ZeroShot_CODEBRIM.ipynb

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        "!pip install datasets transformers timm\n",
        "!pip install kaggle --upgrade\n",
        "\n",
        "import os\n",
        "import cv2\n",
        "import gc\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import itertools\n",
        "from tqdm.autonotebook import tqdm\n",
        "import albumentations as A\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "import timm\n",
        "from transformers import DistilBertModel, DistilBertConfig, DistilBertTokenizer\n",
        "from transformers import CLIPProcessor, CLIPModel\n",
        "from PIL import Image\n",
        "from torchvision import transforms\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.models as models\n",
        "import torch.nn as nn\n",
        "from torchvision.models import ResNet50_Weights"
      ]
    },
    {
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!ls \"/content/drive/My Drive/CODEBRIM/classification_dataset\""
      ],
      "metadata": {
        "id": "F8X4YAElpLo7",
        "colab": {
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        },
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      },
      "execution_count": 2,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Images\tmetadata  test\tval\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_path = \"/content/drive/My Drive/CODEBRIM/\"\n",
        "test_path = \"/content/drive/My Drive/CODEBRIM/\"\n",
        "image_path = \"/content/drive/My Drive/CODEBRIM/classification_dataset/Images\""
      ],
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      },
      "execution_count": 3,
      "outputs": []
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      "source": [
        "train_val_path_act = \"/content/drive/My Drive/CODEBRIM/train_val.csv\"\n",
        "df = pd.read_csv(train_val_path_act)\n",
        "df\n",
        "#This df has train and validation images\n",
        "#rest data analysis done in other notebook"
      ],
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              "7092           The photo of a concrete having no faults  \n",
              "7093           The photo of a concrete having no faults  \n",
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              "      <td>image_0001280_crop_0000001.png</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1418</td>\n",
              "      <td>The photo of a concrete having no faults</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7093</th>\n",
              "      <td>612</td>\n",
              "      <td>image_0001280_crop_0000002.png</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1418</td>\n",
              "      <td>The photo of a concrete having no faults</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7094</th>\n",
              "      <td>613</td>\n",
              "      <td>image_0001280_crop_0000003.png</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1418</td>\n",
              "      <td>The photo of a concrete having no faults</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7095 rows × 6 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-b7534f27-4594-4cbe-a551-0d6e782fee96')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </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-b7534f27-4594-4cbe-a551-0d6e782fee96 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-b7534f27-4594-4cbe-a551-0d6e782fee96');\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-8a0936a4-857a-467e-bd6a-c7660408360d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a0936a4-857a-467e-bd6a-c7660408360d')\"\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",
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              "    </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-8a0936a4-857a-467e-bd6a-c7660408360d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class CFG:\n",
        "    debug = False\n",
        "    image_path = image_path\n",
        "    train_val_path = train_val_path\n",
        "    test_path = test_path\n",
        "    batch_size = 32\n",
        "    num_workers = 2\n",
        "    head_lr = 1e-3\n",
        "    image_encoder_lr = 1e-4\n",
        "    text_encoder_lr = 1e-5\n",
        "    weight_decay = 1e-3\n",
        "    patience = 1\n",
        "    factor = 0.8\n",
        "    epochs = 10\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "    model_name = 'resnet50'\n",
        "    image_embedding = 2048\n",
        "    text_encoder_model = \"distilbert-base-uncased\"\n",
