5/transfer_learning_with_timm_models_and_pytorch_0_97_Best.ipynb

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   },
   "source": [
    "# Installs"
   ]
  },
  {
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     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m549.1/549.1 KB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
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   "source": [
    "!pip install timm -q"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "tlIdBZiYwn7L"
   },
   "source": [
    "# Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import time\n",
    "import random # for torch seed\n",
    "import os # for torch seed\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.optim import Adam, AdamW, RMSprop # optmizers\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR, ReduceLROnPlateau # Learning rate schedulers\n",
    "\n",
    "import albumentations as A\n",
    "# from albumentations.pytorch import ToTensorV2\n",
    "\n",
    "import timm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "outputId": "1a874e29-97d6-46da-e613-4cf4c0221f95"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "\n",
    "drive.mount('/content/drive')\n",
    "\n",
    "!cp /content/drive/MyDrive/Abdulla_OCR/Character/anotation_28x28/Test.csv .\n",
    "!cp /content/drive/MyDrive/Abdulla_OCR/Character/anotation_28x28/Train.csv .\n",
    "\n",
    "!rm -rf sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:13.946834Z",
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     "shell.execute_reply.started": "2022-04-14T20:48:13.946804Z"
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   "outputs": [],
   "source": [
    "# Load the data\n",
    "INPUT_PATH = './'\n",
    "OUTPUT_PATH = './OUTPUT/'\n",
    "\n",
    "train = pd.read_csv(INPUT_PATH + \"Train.csv\")\n",
    "test = pd.read_csv(INPUT_PATH + \"Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "execution": {
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     "text": [
      "Train shape:  (209966, 785)\n",
      "Test shape:  (34966, 784)\n",
      "\n",
      "Train dataset:\n"
     ]
    },
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     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "Test dataset:\n"
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       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "            + ' to learn more about interactive tables.';\n",
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       "\n",
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       "\n",
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       "\n",
       "[34966 rows x 784 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Train shape: \", train.shape)\n",
    "print(\"Test shape: \", test.shape)\n",
    "\n",
    "print(\"\\nTrain dataset:\")\n",
    "display(train)\n",
    "print(\"\\nTest dataset:\")\n",
    "display(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-HtMh8jEwn7O"
   },
   "source": [
    "# CFG\n",
    "\n",
    "This class enables easy configuration to simulate and understand results.\n",
    "\n",
    "You can tweak some parameters and see how they impact on the metrics, plots, and predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:18.776971Z",
     "iopub.status.busy": "2022-04-14T20:48:18.776524Z",
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     "shell.execute_reply.started": "2022-04-14T20:48:18.776935Z"
    },
    "id": "EXhnCTzVwn7P"
   },
   "outputs": [],
   "source": [
    "class CFG:\n",
    "  DEBUG = False\n",
    "\n",
    "  ### input: not configurable\n",
    "  IMG_HEIGHT = 28\n",
    "  IMG_WIDTH = 28\n",
    "  N_CLASS = len(np.unique(train['label']))\n",
    "\n",
    "  ### split train and validation sets\n",
    "  split_fraction = 0.95\n",
    "\n",
    "  ### model\n",
    "  model_name = 'resnet50' # 'resnet34', 'resnet200d', 'efficientnet_b1_pruned', 'efficientnetv2_m', efficientnet_b7 ...  \n",
    "\n",
    "  ### training\n",
    "  print_freq = 100\n",
    "  BATCH_SIZE = 1024\n",
    "  N_EPOCHS = 40\n",
    "\n",
    "  ### set only one to True\n",
    "  save_best_loss = False\n",
    "  save_best_accuracy = True\n",
    "\n",
    "  ### optimizer\n",
    "  # optimizer = 'adam'\n",
    "  # optimizer = 'adamw'\n",
    "  optimizer = 'rmsprop'\n",
    "  LEARNING_RATE = 1e-3\n",
    "  weight_decay = 0.1 # for adamw\n",
    "  l2_penalty = 0.01 # for RMSprop\n",
    "  rms_momentum = 0 # for RMSprop\n",
    "\n",
    "  ### learning rate scheduler (LRS)\n",
    "  scheduler = 'ReduceLROnPlateau'\n",
    "  # scheduler = 'CosineAnnealingLR'\n",
    "  plateau_factor = 0.5\n",
    "  plateau_patience = 3\n",
    "  cosine_T_max = 4\n",
    "  cosine_eta_min = 1e-8\n",
    "  verbose = True\n",
    "\n",
    "  ### train and validation DataLoaders\n",
    "  shuffle = False\n",
    "\n",
    "  ### albumentations\n",
    "  probability = 0.6\n",
    "\n",
    "  random_seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:18.789517Z",
     "iopub.status.busy": "2022-04-14T20:48:18.789175Z",
     "iopub.status.idle": "2022-04-14T20:48:18.796613Z",
     "shell.execute_reply": "2022-04-14T20:48:18.795732Z",
     "shell.execute_reply.started": "2022-04-14T20:48:18.789471Z"
    },
    "id": "dkoeSNEBwn7R",
    "outputId": "238cfeb1-ee28-44b9-e0dd-de5cbc40f904"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 35 classes to train\n"
     ]
    }
   ],
   "source": [
    "if CFG.DEBUG:\n",
    "  CFG.N_EPOCHS = 3\n",
    "  train = train.sample(frac = 0.1).reset_index(drop=True) # n = 10_000\n",
    "  CFG.N_CLASS = len(np.unique(train['label']))\n",
    "\n",
    "print(f'Using {CFG.N_CLASS} classes to train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:18.79895Z",
     "iopub.status.busy": "2022-04-14T20:48:18.79826Z",
     "iopub.status.idle": "2022-04-14T20:48:18.810073Z",
     "shell.execute_reply": "2022-04-14T20:48:18.809242Z",
     "shell.execute_reply.started": "2022-04-14T20:48:18.79891Z"
    },
    "id": "vWtaIigEwn7S",
    "outputId": "b3d663b4-f656-4090-fa2d-fcf396abf914"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG?:  False\n",
      "Train shape:  (209966, 785)\n",
      "Test shape:  (34966, 784)\n",
      "\n",
      "Image shape (H, W): (28,28)\n",
      "Number of classes in train dataset:  35\n"
     ]
    }
   ],
   "source": [
    "print(\"DEBUG?: \", CFG.DEBUG)\n",
    "print(\"Train shape: \", train.shape)\n",
    "print(\"Test shape: \", test.shape)\n",
    "\n",
    "print(f\"\\nImage shape (H, W): ({CFG.IMG_HEIGHT},{CFG.IMG_WIDTH})\")\n",
    "print(\"Number of classes in train dataset: \", CFG.N_CLASS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:18.812843Z",
