MedSegDiff.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9RO905MEDJNB"
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "dRwGoerdh5m6"
   },
   "outputs": [],
   "source": [
    "# Standard library imports\n",
    "import os\n",
    "import copy\n",
    "import math\n",
    "import glob\n",
    "import random\n",
    "import zipfile\n",
    "import functools\n",
    "from functools import partial\n",
    "\n",
    "# Third-party libraries\n",
    "import albumentations as A\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torchvision\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "from skimage import io\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, normalize\n",
    "from tqdm import auto, tqdm\n",
    "\n",
    "# PyTorch imports\n",
    "from torch import einsum, nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.nn import Module, ModuleList, functional as F\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "from torch.fft import fft2, ifft2\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import models, transforms, utils\n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision.transforms import InterpolationMode\n",
    "from torchvision.utils import make_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0aTXVz_xDSiV"
   },
   "source": [
    "# Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random seed for reproducibility\n",
    "RANDOM_SEED = 42\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "random.seed(RANDOM_SEED)\n",
    "np.random.seed(RANDOM_SEED)\n",
    "\n",
    "# Device configuration\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Dataset and environment configurations\n",
    "DATASET_PARENT_DIR = '../dataset/' # Change this to your desired directory\n",
    "DATASET_API_URI = 'mateuszbuda/lgg-mri-segmentation'\n",
    "DATASET_DIR = f'{DATASET_PARENT_DIR}kaggle_3m/'\n",
    "\n",
    "# Image and batch processing configurations\n",
    "IMAGE_HEIGHT = 32\n",
    "TRAIN_BATCH_SIZE = 16\n",
    "TEST_BATCH_SIZE = 16\n",
    "TEST_SIZE = 0.1\n",
    "\n",
    "# Training configurations\n",
    "NUM_EPOCHS = 50\n",
    "LEARNING_RATE = 2e-4\n",
    "TIMESTEPS = 100\n",
    "NOISE_SCHEDULE = 'cosine'\n",
    "\n",
    "# Learning rate scheduler configurations\n",
    "REDUCE_LR_FACTOR = 0.5\n",
    "REDUCE_LR_PATIENCE = 3\n",
    "\n",
    "# Visualization and model saving configurations\n",
    "VISUALIZATION_SAMPLE_INTERVAL = 5\n",
    "VISUALIZATION_NUM_SAMPLES = 3\n",
    "MODEL_SAVE_DIR = \"models/\"\n",
    "MODEL_SAVE_PATH_TEMPLATE = MODEL_SAVE_DIR + 'best_diffusion_model_epoch_{}.pth'\n",
    "\n",
    "if not os.path.exists(MODEL_SAVE_DIR):\n",
    "    os.makedirs(MODEL_SAVE_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "print(f'Using device: {device}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uUioBQcFqbgn"
   },
   "source": [
    "# Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def device_grad_decorator(device=None, no_grad=False):\n",
    "    \"\"\"\n",
    "    A decorator to handle PyTorch tensor device allocation and gradient computation.\n",
    "\n",
    "    Args:\n",
    "        device (str or torch.device, optional): The device to which all tensor arguments should be moved.\n",
    "                                                Examples: 'cpu', 'cuda', torch.device('cuda').\n",
    "        no_grad (bool, optional): If True, the function will be executed within a torch.no_grad() context, \n",
    "                                  disabling gradient calculations. Defaults to False.\n",
    "\n",
    "    Returns:\n",
    "        Callable: A decorated function with tensor arguments moved to the specified device and\n",
    "                  optionally executed without gradient tracking.\n",
    "\n",
    "    Usage:\n",
    "        @device_grad_decorator(device='cuda', no_grad=True)\n",
    "        def some_function(tensor):\n",
    "            ...\n",
    "    \"\"\"\n",
    "    def decorator(func):\n",
    "        @functools.wraps(func)\n",
    "        def wrapper(*args, **kwargs):\n",
    "            # Move positional tensor arguments to the specified device\n",
    "            if device:\n",
    "                args = tuple(\n",
    "                    arg.to(device) if isinstance(arg, torch.Tensor) else arg \n",
    "                    for arg in args\n",
    "                )\n",
    "                # Move keyword tensor arguments to the specified device\n",
    "                kwargs = {\n",
    "                    k: (v.to(device) if isinstance(v, torch.Tensor) else v)\n",
    "                    for k, v in kwargs.items()\n",
    "                }\n",
    "            # Execute the function in no_grad context if specified\n",
    "            if no_grad:\n",
    "                with torch.no_grad():\n",
    "                    return func(*args, **kwargs)\n",
    "            return func(*args, **kwargs)\n",
    "\n",
    "        return wrapper\n",
    "    return decorator\n",
    "\n",
    "def visualize_img(ax, img, title, is_mask=False):\n",
    "    \"\"\"\n",
    "    Displays an image on the given matplotlib axis with an optional grayscale colormap.\n",
    "\n",
    "    Args:\n",
    "        ax (matplotlib.axes._subplots.AxesSubplot): The axis on which to display the image.\n",
    "        img (numpy.ndarray): The image data to be displayed.\n",
    "        title (str): The title for the image.\n",
    "        is_mask (bool, optional): If True, display the image in grayscale. Defaults to False.\n",
    "    \"\"\"\n",
    "    ax.set_title(title)\n",
    "    ax.imshow(img, cmap='gray' if is_mask else None)\n",
    "    ax.axis('off')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download LGG Segmentation Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is utilized in several significant research studies, including:\n",
    "\n",
    "1. Mateusz Buda, Ashirbani Saha, Maciej A. Mazurowski. <i>\"Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.\"</i> Published in Computers in Biology and Medicine, 2019.\n",
    "\n",
    "2. Maciej A. Mazurowski, Kal Clark, Nicholas M. Czarnek, Parisa Shamsesfandabadi, Katherine B. Peters, Ashirbani Saha. <i>\"Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data.\"</i> Featured in the Journal of Neuro-Oncology, 2017.\n",
    "\n",
    "The dataset comprises brain magnetic resonance (MR) images accompanied by manual FLAIR (Fluid-Attenuated Inversion Recovery) abnormality segmentation masks. These images are sourced from The Cancer Imaging Archive (TCIA) and are specifically selected from 110 patients within The Cancer Genome Atlas (TCGA) lower-grade glioma collection. Each patient's dataset includes a FLAIR sequence along with available genomic cluster data.\n",
    "\n",
    "Key dataset features:\n",
    "- **Image Source**: The Cancer Imaging Archive (TCIA)\n",
    "- **Patient Count**: 110 (from TCGA lower-grade glioma collection)\n",
    "- **Content**: Brain MR Images with FLAIR sequence\n",
    "- **Additional Data**: Tumor genomic clusters and patient data (available in `data.csv` file)\n",
    "\n",
    "Dataset Download Source: [Kaggle - LGG MRI Segmentation](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "QVR5Mcu0bN2u",
    "outputId": "d3e42ced-d673-4bf8-97a8-572c0841bbce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already exists at ../dataset/kaggle_3m/\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(DATASET_DIR):\n",
    "    from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "    \n",
    "    api = KaggleApi()\n",
    "    api.authenticate()\n",
    "    api.dataset_download_files(DATASET_API_URI, path=DATASET_PARENT_DIR,  unzip=True)\n",
    "else:\n",
    "    print(f\"Data already exists at {DATASET_DIR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34m../dataset/kaggle_3m/\u001b[0m\n",
      "├── \u001b[00mdata.csv\u001b[0m\n",
      "├── \u001b[00mREADME.md\u001b[0m\n",
      "├── \u001b[01;34mTCGA_CS_4941_19960909\u001b[0m\n",
      "│   ├── \u001b[01;35mTCGA_CS_4941_19960909_10_mask.tif\u001b[0m\n",
      "│   ├── \u001b[01;35mTCGA_CS_4941_19960909_10.tif\u001b[0m\n",
      "│   ├── \u001b[01;35mTCGA_CS_4941_19960909_11_mask.tif\u001b[0m\n",
      "│   ├── \u001b[01;35mTCGA_CS_4941_19960909_11.tif\u001b[0m\n",
      "│   ├── \u001b[01;35mTCGA_CS_4941_19960909_12_mask.tif\u001b[0m\n",
      "│   ├── \u001b[01;35mTCGA_CS_4941_19960909_12.tif\u001b[0m\n",
      "│   ├── \u001b[01;35mTCGA_CS_4941_19960909_13_mask.tif\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! tree {DATASET_DIR} -L 4 | head -n 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gGWuFGl_Dd9G"
   },
   "source": [
    "## Prepare Labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5f-315mh2Hcf"
   },
   "source": [
    "This dataset contains brain MRI images and manual FLAIR abnormality segmentation masks, where each pixel value of masks indicates the presence or absence of cancer (0 and 1, respectively). The images correspond to 110 patients whose IDs are available in the patient_ids.csv file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "d93Y-QkzxR5i"
   },
   "outputs": [],
   "source": [
    "def pos_neg_diagnosis(mask_path):\n",
    "    \"\"\"\n",
    "    Determines whether a mask image indicates the presence of cancer.\n",
    "\n",
    "    Args:\n",
    "        mask_path (str): Path to the mask image file.\n",
    "\n",
    "    Returns:\n",
    "        int: 1 if the mask contains positive values (cancer detected), 0 otherwise.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        mask = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED)\n",
    "        if mask is None:\n",
    "            raise ValueError(f\"Failed to load image at path: {mask_path}\")\n",
    "        return int(np.max(mask) > 0)\n",
    "    except Exception as e:\n",
    "        raise RuntimeError(f\"Error processing mask at {mask_path}: {e}\")\n",
    "\n",
    "def load_mri_df(mri_scans_path):\n",
    "    \"\"\"\n",
    "    Loads MRI scan data into a DataFrame, including cancer diagnosis based on mask files.\n",
    "\n",
    "    Args:\n",
    "        mri_scans_path (str): Path to the root directory containing subdirectories of MRI scans.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: A DataFrame with columns:\n",
    "                      - 'patient_id': Patient identifier derived from subdirectory names.\n",
    "                      - 'image_path': Path to the MRI image files.\n",
    "                      - 'mask_path': Path to the corresponding mask image files.\n",
    "                      - 'has_cancer': 1 if the mask indicates cancer, 0 otherwise.\n",
    "    \"\"\"\n",
    "    data_records = []\n",
    "    \n",
    "    # Iterate through subdirectories\n",
    "    for sub_dir_path in glob.glob(os.path.join(mri_scans_path, \"*/\")):\n",
    "        dir_name = os.path.basename(os.path.normpath(sub_dir_path))\n",
    "        \n",
    "        try:\n",
    "            # Collect image and mask files\n",
    "            image_files = [f for f in os.listdir(sub_dir_path) if not f.endswith('mask.tif')]\n",
    "            mask_files = [f for f in os.listdir(sub_dir_path) if f.endswith('mask.tif')]\n",
    "            \n",
    "            # Match image and mask pairs\n",
    "            for image_file, mask_file in zip(sorted(image_files), sorted(mask_files)):\n",
    "                image_path = os.path.join(sub_dir_path, image_file)\n",
    "                mask_path = os.path.join(sub_dir_path, mask_file)\n",
    "                data_records.append([dir_name, image_path, mask_path])\n",
    "        except Exception as e:\n",
    "            # Log the specific directory that caused an issue\n",
    "            print(f\"Error processing directory '{sub_dir_path}': {e}\")\n",
