vae_reconstruction.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sbz5iBHD5Txz",
        "outputId": "b441e414-3946-422d-eb61-f6c332c037b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading animals10, 614087302 bytes compressed\n",
            "[==================================================] 614087302 bytes downloaded\n",
            "Downloaded and uncompressed: animals10\n",
            "Data source import complete.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'animals10:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F59760%2F840806%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240510%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240510T190515Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dtq6BS3K5Tx2",
        "outputId": "9233db0b-12f0-4dfc-d328-d990eefd3ae3",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "/kaggle/input/animals10/raw-img/pecora/OIP-pnALVXrejjV0-F8aCH40iQHaEK.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/e83cb90a28fc093ed1584d05fb1d4e9fe777ead218ac104497f5c978a6e8b0b1_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-O9PyFwhzSYFYQzSC6R3_oAHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/ea36b50e2afc053ed1584d05fb1d4e9fe777ead218ac104497f5c978a6e8b0b1_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-u06T6wzFWJU_ZOJ5JDQIngHaE8.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-4F_8O5DuAk1dW-eQQInJegHaE8.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-pwRcIGOR1bzrvIJ3mUdnZwHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-H8O7U4ALr6aahPrZveJXugHaF5.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-z8re0zV1hU2DxjsvTdqYIgHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-vdQbmMeJtO534JFNjZYaTgHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-k9s7WN3xGXQrfizS3flZlAHaLH.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-4F0Ryc8T19WWNuYfbgEMxgHaFJ.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-sy26HF5k6Dco1-qmpOkq1QHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP--5EOVD9HIiHEqNd8PZjFogHaII.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/e830b90c2ef1003ed1584d05fb1d4e9fe777ead218ac104497f5c978a6eab2b0_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/e830b9082df7013ed1584d05fb1d4e9fe777ead218ac104497f5c978a6ebb3bf_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-vFjG2XuJTFGZWv3bd2BPuwHaHa.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-xlTDenDFZ6mRNnJQORAOYgHaFW.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-4BhgjxMua_2Tz0KXpp7NJQHaJ6.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-ze5Ru45P6kk0M0j5usWeNAHaHa.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-Oi5aF5VjArS3G8GBL2tjEgHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/eb3cb30b21f2043ed1584d05fb1d4e9fe777ead218ac104497f5c978a6eab2b0_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-tleKheLB3Wz_RmHzo8sRTgHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/eb34b0062ef7053ed1584d05fb1d4e9fe777ead218ac104497f5c978a6eab2b0_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-MVnQ3c-7txWZiX-DihsDbQFhCw.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/eb36b80b21f6083ed1584d05fb1d4e9fe777ead218ac104497f5c978a6eab2b0_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-LwKsdvuR_SKuppT8xL4TfwHaJ4.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-rs8d8czZQKn99e0b_QxQngHaEK.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/ea37b80f2af5003ed1584d05fb1d4e9fe777ead218ac104497f5c978a6e8b0b1_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-KsSLUP0FIKQrRwkR26XwVAHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-oc_-OGg1j6OtXi48p9NqrwHaFO.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-GE9Nizf-56YhSwzFadt_XgHaF7.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-9oUhQfb9ScPe2c8aHyGlSwHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-d6-GmFe5eUA3Rw3H_Zt8LQHaFq.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-hEPu_TnHYzBXieCMNwq9CgHaEU.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-rSxnaCwQ5SZ7TIyo2x8L6QAAAA.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-G0dXFROtKO5ZgPE1i_LltAAAAA.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-WJhzjP3WJ8QqSfxhR5xBdgHaE8.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-9Xf9ty4muXlbvx2FtSsnkgHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP--SmXJlkSW5VPiDpIRt9PFQHaFj.jpeg\n",
            "/kaggle/input/animals10/raw-img/pecora/eb34b90d2df6053ed1584d05fb1d4e9fe777ead218ac104497f5c978a6e8b0b1_640.jpg\n",
            "/kaggle/input/animals10/raw-img/pecora/OIP-hfxCGH1jncKF6-yBdDKqRQHaFj.jpeg\n"
          ]
        }