     "iopub.status.busy": "2022-04-14T20:48:18.812646Z",
     "iopub.status.idle": "2022-04-14T20:48:18.870006Z",
     "shell.execute_reply": "2022-04-14T20:48:18.869206Z",
     "shell.execute_reply.started": "2022-04-14T20:48:18.812821Z"
    },
    "id": "CrW0yh0iwn7T",
    "outputId": "75a0b8ad-6002-456b-e521-68836c951d24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# detect and define device \n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:18.872049Z",
     "iopub.status.busy": "2022-04-14T20:48:18.871218Z",
     "iopub.status.idle": "2022-04-14T20:48:18.881523Z",
     "shell.execute_reply": "2022-04-14T20:48:18.880742Z",
     "shell.execute_reply.started": "2022-04-14T20:48:18.872013Z"
    },
    "id": "fZzsG8HJwn7U"
   },
   "outputs": [],
   "source": [
    "# for reproducibility\n",
    "def seed_torch(seed):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "seed_torch(seed = CFG.random_seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N_bbW5Yxwn7V"
   },
   "source": [
    "# Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:18.886114Z",
     "iopub.status.busy": "2022-04-14T20:48:18.885853Z",
     "iopub.status.idle": "2022-04-14T20:48:19.075421Z",
     "shell.execute_reply": "2022-04-14T20:48:19.073748Z",
     "shell.execute_reply.started": "2022-04-14T20:48:18.886061Z"
    },
    "id": "VJw4CCZhwn7W",
    "outputId": "175c5a01-d396-44f8-ef6a-c1ee2e5126d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before split:\n",
      "train shape:  (209966, 785)\n",
      "\n",
      "After split:\n",
      "train_df shape:  (199467, 785)\n",
      "valid_df shape:  (10499, 785)\n"
     ]
    }
   ],
   "source": [
    "print('Before split:')\n",
    "print('train shape: ', train.shape)\n",
    "\n",
    "### simple split\n",
    "# split = int(CFG.split_fraction * len(train))\n",
    "# valid_df = train[split:].reset_index(drop = True)\n",
    "# train_df = train[:split].reset_index(drop = True)\n",
    "\n",
    "### random split\n",
    "train_df, valid_df = train_test_split(train, test_size=(1-CFG.split_fraction), random_state=CFG.random_seed)\n",
    "\n",
    "print('\\nAfter split:')\n",
    "print('train_df shape: ', train_df.shape)\n",
    "print('valid_df shape: ', valid_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:19.077155Z",
     "iopub.status.busy": "2022-04-14T20:48:19.07688Z",
     "iopub.status.idle": "2022-04-14T20:48:19.08885Z",
     "shell.execute_reply": "2022-04-14T20:48:19.08807Z",
     "shell.execute_reply.started": "2022-04-14T20:48:19.07712Z"
    },
    "id": "eigVWEpYwn7Y"
   },
   "outputs": [],
   "source": [
    "### for training and validation\n",
    "class DigitDataset(Dataset):\n",
    "    def __init__(self, df, X_col, y_col, augmentations = None):\n",
    "        self.df = df\n",
    "        self.features = df[X_col].values/255 # scale (greyscale) only features. do not scale target\n",
    "        self.targets = df[y_col].values.reshape((-1, 1))\n",
    "        self.augmentations = augmentations\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "      image = self.features[idx].reshape((28, 28))\n",
    "      label = self.targets[idx]\n",
    "\n",
    "      if self.augmentations is not None:\n",
    "        augmented = self.augmentations(image=image)  \n",
    "        image = augmented['image']\n",
    "        image = torch.from_numpy(image).float()\n",
    "        image = torch.stack([image, image, image], dim = 0) # images must have 3 channels to enter timm models, check the model's fisrt layer parameters to confirm        \n",
    "        return image, label\n",
    "      else:\n",
    "        image = torch.from_numpy(image).float()\n",
    "        image = torch.stack([image, image, image], dim = 0) # images must have 3 channels to enter timm models, check the model's fisrt layer parameters to confirm\n",
    "        return image, label\n",
    "\n",
    "\n",
    "### for inference\n",
    "class DigitInferenceDataset(Dataset):\n",
    "    def __init__(self, df, augmentations = None): # for inference we only have the features dataframe\n",
    "        self.df = df\n",
    "        self.features = df[:].values/255 # scale (greyscale) only features. do not scale target\n",
    "        self.augmentations = augmentations\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        image = self.features[idx].reshape((28, 28))\n",
    "        image = torch.from_numpy(image).float()\n",
    "        image = torch.stack([image, image, image], dim = 0) # images must have 3 channels to enter timm models, check the model's fisrt layer parameters to confirm\n",
    "        return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xz9bhA7Own7Z"
   },
   "source": [
    "# Data Augmentation\n",
    "\n",
    "Set parameter p (probability of applying the transformation) to 1 to make sure you will visualize the augmentations.\n",
    "\n",
    "[Albumentations Demo](https://albumentations-demo.herokuapp.com/)\n",
    "\n",
    "[Albumentations docs](https://albumentations.ai/docs/getting_started/transforms_and_targets/)\n",
    "\n",
    "[Albumentations transforms](https://albumentations.ai/docs/getting_started/transforms_and_targets/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:19.090669Z",
     "iopub.status.busy": "2022-04-14T20:48:19.090404Z",
     "iopub.status.idle": "2022-04-14T20:48:19.101702Z",
     "shell.execute_reply": "2022-04-14T20:48:19.100977Z",
     "shell.execute_reply.started": "2022-04-14T20:48:19.090634Z"
    },
    "id": "YMdJ4FPawn7Z"
   },
   "outputs": [],
   "source": [
    "transform_train = A.Compose([\n",
    "                  # A.Resize(CFG.IMG_HEIGHT - 20, CFG.IMG_WIDTH - 20, p=CFG.probability),\n",
    "                  # A.HorizontalFlip(p=CFG.probability), # 0.2\n",
    "                  # A.VerticalFlip(p=CFG.probability), # 0.5\n",
    "                  # A.CenterCrop(23, 23, p=CFG.probability),\n",
    "                  # A.RandomCrop(width=23, height=23, p=CFG.probability),                  \n",
    "                  # A.MotionBlur(p=CFG.probability), # 0.2\n",
    "                  # A.Normalize(p=CFG.probability)                  \n",
    "                  # A.Affine(p=CFG.probability),                  \n",
    "                  # A.Equalize(p=CFG.probability),                  \n",
    "                  # A.CLAHE(p=CFG.probability),                  \n",
    "                  # ToTensorV2(),\n",
    "\n",
    "\n",
    "                  A.Rotate(limit=40, p=CFG.probability), \n",
    "                  A.ShiftScaleRotate(rotate_limit=40, p=CFG.probability),\n",
    "                  # A.IAASharpen(p=CFG.probability),\n",
    "                  A.Downscale(scale_min=0.7, scale_max=0.7, p=CFG.probability)\n",
    "])\n",
    "\n",
