PCa16TileSquareAug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import cv2\n",
    "import time\n",
    "import copy\n",
    "import pickle  # Log dictionary data\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F # stateless functions\n",
    "import torchvision.transforms as T\n",
    "import torchvision.models as models\n",
    "\n",
    "import multiprocessing\n",
    "# We must import this explicitly, it is not imported by the top-level\n",
    "# multiprocessing module.\n",
    "import multiprocessing.pool\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold, train_test_split\n",
    "from sklearn.metrics import cohen_kappa_score,confusion_matrix\n",
    "from tqdm.auto import tqdm\n",
    "from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler\n",
    "from datetime import datetime\n",
    "from multiprocessing import Manager\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CFG:\n",
    "    batch_size = 16\n",
    "    debug = False\n",
    "    device = torch.device('cuda')\n",
    "    dtype = torch.float32\n",
    "    epochs = 12\n",
    "    lr = 1e-4\n",
    "    model_name = 'resnet'\n",
    "    num_classes = 6\n",
    "    nworkers = 1\n",
    "    nfolds = 4\n",
    "    seed = 524\n",
    "    test_split = 516\n",
    "    TRAIN = '../yi_data/panda-16x128x128-tiles-data/train/'\n",
    "    LABELS = '../data/train.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Data"
   ]
  },
  {
   "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>data_provider</th>\n",
       "      <th>isup_grade</th>\n",
       "      <th>gleason_score</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89ce8e0a494db9c7ddc68dbf58729c68</td>\n",
       "      <td>radboud</td>\n",
       "      <td>1</td>\n",
       "      <td>3+3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ef9c72d529806d9ba341476d4c159838</td>\n",
       "      <td>radboud</td>\n",
       "      <td>5</td>\n",
       "      <td>5+4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>139e1e89bca7897799f80da93adcd3c7</td>\n",
       "      <td>karolinska</td>\n",
       "      <td>0</td>\n",
       "      <td>0+0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0abc61a1bc7abe47b9e44a9f69979eb0</td>\n",
       "      <td>karolinska</td>\n",
       "      <td>2</td>\n",
       "      <td>3+4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fe7812f20a38495743cf7bf7f811c108</td>\n",
       "      <td>karolinska</td>\n",
       "      <td>1</td>\n",
       "      <td>3+3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           image_id data_provider  isup_grade gleason_score  \\\n",
       "0  89ce8e0a494db9c7ddc68dbf58729c68       radboud           1           3+3   \n",
       "1  ef9c72d529806d9ba341476d4c159838       radboud           5           5+4   \n",
       "2  139e1e89bca7897799f80da93adcd3c7    karolinska           0           0+0   \n",
       "3  0abc61a1bc7abe47b9e44a9f69979eb0    karolinska           2           3+4   \n",
       "4  fe7812f20a38495743cf7bf7f811c108    karolinska           1           3+3   \n",
       "\n",
       "   split  \n",
       "0      0  \n",
       "1      2  \n",
       "2      1  \n",
       "3      3  \n",
       "4      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(CFG.LABELS).set_index('image_id')\n",
    "files = sorted(set([p[:32] for p in os.listdir(CFG.TRAIN)]))\n",
    "train = train.loc[files].reset_index()\n",
    "\n",
    "train, test = train_test_split(train, test_size=CFG.test_split, random_state=CFG.seed)\n",
    "train = train.reset_index(drop=True)\n",
    "test = test.reset_index(drop=True)\n",
    "\n",
    "if CFG.debug:\n",
    "    df = train.sample(n=50, random_state=CFG.seed).copy()\n",
    "else:\n",
    "    df = train.copy()\n",
    "\n",
    "# Generate train/validation sets containing the same distribution of isup_grade\n",
    "splits = StratifiedKFold(n_splits=CFG.nfolds, random_state=CFG.seed, shuffle=True)\n",
    "splits = list(splits.split(df,df.isup_grade))\n",
    "# Assign split index to training samples\n",
    "folds_splits = np.zeros(len(df)).astype(np.int)\n",
    "for i in range(CFG.nfolds):\n",
    "    folds_splits[splits[i][1]] = i\n",
    "df['split'] = folds_splits\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/yasufuminakama/panda-se-resnext50-regression-baseline\n",
    "class TrainDataset(Dataset):\n",
    "    \"\"\"Prostate Cancer Biopsy Dataset\"\"\"\n",
    "    \n",
    "    def __init__(self, df, labels, transform=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            csv_file (string): Path to the csv file\n",
    "            root_dir (string): Path to the directory with all images\n",
