2021-08-08T15:20:04.780287Z",
     "iopub.status.idle": "2021-08-08T15:20:04.824205Z",
     "shell.execute_reply": "2021-08-08T15:20:04.824586Z"
    },
    "papermill": {
     "duration": 0.115965,
     "end_time": "2021-08-08T15:20:04.824729",
     "exception": false,
     "start_time": "2021-08-08T15:20:04.708764",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 定义数据集结构\n",
    "class SIIMDataset(Dataset):\n",
    "    def __init__(self, df, mode, transform=None):\n",
    "        self.df = df.reset_index(drop=True)\n",
    "        self.mode = mode # 数据集模式（train模式或test模式）\n",
    "        self.transform = transform # 数据增强\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.df) # 获取dataframe行数\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        row = self.df.loc[index] # 获取指定（index）行\n",
    "        \n",
    "        img = cv2.imread(row.file_path) # 用cv2读入图片数据\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2默认读入是BGR格式，现在转换成RGB格式\n",
    "        \n",
    "        # 如果有数据增强，则做数据增强\n",
    "        if self.transform is not None: \n",
    "            img = self.transform(image=img)['image']\n",
    "        \n",
    "        # 调整一下数据格式\n",
    "        img = img.transpose(2,0,1)\n",
    "        img = img.astype(np.float32)\n",
    "        # img = img/255 \n",
    "        \n",
    "        return torch.tensor(img).float() # 如果test模式，则只返回图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "unique-translator",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:04.964322Z",
     "iopub.status.busy": "2021-08-08T15:20:04.963556Z",
     "iopub.status.idle": "2021-08-08T15:20:05.006432Z",
     "shell.execute_reply": "2021-08-08T15:20:05.006791Z"
    },
    "papermill": {
     "duration": 0.117559,
     "end_time": "2021-08-08T15:20:05.006942",
     "exception": false,
     "start_time": "2021-08-08T15:20:04.889383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EfficientNetModel(nn.Module):\n",
    "    def __init__(self, num_classes=1, model_arch=CFG.model_arch, pretrained=True):\n",
    "        super(EfficientNetModel, self).__init__()\n",
    "        e = timm.create_model(model_arch, pretrained=pretrained, drop_rate=0, drop_path_rate=0)\n",
    "        \n",
    "        self.logit = nn.Linear(e.classifier.in_features, num_classes)\n",
    "        self.e1 = nn.Sequential(e.conv_stem, e.bn1, e.act1,)\n",
    "        \n",
    "        self.b0 = e.blocks[0]\n",
    "        self.b1 = e.blocks[1]\n",
    "        self.b2 = e.blocks[2]\n",
    "        self.b3 = e.blocks[3]\n",
    "        self.b4 = e.blocks[4] # channel = 224\n",
    "        self.b5 = e.blocks[5]\n",
    "        self.b6 = e.blocks[6]\n",
    "        \n",
    "        self.mask = nn.Sequential(\n",
    "            nn.Conv2d(224, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 1, kernel_size=1, padding=0),\n",
    "        )\n",
    "        self.e2 = nn.Sequential(e.conv_head, e.bn2, e.act2,)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = len(x)\n",
    "        x = self.e1(x)\n",
    "        x = self.b0(x)\n",
    "        x = self.b1(x)\n",
    "        x = self.b2(x)\n",
    "        x = self.b3(x)\n",
    "        x = self.b4(x)\n",
    "        # ==========================\n",
    "        mask = self.mask(x)\n",
    "        # ==========================\n",
    "        x = self.b5(x)\n",
    "        x = self.b6(x)\n",
    "        x = self.e2(x)\n",
    "        x = F.adaptive_avg_pool2d(x,1).reshape(batch_size,-1)\n",
    "        logit = self.logit(x)\n",
    "\n",
    "        return logit, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "human-dubai",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:05.141969Z",
     "iopub.status.busy": "2021-08-08T15:20:05.141204Z",
     "iopub.status.idle": "2021-08-08T15:20:49.556921Z",
     "shell.execute_reply": "2021-08-08T15:20:49.556467Z"
    },
    "papermill": {
     "duration": 44.485633,
     "end_time": "2021-08-08T15:20:49.557071",
     "exception": false,
     "start_time": "2021-08-08T15:20:05.071438",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/2class-pth/stage1_4632_fold0_tf_efficientnetv2_l_21k_512_best_9_9685.pth\n",
      "/kaggle/input/2class-pth/stage1_5812_fold1_tf_efficientnetv2_l_21k_512_best_5_9688.pth\n",
      "/kaggle/input/2class-pth/stage1_5812_fold2_tf_efficientnetv2_l_21k_512_best_7_9729.pth\n",
      "/kaggle/input/2class-pth/stage1_5812_fold3_tf_efficientnetv2_l_21k_512_best_8_9691.pth\n",
      "/kaggle/input/2class-pth/stage1_5812_fold4_tf_efficientnetv2_l_21k_512_best_9_9682.pth\n",
      "multi_gpu:  True\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "models_list = []\n",
    "def load_study_model():\n",
    "    for one_model_pth in CFG.class2_path:\n",
    "        if('v2_m' in one_model_pth):\n",
    "            model = EfficientNetB7(pretrained=False)\n",
    "        else:\n",
    "            model = EfficientNetModel(pretrained=False)\n",
    "        print(one_model_pth)\n",
    "        state = torch.load(one_model_pth, map_location='cuda:0') \n",
    "        if 1:\n",
    "            new_state_dict = OrderedDict()\n",
    "            for k, v in state.items():\n",
    "                k=k[7:]\n",
    "                new_state_dict[k]=v\n",
    "            model.load_state_dict(new_state_dict)\n",
    "        else:\n",
    "            model.load_state_dict(state)\n",
    "        model.to(device);\n",
    "        model.eval()\n",
    "        models_list.append(model)\n",
    "\n",
    "    print(\"multi_gpu: \", CFG.multi_gpu)\n",
    "    print(len(models_list))\n",
    "    \n",
    "load_study_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "boring-weather",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:49.711375Z",
     "iopub.status.busy": "2021-08-08T15:20:49.710607Z",
     "iopub.status.idle": "2021-08-08T15:20:49.799821Z",
     "shell.execute_reply": "2021-08-08T15:20:49.799357Z"
    },
    "papermill": {
     "duration": 0.169833,
     "end_time": "2021-08-08T15:20:49.799979",
     "exception": false,
     "start_time": "2021-08-08T15:20:49.630146",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def test_func(models_list,test_loader):\n",
    "    model.eval() # 模型调整到评估模式\n",
    "    all_logits = []\n",
    "    with torch.no_grad():\n",
    "        for images in tqdm(test_loader): \n",
    "            avg_model_preds = []\n",
    "            for model in models_list:\n",
    "                images = images.to(device).float() \n",
    "                logits, mask = model(images)  \n",
    "                avg_model_preds.append(logits)\n",
    "            avg_model_preds = np.mean(avg_model_preds, axis=0)\n",
    "        all_logits += [avg_model_preds.detach().cpu()] \n",
    "        all_logits = torch.cat(all_logits).cpu().numpy() \n",
    "        return all_logits\n",
    "\n",
    "def test_func_tta(models_list, test_loader):\n",
    "     # 模型调整到评估模式\n",
    "    all_logits = []\n",
    "    with torch.no_grad():\n",
    "        for images in tqdm(test_loader): \n",
    "            images = images.to(device).float() \n",
    "            avg_model_preds = []\n",
    "            for model in models_list:\n",
    "                model.eval()\n",
    "                logits1, mask1 = model(images)  \n",
    "                logits2, mask2 = model(images.flip(-1))\n",
    "                logits = (logits1.sigmoid().to('cpu').numpy() + logits2.sigmoid().to('cpu').numpy()) / 2\n",
    "                avg_model_preds.append(logits)\n",
    "            avg_model_preds = np.mean(avg_model_preds, axis=0)\n",
    "            all_logits.append(avg_model_preds)\n",
    "        all_logits = np.concatenate(all_logits)\n",
    "        return all_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "vulnerable-ensemble",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:49.944384Z",
     "iopub.status.busy": "2021-08-08T15:20:49.943632Z",
     "iopub.status.idle": "2021-08-08T15:20:53.238564Z",
     "shell.execute_reply": "2021-08-08T15:20:53.239056Z"
    },
    "papermill": {
     "duration": 3.371474,
     "end_time": "2021-08-08T15:20:53.239239",
     "exception": false,
     "start_time": "2021-08-08T15:20:49.867765",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0da7cfbe73394f7aa813c15ae9ea0464",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_test = SIIMDataset(class2_sub_df, 'test', transform = transforms_test)\n",
    "test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=CFG.valid_bs, shuffle=False, num_workers = CFG.n_worker, drop_last=False)\n",
    "\n",
    "if CFG.tta:\n",
    "    class2_sub_df[label_cols] = test_func_tta(models_list,test_loader)\n",
    "else:\n",
    "    class2_sub_df[label_cols] = test_func(models_list,test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "lovely-people",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:53.385472Z",
     "iopub.status.busy": "2021-08-08T15:20:53.384721Z",
     "iopub.status.idle": "2021-08-08T15:20:53.488498Z",
     "shell.execute_reply": "2021-08-08T15:20:53.489178Z"
    },
    "papermill": {
     "duration": 0.180382,
     "end_time": "2021-08-08T15:20:53.489377",
     "exception": false,
     "start_time": "2021-08-08T15:20:53.308995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "      <th>file_path</th>\n",
       "      <th>pred_2class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>/kaggle/tmp/test/image/65761e66de9f_image.png</td>\n",
       "      <td>0.890850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>/kaggle/tmp/test/image/51759b5579bc_image.png</td>\n",
       "      <td>0.645077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id PredictionString  \\\n",
       "2  65761e66de9f_image   none 1 0 0 1 1   \n",
       "3  51759b5579bc_image   none 1 0 0 1 1   \n",
       "\n",
       "                                       file_path  pred_2class  \n",
       "2  /kaggle/tmp/test/image/65761e66de9f_image.png     0.890850  \n",
       "3  /kaggle/tmp/test/image/51759b5579bc_image.png     0.645077  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class2_sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "going-electricity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:53.769983Z",
     "iopub.status.busy": "2021-08-08T15:20:53.766804Z",
     "iopub.status.idle": "2021-08-08T15:20:53.859681Z",
     "shell.execute_reply": "2021-08-08T15:20:53.860661Z"
    },
    "papermill": {
     "duration": 0.241234,
     "end_time": "2021-08-08T15:20:53.860854",
     "exception": false,