    "\n",
    "transform_valid = A.Compose([\n",
    "                  # A.Resize(CFG.IMG_HEIGHT, CFG.IMG_WIDTH, p=CFG.probability),\n",
    "                  # ToTensorV2(),\n",
    "\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:19.103506Z",
     "iopub.status.busy": "2022-04-14T20:48:19.103208Z",
     "iopub.status.idle": "2022-04-14T20:48:19.515528Z",
     "shell.execute_reply": "2022-04-14T20:48:19.514771Z",
     "shell.execute_reply.started": "2022-04-14T20:48:19.10347Z"
    },
    "id": "WLdm1Vh3wn7a",
    "outputId": "92b2e35d-d8ec-484b-c80a-a065996d79ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length train_dataset (td):  209966\n",
      "Length train_dataloader (tl):  206\n",
      "Batch size:  1024\n",
      "Label:  [0]\n"
     ]
    }
   ],
   "source": [
    "# check if Dataset class is working\n",
    "# check some augmentations\n",
    "y_col = \"label\"\n",
    "X_col = [c for c in train.columns if c != 'label']\n",
    "\n",
    "# instantiate Dataset class\n",
    "td = DigitDataset(train, X_col, y_col, augmentations = None) # no augmentation\n",
    "td_aug = DigitDataset(train, X_col, y_col, augmentations = transform_train) # with data augmentation\n",
    "\n",
    "# DataLoaders\n",
    "tl = DataLoader(td, batch_size = CFG.BATCH_SIZE, shuffle = True) # no augmentation\n",
    "tl_aug = DataLoader(td_aug, batch_size = CFG.BATCH_SIZE, shuffle = True) # with data augmentation\n",
    "\n",
    "print('Length train_dataset (td): ', len(td))\n",
    "print('Length train_dataloader (tl): ', len(tl))\n",
    "print('Batch size: ', CFG.BATCH_SIZE)\n",
    "\n",
    "img_aug, lab_aug = td_aug.__getitem__(8) # with data augmentation\n",
    "print('Label: ', lab_aug)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bfhvOycKwn7b"
   },
   "source": [
    "# Transfer Learning: timm\n",
    "\n",
    "[timm docs](https://rwightman.github.io/pytorch-image-models/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:19.517455Z",
     "iopub.status.busy": "2022-04-14T20:48:19.516731Z",
     "iopub.status.idle": "2022-04-14T20:48:19.523738Z",
     "shell.execute_reply": "2022-04-14T20:48:19.522763Z",
     "shell.execute_reply.started": "2022-04-14T20:48:19.517414Z"
    },
    "id": "gpTseyP3wn7c"
   },
   "outputs": [],
   "source": [
    "class DigitModel(nn.Module):\n",
    "    def __init__(self, model_name = CFG.model_name, pretrained = True):\n",
    "        super().__init__()\n",
    "\n",
    "        self.model_name = model_name\n",
    "        self.cnn = timm.create_model(self.model_name, pretrained = pretrained, num_classes = CFG.N_CLASS)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.cnn(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:19.525571Z",
     "iopub.status.busy": "2022-04-14T20:48:19.525138Z",
     "iopub.status.idle": "2022-04-14T20:48:24.072601Z",
     "shell.execute_reply": "2022-04-14T20:48:24.071876Z",
     "shell.execute_reply.started": "2022-04-14T20:48:19.525537Z"
    },
    "id": "5fIydmMkwn7c",
    "outputId": "f159cca8-7d3d-466b-d14d-1bd2fb28b9e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth\" to /root/.cache/torch/hub/checkpoints/resnet50_a1_0-14fe96d1.pth\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DigitModel(\n",
       "  (cnn): ResNet(\n",
       "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act1): ReLU(inplace=True)\n",
       "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (layer1): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer2): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer3): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (4): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (5): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer4): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act1): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (drop_block): Identity()\n",
       "        (act2): ReLU(inplace=True)\n",
       "        (aa): Identity()\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (act3): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
       "    (fc): Linear(in_features=2048, out_features=35, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### check model structure\n",
    "\n",
    "m = DigitModel()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Aiha_YWSwn7d"
   },
   "source": [
    "## List of timm models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:24.074311Z",
     "iopub.status.busy": "2022-04-14T20:48:24.073885Z",
     "iopub.status.idle": "2022-04-14T20:48:24.085439Z",
     "shell.execute_reply": "2022-04-14T20:48:24.084695Z",
     "shell.execute_reply.started": "2022-04-14T20:48:24.074274Z"
    },
    "id": "L4HMLzo7wn7d",
    "outputId": "f04f885c-7204-466e-ca3d-174f8e55eda7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of models available:  770\n",
      "\n",
      "Densenet models:  ['densenet121', 'densenet121d', 'densenet161', 'densenet169', 'densenet201', 'densenet264', 'densenet264d_iabn', 'densenetblur121d', 'tv_densenet121']\n",
      "\n",
      "EfficientNet models:  ['efficientnet_b0', 'efficientnet_b0_g8_gn', 'efficientnet_b0_g16_evos', 'efficientnet_b0_gn', 'efficientnet_b1']\n"
     ]
    }
   ],
   "source": [
    "print('Number of models available: ', len(timm.list_models(pretrained=True)))\n",
    "print('\\nDensenet models: ', timm.list_models('*densenet*'))\n",
    "print('\\nEfficientNet models: ', timm.list_models('efficientnet*')[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:24.087679Z",
     "iopub.status.busy": "2022-04-14T20:48:24.087003Z",
     "iopub.status.idle": "2022-04-14T20:48:24.092958Z",
     "shell.execute_reply": "2022-04-14T20:48:24.092253Z",
     "shell.execute_reply.started": "2022-04-14T20:48:24.087599Z"
    },
    "id": "VfdsxdFcwn7e"
   },
   "outputs": [],
   "source": [
    "### all models\n",
    "# timm.list_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:24.095945Z",
     "iopub.status.busy": "2022-04-14T20:48:24.095232Z",
     "iopub.status.idle": "2022-04-14T20:48:26.36453Z",
     "shell.execute_reply": "2022-04-14T20:48:26.363882Z",
     "shell.execute_reply.started": "2022-04-14T20:48:24.095894Z"
    },
    "id": "7w8qn02rwn7e",
    "outputId": "bf3578ab-48c7-46c6-f32a-8f9e1a864df0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=4096, out_features=1000, bias=True)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### checking model architecture and number of input neurons in the last layer\n",
    "m = timm.create_model('vgg16') # just testing, vgg will not be used\n",
    "\n",
    "m.head.fc # print last layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2woCD-e8wn7f"
   },
   "source": [
    "# Optimizer\n",
    "\n",
    "[torch optimizer documentation](https://pytorch.org/docs/stable/optim.html#)\n",
