training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQx_iy3gbZwQ"
      },
      "source": [
        "# **Importing All Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuAMNF8YF67R",
        "outputId": "e85d45e5-5bfb-4b00-a4cf-3998846db8a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting openai-clip\n",
            "  Downloading openai-clip-1.0.1.tar.gz (1.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting ftfy (from openai-clip)\n",
            "  Downloading ftfy-6.2.0-py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from openai-clip) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai-clip) (4.66.4)\n",
            "Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /usr/local/lib/python3.10/dist-packages (from ftfy->openai-clip) (0.2.13)\n",
            "Building wheels for collected packages: openai-clip\n",
            "  Building wheel for openai-clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai-clip: filename=openai_clip-1.0.1-py3-none-any.whl size=1368605 sha256=be93fb4297f69cc75429ff09500fcd6c51eaded98e230801d5094a98e5ebe7e5\n",
            "  Stored in directory: /root/.cache/pip/wheels/08/77/8e/8d2f862df6bf7fb4e2007062d2cbaeae49862ec7b56d041229\n",
            "Successfully built openai-clip\n",
            "Installing collected packages: ftfy, openai-clip\n",
            "Successfully installed ftfy-6.2.0 openai-clip-1.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install openai-clip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4KMQqRnobf83"
      },
      "outputs": [],
      "source": [
        "# Essential imports\n",
        "import os\n",
        "import zipfile\n",
        "import urllib.request\n",
        "\n",
        "import json\n",
        "from io import open\n",
        "\n",
        "# PyTorch imports\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, Dataset\n",
        "\n",
        "# Transformers and CLIP imports\n",
        "import clip\n",
        "from transformers import CLIPProcessor, CLIPModel, GPT2Tokenizer, GPT2LMHeadModel, GPT2Config\n",
        "from torchvision import transforms\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from tqdm import tqdm\n",
        "\n",
        "# PIL import\n",
        "from PIL import Image\n",
        "\n",
        "# Google Colab import\n",
        "from google.colab import drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KHIq0Ph1YRW"
      },
      "source": [
        "# **Necessary Setup**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFJuutKKm-_1",
        "outputId": "013bb99c-0acc-48e2-ec1b-b3f746ecd837"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1px_b7p91WTI",
        "outputId": "45fb6059-778b-4b00-d24f-682f91a18756"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading train2014.zip...\n",
            "Downloaded train2014.zip\n",
            "Unzipping train2014.zip...\n",
            "Unzipped train2014.zip\n",
            "Deleting train2014.zip...\n",
            "Deleted train2014.zip\n",
            "Downloading v2_Questions_Train_mscoco.zip...\n",
            "Downloaded v2_Questions_Train_mscoco.zip\n",
            "Unzipping v2_Questions_Train_mscoco.zip...\n",
            "Unzipped v2_Questions_Train_mscoco.zip\n",
            "Deleting v2_Questions_Train_mscoco.zip...\n",
            "Deleted v2_Questions_Train_mscoco.zip\n",
            "Downloading v2_Annotations_Train_mscoco.zip...\n",
            "Downloaded v2_Annotations_Train_mscoco.zip\n",
            "Unzipping v2_Annotations_Train_mscoco.zip...\n",
            "Unzipped v2_Annotations_Train_mscoco.zip\n",
            "Deleting v2_Annotations_Train_mscoco.zip...\n",
            "Deleted v2_Annotations_Train_mscoco.zip\n",
            "All files downloaded, unzipped, and cleaned up successfully.\n"
          ]
        }
      ],
      "source": [
        "def download_and_unzip(url, extract_to='.'):\n",
        "    filename = os.path.basename(url)\n",
        "    print(f\"Downloading {filename}...\")\n",
        "    urllib.request.urlretrieve(url, filename)\n",
        "    print(f\"Downloaded {filename}\")\n",
        "\n",
        "    print(f\"Unzipping {filename}...\")\n",