    "\n",
    "    # Create a DataFrame and compute cancer diagnosis\n",
    "    mri_df = pd.DataFrame(data_records, columns=['patient_id', 'image_path', 'mask_path'])\n",
    "    mri_df['has_cancer'] = mri_df['mask_path'].apply(pos_neg_diagnosis)\n",
    "    \n",
    "    return mri_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "id": "nIomw-OAqbgn",
    "outputId": "d46d2fae-09b7-49f2-eaac-3271a40ff596"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>image_path</th>\n",
       "      <th>mask_path</th>\n",
       "      <th>has_cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA_HT_A5RC_19990831</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA_HT_A5RC_19990831</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA_HT_A5RC_19990831</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA_HT_A5RC_19990831</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA_HT_A5RC_19990831</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              patient_id                                         image_path  \\\n",
       "0  TCGA_HT_A5RC_19990831  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...   \n",
       "1  TCGA_HT_A5RC_19990831  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...   \n",
       "2  TCGA_HT_A5RC_19990831  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...   \n",
       "3  TCGA_HT_A5RC_19990831  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...   \n",
       "4  TCGA_HT_A5RC_19990831  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...   \n",
       "\n",
       "                                           mask_path  has_cancer  \n",
       "0  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...           0  \n",
       "1  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...           0  \n",
       "2  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...           0  \n",
       "3  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...           0  \n",
       "4  ../dataset/kaggle_3m/TCGA_HT_A5RC_19990831/TCG...           0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mri_df = load_mri_df(mri_scans_path=DATASET_DIR)\n",
    "mri_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explatory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1800 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def process_and_visualize_cases(mri_df, max_rows, axs):\n",
    "    \"\"\"\n",
    "    Processes and visualizes MRI and mask images for cases with cancer.\n",
    "\n",
    "    Args:\n",
    "        mri_df (pd.DataFrame): DataFrame containing MRI image and mask paths, and diagnosis.\n",
    "        max_rows (int): Maximum number of rows to display in the visualization.\n",
    "        axs (np.ndarray): Array of matplotlib Axes for displaying the images.\n",
    "\n",
    "    Returns:\n",
    "        int: Number of rows visualized.\n",
    "    \"\"\"\n",
    "    row_count = 0\n",
    "\n",
    "    for idx in mri_df.index:\n",
    "        try:\n",
    "            # Check if the current case has cancer\n",
    "            has_cancer = mri_df.at[idx, 'has_cancer']\n",
    "            if has_cancer == 1:\n",
    "                # Load the MRI image and mask\n",
    "                img_path = mri_df.at[idx, 'image_path']\n",
    "                mask_path = mri_df.at[idx, 'mask_path']\n",
    "                img = io.imread(img_path)\n",
    "                mask = io.imread(mask_path)\n",
    "\n",
    "                # Visualize the images\n",
    "                visualize_img(axs[row_count][0], img, 'Brain MRI')\n",
    "                visualize_img(axs[row_count][1], mask, 'Mask', is_mask=True)\n",
    "\n",
    "                # Overlay mask on the MRI image\n",
    "                overlayed_img = img.copy()\n",
    "                overlayed_img[mask == 255] = (0, 255, 150)\n",
    "                visualize_img(axs[row_count][2], overlayed_img, 'MRI with Mask')\n",
    "\n",
    "                # Increment the row count\n",
    "                row_count += 1\n",
    "\n",
    "            # Stop when maximum rows are filled\n",
    "            if row_count == max_rows:\n",
    "                break\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Error visualizing row {idx}: {e}\")\n",
    "\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Main visualization logic\n",
    "max_rows = 6\n",
    "fig, axs = plt.subplots(max_rows, 3, figsize=(9, 3 * max_rows))\n",
    "\n",
    "# Process and visualize cases\n",
    "process_and_visualize_cases(mri_df, max_rows, axs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_cancerous_rate(mri_df):\n",
    "    \"\"\"\n",
    "    Displays the distribution of cancerous and non-cancerous cases in the dataset.\n",
    "\n",
    "    Args:\n",
    "        mri_df (pd.DataFrame): DataFrame containing MRI image and mask paths, and diagnosis.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    ax = sns.countplot(x='has_cancer', data=mri_df, palette=['#3498db', '#e74c3c'])\n",
    "    plt.title('Distribution of Cancerous vs Non-Cancerous Cases', fontsize=16)\n",
    "    plt.xlabel('Diagnosis', fontsize=14)\n",
    "    plt.ylabel('Count', fontsize=14)\n",
    "    ax.set_xticklabels(['Non-Cancerous', 'Cancerous'], fontsize=12)\n",
    "    ax.set_ylim(0, 3000)  # Set y-axis height to 3000\n",
    "\n",
    "    # Calculate percentages\n",
    "    total = len(mri_df)\n",
    "    for p in ax.patches:\n",
    "        percentage = f'{100 * p.get_height() / total:.1f}%'\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = p.get_height() / 2\n",
    "        ax.annotate(percentage, (x, y), ha='center', va='center', fontsize=18, color='black', fontweight='bold')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "# Display the cancerous rate\n",
    "display_cancerous_rate(mri_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MedSegDiff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KRrQJRbUqbgo",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "Following the standard implementation of Denoising Diffusion Probabilistic Models (DDPMs), a U-Net architecture is employed for learning. To achieve segmentation, the step estimation function ($\\epsilon_{\\theta}$) is conditioned on the raw image prior, described by:\n",
    "\n",
    "$$\n",
    "\\epsilon_{\\theta}(x_t, I, t) = D((E_{t}^{I} + E_{t}^{x}, t))\n",
    "$$\n",
    "\n",
    "where $E_{t}^{I}$ is the conditional feature embedding (the raw image embedding), and $E_{t}^{x}$ is the segmentation map feature embedding at the current step. These components are combined and forwarded to a U-Net decoder $D$ for reconstruction. The step index $t$ is integrated with the embeddings and decoder features, leveraging a shared learned lookup table.\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"https://raw.githubusercontent.com/deepmancer/medseg-diffusion/main/images/model_overview.png\" style=\"display: block; margin: auto;\" width=\"800\"/>\n",
    "</p>\n",
    "\n",
    "\n",
    "The loss of our model is represented by the following equation: $\\mathcal{L} = \\mathbb{E}_{mask_0,\\epsilon,t}[\\lVert \\epsilon - \\epsilon_\\theta(\\sqrt{\\bar{a}_t}mask_0 + \\sqrt{1 - \\bar{a}_t}\\epsilon, I_i,t) \\rVert^2]$\n",
    "\n",
    "\n",
    "In each iteration, a random pair of raw image $I_i$ and segmentation label $mask_i$ are sampled for training. The iteration number is sampled from a uniform distribution and $\\epsilon$ from a Gaussian distribution. The main architecture of the model is a modified ResUNet, which we implement with a ResNet encoder followed by a UNet decoder. $I$ and $x_t$ (noisy mask at the step=$t$) are encoded with two individual encoders.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review Denoising Diffusion Probabilistic Models (DDPMs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will implement a **Denoising Diffusion Probabilistic Model (DDPM)** for brain tumor segmentation on MRI images. Diffusion models are generative models consisting of two main stages: **Forward Diffusion** and **Reverse Diffusion**. In the following sections, we will break down these stages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🔄 Forward Diffusion Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The forward diffusion process transforms a clean segmentation mask into progressively noisier representations. The steps are as follows:\n",
    "\n",
    "1. **Noise Addition**: \n",
    "   - Starting with an original segmentation mask $\\text{mask}_0$ sampled from the real data distribution, Gaussian noise is iteratively added at each step $t$ over $T$ steps.\n",
    "   - The noise is scaled based on a variance schedule $\\beta_t$, ensuring controlled perturbation at each step.\n",
    "\n",
    "2. **Progressive Degradation**:\n",
    "   - Each step produces a noisier version of the mask, denoted as $\\text{mask}_t$, where features gradually become indistinguishable as $t$ increases.\n",
    "   - This progression creates a sequence $\\text{mask}_0, \\text{mask}_1, \\dots, \\text{mask}_T$.\n",
    "\n",
    "3. **Convergence to Gaussian Noise**:\n",
    "   - As $T \\to \\infty$, the segmentation mask $\\text{mask}_T$ becomes indistinguishable from an isotropic Gaussian distribution, effectively losing all original information.\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"https://raw.githubusercontent.com/deepmancer/medseg-diffusion/main/images/diff_forward.png\" style=\"display: block; margin: auto;\" width=\"900\"/>\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"https://raw.githubusercontent.com/deepmancer/medseg-diffusion/main/images/diff_forward2.png\" style=\"display: block; margin: auto;\" width=\"650\"/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🔁 Reverse Diffusion Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "The reverse diffusion process reconstructs the original segmentation mask from the noisy data $\\text{mask}_T$ by iteratively denoising. The steps are as follows:\n",
    "\n",
    "1. **Noise Prediction**:\n",
    "   - A **U-Net architecture** is trained to predict the Gaussian noise $\\epsilon_t$ added to the segmentation mask during the forward diffusion process.\n",
    "   - The model learns a conditional mapping $\\epsilon_\\theta(\\text{mask}_t, t)$, where $\\theta$ represents the model parameters.\n",
    "\n",
    "2. **Stepwise Denoising**:\n",
    "   - Starting with $\\text{mask}_T$, the model iteratively refines the noisy mask by subtracting the predicted noise at each step $t$, moving from $t = T$ to $t = 0$.\n",
    "   - At each step, the predicted distribution $q(\\text{mask}_{t-1} | \\text{mask}_t)$ guides the reconstruction process.\n",
    "\n",
    "3. **Final Reconstruction**:\n",
    "   - After $T$ reverse steps, the output $\\text{mask}_0$ closely approximates the original segmentation mask sampled from the real distribution.\n",
    "   - This process effectively reverses the effects of forward noise addition, bridging the gap between isotropic Gaussian noise and meaningful segmentation masks.\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"https://raw.githubusercontent.com/deepmancer/medseg-diffusion/main/images/diff_reverse.png\" style=\"display: block; margin: auto;\" width=\"900\"/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wqzFs-Uwqbgo"
   },
   "source": [
    "## Architecture Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sNEp6iBDqbgo",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "Please find below a description of the components of the U-Net architecture that will be implemented for this task:\n",
    "\n",
    "### 1) Dynamic Encoding Process\n",
    "\n",