    "\n",
    "Function to get the optimizer to be used (can be tuned in CFG class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:26.366296Z",
     "iopub.status.busy": "2022-04-14T20:48:26.365879Z",
     "iopub.status.idle": "2022-04-14T20:48:26.373412Z",
     "shell.execute_reply": "2022-04-14T20:48:26.372639Z",
     "shell.execute_reply.started": "2022-04-14T20:48:26.366259Z"
    },
    "id": "cUba6rpJwn7f"
   },
   "outputs": [],
   "source": [
    "def get_optimizer(lr = CFG.LEARNING_RATE):\n",
    "\n",
    "  if CFG.optimizer == 'adam':\n",
    "      optimizer = Adam(model.parameters(), lr=lr, weight_decay = CFG.weight_decay, amsgrad = False)\n",
    "\n",
    "  elif CFG.optimizer == 'adamw':\n",
    "      optimizer = AdamW(model.parameters(), lr = lr, weight_decay = CFG.weight_decay)\n",
    "\n",
    "  elif CFG.optimizer == 'rmsprop':\n",
    "      optimizer = RMSprop(model.parameters(), lr = lr, weight_decay = CFG.l2_penalty, momentum = CFG.rms_momentum)\n",
    "\n",
    "  else:\n",
    "      print('Optimizer is not defined')      \n",
    "\n",
    "  return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S4xoAMY8wn7g"
   },
   "source": [
    "# LR Scheduler\n",
    "\n",
    "[torch LRS documentation](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate)\n",
    "\n",
    "Function to get the Learning Rate Scheduler to be used (can be tuned in CFG class).\n",
    "\n",
    "torch.optim.lr_scheduler provides several methods to adjust the learning rate based on the number of epochs. \n",
    "\n",
    "torch.optim.lr_scheduler.ReduceLROnPlateau allows dynamic learning rate reducing based on some validation measurements.\n",
    "\n",
    "Learning rate scheduling should be applied after optimizer’s update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:26.374974Z",
     "iopub.status.busy": "2022-04-14T20:48:26.374639Z",
     "iopub.status.idle": "2022-04-14T20:48:26.383833Z",
     "shell.execute_reply": "2022-04-14T20:48:26.383054Z",
     "shell.execute_reply.started": "2022-04-14T20:48:26.374935Z"
    },
    "id": "kUnKMDCWwn7g"
   },
   "outputs": [],
   "source": [
    "def get_scheduler(optimizer):\n",
    "\n",
    "  if CFG.scheduler=='ReduceLROnPlateau':\n",
    "      scheduler = ReduceLROnPlateau(optimizer, mode='max', factor = CFG.plateau_factor, patience = CFG.plateau_patience, verbose = CFG.verbose)\n",
    "\n",
    "  elif CFG.scheduler=='CosineAnnealingLR':\n",
    "      scheduler = CosineAnnealingLR(optimizer, T_max = CFG.cosine_T_max, eta_min = CFG.cosine_eta_min)\n",
    "\n",
    "  else:\n",
    "      print('LR Scheduler is not defined')\n",
    "\n",
    "  return scheduler "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9kg1YSqGwn7h"
   },
   "source": [
    "# Train function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:26.387385Z",
     "iopub.status.busy": "2022-04-14T20:48:26.387146Z",
     "iopub.status.idle": "2022-04-14T20:48:26.396431Z",
     "shell.execute_reply": "2022-04-14T20:48:26.395785Z",
     "shell.execute_reply.started": "2022-04-14T20:48:26.38736Z"
    },
    "id": "JuvJz2Okwn7h"
   },
   "outputs": [],
   "source": [
    "def train_fn(train_loader, model, criterion, optmizer, device):\n",
    "  # switch to train mode\n",
    "  model.train()\n",
    "    \n",
    "    \n",
    "  size = len(train_loader.dataset)\n",
    "  num_batches = len(train_loader)\n",
    "\n",
    "  loss, correct = 0, 0\n",
    "\n",
    "  ################################# train #################################\n",
    "\n",
    "  for batch, (X, y) in enumerate(train_loader):\n",
    "\n",
    "    start = time.time()\n",
    "\n",
    "    device = torch.device(device)\n",
    "    X, y = X.to(device), y.to(device)  \n",
    "\n",
    "    # compute predictions and loss\n",
    "    optimizer.zero_grad()\n",
    "    pred = model(X)\n",
    "    loss = criterion(pred, y.long().squeeze()) \n",
    "    current = batch * len(X)\n",
    "\n",
    "    # Backpropagation: only in train function, not done in validation function\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # sum correct predictions\n",
    "    y_pred, y_true = torch.argmax(pred, axis=1), y.long().squeeze()\n",
    "    correct += (y_pred == y_true).type(torch.float).sum().item()\n",
    "\n",
    "    end = time.time()\n",
    "    time_delta = np.round(end - start, 3)\n",
    "\n",
    "    # log\n",
    "    loss, current = np.round(loss.item(), 5), batch * len(X)\n",
    "    # if batch % (CFG.print_freq) == 0:\n",
    "    #   print(f\"Train Batch: {current:>5d}/{size:>5d}: loss: {loss:>5f} Elapsed Time: {time_delta} s\")\n",
    "  \n",
    "  # metrics: calculate accuracy and loss for epoch (all batches)\n",
    "  correct /= size # epoch accuracy\n",
    "  loss /= num_batches # epoch loss\n",
    "\n",
    "  print(f\"Train: Accuracy: {(100*correct):>0.2f}%, Avg loss: {loss:>5f} \\n\")\n",
    "\n",
    "  return loss, correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s9uATQ8bwn7i"
   },
   "source": [
    "# Validation function\n",
    "\n",
    "[torch.no_grad documentation](https://pytorch.org/docs/stable/generated/torch.no_grad.html)\n",
    "\n",
    "Use it when you are sure you will not call Tensor.backward(). It reduces memory and time consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:26.398817Z",
     "iopub.status.busy": "2022-04-14T20:48:26.397905Z",
     "iopub.status.idle": "2022-04-14T20:48:26.409841Z",
     "shell.execute_reply": "2022-04-14T20:48:26.409139Z",
     "shell.execute_reply.started": "2022-04-14T20:48:26.398779Z"
    },
    "id": "x89PQ8ixwn7j"
   },
   "outputs": [],
   "source": [
    "def valid_fn(valid_loader, model, criterion, device):\n",
    "  model.eval()\n",
    "\n",
    "  size = len(valid_loader.dataset)\n",
    "  num_batches = len(valid_loader)\n",
    "\n",
    "  loss, correct = 0, 0\n",
    "\n",
    "  ################################# validation #################################\n",
    "\n",
    "  with torch.no_grad(): # disable gradients\n",
    "    for batch, (X, y) in enumerate(valid_loader):\n",
    "\n",
    "      start = time.time()\n",
    "\n",
    "      device = torch.device(device)\n",
    "      X, y = X.to(device), y.to(device)\n",
    "\n",
    "      # compute predictions and loss\n",
    "      pred = model(X)\n",
    "      loss = criterion(pred, y.long().squeeze()) \n",
    "      current = batch * len(X)\n",
    "      \n",
    "      # sum correct predictions\n",
    "      y_pred, y_true = torch.argmax(pred, axis=1), y.long().squeeze()\n",
    "      correct += (y_pred == y_true).type(torch.float).sum().item()\n",
    "\n",
    "      end = time.time()\n",
    "      time_delta = np.round(end - start, 3)\n",
