CNN_Denoising.ipynb

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      "collapsed_sections": [
        "4Z3yFUGTSpMV",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# CNN models \n",
        "\n",
        "\n",
        "This work is part of the experimental analysis for the paper Low Latency Video Denoising for Video Conferencing Using CNN Architectures \n",
        "\n",
        "Authors of this notebook, Masters Computer Science students, Seattle University Fall, 2022\n",
        " - Altanai Bisht\n",
        " - Ana Carolina Mendes\n",
        " - Justin Thoreson\n",
        "Faculty Advisor \n",
        "- Dr Shadrokh Samavi\n",
        "\n",
        "This work follows Licenses for the respective models\n",
        "\n",
        "----------------------------------------"
      ],
      "metadata": {
        "id": "Dzt_A1DkS8Sp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import cv2\n",
        "import argparse\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "import torchvision as tv\n",
        "from torchvision.utils import save_image, make_grid\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as td\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "import os\n",
        "import time"
      ],
      "metadata": {
        "id": "6Ip0Q7Z6SIlW"
      },
      "execution_count": 305,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Model\n",
        "from keras.layers import Conv1D,Conv2D, MaxPooling2D, Dense, Input, Conv2D, Conv2DTranspose, UpSampling2D, BatchNormalization, concatenate, AveragePooling2D, Add\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "metadata": {
        "id": "6ISwtba6gTsh"
      },
      "execution_count": 306,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_root_dir = '/content/'\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQP6lvIPknGO",
        "outputId": "e96ca52b-9667-4e4e-8da0-cb4c2150114e"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Helpers : Visualize the results "
      ],
      "metadata": {
        "id": "535OT2QEFFVC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Custom function to plot/visualize noisy, ground truth and predicted images\n",
        "def visualize(sample,model):\n",
        "  fig,ax = plt.subplots(len(sample),3,figsize=(30,30))\n",
        "  for i in range(len(sample)):\n",
        "    path = sample['Ground Truth Images'].iloc[i]\n",
        "    test_img_gt = cv2.imread(path)\n",
        "    test_img_gt = cv2.cvtColor(test_img_gt, cv2.COLOR_BGR2RGB)\n",
        "    test_img_gt = cv2.resize(test_img_gt,(512,512))\n",
        "    test_img_gt = test_img_gt.astype(\"float32\") / 255.0\n",
        "  \n",
        "    path = sample['Noisy Images'].iloc[i]\n",
        "    test_img_nsy = cv2.imread(path)\n",
        "    pred_img = prediction(test_img_nsy,model)\n",
        "    pred_img = cv2.resize(pred_img,(512,512))\n",
        "\n",
        "    test_img_nsy = cv2.cvtColor(test_img_nsy, cv2.COLOR_BGR2RGB)\n",
        "    test_img_nsy = cv2.resize(test_img_nsy,(512,512))\n",
        "    test_img_nsy = test_img_nsy.astype(\"float32\") / 255.0\n",
        "    \n",
        "    ax[i][0].imshow(test_img_nsy)\n",
        "    ax[i][0].get_xaxis().set_visible(False)\n",
        "    ax[i][0].get_yaxis().set_visible(False)\n",
        "    ax[i][0].title.set_text(\"Noisy Image\")\n",
        "\n",
        "    ax[i][1].imshow(test_img_gt)\n",
        "    ax[i][1].get_xaxis().set_visible(False)\n",
        "    ax[i][1].get_yaxis().set_visible(False)\n",
        "    ax[i][1].title.set_text(\"Ground Truth Image\")\n",
        "\n",
        "    ax[i][2].imshow(pred_img)\n",
        "    ax[i][2].get_xaxis().set_visible(False)\n",
        "    ax[i][2].get_yaxis().set_visible(False)\n",
        "    ax[i][2].title.set_text(\"Predicted Image\")\n",
        "\n",
        "# matplotlib displays color in BGR format, we are converting that to RGB\n",
        "def plot_rgb_img(img):\n",
        "  plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
        "  plt.axis('off')\n",
        "  plt.show()\n",
        "\n",
        "def show_img(dataset):\n",
        "    f, ax = plt.subplots(1,5)\n",
        "    f.set_size_inches(40, 20)\n",
        "    for i in range(1,3):\n",
        "        ax[i].imshow(dataset[i])\n",
        "    plt.show()\n",
        "\n",
        "def myimshow(image, ax=plt):\n",
        "    image = image.to('cpu').numpy()\n",
        "    image = np.moveaxis(image, [0, 1, 2], [2, 0, 1])\n",
        "    image = (image + 1) / 2\n",
        "    image[image < 0] = 0\n",
        "    image[image > 1] = 1\n",
        "    h = ax.imshow(image)\n",
        "    ax.axis('off')\n",
        "    return h"
      ],
      "metadata": {
        "id": "-MZfrXlFDtfB"
      },
      "execution_count": 308,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import skimage.metrics as measure\n",
        "\n",
        "def plot_predictions(Noisy,Denoised):    \n",
        "    f, ax = plt.subplots(3, 3)\n",
        "    f.set_size_inches(12,10)\n",
        "    for i in range(3):\n",
        "        # ax[0][i].imshow(tf.squeeze(GT[i]))\n",
        "        # ax[0][i].set_title(\"original\")\n",
        "        # ax[0][i].axis('off')\n",
        "        psnr1=measure.peak_signal_noise_ratio(train_df[i],Noisy[i] )\n",
        "        psnr2=measure.peak_signal_noise_ratio(train_df[i],Denoised[i])\n",
        "        ssim1=measure.structural_similarity(train_df[i],Noisy[i],multichannel=True)\n",
        "        ssim2= measure.structural_similarity(train_df[i],Denoised[i],multichannel=True)\n",
        "        ax[0][i].text(0, 0, ' PSNR before denoising :'+ str(psnr1)+ \"\\n\"+\n",
        "            ' SSIM before denoising :'+ str(ssim1) + \"\\n\"+\n",
        "            ' PSNR after denoising :'+str(psnr2)   + \"\\n\"+\n",
        "            ' SSIM after denoising :'+str(ssim2) + \"\\n\" +\n",
        "            ' PSNR Improvement : '+ str(psnr2-psnr1))\n",
        "        ax[0][i].axis('off')\n",
        "\n",
        "        ax[1][i].imshow(tf.squeeze(Noisy[i]))\n",
        "        ax[1][i].set_title(\"noisy\")\n",
        "        ax[1][i].axis('off')\n",
        "\n",
        "        ax[2][i].imshow(tf.squeeze(Denoised[i]))\n",
        "        ax[2][i].set_title(\"denoised_imgs\")\n",
        "        ax[2][i].axis('off')\n",
        "\n",
        "\n",
        "    plt.tight_layout()"
      ],
      "metadata": {
        "id": "Swmopwdqjlay"
      },
      "execution_count": 309,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data in test , train and validation df "
      ],
      "metadata": {
        "id": "4Z3yFUGTSpMV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# imgsize=(1080,1920,3)\n",
        "imgsize=(256,256,3)\n",
        "print(imgsize[0],imgsize[1],imgsize[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYqKGHS-JlyI",
        "outputId": "3dc633d4-e295-4818-f8c9-279d3241ebc8"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "256 256 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## training images "
      ],
      "metadata": {
        "id": "BSXdxQEjsRc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_images = sorted(os.listdir(dataset_root_dir+\"GT\"))\n",
        "train_image = []\n",
        "for im in train_images:\n",
        "    # img = tf.keras.utils.load_img(dataset_root_dir+\"train/\"+ im)\n",
        "    img = cv2.imread(dataset_root_dir+\"GT/\"+ im)\n",
        "    img = cv2.resize(img,(imgsize[0],imgsize[1]))\n",
        "    img = tf.keras.utils.img_to_array(img)\n",
        "    img = img/255\n",
        "    train_image.append(img)\n",
        "train_df = np.array(train_image)\n",
