sorghum-pytorch-lightning-starter-training.ipynb

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   "source": [
    "# package_paths = ['./pytorch-image-models-master',]\n",
    "\n",
    "import sys\n",
    "# for pth in package_paths:\n",
    "#     sys.path.append(pth)"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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    "tags": []
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import cv2\n",
    "import timm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import albumentations as A\n",
    "import pytorch_lightning as pl\n",
    "import matplotlib.pyplot as plt\n",
    "import torchmetrics\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from albumentations.core.composition import Compose, OneOf\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "\n",
    "from pytorch_lightning import Trainer, seed_everything\n",
    "from pytorch_lightning import Callback\n",
    "from pytorch_lightning.loggers import CSVLogger\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch Lightning version: 1.6.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"PyTorch Lightning version: {pl.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
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    },
    "tags": []
   },
   "source": [
    "# Config"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "DEBUG = False\n",
    "\n",
    "class CFG:\n",
    "    data_dir = \"data\"\n",
    "    train_dir = os.path.join(data_dir, \"train\")\n",
    "    test_dir = os.path.join(data_dir, \"test\")\n",
    "    class_mapping_file = os.path.join(data_dir, \"train_cultivar_mapping.csv\")\n",
    "    seed = 42\n",
    "    model_name = 'tf_efficientnet_b3_ns'\n",
    "    pretrained = True\n",
    "    img_size = 512\n",
    "    num_classes = 100\n",
    "    lr = 1e-4\n",
    "    max_lr = 1e-3\n",
    "    pct_start = 0.2\n",
    "    div_factor = 1.0e+3\n",
    "    final_div_factor = 1.0e+3\n",
    "    num_epochs = 40\n",
    "    batch_size = 16\n",
    "    accum = 1\n",
    "    precision = 16\n",
    "    n_fold = 4\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed_everything(CFG.seed)\n",
    "CFG.device"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   "source": [
    "PATH = \"data\"\n",
    "\n",
    "# TRAIN_DIR = PATH + 'train_images/'\n",
    "TRAIN_DIR = \"../input/sorghum-cultivar-identification-512512/train/\"\n",
    "TEST_DIR = PATH + 'test/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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     "name": "stdout",
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     "text": [
      "22193\n"
     ]
    }
   ],
   "source": [
    "df_all = pd.read_csv(CFG.class_mapping_file).dropna(inplace=False)\n",
    "print(len(df_all))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "unique_cultivars = list(df_all[\"cultivar\"].unique())\n",
    "num_classes = len(unique_cultivars)\n",
    "\n",
    "CFG.num_classes = num_classes\n",
    "print(num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
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       "<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>image</th>\n",
       "      <th>cultivar</th>\n",
       "      <th>file_path</th>\n",
       "      <th>cultivar_index</th>\n",
       "      <th>is_exist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-16__12-24-20-930.png</td>\n",
       "      <td>PI_257599</td>\n",
       "      <td>data/train/2017-06-16__12-24-20-930.png</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-02__16-48-57-866.png</td>\n",
       "      <td>PI_154987</td>\n",
       "      <td>data/train/2017-06-02__16-48-57-866.png</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-12__13-18-07-707.png</td>\n",
       "      <td>PI_92270</td>\n",
       "      <td>data/train/2017-06-12__13-18-07-707.png</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-22__13-18-06-841.png</td>\n",
       "      <td>PI_152651</td>\n",
       "      <td>data/train/2017-06-22__13-18-06-841.png</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-26__12-56-48-642.png</td>\n",
       "      <td>PI_176766</td>\n",
       "      <td>data/train/2017-06-26__12-56-48-642.png</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          image   cultivar  \\\n",
       "0  2017-06-16__12-24-20-930.png  PI_257599   \n",
       "1  2017-06-02__16-48-57-866.png  PI_154987   \n",
       "2  2017-06-12__13-18-07-707.png   PI_92270   \n",
       "3  2017-06-22__13-18-06-841.png  PI_152651   \n",
       "4  2017-06-26__12-56-48-642.png  PI_176766   \n",
       "\n",