    "      \n",
    "      # log\n",
    "      loss, current = np.round(loss.item(), 5), batch * len(X)\n",
    "      # if batch % (CFG.print_freq) == 0:\n",
    "      #   print(f\"Valid Batch: {current:>5d}/{size:>5d}: loss: {loss:>5f} Elapsed Time: {time_delta} s\")\n",
    "\n",
    "  # metrics: calculate accuracy and loss for epoch (all batches)\n",
    "  correct /= size # epoch accuracy\n",
    "  loss /= num_batches # epoch loss\n",
    "\n",
    "  print(f\"Valid: Accuracy: {(100*correct):>0.2f}%, Avg loss: {loss:>5f} \\n\")\n",
    "\n",
    "  return loss, correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xspLxqClwn7k"
   },
   "source": [
    "# Run training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:48:26.411603Z",
     "iopub.status.busy": "2022-04-14T20:48:26.41125Z",
     "iopub.status.idle": "2022-04-14T20:58:06.91537Z",
     "shell.execute_reply": "2022-04-14T20:58:06.914653Z",
     "shell.execute_reply.started": "2022-04-14T20:48:26.411568Z"
    },
    "id": "QDQ9kdOuwn7k",
    "outputId": "e8a4678e-cc1b-4154-b7d5-57f9a1e081a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ******************************* Using backbone:  resnet50  ******************************* \n",
      "\n",
      "Starting Training...\n",
      "\n",
      "\n",
      "-------------------------------   Epoch 1   -------------------------------\n",
      "\n",
      "Train: Accuracy: 51.31%, Avg loss: 0.005126 \n",
      "\n",
      "Valid: Accuracy: 61.89%, Avg loss: 0.112740 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 147.207 s\n",
      "\n",
      "-------------------------------   Epoch 2   -------------------------------\n",
      "\n",
      "Train: Accuracy: 73.04%, Avg loss: 0.005170 \n",
      "\n",
      "Valid: Accuracy: 63.91%, Avg loss: 0.106495 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 142.29 s\n",
      "\n",
      "-------------------------------   Epoch 3   -------------------------------\n",
      "\n",
      "Train: Accuracy: 75.95%, Avg loss: 0.004225 \n",
      "\n",
      "Valid: Accuracy: 74.18%, Avg loss: 0.090438 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 142.365 s\n",
      "\n",
      "-------------------------------   Epoch 4   -------------------------------\n",
      "\n",
      "Train: Accuracy: 77.11%, Avg loss: 0.005637 \n",
      "\n",
      "Valid: Accuracy: 46.97%, Avg loss: 0.180160 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 141.189 s\n",
      "\n",
      "-------------------------------   Epoch 5   -------------------------------\n",
      "\n",
      "Train: Accuracy: 78.66%, Avg loss: 0.003928 \n",
      "\n",
      "Valid: Accuracy: 70.29%, Avg loss: 0.109359 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 141.096 s\n",
      "\n",
      "-------------------------------   Epoch 6   -------------------------------\n",
      "\n",
      "Train: Accuracy: 79.47%, Avg loss: 0.003368 \n",
      "\n",
      "Valid: Accuracy: 41.75%, Avg loss: 0.204163 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 140.029 s\n",
      "\n",
      "-------------------------------   Epoch 7   -------------------------------\n",
      "\n",
      "Train: Accuracy: 80.56%, Avg loss: 0.003629 \n",
      "\n",
      "Valid: Accuracy: 2.76%, Avg loss: 144.961004 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 140.12 s\n",
      "\n",
      "-------------------------------   Epoch 8   -------------------------------\n",
      "\n",
      "Train: Accuracy: 81.49%, Avg loss: 0.003619 \n",
      "\n",
      "Valid: Accuracy: 64.15%, Avg loss: 0.131697 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 140.081 s\n",
      "\n",
      "-------------------------------   Epoch 9   -------------------------------\n",
      "\n",
      "Train: Accuracy: 82.19%, Avg loss: 0.002921 \n",
      "\n",
      "Valid: Accuracy: 63.19%, Avg loss: 0.117319 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 138.911 s\n",
      "\n",
      "-------------------------------   Epoch 10   -------------------------------\n",
      "\n",
      "Train: Accuracy: 82.86%, Avg loss: 0.003209 \n",
      "\n",
      "Valid: Accuracy: 67.80%, Avg loss: 0.118510 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 139.103 s\n",
      "\n",
      "-------------------------------   Epoch 11   -------------------------------\n",
      "\n",
      "Train: Accuracy: 83.46%, Avg loss: 0.002745 \n",
      "\n",
      "Valid: Accuracy: 24.27%, Avg loss: 0.277466 \n",
      "\n",
      "Epoch 00011: reducing learning rate of group 0 to 5.0000e-04.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 138.893 s\n",
      "\n",
      "-------------------------------   Epoch 12   -------------------------------\n",
      "\n",
      "Train: Accuracy: 87.72%, Avg loss: 0.002408 \n",
      "\n",
      "Valid: Accuracy: 27.40%, Avg loss: 0.268879 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 136.752 s\n",
      "\n",
      "-------------------------------   Epoch 13   -------------------------------\n",
      "\n",
      "Train: Accuracy: 87.77%, Avg loss: 0.002276 \n",
      "\n",
      "Valid: Accuracy: 38.02%, Avg loss: 0.219310 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 137.443 s\n",
      "\n",
      "-------------------------------   Epoch 14   -------------------------------\n",
      "\n",
      "Train: Accuracy: 87.89%, Avg loss: 0.002199 \n",
      "\n",
      "Valid: Accuracy: 26.19%, Avg loss: 0.275855 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 136.855 s\n",
      "\n",
      "-------------------------------   Epoch 15   -------------------------------\n",
      "\n",
      "Train: Accuracy: 88.21%, Avg loss: 0.002316 \n",
      "\n",
      "Valid: Accuracy: 66.60%, Avg loss: 0.114428 \n",
      "\n",
      "Epoch 00015: reducing learning rate of group 0 to 2.5000e-04.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 136.783 s\n",
      "\n",
      "-------------------------------   Epoch 16   -------------------------------\n",
      "\n",
      "Train: Accuracy: 90.30%, Avg loss: 0.001909 \n",
      "\n",
      "Valid: Accuracy: 38.32%, Avg loss: 0.212470 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 134.772 s\n",
      "\n",
      "-------------------------------   Epoch 17   -------------------------------\n",
      "\n",
      "Train: Accuracy: 90.41%, Avg loss: 0.001625 \n",
      "\n",
      "Valid: Accuracy: 70.02%, Avg loss: 0.091482 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 135.368 s\n",
      "\n",
      "-------------------------------   Epoch 18   -------------------------------\n",
      "\n",
      "Train: Accuracy: 90.68%, Avg loss: 0.002057 \n",
      "\n",
      "Valid: Accuracy: 49.51%, Avg loss: 0.181692 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 135.503 s\n",
