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install -q git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git\n!pip install -q efficientnet_pytorch","metadata":{"papermill":{"duration":15.150617,"end_time":"2023-10-15T04:49:08.056189","exception":false,"start_time":"2023-10-15T04:48:52.905572","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training pipeline for arutema47 model.\n\nThe model and image tile preparation is based on public kernels.\n\n* Most important part is using the denoised training labels.\n* Model efficientnet-b0\n* Original augumentations for better generization (specifics in the dataloader sections)","metadata":{"papermill":{"duration":0.015425,"end_time":"2023-10-15T04:49:08.088215","exception":false,"start_time":"2023-10-15T04:49:08.07279","status":"completed"},"tags":[]}},{"cell_type":"code","source":"DEBUG = False","metadata":{"papermill":{"duration":0.023674,"end_time":"2023-10-15T04:49:08.127786","exception":false,"start_time":"2023-10-15T04:49:08.104112","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"FOLD = 3","metadata":{"papermill":{"duration":0.021185,"end_time":"2023-10-15T04:49:08.164969","exception":false,"start_time":"2023-10-15T04:49:08.143784","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"kernel_type = 'efficientnet-b0'\nmodelname = kernel_type\n\n\ntile_size = 256\nimage_size = 256\nn_tiles = 36\nbatch_size = 6\nnum_workers = 2\nout_dim = 5\ninit_lr = 1e-4\nwarmup_factor = 10\nn_epochs = 25\npatience = 3\n\nload_raw_png = False\nload_jpg = False\n\nmixup = True\ncutmix = False\n\n# Cosine annealing or exp scheduler\nCOSINE = True\nEXP = False\nif not COSINE:\n    EXP = True\n\npoolmethod = \"avgpool\"\n\nkernel_type += \"_tile{}_imsize{}\".format(n_tiles, image_size)\nif mixup:\n    kernel_type += \"_mixup\"\nif cutmix:\n    kernel_type += \"_cutmix\"","metadata":{"papermill":{"duration":0.023931,"end_time":"2023-10-15T04:49:08.204524","exception":false,"start_time":"2023-10-15T04:49:08.180593","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport sys\n\n# make dirs\nos.makedirs(\"models\", exist_ok=True)\nos.makedirs(\"log\", exist_ok=True)","metadata":{"papermill":{"duration":0.022133,"end_time":"2023-10-15T04:49:08.241845","exception":false,"start_time":"2023-10-15T04:49:08.219712","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from datetime import datetime\nimport pytz\nfrom shutil import copyfile\nimport skimage.io\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport PIL.Image\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm_notebook as tqdm\n\nimport torchvision\nfrom torchvision.ops import misc as misc_nn_ops\nfrom torchvision.models import resnet\nfrom efficientnet_pytorch import EfficientNet\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SubsetRandomSampler, RandomSampler, SequentialSampler\n\nfrom warmup_scheduler import GradualWarmupScheduler\nimport albumentations\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import cohen_kappa_score, confusion_matrix\n\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":3.106461,"end_time":"2023-10-15T04:49:11.363587","exception":false,"start_time":"2023-10-15T04:49:08.257126","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Fix SEED\ntorch.manual_seed(42)\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\nnp.random.seed(42)","metadata":{"papermill":{"duration":0.024477,"end_time":"2023-10-15T04:49:11.403973","exception":false,"start_time":"2023-10-15T04:49:11.379496","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# We use the denoised training