CLIP_SKRIPSI_RN50x4 (3).ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Px7KnkTEPtfN"
      },
      "source": [
        "Download and preprocess dataset skripsi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v-jDQ7ISyk_6"
      },
      "outputs": [],
      "source": [
        "import glob\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from time import time\n",
        "from numpy import array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_XQtDzAvxmd",
        "colab": {
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        "outputId": "e67a0b6a-6322-43e1-a0bf-2f4be5f44792"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 423
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        "id": "aRgupW-nyKb7",
        "outputId": "c9a07e2b-c72b-4f66-8bea-c794f813c037"
      },
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              "             image                                            caption\n",
              "0         gl_1.jpg  overcoming the challenge of a deep roadside di...\n",
              "1         gl_1.jpg        careful there is a ditch in side of a road.\n",
              "2         gl_1.jpg  taking the leap conquering the hurdle of a tre...\n",
              "3         gl_1.jpg  careful navigating through the obstacle of a d...\n",
              "4         gl_2.jpg  obstacle on side of a street  there is deep ca...\n",
              "...            ...                                                ...\n",
              "15195  gl_3799.jpg  stay vigilant while entering the traffic light...\n",
              "15196  gl_3800.jpg  exercise caution as there are multiple motorcy...\n",
              "15197  gl_3800.jpg  stay alert for the presence of numerous motorc...\n",
              "15198  gl_3800.jpg  exercise caution due to the high number of mot...\n",
              "15199  gl_3800.jpg  be mindful of the abundance of motorcycles and...\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/preprocessed_dataset_caption_skripsi.txt\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EFtYDUXI9_de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a413ea4d-f415-44ae-eb1c-efaca4c26e7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ftfy\n",
            "  Downloading ftfy-6.1.1-py3-none-any.whl (53 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/53.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: ftfy\n",
            "Successfully installed ftfy-6.1.1\n",
            "Collecting git+https://github.com/openai/CLIP.git\n",
            "  Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-pjyl0vmo\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /tmp/pip-req-build-pjyl0vmo\n",
            "  Resolved https://github.com/openai/CLIP.git to commit a1d071733d7111c9c014f024669f959182114e33\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: ftfy in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (6.1.1)\n",
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            "Building wheels for collected packages: clip\n",
            "  Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369497 sha256=ce4030f9a860e83fd3163f462583e4959daa300c4e36d3ebd04ec42edebba563\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-cbtcoicm/wheels/da/2b/4c/d6691fa9597aac8bb85d2ac13b112deb897d5b50f5ad9a37e4\n",
            "Successfully built clip\n",
            "Installing collected packages: clip\n",
            "Successfully installed clip-1.0\n"
          ]
        }
      ],
      "source": [
        "# $ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0\n",
        "! pip install ftfy regex tqdm\n",
        "! pip install git+https://github.com/openai/CLIP.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXc4RKGlbyKR",
        "outputId": "1b7f0e93-a07d-4679-9faf-c3fa3f0b2bd2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Installing collected packages: safetensors, huggingface-hub, tokenizers, transformers\n",
            "Successfully installed huggingface-hub-0.17.3 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.34.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RXh8smZzyvgP"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import skimage.io as io\n",
        "import clip\n",
        "from PIL import Image\n",
        "import pickle\n",
        "import json\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import argparse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gUtdb_o6-CRE"
      },
      "outputs": [],
      "source": [
        "\n",
        "import IPython.display\n",
        "from IPython.display import Markdown, display\n",
        "import ipywidgets as widgets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FF98n4HEm9Q-"
      },
      "outputs": [],
      "source": [
        "def parse(clip_model_type: str):\n",
        "    device = torch.device('cuda:0')\n",
        "    clip_model_name = clip_model_type.replace('/', '_')\n",
        "    out_path = f\"./dataskripsi_{clip_model_name}_train.pkl\"\n",
        "    clip_model, preprocess = clip.load(clip_model_type, device=device, jit=False)\n",
        "    all_embeddings = []\n",
        "    all_captions = []\n",
        "    for i in tqdm(range(len(df))):\n",
        "        d=df.iloc[i]\n",
        "        img_id = d[\"image\"]\n",
        "        filename = f\"/content/drive/MyDrive/Dataset SKRIPSI/Resize/{img_id}\"\n",
        "        image = Image.open(filename).convert(\"RGB\")\n",
        "        image = preprocess(image).unsqueeze(0).to(device)\n",
        "        with torch.no_grad():\n",
        "            prefix = clip_model.encode_image(image).cpu()\n",
        "        d[\"clip_embedding\"] = i\n",
        "        all_embeddings.append(prefix)\n",
        "        all_captions.append(d)\n",
        "        if (i + 1) % 10000 == 0:\n",
        "            with open(out_path, 'wb') as f:\n",
        "                pickle.dump({\"clip_embedding\": torch.cat(all_embeddings, dim=0), \"captions\": all_captions}, f)\n",
        "\n",
        "    with open(out_path, 'wb') as f:\n",
        "        pickle.dump({\"clip_embedding\": torch.cat(all_embeddings, dim=0), \"captions\": all_captions}, f)\n",
        "\n",
        "    print('Done')\n",
        "    print(\"%0d embeddings saved \" % len(all_embeddings))\n",
        "    return 0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9v83aDhzB1WU",
        "outputId": "8a773e8c-c704-4c39-e4b8-fbcbce1f7629"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['RN50',\n",
              " 'RN101',\n",
              " 'RN50x4',\n",
              " 'RN50x16',\n",
              " 'RN50x64',\n",
              " 'ViT-B/32',\n",
              " 'ViT-B/16',\n",
              " 'ViT-L/14',\n",
              " 'ViT-L/14@336px']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "clip.available_models()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b67NJJ-FB1WU"
      },
      "outputs": [],
      "source": [
        "parse('RN50x4')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "idX81XcwB1WV"
      },
      "outputs": [],
      "source": [
        "import clip\n",
        "import os\n",
        "from torch import nn\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as nnf\n",
        "import sys\n",
        "from typing import Tuple, List, Union, Optional\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup\n",
        "from tqdm import tqdm, trange\n",
        "# from google.colab import files\n",
        "import skimage.io as io\n",
        "import PIL.Image\n",
        "# from IPython.display import Image\n",
        "from enum import Enum\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VXTiHaH4B1WV"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import functional as nnf\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from enum import Enum\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "import pickle\n",
        "import sys\n",
        "import argparse\n",
        "import json\n",
        "from typing import Tuple, Optional, Union\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CZXE6o-oB1WV"
      },
      "outputs": [],
      "source": [
        "class ClipDataset(Dataset):\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return len(self.captions_tokens)\n",
        "\n",
        "    def pad_tokens(self, item: int):\n",
        "        tokens = self.captions_tokens[item]\n",
        "        padding = self.max_seq_len - tokens.shape[0]\n",
        "        if padding > 0:\n",
        "            tokens = torch.cat((tokens, torch.zeros(padding, dtype=torch.int64) - 1))\n",
        "            self.captions_tokens[item] = tokens\n",
        "        elif padding < 0:\n",
        "            tokens = tokens[:self.max_seq_len]\n",
        "            self.captions_tokens[item] = tokens\n",
        "        mask = tokens.ge(0)  # mask is zero where we out of sequence\n",
        "        tokens[~mask] = 0\n",
        "        mask = mask.float()\n",
        "        mask = torch.cat((torch.ones(self.prefix_length), mask), dim=0)  # adding prefix mask\n",
        "        return tokens, mask\n",
        "\n",
        "    def __getitem__(self, item: int) -> Tuple[torch.Tensor, ...]:\n",
        "        tokens, mask = self.pad_tokens(item)\n",
        "        prefix = self.prefixes[self.caption2embedding[item]]\n",
        "        if self.normalize_prefix:\n",
        "            prefix = prefix.float()\n",
        "            prefix = prefix / prefix.norm(2, -1)\n",
        "        return tokens, mask, prefix\n",
        "\n",
        "    def __init__(self, data_path: str,  prefix_length: int, gpt2_type: str = \"gpt2\",\n",
        "                 normalize_prefix=False):\n",
        "        self.tokenizer = GPT2Tokenizer.from_pretrained(gpt2_type)\n",
        "        self.prefix_length = prefix_length\n",
        "        self.normalize_prefix = normalize_prefix\n",
        "        with open(data_path, 'rb') as f:\n",
        "            all_data = pickle.load(f)\n",
        "        print(\"Data size is %0d\" % len(all_data[\"clip_embedding\"]))\n",
        "        sys.stdout.flush()\n",
        "        self.prefixes = all_data[\"clip_embedding\"]\n",
        "        captions_raw = all_data[\"captions\"]\n",
        "        self.image_ids = [caption[\"image\"] for caption in captions_raw]\n",
        "        self.captions = [caption['caption'] for caption in captions_raw]\n",
        "        if os.path.isfile(f\"{data_path[:-4]}_tokens.pkl\"):\n",
        "            with open(f\"{data_path[:-4]}_tokens.pkl\", 'rb') as f:\n",
        "                self.captions_tokens, self.caption2embedding, self.max_seq_len = pickle.load(f)\n",
        "        else:\n",
        "            self.captions_tokens = []\n",
        "            self.caption2embedding = []\n",
        "            max_seq_len = 0\n",
        "            for caption in captions_raw:\n",
        "                self.captions_tokens.append(torch.tensor(self.tokenizer.encode(caption['caption']), dtype=torch.int64))\n",
        "                self.caption2embedding.append(caption[\"clip_embedding\"])\n",
        "                max_seq_len = max(max_seq_len, self.captions_tokens[-1].shape[0])\n",
        "            # self.max_seq_len = max_seq_len\n",
        "            with open(f\"{data_path[:-4]}_tokens.pkl\", 'wb') as f:\n",
        "                pickle.dump([self.captions_tokens, self.caption2embedding, max_seq_len], f)\n",
        "        all_len = torch.tensor([len(self.captions_tokens[i]) for i in range(len(self))]).float()\n",
        "        self.max_seq_len = min(int(all_len.mean() + all_len.std() * 10), int(all_len.max()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_LyQwFeSOqUG"
      },
      "outputs": [],
      "source": [
        "#@title Model\n",
        "\n",
        "\n",
        "class MappingType(Enum):\n",
        "    MLP = 'mlp'\n",
        "    Transformer = 'transformer'\n",
        "\n",
        "\n",
        "class MlpTransformer(nn.Module):\n",
        "     def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.):\n",
        "         super().__init__()\n",
        "         out_d = out_d if out_d is not None else in_dim\n",
        "         self.fc1 = nn.Linear(in_dim, h_dim)\n",
        "         self.act = act\n",
        "         self.fc2 = nn.Linear(h_dim, out_d)\n",
        "         self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "     def forward(self, x):\n",
        "         x = self.fc1(x)\n",
        "         x = self.act(x)\n",
        "         x = self.dropout(x)\n",
        "         x = self.fc2(x)\n",
        "         x = self.dropout(x)\n",
        "         return x\n",
        "\n",
