Cait with Mixup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9d3ffb9-4188-4bc5-ad79-5b4f0e429c70",
   "metadata": {},
   "source": [
    "# Cait with Mixup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98054744",
   "metadata": {
    "papermill": {
     "duration": 31.960769,
     "end_time": "2022-04-16T14:38:33.289479",
     "exception": false,
     "start_time": "2022-04-16T14:38:01.328710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install python-box timm pytorch-lightning ttach torchmetrics torchtoolbox albumentations wandb -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2fb229a-bec1-4a72-8d2d-fc5d7999623f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove 'cait_m36_384': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!rm -r cait_m36_384"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3bd1dc5",
   "metadata": {
    "papermill": {
     "duration": 6.787705,
     "end_time": "2022-04-16T14:38:40.107058",
     "exception": false,
     "start_time": "2022-04-16T14:38:33.319353",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71c51ac8",
   "metadata": {
    "papermill": {
     "duration": 2.144838,
     "end_time": "2022-04-16T14:38:42.277528",
     "exception": false,
     "start_time": "2022-04-16T14:38:40.132690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "from pprint import pprint\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import torchmetrics\n",
    "\n",
    "import numpy\n",
    "\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import cv2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision.transforms as T\n",
    "from box import Box\n",
    "from timm import create_model\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from torchvision.io import read_image\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from pytorch_grad_cam import GradCAMPlusPlus\n",
    "from pytorch_grad_cam.utils.image import show_cam_on_image\n",
    "\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.utilities.seed import seed_everything\n",
    "from pytorch_lightning import callbacks\n",
    "from pytorch_lightning.callbacks.progress import ProgressBarBase\n",
    "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
    "from pytorch_lightning.loggers import WandbLogger\n",
    "from pytorch_lightning import LightningDataModule, LightningModule\n",
    "\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "149cde34",
   "metadata": {
    "papermill": {
     "duration": 0.036495,
     "end_time": "2022-04-16T14:38:42.342047",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.305552",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.10.0+cu113\n"
     ]
    }
   ],
   "source": [
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9857c45a",
   "metadata": {
    "papermill": {
     "duration": 0.034408,
     "end_time": "2022-04-16T14:38:42.402999",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.368591",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import timm\n",
    "# timm.list_models(pretrained=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c0d303",
   "metadata": {
    "papermill": {
     "duration": 0.026112,
     "end_time": "2022-04-16T14:38:42.455346",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.429234",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22a8aa1c",
   "metadata": {
    "papermill": {
     "duration": 0.034034,
     "end_time": "2022-04-16T14:38:42.516210",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.482176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !rm ./convit_base -r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b885addf",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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     "duration": 0.040889,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "config = {'seed': 42,\n",
    "          'root': '/hy-tmp', \n",
    "          'n_splits': 5,\n",
    "          'epoch': 30,\n",
    "          'trainer': {\n",
    "              'gpus': 1,\n",
    "              'accumulate_grad_batches': 4,\n",
    "              'progress_bar_refresh_rate': 0,\n",
    "              'fast_dev_run': False,\n",
    "              'num_sanity_val_steps': 0,\n",
    "              'resume_from_checkpoint': None,\n",
    "          },\n",
    "          'transform':{\n",
    "              'name': 'get_default_transforms',\n",
    "              'image_size': 384\n",
    "          },\n",
    "          'train_loader':{\n",
    "              'batch_size': 4,\n",
    "              'shuffle': True,\n",
    "              'num_workers': 4,\n",
    "              'pin_memory': False,\n",
    "              'drop_last': True,\n",
    "          },\n",
    "          'val_loader': {\n",
    "              'batch_size': 8,\n",
    "              'shuffle': False,\n",
    "              'num_workers': 4,\n",
    "              'pin_memory': False,\n",
    "              'drop_last': False\n",
    "         },\n",
    "          'model':{\n",
    "              'name': 'cait_m36_384',\n",
    "              'output_dim': 6\n",
    "          },\n",
    "          'optimizer':{\n",
    "              'name': 'optim.AdamW',\n",
    "              'params':{\n",
    "                  'lr': 1e-5\n",
    "              },\n",
    "          },\n",
    "          'scheduler':{\n",
    "              'name': 'optim.lr_scheduler.CosineAnnealingWarmRestarts',\n",
    "              'params':{\n",
    "                  'T_0': 20,\n",
    "                  'eta_min': 3e-6,\n",
    "              }\n",
    "          },\n",
    "          'loss': 'nn.CrossEntropyLoss',\n",
    "}\n",
    "\n",
    "config = Box(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72e550eb",
   "metadata": {
    "papermill": {
     "duration": 0.050462,
     "end_time": "2022-04-16T14:38:42.661085",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.610623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 30,\n",
      " 'loss': 'nn.CrossEntropyLoss',\n",
      " 'model': Box({'name': 'cait_m36_384', 'output_dim': 6}),\n",
      " 'n_splits': 5,\n",
      " 'optimizer': Box({'name': 'optim.AdamW', 'params': {'lr': 1e-05}}),\n",
      " 'root': '/hy-tmp',\n",
      " 'scheduler': {'name': 'optim.lr_scheduler.CosineAnnealingWarmRestarts',\n",
      "               'params': Box({'T_0': 20, 'eta_min': 3e-06})},\n",
      " 'seed': 42,\n",
      " 'train_loader': {'batch_size': 4,\n",
      "                  'drop_last': True,\n",
      "                  'num_workers': 4,\n",
      "                  'pin_memory': False,\n",
      "                  'shuffle': True},\n",
      " 'trainer': {'accumulate_grad_batches': 4,\n",
      "             'fast_dev_run': False,\n",
      "             'gpus': 1,\n",
      "             'num_sanity_val_steps': 0,\n",
      "             'progress_bar_refresh_rate': 0,\n",
      "             'resume_from_checkpoint': None},\n",
      " 'transform': Box({'name': 'get_default_transforms', 'image_size': 384}),\n",
      " 'val_loader': {'batch_size': 8,\n",
      "                'drop_last': False,\n",
      "                'num_workers': 4,\n",
      "                'pin_memory': False,\n",
      "                'shuffle': False}}\n"
     ]
    }
   ],
   "source": [
    "pprint(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3f627f",
   "metadata": {
    "papermill": {
     "duration": 0.027126,
     "end_time": "2022-04-16T14:38:42.715683",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.688557",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d022335",
   "metadata": {
    "papermill": {
     "duration": 0.043645,
     "end_time": "2022-04-16T14:38:42.786177",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.742532",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EMSIGDataset(Dataset):\n",
    "    def __init__(self, df, image_size=384, transform=None):\n",
    "        self._X = df[\"id\"].values\n",
    "        self._y = None\n",
    "        if \"target\" in df.keys():\n",
    "            self._y = df[\"target\"].values\n",
    "#         self._transform = T.Resize([image_size, image_size])\n",
    "        self._transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self._X)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image_path = self._X[idx]\n",
    "#         image = read_image(image_path)\n",
    "#         image = self._transform(image)\n",
    "        image = cv2.imread(image_path)\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        augmented = self._transform(image=image)\n",
    "#         print('augment')\n",
    "        image = augmented['image']\n",
    "#         print('augmented ')\n",
    "        if self._y is not None:\n",
    "            label = self._y[idx]\n",
    "            return image, label\n",
    "        return image\n",
    "\n",
    "class EMSIGDataModule(LightningDataModule):\n",
    "    def __init__(\n",
    "        self,\n",
    "        train_df,\n",
    "        val_df,\n",
    "        cfg,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self._train_df = train_df\n",
    "        self._val_df = val_df\n",
    "        self._cfg = cfg\n",
    "\n",
    "    def __create_dataset(self, train=True):\n",
    "        return (\n",
    "            EMSIGDataset(self._train_df, self._cfg.transform.image_size, transform = get_default_transforms()['train'])\n",
    "            if train\n",
    "            else EMSIGDataset(self._val_df, self._cfg.transform.image_size, transform = get_default_transforms()['val'])\n",
    "        )\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        dataset = self.__create_dataset(True)\n",
    "        return DataLoader(dataset, **self._cfg.train_loader)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        dataset = self.__create_dataset(False)\n",
    "        return DataLoader(dataset, **self._cfg.val_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5bbab99",
   "metadata": {
    "papermill": {
     "duration": 0.025902,
     "end_time": "2022-04-16T14:38:42.838194",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.812292",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99bed4b6",
