AI_OF_GOD_5_0.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EbiMKmw0x3WA"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7Rhee3Ey2-e",
        "outputId": "eea1b859-b046-4fcd-9043-bfaa12ff6dd6"
      },
      "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": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yN8_hSqly6Lb",
        "outputId": "7d3f626a-2abc-4188-c997-735a2e61c1c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: train/984.jpg           \n",
            "  inflating: train/985.jpg           \n",
            "  inflating: train/986.jpg           \n",
            "  inflating: train/987.jpg           \n",
            "  inflating: train/988.jpg           \n",
            "  inflating: train/989.jpg           \n",
            "  inflating: train/99.jpg            \n",
            "  inflating: train/990.jpg           \n",
            "  inflating: train/991.jpg           \n",
            "  inflating: train/992.jpg           \n",
            "  inflating: train/993.jpg           \n",
            "  inflating: train/994.jpg           \n",
            "  inflating: train/995.jpg           \n",
            "  inflating: train/996.jpg           \n",
            "  inflating: train/997.jpg           \n",
            "  inflating: train/998.jpg           \n",
            "  inflating: train/999.jpg           \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/drive/MyDrive/GOD_OF_AI/ai-of-god-v20.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fnGU5uNm0XqP"
      },
      "source": [
        "### Checking Our Training Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "h78emH5uzNvW",
        "outputId": "e658cd80-7982-406e-de6e-567d0d932c0b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FileName    481.714286\n",
            "dtype: float64\n",
            "FileName    327.833333\n",
            "dtype: float64\n",
            "       FileName\n",
            "Class          \n",
            "0          4829\n",
            "1          1405\n",
            "2           285\n",
            "3           443\n",
            "4           789\n",
            "5            68\n",
            "6           295\n",
            "7            87\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Step 1: Read the CSV file into a pandas DataFrame\n",
        "csv_file_path = '/content/train.csv'  # Replace with the path to your CSV file\n",
        "df = pd.read_csv(csv_file_path)\n",
        "\n",
        "# Step 2: Group the data by the class column and count the occurrences\n",
        "class_counts = df['Class'].value_counts() / 481\n",
        "\n",
        "# Step 3: Plot the class-wise distribution\n",
        "plt.figure(figsize=(10, 6))  # Adjust the figure size as needed\n",
        "plt.bar(class_counts.index, class_counts.values)\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Class-wise Distribution')\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for readability\n",
        "plt.tight_layout()\n",
        "\n",
        "# Step 4: Show the plot or save it to a file\n",
        "plt.show()\n",
        "mean_without_0=df[df['Class'] != 0].groupby('Class').count().mean()\n",
        "mean_without_0_1 = df[(df['Class'] != 0) & (df['Class'] != 1)].groupby('Class').count().mean()\n",
        "class_counts = df.groupby('Class').count()\n",
        "print(mean_without_0)\n",
        "print(mean_without_0_1)\n",
        "print(class_counts)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating Validation.csv File"
      ],
      "metadata": {
        "id": "-xrziMsK1tgw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Step 1: Read the CSV file into a pandas DataFrame\n",
        "csv_file_path = '/content/train.csv'  # Replace with the path to your CSV file\n",
        "df = pd.read_csv(csv_file_path)\n",
        "\n",
        "# Step 2: Split the DataFrame into training and validation sets\n",
        "train_df, val_df = train_test_split(df, test_size=0.1, random_state=42)\n",
        "\n",
        "# Step 3: Save the training and validation sets to separate CSV files if needed\n",
        "train_df.to_csv('/content/train_split.csv', index=False)\n",
        "val_df.to_csv('/content/validation_split.csv', index=False)\n",
        "\n",
        "# Now, train_df contains your training data, and val_df contains your validation data.\n",
        "\n"
      ],
      "metadata": {
        "id": "bCt8OrX81xuF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Yy5P1yy0V_6"
      },
      "source": [
        "### Creating Sampled Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z9U6NT750hm7"
      },
      "outputs": [],
      "source": [
        "# Import pandas library\n",
        "import pandas as pd\n",
        "\n",
        "# Load your csv file as a pandas DataFrame\n",
        "df = pd.read_csv(\"/content/train_split.csv\")\n",
        "\n",
        "# Filter out the rows that have class 0\n",
        "df_0 = df[df['Class'] == 0]\n",
        "# df_1 = df[df['Class'] == 1]\n",
        "# Randomly sample 600 rows from the filtered DataFrame\n",
        "df_0_2500 = df_0.sample(n=2500)\n",
        "remaining_df_0_2500 = df_0.drop(df_0_2500.index)\n",
        "remaining_df_0_2500 = remaining_df_0_2500.reset_index(drop=True)\n",
        "# df_1 = df_1.sample (n=600)\n",
        "# Concatenate the sampled DataFrame with the original DataFrame without class 0\n",
        "df = pd.concat ([df[df['Class'] != 0], df_0_2500])\n",
        "remaining_df = pd.concat ([df[df['Class'] != 0], remaining_df_0_2500])\n",
        "# df = pd.concat ([df[df['Class'] != 1], df_1])\n",
        "# Reset the index and drop the old one\n",
        "df = df.reset_index(drop=True)\n",
        "remaining_df = remaining_df.reset_index(drop=True)\n",
        "\n",
        "# Save the new DataFrame as a csv file\n",
        "df.to_csv (\"/content/drive/MyDrive/GOD_OF_AI/new_train_2500.csv\", index=False)\n",
        "# Save the new DataFrame as a csv file\n",