     "start_time": "2021-08-08T15:20:53.619620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "      <th>file_path</th>\n",
       "      <th>pred_2class</th>\n",
       "      <th>dim0</th>\n",
       "      <th>dim1</th>\n",
       "      <th>split</th>\n",
       "      <th>none</th>\n",
       "      <th>image_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>/kaggle/tmp/test/image/65761e66de9f_image.png</td>\n",
       "      <td>0.890850</td>\n",
       "      <td>2330</td>\n",
       "      <td>2783</td>\n",
       "      <td>test</td>\n",
       "      <td>0.109150</td>\n",
       "      <td>/kaggle/tmp/test/image/65761e66de9f_image.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>/kaggle/tmp/test/image/51759b5579bc_image.png</td>\n",
       "      <td>0.645077</td>\n",
       "      <td>3093</td>\n",
       "      <td>2850</td>\n",
       "      <td>test</td>\n",
       "      <td>0.354923</td>\n",
       "      <td>/kaggle/tmp/test/image/51759b5579bc_image.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id PredictionString  \\\n",
       "0  65761e66de9f_image   none 1 0 0 1 1   \n",
       "1  51759b5579bc_image   none 1 0 0 1 1   \n",
       "\n",
       "                                       file_path  pred_2class  dim0  dim1  \\\n",
       "0  /kaggle/tmp/test/image/65761e66de9f_image.png     0.890850  2330  2783   \n",
       "1  /kaggle/tmp/test/image/51759b5579bc_image.png     0.645077  3093  2850   \n",
       "\n",
       "  split      none                                     image_path  \n",
       "0  test  0.109150  /kaggle/tmp/test/image/65761e66de9f_image.png  \n",
       "1  test  0.354923  /kaggle/tmp/test/image/51759b5579bc_image.png  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2class = class2_sub_df.merge(meta,how=\"left\",on='id')\n",
    "df_2class[\"none\"] = 1-df_2class[label_cols]\n",
    "df_2class[\"image_path\"] = df_2class[\"file_path\"]\n",
    "df_2class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "final-little",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:54.084121Z",
     "iopub.status.busy": "2021-08-08T15:20:54.083284Z",
     "iopub.status.idle": "2021-08-08T15:20:54.431702Z",
     "shell.execute_reply": "2021-08-08T15:20:54.431209Z"
    },
    "papermill": {
     "duration": 0.4377,
     "end_time": "2021-08-08T15:20:54.431851",
     "exception": false,
     "start_time": "2021-08-08T15:20:53.994151",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del models_list\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorrect-cambodia",
   "metadata": {
    "papermill": {
     "duration": 0.073739,
     "end_time": "2021-08-08T15:20:54.575185",
     "exception": false,
     "start_time": "2021-08-08T15:20:54.501446",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Yolov5 predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "decimal-moscow",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:54.728675Z",
     "iopub.status.busy": "2021-08-08T15:20:54.727744Z",
     "iopub.status.idle": "2021-08-08T15:20:54.787242Z",
     "shell.execute_reply": "2021-08-08T15:20:54.787665Z"
    },
    "papermill": {
     "duration": 0.13736,
     "end_time": "2021-08-08T15:20:54.787841",
     "exception": false,
     "start_time": "2021-08-08T15:20:54.650481",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>id_last_str</th>\n",
       "      <th>dim0</th>\n",
       "      <th>dim1</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>e</td>\n",
       "      <td>2330</td>\n",
       "      <td>2783</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>e</td>\n",
       "      <td>3093</td>\n",
       "      <td>2850</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id id_last_str  dim0  dim1 split\n",
       "0  65761e66de9f_image           e  2330  2783  test\n",
       "1  51759b5579bc_image           e  3093  2850  test"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yolov5_df = all_sub_df[study_len:].reset_index(drop=True) \n",
    "yolov5_df = pd.merge(yolov5_df, meta, on='id', how='left')\n",
    "yolov5_df = yolov5_df.drop(['PredictionString'], axis=1)\n",
    "yolov5_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "unable-affiliate",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:54.953839Z",
     "iopub.status.busy": "2021-08-08T15:20:54.953257Z",
     "iopub.status.idle": "2021-08-08T15:20:55.547898Z",
     "shell.execute_reply": "2021-08-08T15:20:55.547076Z"
    },
    "papermill": {
     "duration": 0.679683,
     "end_time": "2021-08-08T15:20:55.548042",
     "exception": false,
     "start_time": "2021-08-08T15:20:54.868359",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dim = 512\n",
    "test_dir = f'/kaggle/tmp/{split}/image'\n",
    "shutil.copytree('/kaggle/input/d/kongbai97/yolov5-repo/yolov5-master', '/kaggle/working/yolov5')\n",
    "os.chdir('/kaggle/working/yolov5')\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "rural-image",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:20:55.715861Z",
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     "shell.execute_reply": "2021-08-08T15:21:05.049098Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[34m\u001B[1mdetect: \u001B[0mweights=['/kaggle/input/siim-cov19-yolov5l-train/yolov5/runs/train/exp/weights/best.pt'], source=/kaggle/tmp/test/image, imgsz=672, conf_thres=0.001, iou_thres=0.5, max_det=1000, device=, view_img=False, save_txt=True, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=True, visualize=False, update=False, project=runs/detect, name=exp2, exist_ok=True, line_thickness=3, hide_labels=False, hide_conf=False, half=False\r\n",
      "Results saved to runs/detect/exp2\r\n",
      "2 labels saved to runs/detect/exp2/labels\r\n",
      "Done. (0.366s)\r\n"
     ]
    }
   ],
   "source": [
    "weights_dir = '/kaggle/input/siim-cov19-yolov5l-train/yolov5/runs/train/exp/weights/best.pt'\n",
    "!python detect.py --weights $weights_dir\\\n",
    "--img 672\\\n",
    "--augment\\\n",
    "--conf 0.001\\\n",
    "--iou 0.5\\\n",
    "--source $test_dir\\\n",
    "--name exp2\\\n",
    "--save-txt --save-conf --exist-ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "continuous-yahoo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:05.195313Z",
     "iopub.status.busy": "2021-08-08T15:21:05.194501Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[34m\u001B[1mdetect: \u001B[0mweights=['/kaggle/input/new-yolov5-weights/5fold/fold0.pt'], source=/kaggle/tmp/test/image, imgsz=672, conf_thres=0.001, iou_thres=0.5, max_det=1000, device=, view_img=False, save_txt=True, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=True, visualize=False, update=False, project=runs/detect, name=expf0, exist_ok=True, line_thickness=3, hide_labels=False, hide_conf=False, half=False\r\n",
      "Results saved to runs/detect/expf0\r\n",
      "2 labels saved to runs/detect/expf0/labels\r\n",
      "Done. (0.420s)\r\n"
     ]
    }
   ],
   "source": [
    "weights_dir = '/kaggle/input/new-yolov5-weights/5fold/fold0.pt'\n",
    "!python detect.py --weights $weights_dir\\\n",
    "--img 672\\\n",
    "--augment\\\n",
    "--conf 0.001\\\n",
    "--iou 0.5\\\n",
    "--source $test_dir\\\n",
    "--name expf0\\\n",
    "--save-txt --save-conf --exist-ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "wicked-regular",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:15.233574Z",
     "iopub.status.busy": "2021-08-08T15:21:15.232809Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[34m\u001B[1mdetect: \u001B[0mweights=['/kaggle/input/new-yolov5-weights/5fold/fold1.pt'], source=/kaggle/tmp/test/image, imgsz=672, conf_thres=0.001, iou_thres=0.5, max_det=1000, device=, view_img=False, save_txt=True, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=True, visualize=False, update=False, project=runs/detect, name=expf1, exist_ok=True, line_thickness=3, hide_labels=False, hide_conf=False, half=False\r\n",
      "Results saved to runs/detect/expf1\r\n",
      "2 labels saved to runs/detect/expf1/labels\r\n",
      "Done. (0.406s)\r\n"
     ]
    }
   ],
   "source": [
    "weights_dir = '/kaggle/input/new-yolov5-weights/5fold/fold1.pt'\n",
    "import torch\n",
    "!python detect.py --weights $weights_dir\\\n",
    "--img 672\\\n",
    "--augment\\\n",
    "--conf 0.001\\\n",
    "--iou 0.5\\\n",
    "--source $test_dir\\\n",
    "--name expf1\\\n",
    "--save-txt --save-conf --exist-ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "successful-magic",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:25.178406Z",
     "iopub.status.busy": "2021-08-08T15:21:25.177587Z",
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     "shell.execute_reply": "2021-08-08T15:21:34.171553Z"
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     "exception": false,
     "start_time": "2021-08-08T15:21:25.101121",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[34m\u001B[1mdetect: \u001B[0mweights=['/kaggle/input/new-yolov5-weights/5fold/fold2.pt'], source=/kaggle/tmp/test/image, imgsz=672, conf_thres=0.001, iou_thres=0.5, max_det=1000, device=, view_img=False, save_txt=True, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=True, visualize=False, update=False, project=runs/detect, name=expf2, exist_ok=True, line_thickness=3, hide_labels=False, hide_conf=False, half=False\r\n",
      "Results saved to runs/detect/expf2\r\n",
      "2 labels saved to runs/detect/expf2/labels\r\n",
      "Done. (0.440s)\r\n"
     ]
    }
   ],
   "source": [
    "weights_dir = '/kaggle/input/new-yolov5-weights/5fold/fold2.pt'\n",
    "import torch\n",
    "!python detect.py --weights $weights_dir\\\n",
    "--img 672\\\n",
    "--augment\\\n",
    "--conf 0.001\\\n",
    "--iou 0.5\\\n",
    "--source $test_dir\\\n",
    "--name expf2\\\n",
    "--save-txt --save-conf --exist-ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "graphic-purchase",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:34.324185Z",
     "iopub.status.busy": "2021-08-08T15:21:34.318986Z",
     "iopub.status.idle": "2021-08-08T15:21:44.398515Z",
     "shell.execute_reply": "2021-08-08T15:21:44.397981Z"
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     "exception": false,
     "start_time": "2021-08-08T15:21:34.243612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[34m\u001B[1mdetect: \u001B[0mweights=['/kaggle/input/new-yolov5-weights/5fold/fold3.pt'], source=/kaggle/tmp/test/image, imgsz=672, conf_thres=0.001, iou_thres=0.5, max_det=1000, device=, view_img=False, save_txt=True, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=True, visualize=False, update=False, project=runs/detect, name=expf3, exist_ok=True, line_thickness=3, hide_labels=False, hide_conf=False, half=False\r\n",
