DnCNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4",
      "cell_execution_strategy": "setup",
      "authorship_tag": "ABX9TyMV+zQ94bS94obnIJ3+SsUY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Voyageran/StartNN/blob/main/DnCNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7Bg-FF7VaSG",
        "outputId": "b99d372f-20c8-4ead-f03f-9d69cb4fc5e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l' -> '/content/d2l'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/paddle.py' -> '/content/d2l/paddle.py'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/mxnet.py' -> '/content/d2l/mxnet.py'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/tensorflow.py' -> '/content/d2l/tensorflow.py'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/torch.py' -> '/content/d2l/torch.py'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/__init__.py' -> '/content/d2l/__init__.py'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/__pycache__' -> '/content/d2l/__pycache__'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/__pycache__/mxnet.cpython-310.pyc' -> '/content/d2l/__pycache__/mxnet.cpython-310.pyc'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/__pycache__/__init__.cpython-310.pyc' -> '/content/d2l/__pycache__/__init__.cpython-310.pyc'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/__pycache__/torch.cpython-310.pyc' -> '/content/d2l/__pycache__/torch.cpython-310.pyc'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/__pycache__/tensorflow.cpython-310.pyc' -> '/content/d2l/__pycache__/tensorflow.cpython-310.pyc'\n",
            "'/content/gdrive/MyDrive/Colab Notebooks/d2l/__pycache__/paddle.cpython-310.pyc' -> '/content/d2l/__pycache__/paddle.cpython-310.pyc'\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "sys.path.insert(0,\"/content/content/notebooks/colabInstallPackage\")\n",
        "\n",
        "!cp -av '/content/gdrive/MyDrive/Colab Notebooks/d2l' '/content/'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import tensorflow as tf\n",
        "\n",
        "torch.__version__, tf.__version__"
      ],
      "metadata": {
        "id": "7U0nmQO6VdMU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4246b2e1-f816-46d7-cc2e-a28e869d9479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('2.0.1+cu118', '2.12.0')"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pwd\n",
        "%cd '/content/gdrive/MyDrive/Dn'\n",
        "!pwd\n",
        "# !git clone https://github.com/yjn870/DnCNN-pytorch.git\n",
        "# !git clone https://github.com/SaoYan/DnCNN-PyTorch.git\n",
        "!pip install tensorboardX"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llDH4pc6HFKM",
        "outputId": "951b9163-343f-4de1-9711-61f32d209f79"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/Dn\n",
            "/content/gdrive/MyDrive/Dn\n",
            "Collecting tensorboardX\n",
            "  Downloading tensorboardX-2.6.2-py2.py3-none-any.whl (101 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.6/101.6 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tensorboardX) (1.22.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorboardX) (23.1)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from tensorboardX) (3.20.3)\n",
            "Installing collected packages: tensorboardX\n",
            "Successfully installed tensorboardX-2.6.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd '/content/gdrive/MyDrive/Dn/DnCNN-PyTorch/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OE2xGGz2MAo7",
        "outputId": "4b010636-6e1f-40da-e062-cb94bfc838a1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/Dn/DnCNN-PyTorch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import os\n",
        "# checkpoint_path = \"/content/gdrive/MyDrive/Dn/DnCNN-PyTorch/checkpoint\"\n",
        "# checkpoint_dir = os.path.dirname(checkpoint_path)\n",