        "remaining_df.to_csv (\"/content/drive/MyDrive/GOD_OF_AI/new_remaining_train_2500.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "4IA_LTqt08AO",
        "outputId": "3562cae6-4779-452d-be1e-dd685a332b75"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FileName    129.285714\n",
            "dtype: float64\n",
            "FileName    55.0\n",
            "dtype: float64\n",
            "       FileName\n",
            "Class          \n",
            "0          1789\n",
            "1           575\n",
            "2             8\n",
            "3           218\n",
            "4            20\n",
            "5            10\n",
            "6            72\n",
            "7             2\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "csv_file_path = '/content/submission.csv'  # Replace with the path to your CSV file\n",
        "df = pd.read_csv(csv_file_path)\n",
        "# Step 2: Group the data by the class column and count the occurrences\n",
        "class_counts = df['Class'].value_counts()\n",
        "\n",
        "# Step 3: Plot the class-wise distribution\n",
        "plt.figure(figsize=(10, 6))  # Adjust the figure size as needed\n",
        "plt.bar(class_counts.index, class_counts.values)\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Class-wise Distribution')\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for readability\n",
        "plt.tight_layout()\n",
        "\n",
        "# Step 4: Show the plot or save it to a file\n",
        "plt.show()\n",
        "mean_without_0=df[df['Class'] != 0].groupby('Class').count().mean()\n",
        "mean_without_0_1 = df[(df['Class'] != 0) & (df['Class'] != 1)].groupby('Class').count().mean()\n",
        "class_counts = df.groupby('Class').count()\n",
        "print(mean_without_0)\n",
        "print(mean_without_0_1)\n",
        "print(class_counts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLsFs9N00OIQ"
      },
      "source": [
        "### Creating Organized Training Directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_-Au8CfzRnP",
        "outputId": "82e4e781-6cde-4344-8bf5-40eabb1e9f47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data organization completed.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "# Load the CSV file into a pandas DataFrame\n",
        "csv_file_path = '/content/drive/MyDrive/GOD_OF_AI/new_train_2500.csv'  # Replace with the path to your CSV file\n",
        "df = pd.read_csv(csv_file_path)\n",
        "\n",
        "# Image directory containing your images\n",
        "image_dir = '/content/train'  # Replace with the path to your image directory\n",
        "\n",
        "# Create 8 class-specific directories if they don't exist\n",
        "output_dir_base = '/content/drive/MyDrive/GOD_OF_AI/train_split_directory_2500'\n",
        "class_labels = df['Class'].unique()\n",
        "for label in class_labels:\n",
        "    output_dir = os.path.join(output_dir_base, str(label))\n",
        "    os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "# Iterate through the DataFrame and move images to the corresponding class-specific directory\n",
        "for index, row in df.iterrows():\n",
        "    image_filename = row['FileName']\n",
        "    class_label = row['Class']\n",
        "    source_image_path = os.path.join(image_dir, image_filename)\n",
        "    destination_dir = os.path.join(output_dir_base, str(class_label))\n",
        "    destination_image_path = os.path.join(destination_dir, image_filename)\n",
        "\n",
        "    # Move the image to the appropriate directory\n",
        "    shutil.copy(source_image_path, destination_image_path)\n",
        "\n",
        "print(\"Data organization completed.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Remaining train split data directory"
      ],
      "metadata": {
        "id": "-4eFtsMV9Mse"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "# Load the CSV file into a pandas DataFrame\n",
        "csv_file_path = '/content/drive/MyDrive/GOD_OF_AI/new_remaining_train_2500.csv'  # Replace with the path to your CSV file\n",
        "df = pd.read_csv(csv_file_path)\n",
        "\n",
        "# Image directory containing your images\n",
        "image_dir = '/content/train'  # Replace with the path to your image directory\n",
        "\n",
        "# Create 8 class-specific directories if they don't exist\n",
        "output_dir_base = '/content/drive/MyDrive/GOD_OF_AI/remaining_train_split_directory_2500'\n",
        "class_labels = df['Class'].unique()\n",
        "for label in class_labels:\n",
        "    output_dir = os.path.join(output_dir_base, str(label))\n",
        "    os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "# Iterate through the DataFrame and move images to the corresponding class-specific directory\n",
        "for index, row in df.iterrows():\n",
        "    image_filename = row['FileName']\n",
        "    class_label = row['Class']\n",
        "    source_image_path = os.path.join(image_dir, image_filename)\n",
        "    destination_dir = os.path.join(output_dir_base, str(class_label))\n",
        "    destination_image_path = os.path.join(destination_dir, image_filename)\n",
        "\n",
        "    # Move the image to the appropriate directory\n",
        "    shutil.copy(source_image_path, destination_image_path)\n",
        "\n",
        "print(\"Data organization completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFo1QfDt9Law",
        "outputId": "8d9c7543-7bac-4606-fcfa-6aa27584263a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data organization completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* validation directory"
      ],
      "metadata": {
        "id": "jKIrzgeT-s6l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "# Load the CSV file into a pandas DataFrame\n",
        "csv_file_path = '/content/validation_split.csv'  # Replace with the path to your CSV file\n",
        "df = pd.read_csv(csv_file_path)\n",
        "\n",
        "# Image directory containing your images\n",
