직쏘_dacon lossi.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "peyRsM3QknxQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfba2bee-e747-4b42-b39f-bcd2988d182a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting timm\n",
            "  Downloading timm-0.9.12-py3-none-any.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m37.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: timm\n",
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          ]
        }
      ],
      "source": [
        "!pip install pandas\n",
        "!pip install timm\n",
        "!pip install --upgrade timm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KN12aRLtfoAM",
        "outputId": "d8dfe7d2-1e31-42bb-8f4e-b94b1c0baed3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "import torch\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vJXmMN61dTQc"
      },
      "outputs": [],
      "source": [
        "!mkdir jigsaw\n",
        "!unzip -qq \"/content/drive/MyDrive/open.zip\" -d /content/jigsaw"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NJQMGy2ZvG12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f264ada-5178-4f35-b18f-a32bf6e8656e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ID                      TRAIN_00003\n",
            "img_path    ./train/TRAIN_00003.jpg\n",
            "1                                14\n",
            "2                                 1\n",
            "3                                 6\n",
            "4                                15\n",
            "5                                 9\n",
            "6                                 4\n",
            "7                                 8\n",
            "8                                 5\n",
            "9                                16\n",
            "10                               10\n",
            "11                               13\n",
            "12                                7\n",
            "13                                2\n",
            "14                                3\n",
            "15                               11\n",
            "16                               12\n",
            "Name: 3, dtype: object\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df =pd.read_csv('jigsaw/train.csv')\n",
        "# print(df.iloc[13:16])\n",
        "row=df.iloc[3]\n",
        "# image = read_image(os.path.join(self.data_path, row['img_path']))\n",
        "#train.csv의 jigsaw puzzle 풀기전 order\n",
        "preseq = row[[str(i) for i in range(1, 17)]]\n",
        "print(row)\n",
        "# print(preseq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hZ0BbNmYl4h6"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "image_path = \"jigsaw/train/TRAIN_00000.jpg\"\n",
        "image = Image.open(image_path)\n",
        "# image.shape\n",
        "width, height =image.size\n",
        "print(width,height)  #512,512 pixel 사이즈"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zHmJFN887XyZ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "# image = read_image(os.path.join(self.data_path, row['img_path']))\n",
        "traindir = 'jigsaw/train'\n",
        "images = sorted(os.listdir(traindir))\n",
        "for i in images:\n",
        "    image_path = os.path.join(traindir, i)\n",
        "    image = cv2.imread(image_path)\n",
        "\n",
        "    if i == 'TRAIN_00003.jpg':\n",
        "        break\n",
        "\n",
        "print(image.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DjGQ6GCqA9nT"
      },
      "outputs": [],
      "source": [
        "images[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o9sFFnsiz_hj"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import timm\n",
        "from timm.data import create_transform\n",
        "from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD\n",
        "class model(nn.Module):\n",
        "  def __init__(self,embeddingdim=768,imgsize=384,patchsize=16,batchsize=16,mask_ratio=1.0):\n",
        "    super().__init__()\n",
        "    deit = timm.create_model('deit_base_distilled_patch16_384', pretrained = True)\n",
        "    # teacher-student strategy 학생이 선생님의 attention 학습\n",
        "    # distilled patch embedding Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
        "    self.blk = deit.blocks\n",
        "    self.LN = deit.norm\n",
        "    self.patcher = deit.patch_embed\n",
        "    self.mask_ratio = mask_ratio\n",
        "    self.clstoken = deit.cls_token\n",
        "    self.numpatches = (imgsize*imgsize)//patchsize**2\n",
        "\n",
        "    self.jigsaw = torch.nn.Sequential(*[torch.nn.Linear(embeddingdim, embeddingdim),\n",
        "                                          torch.nn.ReLU(),\n",
        "                                          torch.nn.Linear(embeddingdim, embeddingdim),\n",
        "                                          torch.nn.ReLU(),\n",
        "                                          torch.nn.Linear(embeddingdim,(imgsize*imgsize)//patchsize**2)])\n",
        "                                          # num_patches = (imgsize*imgsize) //patchsize**2\n",
        "\n",
        "  def random_masking(self, x, mask_ratio):\n",
        "      \"\"\"\n",
        "      Perform per-sample random masking by per-sample shuffling.\n",
        "      Per-sample shuffling is done by argsort random noise.\n",
        "      x: [N, L, D], sequence\n",
        "      \"\"\"\n",
        "\n",
        "      N, L, D = x.shape  # batch, length, dim\n",
        "      len_keep = int(L * (1 - mask_ratio))\n",
        "\n",
        "      noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]\n",
        "\n",
        "      # sort noise for each sample\n",
        "      ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove\n",
        "      # target = einops.repeat(self.target, 'L -> N L', N=N)\n",
        "      # target = target.to(x.device)\n",
        "\n",
        "      ids_keep = ids_shuffle[:, :len_keep] # N, len_keep\n",
        "      x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))\n",
        "      target_masked = ids_keep\n",
        "\n",
        "      return x_masked, target_masked\n",
        "\n",
        "  def __call__(self,x):\n",
        "    batchsize = x.shape[0]\n",
        "    x  = self.patcher(x)\n",
        "    x, target = self.random_masking(x, self.mask_ratio)\n",
        "    adjustedclstoken = self.clstoken.expand(batchsize,-1,-1)\n",
        "    x= torch.cat((adjustedclstoken,x),dim=1)\n",
        "    x= self.blk(x)\n",
        "    x = self.jigsaw(x[:, 1:])\n",
        "    return x.reshape(-1, self.numpatches), target.reshape(-1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11k4yo8OzJF0"
      },
      "outputs": [],
      "source": [
        "model1 =model(mask_ratio=0.5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vHxYmU84zk01"
      },
      "outputs": [],
      "source": [
        "demo_img = torch.randn(32,3,384,384)\n",
        "model1(demo_img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z8BlNETvQv1d"
      },
      "outputs": [],
      "source": [
        "# swin = timm.create_model('swinv2_base_window12to24_192to384',pretrained=True)\n",
        "# swin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4YyFQ8UDarvl"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "from tqdm import tqdm\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# def imgtransform():\n",