        "train_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UK_GhquXiWRD",
        "outputId": "d7ef6a2b-71ee-4953-a9b7-29b9f5a40938"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 256, 256, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## training noisy images"
      ],
      "metadata": {
        "id": "_GvJgEqNsd3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# noisy_imgs = sorted(os.listdir(dataset_root_dir+\"train_noisy_gauss\"))\n",
        "# for img in train_df:\n",
        "#     noised_df.append(img)\n",
        "\n",
        "noise_level= 40\n",
        "def add_noise(image):\n",
        "    row,col,ch= imgsize[0],imgsize[1],imgsize[2]\n",
        "    mean = 0\n",
        "    sigma = 1\n",
        "    gauss = np.random.normal(mean,sigma,(row,col,ch))\n",
        "    gauss = gauss.reshape(row,col,ch)\n",
        "    noisy = image + gauss*noise_level\n",
        "    return noisy\n",
        "\n",
        "noise_type=\"gauss\"\n",
        "noisy_imgs = sorted(os.listdir(dataset_root_dir+\"train\"))\n",
        "\n",
        "noisy_img= []\n",
        "for im in noisy_imgs:\n",
        "    # img = tf.keras.utils.load_img(dataset_root_dir+\"train/\"+ im)\n",
        "    img = cv2.imread(dataset_root_dir+\"train/\"+ im)\n",
        "    img = cv2.resize(img,(imgsize[0],imgsize[1]))\n",
        "    img = add_noise(img)\n",
        "    img = tf.keras.utils.img_to_array(img)\n",
        "    img = img/255\n",
        "    noisy_img.append(img)\n",
        "noised_df = np.array(noisy_img)\n",
        "noised_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i24QztCHiY8n",
        "outputId": "6404aa17-c49e-4db5-9f5a-1316e13177e1"
      },
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 256, 256, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=3\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2)\n",
        "axes[0].imshow(noised_df[x])\n",
        "axes[0].axis('off')\n",
        "axes[0].set_title('Noisy')\n",
        "\n",
        "axes[1].imshow(train_df[x])\n",
        "axes[1].axis('off')\n",
        "axes[1].set_title('Clean')\n",
        "\n",
        "# print('PSNR between noised and GT  :', measure.peak_signal_noise_ratio(noised_df[x],train_df[x]))\n",
        "# print('SSIM between noised and GT  :', measure.structural_similarity(noised_df[x],train_df[x],multichannel=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "lcHLTZXyoytn",
        "outputId": "18e68746-2537-4cb4-f46d-d4b21b4f44e7"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Clean')"
            ]
          },
          "metadata": {},
          "execution_count": 313
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot_predictions(train_df, noised_df)"
      ],
      "metadata": {
        "id": "A5DOgA87hAR6"
      },
      "execution_count": 314,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## validation images \n",
        "\n",
        "validation_images = sorted(os.listdir(dataset_root_dir+\"validation\"))\n",
        "validation_image = []\n",
        "for im in validation_images:\n",
        "    print(im)\n",
        "    # img = tf.keras.utils.load_img(dataset_root_dir+\"validation/\"+ im)\n",
        "    img = cv2.imread(dataset_root_dir+\"validation/\"+ im)\n",
        "    img = cv2.resize(img,(imgsize[0],imgsize[1]))\n",
        "    img = tf.keras.utils.img_to_array(img)\n",
        "    img = img/255\n",
        "    validation_image.append(img)\n",
        "validation_df = np.array(validation_image)\n",
        "validation_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tZkZOnwSIBE",
        "outputId": "1ea517d5-87cf-4bb3-fdb7-6ecfae41c0db"
      },
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SaltPepperNoise_gnimg_1.jpg\n",
            "SaltPepperNoise_gnimg_2.jpg\n",
            "SaltPepperNoise_gnimg_3.jpg\n",
            "SaltPepperNoise_gnimg_4.jpg\n",
            "SaltPepperNoise_gnimg_5.jpg\n",
            "SaltPepperNoise_gnimg_6.jpg\n",
            "SaltPepperNoise_gnimg_7.jpg\n",
            "SaltPepperNoise_gnimg_8.jpg\n",
            "SaltPepperNoise_gnimg_9.jpg\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 256, 256, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain= train_df[0:7]\n",
        "xtrain_noisy = noised_df[0:7]\n",
        "# show_img(xtrain)\n",
        "print(xtrain.shape)\n",
        "print(xtrain_noisy.shape)\n",
        "\n",
        "xtest= train_df[8:]\n",
        "xtest_noisy= noised_df[8:]\n",
        "# show_img(xtest)\n",
        "print(xtest.shape)\n",
        "print(xtest_noisy.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Z86BEFoiadQ",
        "outputId": "c9591678-bff2-40ab-8ec7-658c146f828d"
      },
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7, 256, 256, 3)\n",
            "(7, 256, 256, 3)\n",
            "(1, 256, 256, 3)\n",
            "(1, 256, 256, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Median Blur"
      ],
      "metadata": {
        "id": "_gMLfiYULwcZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "denoised_medianblur = []\n",
        "for i in range(len(train_df)):\n",
        "    denoised_medianblur.append (cv2.medianBlur(train_df[i],5))"
      ],
      "metadata": {
        "id": "Av8fu1KFLydC"
      },
      "execution_count": 317,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n CBD Densoing analysis for noise \" + str(noise_level))\n",
        "\n",
        "psnr1=measure.peak_signal_noise_ratio(train_df[x],noised_df[x])\n",
        "psnr2= measure.peak_signal_noise_ratio(train_df[x],denoised_medianblur[x])\n",
        "\n",
        "print('PSNR before denoising :',str(psnr1))\n",
        "print('PSNR after denoising :', str(psnr2))\n",
        "\n",
        "print('SSIM before denoising :',measure.structural_similarity(train_df[x],noised_df[x],multichannel=True))\n",
        "print('SSIM after denoising :', measure.structural_similarity(train_df[x],denoised_medianblur[x],multichannel=True))\n",
        "\n",
        "print(\" PSNR Improvement : \"+ str(psnr2- psnr1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdV0MgVgyWqj",
        "outputId": "9fcf8727-6f7c-4b63-9bce-f66fa40bcc41"
      },
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " CBD Densoing analysis for noise 40\n",
            "PSNR before denoising : 10.720522825419136\n",
            "PSNR after denoising : 23.584778666589358\n",
            "SSIM before denoising : 0.12380560948753914\n",
            "SSIM after denoising : 0.9198650137067803\n",
            " PSNR Improvement : 12.864255841170221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(xtrain, denoised_medianblur)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "lzCxDMDvMa9J",
        "outputId": "a82a16ce-ce21-4296-fa0a-e54315d7e990"
      },
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CBD Net"
      ],
      "metadata": {
        "id": "o_o3KSrfhU8l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.backend.clear_session()\n",
        "input = Input(shape=imgsize)\n",
        "\n",
        "#Noise estimation subnetwork\n",
        "x = Conv2D(32, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(input)\n",
        "x = Conv2D(32, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(x)\n",
        "x = Conv2D(32, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(x)\n",
        "x = Conv2D(32, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(x)\n",
        "x = Conv2D(3, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(x)\n",
        "\n",
        "#Non Blind denoising subnetwork\n",
        "x = concatenate([x,input])\n",
        "conv1 = Conv2D(64, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(x)\n",