       "                                 file_path  cultivar_index  is_exist  \n",
       "0  data/train/2017-06-16__12-24-20-930.png               0      True  \n",
       "1  data/train/2017-06-02__16-48-57-866.png               1      True  \n",
       "2  data/train/2017-06-12__13-18-07-707.png               2      True  \n",
       "3  data/train/2017-06-22__13-18-06-841.png               3      True  \n",
       "4  data/train/2017-06-26__12-56-48-642.png               4      True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all[\"file_path\"] = df_all[\"image\"].apply(lambda image: os.path.join(CFG.train_dir, image))\n",
    "df_all[\"cultivar_index\"] = df_all[\"cultivar\"].map(lambda item: unique_cultivars.index(item))\n",
    "df_all[\"is_exist\"] = df_all[\"file_path\"].apply(lambda file_path: os.path.exists(file_path))\n",
    "df_all = df_all[df_all.is_exist==True]\n",
    "df_all.head()"
   ]
  },
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   "execution_count": 10,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if DEBUG == True:\n",
    "    df_all = df_all[:200]\n",
    "    CFG.num_epochs = 10"
   ]
  },
  {
   "cell_type": "markdown",
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     "start_time": "2022-03-27T08:10:32.479902",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train size: 16645\n",
      "valid size: 5548\n",
      "PI_156393    223\n",
      "PI_155760    211\n",
      "PI_157030    204\n",
      "PI_329299    202\n",
      "PI_152771    196\n",
      "            ... \n",
      "PI_213900    102\n",
      "PI_152816    102\n",
      "PI_195754    102\n",
      "PI_257600    101\n",
      "PI_152971    101\n",
      "Name: cultivar, Length: 100, dtype: int64\n",
      "PI_156393    75\n",
      "PI_155760    71\n",
      "PI_157030    68\n",
      "PI_329299    67\n",
      "PI_152771    66\n",
      "             ..\n",
      "PI_152816    34\n",
      "PI_181080    34\n",
      "PI_155885    34\n",
      "PI_213900    33\n",
      "PI_257600    33\n",
      "Name: cultivar, Length: 100, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "skf = StratifiedKFold(n_splits=CFG.n_fold, shuffle=True, random_state=CFG.seed)\n",
    "\n",
    "for train_idx, valid_idx in skf.split(df_all['image'], df_all[\"cultivar_index\"]):\n",
    "    df_train = df_all.iloc[train_idx]\n",
    "    df_valid = df_all.iloc[valid_idx]\n",
    "\n",
    "print(f\"train size: {len(df_train)}\")\n",
    "print(f\"valid size: {len(df_valid)}\")\n",
    "\n",
    "print(df_train.cultivar.value_counts())\n",
    "print(df_valid.cultivar.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
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     "start_time": "2022-03-27T08:10:32.644142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Define Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SorghumDataset(Dataset):\n",
    "    def __init__(self, df, transform=None):\n",
    "        self.image_path = df['file_path'].values\n",
    "        self.labels = df[\"cultivar_index\"].values\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "#         image_id = self.image_id[idx]\n",
    "        label = torch.tensor(self.labels[idx], dtype=torch.float32)\n",
    "        \n",
    "        image_path = self.image_path[idx]\n",
    "        image = cv2.imread(image_path)\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        \n",
    "        augmented = self.transform(image=image)\n",
    "        image = augmented['image']\n",
    "        return {'image':image, 'target': label}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032589,
     "end_time": "2022-03-27T08:10:32.817158",
     "exception": false,
     "start_time": "2022-03-27T08:10:32.784569",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Augmant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:32.894715Z",
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     "shell.execute_reply.started": "2022-03-27T06:59:10.42358Z"
    },
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     "duration": 0.04579,
     "end_time": "2022-03-27T08:10:32.896436",
     "exception": false,
     "start_time": "2022-03-27T08:10:32.850646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_transform(phase: str):\n",
    "    if phase == \"train\":\n",
    "        return Compose([\n",
    "            A.RandomResizedCrop(height=CFG.img_size, width=CFG.img_size),\n",
    "            A.Flip(p=0.5),\n",
    "            A.RandomRotate90(p=0.5),\n",
    "            A.ShiftScaleRotate(p=0.5),\n",
    "            A.HueSaturationValue(p=0.5),\n",
    "            A.OneOf([\n",
