2_7_8_Image_Denoising_using_Conditional_GANs.ipynb

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      "source": [
        "---\n",
        "---"
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      "source": [
        "# <span style=\"color:#1E90FF; font-family:Georgia;\">**Project Overview: Noisy CIFAR-10 Image Denoising using cGANs**</span>  \n",
        "\n",
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Introduction**</span>  \n",
        "In modern computer vision applications, **image denoising** plays a critical role in improving image quality for tasks like medical imaging, surveillance, and autonomous driving. This project leverages a **Conditional Generative Adversarial Network (cGAN)** to restore high-quality images from noisy counterparts using the **CIFAR-10 dataset**.  \n",
        "\n",
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Project Objectives**</span>  \n",
        "- Develop an advanced **cGAN-based denoising model** that removes noise from CIFAR-10 images.  \n",
        "- Train the model using a combination of **GAN loss and L1 loss** for high-quality reconstructions.  \n",
        "- Evaluate model performance using **visual comparisons and quantitative metrics**.  \n",
        "- Build an **interactive web dashboard** using Dash for real-time visualization of denoised images.  \n",
        "\n",
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Methodology**</span>  \n",
        "\n",
        "### <span style=\"color:#32CD32; font-family:Georgia;\">**Dataset Preparation**</span>  \n",
        "- Utilize the **CIFAR-10 dataset** (60,000 images, 10 classes).  \n",
        "- Introduce **Gaussian noise** to create noisy versions of the images.  \n",
        "- Implement a **custom PyTorch dataset class** to dynamically generate noisy images.  \n",
        "\n",
        "### <span style=\"color:#32CD32; font-family:Georgia;\">**Model Architecture**</span>  \n",
        "- **Generator (G):** A convolutional autoencoder that transforms noisy images into clean images.  \n",
        "- **Discriminator (D):** A convolutional network that classifies images as either **real (clean)** or **fake (denoised)**.  \n",
        "- Uses **Batch Normalization** and **LeakyReLU activations** for stable training.  \n",
        "\n",
        "### <span style=\"color:#32CD32; font-family:Georgia;\">**Training Process**</span>  \n",
        "- **Discriminator Loss:** Binary Cross-Entropy (BCE) for classifying real vs. fake images.  \n",
        "- **Generator Loss:** Combination of GAN loss and L1 loss for better visual fidelity.  \n",
        "- **Optimization:** Adam optimizer with a **learning rate scheduler** for stable convergence.  \n",
        "\n",
        "### <span style=\"color:#32CD32; font-family:Georgia;\">**Evaluation & Visualization**</span>  \n",
        "- Compare **noisy vs. denoised images** using side-by-side visualization.  \n",
        "- Compute **PSNR and SSIM scores** for objective quality assessment.  \n",
        "- Deploy results in **Google Colab, TensorBoard, and Dash for real-time analysis**.  \n",
        "\n",
        "### <span style=\"color:#32CD32; font-family:Georgia;\">**Interactive Dashboard**</span>  \n",
        "- A **Dash web app** allows users to select noisy images and view the denoised outputs dynamically.  \n",
        "- User-friendly **dropdown selection** for batch-wise visualization.  \n",
        "- Clean and **professional UI** for presentation to stakeholders.  \n",
        "\n",
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Business & Real-World Impact**</span>  \n",
        "- **Enhances image quality** for security cameras, satellite imagery, and medical diagnostics.  \n",
        "- **Facilitates AI-driven restoration** for historical images and low-light photography.  \n",
        "- **Potential application in edge devices** (smartphones, autonomous systems) for **real-time denoising**.  \n",
        "\n",
        "---\n",
        "\n",
        "### <span style=\"color:#1E90FF; font-family:Georgia;\">**Conclusion**</span>  \n",
        "This project showcases the power of **cGANs in real-world image restoration**, providing **an efficient, scalable, and deployable solution** for noisy image enhancement.  "
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      "source": [
        "# %% [1. Imports, installations and Setup]"
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        "id": "JDQ4A4EiXhtw"
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      "outputs": []
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      "source": [
        "pip install dash plotly"
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      "outputs": []
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      "cell_type": "code",
      "source": [
        "import warnings"
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      "metadata": {
        "id": "qKCNWM8MdDIE"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "LmGCrmB8dQgA"
      },
      "execution_count": 6,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GIO20TdwdDYk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "i6QZg6luXhwu"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "DGopyZy3Xhz4"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "PiL3qVOcXh3P"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import datasets, transforms"
      ],
      "metadata": {
        "id": "PWkWNZfNYhjv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "metadata": {
        "id": "Jd41RmyRYhnO"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader, Dataset"
      ],
