{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About this notebook  \n",
    "\n",
    "TBD..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['train_tfrecords',\n",
       " 'train_images',\n",
       " 'test_images',\n",
       " 'label_num_to_disease_map.json',\n",
       " 'sample_submission.csv',\n",
       " 'train.csv',\n",
       " 'test_tfrecords']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(\"../input/cassava-leaf-disease-classification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>image_id</th>\n",
       "      <th>label</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000015157.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000201771.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100042118.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000723321.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000812911.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         image_id  label  source\n",
       "0  1000015157.jpg      0    2020\n",
       "1  1000201771.jpg      3    2020\n",
       "2   100042118.jpg      1    2020\n",
       "3  1000723321.jpg      1    2020\n",
       "4  1000812911.jpg      3    2020"
      ]
     },
     "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>image_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2216849948.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         image_id  label\n",
       "0  2216849948.jpg      4"
      ]
     },
     "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cassava Bacterial Blight (CBB)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cassava Brown Streak Disease (CBSD)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cassava Green Mottle (CGM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cassava Mosaic Disease (CMD)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Healthy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0\n",
       "0       Cassava Bacterial Blight (CBB)\n",
       "1  Cassava Brown Streak Disease (CBSD)\n",
       "2           Cassava Green Mottle (CGM)\n",
       "3         Cassava Mosaic Disease (CMD)\n",
       "4                              Healthy"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train = pd.read_csv(\"../input/cassava-leaf-disease-classification/train.csv\")\n",
    "train = pd.read_csv(\"../input/cassava-leaf-disease-merged/merged.csv\")\n",
    "# train = pd.read_csv(\"../input/cassava-leaf-disease-merged/oversample-0124.csv\")  # label 0124 x3\n",
    "test = pd.read_csv(\"../input/cassava-leaf-disease-classification/sample_submission.csv\")\n",
    "label_map = pd.read_json(\"../input/cassava-leaf-disease-classification/label_num_to_disease_map.json\", orient=\"index\")\n",
    "display(train.head())\n",
    "display(test.head())\n",
    "display(label_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[\"label\"], kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Directory settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# Directory settings\n",
    "# ====================================================\n",
    "import os\n",
    "\n",
    "OUTPUT_DIR = \"./\"\n",
    "if not os.path.exists(OUTPUT_DIR):\n",
    "    os.makedirs(OUTPUT_DIR)\n",
    "\n",
    "# TRAIN_PATH = \"../input/cassava-leaf-disease-classification/train_images\"\n",
    "TRAIN_PATH = \"../input/cassava-leaf-disease-merged/train\"\n",
    "TEST_PATH = \"../input/cassava-leaf-disease-classification/test_images\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CFG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# CFG\n",
    "# ====================================================\n",
    "class CFG:\n",
    "    debug = False\n",
    "    apex = True\n",
    "    print_freq = 100\n",
    "    num_workers = 4\n",
    "    model_name = \"seresnext50_32x4d\"  # resnext50_32x4d, seresnext50_32x4d, tf_efficientnet_b3_ns, tf_efficientnet_b4_ns, vit_base_patch16_384, deit_base_patch16_384\n",
    "    batch_size = 24\n",
    "    gradient_accumulation_steps = 1\n",
    "    size = 384 if \"it_base_\" in model_name else 512\n",
    "    n_fold = 5\n",
    "    trn_fold = [0, 1, 2, 3, 4]\n",
    "    criterion = \"BiTemperedLoss\"  # ['CrossEntropyLoss', 'BiTemperedLoss']\n",
    "    btl_t1 = 0.3  # Bi-Tempered Logistic Loss\n",
    "    btl_t2 = 1.0\n",
    "    label_smoothing = 0.2\n",
    "    scheduler = \"CosineAnnealingWarmRestarts\"  # ['ReduceLROnPlateau', 'CosineAnnealingLR', 'CosineAnnealingWarmRestarts', 'CosineAnnealingWarmupRestarts']\n",
    "    scheduler_batch_update = True\n",
    "    epochs = 10\n",
    "    # factor = 0.2  # ReduceLROnPlateau\n",
    "    # patience = 4  # ReduceLROnPlateau\n",
    "    # eps = 1e-6  # ReduceLROnPlateau\n",
    "    # T_max = 10  # CosineAnnealingLR\n",
    "    T_0 = (\n",
    "        len(train) // n_fold * (n_fold - 1) // batch_size // gradient_accumulation_steps * epochs + 5\n",
    "    )  # CosineAnnealingWarmRestarts\n",
    "    # first_cycle_steps = (\n",
    "    #     len(train) // n_fold * (n_fold - 1) // batch_size // gradient_accumulation_steps * epochs + 5\n",
    "    # )  # CosineAnnealingWarmupRestarts for batch update\n",
    "    # warmup_steps = first_cycle_steps // 10  # CosineAnnealingWarmupRestarts\n",
    "    # gamma = 0.8  # CosineAnnealingWarmupRestarts\n",
    "    lr = 1e-4\n",
    "    min_lr = 2e-6\n",
    "    weight_decay = 1e-6\n",
    "    max_grad_norm = 1000\n",
    "    seed = 3456\n",
    "    target_size = 5\n",
    "    target_col = \"label\"\n",
    "    train = True\n",