      "\n",
      "-------------------------------   Epoch 19   -------------------------------\n",
      "\n",
      "Train: Accuracy: 90.67%, Avg loss: 0.001657 \n",
      "\n",
      "Valid: Accuracy: 48.44%, Avg loss: 0.177650 \n",
      "\n",
      "Epoch 00019: reducing learning rate of group 0 to 1.2500e-04.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 135.582 s\n",
      "\n",
      "-------------------------------   Epoch 20   -------------------------------\n",
      "\n",
      "Train: Accuracy: 91.77%, Avg loss: 0.001612 \n",
      "\n",
      "Valid: Accuracy: 94.04%, Avg loss: 0.018100 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 134.46 s\n",
      "\n",
      "-------------------------------   Epoch 21   -------------------------------\n",
      "\n",
      "Train: Accuracy: 91.92%, Avg loss: 0.001650 \n",
      "\n",
      "Valid: Accuracy: 95.51%, Avg loss: 0.015580 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 137.516 s\n",
      "\n",
      "-------------------------------   Epoch 22   -------------------------------\n",
      "\n",
      "Train: Accuracy: 92.03%, Avg loss: 0.001566 \n",
      "\n",
      "Valid: Accuracy: 54.79%, Avg loss: 0.149434 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 135.789 s\n",
      "\n",
      "-------------------------------   Epoch 23   -------------------------------\n",
      "\n",
      "Train: Accuracy: 92.10%, Avg loss: 0.001552 \n",
      "\n",
      "Valid: Accuracy: 93.50%, Avg loss: 0.024675 \n",
      "\n",
      "Epoch 00023: reducing learning rate of group 0 to 6.2500e-05.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 134.059 s\n",
      "\n",
      "-------------------------------   Epoch 24   -------------------------------\n",
      "\n",
      "Train: Accuracy: 92.72%, Avg loss: 0.001428 \n",
      "\n",
      "Valid: Accuracy: 92.86%, Avg loss: 0.024798 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 132.514 s\n",
      "\n",
      "-------------------------------   Epoch 25   -------------------------------\n",
      "\n",
      "Train: Accuracy: 92.78%, Avg loss: 0.001408 \n",
      "\n",
      "Valid: Accuracy: 84.33%, Avg loss: 0.050515 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 134.001 s\n",
      "\n",
      "-------------------------------   Epoch 26   -------------------------------\n",
      "\n",
      "Train: Accuracy: 92.91%, Avg loss: 0.001367 \n",
      "\n",
      "Valid: Accuracy: 94.30%, Avg loss: 0.022035 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 134.619 s\n",
      "\n",
      "-------------------------------   Epoch 27   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.00%, Avg loss: 0.001449 \n",
      "\n",
      "Valid: Accuracy: 95.71%, Avg loss: 0.014132 \n",
      "\n",
      "Epoch 00027: reducing learning rate of group 0 to 3.1250e-05.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 134.898 s\n",
      "\n",
      "-------------------------------   Epoch 28   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.28%, Avg loss: 0.001269 \n",
      "\n",
      "Valid: Accuracy: 95.92%, Avg loss: 0.014735 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 133.289 s\n",
      "\n",
      "-------------------------------   Epoch 29   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.33%, Avg loss: 0.001537 \n",
      "\n",
      "Valid: Accuracy: 96.44%, Avg loss: 0.013126 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 133.919 s\n",
      "\n",
      "-------------------------------   Epoch 30   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.34%, Avg loss: 0.001339 \n",
      "\n",
      "Valid: Accuracy: 95.40%, Avg loss: 0.017112 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 137.833 s\n",
      "\n",
      "-------------------------------   Epoch 31   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.43%, Avg loss: 0.001339 \n",
      "\n",
      "Valid: Accuracy: 96.21%, Avg loss: 0.015835 \n",
      "\n",
      "Epoch 00031: reducing learning rate of group 0 to 1.5625e-05.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 136.054 s\n",
      "\n",
      "-------------------------------   Epoch 32   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.53%, Avg loss: 0.001341 \n",
      "\n",
      "Valid: Accuracy: 96.98%, Avg loss: 0.010847 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 133.761 s\n",
      "\n",
      "-------------------------------   Epoch 33   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.57%, Avg loss: 0.001320 \n",
      "\n",
      "Valid: Accuracy: 96.96%, Avg loss: 0.013201 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 133.395 s\n",
      "\n",
      "-------------------------------   Epoch 34   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.62%, Avg loss: 0.001211 \n",
      "\n",
      "Valid: Accuracy: 96.73%, Avg loss: 0.012411 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 133.228 s\n",
      "\n",
      "-------------------------------   Epoch 35   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.68%, Avg loss: 0.001195 \n",
      "\n",
      "Valid: Accuracy: 95.99%, Avg loss: 0.017712 \n",
      "\n",
      "Epoch 00035: reducing learning rate of group 0 to 7.8125e-06.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 133.483 s\n",
      "\n",
      "-------------------------------   Epoch 36   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.75%, Avg loss: 0.001276 \n",
      "\n",
      "Valid: Accuracy: 97.20%, Avg loss: 0.011475 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 132.644 s\n",
      "\n",
      "-------------------------------   Epoch 37   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.75%, Avg loss: 0.001320 \n",
      "\n",
      "Valid: Accuracy: 97.02%, Avg loss: 0.012741 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 132.802 s\n",
      "\n",
      "-------------------------------   Epoch 38   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.71%, Avg loss: 0.001483 \n",
      "\n",
      "Valid: Accuracy: 97.37%, Avg loss: 0.011624 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 132.983 s\n",
      "\n",
      "-------------------------------   Epoch 39   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.78%, Avg loss: 0.001148 \n",
      "\n",
      "Valid: Accuracy: 97.22%, Avg loss: 0.011487 \n",
      "\n",
      "Epoch 00039: reducing learning rate of group 0 to 3.9063e-06.\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 132.379 s\n",
      "\n",
      "-------------------------------   Epoch 40   -------------------------------\n",
      "\n",
      "Train: Accuracy: 93.87%, Avg loss: 0.001327 \n",
      "\n",
      "Valid: Accuracy: 97.39%, Avg loss: 0.011370 \n",