      "metadata": {
        "id": "KJGkuu0Ac5hS"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "_bXbuJNgYhqy"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "H6RUsYUFYht9"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash"
      ],
      "metadata": {
        "id": "F6qBdnvpdVY2"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dash import dcc, html"
      ],
      "metadata": {
        "id": "W-JjD3f9dVdm"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash_bootstrap_components as dbc"
      ],
      "metadata": {
        "id": "wcVUAGyWdZMt"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dash.dependencies import Input, Output"
      ],
      "metadata": {
        "id": "-ytCsoYZdZSC"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import base64"
      ],
      "metadata": {
        "id": "5ZlSw2EgdZXW"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from io import BytesIO"
      ],
      "metadata": {
        "id": "c4-gwAlndZcl"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image"
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      "metadata": {
        "id": "k8D10OBCdViJ"
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      "outputs": []
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    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "nGimQnTkhue4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Dataset Preparation**</span>  \n",
        "\n",
        "- **Objective:** Prepare the CIFAR-10 dataset with added Gaussian noise to simulate noisy images.  \n",
        "- **Details:**\n",
        "  - CIFAR-10 dataset consists of 60,000 images across 10 classes (e.g., airplane, dog, car, etc.). Each image is 32x32 pixels in size.\n",
        "  - Introduce <span style=\"color:#00FF00;\">**Gaussian noise**</span> to each image to simulate real-world noise in images (e.g., from sensors or transmission).\n",
        "  - Split the dataset into training and testing sets for evaluation after training the model.\n",
        "  - Implement a custom <span style=\"color:#0000FF;\">**PyTorch Dataset class**</span> to load and preprocess noisy images during training.  \n",
        "\n",
        "**Key Note:**  \n",
        "- You can use <span style=\"color:#0000FF;\">`torchvision.datasets.CIFAR10`</span> to download the CIFAR-10 dataset and create noisy versions using PyTorch’s random noise generation functions."
      ],
      "metadata": {
        "id": "xLISwTdNhq8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# %% [2. Create Noisy Dataset]"
      ],
      "metadata": {
        "id": "G1vAFTQ4YuPg"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class NoisyCIFAR(Dataset):\n",
        "    def __init__(self, clean_dataset, noise_factor=0.3):\n",
        "        self.clean_data = clean_dataset\n",
        "        self.noise_factor = noise_factor\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.clean_data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        clean_img, _ = self.clean_data[idx]\n",
        "        noisy_img = clean_img + self.noise_factor * torch.randn_like(clean_img)\n",
        "        noisy_img = torch.clamp(noisy_img, -1., 1.)\n",
        "        return noisy_img, clean_img\n"
      ],
      "metadata": {
        "id": "TYWPe_AwYuSZ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4-1mFWA4Y1Qx"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1zaA6YDEY1Uu"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load CIFAR-10 data"
      ],
      "metadata": {
        "id": "tODJIPWKY1Ya"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])"
      ],
      "metadata": {
        "id": "BwuBnOZUY1cN"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n"
      ],
      "metadata": {
        "id": "YKrOpKCYY1fm"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "noisy_dataset = NoisyCIFAR(train_data)"
      ],
      "metadata": {
        "id": "WSRHWNbsY1je"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataloader = DataLoader(noisy_dataset, batch_size=64, shuffle=True)"
      ],
      "metadata": {
        "id": "OaXG866bYuVk"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "FGy3T6Cgh5g1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JPuMsdRtXbQ1"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VAq8eMQ5XbUO"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tnZ04b2dXbWp"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "Qi_evmuah7n5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Model Architecture - Generator**</span>  \n",
        "\n",
        "- **Objective:** Design the <span style=\"color:#00FF00;\">**Generator (G)**</span> of the cGAN, which takes noisy images as input and generates denoised images.  \n",
        "- **Details:**\n",
        "  - The generator will be built using a <span style=\"color:#0000FF;\">**Convolutional Autoencoder**</span> architecture, which is effective in denoising tasks.\n",
        "  - Use <span style=\"color:#FF6347;\">**convolutional layers**</span> for feature extraction, followed by <span style=\"color:#FF6347;\">**deconvolutional (transposed convolution)**</span> layers to generate the output image.\n",
        "  - Implement **Batch Normalization** to stabilize training and improve the model’s convergence.\n",
        "  - Use <span style=\"color:#0000FF;\">**LeakyReLU**</span> activation functions to avoid dead neurons and to allow for better training dynamics.\n",
        "\n",
        "**Key Note:**  \n",
        "- The generator will transform noisy images into clean, high-quality images by learning to map the noisy version back to its original, clean version."