      "Results saved to runs/detect/expf3\r\n",
      "2 labels saved to runs/detect/expf3/labels\r\n",
      "Done. (0.459s)\r\n"
     ]
    }
   ],
   "source": [
    "weights_dir = '/kaggle/input/new-yolov5-weights/5fold/fold3.pt'\n",
    "import torch\n",
    "!python detect.py --weights $weights_dir\\\n",
    "--img 672\\\n",
    "--augment\\\n",
    "--conf 0.001\\\n",
    "--iou 0.5\\\n",
    "--source $test_dir\\\n",
    "--name expf3\\\n",
    "--save-txt --save-conf --exist-ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "controversial-identification",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:44.558897Z",
     "iopub.status.busy": "2021-08-08T15:21:44.558131Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[34m\u001B[1mdetect: \u001B[0mweights=['/kaggle/input/new-yolov5-weights/5fold/fold4.pt'], source=/kaggle/tmp/test/image, imgsz=672, conf_thres=0.001, iou_thres=0.5, max_det=1000, device=, view_img=False, save_txt=True, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=True, visualize=False, update=False, project=runs/detect, name=expf4, exist_ok=True, line_thickness=3, hide_labels=False, hide_conf=False, half=False\r\n",
      "Results saved to runs/detect/expf4\r\n",
      "2 labels saved to runs/detect/expf4/labels\r\n",
      "Done. (0.427s)\r\n"
     ]
    }
   ],
   "source": [
    "weights_dir = '/kaggle/input/new-yolov5-weights/5fold/fold4.pt'\n",
    "import torch\n",
    "!python detect.py --weights $weights_dir\\\n",
    "--img 672\\\n",
    "--augment\\\n",
    "--conf 0.001\\\n",
    "--iou 0.5\\\n",
    "--source $test_dir\\\n",
    "--name expf4\\\n",
    "--save-txt --save-conf --exist-ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "polyphonic-scout",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:54.748258Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def yolo2voc(bboxes):\n",
    "    \"\"\"\n",
    "    yolo => [xmid, ymid, w, h] (normalized)\n",
    "    voc  => [x1, y1, x2, y1]\n",
    "\n",
    "    \"\"\" \n",
    "    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n",
    "\n",
    "    bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]]/2\n",
    "    bboxes[..., [2, 3]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]\n",
    "    \n",
    "    return bboxes\n",
    "\n",
    "def yolo2voc1(image_height, image_width, bboxes):\n",
    "    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n",
    "\n",
    "    bboxes[..., [0, 2]] = bboxes[..., [0, 2]]* image_width\n",
    "    bboxes[..., [1, 3]] = bboxes[..., [1, 3]]* image_height\n",
    "    \n",
    "    return bboxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "pregnant-engineer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:54.993761Z",
     "iopub.status.busy": "2021-08-08T15:21:54.986486Z",
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     "shell.execute_reply": "2021-08-08T15:21:55.567490Z",
     "shell.execute_reply.started": "2021-08-06T15:23:10.925499Z"
    },
    "papermill": {
     "duration": 0.683417,
     "end_time": "2021-08-08T15:21:55.568218",
     "exception": false,
     "start_time": "2021-08-08T15:21:54.884801",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights: [1, 1, 2, 1, 1]\n",
      "weights: [1, 1, 2, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "from ensemble_boxes import weighted_boxes_fusion\n",
    "image_ids = []\n",
    "PredictionStrings = []\n",
    "\n",
    "for i in range(len(yolov5_df)):\n",
    "    boxes_list = []\n",
    "    scores_list = []\n",
    "    labels_list = []\n",
    "    is_exist1 = True\n",
    "    is_exist2 = True\n",
    "    \n",
    "    iou_thr = 0.5\n",
    "    skip_box_thr = 0.0001\n",
    "    image_id = yolov5_df.loc[i,'id']\n",
    "    w, h = yolov5_df.loc[yolov5_df.id==image_id,['dim1', 'dim0']].values[0]\n",
    "    \n",
    "    boxes_list = []\n",
    "    scores_list = []\n",
    "    labels_list = []\n",
    "    weights = [1, 1, 1, 1, 1]\n",
    "    file_pathf0 = '/kaggle/working/yolov5/runs/detect/expf0/labels/{}.txt'.format(image_id)\n",
    "    if os.path.exists(file_pathf0):\n",
    "        f = open(file_pathf0, 'r')\n",
    "        data = np.array(f.read().replace('\\n', ' ').strip().split(' ')).astype(np.float32).reshape(-1, 6)\n",
    "        data = data[:, [0, 5, 1, 2, 3, 4]]\n",
    "        data[:,2:] = yolo2voc(data[:, 2:])\n",
    "        boxes_list.append(data[:,2:].tolist())\n",
    "        scores_list.append(data[:,1].tolist())\n",
    "        labels_list.append(data[:,0].tolist())\n",
    "        \n",
    "    file_pathf1 = '/kaggle/working/yolov5/runs/detect/expf1/labels/{}.txt'.format(image_id)\n",
    "    if os.path.exists(file_pathf1):\n",
    "        f = open(file_pathf1, 'r')\n",
    "        data = np.array(f.read().replace('\\n', ' ').strip().split(' ')).astype(np.float32).reshape(-1, 6)\n",
    "        data = data[:, [0, 5, 1, 2, 3, 4]]\n",
    "        data[:,2:] = yolo2voc(data[:, 2:])\n",
    "        boxes_list.append(data[:,2:].tolist())\n",
    "        scores_list.append(data[:,1].tolist())\n",
    "        labels_list.append(data[:,0].tolist())\n",
    "        \n",
    "    file_pathf2 = '/kaggle/working/yolov5/runs/detect/expf2/labels/{}.txt'.format(image_id)\n",
    "    if os.path.exists(file_pathf2):\n",
    "        f = open(file_pathf2, 'r')\n",
    "        data = np.array(f.read().replace('\\n', ' ').strip().split(' ')).astype(np.float32).reshape(-1, 6)\n",
    "        data = data[:, [0, 5, 1, 2, 3, 4]]\n",
    "        data[:,2:] = yolo2voc(data[:, 2:])\n",
    "        boxes_list.append(data[:,2:].tolist())\n",
    "        scores_list.append(data[:,1].tolist())\n",
    "        labels_list.append(data[:,0].tolist())\n",
    "        \n",
    "    file_pathf3 = '/kaggle/working/yolov5/runs/detect/expf3/labels/{}.txt'.format(image_id)\n",
    "    if os.path.exists(file_pathf3):\n",
    "        f = open(file_pathf3, 'r')\n",
    "        data = np.array(f.read().replace('\\n', ' ').strip().split(' ')).astype(np.float32).reshape(-1, 6)\n",
    "        data = data[:, [0, 5, 1, 2, 3, 4]]\n",
    "        data[:,2:] = yolo2voc(data[:, 2:])\n",
    "        boxes_list.append(data[:,2:].tolist())\n",
    "        scores_list.append(data[:,1].tolist())\n",
    "        labels_list.append(data[:,0].tolist())\n",
    "        \n",
    "    file_pathf4 = '/kaggle/working/yolov5/runs/detect/expf4/labels/{}.txt'.format(image_id)\n",
    "    if os.path.exists(file_pathf4):\n",
    "        f = open(file_pathf4, 'r')\n",
    "        data = np.array(f.read().replace('\\n', ' ').strip().split(' ')).astype(np.float32).reshape(-1, 6)\n",
    "        data = data[:, [0, 5, 1, 2, 3, 4]]\n",
    "        data[:,2:] = yolo2voc(data[:, 2:])\n",
    "        boxes_list.append(data[:,2:].tolist())\n",
    "        scores_list.append(data[:,1].tolist())\n",
    "        labels_list.append(data[:,0].tolist())\n",
    "        \n",
    "    \n",
    "    if len(boxes_list)>1:\n",
    "        if len(boxes_list)==2:\n",
    "            weights = [1, 1]\n",
    "        elif len(boxes_list)==3:\n",
    "            weights = [1, 1, 1]\n",
    "        elif len(boxes_list)==4:\n",
    "            weights = [1, 1, 1, 1]\n",
    "        else:\n",
    "            weights = [1, 1, 2, 1, 1] ####\n",
    "        print(\"weights:\",weights)\n",
    "        boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr)\n",
    "        data1 = np.zeros((boxes.shape[0],6))\n",
    "        data1[:,0] = labels\n",
    "        data1[:,1] = scores\n",
    "        data1[:,2:] = boxes\n",
    "    elif len(boxes_list)==1:\n",
    "        data1 = data\n",
    "    else:\n",
    "        is_exist1 = False\n",
    "        \n",
    "    file_path = '/kaggle/working/yolov5/runs/detect/exp2/labels/{}.txt'.format(image_id)\n",
    "    if os.path.exists(file_path):\n",
    "        f = open(file_path, 'r')\n",
    "        data = np.array(f.read().replace('\\n', ' ').strip().split(' ')).astype(np.float32).reshape(-1, 6)\n",
    "        data = data[:, [0, 5, 1, 2, 3, 4]]\n",
    "        data[:,2:] = yolo2voc(data[:, 2:])\n",
    "            \n",
    "    boxes_list = []\n",
    "    scores_list = []\n",
    "    labels_list = []\n",
    "    weights = [1, 6]\n",
    "    if os.path.exists(file_path) and is_exist1:\n",
    "        boxes_list.append(data[:,2:].tolist())\n",
    "        scores_list.append(data[:,1].tolist())\n",
    "        labels_list.append(data[:,0].tolist())\n",
    "        boxes_list.append(data1[:,2:].tolist())\n",
    "        scores_list.append(data1[:,1].tolist())\n",
    "        labels_list.append(data1[:,0].tolist())\n",
    "        boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr)\n",
    "        data2 = np.zeros((boxes.shape[0],6))\n",
    "        data2[:,0] = labels\n",
    "        data2[:,1] = scores\n",
    "        data2[:,2:] = boxes\n",
    "    elif os.path.exists(file_path) or is_exist1:\n",
    "        data2 = data\n",
    "    else:\n",
    "        is_exist2 = False\n",
    "        \n",
    "    if is_exist1==False and is_exist2==False:\n",
    "        continue\n",
    "            \n",
    "    bboxes = list(np.round(np.concatenate((data2[:, :2], np.round(yolo2voc1(h, w, data2[:, 2:]))), axis =1).reshape(-1), 12).astype(str))\n",
    "    for idx in range(len(bboxes)):\n",
    "        bboxes[idx] = str(int(float(bboxes[idx]))) if idx%6!=1 else bboxes[idx]\n",
    "    image_ids.append(image_id)\n",
    "    PredictionStrings.append(' '.join(bboxes))\n",
    "\n",
    "image_sub_2_df = pd.DataFrame({'id':image_ids,\n",
    "                        'PredictionString':PredictionStrings})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "stuck-spencer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:55.736443Z",
     "iopub.status.busy": "2021-08-08T15:21:55.735666Z",
     "iopub.status.idle": "2021-08-08T15:21:55.784815Z",
     "shell.execute_reply": "2021-08-08T15:21:55.784336Z"
    },
    "papermill": {
     "duration": 0.131967,
     "end_time": "2021-08-08T15:21:55.784956",
     "exception": false,
     "start_time": "2021-08-08T15:21:55.652989",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
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       "                   id                                   PredictionString\n",
       "0  65761e66de9f_image  0 0.185589805245 669 751 1261 1441 0 0.1627866...\n",
       "1  51759b5579bc_image  0 0.110834948719 1900 1235 2565 2095 0 0.08008..."