    "- *FF-Parser Input*: The segmentation map is first input into the Feature Frequency Parser (FF-Parser), which helps to reduce high-frequency noise and refine the feature representation.\n",
    "- *Attentive Fusion*: After FF-Parser processing, the denoised feature map is combined synergistically with the prior image embeddings. An attentive-like mechanism is employed to enhance regional attention and feature saliency.\n",
    "- *Iterative Refinement*: The enriched feature map is then subject to iterative refinement through the FF-Parser and the attentive mechanism, culminating at the bottleneck phase.\n",
    "- *Bottleneck Convergence*: At this point, the processed feature map is added to the UNet encoder's outputs, resulting in an improved segmentation map that proceeds to the final encoding stage.\n",
    "\n",
    "### 2) Time Encoding Block\n",
    "- *Sinusoidal Embedding Calculation*: This step begins with the calculation of sinusoidal timestep embeddings.\n",
    "- *Initial Processing Layers*: These embeddings are then passed through a linear layer, followed by SiLU activation, and another linear layer.\n",
    "- *Integration into Residual Blocks*: Time features are then integrated into residual blocks. This is achieved either through straightforward spatial addition or via adaptive group normalization.\n",
    "\n",
    "### 3) Bottleneck Block\n",
    "- *Embedding Fusion*: At the core of the U-Net, this phase combines the embeddings from the UNet's encoder with those from the Dynamic Encoding, preparing them for the final encoding stage.\n",
    "\n",
    "### 4) Encoder & Decoder Blocks\n",
    "+ *Initial Convolution*: The encoder begins with separate initial convolutional layers for the mask and input image, preparing the features for downstream processing.\n",
    "+ *Residual Blocks:* As a base sub-module, we define each ResNet block as two consecutive convolutional layers with a SiLU activation in between and Group Normalization after each convolutional layer. You can use this module in Down/Up blocks. Also, by removing the residual connection, you can use this block as a convolutional network throughout the network.\n",
    "+ *Attention:* This is also a sub-module consisting of a Layer Normalization, Multi-head Attention, a residual connection, a feed-forward network, and another residual connection.\n",
    "+ *Time Embedding Integration*: Each residual block in the downsampling and upsampling stages integrate time embeddings.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MedSegDiff Model Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submodules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9ZFpn5Ufqbgp"
   },
   "source": [
    "### 1) Dynamic Encoding Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6pZ01PzBqbgp"
   },
   "source": [
    "\n",
    "To achieve segmentation, we condition the step estimation function by using a  **Dynamic Conditional Encoding** process. At step $t$, we add the segmentation map feature embedding and the raw image embedding and send them to a UNet decoder $D$ for reconstruction. In most conditional DPM, the conditional prior is a unique given information. However, medical image segmentation is more challenging due to its ambiguous objects, where lesions or tissues are often difficult to differentiate from the background. Moreover, low-contrast image modalities such as MRI images make it even more challenging. Hence, given only a static image $I$ as the condition for each step is hard to learn.\n",
    "\n",
    "We will implement a dynamic conditional encoding for each step to address this issue. On one hand, the raw image contains accurate segmentation target information but is hard to differentiate from the background. On the other hand, the current-step segmentation map contains enhanced target regions but is not accurate. Therefore, integrating the current-step segmentation information $x_t$ into the conditional raw image encoding for the mutual complement is a reasonable response. To be specific, we will integrate this on the feature level by fusing conditional feature maps and image encoding features through an **attentive-like mechanism**. This process helps the model to localize and calibrate the segmentation dynamically. In particular, two feature maps are first applied layer normalization and multiplied together to get an affinity map. Then we multiply the affinity map with the condition encoding features to enhance the attentive region, which is: \n",
    "$$\n",
    "A(m_I^k, m_x^k) = (\\text{LayerNorm}(m_I^k) \\otimes \\text{LayerNorm}(m_x^k)) \\otimes m_I^k\n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xqdKXxTQqbgp"
   },
   "source": [
    "#### Attention-like Mechanism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "mvYMv8KZqbgp"
   },
   "outputs": [],
   "source": [
    "from unet_util import LayerNorm\n",
    "\n",
    "class AttentionLikeMechanism(nn.Module):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            dim (int): The number of features in the input tensors, typically the channel dimension.\n",
    "\n",
    "        Attributes:\n",
    "            layer_norm_i (LayerNorm): Layer normalization for the image feature tensor.\n",
    "            layer_norm_x (LayerNorm): Layer normalization for the input segmentation mask tensor.\n",
    "        \"\"\"\n",
    "        self.layer_norm_i = LayerNorm(dim, bias=True)\n",
    "        self.layer_norm_x = LayerNorm(dim, bias=True)\n",
    "\n",
    "    def forward(self, x, image):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x (Tensor): The noisy segmentation mask at step t and layer l of the U-Net's encoder with shape [batch_size, channels, height, width].\n",
    "            image (Tensor): The embedding of the image combined with feature maps from a previous fusion layer with shape [batch_size, channels, height, width].\n",
    "\n",
    "        Returns:\n",
    "            Tensor: The refined segmentation mask, having the same shape as the input tensors with shape [batch_size, channels, height, width].\n",
    "        \"\"\"\n",
    "        normed_x = self.layer_norm_x(x)\n",
    "        normed_i = self.layer_norm_i(image)\n",
    "\n",
    "        return (normed_x * normed_i) * normed_i"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an issue with integrating the embedding of $x_t$ as it generates additional high-frequency noise. To overcome this problem, we need to restrict the high-frequency elements in the features. We can achieve this by training an attentive (weight) map with parameters, which will be applied to the features in **Fourier space**. The following is a step-by-step explanation of this process:\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"./images/ff_parser.png\" style=\"display: block; margin: auto;\" width=\"600\"/>\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "1. **Performing 2D FFT (fast Fourier transform) along the spatial dimensions**: \n",
    "  $$\n",
    "  M = \\mathcal{F}[m] \\in \\mathbb{C}^{H \\times W \\times C}\n",
    "  $$\n",
    "\n",
    "2. **Modulating the spectrum of $m$ by multiplying a parameterized attentive map $A$ to $M$**: \n",
    "  $$\n",
    "  A \\in \\mathbb{C}^{H \\times W \\times C}, \\  M' = A \\otimes M \\ (\\text{elementwise product})\n",
    "  $$\n",
    "\n",
    "3. **Reversing $M'$ back to the spatial domain by adopting inverse FFT**: \n",
    "  $$\n",
    "  m' = \\mathcal{F}^{-1}[M']\n",
    "  $$\n",
    "\n",
    "\n",
    "This FF-Parser can be regarded as a learnable version of frequency filters which are wildly applied in the digital image processing. Different from the spatial attention, it globally adjusts the components of the specific frequencies. Thus it can be learn to constrain the high-frequency component for the adaptive integration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z4ITY2LXqbgp"
   },
   "source": [
    "#### Fourier-space Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "APNetuWTqbgp"
   },
   "outputs": [],
   "source": [
    "class FFParser(nn.Module):\n",
    "    def __init__(self, feature_map_size, dim):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            feature_map_size (int): The size of the spatial dimensions (height and width) of the feature maps.\n",
    "            dim (int): The number of channels in the feature maps.\n",
    "\n",
    "        Attributes:\n",
    "            ff_parser_attn_map (Parameter): A learnable tensor that serves as the attentive map in the Fourier space. Initialized to ones.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.ff_parser_attn_map = torch.nn.Parameter(torch.ones(dim, feature_map_size, feature_map_size))\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x (Tensor): The input feature map tensor with shape [batch_size, channels, height, width].\n",
    "\n",
    "        Returns:\n",
    "            Tensor: The processed feature map, having the same shape as the input, with high-frequency components modulated.\n",
    "        \"\"\"\n",
    "        dtype = x.dtype\n",
    "\n",
    "        x = fft2(x) * self.ff_parser_attn_map\n",
    "\n",
    "        x = ifft2(x).real.type(dtype)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dZJKogjiqbgv"
   },
   "source": [
    "#### Putting All Together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "uZdY-iKOqbgv"
   },
   "outputs": [],
   "source": [
    "from unet_util import ResnetBlock\n",
    "\n",
    "class DynamicFusionLayer(nn.Module):\n",
    "    def __init__(self, feature_map_size, dim):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            feature_map_size (int): The size of the spatial dimensions (height and width) of the feature maps.\n",
    "            dim (int): The number of channels in the feature maps.\n",
    "        Attributes:\n",
    "            ff_parser (FFParser): Module to modulate high-frequency components in the feature map.\n",
    "            attention (AttentionLikeMechanism): Module to apply an attention-like mechanism for feature enhancement.\n",
    "            block (ResnetBlock): A ResNet block for additional feature processing and refinement.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.ff_parser = FFParser(feature_map_size, dim)\n",
    "        self.attention = AttentionLikeMechanism(dim)\n",
    "        self.block = ResnetBlock(dim, dim)\n",
    "\n",
    "    def forward(self, x, image):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x (Tensor): The input segmentation map tensor with shape [batch_size, channels, height, width].\n",
    "            image (Tensor): The prior image embedding tensor with shape [batch_size, channels, height, width].\n",
    "\n",
    "        Returns:\n",
    "            Tensor: The output of the Dynamic Fusion Layer, representing the enhanced and refined feature map.\n",
    "        \"\"\"\n",
    "        x = self.ff_parser(x)\n",
    "\n",
    "        attention_out = self.attention(x, image)\n",
    "\n",
    "        return self.block(attention_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4OCvM8vEqbgv"
   },
   "source": [
    "### 2) Time Encoding Block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This block is responsible for encoding the time information into the model. The time embedding is calculated using a sinusoidal function, which is then processed through a series of linear layers and *SiLU activations*. The time embeddings are then integrated into the residual blocks of the model. This block is crucial for the model to learn the temporal dynamics of the diffusion process.\n",
    "\n",
    "<img \n",
    "src=\"https://raw.githubusercontent.com/deepmancer/medseg-diffusion/main/images/time_pe.png\" style=\"display: block; margin: auto;\" width=\"600\"/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QDvm32O1qbgv"
   },
   "source": [