        "train_transform = A.Compose(\n",
        "    [A.Resize(height =384,width=384, interpolation=Image.BICUBIC),\n",
        "      A.Rotate(limit=35,p=1.0),  #0~35의 randn에서 angle 결정, 100%확률 적용\n",
        "      A.HorizontalFlip(p=0.5), #50%확률로 가로 뒤집기\n",
        "      A.VerticalFlip(p=0.1),\n",
        "      A.Normalize(mean=[0.0,0.0,0.0],#이미지 데이터 정규화\n",
        "                  std=[1.0,1.0,1.0],\n",
        "                  max_pixel_value=255.0),ToTensorV2()#이미지를 tensor화시킴\n",
        "    ])\n",
        "val_transform =  A.Compose(\n",
        "  [\n",
        "    A.Resize(height =384,width=384),\n",
        "    A.Normalize(mean=[0.0,0.0,0.0],#이미지 데이터 정규화\n",
        "                std=[1.0,1.0,1.0],\n",
        "                max_pixel_value=255.0,),ToTensorV2()#이미지를 tensor화시킴\n",
        "  ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QCcMYhML4q_y"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader,Dataset\n",
        "import os\n",
        "import numpy as np\n",
        "class jigsawdataset(Dataset):\n",
        "  def __init__(self,csv,imgdir,transform=None):\n",
        "    self.csv = csv #train.csv\n",
        "    self.imgdir = imgdir\n",
        "    self.transform = transform\n",
        "    self.images = sorted(os.listdir(imgdir))\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.images)\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    #dataloader가 데이터 로드할떄 __getitem__호출하며 idx가 내부적으로 제공\n",
        "    imgpath = os.path.join(self.imgdir,self.images[idx])\n",
        "    row = self.csv[idx]\n",
        "    image = cv2.imread(os.path.join(self.imgdir, row['img_path']))\n",
        "    # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "    jigsaworder = np.array([row[str(i)] - 1 for i in range(1, 17)])\n",
        "    img = self.reset_image(self.image,jigsaworder)\n",
        "    image = Image.fromarray(image)\n",
        "\n",
        "    if self.transform is not None:\n",
        "      augmentations = self.transform(image=image)\n",
        "      image = augmentations['image']\n",
        "    return image\n",
        "\n",
        "  def reset_image(self, image, shuffle_order):\n",
        "      c, h, w = image.shape\n",
        "      block_h, block_w = h//4, w//4\n",
        "      image_src = [[0 for _ in range(4)] for _ in range(4)]\n",
        "      for idx, order in enumerate(shuffle_order):\n",
        "          h_idx, w_idx = divmod(order,4)\n",
        "          h_idx_shuffle, w_idx_shuffle = divmod(idx, 4)\n",
        "          image_src[h_idx][w_idx] = image[:, block_h * h_idx_shuffle : block_h * (h_idx_shuffle+1), block_w * w_idx_shuffle : block_w * (w_idx_shuffle+1)]\n",
        "      image_src = np.concatenate([np.concatenate(image_row, -1) for image_row in image_src], -2)\n",
        "      return image_src.transpose(1, 2, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jb8-QbQfRgWd"
      },
      "outputs": [],
      "source": [
        "row = df.iloc[2]\n",
        "jigsaworder = np.array([row[str(i)] - 1 for i in range(1, 17)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Byd1zLTRSCnT"
      },
      "outputs": [],
      "source": [
        "jigsaworder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uLdDWLMo9agr"
      },
      "outputs": [],
      "source": [
        "# jigsaw=jigsawdataset(df,'./jigsaw/train',transform=train_transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jK5jIP3Yl2QL"
      },
      "outputs": [],
      "source": [
        "\n",
        "point = int(0.9*len(df))\n",
        "train = df.iloc[:point]\n",
        "valid = df.iloc[point:]\n",
        "point\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YXupZ-tur-JT"
      },
      "outputs": [],
      "source": [
        "# lr = 1e-4\n",
        "# device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "# batchsize =32\n",
        "# epochs = 200\n",
        "# num_workers = 2\n",
        "# # 데이터를 로드할 때 병렬로 데이터를 읽어오는 프로세스의 수를 지정하는 parameter\n",
        "# image_width =240\n",
        "# image_height = 160\n",
        "# pin_memory = True\n",
        "# # dataloader 클래스에서 사용 gpu로 데이터 전송전 호스트 메모리에서 gpu 메모리로 복사할지 여부\n",
        "# load_model = True\n",
        "# # 저장된 모델 가중치를 불러와서 이어서 학습하거나 추론에 사용할 수 있습니다.\n",
        "# train_imgdir = 'data/train_dir/'\n",
        "# train_maskdir = 'data/mask_dir/'\n",
        "# val_imgdir = 'data/val_images/'\n",
        "# val_maskdir = 'data/val_masks/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O_0mD8cJjmWI"
      },
      "outputs": [],
      "source": [
        "batchsize=16\n",
        "train_ds = jigsawdataset(csv=train,imgdir='./jigsaw/train/',\n",
        "                    transform =train_transform)\n",
        "train_loader = DataLoader(train_ds,   #import한 module\n",
        "                          batch_size =batchsize,\n",
        "                          # num_workers=num_workers,\n",
        "                          pin_memory =True,\n",
        "                          shuffle=True)\n",
        "val_ds = jigsawdataset(csv=valid,imgdir='./jigsaw/test/', #__init__(self,csv,imgdir,transform=None):\n",
        "                    transform =val_transform)\n",
        "val_loader = DataLoader(val_ds,\n",
        "                        batch_size =batchsize,\n",
        "                        # num_workers=num_workers,\n",
        "                        pin_memory =True,\n",
        "                        shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PVecwdosUVdb"
      },
      "outputs": [],
      "source": [
        "print(train_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L_1O8cDsmf7B"
      },
      "outputs": [],
      "source": [
        "from torch.autograd import grad, grad_scaler\n",
        "\n",
        "loop = tqdm(train_loader)\n",
        "optimizer = optim.Adam(model1.parameter(),\n",
        "                       lr=2e-6,\n",
        "                       weight_decay = 0.05)\n",
        "scaler = torch.cuda.amp.GradScaler()\n",
        "\n",
        "#loader = torch.utils.data , Dataloader의 instance dataloader의 argument는 만든 dataset\n",
        "for batch_idx,  in enumerate(loop):\n",
        "  # targets = targets.float().unsqueeze(1).to(device = device)\n",
        "#data 불러오는 과정 targets 등\n",
        "  #forward pass\n",
        "  with torch.cuda.amp.autocast():\n",
        "    # 실행되는 코드 블록은 자동으로 FP16 형식으로 연산이 수행 16-bit floating point\n",
        "    #메모리 사용량 적어지는 대신 정밀도 낮아짐\n",
        "    predictions =  model1(data) # f.cross_entropy(logits,yb) 와 유사\n",
        "    loss = F.cross_entropy(predictions,targets)\n",
        "\n",
        "  #backward pass\n",
        "  optimizer.zero_grad() #초기값 -\n",
        "  scaler.scale(loss).backward() #gradient 소실 방지 scaler\n",
        "  scaler.step(optimizer)\n",
        "  scaler.update()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xwDd6ycKhfQC"
      },
      "outputs": [],
      "source": [
        "def calc_puzzle(answer_df, submission_df):\n",
        "    # Check for missing values in submission_df\n",
        "    if submission_df.isnull().values.any():\n",
        "        raise ValueError(\"The submission dataframe contains missing values.\")\n",
        "\n",
        "    # Public or Private answer Sample and Sorting by 'ID'\n",