        "class MLP(nn.Module):\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "        return self.model(x)\n",
        "\n",
        "    def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):\n",
        "        super(MLP, self).__init__()\n",
        "        layers = []\n",
        "        for i in range(len(sizes) - 1):\n",
        "            layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))\n",
        "            if i < len(sizes) - 2:\n",
        "                layers.append(act())\n",
        "        self.model = nn.Sequential(*layers)\n",
        "\n",
        "\n",
        "class MultiHeadAttention(nn.Module):\n",
        "\n",
        "    def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.):\n",
        "        super().__init__()\n",
        "        self.num_heads = num_heads\n",
        "        head_dim = dim_self // num_heads\n",
        "        self.scale = head_dim ** -0.5\n",
        "        self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)\n",
        "        self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)\n",
        "        self.project = nn.Linear(dim_self, dim_self)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, x, y=None, mask=None):\n",
        "        y = y if y is not None else x\n",
        "        b, n, c = x.shape\n",
        "        _, m, d = y.shape\n",
        "        # b n h dh\n",
        "        queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads)\n",
        "        # b m 2 h dh\n",
        "        keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads)\n",
        "        keys, values = keys_values[:, :, 0], keys_values[:, :, 1]\n",
        "        attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale\n",
        "        if mask is not None:\n",
        "            if mask.dim() == 2:\n",
        "                mask = mask.unsqueeze(1)\n",
        "            attention = attention.masked_fill(mask.unsqueeze(3), float(\"-inf\"))\n",
        "        attention = attention.softmax(dim=2)\n",
        "        out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c)\n",
        "        out = self.project(out)\n",
        "        return out, attention\n",
        "\n",
        "\n",
        "class TransformerLayer(nn.Module):\n",
        "\n",
        "    def forward_with_attention(self, x, y=None, mask=None):\n",
        "        x_, attention = self.attn(self.norm1(x), y, mask)\n",
        "        x = x + x_\n",
        "        x = x + self.mlp(self.norm2(x))\n",
        "        return x, attention\n",
        "\n",
        "    def forward(self, x, y=None, mask=None):\n",
        "        x = x + self.attn(self.norm1(x), y, mask)[0]\n",
        "        x = x + self.mlp(self.norm2(x))\n",
        "        return x\n",
        "\n",
        "    def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu,\n",
        "                 norm_layer: nn.Module = nn.LayerNorm):\n",
        "        super().__init__()\n",
        "        self.norm1 = norm_layer(dim_self)\n",
        "        self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout)\n",
        "        self.norm2 = norm_layer(dim_self)\n",
        "        self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout)\n",
        "\n",
        "\n",
        "class Transformer(nn.Module):\n",
        "\n",
        "    def forward_with_attention(self, x, y=None, mask=None):\n",
        "        attentions = []\n",
        "        for layer in self.layers:\n",
        "            x, att = layer.forward_with_attention(x, y, mask)\n",
        "            attentions.append(att)\n",
        "        return x, attentions\n",
        "\n",
        "    def forward(self, x, y=None, mask=None):\n",
        "        for i, layer in enumerate(self.layers):\n",
        "            if i % 2 == 0 and self.enc_dec: # cross\n",
        "                x = layer(x, y)\n",
        "            elif self.enc_dec:  # self\n",
        "                x = layer(x, x, mask)\n",
        "            else:  # self or cross\n",
        "                x = layer(x, y, mask)\n",
        "        return x\n",
        "\n",
        "    def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None,\n",
        "                 mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False):\n",
        "        super(Transformer, self).__init__()\n",
        "        dim_ref = dim_ref if dim_ref is not None else dim_self\n",
        "        self.enc_dec = enc_dec\n",
        "        if enc_dec:\n",
        "            num_layers = num_layers * 2\n",
        "        layers = []\n",
        "        for i in range(num_layers):\n",
        "            if i % 2 == 0 and enc_dec:  # cross\n",
        "                layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n",
        "            elif enc_dec:  # self\n",
        "                layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n",
        "            else:  # self or cross\n",
        "                layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n",
        "        self.layers = nn.ModuleList(layers)\n",
        "\n",
        "\n",
        "class TransformerMapper(nn.Module):\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.linear(x).view(x.shape[0], self.clip_length, -1)\n",
        "        prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape)\n",
        "        prefix = torch.cat((x, prefix), dim=1)\n",
        "        out = self.transformer(prefix)[:, self.clip_length:]\n",
        "        return out\n",
        "\n",
        "    def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8):\n",
        "        super(TransformerMapper, self).__init__()\n",
        "        self.clip_length = clip_length\n",
        "        self.transformer = Transformer(dim_embedding, 8, num_layers)\n",
        "        self.linear = nn.Linear(dim_clip, clip_length * dim_embedding)\n",
        "        self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)\n",
        "\n",
        "\n",
        "class ClipCaptionModel(nn.Module):\n",
        "\n",
        "    def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:\n",
        "        return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)\n",
        "\n",
        "    def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None,\n",
        "                labels: Optional[torch.Tensor] = None):\n",
        "        embedding_text = self.gpt.transformer.wte(tokens)\n",
        "        prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)\n",
        "        embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)\n",
        "        if labels is not None:\n",
        "            dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)\n",
        "            labels = torch.cat((dummy_token, tokens), dim=1)\n",
        "        out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)\n",
        "        return out\n",
        "\n",
        "    def __init__(self, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512,\n",
        "                 num_layers: int = 8, mapping_type: MappingType = MappingType.MLP):\n",
        "        super(ClipCaptionModel, self).__init__()\n",
        "        self.prefix_length = prefix_length\n",
        "        self.gpt = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "        self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]\n",
        "        if mapping_type == MappingType.MLP:\n",
        "            self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2,\n",
        "                                     self.gpt_embedding_size * prefix_length))\n",
        "        else:\n",
        "            self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length,\n",
        "                                                                     clip_length, num_layers)\n",
        "\n",
        "\n",
        "class ClipCaptionPrefix(ClipCaptionModel):\n",
        "\n",
        "    def parameters(self, recurse: bool = True):\n",
        "        return self.clip_project.parameters()\n",
        "\n",
        "    def train(self, mode: bool = True):\n",
        "        super(ClipCaptionPrefix, self).train(mode)\n",
        "        self.gpt.eval()\n",
        "        return self\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wKhl6bIcB1WX"
      },
      "outputs": [],
      "source": [
        "def train(dataset: ClipDataset, model: ClipCaptionModel,\n",
        "          bs=32,epochs=30,save_every=5,\n",
        "          lr: float = 2e-5, warmup_steps: int = 5000, output_dir: str = \".\", output_prefix: str = \"model_prefix\"):\n",
        "    history = []\n",
        "    device = torch.device('cuda:0')\n",
        "    batch_size = bs\n",
        "    epochs = epochs\n",
        "    if not os.path.exists(output_dir):\n",
        "        os.makedirs(output_dir)\n",
        "    model = model.to(device)\n",
        "    model.train()\n",
        "    optimizer = AdamW(model.parameters(), lr=lr)\n",
        "    train_dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)\n",
        "    scheduler = get_linear_schedule_with_warmup(\n",
        "        optimizer, num_warmup_steps=warmup_steps, num_training_steps=epochs * len(train_dataloader)\n",
        "    )\n",
        "    # save_config(args)\n",
        "    for epoch in range(epochs):\n",
        "        print(f\">>> Training epoch {epoch}\")\n",
        "        sys.stdout.flush()\n",
        "        progress = tqdm(total=len(train_dataloader), desc=output_prefix)\n",
        "        for idx, (tokens, mask, prefix) in enumerate(train_dataloader):\n",
        "            model.zero_grad()\n",
        "            tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float32)\n",
        "            outputs = model(tokens, prefix, mask)\n",
        "            logits = outputs.logits[:, dataset.prefix_length - 1: -1]\n",
        "            loss = nnf.cross_entropy(logits.reshape(-1, logits.shape[-1]), tokens.flatten(), ignore_index=0)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            scheduler.step()\n",
        "            optimizer.zero_grad()\n",
        "            progress.set_postfix({\"loss\": loss.item()})\n",
        "            progress.update()\n",
        "            if (idx + 1) % 10000 == 0:\n",
        "                torch.save(\n",
        "                    model.state_dict(),\n",
        "                    os.path.join(output_dir, f\"{output_prefix}_latest.pt\"),\n",
        "                )\n",
        "        progress.close()\n",
        "        if epoch % save_every == 0 or epoch == epochs - 1:\n",
        "            torch.save(\n",
        "                model.state_dict(),\n",
        "                os.path.join(output_dir, f\"{output_prefix}-{epoch:03d}.pt\"),\n",
        "            )\n",
        "        history.append({'epoch': epoch, 'loss': loss.item()})\n",
        "    return model,history\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FV1Nxpl_B1WX"
      },
      "outputs": [],
      "source": [
        "prefix_length = 40\n",
        "prefix_dim = 640\n",
        "mapping_type = MappingType.Transformer\n",
        "data_path= '/content/dataskripsi_RN50x4_train.pkl'\n",
        "only_prefix = True\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "covmzUEiB1WX"
      },
      "outputs": [],
      "source": [
        "dataset = ClipDataset(data_path, prefix_length)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_loss(history):\n",
        "    epochs = len(history)\n",
        "    loss_values = [history[i]['loss'] for i in range(epochs)]\n",
        "    plt.plot(range(1, epochs + 1), loss_values, label='Training Loss Model Skripsi CLIP-Daffa')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.title('Training Loss')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "AWyw-xK1Lx4N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "4b2fd0b24ce7489c898c982c54a77d44",
            "78da7db4b3b54cac8dffb379ab23ebb4",
            "6aa3e40bde1648d088f13bad9816fbe1",
            "62665c96f324476a8616949b0f0c40ba",
            "d83077fd26794cfbababf6380beb0be4",
            "88174f905dde4486876da739ce7283b5",
            "d3d7065214ec4f69a5d47132a4986b1f",
            "40f4a8e6eeb64162a4f60ea1480e8b13",
            "e15aa592e95d47da8e1b107de425529d",
            "1ced66861cb4419bb993c29a1860d9cd",
            "3443c2af45d34d2ba60d827032851ed4",
            "606de85ced5b4bdb98f213efdbdfe8af",
            "f86643abb4bb45c0b24e093c4d4ae0f3",
            "39ae5626c6794cf19540fd84107d533f",
            "9fe0b8c8831f4ee09bfacce8dd9f4a87",
            "f2f0964360fc472ea40397f5fc00e12b",
            "38161e49e34541c3b329f252579b8318",
            "f91ad8b7a29b4043bc18d091dac4edfc",