        "# Create a callback that saves the model's weights\n",
        "# cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
        "#                                                  save_weights_only=True,\n",
        "#                                                  verbose=1)\n",
        "\n",
        "# save checkpoint\n",
        "# checkpoint = {\n",
        "#     'epoch': epoch,\n",
        "#     'model_state_dict': model.state_dict(),\n",
        "#     'optimizer_state_dict': optimizer.state_dict(),\n",
        "#     'loss': loss,\n",
        "# }\n",
        "# torch.save(checkpoint, '/content/gdrive/My Drive/Dn/DnCNN-PyTorch/checkpoint')"
      ],
      "metadata": {
        "id": "3OnAiLxmMiyi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py \\\n",
        "  --preprocess False \\\n",
        "  --num_of_layers 17 \\\n",
        "  --epochs 5 \\\n",
        "  --mode S \\\n",
        "  --noiseL 25 \\\n",
        "  --val_noiseL 25\\\n",
        "  # --project \"/content/gdrive/MyDrive/Dn/DnCNN-PyTorch/checkpoint\"\\\n",
        "  # --name checkpoint_31July23"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2eepCLaSTdH",
        "outputId": "1af7adb6-6bd8-4b41-e01b-287f7c56e6cd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001b[0m\n",
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            "\n",
            "[epoch 3] PSNR_val: 30.0271\n",
            "learning rate 0.001000\n",
            "[epoch 4][1/1863] loss: 1.2175 PSNR_train: 29.2676\n",
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            "\n",
            "[epoch 4] PSNR_val: 30.0410\n",
            "learning rate 0.001000\n",
            "[epoch 5][1/1863] loss: 1.3672 PSNR_train: 28.6233\n",
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            "[epoch 5][1715/1863] loss: 1.3583 PSNR_train: 27.5929\n",
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            "[epoch 5][1717/1863] loss: 1.2438 PSNR_train: 28.6273\n",
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            "[epoch 5][1723/1863] loss: 1.3212 PSNR_train: 29.0998\n",
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            "[epoch 5][1730/1863] loss: 1.3771 PSNR_train: 28.8422\n",
            "[epoch 5][1731/1863] loss: 1.3174 PSNR_train: 28.7798\n",
            "[epoch 5][1732/1863] loss: 1.2898 PSNR_train: 28.9601\n",
            "[epoch 5][1733/1863] loss: 1.2981 PSNR_train: 28.8619\n",
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            "[epoch 5][1768/1863] loss: 1.3417 PSNR_train: 28.7592\n",
            "[epoch 5][1769/1863] loss: 1.4221 PSNR_train: 28.4111\n",
            "[epoch 5][1770/1863] loss: 1.1964 PSNR_train: 29.3953\n",
            "[epoch 5][1771/1863] loss: 1.3130 PSNR_train: 28.7426\n",
            "[epoch 5][1772/1863] loss: 1.2919 PSNR_train: 29.0038\n",
            "[epoch 5][1773/1863] loss: 1.2370 PSNR_train: 29.4900\n",
            "[epoch 5][1774/1863] loss: 1.2590 PSNR_train: 29.1544\n",
            "[epoch 5][1775/1863] loss: 1.2601 PSNR_train: 29.0753\n",
            "[epoch 5][1776/1863] loss: 1.3275 PSNR_train: 29.0803\n",
            "[epoch 5][1777/1863] loss: 1.3172 PSNR_train: 29.0693\n",
            "[epoch 5][1778/1863] loss: 1.3641 PSNR_train: 28.8526\n",
            "[epoch 5][1779/1863] loss: 1.1679 PSNR_train: 29.6450\n",
            "[epoch 5][1780/1863] loss: 1.3495 PSNR_train: 28.8458\n",
            "[epoch 5][1781/1863] loss: 1.2120 PSNR_train: 29.2242\n",
            "[epoch 5][1782/1863] loss: 1.2307 PSNR_train: 28.8327\n",
            "[epoch 5][1783/1863] loss: 1.3423 PSNR_train: 28.7384\n",
            "[epoch 5][1784/1863] loss: 1.3546 PSNR_train: 28.7332\n",
            "[epoch 5][1785/1863] loss: 1.2094 PSNR_train: 29.1385\n",
            "[epoch 5][1786/1863] loss: 1.2277 PSNR_train: 29.3484\n",