        "    with zipfile.ZipFile(filename, 'r') as zip_ref:\n",
        "        zip_ref.extractall(extract_to)\n",
        "    print(f\"Unzipped {filename}\")\n",
        "\n",
        "    print(f\"Deleting {filename}...\")\n",
        "    os.remove(filename)\n",
        "    print(f\"Deleted {filename}\")\n",
        "\n",
        "train_images_link = \"http://images.cocodataset.org/zips/train2014.zip\"\n",
        "val_images_link = \"http://images.cocodataset.org/zips/val2014.zip\"\n",
        "test_images_link = \"http://images.cocodataset.org/zips/test2015.zip\"\n",
        "download_and_unzip(train_images_link, 'train2014')\n",
        "# download_and_unzip(val_images_link, 'val2014')\n",
        "# download_and_unzip(test_images_link, 'test2015')\n",
        "\n",
        "train_questions_link = \"https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Train_mscoco.zip\"\n",
        "val_questions_link = \"https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Val_mscoco.zip\"\n",
        "test_questions_link = \"https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Test_mscoco.zip\"\n",
        "download_and_unzip(train_questions_link, 'train_questions')\n",
        "# download_and_unzip(val_questions_link, 'val_questions')\n",
        "# download_and_unzip(test_questions_link, 'test_questions')\n",
        "\n",
        "train_annotations_link = \"https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Train_mscoco.zip\"\n",
        "val_annotations_link = \"https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip\"\n",
        "download_and_unzip(train_annotations_link, 'train_annotations')\n",
        "# download_and_unzip(val_annotations_link, 'val_annotations')\n",
        "\n",
        "print(\"All files downloaded, unzipped, and cleaned up successfully.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JuFdE4op11EO"
      },
      "outputs": [],
      "source": [
        "# SETTING UP PATHS\n",
        "train_images_path = \"/content/train2014/train2014\"\n",
        "val_images_path = \"/content/val2014/val2014\"\n",
        "test_images_path = \"/content/test2015/test2015\"\n",
        "\n",
        "train_questions_path = \"/content/train_questions/v2_OpenEnded_mscoco_train2014_questions.json\"\n",
        "val_questions_path = \"/content/val_questions/v2_OpenEnded_mscoco_val2014_questions.json\"\n",
        "test_questions_path = \"/content/test_questions/v2_OpenEnded_mscoco_test2015_questions.json\"\n",
        "test_questions_dev_path = \"/content/test_questions/v2_OpenEnded_mscoco_test-dev2015_questions.json\"\n",
        "\n",
        "train_annotations_path = \"/content/train_annotations/v2_mscoco_train2014_annotations.json\"\n",
        "val_annotations_path = \"/content/val_annotations/v2_mscoco_val2014_annotations.json\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mp0_35FomSQg"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_EBBMpC71_Ug"
      },
      "outputs": [],
      "source": [
        "# Get LISTS of Train and Val Answers\n",
        "with open(train_annotations_path, 'r') as f:\n",
        "    train_annotations = json.load(f)\n",
        "\n",
        "# with open(val_annotations_path, 'r') as f:\n",
        "#     val_annotations = json.load(f)\n",
        "\n",
        "list_train_annotations = train_annotations['annotations']\n",
        "# list_val_annotations = val_annotations['annotations']\n",
        "\n",
        "# the extracted lists contain the answers of an image, the question_id and the image_id\n",
        "extracted_train_annotations =  [{'answers': item['answers'], 'image_id': item['image_id'], 'question_id': item['question_id']} for item in list_train_annotations]\n",
        "# extracted_val_annotations = [{'answers': item['answers'], 'image_id': item['image_id'], 'question_id': item['question_id']} for item in list_val_annotations]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3oiDnSudd5cs"
      },
      "outputs": [],
      "source": [
        "with open(train_questions_path, 'r') as f:\n",
        "    train_questions = json.load(f)\n",
        "# with open(val_questions_path, 'r') as f:\n",
        "#     val_questions = json.load(f)\n",