   "metadata": {
    "papermill": {
     "duration": 0.076922,
     "end_time": "2022-04-16T14:38:42.941072",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.864150",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 42\n"
     ]
    }
   ],
   "source": [
    "torch.autograd.set_detect_anomaly(True)\n",
    "seed_everything(config.seed)\n",
    "\n",
    "df = pd.read_csv('/hy-tmp/train_labels.csv')\n",
    "df[\"id\"] = df[\"id\"].apply(lambda x: os.path.join(config.root, \"crop_spectrum_train_pic\", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "801503d1",
   "metadata": {
    "papermill": {
     "duration": 0.050789,
     "end_time": "2022-04-16T14:38:43.019481",
     "exception": false,
     "start_time": "2022-04-16T14:38:42.968692",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['target'] = df['target'] - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8007446d",
   "metadata": {
    "papermill": {
     "duration": 0.026991,
     "end_time": "2022-04-16T14:38:43.127924",
     "exception": false,
     "start_time": "2022-04-16T14:38:43.100933",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a40497db",
   "metadata": {
    "papermill": {
     "duration": 0.040715,
     "end_time": "2022-04-16T14:38:43.260439",
     "exception": false,
     "start_time": "2022-04-16T14:38:43.219724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "IMAGENET_MEAN = [0.485, 0.456, 0.406]  # RGB\n",
    "IMAGENET_STD = [0.229, 0.384, 0.225]  # RGB\n",
    "IMAGE_SIZE = config.transform.image_size\n",
    "\n",
    "def get_default_transforms():\n",
    "    transform = {\n",
    "        \"train\": A.Compose(\n",
    "            [\n",
    "                A.Resize(IMAGE_SIZE, IMAGE_SIZE),\n",
    "#                 A.Cutout(num_holes=1, max_h_size=IMAGE_SIZE, max_w_size=int(IMAGE_SIZE/8)),\n",
    "#                 A.Cutout(num_holes=1, max_h_size=int(IMAGE_SIZE/8), max_w_size=IMAGE_SIZE),\n",
    "                A.CoarseDropout(max_holes=2, max_height=IMAGE_SIZE, max_width=int(IMAGE_SIZE/8), min_holes=1, min_height=None, min_width=int(IMAGE_SIZE/16), fill_value=[0, 0, 225]),\n",
    "                A.CoarseDropout(max_holes=2, max_height=int(IMAGE_SIZE/8), max_width=IMAGE_SIZE, min_holes=1, min_height=int(IMAGE_SIZE/16), min_width=None, fill_value=[0, 0, 225]),\n",
    "                A.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),\n",
    "                ToTensorV2(),\n",
    "            ]\n",
    "        ),\n",
    "        \"val\": A.Compose(\n",
    "            [\n",
    "                A.Resize(IMAGE_SIZE, IMAGE_SIZE),\n",
    "                A.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),\n",
    "                ToTensorV2()\n",
    "            ]\n",
    "        ),\n",
    "    }\n",
    "    return transform\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8bfb4578",
   "metadata": {
    "papermill": {
     "duration": 3.183724,
     "end_time": "2022-04-16T14:38:46.470942",
     "exception": false,
     "start_time": "2022-04-16T14:38:43.287218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_dataloader = EMSIGDataModule(df, df, config).train_dataloader()\n",
    "images, labels = iter(sample_dataloader).next()\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "for it, (image, label) in enumerate(zip(images[:16], labels[:16])):\n",
    "    plt.subplot(4, 4, it+1)\n",
    "    plt.imshow(image.permute(1, 2, 0))\n",
    "    plt.axis('off')\n",
    "    plt.title(f'target: {int(label)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49829396",
   "metadata": {
    "papermill": {
     "duration": 0.031305,
     "end_time": "2022-04-16T14:38:46.534852",
     "exception": false,
     "start_time": "2022-04-16T14:38:46.503547",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f67e318a",
   "metadata": {
    "papermill": {
     "duration": 0.041549,
     "end_time": "2022-04-16T14:38:46.607826",
     "exception": false,
     "start_time": "2022-04-16T14:38:46.566277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def mixup(x: torch.Tensor, y: torch.Tensor, alpha: float = 1.0):\n",
    "    assert alpha > 0, \"alpha should be larger than 0\"\n",
    "    assert x.size(0) > 1, \"Mixup cannot be applied to a single instance.\"\n",
    "\n",
    "    lam = np.random.beta(alpha, alpha)\n",
    "    rand_index = torch.randperm(x.size()[0])\n",
    "    mixed_x = lam * x + (1 - lam) * x[rand_index, :]\n",
    "    target_a, target_b = y, y[rand_index]\n",
    "    return mixed_x, target_a, target_b, lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4501e622",
   "metadata": {
    "papermill": {
     "duration": 0.039167,
     "end_time": "2022-04-16T14:38:46.678447",
     "exception": false,
     "start_time": "2022-04-16T14:38:46.639280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fuc = get_default_transforms()['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32a68773",
   "metadata": {
    "papermill": {
     "duration": 0.058901,
     "end_time": "2022-04-16T14:38:46.830765",
     "exception": false,
     "start_time": "2022-04-16T14:38:46.771864",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Model(pl.LightningModule):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.cfg = cfg\n",
    "        self.__build_model()\n",
    "        self._criterion = eval(self.cfg.loss)()\n",
    "        self.transform = get_default_transforms()\n",
    "        self.save_hyperparameters(cfg)\n",
    "\n",
    "    def __build_model(self):\n",
    "        self.backbone = create_model(\n",
    "            self.cfg.model.name, pretrained=True, num_classes=0, in_chans=3\n",
    "        )\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Dropout(0.5), \n",
    "            nn.Linear(self.backbone.num_features, 64),\n",
    "            nn.Dropout(0.5), \n",
    "            nn.Linear(64, self.cfg.model.output_dim)\n",
    "        )\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        f = self.backbone(x)\n",
    "        out = self.fc(f)\n",
    "        return out\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, pred, labels = self.__share_step(batch, 'train')\n",
    "        # print('train loss: ', loss)\n",
    "        return {'loss': loss, 'pred': pred, 'labels': labels}\n",
    "        \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, pred, labels = self.__share_step(batch, 'val')\n",
    "        # print('valid loss: ', loss)\n",
    "        return {'pred': pred, 'labels': labels}\n",
    "    \n",
    "    def __share_step(self, batch, mode):\n",
    "        images, labels = batch\n",
    "    \n",
    "    # mixup\n",
    "        if torch.rand(1)[0] < 0.5 and mode == 'train':\n",
    "            mix_images, target_a, target_b, lam = mixup(images, labels, alpha=0.5)\n",
    "            pred = self.forward(mix_images)\n",
    "            loss = self._criterion(pred, target_a) * lam + \\\n",
    "                (1 - lam) * self._criterion(pred, target_b)\n",
    "        else:\n",
    "            pred = self.forward(images)\n",
    "            loss = self._criterion(pred, labels)\n",
    "        \n",
    "        # pred = self.forward(images)\n",
    "        # loss = self._criterion(pred, labels)\n",
    "        \n",
    "        pred_cpu = pred.detach().cpu()# * 100.\n",
    "        labels = labels.detach().cpu()# * 100.\n",
    "        return loss, pred_cpu, labels\n",
    "        \n",
    "    def training_epoch_end(self, outputs):\n",
    "        print('-----train-----')\n",
    "        self.__share_epoch_end(outputs, 'train')\n",
    "\n",
    "    def validation_epoch_end(self, outputs):\n",
    "        print('-----valid-----')\n",
    "        self.__share_epoch_end(outputs, 'val')    \n",
    "        \n",
    "    def __share_epoch_end(self, outputs, mode):\n",
    "        preds = []\n",
    "        labels = []\n",
    "        for out in outputs:\n",
    "            pred, label = out['pred'], out['labels']\n",
    "            preds.append(pred)\n",
    "            labels.append(label)\n",
    "        preds = torch.cat(preds)\n",
    "        labels = torch.cat(labels)\n",
    "        loss = self._criterion(preds, labels)\n",
    "        metrics = torchmetrics.functional.accuracy(preds, labels, average='macro', num_classes=6)\n",
    "        sfmx = nn.Softmax(dim = 1)\n",
    "        preds_prob = sfmx(preds)\n",
    "        cm = torchmetrics.functional.confusion_matrix(preds_prob, labels, num_classes=6, normalize=None, threshold=0.5, multilabel=False)\n",
    "        print(cm.numpy().tolist())\n",
    "        print('Accuracy： ', float(metrics.numpy()))\n",
    "        print('Epoch loss: ', float(loss.numpy()))\n",
    "        self.log(f'Fold_{FOLD}_{mode}_loss', float(loss.numpy()))\n",
    "        self.log(f'{mode}_loss', float(loss.numpy()))\n",
    "        self.log(f'Fold_{FOLD}_{mode}_acc', float(metrics.numpy()))\n",
    "    \n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = eval(self.cfg.optimizer.name)(\n",
    "            self.parameters(), **self.cfg.optimizer.params\n",
    "        )\n",
    "        scheduler = eval(self.cfg.scheduler.name)(\n",
    "            optimizer,\n",
    "            **self.cfg.scheduler.params\n",
    "        )\n",
    "        return [optimizer], [scheduler]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993a3ad7",
   "metadata": {
    "papermill": {
     "duration": 0.031749,
     "end_time": "2022-04-16T14:38:46.894253",
     "exception": false,
     "start_time": "2022-04-16T14:38:46.862504",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab9d212d",
   "metadata": {
    "papermill": {
     "duration": 5175.157798,
     "end_time": "2022-04-16T16:05:02.082646",
     "exception": false,