            "[epoch 5][1787/1863] loss: 1.2720 PSNR_train: 29.1508\n",
            "[epoch 5][1788/1863] loss: 1.3397 PSNR_train: 28.9695\n",
            "[epoch 5][1789/1863] loss: 1.3434 PSNR_train: 28.7761\n",
            "[epoch 5][1790/1863] loss: 1.2760 PSNR_train: 29.2026\n",
            "[epoch 5][1791/1863] loss: 1.2151 PSNR_train: 29.2228\n",
            "[epoch 5][1792/1863] loss: 1.2755 PSNR_train: 29.1051\n",
            "[epoch 5][1793/1863] loss: 1.3215 PSNR_train: 28.7788\n",
            "[epoch 5][1794/1863] loss: 1.3332 PSNR_train: 28.7251\n",
            "[epoch 5][1795/1863] loss: 1.1339 PSNR_train: 29.5160\n",
            "[epoch 5][1796/1863] loss: 1.2422 PSNR_train: 29.0201\n",
            "[epoch 5][1797/1863] loss: 1.2390 PSNR_train: 28.8142\n",
            "[epoch 5][1798/1863] loss: 1.1783 PSNR_train: 29.1755\n",
            "[epoch 5][1799/1863] loss: 1.2629 PSNR_train: 29.0327\n",
            "[epoch 5][1800/1863] loss: 1.3224 PSNR_train: 28.9948\n",
            "[epoch 5][1801/1863] loss: 1.3190 PSNR_train: 28.6740\n",
            "[epoch 5][1802/1863] loss: 1.2496 PSNR_train: 29.1726\n",
            "[epoch 5][1803/1863] loss: 1.2892 PSNR_train: 29.0799\n",
            "[epoch 5][1804/1863] loss: 1.3668 PSNR_train: 28.6301\n",
            "[epoch 5][1805/1863] loss: 1.2544 PSNR_train: 29.1972\n",
            "[epoch 5][1806/1863] loss: 1.4598 PSNR_train: 28.3773\n",
            "[epoch 5][1807/1863] loss: 1.3263 PSNR_train: 28.7322\n",
            "[epoch 5][1808/1863] loss: 1.3225 PSNR_train: 28.8031\n",
            "[epoch 5][1809/1863] loss: 1.1595 PSNR_train: 29.5847\n",
            "[epoch 5][1810/1863] loss: 1.2271 PSNR_train: 29.4454\n",
            "[epoch 5][1811/1863] loss: 1.4854 PSNR_train: 28.7180\n",
            "[epoch 5][1812/1863] loss: 1.1781 PSNR_train: 29.5840\n",
            "[epoch 5][1813/1863] loss: 1.3848 PSNR_train: 28.4886\n",
            "[epoch 5][1814/1863] loss: 1.4167 PSNR_train: 28.5349\n",
            "[epoch 5][1815/1863] loss: 1.2165 PSNR_train: 29.3629\n",
            "[epoch 5][1816/1863] loss: 1.3179 PSNR_train: 28.7484\n",
            "[epoch 5][1817/1863] loss: 1.2689 PSNR_train: 28.8986\n",
            "[epoch 5][1818/1863] loss: 1.1654 PSNR_train: 29.3094\n",
            "[epoch 5][1819/1863] loss: 1.3130 PSNR_train: 28.9281\n",
            "[epoch 5][1820/1863] loss: 1.2949 PSNR_train: 28.7465\n",
            "[epoch 5][1821/1863] loss: 1.1814 PSNR_train: 29.4089\n",
            "[epoch 5][1822/1863] loss: 1.3343 PSNR_train: 28.9211\n",
            "[epoch 5][1823/1863] loss: 1.4287 PSNR_train: 28.3257\n",
            "[epoch 5][1824/1863] loss: 1.3394 PSNR_train: 28.6020\n",
            "[epoch 5][1825/1863] loss: 1.2094 PSNR_train: 29.1962\n",
            "[epoch 5][1826/1863] loss: 1.2448 PSNR_train: 29.0441\n",
            "[epoch 5][1827/1863] loss: 1.3116 PSNR_train: 28.9179\n",
            "[epoch 5][1828/1863] loss: 1.3099 PSNR_train: 28.9771\n",
            "[epoch 5][1829/1863] loss: 1.3661 PSNR_train: 28.6472\n",
            "[epoch 5][1830/1863] loss: 1.3504 PSNR_train: 29.1405\n",
            "[epoch 5][1831/1863] loss: 1.1901 PSNR_train: 29.5651\n",
            "[epoch 5][1832/1863] loss: 1.3311 PSNR_train: 28.7731\n",
            "[epoch 5][1833/1863] loss: 1.3493 PSNR_train: 29.1058\n",
            "[epoch 5][1834/1863] loss: 1.2568 PSNR_train: 29.4699\n",
            "[epoch 5][1835/1863] loss: 1.3986 PSNR_train: 28.5324\n",
            "[epoch 5][1836/1863] loss: 1.2530 PSNR_train: 28.6967\n",
            "[epoch 5][1837/1863] loss: 1.2199 PSNR_train: 28.9409\n",
            "[epoch 5][1838/1863] loss: 1.3325 PSNR_train: 28.6442\n",
            "[epoch 5][1839/1863] loss: 1.3831 PSNR_train: 28.3588\n",
            "[epoch 5][1840/1863] loss: 1.4414 PSNR_train: 28.1503\n",