    "            transform (callable, optional): Optional transform to be applied on an image sample\n",
    "        \"\"\"\n",
    "        # Shuffle dataframes with fixed seed; otherwise, validation set only get cancerous samples\n",
    "        self.df = df\n",
    "        self.labels = labels\n",
    "        self.transform = transform\n",
    "        # For weighted sampler\n",
    "        class_counts = [len(labels[labels==g]) for g in range(CFG.num_classes)]\n",
    "        self.num_samples = sum(class_counts)\n",
    "        class_weights = [self.num_samples/class_counts[i] for i in range(len(class_counts))]\n",
    "        self.weights = [class_weights[labels[i]] for i in range(self.num_samples)]\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        #worker = torch.utils.data.get_worker_info()\n",
    "        #worker_id = worker.id if worker is not None else -1\n",
    "        #start = time.time()\n",
    "        # https://stackoverflow.com/questions/33369832/read-multiple-images-on-a-folder-in-opencv-python\n",
    "        img_fns = [fn for fn in glob.glob(f\"{CFG.TRAIN}/{self.df['image_id'][idx]}_*.png\")]\n",
    "        imgs = [cv2.imread(fn) for fn in img_fns]\n",
    "        # (D,W,H)\n",
    "        img = cv2.hconcat([cv2.vconcat([imgs[0], imgs[1], imgs[2], imgs[3]]),\n",
    "                           cv2.vconcat([imgs[4], imgs[5], imgs[6], imgs[7]]),\n",
    "                           cv2.vconcat([imgs[8], imgs[9], imgs[10], imgs[11]]),\n",
    "                           cv2.vconcat([imgs[12], imgs[13], imgs[14], imgs[15]])])\n",
    "        img = Image.fromarray(img)\n",
    "        \n",
    "        if self.transform:\n",
    "            img = self.transform(img)\n",
    "            \n",
    "        label = self.labels[idx]\n",
    "        #end = time.time()\n",
    "        return img, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_transforms(phase):\n",
    "    assert phase in {'train', 'val'}\n",
    "    \n",
    "    if phase == 'train':\n",
    "        return T.Compose([\n",
    "            T.RandomHorizontalFlip(),\n",
    "            T.RandomVerticalFlip(),\n",
    "            T.RandomRotation(15, fill=255),\n",
    "            T.ToTensor(),\n",
    "            T.Normalize(\n",
    "                mean=[0.8776, 0.8186, 0.9090],\n",
    "                std=[0.1659, 0.2507, 0.1357],\n",
    "            ),\n",
    "        ])\n",
    "    else:\n",
    "        return T.Compose([\n",
    "            T.ToTensor(),\n",
    "            T.Normalize(\n",
    "                mean=[0.8776, 0.8186, 0.9090],\n",
    "                std=[0.1659, 0.2507, 0.1357],\n",
    "            ),\n",
    "        ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use fold idx as validation set\n",
    "def data_loader(fold_idx):\n",
    "    train_idx = df[df['split'] != fold_idx].index\n",
    "    val_idx = df[df['split'] == fold_idx].index\n",
    "\n",
    "    train_dataset = TrainDataset(df.loc[train_idx].reset_index(drop=True),\n",
    "                                 df.loc[train_idx].reset_index(drop=True)['isup_grade'],\n",
    "                                 transform = get_transforms(phase='train'))\n",
    "    val_dataset = TrainDataset(df.loc[val_idx].reset_index(drop=True),\n",
    "                               df.loc[val_idx].reset_index(drop=True)['isup_grade'],\n",
    "                               transform = get_transforms(phase='val'))\n",
    "    \n",
    "    train_sampler = WeightedRandomSampler(weights=train_dataset.weights, num_samples=train_dataset.num_samples)\n",
    "    train_loader = DataLoader(train_dataset, batch_size=CFG.batch_size, sampler=train_sampler, num_workers=CFG.nworkers)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=CFG.batch_size, shuffle=False, num_workers=CFG.nworkers)\n",
    "    return train_loader, val_loader\n",
    "\n",
    "\n",
    "def data_loader_test(train_df, test_df):\n",
    "    train_dataset = TrainDataset(train_df, train_df['isup_grade'],\n",
    "                                 transform = get_transforms(phase='train'))\n",
    "    test_dataset = TrainDataset(test_df, test_df['isup_grade'],\n",
    "                               transform = get_transforms(phase='val'))\n",
    "    \n",
    "    train_sampler = WeightedRandomSampler(weights=train_dataset.weights, num_samples=train_dataset.num_samples)\n",
    "    train_loader = DataLoader(train_dataset, batch_size=CFG.batch_size, sampler=train_sampler, num_workers=CFG.nworkers)\n",
    "    test_loader = DataLoader(test_dataset, batch_size=CFG.batch_size, shuffle=False, num_workers=CFG.nworkers)\n",