data","metadata":{"papermill":{"duration":0.015196,"end_time":"2023-10-15T04:49:11.434612","exception":false,"start_time":"2023-10-15T04:49:11.419416","status":"completed"},"tags":[]}},{"cell_type":"code","source":"data_dir = '/kaggle/input/'\n# load clean labels\ndf_train = pd.read_csv(os.path.join(data_dir,\"panda-split/train_9k.csv\"))\nimage_folder = os.path.join(data_dir, 'train-256-36/train_256_36')\n\nwarmup_epo = 1\n\ndf_train = df_train.sample(100).reset_index(drop=True) if DEBUG else df_train\n\ndevice = torch.device('cuda')\n\nprint(image_folder)","metadata":{"papermill":{"duration":0.064736,"end_time":"2023-10-15T04:49:11.51487","exception":false,"start_time":"2023-10-15T04:49:11.450134","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"papermill":{"duration":0.035878,"end_time":"2023-10-15T04:49:11.566475","exception":false,"start_time":"2023-10-15T04:49:11.530597","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Create Folds","metadata":{"papermill":{"duration":0.016444,"end_time":"2023-10-15T04:49:11.600595","exception":false,"start_time":"2023-10-15T04:49:11.584151","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def erase(df_train):\n    df_train2 = df_train\n    erase = []\n    for i, id in enumerate(df_train2[\"image_id\"].to_numpy()):\n        if not os.path.isfile(os.path.join(image_folder, f'{id}.npz')):\n            erase.append(i)\n            pass\n        #img = cv2.imread(os.path.join(image_folder, f'{id}.png'))\n        \n    return df_train.drop(erase)\n\ndf_train = erase(df_train).reset_index()","metadata":{"papermill":{"duration":28.032978,"end_time":"2023-10-15T04:49:39.649628","exception":false,"start_time":"2023-10-15T04:49:11.61665","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = df_train.drop(\"index\", 1)\nlen(df_train)","metadata":{"papermill":{"duration":0.028193,"end_time":"2023-10-15T04:49:39.694286","exception":false,"start_time":"2023-10-15T04:49:39.666093","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"skf = StratifiedKFold(5, shuffle=True, random_state=42)\ndf_train['fold'] = -1\nfor i, (train_idx, valid_idx) in enumerate(skf.split(df_train, df_train['isup_grade'])):\n    df_train.loc[valid_idx, 'fold'] = i\n\ndf_train.tail()","metadata":{"papermill":{"duration":0.042517,"end_time":"2023-10-15T04:49:39.753101","exception":false,"start_time":"2023-10-15T04:49:39.710584","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_idx = np.where((df_train['fold'] != FOLD))[0]\nvalid_idx = np.where((df_train['fold'] == FOLD))[0]\n\ndf_this  = df_train.loc[train_idx]\ndf_valid = df_train.loc[valid_idx]","metadata":{"papermill":{"duration":0.027517,"end_time":"2023-10-15T04:49:39.797363","exception":false,"start_time":"2023-10-15T04:49:39.769846","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model.\n\nThe model is based on a public kernel:\n\nhttps://www.kaggle.com/haqishen/train-efficientnet-b0-w-36-tiles-256-lb0-87","metadata":{"papermill":{"duration":0.016847,"end_time":"2023-10-15T04:49:39.830802","exception":false,"start_time":"2023-10-15T04:49:39.813955","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class enetv2(nn.Module):\n    def __init__(self, out_dim=5, freeze_bn=True):\n        super(enetv2, self).__init__()\n        self.basemodel = EfficientNet.from_pretrained(modelname) \n        self.myfc = nn.Linear(self.basemodel._fc.in_features, out_dim)\n        self.basemodel._fc = nn.Identity()        \n            \n    def extract(self, x):\n        return self.basemodel(x)\n\n    def forward(self, x):\n        x = self.basemodel(x)\n        x = self.myfc(x)\n        return x","metadata":{"papermill":{"duration":0.02543,"end_time":"2023-10-15T04:49:39.872811","exception":false,"start_time":"2023-10-15T04:49:39.847381","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataset\n\nTile preparation is also based on the public kernel.