      ],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "WEFPouY45Z-Y"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split\n",
        "import random\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8XAs6qn85nCe",
        "outputId": "72fb8f10-8c46-4703-8020-76dd87949de5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of images read: 2623\n"
          ]
        }
      ],
      "source": [
        "image_dir = \"/kaggle/input/animals10/raw-img/cavallo\"\n",
        "\n",
        "images = []\n",
        "for filename in os.listdir(image_dir):\n",
        "    image_path = os.path.join(image_dir, filename)\n",
        "    image = cv2.imread(image_path)\n",
        "\n",
        "    if image is not None:\n",
        "        image = cv2.resize(image, (120, 120))\n",
        "        images.append(image)\n",
        "\n",
        "print(\"Number of images read:\", len(images))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "pXhSMkXQ5q2K"
      },
      "outputs": [],
      "source": [
        "def divide_image(image):\n",
        "    parts = []\n",
        "    height, width, _ = image.shape\n",
        "    part_height = height // 3\n",
        "    part_width = width // 3\n",
        "\n",
        "    for i in range(3):\n",
        "        for j in range(3):\n",
        "            part = image[i*part_height:(i+1)*part_height, j*part_width:(j+1)*part_width]\n",
        "            parts.append(part)\n",
        "\n",
        "    return parts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "MKn-CImi5tMI"
      },
      "outputs": [],
      "source": [
        "def generate_combinations(parts, num_combinations):\n",
        "    combinations = []\n",
        "    original_positions = []\n",
        "    indices = list(range(len(parts)))\n",
        "\n",
        "    for _ in range(num_combinations):\n",
        "        random.shuffle(indices)\n",
        "        combination = [parts[i] for i in indices]\n",
        "        combinations.append(combination)\n",
        "        original_positions.append(indices.copy())\n",
        "\n",
        "    return combinations, original_positions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "FUeweXfJ5vgC"
      },
      "outputs": [],
      "source": [
        "def stitch_shuffled_image(parts):\n",
        "    num_parts = len(parts)\n",
        "    part_size = parts[0].shape[0]  # Assuming all parts are square\n",
        "\n",
        "    stitched_image_size = int(np.sqrt(num_parts) * part_size)\n",
        "    stitched_image = np.zeros((stitched_image_size, stitched_image_size, parts[0].shape[2]), dtype=np.uint8)\n",
        "\n",
        "    for i in range(stitched_image.shape[0] // part_size):\n",
        "        for j in range(stitched_image.shape[1] // part_size):\n",
        "            part_index = i * int(stitched_image.shape[0] / part_size) + j\n",
        "            stitched_image[i*part_size:(i+1)*part_size, j*part_size:(j+1)*part_size] = parts[part_index]\n",
        "\n",
        "    return stitched_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "6at9FJ1d7X-2"
      },
      "outputs": [],
      "source": [
        "\n",
        "input_data = []\n",
        "original_images = []\n",
        "target_data = []\n",
        "\n",
        "for image in images:\n",
        "    parts = divide_image(image)\n",
        "    combinations, original_positions = generate_combinations(parts, 10)\n",
        "\n",
        "    for idx, combination in enumerate(combinations):\n",
        "        shuffled_image = stitch_shuffled_image(combination)\n",
        "        input_data.append(shuffled_image)\n",
        "        original_images.append(image)  # Append the original image for reconstruction comparison\n",
        "\n",
        "        dummy_target = np.zeros((9, 9), dtype=np.uint8)\n",
        "        for i in range(9):\n",
        "            dummy_target[i, original_positions[idx][i]] = 1\n",
        "\n",
        "        target_data.append(dummy_target.flatten())\n",
        "# Assuming dummy_target is 9x9 and represents a grid where only one element is '1' in each row.\n",
        "converted_target_data = [np.argmax(dummy_target.reshape(-1, 9), axis=1) for dummy_target in target_data]\n",
        "target_data = np.array([np.argmax(row) for row in converted_target_data])  # Convert to single indices\n",
        "\n",
        "\n",
        "original_images = np.array(original_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "eJes4pwe7_kC"
      },
      "outputs": [],
      "source": [
        "# import torch\n",
        "# import torch.nn as nn\n",