            "c6b9a79aa0e84e2fb2929a711937fb99",
            "b34f6372fbfb46ec9e362a492e470289",
            "49f9a48d06f9425d9d7ba656929045dc",
            "c57fe46626c64bc3b49b4d124b3d59fc"
          ]
        },
        "id": "OfKfbU8mB1WY",
        "outputId": "50d8df80-cd6d-4865-94dc-0025e80119bb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b2fd0b24ce7489c898c982c54a77d44"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "606de85ced5b4bdb98f213efdbdfe8af"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model=ClipCaptionPrefix(40, clip_length=40, prefix_size=640,\n",
        "                        num_layers=8, mapping_type='transformer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "q-r-cOx2B1WY",
        "outputId": "f85e40d1-d79c-42ba-e966-c8dd3d0b6204"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 0\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:04<00:00,  1.12it/s, loss=3.65]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 1\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:02<00:00,  1.12it/s, loss=3.21]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 2\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=2.65]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 3\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:02<00:00,  1.12it/s, loss=2.41]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 4\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=2.35]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 5\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.92]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 6\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.92]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 7\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.96]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 8\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.85]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 9\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=2.01]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 10\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.52]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 11\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.64]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 12\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:04<00:00,  1.12it/s, loss=1.81]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 13\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.38]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 14\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.47]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 15\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.3]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 16\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.29]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 17\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.956]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 18\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.17]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 19\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=1.04]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 20\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.83]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 21\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.855]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Training epoch 22\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.813]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Training epoch 23\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.712]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Training epoch 24\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.922]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Training epoch 25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:02<00:00,  1.12it/s, loss=0.609]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Training epoch 26\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:02<00:00,  1.12it/s, loss=0.652]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Training epoch 27\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.474]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Training epoch 28\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:03<00:00,  1.12it/s, loss=0.54]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Training epoch 29\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "model_skrpsi_RNx64: 100%|██████████| 475/475 [07:02<00:00,  1.12it/s, loss=0.4]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model, history=train( dataset,model,\n",
        "                      bs=32,epochs=30,save_every=5,\n",
        "                       lr= 2e-5, warmup_steps = 5000, output_dir = \"./\", output_prefix = \"model_skrpsi_RNx64\")\n",
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_loss(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "GMBt_nO2ABGg",
        "outputId": "79347c3b-27ec-4d44-a4db-e4e0b1ebb8b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lI_RLPKYB1WY"
      },
      "source": [
        "# predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oySSLnfpB1WY"
      },
      "outputs": [],
      "source": [
        "#@title Caption prediction\n",
        "\n",
        "def generate_beam(model, tokenizer, beam_size: int = 5, prompt=None, embed=None,\n",
        "                  entry_length=40, temperature=1., stop_token: str = '.'):\n",
        "\n",
        "    model.eval()\n",
        "    stop_token_index = tokenizer.encode(stop_token)[0]\n",
        "    tokens = None\n",
        "    scores = None\n",
        "    device = next(model.parameters()).device\n",
        "    seq_lengths = torch.ones(beam_size, device=device)\n",
        "    is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)\n",
        "    with torch.no_grad():\n",
        "        if embed is not None:\n",
        "            generated = embed\n",
        "        else:\n",
        "            if tokens is None:\n",
        "                tokens = torch.tensor(tokenizer.encode(prompt))\n",
        "                tokens = tokens.unsqueeze(0).to(device)\n",
        "                generated = model.gpt.transformer.wte(tokens)\n",
        "        for i in range(entry_length):\n",
        "            outputs = model.gpt(inputs_embeds=generated)\n",
        "            logits = outputs.logits\n",
        "            logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n",
        "            logits = logits.softmax(-1).log()\n",
        "            if scores is None:\n",
        "                scores, next_tokens = logits.topk(beam_size, -1)\n",
        "                generated = generated.expand(beam_size, *generated.shape[1:])\n",
        "                next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)\n",
        "                if tokens is None:\n",
        "                    tokens = next_tokens\n",
        "                else:\n",
        "                    tokens = tokens.expand(beam_size, *tokens.shape[1:])\n",
        "                    tokens = torch.cat((tokens, next_tokens), dim=1)\n",
        "            else:\n",
        "                logits[is_stopped] = -float(np.inf)\n",
        "                logits[is_stopped, 0] = 0\n",
        "                scores_sum = scores[:, None] + logits\n",
        "                seq_lengths[~is_stopped] += 1\n",
        "                scores_sum_average = scores_sum / seq_lengths[:, None]\n",
        "                scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1)\n",
        "                next_tokens_source = next_tokens // scores_sum.shape[1]\n",
        "                seq_lengths = seq_lengths[next_tokens_source]\n",
        "                next_tokens = next_tokens % scores_sum.shape[1]\n",
        "                next_tokens = next_tokens.unsqueeze(1)\n",
        "                tokens = tokens[next_tokens_source]\n",
        "                tokens = torch.cat((tokens, next_tokens), dim=1)\n",
        "                generated = generated[next_tokens_source]\n",
        "                scores = scores_sum_average * seq_lengths\n",
        "                is_stopped = is_stopped[next_tokens_source]\n",
        "            next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1)\n",
        "            generated = torch.cat((generated, next_token_embed), dim=1)\n",
        "            is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()\n",
        "            if is_stopped.all():\n",
        "                break\n",
        "    scores = scores / seq_lengths\n",
        "    output_list = tokens.cpu().numpy()\n",
        "    output_texts = [tokenizer.decode(output[:int(length)]) for output, length in zip(output_list, seq_lengths)]\n",
        "    order = scores.argsort(descending=True)\n",
        "    #output_texts = [output_texts]\n",
        "    output_texts = [output_texts[i] for i in order]\n",
        "    return output_texts\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q0OaKtjD0uWM"
      },
      "outputs": [],
      "source": [
        "def generate2(\n",
        "        model,\n",
        "        tokenizer,\n",
        "        tokens=None,\n",
        "        prompt=None,\n",
        "        embed=None,\n",
        "        entry_count=1,\n",
        "        entry_length=35,  # maximum number of words\n",
        "        top_p=0.8,\n",
        "        temperature=1,\n",
        "        stop_token: str = '.',\n",
        "):\n",
        "    model.eval()\n",
        "    generated_num = 0\n",
        "    generated_list = []\n",
        "    stop_token_index = tokenizer.encode(stop_token)[0]\n",
        "    filter_value = -float(\"Inf\")\n",
        "    device = next(model.parameters()).device\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        for entry_idx in trange(entry_count):\n",
        "            if embed is not None:\n",
        "                generated = embed\n",
        "            else:\n",
        "                if tokens is None:\n",
        "                    tokens = torch.tensor(tokenizer.encode(prompt))\n",
        "                    tokens = tokens.unsqueeze(0).to(device)\n",
        "\n",
        "                generated = model.gpt.transformer.wte(tokens)\n",
        "\n",
        "            for i in range(entry_length):\n",
        "\n",
        "                outputs = model.gpt(inputs_embeds=generated)\n",
        "                logits = outputs.logits\n",
        "                logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n",
        "                sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n",
        "                cumulative_probs = torch.cumsum(nnf.softmax(sorted_logits, dim=-1), dim=-1)\n",
        "                sorted_indices_to_remove = cumulative_probs > top_p\n",
        "                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[\n",
        "                                                    ..., :-1\n",
        "                                                    ].clone()\n",
        "                sorted_indices_to_remove[..., 0] = 0\n",
        "\n",
        "                indices_to_remove = sorted_indices[sorted_indices_to_remove]\n",
        "                logits[:, indices_to_remove] = filter_value\n",
        "                next_token = torch.argmax(logits, -1).unsqueeze(0)\n",
        "                next_token_embed = model.gpt.transformer.wte(next_token)\n",
        "                if tokens is None:\n",
        "                    tokens = next_token\n",
        "                else:\n",
        "                    tokens = torch.cat((tokens, next_token), dim=1)\n",
        "                generated = torch.cat((generated, next_token_embed), dim=1)\n",
        "                if stop_token_index == next_token.item():\n",
        "                    break\n",
        "\n",
        "            output_list = list(tokens.squeeze().cpu().numpy())\n",
        "            output_text = tokenizer.decode(output_list)\n",
        "            generated_list.append(output_text)\n",
        "\n",
        "    return generated_list[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "izPa51ovB1WZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
            "e383ae87c3314b6f8d232334c7342879",
            "9be34c03373e44ee867c54c844623337",
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            "20123d6fdd1746ea998dabd1d79107b8",
            "18dc7c12cf614a198d4ebb657ec1d3e9",
            "c8a7747368cf4e3f9c39349291e0b4f4",
            "40807e77679b41c7a97e64d2aeef3a71",
            "ab6e89eb511b4e08abc9eee1d298f439",
            "40d2ab274c8249528c0c0a83bcf12d6c",