\nhttps://www.kaggle.com/haqishen/train-efficientnet-b0-w-36-tiles-256-lb0-87\n\n\nAugumentations are different and important.\n\n* We add cutouts, rotations augumentation for better generization.\n* We add mixup augumentation for training for better generization.\n\nmixup: Beyond Empirical Risk Minimization\n\nhttps://arxiv.org/abs/1710.09412\n\nFor mixup, I simply made an another tile and mixed two images.\nI just implemented mixup from the original paper, but some implementation can be found from Bengali contests.","metadata":{"papermill":{"duration":0.016425,"end_time":"2023-10-15T04:49:39.905613","exception":false,"start_time":"2023-10-15T04:49:39.889188","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def rand_bbox(size, lam):\n    W = size[1]\n    H = size[2]\n    cut_rat = np.sqrt(1. - lam)\n    cut_w = np.int(W * cut_rat)\n    cut_h = np.int(H * cut_rat)\n\n    # uniform\n    cx = np.random.randint(W)\n    cy = np.random.randint(H)\n\n    bbx1 = np.clip(cx - cut_w // 2, 0, W)\n    bby1 = np.clip(cy - cut_h // 2, 0, H)\n    bbx2 = np.clip(cx + cut_w // 2, 0, W)\n    bby2 = np.clip(cy + cut_h // 2, 0, H)\n\n    return bbx1, bby1, bbx2, bby2","metadata":{"papermill":{"duration":0.025935,"end_time":"2023-10-15T04:49:39.948099","exception":false,"start_time":"2023-10-15T04:49:39.922164","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_tiles(img, mode=0, transform=None):\n        result = []\n        h, w, c = img.shape\n        pad_h = (tile_size - h % tile_size) % tile_size + ((tile_size * mode) // 2)\n        pad_w = (tile_size - w % tile_size) % tile_size + ((tile_size * mode) // 2)\n\n        img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)\n        img3 = img2.reshape(\n            img2.shape[0] // tile_size,\n            tile_size,\n            img2.shape[1] // tile_size,\n            tile_size,\n            3\n        ).astype(np.float32)\n\n        img3 = img3.transpose(0,2,1,3,4).reshape(-1, tile_size, tile_size,3)\n        n_tiles_with_info = (img3.reshape(img3.shape[0],-1).sum(1) < tile_size ** 2 * 3 * 255).sum()\n        if len(img3) < n_tiles:\n            img3 = np.pad(img3,[[0,n_tiles-len(img3)],[0,0],[0,0],[0,0]], constant_values=255)\n        idxs = np.argsort(img3.reshape(img3.shape[0],-1).sum(-1))[:n_tiles]\n        img3 = img3[idxs]\n        img3 = (img3*255).astype(np.uint8)\n        for i in range(len(img3)):\n            if transform is not None:\n                img3[i] = transform(image=img3[i])['image']\n            result.append({'img':img3[i], 'idx':i})\n        return result, n_tiles_with_info >= n_tiles\n\n\nclass PANDADataset(Dataset):\n    def __init__(self,\n                 df,\n                 image_size,\n                 n_tiles=n_tiles,\n                 tile_mode=0,\n                 rand=False,\n                 transform=None,\n                ):\n\n        self.df = df.reset_index(drop=True)\n        self.image_size = image_size\n        self.n_tiles = n_tiles\n        self.tile_mode = tile_mode\n        self.rand = rand\n        self.transform = transform\n\n    def __len__(self):\n        return self.df.shape[0]\n\n    def __getitem__(self, index):\n        row = self.df.iloc[index]\n        img_id = row.image_id\n        \n        if load_raw_png:\n            tiff_file = os.path.join(image_folder, f'{img_id}.jpg')\n            image = cv2.imread(tiff_file)\n            if self.transform is not None:\n                tiles, OK = get_tiles(image, self.tile_mode, self.transform)\n            else:\n                tiles, OK = get_tiles(image, self.tile_mode)\n\n            