    "    inference = False\n",
    "\n",
    "\n",
    "if CFG.debug:\n",
    "    CFG.epochs = 1\n",
    "    train = train.sample(n=1000, random_state=CFG.seed).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# Library\n",
    "# ====================================================\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../input/pytorch-image-models/pytorch-image-models-master\")\n",
    "sys.path.append(\"../input/pytorchcosineannealingwithwarmup\")\n",
    "sys.path.append(\"../input/bitemperedlogloss/\")\n",
    "sys.path.append(\"../input/image-fmix/FMix-master\")\n",
    "\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "import shutil\n",
    "import time\n",
    "import warnings\n",
    "from collections import Counter, defaultdict\n",
    "from contextlib import contextmanager\n",
    "from functools import partial\n",
    "from pathlib import Path\n",
    "\n",
    "import bi_tempered_loss_pytorch as btl\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import timm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision.models as models\n",
    "from albumentations import (\n",
    "    CenterCrop,\n",
    "    CoarseDropout,\n",
    "    Compose,\n",
    "    Cutout,\n",
    "    HorizontalFlip,\n",
    "    HueSaturationValue,\n",
    "    IAAAdditiveGaussianNoise,\n",
    "    ImageOnlyTransform,\n",
    "    Normalize,\n",
    "    OneOf,\n",
    "    RandomBrightness,\n",
    "    RandomBrightnessContrast,\n",
    "    RandomContrast,\n",
    "    RandomCrop,\n",
    "    RandomResizedCrop,\n",
    "    Resize,\n",
    "    Rotate,\n",
    "    ShiftScaleRotate,\n",
    "    Transpose,\n",
    "    VerticalFlip,\n",
    ")\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "from cosine_annearing_with_warmup import CosineAnnealingWarmupRestarts\n",
    "from fmix import sample_mask\n",
    "from PIL import Image\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from torch.nn.parameter import Parameter\n",
    "from torch.optim import SGD, Adam\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR, CosineAnnealingWarmRestarts, ReduceLROnPlateau\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "if CFG.apex:\n",
    "    from apex import amp\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# Utils\n",
    "# ====================================================\n",
    "def get_score(y_true, y_pred):\n",
    "    return accuracy_score(y_true, y_pred)\n",
    "\n",
    "\n",
    "@contextmanager\n",
    "def timer(name):\n",
    "    t0 = time.time()\n",
    "    LOGGER.info(f\"[{name}] start\")\n",
    "    yield\n",
    "    LOGGER.info(f\"[{name}] done in {time.time() - t0:.0f} s.\")\n",
    "\n",
    "\n",
    "def init_logger(log_file=OUTPUT_DIR + \"train.log\"):\n",
    "    from logging import INFO, FileHandler, Formatter, StreamHandler, getLogger\n",
    "\n",
    "    logger = getLogger(__name__)\n",
    "    logger.setLevel(INFO)\n",
    "    handler1 = StreamHandler()\n",
    "    handler1.setFormatter(Formatter(\"%(message)s\"))\n",
    "    handler2 = FileHandler(filename=log_file)\n",
    "    handler2.setFormatter(Formatter(\"%(message)s\"))\n",
    "    logger.addHandler(handler1)\n",
    "    logger.addHandler(handler2)\n",
    "    return logger\n",
    "\n",
    "\n",
    "LOGGER = init_logger()\n",
    "\n",
    "\n",
    "def seed_torch(seed=42):\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",
    "\n",
    "\n",
    "seed_torch(seed=CFG.seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CV split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold  label\n",
      "0     0         299\n",
      "      1         695\n",
      "      2         604\n",
      "      3        3092\n",
      "      4         578\n",
      "1     0         299\n",
      "      1         695\n",
      "      2         604\n",
      "      3        3092\n",
      "      4         578\n",
      "2     0         298\n",
      "      1         695\n",
      "      2         603\n",
      "      3        3093\n",
      "      4         578\n",
      "3     0         298\n",
      "      1         695\n",
      "      2         603\n",
      "      3        3093\n",
      "      4         578\n",
      "4     0         298\n",
      "      1         696\n",
      "      2         603\n",
      "      3        3092\n",
      "      4         578\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "folds = train.copy()\n",
    "Fold = StratifiedKFold(n_splits=CFG.n_fold, shuffle=True, random_state=CFG.seed)\n",
    "for n, (train_index, val_index) in enumerate(Fold.split(folds, folds[CFG.target_col])):\n",
    "    folds.loc[val_index, \"fold\"] = int(n)\n",
    "folds[\"fold\"] = folds[\"fold\"].astype(int)\n",
    "print(folds.groupby([\"fold\", CFG.target_col]).size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# Dataset\n",
    "# ====================================================\n",
    "class TrainDataset(Dataset):\n",
    "    def __init__(self, df, transform=None):\n",
    "        self.df = df\n",
    "        self.file_names = df[\"image_id\"].values\n",
    "        self.labels = df[\"label\"].values\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file_name = self.file_names[idx]\n",
    "        file_path = f\"{TRAIN_PATH}/{file_name}\"\n",
    "        image = cv2.imread(file_path)\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        if self.transform:\n",
    "            augmented = self.transform(image=image)\n",
    "            image = augmented[\"image\"]\n",