            "a9d5d9d784b64c088aa7a27ab7ed9b15",
            "310fb3d9bd2d435a98418c03db15e211",
            "52a4cc55b9dc4a9fb543a1aede933041",
            "368c29c0cc0e4ec599772821f7542ccf",
            "3eb781acd54f4c6d96235af209253d87",
            "a037cd1a7693437ca288087a0a01f68e",
            "b6daed65cfa9462e819a959382a13360",
            "6a153c51759c4b24b66381b896aff0f6",
            "48c89ffef10a40d5bd60bfafef8e69be",
            "6be85732f28b4685981ae6a01e8f0d5b",
            "39d3a0f124d84d4b9d7945991b4b2b95",
            "c140f5d1e42b4698b3fb94038e94226a",
            "acddbdec047f42c487d99e736a12ba25",
            "91db71734a5c4cf281a98fa0de424807",
            "a615f4497f304a8ab46bb9efa8b8db08",
            "937127cfa2d2459fa61648ea8b4ffbcc",
            "58c626e969834f8fbbb5d51643fb8357",
            "e97a50d5ecdb470395993145597ec601",
            "d602963ec9e642dc886e2c1a479d4565",
            "a7b057009169452d8a886df653c5db73",
            "2c0dce7c831c49d4aa7eaa515aa50a93",
            "0170c309c1c64af1aa6ac05def6989b2",
            "4f9782c5c9f2493ba76ccef6422554be",
            "e3aa48664faa4d8aa2ea4ab3b6d3836b",
            "e694e7d679bd4120a2fa8b9ffac86659",
            "7fefe225aa1b413b869406e2d840e365",
            "0cf9bbb628c744ae99d8aad1667787e5",
            "ee7f67371e584c3ca0a9c6c7f8dd0f18",
            "8ebb32afa7ac4c0e8a52ce3fe4c62e49",
            "c0b6f9f72e9e444782fdb4e451f90b3a",
            "a300aa78e8034940bfef48628506079a",
            "4977f40e48e443a18d3f1886b9c91686",
            "5466c727f32f40abbc2bd613656d195e",
            "18e08b4c6f524600af6ea567ebf46003",
            "2ac81a165c5e4975b599a13e09d1568b"
          ]
        },
        "outputId": "85389971-b068-423e-92f0-0aa906bfcdc4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|████████████████████████████████████████| 402M/402M [00:04<00:00, 103MiB/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e383ae87c3314b6f8d232334c7342879"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52a4cc55b9dc4a9fb543a1aede933041"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91db71734a5c4cf281a98fa0de424807"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e694e7d679bd4120a2fa8b9ffac86659"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "device = torch.device('cuda:0')\n",
        "clip_model, preprocess = clip.load(\"RN50x4\", device=device, jit=False)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aGdBCKf6B1WZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "f272e3bb0e9c42d5add9e8a5ec75bc3c",
            "34f98e286fc4449aab629b1c887260f6",
            "83f691a3228e45e4b2d9d999fa33a708",
            "999de08a62f84b07a2f73349d9987c0a",
            "524704aaebb04afe9057d3e6ddc72dc0",
            "788eefd88cbf46f492d39f172bf64ca5",
            "eb347530ea49435199e9ab1f8b0e8cc2",
            "e9f3aef0c9534936859d9304e7a91e53",
            "a86a249c88e74fd9adfc6646b473df7a",
            "9d23ef92064641cda3787ba1cac678c4",
            "38ca9d91bdf544cbabb4ee106121e199",
            "eb326b87c3ed4577a6bf757bd622eadc",
            "7543e1c3060e4b10984f4ec9bec8aac7",
            "d58548c4be8b498caa653f033539dce8",
            "f38827849f6e4897bce41318641aa64c",
            "7c8c2afbc7d14e52b6561d86c76a60fa",
            "f9efd5e295414885a9de9ee62f352512",
            "18fe193ce933440abdc17ac2aee31b49",
            "9bb4bfd54d264a19a10e4257765ebce0",
            "c203d089af094459b9a1e304f601a101",
            "9069c5baeee44192ae4a4e461dc66764",
            "a8dc066f047b4b1bb387d7d71b8dc08a"
          ]
        },
        "outputId": "2211b3bd-5959-4bab-cb89-6f4f752ec711"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f272e3bb0e9c42d5add9e8a5ec75bc3c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb326b87c3ed4577a6bf757bd622eadc"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "prefix_length = 40\n",
        "\n",
        "model = ClipCaptionPrefix(prefix_length, clip_length=40, prefix_size=640,\n",
        "                                  num_layers=8, mapping_type='transformer')\n",
        "model.load_state_dict(torch.load('/content/drive/MyDrive/SKRIPSI/Hasil,Coding/Coding/model_skrpsi_RN50x4-029.pt', map_location='cpu'))\n",
        "\n",
        "model = model.eval()\n",
        "# device = CUDA(0) if is_gpu else \"cpu\"\n",
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BFdNMCE7rre"
      },
      "source": [
        "## Dataset Predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AIET_Df7u5G",
        "outputId": "a98ba07a-255f-411b-a037-6e2da7970e4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1062.jpg\n",
            "be mindful of the parked pole in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1060.jpg\n",
            "thepassage is temporarily blocked by a car.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1059.jpg\n",
            "the way is temporarily blocked by a motorcycle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1061.jpg\n",
            "the way is temporarily blocked by a motorcycle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1058.jpg\n",
            "there is a motorcycle blocking the way.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1055.jpg\n",
            "witness the obstructed pathway obstructed by a parked car.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1056.jpg\n",
            "be mindful of the parked car obstructing the way.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1054.jpg\n",
            "be mindful of the parked car in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1053.jpg\n",
            "please watch for the parked car ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1051.jpg\n",
            "stay alert and aware there is a motorcycle blocking the way.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1035.jpg\n",
            "watch out for the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1029.jpg\n",
            "exercise extra care when walking through the big puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1050.jpg\n",
            "exercise caution and be patient as there is a motorcycle blocking the way.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1027.jpg\n",
            "exercise extra care when walking through the big puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1010.jpg\n",
            "watch out for the tree on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1026.jpg\n",
            "exercise extra care when walking through the substantial puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1025.jpg\n",
            "exercise extra care when walking through the substantial puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1017.jpg\n",
            "exercise extra care when walking through the substantial puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1021.jpg\n",
            "exercise extra care when encountering the big puddles on the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1009.jpg\n",
            "exercise extra care when encountering the big puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1397.jpg\n",
            "navigate the busy crosswalk by navigating the busy intersection on the right side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1376.jpg\n",
            "watch out for the vibrant intersection on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1600.jpg\n",
            "approaching the crosswalk with heavy traffic.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1387.jpg\n",
            "navigate the bustling street with care and care.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1375.jpg\n",
            "watch out for the bustling intersection on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1082.jpg\n",
            "there is a motorcycle blocking the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1361.jpg\n",
            "be mindful of the car and the environment that includes a car with a car with a car with a car with a car with a car with a motorcycle with a walker and a car with a walk\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1362.jpg\n",
            "prepare for the busy intersection on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1374.jpg\n",
            "pay attention to the crowded street with a motorcycle and car.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1267.jpg\n",
            "navigate the unevenness of the obstacle skirt by navigating the busy intersection on the right.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1266.jpg\n",
            "take care when step by step destination centering your foot on the left side slope.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1095.jpg\n",
            "take care to navigate around the obstacle created by the motorcycle on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1264.jpg\n",
            "navigate the largely deserted crosswalk by navigating the busy intersection on the right side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1087.jpg\n",
            "stay alert for the delayed turn around on the right side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1263.jpg\n",
            "pay attention there is a skirt on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1265.jpg\n",
            "navigate the traffic busters.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1083.jpg\n",
            "take care onto the deserted sidewalk as there is a traffic jam caused by a motorcycle obstructing the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1084.jpg\n",
            "take care that the path is not blocked by a motorcycle on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1085.jpg\n",
            "motorcycle blocking the way please feel free to route around it find an alternative route and proceed  finding an alternative route.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1086.jpg\n",
            "take necessary measures to navigate around the blocked path obstructed by a motorcycle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1075.jpg\n",
            "take necessary measures to navigate around the blocked path caused by the motorbike.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1072.jpg\n",
            "stay alert for the blocked path caused by the parked car.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1070.jpg\n",
            "stay alert to the sidewalk full of traffic caused by the motorcycle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1073.jpg\n",
            "stay alert for the blocked path caused by the motorcycle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1074.jpg\n",
            "be mindful of the blocked path caused by the parked car and the path is not smooth.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1071.jpg\n",
            "take care due to the blocked path caused by the motorcycle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1069.jpg\n",
            "stay alert to the sidewalk obstructing the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1067.jpg\n",
            "stay alert for the blocked path caused by the motorbike.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1066.jpg\n",
            "stay alert for the blocked path caused by the parked car.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1065.jpg\n",
            "take extra care to navigate around the blocked path caused by the motorcycle parked on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1783.jpg\n",
            "take heed of the ditch situated on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1782.jpg\n",
            "approach the ravine with the ditch on the left side of the road with the ditch on the right.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1784.jpg\n",
            "stay alert to the presence of the ditch on the left side of the road that can be hazardous.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1778.jpg\n",
            "navigate the busy crosswalk with care.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1774.jpg\n",
            "beware of the ditch positioned by the right side of the road that runs parallel to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1776.jpg\n",
            "stay alert to the left side of the path there is a ditch running parallel to the right side of the street.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1775.jpg\n",
            "take notice of the ditch on the right side of the road that exists in the right side of the road that exists in the right side of the road that exists in the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1773.jpg\n",
            "stay alert to the presence of a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1772.jpg\n",