    "                A.RandomBrightnessContrast(p=0.5),\n",
    "                A.RandomGamma(p=0.5),\n",
    "            ], p=0.5),\n",
    "            A.OneOf([\n",
    "                A.Blur(p=0.1),\n",
    "                A.GaussianBlur(p=0.1),\n",
    "                A.MotionBlur(p=0.1),\n",
    "            ], p=0.1),\n",
    "            A.OneOf([\n",
    "                A.GaussNoise(p=0.1),\n",
    "                A.ISONoise(p=0.1),\n",
    "                A.GridDropout(ratio=0.5, p=0.2),\n",
    "                A.CoarseDropout(max_holes=16, min_holes=8, max_height=16, max_width=16, min_height=8, min_width=8, p=0.2)\n",
    "            ], p=0.2),\n",
    "            A.Normalize(\n",
    "                mean=[0.485, 0.456, 0.406],\n",
    "                std=[0.229, 0.224, 0.225],\n",
    "            ),\n",
    "            ToTensorV2(),\n",
    "        ])\n",
    "    elif phase == \"valid\":\n",
    "        return Compose([\n",
    "            A.Resize(height=CFG.img_size, width=CFG.img_size),\n",
    "            A.Normalize(\n",
    "                mean=[0.485, 0.456, 0.406],\n",
    "                std=[0.229, 0.224, 0.225],\n",
    "            ),\n",
    "            ToTensorV2(),\n",
    "        ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:32.969013Z",
     "iopub.status.busy": "2022-03-27T08:10:32.968251Z",
     "iopub.status.idle": "2022-03-27T08:10:32.970800Z",
     "shell.execute_reply": "2022-03-27T08:10:32.970216Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.43869Z"
    },
    "papermill": {
     "duration": 0.041283,
     "end_time": "2022-03-27T08:10:32.971012",
     "exception": false,
     "start_time": "2022-03-27T08:10:32.929729",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset = SorghumDataset(df_train, get_transform('train'))\n",
    "valid_dataset = SorghumDataset(df_valid, get_transform('valid'))\n",
    "\n",
    "train_loader = DataLoader(train_dataset, \n",
    "                          batch_size=CFG.batch_size, \n",
    "                          shuffle=True, \n",
    "                          pin_memory=True, \n",
    "                          drop_last=True, \n",
    "                          num_workers=16)\n",
    "valid_loader = DataLoader(valid_dataset, \n",
    "                          batch_size=CFG.batch_size, \n",
    "                          shuffle=False, \n",
    "                          pin_memory=True, \n",
    "                          num_workers=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:33.042723Z",
     "iopub.status.busy": "2022-03-27T08:10:33.042100Z",
     "iopub.status.idle": "2022-03-27T08:10:33.044892Z",
     "shell.execute_reply": "2022-03-27T08:10:33.045287Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.454288Z"
    },
    "papermill": {
     "duration": 0.041027,
     "end_time": "2022-03-27T08:10:33.045415",
     "exception": false,
     "start_time": "2022-03-27T08:10:33.004388",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1040"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CFG.steps_per_epoch = len(train_loader)\n",
    "CFG.steps_per_epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.033267,
     "end_time": "2022-03-27T08:10:33.112511",
     "exception": false,
     "start_time": "2022-03-27T08:10:33.079244",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:33.186666Z",
     "iopub.status.busy": "2022-03-27T08:10:33.186014Z",
     "iopub.status.idle": "2022-03-27T08:10:33.188519Z",
     "shell.execute_reply": "2022-03-27T08:10:33.188112Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.466214Z"
    },
    "papermill": {
     "duration": 0.041944,
     "end_time": "2022-03-27T08:10:33.188626",
     "exception": false,
     "start_time": "2022-03-27T08:10:33.146682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CustomEffNet(nn.Module):\n",
    "    def __init__(self, model_name='tf_efficientnet_b0_ns', pretrained=True):\n",
    "        super().__init__()\n",
    "        self.model = timm.create_model(model_name, pretrained=pretrained)\n",
    "        in_features = self.model.get_classifier().in_features\n",
    "#         self.model.fc = nn.Linear(in_features, CFG.num_classes)\n",
    "        self.model.classifier = nn.Sequential(\n",
    "            nn.Linear(in_features, in_features),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(in_features, CFG.num_classes)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.model(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:33.270697Z",
     "iopub.status.busy": "2022-03-27T08:10:33.269156Z",
     "iopub.status.idle": "2022-03-27T08:10:33.271284Z",
