method3.ipynb

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      "gpuType": "T4"
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "tvK7C5tzhedc"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import spacy\n",
        "from transformers import CLIPModel, CLIPProcessor\n",
        "from datasets import load_dataset\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import re\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KU6UbL8Ur3Rs",
        "outputId": "f841d6ea-4f32-419e-9005-c3583a83f6a4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load CLIP Model and Processor\n",
        "def load_clip_model(device):\n",
        "    model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
        "    processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
        "    model.to(device)\n",
        "    return model, processor\n",
        "\n",
        "# Compute CLIP Embedding for Image\n",
        "def get_image_embedding(model, processor, image, device):\n",
        "    inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
        "    with torch.no_grad():\n",
        "        image_embedding = model.get_image_features(**inputs)\n",
        "    return image_embedding.cpu().numpy()\n",
        "\n",
        "# Compute CLIP Embedding for Text\n",
        "def get_text_embedding(model, processor, text, device):\n",
        "    inputs = processor(text=text, return_tensors=\"pt\").to(device)\n",
        "    with torch.no_grad():\n",
        "        text_embedding = model.get_text_features(**inputs)\n",
        "    return text_embedding.cpu().numpy()\n",
        "\n",
        "# Calculate Cosine Similarity\n",
        "def calculate_similarity(embedding1, embedding2):\n",
        "    return cosine_similarity(embedding1, embedding2)[0][0]\n",
        "\n",
        "# Extract adjectives from a given caption\n",
        "spacy.require_gpu()\n",
        "nlp = spacy.load(\"en_core_web_sm\", disable=[\"ner\", \"parser\"])\n",
        "\n",
        "def extract_adjectives(caption):\n",
        "    doc = nlp(caption)\n",
        "    return [token.text for token in doc if token.pos_ == \"ADJ\"]\n",
        "    #return list(set([token.text for token in doc if token.pos_ == \"ADJ\"]))\n",
        "\n",
        "# Compare Adjective-Modified Captions\n",
        "def compare_adjective_embeddings(image_id, original_caption, generated_caption, adjectives, image_embedding, model, processor, device):\n",
        "    \"\"\"\n",
        "    Compare angular distance (angle) of image embedding with each adjective vs. adjective-free text embeddings.\n",
        "    Returns a dictionary with image_id, original caption, adjectives, and hallucination status for the caption.\n",
        "    \"\"\"\n",
        "    adjectives_list = []\n",
        "    hallucinated_list = []\n",
        "\n",
        "    for adjective in adjectives:\n",
        "        # Remove the selected adjective from the caption\n",
        "        pattern = r'\\b' + re.escape(adjective) + r'\\b'\n",
        "        adjective_free_caption = re.sub(pattern, '', generated_caption).strip()\n",
        "\n",
        "        # Get embeddings for the generated and adjective-free captions\n",
        "        generated_embedding = get_text_embedding(model, processor, generated_caption, device)\n",
        "        adjective_free_embedding = get_text_embedding(model, processor, adjective_free_caption, device)\n",
        "\n",
        "        # Calculate cosine similarities\n",
        "        sim_with_adjective = calculate_similarity(image_embedding, generated_embedding)\n",
        "        sim_without_adjective = calculate_similarity(image_embedding, adjective_free_embedding)\n",
        "\n",
        "        # Convert cosine similarity to angular distance (in radians)\n",
        "        angle_with_adjective = np.arccos(sim_with_adjective)\n",
        "        angle_without_adjective = np.arccos(sim_without_adjective)\n",
        "\n",
        "        # Determine if adjective is hallucinated\n",
        "        hallucinated = angle_with_adjective > angle_without_adjective\n",
        "\n",
        "        # Append the adjective and hallucination result to the lists\n",
        "        adjectives_list.append(adjective)\n",
        "        hallucinated_list.append(hallucinated)\n",
        "\n",
        "    # Create a dictionary for the image_id, original caption, adjectives, and hallucinated status\n",
        "    result = {\n",
        "        \"image_id\": image_id,\n",
        "        \"original_caption\": original_caption,\n",
        "        \"generated_caption\": generated_caption,\n",
        "        \"adjectives\": f\"[{', '.join(adjectives_list)}]\",  # Format adjectives as a list\n",
        "        \"hallucinated\": f\"[{', '.join([str(hall) for hall in hallucinated_list])}]\"  # Format hallucinated status as a list\n",
        "    }\n",
        "\n",
        "    return result\n"
      ],
      "metadata": {