            "stay alert to the presence of the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1771.jpg\n",
            "there is a ditch on the left side of the road that needs your attention.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1686.jpg\n",
            "stay alert to the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1683.jpg\n",
            "stay alert to the presence of the ditch on the left side of the road that stretches along the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1684.jpg\n",
            "beware of the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1685.jpg\n",
            "there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1770.jpg\n",
            "take notice of the ditch on the left side of the road that runs parallel to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1687.jpg\n",
            "beware of the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1746.jpg\n",
            "please be aware of the approaching crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1675.jpg\n",
            "take notice of the ditch positioned on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1682.jpg\n",
            "stay alert there is a ditch on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1674.jpg\n",
            "take notice of the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1673.jpg\n",
            "stay alert to the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1601.jpg\n",
            "take your time to cross the crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1651.jpg\n",
            "beware of the heavy traffic that goes through the crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1672.jpg\n",
            "deep canal on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1663.jpg\n",
            "take your time to cross the marked crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1650.jpg\n",
            "beware of the crosswalk with heavy traffic.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1664.jpg\n",
            "take care when approaching the crosswalk that has a high volume of traffic.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1671.jpg\n",
            "beware of the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1652.jpg\n",
            "beware of the heavy traffic that goes through the crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1603.jpg\n",
            "take care when approaching the crosswalk with high volume of traffic.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1931.jpg\n",
            "traveling near the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1928.jpg\n",
            "there is a ditch on the left side of the road that can be hazardous.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1929.jpg\n",
            "watch out for the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1930.jpg\n",
            "there is a ditch on the left side of the road that you should avoid.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1926.jpg\n",
            "traveling near the ditch on the left side of the road be careful and avoid the hazard of the ditch on the side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1925.jpg\n",
            "mind there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1922.jpg\n",
            "approach the road with the road with the ditch on the left side cautiously.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1921.jpg\n",
            "watch out for the ditch on the left side of the road met on the right side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1923.jpg\n",
            "traveling near the ditch on the left side of the road and beware of the ditch that runs parallel to the road on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1920.jpg\n",
            "watch out for the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1919.jpg\n",
            "traveling near the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1918.jpg\n",
            "traveling near the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1916.jpg\n",
            "deep canal on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1909.jpg\n",
            "be cautious of the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1917.jpg\n",
            "traveling near the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1899.jpg\n",
            "take notice of the ditch that runs parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1910.jpg\n",
            "mind the presence of the ditch on the left side of the road that poses a barrier to navigate.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1881.jpg\n",
            "stay vigilant on the right side of the road as there is a deep trench running along the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1816.jpg\n",
            "take care when entering the crosswalk area with a crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1877.jpg\n",
            "be mindful of the crosswalk ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1792.jpg\n",
            "take extra care when entering the area with a steady foot.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1788.jpg\n",
            "stay alert to the presence of the ditch on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1865.jpg\n",
            "watch your step there is a crosswalk with a crosswalk ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1804.jpg\n",
            "navigate the busy crosswalk with care.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1900.jpg\n",
            "take extra care when walking near the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1898.jpg\n",
            "watch out for the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1897.jpg\n",
            "beware of the ditch that runs parallel to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1893.jpg\n",
            "watch out for the hazardous ditch on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1786.jpg\n",
            "deep canal on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1785.jpg\n",
            "stay alert to the presence of the ditch on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1787.jpg\n",
            "stay alert to the presence of the ditch on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2274.jpg\n",
            "prepare for the obstruction caused by a stationary vehicle obstructing the roadway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2222.jpg\n",
            "watch your step there is a crosswalk ahead with a crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2209.jpg\n",
            "take your time and wait for the busy crosswalk to become closed before proceeding.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2096.jpg\n",
            "take heed of the substantial crevasse bordering the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2173.jpg\n",
            "watch your step there is a crosswalk with a crosswalk ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2272.jpg\n",
            "prepare for the obstruction in your path created by a parked car hindering forward progress.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2260.jpg\n",
            "take heed of the hindrance created by a parked car obstructing the way.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2094.jpg\n",
            "take heed of the substantial trench running parallel to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2093.jpg\n",
            "take heed of the substantial crevices adjacent to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2095.jpg\n",
            "exercise vigilance near the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2083.jpg\n",
            "watch out for the deep ravine running parallel to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2084.jpg\n",
            "take heed of the substantial crevasse bordering the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2090.jpg\n",
            "take heed of the substantial crevasse bordering the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2082.jpg\n",
            "take heed of the substantial crevasse bordering the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2089.jpg\n",
            "take heed of the sizable ditch adjacent to the road on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2091.jpg\n",
            "watch out for the deep ravine running parallel to the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2092.jpg\n",
            "take heed of the substantial crevasse bordering the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2076.jpg\n",
            "take heed of the substantial crevasse bordering the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2081.jpg\n",
            "take heed of the substantial crevasse bordering the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2078.jpg\n",
            "take heed of the substantial crevasse bordering the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2080.jpg\n",
            "watch out for the deep ditch on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1963.jpg\n",
            "watch out for thepolicing on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2065.jpg\n",
            "take heed of the substantial crevasse bordering the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2063.jpg\n",
            "take heed of the substantial crevasse adjacent to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2064.jpg\n",
            "navigate the treacherous void that stretches along the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1934.jpg\n",
            "be mindful of the ditch running parallel to the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1987.jpg\n",
            "navigate the busy crosswalk with care and care.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1932.jpg\n",
            "traveling near the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1933.jpg\n",
            "stay alert to the presence of the ditch on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_1975.jpg\n",
            "be mindful of the danger caused by the presence of the parked car and the presence of the parked car.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2645.jpg\n",
            "stay alert to the presence of gaps and depressions on the pavement adapting your steps to ensure a smooth journey.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2642.jpg\n",
            "watch out for the intermittent holes and depressions that surround the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2646.jpg\n",
            "watch out for the uneven sections of the pathway characterized by holes and depressions that can present challenges while walking.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2644.jpg\n",
            "watch out for the intermittent holes and hollows that surround the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2643.jpg\n",
            "there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2678.jpg\n",
            "watch out for the intermittent holes and hollows on the walkway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2638.jpg\n",
            "watch out for the intermittent holes and hollows that surround the pathway on the left side of the walkway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2636.jpg\n",
            "mind the irregular surface of the walkway marked by intermittent holes and depressions that may require careful navigation.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2637.jpg\n",
            "exercise mindfulness on the hole studded pavement djusting your stride to avoid any potential trip hazards.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2557.jpg\n",
            "revel in the uninterrupted and meticulously maintained footpath.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2639.jpg\n",
            "watch out for the intermittent holes and hollows that surround the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2653.jpg\n",
            "watch out for the hollow and depressured footpaths on the left side of the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2648.jpg\n",
            "watch out for the uneven sections of the pathway characterized by holes and depressions that can present challenges while walking.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2647.jpg\n",
            "watch out for the uneven sections of the pathway characterized by holes and depressions that can present challenges while walking.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2667.jpg\n",
            "watch your footing on the hole riddled pavement as uneven surfaces and cavities demand careful attention.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2641.jpg\n",