if self.rand:\n                idxes = np.random.choice(list(range(self.n_tiles)), self.n_tiles, replace=False)\n            else:\n                idxes = list(range(self.n_tiles))\n\n            n_row_tiles = int(np.sqrt(self.n_tiles))\n            images = np.zeros((image_size * n_row_tiles, image_size * n_row_tiles, 3))\n            for h in range(n_row_tiles):\n                for w in range(n_row_tiles):\n                    i = h * n_row_tiles + w\n\n                    if len(tiles) > idxes[i]:\n                        this_img = tiles[idxes[i]]['img']\n                    else:\n                        this_img = np.ones((self.image_size, self.image_size, 3)).astype(np.uint8) * 255\n                    this_img = 255 - this_img\n                    if self.transform is not None:\n                        this_img = self.transform(image=this_img)['image']\n                    h1 = h * image_size\n                    w1 = w * image_size\n                    images[h1:h1+image_size, w1:w1+image_size] = this_img\n                    images = (images*255).astype(np.uint8)\n        elif load_jpg:            \n            file = os.path.join(\"train_{}_{}_aug\".format(self.image_size, self.n_tiles), f'{img_id}_{np.random.randint(0,9)}.jpg')\n            images = cv2.imread(file)\n        else:\n            file = os.path.join(image_folder, f'{img_id}.npz')\n            images = np.load(file)[\"arr_0\"]\n            images = images.transpose(2, 0, 1)\n        \n        # Load labels\n        label = np.zeros(5).astype(np.float32)\n        label[:row.isup_grade] = 1.\n        \n        # aug\n        if self.transform is not None:\n            images = self.transform(image=images.transpose())['image']\n        else:\n            images=images.transpose()\n        \n        # Mixup part\n        rd = np.random.rand()\n        if mixup and rd < 0.3 and self.transform is not None:\n            mix_idx = np.random.random_integers(0, len(self.df))\n            row2 = self.df.iloc[mix_idx]\n            img_id2 = row2.image_id\n            file = os.path.join(image_folder, f'{img_id2}.npz')\n            images2 = np.load(file)[\"arr_0\"]\n            images2 = images2.transpose(2, 0, 1)\n            \n            if self.transform is not None:\n                images2 = self.transform(image=images2.transpose())['image']\n            else:\n                images2=images2.transpose()\n            \n            # blend image\n            gamma = np.random.beta(1,1)\n            images = ((images*gamma + images2*(1-gamma))).astype(np.uint8)\n            # blend labels\n            label2 = np.zeros(5).astype(np.float32)\n            label2[:row2.isup_grade] = 1.\n            label = (label*gamma+label2*(1-gamma))\n        \n        # Also tried cutmix, does not work well. Maybe because tile includes important information partially.\n        elif cutmix and rd > 0.7 and self.transform is not None:\n            #print(\"cutmix\")\n            mix_idx = np.random.random_integers(0, len(self.df))\n            row2 = self.df.iloc[mix_idx]\n            img_id2 = row2.image_id\n            file = os.path.join(image_folder, f'{img_id2}.npz')\n            images2 = np.load(file)[\"arr_0\"]\n            images2 = images2.transpose(2, 0, 1)\n            \n            # blend image\n            gamma = np.random.beta(1,1)\n            bbx1, bby1, bbx2, bby2 = rand_bbox(images.shape, gamma)\n            images[:, bbx1:bbx2, bby1:bby2] = images2[:, bbx1:bbx2, bby1:bby2]\n            # adjust lambda to exactly match pixel ratio\n            gamma = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (images.shape[-1] * images.shape[-2]))\n            # blend labels\n            label2 = np.zeros(5).astype(np.float32)\n            label2[:row2.isup_grade] = 1.