arcface_look_alike.ipynb

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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/forminju/youtuber-look-alike-crawler/blob/main/arcface_look_alike.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 모델 학습을 위한 전처리 과정"
      ],
      "metadata": {
        "id": "SPIf7UHWKZdt"
      }
    },
    {
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      "execution_count": 1,
      "metadata": {
        "colab": {
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        },
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.13.1+cu116\n"
          ]
        }
      ],
      "source": [
        "# 토치 버전확인\n",
        "import torch\n",
        "print(torch.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 라이브러리 세팅\n",
        "\n",
        "import random\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "import albumentations as A #증강\n",
        "from albumentations.pytorch.transforms import ToTensorV2\n",
        "import torchvision.models as models\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')\n",
        "\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "SCB5d3Ygr1tJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# GPU 확인\n",
        "print(torch.cuda.is_available())\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2001FDZ1r-DP",
        "outputId": "47a886ae-561f-4dbc-876c-fea53a86d98e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# EfficientNet 오류 안나게..\n",
        "torch.backends.cudnn.enabled = False"
      ],
      "metadata": {
        "id": "rRt1ynbcsFXn"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CFG = {\n",
        "    'IMG_SIZE':456,\n",
        "    'EPOCHS':10,\n",
        "    'LEARNING_RATE':3e-4,\n",
        "    'BATCH_SIZE':16,\n",
        "    'SEED':42\n",
        "}\n",
        "\n",
        "# chatGPT 추천\n",
        "# CFG = {\n",
        "#     'embedding_size': 512,\n",
        "#     'num_classes': num_classes,\n",
        "#     'margin': 0.5,\n",
        "#     'scale': 64,\n",
        "#     'batch_size': 32,\n",
        "#     'learning_rate': 0.0001,\n",
        "#     'num_epochs': 50\n",
        "# }"
      ],
      "metadata": {
        "id": "nCI0U0XPsaX2"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "\n",
        "seed_everything(CFG['SEED'])"
      ],
      "metadata": {
        "id": "-AU5-vKfMQ4N"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lFbarPJD2eo",
        "outputId": "3cc0a069-fec5-4b57-85d7-da281ea513d3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/glory_match/look-alike/glory_train_df.csv')\n",
        "image_root_dir = '/content/drive/MyDrive/glory_match/look-alike/pre-processed-image'"
      ],
      "metadata": {
        "id": "ao-KyufkMX1k"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train, Test dataset 분리\n",
        "항목별로 70%를 train, 30%를 valid로 사용합니다."
      ],
      "metadata": {
        "id": "uaHH1SInGT49"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_name_list = []"
      ],
      "metadata": {
        "id": "puWm5xo4K7V5"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tmp=df.copy()\n",
        "tmp['class'].shape\n",
        "for initial in tmp['class']:\n",
        "  if initial not in class_name_list:\n",
        "    class_name_list.append(initial)\n",
        "class_name_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8E2VOC7NDf3",
        "outputId": "aca933d2-9e9c-4581-981b-35d749aa39ee"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SMO', 'CHJ', 'CDE', 'HAR', 'JJJ', 'JSI', 'OJY', 'SHE', 'SHG', 'IDH', 'IJH']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_name_to_lower_case_list = [class_name.lower() for class_name in class_name_list]\n",
        "class_name_to_lower_case_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oit8YbHsESj2",
        "outputId": "343670f1-278d-46d4-9fb6-90ba9ab8b190"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['smo', 'chj', 'cde', 'har', 'jjj', 'jsi', 'ojy', 'she', 'shg', 'idh', 'ijh']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for initial in class_name_to_lower_case_list:\n",
        "  image_root_path = os.path.join(image_root_dir, initial)\n",
        "  for i in range(len(tmp)):\n",
        "    if (initial.upper() == tmp['class'][i]):\n",
        "      tmp['file_name'][i] = os.path.join(image_root_path, tmp['file_name'][i])\n",
        "tmp['file_name'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wiq2WN4uLGgB",
        "outputId": "3099b5ea-daec-4993-c932-bfe21fe12569"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    /content/drive/MyDrive/glory_match/look-alike/...\n",
              "1    /content/drive/MyDrive/glory_match/look-alike/...\n",
              "2    /content/drive/MyDrive/glory_match/look-alike/...\n",
              "3    /content/drive/MyDrive/glory_match/look-alike/...\n",
              "4    /content/drive/MyDrive/glory_match/look-alike/...\n",
              "Name: file_name, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.DataFrame(columns=tmp.columns)\n",
        "valid = pd.DataFrame(columns=tmp.columns)\n",
        "\n",
        "for class_name in class_name_list:\n",
