real_esrgan_notebook.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zP_IVDLocew2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import nibabel as nib\n",
        "import math\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import argparse\n",
        "import cv2\n",
        "import glob\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mu7MAAlQ6q2x",
        "outputId": "17ab0e7a-feec-4bea-816f-0f347bd393d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOK5Z3JcO5HE",
        "outputId": "02f44084-1cf3-4077-d937-42b4ef7be7be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'Real-ESRGAN'...\n",
            "remote: Enumerating objects: 431, done.\u001b[K\n",
            "remote: Counting objects: 100% (431/431), done.\u001b[K\n",
            "remote: Compressing objects: 100% (277/277), done.\u001b[K\n",
            "remote: Total 431 (delta 235), reused 297 (delta 119), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (431/431), 3.22 MiB | 14.32 MiB/s, done.\n",
            "Resolving deltas: 100% (235/235), done.\n"
          ]
        }
      ],
      "source": [
        "#clone repository\n",
        "!git clone https://github.com/xinntao/Real-ESRGAN.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0notKJVPafz",
        "outputId": "024d7bcc-18a5-4124-e1e4-b229407ef7d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN\n"
          ]
        }
      ],
      "source": [
        "#change directory to the folder Real-ESRGAN\n",
        "%cd /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3YSj3Xx4pBbl",
        "outputId": "d81455df-ffa3-4118-ba05-10d7cf39e1e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: basicsr\n",
            "  Building wheel for basicsr (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for basicsr: filename=basicsr-1.3.5-py3-none-any.whl size=194484 sha256=bd2547ff74032ddc8eb19ff832216b61c0ca779e63f56fb48c4ab794788789d3\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/1b/d0/8659cf028233dd1e3bf282271009fbf037dfc4ab761f32a032\n",
            "Successfully built basicsr\n",
            "Installing collected packages: yapf, tb-nightly, addict, basicsr\n",
            "Successfully installed addict-2.4.0 basicsr-1.3.5 tb-nightly-2.10.0a20220604 yapf-0.32.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (4.11.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.8.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.2.0)\n",
            "Building wheels for collected packages: filterpy\n",
            "  Building wheel for filterpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110474 sha256=75182a1f7505b5793b2a7d1b9f60be9a5980a16123744db14f49e2aeb7cd45b1\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/e0/ee/a2b3c5caab3418c1ccd8c4de573d4cbe13315d7e8b0a55fbc2\n",
            "Successfully built filterpy\n",
            "Installing collected packages: numpy, filterpy, facexlib, gfpgan\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.21.6\n",
            "    Uninstalling numpy-1.21.6:\n",
            "      Successfully uninstalled numpy-1.21.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.20.3 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed facexlib-0.2.3 filterpy-1.4.5 gfpgan-1.3.2 numpy-1.20.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "running develop\n",
            "running egg_info\n",
            "writing realesrgan.egg-info/PKG-INFO\n",
            "writing dependency_links to realesrgan.egg-info/dependency_links.txt\n",
            "writing requirements to realesrgan.egg-info/requires.txt\n",
            "writing top-level names to realesrgan.egg-info/top_level.txt\n",
            "reading manifest template 'MANIFEST.in'\n",
            "adding license file 'LICENSE'\n",
            "writing manifest file 'realesrgan.egg-info/SOURCES.txt'\n",
            "running build_ext\n",
            "Creating /usr/local/lib/python3.7/dist-packages/realesrgan.egg-link (link to .)\n",
            "Adding realesrgan 0.2.2.5 to easy-install.pth file\n",
            "\n",
            "Installed /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN\n",
            "Processing dependencies for realesrgan==0.2.2.5\n",
            "Searching for tqdm==4.64.0\n",
            "Best match: tqdm 4.64.0\n",
            "Adding tqdm 4.64.0 to easy-install.pth file\n",
            "Installing tqdm script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for torchvision==0.12.0+cu113\n",
            "Best match: torchvision 0.12.0+cu113\n",
            "Adding torchvision 0.12.0+cu113 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for torch==1.11.0+cu113\n",
            "Best match: torch 1.11.0+cu113\n",
            "Adding torch 1.11.0+cu113 to easy-install.pth file\n",
            "Installing convert-caffe2-to-onnx script to /usr/local/bin\n",
            "Installing convert-onnx-to-caffe2 script to /usr/local/bin\n",
            "Installing torchrun script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Pillow==7.1.2\n",
            "Best match: Pillow 7.1.2\n",
            "Adding Pillow 7.1.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for opencv-python==4.1.2.30\n",
            "Best match: opencv-python 4.1.2.30\n",
            "Adding opencv-python 4.1.2.30 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for numpy==1.20.3\n",