\n            label = (label*gamma+label2*(1-gamma))\n            #print(label)\n        \n        \n        images = images.astype(np.float32)\n        images /= 255\n        images = images.transpose(2, 0, 1)\n        \n        return torch.tensor(images), torch.tensor(label)","metadata":{"papermill":{"duration":0.051271,"end_time":"2023-10-15T04:49:40.015649","exception":false,"start_time":"2023-10-15T04:49:39.964378","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.random.beta(1,1)","metadata":{"papermill":{"duration":0.024105,"end_time":"2023-10-15T04:49:40.057056","exception":false,"start_time":"2023-10-15T04:49:40.032951","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset_show = PANDADataset(df_train, image_size, n_tiles, 0, transform=None)\nimages, label = dataset_show[0]","metadata":{"papermill":{"duration":0.155388,"end_time":"2023-10-15T04:49:40.229124","exception":false,"start_time":"2023-10-15T04:49:40.073736","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(images.shape)","metadata":{"papermill":{"duration":0.024381,"end_time":"2023-10-15T04:49:40.270911","exception":false,"start_time":"2023-10-15T04:49:40.24653","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Augmentations by Albumentations","metadata":{"papermill":{"duration":0.016897,"end_time":"2023-10-15T04:49:40.305863","exception":false,"start_time":"2023-10-15T04:49:40.288966","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import albumentations as A\n\ntransforms_train = A.Compose([\n    A.ShiftScaleRotate(scale_limit=0.25, rotate_limit=180,p=0.5),\n    A.OneOf([\n        A.HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit= 0.2, val_shift_limit=0.2, p=0.5),\n        A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),\n    ],p=0.9),\n    A.Cutout(num_holes=36, max_h_size=128, max_w_size=128, fill_value=0, p=0.5),\n    A.Transpose(p=0.5),\n    A.VerticalFlip(p=0.5),\n    A.HorizontalFlip(p=0.5)  \n])\n\ntransforms_val = albumentations.Compose([])","metadata":{"papermill":{"duration":0.028993,"end_time":"2023-10-15T04:49:40.351983","exception":false,"start_time":"2023-10-15T04:49:40.32299","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset_show = PANDADataset(df_train, image_size, n_tiles, 0, transform=transforms_train)\nfrom pylab import rcParams\nrcParams['figure.figsize'] = 20,10\nfor i in range(3):\n    f, axarr = plt.subplots(1,5)\n    for p in range(5):\n        idx = np.random.randint(0, len(dataset_show))\n        img, label = dataset_show[idx]\n        axarr[p].imshow(1. - img.transpose(0, 1).transpose(1,2).squeeze())\n        axarr[p].set_title(str(sum(label)))","metadata":{"papermill":{"duration":9.529149,"end_time":"2023-10-15T04:49:49.898123","exception":false,"start_time":"2023-10-15T04:49:40.368974","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Loss","metadata":{"papermill":{"duration":0.051137,"end_time":"2023-10-15T04:49:50.000485","exception":false,"start_time":"2023-10-15T04:49:49.949348","status":"completed"},"tags":[]}},{"cell_type":"code","source":"criterion = nn.BCEWithLogitsLoss()","metadata":{"papermill":{"duration":0.057429,"end_time":"2023-10-15T04:49:50.107684","exception":false,"start_time":"2023-10-15T04:49:50.050255","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Train & Val","metadata":{"papermill":{"duration":0.049527,"end_time":"2023-10-15T04:49:50.206118","exception":false,"start_time":"2023-10-15T04:49:50.156591","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def train_epoch(loader, optimizer):\n\n    model.train()\n    train_loss = []\n    