    "    return train_loader, test_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, fold, dataloaders, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "    \n",
    "    # Send the model to GPU/CPU\n",
    "    model = model.to(device=CFG.device)\n",
    "    \n",
    "    train_acc_history = []\n",
    "    val_acc_history = []\n",
    "    loss_history = []\n",
    "    \n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        \n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train()   # Set model to training phase\n",
    "            else:\n",
    "                # Skip evaluation for test dataset except for the last epoch\n",
    "                if (len(dataloaders[phase].dataset) != CFG.test_split) or (num_epochs == epoch+1):\n",
    "                    model.eval()    # Set model to evaluate phase\n",
    "                else:\n",
    "                    continue\n",
    "            \n",
    "            avg_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            \n",
    "            print(' ', end='', flush=True)  # To workaround tqdm issue in multiprocess\n",
    "            for inputs, labels in tqdm(dataloaders[phase],\n",
    "                                       desc='[{}] {}/{}({:5s})'.format(fold, epoch+1,num_epochs,phase)):\n",
    "                inputs = inputs.to(device=CFG.device, dtype=CFG.dtype)\n",
    "                labels = labels.to(device=CFG.device, dtype=torch.long)\n",
    "                #print(labels)\n",
    "                \n",
    "                # Zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "                \n",
    "                # Forward, track history if only in training\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    loss = criterion(outputs, labels)\n",
    "                    \n",
    "                    pred = torch.argmax(outputs, 1)\n",
    "\n",
    "                    # Backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "                    \n",
    "                # Statistics\n",
    "                avg_loss += loss.item()*(inputs.size(0)/len(dataloaders[phase].dataset))  # len(dataloaders[phase].dataset) not len(dataloaders[phase])\n",
    "                running_corrects += torch.sum(pred == labels)\n",
    "            \n",
    "            # End of epoch\n",
    "            with torch.no_grad():\n",
    "                epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
    "\n",
    "                if phase == 'val':\n",
    "                    val_acc_history.append(epoch_acc)\n",
    "                    # deep copy the model\n",
    "                    if epoch_acc > best_acc:\n",
    "                        best_acc = epoch_acc\n",
    "                        best_model_wts = copy.deepcopy(model.state_dict())\n",
    "                    # Apply lr_scheduler\n",
    "                    if scheduler is not None:\n",
    "                        scheduler.step(avg_loss)\n",
    "                else:\n",
    "                    train_acc_history.append(epoch_acc)\n",
    "                    loss_history.append(avg_loss)\n",
    "                print('[{}] {} Loss: {:4f} Acc: {:4f}'.format(fold, phase, avg_loss, epoch_acc))\n",
    "    \n",
    "    time_elapsed = time.time() - since\n",
    "    print('[{}] Training complete in {:.0f}m {:0f}s'.format(fold, time_elapsed//60, time_elapsed%60))\n",
    "    print('[{}] Best val Acc: {:4f}'.format(fold, best_acc))\n",
    "    print()\n",
    "    \n",
    "    model.load_state_dict(best_model_wts)\n",
    "                      \n",
    "    return model, loss_history, train_acc_history, val_acc_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Multiprocessing\n",
    "\"\"\"\n",
    "class NoDaemonProcess(multiprocessing.Process):\n",
    "    # make 'daemon' attribute always return False\n",
    "    def _get_daemon(self):\n",
    "        return False\n",
    "    def _set_daemon(self, value):\n",
    "        pass\n",
    "    daemon = property(_get_daemon, _set_daemon)\n",
    "\n",
    "# We sub-class multiprocessing.pool.Pool instead of multiprocessing.Pool\n",
    "# because the latter is only a wrapper function, not a proper class.\n",
    "class MyPool(multiprocessing.pool.Pool):\n",
    "    Process = NoDaemonProcess\n",
    "\n",
    "def train_fn(fold):\n",
    "    model_ft = initialize_model(CFG.model_name, CFG.num_classes, use_pretrained=True)\n",
    "    \"\"\"\n",
    "    optimizer = optim.SGD(model_ft.parameters(),\n",
    "                          lr=CFG.lr,\n",
    "                          momentum=.9,\n",
    "                          nesterov=True)\n",
    "    \"\"\"\n",