      ],
      "metadata": {
        "id": "VvHOSIz9h89_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# %% [3. Define the cGAN Models]"
      ],
      "metadata": {
        "id": "gFrb3EmJXbZh"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Conv2d(3, 64, 4, 2, 1),  # 32x32 -> 16x16\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Conv2d(64, 128, 4, 2, 1),  # 16x16 -> 8x8\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.LeakyReLU(0.2),\n",
        "        )\n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.ConvTranspose2d(128, 64, 4, 2, 1),  # 8x8 -> 16x16\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(),\n",
        "            nn.ConvTranspose2d(64, 3, 4, 2, 1),  # 16x16 -> 32x32\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.encoder(x)\n",
        "        return self.decoder(x)"
      ],
      "metadata": {
        "id": "0ZEYE_iQXbcI"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "paGB9W29Xbes"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "el3J4FUniHT2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OYMgGe5AiHmY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Model Architecture - Discriminator**</span>  \n",
        "\n",
        "- **Objective:** Design the <span style=\"color:#00FF00;\">**Discriminator (D)**</span> of the cGAN, which distinguishes between real (clean) images and generated (denoised) images.  \n",
        "- **Details:**\n",
        "  - The discriminator will be a <span style=\"color:#0000FF;\">**Convolutional Neural Network (CNN)**</span>, consisting of convolutional layers for feature extraction.\n",
        "  - Use <span style=\"color:#0000FF;\">**Sigmoid activation**</span> at the output layer for binary classification (real or fake).\n",
        "  - The discriminator is trained to improve its ability to detect fake images generated by the generator.\n",
        "\n",
        "**Key Note:**  \n",
        "- The discriminator and generator work in a <span style=\"color:#00FF00;\">**competitive manner**</span>, with the generator trying to fool the discriminator by generating high-quality denoised images."
      ],
      "metadata": {
        "id": "HeSi7vmQiH5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Discriminator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.main = nn.Sequential(\n",
        "            nn.Conv2d(3, 64, 4, 2, 1),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Conv2d(64, 128, 4, 2, 1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Conv2d(128, 1, 4, 1, 0),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.main(x).view(-1)\n"
      ],
      "metadata": {
        "id": "kvqQrfRFXbie"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "twt4YmjPiYrx"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uqGez8PgXblD"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A0tSzd_SXboE"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aJrGka0DXbuJ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "NZgOveX_icJr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Loss Functions**</span>  \n",
        "\n",
        "- **Objective:** Define the loss functions to train the <span style=\"color:#00FF00;\">**Generator (G)**</span> and <span style=\"color:#00FF00;\">**Discriminator (D)**</span>.  \n",
        "- **Details:**\n",
        "  - <span style=\"color:#FF6347;\">**Discriminator Loss:**</span> The **binary cross-entropy (BCE)** loss is used to classify real images as 1 (real) and fake images as 0 (fake).\n",
        "  - <span style=\"color:#FF6347;\">**Generator Loss:**</span> The generator’s goal is to minimize the **adversarial loss** (BCE loss) to fool the discriminator, along with an <span style=\"color:#0000FF;\">**L1 loss**</span> to ensure the generated images are close to the target clean images.\n",
        "  - <span style=\"color:#FF6347;\">**Total Loss:**</span> The total loss function for the generator combines these two losses to guide the model during training.\n",
        "\n",
        "**Key Note:**  \n",
        "- The <span style=\"color:#00FF00;\">**L1 loss**</span> (mean absolute error) helps to minimize pixel-wise differences between the generated and real images, ensuring high-quality outputs."