        "# with open(test_questions_path, 'r') as f:\n",
        "#     test_questions = json.load(f)\n",
        "\n",
        "list_train_questions = train_questions['questions']\n",
        "# list_val_questions = val_questions['questions']\n",
        "# list_test_questions = test_questions['questions']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtAONPDK6nvc",
        "outputId": "f6fbf6fd-d54b-43fd-da34-0c4405ec5e8a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'answers': [{'answer': 'net', 'answer_confidence': 'maybe', 'answer_id': 1},\n",
              "  {'answer': 'net', 'answer_confidence': 'yes', 'answer_id': 2},\n",
              "  {'answer': 'net', 'answer_confidence': 'yes', 'answer_id': 3},\n",
              "  {'answer': 'netting', 'answer_confidence': 'yes', 'answer_id': 4},\n",
              "  {'answer': 'net', 'answer_confidence': 'yes', 'answer_id': 5},\n",
              "  {'answer': 'net', 'answer_confidence': 'yes', 'answer_id': 6},\n",
              "  {'answer': 'mesh', 'answer_confidence': 'maybe', 'answer_id': 7},\n",
              "  {'answer': 'net', 'answer_confidence': 'yes', 'answer_id': 8},\n",
              "  {'answer': 'net', 'answer_confidence': 'yes', 'answer_id': 9},\n",
              "  {'answer': 'net', 'answer_confidence': 'yes', 'answer_id': 10}],\n",
              " 'image_id': 458752,\n",
              " 'question_id': 458752000}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "extracted_train_annotations[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UFIOC6Ka86Ug",
        "outputId": "c50585fb-e925-4b89-ce0e-b01cf09c12b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'image_id': 458752,\n",
              " 'question': 'What position is this man playing?',\n",
              " 'question_id': 458752001}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_train_questions[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_b9LQeQX6QNf"
      },
      "outputs": [],
      "source": [
        "def idAsDicKey(questions_list):\n",
        "\n",
        "  questions_dict = {}\n",
        "  for sample in questions_list:\n",
        "    questions_dict[sample[\"question_id\"]] = {\"question\": sample[\"question\"] ,\"image_id\":sample[\"image_id\"] }\n",
        "\n",
        "  return questions_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eGT92Jsk-Psv"
      },
      "outputs": [],
      "source": [
        "train_questions_dict = idAsDicKey(list_train_questions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6KUw-wt6pSy"
      },
      "source": [
        "# **Dataset and Dataloaders**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52JAOb_3JeL9"
      },
      "outputs": [],
      "source": [
        "MAX_LENGTH_ANNOTATIONS = 7\n",
        "MAX_LENGTH_QUESTIONS = 15"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ee6i_y04_3yh"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2G4WZjsH67fX"
      },
      "outputs": [],
      "source": [
        "class VqaDataset(Dataset):\n",
        "  def __init__(self,base_img_path,num_samples,annotation_list,questions_dict):\n",
        "\n",
        "\n",
        "    super(VqaDataset, self).__init__()\n",
        "    self.num_samples = num_samples\n",
        "    self.annotations = annotation_list\n",
        "    self.questions_dict = questions_dict\n",
        "    self.base_img_path = base_img_path\n",
        "    self.total_id_len = len(\"000000000009\")\n",
        "\n",
        "    self.clip_encoder, self.preprocess = clip.load(\"ViT-B/32\",device=device)\n",
        "\n",
        "\n",
        "\n",
        "  def __len__(self):\n",
        "      \"\"\"\n",
        "      Returns the total number of samples in the dataset.\n",
        "      \"\"\"\n",
        "      return self.num_samples\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "\n",
        "\n",
        "      sample = self.annotations[idx]\n",
        "      answer = sample[\"answers\"][0][\"answer\"]\n",
        "\n",
        "      question_id = sample[\"question_id\"]\n",
        "      question = self.questions_dict[question_id]\n",
        "\n",
        "\n",
        "      image_id = question[\"image_id\"]\n",