    "#### Sinusoidal Positional Encoding for the Time Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "g0SBiDsNqbgv"
   },
   "outputs": [],
   "source": [
    "class TimeSinusoidalPE(nn.Module):\n",
    "    def __init__(self, dim, out_dim):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            dim (int): The dimension of the sinusoidal embedding's space.\n",
    "            out_dim (int): The dimension of the output after projection.\n",
    "\n",
    "        Attributes:\n",
    "            projector (nn.Sequential): Projection layers to map the encoding to the output dimension.\n",
    "            sin_cos_emb (Tensor): Precomputed sinusoidal embeddings.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        half_dim = dim // 2\n",
    "        emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -(math.log(10000) / (half_dim - 1)))\n",
    "        emb = torch.cat((emb.sin(), emb.cos())).view(1, -1)\n",
    "        self.register_buffer('sin_cos_emb', emb)\n",
    "\n",
    "        self.projector = nn.Sequential(\n",
    "            nn.Linear(dim, out_dim),\n",
    "            nn.SiLU(),\n",
    "            nn.Linear(out_dim, out_dim)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x (Tensor): Input tensor representing time or a one-dimensional sequence, shape [batch_size].\n",
    "\n",
    "        Returns:\n",
    "            Tensor: Projected sinusoidal encoding, shape [batch_size, out_dim].\n",
    "        \"\"\"\n",
    "        x = x.unsqueeze(-1) * self.sin_cos_emb\n",
    "\n",
    "        return self.projector(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S3eatQxQqbgw"
   },
   "source": [
    "### 3) Bottleneck Block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Qv7_D9Nqbgw"
   },
   "source": [
    "#### Bottleneck Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "5S1JtXx3qbgw"
   },
   "outputs": [],
   "source": [
    "from unet_util import ResnetBlock, Transformer\n",
    "\n",
    "class BottleNeck(nn.Module):\n",
    "    def __init__(self, dim, time_embedding_dim, transformer_layers, groups=8, **attention_configs):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            dim (int): The number of channels in the feature maps.\n",
    "            time_embedding_dim (int): The dimension of the time embedding.\n",
    "            groups (int, optional): The number of groups for grouped convolutions in ResnetBlocks. Defaults to 8.\n",
    "            **transformer_configs: Additional configurations for the Transformer layer.\n",
    "\n",
    "        Attributes:\n",
    "            conv_mask (ResnetBlock): ResNet block for processing the mask.\n",
    "            conv_img (ResnetBlock): ResNet block for processing the image.\n",
    "            transformer_fuser (Transformer): Transformer layer for feature fusion.\n",
    "            conv_fuser (ResnetBlock): ResNet block for processing the fused features.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        self.conv_mask = ResnetBlock(dim, dim, groups=groups, time_embedding_dim=time_embedding_dim)\n",
    "        self.conv_img = ResnetBlock(dim, dim, groups=groups, time_embedding_dim=time_embedding_dim)\n",
    "\n",
    "        self.transformer_fuser = Transformer(dim, depth=transformer_layers, **attention_configs)\n",
    "\n",
    "        self.conv_fuser = ResnetBlock(dim, dim, groups=groups, time_embedding_dim=time_embedding_dim)\n",
    "\n",
    "    def forward(self, x, image, time_embedding):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x (Tensor): The input mask tensor with shape [batch_size, channels, height, width].\n",
    "            image (Tensor): The input image tensor with shape [batch_size, channels, height, width].\n",
    "            time_embedding (Tensor): The time embedding tensor with shape [batch_size, time_embedding_dim].\n",
    "\n",
    "        Returns:\n",
    "            Tensor: The output tensor representing fused and refined features, with shape [batch_size, channels, height, width].\n",
    "        \"\"\"\n",
    "        x = self.conv_mask(x, time_embedding)\n",
    "        image = self.conv_img(image, time_embedding)\n",
    "\n",
    "        x = x + image\n",
    "\n",
    "        x = self.transformer_fuser(x)\n",
    "\n",
    "        x = self.conv_fuser(x, time_embedding)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NVzmdQZ5qbgw"
   },
   "source": [
    "## U-Net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QbjIXgZ1qbgw",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Data Flow Details\n",
    "\n",
    "1. **Preprocess Input:**\n",
    "   - Apply the initial convolution layers to mask and image.\n",
    "\n",
    "2. **Encoding Path:**\n",
    "   - Initialize an empty list `skip_connections` for storing features for skip connections.\n",
    "   - Iterate through the encoder layers (`mask_encoders`, `image_encoders`, and `dynamic_fusion_layers`):\n",
    "     - For each layer:\n",
    "       - Apply the first ResNet block to `x` (mask) and `image_emb` (image embedding), incorporating the time embedding `t`.\n",
    "       - Append `[x, image_emb]` to `h` for skip connections (if `skip_connect_image_feature_maps` is enabled, otherwise append `[x]`).\n",
    "       - Apply the second ResNet block to both `x` and `image_emb`.\n",
    "       - Apply the attention mechanism to both `x` and `image_emb`.\n",
    "       - Fuse `x` and `image_emb` using `dynamic_fusion_layer`.\n",
    "       - Append `[x, image_emb]` to `h` for skip connections (if `skip_connect_image_feature_maps` is enabled, otherwise append `[x]`).\n",
    "       - Downsample `x` and `image_emb` using the last layer in `mask_encoder` and `image_encoder` respectively.\n",
    "\n",
    "\n",
    "3. **Bottleneck:**\n",
    "   - Pass `x`, `image_emb`, and `t` through the `bottleneck` layer for processing.\n",
    "\n",
    "4. **Decoding Path:**\n",
    "   - Iterate through the decoder layers in reverse order:\n",
    "     - For each layer:\n",
    "       - Concatenate `x` with the corresponding features from `h` (popped from the end).\n",
    "       - Apply the first ResNet blocks.\n",
    "       - Concatenate the result with the corresponding features from `h` (popped from the end).\n",
    "       - Apply the second Resnet block, following by an attention mechanism.\n",
    "       - Upsample `x` using the last layer in the decoder.\n",
    "\n",
    "\n",
    "5. **Final Output:**\n",
    "   - Concatenate the final `x` with the output of the first conv layer.\n",
    "   - Pass through the `final_res_block` and `final_conv` to produce the output segmentation mask.\n",
    "\n",
    "The implementation of the `forward` function should closely follow the above steps, ensuring proper data flow and feature processing through the U-Net architecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SIinUkRWqbgw"
   },
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "M7s1EE9fqbgw"
   },
   "outputs": [],
   "source": [
    "from unet_util import Attention, LinearAttention, Downsample, Upsample, Residual, ResnetBlock, InitWeights_He\n",
    "\n",
    "class Unet(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        image_size,\n",
    "        num_layers,\n",
    "        weight_initializer=InitWeights_He,\n",
    "        linear_attn_layers=None,\n",
    "        skip_connect_image_feature_maps=False,\n",
    "        first_conv_channels=16,\n",
    "        time_embedding_dim=128,\n",
    "        mask_channels=1,\n",
    "        output_channels=1,\n",
    "        image_channels=3,\n",
    "        **kwargs,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            image_size (int): Size of the input image.\n",
    "            num_layers (int): Number of encoder/decoder blocks.\n",
    "            weight_initializer (Class): A class to initialize the weights of the layers.\n",
    "            linear_attn_layers (list of bool): Specifies layers using linear attention in each encoder/decoder block. True for layer indices with linear attention.\n",
    "            skip_connect_image_feature_maps (bool): If True, concatenate connections for image feature maps.\n",
    "            config (dict): Configuration dictionary with model parameters.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        # Basic model configuration and parameters\n",
    "        self.num_layers = num_layers\n",
    "        self.image_size = image_size\n",
    "        self.skip_connect_image_feature_maps = skip_connect_image_feature_maps\n",
    "        self.weight_initializer = weight_initializer\n",
    "        self.first_conv_channels = first_conv_channels\n",
    "        self.image_channels = image_channels\n",
    "        self.mask_channels = mask_channels\n",
    "        self.output_channels = output_channels\n",
    "        self.time_embedding_dim = time_embedding_dim\n",
    "        self.linear_attn_layers = linear_attn_layers or [i < (self.num_layers - 1) for i in range(self.num_layers)] # [T, T, T, F]\n",
    "\n",
    "        self.mask_initial_conv = nn.Conv2d(self.mask_channels, self.first_conv_channels, 7, padding=3)\n",
    "        self.img_initial_conv = nn.Conv2d(self.image_channels, self.first_conv_channels, 7, padding=3)\n",
    "\n",
    "\n",
    "        self.time_embedding_layer = TimeSinusoidalPE(self.first_conv_channels, self.time_embedding_dim)\n",
    "\n",
    "        attention_heads = kwargs.get('att_heads', 4)\n",
    "        attention_head_dim = kwargs.get('att_head_dim', 32)\n",
    "        bottleneck_transformer_num_layers = kwargs.get('bottleneck_transformer_layers', 1)\n",
    "\n",
    "        channels_num_factors = [int(2 ** i) for i in range(len(self.linear_attn_layers))]\n",
    "        hidden_channels =  [self.first_conv_channels, *map(lambda m: self.first_conv_channels * m, channels_num_factors)]\n",
    "        in_out = list(zip(hidden_channels[:-1], hidden_channels[1:]))\n",
    "\n",
    "        num_resolutions = len(in_out)\n",
    "\n",
    "        self.dynamic_fusion_layers = ModuleList([])\n",
    "        self.mask_encoders = ModuleList([])\n",
    "        self.image_encoders = ModuleList([])\n",
    "\n",
    "\n",
    "        # This loop constructs each encoder layer with two ResnetBlocks, a Residual Attention module (full or linear),\n",
    "        # and concludes with a Downsample layer, except for the last layer which uses a Conv2D layer.\n",
    "        current_feature_map_size = self.image_size\n",
    "        for i, ((dim_in, dim_out), use_linear) in enumerate(zip(in_out, self.linear_attn_layers)):\n",
    "            is_last_layer = i == (num_resolutions - 1)\n",
    "\n",
    "            first_resnet_block = ResnetBlock(dim_in, dim_in, time_embedding_dim=self.time_embedding_dim)\n",
    "            second_resnet_block = ResnetBlock(dim_in, dim_in, time_embedding_dim=self.time_embedding_dim)\n",
    "\n",
    "            attention_class = LinearAttention if use_linear else Attention\n",
    "            attention_configs = dict(\n",
    "                dim_head=attention_head_dim,\n",
    "                heads=attention_heads,\n",
    "            )\n",
    "            attention = attention_class(dim_in, **attention_configs)\n",
    "\n",
    "            dynamic_fusion_layer = DynamicFusionLayer(current_feature_map_size, dim_in)\n",
    "            last_conv_layer = nn.Conv2d(dim_in, dim_out, 3, padding=1) if is_last_layer else Downsample(dim_in, dim_out)\n",
    "\n",
    "            current_feature_map_size = current_feature_map_size // 2 if not is_last_layer else current_feature_map_size\n",
    "\n",
    "            self.dynamic_fusion_layers.append(dynamic_fusion_layer)\n",
    "            self.mask_encoders.append(ModuleList([\n",