     "shell.execute_reply": "2022-03-27T08:10:33.271715Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.476691Z"
    },
    "papermill": {
     "duration": 0.049897,
     "end_time": "2022-03-27T08:10:33.271864",
     "exception": false,
     "start_time": "2022-03-27T08:10:33.221967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class LitSorghum(pl.LightningModule):\n",
    "    def __init__(self, model):\n",
    "        super(LitSorghum, self).__init__()\n",
    "        self.model = model\n",
    "        self.metric = torchmetrics.Accuracy(threshold=0.5, num_classes=CFG.num_classes)\n",
    "        self.criterion = nn.CrossEntropyLoss()\n",
    "        self.lr = CFG.lr\n",
    "\n",
    "    def forward(self, x, *args, **kwargs):\n",
    "        return self.model(x)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)\n",
    "        self.scheduler = torch.optim.lr_scheduler.OneCycleLR(self.optimizer, \n",
    "                                                             epochs=CFG.num_epochs, steps_per_epoch=CFG.steps_per_epoch,\n",
    "                                                             max_lr=CFG.max_lr, pct_start=CFG.pct_start, \n",
    "                                                             div_factor=CFG.div_factor, final_div_factor=CFG.final_div_factor)\n",
    "        scheduler = {'scheduler': self.scheduler, 'interval': 'step',}\n",
    "\n",
    "        return [self.optimizer], [scheduler]\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        image = batch['image']\n",
    "        target = batch['target'].long()\n",
    "        output = self.model(image)\n",
    "        loss = self.criterion(output, target)\n",
    "        score = self.metric(output.argmax(1), target)\n",
    "        logs = {'train_loss': loss, 'train_acc': score, 'lr': self.optimizer.param_groups[0]['lr']}\n",
    "        self.log_dict(\n",
    "            logs,\n",
    "            on_step=False, on_epoch=True, prog_bar=True, logger=True\n",
    "        )\n",
    "        return loss\n",
    "    \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        image = batch['image']\n",
    "        target = batch['target'].long()\n",
    "        output = self.model(image)\n",
    "        loss = self.criterion(output, target)\n",
    "        score = self.metric(output.argmax(1), target)\n",
    "        logs = {'valid_loss': loss, 'valid_acc': score}\n",
    "        self.log_dict(\n",
    "            logs,\n",
    "            on_step=False, on_epoch=True, prog_bar=True, logger=True\n",
    "        )\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:33.345743Z",
     "iopub.status.busy": "2022-03-27T08:10:33.345206Z",
     "iopub.status.idle": "2022-03-27T08:10:37.020266Z",
     "shell.execute_reply": "2022-03-27T08:10:37.019675Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.496651Z"
    },
    "papermill": {
     "duration": 3.714679,
     "end_time": "2022-03-27T08:10:37.020398",
     "exception": false,
     "start_time": "2022-03-27T08:10:33.305719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = CustomEffNet(model_name=CFG.model_name, pretrained=CFG.pretrained)\n",
    "\n",
    "# checkpoint = \"../input/plant2021-efficinenetb5/results/logs/tf_efficientnet_b5_ns/version_0/checkpoints/last.ckpt\"\n",
    "# model.load_state_dict(torch.load(checkpoint)['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:37.096381Z",
     "iopub.status.busy": "2022-03-27T08:10:37.094241Z",
     "iopub.status.idle": "2022-03-27T08:10:37.098475Z",
     "shell.execute_reply": "2022-03-27T08:10:37.098937Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.860714Z"
    },
    "papermill": {
     "duration": 0.043693,
     "end_time": "2022-03-27T08:10:37.099073",
     "exception": false,
     "start_time": "2022-03-27T08:10:37.055380",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lit_model = LitSorghum(model.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:37.173298Z",
     "iopub.status.busy": "2022-03-27T08:10:37.172742Z",
     "iopub.status.idle": "2022-03-27T08:10:37.183295Z",
     "shell.execute_reply": "2022-03-27T08:10:37.183680Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.869973Z"
    },
    "papermill": {
     "duration": 0.050565,
     "end_time": "2022-03-27T08:10:37.183841",
     "exception": false,
     "start_time": "2022-03-27T08:10:37.133276",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n"
     ]
    },
    {
     "ename": "MisconfigurationException",