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     "execution_count": 34,
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   ],
   "source": [
    "image_sub_2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "understanding-maximum",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:55.948083Z",
     "iopub.status.busy": "2021-08-08T15:21:55.947301Z",
     "iopub.status.idle": "2021-08-08T15:21:55.998579Z",
     "shell.execute_reply": "2021-08-08T15:21:55.998193Z"
    },
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     "end_time": "2021-08-08T15:21:55.998683",
     "exception": false,
     "start_time": "2021-08-08T15:21:55.864903",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>0 0.185589805245 669 751 1261 1441 0 0.1627866...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>0 0.110834948719 1900 1235 2565 2095 0 0.08008...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                   PredictionString\n",
       "0  65761e66de9f_image  0 0.185589805245 669 751 1261 1441 0 0.1627866...\n",
       "1  51759b5579bc_image  0 0.110834948719 1900 1235 2565 2095 0 0.08008..."
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yolov5_df = pd.merge(yolov5_df, image_sub_2_df, on = 'id', how = 'left').fillna(\"none 1 0 0 1 1\")\n",
    "yolov5_df = yolov5_df[['id', 'PredictionString']]\n",
    "yolov5_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "mounted-intention",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:56.166065Z",
     "iopub.status.busy": "2021-08-08T15:21:56.165311Z",
     "iopub.status.idle": "2021-08-08T15:21:56.215554Z",
     "shell.execute_reply": "2021-08-08T15:21:56.215923Z"
    },
    "papermill": {
     "duration": 0.137445,
     "end_time": "2021-08-08T15:21:56.216057",
     "exception": false,
     "start_time": "2021-08-08T15:21:56.078612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>opacity 0.185589805245 669 751 1261 1441 opaci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>opacity 0.110834948719 1900 1235 2565 2095 opa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                   PredictionString\n",
       "0  65761e66de9f_image  opacity 0.185589805245 669 751 1261 1441 opaci...\n",
       "1  51759b5579bc_image  opacity 0.110834948719 1900 1235 2565 2095 opa..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(yolov5_df.shape[0]):\n",
    "    if yolov5_df.loc[i,'PredictionString'] == \"none 1 0 0 1 1\":\n",
    "        continue\n",
    "    sub_df_split = yolov5_df.loc[i,'PredictionString'].split()\n",
    "    sub_df_list = []\n",
    "    for j in range(int(len(sub_df_split) / 6)):\n",
    "        sub_df_list.append('opacity')\n",
    "        sub_df_list.append(sub_df_split[6 * j + 1])\n",
    "        sub_df_list.append(sub_df_split[6 * j + 2])\n",
    "        sub_df_list.append(sub_df_split[6 * j + 3])\n",
    "        sub_df_list.append(sub_df_split[6 * j + 4])\n",
    "        sub_df_list.append(sub_df_split[6 * j + 5])\n",
    "    yolov5_df.loc[i,'PredictionString'] = ' '.join(sub_df_list)\n",
    "\n",
    "yolov5_df['none'] = df_2class['none'] \n",
    "for i in range(yolov5_df.shape[0]):\n",
    "    if yolov5_df.loc[i,'PredictionString'] != 'none 1 0 0 1 1':\n",
    "        yolov5_df.loc[i,'PredictionString'] = yolov5_df.loc[i,'PredictionString'] + ' none ' + str(yolov5_df.loc[i,'none']) + ' 0 0 1 1'\n",
    "\n",
    "yolov5_df = yolov5_df[['id', 'PredictionString']] \n",
    "yolov5_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "imported-wiring",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:56.381781Z",
     "iopub.status.busy": "2021-08-08T15:21:56.381010Z",
     "iopub.status.idle": "2021-08-08T15:21:56.425423Z",
     "shell.execute_reply": "2021-08-08T15:21:56.425780Z"
    },
    "papermill": {
     "duration": 0.129241,
     "end_time": "2021-08-08T15:21:56.425926",
     "exception": false,
     "start_time": "2021-08-08T15:21:56.296685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "image_all_df = yolov5_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjustable-chorus",
   "metadata": {
    "papermill": {
     "duration": 0.080068,
     "end_time": "2021-08-08T15:21:56.587302",
     "exception": false,
     "start_time": "2021-08-08T15:21:56.507234",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# YOLOV5 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "identical-absorption",
   "metadata": {
    "papermill": {
     "duration": 0.079375,
     "end_time": "2021-08-08T15:21:56.744177",
     "exception": false,
     "start_time": "2021-08-08T15:21:56.664802",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# MMDET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "pretty-return",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:21:56.913079Z",
     "iopub.status.busy": "2021-08-08T15:21:56.912308Z",
     "iopub.status.idle": "2021-08-08T15:22:12.395130Z",
     "shell.execute_reply": "2021-08-08T15:22:12.394657Z"
    },
    "papermill": {
     "duration": 15.570458,
     "end_time": "2021-08-08T15:22:12.395264",
     "exception": false,
     "start_time": "2021-08-08T15:21:56.824806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "1.7.0 True\n",
      "11.0\n",
      "GCC 7.3\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import torch\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device.type)\n",
    "\n",
    "import torchvision\n",
    "print(torch.__version__, torch.cuda.is_available())\n",
    "\n",
    "# Check mmcv installation\n",
    "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n",
    "print(get_compiling_cuda_version())\n",
    "print(get_compiler_version())\n",
    "\n",
    "# Check MMDetection installation\n",
    "from mmdet.apis import set_random_seed\n",
    "\n",
    "# Imports\n",
    "import mmdet\n",
    "from mmdet.apis import set_random_seed\n",
    "from mmdet.datasets import build_dataset\n",
    "from mmdet.models import build_detector\n",
    "\n",
    "import mmcv\n",
    "from mmcv import Config\n",
    "from mmcv.runner import load_checkpoint\n",
    "from mmcv.parallel import MMDataParallel\n",
    "from mmdet.apis import inference_detector, init_detector, show_result_pyplot\n",
    "from mmdet.apis import single_gpu_test\n",
    "from mmdet.datasets import build_dataloader, build_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "united-plasma",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:12.565802Z",
     "iopub.status.busy": "2021-08-08T15:22:12.564985Z",
     "iopub.status.idle": "2021-08-08T15:22:12.616926Z",
     "shell.execute_reply": "2021-08-08T15:22:12.616497Z"
    },
    "papermill": {
     "duration": 0.14585,
     "end_time": "2021-08-08T15:22:12.617054",
     "exception": false,
     "start_time": "2021-08-08T15:22:12.471204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "label2color = [[59, 238, 119]]\n",
    "IMAGE_DIMS = (512, 512)\n",
    "\n",
    "viz_labels =  [\"Covid_Abnormality\"]\n",
    "\n",
    "def plot_img(img, size=(18, 18), is_rgb=True, title=\"\", cmap=None):\n",
    "    plt.figure(figsize=size)\n",
    "    plt.imshow(img, cmap=cmap)\n",
    "    plt.suptitle(title)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_imgs(imgs, cols=2, size=10, is_rgb=True, title=\"\", cmap=None, img_size=None):\n",
    "    rows = len(imgs)//cols + 1\n",
    "    fig = plt.figure(figsize=(cols*size, rows*size))\n",
    "    for i, img in enumerate(imgs):\n",
    "        if img_size is not None:\n",
    "            img = cv2.resize(img, img_size)\n",
    "        fig.add_subplot(rows, cols, i+1)\n",
    "        plt.imshow(img, cmap=cmap)\n",