            "Best match: numpy 1.20.3\n",
            "Adding numpy 1.20.3 to easy-install.pth file\n",
            "Installing f2py script to /usr/local/bin\n",
            "Installing f2py3 script to /usr/local/bin\n",
            "Installing f2py3.7 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for gfpgan==1.3.2\n",
            "Best match: gfpgan 1.3.2\n",
            "Adding gfpgan 1.3.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for facexlib==0.2.3\n",
            "Best match: facexlib 0.2.3\n",
            "Adding facexlib 0.2.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for basicsr==1.3.5\n",
            "Best match: basicsr 1.3.5\n",
            "Adding basicsr 1.3.5 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for requests==2.23.0\n",
            "Best match: requests 2.23.0\n",
            "Adding requests 2.23.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for typing-extensions==4.2.0\n",
            "Best match: typing-extensions 4.2.0\n",
            "Adding typing-extensions 4.2.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for lmdb==0.99\n",
            "Best match: lmdb 0.99\n",
            "Adding lmdb 0.99 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for scipy==1.4.1\n",
            "Best match: scipy 1.4.1\n",
            "Adding scipy 1.4.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for yapf==0.32.0\n",
            "Best match: yapf 0.32.0\n",
            "Adding yapf 0.32.0 to easy-install.pth file\n",
            "Installing yapf script to /usr/local/bin\n",
            "Installing yapf-diff script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tb-nightly==2.10.0a20220604\n",
            "Best match: tb-nightly 2.10.0a20220604\n",
            "Adding tb-nightly 2.10.0a20220604 to easy-install.pth file\n",
            "Installing tensorboard script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for PyYAML==3.13\n",
            "Best match: PyYAML 3.13\n",
            "Adding PyYAML 3.13 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for numba==0.51.2\n",
            "Best match: numba 0.51.2\n",
            "Adding numba 0.51.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for filterpy==1.4.5\n",
            "Best match: filterpy 1.4.5\n",
            "Adding filterpy 1.4.5 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for addict==2.4.0\n",
            "Best match: addict 2.4.0\n",
            "Adding addict 2.4.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for scikit-image==0.18.3\n",
            "Best match: scikit-image 0.18.3\n",
            "Adding scikit-image 0.18.3 to easy-install.pth file\n",
            "Installing skivi script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for future==0.16.0\n",
            "Best match: future 0.16.0\n",
            "Adding future 0.16.0 to easy-install.pth file\n",
            "Installing futurize script to /usr/local/bin\n",
            "Installing pasteurize script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for idna==2.10\n",
            "Best match: idna 2.10\n",
            "Adding idna 2.10 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for chardet==3.0.4\n",
            "Best match: chardet 3.0.4\n",
            "Adding chardet 3.0.4 to easy-install.pth file\n",
            "Installing chardetect script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for certifi==2022.5.18.1\n",
            "Best match: certifi 2022.5.18.1\n",
            "Adding certifi 2022.5.18.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for urllib3==1.24.3\n",
            "Best match: urllib3 1.24.3\n",
            "Adding urllib3 1.24.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for wheel==0.37.1\n",
            "Best match: wheel 0.37.1\n",
            "Adding wheel 0.37.1 to easy-install.pth file\n",
            "Installing wheel script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tensorboard-plugin-wit==1.8.1\n",
            "Best match: tensorboard-plugin-wit 1.8.1\n",
            "Adding tensorboard-plugin-wit 1.8.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for grpcio==1.46.3\n",
            "Best match: grpcio 1.46.3\n",
            "Adding grpcio 1.46.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Werkzeug==1.0.1\n",
            "Best match: Werkzeug 1.0.1\n",
            "Adding Werkzeug 1.0.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for google-auth==1.35.0\n",
            "Best match: google-auth 1.35.0\n",
            "Adding google-auth 1.35.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for setuptools==57.4.0\n",
            "Best match: setuptools 57.4.0\n",
            "Adding setuptools 57.4.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for protobuf==3.17.3\n",
            "Best match: protobuf 3.17.3\n",
            "Adding protobuf 3.17.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for google-auth-oauthlib==0.4.6\n",
            "Best match: google-auth-oauthlib 0.4.6\n",
            "Adding google-auth-oauthlib 0.4.6 to easy-install.pth file\n",
            "Installing google-oauthlib-tool script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for absl-py==1.0.0\n",
            "Best match: absl-py 1.0.0\n",
            "Adding absl-py 1.0.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Markdown==3.3.7\n",
