{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyM5Fah77XuALjsYEAXaCFqE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mobadara/precision-diagnostics-xai/blob/main/notebooks/01_model_training_and_fine_tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Training and Fine-Tuning**\n",
        "\n",
        "**Objective:** Ingest the Kaggle Chest X-Ray dataset, apply strategic data augmentation, handle severe class imbalance via loss weighting, and fine-tune a pre-trained DenseNet121 architecture to detect pneumonia.\n",
        "\n",
        "## **Environment Setup**\n",
        "First, we import the necessary PyTorch libraries and configure our hardware accelerator. Because image processing is computationally expensive, we must ensure PyTorch detects and utilizes the Colab GPU (CUDA)."
      ],
      "metadata": {
        "id": "24IiOoFPN2oN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGGkAlCRNlro",
        "outputId": "919ad755-854c-49b1-a812-43d413128bb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Compute Device: cuda\n",
            "GPU Model: Tesla T4\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms, models\n",
        "from torch.utils.data import DataLoader\n",
        "import os\n",
        "import numpy as np\n",
        "\n",
        "# Configure the device\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(f'Compute Device: {device}')\n",
        "if device.type == 'cuda':\n",
        "    print(f'GPU Model: {torch.cuda.get_device_name(0)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preprocessing and Augmentation Pipeline**\n",
        "As determined in our EDA, we must standardize all input resolutions to 224x224.\n",
        "\n",
        "To prevent overfitting without introducing artificial artifacts, we apply light data augmentation **only to the training set** (rotation and color jitter). The validation and test sets remain strictly unaltered to provide an honest evaluation metric."
      ],
      "metadata": {
        "id": "2SGyO1PxOmDX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dataset paths\n",
        "data_dir = '../dataset/chest_xray'\n",
        "train_dir = os.path.join(data_dir, 'train')\n",
        "val_dir = os.path.join(data_dir, 'val')\n",
        "test_dir = os.path.join(data_dir, 'test')\n",
        "\n",
        "# 1. Training Transforms (with Augmentation)\n",
        "train_transforms = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomRotation(10), # Slight rotation to simulate patient misalignment\n",
        "    transforms.ColorJitter(brightness=0.2, contrast=0.2), # Account for different X-ray exposures\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Standard ImageNet normalization\n",
        "])\n",
        "\n",
        "# 2. Validation/Testing Transforms (Strictly unaltered)\n",
        "eval_transforms = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "print(\"Transforms configured successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikceEotXOU_1",
        "outputId": "367466cb-bd4f-4de8-b8c6-1142abda4b49"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transforms configured successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I6obJOa1QYMy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}