        "    submission_df = submission_df[submission_df.iloc[:, 0].isin(answer_df.iloc[:, 0])]\n",
        "    submission_df = submission_df.sort_values(by='ID').reset_index(drop=True)\n",
        "\n",
        "    # Check for length in submission_df\n",
        "    if len(submission_df) != len(answer_df):\n",
        "        raise ValueError(\"The submission dataframe wrong length.\")\n",
        "\n",
        "    # Convert position data to numpy arrays for efficient computation\n",
        "    answer_positions = answer_df.iloc[:, 2:].to_numpy()  # Excluding ID, img_path, and type columns\n",
        "    submission_positions = submission_df.iloc[:, 1:].to_numpy()  # Excluding ID column\n",
        "\n",
        "    # Initialize the dictionary to hold accuracies\n",
        "    accuracies = {}\n",
        "\n",
        "    # Define combinations for 2x2 and 3x3 puzzles\n",
        "    combinations_2x2 = [(i, j) for i in range(3) for j in range(3)]\n",
        "    combinations_3x3 = [(i, j) for i in range(2) for j in range(2)]\n",
        "\n",
        "    # 1x1 Puzzle Accuracy\n",
        "    accuracies['1x1'] = np.mean(answer_positions == submission_positions)\n",
        "\n",
        "    # Calculate accuracies for 2x2, 3x3, and 4x4 puzzles\n",
        "    for size in range(2, 5):  # Loop through sizes 2, 3, 4\n",
        "        correct_count = 0  # Initialize counter for correct full sub-puzzles\n",
        "        total_subpuzzles = 0\n",
        "\n",
        "        # Iterate through each sample's puzzle\n",
        "        for i in range(len(answer_df)):\n",
        "            puzzle_a = answer_positions[i].reshape(4, 4)\n",
        "            puzzle_s = submission_positions[i].reshape(4, 4)\n",
        "            combinations = combinations_2x2 if size == 2 else combinations_3x3 if size == 3 else [(0, 0)]\n",
        "\n",
        "            # Calculate the number of correct sub-puzzles for this size within a 4x4\n",
        "            for start_row, start_col in combinations:\n",
        "                rows = slice(start_row, start_row + size)\n",
        "                cols = slice(start_col, start_col + size)\n",
        "                if np.array_equal(puzzle_a[rows, cols], puzzle_s[rows, cols]):\n",
        "                    correct_count += 1\n",
        "                total_subpuzzles += 1\n",
        "\n",
        "        accuracies[f'{size}x{size}'] = correct_count / total_subpuzzles\n",
        "\n",
        "    score = (accuracies['1x1'] + accuracies['2x2'] + accuracies['3x3'] + accuracies['4x4']) / 4.\n",
        "    return score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GmRN6o3n4huh"
      },
      "outputs": [],
      "source": [
        "# deit = timm.create_model('deit_base_distilled_patch16_384', pretrained = True)\n",
        "# print(deit)\n",
        "# deit.blocks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-36MsnEETjyP"
      },
      "outputs": [],
      "source": [
        "# swin2 = timm.create_model('swinv2_cr_huge_384',pretrained=True)\n",
        "# swin2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TXTQDLWz4ux5"
      },
      "outputs": [],
      "source": [
        "print(timm.list_models())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JSlX1jb3uhnG"
      },
      "outputs": [],
      "source": [
        "# Copyright (c) Meta Platforms, Inc. and affiliates.\n",
        "# All rights reserved.\n",
        "\n",
        "# This source code is licensed under the license found in the\n",
        "# LICENSE file in the root directory of this source tree.\n",
        "# --------------------------------------------------------\n",
        "# References:\n",
        "# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm\n",
        "# DeiT: https://github.com/facebookresearch/deit\n",
        "# --------------------------------------------------------\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jWX-I-cGWTuO"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "from torchvision.io import read_image\n",
        "\n",
        "import timm\n",
        "from timm.data import create_transform\n",
        "from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from tqdm.auto import tqdm\n",
        "import os\n",
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JqBchYqhWUcD"
      },
      "outputs": [],
      "source": [
        "class JigsawDataset(Dataset):\n",
        "    def __init__(self, df, data_path, mode='train', transform=None):\n",
        "        self.df = df\n",
        "        self.data_path = data_path\n",
        "        self.mode = mode\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if self.mode == 'train':\n",
        "            row = self.df.iloc[idx]\n",
        "            image = read_image(os.path.join(self.data_path, row['img_path']))\n",
        "            shuffle_order = row[[str(i) for i in range(1, 17)]].values-1\n",
        "            image = self.reset_image(image, shuffle_order)\n",
        "            image = Image.fromarray(image)\n",
        "\n",
        "            if self.transform:\n",
        "              image = self.transform(image)\n",
        "\n",
        "\n",
        "            return image\n",
        "        elif self.mode == 'test':\n",
        "            row = self.df.iloc[idx]\n",
        "            image = Image.open(os.path.join(self.data_path, row['img_path']))\n",
        "            if self.transform:\n",
        "              image = self.transform(image)\n",
        "            return image\n",
        "\n",
        "    def reset_image(self, image, shuffle_order):\n",
        "        c, h, w = image.shape\n",
        "        block_h, block_w = h//4, w//4\n",
        "        image_src = [[0 for _ in range(4)] for _ in range(4)]\n",
        "        for idx, order in enumerate(shuffle_order):\n",
        "            h_idx, w_idx = divmod(order,4)\n",
        "            h_idx_shuffle, w_idx_shuffle = divmod(idx, 4)\n",
        "            image_src[h_idx][w_idx] = image[:, block_h * h_idx_shuffle : block_h * (h_idx_shuffle+1), block_w * w_idx_shuffle : block_w * (w_idx_shuffle+1)]\n",
        "        image_src = np.concatenate([np.concatenate(image_row, -1) for image_row in image_src], -2)\n",
        "        return image_src.transpose(1, 2, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MMbPYg9tWa4I"
      },
      "outputs": [],
      "source": [
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "def build_transform(is_train):\n",
        "  if is_train:\n",
        "    # transform = A.Compose(\n",
        "    #   [A.Resize(height =384,width=384, interpolation=Image.BICUBIC),\n",
        "    #     A.Rotate(limit=35,p=1.0),  #0~35의 randn에서 angle 결정, 100%확률 적용\n",
        "    #     A.HorizontalFlip(p=0.4), #50%확률로 가로 뒤집기\n",
        "    #     A.VerticalFlip(p=0.2),\n",
        "    #     A.ColorJitter(p=0.3),\n",
        "    #     A.GaussianBlur(p=0.3),\n",
        "    #     A.Normalize(mean=IMAGENET_DEFAULT_MEAN,#이미지 데이터 정규화\n",
        "    #                 std=[1.0,1.0,1.0],\n",
        "    #                 max_pixel_value=255.0),ToTensorV2()])#이미지를 tensor화시킴\n",
        "\n",
        "    transform = create_transform(\n",
        "        input_size = (384, 384),\n",
        "        is_training = True,\n",
        "        color_jitter = 0.3,\n",