            "Best match: Markdown 3.3.7\n",
            "Adding Markdown 3.3.7 to easy-install.pth file\n",
            "Installing markdown_py script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tensorboard-data-server==0.6.1\n",
            "Best match: tensorboard-data-server 0.6.1\n",
            "Adding tensorboard-data-server 0.6.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for llvmlite==0.34.0\n",
            "Best match: llvmlite 0.34.0\n",
            "Adding llvmlite 0.34.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for matplotlib==3.2.2\n",
            "Best match: matplotlib 3.2.2\n",
            "Adding matplotlib 3.2.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for PyWavelets==1.3.0\n",
            "Best match: PyWavelets 1.3.0\n",
            "Adding PyWavelets 1.3.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for imageio==2.4.1\n",
            "Best match: imageio 2.4.1\n",
            "Adding imageio 2.4.1 to easy-install.pth file\n",
            "Installing imageio_download_bin script to /usr/local/bin\n",
            "Installing imageio_remove_bin script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tifffile==2021.11.2\n",
            "Best match: tifffile 2021.11.2\n",
            "Adding tifffile 2021.11.2 to easy-install.pth file\n",
            "Installing lsm2bin script to /usr/local/bin\n",
            "Installing tiff2fsspec script to /usr/local/bin\n",
            "Installing tiffcomment script to /usr/local/bin\n",
            "Installing tifffile script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for networkx==2.6.3\n",
            "Best match: networkx 2.6.3\n",
            "Adding networkx 2.6.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for six==1.15.0\n",
            "Best match: six 1.15.0\n",
            "Adding six 1.15.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for cachetools==4.2.4\n",
            "Best match: cachetools 4.2.4\n",
            "Adding cachetools 4.2.4 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for pyasn1-modules==0.2.8\n",
            "Best match: pyasn1-modules 0.2.8\n",
            "Adding pyasn1-modules 0.2.8 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for rsa==4.8\n",
            "Best match: rsa 4.8\n",
            "Adding rsa 4.8 to easy-install.pth file\n",
            "Installing pyrsa-decrypt script to /usr/local/bin\n",
            "Installing pyrsa-encrypt script to /usr/local/bin\n",
            "Installing pyrsa-keygen script to /usr/local/bin\n",
            "Installing pyrsa-priv2pub script to /usr/local/bin\n",
            "Installing pyrsa-sign script to /usr/local/bin\n",
            "Installing pyrsa-verify script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for requests-oauthlib==1.3.1\n",
            "Best match: requests-oauthlib 1.3.1\n",
            "Adding requests-oauthlib 1.3.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for importlib-metadata==4.11.4\n",
            "Best match: importlib-metadata 4.11.4\n",
            "Adding importlib-metadata 4.11.4 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for cycler==0.11.0\n",
            "Best match: cycler 0.11.0\n",
            "Adding cycler 0.11.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for kiwisolver==1.4.2\n",
            "Best match: kiwisolver 1.4.2\n",
            "Adding kiwisolver 1.4.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for pyparsing==3.0.9\n",
            "Best match: pyparsing 3.0.9\n",
            "Adding pyparsing 3.0.9 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for python-dateutil==2.8.2\n",
            "Best match: python-dateutil 2.8.2\n",
            "Adding python-dateutil 2.8.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for pyasn1==0.4.8\n",
            "Best match: pyasn1 0.4.8\n",
            "Adding pyasn1 0.4.8 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for oauthlib==3.2.0\n",
            "Best match: oauthlib 3.2.0\n",
            "Adding oauthlib 3.2.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for zipp==3.8.0\n",
            "Best match: zipp 3.8.0\n",
            "Adding zipp 3.8.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Finished processing dependencies for realesrgan==0.2.2.5\n"
          ]
        }
      ],
      "source": [
        "!pip install basicsr\n",
        "!pip install -r requirements.txt\n",
        "!python setup.py develop\n",
        "from basicsr.archs.rrdbnet_arch import RRDBNet\n",
        "from realesrgan import RealESRGANer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_kFG2_4OjND",
        "outputId": "ee84f573-8980-49fa-e56e-0db3798030c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DIV2K_train_HR_sub/100_slc0_org.npy\n",
            "DIV2K_train_HR_sub/100_slc10_org.npy\n",
            "DIV2K_train_HR_sub/100_slc11_org.npy\n",
            "DIV2K_train_HR_sub/100_slc12_org.npy\n",
            "DIV2K_train_HR_sub/100_slc13_org.npy\n",
            "DIV2K_train_HR_sub/100_slc14_org.npy\n",
            "DIV2K_train_HR_sub/100_slc15_org.npy\n",
            "DIV2K_train_HR_sub/100_slc16_org.npy\n",
            "DIV2K_train_HR_sub/100_slc17_org.npy\n",
            "DIV2K_train_HR_sub/100_slc18_org.npy\n",
            "DIV2K_train_HR_sub/100_slc19_org.npy\n",