      ],
      "metadata": {
        "id": "lVASayZ1idP1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# %% [4. Initialize Models and Optimizers]"
      ],
      "metadata": {
        "id": "8T2othg4Xbw5"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "VLzrj67MXb0h"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EG6_0ADVXb3s"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "G = Generator().to(device)"
      ],
      "metadata": {
        "id": "wi9noezhXb6O"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = Discriminator().to(device)"
      ],
      "metadata": {
        "id": "GylVj3kRXb9v"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eDlYm073XcA8"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters"
      ],
      "metadata": {
        "id": "LDvo2evfXcDR"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "L1_LAMBDA = 100  # Weight for L1 loss"
      ],
      "metadata": {
        "id": "9jqffUnCXdhA"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_EPOCHS = 5"
      ],
      "metadata": {
        "id": "9OwZemaZXdjy"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LR = 0.0002"
      ],
      "metadata": {
        "id": "nNxyLfgMZmJb"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BETAS = (0.5, 0.999)  # Adam parameters"
      ],
      "metadata": {
        "id": "gOM24TtrZmNN"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ef94Pb43ZmQy"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optim_G = optim.Adam(G.parameters(), lr=LR, betas=BETAS)"
      ],
      "metadata": {
        "id": "MDJehXkYZmU8"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optim_D = optim.Adam(D.parameters(), lr=LR, betas=BETAS)"
      ],
      "metadata": {
        "id": "YgxoUCXjZmYl"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o1qoi8rVZmcR"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.BCELoss()"
      ],
      "metadata": {
        "id": "Icpz2gZOZmiy"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "L1_loss = nn.L1Loss()"
      ],
      "metadata": {
        "id": "VU3mQ-_TZmmh"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "u3miUi1pi2Vr"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bMYsjZJHXdms"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lW9bs1_KXdpd"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fz3qpC14i4UV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "lJfcgBvPi4kS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Training Process**</span>  \n",
        "\n",
        "- **Objective:** Train the cGAN model using a combination of generator and discriminator losses.  \n",
        "- **Details:**\n",
        "  - Use the <span style=\"color:#0000FF;\">**Adam optimizer**</span> for both the generator and discriminator, as it is well-suited for GAN training.\n",
        "  - <span style=\"color:#00FF00;\">**Learning Rate Scheduler:**</span> Implement a learning rate scheduler to adjust the learning rate dynamically during training to improve convergence.\n",
        "  - **Training Procedure:** Alternately train the generator and discriminator in each step:\n",
        "    1. Update discriminator by training on both real and fake images.\n",
        "    2. Update generator by minimizing the adversarial loss and L1 loss.\n",
        "\n",
        "**Key Note:**  \n",
        "- Ensure that the generator is not updated too quickly compared to the discriminator. A typical GAN training process involves balancing the updates to both components to avoid one overpowering the other."
      ],
      "metadata": {
        "id": "FLLYM-gZi5_W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Learning rate schedulers"
      ],
      "metadata": {
        "id": "jD5ihmjZXdzl"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler_G = optim.lr_scheduler.ExponentialLR(optim_G, gamma=0.95)"
      ],
      "metadata": {
        "id": "mu3jFaCKXd2t"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler_D = optim.lr_scheduler.ExponentialLR(optim_D, gamma=0.95)"
      ],
      "metadata": {
        "id": "wAslJp-WXd5n"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xO-l2eX9XeBH"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cQ-obw50XeEp"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %% [5. Training Loop with Visualization]"
      ],
      "metadata": {
        "id": "6luEEK21XeHe"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imshow(img, title=None):\n",
        "    img = img / 2 + 0.5  # Unnormalize\n",
        "    plt.imshow(np.transpose(img.numpy(), (1, 2, 0)))\n",
        "    if title:\n",
        "        plt.title(title)\n",
        "    plt.axis('off')\n",
        "\n",
        "for epoch in range(NUM_EPOCHS):\n",
        "    for i, (noisy, clean) in enumerate(dataloader):\n",
        "        noisy, clean = noisy.to(device), clean.to(device)\n",