        "conv2 = Conv2D(64, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv1)\n",
        "\n",
        "pool1 = AveragePooling2D(pool_size=(2,2),padding='same')(conv2)\n",
        "conv3 = Conv2D(128, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(pool1)\n",
        "conv4 = Conv2D(128, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv3)\n",
        "conv5 = Conv2D(128, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv4)\n",
        "\n",
        "pool2 = AveragePooling2D(pool_size=(2,2),padding='same')(conv5)\n",
        "conv6 = Conv2D(256, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(pool2)\n",
        "conv7 = Conv2D(256, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv6)\n",
        "conv8 = Conv2D(256, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv7)\n",
        "conv9 = Conv2D(256, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv8)\n",
        "conv10 = Conv2D(256, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv9)\n",
        "conv11 = Conv2D(256, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv10)\n",
        "\n",
        "upsample1 = Conv2DTranspose(128, (3, 3), strides=2, activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv11)\n",
        "add1 = Add()([upsample1,conv5])\n",
        "conv12 = Conv2D(128, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(add1)\n",
        "conv13 = Conv2D(128, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv12)\n",
        "conv14 = Conv2D(128, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv13)\n",
        "\n",
        "upsample2 = Conv2DTranspose(64, (3, 3), strides=2, activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv14)\n",
        "add1 = Add()([upsample2,conv2])\n",
        "conv15 = Conv2D(64, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(add1)\n",
        "conv16 = Conv2D(64, (3, 3), activation=\"relu\", kernel_initializer='he_normal',padding=\"same\")(conv15)\n",
        "\n",
        "out = Conv2D(3, (1,1), kernel_initializer='he_normal',padding=\"same\")(conv16)\n",
        "out = Add()([out,input])\n",
        "\n",
        "CBDNet = Model(input,out)\n",
        "CBDNet.compile(optimizer=tf.keras.optimizers.Adam(1e-03), loss=tf.keras.losses.MeanSquaredError())"
      ],
      "metadata": {
        "id": "5yxb1-PvhUtn"
      },
      "execution_count": 320,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CBDNet.fit(xtrain_noisy, xtrain, \n",
        "                epochs=10, \n",
        "                shuffle=True, \n",
        "                validation_data=(xtest_noisy, xtest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReEFJSBKhwDk",
        "outputId": "ea9c3ad6-3c25-4070-b87f-b41c03aba14e"
      },
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.4113 - val_loss: 4.7589\n",
            "Epoch 2/10\n",
            "1/1 [==============================] - 0s 378ms/step - loss: 4.7218 - val_loss: 0.2679\n",
            "Epoch 3/10\n",
            "1/1 [==============================] - 0s 376ms/step - loss: 0.2664 - val_loss: 0.3297\n",
            "Epoch 4/10\n",
            "1/1 [==============================] - 0s 378ms/step - loss: 0.3275 - val_loss: 0.2292\n",
            "Epoch 5/10\n",
            "1/1 [==============================] - 0s 372ms/step - loss: 0.2279 - val_loss: 0.1496\n",
            "Epoch 6/10\n",
            "1/1 [==============================] - 0s 373ms/step - loss: 0.1489 - val_loss: 0.1171\n",
            "Epoch 7/10\n",
            "1/1 [==============================] - 0s 375ms/step - loss: 0.1166 - val_loss: 0.1027\n",
            "Epoch 8/10\n",
            "1/1 [==============================] - 0s 376ms/step - loss: 0.1022 - val_loss: 0.0965\n",
            "Epoch 9/10\n",
            "1/1 [==============================] - 0s 366ms/step - loss: 0.0960 - val_loss: 0.0937\n",
            "Epoch 10/10\n",
            "1/1 [==============================] - 0s 373ms/step - loss: 0.0931 - val_loss: 0.0924\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc34e535190>"
            ]
          },
          "metadata": {},
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CBDNet.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCacksZ9tYCk",
        "outputId": "e76f57c9-9b21-48cb-b783-072f571ce7f2"
      },
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)                (None, 256, 256, 32  896         ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)              (None, 256, 256, 32  9248        ['conv2d[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)              (None, 256, 256, 32  9248        ['conv2d_1[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)              (None, 256, 256, 32  9248        ['conv2d_2[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)              (None, 256, 256, 3)  867         ['conv2d_3[0][0]']               \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 256, 256, 6)  0           ['conv2d_4[0][0]',               \n",
            "                                                                  'input_1[0][0]']                \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)              (None, 256, 256, 64  3520        ['concatenate[0][0]']            \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)              (None, 256, 256, 64  36928       ['conv2d_5[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " average_pooling2d (AveragePool  (None, 128, 128, 64  0          ['conv2d_6[0][0]']               \n",
            " ing2D)                         )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)              (None, 128, 128, 12  73856       ['average_pooling2d[0][0]']      \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)              (None, 128, 128, 12  147584      ['conv2d_7[0][0]']               \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)              (None, 128, 128, 12  147584      ['conv2d_8[0][0]']               \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (AveragePo  (None, 64, 64, 128)  0          ['conv2d_9[0][0]']               \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)             (None, 64, 64, 256)  295168      ['average_pooling2d_1[0][0]']    \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)             (None, 64, 64, 256)  590080      ['conv2d_10[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 64, 64, 256)  590080      ['conv2d_11[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 64, 64, 256)  590080      ['conv2d_12[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)             (None, 64, 64, 256)  590080      ['conv2d_13[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)             (None, 64, 64, 256)  590080      ['conv2d_14[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_transpose (Conv2DTransp  (None, 128, 128, 12  295040     ['conv2d_15[0][0]']              \n",
            " ose)                           8)                                                                \n",
            "                                                                                                  \n",
            " add (Add)                      (None, 128, 128, 12  0           ['conv2d_transpose[0][0]',       \n",
            "                                8)                                'conv2d_9[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)             (None, 128, 128, 12  147584      ['add[0][0]']                    \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)             (None, 128, 128, 12  147584      ['conv2d_16[0][0]']              \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)             (None, 128, 128, 12  147584      ['conv2d_17[0][0]']              \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " conv2d_transpose_1 (Conv2DTran  (None, 256, 256, 64  73792      ['conv2d_18[0][0]']              \n",