            "DIV2K_train_HR_sub/100_slc1_org.npy\n",
            "DIV2K_train_HR_sub/100_slc2_org.npy\n",
            "DIV2K_train_HR_sub/100_slc3_org.npy\n",
            "DIV2K_train_HR_sub/100_slc4_org.npy\n",
            "DIV2K_train_HR_sub/100_slc5_org.npy\n",
            "DIV2K_train_HR_sub/100_slc6_org.npy\n",
            "DIV2K_train_HR_sub/100_slc7_org.npy\n",
            "DIV2K_train_HR_sub/100_slc8_org.npy\n",
            "DIV2K_train_HR_sub/100_slc9_org.npy\n",
            "DIV2K_train_HR_sub/101_slc0_org.npy\n",
            "DIV2K_train_HR_sub/101_slc10_org.npy\n",
            "DIV2K_train_HR_sub/101_slc11_org.npy\n",
            "DIV2K_train_HR_sub/101_slc12_org.npy\n",
            "DIV2K_train_HR_sub/101_slc13_org.npy\n",
            "DIV2K_train_HR_sub/101_slc14_org.npy\n",
            "DIV2K_train_HR_sub/101_slc15_org.npy\n",
            "DIV2K_train_HR_sub/101_slc16_org.npy\n",
            "DIV2K_train_HR_sub/101_slc17_org.npy\n",
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            "DIV2K_train_HR_sub/9_slc13_org.npy\n",
            "DIV2K_train_HR_sub/9_slc14_org.npy\n",
            "DIV2K_train_HR_sub/9_slc15_org.npy\n",
            "DIV2K_train_HR_sub/9_slc16_org.npy\n",
            "DIV2K_train_HR_sub/9_slc17_org.npy\n",
            "DIV2K_train_HR_sub/9_slc18_org.npy\n",
            "DIV2K_train_HR_sub/9_slc19_org.npy\n",
            "DIV2K_train_HR_sub/9_slc1_org.npy\n",
            "DIV2K_train_HR_sub/9_slc2_org.npy\n",
            "DIV2K_train_HR_sub/9_slc3_org.npy\n",
            "DIV2K_train_HR_sub/9_slc4_org.npy\n",
            "DIV2K_train_HR_sub/9_slc5_org.npy\n",
            "DIV2K_train_HR_sub/9_slc6_org.npy\n",
            "DIV2K_train_HR_sub/9_slc7_org.npy\n",
            "DIV2K_train_HR_sub/9_slc8_org.npy\n",
            "DIV2K_train_HR_sub/9_slc9_org.npy\n"
          ]
        }
      ],
      "source": [
        "!python scripts/generate_meta_info.py --input datasets/DIV2K/DIV2K_train_HR_sub --root datasets/DIV2K --meta_info datasets/DIV2K/meta_info/meta_info_DF2Kmultiscale.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7Eo1Y2Oem8l",
        "outputId": "79f6c0f6-7213-4c87-b13d-ee6b0fc59db5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-17 04:31:10--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\n",
            "Resolving github.com (github.com)... 140.82.121.3\n",
            "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/387326890/08f0e941-ebb7-48f0-9d6a-73e87b710e7e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211117%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211117T043110Z&X-Amz-Expires=300&X-Amz-Signature=9c93b3efea4d32d2247d0af2ecd6789a1c2bd2b3c7bd81800aaa9bb538383d5d&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=387326890&response-content-disposition=attachment%3B%20filename%3DRealESRGAN_x4plus.pth&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-11-17 04:31:10--  https://github-releases.githubusercontent.com/387326890/08f0e941-ebb7-48f0-9d6a-73e87b710e7e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211117%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211117T043110Z&X-Amz-Expires=300&X-Amz-Signature=9c93b3efea4d32d2247d0af2ecd6789a1c2bd2b3c7bd81800aaa9bb538383d5d&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=387326890&response-content-disposition=attachment%3B%20filename%3DRealESRGAN_x4plus.pth&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 67040989 (64M) [application/octet-stream]\n",
            "Saving to: ‘experiments/pretrained_models/RealESRGAN_x4plus.pth’\n",
            "\n",
            "RealESRGAN_x4plus.p 100%[===================>]  63.93M  34.1MB/s    in 1.9s    \n",
            "\n",
            "2021-11-17 04:31:12 (34.1 MB/s) - ‘experiments/pretrained_models/RealESRGAN_x4plus.pth’ saved [67040989/67040989]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#save the weights\n",
        "!wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBPynt4ku0eP",
        "outputId": "e340a572-d634-4ab1-b274-f13b67e692db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-17 04:31:14--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth\n",
            "Resolving github.com (github.com)... 140.82.121.4\n",
            "Connecting to github.com (github.com)|140.82.121.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/387326890/65cdf89f-e159-4ad1-abe7-4bb7400b8077?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211117%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211117T043114Z&X-Amz-Expires=300&X-Amz-Signature=23122afebd798c8c289477a0ca8e5893e0c1b54c4df7bb40f44f6bafa6eb4344&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=387326890&response-content-disposition=attachment%3B%20filename%3DRealESRNet_x4plus.pth&response-content-type=application%2Foctet-stream [following]\n",
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            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 67040989 (64M) [application/octet-stream]\n",
            "Saving to: ‘experiments/pretrained_models/RealESRNet_x4plus.pth’\n",
            "\n",
            "RealESRNet_x4plus.p 100%[===================>]  63.93M  45.1MB/s    in 1.4s    \n",
            "\n",