        "  tmp_with_class = tmp.loc[tmp['class'] == class_name]\n",
        "  train_tmp, valid_tmp = train_test_split(tmp_with_class, test_size = 0.3, random_state = 42)\n",
        "\n",
        "  train_tmp['class'] = class_name\n",
        "  valid_tmp['class'] = class_name\n",
        "  train = pd.concat([train, train_tmp])\n",
        "  valid = pd.concat([valid, valid_tmp])"
      ],
      "metadata": {
        "id": "sAjirL2WOS7X"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MDUnpl2CxH6O",
        "outputId": "c38d16a4-5a09-4e3b-e601-cf07d1ce62d3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     index                                          file_name class\n",
              "41      41  /content/drive/MyDrive/glory_match/look-alike/...   SMO\n",
              "19      19  /content/drive/MyDrive/glory_match/look-alike/...   SMO\n",
              "30      30  /content/drive/MyDrive/glory_match/look-alike/...   SMO\n",
              "49      49  /content/drive/MyDrive/glory_match/look-alike/...   SMO\n",
              "50      50  /content/drive/MyDrive/glory_match/look-alike/...   SMO\n",
              "...    ...                                                ...   ...\n",
              "1213  1213  /content/drive/MyDrive/glory_match/look-alike/...   IJH\n",
              "1121  1121  /content/drive/MyDrive/glory_match/look-alike/...   IJH\n",
              "1199  1199  /content/drive/MyDrive/glory_match/look-alike/...   IJH\n",
              "1286  1286  /content/drive/MyDrive/glory_match/look-alike/...   IJH\n",
              "1209  1209  /content/drive/MyDrive/glory_match/look-alike/...   IJH\n",
              "\n",
              "[910 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4adee82-2857-4541-be3f-48a0853bed90\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>index</th>\n",
              "      <th>file_name</th>\n",
              "      <th>class</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>41</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>SMO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>SMO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>30</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>SMO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>49</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>SMO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>50</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>SMO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1213</th>\n",
              "      <td>1213</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>IJH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1121</th>\n",
              "      <td>1121</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>IJH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1199</th>\n",
              "      <td>1199</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>IJH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1286</th>\n",
              "      <td>1286</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>IJH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1209</th>\n",
              "      <td>1209</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>IJH</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>910 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4adee82-2857-4541-be3f-48a0853bed90')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-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-a4adee82-2857-4541-be3f-48a0853bed90 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-a4adee82-2857-4541-be3f-48a0853bed90');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### label을 tensor로 반환하기 위해 one-hot-encoding 적용"
      ],
      "metadata": {
        "id": "PhVR42_-v_vs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encoded = pd.get_dummies(tmp['class'])\n",
        "train_one_hot_encoded = pd.get_dummies(train['class'])\n",
        "valid_one_hot_encoded = pd.get_dummies(valid['class'])"
      ],
      "metadata": {
        "id": "8dUpol-rqUPH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.concat([tmp, one_hot_encoded], axis=1)\n",
        "data = data.drop(['class'], axis=1)\n",
        "train = pd.concat([train, train_one_hot_encoded], axis=1)\n",
        "train = train.drop(['class'], axis=1)\n",
        "valid = pd.concat([valid, valid_one_hot_encoded], axis=1)\n",
        "valid = valid.drop(['class'], axis=1)"
      ],
      "metadata": {
        "id": "LcOPPWVqiXjV"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "U3vUhbozGlC5",
        "outputId": "1a0329aa-634c-449a-e9e0-f8af90ec515c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index                                          file_name  CDE  CHJ  HAR  \\\n",
              "41    41  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "19    19  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "30    30  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "49    49  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "50    50  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "\n",
              "    IDH  IJH  JJJ  JSI  OJY  SHE  SHG  SMO  \n",
              "41    0    0    0    0    0    0    0    1  \n",