    "                first_resnet_block,\n",
    "                second_resnet_block,\n",
    "                Residual(attention),\n",
    "                last_conv_layer,\n",
    "            ]))\n",
    "\n",
    "\n",
    "        self.image_encoders = copy.deepcopy(self.mask_encoders)\n",
    "\n",
    "        self.bottleneck = BottleNeck(\n",
    "            hidden_channels[-1],\n",
    "            self.time_embedding_dim,\n",
    "            transformer_layers=bottleneck_transformer_num_layers,\n",
    "            **dict(\n",
    "                dim_head=attention_head_dim,\n",
    "                heads=attention_heads,\n",
    "            ),\n",
    "        )\n",
    "\n",
    "        self.decoders = ModuleList([])\n",
    "        for i, ((dim_in, dim_out), use_linear) in enumerate(zip(reversed(in_out), reversed(self.linear_attn_layers))):\n",
    "            is_last_layer = i == (num_resolutions - 1)\n",
    "\n",
    "            skip_connect_dim = dim_in * (2 if self.skip_connect_image_feature_maps else 1)\n",
    "\n",
    "            first_resnet_block = ResnetBlock(dim_out + skip_connect_dim, dim_out, time_embedding_dim=self.time_embedding_dim)\n",
    "            second_resnet_block = ResnetBlock(dim_out + skip_connect_dim, dim_out, time_embedding_dim=self.time_embedding_dim)\n",
    "\n",
    "            attention_class = LinearAttention if use_linear else Attention\n",
    "            attention_configs = dict(\n",
    "                dim_head=attention_head_dim,\n",
    "                heads=attention_heads,\n",
    "            )\n",
    "            attention = attention_class(dim_out, **attention_configs)\n",
    "\n",
    "            last_conv_layer = nn.Conv2d(dim_out, dim_in, 3, padding=1) if is_last_layer else Upsample(dim_out, dim_in)\n",
    "\n",
    "            self.decoders.append(ModuleList([\n",
    "                first_resnet_block,\n",
    "                second_resnet_block,\n",
    "                Residual(attention),\n",
    "                last_conv_layer,\n",
    "            ]))\n",
    "\n",
    "\n",
    "        self.final_res_block = ResnetBlock(self.first_conv_channels * 2, self.first_conv_channels, time_embedding_dim=self.time_embedding_dim)\n",
    "\n",
    "        self.final_conv = nn.Conv2d(self.first_conv_channels, self.output_channels, 1)\n",
    "\n",
    "        # Apply the weight initializer if provided\n",
    "        if self.weight_initializer is not None:\n",
    "            self.apply(self.weight_initializer)\n",
    "\n",
    "    @device_grad_decorator(device=device)\n",
    "    def forward(self, x, time, image):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x (torch.Tensor): The input mask tensor.\n",
    "            time (torch.Tensor): The tensor representing time embeddings.\n",
    "            image (torch.Tensor): The input image tensor.\n",
    "\n",
    "        Returns:\n",
    "            torch.Tensor: The output tensor after segmentation.\n",
    "        \"\"\"\n",
    "        dtype, skip_connect_image = x.dtype, self.skip_connect_image_feature_maps\n",
    "\n",
    "        x = self.mask_initial_conv(x)\n",
    "        image_emb = self.img_initial_conv(image)\n",
    "\n",
    "        x_clone = x.clone()\n",
    "\n",
    "        t = self.time_embedding_layer(time)\n",
    "        skip_connections = []\n",
    "\n",
    "        # Encoding path: iterate through each layer in mask and image encoders\n",
    "        for mask_encoder, image_encoder, dynamic_fusion_layer in zip(self.mask_encoders, self.image_encoders, self.dynamic_fusion_layers):\n",
    "            block_1, block_2, attention, downsample = mask_encoder\n",
    "            image_block_1, image_block_2, image_attention, image_downsample = image_encoder\n",
    "\n",
    "            x = block_1(x, t)\n",
    "            image_emb = image_block_1(image_emb, t)\n",
    "\n",
    "            skip_connections.append([x, image_emb] if skip_connect_image else [x])\n",
    "\n",
    "            x = block_2(x, t)\n",
    "            image_emb = image_block_2(image_emb, t)\n",
    "\n",
    "            x = attention(x)\n",
    "            image_emb = image_attention(image_emb)\n",
    "\n",
    "            image_emb = dynamic_fusion_layer(x, image_emb)\n",
    "\n",
    "            skip_connections.append([x, image_emb] if skip_connect_image else [x])\n",
    "\n",
    "            x = downsample(x)\n",
    "            image_emb = image_downsample(image_emb)\n",
    "\n",
    "        # Processing through the bottleneck\n",
    "        x = self.bottleneck(x, image_emb, t)\n",
    "\n",
    "        # Decoding path: processing through decoders\n",
    "        for block_1, block_2, attention, upsample in self.decoders:\n",
    "            x = torch.cat((x, *skip_connections.pop()), dim=1)\n",
    "\n",
    "            x = block_1(x, t)\n",
    "\n",
    "            x = torch.cat((x, *skip_connections.pop()), dim=1)\n",
    "\n",
    "            x = block_2(x, t)\n",
    "            x = attention(x)\n",
    "            x = upsample(x)\n",
    "\n",
    "        x = torch.cat((x, x_clone), dim = 1)\n",
    "        x = self.final_res_block(x, t)\n",
    "        return self.final_conv(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gJqoXVwWqbgx"
   },
   "source": [
    "# MedSegDiff Diffusion Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u-p8OQqLqbgx"
   },
   "source": [
    "## Noise Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KRrGvUhGqbgx"
   },
   "source": [
    "In the diffusion process, the noise variance schedule $\\beta_t$ controls the amount of noise added at each step $t$. The schedule is typically designed to decrease over time, starting with a high variance to introduce significant noise and gradually reducing it to allow the model to denoise the data. Common variance scheduling functions include **Linear**, **Cosine**, and **Quadratic** variance schedules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "P8oEyFz4qbgx"
   },
   "outputs": [],
   "source": [
    "class NoiseScheduler:\n",
    "    def __init__(self, timesteps, beta_min=0.0001, beta_max=0.02, mode='linear', cosine_s=8e-3):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            timesteps (int): The number of timesteps for the noise schedule.\n",
    "            beta_min (float): The minimum value of beta in the schedule.\n",
    "            beta_max (float): The maximum value of beta in the schedule.\n",
    "            mode (str): The type of scheduling to use. Options are 'quad', 'linear', 'cosine'.\n",
    "            cosine_s (float): S parameter for cosine scheduling. Defaults to 8e-3.\n",
    "        \"\"\"\n",
    "        self.timesteps = timesteps\n",
    "        self.beta_min = beta_min\n",
    "        self.beta_max = beta_max\n",
    "        self.mode = mode\n",
    "        self.cosine_s = cosine_s\n",
    "        assert mode in ['quad', 'linear', 'cosine'], '\"Unsupported scheduling mode'\n",
    "\n",
    "    def get_beta_schedule(self):\n",
    "        if self.mode == \"quad\":\n",
    "            betas = (\n",
    "                torch.linspace(\n",
    "                    self.beta_min ** 0.5, self.beta_max ** 0.5, self.timesteps, dtype=torch.float64\n",
    "                )\n",
    "                ** 2\n",
    "            )\n",
    "        elif self.mode == \"linear\":\n",
    "            betas = torch.linspace(\n",
    "                self.beta_min, self.beta_max, self.timesteps, dtype=torch.float64\n",
    "            )\n",
    "        elif self.mode == \"cosine\":\n",
    "            timesteps = (\n",
    "                torch.arange(self.timesteps + 1, dtype=torch.float64) / self.timesteps + self.cosine_s\n",
    "            )\n",
    "            alphas = timesteps / (1 + self.cosine_s) * math.pi / 2\n",
    "            alphas = torch.cos(alphas).pow(2)\n",
    "            alphas = alphas / alphas[0]\n",
    "            betas = 1 - alphas[1:] / alphas[:-1]\n",
    "            betas = betas.clamp(min=0 ,max=0.999)\n",
    "\n",
    "        return betas.numpy()\n",
    "\n",
    "    def get_alpha_schedule(self):\n",
    "        beta_schedule = self.get_beta_schedule()\n",
    "        alpha_schedule = 1 - beta_schedule\n",
    "        return alpha_schedule\n",
    "\n",
    "    def get_alpha_bar_schedule(self):\n",
    "        alpha_schedule = self.get_alpha_schedule()\n",
    "        alpha_bar_schedule = np.cumprod(alpha_schedule)\n",
    "        return alpha_bar_schedule\n",
    "\n",
    "    def get_alphas_bar_previous_schedule(self):\n",
    "        alpha_bar_schedule = self.get_alpha_bar_schedule()\n",
    "        alpha_bars_prev = np.pad(alpha_bar_schedule[:-1], (1, 0), mode='constant', constant_values=1)\n",
    "        return alpha_bars_prev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "aNIoEf3Gqbgx",
    "outputId": "9e31a0f5-9329-46cb-d294-2a3c1cb11664"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_noise_scheduler_dataframe(timesteps, beta_min, beta_max, modes):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        timesteps (int): Number of timesteps for the schedule.\n",
    "        beta_min (float): Minimum beta value.\n",
    "        beta_max (float): Maximum beta value.\n",
    "        modes (list): List of scheduling modes to generate schedules for.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: DataFrame containing beta, alpha, and alpha_bar schedules.\n",
    "    \"\"\"\n",
    "    df = pd.DataFrame()\n",
    "    df['timestep'] = np.arange(timesteps)\n",
    "\n",
    "    for mode in modes:\n",
    "        scheduler = NoiseScheduler(timesteps, beta_min, beta_max, mode)\n",
    "        df[f'{mode}_beta'] = scheduler.get_beta_schedule()\n",
    "        df[f'{mode}_alpha'] = scheduler.get_alpha_schedule()\n",
    "        df[f'{mode}_alpha_bar'] = scheduler.get_alpha_bar_schedule()\n",
    "\n",
    "    return df\n",
    "\n",
    "def plot_scheduler_data(df, x_col, y_cols, titles):\n",
    "    \"\"\"\n",
    "   Args:\n",
    "        df (pd.DataFrame): DataFrame containing the scheduler data.\n",
    "        x_col (str): Column name for the x-axis.\n",
    "        y_cols (list of list): Lists of column names for the y-axis for each subplot.\n",
    "        titles (list): Titles for each subplot.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(18, 6))\n",
    "\n",
    "    for i, (y_col, title) in enumerate(zip(y_cols, titles), 1):\n",
    "        plt.subplot(1, len(y_cols), i)\n",
    "        for col in y_col:\n",
    "            plt.plot(df[x_col], df[col], label=col)\n",
    "        plt.xlabel(x_col)\n",
    "        plt.ylabel('Value')\n",
    "        plt.title(title)\n",
    "        plt.legend()\n",
    "\n",
    "\n",
    "def visualize_noise_schedulers():\n",
    "    modes = ['linear', 'cosine', 'quad']\n",
    "    noise_df = create_noise_scheduler_dataframe(timesteps=1000, beta_min=0.0001, beta_max=0.02, modes=modes)\n",
    "\n",
    "    y_cols = [\n",
    "        [f'{mode}_beta' for mode in modes],\n",
    "        [f'{mode}_alpha' for mode in modes],\n",
    "        [f'{mode}_alpha_bar' for mode in modes]\n",
    "    ]\n",
    "    titles = ['Beta vs Timestep', 'Alpha vs Timestep', 'Alpha Bar vs Timestep']\n",
    "\n",
    "    plot_scheduler_data(noise_df, 'timestep', y_cols, titles)\n",
    "    plt.show()\n",
    "\n",
    "visualize_noise_schedulers()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cW0F-3Hbqbgx"
   },
   "source": [
    "## Diffusion Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G_aCRfwDqbgx"
   },
   "source": [
    "### Functionalities\n",
    "\n",
    "\n",
