{
 "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 = \"vit_base_patch16_384\"  # resnext50_32x4d, seresnext50_32x4d, tf_efficientnet_b3_ns, vit_base_patch16_384, deit_base_patch16_384\n",
    "    batch_size = 16\n",
    "    gradient_accumulation_steps = 2\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 = 4012\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([[-0.0045, -0.7661, -0.0946, -0.1649, -0.0217],\n",
      "        [-0.0315, -0.5681, -0.2877, -0.5274, -0.9344],\n",
      "        [-0.7614, -0.4533, -0.2327, -0.3871, -0.4727],\n",
      "        [-0.4639, -0.4113, -0.1818, -0.3499, -0.5625]],\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1][0/1316] Elapsed 0m 1s (remain 33m 14s) Loss: 0.5794(0.5794) Grad: 1.5508 LR: 0.000100  \n",
      "Epoch: [1][100/1316] Elapsed 0m 29s (remain 5m 52s) Loss: 0.0620(0.1618) Grad: 0.6770 LR: 0.000100  \n",
      "Epoch: [1][200/1316] Elapsed 0m 56s (remain 5m 16s) Loss: 0.1746(0.1321) Grad: 0.6205 LR: 0.000100  \n",
      "Epoch: [1][300/1316] Elapsed 1m 24s (remain 4m 46s) Loss: 0.0775(0.1234) Grad: 0.3096 LR: 0.000100  \n",
      "Epoch: [1][400/1316] Elapsed 1m 52s (remain 4m 17s) Loss: 0.0577(0.1178) Grad: 0.7273 LR: 0.000100  \n",
      "Epoch: [1][500/1316] Elapsed 2m 20s (remain 3m 48s) Loss: 0.1269(0.1127) Grad: 0.4751 LR: 0.000100  \n",
      "Epoch: [1][600/1316] Elapsed 2m 48s (remain 3m 20s) Loss: 0.0861(0.1091) Grad: 0.5093 LR: 0.000099  \n",
      "Epoch: [1][700/1316] Elapsed 3m 16s (remain 2m 52s) Loss: 0.0807(0.1056) Grad: 0.6507 LR: 0.000099  \n",
      "Epoch: [1][800/1316] Elapsed 3m 43s (remain 2m 23s) Loss: 0.0916(0.1033) Grad: 0.4107 LR: 0.000099  \n",
      "Epoch: [1][900/1316] Elapsed 4m 11s (remain 1m 55s) Loss: 0.0680(0.1001) Grad: 0.3385 LR: 0.000099  \n",
      "Epoch: [1][1000/1316] Elapsed 4m 39s (remain 1m 27s) Loss: 0.0480(0.0983) Grad: 0.5507 LR: 0.000099  \n",
      "Epoch: [1][1100/1316] Elapsed 5m 7s (remain 1m 0s) Loss: 0.0940(0.0968) Grad: 0.6482 LR: 0.000098  \n",
      "Epoch: [1][1200/1316] Elapsed 5m 35s (remain 0m 32s) Loss: 0.2296(0.0955) Grad: 0.6231 LR: 0.000098  \n",
      "Epoch: [1][1300/1316] Elapsed 6m 3s (remain 0m 4s) Loss: 0.1272(0.0944) Grad: 0.4961 LR: 0.000098  \n",
      "Epoch: [1][1315/1316] Elapsed 6m 7s (remain 0m 0s) Loss: 0.1169(0.0942) Grad: 0.4749 LR: 0.000098  \n",
      "EVAL: [0/330] Elapsed 0m 0s (remain 4m 6s) Loss: 0.0405(0.0405) \n",
      "EVAL: [100/330] Elapsed 0m 10s (remain 0m 22s) Loss: 0.0450(0.0812) \n",
      "EVAL: [200/330] Elapsed 0m 19s (remain 0m 12s) Loss: 0.0472(0.0759) \n",
      "EVAL: [300/330] Elapsed 0m 28s (remain 0m 2s) Loss: 0.0809(0.0746) \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1 - avg_train_loss: 0.0942  avg_val_loss: 0.0750  time: 399s\n",
      "Epoch 1 - Accuracy: 0.8747152619589977\n",
      "Epoch 1 - Save Best Score: 0.8747 Model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EVAL: [329/330] Elapsed 0m 31s (remain 0m 0s) Loss: 0.0119(0.0750) \n",
      "Epoch: [2][0/1316] Elapsed 0m 0s (remain 21m 36s) Loss: 0.0518(0.0518) Grad: 0.2093 LR: 0.000098  \n",
      "Epoch: [2][100/1316] Elapsed 0m 28s (remain 5m 46s) Loss: 0.0607(0.0613) Grad: 0.6515 LR: 0.000097  \n",
      "Epoch: [2][200/1316] Elapsed 0m 56s (remain 5m 14s) Loss: 0.1083(0.0578) Grad: 0.4569 LR: 0.000097  \n",
      "Epoch: [2][300/1316] Elapsed 1m 24s (remain 4m 44s) Loss: 0.0052(0.0610) Grad: 0.1117 LR: 0.000096  \n",
      "Epoch: [2][400/1316] Elapsed 1m 52s (remain 4m 16s) Loss: 0.0982(0.0612) Grad: 0.3762 LR: 0.000096  \n",