            "2021-11-17 04:31:18 (45.1 MB/s) - ‘experiments/pretrained_models/RealESRNet_x4plus.pth’ saved [67040989/67040989]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth -P experiments/pretrained_models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5cmVs-IvYnP",
        "outputId": "b6dee699-3c1d-4197-ecf1-78e63d9d0d8f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-17 04:31:22--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth\n",
            "Resolving github.com (github.com)... 140.82.121.3\n",
            "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/387326890/aa6a016d-5b71-4376-a19d-0ba389fe2dbe?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211117%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211117T043122Z&X-Amz-Expires=300&X-Amz-Signature=5195df80d22b9682a3f5d5cdcae483307f33a4a35949938fa1ee15203c5bccf3&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=387326890&response-content-disposition=attachment%3B%20filename%3DRealESRGAN_x4plus_netD.pth&response-content-type=application%2Foctet-stream [following]\n",
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            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.110.154, 185.199.109.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 17587089 (17M) [application/octet-stream]\n",
            "Saving to: ‘experiments/pretrained_models/RealESRGAN_x4plus_netD.pth’\n",
            "\n",
            "RealESRGAN_x4plus_n 100%[===================>]  16.77M  38.8MB/s    in 0.4s    \n",
            "\n",
            "2021-11-17 04:31:23 (38.8 MB/s) - ‘experiments/pretrained_models/RealESRGAN_x4plus_netD.pth’ saved [17587089/17587089]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqYBH5vcSlYF",
        "outputId": "8b246513-e72c-413a-8c7d-16c3a4db487c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Disable distributed.\n",
            "Set pretrain_network_g to /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_g_245000.pth\n",
            "Set pretrain_network_d to /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_d_245000.pth\n",
            "Set param_key_g to params\n",
            "2022-06-03 14:12:58,675 INFO: \n",
            "                ____                _       _____  ____\n",
            "               / __ ) ____ _ _____ (_)_____/ ___/ / __ \\\n",
            "              / __  |/ __ `// ___// // ___/\\__ \\ / /_/ /\n",
            "             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/\n",
            "            /_____/ \\__,_//____//_/ \\___//____//_/ |_|\n",
            "     ______                   __   __                 __      __\n",
            "    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /\n",
            "   / / __ / __ \\ / __ \\ / __  /  / /   / / / // ___// //_/  / /\n",
            "  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/\n",
            "  \\____/ \\____/ \\____/ \\____/  /_____/\\____/ \\___//_/|_|  (_)\n",
            "    \n",
            "Version Information: \n",
            "\tBasicSR: 1.3.5\n",
            "\tPyTorch: 1.11.0+cu113\n",
            "\tTorchVision: 0.12.0+cu113\n",
            "2022-06-03 14:12:58,675 INFO: \n",
            "  name: train_RealESRGAN_in_ou_1_fft_new\n",
            "  model_type: RealESRGANModel\n",
            "  scale: 4\n",
            "  num_gpu: 1\n",
            "  manual_seed: 0\n",
            "  l1_gt_usm: True\n",
            "  percep_gt_usm: True\n",
            "  gan_gt_usm: False\n",
            "  resize_prob: [0.2, 0.7, 0.1]\n",
            "  resize_range: [0.15, 1.5]\n",
            "  gaussian_noise_prob: 0.5\n",
            "  noise_range: [1, 30]\n",
            "  poisson_scale_range: [0.05, 3]\n",
            "  gray_noise_prob: 0.4\n",
            "  jpeg_range: [30, 95]\n",
            "  second_blur_prob: 0.8\n",
            "  resize_prob2: [0.3, 0.4, 0.3]\n",
            "  resize_range2: [0.3, 1.2]\n",
            "  gaussian_noise_prob2: 0.5\n",
            "  noise_range2: [1, 25]\n",
            "  poisson_scale_range2: [0.05, 2.5]\n",
            "  gray_noise_prob2: 0.4\n",
            "  jpeg_range2: [30, 95]\n",
            "  gt_size: 256\n",
            "  queue_size: 180\n",
            "  datasets:[\n",
            "    train:[\n",
            "      name: DIV2K\n",
            "      type: RealESRGANDataset\n",
            "      dataroot_gt: datasets/DIV2K\n",
            "      meta_info: datasets/DIV2K/meta_info/meta_info_DF2Kmultiscale.txt\n",
            "      io_backend:[\n",
            "        type: disk\n",
            "      ]\n",
            "      blur_kernel_size: 21\n",
            "      kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']\n",
            "      kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]\n",
            "      sinc_prob: 0.1\n",
            "      blur_sigma: [0.2, 3]\n",
            "      betag_range: [0.5, 4]\n",
            "      betap_range: [1, 2]\n",
            "      blur_kernel_size2: 21\n",
            "      kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']\n",
            "      kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]\n",
            "      sinc_prob2: 0.1\n",
            "      blur_sigma2: [0.2, 1.5]\n",
            "      betag_range2: [0.5, 4]\n",
            "      betap_range2: [1, 2]\n",
            "      final_sinc_prob: 0.8\n",
            "      gt_size: 256\n",