      "\n",
      "\n",
      "\n",
      "Epoch Elapsed Time: 132.469 s\n",
      "\n",
      "\n",
      "Total Elapsed Time: 90.941 min\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "# define loss function\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "# instantiate model\n",
    "device = torch.device(device)\n",
    "model = DigitModel().to(device) # move the model to GPU before constructing optimizers for it\n",
    "\n",
    "print('\\n ******************************* Using backbone: ', CFG.model_name, \" ******************************* \\n\")\n",
    "\n",
    "# define optimizer\n",
    "optimizer = get_optimizer(lr = CFG.LEARNING_RATE)\n",
    "\n",
    "# define scheduler\n",
    "scheduler = get_scheduler(optimizer)\n",
    "\n",
    "# prepare dataset\n",
    "y_col = \"label\"\n",
    "X_col = [c for c in train.columns if c != 'label']\n",
    "\n",
    "train_dataset = DigitDataset(train_df, X_col, y_col, augmentations=transform_train) # data augmentation. set augmentations = None to disable augmentations\n",
    "valid_dataset = DigitDataset(valid_df, X_col, y_col, augmentations=transform_valid) # data augmentation. set augmentations = None to disable augmentations\n",
    "\n",
    "train_dataloader = DataLoader(train_dataset,\n",
    "                              batch_size = CFG.BATCH_SIZE,\n",
    "                              shuffle = CFG.shuffle)\n",
    "\n",
    "valid_dataloader = DataLoader(valid_dataset,\n",
    "                              batch_size = CFG.BATCH_SIZE,\n",
    "                              shuffle = CFG.shuffle)\n",
    "\n",
    "\n",
    "train_loss_history = []\n",
    "train_acc_history = []\n",
    "valid_loss_history = []\n",
    "valid_acc_history = []\n",
    "LR_history = []\n",
    "\n",
    "best_loss = np.inf\n",
    "best_epoch_loss = 0\n",
    "best_acc = 0\n",
    "best_epoch_acc = 0\n",
    "\n",
    "print('Starting Training...\\n')\n",
    "\n",
    "start_train_time = time.time()\n",
    "\n",
    "for epoch in range(0, CFG.N_EPOCHS):\n",
    "  print(f\"\\n-------------------------------   Epoch {epoch + 1}   -------------------------------\\n\")\n",
    "  start_epoch_time = time.time()\n",
    "\n",
    "  # train\n",
    "  train_loss, train_acc = train_fn(train_dataloader, model, loss_fn, optimizer, device)\n",
    "  train_loss_history.append(train_loss)\n",
    "  train_acc_history.append(train_acc)\n",
    "\n",
    "  # validation\n",
    "  valid_loss, valid_acc = valid_fn(valid_dataloader, model, loss_fn, device)\n",
    "  valid_loss_history.append(valid_loss)\n",
    "  valid_acc_history.append(valid_acc)\n",
    "\n",
    "  # apply LR scheduler after each epoch\n",
    "  if isinstance(scheduler, ReduceLROnPlateau):\n",
    "      scheduler.step(valid_loss)\n",
    "\n",
    "  elif isinstance(scheduler, CosineAnnealingLR):\n",
    "      scheduler.step()\n",
    "\n",
    "  # save LR value to plot later\n",
    "  for param_group in optimizer.param_groups:\n",
    "    LR_history.append(param_group['lr'])\n",
    "\n",
    "  # save validation loss if it was improved (reduced)\n",
    "  if valid_loss < best_loss:\n",
    "    best_epoch_loss = epoch + 1\n",
    "    best_loss = valid_loss\n",
    "    if CFG.save_best_loss:\n",
    "      # save the model's weights and biases only if CFG.save_best_loss == True\n",
    "      torch.save(model.state_dict(), OUTPUT_PATH + f\"DigitModel_ep{best_epoch_loss}.pth\")\n",
    "\n",
    "  # save validation accuracy if it was improved (increased)\n",
    "  if valid_acc > best_acc:\n",
    "    best_epoch_acc = epoch + 1\n",
    "    best_acc = valid_acc\n",
    "    if CFG.save_best_accuracy:\n",
    "      # save the model's weights and biases only if CFG.save_best_accuracy == True\n",
    "      torch.save(model.state_dict(), OUTPUT_PATH + f\"DigitModel_ep{best_epoch_acc}.pth\")    \n",
    "\n",
    "  end_epoch_time = time.time()\n",
    "  time_delta = np.round(end_epoch_time - start_epoch_time, 3)\n",
    "  print(\"\\n\\nEpoch Elapsed Time: {} s\".format(time_delta))\n",
    "\n",
    "end_train_time = time.time()\n",
    "print(\"\\n\\nTotal Elapsed Time: {} min\".format(np.round((end_train_time - start_train_time)/60, 3)))\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "qRSrJvkC4hMq"
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), f\"DigitModel_ep{best_epoch_acc}.pth\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "dulhiLtMGVnV"
   },
   "outputs": [],
   "source": [
    "!cp /content/DigitModel_ep40.pth /content/drive/MyDrive/Model/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JBtdNawVwn7m"
   },
   "source": [
    "# Plot Epochs\n",
    "\n",
    "Plot Train and Validation Loss and Accuracy for each Epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:58:06.917272Z",
     "iopub.status.busy": "2022-04-14T20:58:06.916837Z",
     "iopub.status.idle": "2022-04-14T20:58:06.924187Z",
     "shell.execute_reply": "2022-04-14T20:58:06.923408Z",
     "shell.execute_reply.started": "2022-04-14T20:58:06.917234Z"
    },
    "id": "-gT3xNPXwn7n",
    "outputId": "8435f7ff-0122-426a-9e3c-ea91fa3d42bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best loss:  0.010847272727272726\n",
      "Best epoch (loss criteria):  32\n",
      "\n",
      "\n",
      "Best accuracy:  0.9739022764072769\n",
      "Best epoch (accuracy criteria):  40\n"
     ]
    }
   ],
   "source": [
    "print('Best loss: ', best_loss)\n",
    "print('Best epoch (loss criteria): ', best_epoch_loss)\n",
    "print('\\n')\n",
    "print('Best accuracy: ', best_acc)\n",
    "print('Best epoch (accuracy criteria): ', best_epoch_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 358
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:58:06.926082Z",
     "iopub.status.busy": "2022-04-14T20:58:06.925634Z",
     "iopub.status.idle": "2022-04-14T20:58:07.727617Z",
     "shell.execute_reply": "2022-04-14T20:58:07.726942Z",
     "shell.execute_reply.started": "2022-04-14T20:58:06.926023Z"
    },
    "id": "Q-XHbVhYwn7n",
    "outputId": "598ba5bf-36b6-4166-d7d7-2fb92cca0f5a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (18, 8))\n",
    "fig.suptitle('Epoch Results', fontsize = 18)\n",
    "\n",
    "abscissa = np.arange(1, CFG.N_EPOCHS + 1, 1)\n",
    "\n",
    "# x_ticks according to CFG.N_EPOCHS for better visuailzation\n",
    "if CFG.N_EPOCHS <= 20:\n",
    "  x_ticks = np.arange(1, CFG.N_EPOCHS + 1, 1)\n",
    "else:\n",
    "  x_ticks = np.arange(1, CFG.N_EPOCHS + 1, int(CFG.N_EPOCHS/20) + 1)\n",
    "\n",
    "# Loss plot\n",
    "ax1 = plt.subplot(1, 2, 1)\n",
    "ax1.plot(abscissa, train_loss_history, label='Training', color = 'black')\n",
    "ax1.plot(abscissa, valid_loss_history, label='Validation', color = 'red')\n",