    "- ```def forward(self, mask, conditioned_image, *args, **kwargs)```\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"./images/train_algo.png\" style=\"display: block; margin: auto;\" width=\"400\"/>\n",
    "</p>\n",
    "\n",
    "- ```def sample(self, conditioned_image)```\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"./images/sample_algo.png\" style=\"display: block; margin: auto;\" width=\"400\"/>\n",
    "</p>\n",
    "\n",
    "- ```def calculate_xt_from_x0(self, x_0, t, noise)```\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{x}_t &= \\sqrt{\\bar{\\alpha}_t}\\mathbf{x}_0 + \\sqrt{1 - \\bar{\\alpha}_t}\\boldsymbol{\\epsilon}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "- ```def calculate_x0_from_xt(self, x_t, t, noise)```\n",
    "$$\n",
    "\\mathbf{x}_0 = \\frac{1}{\\sqrt{\\bar{\\alpha}_t}}(\\mathbf{x}_t - \\sqrt{1 - \\bar{\\alpha}_t}\\epsilon_t)\n",
    "$$\n",
    "\n",
    "- ```def estimate_mean(self, predicted_noise, x_t, t)```\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mu_\\theta(\\mathbf{x}_t, t) &= \\frac{1}{\\sqrt{\\alpha_t}} \\Big( \\mathbf{x}_t - \\frac{1 - \\alpha_t}{\\sqrt{1 - \\bar{\\alpha}_t}} \\epsilon_\\theta(\\mathbf{x}_t, \\; t) \\Big)\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "- ```def estimate_variance(self, t)```:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    \\Sigma_\\theta(\\mathbf{x}_t, t) = \\sigma^2_t \\cdot \\mathbf{I} =\n",
    "    \\begin{cases}\n",
    "        \\tilde{\\beta}_t = \\frac{1 - \\bar{\\alpha}_{t-1}}{1 - \\bar{\\alpha}_t} \\cdot \\beta_t \\;\\; \\text{or} \\;\\; \\beta_t & \\text{(Ho et al., 2020)} \\\\[2.5ex]\n",
    "        \\exp(\\mathbf{v} \\log \\beta_t + (1-\\mathbf{v}) \\log \\tilde{\\beta}_t) & \\text{(Nichol \\& Dhariwal, 2021)}\n",
    "    \\end{cases}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "- ```def reverse_one_step(self, mask, t, conditioned_image)```\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "Calculates \\;\\; p_\\theta(\\mathbf{x}_{t-1} \\vert \\mathbf{x}_t)\n",
    "\\end{aligned}\n",
    "$$\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{x}_{t-1} &= \\mathcal{N}(\\mathbf{x}_{t-1}; \\frac{1}{\\sqrt{\\alpha_t}} \\Big( \\mathbf{x}_t - \\frac{1 - \\alpha_t}{\\sqrt{1 - \\bar{\\alpha}_t}} \\epsilon_\\theta(\\mathbf{x}_t, t) \\Big), \\Sigma_\\theta(\\mathbf{x}_t, t))\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "- ```def _setup_diffusion_tensors(self, betas, alphas, alpha_bars, alpha_bars_prev)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "eEssVVLSqbgy"
   },
   "outputs": [],
   "source": [
    "from unet_util import normalize_to_neg_one_to_one, unnormalize_to_zero_to_one, default, identity, ModelPrediction\n",
    "\n",
    "class Diffusion(nn.Module):\n",
    "    \"\"\"\n",
    "    A denoising probabilistic Gaussian diffusion model (DDPM).\n",
    "\n",
    "    Attributes:\n",
    "        model (nn.Module): Neural network model for generating predictions.\n",
    "        image_channels (int): Number of input image channels.\n",
    "        mask_channels (int): Number of mask channels.\n",
    "        image_size (int): Size of the image.\n",
    "        device (torch.device): Device to run the model (CPU or GPU).\n",
    "        num_timesteps (int): Number of timesteps for the diffusion process.\n",
    "        Various other attributes for the diffusion process.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model, device=device, timesteps=1000, noise_schedule='cosine'):\n",
    "        \"\"\"\n",
    "        Initialize the Diffusion model.\n",
    "\n",
    "        Args:\n",
    "            model (nn.Module): Neural network model for generating predictions.\n",
    "            device (torch.device): Device to run the model (CPU or GPU).\n",
    "            timesteps (int): Number of timesteps for the diffusion process.\n",
    "            noise_schedule (str): Type of noise scheduling ('linear', 'cosine', etc.).\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.image_channels = model.image_channels\n",
    "        self.mask_channels = model.mask_channels\n",
    "        self.image_size = model.image_size\n",
    "        self.device = device\n",
    "        self._setup_diffusion_parameters(timesteps, noise_schedule)\n",
    "\n",
    "    def _setup_diffusion_parameters(self, timesteps, noise_schedule):\n",
    "        \"\"\"\n",
    "        Setup the diffusion process parameters.\n",
    "\n",
    "        Args:\n",
    "            timesteps (int): Number of timesteps for the diffusion process.\n",
    "            noise_schedule (str): Type of noise scheduling.\n",
    "        \"\"\"\n",
    "        noise_scheduler = NoiseScheduler(timesteps, mode=noise_schedule)\n",
    "        betas = torch.tensor(noise_scheduler.get_beta_schedule())\n",
    "        alphas = torch.tensor(noise_scheduler.get_alpha_schedule())\n",
    "        alpha_bars = torch.tensor(noise_scheduler.get_alpha_bar_schedule())\n",
    "        alpha_bars_prev = torch.tensor(noise_scheduler.get_alphas_bar_previous_schedule())\n",
    "\n",
    "        self.num_timesteps = betas.shape[0]\n",
    "        self._setup_diffusion_tensors(betas, alphas, alpha_bars, alpha_bars_prev)\n",
    "\n",
    "    def _setup_diffusion_tensors(self, betas, alphas, alpha_bars, alpha_bars_prev):\n",
    "        \"\"\"\n",
    "        Setup and convert diffusion tensors to the correct type and device, and register them as buffers.\n",
    "\n",
    "        Args:\n",
    "            betas, alphas, alpha_bars, alpha_bars_prev: Tensors for diffusion.\n",
    "        \"\"\"\n",
    "        #       register a buffer for the following parameters for later use ( pts):\n",
    "        #       beta(t), log(beta(t)), alpha_bar(t), alpha_bar(t-1), sqrt(alpha_bar(t)), sqrt(1 - alpha_bar(t)),\n",
    "        #       1 / sqrt(alpha(t)), 1 / sqrt(alpha_bar(t)), beta(t) / sqrt(1 - alpha_bar(t)), beta_tilda(t), log(beta_tilda(t)).\n",
    "\n",
    "        beta_tilda = betas * (1. - alpha_bars_prev) / (1. - alpha_bars)\n",
    "        self.register_buffer('betas', betas.float())\n",
    "\n",
    "        self.register_buffer('alpha_bars', alpha_bars.float())\n",
    "        self.register_buffer('alpha_bars_prev', alpha_bars_prev.float())\n",
    "        self.register_buffer('sqrt_alpha_bars', torch.sqrt(alpha_bars).float())\n",
    "        self.register_buffer('sqrt_one_minus_alpha_bars', torch.sqrt(1. - alpha_bars).float())\n",
    "        self.register_buffer('sqrt_inverse_alpha_bars', torch.sqrt(1. / alpha_bars).float())\n",
    "\n",
    "        self.register_buffer('sqrt_inverse_alphas', torch.sqrt(1. / alphas).float())\n",
    "        self.register_buffer('estimated_mean_noise_coefficient', (betas / torch.sqrt(1. - alpha_bars)).float())\n",
    "\n",
    "        self.register_buffer('estimated_log_variance_beta_tilda', torch.log(beta_tilda.clamp(min=1e-20)).float())\n",
    "        self.register_buffer('estimated_log_variance_beta', torch.log(betas).float())\n",
    "\n",
    "    @device_grad_decorator(device=device, no_grad=True)\n",
    "    def batch_select_time_indices(self, tensor, indices):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            tensor (torch.Tensor): The input tensor from which elements are to be extracted.\n",
    "            indices (torch.Tensor): The tensor containing indices at which to extract elements.\n",
    "        Returns:\n",
    "            torch.Tensor: The reshaped tensor after extraction with shape [batch_size, 1, 1, 1]\n",
    "        \"\"\"\n",
    "        return tensor.gather(dim=-1, index=indices).reshape(indices.shape[0], 1, 1, 1)\n",
    "\n",
    "    @device_grad_decorator(device=device, no_grad=True)\n",
    "    def calculate_x0_from_xt(self, x_t, t, noise):\n",
    "        \"\"\"\n",
    "        Predict the start image from noise.\n",
    "\n",
    "        Args:\n",
    "            x_t (tensor): The noisy image at timestep t.\n",
    "            t (int): The current timestep.\n",
    "            noise (tensor): The noise tensor.\n",
    "\n",
    "        Returns:\n",
    "            The predicted start image at t=0.\n",
    "        \"\"\"\n",
    "        sqrt_inverse_alpha_bars = self.batch_select_time_indices(self.sqrt_inverse_alpha_bars, t)\n",
    "        sqrt_one_minus_alpha_bar_t = self.batch_select_time_indices(self.sqrt_one_minus_alpha_bars, t)\n",
    "        return (x_t - sqrt_one_minus_alpha_bar_t * noise) * sqrt_inverse_alpha_bars\n",
    "\n",
    "    @device_grad_decorator(device=device, no_grad=True)\n",
    "    def estimate_mean(self, predicted_noise, x_t, t):\n",
    "        sqrt_inverse_alpha = self.batch_select_time_indices(self.sqrt_inverse_alphas, t)\n",
    "        noise_coefficient = self.batch_select_time_indices(self.estimated_mean_noise_coefficient, t)\n",
    "        estimated_mean = sqrt_inverse_alpha * (x_t - noise_coefficient * predicted_noise)\n",
    "        return estimated_mean\n",
    "\n",
    "    @device_grad_decorator(device=device, no_grad=True)\n",
    "    def estimate_variance(self, t):\n",
    "        estimated_log_variance_beta_tilda = self.batch_select_time_indices(self.estimated_log_variance_beta_tilda, t)\n",
    "        estimated_log_variance_beta = self.batch_select_time_indices(self.estimated_log_variance_beta, t)\n",
    "        return estimated_log_variance_beta_tilda, estimated_log_variance_beta\n",
    "\n",
    "    @device_grad_decorator(device=device, no_grad=True)\n",
    "    def reverse_one_step(self, noisy_mask, t, conditioned_image):\n",
    "        \"\"\"\n",
    "        Sample from the model.\n",
    "        Args:\n",
    "            mask (tensor): The input tensor.\n",
    "            t (int): The current timestep.\n",
    "            conditioned_image (tensor): Conditioning tensor.\n",
    "        Returns:\n",
    "            The predicted mask and the start mask.\n",
    "        \"\"\"\n",
    "        batched_times = torch.full((noisy_mask.shape[0],), t, dtype=torch.long, device=self.device)\n",
    "\n",
    "        predicted_noise = self.model(noisy_mask, batched_times, conditioned_image)\n",
    "        model_mean = self.estimate_mean(predicted_noise, noisy_mask, batched_times)\n",
    "        log_var_beta_tilda, log_var_beta = self.estimate_variance(batched_times)\n",
    "\n",
    "        noise = torch.randn_like(noisy_mask) if t > 0 else torch.zeros_like(noisy_mask)  # no noise if t == 0\n",
    "        sigma = (0.5 * log_var_beta).exp()\n",
    "        predicted_previous_mask = model_mean + sigma * noise\n",
    "        return predicted_previous_mask\n",
    "\n",
    "    @device_grad_decorator(device=device, no_grad=True)\n",
    "    def sample(self, conditioned_image):\n",
    "        \"\"\"\n",
    "        Generate a sample based on conditioning image.\n",
    "\n",
    "        Args:\n",
    "            conditioned_image (tensor): Conditioning images tensor with the shape of [batch_size, channels, height, width].\n",
    "        Returns:\n",
    "            Generated masks tensor.\n",
    "        \"\"\"\n",
    "        noisy_mask = torch.randn(\n",
    "            (conditioned_image.shape[0], self.mask_channels, self.image_size, self.image_size),\n",
    "            device=self.device,\n",
    "        )\n",
    "\n",
    "        for t in tqdm(reversed(range(self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):\n",
    "            noisy_mask = self.reverse_one_step(noisy_mask, t, conditioned_image)\n",
    "\n",
    "        denoised_mask = unnormalize_to_zero_to_one(noisy_mask)\n",
    "        return denoised_mask\n",
    "\n",
    "    @device_grad_decorator(device=device)\n",
    "    def calculate_xt_from_x0(self, x_0, t, noise):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x_0 (tensor): The start image.\n",
    "            t (int): The current timestep.\n",