            "stay alert to the presence of gaps and hollows on the pavement adapting your steps to ensure a smooth journey.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2635.jpg\n",
            "stay alert to the presence of gaps and depressions on the pavement.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2552.jpg\n",
            "revel in the seamless and flawlessly maintained footpath.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2634.jpg\n",
            "watch out for the intermittent holes and depressions that surround the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2551.jpg\n",
            "take advantage of the impeccable condition of the pedestrian walkway without holes or any obstacle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2550.jpg\n",
            "enjoy the faultless and seamless pavement without holes or any obstacle.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2466.jpg\n",
            "watch out for the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2465.jpg\n",
            "admire the pristine state of the walkway without holes.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2549.jpg\n",
            "enjoy the seamless and flawlessly maintained footpaths without any holes.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2453.jpg\n",
            "admire the pristine state of the walkway without holes.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2464.jpg\n",
            "admire the pristine state of the walkway without holes.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2471.jpg\n",
            "appreciate the impeccable condition of the sidewalk without holes requiring careful maneuvering.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2452.jpg\n",
            "appreciate the flawlessly maintained sidewalk without holes.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2449.jpg\n",
            "take pleasure in the pristine condition of the walkway without holes remaining smooth despite the presence of trees or poles that may require careful navigation.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2276.jpg\n",
            "be mindful of the stationary vehicle ahead presenting a barrier that necessitates finding an alternate route.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2985.jpg\n",
            "take note of the sales sign in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2963.jpg\n",
            "be cautious of the substantial hole in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2950.jpg\n",
            "watch out for the deep gaps in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2913.jpg\n",
            "stay alert to the presence of poles that run parallel to the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2872.jpg\n",
            "mind the presence of sales cart and other obstructions in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2889.jpg\n",
            "exercise mindfulness on the hole studded pavement djusting your stride to avoid any potential trip hazards.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2842.jpg\n",
            "exercise caution as there is a hole in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2855.jpg\n",
            "take note of the sales sign in the middle of the sidewalk and proceed with caution.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2847.jpg\n",
            "take note of the sales sign in the middle of the sidewalk and proceed with caution.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2786.jpg\n",
            "prepare for the obstacle ahead where a car is blocking the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2682.jpg\n",
            "be mindful of the intermittent holes and hollows on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2683.jpg\n",
            "be mindful of the intermittent holes and hollows on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2684.jpg\n",
            "be mindful of the intermittent holes and recesses on the walkway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2685.jpg\n",
            "watch out for the intermittent holes and hollows on the pavement.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2687.jpg\n",
            "watch out for the intermittent holes and hollows on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2785.jpg\n",
            "keep an eye out for the car that is blocking the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2781.jpg\n",
            "prepare for a momentary delay as you pass a car that requires you to pass through the gap in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2784.jpg\n",
            "watch out for the car parked on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2782.jpg\n",
            "prepare for a barrier ahead where a car lies ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2783.jpg\n",
            "prepare for a momentary delay as you encounter a stationary vehicle ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2780.jpg\n",
            "prepare for a barrier ahead requiring you to navigate around it or be prepared for a barrier ahead requiring you to navigate around it or be prepared for a barrier ahead requiring you to navigate around it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2706.jpg\n",
            "watch out for the intermittent holes and hollows that surround the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2705.jpg\n",
            "watch out for the intermittent holes and hollows that surround the middle of the walkway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2704.jpg\n",
            "watch out for the intermittent holes and hollows that line the pathway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2779.jpg\n",
            "prepare for the barrier created by a car ahead requiring you to navigate around it or navigate around it on your own.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2696.jpg\n",
            "watch out for the intermittent holes and hollows on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2693.jpg\n",
            "be mindful of the sporadic holes and hollows on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2686.jpg\n",
            "watch out for the intermittent holes and hollows that await.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2681.jpg\n",
            "watch out for the intermittent holes and hollows that await unsuspecting pedestrians.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_2680.jpg\n",
            "watch your step on the hole riddled pavement as uneven surfaces and cavities demand careful attention.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3081.jpg\n",
            "be mindful of the rocky and rocky terrain that surrounds the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3077.jpg\n",
            "be aware of the rocky and uneven ground on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3079.jpg\n",
            "be aware of the rocky and uneven ground on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3078.jpg\n",
            "be aware of the rocky and uneven ground on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3080.jpg\n",
            "be mindful of the rocky and rocky terrain that surrounds the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3069.jpg\n",
            "watch out for the parked car that is blocking the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3067.jpg\n",
            "watch out for the sales cart on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3054.jpg\n",
            "be mindful of the poles in the right side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3053.jpg\n",
            "be mindful of the poles in the middle of the path that may require careful navigation.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3070.jpg\n",
            "take notice of the parked car in the middle of the sidewalk requiring you to navigate around it safely.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3068.jpg\n",
            "take care there is a parked car obstructing the sidewalk requiring you to navigate around it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3066.jpg\n",
            "be mindful of the sales cart that is blocking the pathway on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3065.jpg\n",
            "be mindful of the sales cart on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3036.jpg\n",
            "watch out for the obstacles on the left side of the path.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3033.jpg\n",
            "pay attention to the pole located on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3035.jpg\n",
            "exercise caution with the obstacles on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3042.jpg\n",
            "mind the presence of poles in the middle of the sidewalk that may require your attention.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3043.jpg\n",
            "watch out for the trees on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3050.jpg\n",
            "be mindful of the sales sign positioned on the right side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3041.jpg\n",
            "mind the presence of trees in the left side of the sidewalk and the presence of poles in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3034.jpg\n",
            "exercise caution when approaching the pillar in the right side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3030.jpg\n",
            "watch out for the sales cart obstructing the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3019.jpg\n",
            "watch your footing on the hole ridden pavement as irregular indentations and cavities demand careful attention.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3029.jpg\n",
            "be cautious of the sales cart positioned on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3025.jpg\n",
            "watch out for the hole in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3031.jpg\n",
            "mind the presence of a sales cart on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3032.jpg\n",
            "exercise caution with the pole located on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3015.jpg\n",
            "watch out for the parked car obstructing the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3014.jpg\n",
            "watch out for the parked parked car obstructing the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3013.jpg\n",
            "take care as there is a parked car obstructing the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3414.jpg\n",
            "keep a safe distance from the car and be prepared to yield if necessary.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3367.jpg\n",
            "mind the presence of sales cart and other obstructions in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3366.jpg\n",
            "exercise caution and navigate around the sales cart on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3354.jpg\n",
            "the presence of the sales cart requires pedestrians to navigate around it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3364.jpg\n",
            "the presence of the sales cart requires pedestrians to navigate around it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3343.jpg\n",
            "the sales cart positioned in front poses a challenge for pedestrians.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3340.jpg\n",
            "the sales cart positioned in front poses a challenge for pedestrians.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3339.jpg\n",
            "the sales cart positioned ahead poses a challenge for pedestrians.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3214.jpg\n",
            "exercise caution as there is a tree blocking the path on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3212.jpg\n",
            "exercise caution when passing the tree on the sidewalk as there is a tree obstructing the path.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3215.jpg\n",
            "exercise care when walking on the mural obstructing the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3324.jpg\n",
            "take note of the sales cart placed in the middle of the road that may require you to adjust your route.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3237.jpg\n",
            "pay attention to the area where there is a motorcycle obstructing the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3235.jpg\n",