     "evalue": "`Trainer(strategy='ddp_spawn')` or `Trainer(accelerator='ddp_spawn')` is not compatible with an interactive environment. Run your code as a script, or choose one of the compatible strategies: Trainer(strategy=None|dp|tpu_spawn). In case you are spawning processes yourself, make sure to include the Trainer creation inside the worker function.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMisconfigurationException\u001b[0m                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-6efbed06bb16>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m                                       mode='min')\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m trainer = Trainer(\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCFG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mgpus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kaggle-env/lib/python3.8/site-packages/pytorch_lightning/utilities/argparse.py\u001b[0m in \u001b[0;36minsert_env_defaults\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         \u001b[0;31m# all args were already moved to kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_T\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minsert_env_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kaggle-env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, logger, checkpoint_callback, enable_checkpointing, callbacks, default_root_dir, gradient_clip_val, gradient_clip_algorithm, process_position, num_nodes, num_processes, devices, gpus, auto_select_gpus, tpu_cores, ipus, log_gpu_memory, progress_bar_refresh_rate, enable_progress_bar, overfit_batches, track_grad_norm, check_val_every_n_epoch, fast_dev_run, accumulate_grad_batches, max_epochs, min_epochs, max_steps, min_steps, max_time, limit_train_batches, limit_val_batches, limit_test_batches, limit_predict_batches, val_check_interval, flush_logs_every_n_steps, log_every_n_steps, accelerator, strategy, sync_batchnorm, precision, enable_model_summary, weights_summary, weights_save_path, num_sanity_val_steps, resume_from_checkpoint, profiler, benchmark, deterministic, reload_dataloaders_every_n_epochs, auto_lr_find, replace_sampler_ddp, detect_anomaly, auto_scale_batch_size, prepare_data_per_node, plugins, amp_backend, amp_level, move_metrics_to_cpu, multiple_trainloader_mode, stochastic_weight_avg, terminate_on_nan)\u001b[0m\n\u001b[1;32m    484\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_connector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataConnector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiple_trainloader_mode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m         self._accelerator_connector = AcceleratorConnector(\n\u001b[0m\u001b[1;32m    487\u001b[0m             \u001b[0mnum_processes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_processes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m             \u001b[0mdevices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kaggle-env/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, devices, num_nodes, accelerator, strategy, plugins, precision, amp_type, amp_level, sync_batchnorm, benchmark, replace_sampler_ddp, deterministic, num_processes, tpu_cores, ipus, gpus, gpu_ids)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;31m# 6. Instantiate Strategy - Part 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lazy_init_strategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_deterministic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeterministic\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kaggle-env/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py\u001b[0m in \u001b[0;36m_lazy_init_strategy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_IS_INTERACTIVE\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlauncher\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlauncher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive_compatible\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 760\u001b[0;31m             raise MisconfigurationException(\n\u001b[0m\u001b[1;32m    761\u001b[0m                 \u001b[0;34mf\"`Trainer(strategy={self.strategy.strategy_name!r})` or\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    762\u001b[0m                 \u001b[0;34mf\" `Trainer(accelerator={self.strategy.strategy_name!r})` is not compatible with an interactive\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMisconfigurationException\u001b[0m: `Trainer(strategy='ddp_spawn')` or `Trainer(accelerator='ddp_spawn')` is not compatible with an interactive environment. Run your code as a script, or choose one of the compatible strategies: Trainer(strategy=None|dp|tpu_spawn). In case you are spawning processes yourself, make sure to include the Trainer creation inside the worker function."