        "      image_id = (self.total_id_len - len(str(image_id))) * '0' + str(image_id)\n",
        "      image_path = self.base_img_path + str(image_id) +'.jpg'\n",
        "      pilImg = Image.open(image_path).convert('L')\n",
        "\n",
        "      img = self.preprocess(pilImg).unsqueeze(0).to(device)\n",
        "\n",
        "      question = clip.tokenize(question[\"question\"]).to(device)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "      with torch.no_grad():\n",
        "        img_features = self.clip_encoder.encode_image(img).squeeze()\n",
        "        question_features = self.clip_encoder.encode_text(question).squeeze()\n",
        "\n",
        "\n",
        "\n",
        "      return img_features, question_features,[answer]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SaN95Sk2KiO"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AnAh2mJ-ydoT"
      },
      "source": [
        "# **Model Architecture**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XNGKS-Auk5jU"
      },
      "outputs": [],
      "source": [
        "class VQAModel(nn.Module):\n",
        "    def __init__(self, gpt2_model_name=\"gpt2\", num_unfrozen_layers=5):\n",
        "        super(VQAModel, self).__init__()\n",
        "\n",
        "        # Load GPT-2 model and tokenizer\n",
        "        self.gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name)\n",
        "        self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)\n",
        "        self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)\n",
        "\n",
        "\n",
        "        self.gpt2_tokenizer.pad_token = self.gpt2_tokenizer.eos_token\n",
        "        config = GPT2Config.from_pretrained(gpt2_model_name)\n",
        "\n",
        "        for param in self.gpt2_model.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "        # Unfreeze last few layers\n",
        "        for name, param in self.gpt2_model.named_parameters():\n",
        "            if any(layer in name for layer in [f\"layer.{i}\" for i in range(config.n_layer - num_unfrozen_layers, config.n_layer)]):\n",
        "                param.requires_grad = True\n",
        "\n",
        "        # Linear layer to project 1024-dim concatenated features to GPT-2's embedding size\n",
        "        self.projection = nn.Linear(1024, self.gpt2_model.config.n_embd)\n",
        "\n",
        "        # Define the loss function\n",
        "        self.loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "    def forward(self, combined_features, target_strings=None, max_length=MAX_LENGTH_ANNOTATIONS):\n",
        "        # Project the combined features to match GPT-2's embedding size\n",
        "        projected_features = self.projection(combined_features)  # Shape: [batch_size, n_embd]\n",
        "\n",
        "        # Initialize the GcPT-2 model input with the projected features\n",
        "        model_inputs = projected_features.unsqueeze(1)  # Shape: [batch_size, 1, n_embd]\n",
        "\n",
        "        generated_tokens = []\n",
        "        all_logits = []\n",
        "\n",
        "        for _ in range(max_length):\n",
        "            outputs = self.gpt2_model(inputs_embeds=model_inputs)\n",
        "            logits = outputs.logits[:, -1, :]\n",
        "            next_token = torch.argmax(logits, dim=-1)\n",
        "            generated_tokens.append(next_token)\n",
        "            all_logits.append(logits)\n",
        "\n",
        "            # Update model inputs\n",
        "            next_token_embeds = self.gpt2_model.transformer.wte(next_token)\n",
        "            model_inputs = torch.cat((model_inputs, next_token_embeds.unsqueeze(1)), dim=1)\n",
        "\n",
        "            if (next_token == self.gpt2_tokenizer.eos_token_id).all():\n",
        "                break\n",
        "\n",
        "        # Stack generated tokens and logits\n",
        "        generated_tokens = torch.stack(generated_tokens, dim=1)  # Shape: [batch_size, seq_len]\n",
        "        all_logits = torch.stack(all_logits, dim=1)  # Shape: [batch_size, seq_len, vocab_size]\n",
        "\n",