    "    optimizer = optim.Adam(model_ft.parameters(),\n",
    "                           lr=CFG.lr)\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=2, verbose=True, eps=1e-06)\n",
    "    #print(f'### FOLD: {fold} ###', flush=True)\n",
    "    loader_train, loader_val = data_loader(fold)\n",
    "    best_model, loss_history, train_acc_history, val_acc_history = train_model(model_ft, fold, {'train': loader_train, 'val': loader_val}, F.cross_entropy, optimizer, scheduler, CFG.epochs)\n",
    "\n",
    "    return best_model, loss_history, train_acc_history, val_acc_history\n",
    "\n",
    "def progressor(fold):\n",
    "    best_model, loss_history, train_acc_history, val_acc_history = train_fn(fold)\n",
    "    return {f'best_mode_{fold}': best_model,\n",
    "            f'loss_history_{fold}': loss_history,\n",
    "            f'train_acc_history_{fold}': train_acc_history,\n",
    "            f'val_acc_history_{fold}': val_acc_history}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resnet18\n",
    "log: resnet_06_01_2020_13_11.pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_parameter_requires_grad(model, feature_extracting):\n",
    "    if feature_extracting:\n",
    "        for param in model.parameters():\n",
    "            param.requires_grad = False\n",
    "            \n",
    "\n",
    "def initialize_model(model_name, num_classes, feature_extract=False, use_pretrained=False):\n",
    "    \"\"\"\n",
    "    Params:\n",
    "        feature_extract\n",
    "            True - fine tunning\n",
    "            False - fix the model\n",
    "    \"\"\"\n",
    "    model_ft = None\n",
    "    \n",
    "    if model_name == 'alexnet':\n",
    "        \"\"\"AlexNet\n",
    "        \"\"\"\n",
    "        model_ft = models.alexnet(pretrained=use_pretrained)\n",
    "        set_parameter_requires_grad(model_ft, feature_extract)\n",
    "        num_ftrs = model_ft.classifier[6].in_features\n",
    "        model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)\n",
    "    elif model_name == 'resnet':\n",
    "        \"\"\"Resnet\n",
    "        \"\"\"\n",
    "        model_ft = models.resnet18(pretrained=use_pretrained)\n",
    "        set_parameter_requires_grad(model_ft, feature_extract)\n",
    "        num_ftrs = model_ft.fc.in_features\n",
    "        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n",
    "    \n",
    "    return model_ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "  "
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      "  "
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      "[2] train Loss: 0.877323 Acc: 0.667200\n",
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      "[0] val Loss: 1.050435 Acc: 0.591200\n",
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      "[3] val Loss: 1.053434 Acc: 0.606000\n",
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      "[2] val Loss: 1.044258 Acc: 0.600800\n",
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      "[2] train Loss: 0.774755 Acc: 0.716800\n",
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      "[2] val Loss: 1.075104 Acc: 0.586000\n",
      " "
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      "[3] val Loss: 1.071824 Acc: 0.599600\n",
      " "
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      " "
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      "\n",
      "[0] train Loss: 0.787452 Acc: 0.701067\n",
      " "
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      "\n",
      "[3] train Loss: 0.836986 Acc: 0.685067\n",
      " "
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    },
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     "output_type": "stream",
     "text": [
      "\n",
      "[1] train Loss: 0.906319 Acc: 0.649600\n",
      " "
     ]
    },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[2] train Loss: 0.754275 Acc: 0.718667\n",
      " "
     ]
    },
    {
     "data": {
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       "model_id": "cdc090e946934991ba7579373ad90575",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[2] 12/12(val  )', max=157.0, style=ProgressStyle(descrip…"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] val Loss: 1.050803 Acc: 0.599200\n",
      "[0] Training complete in 1118m 28.733871s\n",
      "[0] Best val Acc: 0.601600\n",
      "\n",
      "\n",
      "[3] val Loss: 1.054158 Acc: 0.606000\n",
      "[3] Training complete in 1123m 12.016683s\n",
      "[3] Best val Acc: 0.606000\n",
      "\n",
      "\n",
      "[2] val Loss: 1.049828 Acc: 0.588800\n",
      "[2] Training complete in 1123m 14.518635s\n",
      "[2] Best val Acc: 0.600800\n",