            "[epoch 5][1841/1863] loss: 1.4212 PSNR_train: 28.2083\n",
            "[epoch 5][1842/1863] loss: 1.3191 PSNR_train: 28.4672\n",
            "[epoch 5][1843/1863] loss: 1.2571 PSNR_train: 28.8885\n",
            "[epoch 5][1844/1863] loss: 1.3096 PSNR_train: 28.9888\n",
            "[epoch 5][1845/1863] loss: 1.3350 PSNR_train: 28.7797\n",
            "[epoch 5][1846/1863] loss: 1.3756 PSNR_train: 28.8641\n",
            "[epoch 5][1847/1863] loss: 1.3072 PSNR_train: 28.8206\n",
            "[epoch 5][1848/1863] loss: 1.3348 PSNR_train: 28.6495\n",
            "[epoch 5][1849/1863] loss: 1.3633 PSNR_train: 28.9638\n",
            "[epoch 5][1850/1863] loss: 1.4211 PSNR_train: 28.6320\n",
            "[epoch 5][1851/1863] loss: 1.2643 PSNR_train: 29.2204\n",
            "[epoch 5][1852/1863] loss: 1.2237 PSNR_train: 28.7628\n",
            "[epoch 5][1853/1863] loss: 1.2506 PSNR_train: 29.2175\n",
            "[epoch 5][1854/1863] loss: 1.2681 PSNR_train: 28.8857\n",
            "[epoch 5][1855/1863] loss: 1.3957 PSNR_train: 28.3558\n",
            "[epoch 5][1856/1863] loss: 1.3006 PSNR_train: 28.5655\n",
            "[epoch 5][1857/1863] loss: 1.3625 PSNR_train: 28.7384\n",
            "[epoch 5][1858/1863] loss: 1.2390 PSNR_train: 29.1544\n",
            "[epoch 5][1859/1863] loss: 1.3254 PSNR_train: 28.9103\n",
            "[epoch 5][1860/1863] loss: 1.2980 PSNR_train: 28.7231\n",
            "[epoch 5][1861/1863] loss: 1.2016 PSNR_train: 29.0781\n",
            "[epoch 5][1862/1863] loss: 1.2759 PSNR_train: 29.0098\n",
            "[epoch 5][1863/1863] loss: 1.1879 PSNR_train: 29.5087\n",
            "\n",
            "[epoch 5] PSNR_val: 30.1237\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test\n",
        "!python test.py \\\n",
        "  --num_of_layers 17 \\\n",
        "  --logdir logs/DnCNN-S-15 \\\n",
        "  --test_data Set12 \\\n",
        "  --test_noiseL 15"
      ],
      "metadata": {
        "id": "VqBeK_KMzSNZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b136fadb-c601-4855-8920-f0b6535e2f71"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model ...\n",
            "\n",
            "Loading data info ...\n",
            "\n",
            "data/Set12/01.png PSNR 32.484350\n",
            "data/Set12/02.png PSNR 35.038933\n",
            "data/Set12/03.png PSNR 33.286359\n",
            "data/Set12/04.png PSNR 32.228173\n",
            "data/Set12/05.png PSNR 33.101883\n",
            "data/Set12/06.png PSNR 31.727394\n",
            "data/Set12/07.png PSNR 31.815198\n",
            "data/Set12/08.png PSNR 34.521327\n",
            "data/Set12/09.png PSNR 32.522216\n",
            "data/Set12/10.png PSNR 32.384525\n",
            "data/Set12/11.png PSNR 32.366826\n",
            "data/Set12/12.png PSNR 32.360020\n",
            "\n",
            "PSNR on test data 32.819767\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test\n",
        "#net.pth  --save model parameters\n",
        "!python test.py \\\n",
        "  --num_of_layers 17 \\\n",
        "  --logdir logs/DnCNN-S-15 \\\n",
        "  --test_data Set68 \\\n",
        "  --test_noiseL 15"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iTo_no7XBIt",
        "outputId": "19a50da5-9471-4482-9897-8f727f77a686"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model ...\n",
            "\n",
            "Loading data info ...\n",
            "\n",
            "data/Set68/test001.png PSNR 28.411475\n",
            "data/Set68/test002.png PSNR 32.752280\n",
            "data/Set68/test003.png PSNR 32.629821\n",
            "data/Set68/test004.png PSNR 33.363013\n",
            "data/Set68/test005.png PSNR 30.246052\n",
            "data/Set68/test006.png PSNR 35.250490\n",
            "data/Set68/test007.png PSNR 31.396176\n",
            "data/Set68/test008.png PSNR 29.029910\n",