    "            noise (tensor): Optional noise tensor.\n",
    "\n",
    "        Returns:\n",
    "            Sampled image tensor.\n",
    "        \"\"\"\n",
    "        first_term = self.batch_select_time_indices(self.sqrt_alpha_bars, t) * x_0\n",
    "        second_term = self.batch_select_time_indices(self.sqrt_one_minus_alpha_bars, t) * noise\n",
    "        return first_term + second_term\n",
    "\n",
    "    @device_grad_decorator(device=device)\n",
    "    def forward(self, mask, conditioned_image, *args, **kwargs):\n",
    "        \"\"\"\n",
    "        Forward pass through the Diffusion model.\n",
    "\n",
    "        Args:\n",
    "            img (tensor): Input image tensor.\n",
    "            conditioned_image (tensor): Conditioning image tensor.\n",
    "            args: Additional arguments.\n",
    "            kwargs: Keyword arguments.\n",
    "\n",
    "        Returns:\n",
    "            The loss for the model.\n",
    "        \"\"\"\n",
    "        mask, conditioned_image = mask.to(self.device), conditioned_image.to(self.device)\n",
    "        t = torch.randint(0, self.num_timesteps, [mask.shape[0]], device=self.device).long()\n",
    "\n",
    "        mask = normalize_to_neg_one_to_one(mask)\n",
    "        noise = torch.randn(mask.shape, device=self.device)\n",
    "\n",
    "        x_t = self.calculate_xt_from_x0(mask, t, noise)\n",
    "        noise_hat = self.model(x_t, t, conditioned_image)\n",
    "\n",
    "        loss = F.mse_loss(noise_hat.reshape(mask.shape[0], -1), noise.reshape(mask.shape[0], -1))\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XYzwX_fiEzyk"
   },
   "source": [
    "# Dataset & DataLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ziS_kiwu-Gf5"
   },
   "source": [
    "## Splitting To Train/Test/Val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "yp0hlaVvqbgy"
   },
   "outputs": [],
   "source": [
    "def split_dataset(mri_df, x_col, y_col, stratify_col, test_size=TEST_SIZE, random_state=RANDOM_SEED):\n",
    "    \"\"\"\n",
    "    Splits the dataset into training and testing sets.\n",
    "\n",
    "    Args:\n",
    "        mri_df (pd.DataFrame): The dataframe containing MRI data.\n",
    "        x_col (str): The column name for the input features.\n",
    "        y_col (str): The column name for the target variable.\n",
    "        stratify_col (str): The column name for stratification.\n",
    "        test_size (float, optional): The proportion of the dataset to include in the test split. Defaults to TEST_SIZE.\n",
    "        random_state (int, optional): The random seed for reproducibility. Defaults to RANDOM_SEED.\n",
    "\n",
    "    Returns:\n",
    "        tuple: A tuple containing the training and testing dataframes (train_df, test_df).\n",
    "    \"\"\"\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        mri_df[[x_col]],\n",
    "        mri_df[[y_col, 'has_cancer']],\n",
    "        test_size=test_size,\n",
    "        random_state=random_state,\n",
    "        stratify=mri_df[stratify_col],\n",
    "    )\n",
    "\n",
    "    train_df = pd.concat([X_train, y_train], axis=1).reset_index(drop=True)\n",
    "    test_df = pd.concat([X_test, y_test], axis=1).reset_index(drop=True)\n",
    "    return train_df, test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uFVxQAfjIpAV",
    "outputId": "af5cc7bf-cb63-4636-f202-4a71a601714b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train df count: 3536\n",
      "test df count: 393\n"
     ]
    }
   ],
   "source": [
    "train_df, test_df = split_dataset(\n",
    "    mri_df,\n",
    "    'image_path',\n",
    "    'mask_path',\n",
    "    'has_cancer',\n",
    ")\n",
    "\n",
    "print(f'train df count: {len(train_df)}')\n",
    "print(f'test df count: {len(test_df)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Resize((IMAGE_HEIGHT, IMAGE_HEIGHT), interpolation=transforms.InterpolationMode.NEAREST),\n",
    "    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n",
    "])\n",
    "\n",
    "mask_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Resize((IMAGE_HEIGHT, IMAGE_HEIGHT), interpolation=transforms.InterpolationMode.NEAREST),\n",
    "])\n",
    "\n",
    "joint_transform = A.Compose(\n",
    "    [\n",
    "        A.HorizontalFlip(p=0.5),\n",
    "        A.VerticalFlip(p=0.5),\n",
    "    ],\n",
    "    p=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Torch Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "xf7jCMppE_nb"
   },
   "outputs": [],
   "source": [
    "class BrainMRIDataset(Dataset):\n",
    "    def __init__(self, dataframe, image_transform, mask_transform, joint_transform):\n",
    "        self.dataframe = dataframe\n",
    "        self.image_transform = image_transform\n",
    "        self.mask_transform = mask_transform\n",
    "        self.joint_transform = joint_transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dataframe)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image = self.get_unprocessed_image(idx)\n",
    "        mask = self.get_unprocessed_mask(idx)\n",
    "        label = int(self.dataframe['has_cancer'][idx])\n",
    "        transformed = self.joint_transform(image=image, mask=mask)\n",
    "\n",
    "        image = transformed['image']\n",
    "        mask = transformed['mask']\n",
    "\n",
    "        image = self.image_transform(image)\n",
    "        mask = self.mask_transform(mask)\n",
    "\n",
    "        return image, mask, torch.tensor(label).int()\n",
    "\n",
    "    def get_unprocessed_mask(self, idx):\n",
    "        mask_path = self.dataframe['mask_path'][idx]\n",
    "        mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n",
    "        return mask\n",
    "\n",
    "    def get_unprocessed_image(self, idx):\n",
    "        image_path = self.dataframe['image_path'][idx]\n",
    "        image = cv2.imread(image_path)\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        return image\n",
    "\n",
    "train_dataset = BrainMRIDataset(train_df, image_transform, mask_transform, joint_transform)\n",
    "test_dataset = BrainMRIDataset(test_df, image_transform, mask_transform, joint_transform)\n",
    "\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=TRAIN_BATCH_SIZE, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=TEST_BATCH_SIZE, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "KlB7rT-kqbgz",
    "outputId": "813ea681-43eb-4b92-dd96-5b8494b07efe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test_joint_transform(\n",
    "    dataset_class=BrainMRIDataset,\n",
    "    df=train_df,\n",
    "    joint_transform=joint_transform,\n",
    "    mock_joint_transform=A.Compose([], p=1),\n",
    "    image_transform=image_transform,\n",
    "    mask_transform=mask_transform,\n",
    "    num_samples=5\n",
    "):\n",
    "    partial_class = partial(dataset_class, dataframe=df ,image_transform=image_transform, mask_transform=mask_transform)\n",
    "    org_dataset = partial_class(joint_transform=mock_joint_transform)\n",
    "    flipped_dataset = partial_class(joint_transform=joint_transform)\n",
    "    random_indices = df[df['has_cancer'] == 1][['has_cancer']].sample(num_samples).reset_index()['index'].tolist()\n",
    "    plt.figure(figsize=(10, 4 * num_samples))\n",
    "\n",
    "    for i, index in enumerate(random_indices):\n",
    "        org_image, org_mask, _ = org_dataset[index]\n",
    "        org_image, org_mask = org_image.permute(1, 2, 0).numpy(), org_mask.permute(1, 2, 0).squeeze(2).numpy()\n",
    "\n",
    "        flipped_image, flipped_mask, _ = flipped_dataset[index]\n",
    "        flipped_image, flipped_mask = flipped_image.permute(1, 2, 0).numpy(), flipped_mask.permute(1, 2, 0).squeeze(2).numpy()\n",
    "        org_image[org_mask==1] = (0, 1, .9)\n",
    "        flipped_image[flipped_mask==1] = (0, 1, .9)\n",
    "        org_image = unnormalize_to_zero_to_one(org_image)\n",
    "        flipped_image = unnormalize_to_zero_to_one(flipped_image)\n",
    "        plt.subplot(num_samples, 2, 2 * i + 1)\n",
    "        plt.imshow(org_image)\n",
    "        plt.title(f\"Image\")\n",
    "        plt.axis(\"off\")\n",
    "\n",
    "        plt.subplot(num_samples, 2, 2 * i + 2)\n",
    "        plt.imshow(flipped_image)\n",
    "        plt.title(f\"Flippd Image\")\n",
    "        plt.axis(\"off\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "test_joint_transform()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7r2tflTHqbgz"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pf3szl93qbgz",
    "outputId": "6f819ed8-f408-4e86-a15e-014c5f25bd5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of parameters: 405041\n"
     ]
    }
   ],
   "source": [
    "# Initialize the U-Net model\n",
    "unet = Unet(\n",
    "    image_size=IMAGE_HEIGHT,\n",
    "    num_layers=2,\n",
    "    skip_connect_image_feature_maps=False\n",
    ").to(device)\n",
    "\n",
    "# Initialize the diffusion model\n",
    "diffusion = Diffusion(\n",
    "    model=unet,\n",
    "    device=device,\n",
    "    timesteps=TIMESTEPS,\n",
    "    noise_schedule=NOISE_SCHEDULE\n",
    ").to(device)\n",
    "\n",
    "# Display model parameter count\n",
    "num_parameters = sum(p.numel() for p in unet.parameters())\n",
    "print(f'Number of parameters: {num_parameters}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "8g7djNjBqbgz"
   },
   "outputs": [],
   "source": [
    "# Configure optimizer and scheduler\n",
    "optimizer = torch.optim.Adam(diffusion.parameters(), lr=LEARNING_RATE)\n",
    "scheduler = ReduceLROnPlateau(\n",
    "    optimizer,\n",
    "    mode='min',\n",
    "    factor=REDUCE_LR_FACTOR,\n",
    "    patience=REDUCE_LR_PATIENCE,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "u_fiFFnZqbgz"
   },
   "outputs": [],
   "source": [
    "def plot_prediction(axs, index, dataset, prediction):\n",
    "    \"\"\"\n",
    "    Plot an MRI image, its corresponding mask, and the predicted mask.\n",
    "\n",
    "    Args:\n",
    "        axs: List of matplotlib Axes objects for plotting.\n",
    "        index: Index of the sample in the dataset.\n",
    "        dataset: The dataset containing the images and masks.\n",
    "        prediction: The predicted mask (Tensor).\n",
    "    \"\"\"\n",
    "    # Retrieve the unprocessed image and corresponding label\n",
    "    img = dataset.get_unprocessed_image(index)\n",
    "    _, mask, label = dataset[index]\n",
    "\n",
    "    # Set up the MRI image\n",
    "    label = int(label)\n",
    "    status = 'Cancerous' if label == 1 else 'Non-cancerous'\n",
    "    axs[0].imshow(img)\n",
    "    axs[0].set_title(f'A {status} MRI')\n",
    "    axs[0].axis('off')\n",
    "\n",
    "    # Plot the ground truth mask\n",
    "    mask = mask.permute(1, 2, 0).numpy()\n",
    "    axs[1].imshow(mask, cmap='gray')\n",
    "    axs[1].set_title(\"Mask\")\n",
    "    axs[1].axis('off')\n",
    "\n",
    "    # Plot the predicted mask\n",
    "    prediction = prediction.detach().cpu().permute(1, 2, 0).numpy()\n",
    "    axs[2].imshow(prediction, cmap='gray')\n",
    "    axs[2].set_title(\"Mask Prediction\")\n",
    "    axs[2].axis('off')\n",
    "\n",
    "\n",
    "@device_grad_decorator(no_grad=True)\n",
    "def sample_and_visualize(diffusion, dataset, num_samples, device=device):\n",
    "    \"\"\"\n",
    "    Sample and visualize predictions for a subset of the dataset.\n",
    "\n",
    "    Args:\n",
    "        diffusion: The diffusion model for generating samples.\n",
    "        dataset: The dataset containing images and masks.\n",
    "        num_samples: Number of samples to visualize.\n",
    "        device: The device to run computations on.\n",
    "    \"\"\"\n",