        "    text_embedding = 768\n",
        "    text_tokenizer = \"distilbert-base-uncased\"\n",
        "    max_length = 200\n",
        "\n",
        "    pretrained = True # for both image encoder and text encoder\n",
        "    trainable = True # for both image encoder and text encoder\n",
        "    temperature = 1.0\n",
        "\n",
        "    # image size\n",
        "    size = 224\n",
        "\n",
        "    # for projection head; used for both image and text encoders\n",
        "    num_projection_layers = 1\n",
        "    projection_dim = 256\n",
        "    dropout = 0.1"
      ],
      "metadata": {
        "id": "t120duNfpocj"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AvgMeter:\n",
        "    def __init__(self, name=\"Metric\"):\n",
        "        self.name = name\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.avg, self.sum, self.count = [0] * 3\n",
        "\n",
        "    def update(self, val, count=1):\n",
        "        self.count += count\n",
        "        self.sum += val * count\n",
        "        self.avg = self.sum / self.count\n",
        "\n",
        "    def __repr__(self):\n",
        "        text = f\"{self.name}: {self.avg:.4f}\"\n",
        "        return text\n",
        "\n",
        "def get_lr(optimizer):\n",
        "    for param_group in optimizer.param_groups:\n",
        "        return param_group[\"lr\"]"
      ],
      "metadata": {
        "id": "oGNyUiHSpsVq"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CLIPDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, image_filenames, captions, tokenizer, transforms):\n",
        "        \"\"\"\n",
        "        image_filenames and cpations must have the same length; so, if there are\n",
        "        multiple captions for each image, the image_filenames must have repetitive\n",
        "        file names\n",
        "        \"\"\"\n",
        "\n",
        "        self.image_filenames = image_filenames\n",
        "        self.captions = list(captions)\n",
        "        self.encoded_captions = tokenizer(\n",
        "            list(captions), padding=True, truncation=True, max_length=CFG.max_length\n",
        "        )\n",
        "        self.transforms = transforms\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {\n",
        "            key: torch.tensor(values[idx])\n",
        "            for key, values in self.encoded_captions.items()\n",
        "        }\n",
        "\n",
        "        image = cv2.imread(f\"{CFG.image_path}/{self.image_filenames[idx]}\")\n",
        "        image = self.transforms(image=image)['image']\n",
        "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "        item['image'] = torch.tensor(image).permute(2, 0, 1).float()\n",
        "        item['caption'] = self.captions[idx]\n",
        "\n",
        "        return item\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.captions)\n",
        "\n",
        "\n",
        "\n",
        "def get_transforms(mode=\"train\"):\n",
        "    if mode == \"train\":\n",
        "        return A.Compose(\n",
        "            [\n",
        "                A.Resize(CFG.size, CFG.size, always_apply=True),\n",
        "                A.Normalize(max_pixel_value=255.0, always_apply=True),\n",
        "            ]\n",
        "        )\n",
        "    else:\n",
        "        return A.Compose(\n",
        "            [\n",
        "                A.Resize(CFG.size, CFG.size, always_apply=True),\n",
        "                A.Normalize(max_pixel_value=255.0, always_apply=True),\n",
        "            ]\n",
        "        )"
      ],
      "metadata": {
        "id": "yGl7ulqdpt26"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ImageEncoder(nn.Module):\n",
        "    \"\"\"\n",
        "    Encode images to a fixed size vector\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self, model_name=CFG.model_name, pretrained=CFG.pretrained, trainable=CFG.trainable\n",
        "    ):\n",
        "        super().__init__()\n",
        "        self.model = timm.create_model(\n",
        "            model_name, pretrained, num_classes=0, global_pool=\"avg\"\n",
        "        )\n",
        "        for p in self.model.parameters():\n",
        "            p.requires_grad = trainable\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)"
      ],
      "metadata": {
        "id": "rbeILw7HpvQT"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TextEncoder(nn.Module):\n",
        "    def __init__(self, model_name=CFG.text_encoder_model, pretrained=CFG.pretrained, trainable=CFG.trainable):\n",