        "# import torch.nn.functional as F\n",
        "# import torch.optim as optim\n",
        "\n",
        "# class JigsawVAE(nn.Module):\n",
        "#     def __init__(self):\n",
        "#         super(JigsawVAE, self).__init__()\n",
        "#         # Encoder\n",
        "#         self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1)\n",
        "#         self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1)\n",
        "#         self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)\n",
        "\n",
        "#         # Fully connected layers for classification\n",
        "#         self.fc1 = nn.Linear(64 * 32 * 32, 512)\n",
        "#         self.fc2 = nn.Linear(512, 9)  # Assuming 9 classes for the 3x3 puzzle\n",
        "\n",
        "#         # Decoder\n",
        "#         self.deconv1 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)\n",
        "#         self.deconv2 = nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1)\n",
        "#         self.deconv3 = nn.ConvTranspose2d(16, 3, kernel_size=3, stride=2, padding=1, output_padding=1)\n",
        "\n",
        "#     def forward(self, x):\n",
        "#         # Encode\n",
        "#         x = F.relu(self.conv1(x))\n",
        "#         x = F.relu(self.conv2(x))\n",
        "#         encoded = F.relu(self.conv3(x))\n",
        "\n",
        "#         # Classification\n",
        "#         flat = torch.flatten(encoded, start_dim=1)\n",
        "#         fc = F.relu(self.fc1(flat))\n",
        "#         classes = self.fc2(fc)\n",
        "\n",
        "#         # Decode\n",
        "#         x = F.relu(self.deconv1(encoded))\n",
        "#         x = F.relu(self.deconv2(x))\n",
        "#         reconstructed = torch.sigmoid(self.deconv3(x))  # Use sigmoid if inputs are normalized to [0,1]\n",
        "\n",
        "#         return classes, reconstructed\n",
        "\n",
        "# model = JigsawVAE()\n",
        "class JigsawVAE(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(JigsawVAE, self).__init__()\n",
        "        # Encoder\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1)  # Output size: (16, 128, 128) for 256x256 input\n",
        "        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1) # Output size: (32, 64, 64)\n",
        "        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1) # Output size: (64, 32, 32)\n",
        "\n",
        "        # Adjust the number of flat features according to the actual output size\n",
        "        self.num_flat_features = 64 * 15 * 15  # Corrected to match the output from conv3: 64 channels, each 15x15\n",
        "\n",
        "        # Fully connected layers for classification\n",
        "        self.fc1 = nn.Linear(self.num_flat_features, 512)\n",
        "        self.fc2 = nn.Linear(512, 9)  # Assuming 9 classes for the 3x3 puzzle\n",
        "\n",
        "        # Decoder\n",
        "        self.deconv1 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)\n",
        "        self.deconv2 = nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1)\n",
        "        self.deconv3 = nn.ConvTranspose2d(16, 3, kernel_size=3, stride=2, padding=1, output_padding=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Encode\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = F.relu(self.conv3(x))\n",
        "\n",
        "        # Flatten for the fully connected layers\n",
        "        x = x.view(-1, self.num_flat_features)\n",
        "\n",
        "        # Classification\n",
        "        fc = F.relu(self.fc1(x))\n",
        "        classes = self.fc2(fc)\n",
        "\n",
        "        # Decode\n",
        "        x = x.view(-1, 64, 15, 15)  # Reshape x to the expected shape for deconvolution\n",
        "        x = F.relu(self.deconv1(x))\n",
        "        x = F.relu(self.deconv2(x))\n",
        "        reconstructed = torch.sigmoid(self.deconv3(x))  # Use sigmoid if inputs are normalized to [0,1]\n",
        "\n",
        "        return classes, reconstructed\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "-Zub1_rKcIKn"
      },
      "outputs": [],
      "source": [
        "# class JigsawDataset(Dataset):\n",
        "#     def __init__(self, input_data, target_data, original_images, transform=None):\n",
        "#         self.input_data = input_data\n",
        "#         self.target_data = target_data  # This is still one-hot encoded here\n",
        "#         self.original_images = original_images\n",
        "#         self.transform = transform\n",
        "\n",
        "#     def __len__(self):\n",