    "plt.xticks(x_ticks)\n",
    "plt.axhline(0, linestyle = 'dashed', color = 'grey')\n",
    "plt.axvline(best_epoch_loss, linestyle = 'dashed', color = 'blue', label = 'Best val loss: ep ' + str(best_epoch_loss))\n",
    "plt.axvline(best_epoch_acc, linestyle = 'dashed', color = 'green', label = 'Best val acc: ep ' + str(best_epoch_acc))\n",
    "plt.title(\"Loss\")\n",
    "ax1.legend(frameon=False);\n",
    "\n",
    "# Accuracy plot\n",
    "ax2 = plt.subplot(1, 2, 2)\n",
    "ax2.plot(abscissa, train_acc_history, label='Training', color = 'black')\n",
    "ax2.plot(abscissa, valid_acc_history, label='Validation', color = 'red')\n",
    "plt.xticks(x_ticks)\n",
    "plt.axhline(0.99, linestyle = 'dashed', color = 'grey')\n",
    "plt.axvline(best_epoch_loss, linestyle = 'dashed', color = 'blue', label = 'Best val loss: ep ' + str(best_epoch_loss))\n",
    "plt.axvline(best_epoch_acc, linestyle = 'dashed', color = 'green', label = 'Best val acc: ep ' + str(best_epoch_acc))\n",
    "plt.title(\"Accuracy\")\n",
    "ax2.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:58:07.729562Z",
     "iopub.status.busy": "2022-04-14T20:58:07.729066Z",
     "iopub.status.idle": "2022-04-14T20:58:08.041919Z",
     "shell.execute_reply": "2022-04-14T20:58:08.041261Z",
     "shell.execute_reply.started": "2022-04-14T20:58:07.729525Z"
    },
    "id": "xReyiXnYwn7o",
    "outputId": "ce931cf3-94b5-4a97-d41e-dff8ffcf337c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (14, 8))\n",
    "\n",
    "abscissa = np.arange(1, CFG.N_EPOCHS + 1, 1)\n",
    "\n",
    "# x_ticks according to CFG.N_EPOCHS for better visuailzation\n",
    "if CFG.N_EPOCHS <= 20:\n",
    "  x_ticks = np.arange(1, CFG.N_EPOCHS + 1, 1)\n",
    "else:\n",
    "  x_ticks = np.arange(1, CFG.N_EPOCHS + 1, int(CFG.N_EPOCHS/20) + 1)\n",
    "\n",
    "# LR plot\n",
    "plt.plot(abscissa, LR_history, label='LR', color = 'orange')\n",
    "plt.xticks(x_ticks)\n",
    "plt.axhline(CFG.LEARNING_RATE, linestyle = 'dashed', color = 'grey')\n",
    "plt.axhline(0, linestyle = 'dashed', color = 'grey')\n",
    "plt.axvline(best_epoch_loss, linestyle = 'dashed', color = 'blue', label = 'Best val loss: ep ' + str(best_epoch_loss))\n",
    "plt.axvline(best_epoch_acc, linestyle = 'dashed', color = 'green', label = 'Best val acc: ep ' + str(best_epoch_acc))\n",
    "plt.title(f\"Learning Rate vs Epochs: {CFG.scheduler}\", fontsize = 16, color = 'orange')\n",
    "plt.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUsWS0EOwn7p"
   },
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:58:08.04379Z",
     "iopub.status.busy": "2022-04-14T20:58:08.04314Z",
     "iopub.status.idle": "2022-04-14T20:58:08.054236Z",
     "shell.execute_reply": "2022-04-14T20:58:08.053387Z",
     "shell.execute_reply.started": "2022-04-14T20:58:08.043748Z"
    },
    "id": "0g5pg2mIwn7p"
   },
   "outputs": [],
   "source": [
    "def softmax(x):\n",
    "    return np.exp(x)/np.sum(np.exp(x), axis=1)[:, None]\n",
    "\n",
    "def inference(test_loader, model):\n",
    "    model.eval()\n",
    "\n",
    "    predictions = []\n",
    "\n",
    "    size = len(test_loader.dataset)\n",
    "    num_batches = len(test_loader)    \n",
    "\n",
    "    model = DigitModel().to(device)\n",
    "\n",
    "    if CFG.save_best_loss: # load model with best validation loss\n",
    "      model.load_state_dict(torch.load(OUTPUT_PATH + f\"DigitModel_ep{best_epoch_loss}.pth\"))\n",
    "    else: # load model with best validation accuracy\n",
    "      model.load_state_dict(torch.load(OUTPUT_PATH + f\"DigitModel_ep{best_epoch_acc}.pth\"))\n",
    "\n",
    "    # disable gradients for inference\n",
    "    with torch.no_grad():\n",
    "      for batch, X in enumerate(test_loader):\n",
    "        \n",
    "        ################################# inference #################################\n",
    "        start = time.time()\n",
    "        current = batch * len(X)\n",
    "\n",
    "        X = X.to(device)\n",
    "\n",
    "        # compute predictions\n",
    "        pred = model(X)       \n",
    "        # softmax\n",
    "        y_pred = softmax(pred.detach().cpu().numpy()) # convert tensor to numpy and apply softmax \n",
    "        y_pred = np.argmax(y_pred, axis = 1) # take the indice of the max value (higher probability: predicted class)\n",
    "\n",
    "        # store results\n",
    "        predictions.append(y_pred)\n",
    "\n",
    "        # log\n",
    "        end = time.time()\n",
    "        time_delta = np.round(end - start, 5)\n",
    "\n",
    "        # if batch % (CFG.print_freq) == 0:\n",
    "        #   print(f\"Inference Batch: {current:>5d}/{size:>5d}: Elapsed Time: {time_delta} s\")          \n",
    "\n",
    "    test_predictions = np.concatenate(predictions, axis = 0) # join sequence of arrays along axis 0\n",
    "    return test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-14T20:58:08.058919Z",
     "iopub.status.busy": "2022-04-14T20:58:08.058634Z",
     "iopub.status.idle": "2022-04-14T20:58:08.133532Z",
     "shell.execute_reply": "2022-04-14T20:58:08.132784Z",
     "shell.execute_reply.started": "2022-04-14T20:58:08.058881Z"
    },
    "id": "fDP-hfAkwn7q"
   },
   "outputs": [],
   "source": [
    "# instantiate Inference Dataset class (create inference Dataset)\n",
    "inference_dataset = DigitInferenceDataset(test, augmentations=None)\n",
    "\n",
    "# create Inference DataLoader object from Dataset class object\n",
    "inference_dataloader = DataLoader(inference_dataset,\n",
    "                                  batch_size = CFG.BATCH_SIZE,\n",
    "                                  shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-04-14T20:58:08.135234Z",
     "iopub.status.busy": "2022-04-14T20:58:08.134927Z",
     "iopub.status.idle": "2022-04-14T20:58:10.663678Z",
     "shell.execute_reply": "2022-04-14T20:58:10.662996Z",
     "shell.execute_reply.started": "2022-04-14T20:58:08.135198Z"
    },
    "id": "SSdKNOZqwn7r",
    "outputId": "66acc296-4816-43d9-89ca-b82c6ee26d88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.95 s, sys: 1.18 s, total: 6.13 s\n",
      "Wall time: 10.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 6, 20, 24, ...,  4, 19, 29])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# run inference\n",
    "predictions = inference(inference_dataloader, model)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S2reIKsd37pM"
   },
   "outputs": [],
   "source": [
    "!cp -r OUTPUT /content/drive/MyDrive/abdulla/Number/anotation_28x28"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
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 },
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