        "        super().__init__()\n",
        "        if pretrained:\n",
        "            self.model = DistilBertModel.from_pretrained(model_name)\n",
        "        else:\n",
        "            self.model = DistilBertModel(config=DistilBertConfig())\n",
        "\n",
        "        for p in self.model.parameters():\n",
        "            p.requires_grad = trainable\n",
        "\n",
        "        # we are using the CLS token hidden representation as the sentence's embedding\n",
        "        self.target_token_idx = 0\n",
        "\n",
        "    def forward(self, input_ids, attention_mask):\n",
        "        output = self.model(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        last_hidden_state = output.last_hidden_state\n",
        "        return last_hidden_state[:, self.target_token_idx, :]"
      ],
      "metadata": {
        "id": "BDrzpcfcpwrb"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ProjectionHead(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        embedding_dim,\n",
        "        projection_dim=CFG.projection_dim,\n",
        "        dropout=CFG.dropout\n",
        "    ):\n",
        "        super().__init__()\n",
        "        self.projection = nn.Linear(embedding_dim, projection_dim)\n",
        "        self.gelu = nn.GELU()\n",
        "        self.fc = nn.Linear(projection_dim, projection_dim)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.layer_norm = nn.LayerNorm(projection_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        projected = self.projection(x)\n",
        "        x = self.gelu(projected)\n",
        "        x = self.fc(x)\n",
        "        x = self.dropout(x)\n",
        "        x = x + projected\n",
        "        x = self.layer_norm(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "ZTKu6oYYpyAK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CLIPModel(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        temperature=CFG.temperature,\n",
        "        image_embedding=CFG.image_embedding,\n",
        "        text_embedding=CFG.text_embedding,\n",
        "    ):\n",
        "        super().__init__()\n",
        "        self.image_encoder = ImageEncoder()\n",
        "        self.text_encoder = TextEncoder()\n",
        "        self.image_projection = ProjectionHead(embedding_dim=image_embedding)\n",
        "        self.text_projection = ProjectionHead(embedding_dim=text_embedding)\n",
        "        self.temperature = temperature\n",
        "\n",
        "    def forward(self, batch):\n",
        "        # Getting Image and Text Features\n",
        "        image_features = self.image_encoder(batch[\"image\"])\n",
        "        text_features = self.text_encoder(\n",
        "            input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
        "        )\n",
        "        # Getting Image and Text Embeddings (with same dimension)\n",
        "        image_embeddings = self.image_projection(image_features)\n",
        "        text_embeddings = self.text_projection(text_features)\n",
        "\n",
        "        # Calculating the Loss\n",
        "        logits = (text_embeddings @ image_embeddings.T) / self.temperature\n",
        "        images_similarity = image_embeddings @ image_embeddings.T\n",
        "        texts_similarity = text_embeddings @ text_embeddings.T\n",
        "        targets = F.softmax(\n",
        "            (images_similarity + texts_similarity) / 2 * self.temperature, dim=-1\n",
        "        )\n",
        "        texts_loss = cross_entropy(logits, targets, reduction='none')\n",
        "        images_loss = cross_entropy(logits.T, targets.T, reduction='none')\n",
        "        loss =  (images_loss + texts_loss) / 2.0 # shape: (batch_size)\n",
        "        return loss.mean()\n",
        "\n",
        "\n",
        "def cross_entropy(preds, targets, reduction='none'):\n",
        "    log_softmax = nn.LogSoftmax(dim=-1)\n",
        "    loss = (-targets * log_softmax(preds)).sum(1)\n",
        "    if reduction == \"none\":\n",
        "        return loss\n",
        "    elif reduction == \"mean\":\n",
        "        return loss.mean()"
      ],
      "metadata": {
        "id": "4dSn7KTGpzma"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_train_valid_dfs():\n",
        "    dataframe = pd.read_csv(f\"{CFG.train_val_path}/train_val.csv\")\n",
        "    max_id = dataframe[\"id\"].max() + 1 if not CFG.debug else 100\n",
        "    image_ids = np.arange(0, max_id)\n",
        "    np.random.seed(42)\n",