            "exercise extra care as you encounter the parked car and the obstacles on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3213.jpg\n",
            "mind the tree obstructing the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3200.jpg\n",
            "exercise caution as there is a pole in the middle of the sidewalk that requires careful navigation.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3185.jpg\n",
            "exercise mindful as you encounter the pillar in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3186.jpg\n",
            "be mindful of the pillar positioned in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3198.jpg\n",
            "pay attention to the pillar in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3199.jpg\n",
            "exercise caution as there is an pole obstructing the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3184.jpg\n",
            "be mindful of the pole positioned on the right side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3164.jpg\n",
            "approach the sidewalk carefully as there is a sales sign placed in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3173.jpg\n",
            "be mindful of the sales sign in the middle of the sidewalk and the sales cart in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3171.jpg\n",
            "watch out for the sales sign in the middle of the sidewalk and watch out for the sales cart in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3163.jpg\n",
            "take note of the sales sign on the sidewalk and proceed with caution.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3162.jpg\n",
            "watch out for the sales sign on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3161.jpg\n",
            "exercise caution as there is a sales sign placed in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3159.jpg\n",
            "be mindful of the sales sign in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3160.jpg\n",
            "be mindful of the sales sign in the middle of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3082.jpg\n",
            "be mindful of the rocky and rocky terrain that surrounds the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3785.jpg\n",
            "take note of the tree ahead and be mindful of the obstacles on the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3775.jpg\n",
            "be aware of the traffic light ahead and navigate carefully.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3774.jpg\n",
            "be mindful of the traffic signs in the traffic light.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3773.jpg\n",
            "be mindful of the heavy traffic ahead as you approach the traffic light.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3771.jpg\n",
            "stay alert and navigate through the traffic light area as you approach the traffic light zone.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3760.jpg\n",
            "be mindful of the traffic light ahead and navigate with caution.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3772.jpg\n",
            "be mindful of the traffic light ahead and navigate with caution when approaching the traffic light.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3762.jpg\n",
            "exercise care when navigating around the car ahead of you.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3766.jpg\n",
            "be mindful of the traffic light ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3763.jpg\n",
            "be mindful of the obstacles ahead and be aware of your surroundings.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3754.jpg\n",
            "be mindful of the surrounding traffic as you enter the traffic light zone.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3759.jpg\n",
            "be mindful of the traffic light ahead and navigate with caution when navigating through the traffic light.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3749.jpg\n",
            "exercise extra care when navigating around the car ahead and watch for obstacles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3725.jpg\n",
            "take into consideration the presence of a parked car on the right side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3739.jpg\n",
            "be cautious of the sales cart ahead and be prepared to navigate around it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3755.jpg\n",
            "exercise care when approaching the traffic light area with a lack of traffic.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3753.jpg\n",
            "pay attention to the traffic light ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3746.jpg\n",
            "be cautious of the sales cart on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3738.jpg\n",
            "exercise extra care when navigating around the sales cart on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3737.jpg\n",
            "be mindful of the sales cart on the right side and be prepared to navigate around it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3624.jpg\n",
            "watch out for the distant pennant on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3623.jpg\n",
            "watch out for the distant sea on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3565.jpg\n",
            "stay alert there is a sales cart ahead requiring careful navigation.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3564.jpg\n",
            "mind the sales cart up there is a sales cart and there is a sales cart ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3417.jpg\n",
            "take note of the oncoming car and adjust your path accordingly.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3420.jpg\n",
            "take note of the oncoming car and navigate around it with or without it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3418.jpg\n",
            "keep a safe distance from the car and be prepared to navigate around it with or without it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3566.jpg\n",
            "caution is in order to navigate around the sales cart and highway.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3419.jpg\n",
            "keep a safe distance from the car and be mindful of your surroundings.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3421.jpg\n",
            "take note of the presence of the car and be prepared to navigate around it.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3416.jpg\n",
            "take note of the incoming car and be prepared to navigate around it properly.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3415.jpg\n",
            "take note of the parked car and maintain a safe distance from the parked car.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_653.jpg\n",
            "watch out there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_64.jpg\n",
            "be mindful of the parked car on the roadside.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_63.jpg\n",
            "watch out for the parked car on the side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_62.jpg\n",
            "be mindful of the parked car that is blocking the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_556.jpg\n",
            "exercise extra care when entering the crosswalk area.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_566.jpg\n",
            "exercise extra care while navigating through the presence of the parked car and/or motorbike.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_575.jpg\n",
            "navigate the busy crosswalk with care.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_442.jpg\n",
            "be aware of the presence of the crowd at the designated pedestrian zone on the right side of the street.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_542.jpg\n",
            "navigate the busy crosswalk with care.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_61.jpg\n",
            "take care of the car parked on the pathway that is blocked by parked cars on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_576.jpg\n",
            "watch your step there is a crosswalk running ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_545.jpg\n",
            "take your time to cross the busy crosswalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_553.jpg\n",
            "take care when entering the crosswalk area look both ways.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_574.jpg\n",
            "take care when entering the crosswalk that is ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_555.jpg\n",
            "take immediate precautions when entering the crosswalk area.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_454.jpg\n",
            "approaching the neglected left side of the path on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_449.jpg\n",
            "be careful there is a crosswalk running ahead.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_451.jpg\n",
            "watch out for the wall of traffic on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_453.jpg\n",
            "navigate the busy left side of the road with care.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_452.jpg\n",
            "be aware there is a solid barrier on the left side of the path.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_448.jpg\n",
            "there is a crosswalk on the left side of the street.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_441.jpg\n",
            "use caution when approaching the area with high volume of traffic.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_440.jpg\n",
            "be aware of the vehicle's presence on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_439.jpg\n",
            "navigate the busy crosswalk with care and be aware of your surroundings.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3792.jpg\n",
            "stay alert and navigate around the car ahead without any obstacles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_428.jpg\n",
            "navigate the crowded left side of the road with caution.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3790.jpg\n",
            "be mindful of the large amounts of vehicles waiting for you in the traffic light area.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_429.jpg\n",
            "navigate the crowded street with care at all times.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3791.jpg\n",
            "exercise care when navigating around the traffic-free zone on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_427.jpg\n",
            "watch out for the huge crowd at the busy intersection on the left side.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3800.jpg\n",
            "stay alert for the delayed turn around on the right side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_81.jpg\n",
            "stay alert and navigate around the car parked on the side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_810.jpg\n",
            "exercise extra care when encountering the big puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_679.jpg\n",
            "exercise caution on the uneven terrain                                 \n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_79.jpg\n",
            "watch out for the car parked on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_76.jpg\n",
            "watch out for the parked car that is trying to cause a problem on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_78.jpg\n",
            "watch out for the car parked on the left side of the sidewalk be careful! also be careful about the parked car on the left side of the sidewalk be careful! be careful about the parked car on\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_75.jpg\n",
            "watch out for the parked car parked on the side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_662.jpg\n",
            "watch out there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_661.jpg\n",
            "there is a ditch on the left side of the road that can be seen on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_74.jpg\n",
            "watch out for the parked car that blocks the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_72.jpg\n",
            "watch out for the parked car navigating around the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_68.jpg\n",
            "be mindful of the parked cars that are blocking the way there is a parked car that is blocking the way.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_73.jpg\n",