              "19    0    0    0    0    0    0    0    1  \n",
              "30    0    0    0    0    0    0    0    1  \n",
              "49    0    0    0    0    0    0    0    1  \n",
              "50    0    0    0    0    0    0    0    1  "
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              "      <th>file_name</th>\n",
              "      <th>CDE</th>\n",
              "      <th>CHJ</th>\n",
              "      <th>HAR</th>\n",
              "      <th>IDH</th>\n",
              "      <th>IJH</th>\n",
              "      <th>JJJ</th>\n",
              "      <th>JSI</th>\n",
              "      <th>OJY</th>\n",
              "      <th>SHE</th>\n",
              "      <th>SHG</th>\n",
              "      <th>SMO</th>\n",
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              "      <th>41</th>\n",
              "      <td>41</td>\n",
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              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>30</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>49</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>50</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82abc1c4-d72a-4ef2-bb36-8f0fd23dfa6e')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      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",
              "    [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-82abc1c4-d72a-4ef2-bb36-8f0fd23dfa6e 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-82abc1c4-d72a-4ef2-bb36-8f0fd23dfa6e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "UTKa4Vi7fzfu",
        "outputId": "529ec6e6-6db8-47b6-9116-783b84f5a452"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index                                          file_name  CDE  CHJ  HAR  \\\n",
              "0      0  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "5      5  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
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              "13    13  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "44    44  /content/drive/MyDrive/glory_match/look-alike/...    0    0    0   \n",
              "\n",
              "    IDH  IJH  JJJ  JSI  OJY  SHE  SHG  SMO  \n",
              "0     0    0    0    0    0    0    0    1  \n",
              "5     0    0    0    0    0    0    0    1  \n",
              "34    0    0    0    0    0    0    0    1  \n",
              "13    0    0    0    0    0    0    0    1  \n",
              "44    0    0    0    0    0    0    0    1  "
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            "text/html": [
              "\n",
              "  <div id=\"df-940d35fd-da69-4c97-8cf2-249f7f73bc49\">\n",
              "    <div 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",
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              "\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>index</th>\n",
              "      <th>file_name</th>\n",
              "      <th>CDE</th>\n",
              "      <th>CHJ</th>\n",
              "      <th>HAR</th>\n",
              "      <th>IDH</th>\n",
              "      <th>IJH</th>\n",
              "      <th>JJJ</th>\n",
              "      <th>JSI</th>\n",
              "      <th>OJY</th>\n",
              "      <th>SHE</th>\n",
              "      <th>SHG</th>\n",
              "      <th>SMO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>34</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>13</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>44</td>\n",
              "      <td>/content/drive/MyDrive/glory_match/look-alike/...</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-940d35fd-da69-4c97-8cf2-249f7f73bc49')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      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",
              "    [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-940d35fd-da69-4c97-8cf2-249f7f73bc49 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-940d35fd-da69-4c97-8cf2-249f7f73bc49');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CustomDataset"
      ],
      "metadata": {
        "id": "po2U31U2xRqx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, img_path_list, label_list, transforms=None):\n",
        "        self.img_path_list = img_path_list\n",
        "        self.label_list = label_list\n",
        "        self.transforms = transforms\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        img_path = self.img_path_list[index]\n",
        "        \n",
        "        image = cv2.imread(img_path)\n",
        "        \n",
        "        if self.transforms is not None:\n",
        "            image = self.transforms(image=image)['image']\n",
        "        \n",
        "        if self.label_list is not None:\n",
        "            label = torch.FloatTensor(self.label_list[index])\n",
        "            return image, label\n",
        "        else:\n",
        "            return image\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.img_path_list)"
      ],
      "metadata": {