        "        # Pad logits to max_length if needed\n",
        "        if all_logits.size(1) < max_length:\n",
        "            pad_size = max_length - all_logits.size(1)\n",
        "            pad_logits = torch.full((all_logits.size(0), pad_size, all_logits.size(2)), float('-inf'), device=all_logits.device)\n",
        "            all_logits = torch.cat((all_logits, pad_logits), dim=1)\n",
        "\n",
        "        # Decode generated tokens to get the answer\n",
        "        decoded_answers = [self.gpt2_tokenizer.decode(tokens, skip_special_tokens=True) for tokens in generated_tokens]\n",
        "\n",
        "        # Calculate the loss if target strings are provided\n",
        "        if target_strings is not None:\n",
        "            # Tokenize the target strings\n",
        "            target_tokens = self.gpt2_tokenizer(target_strings, padding='max_length', truncation=True, max_length=max_length, return_tensors=\"pt\")\n",
        "            target_ids = target_tokens.input_ids.to(combined_features.device)  # Shape: [batch_size, seq_len]\n",
        "\n",
        "            # Flatten the logits and target ids for loss computation\n",
        "            loss = self.loss_fn(all_logits.view(-1, all_logits.size(-1)), target_ids.view(-1))\n",
        "        else:\n",
        "            loss = None\n",
        "\n",
        "        return decoded_answers, loss\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEeFAtK3g_ls"
      },
      "source": [
        "# **Loss Function**\n",
        "*not used*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "TT--iZb9j-HJ",
        "outputId": "cac7f52c-ac2d-4912-d3c7-9473afabd4f8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "8987071b462e4112897ea461a99df25b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/4.19k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c70a841cfce425c8ef293e39e702c84",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/605M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "76f41b083105400e98b04c1081d08227",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cac0b7d91e164540ac3b3d66254cee1d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/592 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "13c16d28c05544dea408cc9b9a5d55d6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/862k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17d6282c627a4a1eb107e3711cd76193",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "80b7209aa9bc4ffba41a0bbd3da0a5e0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.22M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "21719d9e53264fb6ab7b6d8e16a1e65a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "clip_model_name=\"openai/clip-vit-base-patch32\"\n",
        "clip_encoder = CLIPModel.from_pretrained(clip_model_name)\n",
        "clip_processor = CLIPProcessor.from_pretrained(clip_model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-A8R1ZJPg-vI"
      },
      "outputs": [],
      "source": [
        "def compute_loss(generated_answers, real_answers):\n",
        "  # print(\"generated \",generated_answers)\n",
        "  # print(\"real \",real_answer)\n",
        "  # Encode generated and real answers using CLIP text encoder\n",
        "  generated_inputs =clip_processor(text=generated_answers, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "  real_inputs = clip_processor(text=real_answers, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "\n",
        "  with torch.no_grad():  # No need to compute gradients for CLIP encoding\n",
        "      generated_features = clip_encoder.get_text_features(**generated_inputs)\n",
        "      real_features = clip_encoder.get_text_features(**real_inputs)\n",
        "\n",
        "  # Compute cosine similarity\n",
        "  cosine_sim = cosine_similarity(generated_features, real_features)\n",
        "\n",
        "  # Compute the loss (1 - cosine similarity)\n",
        "  loss = 1 - cosine_sim\n",
        "\n",
        "  # Return the mean loss over the batch\n",
        "  return loss.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yuva2kw9yj12"
      },
      "source": [