            " spose)                         )                                                                 \n",
            "                                                                                                  \n",
            " add_1 (Add)                    (None, 256, 256, 64  0           ['conv2d_transpose_1[0][0]',     \n",
            "                                )                                 'conv2d_6[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)             (None, 256, 256, 64  36928       ['add_1[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)             (None, 256, 256, 64  36928       ['conv2d_19[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)             (None, 256, 256, 3)  195         ['conv2d_20[0][0]']              \n",
            "                                                                                                  \n",
            " add_2 (Add)                    (None, 256, 256, 3)  0           ['conv2d_21[0][0]',              \n",
            "                                                                  'input_1[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 4,570,182\n",
            "Trainable params: 4,570,182\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CBDNet_denoised_imgs= CBDNet.predict(noised_df)"
      ],
      "metadata": {
        "id": "NO3d0l61R5Lb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ade089d3-04ab-4370-c4c3-34a413f05b3c"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 316ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CBD Densoing analysis"
      ],
      "metadata": {
        "id": "xkMr7fvdioKR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n CBD Densoing analysis for noise \" + str(noise_level))\n",
        "\n",
        "psnr1= measure.peak_signal_noise_ratio(train_df[x],noised_df[x])\n",
        "psnr2= measure.peak_signal_noise_ratio(train_df[x],CBDNet_denoised_imgs[x])\n",
        "\n",
        "print('PSNR before denoising :',str(psnr1))\n",
        "print('PSNR after denoising :', str(psnr2))\n",
        "\n",
        "print('SSIM before denoising :',measure.structural_similarity(train_df[x],noised_df[x],multichannel=True))\n",
        "print('SSIM after denoising :', measure.structural_similarity(train_df[x],CBDNet_denoised_imgs[x],multichannel=True))\n",
        "\n",
        "print(\" PSNR Improvement : \"+ str(psnr2- psnr1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "Ywz7l3Vsy8lD",
        "outputId": "bde85044-b067-4eb6-900a-2998b671df3f"
      },
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " CBD Densoing analysis for noise 40\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-324-a3d5ed209485>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n CBD Densoing analysis for noise \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpsnr1\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mmeasure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpeak_signal_noise_ratio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnoised_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mpsnr2\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mmeasure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpeak_signal_noise_ratio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mCBDNet_denoised_imgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/keras_tensor.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m     raise TypeError(\n\u001b[0m\u001b[1;32m    255\u001b[0m         \u001b[0;34mf'You are passing {self}, an intermediate Keras symbolic input/output, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;34m'to a TF API that does not allow registering custom dispatchers, such '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: You are passing KerasTensor(type_spec=TensorSpec(shape=(None, 256, 256, 6), dtype=tf.float32, name=None), name='concatenate/concat:0', description=\"created by layer 'concatenate'\"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as `tf.cond`, `tf.function`, gradient tapes, or `tf.map_fn`. Keras Functional model construction only supports TF API calls that *do* support dispatching, such as `tf.math.add` or `tf.reshape`. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer `call` and calling that layer on this symbolic input/output."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=2\n",
        "\n",
        "fig, axes = plt.subplots(ncols=3)\n",
        "fig.set_size_inches(20,20)\n",
        "axes[0].imshow(train_df[x])\n",
        "axes[0].axis('off')\n",
        "axes[0].set_title('GT')\n",
        "\n",
        "axes[1].imshow(noised_df[x])\n",
        "axes[1].axis('off')\n",
        "axes[1].set_title('Noisy')\n",
        "\n",
        "axes[2].imshow(CBDNet_denoised_imgs[x])\n",
        "axes[2].axis('off')\n",
        "axes[2].set_title('Clean')"
      ],
      "metadata": {
        "id": "WHKlZUBtPoZl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n CBDNet\")\n",
        "plot_predictions(train_df[:3], CBDNet_denoised_imgs[:3])"
      ],
      "metadata": {
        "id": "kRSTnWOs41xs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RIDNET\n",
        "\n",
        "Residual Image Denoising Network Refer the research paper for detailed explanation : https://arxiv.org/pdf/1904.07396v2.pdf"
      ],
      "metadata": {
        "id": "l5oVCEye65Ys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, BatchNormalization, Activation, Flatten, Dense, Input, MaxPooling2D, Add, Reshape, concatenate, AveragePooling2D, Multiply, GlobalAveragePooling2D, UpSampling2D, MaxPool2D,Softmax\n",
        "import pandas as pd\n",
        "\n",
        "class EAM(tf.keras.layers.Layer):\n",
        "  def __init__(self,**kwargs):\n",
        "    super().__init__(**kwargs)\n",
        "    \n",
        "    self.conv1 = Conv2D(64, (3,3), dilation_rate=1,padding='same',activation='relu')\n",
        "    self.conv2 = Conv2D(64, (3,3), dilation_rate=2,padding='same',activation='relu') \n",
        "\n",
        "    self.conv3 = Conv2D(64, (3,3), dilation_rate=3,padding='same',activation='relu')\n",
        "    self.conv4 = Conv2D(64, (3,3), dilation_rate=4,padding='same',activation='relu')\n",
        "\n",
        "    self.conv5 = Conv2D(64, (3,3),padding='same',activation='relu')\n",
        "\n",
        "    self.conv6 = Conv2D(64, (3,3),padding='same',activation='relu')\n",
        "    self.conv7 = Conv2D(64, (3,3),padding='same')\n",
        "\n",
        "    self.conv8 = Conv2D(64, (3,3),padding='same',activation='relu')\n",
        "    self.conv9 = Conv2D(64, (3,3),padding='same',activation='relu')\n",
        "    self.conv10 = Conv2D(64, (1,1),padding='same')\n",
        "\n",
        "    self.gap = GlobalAveragePooling2D()\n",
        "\n",
        "    self.conv11 = Conv2D(64, (3,3),padding='same',activation='relu')\n",
        "    self.conv12 = Conv2D(64, (3,3),padding='same',activation='sigmoid')\n",
        "\n",
        "  def call(self,input):\n",
        "    conv1 = self.conv1(input)\n",
        "    conv1 = self.conv2(conv1)\n",
        "\n",
        "    conv2 = self.conv3(input)\n",
        "    conv2 = self.conv4(conv2)\n",
        "\n",
        "    concat = concatenate([conv1,conv2])\n",
        "    conv3 = self.conv5(concat)\n",
        "    add1 = Add()([input,conv3])\n",
        "\n",
        "    conv4 = self.conv6(add1)\n",
        "    conv4 = self.conv7(conv4)\n",
        "    add2 = Add()([conv4,add1])\n",
        "    add2 = Activation('relu')(add2)\n",