    "    plt.suptitle(title)\n",
    "    return fig\n",
    "    \n",
    "def draw_bbox(image, box, label, color):   \n",
    "    alpha = 0.1\n",
    "    alpha_font = 0.6\n",
    "    thickness = 8\n",
    "    font_size = 2.0\n",
    "    font_weight = 1\n",
    "    overlay_bbox = image.copy()\n",
    "    overlay_text = image.copy()\n",
    "    output = image.copy()\n",
    "\n",
    "    text_width, text_height = cv2.getTextSize(label.upper(), cv2.FONT_HERSHEY_SIMPLEX, font_size, font_weight)[0]\n",
    "    cv2.rectangle(overlay_bbox, (box[0], box[1]), (box[2], box[3]),\n",
    "                color, -1)\n",
    "    cv2.addWeighted(overlay_bbox, alpha, output, 1 - alpha, 0, output)\n",
    "    cv2.rectangle(overlay_text, (box[0], box[1]-18-text_height), (box[0]+text_width+8, box[1]),\n",
    "                (0, 0, 0), -1)\n",
    "    cv2.addWeighted(overlay_text, alpha_font, output, 1 - alpha_font, 0, output)\n",
    "    cv2.rectangle(output, (box[0], box[1]), (box[2], box[3]),\n",
    "                    color, thickness)\n",
    "    cv2.putText(output, label.upper(), (box[0], box[1]-12),\n",
    "            cv2.FONT_HERSHEY_SIMPLEX, font_size, (255, 255, 255), font_weight, cv2.LINE_AA)\n",
    "    return output\n",
    "\n",
    "def draw_bbox_small(image, box, label, color):   \n",
    "    alpha = 0.1\n",
    "    alpha_text = 0.3\n",
    "    thickness = 1\n",
    "    font_size = 0.4\n",
    "    overlay_bbox = image.copy()\n",
    "    overlay_text = image.copy()\n",
    "    output = image.copy()\n",
    "\n",
    "    text_width, text_height = cv2.getTextSize(label.upper(), cv2.FONT_HERSHEY_SIMPLEX, font_size, thickness)[0]\n",
    "    cv2.rectangle(overlay_bbox, (box[0], box[1]), (box[2], box[3]),\n",
    "                color, -1)\n",
    "    cv2.addWeighted(overlay_bbox, alpha, output, 1 - alpha, 0, output)\n",
    "    cv2.rectangle(overlay_text, (box[0], box[1]-7-text_height), (box[0]+text_width+2, box[1]),\n",
    "                (0, 0, 0), -1)\n",
    "    cv2.addWeighted(overlay_text, alpha_text, output, 1 - alpha_text, 0, output)\n",
    "    cv2.rectangle(output, (box[0], box[1]), (box[2], box[3]),\n",
    "                    color, thickness)\n",
    "    cv2.putText(output, label.upper(), (box[0], box[1]-5),\n",
    "            cv2.FONT_HERSHEY_SIMPLEX, font_size, (255, 255, 255), thickness, cv2.LINE_AA)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "latest-scout",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:12.777114Z",
     "iopub.status.busy": "2021-08-08T15:22:12.776328Z",
     "iopub.status.idle": "2021-08-08T15:22:21.774015Z",
     "shell.execute_reply": "2021-08-08T15:22:21.774981Z"
    },
    "papermill": {
     "duration": 9.083261,
     "end_time": "2021-08-08T15:22:21.775185",
     "exception": false,
     "start_time": "2021-08-08T15:22:12.691924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use load_from_local loader\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/kaggle/working/mmdetection/mmdet/apis/inference.py:47: UserWarning: Class names are not saved in the checkpoint's meta data, use COCO classes by default.\n",
      "  warnings.warn('Class names are not saved in the checkpoint\\'s '\n"
     ]
    }
   ],
   "source": [
    "baseline_cfg_path = \"/kaggle/input/siim-mmdetection-cascadercnn-weight-bias/job4_cascade_rcnn_x101_32x4d_fpn_1x_fold0/job4_cascade_rcnn_x101_32x4d_fpn_1x_coco.py\"\n",
    "cfg = Config.fromfile(baseline_cfg_path)\n",
    "\n",
    "cfg.classes = (\"Covid_Abnormality\")\n",
    "cfg.data.test.img_prefix = ''\n",
    "cfg.data.test.classes = cfg.classes\n",
    "\n",
    "# cfg.model.roi_head.bbox_head.num_classes = 1\n",
    "# cfg.model.bbox_head.num_classes = 1\n",
    "for head in cfg.model.roi_head.bbox_head:\n",
    "    head.num_classes = 1\n",
    "\n",
    "# Set seed thus the results are more reproducible\n",
    "cfg.seed = 211\n",
    "set_random_seed(211, deterministic=False)\n",
    "cfg.gpu_ids = [0]\n",
    "\n",
    "cfg.data.test.pipeline=[\n",
    "            dict(type='LoadImageFromFile'),\n",
    "            dict(\n",
    "                type='MultiScaleFlipAug',\n",
    "                img_scale=(1333, 800),\n",
    "                flip=False,\n",
    "                transforms=[\n",
    "                    dict(type='Resize', keep_ratio=True),\n",
    "                    dict(type='RandomFlip', direction='horizontal'),\n",
    "                    dict(\n",
    "                        type='Normalize',\n",
    "                        mean=[123.675, 116.28, 103.53],\n",
    "                        std=[58.395, 57.12, 57.375],\n",
    "                        to_rgb=True),\n",
    "                    dict(type='Pad', size_divisor=32),\n",
    "                    dict(type='DefaultFormatBundle'),\n",
    "                    dict(type='Collect', keys=['img'])\n",
    "                ])\n",
    "        ]\n",
    "\n",
    "cfg.test_pipeline = [\n",
    "            dict(type='LoadImageFromFile'),\n",
    "            dict(\n",
    "                type='MultiScaleFlipAug',\n",
    "                img_scale=(1333, 800),\n",
    "                flip=False,\n",
    "                transforms=[\n",
    "                    dict(type='Resize', keep_ratio=True),\n",
    "                    dict(type='RandomFlip', direction='horizontal'),\n",
    "                    dict(\n",
    "                        type='Normalize',\n",
    "                        mean=[123.675, 116.28, 103.53],\n",
    "                        std=[58.395, 57.12, 57.375],\n",
    "                        to_rgb=True),\n",
    "                    dict(type='Pad', size_divisor=32),\n",
    "                    dict(type='DefaultFormatBundle'),\n",
    "                    dict(type='Collect', keys=['img'])\n",
    "                ])\n",
    "        ]\n",
    "\n",
    "\n",
    "cfg.model.test_cfg.rcnn.score_thr = 0.001\n",
    "\n",
    "WEIGHTS_FILE = '/kaggle/input/siim-mmdetection-cascadercnn-weight-bias/job4_cascade_rcnn_x101_32x4d_fpn_1x_fold0/epoch_10.pth'\n",
    "options = dict(classes = (\"Covid_Abnormality\"))\n",
    "model = init_detector(cfg, WEIGHTS_FILE, device='cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "coupled-distinction",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:22.041368Z",
     "iopub.status.busy": "2021-08-08T15:22:22.040560Z",
     "iopub.status.idle": "2021-08-08T15:22:23.827011Z",
     "shell.execute_reply": "2021-08-08T15:22:23.827423Z"
    },
    "papermill": {
     "duration": 1.916678,
     "end_time": "2021-08-08T15:22:23.827566",
     "exception": false,
     "start_time": "2021-08-08T15:22:21.910888",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85a5322b112748a0b4834249ce3452ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/kaggle/working/mmdetection/mmdet/core/anchor/anchor_generator.py:323: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors`` \n",
      "  warnings.warn('``grid_anchors`` would be deprecated soon. '\n",
      "/kaggle/working/mmdetection/mmdet/core/anchor/anchor_generator.py:360: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors`` \n",
      "  '``single_level_grid_anchors`` would be deprecated soon. '\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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      "text/plain": [
       "                   id                                   PredictionString\n",
       "1  51759b5579bc_image  opacity 0.7418073415756226 145 1851 998 2550 o...\n",
       "0  65761e66de9f_image  opacity 0.8058207631111145 1821 813 2418 1529 ..."