        "image_dir = '/content/train'  # Replace with the path to your image directory\n",
        "\n",
        "# Create 8 class-specific directories if they don't exist\n",
        "output_dir_base = '/content/drive/MyDrive/GOD_OF_AI/validation_split_directory'\n",
        "class_labels = df['Class'].unique()\n",
        "for label in class_labels:\n",
        "    output_dir = os.path.join(output_dir_base, str(label))\n",
        "    os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "# Iterate through the DataFrame and move images to the corresponding class-specific directory\n",
        "for index, row in df.iterrows():\n",
        "    image_filename = row['FileName']\n",
        "    class_label = row['Class']\n",
        "    source_image_path = os.path.join(image_dir, image_filename)\n",
        "    destination_dir = os.path.join(output_dir_base, str(class_label))\n",
        "    destination_image_path = os.path.join(destination_dir, image_filename)\n",
        "\n",
        "    # Move the image to the appropriate directory\n",
        "    shutil.copy(source_image_path, destination_image_path)\n",
        "\n",
        "print(\"Data organization completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuNDcw_5-yId",
        "outputId": "5c0d0557-0e19-4222-be21-fcf73bff136a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data organization completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRs0wSq0kevy"
      },
      "source": [
        "### Creating Dummy Images for classes with less data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZtnW9L8hcBXs"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torchvision import transforms\n",
        "from PIL import Image\n",
        "import torch\n",
        "import torchvision\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import torchvision.models as models\n",
        "\n",
        "# Define the paths to your original dataset and train directory\n",
        "original_dataset_path = '/content/drive/MyDrive/GOD_OF_AI/train_directory_500'\n",
        "train_directory = '/content/drive/MyDrive/GOD_OF_AI/train_directory_500'\n",
        "\n",
        "# Define the classes for which you want to create dummy images\n",
        "target_classes = ['5', '7']  # Replace with your specific class names\n",
        "\n",
        "# Set the number of dummy images to generate per class\n",
        "num_dummy_images_per_class = 100\n",
        "\n",
        "# Ensure the train directory exists\n",
        "if not os.path.exists(train_directory):\n",
        "    os.makedirs(train_directory)\n",
        "\n",
        "def get_randaugment_transform(rotatRand, cont, sat, hu, n, m):\n",
        "    # Define the base transformations without RandAugment\n",
        "    base_transform = transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.RandomRotation(rotatRand),\n",
        "        transforms.ColorJitter(brightness=0.2, contrast=cont, saturation=sat, hue=hu),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "    ])\n",
        "\n",
        "    # Apply RandAugment on top of the base transformations\n",
        "    randaug_transform = transforms.Compose([\n",
        "        transforms.RandAugment(n, m),\n",
        "        base_transform,\n",
        "    ])\n",
        "\n",
        "    return randaug_transform\n",
        "\n",
        "def get_randaugment_bw_transform(rotatRand, n, m):\n",
        "    # Define the base transformations without RandAugment\n",
        "    base_transform = transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.RandomRotation(rotatRand),\n",
        "        transforms.Grayscale(num_output_channels=3),  # Convert image to grayscale\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "    ])\n",
        "\n",
        "    # Apply RandAugment on top of the base transformations\n",
        "    randaug_transform = transforms.Compose([\n",
        "        transforms.RandAugment(n, m),\n",
        "        base_transform,\n",
        "    ])\n",
        "\n",
        "    return randaug_transform\n",
        "\n",
        "# Function to load an image from the original dataset\n",
        "def load_image(image_path):\n",
        "    return Image.open(image_path).convert('RGB')\n",
        "\n",
        "# Function to generate dummy images with data augmentation\n",
        "def generate_dummy_images(class_name, num_images):\n",
        "    class_dir = os.path.join(train_directory, class_name)\n",
        "    if not os.path.exists(class_dir):\n",
        "        os.makedirs(class_dir)\n",
        "\n",
        "    image_files = os.listdir(os.path.join(original_dataset_path, class_name))\n",
        "    print(image_files)\n",
        "\n",
        "    rotatRand = random.randint(25, 35)  # Define your rotation angle\n",
        "    cont = 0.2      # Define your contrast factor\n",
        "    sat = 0.2       # Define your saturation factor\n",
        "    hu = 0.2        # Define your hue factor\n",
        "    n = 2           # Number of transformations to apply\n",
        "    m = 10          # Magnitude of transformations\n",
        "\n",
        "    # Get the RandAugment transform\n",
        "    transform = get_randaugment_bw_transform(rotatRand, n, m)\n",
        "\n",
        "    for i in range(num_images):\n",
        "        # Randomly select an existing image\n",
        "        existing_image_path = os.path.join(original_dataset_path, class_name, random.choice(image_files))\n",
        "        existing_image = load_image(existing_image_path)\n",
        "\n",
        "        # Apply the defined transformations to the image\n",
        "        augmented_image = transform(existing_image)\n",
        "\n",
        "        # Save the augmented image as a dummy image\n",
        "        save_path = os.path.join(class_dir, f'dummy{class_name}_{i}.jpg')\n",
        "        torchvision.utils.save_image(augmented_image, save_path)\n",
        "\n",
        "# Loop through the target classes\n",
        "for class_name in target_classes:\n",