            "data/Set68/test009.png PSNR 31.615096\n",
            "data/Set68/test010.png PSNR 32.309545\n",
            "data/Set68/test011.png PSNR 31.865885\n",
            "data/Set68/test012.png PSNR 31.027726\n",
            "data/Set68/test013.png PSNR 32.084984\n",
            "data/Set68/test014.png PSNR 33.349179\n",
            "data/Set68/test015.png PSNR 29.979530\n",
            "data/Set68/test016.png PSNR 31.040912\n",
            "data/Set68/test017.png PSNR 35.030890\n",
            "data/Set68/test018.png PSNR 35.342731\n",
            "data/Set68/test019.png PSNR 30.816749\n",
            "data/Set68/test020.png PSNR 31.616919\n",
            "data/Set68/test021.png PSNR 29.470177\n",
            "data/Set68/test022.png PSNR 29.838871\n",
            "data/Set68/test023.png PSNR 31.922232\n",
            "data/Set68/test024.png PSNR 31.454920\n",
            "data/Set68/test025.png PSNR 32.215073\n",
            "data/Set68/test026.png PSNR 31.016216\n",
            "data/Set68/test027.png PSNR 32.568702\n",
            "data/Set68/test028.png PSNR 35.580687\n",
            "data/Set68/test029.png PSNR 27.615105\n",
            "data/Set68/test030.png PSNR 32.431351\n",
            "data/Set68/test031.png PSNR 27.584524\n",
            "data/Set68/test032.png PSNR 29.459015\n",
            "data/Set68/test033.png PSNR 30.762440\n",
            "data/Set68/test034.png PSNR 35.399046\n",
            "data/Set68/test035.png PSNR 30.561768\n",
            "data/Set68/test036.png PSNR 29.231051\n",
            "data/Set68/test037.png PSNR 30.839642\n",
            "data/Set68/test038.png PSNR 31.730873\n",
            "data/Set68/test039.png PSNR 36.131793\n",
            "data/Set68/test040.png PSNR 31.735567\n",
            "data/Set68/test041.png PSNR 30.634444\n",
            "data/Set68/test042.png PSNR 32.206793\n",
            "data/Set68/test043.png PSNR 33.213581\n",
            "data/Set68/test044.png PSNR 30.574774\n",
            "data/Set68/test045.png PSNR 35.839641\n",
            "data/Set68/test046.png PSNR 29.175935\n",
            "data/Set68/test047.png PSNR 29.353164\n",
            "data/Set68/test048.png PSNR 33.014596\n",
            "data/Set68/test049.png PSNR 33.427670\n",
            "data/Set68/test050.png PSNR 29.456029\n",
            "data/Set68/test051.png PSNR 31.152763\n",
            "data/Set68/test052.png PSNR 34.029012\n",
            "data/Set68/test053.png PSNR 33.128156\n",
            "data/Set68/test054.png PSNR 31.809224\n",
            "data/Set68/test055.png PSNR 30.304758\n",
            "data/Set68/test056.png PSNR 27.048952\n",
            "data/Set68/test057.png PSNR 30.803442\n",
            "data/Set68/test058.png PSNR 32.631837\n",
            "data/Set68/test059.png PSNR 32.384764\n",
            "data/Set68/test060.png PSNR 33.099370\n",
            "data/Set68/test061.png PSNR 32.558160\n",
            "data/Set68/test062.png PSNR 36.564372\n",
            "data/Set68/test063.png PSNR 29.471573\n",
            "data/Set68/test064.png PSNR 29.027278\n",
            "data/Set68/test065.png PSNR 31.434091\n",
            "data/Set68/test066.png PSNR 40.786700\n",
            "data/Set68/test067.png PSNR 27.666375\n",
            "data/Set68/test068.png PSNR 29.104894\n",
            "\n",
            "PSNR on test data 31.714864\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load checkpoint\n",
        "# checkpoint = torch.load('/content/gdrive/My Drive/Dn/DnCNN-PyTorch/checkpoint')\n",
        "# model.load_state_dict(checkpoint['model_state_dict'])\n",
        "# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "# epoch = checkpoint['epoch']\n",
        "# loss = checkpoint['loss']"
      ],
      "metadata": {
        "id": "XnaQLUiFOnJ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "YIMRN_2NmNLA"
      }
    }
  ]
}