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz_images = []\n",
    "results = []\n",
    "score_threshold = cfg.model.test_cfg.rcnn.score_thr\n",
    "\n",
    "def format_pred(boxes: np.ndarray, scores: np.ndarray, labels: np.ndarray) -> str:\n",
    "    pred_strings = []\n",
    "    label_str = ['opacity']\n",
    "    for label, score, bbox in zip(labels, scores, boxes):\n",
    "        xmin, ymin, xmax, ymax = bbox.astype(np.int64)\n",
    "        pred_strings.append(f\"{label_str[int(label)]} {score:.16f} {xmin} {ymin} {xmax} {ymax}\")\n",
    "    return \" \".join(pred_strings)\n",
    "\n",
    "model.to(device)\n",
    "model.eval()\n",
    "\n",
    "viz_images = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for index, row in tqdm(df_2class.iterrows(), total=df_2class.shape[0]):\n",
    "        original_H, original_W = (int(row.dim0), int(row.dim1))\n",
    "        predictions = inference_detector(model, row.image_path)\n",
    "        boxes, scores, labels = (list(), list(), list())\n",
    "\n",
    "        for k, cls_result in enumerate(predictions):\n",
    "#             print(\"cls_result\", cls_result)\n",
    "            if cls_result.size != 0:\n",
    "                if len(labels)==0:\n",
    "                    boxes = np.array(cls_result[:, :4])\n",
    "                    scores = np.array(cls_result[:, 4])\n",
    "                    labels = np.array([k]*len(cls_result[:, 4]))\n",
    "                else:    \n",
    "                    boxes = np.concatenate((boxes, np.array(cls_result[:, :4])))\n",
    "                    scores = np.concatenate((scores, np.array(cls_result[:, 4])))\n",
    "                    labels = np.concatenate((labels, [k]*len(cls_result[:, 4])))\n",
    "                    \n",
    "            if fast_sub:\n",
    "                img_viz = cv2.imread(row.image_path)\n",
    "                for box, label, score in zip(boxes, labels, scores):\n",
    "                    color = label2color[int(label)]\n",
    "                    img_viz = draw_bbox_small(img_viz, box.astype(np.int32), f'opacity_{score:.4f}', color)\n",
    "                viz_images.append(img_viz)\n",
    "\n",
    "        indexes = np.where(scores > score_threshold)\n",
    "#         print(indexes)\n",
    "        boxes = boxes[indexes]\n",
    "        scores = scores[indexes]\n",
    "        labels = labels[indexes]\n",
    "\n",
    "        if len(labels) != 0:\n",
    "            h_ratio = original_H/IMAGE_DIMS[0]\n",
    "            w_ratio = original_W/IMAGE_DIMS[1]\n",
    "            boxes[:, [0, 2]] *= w_ratio\n",
    "            boxes[:, [1, 3]] *= h_ratio\n",
    "\n",
    "            result = {\n",
    "                \"id\": row.id,\n",
    "                \"PredictionString\": format_pred(\n",
    "                    boxes, scores, labels\n",
    "                ),\n",
    "            }\n",
    "\n",
    "            results.append(result)\n",
    "del model\n",
    "gc.collect()\n",
    "\n",
    "mmdet_df = pd.DataFrame(results, columns=['id', 'PredictionString'])\n",
    "\n",
    "if fast_sub:\n",
    "    display(mmdet_df.sample(2))\n",
    "    # Plot sample images\n",
    "    plot_imgs(viz_images, cmap=None)\n",
    "    plt.savefig('viz_fig_siim.png', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "strategic-contributor",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:24.020656Z",
     "iopub.status.busy": "2021-08-08T15:22:24.020135Z",
     "iopub.status.idle": "2021-08-08T15:22:24.125150Z",
     "shell.execute_reply": "2021-08-08T15:22:24.124717Z"
    },
    "papermill": {
     "duration": 0.203291,
     "end_time": "2021-08-08T15:22:24.125264",
     "exception": false,
     "start_time": "2021-08-08T15:22:23.921973",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>opacity 0.8058207631111145 1821 813 2418 1529 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>opacity 0.7418073415756226 145 1851 998 2550 o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                   PredictionString\n",
       "0  65761e66de9f_image  opacity 0.8058207631111145 1821 813 2418 1529 ...\n",
       "1  51759b5579bc_image  opacity 0.7418073415756226 145 1851 998 2550 o..."
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmdet_df = mmdet_df.merge(df_2class[['id', 'none']], on='id', how='left')\n",
    "\n",
    "for i in range(mmdet_df.shape[0]):\n",
    "    if mmdet_df.loc[i,'PredictionString'] != 'none 1 0 0 1 1':\n",
    "        mmdet_df.loc[i,'PredictionString'] = mmdet_df.loc[i,'PredictionString'] + ' none ' + str(mmdet_df.loc[i,'none']) + ' 0 0 1 1'\n",
    "mmdet_df = mmdet_df[['id', 'PredictionString']]\n",
    "\n",
    "mmdet_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "generic-jesus",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:24.316287Z",
     "iopub.status.busy": "2021-08-08T15:22:24.315751Z",
     "iopub.status.idle": "2021-08-08T15:22:24.571498Z",
     "shell.execute_reply": "2021-08-08T15:22:24.570990Z"
    },
    "papermill": {
     "duration": 0.353163,
     "end_time": "2021-08-08T15:22:24.571620",
     "exception": false,
     "start_time": "2021-08-08T15:22:24.218457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "mmdet_df.to_csv(\"/kaggle/working/mmdet_df.csv\",index=False)\n",
    "!rm -r /kaggle/working/mmdetection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-twins",
   "metadata": {
    "papermill": {
     "duration": 0.094147,
     "end_time": "2021-08-08T15:22:24.761086",
     "exception": false,
     "start_time": "2021-08-08T15:22:24.666939",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Study_Level Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "younger-frank",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:24.954368Z",
     "iopub.status.busy": "2021-08-08T15:22:24.953658Z",
     "iopub.status.idle": "2021-08-08T15:22:25.017125Z",
     "shell.execute_reply": "2021-08-08T15:22:25.016708Z"
    },
    "papermill": {
     "duration": 0.162796,
     "end_time": "2021-08-08T15:22:25.017239",
     "exception": false,
     "start_time": "2021-08-08T15:22:24.854443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['negative', 'typical', 'indeterminate', 'atypical'], dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if fast_sub:\n",
    "    study_sub_df = fast_df.copy()\n",
    "else:\n",
    "    study_sub_df = pd.read_csv('/kaggle/input/siim-covid19-detection/sample_submission.csv')\n",
    "    \n",
    "    \n",
    "study_sub_df = study_sub_df[:study_len]\n",
    "\n",
    "study_sub_df[\"file_path\"] = save_study_dir + study_sub_df[\"id\"] + \".png\"\n",
    "study_sub_df['negative'] = 0\n",
    "study_sub_df['typical'] = 0\n",
    "study_sub_df['indeterminate'] = 0\n",
    "study_sub_df['atypical'] = 0\n",
    "\n",
    "label_cols = study_sub_df.columns[3:]\n",
    "label_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "elder-robin",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:25.211675Z",
     "iopub.status.busy": "2021-08-08T15:22:25.210903Z",
     "iopub.status.idle": "2021-08-08T15:22:25.259336Z",
     "shell.execute_reply": "2021-08-08T15:22:25.258899Z"
    },
    "papermill": {
     "duration": 0.148401,
     "end_time": "2021-08-08T15:22:25.259447",
     "exception": false,
     "start_time": "2021-08-08T15:22:25.111046",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 对 train set 和 valid set 做数据增强\n",
    "from albumentations import (\n",
    "    HorizontalFlip, VerticalFlip, IAAPerspective, CLAHE, RandomRotate90,\n",
    "    Transpose, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,\n",
    "    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,\n",
    "    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose, Normalize, Cutout, CoarseDropout, ShiftScaleRotate, \n",
    "    CenterCrop, Resize, RandomCrop, GaussianBlur, JpegCompression, Downscale, ElasticTransform, ImageCompression\n",
    ")\n",
    "import albumentations\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "\n",
    "transforms_test = Compose([\n",
    "    Resize(CFG.image_size, CFG.image_size),\n",
    "    Normalize()\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fixed-pilot",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:25.454877Z",
     "iopub.status.busy": "2021-08-08T15:22:25.454123Z",
     "iopub.status.idle": "2021-08-08T15:22:25.501753Z",
     "shell.execute_reply": "2021-08-08T15:22:25.502156Z"
    },
    "papermill": {
     "duration": 0.149173,
     "end_time": "2021-08-08T15:22:25.502287",
     "exception": false,
     "start_time": "2021-08-08T15:22:25.353114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 定义数据集结构\n",
    "class SIIMDataset(Dataset):\n",
    "    def __init__(self, df, mode, transform=None):\n",
    "        self.df = df.reset_index(drop=True)\n",
    "        self.mode = mode # 数据集模式（train模式或test模式）\n",
    "        self.transform = transform # 数据增强\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.df) # 获取dataframe行数\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        row = self.df.loc[index] # 获取指定（index）行\n",
    "        \n",
    "        img = cv2.imread(row.file_path) # 用cv2读入图片数据\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2默认读入是BGR格式，现在转换成RGB格式\n",
    "        \n",
    "        # 如果有数据增强，则做数据增强\n",
    "        if self.transform is not None: \n",
    "            img = self.transform(image=img)['image']\n",
    "        \n",
    "        # 调整一下数据格式\n",
    "        img = img.transpose(2,0,1)\n",
    "        img = img.astype(np.float32)\n",
    "        # img = img/255 \n",
    "        \n",
    "        # 返回 获取到的图片\n",
    "        if self.mode == 'test':\n",
    "            return torch.tensor(img).float() # 如果test模式，则只返回图片\n",
    "        else:\n",
    "            return torch.tensor(img).float(), torch.tensor(row[:4]).float() # 如果其他模式，则返回 图片 和 其所属类别标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "supposed-amount",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:25.724300Z",
     "iopub.status.busy": "2021-08-08T15:22:25.723343Z",
     "iopub.status.idle": "2021-08-08T15:22:25.782750Z",
     "shell.execute_reply": "2021-08-08T15:22:25.782260Z"
    },
    "papermill": {
     "duration": 0.185566,
     "end_time": "2021-08-08T15:22:25.782897",
     "exception": false,
     "start_time": "2021-08-08T15:22:25.597331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EfficientNetModel(nn.Module):\n",
    "    def __init__(self, num_classes=4, model_arch=CFG.model_arch, pretrained=True):\n",
    "        super(EfficientNetModel, self).__init__()\n",
    "        e = timm.create_model(model_arch, pretrained=pretrained, drop_rate=0, drop_path_rate=0)\n",
    "        \n",
    "        self.logit = nn.Linear(e.classifier.in_features, num_classes)\n",
    "        self.e1 = nn.Sequential(e.conv_stem, e.bn1, e.act1,)\n",
    "        \n",
    "        self.b0 = e.blocks[0]\n",
    "        self.b1 = e.blocks[1]\n",
    "        self.b2 = e.blocks[2]\n",
    "        self.b3 = e.blocks[3]\n",
    "        self.b4 = e.blocks[4] # channel = 224\n",
    "        self.b5 = e.blocks[5]\n",
    "        self.b6 = e.blocks[6]\n",
    "        \n",
    "        self.mask = nn.Sequential(\n",
    "            nn.Conv2d(224, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 1, kernel_size=1, padding=0),\n",