      "Epoch: [2][500/1316] Elapsed 2m 20s (remain 3m 48s) Loss: 0.0025(0.0612) Grad: 0.0627 LR: 0.000095  \n",
      "Epoch: [2][600/1316] Elapsed 2m 48s (remain 3m 20s) Loss: 0.0654(0.0619) Grad: 0.3446 LR: 0.000095  \n",
      "Epoch: [2][700/1316] Elapsed 3m 16s (remain 2m 51s) Loss: 0.0478(0.0616) Grad: 0.3664 LR: 0.000094  \n",
      "Epoch: [2][800/1316] Elapsed 3m 44s (remain 2m 24s) Loss: 0.0261(0.0619) Grad: 0.3881 LR: 0.000094  \n",
      "Epoch: [2][900/1316] Elapsed 4m 12s (remain 1m 56s) Loss: 0.0022(0.0615) Grad: 0.0438 LR: 0.000093  \n",
      "Epoch: [2][1000/1316] Elapsed 4m 40s (remain 1m 28s) Loss: 0.0361(0.0610) Grad: 0.3850 LR: 0.000093  \n",
      "Epoch: [2][1100/1316] Elapsed 5m 8s (remain 1m 0s) Loss: 0.0615(0.0617) Grad: 0.4590 LR: 0.000092  \n",
      "Epoch: [2][1200/1316] Elapsed 5m 36s (remain 0m 32s) Loss: 0.1092(0.0621) Grad: 0.5148 LR: 0.000091  \n",
      "Epoch: [2][1300/1316] Elapsed 6m 4s (remain 0m 4s) Loss: 0.0020(0.0630) Grad: 0.0387 LR: 0.000091  \n",
      "Epoch: [2][1315/1316] Elapsed 6m 8s (remain 0m 0s) Loss: 0.0842(0.0631) Grad: 0.7475 LR: 0.000091  \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2 - avg_train_loss: 0.0631  avg_val_loss: 0.0759  time: 401s\n",
      "Epoch 2 - Accuracy: 0.8714882308276386\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EVAL: [329/330] Elapsed 0m 31s (remain 0m 0s) Loss: 0.0074(0.0759) \n",
      "Epoch: [3][0/1316] Elapsed 0m 1s (remain 24m 14s) Loss: 0.0811(0.0811) Grad: 0.4605 LR: 0.000091  \n",
      "Epoch: [3][100/1316] Elapsed 0m 29s (remain 6m 0s) Loss: 0.1928(0.1346) Grad: 0.5552 LR: 0.000090  \n",
      "Epoch: [3][200/1316] Elapsed 0m 59s (remain 5m 28s) Loss: 0.1511(0.1324) Grad: 0.7042 LR: 0.000089  \n",
      "Epoch: [3][300/1316] Elapsed 1m 28s (remain 4m 57s) Loss: 0.1278(0.1355) Grad: 0.4176 LR: 0.000088  \n",
      "Epoch: [3][400/1316] Elapsed 1m 57s (remain 4m 28s) Loss: 0.0663(0.1341) Grad: 0.3144 LR: 0.000088  \n",
      "Epoch: [3][500/1316] Elapsed 2m 27s (remain 3m 59s) Loss: 0.1598(0.1353) Grad: 0.4819 LR: 0.000087  \n",
      "Epoch: [3][600/1316] Elapsed 2m 56s (remain 3m 29s) Loss: 0.0939(0.1361) Grad: 0.4698 LR: 0.000086  \n",
      "Epoch: [3][700/1316] Elapsed 3m 24s (remain 2m 59s) Loss: 0.1890(0.1353) Grad: 0.3756 LR: 0.000085  \n",
      "Epoch: [3][800/1316] Elapsed 3m 54s (remain 2m 30s) Loss: 0.1341(0.1349) Grad: 0.2458 LR: 0.000084  \n",
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      "Epoch: [3][1000/1316] Elapsed 4m 52s (remain 1m 31s) Loss: 0.1961(0.1332) Grad: 0.3775 LR: 0.000083  \n",
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      "Epoch: [3][1200/1316] Elapsed 5m 49s (remain 0m 33s) Loss: 0.2012(0.1329) Grad: 0.3040 LR: 0.000081  \n",
      "Epoch: [3][1300/1316] Elapsed 6m 18s (remain 0m 4s) Loss: 0.1879(0.1316) Grad: 0.4014 LR: 0.000080  \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3 - avg_train_loss: 0.1320  avg_val_loss: 0.0727  time: 415s\n",
      "Epoch 3 - Accuracy: 0.8813591495823843\n",
      "Epoch 3 - Save Best Score: 0.8814 Model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EVAL: [329/330] Elapsed 0m 31s (remain 0m 0s) Loss: 0.0375(0.0727) \n",
      "Epoch: [4][0/1316] Elapsed 0m 1s (remain 22m 57s) Loss: 0.2282(0.2282) Grad: 0.2311 LR: 0.000080  \n",
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      "Epoch: [4][1200/1316] Elapsed 5m 47s (remain 0m 33s) Loss: 0.1657(0.1275) Grad: 0.5266 LR: 0.000067  \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4 - avg_train_loss: 0.1276  avg_val_loss: 0.0782  time: 411s\n",