    "    # Randomly select indices from the dataset\n",
    "    sampled_indices = np.random.choice(len(dataset), size=num_samples, replace=False)\n",
    "    \n",
    "    # Load the sampled images and move them to the specified device\n",
    "    sampled_images = torch.stack([dataset[i][0] for i in sampled_indices]).to(device)\n",
    "    \n",
    "    # Generate predictions using the diffusion model\n",
    "    predictions = diffusion.sample(sampled_images)\n",
    "    \n",
    "    # Create a figure with subplots\n",
    "    fig, axs = plt.subplots(num_samples, 3, figsize=(15, 5 * num_samples))\n",
    "    \n",
    "    # Ensure axs is always iterable (even for one sample)\n",
    "    if num_samples == 1:\n",
    "        axs = [axs]\n",
    "    \n",
    "    # Plot each sample's MRI image, mask, and prediction\n",
    "    for i, sample_idx in enumerate(sampled_indices):\n",
    "        plot_prediction(axs[i], sample_idx, dataset, predictions[i])\n",
    "    \n",
    "    # Adjust layout and show the visualization\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9NgrYkT8qbgz"
   },
   "source": [
    "## Training & Evaluation Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_one_epoch(diffusion, train_dataloader, optimizer, scheduler, device=device):\n",
    "    \"\"\"\n",
    "    Train the diffusion model for one epoch.\n",
    "\n",
    "    Args:\n",
    "        diffusion: The diffusion model.\n",
    "        train_dataloader: DataLoader for the training dataset.\n",
    "        optimizer: Optimizer for updating model parameters.\n",
    "        scheduler: Learning rate scheduler.\n",
    "        device: Device to run computations on.\n",
    "\n",
    "    Returns:\n",
    "        train_mean_loss: Average training loss for the epoch.\n",
    "    \"\"\"\n",
    "    diffusion.train()\n",
    "    total_loss = 0.0\n",
    "\n",
    "    for img, mask, label in tqdm(train_dataloader, desc=\"Training\", leave=False):\n",
    "        # Move data to the appropriate device\n",
    "        img, mask = img.to(device), mask.to(device)\n",
    "\n",
    "        # Forward pass and compute loss\n",
    "        optimizer.zero_grad()\n",
    "        loss = diffusion(mask, img)\n",
    "\n",
    "        # Backward pass and optimization\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Accumulate loss\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    # Compute average loss and update scheduler\n",
    "    train_mean_loss = total_loss / len(train_dataloader)\n",
    "    scheduler.step(train_mean_loss)\n",
    "\n",
    "    return train_mean_loss\n",
    "\n",
    "\n",
    "@device_grad_decorator(no_grad=True)\n",
    "def test_one_epoch(diffusion, test_dataloader, device=device):\n",
    "    \"\"\"\n",
    "    Evaluate the diffusion model for one epoch.\n",
    "\n",
    "    Args:\n",
    "        diffusion: The diffusion model.\n",
    "        test_dataloader: DataLoader for the test dataset.\n",
    "        device: Device to run computations on.\n",
    "\n",
    "    Returns:\n",
    "        test_mean_loss: Average test loss for the epoch.\n",
    "    \"\"\"\n",
    "    diffusion.eval()\n",
    "    total_loss = 0.0\n",
    "\n",
    "    for img, mask, label in tqdm(test_dataloader, desc=\"Testing\", leave=False):\n",
    "        # Move data to the appropriate device\n",
    "        img, mask = img.to(device), mask.to(device)\n",
    "\n",
    "        # Forward pass and compute loss\n",
    "        loss = diffusion(mask, img)\n",
    "\n",
    "        # Accumulate loss\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    # Compute average loss\n",
    "    test_mean_loss = total_loss / len(test_dataloader)\n",
    "\n",
    "    return test_mean_loss\n",
    "\n",
    "def save_model(diffusion, old_best_epoch, new_best_epoch, path_template=MODEL_SAVE_PATH_TEMPLATE):\n",
    "    \"\"\"\n",
    "    Save the current best model, removing the previous best model file.\n",
    "\n",
    "    Args:\n",
    "        diffusion: The diffusion model.\n",
    "        old_best_epoch: The epoch number of the previous best model.\n",
    "        new_best_epoch: The epoch number of the current best model.\n",
    "        path_template: Template for the model file path.\n",
    "    \"\"\"\n",
    "    # Remove old best model if it exists\n",
    "    old_model_path = path_template.format(old_best_epoch)\n",
    "    if os.path.exists(old_model_path):\n",
    "        os.remove(old_model_path)\n",
    "\n",
    "    # Save the new best model\n",
    "    new_model_path = path_template.format(new_best_epoch)\n",
    "    torch.save(diffusion.state_dict(), new_model_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_and_evaluate(\n",
    "    diffusion,\n",
    "    train_dataloader,\n",
    "    test_dataloader,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    num_epochs,\n",
    "    model_save_path_template,\n",
    "    visualization_interval=VISUALIZATION_SAMPLE_INTERVAL,\n",
    "    visualization_num_samples=VISUALIZATION_NUM_SAMPLES,\n",
    "    device=device,\n",
    "):\n",
    "    \"\"\"\n",
    "    Train and evaluate the diffusion model.\n",
    "\n",
    "    Args:\n",
    "        diffusion: The diffusion model.\n",
    "        train_dataloader: DataLoader for the training dataset.\n",
    "        test_dataloader: DataLoader for the test dataset.\n",
    "        optimizer: Optimizer for training.\n",
    "        scheduler: Learning rate scheduler.\n",
    "        num_epochs: Number of epochs to train for.\n",
    "        model_save_path_template: Template string for saving model checkpoints.\n",
    "        visualization_interval: Interval for visualizing results.\n",
    "        visualization_num_samples: Number of samples to visualize.\n",
    "\n",
    "    Returns:\n",
    "        train_losses: List of average training losses for each epoch.\n",
    "        test_losses: List of average test losses for each epoch.\n",
    "    \"\"\"\n",
    "    best_test_loss = float('inf')\n",
    "    best_test_loss_epoch = -1\n",
    "    train_losses = []\n",
    "    test_losses = []\n",
    "\n",
    "    for epoch in range(1, num_epochs + 1):\n",
    "        print(f\"\\nEpoch {epoch}/{num_epochs}\")\n",
    "        print(\"-\" * 30)\n",
    "\n",
    "        # Train for one epoch\n",
    "        train_loss = train_one_epoch(diffusion, train_dataloader, optimizer, scheduler)\n",
    "        train_losses.append(train_loss)\n",
    "\n",
    "        # Evaluate for one epoch\n",
    "        test_loss = test_one_epoch(diffusion, test_dataloader)\n",
    "        test_losses.append(test_loss)\n",
    "\n",
    "        # Save model if it's the best test loss\n",
    "        if test_loss < best_test_loss:\n",
    "            save_model(diffusion, best_test_loss_epoch, epoch, model_save_path_template)\n",
    "            best_test_loss = test_loss\n",
    "            best_test_loss_epoch = epoch\n",
    "\n",
    "        # Log progress\n",
    "        print(f\"Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}, \"\n",
    "              f\"Best Test Loss: {best_test_loss:.4f} (Epoch {best_test_loss_epoch})\")\n",
    "\n",
    "        # Visualization\n",
    "        if epoch % visualization_interval == 0 or epoch == num_epochs:\n",
    "            sample_and_visualize(\n",
    "                diffusion,\n",
    "                test_dataloader.dataset,\n",
    "                num_samples=visualization_num_samples,\n",
    "                device=device,\n",
    "            )\n",
    "\n",
    "    print(f\"\\nTraining complete. Best Test Loss: {best_test_loss:.4f} at Epoch {best_test_loss_epoch}.\")\n",
    "    return train_losses, test_losses, best_test_loss_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and evaluate the model\n",
    "train_losses, test_losses, best_epoch = train_and_evaluate(\n",
    "    diffusion=diffusion,\n",
    "    train_dataloader=train_dataloader,\n",
    "    test_dataloader=test_dataloader,\n",
    "    optimizer=optimizer,\n",
    "    scheduler=scheduler,\n",
    "    num_epochs=NUM_EPOCHS,\n",
    "    model_save_path_template=MODEL_SAVE_PATH_TEMPLATE,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RJNNKxZGqbgz"
   },
   "source": [
    "## Visualize Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "4Tyghb2qqbg0",
    "outputId": "9b2ba837-b9d3-4197-fe03-be2d9fa402d9"
   },
   "outputs": [],
   "source": [
    "def plot_losses(train_losses, test_losses):\n",
    "    \"\"\"\n",
    "    Plot the training and test losses over epochs.\n",
    "\n",
    "    Args:\n",
    "        train_losses: List of training losses for each epoch.\n",
    "        test_losses: List of test losses for each epoch.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    epochs = range(1, len(train_losses) + 1)  # Epochs start at 1 for better clarity\n",
    "\n",
    "    plt.plot(epochs, train_losses, label='Train Loss', linewidth=2)\n",
    "    plt.plot(epochs, test_losses, label='Test Loss', linewidth=2)\n",
    "\n",
    "    plt.title('Training vs Test Loss', fontsize=16)\n",
    "    plt.xlabel('Epochs', fontsize=12)\n",
    "    plt.ylabel('Loss (Log Scale)', fontsize=12)\n",
    "    plt.yscale('log')  # Logarithmic scale for better visualization\n",
    "    plt.legend(fontsize=12)\n",
    "    plt.grid(True, which=\"both\", linestyle='--', linewidth=0.5)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_losses(train_losses, test_losses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yxLtkcT-qbg0"
   },
   "source": [
    "## Sample Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GfaETp5yqbg0",
    "outputId": "7aa1f1dd-ca4b-4584-e8f6-fce7d7102a93"
   },
   "outputs": [],
   "source": [
    "def load_best_model(diffusion, model_save_path_template, best_epoch, device=device):\n",
    "    \"\"\"\n",
    "    Load the best saved diffusion model from the specified epoch.\n",
    "\n",
    "    Args:\n",
    "        diffusion: The diffusion model instance.\n",
    "        model_save_path_template: Template for the model file path.\n",
    "        best_epoch: The epoch number of the best model.\n",
    "        device: Device to run computations on.\n",
    "\n",
    "    Returns:\n",
    "        diffusion: The diffusion model with loaded weights.\n",
    "    \"\"\"\n",
    "    best_model_path = model_save_path_template.format(str(int(best_epoch)))\n",
    "    diffusion.load_state_dict(\n",
    "        torch.load(\n",
    "            best_model_path,\n",
    "            map_location=device,\n",
    "        ),\n",
    "        strict=False,\n",
    "    )\n",
    "    diffusion.eval()\n",
    "    print(f\"Best model from epoch {best_epoch} loaded successfully.\")\n",
    "    return diffusion\n",
    "\n",
    "def visualize_best_model_predictions(diffusion, dataset, num_samples, device=device):\n",
    "    \"\"\"\n",
    "    Visualize predictions of the best diffusion model on a subset of the dataset.\n",
    "\n",
    "    Args:\n",
    "        diffusion: The best diffusion model with loaded weights.\n",
    "        dataset: The dataset containing images and masks.\n",
    "        num_samples: Number of samples to visualize.\n",
    "        device: Device to run computations on.\n",
    "    \"\"\"\n",
    "    print(\"\\nVisualizing predictions from the best model...\")\n",
    "    sample_and_visualize(diffusion, dataset, num_samples, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_diffusion_model = load_best_model(diffusion, MODEL_SAVE_PATH_TEMPLATE, best_epoch)\n",
    "visualize_best_model_predictions(best_diffusion_model, test_dataloader.dataset, num_samples=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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