        "id": "Pn2OvMX3LAsL"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_transform = A.Compose([\n",
        "    A.Resize(180, 180),\n",
        "    A.HorizontalFlip(p=0.5),\n",
        "    A.RandomBrightnessContrast(p=0.5),\n",
        "    A.RandomBrightnessContrast(brightness_limit=(-0.3, 0.3), contrast_limit=(-0.3, 0.3), p=1),\n",
        "    A.ChannelShuffle(p=0.2),\n",
        "    ToTensorV2()\n",
        "])\n",
        "\n",
        "valid_transform = A.Compose([\n",
        "    A.Resize(180, 180),\n",
        "    ToTensorV2()\n",
        "])"
      ],
      "metadata": {
        "id": "jeBV7xxVOy4Q"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_labels(df):\n",
        "    return df.iloc[:,2:].values"
      ],
      "metadata": {
        "id": "7nck9Kh71rSL"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels=get_labels(train)\n",
        "valid_labels=get_labels(valid)"
      ],
      "metadata": {
        "id": "IPnVRY301sjL"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = CustomDataset(train['file_name'].values, train_labels, transforms=train_transform)\n",
        "valid_dataset = CustomDataset(valid['file_name'].values, valid_labels, transforms=valid_transform)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=CFG['BATCH_SIZE'], shuffle=True, num_workers=4)\n",
        "valid_loader = DataLoader(valid_dataset, batch_size=CFG['BATCH_SIZE'], shuffle=False, num_workers=4)"
      ],
      "metadata": {
        "id": "y9PTL15dPXQx"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x,y in train_loader:\n",
        "  print(f'Image Shape: {x.shape}')\n",
        "  print(f'Label Shape: {y.shape}')\n",
        "  break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxTG3vgfRDRi",
        "outputId": "d6de58f6-e975-41bf-94f1-26fdaf67a8f8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Shape: torch.Size([16, 3, 180, 180])\n",
            "Label Shape: torch.Size([16, 11])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ArcFace"
      ],
      "metadata": {
        "id": "PiUC4YbRGRLB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.models as models\n",
        "import math\n",
        "\n",
        "class ArcMarginProduct(nn.Module):\n",
        "    def __init__(self, in_features, out_features, scale=30.0, margin=0.50, easy_margin=False, device='cuda'):\n",
        "        super(ArcMarginProduct, self).__init__()\n",
        "        self.in_features = in_features\n",
        "        self.out_features = out_features\n",
        "        self.scale = scale\n",
        "        self.margin = margin\n",
        "        self.device = device\n",
        "        self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))\n",
        "        nn.init.xavier_uniform_(self.weight)\n",
        "\n",
        "        self.easy_margin = easy_margin\n",
        "        self.cos_m = math.cos(margin)\n",
        "        self.sin_m = math.sin(margin)\n",
        "        self.th = math.cos(math.pi - margin)\n",
        "        self.mm = math.sin(math.pi - margin) * margin\n",
        "\n",
        "    def forward(self, input, label):\n",
        "        cosine = F.linear(F.normalize(input), F.normalize(self.weight))\n",
        "        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))\n",
        "        phi = cosine * self.cos_m - sine * self.sin_m\n",
        "        if self.easy_margin:\n",
        "            phi = torch.where(cosine > 0, phi, cosine)\n",
        "        else:\n",
        "            phi = torch.where(cosine > self.th, phi, cosine - self.mm)\n",
        "\n",
        "        one_hot = torch.zeros(cosine.size(), device=input.device)\n",
        "        one_hot.scatter_(1, label.view(-1, 1).long(), 1)\n",
        "        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)\n",
        "        output *= self.scale\n",
        "\n",
        "        return output\n",
        "\n",
        "class CustomArcFaceModel(nn.Module):\n",
        "    def __init__(self, num_classes=11, device='cuda'):\n",
        "        super(CustomArcFaceModel, self).__init__()\n",
        "        self.device = device\n",
        "        self.backbone = nn.Sequential(*list(models.resnet50(pretrained=True).children())[:-1])\n",
        "        self.arc_margin_product = ArcMarginProduct(2048, num_classes, device=self.device)\n",
        "        self.classifier = nn.Linear(2048, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        features = self.backbone(x)\n",
        "        features = F.normalize(features)\n",
        "        features = features.view(features.size(0), -1)\n",
        "        features = F.sigmoid(self.classifier(features))\n",
        "\n",
        "        return features        \n"
      ],
      "metadata": {
        "id": "QD9_t-NkGTaM"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model, optimizer, train_loader, valid_loader, scheduler, device):\n",
        "    model.to(device)\n",
        "    criterion = nn.BCELoss().to(device)\n",
        "    \n",
        "    best_valid_acc = 0\n",
        "    best_model = None\n",
        "    \n",
        "    for epoch in range(1, CFG['EPOCHS']+1):\n",
        "        model.train()\n",