        "    valid_ids = np.random.choice(\n",
        "        image_ids, size=int(0.2 * len(image_ids)), replace=False\n",
        "    )\n",
        "    train_ids = [id_ for id_ in image_ids if id_ not in valid_ids]\n",
        "    train_dataframe = dataframe[dataframe[\"id\"].isin(train_ids)].reset_index(drop=True)\n",
        "    valid_dataframe = dataframe[dataframe[\"id\"].isin(valid_ids)].reset_index(drop=True)\n",
        "    return train_dataframe, valid_dataframe\n",
        "\n",
        "\n",
        "def build_loaders(dataframe, tokenizer, mode):\n",
        "    transforms = get_transforms(mode=mode)\n",
        "    dataset = CLIPDataset(\n",
        "        dataframe[\"image\"].values,\n",
        "        dataframe[\"caption\"].values,\n",
        "        tokenizer=tokenizer,\n",
        "        transforms=transforms,\n",
        "    )\n",
        "    dataloader = torch.utils.data.DataLoader(\n",
        "        dataset,\n",
        "        batch_size=CFG.batch_size,\n",
        "        num_workers=CFG.num_workers,\n",
        "        shuffle=True if mode == \"train\" else False,\n",
        "    )\n",
        "    return dataloader"
      ],
      "metadata": {
        "id": "0N3IaszSp05y"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_epoch(model, train_loader, optimizer, lr_scheduler, step):\n",
        "    loss_meter = AvgMeter()\n",
        "    tqdm_object = tqdm(train_loader, total=len(train_loader))\n",
        "    for batch in tqdm_object:\n",
        "        batch = {k: v.to(CFG.device) for k, v in batch.items() if k != \"caption\"}\n",
        "        loss = model(batch)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if step == \"batch\":\n",
        "            lr_scheduler.step()\n",
        "\n",
        "        count = batch[\"image\"].size(0)\n",
        "        loss_meter.update(loss.item(), count)\n",
        "\n",
        "        tqdm_object.set_postfix(train_loss=loss_meter.avg, lr=get_lr(optimizer))\n",
        "    return loss_meter\n",
        "\n",
        "\n",
        "def valid_epoch(model, valid_loader):\n",
        "    loss_meter = AvgMeter()\n",
        "\n",
        "    tqdm_object = tqdm(valid_loader, total=len(valid_loader))\n",
        "    for batch in tqdm_object:\n",
        "        batch = {k: v.to(CFG.device) for k, v in batch.items() if k != \"caption\"}\n",
        "        loss = model(batch)\n",
        "\n",
        "        count = batch[\"image\"].size(0)\n",
        "        loss_meter.update(loss.item(), count)\n",
        "\n",
        "        tqdm_object.set_postfix(valid_loss=loss_meter.avg)\n",
        "    return loss_meter\n",
        "\n",
        "\n",
        "def main():\n",
        "    train_df, valid_df = make_train_valid_dfs()\n",
        "    tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)\n",
        "    train_loader = build_loaders(train_df, tokenizer, mode=\"train\")\n",
        "    valid_loader = build_loaders(valid_df, tokenizer, mode=\"valid\")\n",
        "\n",
        "\n",
        "    model = CLIPModel().to(CFG.device)\n",
        "    params = [\n",
        "        {\"params\": model.image_encoder.parameters(), \"lr\": CFG.image_encoder_lr},\n",
        "        {\"params\": model.text_encoder.parameters(), \"lr\": CFG.text_encoder_lr},\n",
        "        {\"params\": itertools.chain(\n",
        "            model.image_projection.parameters(), model.text_projection.parameters()\n",
        "        ), \"lr\": CFG.head_lr, \"weight_decay\": CFG.weight_decay}\n",
        "    ]\n",
        "    optimizer = torch.optim.AdamW(params, weight_decay=0.)\n",
        "    lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
        "        optimizer, mode=\"min\", patience=CFG.patience, factor=CFG.factor\n",
        "    )\n",
        "    step = \"epoch\"\n",
        "\n",
        "    best_loss = float('inf')\n",
        "    for epoch in range(CFG.epochs):\n",
        "        print(f\"Epoch: {epoch + 1}\")\n",
        "        model.train()\n",
        "        train_loss = train_epoch(model, train_loader, optimizer, lr_scheduler, step)\n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            valid_loss = valid_epoch(model, valid_loader)\n",
        "\n",
        "        if valid_loss.avg < best_loss:\n",
        "            best_loss = valid_loss.avg\n",
        "            torch.save(model.state_dict(), \"best.pt\")\n",
        "            print(\"Saved Best Model!\")\n",
        "\n",
        "        lr_scheduler.step(valid_loss.avg)"
      ],
      "metadata": {