     ]
    }
   ],
   "source": [
    "logger = CSVLogger(save_dir='logs/', name=CFG.model_name)\n",
    "logger.log_hyperparams(CFG.__dict__)\n",
    "checkpoint_callback = ModelCheckpoint(monitor='valid_loss',\n",
    "                                      save_top_k=1,\n",
    "                                      save_last=True,\n",
    "                                      save_weights_only=True,\n",
    "                                      filename='{epoch:02d}-{valid_loss:.4f}-{valid_acc:.4f}',\n",
    "                                      verbose=False,\n",
    "                                      mode='min')\n",
    "\n",
    "trainer = Trainer(\n",
    "    max_epochs=CFG.num_epochs,\n",
    "    gpus=[0,1],\n",
    "    accumulate_grad_batches=CFG.accum,\n",
    "    precision=CFG.precision,\n",
    "    callbacks=[checkpoint_callback], \n",
    "    logger=logger,\n",
    "    weights_summary='top',)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.033433,
     "end_time": "2022-03-27T08:10:37.251258",
     "exception": false,
     "start_time": "2022-03-27T08:10:37.217825",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T08:10:37.327834Z",
     "iopub.status.busy": "2022-03-27T08:10:37.327060Z",
     "iopub.status.idle": "2022-03-27T14:17:36.969501Z",
     "shell.execute_reply": "2022-03-27T14:17:36.968584Z",
     "shell.execute_reply.started": "2022-03-27T06:59:10.883811Z"
    },
    "papermill": {
     "duration": 22019.684556,
     "end_time": "2022-03-27T14:17:36.969671",
     "exception": false,
     "start_time": "2022-03-27T08:10:37.285115",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "trainer.fit(lit_model, train_dataloaders=train_loader, val_dataloaders=valid_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.05777,
     "end_time": "2022-03-27T14:17:37.083373",
     "exception": false,
     "start_time": "2022-03-27T14:17:37.025603",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:17:37.220716Z",
     "iopub.status.busy": "2022-03-27T14:17:37.214080Z",
     "iopub.status.idle": "2022-03-27T14:17:38.124163Z",
     "shell.execute_reply": "2022-03-27T14:17:38.123646Z",
     "shell.execute_reply.started": "2022-03-27T07:01:24.267068Z"
    },
    "papermill": {
     "duration": 0.983608,
     "end_time": "2022-03-27T14:17:38.124296",
     "exception": false,
     "start_time": "2022-03-27T14:17:37.140688",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "metrics = pd.read_csv(f'{trainer.logger.log_dir}/metrics.csv')\n",
    "\n",
    "train_acc = metrics['train_acc'].dropna().reset_index(drop=True)\n",
    "valid_acc = metrics['valid_acc'].dropna().reset_index(drop=True)\n",
    "    \n",
    "fig = plt.figure(figsize=(7, 6))\n",
    "plt.grid(True)\n",
    "plt.plot(train_acc, color=\"r\", marker=\"o\", label='train/acc')\n",
    "plt.plot(valid_acc, color=\"b\", marker=\"x\", label='valid/acc')\n",
    "plt.ylabel('Accuracy', fontsize=24)\n",
    "plt.xlabel('Epoch', fontsize=24)\n",
    "plt.legend(loc='lower right', fontsize=18)\n",
    "plt.savefig(f'{trainer.logger.log_dir}/acc.png')\n",
    "\n",
    "train_loss = metrics['train_loss'].dropna().reset_index(drop=True)\n",
    "valid_loss = metrics['valid_loss'].dropna().reset_index(drop=True)\n",
    "\n",
    "fig = plt.figure(figsize=(7, 6))\n",
    "plt.grid(True)\n",
    "plt.plot(train_loss, color=\"r\", marker=\"o\", label='train/loss')\n",
    "plt.plot(valid_loss, color=\"b\", marker=\"x\", label='valid/loss')\n",
    "plt.ylabel('Loss', fontsize=24)\n",
    "plt.xlabel('Epoch', fontsize=24)\n",
    "plt.legend(loc='upper right', fontsize=18)\n",
    "plt.savefig(f'{trainer.logger.log_dir}/loss.png')\\\n",
    "\n",
    "lr = metrics['lr'].dropna().reset_index(drop=True)\n",
    "\n",
    "fig = plt.figure(figsize=(7, 6))\n",
    "plt.grid(True)\n",
    "plt.plot(lr, color=\"g\", marker=\"o\", label='learning rate')\n",
    "plt.ylabel('LR', fontsize=24)\n",
    "plt.xlabel('Epoch', fontsize=24)\n",
    "plt.legend(loc='upper right', fontsize=18)\n",
    "plt.savefig(f'{trainer.logger.log_dir}/lr.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.059345,
     "end_time": "2022-03-27T14:17:38.243280",
     "exception": false,
     "start_time": "2022-03-27T14:17:38.183935",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:17:38.367087Z",
     "iopub.status.busy": "2022-03-27T14:17:38.366453Z",
     "iopub.status.idle": "2022-03-27T14:17:39.152509Z",
     "shell.execute_reply": "2022-03-27T14:17:39.152023Z",
     "shell.execute_reply.started": "2022-03-27T07:00:35.002845Z"
    },
    "papermill": {
     "duration": 0.849217,