        "    generate_dummy_images(class_name, num_dummy_images_per_class)\n",
        "\n",
        "# Now you have dummy images for specific classes with data augmentation in the train directory.\n",
        "# You can use these images for training your image classification model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XtTcLxaB1jtn"
      },
      "source": [
        "# Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_ZDQWSl2D2p"
      },
      "source": [
        "### Creating DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "swVuifUx1pl0"
      },
      "outputs": [],
      "source": [
        "train_dataset_directory = '/content/drive/MyDrive/GOD_OF_AI/train_split_directory_2500'\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "\n",
        "input_shape = (256, 256, 3) # define the input shape\n",
        "num_classes = 8 # define the number of classes\n",
        "batch_size = 256 # define the batch size\n",
        "\n",
        "# define the data augmentation for the training set\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(input_shape[:2]), # randomly crop and resize the images\n",
        "    transforms.RandomHorizontalFlip(), # randomly flip the images horizontally\n",
        "    transforms.RandomRotation(20), # randomly rotate the images by 20 degrees\n",
        "    transforms.RandomVerticalFlip(), # randomly flip the images vertically\n",
        "    transforms.ToTensor(), # convert the images to tensors\n",
        "    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) # normalize the images using the mean and standard deviation of ImageNet\n",
        "])\n",
        "\n",
        "# create the training dataset from a directory of images\n",
        "train_dataset = torchvision.datasets.ImageFolder(\n",
        "    root=train_dataset_directory, # specify the directory of the training images\n",
        "    transform=train_transform, # apply the data augmentation for the training set\n",
        ")\n",
        "\n",
        "# create the data loaders for the training and test sets\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    train_dataset, # use the training dataset\n",
        "    batch_size=batch_size, # use the batch size\n",
        "    shuffle=True, # shuffle the data\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XyFpXhlW2HQG"
      },
      "source": [
        "### Creating Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UI33ZMuRTDoN"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "def create_efficientNetV2_model(input_shape, num_classes):\n",
        "    base_model = models.efficientnet_v2_s(pretrained=False, dropout=0.2) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "def create_shufflenet_model(input_shape, num_classes):\n",
        "    base_model = models.shufflenet_v2_x0_5(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    #base_model.features[0] = nn.Conv2d(input_shape[0], 64, kernel_size=2, stride =1 , padding=1) # replace the first convolutional layer to match the input shape\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "def create_mobilenet_model(input_shape, num_classes):\n",
        "    base_model = models.mobilenet_v3_large(pretrained=True)\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yzjXScAku4sm"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class CosineLoss(nn.Module):\n",
        "    def __init__(self, xent=.1, reduction=\"mean\"):\n",
        "        super(CosineLoss, self).__init__()\n",
        "        self.xent = xent\n",
        "        self.reduction = reduction\n",
        "\n",
        "        self.y = torch.Tensor([1]).cuda()\n",
        "\n",
        "    def forward(self, input, target):\n",
        "        cosine_loss = F.cosine_embedding_loss(input, F.one_hot(target, num_classes=input.size(-1)), self.y, reduction=self.reduction)\n",
        "        cent_loss = F.cross_entropy(F.normalize(input), target, reduction=self.reduction)\n",
        "\n",
        "        return cosine_loss + self.xent * cent_loss\n",
        "\n",
        "class FocalCosineLoss(nn.Module):\n",
        "    def __init__(self, alpha=1, gamma=2, xent=.1, reduction=\"mean\"):\n",
        "        super(FocalCosineLoss, self).__init__()\n",
        "        self.alpha = alpha\n",
        "        self.gamma = gamma\n",
        "\n",
        "        self.xent = xent\n",
        "        self.reduction = reduction\n",
        "\n",
        "        self.y = torch.Tensor([1])\n",
        "\n",
        "    def forward(self, input, target):\n",
        "        cosine_loss = F.cosine_embedding_loss(input, F.one_hot(target, num_classes=input.size(-1)), self.y, reduction=self.reduction)\n",
        "\n",
        "        cent_loss = F.cross_entropy(F.normalize(input), target, reduce=False)\n",
        "        pt = torch.exp(-cent_loss)\n",
        "        focal_loss = self.alpha * (1-pt)**self.gamma * cent_loss\n",
        "\n",
        "        if self.reduction == \"mean\":\n",
        "            focal_loss = torch.mean(focal_loss)\n",
        "\n",
        "        return cosine_loss + self.xent * focal_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Focal loss for multiclassification"
      ],
      "metadata": {
        "id": "DoRX-XRcvEtK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class FocalLoss(nn.Module):\n",
        "    def __init__(self, gamma=2, reduction='mean'):\n",
        "        super(FocalLoss, self).__init__()\n",
        "        self.gamma = gamma\n",
        "        self.reduction = reduction\n",
        "\n",
        "    def forward(self, inputs, targets):\n",
        "        # Compute cross-entropy loss\n",
        "        ce_loss = F.cross_entropy(inputs, targets, reduction='none')\n",
        "\n",
        "        # Compute the class probabilities\n",
        "        probs = torch.exp(-ce_loss)\n",
        "\n",
        "        # Compute the frequency of each class in the batch\n",
        "        class_counts = torch.bincount(targets)\n",
        "\n",
        "        # Compute the alpha values based on class frequencies\n",