        "        auto_augment = 'rand-m9-mstd0.5-inc1',\n",
        "        interpolation= 'bicubic',\n",
        "        re_prob= 0.25,\n",
        "        re_mode= 'pixel',\n",
        "        re_count= 1,\n",
        "        # vflip=0.3,\n",
        "        # hflip=0.3,\n",
        "        # separate=True\n",
        "\n",
        "    )\n",
        "    return transform\n",
        "  # transform = A.Compose(\n",
        "  #     [A.Resize(height =384,width=384, interpolation=Image.BICUBIC),\n",
        "\n",
        "  #       A.Normalize(mean=IMAGENET_DEFAULT_MEAN,\n",
        "  #                   std= IMAGENET_DEFAULT_STD,\n",
        "  #                   max_pixel_value=255.0),ToTensorV2()])\n",
        "  t = []\n",
        "  t.append(transforms.Resize((384,384), interpolation=3))\n",
        "  t.append(transforms.ToTensor())\n",
        "  t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))\n",
        "  return transforms.Compose(t)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q9gSlFTaWeeA"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('./jigsaw/train.csv')\n",
        "threshold = int(0.9*len(df))\n",
        "train_df = df.iloc[:]\n",
        "valid_df = df.iloc[threshold:]\n",
        "\n",
        "train_transform = build_transform(is_train = True)\n",
        "valid_transform = build_transform(is_train = False)\n",
        "\n",
        "train_set = JigsawDataset(df = train_df,\n",
        "                              data_path = './jigsaw',\n",
        "                              mode = 'train',\n",
        "                              transform = train_transform)\n",
        "valid_set = JigsawDataset(df = valid_df,\n",
        "                              data_path = './jigsaw',\n",
        "                              mode = 'test',\n",
        "                              transform = valid_transform)\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "    train_set,\n",
        "    batch_size = 16,\n",
        "    shuffle = True\n",
        ")\n",
        "valid_dataloader = DataLoader(\n",
        "    valid_set,\n",
        "    batch_size = 16,\n",
        "    shuffle = False\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import requests\n",
        "\n",
        "from transformers import CLIPProcessor, CLIPModel\n",
        "\n",
        "model2 = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
        "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")"
      ],
      "metadata": {
        "id": "yn57xNRLbZp6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vit = timm.create_model('vit_base_patch32_clip_384.laion2b_ft_in12k_in1k', pretrained=True)\n",
        "vit"
      ],
      "metadata": {
        "id": "Jkpx9XrJNIuJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "from transformers import AutoFeatureExtractor, DeiTForImageClassificationWithTeacher\n",
        "\n",
        "model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-384')\n"
      ],
      "metadata": {
        "id": "4hiOBMQjK5oU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
        "class Model(nn.Module):\n",
        "    def __init__(self, mask_ratio = 0.0, pretrained = True):\n",
        "        super().__init__()\n",
        "\n",
        "        self.mask_ratio = mask_ratio\n",
        "        self.pretrained = pretrained\n",
        "        # self.feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-384')\n",
        "        # deit3 = ViTForImageClassification.from_pretrained('google/vit-base-patch16-384')\n",
        "        # deit3 = timm.create_model(\"hf-hub:BVRA/vit_base_patch16_384.ft_df20m_384\",pretrained = pretrained)\n",
        "        # deit3 = timm.create_model('vit_base_patch16_clip_384.laion2b_ft_in16k_in1k', pretrained=True)\n",
        "        # deit3  = ViTForImageClassification.from_pretrained('google/vit-large-patch16-384')\n",
        "        deit3 = timm.create_model('vit_base_patch32_clip_384.laion2b_ft_in12k_in1k', pretrained=True)\n",
        "        self.patch_embed = deit3.patch_embed\n",
        "        self.norm = deit3.norm\n",
        "        self.blocks = deit3.blocks\n",
        "        self.mask_ratio = mask_ratio\n",
        "        self.cls_token = deit3.cls_token\n",
        "        self.jigsaw = nn.Sequential(\n",
        "            nn.Linear(768, 768),\n",
        "            nn.SiLU(),\n",
        "            nn.Linear(768, 768),\n",
        "            nn.SiLU(),\n",
        "            nn.Linear(768, 768),\n",
        "            nn.SiLU(),\n",
        "            nn.Linear(768, 16*16)\n",
        "        )\n",
        "\n",
        "    def random_masking(self, x, mask_ratio):\n",
        "        \"\"\"\n",
        "        Perform per-sample random masking by per-sample shuffling.\n",
        "        Per-sample shuffling is done by argsort random noise.\n",
        "        x: [N, L, D], sequence\n",
        "        \"\"\"\n",
        "        N, L, D = x.shape  # batch, length, dim\n",
        "        len_keep = int(L * (1 - mask_ratio))\n",
        "\n",
        "        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]\n",
        "\n",
        "        # sort noise for each sample\n",
        "        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove\n",
        "        # target = einops.repeat(self.target, 'L -> N L', N=N)\n",
        "        # target = target.to(x.device)\n",
        "\n",
        "        # keep the first subset\n",
        "        ids_keep = ids_shuffle[:, :len_keep] # N, len_keep\n",
        "        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))\n",
        "        target_masked = ids_keep\n",
        "\n",
        "        return x_masked, target_masked\n",
        "\n",
        "    def forward(self, x):\n",
        "\n",
        "        x = self.patch_embed(x)\n",
        "        x, target = self.random_masking(x, self.mask_ratio)\n",
        "\n",
        "        # append cls token\n",
        "        clstokens = self.cls_token.expand(x.shape[0], -1, -1)\n",
        "        x = torch.cat((clstokens, x), dim=1)\n",
        "\n",
        "        # apply Transformer blocks\n",
        "        x = self.blocks(x)\n",
        "        x = self.norm(x)\n",
        "        x = self.jigsaw(x[:, 1:])\n",
        "        return x.reshape(-1, 16*16), target.reshape(-1)\n",
        "\n",
        "    def forward_test(self, x):\n",
        "        x = self.patch_embed(x)\n",
        "\n",
        "        # append cls token\n",
        "        clstokens = self.cls_token.expand(x.shape[0], -1, -1)\n",
        "        x = torch.cat((clstokens, x), dim=1)\n",
        "\n",
        "        # apply Transformer blocks\n",
        "        x = self.blocks(x)\n",
        "        x = self.norm(x)\n",
        "        x = self.jigsaw(x[:, 1:])\n",
        "        return x"
      ],
      "metadata": {
        "id": "XUs-V5Dw-aRF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "linear = nn.Linear(768,768)\n"
      ],
      "metadata": {
        "id": "i_2qw3dMFBrd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "demo = torch.randn(8,3,768)\n",
        "demo = linear(demo)\n",
        "demo.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isCiT_DALilt",
        "outputId": "aead28d4-4d4b-4417-b459-fc8b015a9907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8, 3, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_ATyq2VrWsHq"