    "        label = torch.tensor(self.labels[idx]).long()\n",
    "        return image, label\n",
    "\n",
    "\n",
    "class TestDataset(Dataset):\n",
    "    def __init__(self, df, transform=None):\n",
    "        self.df = df\n",
    "        self.file_names = df[\"image_id\"].values\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file_name = self.file_names[idx]\n",
    "        file_path = f\"{TEST_PATH}/{file_name}\"\n",
    "        image = cv2.imread(file_path)\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        if self.transform:\n",
    "            augmented = self.transform(image=image)\n",
    "            image = augmented[\"image\"]\n",
    "        return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_dataset = TrainDataset(train, transform=None)\n",
    "\n",
    "for i in range(1):\n",
    "    image, label = train_dataset[i]\n",
    "    plt.imshow(image)\n",
    "    plt.title(f\"label: {label}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# Transforms\n",
    "# ====================================================\n",
    "def get_transforms(*, data):\n",
    "\n",
    "    if data == \"train\":\n",
    "        return Compose(\n",
    "            [\n",
    "                # Resize(CFG.size, CFG.size),\n",
    "                RandomResizedCrop(CFG.size, CFG.size),\n",
    "                Transpose(p=0.5),\n",
    "                HorizontalFlip(p=0.5),\n",
    "                VerticalFlip(p=0.5),\n",
    "                ShiftScaleRotate(p=0.5),\n",
    "                HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5),\n",
    "                RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.1, 0.1), p=0.5),\n",
    "                CoarseDropout(p=0.5),\n",
    "                Cutout(p=0.5),\n",
    "                Normalize(\n",
    "                    mean=[0.485, 0.456, 0.406],\n",
    "                    std=[0.229, 0.224, 0.225],\n",
    "                ),\n",
    "                ToTensorV2(),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    elif data == \"valid\":\n",
    "        return Compose(\n",
    "            [\n",
    "                Resize(CFG.size, CFG.size),\n",
    "                CenterCrop(CFG.size, CFG.size),\n",
    "                Normalize(\n",
    "                    mean=[0.485, 0.456, 0.406],\n",
    "                    std=[0.229, 0.224, 0.225],\n",
    "                ),\n",
    "                ToTensorV2(),\n",
    "            ]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_dataset = TrainDataset(train, transform=get_transforms(data=\"train\"))\n",
    "\n",
    "for i in range(1):\n",
    "    image, label = train_dataset[i]\n",
    "    plt.imshow(image[0])\n",
    "    plt.title(f\"label: {label}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CutMix / FMix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rand_bbox(size, lam):\n",
    "    W = size[2]\n",
    "    H = size[3]\n",
    "    cut_rat = np.sqrt(1.0 - lam)\n",
    "    cut_w = np.int(W * cut_rat)\n",
    "    cut_h = np.int(H * cut_rat)\n",
    "\n",
    "    # uniform\n",
    "    cx = np.random.randint(W)\n",
    "    cy = np.random.randint(H)\n",
    "\n",
    "    bbx1 = np.clip(cx - cut_w // 2, 0, W)\n",
    "    bby1 = np.clip(cy - cut_h // 2, 0, H)\n",
    "    bbx2 = np.clip(cx + cut_w // 2, 0, W)\n",
    "    bby2 = np.clip(cy + cut_h // 2, 0, H)\n",
    "    return bbx1, bby1, bbx2, bby2\n",
    "\n",
    "\n",
    "def cutmix(data, target, alpha):\n",
    "    indices = torch.randperm(data.size(0))\n",
    "    shuffled_data = data[indices]\n",
    "    shuffled_target = target[indices]\n",
    "\n",
    "    lam = np.clip(np.random.beta(alpha, alpha), 0.3, 0.4)\n",
    "    bbx1, bby1, bbx2, bby2 = rand_bbox(data.size(), lam)\n",
    "    new_data = data.clone()\n",
    "    new_data[:, :, bby1:bby2, bbx1:bbx2] = data[indices, :, bby1:bby2, bbx1:bbx2]\n",
    "    # adjust lambda to exactly match pixel ratio\n",
    "    lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (data.size()[-1] * data.size()[-2]))\n",
    "    targets = (target, shuffled_target, lam)\n",
    "\n",
    "    return new_data, targets\n",
    "\n",
    "\n",
    "def fmix(data, targets, alpha, decay_power, shape, max_soft=0.0, reformulate=False):\n",
    "    lam, mask = sample_mask(alpha, decay_power, shape, max_soft, reformulate)\n",
    "    if CFG.apex:\n",
    "        # mask = torch.tensor(mask, device=device).float()\n",
    "        mask = mask.astype(np.float32)\n",
    "    indices = torch.randperm(data.size(0))\n",
    "    shuffled_data = data[indices]\n",
    "    shuffled_targets = targets[indices]\n",
    "    x1 = torch.from_numpy(mask).to(device) * data\n",
    "    x2 = torch.from_numpy(1 - mask).to(device) * shuffled_data\n",
    "    targets = (targets, shuffled_targets, lam)\n",
    "\n",
    "    return (x1 + x2), targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MixUp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/yuhao318/mwh/blob/e9e2da8fc6/utils.py\n",
    "def mixup(x, y, alpha=1.0, use_cuda=True):\n",
    "\n",
    "    \"\"\"Compute the mixup data. Return mixed inputs, pairs of targets, and lambda\"\"\"\n",
    "    if alpha > 0.0:\n",
    "        lam = np.random.beta(alpha, alpha)\n",
    "        lam = max(lam, 1 - lam)\n",
    "        # lam = min(lam, 1-lam)\n",
    "    else:\n",
    "        lam = 1.0\n",
    "    batch_size = x.size()[0]\n",
    "    if use_cuda:\n",
    "        index = torch.randperm(batch_size).cuda()\n",
    "    else:\n",