        "        alpha = 1 / (class_counts + 1e-8)  # Adding a small epsilon to prevent division by zero\n",
        "\n",
        "        # Gather the alpha values for the target classes\n",
        "        alpha = alpha[targets]\n",
        "\n",
        "        # Compute the focal loss\n",
        "        focal_loss = (alpha * (1 - probs) ** self.gamma * ce_loss)\n",
        "\n",
        "        # Apply reduction\n",
        "        if self.reduction == 'mean':\n",
        "            return focal_loss.mean()\n",
        "        elif self.reduction == 'sum':\n",
        "            return focal_loss.sum()\n",
        "        else:\n",
        "            return focal_loss"
      ],
      "metadata": {
        "id": "_3MXXqyHvD3l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwXeVGLNw7S_",
        "outputId": "fe201a02-16ea-49c1-a817-73b0ee6e756a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchmetrics\n",
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            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.8.1->torchmetrics) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.8.1->torchmetrics) (17.0.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.1->torchmetrics) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.1->torchmetrics) (1.3.0)\n",
            "Installing collected packages: lightning-utilities, torchmetrics\n",
            "Successfully installed lightning-utilities-0.9.0 torchmetrics-1.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install torchmetrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfNr2ZYQMlr8",
        "outputId": "cfef321a-252c-409e-b551-306ec365a32c"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100, Loss: 0.283859521150589, Train accuracy: 0.6186662316322327, Train macro F1 score: 0.391615092754364, Learning Rate: [0.001]\n",
            "Epoch 2/100, Loss: 0.2880855202674866, Train accuracy: 0.7428173422813416, Train macro F1 score: 0.6083970069885254, Learning Rate: [0.001]\n",
            "Epoch 3/100, Loss: 0.24039065837860107, Train accuracy: 0.7788612246513367, Train macro F1 score: 0.6937605142593384, Learning Rate: [0.001]\n",
            "Epoch 4/100, Loss: 0.21209672093391418, Train accuracy: 0.7917464971542358, Train macro F1 score: 0.7359111905097961, Learning Rate: [0.001]\n",
            "Epoch 5/100, Loss: 0.25954848527908325, Train accuracy: 0.8006268739700317, Train macro F1 score: 0.7610572576522827, Learning Rate: [0.001]\n",
            "Epoch 6/100, Loss: 0.27506232261657715, Train accuracy: 0.7997562289237976, Train macro F1 score: 0.7568023800849915, Learning Rate: [0.001]\n",
            "Epoch 7/100, Loss: 0.23515331745147705, Train accuracy: 0.80689537525177, Train macro F1 score: 0.7709423303604126, Learning Rate: [0.001]\n",
            "Epoch 8/100, Loss: 0.23155350983142853, Train accuracy: 0.8257008790969849, Train macro F1 score: 0.7973708510398865, Learning Rate: [0.001]\n",
            "Epoch 9/100, Loss: 0.2195812165737152, Train accuracy: 0.8192582130432129, Train macro F1 score: 0.7872310876846313, Learning Rate: [0.001]\n",
            "Epoch 10/100, Loss: 0.187921941280365, Train accuracy: 0.8234372138977051, Train macro F1 score: 0.7973896861076355, Learning Rate: [0.001]\n",
            "Epoch 11/100, Loss: 0.2302999347448349, Train accuracy: 0.8258749842643738, Train macro F1 score: 0.797242283821106, Learning Rate: [0.001]\n",
            "Epoch 12/100, Loss: 0.22091761231422424, Train accuracy: 0.8276162147521973, Train macro F1 score: 0.8007580041885376, Learning Rate: [0.001]\n",
            "Epoch 13/100, Loss: 0.21107491850852966, Train accuracy: 0.8239595890045166, Train macro F1 score: 0.806094765663147, Learning Rate: [0.001]\n",
            "Epoch 14/100, Loss: 0.18853333592414856, Train accuracy: 0.8354518413543701, Train macro F1 score: 0.8154882788658142, Learning Rate: [0.001]\n",
            "Epoch 15/100, Loss: 0.20007267594337463, Train accuracy: 0.8307504653930664, Train macro F1 score: 0.8090870976448059, Learning Rate: [0.001]\n",
            "Epoch 16/100, Loss: 0.2216358482837677, Train accuracy: 0.8319693803787231, Train macro F1 score: 0.8128417730331421, Learning Rate: [0.001]\n",
            "Epoch 17/100, Loss: 0.22365370392799377, Train accuracy: 0.840849757194519, Train macro F1 score: 0.8176907300949097, Learning Rate: [0.001]\n",
            "Epoch 18/100, Loss: 0.24984586238861084, Train accuracy: 0.8396308422088623, Train macro F1 score: 0.815936803817749, Learning Rate: [0.001]\n",
            "Epoch 19/100, Loss: 0.2672343850135803, Train accuracy: 0.838760256767273, Train macro F1 score: 0.8201613426208496, Learning Rate: [0.001]\n",
            "Epoch 20/100, Loss: 0.20835523307323456, Train accuracy: 0.841023862361908, Train macro F1 score: 0.8158797025680542, Learning Rate: [0.001]\n",
            "Epoch 21/100, Loss: 0.1968822479248047, Train accuracy: 0.8464217185974121, Train macro F1 score: 0.8324790596961975, Learning Rate: [0.001]\n",
            "Epoch 22/100, Loss: 0.23322629928588867, Train accuracy: 0.8345812559127808, Train macro F1 score: 0.8180674314498901, Learning Rate: [0.001]\n",
            "Epoch 23/100, Loss: 0.26096320152282715, Train accuracy: 0.8462476134300232, Train macro F1 score: 0.8311206102371216, Learning Rate: [0.001]\n"
          ]
        }
      ],
      "source": [
        "epochs = 100\n",
        "save_dir = '/content/drive/MyDrive/GOD_OF_AI/mobilenet_wts'\n",
        "import os\n",
        "import torch\n",
        "import torchmetrics\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim.lr_scheduler import MultiStepLR\n",
        "\n",
        "model = create_mobilenet_model(input_shape, num_classes)\n",
        "# model.load_state_dict(torch.load('/content/drive/MyDrive/GOD_OF_AI/Training_eNetV2s_wts/model_weights_epoch_5.pt'))\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "# loss_fn = nn.CrossEntropyLoss()\n",
        "loss_fn = FocalCosineLoss()\n",
        "# loss_fn2 = FocalLoss(gamma=2)\n",