      "\n",
      "\n",
      "[1] val Loss: 1.060938 Acc: 0.592000\n",
      "[1] Training complete in 1123m 14.939176s\n",
      "[1] Best val Acc: 0.597200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "log_dict = {'batch_size': CFG.batch_size,\n",
    "            'epochs': CFG.epochs,\n",
    "            'learning_rate': CFG.lr,\n",
    "            'model': CFG.model_name,\n",
    "            'nworkers': CFG.nworkers,\n",
    "            'nfolds': CFG.nfolds,\n",
    "            'random_seed': CFG.seed}\n",
    "\n",
    "nfold = range(CFG.nfolds)\n",
    "result_list = list(MyPool(CFG.nfolds).map(progressor, nfold))\n",
    "\n",
    "# Accumulate result from each process\n",
    "for result in result_list:\n",
    "    log_dict.update(result)\n",
    "\n",
    "# Log results\n",
    "log_file = f'{CFG.model_name}_{datetime.now().strftime(\"%m_%d_%Y_%H_%M\")}.pkl'\n",
    "with open(log_file, 'wb') as pkl_file:\n",
    "    pickle.dump(log_dict, pkl_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfor fold in range(CFG.nfolds):\\n    best_model, loss_history, train_acc_history, val_acc_history = train_fn(fold)\\n    log_dict[f'best_mode_{fold}'] = best_model\\n    log_dict[f'loss_history_{fold}'] = loss_history\\n    log_dict[f'train_acc_history_{fold}'] = train_acc_history\\n    log_dict[f'val_acc_history_{fold}'] = val_acc_history\\n\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "for fold in range(CFG.nfolds):\n",
    "    best_model, loss_history, train_acc_history, val_acc_history = train_fn(fold)\n",
    "    log_dict[f'best_mode_{fold}'] = best_model\n",
    "    log_dict[f'loss_history_{fold}'] = loss_history\n",
    "    log_dict[f'train_acc_history_{fold}'] = train_acc_history\n",
    "    log_dict[f'val_acc_history_{fold}'] = val_acc_history\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "Graphs\n",
    "1. loss vs. iterations\n",
    "2. Train/Validation accuracy along epoch\n",
    "\"\"\"\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(log_dict['loss_history_0'], 'o')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(log_dict['train_acc_history_0'], '-o')\n",
    "plt.plot(log_dict['val_acc_history_0'], '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "\n",
    "plt.tight_layout(pad=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read python dict back from the file\n",
    "with open('resnet_06_06_2020_19_27.pkl', 'rb') as pfile:\n",
    "    log_dict = pickle.load(pfile)\n",
    "\"\"\"\n",
    "Graphs\n",
    "1. loss vs. iterations\n",
    "2. Train/Validation accuracy along epoch\n",
    "\"\"\"\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(log_dict['loss_history_0'], 'o')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Loss')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(log_dict['train_acc_history_0'], '-o')\n",
    "plt.plot(log_dict['val_acc_history_0'], '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Valid Accuracy')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07e8208abfe645c79cc6b00c2256c112",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=157.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "438903f3894e47b498fa0f33a8ad3c6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=157.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cb895ffbcb245c0828378a68d75d0e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=157.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82af408cbc83465a9c2f8e4ec328310d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=157.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "preds, targets = [], []\n",
    "for fold in range(CFG.nfolds):\n",
    "    model_fd = log_dict[f'best_mode_{fold}']\n",
    "    _, loader_val = data_loader(fold)\n",
    "    for inputs, labels in tqdm(loader_val):\n",
    "        inputs = inputs.to(device=CFG.device, dtype=CFG.dtype)\n",
    "        labels = labels.to(device=CFG.device, dtype=torch.long)\n",
    "                \n",
    "        # Forward, track history if only in training\n",
    "        with torch.no_grad():\n",
    "            outputs = model_fd(inputs)\n",
    "            pred = torch.argmax(outputs, 1)\n",
    "        preds.append(pred)\n",
    "        targets.append(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kappa: 0.7414910361034592\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'True label')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = torch.cat(preds).cpu()\n",
    "t = torch.cat(targets).cpu()\n",
    "kappa = cohen_kappa_score(t, p, weights='quadratic')\n",
    "print(f'Kappa: {kappa}')\n",
    "conf_mat = confusion_matrix(t,p)\n",