        "id": "aC8D7Y4Pp2hv"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main()"
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        "id": "n8eL02Vip33i",
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          "height": 308,
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        "outputId": "a80d5d73-f39d-47cd-a8d7-f4f966fc5e66"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading model.safetensors:   0%|          | 0.00/102M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3e918f8fb22d40d79531474e9882551b"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "4497c8fe3ead47539a4dafc13986e969"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved Best Model!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_test_dfs():\n",
        "    dataframe = pd.read_csv(f\"{CFG.test_path}/test.csv\")\n",
        "    max_id = dataframe[\"id\"].max() + 1 if not CFG.debug else 100\n",
        "    image_ids = np.arange(0, max_id)\n",
        "    test_ids = [id_ for id_ in image_ids]\n",
        "    test_dataframe = dataframe[dataframe[\"id\"].isin(test_ids)].reset_index(drop=True)\n",
        "    return test_dataframe"
      ],
      "metadata": {
        "id": "SCorG9jmOR78"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = make_test_dfs()"
      ],
      "metadata": {
        "id": "ySDMajitRCv1"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['Crack','Spallation','Efflorescence','ExposedBars','CorrosionStain','No faults']\n",
        "clip_labels = [f\"a photo of concrete having {label}\" for label in labels]\n",
        "clip_labels\n",
        "np.shape(clip_labels)"
      ],
      "metadata": {
        "id": "5gSG6hq7RMWG",
        "outputId": "5fc239b3-8a7d-44ed-c00b-a5ee9cd9b321",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6,)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)\n",
        "model = CLIPModel().to(CFG.device)\n",
        "model.load_state_dict(torch.load(\"best.pt\", map_location=CFG.device))\n",
        "model.eval()\n",
        "\n",
        "encoded_query = tokenizer([clip_labels[0]])\n",
        "batch = {\n",
        "    key: torch.tensor(values).to(CFG.device)\n",
        "    for key, values in encoded_query.items()\n",
        "}\n",
        "with torch.no_grad():\n",
        "    text_features = model.text_encoder(\n",
        "        input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
        "    )\n",
        "    T1 = model.text_projection(text_features)\n",
        "\n",
        "encoded_query = tokenizer([clip_labels[1]])\n",
        "batch = {\n",
        "    key: torch.tensor(values).to(CFG.device)\n",
        "    for key, values in encoded_query.items()\n",
        "}\n",
        "with torch.no_grad():\n",
        "    text_features = model.text_encoder(\n",
        "        input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
        "    )\n",
        "    T2 = model.text_projection(text_features)\n",
        "\n",
        "encoded_query = tokenizer([clip_labels[2]])\n",
        "batch = {\n",
        "    key: torch.tensor(values).to(CFG.device)\n",
        "    for key, values in encoded_query.items()\n",
        "}\n",
        "with torch.no_grad():\n",
        "    text_features = model.text_encoder(\n",
        "        input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
        "    )\n",
        "    T3 = model.text_projection(text_features)\n",
        "\n",
        "encoded_query = tokenizer([clip_labels[3]])\n",
        "batch = {\n",
        "    key: torch.tensor(values).to(CFG.device)\n",
        "    for key, values in encoded_query.items()\n",
        "}\n",
        "with torch.no_grad():\n",
        "    text_features = model.text_encoder(\n",
        "        input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
        "    )\n",
        "    T4 = model.text_projection(text_features)\n",
        "\n",
        "encoded_query = tokenizer([clip_labels[4]])\n",
        "batch = {\n",
        "    key: torch.tensor(values).to(CFG.device)\n",
        "    for key, values in encoded_query.items()\n",
        "}\n",
        "with torch.no_grad():\n",
        "    text_features = model.text_encoder(\n",
        "        input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
        "    )\n",
        "    T5 = model.text_projection(text_features)\n",
        "\n",
        "encoded_query = tokenizer([clip_labels[5]])\n",
        "batch = {\n",
        "    key: torch.tensor(values).to(CFG.device)\n",
        "    for key, values in encoded_query.items()\n",
        "}\n",
        "with torch.no_grad():\n",