        "        train_loss = []\n",
        "        for imgs, labels in tqdm(iter(train_loader)):\n",
        "            imgs = imgs.float().to(device)\n",
        "            labels = labels.to(device)\n",
        "            \n",
        "            optimizer.zero_grad()\n",
        "            \n",
        "            output = model(imgs)\n",
        "            loss = criterion(output, labels)\n",
        "            \n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            \n",
        "            train_loss.append(loss.item())\n",
        "                    \n",
        "        _valid_loss, _valid_acc = validation(model, criterion, valid_loader, device)\n",
        "        _train_loss = np.mean(train_loss)\n",
        "        print(f'Epoch [{epoch}], Train Loss : [{_train_loss:.5f}] Valid Loss : [{_valid_loss:.5f}] Valid ACC : [{_valid_acc:.5f}]')\n",
        "        \n",
        "        if scheduler is not None:\n",
        "            scheduler.step(_valid_acc)\n",
        "            \n",
        "        if best_valid_acc < _valid_acc:\n",
        "            best_valid_acc = _valid_acc\n",
        "            best_model = model\n",
        "    \n",
        "    return best_model"
      ],
      "metadata": {
        "id": "DmPs2I8r3HcY"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validation(model, criterion, valid_loader, device):\n",
        "    model.eval()\n",
        "    valid_loss = []\n",
        "    valid_acc = []\n",
        "    with torch.no_grad():\n",
        "        for imgs, labels in tqdm(iter(valid_loader)):\n",
        "            imgs = imgs.float().to(device)\n",
        "            labels = labels.to(device)\n",
        "            \n",
        "            probs = model(imgs)\n",
        "            \n",
        "            loss = criterion(probs, labels)\n",
        "            \n",
        "            probs  = probs.cpu().detach().numpy()\n",
        "            labels = labels.cpu().detach().numpy()\n",
        "            preds = probs > 0.5\n",
        "            batch_acc = (labels == preds).mean()\n",
        "            \n",
        "            valid_acc.append(batch_acc)\n",
        "            valid_loss.append(loss.item())\n",
        "        \n",
        "        _valid_loss = np.mean(valid_loss)\n",
        "        _valid_acc = np.mean(valid_acc)\n",
        "    \n",
        "    return _valid_loss, _valid_acc"
      ],
      "metadata": {
        "id": "ZUnGWWy53JYR"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = CustomArcFaceModel()\n",
        "model.eval()\n",
        "optimizer = torch.optim.Adam(params = model.parameters(), lr = CFG[\"LEARNING_RATE\"])\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=2,threshold_mode='abs',min_lr=1e-8, verbose=True)\n",
        "\n",
        "infer_model = train(model, optimizer, train_loader, valid_loader, scheduler, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896,
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        },
        "id": "-Mb4QYop3LOZ",
        "outputId": "1c4fc6dd-2fda-4fdd-b909-28ea5d67b72f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/57 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e23160b6f70044e585485dd68d8a4866"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01c12bfc5f53418cb96c47272c0116ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1], Train Loss : [0.56055] Valid Loss : [0.45991] Valid ACC : [0.90909]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/57 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8772d261cce4258973db3a9f85efd22"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e46f644ad914135bf536723685a9209"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [2], Train Loss : [0.40189] Valid Loss : [0.35865] Valid ACC : [0.90909]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "model_id": "c72c37377c9b44a883603339fb51cc15"
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        {
          "output_type": "display_data",
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [3], Train Loss : [0.33556] Valid Loss : [0.31893] Valid ACC : [0.90909]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "f31ef1c85feb462099b1905f8fcdf77e"
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        },
        {
          "output_type": "display_data",
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              "model_id": "0a1244ce2a324026ab8e6c6d5fce2903"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [4], Train Loss : [0.30694] Valid Loss : [0.29977] Valid ACC : [0.90909]\n",
            "Epoch 00004: reducing learning rate of group 0 to 1.5000e-04.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ab813cb88464ea4a77323648b8a3b39"
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        },
        {
          "output_type": "display_data",
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              "model_id": "a66c0ea0a06d43a789ef99cc51e266c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [5], Train Loss : [0.29195] Valid Loss : [0.28881] Valid ACC : [0.90909]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c294afac9bb74d9d811f89b554105880"