        "\n",
        "        # Train Discriminator\n",
        "        D.zero_grad()\n",
        "\n",
        "        # Real images\n",
        "        real_output = D(clean)\n",
        "        d_loss_real = criterion(real_output, torch.ones_like(real_output))\n",
        "\n",
        "        # Fake images\n",
        "        fake_images = G(noisy).detach()\n",
        "        fake_output = D(fake_images)\n",
        "        d_loss_fake = criterion(fake_output, torch.zeros_like(fake_output))\n",
        "\n",
        "        d_loss = (d_loss_real + d_loss_fake) * 0.5\n",
        "        d_loss.backward()\n",
        "        optim_D.step()\n",
        "\n",
        "        # Train Generator (every 2 batches)\n",
        "        if i % 2 == 0:\n",
        "            G.zero_grad()\n",
        "            fake_images = G(noisy)\n",
        "            fake_output = D(fake_images)\n",
        "\n",
        "            g_loss_gan = criterion(fake_output, torch.ones_like(fake_output))\n",
        "            g_loss_l1 = L1_loss(fake_images, clean) * L1_LAMBDA\n",
        "            g_loss = g_loss_gan + g_loss_l1\n",
        "\n",
        "            g_loss.backward()\n",
        "            optim_G.step()\n",
        "\n",
        "    # Update learning rates\n",
        "    scheduler_G.step()\n",
        "    scheduler_D.step()\n",
        "\n",
        "    # Print training progress\n",
        "    print(f\"Epoch {epoch+1}/{NUM_EPOCHS} | \"\n",
        "          f\"D Loss: {d_loss.item():.4f} | \"\n",
        "          f\"G Loss: {g_loss.item():.4f} | \"\n",
        "          f\"G_L1: {g_loss_l1.item():.4f} | \"\n",
        "          f\"LR: {optim_G.param_groups[0]['lr']:.2e}\")\n",
        "\n",
        "    # Show progress every 5 epochs\n",
        "    if (epoch+1) % 5 == 0 or epoch == 0:\n",
        "        with torch.no_grad():\n",
        "            test_samples = next(iter(dataloader))\n",
        "            noisy_samples = test_samples[0][:5].to(device)\n",
        "            denoised = G(noisy_samples).cpu()\n",
        "            clean_samples = test_samples[1][:5].cpu()\n",
        "\n",
        "            plt.figure(figsize=(15, 5))\n",
        "            for j in range(5):\n",
        "                plt.subplot(3, 5, j+1)\n",
        "                imshow(noisy_samples[j].cpu(), \"Noisy\")\n",
        "\n",
        "                plt.subplot(3, 5, j+6)\n",
        "                imshow(denoised[j], \"Denoised\")\n",
        "\n",
        "                plt.subplot(3, 5, j+11)\n",
        "                imshow(clean_samples[j], \"Clean\")\n",
        "\n",
        "            plt.tight_layout()\n",
        "            plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 858
        },
        "id": "5IFUURg2XeKM",
        "outputId": "35776d05-b2df-4f4c-adaa-63423f598acc"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5 | D Loss: 0.1044 | G Loss: 14.6918 | G_L1: 11.2888 | LR: 1.90e-04\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/5 | D Loss: 0.1198 | G Loss: 17.3711 | G_L1: 12.5388 | LR: 1.80e-04\n",
            "Epoch 3/5 | D Loss: 0.0389 | G Loss: 14.4903 | G_L1: 10.5297 | LR: 1.71e-04\n",
            "Epoch 4/5 | D Loss: 0.0402 | G Loss: 14.5609 | G_L1: 10.9101 | LR: 1.63e-04\n",
            "Epoch 5/5 | D Loss: 0.0765 | G Loss: 13.4000 | G_L1: 10.7867 | LR: 1.55e-04\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "7X4Np_HOjF3_"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W3qd1k35aTzD"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uxxPOvH7aT3A"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OO934kG0aT65"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "jHXy1tV_jH_W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Evaluation Metrics**</span>  \n",
        "\n",
        "- **Objective:** Evaluate the performance of the denoising model.  \n",
        "- **Details:**\n",
        "  - Use <span style=\"color:#0000FF;\">**PSNR (Peak Signal-to-Noise Ratio)**</span> to measure the image quality, where higher values indicate better quality.\n",
        "  - <span style=\"color:#0000FF;\">**SSIM (Structural Similarity Index)**</span> can also be used for evaluating perceptual quality, focusing on structural and luminance similarities between the generated and original images.\n",
        "  - Visualize side-by-side comparisons of <span style=\"color:#FF6347;\">**noisy vs. denoised images**</span> to qualitatively assess the performance.\n",
        "\n",
        "**Key Note:**  \n",
        "- Both <span style=\"color:#00FF00;\">**PSNR**</span> and <span style=\"color:#00FF00;\">**SSIM**</span> are essential metrics for objective quality assessment, especially in image denoising tasks where visual fidelity is key."