    "        )\n",
    "        self.e2 = nn.Sequential(e.conv_head, e.bn2, e.act2,)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = len(x)\n",
    "        x = self.e1(x)\n",
    "        x = self.b0(x)\n",
    "        x = self.b1(x)\n",
    "        x = self.b2(x)\n",
    "        x = self.b3(x)\n",
    "        x = self.b4(x)\n",
    "        # ==========================\n",
    "        mask = self.mask(x)\n",
    "        # ==========================\n",
    "        x = self.b5(x)\n",
    "        x = self.b6(x)\n",
    "        x = self.e2(x)\n",
    "        x = F.adaptive_avg_pool2d(x,1).reshape(batch_size,-1)\n",
    "        logit = self.logit(x)\n",
    "\n",
    "        return logit, mask\n",
    "    \n",
    "    \n",
    "class EfficientNetB7(nn.Module):\n",
    "    def __init__(self, model_name='tf_efficientnet_b7_ns',pretrained=False):\n",
    "        super().__init__()        \n",
    "        e = timm.create_model(model_name, pretrained=False)\n",
    "#         print(e)\n",
    "        self.logit = nn.Linear(e.classifier.in_features, 4)\n",
    "        self.e1 = nn.Sequential(e.conv_stem, e.bn1, e.act1,)\n",
    "        \n",
    "        self.b0 = e.blocks[0]\n",
    "        self.b1 = e.blocks[1]\n",
    "        self.b2 = e.blocks[2]\n",
    "        self.b3 = e.blocks[3]\n",
    "        self.b4 = e.blocks[4] # channel = 224\n",
    "        self.b5 = e.blocks[5]\n",
    "        self.b6 = e.blocks[6]\n",
    "        \n",
    "        self.mask = nn.Sequential(\n",
    "            nn.Conv2d(224, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 1, kernel_size=1, padding=0),\n",
    "        )\n",
    "        \n",
    "        self.e2 = nn.Sequential(e.conv_head, e.bn2, e.act2,)\n",
    "    def forward(self, x):\n",
    "        batch_size = len(x)\n",
    "        x = self.e1(x)\n",
    "        x = self.b0(x)\n",
    "        x = self.b1(x)\n",
    "        x = self.b2(x)\n",
    "        x = self.b3(x)\n",
    "        x = self.b4(x)\n",
    "        # ==========================\n",
    "        mask = self.mask(x)\n",
    "        # ==========================\n",
    "        x = self.b5(x)\n",
    "        x = self.b6(x)\n",
    "        x = self.e2(x)\n",
    "        x = F.adaptive_avg_pool2d(x,1).reshape(batch_size,-1)\n",
    "        logit = self.logit(x)\n",
    "#         print(mask.size())\n",
    "#         print(logit.size())\n",
    "        return logit, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "victorian-paintball",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:22:25.991410Z",
     "iopub.status.busy": "2021-08-08T15:22:25.990682Z",
     "iopub.status.idle": "2021-08-08T15:23:31.266182Z",
     "shell.execute_reply": "2021-08-08T15:23:31.265669Z"
    },
    "papermill": {
     "duration": 65.376235,
     "end_time": "2021-08-08T15:23:31.266323",
     "exception": false,
     "start_time": "2021-08-08T15:22:25.890088",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/study-model-2/stage1_4749_fold0_tf_efficientnetv2_l_21k_512_best_9_5701.pth\n",
      "/kaggle/input/study-model-2/stage1_1852_fold1_tf_efficientnetv2_l_21k_512_best_9_5724.pth\n",
      "/kaggle/input/study-model/stage1_0218_fold2_tf_efficientnetv2_l_21k_512_best_7_6025.pth\n",
      "/kaggle/input/study-model/stage1_0218_fold3_tf_efficientnetv2_l_21k_512_best_9_5855.pth\n",
      "/kaggle/input/study-model/stage1_0218_fold4_tf_efficientnetv2_l_21k_512_best_9_5945.pth\n",
      "/kaggle/input/simeffb7/stage1_1852_fold0_tf_efficientnetv2_m_512_best_5_5800.pth\n",
      "/kaggle/input/simeffb7/stage1_1852_fold1_tf_efficientnetv2_m_512_best_6_5684.pth\n",
      "/kaggle/input/simeffb7/stage1_1852_fold2_tf_efficientnetv2_m_512_best_6_6087.pth\n",
      "/kaggle/input/simeffb7/stage1_1852_fold3_tf_efficientnetv2_m_512_best_5_5798.pth\n",
      "/kaggle/input/simeffb7/stage1_1852_fold4_tf_efficientnetv2_m_512_best_3_5965.pth\n",
      "multi_gpu:  True\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "models_list = []\n",
    "def load_study_model():\n",
    "    for one_model_pth in CFG.model_path:\n",
    "        if('v2_m' in one_model_pth):\n",
    "            model = EfficientNetB7(pretrained=False)\n",
    "        else:\n",
    "            model = EfficientNetModel(pretrained=False)\n",
    "        print(one_model_pth)\n",
    "        state = torch.load(one_model_pth, map_location='cuda:0') \n",
    "        if 1:\n",
    "            new_state_dict = OrderedDict()\n",
    "            for k, v in state.items():\n",
    "                k=k[7:]\n",
    "                new_state_dict[k]=v\n",
    "            model.load_state_dict(new_state_dict)\n",
    "        else:\n",
    "            model.load_state_dict(state)\n",
    "        model.to(device);\n",
    "        model.eval()\n",
    "        models_list.append(model)\n",
    "\n",
    "    print(\"multi_gpu: \", CFG.multi_gpu)\n",
    "    print(len(models_list))\n",
    "    \n",
    "load_study_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "excellent-charity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:23:31.471315Z",
     "iopub.status.busy": "2021-08-08T15:23:31.470528Z",
     "iopub.status.idle": "2021-08-08T15:23:31.569168Z",
     "shell.execute_reply": "2021-08-08T15:23:31.568157Z"
    },
    "papermill": {
     "duration": 0.204901,
     "end_time": "2021-08-08T15:23:31.569285",
     "exception": false,
     "start_time": "2021-08-08T15:23:31.364384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def test_func(models_list,test_loader):\n",
    "    model.eval() # 模型调整到评估模式\n",
    "    all_logits = []\n",
    "    with torch.no_grad():\n",
    "        for images in tqdm(test_loader): \n",
    "            avg_model_preds = []\n",
    "            for model in models_list:\n",
    "                images = images.to(device).float() \n",
    "                logits, mask = model(images)  \n",
    "                avg_model_preds.append(logits)\n",
    "            avg_model_preds = np.mean(avg_model_preds, axis=0)\n",
    "        all_logits += [avg_model_preds.detach().cpu()] \n",
    "        all_logits = torch.cat(all_logits).cpu().numpy() \n",
    "        return all_logits\n",
    "\n",
    "def test_func_tta(models_list, test_loader):\n",
    "     # 模型调整到评估模式\n",
    "    all_logits = []\n",
    "    with torch.no_grad():\n",
    "        for images in tqdm(test_loader): \n",
    "            images = images.to(device).float() \n",
    "            avg_model_preds = []\n",
    "            for model in models_list:\n",
    "                model.eval()\n",
    "                logits1, mask1 = model(images)  \n",
    "                logits2, mask2 = model(images.flip(-1))\n",
    "                logits = (logits1.sigmoid().to('cpu').numpy() + logits2.sigmoid().to('cpu').numpy()) / 2\n",
    "                avg_model_preds.append(logits)\n",
    "            avg_model_preds = np.average(avg_model_preds, axis=0, weights=CFG.class_weight) ####\n",
    "            all_logits.append(avg_model_preds)\n",
    "        all_logits = np.concatenate(all_logits)\n",
    "        return all_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "molecular-rebecca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:23:31.771125Z",
     "iopub.status.busy": "2021-08-08T15:23:31.770343Z",
     "iopub.status.idle": "2021-08-08T15:23:35.889375Z",
     "shell.execute_reply": "2021-08-08T15:23:35.888891Z"
    },
    "papermill": {
     "duration": 4.223111,
     "end_time": "2021-08-08T15:23:35.889499",
     "exception": false,
     "start_time": "2021-08-08T15:23:31.666388",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fc53e5ac3d444e8aa8d426bac62dd6c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "      <th>id_last_str</th>\n",
       "      <th>PredictionString1</th>\n",
       "      <th>negative</th>\n",
       "      <th>typical</th>\n",
       "      <th>indeterminate</th>\n",
       "      <th>atypical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00086460a852_study</td>\n",
       "      <td>negative 1 0 0 1 1</td>\n",
       "      <td>y</td>\n",
       "      <td>negative 1 0 0 1 1</td>\n",
       "      <td>0.082841</td>\n",
       "      <td>0.378069</td>\n",
       "      <td>0.203301</td>\n",
       "      <td>0.335101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000c9c05fd14_study</td>\n",
       "      <td>negative 1 0 0 1 1</td>\n",
       "      <td>y</td>\n",
       "      <td>negative 1 0 0 1 1</td>\n",
       "      <td>0.043461</td>\n",
       "      <td>0.065455</td>\n",
       "      <td>0.068631</td>\n",
       "      <td>0.862181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>none 1 0 0 1 1</td>\n",
       "      <td>e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id    PredictionString id_last_str   PredictionString1  \\\n",
       "0  00086460a852_study  negative 1 0 0 1 1           y  negative 1 0 0 1 1   \n",
       "1  000c9c05fd14_study  negative 1 0 0 1 1           y  negative 1 0 0 1 1   \n",
       "2  65761e66de9f_image      none 1 0 0 1 1           e                 NaN   \n",
       "3  51759b5579bc_image      none 1 0 0 1 1           e                 NaN   \n",
       "\n",
       "   negative   typical  indeterminate  atypical  \n",
       "0  0.082841  0.378069       0.203301  0.335101  \n",
       "1  0.043461  0.065455       0.068631  0.862181  \n",
       "2       NaN       NaN            NaN       NaN  \n",
       "3       NaN       NaN            NaN       NaN  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test = SIIMDataset(study_sub_df, 'test', transform = transforms_test)\n",
    "test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=CFG.valid_bs, shuffle=False, num_workers = CFG.n_worker, drop_last=False)\n",
    "\n",
    "if CFG.tta:\n",
    "    study_sub_df[label_cols] = test_func_tta(models_list,test_loader)\n",
    "else:\n",
    "    study_sub_df[label_cols] = test_func(models_list,test_loader)\n",
    "    \n",
    "    \n",
    "study_sub_df = study_sub_df.drop(\"file_path\",axis=1)\n",
    "study_sub_df.columns = ['id', 'PredictionString1', 'negative', 'typical', 'indeterminate', 'atypical']\n",
    "all_sub_df = pd.merge(all_sub_df, study_sub_df, on = 'id', how = 'left')\n",
    "all_sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "concrete-parking",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:23:36.113967Z",
     "iopub.status.busy": "2021-08-08T15:23:36.113182Z",
     "iopub.status.idle": "2021-08-08T15:23:36.178103Z",
     "shell.execute_reply": "2021-08-08T15:23:36.178499Z"
    },
    "papermill": {
     "duration": 0.183813,
     "end_time": "2021-08-08T15:23:36.178637",
     "exception": false,
     "start_time": "2021-08-08T15:23:35.994824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00086460a852_study</td>\n",
       "      <td>negative 0.08284110790118575 0 0 1 1 typical 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000c9c05fd14_study</td>\n",
       "      <td>negative 0.04346077236259589 0 0 1 1 typical 0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                   PredictionString\n",
       "0  00086460a852_study  negative 0.08284110790118575 0 0 1 1 typical 0...\n",
       "1  000c9c05fd14_study  negative 0.04346077236259589 0 0 1 1 typical 0..."