        "# Define the learning rate scheduler\n",
        "scheduler = MultiStepLR(optimizer, milestones=[50], gamma=0.95)\n",
        "\n",
        "# Define a function to create one-hot encoded labels\n",
        "def one_hot_encode(labels):\n",
        "    one_hot = torch.zeros(labels.shape[0], num_classes)\n",
        "    one_hot.scatter_(1, labels.unsqueeze(1), 1)\n",
        "    return one_hot\n",
        "\n",
        "# Create an instance of the MulticlassF1Score metric\n",
        "f1 = torchmetrics.classification.MulticlassF1Score(num_classes=num_classes, average='macro')\n",
        "\n",
        "# Train the shufflenetV2 model on the resampled data\n",
        "for epoch in range(epochs):\n",
        "    total_correct = 0\n",
        "    total_samples = 0\n",
        "    f1.reset()\n",
        "    for images, labels in train_loader: #r\n",
        "        optimizer.zero_grad() # zero out the gradients\n",
        "        images = images.permute(0, 1, 3, 2)\n",
        "        predictions = model(images) # get the model predictions\n",
        "        loss = loss_fn(predictions, labels) # compute the loss\n",
        "        # loss = loss1 + loss_fn2(predictions, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step() # update the model parameters\n",
        "        pred_labels = torch.argmax(predictions, dim=1) # get the predicted labels from the predictions\n",
        "        labels = labels.to(torch.int64) # cast the true labels to int64 type\n",
        "        total_correct += torch.sum(pred_labels == labels) # count the number of correct predictions\n",
        "        total_samples += labels.shape[0] # count the number of samples\n",
        "        f1.update(pred_labels, labels) # update the metric with the predicted and true labels\n",
        "    train_acc = total_correct / total_samples # compute the train accuracy\n",
        "    train_f1 = f1.compute() # compute the macro F1 score at the end of each epoch\n",
        "    # Step the learning rate scheduler at the end of each epoch\n",
        "    save_path = os.path.join(save_dir, f'mobilenet_wts_split2500_epoch{epoch}.pt')\n",
        "    torch.save(model.state_dict(), save_path)\n",
        "    scheduler.step()\n",
        "\n",
        "    print(f\"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}, Train accuracy: {train_acc}, Train macro F1 score: {train_f1}, Learning Rate: {scheduler.get_last_lr()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### submission.csv file generator"
      ],
      "metadata": {
        "id": "liXYAAipdsy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!unzip /content/drive/MyDrive/ai-of-god-v20.zip\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models, transforms\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.autograd import Variable\n",
        "from PIL import Image\n",
        "import os\n",
        "input_shape = (256, 256, 3) # define the input shape\n",
        "num_classes = 8 # define the number of classes\n",
        "batch_size = 16 # define the batch size\n",
        "\n",
        "# define the data augmentation for the training set\n",
        "transform = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(input_shape[:2]), # randomly crop and resize the images\n",
        "    transforms.RandomHorizontalFlip(), # randomly flip the images horizontally\n",
        "    transforms.RandomRotation(20), # randomly rotate the images by 20 degrees\n",
        "    transforms.RandomVerticalFlip(), # randomly flip the images vertically\n",
        "    transforms.ToTensor() # normalize the images using the mean and standard deviation of ImageNet\n",
        "])\n",
        "\n",
        "normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n",
        "def create_shufflenet_model(input_shape, num_classes):\n",
        "    base_model = models.shufflenet_v2_x0_5(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    #base_model.features[0] = nn.Conv2d(input_shape[0], 64, kernel_size=2, stride =1 , padding=1) # replace the first convolutional layer to match the input shape\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "def create_mobilenet_model(input_shape, num_classes):\n",
        "    base_model = models.mobilenet_v3_large(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "def create_efficientNetV2_model(input_shape, num_classes):\n",
        "    base_model = models.efficientnet_v2_s(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "# Define the transformation for input images (modify as needed)\n",
        "\n",
        "\n",
        "# Load the custom model\n",
        "input_shape = (3,256,256) # define the input shape\n",
        "num_classes = 8 # define the number of classes\n",
        "model = create_mobilenet_model(input_shape, num_classes)\n",
        "model.load_state_dict(torch.load('/content/drive/MyDrive/GOD_OF_AI/mobilenet_wts/mobilenet_wts_remaining_epoch58.pt'))\n",
        "model.eval()\n",
        "\n",
        "# Load the CSV file containing image file paths\n",
        "image_df = pd.read_csv('/content/test.csv')\n",
        "\n",
        "# Create empty lists to store image names and classes\n",
        "image_names = []\n",
        "image_classes = []\n",
        "root_directory = '/content/test'  # Replace with your actual root directory path\n",
        "# Loop through each row in the CSV file\n",
        "for index, row in image_df.iterrows():\n",
        "    image_path = row['FileName']  # Assuming your CSV has a column named 'image_path'\n",
        "    image_path = os.path.join(root_directory, image_path)\n",
        "    image = Image.open(image_path)\n",
        "    dummy_image = transform(image).expand(3,-1,-1)\n",
        "    dummy_image= normalize(dummy_image)\n",
        "    dummy_image = Variable(dummy_image.unsqueeze(0))\n",
        "\n",
        "    # Predict the class of the image\n",
        "    with torch.no_grad():\n",
        "        output = model(dummy_image)\n",
        "        predicted_class = torch.argmax(output).item()\n",