    "        index = torch.randperm(batch_size)\n",
    "\n",
    "    ## SYM\n",
    "    # mixed_x = lam * x + (1 - lam) * x[index,:]\n",
    "    # mixed_y = (1 - lam) * x + lam * x[index,:]\n",
    "    # mixed_image  = torch.cat([mixed_x,mixed_y], 0)\n",
    "    # y_a, y_b = y, y[index]\n",
    "    # mixed_label  = torch.cat([y_a,y_b], 0)\n",
    "\n",
    "    ## Reduce batch size\n",
    "    # new_batch_size = batch_size // 2\n",
    "    # x_i = x[ : new_batch_size]\n",
    "    # x_j = x[new_batch_size : ]\n",
    "    # y_a = y[ : new_batch_size]\n",
    "    # y_b = y[new_batch_size : ]\n",
    "    # mixed_x = lam * x_i + (1 - lam) * x_j\n",
    "\n",
    "    ## NO SYM\n",
    "    mixed_x = lam * x + (1 - lam) * x[index, :]\n",
    "    y_a, y_b = y, y[index]\n",
    "\n",
    "    ## Only Alpha\n",
    "    # mixed_x = 0.5 * x + (1 - 0.5) * x[index,:]\n",
    "    # mixed_image  = mixed_x\n",
    "    # y_a, y_b = y, y[index]\n",
    "    # ind_label = torch.randint_like(y, 0,2)\n",
    "    # mixed_label  = ind_label * y_a + (1-ind_label) * y_b\n",
    "\n",
    "    ## Reduce batch size and SYM\n",
    "    # new_batch_size = batch_size // 2\n",
    "    # x_i = x[ : new_batch_size]\n",
    "    # x_j = x[new_batch_size : ]\n",
    "    # y_a = y[ : new_batch_size]\n",
    "    # y_b = y[new_batch_size : ]\n",
    "    # mixed_x = lam * x_i + (1 - lam) * x_j\n",
    "    # mixed_y = (1 - lam) * x_i + lam * x_j\n",
    "    # mixed_x  = torch.cat([mixed_x,mixed_y], 0)\n",
    "    # y_b = torch.cat([y_b,y_a], 0)\n",
    "    # y_a = y\n",
    "\n",
    "    # return mixed_image, mixed_label, lam\n",
    "    return mixed_x, (y_a, y_b, lam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# MODEL\n",
    "# ====================================================\n",
    "class CassvaImgClassifier(nn.Module):\n",
    "    def __init__(self, model_name=\"resnext50_32x4d\", pretrained=False):\n",
    "        super().__init__()\n",
    "        self.model_name = model_name\n",
    "\n",
    "        if model_name.startswith(\"deit_\"):\n",
    "            self.model = torch.hub.load(\"facebookresearch/deit:main\", model_name, pretrained=True)\n",
    "\n",
    "            if model_name == \"deit_base_patch16_384\":\n",
    "                n_features = self.model.head.in_features\n",
    "                self.model.head = nn.Linear(n_features, CFG.target_size)\n",
    "\n",
    "        else:\n",
    "            self.model = timm.create_model(model_name, pretrained=pretrained)\n",
    "\n",
    "            if \"resnext50_32x4d\" in model_name:\n",
    "                n_features = self.model.fc.in_features\n",
    "                self.model.fc = nn.Linear(n_features, CFG.target_size)\n",
    "\n",
    "            elif model_name.startswith(\"tf_efficientnet\"):\n",
    "                n_features = self.model.classifier.in_features\n",
    "                self.model.classifier = nn.Linear(n_features, CFG.target_size)\n",
    "\n",
    "            elif model_name.startswith(\"vit_\"):\n",
    "                n_features = self.model.head.in_features\n",
    "                self.model.head = nn.Linear(n_features, CFG.target_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.model(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freeze_batch_normalization(model):\n",
    "    if CFG.model_name.startswith(\"tf_efficientnet_\"):\n",
    "        for name1, child1 in model.named_children():\n",
    "            for name2, child2 in child1.named_children():\n",
    "                # print(f\"===== {name2} =====\")\n",
    "                if name2.startswith(\"bn\"):\n",
    "                    for param in child2.parameters():\n",
    "                        param.requires_grad = False\n",
    "                        # print(param.requires_grad)\n",
    "\n",
    "                for child3 in child2.children():\n",
    "                    if isinstance(child3, nn.modules.container.Sequential):\n",
    "                        for child4 in child3.children():\n",
    "                            for child5 in child4.children():\n",
    "                                if isinstance(child5, nn.BatchNorm2d):\n",
    "                                    # print(child5)\n",
    "                                    for param in child5.parameters():\n",
    "                                        param.requires_grad = False\n",
    "                                        # print(param.requires_grad)\n",
    "\n",
    "    if CFG.model_name.startswith(\"vit_\"):\n",
    "        try:\n",
    "            for m in model.modules():\n",
    "                if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.LayerNorm):\n",
    "                    m.eval()\n",
    "        except ValuError:\n",
    "            print(\"error with batchnorm2d or layernorm\")\n",
    "            return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.0300,  0.6243,  0.1679, -0.1099, -0.4241],\n",
      "        [-0.0866,  0.6408,  0.2015, -0.1033, -0.3610],\n",
      "        [-0.1117,  0.5691,  0.2299, -0.0624, -0.3747],\n",
      "        [-0.2299,  0.6055,  0.2559,  0.0412, -0.4098]],\n",
      "       grad_fn=<AddmmBackward>)\n"
     ]
    }
   ],
   "source": [
    "model = CassvaImgClassifier(model_name=CFG.model_name, pretrained=False)\n",
    "freeze_batch_normalization(model)\n",
    "# print(model)\n",
    "\n",
    "train_dataset = TrainDataset(train, transform=get_transforms(data=\"train\"))\n",