        "\n",
        "    conv5 = self.conv8(add2)\n",
        "    conv5 = self.conv9(conv5)\n",
        "    conv5 = self.conv10(conv5)\n",
        "    add3 = Add()([add2,conv5])\n",
        "    add3 = Activation('relu')(add3)\n",
        "\n",
        "    gap = self.gap(add3)\n",
        "    gap = Reshape((1,1,64))(gap)\n",
        "    conv6 = self.conv11(gap)\n",
        "    conv6 = self.conv12(conv6)\n",
        "    \n",
        "    mul = Multiply()([conv6, add3])\n",
        "    out = Add()([input,mul]) # This is not included in the reference code\n",
        "    return out\n",
        "  \n",
        "tf.keras.backend.clear_session()\n",
        "input = Input(shape=imgsize)\n",
        "\n",
        "conv1 = Conv2D(64, (3,3),padding='same')(input)\n",
        "eam1 = EAM()(conv1)\n",
        "eam2 = EAM()(eam1)\n",
        "eam3 = EAM()(eam2)\n",
        "eam4 = EAM()(eam3) \n",
        "conv2 = Conv2D(3, (3,3),padding='same')(eam4)\n",
        "out = Add()([conv2,input])\n",
        "\n",
        "RIDNet = Model(input,out)\n",
        "RIDNet.compile(optimizer=tf.keras.optimizers.Adam(1e-03), loss=tf.keras.losses.MeanSquaredError())\n"
      ],
      "metadata": {
        "id": "liiyYSxx7CC0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.backend.clear_session()\n",
        "input = Input(shape=imgsize)\n",
        "\n",
        "conv1 = Conv2D(64, (3,3),padding='same')(input)\n",
        "eam1 = EAM()(conv1)\n",
        "eam2 = EAM()(eam1)\n",
        "eam3 = EAM()(eam2)\n",
        "eam4 = EAM()(eam3)\n",
        "#add = Add()([eam4,conv1])  \n",
        "conv2 = Conv2D(3, (3,3),padding='same')(eam4)\n",
        "out = Add()([conv2,input])\n",
        "\n",
        "RIDNet = Model(input,out)\n",
        "RIDNet.compile(optimizer=tf.keras.optimizers.Adam(1e-03), loss=tf.keras.losses.MeanSquaredError())\n",
        "RIDNet.summary()"
      ],
      "metadata": {
        "id": "iZ02LpbMhX38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# batch_size=8\n",
        "# train_dataloader = Dataloder(xtrain_noisy,xtarin, batch_size, shuffle=True)\n",
        "# test_dataloader = Dataloder(xtest_noisy,xtest,batch_size, shuffle=True)"
      ],
      "metadata": {
        "id": "NQ7KgYkEhnpE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# log_dir=\"logs/model_4\"\n",
        "# tensorboard = tf.keras.callbacks.TensorBoard(log_dir=log_dir,histogram_freq=1, write_graph=True,write_grads=True)\n",
        "# reducelr = ReduceLROnPlateau(monitor='val_loss', factor=0.1,verbose=1,patience=4,min_delta=0.00001)\n",
        "# callback = [tensorboard,reducelr]\n",
        "# RIDNet.fit(train_dataloader,shuffle=True,epochs=20,validation_data= test_dataloader, callbacks=callback)"
      ],
      "metadata": {
        "id": "TeJAs44oh5jc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# RIDNet.fit(train_dataloader,shuffle=True,epochs=25,initial_epoch=20,validation_data= test_dataloader, callbacks=callback)\n",
        "# RIDNet.save('RIDNet.h5')"
      ],
      "metadata": {
        "id": "FUTtAtGuh7uK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RIDNet = tf.keras.models.load_model('/content/RIDNet.h5',custom_objects={'EAM':EAM})\n",
        "#  Model downloaded fom https://github.com/sharathsolomon/ImageDenoising"
      ],
      "metadata": {
        "id": "Q8Iw0WjyiG1u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RIDNet Densoing analysis"
      ],
      "metadata": {
        "id": "g-sCG29HkwK_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start = time.time()\n",
        "RIDNet_denoised_imgs= RIDNet.predict(noised_df)\n",
        "end = time.time()\n",
        "print(\"RIDNet_denoised_imgs Elapsed time:\"+ str(end-start))"
      ],
      "metadata": {
        "id": "7Vaa0GWBiAXq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n RIDNet Densoing analysis for noise \" + str(noise_level))\n",
        "\n",
        "psnr1= measure.peak_signal_noise_ratio(train_df[x],noised_df[x])\n",
        "psnr2= measure.peak_signal_noise_ratio(train_df[x],RIDNet_denoised_imgs[x])\n",
        "\n",
        "print('PSNR before denoising :',str(psnr1))\n",
        "print('PSNR after denoising :', str(psnr2))\n",
        "\n",
        "print('SSIM before denoising :',measure.structural_similarity(train_df[x],noised_df[x],multichannel=True))\n",
        "print('SSIM after denoising :', measure.structural_similarity(train_df[x],RIDNet_denoised_imgs[x],multichannel=True))\n",
        "\n",
        "print(\" PSNR Improvement : \"+ str(psnr2- psnr1))"
      ],
      "metadata": {
        "id": "Tpsu60iEzKCy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n RIDNet Densoing analysis\")\n",
        "fig, axes = plt.subplots(ncols=2)\n",
        "axes[0].imshow(noised_df[0])\n",
        "axes[0].axis('off')\n",
        "axes[0].set_title('Noisy')\n",
        "\n",
        "axes[1].imshow(RIDNet_denoised_imgs[0])\n",
        "axes[1].axis('off')\n",
        "axes[1].set_title('Clean')"
      ],
      "metadata": {
        "id": "vFE9nH0mj9t_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n RIDNet Densoing analysis for noise level \"+ str(noise_level) )\n",
        "plot_predictions(noised_df[:3], RIDNet_denoised_imgs[:3])"
      ],
      "metadata": {
        "id": "KjMDEKH-BYH8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# HiGAN"
      ],
      "metadata": {
        "id": "WnJTXLNBfRQ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class single_conv(nn.Module):\n",
        "    def __init__(self, in_ch, out_ch):\n",
        "        super(single_conv, self).__init__()\n",
        "        self.conv = nn.Sequential(\n",
        "            nn.Conv2d(in_ch, out_ch, 3, padding=1),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        return x\n",
        "\n",
        "class up(nn.Module):\n",
        "    def __init__(self, in_ch):\n",
        "        super(up, self).__init__()\n",
        "        self.up = nn.ConvTranspose2d(in_ch, in_ch // 2, 2, stride=2)\n",
        "\n",
        "    def forward(self, x1, x2):\n",
        "        x1 = self.up(x1)\n",
        "\n",
        "        # input is CHW\n",
        "        diffY = x2.size()[2] - x1.size()[2]\n",
        "        diffX = x2.size()[3] - x1.size()[3]\n",
        "\n",
        "        x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2,\n",
        "                        diffY // 2, diffY - diffY // 2))\n",
        "\n",
        "        x = x2 + x1\n",
        "        return x\n",
        "\n",
        "class outconv(nn.Module):\n",
        "    def __init__(self, in_ch, out_ch):\n",
        "        super(outconv, self).__init__()\n",
        "        self.conv = nn.Conv2d(in_ch, out_ch, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        return x\n",
        "\n",
        "class RDB(nn.Module):\n",
        "    def __init__(self, nChannels, nDenselayer, growthRate):\n",
        "        super(RDB, self).__init__()\n",
        "        nChannels_ = nChannels\n",
        "        modules = []\n",
        "        for i in range(nDenselayer):\n",
        "            modules.append(make_dense(nChannels_, growthRate))\n",
        "            nChannels_ += growthRate\n",
        "        self.dense_layers = nn.Sequential(*modules)\n",
        "        self.conv_1x1 = nn.Conv2d(nChannels_, nChannels, kernel_size=1, padding=0, bias=False)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.dense_layers(x)\n",
        "        out = self.conv_1x1(out)\n",
        "        out = out + x\n",
        "        return out\n",
        "\n",
        "class make_dense(nn.Module):\n",
        "    def __init__(self, nChannels, growthRate, kernel_size=3):\n",