    "#plt.matshow()\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(conf_mat, annot=True)\n",
    "plt.xlabel('Predicted label')\n",
    "plt.ylabel('True label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We use model trained in CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read python dict back from the file\n",
    "#with open('resnet_06_06_2020_19_27.pkl', 'rb') as pfile:\n",
    "#    log_dict_fold = pickle.load(pfile)\n",
    "\"\"\"\n",
    "Graphs\n",
    "1. loss vs. iterations\n",
    "2. Train/Validation accuracy along epoch\n",
    "\"\"\"\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(log_dict_fold['loss_history_0'], 'o')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Loss')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(log_dict_fold['train_acc_history_0'], '-o')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Accuracy')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "935a62e737614d68b1b76dbf6ebad4ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=33.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "preds_test_fold, targets_test_fold = [], []\n",
    "model_fold = log_dict_fold['best_mode_0']\n",
    "model_fold = model_fold.to(device=CFG.device, dtype=CFG.dtype)\n",
    "_, loader_test = data_loader_test(train, test)\n",
    "for inputs, labels in tqdm(loader_test):\n",
    "    inputs = inputs.to(device=CFG.device, dtype=CFG.dtype)\n",
    "    labels = labels.to(device=CFG.device, dtype=torch.long)\n",
    "\n",
    "    # Forward, track history if only in training\n",
    "    with torch.no_grad():\n",
    "        outputs = model_fold(inputs)\n",
    "        pred = torch.argmax(outputs, 1)\n",
    "    preds_test_fold.append(pred)\n",
    "    targets_test_fold.append(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kappa: 0.4993024275521186\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'True label')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_test = torch.cat(preds_test_fold).cpu()\n",
    "t_test = torch.cat(targets_test_fold).cpu()\n",
    "kappa = cohen_kappa_score(t_test[t_test>=2], p_test[t_test>=2], weights='quadratic')\n",
    "print(f'Kappa: {kappa}')\n",
    "conf_mat = confusion_matrix(t_test,p_test)\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(conf_mat, annot=True)\n",
    "plt.xlabel('Predicted label')\n",
    "plt.ylabel('True label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(119)\n",
      "237\n"
     ]
    }
   ],
   "source": [
    "print(torch.sum(t_test[t_test>=2]==p_test[t_test>=2], 0))\n",
    "print(len(t_test[t_test>=2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results of Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ebc5d7bb70644458118c128c75dbef7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 1/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.632781 Acc: 0.302000\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0caa594910e34cd6a8434fab9878e64a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 2/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.523161 Acc: 0.351300\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86a839ce97c34070a65f9f8c57662ab2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 3/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.487541 Acc: 0.378800\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3a41d076fd64152ac01c8d90ad99e30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 4/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.478498 Acc: 0.382400\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53935979d2ba47e9bad33f950aec5eb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 5/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.443825 Acc: 0.397000\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af6f986cc11f49629a0ae0f2944a9a61",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 6/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.423525 Acc: 0.416100\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2c7ca1e591b4ac78eecccb345b65238",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 7/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.390200 Acc: 0.427100\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4695ab20f1744f188bd147eaa09c5860",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 8/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.385322 Acc: 0.427500\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5cdc9101729474c9121902317d91a90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 9/12(train)', max=625.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.367749 Acc: 0.430600\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f823f7501a449438b2326f2a3e3bcdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 10/12(train)', max=625.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.353327 Acc: 0.441200\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2e5097df4ec4c4181e091e20fea25b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 11/12(train)', max=625.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.342586 Acc: 0.452300\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c16289db4f754a9890f61d6880a6af45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 12/12(train)', max=625.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] train Loss: 1.328782 Acc: 0.454500\n",