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(study_len):\n",
    "    negative = all_sub_df.loc[i,'negative']\n",
    "    typical = all_sub_df.loc[i,'typical']\n",
    "    indeterminate = all_sub_df.loc[i,'indeterminate']\n",
    "    atypical = all_sub_df.loc[i,'atypical']\n",
    "    all_sub_df.loc[i, 'PredictionString'] = f'negative {negative} 0 0 1 1 typical {typical} 0 0 1 1 indeterminate {indeterminate} 0 0 1 1 atypical {atypical} 0 0 1 1'\n",
    "    \n",
    "study_sub_df = all_sub_df[['id', 'PredictionString']][:study_len]\n",
    "study_sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "finite-slide",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:23:36.382093Z",
     "iopub.status.busy": "2021-08-08T15:23:36.381315Z",
     "iopub.status.idle": "2021-08-08T15:23:36.837304Z",
     "shell.execute_reply": "2021-08-08T15:23:36.836508Z"
    },
    "papermill": {
     "duration": 0.559234,
     "end_time": "2021-08-08T15:23:36.837435",
     "exception": false,
     "start_time": "2021-08-08T15:23:36.278201",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del models_list\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-stationery",
   "metadata": {
    "papermill": {
     "duration": 0.099537,
     "end_time": "2021-08-08T15:23:37.037189",
     "exception": false,
     "start_time": "2021-08-08T15:23:36.937652",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Merge study and image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "wanted-election",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:23:37.243037Z",
     "iopub.status.busy": "2021-08-08T15:23:37.242213Z",
     "iopub.status.idle": "2021-08-08T15:23:37.303010Z",
     "shell.execute_reply": "2021-08-08T15:23:37.302582Z"
    },
    "papermill": {
     "duration": 0.165598,
     "end_time": "2021-08-08T15:23:37.303126",
     "exception": false,
     "start_time": "2021-08-08T15:23:37.137528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dim0</th>\n",
       "      <th>dim1</th>\n",
       "      <th>split</th>\n",
       "      <th>PredictionString_x</th>\n",
       "      <th>PredictionString_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>2330</td>\n",
       "      <td>2783</td>\n",
       "      <td>test</td>\n",
       "      <td>opacity 0.185589805245 669 751 1261 1441 opaci...</td>\n",
       "      <td>opacity 0.8058207631111145 1821 813 2418 1529 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>3093</td>\n",
       "      <td>2850</td>\n",
       "      <td>test</td>\n",
       "      <td>opacity 0.110834948719 1900 1235 2565 2095 opa...</td>\n",
       "      <td>opacity 0.7418073415756226 145 1851 998 2550 o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  dim0  dim1 split  \\\n",
       "0  65761e66de9f_image  2330  2783  test   \n",
       "1  51759b5579bc_image  3093  2850  test   \n",
       "\n",
       "                                  PredictionString_x  \\\n",
       "0  opacity 0.185589805245 669 751 1261 1441 opaci...   \n",
       "1  opacity 0.110834948719 1900 1235 2565 2095 opa...   \n",
       "\n",
       "                                  PredictionString_y  \n",
       "0  opacity 0.8058207631111145 1821 813 2418 1529 ...  \n",
       "1  opacity 0.7418073415756226 145 1851 998 2550 o...  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_all_df = meta.merge(yolov5_df,on=\"id\").merge(mmdet_df,on=\"id\")\n",
    "image_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "multiple-perspective",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-08T15:23:37.523354Z",
     "iopub.status.busy": "2021-08-08T15:23:37.522563Z",
     "iopub.status.idle": "2021-08-08T15:23:37.618171Z",
     "shell.execute_reply": "2021-08-08T15:23:37.618569Z"
    },
    "papermill": {
     "duration": 0.212404,
     "end_time": "2021-08-08T15:23:37.618791",
     "exception": false,
     "start_time": "2021-08-08T15:23:37.406387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>PredictionString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65761e66de9f_image</td>\n",
       "      <td>opacity 0.3235451877117157 1825 827 2414 1518 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51759b5579bc_image</td>\n",
       "      <td>opacity 0.2455153912305832 163 1824 1004 2527 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                   PredictionString\n",
       "0  65761e66de9f_image  opacity 0.3235451877117157 1825 827 2414 1518 ...\n",
       "1  51759b5579bc_image  opacity 0.2455153912305832 163 1824 1004 2527 ..."
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ensemble_boxes import *\n",
    "\n",
    "weights = [3, 1]\n",
    "iou_thr = 0.6\n",
    "skip_box_thr = 0.0001\n",
    "image_output_list = []\n",
    "\n",
    "for index, id_, dim0, dim1, split, yolo5_boxes, mmdet_boxes in image_all_df.itertuples():\n",
    "    yolo5_boxes_list = yolo5_boxes.split(\" \"); mmdet_boxes_list = mmdet_boxes.split(\" \")\n",
    "    assert len(yolo5_boxes_list) % 6 == 0;    assert len(mmdet_boxes_list) % 6 == 0\n",
    "    none_list = yolo5_boxes_list[-6:]\n",
    "    yolo5_boxes_list = yolo5_boxes_list[:-6];  mmdet_boxes_list = mmdet_boxes_list[:-6]\n",
    "    boxes_list = [[],[]]\n",
    "    scores_list = [[],[]]\n",
    "    for i in range(len(yolo5_boxes_list)):\n",
    "        if i % 6 == 1:\n",
    "            scores_list[0].append(float(yolo5_boxes_list[i]))\n",
    "        if i % 6 == 2:\n",
    "            boxes_list[0].append([float(yolo5_boxes_list[i])/dim1,\n",
    "                                  float(yolo5_boxes_list[i+1])/dim0,\n",
    "                                  float(yolo5_boxes_list[i+2])/dim1,\n",
    "                                  float(yolo5_boxes_list[i+3])/dim0])\n",
    "            \n",
    "    for i in range(len(mmdet_boxes_list)):\n",
    "        if i % 6 == 1:\n",
    "            scores_list[1].append(float(mmdet_boxes_list[i]))\n",
    "        if i % 6 == 2:\n",
    "            boxes_list[1].append([float(mmdet_boxes_list[i])/dim1,\n",
    "                                  float(mmdet_boxes_list[i+1])/dim0,\n",
    "                                  float(mmdet_boxes_list[i+2])/dim1,\n",
    "                                  float(mmdet_boxes_list[i+3])/dim0]) \n",
    "            \n",
    "    labels_list = [[0]*len(scores_list[0]), [0]*len(scores_list[1])]        \n",
    "    boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr)\n",
    "    \n",
    "    output_str = \"\"\n",
    "    for box, score in zip(boxes, scores): \n",
    "        output_str += \"opacity \"\n",
    "        output_str += f\"{score} \"\n",
    "        for b_i in range(4):\n",
    "            if b_i in [0,2]:\n",
    "                output_str += f\"{int(round(box[b_i]*dim1,0))} \"\n",
    "            elif b_i in [1,3]:\n",
    "                output_str += f\"{int(round(box[b_i]*dim0,0))} \"\n",
    "\n",
    "    output_str += \" \".join(none_list)\n",
    "    image_output_list.append(output_str)\n",
    "\n",
    "    \n",
    "image_all_df[\"PredictionString\"] = image_output_list\n",
    "image_all_df = image_all_df[[\"id\", \"PredictionString\"]]\n",
    "image_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "comfortable-sunday",
   "metadata": {
    "execution": {
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      "/opt/conda/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
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       "0  00086460a852_study  negative 0.08284110790118575 0 0 1 1 typical 0...\n",
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    "study_sub_df = study_sub_df.append(image_all_df).reset_index(drop=True)\n",
    "study_sub_df.to_csv('/kaggle/working/submission.csv',index = False)\n",
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      "  and should_run_async(code)\n"
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