    "train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4, pin_memory=True, drop_last=True)\n",
    "\n",
    "for image, label in train_loader:\n",
    "    output = model(image)\n",
    "    print(output)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BiTemperedLogisticLoss(nn.Module):\n",
    "    def __init__(self, t1, t2, smoothing=0.0):\n",
    "        super(BiTemperedLogisticLoss, self).__init__()\n",
    "        self.t1 = t1\n",
    "        self.t2 = t2\n",
    "        self.smoothing = smoothing\n",
    "\n",
    "    def forward(self, logit_label, truth_label):\n",
    "        loss_label = btl.bi_tempered_logistic_loss(\n",
    "            logit_label, truth_label, t1=self.t1, t2=self.t2, label_smoothing=self.smoothing, reduction=\"none\"\n",
    "        )\n",
    "\n",
    "        loss_label = loss_label.mean()\n",
    "        return loss_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# Helper functions\n",
    "# ====================================================\n",
    "class AverageMeter(object):\n",
    "    \"\"\"Computes and stores the average and current value\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.count = 0\n",
    "\n",
    "    def update(self, val, n=1):\n",
    "        self.val = val\n",
    "        self.sum += val * n\n",
    "        self.count += n\n",
    "        self.avg = self.sum / self.count\n",
    "\n",
    "\n",
    "def asMinutes(s):\n",
    "    m = math.floor(s / 60)\n",
    "    s -= m * 60\n",
    "    return \"%dm %ds\" % (m, s)\n",
    "\n",
    "\n",
    "def timeSince(since, percent):\n",
    "    now = time.time()\n",
    "    s = now - since\n",
    "    es = s / (percent)\n",
    "    rs = es - s\n",
    "    return \"%s (remain %s)\" % (asMinutes(s), asMinutes(rs))\n",
    "\n",
    "\n",
    "def train_fn(train_loader, model, criterion, optimizer, epoch, scheduler, device, scheduler_batch_update=True):\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    scores = AverageMeter()\n",
    "    # switch to train mode\n",
    "    model.train()\n",
    "    start = end = time.time()\n",
    "    global_step = 0\n",
    "    for step, (images, labels) in enumerate(train_loader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        batch_size = labels.size(0)\n",
    "\n",
    "        # CutMix, FMix\n",
    "        if epoch <= 1 or epoch >= CFG.epochs - 1:\n",
    "            mix_decision = 0.75  # Disable CutMix, FMix for final epoch\n",
    "        else:\n",
    "            mix_decision = np.random.rand()\n",
    "\n",
    "        if epoch >= CFG.epochs - 4:\n",
    "            mix_decision *= 2  # Reduce probability\n",
    "\n",
    "        if mix_decision < 0.25:\n",
    "            images, labels = cutmix(images, labels, 1.0)\n",
    "        elif mix_decision >= 0.25 and mix_decision < 0.5:\n",
    "            images, labels = fmix(images, labels, alpha=1.0, decay_power=5.0, shape=(CFG.size, CFG.size))\n",
    "        elif mix_decision >= 0.5 and mix_decision < 0.75:\n",
    "            images, labels = mixup(images, labels, alpha=0.5)\n",
    "\n",
    "        y_preds = model(images.float())\n",
    "\n",
    "        if mix_decision < 0.75:\n",
    "            loss = criterion(y_preds, labels[0]) * labels[2] + criterion(y_preds, labels[1]) * (1.0 - labels[2])\n",
    "        else:\n",
    "            loss = criterion(y_preds, labels)\n",
    "\n",
    "        # record loss\n",
    "        losses.update(loss.item(), batch_size)\n",
    "        if CFG.gradient_accumulation_steps > 1:\n",
    "            loss = loss / CFG.gradient_accumulation_steps\n",
    "        if CFG.apex:\n",
    "            with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
    "                scaled_loss.backward()\n",
    "        else:\n",
    "            loss.backward()\n",
    "        grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), CFG.max_grad_norm)\n",
    "        if (step + 1) % CFG.gradient_accumulation_steps == 0:\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            if CFG.scheduler_batch_update:\n",
    "                scheduler.step()\n",
    "\n",
    "            global_step += 1\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "        if step % CFG.print_freq == 0 or step == (len(train_loader) - 1):\n",
    "            print(\n",
    "                \"Epoch: [{0}][{1}/{2}] \"\n",
    "                # \"Data {data_time.val:.3f} ({data_time.avg:.3f}) \"\n",
    "                # \"Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) \"\n",
    "                \"Elapsed {remain:s} \"\n",
    "                \"Loss: {loss.val:.4f}({loss.avg:.4f}) \"\n",
    "                \"Grad: {grad_norm:.4f} \"\n",
    "                \"LR: {lr:.6f}  \".format(\n",
    "                    epoch + 1,\n",
    "                    step,\n",
    "                    len(train_loader),\n",
    "                    # batch_time=batch_time,\n",
    "                    # data_time=data_time,\n",
    "                    loss=losses,\n",
    "                    remain=timeSince(start, float(step + 1) / len(train_loader)),\n",
    "                    grad_norm=grad_norm,\n",
    "                    lr=scheduler.get_lr()[0],\n",
    "                )\n",
    "            )\n",
    "    return losses.avg\n",
    "\n",
    "\n",
    "def valid_fn(valid_loader, model, criterion, device):\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    scores = AverageMeter()\n",
    "    # switch to evaluation mode\n",
    "    model.eval()\n",
    "    preds = []\n",