        "# **Training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CC768PL3m5cA"
      },
      "outputs": [],
      "source": [
        "def save_checkpoint(model, optimizer, loss, path=\"/content/drive/MyDrive/VQA_Checkpoints/80000_last_checkpoint.pth\"):\n",
        "    checkpoint = {\n",
        "        'model_state_dict': model.state_dict(),\n",
        "        'optimizer_state_dict': optimizer.state_dict(),\n",
        "        'loss': loss\n",
        "    }\n",
        "    torch.save(checkpoint, path)\n",
        "    print(f\"Checkpoint saved to {path}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aTOnpB2LyoFK"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "def train_epoch(dataloader, c_epoch, decoder,decoder_optimizer):\n",
        "\n",
        "    total_loss = 0\n",
        "    for data in tqdm(dataloader, desc=f\"Epoch {c_epoch}\"):\n",
        "        # image , question , answer\n",
        "        image_tensor, question_tensor, answer_list = data\n",
        "\n",
        "\n",
        "        answer_list = answer_list[0]\n",
        "\n",
        "        image_tensor = image_tensor.to(device)\n",
        "        question_tensor = question_tensor.to(device)\n",
        "\n",
        "        decoder_optimizer.zero_grad()\n",
        "\n",
        "\n",
        "        fused_input = torch.cat((image_tensor,question_tensor),dim=1).to(torch.float32)\n",
        "\n",
        "        outputs,loss = decoder(fused_input,answer_list)\n",
        "\n",
        "\n",
        "        loss.backward()\n",
        "\n",
        "        decoder_optimizer.step()\n",
        "\n",
        "\n",
        "\n",
        "        total_loss += loss.item()\n",
        "    print('truth -> ',answer_list, \"preds -> \",outputs)\n",
        "    print(\"total loss \",total_loss)\n",
        "\n",
        "    return total_loss / len(dataloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R0emkZg0lJLx"
      },
      "outputs": [],
      "source": [
        "def train(train_dataloader, decoder, n_epochs, learning_rate=0.001,\n",
        "               print_every=100, plot_every=100,load_checkpoint=False):\n",
        "\n",
        "\n",
        "    plot_losses = []\n",
        "    print_loss_total = 0  # Reset every print_every\n",
        "    plot_loss_total = 0  # Reset every plot_every\n",
        "\n",
        "    decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)\n",
        "\n",
        "    if(load_checkpoint==True):\n",
        "\n",
        "      chkpnt = load_from_checkpoint()\n",
        "\n",
        "      decoder.load_state_dict(chkpnt[\"model_state_dict\"])\n",
        "      decoder_optimizer.load_state_dict(chkpnt[\"optimizer_state_dict\"])\n",
        "      plot_losses = chkpnt[\"loss\"]\n",
        "\n",
        "\n",
        "\n",
        "    for epoch in range(1, n_epochs + 1):\n",
        "        loss = train_epoch(train_dataloader, epoch , decoder, decoder_optimizer)\n",
        "        print_loss_total += loss\n",
        "        plot_loss_total += loss\n",
        "\n",
        "        print(\"loss \",loss)\n",
        "        save_checkpoint(decoder, decoder_optimizer, plot_losses,path=f\"/content/drive/MyDrive/VQA_Checkpoints/80000_last_checkpoint_{epoch}.pth\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "buvpky67oAFV"
      },
      "outputs": [],
      "source": [
        "def load_from_checkpoint(path=\"/content/drive/MyDrive/VQA_Checkpoints/80000_last_checkpoint.pth\"):\n",
        "  chkpnt = torch.load(path)\n",
        "  print(\"loaded from checkpoint \",path)\n",
        "  return chkpnt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KFCHdW_WEs1",
        "outputId": "dcb8639b-9c97-40c9-f7b1-e281589dbf6b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████| 338M/338M [00:13<00:00, 25.9MiB/s]\n"
          ]
        }
      ],
      "source": [
        "dataset = VqaDataset(\"/content/train2014/train2014/COCO_train2014_\",80000,extracted_train_annotations,train_questions_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dgpv1kewBBnR"
      },
      "outputs": [],
      "source": [
        "trainloader = DataLoader(dataset,batch_size=16)"
      ]
    },