      },
      "outputs": [],
      "source": [
        "model = Model(mask_ratio = 0.1)\n",
        "model.to(device)\n",
        "optimizer = optim.AdamW(model.parameters(),\n",
        "                        lr=3e-5,\n",
        "                        weight_decay = 0.1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(),'model1.pth')\n",
        "# # model.load_state_dict(torch.load('/content/drive/MyDrive/model.pth'))\n",
        "# loaded_state_dict = torch.load('/content/drive/MyDrive/model.pth')\n",
        "\n",
        "# print(model.state_dict().keys())\n",
        "\n",
        "# missing_keys = set(model.state_dict().keys()) - set(loaded_state_dict.keys())\n",
        "# unexpected_keys = set(loaded_state_dict.keys()) - set(model.state_dict().keys())\n",
        "# model.load_state_dict(torch.load('/content/drive/MyDrive/model.pth'))\n"
      ],
      "metadata": {
        "id": "Tefg6Ku1t0Ot"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.mask_ratio = 0.05"
      ],
      "metadata": {
        "id": "hRyV1oSAWbZE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "id": "9gLCVk5fXlrl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e464d10f-238a-4ad4-f3a6-82001fe7c445"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1\n",
            "[0 / 4375] loss: 0.8457428812980652\n",
            "[400 / 4375] loss: 0.9976324439048767\n",
            "[800 / 4375] loss: 0.8630994558334351\n",
            "[1200 / 4375] loss: 0.801385223865509\n",
            "[1600 / 4375] loss: 0.8121039271354675\n",
            "[2000 / 4375] loss: 0.8025326728820801\n",
            "[2400 / 4375] loss: 1.0828694105148315\n",
            "[2800 / 4375] loss: 0.8235419392585754\n",
            "[3200 / 4375] loss: 0.5506572127342224\n",
            "[3600 / 4375] loss: 0.3462061882019043\n",
            "[4000 / 4375] loss: 0.7010009288787842\n",
            "Time elapsed:  2786.291121006012\n"
          ]
        }
      ],
      "source": [
        "optimizer = optim.AdamW(model.parameters(),\n",
        "                        lr=1e-5,\n",
        "                        weight_decay = 0.02)\n",
        "lossi=[]\n",
        "device = 'cuda'\n",
        "for epoch in range(1, 2):\n",
        "    print('Epoch ', epoch)\n",
        "    st = time.time()\n",
        "    model.train()\n",
        "    for i, x in enumerate(train_dataloader):\n",
        "        x = x.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        preds, targets = model(x)\n",
        "\n",
        "        loss = F.cross_entropy(preds, targets)\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lossi.append(loss.item())\n",
        "        if i % 400 == 0:\n",
        "            print(f'[{i} / {len(train_dataloader)}] loss:', loss.item())\n",
        "    et = time.time()\n",
        "    print('Time elapsed: ', et-st)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if loss < 1:\n",
        "  lr = 1e-5\n",
        "  weight_decay=0.01\n",
        "else:\n",
        "  lr = 3e-5\n",
        "  weight_decay = 0.1"
      ],
      "metadata": {
        "id": "OY7MiZvZh3Fi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(),'model3.pth')\n"
      ],
      "metadata": {
        "id": "ulut-7-UuIgk"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(lossi)"
      ],
      "metadata": {
        "id": "WSg-ZBW7vdeT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "ce99761c-ae91-4744-891b-546d4055b6cd"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7cc6416cdf00>]"
            ]
          },
          "metadata": {},
          "execution_count": 176
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VANU_w07MVeU",
        "outputId": "1aeda357-b2eb-44c9-88ef-c5fbbe4859c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7000, 144)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCnOIygcMHs-",
        "outputId": "10dbb2c5-0086-4841-b7e3-e5a2ad22d3a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7000, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "JTT1IkmBNicl",
        "outputId": "92bfab46-04f9-4b6d-bccc-3c9a3a11fecb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ID                 img_path   1   2   3   4   5   6   7   8  \\\n",
              "63000  TRAIN_63000  ./train/TRAIN_63000.jpg  10   2   8   5  12   6   3   4   \n",
              "63001  TRAIN_63001  ./train/TRAIN_63001.jpg   2  14   8   3   7   9   1  10   \n",
              "63002  TRAIN_63002  ./train/TRAIN_63002.jpg  10  11  14   1  12   2  16   7   \n",
              "63003  TRAIN_63003  ./train/TRAIN_63003.jpg   5  16   1   7   3   9   2  13   \n",
              "63004  TRAIN_63004  ./train/TRAIN_63004.jpg   6   9  15   4   1  10  14   2   \n",
              "...            ...                      ...  ..  ..  ..  ..  ..  ..  ..  ..   \n",
              "69995  TRAIN_69995  ./train/TRAIN_69995.jpg   8   4   7   1   2   3  14  10   \n",
              "69996  TRAIN_69996  ./train/TRAIN_69996.jpg  11   9   3  10   4  12   1   6   \n",
              "69997  TRAIN_69997  ./train/TRAIN_69997.jpg   8  16  14  13   5  10   2   3   \n",
              "69998  TRAIN_69998  ./train/TRAIN_69998.jpg   8  15   4   3   2   9  14   7   \n",
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              "\n",
              "        9  10  11  12  13  14  15  16  \n",
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              "63003  12  11   8   4  14   6  15  10  \n",
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              "...    ..  ..  ..  ..  ..  ..  ..  ..  \n",
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              "69997  12   6   7  15   1  11   9   4  \n",
              "69998   6  12   5  11  13   1  16  10  \n",
              "69999   5  12  13   7   3   4  11   9  \n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "<p>7000 rows × 18 columns</p>\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_pred_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "e--DDlUDTFZX",
        "outputId": "be7fb5e1-5c41-485d-9abe-e0e750652988"
      },
      "execution_count": null,
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7907efc5-5976-46d7-9a54-fdbe20630215 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5fbde9cd-81e1-47e2-8239-a51a656f04b1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('valid_pred_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5fbde9cd-81e1-47e2-8239-a51a656f04b1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('valid_pred_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df[train.]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "OopnyWPaSAlL",
        "outputId": "acfaf148-7af5-4113-9d29-58642b65d6c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID               img_path\n",
              "0      TEST_00000  ./test/TEST_00000.jpg\n",
              "1      TEST_00001  ./test/TEST_00001.jpg\n",
              "2      TEST_00002  ./test/TEST_00002.jpg\n",
              "3      TEST_00003  ./test/TEST_00003.jpg\n",
              "4      TEST_00004  ./test/TEST_00004.jpg\n",
              "...           ...                    ...\n",
              "59956  TEST_59956  ./test/TEST_59956.jpg\n",
              "59957  TEST_59957  ./test/TEST_59957.jpg\n",
              "59958  TEST_59958  ./test/TEST_59958.jpg\n",