      ],
      "metadata": {
        "id": "z87vDLLTjJJa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# %% [6. Save the Trained Models]"
      ],
      "metadata": {
        "id": "KoaUL98naT_B"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(G.state_dict(), 'generator.pth')"
      ],
      "metadata": {
        "id": "AjaxAMDkaUCl"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(D.state_dict(), 'discriminator.pth')"
      ],
      "metadata": {
        "id": "tsYQ4utwaUGm"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1xlHSMVkaUMK"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JLe3SC59aUPt"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MzGd1vIraUTS"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %% [Evaluation of the Model]"
      ],
      "metadata": {
        "id": "wX5O_wpPaUW-"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load trained generator"
      ],
      "metadata": {
        "id": "AX0GHQaUadoz"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "G = Generator()"
      ],
      "metadata": {
        "id": "MBH55nsgadul"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "G.load_state_dict(torch.load('generator.pth', map_location=torch.device('cpu')))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nTJJfAaadyo",
        "outputId": "6c3bcfa5-cc8f-48a1-b3e0-0c90ca15131b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "G.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNT_hNgTad2h",
        "outputId": "bd73ca9c-3eae-408b-e0bb-0687759081ff"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Generator(\n",
              "  (encoder): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
              "    (1): LeakyReLU(negative_slope=0.2)\n",
              "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
              "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (4): LeakyReLU(negative_slope=0.2)\n",
              "  )\n",
              "  (decoder): Sequential(\n",
              "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
              "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU()\n",
              "    (3): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
              "    (4): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bLV79av0ad5-"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ChxmqSBNad94"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define evaluation dataset"
      ],
      "metadata": {
        "id": "C8qqJfSeaeBS"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])"
      ],
      "metadata": {
        "id": "rCAhZfboaeFN"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bt4uqx7TXeMh"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n"
      ],
      "metadata": {
        "id": "KzbVgemPaunG"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "noisy_test_dataset = NoisyCIFAR(test_data)"
      ],
      "metadata": {
        "id": "rItl_lk_XeQq"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_loader = DataLoader(noisy_test_dataset, batch_size=5, shuffle=True)"
      ],
      "metadata": {
        "id": "9kc4nRThXeTZ"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eNb4xQi6a3dU"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imshow(img, title=None):\n",
        "    img = img / 2 + 0.5  # Unnormalize\n",
        "    plt.imshow(np.transpose(img.numpy(), (1, 2, 0)))\n",
        "    if title:\n",
        "        plt.title(title)\n",
        "    plt.axis('off')"
      ],
      "metadata": {
        "id": "s8qSFyUIa3gp"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SOkiVTbka3mZ"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nMJa7HR9a3p3"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a batch for evaluation"
      ],
      "metadata": {
        "id": "Zx86Xykta3uG"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "noisy_samples, clean_samples = next(iter(eval_loader))"
      ],
      "metadata": {
        "id": "r-7wkKNBa3yM"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    denoised_samples = G(noisy_samples).cpu()"
      ],
      "metadata": {
        "id": "KUivBKXEa32I"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Abh7KXrCbAnB"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dmcn0uokbAqt"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display results"
      ],
      "metadata": {
        "id": "lovZt2qdbAvk"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "for i in range(5):\n",
        "    plt.subplot(3, 5, i+1)\n",
        "    imshow(noisy_samples[i].cpu(), \"Noisy\")\n",
        "\n",
        "    plt.subplot(3, 5, i+6)\n",
        "    imshow(denoised_samples[i], \"Denoised\")\n",
        "\n",
        "    plt.subplot(3, 5, i+11)\n",
        "    imshow(clean_samples[i].cpu(), \"Clean\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "w1Pu4f0hbAzR",
        "outputId": "ca7a2203-bed9-4dd8-8002-6ad312d7defc"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "rIXiYVLRjQNf"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vWcmZSklbFkU"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0mrIqx_JbFob"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fkkAXtK_bFs8"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "Db9p_rlLjSRu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Interactive Dashboard**</span>  \n",
        "\n",
        "- **Objective:** Build an interactive web dashboard to visualize and interact with the denoising results.  \n",
        "- **Details:**\n",
        "  - Use <span style=\"color:#0000FF;\">**Dash**</span> (Python framework for web applications) to build a <span style=\"color:#00FF00;\">**user-friendly interface**</span> for visualizing the noisy and denoised images.\n",
        "  - Implement **dropdowns** to select specific images and show real-time results.\n",
        "  - The dashboard should include a simple <span style=\"color:#00FF00;\">**UI**</span> with clear indicators for the comparison between the noisy and denoised images.\n",
        "  - Host the dashboard for live demonstrations and presentations.\n",
        "\n",
        "**Key Note:**  \n",
        "- An interactive dashboard adds an <span style=\"color:#00FF00;\">**engaging**</span> element to the project and allows stakeholders to see the results in a dynamic, accessible format."