        "        super(make_dense, self).__init__()\n",
        "        self.conv = nn.Conv2d(nChannels, growthRate, kernel_size=kernel_size, padding=(kernel_size - 1) // 2,\n",
        "                              bias=False)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.conv(x))\n",
        "        out = torch.cat((x, out), 1)\n",
        "        return out"
      ],
      "metadata": {
        "id": "v5S6u2sJthIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MainNet(nn.Module):\n",
        "    \"\"\"B-DenseUNets\"\"\"\n",
        "    def __init__(self, in_nc=12, out_nc=12):\n",
        "        super(MainNet, self).__init__()\n",
        "        self.inc = nn.Sequential(\n",
        "            single_conv(in_nc, 64),\n",
        "            single_conv(64, 64),\n",
        "        )\n",
        "        self.down1 = nn.AvgPool2d(2)\n",
        "        self.conv1 = nn.Sequential(\n",
        "            single_conv(64, 128),\n",
        "            RDB(128, 4, 32),\n",
        "        )\n",
        "        self.down2 = nn.AvgPool2d(2)\n",
        "        self.conv2 = nn.Sequential(\n",
        "            single_conv(128, 256),\n",
        "            RDB(256, 10, 32),\n",
        "        )\n",
        "        self.up1 = up(256)\n",
        "        self.conv3 = nn.Sequential(\n",
        "            RDB(128, 6, 32),\n",
        "        )\n",
        "        self.up2 = up(128)\n",
        "        self.conv4 = nn.Sequential(\n",
        "            RDB(64, 4, 32),\n",
        "        )\n",
        "        self.outc = outconv(64, out_nc)\n",
        "    def forward(self, x):\n",
        "        inx = self.inc(x)\n",
        "\n",
        "        down1 = self.down1(inx)\n",
        "        conv1 = self.conv1(down1)\n",
        "\n",
        "        down2 = self.down2(conv1)\n",
        "        conv2 = self.conv2(down2)\n",
        "\n",
        "        up1 = self.up1(conv2, conv1)\n",
        "        conv3 = self.conv3(up1)\n",
        "\n",
        "        up2 = self.up2(conv3, inx)\n",
        "        conv4 = self.conv4(up2)\n",
        "\n",
        "        out = self.outc(conv4)\n",
        "        return out\n"
      ],
      "metadata": {
        "id": "Ymmu4rWNfQmI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Denoise DND and NAM\n",
        "class UNet_ND(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(UNet_ND, self).__init__()\n",
        "        self.main = MainNet(in_nc=3, out_nc=12)\n",
        "        self.main2 = MainNet(in_nc=15, out_nc=24)\n",
        "        self.out = nn.Conv2d(24, 3, kernel_size=3, padding=1, bias=True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out1 = self.main(x)\n",
        "        out1[:,:3,:,:] = out1[:,:3,:,:] + x\n",
        "        out2 = self.main2(torch.cat([x, out1], dim=1))\n",
        "        out2[:, :3, :, :] = out2[:, :3, :, :] + x\n",
        "        out2[:, 12:, :, :] = out2[:, 12:, :, :] + out1\n",
        "\n",
        "        return self.out(out2) + x\n",
        "\n",
        "class UNet_D(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(UNet_D, self).__init__()\n",
        "        self.main = MainNet(in_nc=3, out_nc=12)\n",
        "        self.main2 = MainNet(in_nc=15, out_nc=24)\n",
        "        self.out = nn.Conv2d(24, 3, kernel_size=3, padding=1, bias=True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out1 = self.main(x)\n",
        "        out1[:,:3,:,:] = out1[:,:3,:,:] + x\n",
        "        out2 = self.main2(torch.cat([x, out1], dim=1))\n",
        "        out2[:, :3, :, :] = out2[:, :3, :, :] + x\n",
        "        out2[:, 12:, :, :] = out2[:, 12:, :, :] + out1\n",
        "\n",
        "        return self.out(out2) + x\n",
        "\n",
        "class HI_GAN(nn.Module):\n",
        "    def __init__(self, ):\n",
        "        super(HI_GAN, self).__init__()\n",
        "        self.main = MainNet(in_nc=6, out_nc=6)\n",
        "        self.main2 = MainNet(in_nc=12, out_nc=12)\n",
        "        self.main3 = MainNet(in_nc=24, out_nc=24)\n",
        "        self.out = nn.Conv2d(24, 3, kernel_size=3, padding=1, bias=True)\n",
        "    def forward(self, unet_nd_dn, unet_d_dn):\n",
        "        concat_img1 = torch.cat([unet_nd_dn, unet_d_dn], dim=1)\n",
        "        out1 = 0.2*self.main(concat_img1) + concat_img1\n",
        "\n",
        "        concat_img2 = torch.cat([concat_img1, out1], dim=1)\n",
        "        out2 = 0.2*self.main2(concat_img2) + concat_img2\n",
        "\n",
        "        concat_img3 = torch.cat([concat_img2, out2], dim=1)\n",
        "        out3 = 0.2*self.main3(concat_img3) + concat_img3\n",
        "\n",
        "        out = 0.2 * self.out(out3) + 0.5 * unet_nd_dn + 0.5 * unet_d_dn\n",
        "        return out\n",
        "\n",
        "# Denoise Cell\n",
        "class UNet_ND_cell(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(UNet_ND_cell, self).__init__()\n",
        "        self.main = MainNet(in_nc=1, out_nc=1)\n",
        "        self.main2 = MainNet(in_nc=2, out_nc=2)\n",
        "        self.out = nn.Conv2d(4, 1, kernel_size=3, padding=1, bias=True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out1 = self.main(x) + x\n",
        "        cat1 = torch.cat([x, out1], dim=1)\n",
        "        out2 = self.main2(cat1) + cat1\n",
        "        cat2 = torch.cat([x,out1, out2], dim=1)\n",
        "        return self.out(cat2) + x\n",
        "\n",
        "class UNet_D_cell(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(UNet_D_cell, self).__init__()\n",
        "        self.main = MainNet(in_nc=1, out_nc=1)\n",
        "        self.main2 = MainNet(in_nc=2, out_nc=2)\n",
        "        self.out = nn.Conv2d(4, 1, kernel_size=3, padding=1, bias=True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out1 = self.main(x) + x\n",
        "        cat1 = torch.cat([x, out1], dim=1)\n",
        "        out2 = self.main2(cat1) + cat1\n",
        "        cat2 = torch.cat([x,out1, out2], dim=1)\n",
        "        return self.out(cat2) + x\n",
        "\n",
        "class HI_GAN_cell(nn.Module):\n",
        "    def __init__(self, ):\n",
        "        super(HI_GAN_cell, self).__init__()\n",
        "        self.main = MainNet(in_nc=2, out_nc=2)\n",
        "        self.main2 = MainNet(in_nc=4, out_nc=4)\n",
        "        self.main3 = MainNet(in_nc=8, out_nc=8)\n",
        "        self.out = nn.Conv2d(8, 1, kernel_size=3, padding=1, bias=True)\n",
        "    def forward(self, unet_nd_dn, unet_d_dn):\n",
        "        cat1 = torch.cat([unet_nd_dn, unet_d_dn], dim=1)\n",
        "        out1 = 0.2*self.main(cat1) + cat1\n",
        "\n",
        "        cat2 = torch.cat([cat1, out1], dim=1)\n",
        "        out2 = 0.2*self.main2(cat2) + cat2\n",
        "\n",
        "        cat3 = torch.cat([cat2, out2], dim=1)\n",
        "        out3 = 0.2*self.main3(cat3) + cat3\n",
        "\n",
        "        out = 0.2 * self.out(out3) + 0.5 * unet_nd_dn + 0.5 * unet_d_dn\n",
        "        return out\n"
      ],
      "metadata": {
        "id": "oojyrHyKfQ1I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# unetnd_path= \"/content/unet_nd_jpeg.pth\"\n",
        "# unetd_path=\"/content/unet_nd_jpeg.pth\"\n",
        "# higan_path=\"/content/hi_gan_jpeg.pth\"\n",
        "\n",
        "unetnd_path= \"/content/unet_nd.pth\"\n",
        "unetd_path=\"/content/unet_nd.pth\"\n",
        "higan_path=\"/content/hi_gan.pth\""
      ],
      "metadata": {
        "id": "XUSxk-KwxOiO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of GPUs available. Use 0 for CPU mode.\n",
        "ngpu = 1\n",