        "    text_features = model.text_encoder(\n",
        "        input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
        "    )\n",
        "    T6 = model.text_projection(text_features)"
      ],
      "metadata": {
        "id": "kht5H2_2ar_n"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "T = torch.cat((T1, T2, T3, T4, T5, T6), 0)\n",
        "T.shape"
      ],
      "metadata": {
        "id": "JoFj7Fb8T-AC",
        "outputId": "1d8fa06e-7d6b-406b-b4f3-e62b8bc4993c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([6, 256])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_image_embeddings(valid_df, model_path):\n",
        "    tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)\n",
        "    valid_loader = build_loaders(valid_df, tokenizer, mode=\"valid\")\n",
        "\n",
        "    model = CLIPModel().to(CFG.device)\n",
        "    model.load_state_dict(torch.load(model_path, map_location=CFG.device))\n",
        "    model.eval()\n",
        "\n",
        "    valid_image_embeddings = []\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(valid_loader):\n",
        "            image_features = model.image_encoder(batch[\"image\"].to(CFG.device))\n",
        "            image_embeddings = model.image_projection(image_features)\n",
        "            valid_image_embeddings.append(image_embeddings)\n",
        "    return model, torch.cat(valid_image_embeddings)"
      ],
      "metadata": {
        "id": "qJxmKQfkbQT4"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model, image_embeddings = get_image_embeddings(test_df, \"best.pt\")"
      ],
      "metadata": {
        "id": "OSbvwLc2fYET",
        "outputId": "595e8798-be4d-49bb-8486-3e558ffea3d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "371e6407113745a1a27954cbcbc184ea",
            "2903b53d94444781ae52a55ed0260f68",
            "bbf629973a164791874854a5dc3702eb",
            "86144e615bef49a78908f0dc80dff043",
            "409a79ea293b4514b139deefcd3401db",
            "30e78b2cbaa14a5ca1860d58b9b54519",
            "a53c312b563c4a2d8da77f4acfbfe2a5",
            "529a50d6ca83489782e2045376022004",
            "a4c9ebd1cecb469eb0a5576065380e62",
            "c668e4c2cfe84400b4c679cc0b6a53f6",
            "c7625fe5483644428ba24af88199cd46"
          ]
        }
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_embeddings.shape"
      ],
      "metadata": {
        "id": "Wv7cZyVkgI9M",
        "outputId": "842e7a24-aa63-4cd7-98d3-ce02dac403eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([630, 256])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df"
      ],
      "metadata": {
        "id": "KwEhqlk1nMxH",
        "outputId": "2779ee38-2dbe-4cd3-ba5b-4b374baaecfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              image  Background  Crack  Spallation  \\\n",
              "0    image_0000077_crop_0000001.png           0      0           0   \n",
              "1    image_0000084_crop_0000001.png           0      0           0   \n",
              "2    image_0000092_crop_0000001.png           0      0           0   \n",
              "3    image_0000119_crop_0000001.png           0      0           0   \n",
              "4    image_0000127_crop_0000001.png           0      0           0   \n",
              "..                              ...         ...    ...         ...   \n",
              "625  image_0001572_crop_0000004.png           1      0           0   \n",
              "626  image_0001572_crop_0000005.png           1      0           0   \n",
              "627  image_0001586_crop_0000001.png           1      0           0   \n",
              "628  image_0001586_crop_0000002.png           1      0           0   \n",
              "629  image_0001586_crop_0000003.png           1      0           0   \n",
              "\n",
              "     Efflorescence  ExposedBars  CorrosionStain  sum   id  \\\n",
              "0                1            0               1    2    0   \n",
              "1                1            0               0    1    0   \n",
              "2                1            0               1    2    0   \n",
              "3                1            0               0    1    0   \n",
              "4                1            0               1    2    0   \n",
              "..             ...          ...             ...  ...  ...   \n",
              "625              0            0               0    0  125   \n",
              "626              0            0               0    0  125   \n",
              "627              0            0               0    0  125   \n",