        "id": "5jHzeh7BhmHV"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Hugging Face Dataset for both images and captions\n",
        "def load_hf_image_and_caption_datasets(image_dataset_name=\"wlsdml357/OpenCHAIR_Adjective\", caption_dataset_name=\"wlsdml357/OpenCHAIR_Adjective_Captions\"):\n",
        "    image_dataset = load_dataset(image_dataset_name)\n",
        "    caption_dataset = load_dataset(caption_dataset_name)\n",
        "    return image_dataset, caption_dataset\n",
        "\n",
        "# Process Images and Captions\n",
        "def process_dataset(image_dataset, caption_dataset, model, processor, device, caption_column_name=\"text\", output_file_name=\"results.csv\"):\n",
        "    results = []\n",
        "    for idx in range(len(caption_dataset[\"train\"])):\n",
        "        image_id = caption_dataset[\"train\"][idx][\"image_id\"]\n",
        "        original_caption = caption_dataset[\"train\"][idx][\"original_caption\"]\n",
        "        image = image_dataset[\"test\"][idx][\"image\"]\n",
        "        generated_caption = caption_dataset[\"train\"][idx][caption_column_name]\n",
        "\n",
        "        # Extract adjectives from the caption\n",
        "        adjectives = extract_adjectives(generated_caption)\n",
        "\n",
        "        # Get image embedding once for each image\n",
        "        image_embedding = get_image_embedding(model, processor, image, device)\n",
        "\n",
        "        # Compare each adjective and check if it's hallucinated\n",
        "        result = compare_adjective_embeddings(image_id, original_caption, generated_caption, adjectives, image_embedding, model, processor, device)\n",
        "        results.append(result)\n",
        "\n",
        "        print(f\"Processed {idx+1}/{len(image_dataset['test'])}\")\n",
        "\n",
        "    save_results_to_csv(results, output_file_name)\n",
        "\n",
        "    return results\n",
        "\n",
        "# Save Results to CSV\n",
        "def save_results_to_csv(results, output_file_name=\"results.csv\"):\n",
        "    df = pd.DataFrame(results)\n",
        "    df.to_csv(output_file_name, index=False)\n",
        "    print(f\"Results saved to {output_file_name}\")"
      ],
      "metadata": {
        "id": "T07ryu5OlSEK"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    model, processor = load_clip_model(device)\n",
        "    image_dataset, caption_dataset = load_hf_image_and_caption_datasets()\n",
        "    caption_column_name = \"BLIP_caption\"\n",
        "    output_file_name = \"method3_BLIP_caption_results.csv\"\n",
        "    results = process_dataset(image_dataset, caption_dataset, model, processor, device, caption_column_name, output_file_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCRUpyxBQNvB",
        "outputId": "367280c4-c45e-412c-9dec-bc9ce82ca4c6"
      },
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            "Results saved to method3_BLIP_caption_results.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    model, processor = load_clip_model(device)\n",
        "    image_dataset, caption_dataset = load_hf_image_and_caption_datasets()\n",
        "    caption_column_name = \"BLIP-Large_caption\"\n",
        "    output_file_name = \"method3_BLIP-Large_caption_results.csv\"\n",
        "    results = process_dataset(image_dataset, caption_dataset, model, processor, device, caption_column_name, output_file_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMSk4CrsQrVq",
        "outputId": "e961f70a-c1cd-4ed6-b1be-65e4048c398d"
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    model, processor = load_clip_model(device)\n",
        "    image_dataset, caption_dataset = load_hf_image_and_caption_datasets()\n",
        "    caption_column_name = \"GIT_caption\"\n",
        "    output_file_name = \"method3_GIT_caption_results.csv\"\n",
        "    results = process_dataset(image_dataset, caption_dataset, model, processor, device, caption_column_name, output_file_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owrUoTS3QzPN",
        "outputId": "18e92ebd-8831-47c6-e2e4-2071664e3c58"
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            "Processed 40/40\n",
            "Results saved to method3_GIT_caption_results.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    model, processor = load_clip_model(device)\n",
        "    image_dataset, caption_dataset = load_hf_image_and_caption_datasets()\n",
        "    caption_column_name = \"VIT-GPT2_caption\"\n",
        "    output_file_name = \"method3_VIT-GPT2_caption_results.csv\"\n",
        "    results = process_dataset(image_dataset, caption_dataset, model, processor, device, caption_column_name, output_file_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwSYYieEQ9c-",
        "outputId": "c7b815e6-ef8a-4f9c-9e90-693a23852b36"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Processed 40/40\n",
            "Results saved to method3_VIT-GPT2_caption_results.csv\n"
          ]
        }
      ]
    }
  ]
}