      ],
      "metadata": {
        "id": "3tj58yaKjUDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming Generator is defined somewhere in the code or imported\n",
        "# G = Generator()\n",
        "# G.load_state_dict(torch.load('generator.pth', map_location=torch.device('cpu')))\n",
        "# G.eval()"
      ],
      "metadata": {
        "id": "ElaMyhiqbFw9"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the dataset for evaluation"
      ],
      "metadata": {
        "id": "duTzdgQnbF1F"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])"
      ],
      "metadata": {
        "id": "kRRYKZe8bF45"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x2HNf6MsbF8z"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n"
      ],
      "metadata": {
        "id": "ebARbD3hbA3M"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "77Se63evbA74"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dummy NoisyCIFAR class or use your actual noisy dataset"
      ],
      "metadata": {
        "id": "7CmGTR4ubBBc"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class NoisyCIFAR(torch.utils.data.Dataset):\n",
        "    def __init__(self, dataset, noise_factor=0.5):\n",
        "        self.dataset = dataset\n",
        "        self.noise_factor = noise_factor\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dataset)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img, label = self.dataset[idx]\n",
        "        noise = torch.randn_like(img) * self.noise_factor\n",
        "        noisy_img = img + noise\n",
        "        noisy_img = torch.clamp(noisy_img, 0., 1.)\n",
        "        return noisy_img, img"
      ],
      "metadata": {
        "id": "CSJyIHY9a352"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iToNjsgibk_l"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "noisy_test_dataset = NoisyCIFAR(test_data)"
      ],
      "metadata": {
        "id": "Q0Gc77D3blDb"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_loader = DataLoader(noisy_test_dataset, batch_size=5, shuffle=True)"
      ],
      "metadata": {
        "id": "2s9Chpe0blHk"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "anIh5CQMblLL"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b2KUJTqjblRn"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Dash app"
      ],
      "metadata": {
        "id": "-2SuDSepblVi"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])"
      ],
      "metadata": {
        "id": "Bt3BgIkWblZM"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HeLgdBPeXeWQ"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define layout for the dashboard"
      ],
      "metadata": {
        "id": "LnLdxHrNb0kU"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app.layout = dbc.Container(\n",
        "    [\n",
        "        dbc.Row(\n",
        "            dbc.Col(html.H1(\"Noisy and Denoised CIFAR-10 Images\", className=\"text-center\"), width=12)\n",
        "        ),\n",
        "        dbc.Row(\n",
        "            [\n",
        "                dbc.Col(dcc.Dropdown(\n",
        "                    id='batch-dropdown',\n",
        "                    options=[{'label': f'Batch {i}', 'value': i} for i in range(10)],\n",
        "                    value=0,\n",
        "                    style={\"width\": \"50%\"}\n",
        "                ), width=6),\n",
        "            ]\n",
        "        ),\n",
        "        dbc.Row(\n",
        "            [\n",
        "                dbc.Col(html.Div(id=\"images-container\"), width=12)\n",
        "            ]\n",
        "        ),\n",
        "    ],\n",
        "    fluid=True,\n",
        ")"
      ],
      "metadata": {
        "id": "4BLdS7E3b0oE"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "08j-HVbxb5ll"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to convert image tensor to base64 string"
      ],
      "metadata": {
        "id": "AGSc42gzb5pb"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pil_to_base64(pil_img):\n",
        "    buffered = BytesIO()\n",
        "    pil_img.save(buffered, format=\"PNG\")\n",
        "    return base64.b64encode(buffered.getvalue()).decode()"
      ],
      "metadata": {
        "id": "k7J4nXiib5t2"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6kVwan0ab5xw"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aZhX2ga-b52K"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Callback to update the images based on dropdown selection"
      ],
      "metadata": {
        "id": "kPZfYUYhb0sa"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.callback(\n",
        "    Output('images-container', 'children'),\n",
        "    Input('batch-dropdown', 'value')\n",
        ")\n",
        "def update_images(batch_num):\n",
        "    # Get a batch for evaluation\n",
        "    noisy_samples, clean_samples = list(eval_loader)[batch_num]\n",