        "device = torch.device(\"cuda\" if (torch.cuda.is_available() and ngpu > 0) else \"cpu\")\n",
        "print(device)\n"
      ],
      "metadata": {
        "id": "JGmz28fGHlLk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create models\n",
        "unetnd = UNet_ND().to(device)\n",
        "unetd = UNet_D().to(device)\n",
        "higan = HI_GAN().to(device)\n",
        "\n",
        "# unetnd.load_state_dict(torch.load(unetnd_path,map_location=torch.device('cpu')), strict=False)\n",
        "# unetd.load_state_dict(torch.load(unetd_path,map_location=torch.device('cpu')), strict=False)\n",
        "unetnd.load_state_dict(torch.load(unetnd_path), strict=False)\n",
        "unetd.load_state_dict(torch.load(unetd_path), strict=False)\n",
        "unetnd.eval()\n",
        "unetd.eval()\n",
        "\n",
        "higan.load_state_dict(torch.load(higan_path), strict=False)\n",
        "# higan.save_weights('higan_chkpoint')\n",
        "# higan.save('higan_model')\n",
        "higan.eval()"
      ],
      "metadata": {
        "id": "VAvrpCHCfDqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### HiGAN Denoising Analysis "
      ],
      "metadata": {
        "id": "e_8GBOyMyXRY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read img\n",
        "# im8 = cv2.imread(dataset_root_dir+\"validation/SaltPepperNoise_gnimg_1.jpg\")\n",
        "im8=xtrain[1]\n",
        "imorig = im8[:, :, ::-1] / 255.0\n",
        "imorig = np.array(imorig).astype('float32')\n",
        "\n",
        "imorig = np.expand_dims(imorig.transpose(2, 0, 1), 0)\n",
        "imorig = torch.Tensor(imorig).to(device)\n",
        "print(imorig.shape)"
      ],
      "metadata": {
        "id": "AS3SkMOQDRfW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "start = time.time()\n",
        "denoised0 = unetnd(imorig)\n",
        "denoised1 = unetd(imorig)\n",
        "higan_denoised_imgs = higan(denoised0, denoised1)\n",
        "end = time.time()\n",
        "print(\"higan_denoised_imgs Elapsed time:\"+ str(end-start))"
      ],
      "metadata": {
        "id": "AC_WztddwkAK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"HiGAN\")\n",
        "plot_predictions(im8, higan_denoised_imgs)"
      ],
      "metadata": {
        "id": "BIWBJ5A3ESl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DnCNN"
      ],
      "metadata": {
        "id": "QoY7urJtTVWT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NNRegressor(nt.NeuralNetwork):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(NNRegressor, self).__init__()\n",
        "        self.mse = nn.MSELoss()\n",
        "\n",
        "    def criterion(self, y, d):\n",
        "        return self.mse(y, d)\n",
        "\n",
        "class DnCNN(NNRegressor):\n",
        "\n",
        "    def __init__(self, D, C=64):\n",
        "        super(DnCNN, self).__init__()\n",
        "        self.D = D\n",
        "        \n",
        "        # convolution layers\n",
        "        self.conv = nn.ModuleList()\n",
        "        self.conv.append(nn.Conv2d(3, C, 3, padding=1))\n",
        "        self.conv.extend([nn.Conv2d(C, C, 3, padding=1) for _ in range(D)])\n",
        "        self.conv.append(nn.Conv2d(C, 3, 3, padding=1))\n",
        "        # apply He's initialization\n",
        "        for i in range(len(self.conv[:-1])):\n",
        "            nn.init.kaiming_normal_(self.conv[i].weight.data, nonlinearity='relu')\n",
        "        \n",
        "        # batch normalization\n",
        "        self.bn = nn.ModuleList()\n",
        "        self.bn.extend([nn.BatchNorm2d(C, C) for _ in range(D)])\n",
        "        # initialize the weights of the Batch normalization layers\n",
        "        for i in range(D):\n",
        "            nn.init.constant_(self.bn[i].weight.data, 1.25 * np.sqrt(C))\n",
        "\n",
        "    def forward(self, x):\n",
        "        D = self.D\n",
        "        h = F.relu(self.conv[0](x))\n",
        "        for i in range(D):\n",
        "            h = F.relu(self.bn[i](self.conv[i+1](h)))\n",
        "        y = self.conv[D+1](h) + x\n",
        "        return y"
      ],
      "metadata": {
        "id": "Q2n4VpYITXfM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x, _ = train_set[-1]\n",
        "x = x.unsqueeze(0).to(device)\n",
        "Ds = [0, 1, 2, 4, 8]\n",
        "\n",
        "fig, axes = plt.subplots(nrows=len(Ds), ncols=3, figsize=(9,9))\n",
        "for i in range(len(Ds)):\n",
        "    with torch.no_grad():\n",
        "        model = DnCNN(Ds[i]).to(device)\n",
        "        y = model.forward(x)\n",
        "    \n",
        "    myimshow(x[0], ax=axes[i][0])\n",
        "    axes[i][0].set_title('x[0]')\n",
        "    myimshow(y[0], ax=axes[i][1])\n",
        "    axes[i][1].set_title(f'y[0] (D={Ds[i]})')\n",
        "    myimshow(x[0]-y[0], ax=axes[i][2])\n",
        "    axes[i][2].set_title(f'x[0]-y[0] (D={Ds[i]})')"
      ],
      "metadata": {
        "id": "lu1rtYWnTffU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DenoisingStatsManager(nt.StatsManager):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(DenoisingStatsManager, self).__init__()\n",
        "\n",
        "    def init(self):\n",
        "        super(DenoisingStatsManager, self).init()\n",
        "        self.running_psnr = 0\n",
        "\n",
        "    def accumulate(self, loss, x, y, d):\n",
        "        super(DenoisingStatsManager, self).accumulate(loss, x, y, d)    \n",
        "        n = x.shape[0] * x.shape[1] * x.shape[2] * x.shape[3]\n",
        "        self.running_psnr += 10*torch.log10(4*n/(torch.norm(y-d)**2))\n",
        "\n",
        "    def summarize(self):\n",
        "        loss = super(DenoisingStatsManager, self).summarize()\n",
        "        psnr = self.running_psnr / self.number_update\n",
        "        return {'loss': loss, 'PSNR': psnr.cpu()}"
      ],
      "metadata": {
        "id": "vN1daL_FTp_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot(exp, fig, axes, noisy, visu_rate=2):\n",
        "    if exp.epoch % visu_rate != 0:\n",
        "        return\n",
        "    with torch.no_grad():\n",
        "        denoised = exp.net(noisy[None].to(net.device))[0]\n",
        "    axes[0][0].clear()\n",
        "    axes[0][1].clear()\n",
        "    axes[1][0].clear()\n",
        "    axes[1][1].clear()\n",
        "    myimshow(noisy, ax=axes[0][0])\n",
        "    axes[0][0].set_title('Noisy image')\n",
        "    \n",
        "    myimshow(denoised, ax=axes[0][1])\n",
        "    axes[0][1].set_title('Denoised image')\n",
        "    \n",
        "    axes[1][0].plot([exp.history[k][0]['loss'] for k in range(exp.epoch)], label='training loss')\n",
        "    axes[1][0].set_ylabel('Loss')\n",
        "    axes[1][0].set_xlabel('Epoch')\n",
        "    axes[1][0].legend()\n",
        "    \n",
        "    axes[1][1].plot([exp.history[k][0]['PSNR'] for k in range(exp.epoch)], label='training psnr')\n",
        "    axes[1][1].set_ylabel('PSNR')\n",
        "    axes[1][1].set_xlabel('Epoch')\n",
        "    axes[1][1].legend()\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    fig.canvas.draw()"
      ],
      "metadata": {
        "id": "MVgdqloATsCJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = 1e-3\n",
        "net = DnCNN(6).to(device)\n",
        "adam = torch.optim.Adam(net.parameters(), lr=lr)\n",
        "stats_manager = DenoisingStatsManager()\n",
        "exp1 = nt.Experiment(net, train_set, test_set, adam, stats_manager, batch_size=4, \n",
        "               output_dir=\"../checkpoints/denoising1\", perform_validation_during_training=True)"
      ],
      "metadata": {