        "#         return len(self.input_data)\n",
        "\n",
        "#     def __getitem__(self, idx):\n",
        "#         shuffled_image = self.input_data[idx]\n",
        "#         original_image = self.original_images[idx]\n",
        "\n",
        "#         if isinstance(shuffled_image, np.ndarray):\n",
        "#             shuffled_image = transforms.ToPILImage()(shuffled_image)\n",
        "#             original_image = transforms.ToPILImage()(original_image)\n",
        "\n",
        "#         if self.transform:\n",
        "#             shuffled_image = self.transform(shuffled_image)\n",
        "#             original_image = self.transform(original_image)\n",
        "\n",
        "#         # Convert one-hot encoding to index\n",
        "#         one_hot_vector = self.target_data[idx]\n",
        "#         class_index = np.argmax(one_hot_vector)  # Convert from one-hot to index\n",
        "#         class_indices = torch.tensor(class_index, dtype=torch.long)  # Ensure it's a single value\n",
        "\n",
        "#         return shuffled_image, original_image, class_indices\n",
        "\n",
        "class JigsawDataset(Dataset):\n",
        "    def __init__(self, input_data, target_data, original_images, transform=None):\n",
        "        \"\"\"\n",
        "        input_data: List of shuffled images (pieces assembled in wrong order)\n",
        "        target_data: List of targets indicating the correct position of each piece.\n",
        "                     This should be a list of indices, not one-hot encoded.\n",
        "        original_images: List of original unshuffled images for reconstruction\n",
        "        transform: Optional transform to be applied on a sample.\n",
        "        \"\"\"\n",
        "        self.input_data = input_data\n",
        "        self.target_data = target_data\n",
        "        self.original_images = original_images\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.input_data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "      shuffled_image = self.input_data[idx]\n",
        "      original_image = self.original_images[idx]\n",
        "\n",
        "      if isinstance(shuffled_image, np.ndarray):  # Ensure image is in correct format\n",
        "          shuffled_image = transforms.ToPILImage()(shuffled_image)\n",
        "          original_image = transforms.ToPILImage()(original_image)\n",
        "\n",
        "      if self.transform:\n",
        "          shuffled_image = self.transform(shuffled_image)\n",
        "          original_image = self.transform(original_image)\n",
        "\n",
        "      # Ensure class_indices is a 1D tensor of type long for cross_entropy\n",
        "      class_indices = torch.tensor(self.target_data[idx], dtype=torch.long)\n",
        "\n",
        "      return shuffled_image, original_image, class_indices\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Define transformations if necessary\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((256, 256)),\n",
        "    transforms.ToTensor()\n",
        "])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "MAMJFaPETRJU"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Assuming JigsawDataset is defined to take input_data, original_images, and target_data\n",
        "\n",
        "# Define transformations\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),  # Ensures input is converted to a tensor and scaled to [0, 1]\n",
        "    # Add any necessary resizing or normalization here if required\n",
        "])\n",
        "\n",
        "# Split data into training and testing sets, including the original_images\n",
        "X_train, X_test, y_train, y_test, original_images_train, original_images_test = train_test_split(\n",
        "    input_data, target_data, original_images, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create instances of the JigsawDataset including original images for VAE reconstruction\n",
        "train_dataset = JigsawDataset(X_train, y_train, original_images_train, transform=transform)\n",
        "test_dataset = JigsawDataset(X_test, y_test, original_images_test, transform=transform)\n",
        "batch_size = 16\n",
        "# Create data loaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2kSMLmTBbwR",
        "outputId": "acc2295e-2d7f-4587-8e4c-191c456c2ca5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "====> Epoch: 9 Average loss: 0.0000\n",
            "Train Epoch: 9 [16000/20984 (76%)]\tLoss: 4.354133\n",
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            "====> Epoch: 9 Average loss: 0.0000\n",