            "      use_hflip: True\n",
            "      use_rot: False\n",
            "      use_shuffle: True\n",
            "      num_worker_per_gpu: 2\n",
            "      batch_size_per_gpu: 1\n",
            "      dataset_enlarge_ratio: 1\n",
            "      prefetch_mode: None\n",
            "      phase: train\n",
            "      scale: 4\n",
            "    ]\n",
            "  ]\n",
            "  network_g:[\n",
            "    type: RRDBNet\n",
            "    num_in_ch: 1\n",
            "    num_out_ch: 1\n",
            "    num_feat: 64\n",
            "    num_block: 23\n",
            "    num_grow_ch: 32\n",
            "    scale: 4\n",
            "  ]\n",
            "  network_d:[\n",
            "    type: UNetDiscriminatorSN\n",
            "    num_in_ch: 1\n",
            "    num_feat: 64\n",
            "    skip_connection: True\n",
            "  ]\n",
            "  path:[\n",
            "    param_key_g: params\n",
            "    strict_load_g: False\n",
            "    resume_state: experiments/train_RealESRGAN_in_ou_1_fft_new/training_states/245000.state\n",
            "    experiments_root: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new\n",
            "    models: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models\n",
            "    training_states: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/training_states\n",
            "    log: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new\n",
            "    visualization: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/visualization\n",
            "    pretrain_network_g: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_g_245000.pth\n",
            "    pretrain_network_d: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_d_245000.pth\n",
            "  ]\n",
            "  train:[\n",
            "    ema_decay: 0.999\n",
            "    optim_g:[\n",
            "      type: Adam\n",
            "      lr: 0.0001\n",
            "      weight_decay: 0\n",
            "      betas: [0.9, 0.99]\n",
            "    ]\n",
            "    optim_d:[\n",
            "      type: Adam\n",
            "      lr: 0.0001\n",
            "      weight_decay: 0\n",
            "      betas: [0.9, 0.99]\n",
            "    ]\n",
            "    scheduler:[\n",
            "      type: MultiStepLR\n",
            "      milestones: [400000]\n",
            "      gamma: 0.5\n",
            "    ]\n",
            "    total_iter: 250000\n",
            "    warmup_iter: -1\n",
            "    pixel_opt:[\n",
            "      type: L1Loss\n",
            "      loss_weight: 1.0\n",
            "      reduction: mean\n",
            "    ]\n",
            "    perceptual_opt:[\n",
            "      type: PerceptualLoss\n",
            "      layer_weights:[\n",
            "        conv1_2: 0.1\n",
            "        conv2_2: 0.1\n",
            "        conv3_4: 1\n",
            "        conv4_4: 1\n",
            "        conv5_4: 1\n",
            "      ]\n",
            "      vgg_type: vgg19\n",
            "      use_input_norm: True\n",
            "      perceptual_weight: 1.0\n",
            "      style_weight: 0\n",
            "      range_norm: False\n",
            "      criterion: l1\n",
            "    ]\n",
            "    gan_opt:[\n",
            "      type: GANLoss\n",
            "      gan_type: vanilla\n",
            "      real_label_val: 1.0\n",
            "      fake_label_val: 0.0\n",
            "      loss_weight: 0.1\n",
            "    ]\n",
            "    net_d_iters: 1\n",
            "    net_d_init_iters: 0\n",
            "  ]\n",
            "  logger:[\n",
            "    print_freq: 100\n",
            "    save_checkpoint_freq: 5000.0\n",
            "    use_tb_logger: True\n",
            "    wandb:[\n",
            "      project: None\n",
            "      resume_id: None\n",
            "    ]\n",
            "  ]\n",
            "  dist_params:[\n",
            "    backend: nccl\n",
            "    port: 29500\n",
            "  ]\n",
            "  dist: False\n",
            "  rank: 0\n",
            "  world_size: 1\n",
            "  auto_resume: True\n",
            "  is_train: True\n",
            "  root_path: /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN\n",
            "\n",
            "2022-06-03 14:13:03,677 INFO: Dataset [RealESRGANDataset] - DIV2K is built.\n",
            "2022-06-03 14:13:03,677 INFO: Training statistics:\n",
            "\tNumber of train images: 1548\n",
            "\tDataset enlarge ratio: 1\n",
            "\tBatch size per gpu: 1\n",
            "\tWorld size (gpu number): 1\n",
            "\tRequire iter number per epoch: 1548\n",
            "\tTotal epochs: 162; iters: 250000.\n",
            "2022-06-03 14:13:03,984 INFO: Network [RRDBNet] is created.\n",
            "2022-06-03 14:13:07,318 INFO: Network: RRDBNet, with parameters: 16,695,681\n",
            "2022-06-03 14:13:07,318 INFO: RRDBNet(\n",
            "  (conv_first): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (body): Sequential(\n",