              "59959  TEST_59959  ./test/TEST_59959.jpg\n",
              "59960  TEST_59960  ./test/TEST_59960.jpg\n",
              "\n",
              "[59961 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb0464ee-f5d3-4629-b662-fc2d65b3c4ee\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>img_path</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TEST_00000</td>\n",
              "      <td>./test/TEST_00000.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TEST_00001</td>\n",
              "      <td>./test/TEST_00001.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TEST_00002</td>\n",
              "      <td>./test/TEST_00002.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TEST_00003</td>\n",
              "      <td>./test/TEST_00003.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TEST_00004</td>\n",
              "      <td>./test/TEST_00004.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59956</th>\n",
              "      <td>TEST_59956</td>\n",
              "      <td>./test/TEST_59956.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59957</th>\n",
              "      <td>TEST_59957</td>\n",
              "      <td>./test/TEST_59957.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59958</th>\n",
              "      <td>TEST_59958</td>\n",
              "      <td>./test/TEST_59958.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59959</th>\n",
              "      <td>TEST_59959</td>\n",
              "      <td>./test/TEST_59959.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59960</th>\n",
              "      <td>TEST_59960</td>\n",
              "      <td>./test/TEST_59960.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59961 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb0464ee-f5d3-4629-b662-fc2d65b3c4ee')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eb0464ee-f5d3-4629-b662-fc2d65b3c4ee button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-eb0464ee-f5d3-4629-b662-fc2d65b3c4ee');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f4d55274-7bc8-4c30-8ab9-225e5a8125fd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4d55274-7bc8-4c30-8ab9-225e5a8125fd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f4d55274-7bc8-4c30-8ab9-225e5a8125fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0a28fcce-4245-46bf-84e8-f1c4bbb8d119\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0a28fcce-4245-46bf-84e8-f1c4bbb8d119 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df"
      ],
      "metadata": {
        "id": "AFL0EPO3NrCG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outs = []\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for x in tqdm(valid_dataloader):\n",
        "        x = x.to('cuda')\n",
        "        out = model.forward_test(x) #return x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "893ee8c099ac49c2af9374b2a453dd3e",
            "15af580990bb4550908702c3bb6e6f94",
            "329b996d9f034098bd3ffb1efdccf6d4",
            "0c54bc07d0d349a98e2fdd0ef1c7b796",
            "e4a7471c9bc14ccf9742db1428331291",
            "7bf1dcba521d44ccb0f893f2f9f4f072",
            "636c84688ceb4c4b95e748922dea52cb",
            "ce190074626246a481d099d99870814a",
            "a68d06996ad9428d9512bfdbdca326e9",
            "d9d1f463e28b4b519d9774fa41ee433d",
            "b05c6f4e6aab411e9cb22a117cbbb5cf"
          ]
        },
        "id": "GXl-qrkRBWG1",
        "outputId": "0c473aa3-db2f-44d4-ced6-fa5b501bc275"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/438 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "893ee8c099ac49c2af9374b2a453dd3e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out.shape #batch, length, dim?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLQhRvLdBp5E",
        "outputId": "d416e366-234c-4cbe-845a-37034bba581c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8, 144, 256])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outs1 = []\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for x in tqdm(valid_dataloader):\n",
        "        x = x.to('cuda')\n",
        "        out = model.forward(x) #return x.reshape(-1,-16*16), target.reshape(-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "f0fbded575c041cfab35905e46f840dd",
            "d28b3d0941a047e5a1ef63bbb92c58a7",
            "f4d72a22cf5644668e3cff7c440b0053",
            "03643e45fc6e4d03b7cb4c90ebff6c6a",
            "2a391a7b073942d8a1f71d7590249bd2",
            "2f1c5a1897ec467c9ea38804b5684ec5",
            "25a8410a4ba1407e9c7739519548dff6",
            "98c9ae9de8324d3190297f6211501f18",
            "9811acf1f9bb477188d0eabfc59ada38",
            "a41153da61ef40018f1a8e8bc23e1b86",
            "b5c332ee32054c149338f0af0cd8a294"
          ]
        },
        "id": "YTX63zqkB4Kl",
        "outputId": "3322230e-dfbe-4f32-bc11-50ba4cda326c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/438 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0fbded575c041cfab35905e46f840dd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWw79FZjDNrX",
        "outputId": "0de193a6-db23-4471-c03d-b6ddc796aebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[-3.1757e+00, -2.8865e+00, -2.9274e+00,  ..., -7.6336e+01,\n",
              "          -7.3505e+01, -7.5695e+01],\n",
              "         [ 6.8997e-02,  7.1812e-02, -9.0699e-01,  ..., -7.5923e+01,\n",
              "          -7.5812e+01, -7.4675e+01],\n",
              "         [ 3.4942e+00,  2.0386e+00,  1.5873e+00,  ..., -1.0624e+02,\n",
              "          -1.0424e+02, -1.0234e+02],\n",
              "         ...,\n",
              "         [-8.3573e+00, -8.4038e+00, -8.6963e+00,  ..., -2.4431e+02,\n",
              "          -2.3775e+02, -2.4408e+02],\n",
              "         [ 1.1731e+01,  1.1572e+01,  1.1156e+01,  ..., -1.0681e+02,\n",
              "          -1.0470e+02, -1.0413e+02],\n",
              "         [ 5.7750e+00,  7.5068e+00,  6.2382e+00,  ..., -1.5749e+02,\n",
              "          -1.5374e+02, -1.5336e+02]], device='cuda:0'),\n",
              " tensor([109, 103,   5,  ...,  41,  44, 113], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outs = []\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for x in tqdm(test_dataloader):\n",
        "        x = x.to('cuda')\n",
        "        out = model.forward_test(x)\n",
        "        # out = out[0].reshape(-1,144,16*16)\n",
        "        out = out.argmax(dim=2).cpu().numpy()\n",
        "        outs.append(out)\n",
        "\n",
        "# outs = np.vstack(outs)\n",
        "# outs\n",
        "# valid_pred_df = valid_df.copy().drop(columns=['img_path'])\n",
        "\n",
        "# for I, (idx, row) in enumerate(tqdm(valid_pred_df.iterrows(), total=len(valid_df))):\n",
        "#     w = outs[I].reshape(12,12)\n",
        "#     CNT_ROW = np.zeros((4,4,4), dtype=np.int32)\n",
        "#     CNT_COL = np.zeros((4,4,4), dtype=np.int32)\n",
        "#     for i in range(12):\n",
        "#         for j in range(12):\n",
        "#             ROW = i // 3\n",
        "#             COL = j // 3\n",
        "#             v = w[i][j]\n",
        "#             CNT_ROW[ROW][COL][v // 12 // 3] += 1\n",
        "#             CNT_COL[ROW][COL][v % 12 // 3] += 1\n",
        "#     ans = CNT_ROW.argmax(2) * 4 + CNT_COL.argmax(2) + 1\n",
        "#     ans = ans.reshape(16)\n",
        "#     ans = list(map(str, ans))\n",
