SampleSubmission_last.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seed_everything(seed = 0):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = False\n",
    "    torch.backends.cudnn.benchmark = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "from glob import glob\n",
    "from albumentations import Compose, Resize, HorizontalFlip, Rotate\n",
    "from tqdm.notebook import tqdm\n",
    "import random\n",
    "import numpy as np\n",
    "import torch\n",
    "import pickle\n",
    "import timm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class param:\n",
    "    num_epoch = 8\n",
    "    batch_size = 64\n",
    "    num_data = 12800\n",
    "    img_size = 384 #224\n",
    "    img_face_crop_mergin = 60\n",
    "    img_center_crop_size = 224\n",
    "    num_workers = 4\n",
    "    lr = 0.0001\n",
    "    k_fold = 5\n",
    "    train_patience = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-13-52b053f2d522>, line 46)",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-52b053f2d522>\"\u001b[0;36m, line \u001b[0;32m46\u001b[0m\n\u001b[0;31m    '''Rotate([5, 5], p=1.0),\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "seed_everything()\n",
    "\n",
    "class path:\n",
    "    train_csv = '/opt/ml/input/data/train/train.csv'\n",
    "    train_images = '/opt/ml/input/data/train/images'\n",
    "    eval_images = '/opt/ml/input/data/eval/images'\n",
    "    model_save = '/opt/ml/code/results'\n",
    "\n",
    "class FaceDataset(Dataset):\n",
    "    def __init__(self, imgs):\n",
    "        self.imgs = imgs\n",
    "        self.bboxes = [pickle.load(open(os.path.splitext(img)[0] + '.txt', 'rb')) for img in self.imgs]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img = Image.open(self.imgs[idx])\n",
    "        img_size = img.size\n",
    "        box = self.bboxes[idx]\n",
    "        if box is not None:\n",
    "            margin = param.img_face_crop_mergin\n",
    "            box = [\n",
    "                int(max(box[0] - margin / 2, 0)),\n",
    "                int(max(box[1] - margin / 2, 0)),\n",
    "                int(min(box[2] + margin / 2, img_size[0])),\n",
    "                int(min(box[3] + margin / 2, img_size[1]))\n",
    "            ]\n",
    "        else:\n",
    "            margin = param.img_center_crop_size\n",
    "            box = [\n",
    "                int(img_size[0] / 2 - margin / 2),\n",
    "                int(img_size[1] / 2 - margin / 2),\n",
    "                int(img_size[0] / 2 + margin / 2),\n",
    "                int(img_size[1] / 2 + margin / 2)\n",
    "            ]\n",
    "        img = img.crop(box)\n",
    "        return img\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.imgs)\n",
    "\n",
    "def get_eval_dataset(images_path):\n",
    "    img_list = glob(images_path + '/*.jpg')\n",
    "    return FaceDataset(img_list)\n",
    "\n",
    "transforms_list = [\n",
    "]\n",
    "    '''Rotate([5, 5], p=1.0),\n",
    "    Rotate([-5, -5], p=1.0),\n",
    "    HorizontalFlip(p=1.0),\n",
    "    Compose([Rotate([5, 5], p=1.0), HorizontalFlip(p=1.0)]),\n",
    "    Compose([Rotate([-5, -5], p=1.0), HorizontalFlip(p=1.0)])'''\n",
    "\n",
    "eval_dataset = get_eval_dataset(path.eval_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "RuntimeError",
     "evalue": "CUDA error: out of memory",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-6a1b0f60e9d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel_paths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'efficientnet_b3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0mmodel_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    592\u001b[0m                     \u001b[0mopened_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_position\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 594\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    595\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_legacy_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)\u001b[0m\n\u001b[1;32m    851\u001b[0m     \u001b[0munpickler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnpickler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m     \u001b[0munpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpersistent_load\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpersistent_load\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 853\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    854\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_loaded_sparse_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mpersistent_load\u001b[0;34m(saved_id)\u001b[0m\n\u001b[1;32m    843\u001b[0m         \u001b[0mdata_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    844\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloaded_storages\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m             \u001b[0mload_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_maybe_decode_ascii\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    846\u001b[0m         \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloaded_storages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    847\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload_tensor\u001b[0;34m(data_type, size, key, location)\u001b[0m\n\u001b[1;32m    832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    833\u001b[0m         \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_storage_from_record\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 834\u001b[0;31m         \u001b[0mloaded_storages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrestore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    835\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpersistent_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaved_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mdefault_restore_location\u001b[0;34m(storage, location)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdefault_restore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_package_registry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_cuda_deserialize\u001b[0;34m(obj, location)\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mstorage_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36m_cuda\u001b[0;34m(self, device, non_blocking, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0mnew_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_new\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m    460\u001b[0m     \u001b[0;31m# We may need to call lazy init again if we are a forked child\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;31m# del _CudaBase.__new__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_CudaBase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: out of memory"