        "\n",
        "    # Append image name and predicted class to lists\n",
        "    image_names.append(row['FileName'])  # Assuming your CSV has a column named 'image_name'\n",
        "    image_classes.append(predicted_class)\n",
        "\n",
        "# Create a new DataFrame with image names and predicted classes\n",
        "result_df = pd.DataFrame({'FileName': image_names, 'Class': image_classes})\n",
        "\n",
        "# Save the result to a new CSV file\n",
        "result_df.to_csv('/content/submission.csv', index=False)\n"
      ],
      "metadata": {
        "id": "vTH-5rmJpO2u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7830732-7c23-4517-8c5d-fa27b31baa93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `submission.csv` from two weight files. - sid"
      ],
      "metadata": {
        "id": "g8rFgYDTEKOL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!unzip /content/drive/MyDrive/ai-of-god-v20.zip\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models, transforms\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.autograd import Variable\n",
        "from PIL import Image\n",
        "import os\n",
        "input_shape = (256, 256, 3) # define the input shape\n",
        "num_classes = 8 # define the number of classes\n",
        "batch_size = 16 # define the batch size\n",
        "\n",
        "# define the data augmentation for the training set\n",
        "transform = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(input_shape[:2]), # randomly crop and resize the images\n",
        "    transforms.RandomHorizontalFlip(), # randomly flip the images horizontally\n",
        "    transforms.RandomRotation(20), # randomly rotate the images by 20 degrees\n",
        "    transforms.RandomVerticalFlip(), # randomly flip the images vertically\n",
        "    transforms.ToTensor() # normalize the images using the mean and standard deviation of ImageNet\n",
        "])\n",
        "\n",
        "normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n",
        "def create_shufflenet_model(input_shape, num_classes):\n",
        "    base_model = models.shufflenet_v2_x0_5(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    #base_model.features[0] = nn.Conv2d(input_shape[0], 64, kernel_size=2, stride =1 , padding=1) # replace the first convolutional layer to match the input shape\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "def create_efficientNet_model(input_shape, num_classes):\n",
        "    base_model = models.efficientnet_b3(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "def create_mobilenet_model(input_shape, num_classes):\n",
        "    base_model = models.mobilenet_v3_large(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "# Define the transformation for input images (modify as needed)\n",
        "\n",
        "\n",
        "# Load the custom model\n",
        "input_shape = (3,256,256) # define the input shape\n",
        "num_classes = 8 # define the number of classes\n",
        "model1 = create_shufflenet_model(input_shape, num_classes)\n",
        "model1.load_state_dict(torch.load('/content/drive/MyDrive/AOG_Rerun_wts/model_split_weights_epoch_8.pt'))\n",
        "model1.eval()\n",
        "\n",
        "model2 = create_mobilenet_model(input_shape, num_classes)\n",
        "model2.load_state_dict(torch.load('/content/drive/MyDrive/GOD_OF_AI/mobilenet_wts/mobilenet_wts_without0_epoch4.pt'))\n",
        "model2.eval()\n",
        "# Load the CSV file containing image file paths\n",
        "image_df = pd.read_csv('/content/test.csv')\n",
        "\n",
        "# Create empty lists to store image names and classes\n",
        "image_names = []\n",
        "image_classes = []\n",
        "root_directory = '/content/test'  # Replace with your actual root directory path\n",
        "# Loop through each row in the CSV file\n",
        "for index, row in image_df.iterrows():\n",
        "    image_path = row['FileName']  # Assuming your CSV has a column named 'image_path'\n",
        "    image_path = os.path.join(root_directory, image_path)\n",
        "    image = Image.open(image_path)\n",
        "    dummy_image = transform(image).expand(3,-1,-1)\n",
        "    dummy_image= normalize(dummy_image)\n",
        "    dummy_image = Variable(dummy_image.unsqueeze(0))\n",
        "\n",
        "    # Predict the class of the image\n",
        "    with torch.no_grad():\n",
        "      output1 = model1(dummy_image)\n",
        "      output2 = model2(dummy_image)\n",
        "\n",
        "      predicted_class1 = torch.argmax(output1).item()\n",
        "      predicted_class2 = torch.argmax(output2).item()\n",
        "      # print(predicted_class1)\n",
        "      # print(predicted_class2)\n",
        "\n",
        "      if output1[0][predicted_class1] > output2[0][predicted_class2]:\n",
        "          predicted_class = predicted_class1\n",
        "      else:\n",
        "          predicted_class = predicted_class2\n",
        "\n",
        "    # Append image name and predicted class to lists\n",
        "    image_names.append(row['FileName'])  # Assuming your CSV has a column named 'image_name'\n",
        "    image_classes.append(predicted_class)\n",
        "\n",
        "# Create a new DataFrame with image names and predicted classes\n",
        "result_df = pd.DataFrame({'FileName': image_names, 'Class': image_classes})\n",
        "\n",
        "# Save the result to a new CSV file\n",
        "result_df.to_csv('/content/submission.csv', index=False)\n"
      ],
      "metadata": {
        "id": "E4EePQm64ydS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cdfe8c9-ea42-4b2e-8f1f-a3340ec75fdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ensemble Method"
      ],
      "metadata": {
        "id": "rmXT6lznKO9k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* submission.csv formation from binary classification of class 0"
      ],
      "metadata": {