              "628              0            0               0    0  125   \n",
              "629              0            0               0    0  125   \n",
              "\n",
              "                                               caption  \n",
              "0    The photo of a concrete having Efflorescence, ...  \n",
              "1         The photo of a concrete having Efflorescence  \n",
              "2    The photo of a concrete having Efflorescence, ...  \n",
              "3         The photo of a concrete having Efflorescence  \n",
              "4    The photo of a concrete having Efflorescence, ...  \n",
              "..                                                 ...  \n",
              "625           The photo of a concrete having no faults  \n",
              "626           The photo of a concrete having no faults  \n",
              "627           The photo of a concrete having no faults  \n",
              "628           The photo of a concrete having no faults  \n",
              "629           The photo of a concrete having no faults  \n",
              "\n",
              "[630 rows x 10 columns]"
            ],
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              "      <td>image_0001586_crop_0000001.png</td>\n",
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              "  </tbody>\n",
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              "<p>630 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7875c883-93fd-41cb-b92e-c63ced8c9417')\"\n",
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              "  <style>\n",
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              "      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-7875c883-93fd-41cb-b92e-c63ced8c9417 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-7875c883-93fd-41cb-b92e-c63ced8c9417');\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",
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              "    </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",
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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-5a4c1492-e910-4799-a4f7-149a9b2b5fc5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arrbase = [ [0]*6 for i in range(len(test_df))]\n",
        "for i in range(len(test_df)):\n",
        "  if(test_df['sum'][i]==0):\n",
        "    arrbase[i][5]=1;\n",
        "  else:\n",
        "    arrbase[i][0]=df['Crack'][i]\n",
        "    arrbase[i][1]=df['Spallation'][i]\n",
        "    arrbase[i][2]=df['Efflorescence'][i]\n",
        "    arrbase[i][3]=df['ExposedBars'][i]\n",
        "    arrbase[i][4]=df['CorrosionStain'][i]"
      ],
      "metadata": {
        "id": "HkY1MSOfuEf-"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arrmain = [ [0]*6 for i in range(len(test_df))]\n",
        "text_embeddings_n = F.normalize(T, p=2, dim=-1)\n",
        "for i in range(len(test_df)):\n",
        "    img_emb = F.normalize(image_embeddings[i], p=2, dim=-1)\n",
        "    scores = text_embeddings_n @ img_emb.T\n",
        "    ts=0\n",
        "    for i in scores:\n",
        "        ts = ts+i\n",
        "    for i in range(6):\n",
        "        scores[i]=(scores[i]*100)/ts\n",
        "    for j in range(5):\n",
        "        if(scores[j]>=16.66):\n",
        "            arrmain[i][j]=1\n",
        "        else:\n",
        "            arrmain[i][j]=0"
      ],
      "metadata": {
        "id": "7yGwnCmYvZ-k"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "count=0\n",
        "k=0;\n",
        "for i in range(len(test_df)):\n",
        "  tempc=0\n",
        "  for j in range(6):\n",
        "    if(arrmain[i][j]==arrbase[i][j]):\n",
        "      tempc+=1\n",
        "      k=k+1\n",
        "  if(tempc>=5):\n",
        "    count+=1\n",
        "acc = count/630 * 100\n",
        "acc"
      ],
      "metadata": {
        "id": "RKfm0D9svOw9",
        "outputId": "ee8ff7ec-ecb8-48c1-ac5e-0c0249af3054",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68.57142857142857"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc2 = k/(630*6)*100"
      ],
      "metadata": {
        "id": "afP36oW_xTjh"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc2"
      ],
      "metadata": {
        "id": "84V16PdPxtn3",
        "outputId": "14c6f937-6edd-41f0-bf9c-24593267a3cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75.37037037037037"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B_nSrTF7xueV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}