        "#     valid_pred_df.loc[idx, '1':'16'] = ans\n",
        "# score = calc_puzzle(valid_df, valid_pred_df)\n",
        "# print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fd11e5e8762e4e5b8ee4805ce58a970a",
            "780c9eef49ec4220ae7caa7302597bcd",
            "1941e817a8c44eb2817ed54f0e49de87",
            "d13e1f30bec84058bc2df3e99b40ec7d",
            "1ac0e720e8db4dc093c62564c6ff2eb9",
            "2f4004238066439087ffc166f80f3885",
            "fe96c5e66a444ed3b3d0d8d3ccdf9f62",
            "28edbbe9f4f6442aa733dc7f17fbd58f",
            "d962a3f0542c467e8645c7d2d0e7a385",
            "9223da75a3cf43fb912bd90c729194ad",
            "d97fef58907747c3a7543ea3f1717148"
          ]
        },
        "id": "Pwns0HLKOZK0",
        "outputId": "a9eb8386-1529-4850-cc10-38cf1c5c4613"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3748 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd11e5e8762e4e5b8ee4805ce58a970a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df\n",
        "submit = pd.read_csv('./jigsaw/sample_submission.csv')"
      ],
      "metadata": {
        "id": "HTOxapBGXVEB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_pred_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Df2y3Sx6Y5GO",
        "outputId": "362f637a-832e-4df5-e5d8-fbbee3c44027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                ID   1   2   3  4   5   6   7   8   9  10  11  12  13  14  15  \\\n",
              "63000  TRAIN_63000  10   3   8  5  12   5   3   4   7   1  14  15  11  16  13   \n",
              "63001  TRAIN_63001   1  14   8  4  11   9   1  10  11  15   4   4  12  13   6   \n",
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              "...            ...  ..  ..  .. ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
              "19810          NaN  13   3   1  1   4  14   7   9   5  12  15  16   8  16  10   \n",
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              "19814          NaN  16   6   5  4  15  10   1   7   9  10  13   4   2   2   7   \n",
              "\n",
              "       16  \n",
              "63000   9  \n",
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              "...    ..  \n",
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              "19814  13  \n",
              "\n",
              "[26815 rows x 17 columns]"
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              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19813</th>\n",
              "      <td>NaN</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19814</th>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26815 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-78e36504-c8cf-4910-b1e2-411cefd87a0a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-78e36504-c8cf-4910-b1e2-411cefd87a0a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8262832d-b5f7-408b-be8f-cad276421d7e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_76ef172b-8562-4aa3-8964-bd1f3776ddf5\">\n",
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              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('valid_pred_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_76ef172b-8562-4aa3-8964-bd1f3776ddf5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('valid_pred_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1ndaNX0gXlYu",
        "outputId": "fdc12a61-d6a7-4ed6-efe7-2e93415ab74d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "0      TEST_00000  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "1      TEST_00001  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "2      TEST_00002  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "3      TEST_00003  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "4      TEST_00004  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "...           ... .. .. .. .. .. .. .. .. ..  ..  ..  ..  ..  ..  ..  ..\n",
              "59956  TEST_59956  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "59957  TEST_59957  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "59958  TEST_59958  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "59959  TEST_59959  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "59960  TEST_59960  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16\n",
              "\n",
              "[59961 rows x 17 columns]"
            ],
            "text/html": [
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TEST_00002</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TEST_00003</td>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>59956</th>\n",
              "      <td>TEST_59956</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59957</th>\n",
              "      <td>TEST_59957</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59958</th>\n",
              "      <td>TEST_59958</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59959</th>\n",
              "      <td>TEST_59959</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59960</th>\n",
              "      <td>TEST_59960</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59961 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      display:flex;\n",
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              "        document.querySelector('#df-28f7dd1b-f9b8-4fb3-a8b4-f6d9786d03a5 button.colab-df-convert');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5a24d800-be29-49f2-a43a-eeeaf5205a5c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1161f837-4240-4177-843c-9e4132802429\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('submit')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('submit');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outs = np.vstack(outs)\n",
        "outs\n",
        "submission = submit.copy()\n",
        "for I, (idx, row) in enumerate(tqdm(submission.iterrows(), total=len(submit))):\n",
        "    w = outs[I].reshape(12,12)\n",
        "    CNT_ROW = np.zeros((4,4,4), dtype=np.int32)\n",
        "    CNT_COL = np.zeros((4,4,4), dtype=np.int32)\n",
        "    for i in range(12):\n",
        "        for j in range(12):\n",
        "            ROW = i // 3\n",
        "            COL = j // 3\n",
        "            v = w[i][j]\n",
        "            CNT_ROW[ROW][COL][v // 12 // 3] += 1\n",
        "            CNT_COL[ROW][COL][v % 12 // 3] += 1\n",
        "    ans = CNT_ROW.argmax(2) * 4 + CNT_COL.argmax(2) + 1\n",
        "    ans = ans.reshape(16)\n",
        "    ans = list(map(str, ans))\n",
        "    submission.loc[idx, '1':'16'] = ans\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "decff2c4e7964bf4abaadfcbab6851f3",
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        "id": "zG5Q7MKdXI7W",
        "outputId": "967939bc-6cda-463f-8bd2-8dd5ec6003fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/59961 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "decff2c4e7964bf4abaadfcbab6851f3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "tbunUFnHZnZs",
        "outputId": "6f5b0564-42c3-4ca0-b710-e35f065a9509"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  \\\n",
              "0      TEST_00000  10   9   5  10  16   1  15  12   5   1   5  14   1   6   1   \n",
              "1      TEST_00001  14   7  15  10  13   1   8  11   3  16   6   5  12   2   4   \n",