     ]
    }
   ],
   "source": [
    "eval_dataloader = DataLoader(eval_dataset, shuffle=True, num_workers=param.num_workers)\n",
    "\n",
    "model_paths = [\n",
    "    '/opt/ml/code/results/last_0_224/005_accuracy_65.43%.ckpt'\n",
    "]\n",
    "\n",
    "'''\n",
    "    ,\n",
    "    '/opt/ml/code/results/last_0_384/007_accuracy_66.80%.ckpt',\n",
    "    '/opt/ml/code/results/last_1_224/007_accuracy_67.19%.ckpt',\n",
    "    '/opt/ml/code/results/last_1_384/006_accuracy_66.60%.ckpt',\n",
    "    '/opt/ml/code/results/last_2_224/006_accuracy_69.53%.ckpt',\n",
    "    '/opt/ml/code/results/last_2_384/006_accuracy_70.70%.ckpt',\n",
    "    '/opt/ml/code/results/last_3_224/007_accuracy_68.55%.ckpt',\n",
    "    '/opt/ml/code/results/last_3_384/004_accuracy_65.62%.ckpt',\n",
    "    '/opt/ml/code/results/last_4_224/006_accuracy_67.97%.ckpt',\n",
    "    '/opt/ml/code/results/last_4_384/005_accuracy_68.95%.ckpt',\n",
    "    '/opt/ml/code/results/last_0_512/007_accuracy_66.41%.ckpt',\n",
    "    '/opt/ml/code/results/last_1_512/006_accuracy_63.87%.ckpt',\n",
    "    '/opt/ml/code/results/last_2_512/007_accuracy_70.12%.ckpt'\n",
    "\n",
    "'''\n",
    "model_list = []\n",
    "for model_path in model_paths:\n",
    "    model = timm.create_model('efficientnet_b3', num_classes=18)\n",
    "    model.load_state_dict(torch.load(model_path))\n",
    "    model.eval()\n",
    "    model_list.append(model)\n",
    "    \n",
    "\n",
    "device = torch.device('cuda')\n",
    "all_predictions = []\n",
    "for images in tqdm(eval_dataloader):\n",
    "    images = images.to(device)\n",
    "    num_predict = 0\n",
    "    for model in path:\n",
    "        pred = model(images)\n",
    "        for transforms in transforms_list:\n",
    "            pred += model(transforms(images))\n",
    "            num_predict += 1\n",
    "    \n",
    "    pred /= num_predict\n",
    "    all_predictions.extend(pred.cpu().numpy())\n",
    "\n",
    "submission['ans'] = np.argmax(all_prediction, axis=1)\n",
    "submission.to_csv(os.path.join(path.eval_images, 'submission.csv'), index=False)\n",
    "print('test inference is done!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Counter({'.jpg': 12600, '.txt': 12600})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from glob import glob\n",
    "img_list = glob(path.eval_images + '/*')\n",
    "ext_list = []\n",
    "for img in img_list:\n",
    "    ext_list.append(os.path.splitext(img)[1])\n",
    "ext_list = Counter(ext_list)\n",
    "print(ext_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "|===========================================================================|\n|                  PyTorch CUDA memory summary, device ID 0                 |\n|---------------------------------------------------------------------------|\n|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n|===========================================================================|\n|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n|---------------------------------------------------------------------------|\n| Allocated memory      |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|---------------------------------------------------------------------------|\n| Active memory         |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|---------------------------------------------------------------------------|\n| GPU reserved memory   |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|---------------------------------------------------------------------------|\n| Non-releasable memory |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |\n|---------------------------------------------------------------------------|\n| Allocations           |       0    |       0    |       0    |       0    |\n|       from large pool |       0    |       0    |       0    |       0    |\n|       from small pool |       0    |       0    |       0    |       0    |\n|---------------------------------------------------------------------------|\n| Active allocs         |       0    |       0    |       0    |       0    |\n|       from large pool |       0    |       0    |       0    |       0    |\n|       from small pool |       0    |       0    |       0    |       0    |\n|---------------------------------------------------------------------------|\n| GPU reserved segments |       0    |       0    |       0    |       0    |\n|       from large pool |       0    |       0    |       0    |       0    |\n|       from small pool |       0    |       0    |       0    |       0    |\n|---------------------------------------------------------------------------|\n| Non-releasable allocs |       0    |       0    |       0    |       0    |\n|       from large pool |       0    |       0    |       0    |       0    |\n|       from small pool |       0    |       0    |       0    |       0    |\n|===========================================================================|\n\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.memory_summary(device=None, abbreviated=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  }
 ]
}