     "start_time": "2022-04-16T14:38:46.924848",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOLD:  0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mbingliangli\u001b[0m (use `wandb login --relogin` to force relogin)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.12.14"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/hy-tmp/wandb/run-20220419_113453-1r72zb2c</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/bingliangli/cait-with-mixup/runs/1r72zb2c\" target=\"_blank\">warm-cherry-1</a></strong> to <a href=\"https://wandb.ai/bingliangli/cait-with-mixup\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name       | Type             | Params\n",
      "------------------------------------------------\n",
      "0 | backbone   | Cait             | 270 M \n",
      "1 | fc         | Sequential       | 49.6 K\n",
      "2 | _criterion | CrossEntropyLoss | 0     \n",
      "------------------------------------------------\n",
      "270 M     Trainable params\n",
      "0         Non-trainable params\n",
      "270 M     Total params\n",
      "1,082.008 Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[62, 0, 0, 0, 1, 0], [1, 52, 0, 3, 6, 0], [1, 44, 8, 2, 8, 0], [0, 13, 0, 35, 13, 0], [0, 5, 0, 9, 47, 1], [0, 0, 0, 1, 1, 38]]\n",
      "Accuracy：  0.7052758932113647\n",
      "Epoch loss:  0.8736026883125305\n",
      "-----train-----\n",
      "[[141, 20, 28, 28, 20, 12], [18, 71, 46, 52, 30, 32], [21, 57, 59, 56, 41, 14], [30, 42, 34, 61, 62, 19], [30, 31, 31, 49, 88, 19], [17, 27, 12, 32, 22, 48]]\n",
      "Accuracy：  0.3323187828063965\n",
      "Epoch loss:  1.6268718242645264\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 1, 0], [0, 62, 0, 0, 0, 0], [0, 1, 61, 0, 1, 0], [0, 4, 1, 47, 9, 0], [0, 2, 2, 6, 51, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9200755953788757\n",
      "Epoch loss:  0.3961757719516754\n",
      "-----train-----\n",
      "[[209, 10, 9, 7, 5, 9], [16, 149, 27, 19, 32, 7], [15, 40, 105, 37, 40, 11], [11, 25, 27, 112, 63, 10], [13, 21, 24, 48, 137, 5], [7, 5, 12, 9, 10, 114]]\n",
      "Accuracy：  0.5981485843658447\n",
      "Epoch loss:  1.2035682201385498\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 1, 0], [0, 60, 0, 1, 1, 0], [0, 0, 60, 1, 2, 0], [0, 2, 0, 50, 9, 0], [0, 0, 0, 5, 56, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9336913824081421\n",
      "Epoch loss:  0.27219292521476746\n",
      "-----train-----\n",
      "[[213, 9, 10, 7, 5, 5], [11, 188, 18, 12, 14, 6], [10, 16, 171, 18, 26, 7], [5, 24, 29, 125, 61, 4], [15, 10, 20, 47, 150, 6], [5, 4, 6, 8, 9, 126]]\n",
      "Accuracy：  0.7010495662689209\n",
      "Epoch loss:  1.0198609828948975\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 59, 0, 2, 1, 0], [0, 0, 61, 0, 2, 0], [0, 0, 0, 49, 11, 1], [0, 0, 0, 4, 57, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9362500905990601\n",
      "Epoch loss:  0.2356831282377243\n",
      "-----train-----\n",
      "[[207, 13, 10, 6, 7, 6], [8, 201, 12, 12, 10, 7], [15, 17, 183, 12, 19, 2], [11, 9, 16, 153, 52, 7], [11, 10, 13, 43, 165, 6], [6, 3, 9, 9, 4, 126]]\n",
      "Accuracy：  0.7430057525634766\n",
      "Epoch loss:  0.9023433327674866\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 60, 0, 2, 0, 0], [0, 0, 62, 1, 0, 0], [0, 0, 0, 55, 6, 0], [0, 0, 0, 11, 50, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9391599893569946\n",
      "Epoch loss:  0.19513696432113647\n",
      "-----train-----\n",
      "[[213, 7, 6, 14, 6, 3], [7, 196, 11, 12, 13, 10], [12, 15, 178, 17, 17, 9], [11, 16, 21, 141, 55, 4], [10, 8, 18, 48, 155, 9], [5, 6, 9, 9, 8, 121]]\n",
      "Accuracy：  0.7199472188949585\n",
      "Epoch loss:  1.0199308395385742\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 63, 0, 0, 0], [0, 1, 0, 57, 3, 0], [0, 0, 0, 12, 49, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9472700357437134\n",
      "Epoch loss:  0.16098827123641968\n",
      "-----train-----\n",
      "[[221, 6, 5, 7, 5, 4], [8, 212, 8, 10, 10, 2], [8, 6, 213, 9, 10, 2], [6, 5, 6, 180, 42, 9], [9, 13, 12, 27, 178, 9], [3, 3, 5, 5, 10, 132]]\n",
      "Accuracy：  0.8128319978713989\n",
      "Epoch loss:  0.7477003931999207\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 60, 0, 2, 0, 0], [0, 0, 58, 1, 4, 0], [0, 0, 0, 50, 11, 0], [0, 0, 0, 1, 60, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9417985677719116\n",
      "Epoch loss:  0.18812812864780426\n",
      "-----train-----\n",
      "[[211, 9, 8, 10, 8, 3], [14, 195, 14, 10, 14, 3], [9, 13, 194, 11, 15, 6], [6, 11, 14, 177, 35, 5], [9, 5, 15, 29, 181, 8], [2, 7, 6, 5, 5, 133]]\n",
      "Accuracy：  0.7829871773719788\n",
      "Epoch loss:  0.8461403846740723\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 63, 0, 0, 0], [0, 0, 0, 57, 4, 0], [0, 0, 0, 8, 53, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9580227136611938\n",
      "Epoch loss:  0.14593066275119781\n",
      "-----train-----\n",
      "[[220, 7, 9, 5, 4, 4], [8, 205, 8, 15, 6, 8], [10, 12, 201, 7, 10, 8], [7, 14, 14, 173, 36, 3], [7, 9, 11, 23, 190, 8], [7, 4, 5, 10, 5, 127]]\n",
      "Accuracy：  0.7973915338516235\n",
      "Epoch loss:  0.8171411156654358\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 63, 0, 0, 0], [0, 1, 0, 59, 1, 0], [0, 0, 0, 20, 41, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9339172840118408\n",
      "Epoch loss:  0.21457046270370483\n",
      "-----train-----\n",
      "[[218, 10, 7, 3, 6, 5], [14, 207, 5, 7, 8, 9], [9, 4, 206, 12, 11, 5], [6, 8, 14, 189, 24, 7], [8, 8, 7, 19, 200, 6], [4, 12, 3, 8, 4, 127]]\n",
      "Accuracy：  0.8183093667030334\n",
      "Epoch loss:  0.7724867463111877\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: Network error (ReadTimeout), entering retry loop.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 63, 0, 0, 0], [0, 0, 0, 51, 10, 0], [0, 0, 0, 1, 60, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9604464769363403\n",
      "Epoch loss:  0.15091271698474884\n",
      "-----train-----\n",
      "[[216, 6, 11, 3, 9, 4], [4, 214, 11, 12, 6, 3], [10, 10, 207, 3, 12, 5], [12, 14, 9, 192, 16, 5], [12, 5, 8, 17, 201, 5], [8, 4, 0, 5, 4, 137]]\n",
      "Accuracy：  0.8355487585067749\n",
      "Epoch loss:  0.6833810806274414\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 60, 1, 2, 0], [0, 0, 0, 55, 6, 0], [0, 0, 0, 4, 57, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9553743600845337\n",
      "Epoch loss:  0.14593099057674408\n",
      "-----train-----\n",
      "[[211, 4, 10, 8, 12, 3], [7, 207, 13, 12, 8, 3], [10, 10, 197, 12, 13, 6], [13, 13, 17, 187, 15, 3], [13, 10, 13, 5, 194, 13], [4, 4, 3, 6, 5, 136]]\n",
      "Accuracy：  0.8117018938064575\n",
      "Epoch loss:  0.8647321462631226\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 63, 0, 0, 0], [0, 1, 0, 57, 3, 0], [0, 0, 0, 9, 52, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9580227136611938\n",
      "Epoch loss:  0.13556532561779022\n",
      "-----train-----\n",
      "[[224, 6, 5, 3, 6, 5], [6, 201, 12, 10, 15, 6], [8, 14, 197, 9, 14, 5], [10, 14, 15, 186, 20, 3], [5, 13, 10, 19, 194, 7], [4, 8, 5, 4, 4, 133]]\n",
      "Accuracy：  0.8125332593917847\n",
      "Epoch loss:  0.7750159502029419\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 1, 0, 0], [0, 0, 0, 55, 6, 0], [0, 0, 0, 3, 58, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9660416841506958\n",
      "Epoch loss:  0.13363544642925262\n",
      "-----train-----\n",
      "[[211, 8, 7, 9, 9, 4], [10, 210, 9, 9, 9, 3], [10, 8, 202, 12, 11, 5], [6, 7, 14, 204, 14, 3], [9, 8, 5, 16, 203, 7], [3, 9, 3, 2, 5, 136]]\n",
      "Accuracy：  0.8345351815223694\n",
      "Epoch loss:  0.7722493410110474\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 63, 0, 0, 0], [0, 1, 0, 57, 3, 0], [0, 0, 0, 4, 57, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9714635610580444\n",
      "Epoch loss:  0.11881964653730392\n",
      "-----train-----\n",
      "[[214, 7, 9, 6, 6, 7], [9, 206, 8, 8, 13, 5], [12, 12, 191, 16, 7, 10], [11, 14, 9, 193, 18, 3], [3, 12, 14, 17, 199, 3], [4, 3, 10, 4, 6, 131]]\n",
      "Accuracy：  0.8111112117767334\n",
      "Epoch loss:  0.8183508515357971\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 62, 1, 0, 0], [0, 0, 0, 57, 4, 0], [0, 0, 0, 7, 54, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9580653309822083\n",
      "Epoch loss:  0.13955643773078918\n",
      "-----train-----\n",
      "[[202, 10, 9, 9, 12, 6], [12, 212, 5, 8, 5, 8], [8, 7, 208, 10, 7, 8], [8, 8, 15, 201, 13, 3], [13, 6, 8, 15, 201, 5], [10, 4, 5, 4, 3, 132]]\n",
      "Accuracy：  0.8262727856636047\n",
      "Epoch loss:  0.7860280275344849\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 63, 0, 0, 0], [0, 0, 0, 54, 7, 0], [0, 0, 0, 2, 59, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9659550189971924\n",
      "Epoch loss:  0.15127910673618317\n",
      "-----train-----\n",
      "[[211, 10, 8, 10, 8, 2], [8, 218, 7, 4, 6, 7], [12, 10, 201, 11, 9, 4], [10, 2, 10, 211, 9, 6], [7, 7, 9, 8, 209, 8], [6, 7, 5, 5, 4, 131]]\n",
      "Accuracy：  0.842636227607727\n",
      "Epoch loss:  0.6751300692558289\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 1, 0], [0, 61, 0, 1, 0, 0], [0, 0, 63, 0, 0, 0], [0, 0, 0, 55, 6, 0], [0, 0, 0, 2, 59, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9660418033599854\n",
      "Epoch loss:  0.14142759144306183\n",
      "-----train-----\n",
      "[[209, 6, 10, 7, 5, 12], [6, 220, 6, 5, 4, 8], [17, 8, 200, 8, 10, 5], [12, 5, 6, 207, 13, 5], [0, 7, 14, 16, 201, 10], [10, 7, 7, 4, 9, 121]]\n",
      "Accuracy：  0.8233879208564758\n",
      "Epoch loss:  0.786585807800293\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 1, 0], [0, 60, 0, 2, 0, 0], [0, 0, 62, 1, 0, 0], [0, 0, 0, 55, 6, 0], [0, 0, 0, 4, 57, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9553316831588745\n",
      "Epoch loss:  0.15510374307632446\n",
      "-----train-----\n",
      "[[213, 11, 9, 6, 5, 5], [11, 208, 11, 8, 6, 6], [7, 7, 218, 7, 7, 2], [8, 12, 5, 199, 13, 11], [8, 8, 5, 6, 217, 3], [7, 5, 3, 9, 3, 131]]\n",
      "Accuracy：  0.8460883498191833\n",
      "Epoch loss:  0.6602727770805359\n",
      "-----valid-----\n",
      "[[62, 1, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 63, 0, 0, 0], [0, 0, 0, 58, 3, 0], [0, 0, 0, 8, 53, 1], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9581094980239868\n",