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        },
        {
          "output_type": "display_data",
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              "model_id": "ba54cd8b985c47afb950551ecdecf121"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [6], Train Loss : [0.28270] Valid Loss : [0.28064] Valid ACC : [0.90909]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee7ede73799f4113860db4f4cc45cdd4"
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        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "86d67ad00b9b4cc081c859e7bd2bf892"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [7], Train Loss : [0.27357] Valid Loss : [0.27015] Valid ACC : [0.90909]\n",
            "Epoch 00007: reducing learning rate of group 0 to 7.5000e-05.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "36ef41eb022f490dbad9d2231a42dcf7"
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        {
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          "data": {
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              "model_id": "5645b0000360426ab3750eaa49ba71de"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [8], Train Loss : [0.26477] Valid Loss : [0.26427] Valid ACC : [0.90909]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "33fc21ef30e643fda8cab7df8c55f044"
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        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3b4390945964cca8ca9decbaaeac889"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [9], Train Loss : [0.25939] Valid Loss : [0.26170] Valid ACC : [0.90909]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "93fca9a8917b4fa6b4d259955c45fd7f"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "721a95cd1a4a487e845808beb84fa420"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [10], Train Loss : [0.25280] Valid Loss : [0.25341] Valid ACC : [0.90909]\n",
            "Epoch 00010: reducing learning rate of group 0 to 3.7500e-05.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PATH = '/content/drive/MyDrive/glory_match/'"
      ],
      "metadata": {
        "id": "wE7VecWy3l-g"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model, PATH + 'arcface.pt') "
      ],
      "metadata": {
        "id": "wLMcY95_48ze"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), PATH + 'arcface_dict.pt')"
      ],
      "metadata": {
        "id": "hlCRfYxv5BuH"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save({\n",
        "    'arcface': model.state_dict(),\n",
        "    'optimizer': optimizer.state_dict()\n",
        "}, PATH + 'arcface_all.tar') "
      ],
      "metadata": {
        "id": "bGTyYjFw5SFO"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HqXBqULr8UEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ResNet"
      ],
      "metadata": {
        "id": "6UzUWoB28ZK0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ResNet50(nn.Module):\n",
        "    def __init__(self, num_classes=11):\n",
        "        super(ResNet50, self).__init__()\n",
        "        self.backbone = models.resnet18(pretrained=True)\n",
        "        self.classifier = nn.Linear(1000, num_classes)\n",
        "        \n",
        "        ######\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.backbone(x)\n",
        "        \n",
        "        ######\n",
        "        \n",
        "        x = F.sigmoid(self.classifier(x))\n",
        "        return x"
      ],
      "metadata": {
        "id": "4MrGDyEh8yT8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model, optimizer, train_loader, valid_loader, scheduler, device):\n",
        "    model.to(device)\n",
        "    criterion = nn.BCELoss().to(device)\n",
        "    \n",
        "    best_valid_acc = 0\n",
        "    best_model = None\n",
        "    \n",
        "    for epoch in range(1, CFG['EPOCHS']+1):\n",
        "        model.train()\n",
        "        train_loss = []\n",
        "        for imgs, labels in tqdm(iter(train_loader)):\n",
        "            imgs = imgs.float().to(device)\n",
        "            labels = labels.to(device)\n",
        "            \n",
        "            optimizer.zero_grad()\n",
        "            \n",
        "            output = model(imgs)\n",
        "            loss = criterion(output, labels)\n",
        "            \n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            \n",
        "            train_loss.append(loss.item())\n",
        "                    \n",
        "        _valid_loss, _valid_acc = validation(model, criterion, valid_loader, device)\n",
        "        _train_loss = np.mean(train_loss)\n",
        "        print(f'Epoch [{epoch}], Train Loss : [{_train_loss:.5f}] Valid Loss : [{_valid_loss:.5f}] Valid ACC : [{_valid_acc:.5f}]')\n",
        "        \n",
        "        if scheduler is not None:\n",
        "            scheduler.step(_valid_acc)\n",
        "            \n",
        "        if best_valid_acc < _valid_acc:\n",
        "            best_valid_acc = _valid_acc\n",
        "            best_model = model\n",
        "    \n",
        "    return best_model"
      ],