     "end_time": "2022-03-27T14:17:39.152631",
     "exception": false,
     "start_time": "2022-03-27T14:17:38.303414",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!ls \"./logs/tf_efficientnet_b3_ns/version_0/checkpoints/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:17:39.273497Z",
     "iopub.status.busy": "2022-03-27T14:17:39.272682Z",
     "iopub.status.idle": "2022-03-27T14:17:39.705132Z",
     "shell.execute_reply": "2022-03-27T14:17:39.704358Z",
     "shell.execute_reply.started": "2022-03-27T07:00:35.718977Z"
    },
    "papermill": {
     "duration": 0.495608,
     "end_time": "2022-03-27T14:17:39.705258",
     "exception": false,
     "start_time": "2022-03-27T14:17:39.209650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = CustomEffNet(model_name=CFG.model_name, pretrained=False)\n",
    "checkpoint = \"./logs/tf_efficientnet_b3_ns/version_0/checkpoints/last.ckpt\"\n",
    "model.load_state_dict(torch.load(checkpoint)['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:17:39.825379Z",
     "iopub.status.busy": "2022-03-27T14:17:39.824621Z",
     "iopub.status.idle": "2022-03-27T14:17:39.861581Z",
     "shell.execute_reply": "2022-03-27T14:17:39.861152Z",
     "shell.execute_reply.started": "2022-03-27T07:00:36.143065Z"
    },
    "papermill": {
     "duration": 0.098292,
     "end_time": "2022-03-27T14:17:39.861702",
     "exception": false,
     "start_time": "2022-03-27T14:17:39.763410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sub = pd.read_csv(PATH + \"sample_submission.csv\")\n",
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:17:39.996895Z",
     "iopub.status.busy": "2022-03-27T14:17:39.996322Z",
     "iopub.status.idle": "2022-03-27T14:17:40.000996Z",
     "shell.execute_reply": "2022-03-27T14:17:40.000525Z",
     "shell.execute_reply.started": "2022-03-27T07:00:36.181329Z"
    },
    "papermill": {
     "duration": 0.081059,
     "end_time": "2022-03-27T14:17:40.001139",
     "exception": false,
     "start_time": "2022-03-27T14:17:39.920080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sub[\"file_path\"] = sub[\"filename\"].apply(lambda image: TEST_DIR + image)\n",
    "sub[\"cultivar_index\"] = 0\n",
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:17:40.120684Z",
     "iopub.status.busy": "2022-03-27T14:17:40.119852Z",
     "iopub.status.idle": "2022-03-27T14:17:40.125385Z",
     "shell.execute_reply": "2022-03-27T14:17:40.124955Z",
     "shell.execute_reply.started": "2022-03-27T07:00:36.202975Z"
    },
    "papermill": {
     "duration": 0.065904,
     "end_time": "2022-03-27T14:17:40.125491",
     "exception": false,
     "start_time": "2022-03-27T14:17:40.059587",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset = SorghumDataset(sub, \n",
    "                              get_transform('valid'))\n",
    "test_loader = DataLoader(test_dataset, \n",
    "                         batch_size=CFG.batch_size, \n",
    "                         shuffle=False, \n",
    "                         num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:17:40.249022Z",
     "iopub.status.busy": "2022-03-27T14:17:40.248176Z",
     "iopub.status.idle": "2022-03-27T14:35:07.716241Z",
     "shell.execute_reply": "2022-03-27T14:35:07.715591Z",
     "shell.execute_reply.started": "2022-03-27T07:00:36.2131Z"
    },
    "papermill": {
     "duration": 1047.531066,
     "end_time": "2022-03-27T14:35:07.716431",
     "exception": false,
     "start_time": "2022-03-27T14:17:40.185365",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "model.cuda()\n",
    "model.eval()\n",
    "\n",
    "predictions = []\n",
    "for batch in tqdm(test_loader):\n",
    "    image = batch['image'].cuda()\n",
    "    with torch.no_grad():\n",
    "        outputs = model(image)\n",
    "        preds = outputs.detach().cpu()\n",
    "        predictions.append(preds.argmax(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-27T14:35:08.888114Z",
     "iopub.status.busy": "2022-03-27T14:35:08.885986Z",
     "iopub.status.idle": "2022-03-27T14:35:08.907155Z",
     "shell.execute_reply": "2022-03-27T14:35:08.907655Z",
     "shell.execute_reply.started": "2022-03-27T07:01:24.261202Z"
    },
    "papermill": {
     "duration": 0.611633,
     "end_time": "2022-03-27T14:35:08.907837",
     "exception": false,
     "start_time": "2022-03-27T14:35:08.296204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tmp = predictions[0]\n",
    "for i in range(len(predictions) - 1):\n",
    "    tmp = torch.cat((tmp, predictions[i+1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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