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      "execution_count": null,
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      "source": [
        "decoder = VQAModel().to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvIuHyphpFIc",
        "outputId": "d20277cf-eb42-4f41-ba43-5daac4225e84"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loaded from checkpoint  /content/drive/MyDrive/VQA_Checkpoints/80000_last_checkpoint.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 1: 100%|██████████| 5000/5000 [1:01:11<00:00,  1.36it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "truth ->  ('up', 'spoon', 'yes', 'back', 'dessert', 'no', 'on table', 'no', 'no', 'no', 'glass', 'pudding', 'cake', 'souffle', 'desert', 'no') preds ->  ['down', 'knife', 'no', 's', 's', 'no', 'on', 'no', 's', 'no', 'sand', 'sand', 'cake', 's', 'break', 'no']\n",
            "total loss  nan\n",
            "loss  nan\n",
            "Checkpoint saved to /content/drive/MyDrive/VQA_Checkpoints/80000_last_checkpoint_1.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 2: 100%|██████████| 5000/5000 [1:02:09<00:00,  1.34it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "truth ->  ('up', 'spoon', 'yes', 'back', 'dessert', 'no', 'on table', 'no', 'no', 'no', 'glass', 'pudding', 'cake', 'souffle', 'desert', 'no') preds ->  ['down', 'knife', 'no', 's', 's', 'no', 'plate', 'no', 's', 'no', 'glass', 'sand', 'cake', 's', 'break', 'no']\n",
            "total loss  nan\n",
            "loss  nan\n",
            "Checkpoint saved to /content/drive/MyDrive/VQA_Checkpoints/80000_last_checkpoint_2.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 3:  41%|████      | 2059/5000 [26:23<43:24,  1.13it/s]"
          ]
        }
      ],
      "source": [
        "train(trainloader, decoder, 50, print_every=5, plot_every=5,load_checkpoint=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTfpdD8mz4X0"
      },
      "source": [
        "# evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPoQg-1T6CB4"
      },
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        "def evaluate(eval_loader, decoder):\n",
        "    decoder.eval()\n",
        "    with torch.no_grad():\n",
        "        sum_acc = 0\n",
        "        total_loss = 0  # Initialize total_loss\n",
        "\n",
        "        for data in tqdm(eval_loader, desc=\"Evaluation\"):\n",
        "            image_tensor, question_tensor, answer_list = data\n",
        "            answer_list = answer_list[0]  # Assuming answer_list is a list of tensors\n",
        "\n",
        "            image_tensor = image_tensor.to(device)\n",
        "            question_tensor = question_tensor.to(device)\n",
        "\n",
        "            fused_input = torch.cat((image_tensor, question_tensor), dim=1).to(torch.float32)\n",
        "\n",
        "            outputs, loss = decoder(fused_input, answer_list)\n",
        "\n",
        "            acc = get_acc(outputs, answer_list)\n",
        "\n",
        "            sum_acc += acc\n",
        "            total_loss += loss.item()  # Accumulate the loss\n",
        "\n",
        "        avg_loss = total_loss / len(eval_loader)\n",
        "        avg_acc = sum_acc / len(eval_loader)\n",
        "        print(\"Average Loss:\", avg_loss)\n",
        "        print(\"Average Accuracy:\", avg_acc)\n",
        "\n",
        "        return avg_loss, avg_acc\n",
        "\n",
        "def get_acc(y, y_hat,threshold=0.6):\n",
        "    generated_inputs = clip_processor(text=y, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "    real_inputs = clip_processor(text=y_hat, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        generated_features = clip_encoder.get_text_features(**generated_inputs)\n",
        "        real_features = clip_encoder.get_text_features(**real_inputs)\n",
        "\n",
        "    cosine_sim = cosine_similarity(generated_features, real_features)\n",
        "\n",
        "    acc = (cosine_sim > threshold).sum().item()  # Compute accuracy over the batch\n",
        "    return acc / len(y_hat)  # Return the mean accuracy\n"
      ]
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