        "id": "nw50KFWfUEjz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(9, 7))\n",
        "exp1.run(num_epochs=200, plot=lambda exp: plot(exp, fig=fig, axes=axes,\n",
        "                                                noisy=test_set[73][0]))"
      ],
      "metadata": {
        "id": "vyAyUGiyUGpa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = []\n",
        "model = exp1.net.to(device)\n",
        "titles = ['clean', 'noise', 'denoise']\n",
        "\n",
        "x, clean = test_set[12]\n",
        "x = x.unsqueeze(0).to(device)\n",
        "img.append(clean)\n",
        "img.append(x[0])\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    y = model.forward(x)\n",
        "img.append(y[0])\n",
        "    \n",
        "fig, axes = plt.subplots(ncols=3, figsize=(9,5), sharex='all', sharey='all')\n",
        "for i in range(len(img)):\n",
        "    myimshow(img[i], ax=axes[i])\n",
        "    axes[i].set_title(f'{titles[i]}')"
      ],
      "metadata": {
        "id": "GZ_Zzt2VUI49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import torch\n",
        "# import torch.nn as nn\n",
        "# import torch.nn.init as init\n",
        "\n",
        "\n",
        "# class DnCNN(nn.Module):\n",
        "#     # reference: https://github.com/cszn/DnCNN/tree/master/TrainingCodes/dncnn_pytorch\n",
        "#     def __init__(self, depth=17, n_channels=64, image_channels=1, \n",
        "#         use_bnorm=True, kernel_size=3):\n",
        "#         super(DnCNN, self).__init__()\n",
        "#         kernel_size = 3\n",
        "#         padding = 1\n",
        "#         layers = []\n",
        "\n",
        "#         layers.append(nn.Conv2d(image_channels, n_channels, \n",
        "#             kernel_size=kernel_size, padding=padding, bias=True))\n",
        "#         layers.append(nn.ReLU(inplace=True))\n",
        "#         for _ in range(depth-2):\n",
        "#             layers.append(nn.Conv2d(n_channels, n_channels, \n",
        "#             kernel_size=kernel_size, padding=padding, bias=False))\n",
        "#             if use_bnorm:\n",
        "#                 layers.append(nn.BatchNorm2d(n_channels, eps=0.0001, momentum = 0.95))\n",
        "#             layers.append(nn.ReLU(inplace=True))\n",
        "#         layers.append(nn.Conv2d(n_channels, image_channels, \n",
        "#             kernel_size=kernel_size, padding=padding, bias=False))\n",
        "#         self.dncnn = nn.Sequential(*layers)\n",
        "#         self._initialize_weights()\n",
        "\n",
        "#         print(f'model size: {self._model_size()}')\n",
        "\n",
        "#     def forward(self, x):\n",
        "#         y = x\n",
        "#         out = self.dncnn(x)\n",
        "#         # out: residual, y: noisy input\n",
        "#         return y - out\n",
        "\n",
        "#     def _initialize_weights(self):\n",
        "#         print('init weight')\n",
        "#         for m in self.modules():\n",
        "#             if isinstance(m, nn.Conv2d):\n",
        "#                 init.orthogonal_(m.weight)\n",
        "#                 if m.bias is not None:\n",
        "#                     init.constant_(m.bias, 0)\n",
        "#             elif isinstance(m, nn.BatchNorm2d):\n",
        "#                 init.constant_(m.weight, 1)\n",
        "#                 init.constant_(m.bias, 0)\n",
        "\n",
        "#     def _model_size(self):\n",
        "#         n_params, n_conv_layers = 0, 0\n",
        "#         for param in self.parameters():\n",
        "#             n_params += param.numel()\n",
        "#         for module in self.modules():\n",
        "#             if 'Conv' in module.__class__.__name__ \\\n",
        "#                     or 'conv' in module.__class__.__name__:\n",
        "#                 n_conv_layers += 1\n",
        "#         return n_params, n_conv_layers"
      ],
      "metadata": {
        "id": "4pqZ4T65YGD0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# class DnCNN_NRL(nn.Module):\n",
        "#     \"\"\"Non-residual learning.\n",
        "#     \"\"\"\n",
        "#     def __init__(self, depth=17, n_channels=64, image_channels=1, \n",
        "#         use_bnorm=True, kernel_size=3):\n",
        "#         super(DnCNN_NRL, self).__init__()\n",
        "#         kernel_size = 3\n",
        "#         padding = 1\n",
        "#         layers = []\n",
        "\n",
        "#         layers.append(nn.Conv2d(image_channels, n_channels, \n",
        "#             kernel_size=kernel_size, padding=padding, bias=True))\n",
        "#         layers.append(nn.ReLU(inplace=True))\n",
        "#         for _ in range(depth-2):\n",
        "#             layers.append(nn.Conv2d(n_channels, n_channels, \n",
        "#             kernel_size=kernel_size, padding=padding, bias=False))\n",
        "#             if use_bnorm:\n",
        "#                 layers.append(nn.BatchNorm2d(n_channels, eps=0.0001, momentum = 0.95))\n",
        "#             layers.append(nn.ReLU(inplace=True))\n",
        "#         layers.append(nn.Conv2d(n_channels, image_channels, \n",
        "#             kernel_size=kernel_size, padding=padding, bias=False))\n",
        "#         self.dncnn = nn.Sequential(*layers)\n",
        "#         self._initialize_weights()\n",
        "\n",
        "#         print(f'model size: {self._model_size()}')\n",
        "\n",
        "#     def forward(self, x):\n",
        "#         # output denoised directly\n",
        "#         return self.dncnn(x)\n",
        "\n",
        "#     def _initialize_weights(self):\n",
        "#         print('init weight')\n",
        "#         for m in self.modules():\n",
        "#             if isinstance(m, nn.Conv2d):\n",
        "#                 init.orthogonal_(m.weight)\n",
        "#                 if m.bias is not None:\n",
        "#                     init.constant_(m.bias, 0)\n",
        "#             elif isinstance(m, nn.BatchNorm2d):\n",
        "#                 init.constant_(m.weight, 1)\n",
        "#                 init.constant_(m.bias, 0)\n",
        "\n",
        "#     def _model_size(self):\n",
        "#         n_params, n_conv_layers = 0, 0\n",
        "#         for param in self.parameters():\n",
        "#             n_params += param.numel()\n",
        "#         for module in self.modules():\n",
        "#             if 'Conv' in module.__class__.__name__ \\\n",
        "#                     or 'conv' in module.__class__.__name__:\n",
        "#                 n_conv_layers += 1\n",
        "#         return n_params, n_conv_layers"
      ],
      "metadata": {
        "id": "8i9F6y_cYGea"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bIEJV8_XFuQC"
      },
      "outputs": [],
      "source": [
        "# model = torch.load('/content/dncnn.pth',map_location=torch.device('cpu'))\n",
        "# print(model)\n",
        "\n",
        "model = DnCNN(depth=17,\n",
        "                  n_channels=64,\n",
        "                  image_channels=1,\n",
        "                  use_bnorm=True,\n",
        "                  kernel_size=3)\n",
        "model.load_state_dict(torch.load('/content/dncnn.pth',map_location=torch.device('cpu')))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()"
      ],
      "metadata": {
        "id": "LGnf9j7EYzfD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start = time.time()\n",
        "denoised = model.dncnn.eval()\n",
        "denoised.(img)\n",
        "end = time.time()\n",
        "print(\"Elapsed time:\"+ end-start)"
      ],
      "metadata": {
        "id": "mhsP1sUFZImJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}