            "watch out for the parked car navigating around the road with caution.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_677.jpg\n",
            "watch out for the tree on the left side of the path.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_678.jpg\n",
            "there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_673.jpg\n",
            "be mindful of the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_675.jpg\n",
            "be mindful of the ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_676.jpg\n",
            "observe the substantial hole on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_674.jpg\n",
            "obey the tree on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_71.jpg\n",
            "watch out for the parked car on the left side of the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_665.jpg\n",
            "there is a ditch on the left side of the road on the right side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_663.jpg\n",
            "watch out there is a long ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_70.jpg\n",
            "watch out for the parked car on the sidewalk.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_67.jpg\n",
            "sidewalk is blocked by a car that is blocking the way there is also a car that is blocking the way there.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_667.jpg\n",
            "be careful to navigate there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_655.jpg\n",
            "there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_656.jpg\n",
            "be mindful of the ditch located on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_654.jpg\n",
            "watch out there is a ditch on the left side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_66.jpg\n",
            "sidewalk is blocked by a car that blocks the path.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_881.jpg\n",
            "exercise extra care when navigating through the significant puddles on the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_877.jpg\n",
            "exercise extra care when navigating through the substantial puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_964.jpg\n",
            "exercise extra caution when walking through the substantial puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_972.jpg\n",
            "exercise extra care when walking through the big puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_962.jpg\n",
            "exercise extra caution when walking through the large puddles in the middle of the middle of the middle of the middle of the middle of the middle of the middle of the middle of the middle of\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_970.jpg\n",
            "exercise extra care when walking through the large puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_969.jpg\n",
            "exercise extra care when walking through the large puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_971.jpg\n",
            "exercise extra care when walking through the jungle on the small side of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_959.jpg\n",
            "exercise extra care when walking on the large puddles in the middle of the middle of the middle of the middle of the middle of the middle of the middle of the middle of the middle of\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_960.jpg\n",
            "exercise extra care when walking through the large puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_961.jpg\n",
            "exercise extra care when walking through the large puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_958.jpg\n",
            "exercise extra care when walking through the big puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_884.jpg\n",
            "exercise extra care when walking through the substantial puddles on the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_882.jpg\n",
            "exercise extra care when walking through the substantial puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_949.jpg\n",
            "exercise extra care when walking through the substantial puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_892.jpg\n",
            "exercise extra care when walking through the large puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_951.jpg\n",
            "exercise extra care when walking through the big puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_952.jpg\n",
            "exercise extra care when walking through the large puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_821.jpg\n",
            "exercise extra care when walking through the substantial puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_822.jpg\n",
            "exercise extra care when walking through the substantial puddles on the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_845.jpg\n",
            "exercise extra care when walking through the big puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_82.jpg\n",
            "watch out for the parked car on the left side of the sidewalk be careful of the parked car on the right side of the sidewalk be careful of the parked car on the left side of the sidewalk be\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_820.jpg\n",
            "watch out for the significant puddles in the middle of the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_865.jpg\n",
            "watch out for the tree on the left side of the street.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_847.jpg\n",
            "exercise extra care when walking through the big puddles.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_819.jpg\n",
            "watch out for the large puddles on the road.\n",
            "\n",
            "\n",
            "/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_808.jpg\n",
            "watch out for the substantial puddles in the middle of the road.\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import glob\n",
        "from PIL import Image\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras import models\n",
        "from PIL import Image\n",
        "from skimage.io import imread\n",
        "import cv2\n",
        "\n",
        "import glob\n",
        "\n",
        "# get the path/directory\n",
        "folder_dir = '/content/drive/MyDrive/SKRIPSI/Gambar Test'\n",
        "\n",
        "# iterate over files in\n",
        "# that directory\n",
        "for images in glob.glob(f'{folder_dir}/*'):\n",
        "\n",
        "    # check if the image ends with png\n",
        "    if (images.endswith(\".jpg\")):\n",
        "\n",
        "        print(images)\n",
        "        imagess = Image.open(images).convert(\"RGB\")\n",
        "        image = preprocess(imagess).unsqueeze(0).to(device)\n",
        "\n",
        "\n",
        "    use_beam_search = True\n",
        "    with torch.no_grad():\n",
        "        prefix = clip_model.encode_image(image).to(device, dtype=torch.float32)\n",
        "        prefix = prefix / prefix.norm(2, -1).item()\n",
        "        prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)\n",
        "    if use_beam_search:\n",
        "        generated_text_prefix = generate_beam(model, tokenizer,embed=prefix_embed,beam_size=7,stop_token='.',entry_length=40)[0]\n",
        "    else:\n",
        "        generated_text_prefix = generate2(model, tokenizer, embed=prefix_embed)\n",
        "\n",
        "\n",
        "    print(generated_text_prefix)\n",
        "    print(\"\\n\")\n",
        "\n",
        "    with open(\"/content/output_caption_skripsi.txt\", \"a\") as f:\n",
        "      print(images,generated_text_prefix, file=f)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7cWeQ6f7pqe"
      },
      "source": [
        "## Single Predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "NYAaSS9IB1Wa",
        "outputId": "ffe2c799-ce07-4278-9b8b-24a3566c7d54"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=500x500>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "images = Image.open('/content/drive/MyDrive/SKRIPSI/Gambar Test/gl_3737.jpg').convert(\"RGB\")\n",
        "image = preprocess(images).unsqueeze(0).to(device)\n",
        "display(images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfXhvMIEZvGP",
        "outputId": "0b9ce108-3024-4352-fc93-c531eea16bb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "be mindful of the sales cart on the right side and be prepared to navigate around it.\n"
          ]
        }
      ],
      "source": [
        "use_beam_search = True\n",
        "\n",
        "# image = io.imread(UPLOADED_FILE)\n",
        "# pil_image = PIL.Image.fromarray(image)\n",
        "# #pil_img = Image(filename=UPLOADED_FILE)\n",
        "# display(pil_image)\n",
        "\n",
        "# image = preprocess(pil_image).unsqueeze(0).to(device)\n",
        "with torch.no_grad():\n",
        "    prefix = clip_model.encode_image(image).to(device, dtype=torch.float32)\n",
        "    prefix = prefix / prefix.norm(2, -1).item()\n",
        "    prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)\n",
        "if use_beam_search:\n",
        "    generated_text_prefix = generate_beam(model, tokenizer,embed=prefix_embed,beam_size=7,stop_token='.',entry_length=40)[0]\n",
        "else:\n",
        "    generated_text_prefix = generate2(model, tokenizer, embed=prefix_embed)\n",
        "print('\\n')\n",
        "print(generated_text_prefix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3F_Ux8lEnOHg"
      },
      "source": [
        "## Evaluasi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXogh6nnrU86",
        "outputId": "94966075-9bf4-4d7a-e63e-bc449ba3668b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'image-caption-metrics'...\n",
            "remote: Enumerating objects: 87, done.\u001b[K\n",
            "remote: Counting objects: 100% (21/21), done.\u001b[K\n",
            "remote: Compressing objects: 100% (21/21), done.\u001b[K\n",
            "remote: Total 87 (delta 9), reused 1 (delta 0), pack-reused 66\u001b[K\n",
            "Receiving objects: 100% (87/87), 93.73 MiB | 26.42 MiB/s, done.\n",
            "Resolving deltas: 100% (11/11), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/EricWWWW/image-caption-metrics.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4EkrvGHsVb7",
        "outputId": "d6c99b18-1f60-4087-f8e7-01cac4e402b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/image-caption-metrics\n"
          ]
        }
      ],
      "source": [
        "cd image-caption-metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_J47qkMFw7ZI"
      },
      "outputs": [],
      "source": [
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5O2XeI3saW4",
        "outputId": "beeec4c0-ee27-459e-be17-121aa7128016"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score meteor yang didapatkan adalah\t0.502059933364088\n",
            "{'testlen': 5029, 'reflen': 4858, 'guess': [5029, 4649, 4269, 3889], 'correct': [4147, 3492, 2990, 2586]}\n",
            "ratio: 1.0351996706461433\n",
            "score bleu yang didapatkan adalah\t[0.8246172201231209, 0.7870159685778834, 0.7570142255776148, 0.7328678579730313]\n"
          ]
        }
      ],
      "source": [
        "import pycocoevalcap.eval as E\n",
        "\n",
        "with open('/content/test_token_RN50x4_2.json', 'r') as f:\n",
        "    gts = json.load(f)\n",
        "with open('/content/output_tes_token_skripsi.json', 'r') as f:\n",
        "    res = json.load(f)\n",
        "\n",
        "meteor = E.get_meteor(gts,res)\n",
        "print(\"score meteor yang didapatkan adalah\"+ \"\\t\" +str(meteor))\n",
        "\n",
        "bleu = E.get_bleu(gts,res)\n",
        "print(\"score bleu yang didapatkan adalah\"+ \"\\t\" +str(bleu))\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "A6P4BL1s8eHz",
        "outputId": "ca549415-f054-4037-e0ba-76c9f49b6782"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Skor BLEU, METEOR, CIDEr, dan ROUGE\n",
        "bleu_scores = [0.8246172201231209, 0.7870159685778834, 0.7570142255776148,  0.7328678579730313]\n",
        "meteor_scores = [meteor]\n",
        "\n",
        "\n",
        "# Nama metrik\n",
        "metric_names = ['BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'METEOR']\n",
        "\n",
        "# Menambahkan skor METEOR, CIDEr, dan ROUGE ke dalam daftar skor BLEU\n",
        "bleu_scores.extend([meteor_scores[0]])\n",
        "\n",
        "# Plotting grafik\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(metric_names, bleu_scores)\n",
        "\n",
        "# Menambahkan label pada sumbu x dan y\n",
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