      "Epoch loss:  0.14373637735843658\n",
      "-----train-----\n",
      "[[216, 6, 7, 7, 8, 4], [6, 225, 6, 2, 7, 4], [10, 6, 211, 8, 5, 8], [9, 6, 6, 209, 14, 4], [7, 5, 10, 12, 211, 3], [4, 2, 6, 8, 5, 133]]\n",
      "Accuracy：  0.8595158457756042\n",
      "Epoch loss:  0.6748884320259094\n",
      "FOLD:  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name       | Type             | Params\n",
      "------------------------------------------------\n",
      "0 | backbone   | Cait             | 270 M \n",
      "1 | fc         | Sequential       | 49.6 K\n",
      "2 | _criterion | CrossEntropyLoss | 0     \n",
      "------------------------------------------------\n",
      "270 M     Trainable params\n",
      "0         Non-trainable params\n",
      "270 M     Total params\n",
      "1,082.008 Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [1, 21, 15, 11, 15, 0], [0, 0, 46, 11, 5, 0], [0, 0, 2, 44, 15, 1], [0, 0, 5, 12, 45, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.7517921924591064\n",
      "Epoch loss:  0.9699860215187073\n",
      "-----train-----\n",
      "[[147, 21, 36, 18, 20, 6], [17, 66, 69, 40, 41, 16], [22, 53, 88, 42, 27, 17], [13, 51, 70, 64, 37, 12], [14, 35, 72, 57, 59, 11], [6, 27, 29, 28, 17, 52]]\n",
      "Accuracy：  0.3392120599746704\n",
      "Epoch loss:  1.610335111618042\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 0, 2, 0], [0, 0, 61, 0, 1, 0], [0, 0, 9, 41, 8, 4], [0, 0, 6, 13, 43, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.8844940066337585\n",
      "Epoch loss:  0.4492086172103882\n",
      "-----train-----\n",
      "[[212, 5, 6, 7, 16, 3], [8, 141, 39, 21, 35, 5], [12, 31, 128, 34, 36, 8], [8, 32, 40, 98, 60, 8], [16, 21, 26, 53, 122, 10], [7, 6, 15, 8, 13, 110]]\n",
      "Accuracy：  0.5856434106826782\n",
      "Epoch loss:  1.1748108863830566\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 55, 4, 3, 0], [0, 0, 1, 44, 16, 1], [0, 1, 0, 9, 52, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9032684564590454\n",
      "Epoch loss:  0.30047017335891724\n",
      "-----train-----\n",
      "[[210, 6, 15, 5, 7, 6], [9, 190, 22, 11, 12, 5], [18, 11, 182, 18, 13, 6], [11, 16, 15, 140, 56, 9], [6, 17, 7, 53, 152, 13], [6, 7, 8, 10, 8, 120]]\n",
      "Accuracy：  0.7124531269073486\n",
      "Epoch loss:  0.9738327264785767\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 58, 0, 0, 5, 0], [0, 0, 58, 2, 2, 0], [0, 0, 3, 45, 14, 0], [0, 0, 0, 6, 56, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9141918420791626\n",
      "Epoch loss:  0.2674119472503662\n",
      "-----train-----\n",
      "[[214, 9, 6, 8, 3, 9], [12, 194, 14, 11, 12, 5], [9, 14, 187, 12, 21, 6], [7, 10, 17, 150, 56, 7], [5, 14, 11, 60, 156, 2], [6, 6, 5, 7, 7, 128]]\n",
      "Accuracy：  0.7390084862709045\n",
      "Epoch loss:  0.8757443428039551\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 59, 2, 1, 0], [0, 0, 2, 46, 14, 0], [0, 0, 0, 5, 57, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9328383207321167\n",
      "Epoch loss:  0.19348546862602234\n",
      "-----train-----\n",
      "[[215, 6, 9, 9, 5, 5], [11, 207, 7, 8, 12, 3], [7, 10, 207, 12, 11, 2], [13, 5, 12, 165, 46, 6], [10, 6, 10, 37, 181, 4], [3, 5, 5, 5, 4, 137]]\n",
      "Accuracy：  0.798157811164856\n",
      "Epoch loss:  0.7636951804161072\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 3, 53, 6, 0], [0, 0, 1, 13, 48, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9328384399414062\n",
      "Epoch loss:  0.19337861239910126\n",
      "-----train-----\n",
      "[[224, 2, 7, 6, 4, 6], [6, 216, 6, 8, 5, 8], [8, 9, 198, 15, 13, 6], [13, 7, 15, 160, 48, 4], [4, 9, 15, 27, 182, 10], [3, 5, 7, 9, 7, 128]]\n",
      "Accuracy：  0.7919825911521912\n",
      "Epoch loss:  0.7746564745903015\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 3, 46, 12, 1], [0, 0, 0, 3, 59, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9462792277336121\n",
      "Epoch loss:  0.15899398922920227\n",
      "-----train-----\n",
      "[[225, 7, 7, 5, 1, 4], [6, 207, 12, 8, 14, 2], [8, 11, 197, 17, 14, 2], [9, 9, 13, 168, 41, 6], [6, 13, 8, 31, 181, 9], [2, 2, 6, 6, 5, 138]]\n",
      "Accuracy：  0.8011324405670166\n",
      "Epoch loss:  0.7951302528381348\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 61, 0, 0, 2, 0], [0, 0, 61, 1, 0, 0], [0, 0, 2, 53, 6, 1], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9490101337432861\n",
      "Epoch loss:  0.1562364548444748\n",
      "-----train-----\n",
      "[[222, 8, 4, 6, 6, 3], [9, 193, 12, 13, 16, 6], [5, 15, 197, 10, 18, 4], [11, 16, 9, 178, 24, 8], [4, 14, 22, 19, 184, 5], [5, 6, 4, 6, 3, 135]]\n",
      "Accuracy：  0.7954001426696777\n",
      "Epoch loss:  0.8445786833763123\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 3, 51, 8, 0], [0, 0, 0, 4, 58, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9543437361717224\n",
      "Epoch loss:  0.1427849382162094\n",
      "-----train-----\n",
      "[[211, 6, 5, 11, 12, 4], [9, 205, 6, 8, 11, 10], [15, 9, 206, 3, 9, 7], [9, 9, 12, 182, 29, 5], [6, 14, 14, 21, 188, 5], [6, 8, 2, 9, 3, 131]]\n",
      "Accuracy：  0.8032988905906677\n",
      "Epoch loss:  0.8176339268684387\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 58, 3, 1, 0], [0, 0, 1, 50, 11, 0], [0, 0, 0, 4, 58, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9435909986495972\n",
      "Epoch loss:  0.150758296251297\n",
      "-----train-----\n",
      "[[221, 4, 9, 3, 8, 4], [7, 214, 9, 7, 9, 3], [15, 7, 197, 11, 15, 4], [5, 9, 15, 185, 24, 8], [8, 12, 12, 32, 178, 6], [4, 1, 8, 5, 4, 137]]\n",
      "Accuracy：  0.811593770980835\n",
      "Epoch loss:  0.743451714515686\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 3, 46, 12, 1], [0, 0, 0, 3, 59, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9462792277336121\n",
      "Epoch loss:  0.14261409640312195\n",
      "-----train-----\n",
      "[[212, 9, 10, 8, 6, 4], [9, 211, 6, 9, 11, 3], [12, 7, 201, 16, 9, 4], [11, 5, 16, 183, 23, 9], [6, 7, 7, 18, 199, 10], [6, 4, 7, 5, 6, 131]]\n",
      "Accuracy：  0.8127471208572388\n",
      "Epoch loss:  0.7641695737838745\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 2, 52, 8, 0], [0, 0, 1, 5, 56, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9543437361717224\n",
      "Epoch loss:  0.1412910670042038\n",
      "-----train-----\n",
      "[[214, 7, 9, 8, 5, 6], [7, 204, 9, 8, 15, 6], [5, 13, 208, 13, 4, 6], [9, 11, 5, 194, 26, 2], [10, 9, 9, 17, 200, 2], [8, 2, 6, 2, 4, 137]]\n",
      "Accuracy：  0.8284722566604614\n",
      "Epoch loss:  0.6873831152915955\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 58, 4, 0, 0], [0, 0, 1, 58, 3, 0], [0, 0, 0, 18, 44, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9274619817733765\n",
      "Epoch loss:  0.20660215616226196\n",
      "-----train-----\n",
      "[[221, 8, 7, 5, 4, 4], [9, 217, 7, 4, 6, 6], [12, 2, 209, 14, 10, 2], [7, 7, 16, 200, 15, 2], [5, 6, 9, 13, 212, 3], [4, 6, 6, 2, 4, 136]]\n",
      "Accuracy：  0.8539514541625977\n",
      "Epoch loss:  0.6188583374023438\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 1, 52, 9, 0], [0, 0, 0, 5, 57, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9543437361717224\n",
      "Epoch loss:  0.14424146711826324\n",
      "-----train-----\n",
      "[[217, 5, 9, 8, 7, 3], [13, 203, 11, 10, 10, 2], [10, 8, 210, 7, 8, 6], [13, 10, 5, 203, 12, 4], [6, 13, 12, 11, 200, 6], [1, 3, 5, 6, 6, 137]]\n",
      "Accuracy：  0.8375872373580933\n",
      "Epoch loss:  0.6845094561576843\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 58, 3, 1, 0], [0, 0, 1, 45, 15, 1], [0, 0, 0, 1, 61, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9382146596908569\n",
      "Epoch loss:  0.1887693554162979\n",
      "-----train-----\n",
      "[[212, 12, 4, 6, 7, 8], [14, 191, 13, 14, 9, 7], [7, 15, 203, 5, 11, 8], [9, 11, 12, 194, 17, 4], [8, 12, 9, 17, 197, 5], [7, 4, 8, 4, 8, 128]]\n",
      "Accuracy：  0.8036065697669983\n",
      "Epoch loss:  0.8507698178291321\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 50, 10, 1], [0, 0, 0, 5, 57, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9516555666923523\n",
      "Epoch loss:  0.1327591985464096\n",
      "-----train-----\n",
      "[[212, 7, 4, 12, 8, 5], [7, 210, 7, 14, 5, 6], [4, 7, 213, 9, 12, 4], [11, 9, 4, 200, 18, 5], [11, 9, 14, 13, 193, 8], [5, 6, 3, 7, 3, 135]]\n",
      "Accuracy：  0.8317722082138062\n",
      "Epoch loss:  0.7137839794158936\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 1, 54, 7, 0], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9543437957763672\n",
      "Epoch loss:  0.1296183466911316\n",
      "-----train-----\n",
      "[[212, 3, 8, 12, 7, 7], [11, 214, 11, 3, 7, 3], [9, 12, 209, 8, 5, 6], [9, 3, 7, 208, 13, 6], [8, 10, 8, 10, 206, 6], [1, 7, 5, 3, 5, 138]]\n",
      "Accuracy：  0.8490498661994934\n",
      "Epoch loss:  0.6300081014633179\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 1, 50, 11, 0], [0, 0, 0, 6, 56, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9435909986495972\n",
      "Epoch loss:  0.1565069854259491\n",
      "-----train-----\n",
      "[[211, 7, 9, 9, 7, 5], [8, 210, 14, 8, 5, 4], [7, 13, 201, 5, 14, 9], [14, 10, 8, 200, 11, 4], [10, 7, 13, 5, 204, 9], [8, 5, 7, 8, 4, 127]]\n",
      "Accuracy：  0.8220747709274292\n",
      "Epoch loss:  0.7501577138900757\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 61, 0, 1, 0], [0, 0, 1, 50, 11, 0], [0, 0, 0, 5, 57, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9489673972129822\n",
      "Epoch loss:  0.15821276605129242\n",
      "-----train-----\n",
      "[[220, 5, 5, 6, 8, 5], [8, 209, 12, 9, 6, 5], [5, 6, 212, 6, 15, 4], [9, 8, 8, 203, 15, 4], [11, 14, 13, 10, 193, 7], [3, 4, 4, 6, 5, 137]]\n",
      "Accuracy：  0.8399089574813843\n",
      "Epoch loss:  0.6889775991439819\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 1, 55, 6, 0], [0, 0, 0, 6, 56, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9570318460464478\n",
      "Epoch loss:  0.12875759601593018\n",
      "-----train-----\n",