    "    start = end = time.time()\n",
    "    for step, (images, labels) in enumerate(valid_loader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        batch_size = labels.size(0)\n",
    "        # compute loss\n",
    "        with torch.no_grad():\n",
    "            y_preds = model(images)\n",
    "        loss = criterion(y_preds, labels)\n",
    "        losses.update(loss.item(), batch_size)\n",
    "        # record accuracy\n",
    "        preds.append(y_preds.softmax(1).to(\"cpu\").numpy())\n",
    "        if CFG.gradient_accumulation_steps > 1:\n",
    "            loss = loss / CFG.gradient_accumulation_steps\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "        if step % CFG.print_freq == 0 or step == (len(valid_loader) - 1):\n",
    "            print(\n",
    "                \"EVAL: [{0}/{1}] \"\n",
    "                # \"Data {data_time.val:.3f} ({data_time.avg:.3f}) \"\n",
    "                # \"Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) \"\n",
    "                \"Elapsed {remain:s} \"\n",
    "                \"Loss: {loss.val:.4f}({loss.avg:.4f}) \".format(\n",
    "                    step,\n",
    "                    len(valid_loader),\n",
    "                    # batch_time=batch_time,\n",
    "                    # data_time=data_time,\n",
    "                    loss=losses,\n",
    "                    remain=timeSince(start, float(step + 1) / len(valid_loader)),\n",
    "                )\n",
    "            )\n",
    "    predictions = np.concatenate(preds)\n",
    "    return losses.avg, predictions\n",
    "\n",
    "\n",
    "def inference(model, states, test_loader, device):\n",
    "    model.to(device)\n",
    "    tk0 = tqdm(enumerate(test_loader), total=len(test_loader))\n",
    "    probs = []\n",
    "    for i, (images) in tk0:\n",
    "        images = images.to(device)\n",
    "        avg_preds = []\n",
    "        for state in states:\n",
    "            model.load_state_dict(state[\"model\"])\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                y_preds = model(images)\n",
    "            avg_preds.append(y_preds.softmax(1).to(\"cpu\").numpy())\n",
    "        avg_preds = np.mean(avg_preds, axis=0)\n",
    "        probs.append(avg_preds)\n",
    "    probs = np.concatenate(probs)\n",
    "    return probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# Train loop\n",
    "# ====================================================\n",
    "def train_loop(folds, fold):\n",
    "\n",
    "    LOGGER.info(f\"========== fold: {fold} training ==========\")\n",
    "\n",
    "    # ====================================================\n",
    "    # loader\n",
    "    # ====================================================\n",
    "    trn_idx = folds[folds[\"fold\"] != fold].index\n",
    "    val_idx = folds[folds[\"fold\"] == fold].index\n",
    "\n",
    "    train_folds = folds.loc[trn_idx].reset_index(drop=True)\n",
    "    valid_folds = folds.loc[val_idx].reset_index(drop=True)\n",
    "\n",
    "    train_dataset = TrainDataset(train_folds, transform=get_transforms(data=\"train\"))\n",
    "    train_dataset_no_aug = TrainDataset(train_folds, transform=get_transforms(data=\"valid\"))\n",
    "    valid_dataset = TrainDataset(valid_folds, transform=get_transforms(data=\"valid\"))\n",
    "\n",
    "    train_loader = DataLoader(\n",
    "        train_dataset,\n",
    "        batch_size=CFG.batch_size,\n",
    "        shuffle=True,\n",
    "        num_workers=CFG.num_workers,\n",
    "        pin_memory=True,\n",
    "        drop_last=True,\n",
    "    )\n",
    "    train_loader_no_aug = DataLoader(\n",
    "        train_dataset_no_aug,\n",
    "        batch_size=CFG.batch_size,\n",
    "        shuffle=True,\n",
    "        num_workers=CFG.num_workers,\n",
    "        pin_memory=True,\n",
    "        drop_last=True,\n",
    "    )\n",
    "    valid_loader = DataLoader(\n",
    "        valid_dataset,\n",
    "        batch_size=CFG.batch_size,\n",
    "        shuffle=False,\n",
    "        num_workers=CFG.num_workers,\n",
    "        pin_memory=True,\n",
    "        drop_last=False,\n",
    "    )\n",
    "\n",
    "    # ====================================================\n",
    "    # scheduler\n",
    "    # ====================================================\n",
    "    def get_scheduler(optimizer):\n",
    "        if CFG.scheduler == \"ReduceLROnPlateau\":\n",
    "            scheduler = ReduceLROnPlateau(\n",
    "                optimizer, mode=\"min\", factor=CFG.factor, patience=CFG.patience, verbose=True, eps=CFG.eps\n",
    "            )\n",
    "        elif CFG.scheduler == \"CosineAnnealingLR\":\n",
    "            scheduler = CosineAnnealingLR(optimizer, T_max=CFG.T_max, eta_min=CFG.min_lr, last_epoch=-1)\n",
    "        elif CFG.scheduler == \"CosineAnnealingWarmRestarts\":\n",
    "            scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=CFG.T_0, T_mult=1, eta_min=CFG.min_lr, last_epoch=-1)\n",
    "        elif CFG.scheduler == \"CosineAnnealingWarmupRestarts\":\n",
    "            scheduler = CosineAnnealingWarmupRestarts(\n",
    "                optimizer,\n",
    "                first_cycle_steps=CFG.first_cycle_steps,\n",
    "                cycle_mult=1.0,\n",
    "                max_lr=CFG.lr,\n",
    "                min_lr=CFG.min_lr,\n",
    "                warmup_steps=CFG.warmup_steps,\n",
    "                gamma=CFG.gamma,\n",
    "            )\n",
    "        return scheduler\n",
    "\n",