            "Train Epoch: 9 [17600/20984 (84%)]\tLoss: 4.344222\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
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            "====> Epoch: 9 Average loss: 0.0000\n",
            "Train Epoch: 9 [19200/20984 (92%)]\tLoss: 4.233050\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
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            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "Train Epoch: 9 [20800/20984 (99%)]\tLoss: 4.348060\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n",
            "====> Epoch: 9 Average loss: 0.0000\n"
          ]
        }
      ],
      "source": [
        "# Ensure your loss function expects the correct parameters:\n",
        "# model = JigsawVAE()\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = JigsawVAE().to(device)\n",
        "def loss_function(class_pred, target_classes, recon_x, original_x):\n",
        "    class_indices = torch.argmax(class_pred, dim=1)\n",
        "\n",
        "    # Convert class indices to one-hot encoding\n",
        "      # total number of classes\n",
        "    one_hot_class = F.one_hot(class_indices, num_classes=81)\n",
        "\n",
        "    classification_loss = F.cross_entropy(one_hot_class.float(), target_classes)\n",
        "    reconstruction_loss = F.mse_loss(recon_x, original_x)\n",
        "    return classification_loss + reconstruction_loss\n",
        "\n",
        "# Example training loop call:\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "num_epochs = 10\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    train_loss = 0\n",
        "\n",
        "    for batch_idx, (data, original, classes) in enumerate(train_loader):\n",
        "      data, original, classes = data.to(device), original.to(device), classes.to(device)\n",
        "      optimizer.zero_grad()\n",
        "      class_pred, recon_batch = model(data)\n",
        "      loss = loss_function(class_pred, classes, recon_batch, original)\n",
        "      loss.backward()\n",
        "      #print('losss',loss)\n",
        "      optimizer.step()\n",
        "      #print('hi')\n",
        "\n",
        "      if batch_idx % 100 == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                100. * batch_idx / len(train_loader), loss.item()))\n",
        "\n",
        "      print('====> Epoch: {} Average loss: {:.4f}'.format(\n",
        "          epoch, train_loss / len(train_loader.dataset)))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHmHWyma8CWF",
        "outputId": "71dcf787-1081-48d5-9fa0-14a66c36f794"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.2720, Accuracy: 599.0/5232 (11%)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "    def calculate_accuracy(y_pred, y_true):\n",
        "      _, predicted = torch.max(y_pred, 1)\n",
        "      correct = (predicted == y_true).sum().item()\n",
        "      return correct / y_true.size(0)\n",
        "    model.eval()\n",
        "    valid_loss = 0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for data, original, classes in test_loader:\n",
        "            data, original, classes = data.to(device), original.to(device), classes.to(device)\n",
        "            class_pred, recon_batch = model(data)\n",
        "            valid_loss += loss_function(class_pred, classes, recon_batch, original).item()\n",
        "            correct += calculate_accuracy(class_pred, classes) * data.size(0)\n",
        "            total += data.size(0)\n",
        "\n",
        "    valid_loss /= len(test_loader.dataset)\n",
        "    accuracy = 100. * correct / total\n",
        "    print('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        valid_loss, correct, total, accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YpFqtybRfrJZ",
        "outputId": "f5973928-3a37-4167-b05e-7eacc244d175"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved model to \"model_vae.pth\"\n"
          ]
        }
      ],
      "source": [
        "torch.save(model.state_dict(), 'model_vae.pth')\n",
        "print('Saved model to \"model_vae.pth\"')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "name": "notebookcad765506b",
      "provenance": []
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "datasetId": 59760,
          "sourceId": 840806,
          "sourceType": "datasetVersion"
        }
      ],
      "isGpuEnabled": false,
      "isInternetEnabled": false,
      "language": "python",
      "sourceType": "notebook"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}