      "metadata": {
        "id": "Xo8pRwRP9-Fa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validation(model, criterion, valid_loader, device):\n",
        "    model.eval()\n",
        "    valid_loss = []\n",
        "    valid_acc = []\n",
        "    with torch.no_grad():\n",
        "        for imgs, labels in tqdm(iter(valid_loader)):\n",
        "            imgs = imgs.float().to(device)\n",
        "            labels = labels.to(device)\n",
        "            \n",
        "            probs = model(imgs)\n",
        "            \n",
        "            loss = criterion(probs, labels)\n",
        "            \n",
        "            probs  = probs.cpu().detach().numpy()\n",
        "            labels = labels.cpu().detach().numpy()\n",
        "            preds = probs > 0.5\n",
        "            batch_acc = (labels == preds).mean()\n",
        "            \n",
        "            valid_acc.append(batch_acc)\n",
        "            valid_loss.append(loss.item())\n",
        "        \n",
        "        _valid_loss = np.mean(valid_loss)\n",
        "        _valid_acc = np.mean(valid_acc)\n",
        "    \n",
        "    return _valid_loss, _valid_acc"
      ],
      "metadata": {
        "id": "m_i8FHz29_XP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = ResNet50()\n",
        "model.eval()\n",
        "optimizer = torch.optim.Adam(params = model.parameters(), lr = CFG[\"LEARNING_RATE\"])\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=2,threshold_mode='abs',min_lr=1e-8, verbose=True)\n",
        "infer_model = train(model, optimizer, train_loader, valid_loader, scheduler, device)"
      ],
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        "id": "MqQ-jLx48jGL",
        "outputId": "d2aa952e-a621-4208-b803-1f886d6693ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
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          "text": [
            "Epoch [1], Train Loss : [0.26212] Valid Loss : [0.18140] Valid ACC : [0.93000]\n"
          ]
        },
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            "Epoch [2], Train Loss : [0.13764] Valid Loss : [0.20180] Valid ACC : [0.92636]\n"
          ]
        },
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            "Epoch [3], Train Loss : [0.09336] Valid Loss : [0.17098] Valid ACC : [0.93968]\n"
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            "Epoch [4], Train Loss : [0.07252] Valid Loss : [0.11213] Valid ACC : [0.96497]\n"
          ]
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            "Epoch [5], Train Loss : [0.04938] Valid Loss : [0.12884] Valid ACC : [0.95529]\n"
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            "Epoch [6], Train Loss : [0.04443] Valid Loss : [0.13167] Valid ACC : [0.96166]\n"
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            "Epoch [7], Train Loss : [0.04044] Valid Loss : [0.10809] Valid ACC : [0.97013]\n"
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            "Epoch [8], Train Loss : [0.03383] Valid Loss : [0.15287] Valid ACC : [0.95078]\n"
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          "name": "stdout",
          "text": [
            "Epoch [9], Train Loss : [0.03098] Valid Loss : [0.11877] Valid ACC : [0.96490]\n"
          ]
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          "text": [
            "Epoch [10], Train Loss : [0.02809] Valid Loss : [0.09940] Valid ACC : [0.97244]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PATH = '/content/drive/MyDrive/glory_match/'"
      ],
      "metadata": {
        "id": "rURAVU93_AkF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model, PATH + 'resnet18.pt') \n",
        "torch.save(model.state_dict(), PATH + 'resnet18_dict.pt')\n",
        "\n",
        "torch.save({\n",
        "    'resnet18': model.state_dict(),\n",
        "    'optimizer': optimizer.state_dict()\n",
        "}, PATH + 'resnet18_all.tar') "
      ],
      "metadata": {
        "id": "jeO9YHEQ-yS3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.models as models\n",
        "import math\n",
        "\n",
        "class ArcMarginProduct(nn.Module):\n",
        "    def __init__(self, in_features, out_features, scale=30.0, margin=0.50, easy_margin=False, device='cuda'):\n",
        "        super(ArcMarginProduct, self).__init__()\n",
        "        self.in_features = in_features\n",
        "        self.out_features = out_features\n",
        "        self.scale = scale\n",
        "        self.margin = margin\n",
        "        self.device = device\n",
        "        self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))\n",
        "        nn.init.xavier_uniform_(self.weight)\n",
        "\n",
        "        self.easy_margin = easy_margin\n",
        "        self.cos_m = math.cos(margin)\n",
        "        self.sin_m = math.sin(margin)\n",
        "        self.th = math.cos(math.pi - margin)\n",
        "        self.mm = math.sin(math.pi - margin) * margin\n",
        "\n",
        "    def forward(self, input, label):\n",
        "        cosine = F.linear(F.normalize(input), F.normalize(self.weight))\n",