        "id": "WCAOngPO8Zko"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!unzip /content/drive/MyDrive/ai-of-god-v20.zip\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models, transforms\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.autograd import Variable\n",
        "from PIL import Image\n",
        "import os\n",
        "input_shape = (256, 256, 3) # define the input shape\n",
        "num_classes = 8 # define the number of classes\n",
        "batch_size = 16 # define the batch size\n",
        "\n",
        "# define the data augmentation for the training set\n",
        "transform = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(input_shape[:2]), # randomly crop and resize the images\n",
        "    transforms.RandomHorizontalFlip(), # randomly flip the images horizontally\n",
        "    transforms.RandomRotation(20), # randomly rotate the images by 20 degrees\n",
        "    transforms.RandomVerticalFlip(), # randomly flip the images vertically\n",
        "    transforms.ToTensor() # normalize the images using the mean and standard deviation of ImageNet\n",
        "])\n",
        "\n",
        "normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n",
        "def create_shufflenet_model(input_shape, num_classes):\n",
        "    base_model = models.shufflenet_v2_x0_5(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    #base_model.features[0] = nn.Conv2d(input_shape[0], 64, kernel_size=2, stride =1 , padding=1) # replace the first convolutional layer to match the input shape\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "def create_shufflenet15_model(input_shape, num_classes):\n",
        "    base_model = models.shufflenet_v2_x1_5(pretrained=False) # load the shufflenet_v2_x1_0 model with pretrained weights\n",
        "    #base_model.features[0] = nn.Conv2d(input_shape[0], 64, kernel_size=2, stride =1 , padding=1) # replace the first convolutional layer to match the input shape\n",
        "    for param in base_model.parameters(): # freeze the parameters of the base model\n",
        "        param.requires_grad = False\n",
        "\n",
        "    model = nn.Sequential(\n",
        "        base_model, # use the feature extractor part of the base model\n",
        "        nn.Flatten(), # flatten the output of the feature extractor\n",
        "        nn.Linear(1000, 256), # add a fully connected layer with 256 units\n",
        "        nn.ReLU(), # add a ReLU activation function\n",
        "        nn.Linear(256, num_classes), # add a fully connected layer with num_classes units\n",
        "        nn.Softmax(dim=1) # add a softmax activation function for the output layer\n",
        "    )\n",
        "    return model\n",
        "\n",
        "\n",
        "# Load the custom model\n",
        "input_shape = (3,256,256) # define the input shape\n",
        "num_classes = 8 # define the number of classes\n",
        "\n",
        "# binary classification of class 0\n",
        "model_binary = create_shufflenet_model(input_shape, 2)\n",
        "model_binary.load_state_dict(torch.load('/content/drive/MyDrive/GOD_OF_AI/shufflenet_only0_wts/CE_only0_snet_epoch_60.pt'))\n",
        "model_binary.eval()\n",
        "\n",
        "# multiple classification\n",
        "model2 = create_shufflenet15_model(input_shape, num_classes)\n",
        "model2.load_state_dict(torch.load('/content/drive/MyDrive/GOD_OF_AI/without0_wts_snet/without0_wts_CE_epoch17.pt'))\n",
        "model2.eval()\n",
        "# Load the CSV file containing image file paths\n",
        "image_df = pd.read_csv('/content/test.csv')\n",
        "\n",
        "# Create empty lists to store image names and classes\n",
        "image_names = []\n",
        "image_classes = []\n",
        "root_directory = '/content/test'  # Replace with your actual root directory path\n",
        "# Loop through each row in the CSV file\n",
        "for index, row in image_df.iterrows():\n",
        "    image_path = row['FileName']  # Assuming your CSV has a column named 'image_path'\n",
        "    image_path = os.path.join(root_directory, image_path)\n",
        "    image = Image.open(image_path)\n",
        "    dummy_image = transform(image).expand(3,-1,-1)\n",
        "    dummy_image= normalize(dummy_image)\n",
        "    dummy_image = Variable(dummy_image.unsqueeze(0))\n",
        "\n",
        "    # Predict the class of the image\n",
        "    with torch.no_grad():\n",
        "      output1 = model_binary(dummy_image)\n",
        "      predicted_class1 = torch.argmax(output1).item()\n",
        "      if predicted_class1 == 1:\n",
        "        predicted_class = 0\n",
        "      else:\n",
        "        output2 = model2(dummy_image)\n",
        "        predicted_class2 = torch.argmax(output2).item()\n",
        "        if predicted_class2 == 0:\n",
        "          # Get the indices of the top two predicted classes\n",
        "          top2_classes = torch.topk(output2, k=2).indices\n",
        "\n",
        "          # The second highest predicted class\n",
        "          second_highest_predicted_class = top2_classes[0][1].item()\n",
        "\n",
        "          predicted_class = second_highest_predicted_class\n",
        "        else:\n",
        "          predicted_class = predicted_class2\n",
        "\n",
        "\n",
        "    # Append image name and predicted class to lists\n",
        "    image_names.append(row['FileName'])  # Assuming your CSV has a column named 'image_name'\n",
        "    image_classes.append(predicted_class)\n",
        "\n",
        "# Create a new DataFrame with image names and predicted classes\n",
        "result_df = pd.DataFrame({'FileName': image_names, 'Class': image_classes})\n",
        "\n",
        "# Save the result to a new CSV file\n",
        "result_df.to_csv('/content/submission.csv', index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuiz_FG5xvDs",
        "outputId": "ca15954b-5ab1-4527-a582-b2aa9de05322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j_2Fs834dp39"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}