      "[[218, 8, 8, 6, 6, 3], [7, 204, 10, 15, 6, 7], [7, 9, 210, 7, 11, 5], [10, 13, 12, 199, 9, 4], [11, 5, 8, 13, 206, 5], [4, 4, 5, 5, 7, 133]]\n",
      "Accuracy：  0.836039662361145\n",
      "Epoch loss:  0.7138755321502686\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 59, 0, 0, 4, 0], [0, 0, 61, 0, 1, 0], [0, 0, 1, 40, 21, 0], [0, 0, 0, 3, 59, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9195255637168884\n",
      "Epoch loss:  0.2334863394498825\n",
      "-----train-----\n",
      "[[202, 10, 5, 14, 11, 7], [10, 199, 11, 14, 10, 5], [10, 11, 203, 8, 11, 6], [9, 12, 12, 192, 17, 4], [15, 11, 14, 11, 188, 9], [10, 5, 5, 1, 8, 130]]\n",
      "Accuracy：  0.7969775199890137\n",
      "Epoch loss:  0.8292832970619202\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 60, 2, 0, 0], [0, 0, 1, 57, 4, 0], [0, 0, 0, 9, 53, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9543436765670776\n",
      "Epoch loss:  0.14133331179618835\n",
      "-----train-----\n",
      "[[205, 13, 9, 7, 8, 7], [11, 197, 8, 15, 14, 4], [10, 9, 206, 10, 10, 3], [9, 11, 13, 188, 18, 8], [15, 12, 6, 18, 193, 4], [8, 5, 0, 3, 7, 136]]\n",
      "Accuracy：  0.8066347241401672\n",
      "Epoch loss:  0.8391714096069336\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 3, 2, 45, 12, 0], [0, 0, 1, 3, 58, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9409028887748718\n",
      "Epoch loss:  0.1596616804599762\n",
      "-----train-----\n",
      "[[209, 11, 2, 12, 10, 5], [12, 197, 14, 11, 9, 6], [5, 8, 201, 15, 12, 8], [13, 7, 14, 180, 24, 8], [13, 10, 12, 25, 182, 6], [8, 11, 3, 6, 4, 127]]\n",
      "Accuracy：  0.7836785912513733\n",
      "Epoch loss:  0.8906357288360596\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 3, 58, 1, 0], [0, 0, 1, 9, 52, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9570319652557373\n",
      "Epoch loss:  0.15193355083465576\n",
      "-----train-----\n",
      "[[215, 10, 5, 7, 9, 3], [7, 212, 7, 9, 9, 5], [6, 9, 203, 12, 12, 6], [11, 9, 14, 194, 16, 3], [11, 9, 12, 14, 195, 7], [4, 4, 4, 6, 6, 135]]\n",
      "Accuracy：  0.8256966471672058\n",
      "Epoch loss:  0.844612717628479\n",
      "-----valid-----\n",
      "[[63, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 1, 58, 3, 0], [0, 0, 1, 8, 53, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9597201347351074\n",
      "Epoch loss:  0.13484129309654236\n",
      "-----train-----\n",
      "[[222, 8, 3, 9, 2, 5], [4, 217, 8, 3, 13, 4], [5, 11, 214, 7, 7, 5], [10, 10, 8, 201, 13, 5], [9, 4, 9, 14, 202, 9], [3, 2, 10, 6, 7, 131]]\n",
      "Accuracy：  0.8463280200958252\n",
      "Epoch loss:  0.6653306484222412\n",
      "FOLD:  2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name       | Type             | Params\n",
      "------------------------------------------------\n",
      "0 | backbone   | Cait             | 270 M \n",
      "1 | fc         | Sequential       | 49.6 K\n",
      "2 | _criterion | CrossEntropyLoss | 0     \n",
      "------------------------------------------------\n",
      "270 M     Trainable params\n",
      "0         Non-trainable params\n",
      "270 M     Total params\n",
      "1,082.008 Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 50, 6, 0, 7, 0], [0, 1, 47, 2, 12, 0], [0, 2, 9, 34, 17, 0], [0, 0, 5, 14, 42, 1], [0, 0, 0, 0, 1, 38]]\n",
      "Accuracy：  0.7919801473617554\n",
      "Epoch loss:  0.9721059799194336\n",
      "-----train-----\n",
      "[[118, 41, 32, 16, 28, 15], [14, 81, 59, 37, 41, 17], [18, 54, 62, 43, 55, 17], [16, 36, 65, 51, 57, 21], [17, 37, 51, 43, 82, 17], [11, 24, 25, 25, 38, 36]]\n",
      "Accuracy：  0.3020022213459015\n",
      "Epoch loss:  1.6576237678527832\n",
      "-----valid-----\n",
      "[[61, 1, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 53, 3, 5, 1], [0, 1, 0, 44, 17, 0], [0, 0, 0, 10, 52, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.8952039480209351\n",
      "Epoch loss:  0.4013558030128479\n",
      "-----train-----\n",
      "[[214, 6, 10, 8, 5, 7], [15, 163, 34, 18, 15, 4], [13, 13, 150, 34, 32, 6], [8, 30, 45, 98, 52, 14], [6, 16, 26, 48, 141, 10], [13, 6, 9, 8, 5, 118]]\n",
      "Accuracy：  0.637534499168396\n",
      "Epoch loss:  1.110771894454956\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 59, 2, 1, 0], [0, 0, 0, 52, 10, 0], [0, 0, 0, 26, 36, 0], [0, 0, 2, 0, 0, 37]]\n",
      "Accuracy：  0.8813232183456421\n",
      "Epoch loss:  0.3167477250099182\n",
      "-----train-----\n",
      "[[215, 6, 7, 9, 8, 5], [7, 191, 18, 18, 14, 1], [7, 16, 192, 13, 10, 11], [9, 15, 20, 138, 53, 12], [7, 19, 9, 43, 163, 5], [7, 5, 8, 8, 3, 128]]\n",
      "Accuracy：  0.7374150156974792\n",
      "Epoch loss:  0.9055424332618713\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 61, 0, 1, 0], [0, 1, 2, 43, 16, 0], [0, 2, 0, 6, 54, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9194402098655701\n",
      "Epoch loss:  0.26907095313072205\n",
      "-----train-----\n",
      "[[211, 9, 7, 10, 9, 3], [10, 190, 16, 13, 15, 5], [12, 13, 195, 8, 13, 8], [10, 16, 23, 149, 44, 5], [11, 18, 7, 38, 165, 8], [2, 6, 4, 8, 5, 134]]\n",
      "Accuracy：  0.7512661218643188\n",
      "Epoch loss:  0.9595688581466675\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 52, 3, 6, 1], [0, 1, 0, 46, 15, 0], [0, 2, 0, 6, 54, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9059566259384155\n",
      "Epoch loss:  0.2896297574043274\n",
      "-----train-----\n",
      "[[208, 9, 8, 10, 5, 10], [11, 203, 7, 9, 13, 6], [7, 11, 209, 8, 8, 5], [6, 9, 11, 166, 49, 6], [9, 12, 9, 33, 179, 6], [6, 4, 4, 3, 7, 134]]\n",
      "Accuracy：  0.7886571884155273\n",
      "Epoch loss:  0.8389695286750793\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 1, 0, 49, 12, 0], [0, 2, 1, 5, 54, 0], [0, 0, 3, 0, 0, 36]]\n",
      "Accuracy：  0.9200605154037476\n",
      "Epoch loss:  0.21761873364448547\n",
      "-----train-----\n",
      "[[212, 9, 9, 6, 9, 5], [7, 207, 12, 10, 10, 2], [6, 12, 200, 14, 10, 7], [9, 10, 14, 173, 35, 6], [11, 10, 11, 34, 176, 6], [6, 2, 3, 7, 8, 132]]\n",
      "Accuracy：  0.7885692715644836\n",
      "Epoch loss:  0.8125458359718323\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 1, 1, 52, 8, 0], [0, 1, 0, 13, 48, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9275047183036804\n",
      "Epoch loss:  0.20670053362846375\n",
      "-----train-----\n",
      "[[204, 10, 10, 9, 10, 5], [14, 200, 8, 8, 12, 7], [10, 14, 201, 16, 4, 4], [8, 10, 13, 174, 37, 5], [10, 10, 10, 26, 185, 7], [8, 4, 7, 2, 5, 133]]\n",
      "Accuracy：  0.7866536378860474\n",
      "Epoch loss:  0.8381108641624451\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 1, 0], [0, 0, 59, 1, 2, 0], [0, 1, 0, 43, 18, 0], [0, 1, 0, 2, 59, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9301501512527466\n",
      "Epoch loss:  0.19629999995231628\n",
      "-----train-----\n",
      "[[208, 5, 11, 7, 11, 8], [9, 195, 14, 10, 14, 7], [9, 6, 215, 6, 8, 4], [16, 10, 10, 172, 34, 5], [5, 14, 10, 28, 185, 5], [4, 7, 5, 9, 3, 131]]\n",
      "Accuracy：  0.7918853163719177\n",
      "Epoch loss:  0.8326565027236938\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 0, 60, 2, 0], [0, 1, 0, 21, 40, 0], [0, 0, 3, 0, 0, 36]]\n",
      "Accuracy：  0.9146842956542969\n",
      "Epoch loss:  0.2273804396390915\n",
      "-----train-----\n",
      "[[206, 8, 9, 13, 7, 7], [10, 201, 17, 9, 8, 3], [17, 14, 202, 3, 9, 4], [13, 6, 8, 189, 25, 6], [7, 14, 5, 19, 189, 14], [5, 5, 5, 6, 11, 126]]\n",
      "Accuracy：  0.7950793504714966\n",
      "Epoch loss:  0.8550106287002563\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 61, 1, 0, 0], [0, 0, 0, 56, 6, 0], [0, 0, 0, 6, 56, 0], [0, 0, 2, 0, 0, 37]]\n",
      "Accuracy：  0.9512158632278442\n",
      "Epoch loss:  0.1585494875907898\n",
      "-----train-----\n",
      "[[212, 5, 9, 11, 7, 5], [6, 205, 9, 12, 10, 7], [9, 7, 218, 5, 7, 3], [13, 11, 4, 195, 18, 5], [7, 11, 8, 15, 202, 5], [9, 6, 5, 4, 5, 130]]\n",
      "Accuracy：  0.8291683197021484\n",
      "Epoch loss:  0.731475830078125\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 0, 59, 3, 0], [0, 0, 0, 14, 48, 0], [0, 0, 1, 0, 0, 38]]\n",
      "Accuracy：  0.9393601417541504\n",
      "Epoch loss:  0.19144372642040253\n",
      "-----train-----\n",
      "[[215, 6, 9, 7, 9, 4], [13, 207, 12, 7, 4, 6], [9, 13, 202, 9, 9, 6], [6, 9, 14, 190, 22, 5], [8, 7, 10, 18, 200, 5], [4, 5, 3, 7, 3, 137]]\n",
      "Accuracy：  0.8243810534477234\n",
      "Epoch loss:  0.7750397324562073\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 59, 1, 2, 0], [0, 0, 0, 38, 24, 0], [0, 0, 0, 1, 61, 0], [0, 0, 2, 0, 0, 37]]\n",
      "Accuracy：  0.9108932018280029\n",
      "Epoch loss:  0.2697893977165222\n",
      "-----train-----\n",
      "[[221, 6, 3, 8, 4, 8], [4, 217, 8, 11, 2, 6], [7, 10, 219, 3, 4, 6], [3, 10, 9, 204, 15, 5], [7, 4, 7, 16, 209, 5], [7, 1, 7, 3, 7, 134]]\n",
      "Accuracy：  0.8588826060295105\n",
      "Epoch loss:  0.6146189570426941\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 0, 45, 17, 0], [0, 0, 0, 2, 60, 0], [0, 0, 1, 0, 0, 38]]\n",
      "Accuracy：  0.9339838027954102\n",
      "Epoch loss:  0.19359706342220306\n",
      "-----train-----\n",
      "[[204, 11, 8, 11, 9, 6], [8, 200, 15, 9, 12, 5], [11, 15, 196, 10, 11, 6], [17, 7, 8, 192, 19, 4], [8, 9, 12, 13, 199, 6], [7, 8, 2, 8, 5, 129]]\n",
      "Accuracy：  0.8006592988967896\n",
      "Epoch loss:  0.823163628578186\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 0, 48, 14, 0], [0, 0, 0, 4, 58, 0], [0, 0, 2, 0, 0, 37]]\n",
      "Accuracy：  0.9323985576629639\n",
      "Epoch loss:  0.1915188431739807\n",
      "-----train-----\n",
      "[[212, 8, 7, 10, 7, 6], [11, 206, 8, 6, 7, 10], [12, 9, 200, 10, 11, 7], [13, 7, 13, 189, 19, 6], [7, 7, 13, 14, 199, 7], [6, 9, 3, 9, 6, 126]]\n",
      "Accuracy：  0.8075268268585205\n",
      "Epoch loss:  0.8404669165611267\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: Network error (ReadTimeout), entering retry loop.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 0, 57, 5, 0], [0, 0, 0, 11, 51, 0], [0, 0, 0, 0, 0, 39]]\n",