        "        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))\n",
        "        phi = cosine * self.cos_m - sine * self.sin_m\n",
        "        if self.easy_margin:\n",
        "            phi = torch.where(cosine > 0, phi, cosine)\n",
        "        else:\n",
        "            phi = torch.where(cosine > self.th, phi, cosine - self.mm)\n",
        "\n",
        "        one_hot = torch.zeros(cosine.size(), device=input.device)\n",
        "        one_hot.scatter_(1, label.view(-1, 1).long(), 1)\n",
        "        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)\n",
        "        output *= self.scale\n",
        "\n",
        "        return output\n",
        "\n",
        "class CustomArcFaceModel(nn.Module):\n",
        "    def __init__(self, num_classes, device='cuda'):\n",
        "        super(CustomArcFaceModel, self).__init__()\n",
        "        self.device = device\n",
        "        self.backbone = nn.Sequential(*list(models.resnet50(pretrained=True).children())[:-1])\n",
        "        self.arc_margin_product = ArcMarginProduct(2048, num_classes, device=self.device)\n",
        "        self.classifier = nn.Linear(1000, num_classes)\n",
        "\n",
        "    def forward(self, x, labels=None):\n",
        "        features = self.backbone(x)\n",
        "        features = F.normalize(features)\n",
        "        features = features.view(features.size(0), -1)\n",
        "        features = F.sigmoid(self.classifier)\n",
        "        if labels is not None:\n",
        "            logits = self.arc_margin_product(features, labels)\n",
        "            return logits\n",
        "        return features"
      ],
      "metadata": {
        "id": "dUYVmHBrBDbB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 11  # 분류할 클래스의 수 (CDE, CHJ, HAR, IDH, IJH, JJJ, JSI, OJY, SHE, SHG, SMO)\n",
        "embedding_size = 2048\n",
        "learning_rate = 0.001\n",
        "epochs = 10\n",
        "\n",
        "# 모델 생성\n",
        "model = CustomArcFaceModel(num_classes, embedding_size)\n",
        "\n",
        "# 옵티마이저 및 손실 함수 설정\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# 모델 학습\n",
        "trained_model = train(model, optimizer, criterion, train_loader, valid_loader, device, epochs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "V09XQA0_B0FX",
        "outputId": "9aea9485-646b-4e35-c727-bc064be11e0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-f92f8319bf07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# 모델 학습\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtrained_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: train() takes 6 positional arguments but 7 were given"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = CustomArcFaceModel()\n",
        "model.eval()\n",
        "optimizer = torch.optim.Adam(params = model.parameters(), lr = CFG[\"LEARNING_RATE\"])\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=2,threshold_mode='abs',min_lr=1e-8, verbose=True)\n",
        "infer_model = train(model, optimizer, train_loader, valid_loader, scheduler, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "_Cdrc3AlBkt2",
        "outputId": "e9831688-c7f7-436e-fda2-ae9bdc17ca8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-7b89e93fc242>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCustomArcFaceModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCFG\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"LEARNING_RATE\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mscheduler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_scheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReduceLROnPlateau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'max'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mthreshold_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'abs'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmin_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0minfer_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'num_classes'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TestAccuracy 확인"
      ],
      "metadata": {
        "id": "6vULEbhS6ebp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to test the model with the test dataset and print the accuracy for the test images\n",
        "def testAccuracy():\n",
        "    \n",
        "    model.eval()\n",
        "    accuracy = 0.0\n",
        "    total = 0.0\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for data in test_loader:\n",
        "            images, labels = data\n",
        "            # run the model on the test set to predict labels\n",
        "            outputs = model(images)\n",
        "            # the label with the highest energy will be our prediction\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            accuracy += (predicted == labels).sum().item()\n",
        "    \n",
        "    # compute the accuracy over all test images\n",
        "    accuracy = (100 * accuracy / total)\n",
        "    return(accuracy)"
      ],
      "metadata": {
        "id": "JAyc8EdX5XzV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}