            "    (0): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (1): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (2): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (3): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (4): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (5): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (6): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (7): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (8): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (9): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (10): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (11): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (12): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (13): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (14): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (15): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (16): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (17): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (18): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (19): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (20): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (21): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (22): RRDB(\n",
            "      (rdb1): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb2): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "      (rdb3): ResidualDenseBlock(\n",
            "        (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (conv_body): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv_up1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv_up2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv_hr): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv_last): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            ")\n",
            "2022-06-03 14:13:19,790 INFO: Loading RRDBNet model from /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_g_245000.pth, with param key: [params].\n",
            "2022-06-03 14:13:19,974 INFO: Use Exponential Moving Average with decay: 0.999\n",
            "2022-06-03 14:13:20,708 INFO: Network [RRDBNet] is created.\n",
            "2022-06-03 14:13:21,134 INFO: Loading RRDBNet model from /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_g_245000.pth, with param key: [params_ema].\n",
            "2022-06-03 14:13:21,334 INFO: Network [UNetDiscriminatorSN] is created.\n",
            "2022-06-03 14:13:21,340 INFO: Network: UNetDiscriminatorSN, with parameters: 4,375,745\n",
            "2022-06-03 14:13:21,340 INFO: UNetDiscriminatorSN(\n",
            "  (conv0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv1): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (conv2): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (conv3): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (conv4): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (conv5): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (conv6): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (conv7): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (conv8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (conv9): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            ")\n",
            "2022-06-03 14:13:22,902 INFO: Loading UNetDiscriminatorSN model from /content/drive/MyDrive/Colab Notebooks/ESRGan/Real-ESRGAN/experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_d_245000.pth, with param key: [params].\n",
            "2022-06-03 14:13:22,919 INFO: Loss [L1Loss] is created.\n",
            "Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n",
            "100% 548M/548M [00:07<00:00, 72.4MB/s]\n",
            "2022-06-03 14:13:32,557 INFO: Loss [PerceptualLoss] is created.\n",
            "2022-06-03 14:13:32,584 INFO: Loss [GANLoss] is created.\n",
            "2022-06-03 14:13:32,623 INFO: Model [RealESRGANModel] is created.\n",
            "2022-06-03 14:13:32,802 INFO: Resuming training from epoch: 13, iter: 245000.\n",
            "2022-06-03 14:13:32,909 INFO: Start training from epoch: 13, iter: 245000\n",
            "2022-06-03 14:14:26,839 INFO: [train..][epoch: 13, iter: 245,100, lr:(1.000e-04,)] [eta: 0:43:40, time (data): 0.539 (0.156)] l_g_pix: 1.5924e-02 l_g_percep: 4.6465e+00 l_g_gan: 2.3298e-01 l_d_real: 4.5408e-01 out_d_real: 1.4330e+00 l_d_fake: 2.5881e-01 out_d_fake: -2.0701e+00 \n",
            "2022-06-03 14:15:15,801 INFO: [train..][epoch: 13, iter: 245,200, lr:(1.000e-04,)] [eta: 0:40:59, time (data): 0.514 (0.142)] l_g_pix: 1.9182e-02 l_g_percep: 4.7388e+00 l_g_gan: 1.7815e-01 l_d_real: 2.2788e-01 out_d_real: 2.6611e+00 l_d_fake: 4.3106e-01 out_d_fake: -1.3499e+00 \n",
            "2022-06-03 14:16:01,303 INFO: [train..][epoch: 13, iter: 245,300, lr:(1.000e-04,)] [eta: 0:38:38, time (data): 0.455 (0.081)] l_g_pix: 2.3398e-02 l_g_percep: 5.7935e+00 l_g_gan: 2.1942e-01 l_d_real: 3.1669e-01 out_d_real: 1.9901e+00 l_d_fake: 2.4527e-01 out_d_fake: -1.9466e+00 \n",
            "2022-06-03 14:16:48,199 INFO: [train..][epoch: 13, iter: 245,400, lr:(1.000e-04,)] [eta: 0:37:20, time (data): 0.462 (0.093)] l_g_pix: 1.4808e-02 l_g_percep: 4.4290e+00 l_g_gan: 1.7725e-01 l_d_real: 5.6621e-01 out_d_real: 1.2041e+00 l_d_fake: 6.1573e-01 out_d_fake: -1.1557e+00 \n",
            "2022-06-03 14:17:34,731 INFO: [train..][epoch: 13, iter: 245,500, lr:(1.000e-04,)] [eta: 0:36:12, time (data): 0.465 (0.099)] l_g_pix: 2.8768e-02 l_g_percep: 5.0905e+00 l_g_gan: 2.1597e-01 l_d_real: 4.2088e-01 out_d_real: 1.2450e+00 l_d_fake: 3.1413e-01 out_d_fake: -1.8445e+00 \n",