      "Accuracy：  0.9463218450546265\n",
      "Epoch loss:  0.17669108510017395\n",
      "-----train-----\n",
      "[[211, 14, 7, 9, 7, 2], [17, 203, 7, 5, 9, 8], [7, 6, 214, 12, 6, 3], [7, 10, 8, 201, 16, 4], [7, 9, 12, 13, 203, 4], [4, 4, 3, 3, 2, 143]]\n",
      "Accuracy：  0.8428595066070557\n",
      "Epoch loss:  0.6807790994644165\n",
      "FOLD:  3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name       | Type             | Params\n",
      "------------------------------------------------\n",
      "0 | backbone   | Cait             | 270 M \n",
      "1 | fc         | Sequential       | 49.6 K\n",
      "2 | _criterion | CrossEntropyLoss | 0     \n",
      "------------------------------------------------\n",
      "270 M     Trainable params\n",
      "0         Non-trainable params\n",
      "270 M     Total params\n",
      "1,082.008 Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[61, 1, 0, 0, 0, 0], [0, 55, 0, 3, 4, 0], [0, 1, 40, 9, 11, 1], [0, 2, 1, 34, 25, 0], [0, 1, 0, 9, 52, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.8130377531051636\n",
      "Epoch loss:  0.9106550216674805\n",
      "-----train-----\n",
      "[[149, 23, 17, 33, 21, 7], [27, 73, 43, 37, 49, 21], [18, 36, 52, 56, 70, 17], [32, 40, 41, 51, 69, 13], [23, 29, 32, 39, 109, 16], [18, 30, 14, 26, 25, 44]]\n",
      "Accuracy：  0.33732056617736816\n",
      "Epoch loss:  1.5999042987823486\n",
      "-----valid-----\n",
      "[[61, 1, 0, 0, 0, 0], [0, 60, 0, 0, 2, 0], [0, 0, 51, 4, 6, 1], [0, 2, 1, 39, 20, 0], [0, 0, 0, 10, 52, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.869489312171936\n",
      "Epoch loss:  0.4216083586215973\n",
      "-----train-----\n",
      "[[212, 6, 8, 10, 11, 3], [11, 178, 19, 16, 20, 5], [13, 13, 144, 32, 32, 15], [12, 24, 31, 106, 58, 16], [15, 17, 18, 58, 133, 6], [2, 7, 14, 7, 10, 118]]\n",
      "Accuracy：  0.6426032781600952\n",
      "Epoch loss:  1.1083954572677612\n",
      "-----valid-----\n",
      "[[61, 1, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 60, 0, 2, 0], [0, 6, 1, 37, 18, 0], [0, 0, 0, 11, 51, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.8883064985275269\n",
      "Epoch loss:  0.31236380338668823\n",
      "-----train-----\n",
      "[[210, 11, 7, 11, 6, 5], [11, 194, 10, 14, 14, 7], [14, 7, 188, 19, 14, 7], [13, 25, 23, 120, 58, 8], [6, 11, 16, 52, 154, 8], [6, 6, 9, 6, 5, 125]]\n",
      "Accuracy：  0.7127517461776733\n",
      "Epoch loss:  0.9729261994361877\n",
      "-----valid-----\n",
      "[[61, 1, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 61, 1, 0, 0], [0, 1, 1, 48, 12, 0], [0, 0, 0, 12, 50, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9178764224052429\n",
      "Epoch loss:  0.23635217547416687\n",
      "-----train-----\n",
      "[[221, 10, 3, 4, 6, 5], [11, 191, 11, 15, 16, 5], [5, 7, 197, 16, 13, 11], [5, 14, 15, 155, 48, 10], [7, 19, 16, 45, 158, 3], [5, 8, 10, 6, 6, 123]]\n",
      "Accuracy：  0.7481486201286316\n",
      "Epoch loss:  0.9589676260948181\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 53, 6, 3, 0], [0, 1, 0, 47, 14, 0], [0, 0, 0, 9, 53, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9044355154037476\n",
      "Epoch loss:  0.27345216274261475\n",
      "-----train-----\n",
      "[[225, 5, 5, 7, 6, 2], [12, 201, 18, 7, 10, 1], [9, 14, 212, 6, 2, 6], [6, 15, 11, 166, 43, 6], [8, 10, 8, 42, 175, 5], [1, 2, 9, 6, 6, 133]]\n",
      "Accuracy：  0.7972463965415955\n",
      "Epoch loss:  0.7587174773216248\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 61, 1, 0, 0], [0, 1, 1, 57, 3, 0], [0, 1, 0, 21, 40, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9178763628005981\n",
      "Epoch loss:  0.23450909554958344\n",
      "-----train-----\n",
      "[[216, 7, 5, 10, 8, 4], [9, 205, 13, 12, 9, 2], [7, 14, 199, 13, 11, 5], [7, 8, 21, 163, 40, 8], [9, 8, 6, 37, 179, 8], [5, 2, 5, 6, 4, 135]]\n",
      "Accuracy：  0.787947416305542\n",
      "Epoch loss:  0.7591454386711121\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 61, 0, 1, 0], [0, 1, 0, 50, 11, 0], [0, 0, 0, 8, 54, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9366936087608337\n",
      "Epoch loss:  0.18497039377689362\n",
      "-----train-----\n",
      "[[208, 11, 8, 7, 9, 7], [8, 201, 15, 10, 10, 6], [18, 13, 196, 6, 9, 5], [6, 12, 11, 181, 31, 6], [12, 11, 16, 30, 177, 2], [5, 4, 2, 7, 7, 133]]\n",
      "Accuracy：  0.7862995862960815\n",
      "Epoch loss:  0.8836531043052673\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 58, 2, 2, 0], [0, 1, 0, 46, 15, 0], [0, 1, 0, 4, 57, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9259408712387085\n",
      "Epoch loss:  0.18997876346111298\n",
      "-----train-----\n",
      "[[215, 8, 5, 9, 3, 9], [13, 217, 5, 4, 8, 3], [7, 9, 199, 16, 12, 6], [9, 7, 20, 170, 28, 12], [4, 10, 14, 27, 189, 4], [4, 5, 6, 8, 7, 128]]\n",
      "Accuracy：  0.7989884614944458\n",
      "Epoch loss:  0.7848672866821289\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 61, 0, 1, 0], [0, 1, 0, 54, 7, 0], [0, 0, 0, 12, 50, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9366936683654785\n",
      "Epoch loss:  0.17464882135391235\n",
      "-----train-----\n",
      "[[223, 5, 3, 6, 6, 7], [12, 210, 3, 10, 8, 6], [1, 11, 216, 10, 6, 5], [4, 9, 12, 191, 24, 6], [4, 8, 7, 28, 200, 1], [7, 2, 4, 5, 4, 136]]\n",
      "Accuracy：  0.841079592704773\n",
      "Epoch loss:  0.6439273357391357\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 60, 0, 2, 0], [0, 1, 0, 41, 20, 0], [0, 0, 0, 1, 61, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9286290407180786\n",
      "Epoch loss:  0.2523758113384247\n",
      "-----train-----\n",
      "[[218, 7, 6, 8, 7, 4], [9, 202, 15, 5, 13, 5], [8, 10, 210, 9, 9, 2], [8, 12, 11, 195, 18, 3], [7, 11, 8, 19, 196, 7], [4, 5, 4, 3, 5, 137]]\n",
      "Accuracy：  0.8294839859008789\n",
      "Epoch loss:  0.7756587266921997\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 60, 0, 2, 0], [0, 1, 0, 47, 14, 0], [0, 0, 0, 3, 59, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9393817186355591\n",
      "Epoch loss:  0.17480358481407166\n",
      "-----train-----\n",
      "[[222, 4, 6, 5, 9, 3], [6, 208, 7, 11, 7, 11], [6, 8, 214, 6, 11, 4], [9, 9, 9, 196, 23, 1], [10, 12, 8, 17, 190, 10], [4, 6, 11, 2, 6, 129]]\n",
      "Accuracy：  0.8270354866981506\n",
      "Epoch loss:  0.7411047220230103\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: Network error (ReadTimeout), entering retry loop.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[61, 1, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 0, 50, 12, 0], [0, 0, 0, 5, 57, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9393817186355591\n",
      "Epoch loss:  0.1615540236234665\n",
      "-----train-----\n",
      "[[206, 16, 10, 6, 6, 5], [14, 205, 9, 6, 13, 3], [11, 7, 196, 12, 12, 11], [9, 4, 15, 193, 18, 7], [9, 11, 13, 16, 194, 5], [8, 7, 3, 6, 5, 129]]\n",
      "Accuracy：  0.8029541373252869\n",
      "Epoch loss:  0.9029321074485779\n",
      "-----valid-----\n",
      "[[61, 1, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 60, 1, 1, 0], [0, 1, 0, 51, 10, 0], [0, 0, 0, 5, 57, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9420698881149292\n",
      "Epoch loss:  0.16991186141967773\n",
      "-----train-----\n",
      "[[209, 14, 10, 7, 5, 5], [11, 208, 7, 10, 9, 4], [14, 10, 206, 5, 9, 5], [11, 7, 14, 194, 12, 8], [8, 5, 10, 15, 204, 6], [4, 5, 7, 6, 7, 129]]\n",
      "Accuracy：  0.8210508823394775\n",
      "Epoch loss:  0.7753897309303284\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 60, 1, 1, 0, 0], [0, 0, 60, 1, 1, 0], [0, 0, 1, 52, 9, 0], [0, 0, 0, 5, 57, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9447580575942993\n",
      "Epoch loss:  0.15326851606369019\n",
      "-----train-----\n",
      "[[220, 11, 4, 8, 5, 2], [6, 210, 11, 12, 8, 2], [6, 8, 206, 13, 8, 8], [10, 11, 11, 197, 13, 5], [8, 14, 8, 12, 202, 3], [6, 2, 8, 3, 3, 136]]\n",
      "Accuracy：  0.8378045558929443\n",
      "Epoch loss:  0.7350605726242065\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 0, 0], [0, 0, 61, 0, 1, 0], [0, 1, 1, 54, 6, 0], [0, 0, 1, 8, 53, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9447581768035889\n",
      "Epoch loss:  0.16804444789886475\n",
      "-----train-----\n",
      "[[215, 10, 10, 4, 3, 8], [10, 203, 12, 11, 8, 5], [13, 10, 201, 6, 9, 9], [10, 11, 8, 203, 14, 1], [8, 9, 11, 11, 199, 10], [3, 5, 10, 5, 8, 127]]\n",
      "Accuracy：  0.8189707398414612\n",
      "Epoch loss:  0.7582250833511353\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 60, 0, 1, 1, 0], [0, 0, 60, 1, 1, 0], [0, 0, 0, 50, 12, 0], [0, 0, 0, 2, 60, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9474462270736694\n",
      "Epoch loss:  0.1712212860584259\n",
      "-----train-----\n",
      "[[216, 2, 5, 9, 11, 7], [10, 208, 12, 5, 10, 5], [4, 13, 216, 9, 4, 3], [10, 11, 9, 195, 11, 10], [11, 11, 8, 15, 199, 3], [4, 7, 7, 9, 8, 123]]\n",
      "Accuracy：  0.8233836889266968\n",
      "Epoch loss:  0.7961664795875549\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 0, 0], [0, 0, 60, 1, 1, 0], [0, 1, 1, 55, 5, 0], [0, 0, 2, 6, 54, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9474462270736694\n",
      "Epoch loss:  0.1456674337387085\n",
      "-----train-----\n",
      "[[202, 12, 10, 13, 7, 6], [9, 208, 8, 14, 6, 4], [10, 6, 200, 9, 17, 6], [14, 12, 13, 189, 14, 5], [12, 9, 10, 8, 204, 5], [7, 2, 4, 6, 6, 133]]\n",
      "Accuracy：  0.8132213354110718\n",
      "Epoch loss:  0.7605144381523132\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 0, 0], [0, 0, 60, 1, 1, 0], [0, 0, 0, 55, 6, 1], [0, 0, 1, 5, 56, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9569892883300781\n",
      "Epoch loss:  0.1380431205034256\n",
      "-----train-----\n",
      "[[222, 2, 8, 7, 5, 6], [5, 218, 4, 5, 10, 7], [10, 9, 210, 7, 11, 2], [8, 7, 7, 201, 15, 8], [6, 10, 10, 18, 197, 7], [7, 6, 5, 6, 5, 129]]\n",
      "Accuracy：  0.8391265273094177\n",