    "    # ====================================================\n",
    "    # model & optimizer\n",
    "    # ====================================================\n",
    "    model = CassvaImgClassifier(CFG.model_name, pretrained=True)\n",
    "    freeze_batch_normalization(model)\n",
    "\n",
    "    model.to(device)\n",
    "\n",
    "    # Use multi GPU\n",
    "    if device == torch.device(\"cuda\") and not CFG.apex:\n",
    "        model = torch.nn.DataParallel(model)  # make parallel\n",
    "        # torch.backends.cudnn.benchmark=True\n",
    "\n",
    "    optimizer = Adam(model.parameters(), lr=CFG.lr, weight_decay=CFG.weight_decay, amsgrad=False)\n",
    "    scheduler = get_scheduler(optimizer)\n",
    "\n",
    "    # ====================================================\n",
    "    # apex\n",
    "    # ====================================================\n",
    "    if CFG.apex:\n",
    "        model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\", verbosity=0)\n",
    "\n",
    "    # ====================================================\n",
    "    # Criterion\n",
    "    # ====================================================\n",
    "    def get_criterion():\n",
    "        if CFG.criterion == \"CrossEntropyLoss\":\n",
    "            criterion = nn.CrossEntropyLoss()\n",
    "        elif CFG.criterion == \"BiTemperedLoss\":\n",
    "            criterion = BiTemperedLogisticLoss(t1=CFG.btl_t1, t2=CFG.btl_t2, smoothing=CFG.label_smoothing)\n",
    "        return criterion\n",
    "\n",
    "    criterion = get_criterion()\n",
    "\n",
    "    # ====================================================\n",
    "    # loop\n",
    "    # ====================================================\n",
    "    best_score = 0.0\n",
    "    best_loss = np.inf\n",
    "\n",
    "    for epoch in range(CFG.epochs):\n",
    "\n",
    "        start_time = time.time()\n",
    "\n",
    "        # train\n",
    "        if epoch <= 1 or epoch >= CFG.epochs - 1:\n",
    "            avg_loss = train_fn(\n",
    "                train_loader_no_aug, model, criterion, optimizer, epoch, scheduler, device, CFG.scheduler_batch_update\n",
    "            )\n",
    "        else:\n",
    "            avg_loss = train_fn(\n",
    "                train_loader, model, criterion, optimizer, epoch, scheduler, device, CFG.scheduler_batch_update\n",
    "            )\n",
    "\n",
    "        # eval\n",
    "        avg_val_loss, preds = valid_fn(valid_loader, model, criterion, device)\n",
    "        valid_labels = valid_folds[CFG.target_col].values\n",
    "\n",
    "        if not CFG.scheduler_batch_update:\n",
    "            if isinstance(scheduler, ReduceLROnPlateau):\n",
    "                scheduler.step(avg_val_loss)\n",
    "            elif isinstance(scheduler, CosineAnnealingLR):\n",
    "                scheduler.step()\n",
    "            elif isinstance(scheduler, CosineAnnealingWarmRestarts):\n",
    "                scheduler.step()\n",
    "\n",
    "        # scoring\n",
    "        score = get_score(valid_labels, preds.argmax(1))\n",
    "\n",
    "        elapsed = time.time() - start_time\n",
    "\n",
    "        LOGGER.info(\n",
    "            f\"Epoch {epoch+1} - avg_train_loss: {avg_loss:.4f}  avg_val_loss: {avg_val_loss:.4f}  time: {elapsed:.0f}s\"\n",
    "        )\n",
    "        LOGGER.info(f\"Epoch {epoch+1} - Accuracy: {score}\")\n",
    "\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            LOGGER.info(f\"Epoch {epoch+1} - Save Best Score: {best_score:.4f} Model\")\n",
    "            torch.save(\n",
    "                {\"model\": model.state_dict(), \"preds\": preds}, OUTPUT_DIR + f\"{CFG.model_name}_fold{fold}_best.pth\"\n",
    "            )\n",
    "\n",
    "        if epoch == CFG.epochs - 1:\n",
    "            LOGGER.info(f\"Epoch {epoch+1} - Save final model\")\n",
    "            torch.save(\n",
    "                {\"model\": model.state_dict(), \"preds\": preds}, OUTPUT_DIR + f\"{CFG.model_name}_fold{fold}_final.pth\"\n",
    "            )\n",
    "\n",
    "    check_point = torch.load(OUTPUT_DIR + f\"{CFG.model_name}_fold{fold}_best.pth\")\n",
    "    valid_folds[[str(c) for c in range(5)]] = check_point[\"preds\"]\n",
    "    valid_folds[\"preds\"] = check_point[\"preds\"].argmax(1)\n",
    "\n",
    "    return valid_folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================================================\n",
    "# main\n",
    "# ====================================================\n",
    "def main():\n",
    "\n",
    "    \"\"\"\n",
    "    Prepare: 1.train  2.test  3.submission  4.folds\n",
    "    \"\"\"\n",
    "\n",
    "    def get_result(result_df):\n",
    "        preds = result_df[\"preds\"].values\n",
    "        labels = result_df[CFG.target_col].values\n",
    "        score = get_score(labels, preds)\n",
    "        LOGGER.info(f\"Score: {score:<.5f}\")\n",
    "\n",
    "    if CFG.train:\n",
    "        # train\n",
    "        oof_df = pd.DataFrame()\n",
    "        for fold in range(CFG.n_fold):\n",
    "            if fold in CFG.trn_fold:\n",
    "                _oof_df = train_loop(folds, fold)\n",
    "                oof_df = pd.concat([oof_df, _oof_df])\n",
    "                LOGGER.info(f\"========== fold: {fold} result ==========\")\n",
    "                get_result(_oof_df)\n",
    "        # CV result\n",
    "        LOGGER.info(f\"========== CV ==========\")\n",
    "        get_result(oof_df)\n",
    "        # save result\n",