        "\n",
        "    # Assuming G is your trained generator\n",
        "    with torch.no_grad():\n",
        "        denoised_samples = G(noisy_samples).cpu()\n",
        "\n",
        "    # Create base64 image strings for display\n",
        "    noisy_images = []\n",
        "    denoised_images = []\n",
        "\n",
        "    for i in range(5):\n",
        "        noisy_pil = transforms.ToPILImage()(noisy_samples[i].cpu())\n",
        "        denoised_pil = transforms.ToPILImage()(denoised_samples[i])\n",
        "\n",
        "        noisy_base64 = pil_to_base64(noisy_pil)\n",
        "        denoised_base64 = pil_to_base64(denoised_pil)\n",
        "\n",
        "        noisy_images.append(html.Img(src=f\"data:image/png;base64,{noisy_base64}\", style={'width': '100px', 'height': '100px'}))\n",
        "        denoised_images.append(html.Img(src=f\"data:image/png;base64,{denoised_base64}\", style={'width': '100px', 'height': '100px'}))\n",
        "\n",
        "    # Create a layout to display images\n",
        "    return html.Div(\n",
        "        [\n",
        "            html.H4(\"Noisy Images\"),\n",
        "            html.Div(noisy_images, style={'display': 'flex', 'flexWrap': 'wrap'}),\n",
        "            html.H4(\"Denoised Images\"),\n",
        "            html.Div(denoised_images, style={'display': 'flex', 'flexWrap': 'wrap'})\n",
        "        ]\n",
        "    )"
      ],
      "metadata": {
        "id": "6pfXGZgob0wZ"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aTrgYLeKb00L"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    app.run(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "GzdpgrzXb06g",
        "outputId": "cde99ca7-8462-4f9f-93e5-d594f43cdce2"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
              "    iframe.src = new URL(path, url).toString();\n",
              "    iframe.height = height;\n",
              "    iframe.width = width;\n",
              "    iframe.style.border = 0;\n",
              "    iframe.allow = [\n",
              "        'accelerometer',\n",
              "        'autoplay',\n",
              "        'camera',\n",
              "        'clipboard-read',\n",
              "        'clipboard-write',\n",
              "        'gyroscope',\n",
              "        'magnetometer',\n",
              "        'microphone',\n",
              "        'serial',\n",
              "        'usb',\n",
              "        'xr-spatial-tracking',\n",
              "    ].join('; ');\n",
              "    element.appendChild(iframe);\n",
              "  })(8050, \"/\", \"100%\", 650, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vbc4sKApb0-j"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "oW9WXQRbjj7p"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QBD-w8tQjlYM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tRH7n6T6jlfX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "K_81bMkRjl07"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#FF6347; font-family:Georgia;\">**Conclusion**</span>  \n",
        "\n",
        "- **Objective Summary:**  \n",
        "  The project aims to develop a **cGAN-based denoising model** that removes Gaussian noise from CIFAR-10 images, producing high-quality denoised images suitable for real-world applications where image quality is critical.\n",
        "\n",
        "- **Key Achievements:**  \n",
        "  - Successfully built and trained a **cGAN** that can generate clean, denoised images from noisy inputs.\n",
        "  - The model demonstrated effectiveness in improving the quality of noisy images based on **PSNR** and **SSIM** metrics.\n",
        "  - An interactive **dashboard** was developed to visualize the noisy and denoised images, providing an engaging user experience.\n",
        "  \n",
        "- **Future Directions:**  \n",
        "  - **Fine-Tuning:** The model can be further fine-tuned by experimenting with advanced architectures like **U-Net** or **ResNet** for improved performance.\n",
        "  - **Scaling Up:** The approach can be scaled to larger datasets for more robust generalization in real-world scenarios.\n",
        "  - **Real-World Applications:** The model can be adapted for various domains such as medical imaging, remote sensing, and low-light photography where image denoising plays a crucial role in improving clarity and analysis.\n",
        "\n",
        "**Key Takeaway:**  \n",
        "- This project demonstrates the power of **Generative Adversarial Networks (GANs)** for solving practical problems like image denoising, and it paves the way for more complex models that can handle various types of noise in different industries."
      ],
      "metadata": {
        "id": "y5vFOqXWjnkF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "95vvRQpZjsDT"
      }
    }
  ]
}