      "Epoch loss:  0.7140925526618958\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 1, 0, 0], [0, 0, 60, 1, 1, 0], [0, 1, 0, 57, 4, 0], [0, 1, 0, 12, 49, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9393817186355591\n",
      "Epoch loss:  0.16568401455879211\n",
      "-----train-----\n",
      "[[207, 11, 11, 6, 9, 6], [9, 211, 11, 6, 8, 5], [16, 13, 198, 11, 5, 6], [6, 10, 7, 204, 13, 6], [7, 11, 14, 9, 199, 7], [8, 2, 7, 6, 9, 126]]\n",
      "Accuracy：  0.816597580909729\n",
      "Epoch loss:  0.8058502078056335\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 0, 0], [0, 0, 61, 0, 1, 0], [0, 1, 1, 52, 8, 0], [0, 0, 0, 4, 58, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9528226256370544\n",
      "Epoch loss:  0.14097118377685547\n",
      "-----train-----\n",
      "[[222, 5, 6, 7, 6, 4], [10, 206, 10, 7, 7, 10], [6, 12, 208, 13, 3, 6], [9, 11, 8, 201, 11, 6], [8, 7, 8, 12, 208, 5], [6, 8, 7, 8, 5, 124]]\n",
      "Accuracy：  0.8318837285041809\n",
      "Epoch loss:  0.7212411761283875\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 59, 0, 1, 2, 0], [0, 0, 60, 0, 2, 0], [0, 0, 0, 51, 11, 0], [0, 0, 1, 3, 58, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9420698881149292\n",
      "Epoch loss:  0.17793723940849304\n",
      "-----train-----\n",
      "[[219, 6, 10, 5, 7, 3], [6, 211, 10, 7, 7, 9], [7, 11, 199, 11, 14, 6], [14, 7, 7, 191, 22, 6], [6, 9, 17, 18, 193, 4], [6, 7, 7, 6, 5, 127]]\n",
      "Accuracy：  0.8134788274765015\n",
      "Epoch loss:  0.7758997678756714\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 56, 0, 3, 3, 0], [0, 0, 61, 0, 1, 0], [0, 0, 0, 55, 7, 0], [0, 0, 1, 6, 55, 0], [0, 0, 0, 1, 0, 39]]\n",
      "Accuracy：  0.9393818378448486\n",
      "Epoch loss:  0.18433324992656708\n",
      "-----train-----\n",
      "[[227, 7, 3, 9, 4, 0], [5, 216, 11, 11, 4, 3], [6, 9, 204, 10, 8, 10], [6, 12, 11, 198, 17, 3], [8, 8, 9, 14, 204, 5], [5, 3, 7, 2, 4, 137]]\n",
      "Accuracy：  0.8481999635696411\n",
      "Epoch loss:  0.6268344521522522\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 1, 0, 0, 0], [0, 0, 61, 0, 1, 0], [0, 0, 1, 55, 6, 0], [0, 0, 1, 10, 51, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9462366104125977\n",
      "Epoch loss:  0.154954731464386\n",
      "-----train-----\n",
      "[[211, 8, 9, 9, 11, 1], [9, 199, 7, 9, 14, 11], [14, 13, 202, 6, 10, 4], [8, 10, 8, 198, 20, 3], [15, 10, 11, 13, 191, 8], [3, 11, 8, 3, 3, 130]]\n",
      "Accuracy：  0.8087327480316162\n",
      "Epoch loss:  0.8374294638633728\n",
      "FOLD:  4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name       | Type             | Params\n",
      "------------------------------------------------\n",
      "0 | backbone   | Cait             | 270 M \n",
      "1 | fc         | Sequential       | 49.6 K\n",
      "2 | _criterion | CrossEntropyLoss | 0     \n",
      "------------------------------------------------\n",
      "270 M     Trainable params\n",
      "0         Non-trainable params\n",
      "270 M     Total params\n",
      "1,082.008 Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 7, 27, 7, 18, 3], [1, 0, 48, 1, 12, 0], [0, 0, 8, 22, 32, 0], [0, 0, 7, 5, 50, 0], [0, 0, 2, 1, 0, 37]]\n",
      "Accuracy：  0.6622312068939209\n",
      "Epoch loss:  1.090577483177185\n",
      "-----train-----\n",
      "[[162, 19, 12, 31, 21, 5], [21, 67, 53, 47, 48, 13], [30, 65, 56, 40, 46, 12], [25, 50, 46, 59, 55, 11], [17, 47, 37, 52, 80, 15], [14, 29, 17, 18, 35, 45]]\n",
      "Accuracy：  0.3315340280532837\n",
      "Epoch loss:  1.6323552131652832\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 60, 0, 0, 2, 0], [1, 0, 60, 1, 0, 0], [0, 2, 5, 38, 17, 0], [0, 1, 3, 11, 47, 0], [0, 0, 1, 1, 0, 38]]\n",
      "Accuracy：  0.876075267791748\n",
      "Epoch loss:  0.4874018728733063\n",
      "-----train-----\n",
      "[[210, 8, 11, 11, 4, 6], [8, 134, 37, 21, 35, 15], [17, 24, 110, 40, 41, 16], [18, 27, 42, 95, 53, 12], [11, 22, 40, 52, 115, 7], [10, 21, 15, 7, 9, 96]]\n",
      "Accuracy：  0.5462242960929871\n",
      "Epoch loss:  1.2598398923873901\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 47, 14, 0], [0, 0, 0, 17, 45, 0], [0, 0, 1, 0, 0, 39]]\n",
      "Accuracy：  0.9071237444877625\n",
      "Epoch loss:  0.2961617708206177\n",
      "-----train-----\n",
      "[[210, 9, 9, 8, 7, 7], [9, 186, 20, 15, 12, 8], [8, 10, 181, 22, 19, 9], [10, 16, 25, 129, 57, 9], [9, 14, 17, 41, 157, 9], [8, 10, 6, 8, 5, 121]]\n",
      "Accuracy：  0.7061246633529663\n",
      "Epoch loss:  0.9841383695602417\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 2, 47, 13, 0], [0, 0, 1, 11, 50, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9247312545776367\n",
      "Epoch loss:  0.23476937413215637\n",
      "-----train-----\n",
      "[[223, 6, 6, 7, 4, 4], [9, 209, 11, 7, 10, 4], [9, 9, 192, 15, 13, 10], [4, 8, 18, 155, 54, 7], [8, 10, 11, 45, 169, 5], [4, 6, 7, 6, 10, 125]]\n",
      "Accuracy：  0.7674776911735535\n",
      "Epoch loss:  0.8553577661514282\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 45, 16, 0], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9327957630157471\n",
      "Epoch loss:  0.20872049033641815\n",
      "-----train-----\n",
      "[[210, 9, 6, 10, 10, 5], [13, 199, 11, 16, 6, 5], [9, 10, 193, 16, 13, 8], [7, 11, 21, 163, 40, 4], [10, 10, 12, 40, 169, 6], [4, 7, 7, 7, 9, 124]]\n",
      "Accuracy：  0.7571204900741577\n",
      "Epoch loss:  0.8930843472480774\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 39, 22, 0], [0, 0, 0, 4, 58, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9232527613639832\n",
      "Epoch loss:  0.2069106549024582\n",
      "-----train-----\n",
      "[[210, 8, 13, 6, 6, 7], [12, 193, 14, 13, 15, 3], [7, 13, 198, 11, 13, 6], [8, 19, 11, 161, 40, 7], [11, 14, 13, 29, 175, 6], [6, 7, 6, 5, 6, 128]]\n",
      "Accuracy：  0.7634384036064148\n",
      "Epoch loss:  0.9258873462677002\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 47, 14, 0], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9340054392814636\n",
      "Epoch loss:  0.18233415484428406\n",
      "-----train-----\n",
      "[[221, 8, 5, 1, 7, 7], [10, 197, 12, 10, 10, 11], [2, 11, 212, 8, 9, 6], [7, 15, 7, 178, 38, 2], [10, 11, 9, 36, 177, 5], [5, 9, 5, 2, 10, 127]]\n",
      "Accuracy：  0.7947573065757751\n",
      "Epoch loss:  0.7897128462791443\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 5, 36, 21, 0], [0, 0, 1, 3, 58, 0], [0, 0, 1, 0, 0, 39]]\n",
      "Accuracy：  0.9125000238418579\n",
      "Epoch loss:  0.23865877091884613\n",
      "-----train-----\n",
      "[[209, 10, 6, 10, 6, 9], [13, 205, 8, 4, 14, 6], [4, 14, 209, 8, 9, 5], [9, 10, 7, 183, 33, 4], [9, 14, 6, 25, 183, 10], [8, 3, 7, 5, 7, 128]]\n",
      "Accuracy：  0.7983795404434204\n",
      "Epoch loss:  0.7939140200614929\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 2, 30, 30, 0], [0, 0, 0, 0, 62, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9071236848831177\n",
      "Epoch loss:  0.29322487115859985\n",
      "-----train-----\n",
      "[[219, 8, 8, 5, 3, 6], [8, 204, 15, 11, 9, 3], [5, 14, 200, 11, 8, 11], [7, 10, 12, 183, 23, 11], [7, 11, 11, 19, 197, 3], [7, 6, 9, 5, 5, 126]]\n",
      "Accuracy：  0.8057427406311035\n",
      "Epoch loss:  0.818953275680542\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 49, 12, 0], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.942069947719574\n",
      "Epoch loss:  0.1644323617219925\n",
      "-----train-----\n",
      "[[221, 6, 6, 10, 7, 0], [6, 211, 7, 14, 8, 4], [6, 6, 215, 10, 9, 3], [8, 11, 14, 192, 19, 3], [10, 11, 11, 13, 192, 10], [5, 3, 4, 2, 9, 134]]\n",
      "Accuracy：  0.8332688808441162\n",
      "Epoch loss:  0.7174878120422363\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 50, 11, 0], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9462366104125977\n",
      "Epoch loss:  0.15460430085659027\n",
      "-----train-----\n",
      "[[202, 10, 9, 12, 7, 10], [10, 206, 9, 7, 10, 7], [8, 7, 207, 11, 11, 5], [8, 12, 7, 195, 19, 6], [13, 9, 12, 15, 192, 6], [9, 4, 8, 4, 8, 125]]\n",
      "Accuracy：  0.8040958642959595\n",
      "Epoch loss:  0.875409722328186\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 59, 2, 0], [0, 0, 0, 21, 41, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9354839324951172\n",
      "Epoch loss:  0.2136780023574829\n",
      "-----train-----\n",
      "[[208, 15, 4, 6, 11, 6], [17, 197, 12, 11, 9, 4], [8, 14, 205, 7, 9, 6], [12, 11, 6, 195, 16, 5], [11, 7, 9, 21, 191, 9], [6, 3, 6, 3, 8, 132]]\n",
      "Accuracy：  0.8074693083763123\n",
      "Epoch loss:  0.8618744015693665\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 42, 19, 0], [0, 0, 1, 3, 58, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9354839324951172\n",
      "Epoch loss:  0.2200246900320053\n",
      "-----train-----\n",
      "[[209, 10, 6, 10, 6, 9], [7, 210, 9, 7, 9, 7], [7, 8, 200, 15, 12, 6], [13, 8, 13, 190, 17, 6], [11, 11, 11, 19, 189, 7], [6, 3, 8, 10, 5, 126]]\n",
      "Accuracy：  0.8024368286132812\n",
      "Epoch loss:  0.8352618217468262\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 53, 8, 0], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9569892883300781\n",
      "Epoch loss:  0.15186680853366852\n",
      "-----train-----\n",
      "[[215, 9, 5, 6, 11, 4], [7, 209, 13, 7, 9, 5], [8, 13, 196, 10, 16, 4], [13, 7, 10, 201, 12, 4], [8, 7, 15, 13, 194, 11], [6, 6, 5, 4, 8, 129]]\n",
      "Accuracy：  0.8170002698898315\n",
      "Epoch loss:  0.7778192162513733\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 50, 11, 0], [0, 0, 0, 5, 57, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9474462866783142\n",
      "Epoch loss:  0.15936625003814697\n",
      "-----train-----\n",
      "[[222, 5, 10, 6, 3, 4], [13, 214, 6, 5, 8, 4], [7, 8, 209, 7, 13, 5], [8, 11, 8, 206, 10, 4], [7, 6, 14, 12, 203, 5], [3, 5, 4, 3, 6, 136]]\n",
      "Accuracy：  0.8509117364883423\n",
      "Epoch loss:  0.6634610295295715\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 51, 10, 0], [0, 0, 0, 3, 59, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9555107951164246\n",