bar = tqdm(loader)\n    for idx,(data, target) in enumerate(bar):\n        data, target = data.to(device), target.to(device)\n        loss_func = criterion\n        optimizer.zero_grad()\n        logits = model(data)\n        loss = loss_func(logits, target)\n        loss.backward()\n        optimizer.step()\n\n        loss_np = loss.detach().cpu().numpy()\n        train_loss.append(loss_np)\n        smooth_loss = sum(train_loss[-100:]) / min(len(train_loss), 100)\n        bar.set_postfix_str(f\"loss: {loss_np:.4f}, smth: {smooth_loss:.4f}, lr: {optimizer.param_groups[0]['lr']:.2e}\")\n    return train_loss\n\n\ndef val_epoch(loader, get_output=False):\n\n    model.eval()\n    val_loss = []\n    LOGITS = []\n    PREDS = []\n    TARGETS = []\n    bar = tqdm(loader)\n    with torch.no_grad():\n        for idx,(data, target) in enumerate(bar):\n            data, target = data.to(device), target.to(device)\n            logits = model(data)\n\n            loss = criterion(logits, target)\n\n            pred = logits.sigmoid().sum(1).detach().round()\n            LOGITS.append(logits)\n            PREDS.append(pred)\n            TARGETS.append(target.sum(1))\n\n            val_loss.append(loss.detach().cpu().numpy())\n            bar.set_postfix_str(f\"val_loss: {np.mean(val_loss):.4f}\")\n        val_loss = np.mean(val_loss)\n        \n    LOGITS = torch.cat(LOGITS).cpu().numpy()\n    PREDS = torch.cat(PREDS).cpu().numpy()\n    TARGETS = torch.cat(TARGETS).cpu().numpy()\n    acc = (PREDS == TARGETS).mean() * 100.\n        \n    qwk = cohen_kappa_score(PREDS, TARGETS, weights='quadratic')\n    qwk_k = cohen_kappa_score(PREDS[df_valid['data_provider'] == 'karolinska'], df_valid[df_valid['data_provider'] == 'karolinska'].isup_grade.values, weights='quadratic')\n    qwk_r = cohen_kappa_score(PREDS[df_valid['data_provider'] == 'radboud'], df_valid[df_valid['data_provider'] == 'radboud'].isup_grade.values, weights='quadratic')\n    \n    if EXP:\n        scheduler.step(val_loss)\n    \n    if get_output:\n        return LOGITS\n    else:\n        return val_loss, acc, qwk, qwk_k, qwk_r, PREDS, TARGETS","metadata":{"papermill":{"duration":0.067457,"end_time":"2023-10-15T04:49:50.323648","exception":false,"start_time":"2023-10-15T04:49:50.256191","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Create Dataloader & Model & Optimizer","metadata":{"papermill":{"duration":0.049517,"end_time":"2023-10-15T04:49:50.42217","exception":false,"start_time":"2023-10-15T04:49:50.372653","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"## 5-fold models are trained","metadata":{"papermill":{"duration":0.049569,"end_time":"2023-10-15T04:49:50.522081","exception":false,"start_time":"2023-10-15T04:49:50.472512","status":"completed"},"tags":[]}},{"cell_type":"code","source":"print(f\"Training Fold {FOLD}...\")\n\ntrain_idx = np.where((df_train['fold'] != FOLD))[0]\nvalid_idx = np.where((df_train['fold'] == FOLD))[0]\n\ndf_this  = df_train.loc[train_idx]\ndf_valid = df_train.loc[valid_idx]\n\ndf_this = df_this.reset_index()\ndf_valid = df_valid.reset_index()\n\ndataset_train = PANDADataset(df_this , image_size, n_tiles, transform=transforms_train)\ndataset_valid = PANDADataset(df_valid, image_size, n_tiles, transform=None)\n\n# Setup dataloader\ntrain_loader = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size, sampler=RandomSampler(dataset_train), num_workers=num_workers)\nvalid_loader = torch.utils.data.DataLoader(dataset_valid, batch_size=batch_size, sampler=SequentialSampler(dataset_valid), num_workers=num_workers)\n\n# Initialize model\nmodel = enetv2(out_dim=out_dim)\nif torch.cuda.device_count() > 1:\n    model = nn.DataParallel(model)\nmodel = model.to(device)\n\nprint(f\"[INFO] Train samples: {len(dataset_train)} - Valid samples: {len(dataset_valid)}\")\n\n# We use Cosine annealing LR scheduling\nif COSINE:\n    optimizer = optim.Adam(model.parameters(), lr=init_lr/warmup_factor)\n    scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, n_epochs-warmup_epo)\n    scheduler = GradualWarmupScheduler(optimizer, multiplier=warmup_factor, total_epoch=warmup_epo, after_scheduler=scheduler_cosine)\nelse:\n    optimizer = optim.Adam(model.parameters(), lr=init_lr)\n    from torch.optim import lr_scheduler\n    scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=4, verbose=True, min_lr=1e-3*1e-5, factor=0.5)","metadata":{"papermill":{"duration":33018.194509,"end_time":"2023-10-15T14:00:08.770252","exception":false,"start_time":"2023-10-15T04:49:50.575743","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.makedirs(\"models\", exist_ok=True)\nos.makedirs(\"log\", exist_ok=True)\ngmt7_timezone = pytz.timezone('Asia/Bangkok')\nbest_file = f'{kernel_type}_best_model.pth'\nlast_file = f'{kernel_type}_last_model.pth'\n\nif os.path.exists(os.path.join('models', last_file)):\n    checkpoint = torch.load(os.path.join('models', last_file), map_location=device)\n    model.load_state_dict(checkpoint['model_state_dict'])\n    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n    epoch_start = checkpoint['epoch'] + 1\n    print(f\"Resuming from epoch {epoch_start}\")\nelse:\n    epoch_start = 0\n    print(f\"First time training!\")\n\nif os.path.exists(os.path.join('models', best_file)):\n    checkpoint = torch.load(os.path.join('models', best_file), map_location=device)\n    qwk_max = checkpoint['qwk']\n    print(f\"Current best qwk: {qwk_max:.5f}\")\nelse:\n    qwk_max = 0.\n\nthreshold = 0\nfor epoch in range(epoch_start, epoch_start + n_epochs):\n    torch.cuda.empty_cache() \n    print(datetime.now(gmt7_timezone).strftime('%Y-%m-%d %H:%M:%S'), 'Epoch:', epoch+1)\n    if COSINE:\n        scheduler.step(epoch)\n    train_loss = train_epoch(train_loader, optimizer)\n    val_loss, acc, qwk, qwk_k, qwk_r, TARGETS, PREDS = val_epoch(valid_loader)\n\n    content = f'Epoch {epoch+1:2d}, lr: {optimizer.param_groups[0][\"lr\"]:.2e}, train loss: {np.mean(train_loss):.5f}, val loss: {np.mean(val_loss):.5f}, acc: {(acc):.5f},     qwk: {(qwk):.5f}, qwk_k: {(qwk_k):.5f}, qwk_r: {(qwk_r):.5f}'\n    print(datetime.now(gmt7_timezone).strftime('%Y-%m-%d %H:%M:%S') + ' ' + content)\n    with open(f'log/log_{kernel_type}.txt', 'a') as appender:\n        appender.write(content + '\\n')\n\n    cmat = confusion_matrix(TARGETS, PREDS).astype(\"uint\")\n    with open(f'log/logcmat_{kernel_type}.txt', 'a') as appender:\n        appender.write(\"Epoch: \" + str(epoch+1) + '\\n')\n        np.savetxt(appender, cmat, fmt='%3d')\n        \n    # Save last model\n    checkpoint = {\n        'epoch': epoch, \n        'qwk': qwk, \n        'model_state_dict': model.state_dict(), \n        'optimizer_state_dict': optimizer.state_dict()}\n    torch.save(checkpoint, os.path.join('models', last_file))\n    \n    # Save best model\n    if qwk > qwk_max:\n        print(f'Best qwk ({qwk_max:.5f} --> {qwk:.5f}).  Saving best model ...')\n        copyfile(os.path.join('models', last_file), os.path.join('models', best_file))\n        qwk_max = qwk\n        threshold = 0\n    else:\n        threshold += 1\n    \n    if threshold > patience:\n        print(f'Early stopping at epoch {epoch+1}')\n        break","metadata":{"papermill":{"duration":0.069798,"end_time":"2023-10-15T14:00:08.908245","exception":false,"start_time":"2023-10-15T14:00:08.838447","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}