              "2      TEST_00002   7   4  14  13   6  15  12  16   5   2   4   1  11   9  10   \n",
              "3      TEST_00003   5  12  11  13  14   4  10   6   2  15  16   9   3   7   8   \n",
              "4      TEST_00004   1   5   7  10   1  14   3  16   9   6  11  13  12   4  15   \n",
              "...           ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
              "59956  TEST_59956   1   4   4  16   1  13   3  16  16   4   1  13  16   4  16   \n",
              "59957  TEST_59957  10   8   2   8   5   7  14   6  16  12   3   1   1  13   9   \n",
              "59958  TEST_59958   9   6  16  13   8  15   5   2  11   1   1  11   7   3  13   \n",
              "59959  TEST_59959   1   6   9  13  13   5   9  12  12   2  14   2  14   2   8   \n",
              "59960  TEST_59960   6   3  16  12  16  12   4   1   9   9  16   7   9   6   5   \n",
              "\n",
              "       16  \n",
              "0      13  \n",
              "1       9  \n",
              "2       8  \n",
              "3       1  \n",
              "4       8  \n",
              "...    ..  \n",
              "59956  16  \n",
              "59957  14  \n",
              "59958  16  \n",
              "59959   7  \n",
              "59960   2  \n",
              "\n",
              "[59961 rows x 17 columns]"
            ],
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              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TEST_00000</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TEST_00001</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TEST_00002</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>12</td>\n",
              "      <td>16</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TEST_00003</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TEST_00004</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59956</th>\n",
              "      <td>TEST_59956</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59957</th>\n",
              "      <td>TEST_59957</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59958</th>\n",
              "      <td>TEST_59958</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59959</th>\n",
              "      <td>TEST_59959</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59960</th>\n",
              "      <td>TEST_59960</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59961 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d1f9766-07e7-4c2e-8310-73b48c0fdfc9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3d1f9766-07e7-4c2e-8310-73b48c0fdfc9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3d1f9766-07e7-4c2e-8310-73b48c0fdfc9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4a4d3154-b2a6-42e3-a1e3-ee89ef43ba60\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a4d3154-b2a6-42e3-a1e3-ee89ef43ba60')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4a4d3154-b2a6-42e3-a1e3-ee89ef43ba60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d745f792-1a1d-4a29-8073-17e9f446fd48\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('submission')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d745f792-1a1d-4a29-8073-17e9f446fd48 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('submission');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: submission csv파일로 저장해줘\n",
        "\n",
        "submission.to_csv('submission.csv', index=False)\n"
      ],
      "metadata": {
        "id": "kfWWIw5qZyV-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, xreandtarget in enumerate(out): #out = x.reshape(-1,-16*16), target.reshape(-1)\n",
        "  print(xreandtarget.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Assbqk00GRmx",
        "outputId": "fba7b74f-ec81-4cab-ba6f-2456dab39721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1032, 256])\n",
            "torch.Size([1032])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model()"
      ],
      "metadata": {
        "id": "Op-F6kk1Edk1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = pd.read_csv('./jigsaw/test.csv')"
      ],
      "metadata": {
        "id": "GiZvIAiTVuBR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_transform = build_transform(is_train = False)\n",
        "\n",
        "test_dataset = JigsawDataset(\n",
        "    df=test_df,\n",
        "    data_path='./jigsaw',\n",
        "    mode='test',\n",
        "    transform = valid_transform\n",
        ")\n",
        "test_dataloader = DataLoader(test_dataset, batch_size = 16,shuffle = True)\n",
        "\n",
        "\n",
        "\n",
        "df = pd.read_csv('./jigsaw/train.csv')\n",
        "\n",
        "\n",
        "train_transform = build_transform(is_train = True)\n",
        "valid_transform = build_transform(is_train = False)\n",
        "\n",
        "train_set = JigsawDataset(df = train_df,\n",
        "                              data_path = './jigsaw',\n",
        "                              mode = 'train',\n",
        "                              transform = train_transform)\n",
        "test_dataset = JigsawDataset(df = test_df,\n",
        "                              data_path = './jigsaw',\n",
        "                              mode = 'test',\n",
        "                              transform = valid_transform)\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "    train_set,\n",
        "    batch_size = 16,\n",
        "    shuffle = True\n",
        ")\n",
        "test_dataloader = DataLoader(\n",
        "    test_dataset,\n",
        "    batch_size = 16,\n",
        "    shuffle = False\n",
        ")"
      ],
      "metadata": {
        "id": "IBeebgJVAWWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# [0 / 4375] loss: 0.8457428812980652\n",
        "# [400 / 4375] loss: 0.9976324439048767\n",
        "# [800 / 4375] loss: 0.8630994558334351\n",
        "# [1200 / 4375] loss: 0.801385223865509\n",
        "# [1600 / 4375] loss: 0.8121039271354675\n",
        "# [2000 / 4375] loss: 0.8025326728820801\n",
        "# [2400 / 4375] loss: 1.0828694105148315\n",
        "# [2800 / 4375] loss: 0.8235419392585754\n",
        "# [3200 / 4375] loss: 0.5506572127342224\n",
        "# [3600 / 4375] loss: 0.3462061882019043\n",
        "# # [4000 / 4375] loss: 0.7010009288787842"
      ],
      "metadata": {
        "id": "AQ50ctuvSqnp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.read_csv('sample_submission')"
      ],
      "metadata": {
        "id": "CqAABgtHsGuY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
        "# # from PIL import Image\n",
        "# # import requests\n",
        "# # image = Image.open(requests.get(url, stream=True).raw)\n",
        "# feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-384')\n",
        "model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-384')\n",
        "# # inputs = feature_extractor(images=image, return_tensors=\"pt\")\n",
        "# # outputs = model(**inputs)\n",
        "# # logits = outputs.logits\n",
        "\n",
        "\n",
        "# from transformers import AutoProcessor, AutoModel\n",
        "# model = AutoModel.from_pretrained(\"google/siglip-large-patch16-384\")\n",
        "# processor = AutoProcessor.from_pretrained(\"google/siglip-large-patch16-384\")"
      ],
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        "id": "FCE6ng4jESrC"
      }
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