      "Epoch loss:  0.15850956737995148\n",
      "-----train-----\n",
      "[[218, 12, 4, 11, 2, 3], [11, 201, 15, 9, 11, 3], [7, 9, 216, 6, 2, 9], [12, 8, 6, 193, 20, 8], [4, 14, 7, 13, 203, 5], [3, 4, 5, 6, 6, 134]]\n",
      "Accuracy：  0.8330251574516296\n",
      "Epoch loss:  0.7483490705490112\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 46, 15, 0], [0, 0, 0, 2, 60, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9447581171989441\n",
      "Epoch loss:  0.17636583745479584\n",
      "-----train-----\n",
      "[[212, 7, 7, 11, 6, 7], [6, 214, 8, 8, 4, 9], [9, 11, 214, 4, 6, 5], [11, 7, 7, 200, 16, 6], [9, 8, 7, 10, 211, 3], [10, 2, 10, 6, 7, 122]]\n",
      "Accuracy：  0.8340781331062317\n",
      "Epoch loss:  0.7543813586235046\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 57, 4, 0], [0, 0, 1, 13, 48, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9489247798919678\n",
      "Epoch loss:  0.17972339689731598\n",
      "-----train-----\n",
      "[[219, 7, 5, 9, 7, 3], [8, 210, 7, 9, 10, 6], [7, 5, 211, 9, 8, 9], [10, 11, 10, 201, 8, 7], [8, 12, 7, 7, 209, 4], [5, 1, 8, 6, 3, 134]]\n",
      "Accuracy：  0.8461352586746216\n",
      "Epoch loss:  0.7681031227111816\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 57, 4, 0], [0, 0, 1, 10, 51, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9569892883300781\n",
      "Epoch loss:  0.14815793931484222\n",
      "-----train-----\n",
      "[[214, 9, 9, 7, 8, 3], [7, 212, 9, 9, 7, 6], [10, 10, 213, 3, 7, 6], [8, 8, 5, 202, 15, 8], [9, 6, 8, 13, 207, 4], [8, 1, 4, 9, 5, 131]]\n",
      "Accuracy：  0.8412883877754211\n",
      "Epoch loss:  0.740772008895874\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 53, 8, 0], [0, 0, 1, 5, 56, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9596774578094482\n",
      "Epoch loss:  0.1510593593120575\n",
      "-----train-----\n",
      "[[211, 8, 13, 6, 7, 5], [17, 196, 7, 9, 14, 6], [8, 14, 194, 13, 10, 10], [8, 12, 13, 197, 14, 3], [10, 15, 7, 9, 199, 7], [7, 2, 11, 5, 5, 128]]\n",
      "Accuracy：  0.8039383888244629\n",
      "Epoch loss:  0.9406574368476868\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 41, 20, 0], [0, 0, 0, 3, 59, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.9327957630157471\n",
      "Epoch loss:  0.21883010864257812\n",
      "-----train-----\n",
      "[[209, 10, 8, 8, 8, 6], [10, 211, 7, 7, 11, 4], [8, 7, 206, 11, 12, 5], [8, 11, 13, 201, 9, 5], [11, 8, 13, 13, 197, 5], [8, 2, 5, 5, 3, 135]]\n",
      "Accuracy：  0.8294054269790649\n",
      "Epoch loss:  0.7622005939483643\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 58, 3, 0], [0, 0, 1, 11, 50, 0], [0, 0, 0, 0, 0, 40]]\n",
      "Accuracy：  0.954301118850708\n",
      "Epoch loss:  0.16043685376644135\n",
      "-----train-----\n",
      "[[210, 10, 9, 9, 8, 4], [12, 194, 17, 7, 14, 6], [6, 11, 202, 9, 13, 7], [12, 9, 8, 197, 16, 4], [7, 14, 7, 16, 197, 7], [6, 8, 2, 3, 5, 134]]\n",
      "Accuracy：  0.8122975826263428\n",
      "Epoch loss:  0.8512935042381287\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 62, 0, 0, 0, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 51, 10, 0], [0, 0, 0, 7, 55, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9474462866783142\n",
      "Epoch loss:  0.16395261883735657\n",
      "-----train-----\n",
      "[[213, 8, 9, 7, 6, 6], [8, 210, 10, 9, 10, 2], [12, 9, 211, 7, 5, 5], [9, 9, 9, 199, 14, 7], [5, 8, 15, 20, 194, 6], [6, 2, 5, 4, 5, 136]]\n",
      "Accuracy：  0.832478404045105\n",
      "Epoch loss:  0.7119481563568115\n",
      "-----valid-----\n",
      "[[62, 0, 0, 0, 0, 0], [0, 61, 0, 0, 1, 0], [0, 0, 62, 0, 0, 0], [0, 0, 1, 59, 2, 0], [0, 0, 0, 15, 47, 0], [0, 0, 0, 0, 1, 39]]\n",
      "Accuracy：  0.9447581768035889\n",
      "Epoch loss:  0.19970622658729553\n",
      "-----train-----\n",
      "[[199, 7, 6, 16, 14, 8], [9, 202, 13, 3, 18, 5], [8, 9, 204, 14, 9, 4], [16, 15, 9, 186, 14, 7], [20, 11, 12, 16, 181, 7], [4, 5, 4, 7, 11, 127]]\n",
      "Accuracy：  0.7860347032546997\n",
      "Epoch loss:  0.9884824156761169\n",
      "CPU times: user 4h 7min 42s, sys: 5h 9min 34s, total: 9h 17min 16s\n",
      "Wall time: 9h 16min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "skf = StratifiedKFold(\n",
    "    n_splits=config.n_splits, shuffle=True, random_state=config.seed\n",
    ")\n",
    "\n",
    "FOLD = 0\n",
    "\n",
    "for fold, (train_idx, val_idx) in enumerate(skf.split(df[\"id\"], df[\"target\"])):\n",
    "    print('FOLD: ', FOLD)\n",
    "    train_df = df.loc[train_idx].reset_index(drop=True)\n",
    "    val_df = df.loc[val_idx].reset_index(drop=True)\n",
    "#     train_name = 'train' + str(FOLD) + '.csv'\n",
    "#     valid_name = 'valid' + str(FOLD) + '.csv'\n",
    "#     train_df.to_csv(train_name)\n",
    "#     val_df.to_csv(valid_name)\n",
    "    datamodule = EMSIGDataModule(train_df, val_df, config)\n",
    "    model = Model(config)\n",
    "    earystopping = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=5)\n",
    "    lr_monitor = callbacks.LearningRateMonitor()\n",
    "    loss_checkpoint = callbacks.ModelCheckpoint(\n",
    "        filename=\"best_loss\",\n",
    "        monitor=\"val_loss\",\n",
    "        save_top_k=1,\n",
    "        mode=\"min\",\n",
    "        save_last=False,\n",
    "    )\n",
    "    logger = WandbLogger(project='cait-with-mixup',\n",
    "                         log_model=False)\n",
    "    \n",
    "    trainer = pl.Trainer(\n",
    "        logger=logger,\n",
    "        max_epochs=config.epoch,\n",
    "        callbacks= earystopping,\n",
    "        **config.trainer,\n",
    "    )\n",
    "    trainer.fit(model, datamodule=datamodule)\n",
    "    # PATH = './' + str(fold)\n",
    "    # torch.save(model.state_dict(), PATH)\n",
    "    FOLD += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c0eacc",
   "metadata": {
    "papermill": {
     "duration": 0.092177,
     "end_time": "2022-04-16T16:05:02.267114",
     "exception": false,
     "start_time": "2022-04-16T16:05:02.174937",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d14c54d5",
   "metadata": {
    "papermill": {
     "duration": 0.105802,
     "end_time": "2022-04-16T16:05:02.657958",
     "exception": false,
     "start_time": "2022-04-16T16:05:02.552156",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model(\n",
      "  (backbone): Cait(\n",
      "    (patch_embed): PatchEmbed(\n",
      "      (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
      "      (norm): Identity()\n",
      "    )\n",
      "    (pos_drop): Dropout(p=0.0, inplace=False)\n",
      "    (blocks): ModuleList(\n",
      "      (0): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (1): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (2): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (3): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (4): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (5): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (6): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (7): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (8): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (9): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (10): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (11): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (12): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (13): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (14): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (15): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (16): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (17): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (18): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (19): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (20): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (21): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (22): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (23): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (24): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (25): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (26): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (27): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (28): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (29): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (30): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (31): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (32): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (33): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (34): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (35): LayerScaleBlock(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): TalkingHeadAttn(\n",
      "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_l): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_w): Linear(in_features=16, out_features=16, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (blocks_token_only): ModuleList(\n",
      "      (0): LayerScaleBlockClassAttn(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): ClassAttn(\n",
      "          (q): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (k): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (v): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (1): LayerScaleBlockClassAttn(\n",
      "        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): ClassAttn(\n",
      "          (q): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (k): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (v): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          (act): GELU()\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
      "    (head): Identity()\n",
      "  )\n",
      "  (fc): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=768, out_features=64, bias=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=64, out_features=6, bias=True)\n",
      "  )\n",
      "  (_criterion): CrossEntropyLoss()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  }
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