      "Epoch 4 - Accuracy: 0.8697798025816249\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EVAL: [329/330] Elapsed 0m 30s (remain 0m 0s) Loss: 0.0193(0.0782) \n",
      "Epoch: [5][0/1316] Elapsed 0m 1s (remain 23m 34s) Loss: 0.0269(0.0269) Grad: 0.4591 LR: 0.000066  \n",
      "Epoch: [5][100/1316] Elapsed 0m 29s (remain 5m 58s) Loss: 0.1449(0.1113) Grad: 0.1427 LR: 0.000065  \n",
      "Epoch: [5][200/1316] Elapsed 0m 58s (remain 5m 23s) Loss: 0.1162(0.1167) Grad: 0.3916 LR: 0.000064  \n",
      "Epoch: [5][300/1316] Elapsed 1m 27s (remain 4m 53s) Loss: 0.1850(0.1232) Grad: 0.2240 LR: 0.000063  \n",
      "Epoch: [5][400/1316] Elapsed 1m 55s (remain 4m 23s) Loss: 0.1377(0.1245) Grad: 0.2900 LR: 0.000062  \n",
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      "Epoch: [5][600/1316] Elapsed 2m 52s (remain 3m 25s) Loss: 0.0754(0.1215) Grad: 0.2239 LR: 0.000059  \n",
      "Epoch: [5][700/1316] Elapsed 3m 21s (remain 2m 56s) Loss: 0.0804(0.1200) Grad: 0.1463 LR: 0.000058  \n",
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      "========== fold: 0 result ==========\n",
      "Score: 0.88933\n",
      "========== fold: 1 training ==========\n"
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      "Epoch 9 - avg_train_loss: 0.0791  avg_val_loss: 0.0685  time: 418s\n",
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      "Epoch 10 - avg_train_loss: 0.0298  avg_val_loss: 0.0687  time: 409s\n",
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      "Epoch 10 - Save final model\n",
      "========== fold: 1 result ==========\n",
      "Score: 0.88573\n",
      "========== fold: 2 training ==========\n"
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      "========== fold: 2 result ==========\n",
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      "Epoch 10 - avg_train_loss: 0.0313  avg_val_loss: 0.0622  time: 425s\n",
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      "Epoch 10 - Save final model\n",
      "========== fold: 3 result ==========\n",
      "Score: 0.89956\n",
      "========== fold: 4 training ==========\n"
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      "Epoch 4 - avg_train_loss: 0.1262  avg_val_loss: 0.0698  time: 447s\n",
      "Epoch 4 - Accuracy: 0.8824757926713499\n"
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      "Epoch 5 - avg_train_loss: 0.1223  avg_val_loss: 0.0657  time: 447s\n",
      "Epoch 5 - Accuracy: 0.8876020505031327\n",
      "Epoch 5 - Save Best Score: 0.8876 Model\n"
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      "Epoch 7 - Save Best Score: 0.8906 Model\n"
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      "Epoch 9 - Accuracy: 0.8912094171254984\n"
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      "Epoch 10 - avg_train_loss: 0.0311  avg_val_loss: 0.0638  time: 435s\n",
      "Epoch 10 - Accuracy: 0.8934877539396241\n",
      "Epoch 10 - Save Best Score: 0.8935 Model\n"
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    },
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     "output_type": "stream",
     "text": [
      "Epoch 10 - Save final model\n",
      "========== fold: 4 result ==========\n",
      "Score: 0.89349\n",
      "========== CV ==========\n",
      "Score: 0.89220\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  }
 ],
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