    "        oof_df.to_csv(OUTPUT_DIR + \"oof_df.csv\", index=False)\n",
    "\n",
    "    if CFG.inference:\n",
    "        # inference\n",
    "        model = CassvaImgClassifier(CFG.model_name, pretrained=False)\n",
    "        states = [torch.load(OUTPUT_DIR + f\"{CFG.model_name}_fold{fold}_best.pth\") for fold in CFG.trn_fold]\n",
    "        test_dataset = TestDataset(test, transform=get_transforms(data=\"valid\"))\n",
    "        test_loader = DataLoader(\n",
    "            test_dataset, batch_size=CFG.batch_size, shuffle=False, num_workers=CFG.num_workers, pin_memory=True\n",
    "        )\n",
    "        predictions = inference(model, states, test_loader, device)\n",
    "        # submission\n",
    "        test[\"label\"] = predictions.argmax(1)\n",
    "        test[[\"image_id\", \"label\"]].to_csv(OUTPUT_DIR + \"submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "========== fold: 0 training ==========\n",
      "Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext50_32x4d_racm-a304a460.pth\" to /root/.cache/torch/hub/checkpoints/seresnext50_32x4d_racm-a304a460.pth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1][0/877] Elapsed 0m 7s (remain 110m 4s) Loss: 0.3193(0.3193) Grad: 0.5409 LR: 0.000100  \n",
      "Epoch: [1][100/877] Elapsed 1m 24s (remain 10m 50s) Loss: 0.1191(0.1704) Grad: 0.4317 LR: 0.000100  \n",
      "Epoch: [1][200/877] Elapsed 2m 41s (remain 9m 2s) Loss: 0.0721(0.1387) Grad: 0.4600 LR: 0.000100  \n",
      "Epoch: [1][300/877] Elapsed 3m 58s (remain 7m 36s) Loss: 0.1291(0.1240) Grad: 0.4978 LR: 0.000100  \n",
      "Epoch: [1][400/877] Elapsed 5m 15s (remain 6m 14s) Loss: 0.0910(0.1150) Grad: 0.5555 LR: 0.000099  \n",
      "Epoch: [1][500/877] Elapsed 6m 32s (remain 4m 54s) Loss: 0.0861(0.1094) Grad: 0.6351 LR: 0.000099  \n",
      "Epoch: [1][600/877] Elapsed 7m 48s (remain 3m 35s) Loss: 0.0751(0.1057) Grad: 0.6176 LR: 0.000099  \n",
      "Epoch: [1][700/877] Elapsed 9m 5s (remain 2m 17s) Loss: 0.0608(0.1015) Grad: 0.3764 LR: 0.000098  \n",
      "Epoch: [1][800/877] Elapsed 10m 22s (remain 0m 59s) Loss: 0.1400(0.0983) Grad: 0.6448 LR: 0.000098  \n",
      "Epoch: [1][876/877] Elapsed 11m 21s (remain 0m 0s) Loss: 0.0339(0.0961) Grad: 0.4891 LR: 0.000098  \n",
      "EVAL: [0/220] Elapsed 0m 1s (remain 4m 52s) Loss: 0.0519(0.0519) \n",
      "EVAL: [100/220] Elapsed 0m 30s (remain 0m 35s) Loss: 0.0884(0.0633) \n",
      "EVAL: [200/220] Elapsed 0m 59s (remain 0m 5s) Loss: 0.0429(0.0656) \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1 - avg_train_loss: 0.0961  avg_val_loss: 0.0663  time: 746s\n",
      "Epoch 1 - Accuracy: 0.8851556567957479\n",
      "Epoch 1 - Save Best Score: 0.8852 Model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EVAL: [219/220] Elapsed 1m 4s (remain 0m 0s) Loss: 0.1302(0.0663) \n",
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      "Epoch: [2][876/877] Elapsed 11m 12s (remain 0m 0s) Loss: 0.1377(0.0562) Grad: 0.7494 LR: 0.000091  \n",
      "EVAL: [0/220] Elapsed 0m 1s (remain 4m 5s) Loss: 0.0387(0.0387) \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2 - avg_train_loss: 0.0562  avg_val_loss: 0.0670  time: 737s\n",
      "Epoch 2 - Accuracy: 0.8899012908124525\n",
      "Epoch 2 - Save Best Score: 0.8899 Model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EVAL: [219/220] Elapsed 1m 4s (remain 0m 0s) Loss: 0.1262(0.0670) \n",
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     ]
    },
    {
     "name": "stderr",
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     "text": [
      "Epoch 3 - avg_train_loss: 0.1345  avg_val_loss: 0.0745  time: 749s\n",
      "Epoch 3 - Accuracy: 0.8809794988610479\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
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     "text": [
      "Epoch 4 - avg_train_loss: 0.1276  avg_val_loss: 0.0668  time: 748s\n",
      "Epoch 4 - Accuracy: 0.8912300683371298\n",
      "Epoch 4 - Save Best Score: 0.8912 Model\n"
     ]
    },
    {
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     ]
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      "Epoch 5 - avg_train_loss: 0.1237  avg_val_loss: 0.0651  time: 747s\n",
      "Epoch 5 - Accuracy: 0.8925588458618071\n",
      "Epoch 5 - Save Best Score: 0.8926 Model\n"
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      "Epoch 6 - avg_train_loss: 0.1227  avg_val_loss: 0.0652  time: 748s\n",
      "Epoch 6 - Accuracy: 0.8925588458618071\n"
     ]
    },
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      "========== fold: 2 result ==========\n",
      "Score: 0.89159\n",
      "========== fold: 3 training ==========\n"
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      "========== fold: 3 result ==========\n",
      "Score: 0.89672\n",
      "========== fold: 4 training ==========\n"
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      "Epoch 4 - avg_train_loss: 0.1274  avg_val_loss: 0.0700  time: 791s\n",
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      "Epoch 10 - avg_train_loss: 0.0371  avg_val_loss: 0.0644  time: 780s\n",
      "Epoch 10 - Accuracy: 0.8948167837478641\n",
      "Epoch 10 - Save Best Score: 0.8948 Model\n"
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     "text": [
      "Epoch 10 - Save final model\n",
      "========== fold: 4 result ==========\n",
      "Score: 0.89482\n",
      "========== CV ==========\n",
      "Score: 0.89532\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  }
 ],
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