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e76c23f708f04f43b69b2f48518b5ac5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='[0] 12/12(val  )', max=33.0, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] val Loss: 1.714642 Acc: 0.381783\n",
      "[0] Training complete in 811m 45.437505s\n",
      "[0] Best val Acc: 0.381783\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def train_full(train_df, test_df):\n",
    "    model_ft = initialize_model(CFG.model_name, CFG.num_classes)\n",
    "    optimizer = optim.Adam(model_ft.parameters(),\n",
    "                           lr=CFG.lr)\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=2, verbose=True, eps=1e-06)\n",
    "    \n",
    "    loader_train, loader_test = data_loader_test(train_df, test_df)\n",
    "    return train_model(model_ft, 0,\n",
    "                       {'train': loader_train, 'val': loader_test},\n",
    "                       F.cross_entropy,\n",
    "                       optimizer, scheduler,\n",
    "                       CFG.epochs)          \n",
    "\n",
    "final_model, loss_history, train_acc_history, test_acc_history = train_full(train, test)\n",
    "log_dict = {\n",
    "            f'best_model_final': final_model.to('cpu'),  # Don't save model as cuda\n",
    "            f'loss_history_final': loss_history,\n",
    "            f'train_acc_history_final': train_acc_history,\n",
    "            f'test_acc_history_final': test_acc_history\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#resnet_final_06_07_2020_15_03.pkl\n",
    "log_file = f'{CFG.model_name}_final_{datetime.now().strftime(\"%m_%d_%Y_%H_%M\")}.pkl'\n",
    "with open(log_file, 'wb') as pkl_file:\n",
    "    pickle.dump(log_dict, pkl_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Graphs\n",
    "1. loss vs. iterations\n",
    "2. Train/Validation accuracy along epoch\n",
    "\"\"\"\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(log_dict['loss_history_final'], 'o')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Loss')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(log_dict['train_acc_history_final'], '-o')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Valid Accuracy')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88636753f66d405cbefecb1748de65f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=33.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "preds_test, targets_test = [], []\n",
    "final_model = final_model.to(device=CFG.device, dtype=CFG.dtype)\n",
    "_, loader_test = data_loader_test(train, test)\n",
    "for inputs, labels in tqdm(loader_test):\n",
    "    inputs = inputs.to(device=CFG.device, dtype=CFG.dtype)\n",
    "    labels = labels.to(device=CFG.device, dtype=torch.long)\n",
    "\n",
    "    # Forward, track history if only in training\n",
    "    with torch.no_grad():\n",
    "        outputs = final_model(inputs)\n",
    "        pred = torch.argmax(outputs, 1)\n",
    "    preds_test.append(pred)\n",
    "    targets_test.append(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kappa: 0.4035605433798548\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'True label')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_test = torch.cat(preds_test).cpu()\n",
    "t_test = torch.cat(targets_test).cpu()\n",
    "kappa = cohen_kappa_score(t_test, p_test, weights='quadratic')\n",
    "print(f'Kappa: {kappa}')\n",
    "conf_mat = confusion_matrix(t_test,p_test)\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(conf_mat, annot=True)\n",
    "plt.xlabel('Predicted label')\n",
    "plt.ylabel('True label')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "00028421c2c74677bf6465c3e81979cc": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "0006a2563aa243c6a73f0dcd03e0374b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "00102b9a53c24fcc8bd98b7bd0f0fb80": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0038ec72fd77434b808987e6a3223aaf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
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