            "2022-06-03 14:18:20,337 INFO: [train..][epoch: 13, iter: 245,600, lr:(1.000e-04,)] [eta: 0:35:04, time (data): 0.461 (0.095)] l_g_pix: 1.4519e-02 l_g_percep: 3.7735e+00 l_g_gan: 1.7601e-01 l_d_real: 3.5393e-01 out_d_real: 2.2116e+00 l_d_fake: 5.1369e-01 out_d_fake: -1.2461e+00 \n",
            "2022-06-03 14:19:06,698 INFO: [train..][epoch: 13, iter: 245,700, lr:(1.000e-04,)] [eta: 0:34:07, time (data): 0.466 (0.100)] l_g_pix: 1.4572e-02 l_g_percep: 3.6782e+00 l_g_gan: 1.9903e-01 l_d_real: 3.3111e-01 out_d_real: 1.9846e+00 l_d_fake: 3.5114e-01 out_d_fake: -1.6386e+00 \n",
            "2022-06-03 14:19:51,558 INFO: [train..][epoch: 13, iter: 245,800, lr:(1.000e-04,)] [eta: 0:33:05, time (data): 0.457 (0.096)] l_g_pix: 1.5715e-02 l_g_percep: 3.7130e+00 l_g_gan: 1.6944e-01 l_d_real: 2.4149e-01 out_d_real: 2.2585e+00 l_d_fake: 5.8667e-01 out_d_fake: -1.1074e+00 \n",
            "2022-06-03 14:20:38,154 INFO: [train..][epoch: 13, iter: 245,900, lr:(1.000e-04,)] [eta: 0:32:15, time (data): 0.468 (0.108)] l_g_pix: 1.5716e-02 l_g_percep: 4.7321e+00 l_g_gan: 1.7573e-01 l_d_real: 3.4348e-01 out_d_real: 1.5531e+00 l_d_fake: 3.4473e-01 out_d_fake: -1.4123e+00 \n",
            "2022-06-03 14:21:23,070 INFO: [train..][epoch: 13, iter: 246,000, lr:(1.000e-04,)] [eta: 0:31:18, time (data): 0.458 (0.098)] l_g_pix: 1.2254e-02 l_g_percep: 4.3823e+00 l_g_gan: 3.1693e-01 l_d_real: 9.4695e-01 out_d_real: -8.2990e-02 l_d_fake: 1.4059e-01 out_d_fake: -3.0279e+00 \n",
            "2022-06-03 14:22:08,636 INFO: [train..][epoch: 13, iter: 246,100, lr:(1.000e-04,)] [eta: 0:30:26, time (data): 0.458 (0.101)] l_g_pix: 1.9123e-02 l_g_percep: 4.7182e+00 l_g_gan: 1.4122e-01 l_d_real: 2.3912e-01 out_d_real: 2.1126e+00 l_d_fake: 5.4870e-01 out_d_fake: -8.6247e-01 \n",
            "2022-06-03 14:22:53,374 INFO: [train..][epoch: 13, iter: 246,200, lr:(1.000e-04,)] [eta: 0:29:33, time (data): 0.453 (0.095)] l_g_pix: 1.3623e-02 l_g_percep: 4.3241e+00 l_g_gan: 1.6660e-01 l_d_real: 3.0431e-01 out_d_real: 1.7660e+00 l_d_fake: 5.2489e-01 out_d_fake: -1.1397e+00 \n",
            "2022-06-03 14:23:38,710 INFO: [train..][epoch: 13, iter: 246,300, lr:(1.000e-04,)] [eta: 0:28:42, time (data): 0.454 (0.095)] l_g_pix: 1.7873e-02 l_g_percep: 5.3764e+00 l_g_gan: 1.9125e-01 l_d_real: 3.1994e-01 out_d_real: 1.5281e+00 l_d_fake: 3.2653e-01 out_d_fake: -1.5851e+00 \n",
            "2022-06-03 14:24:24,807 INFO: [train..][epoch: 13, iter: 246,400, lr:(1.000e-04,)] [eta: 0:27:54, time (data): 0.457 (0.099)] l_g_pix: 1.2534e-02 l_g_percep: 3.5511e+00 l_g_gan: 1.6868e-01 l_d_real: 1.8362e-01 out_d_real: 2.7391e+00 l_d_fake: 4.4273e-01 out_d_fake: -1.2437e+00 \n",
            "2022-06-03 14:25:11,125 INFO: [train..][epoch: 13, iter: 246,500, lr:(1.000e-04,)] [eta: 0:27:07, time (data): 0.466 (0.106)] l_g_pix: 2.1550e-02 l_g_percep: 5.3018e+00 l_g_gan: 1.8469e-01 l_d_real: 2.7955e-01 out_d_real: 1.9593e+00 l_d_fake: 4.0479e-01 out_d_fake: -1.4416e+00 \n",
            "2022-06-03 14:25:52,044 INFO: [train..][epoch: 14, iter: 246,600, lr:(1.000e-04,)] [eta: 0:26:09, time (data): 0.437 (0.078)] l_g_pix: 1.3927e-02 l_g_percep: 4.8003e+00 l_g_gan: 1.6446e-01 l_d_real: 6.3901e-01 out_d_real: 6.7350e-01 l_d_fake: 3.5524e-01 out_d_fake: -1.2890e+00 \n",
            "2022-06-03 14:26:29,158 INFO: [train..][epoch: 14, iter: 246,700, lr:(1.000e-04,)] [eta: 0:25:05, time (data): 0.372 (0.003)] l_g_pix: 1.7526e-02 l_g_percep: 3.7866e+00 l_g_gan: 1.4593e-01 l_d_real: 1.4157e-01 out_d_real: 2.9511e+00 l_d_fake: 6.9798e-01 out_d_fake: -7.6079e-01 \n",
            "2022-06-03 14:27:05,622 INFO: [train..][epoch: 14, iter: 246,800, lr:(1.000e-04,)] [eta: 0:24:03, time (data): 0.368 (0.003)] l_g_pix: 2.0685e-02 l_g_percep: 5.2503e+00 l_g_gan: 2.7391e-01 l_d_real: 2.6214e-01 out_d_real: 2.1297e+00 l_d_fake: 2.0683e-01 out_d_fake: -2.5314e+00 \n",
            "2022-06-03 14:27:42,113 INFO: [train..][epoch: 14, iter: 246,900, lr:(1.000e-04,)] [eta: 0:23:04, time (data): 0.365 (0.003)] l_g_pix: 1.5769e-02 l_g_percep: 4.3598e+00 l_g_gan: 1.1104e-01 l_d_real: 1.4262e-01 out_d_real: 2.7856e+00 l_d_fake: 7.6679e-01 out_d_fake: -3.4320e-01 \n",
            "2022-06-03 14:28:18,626 INFO: [train..][epoch: 14, iter: 247,000, lr:(1.000e-04,)] [eta: 0:22:07, time (data): 0.365 (0.003)] l_g_pix: 2.1137e-02 l_g_percep: 5.1225e+00 l_g_gan: 1.9049e-01 l_d_real: 2.0616e-01 out_d_real: 2.4105e+00 l_d_fake: 5.0423e-01 out_d_fake: -1.3999e+00 \n",
            "2022-06-03 14:28:55,180 INFO: [train..][epoch: 14, iter: 247,100, lr:(1.000e-04,)] [eta: 0:21:12, time (data): 0.366 (0.003)] l_g_pix: 1.6152e-02 l_g_percep: 4.7171e+00 l_g_gan: 2.6247e-01 l_d_real: 4.0110e-01 out_d_real: 1.6538e+00 l_d_fake: 3.0473e-01 out_d_fake: -2.3185e+00 \n",
            "2022-06-03 14:29:31,718 INFO: [train..][epoch: 14, iter: 247,200, lr:(1.000e-04,)] [eta: 0:20:19, time (data): 0.365 (0.003)] l_g_pix: 1.9231e-02 l_g_percep: 4.2815e+00 l_g_gan: 1.4327e-01 l_d_real: 3.1581e-01 out_d_real: 1.5462e+00 l_d_fake: 4.6863e-01 out_d_fake: -9.6346e-01 \n",
            "2022-06-03 14:30:08,217 INFO: [train..][epoch: 14, iter: 247,300, lr:(1.000e-04,)] [eta: 0:19:27, time (data): 0.365 (0.003)] l_g_pix: 1.5333e-02 l_g_percep: 4.6881e+00 l_g_gan: 1.7591e-01 l_d_real: 5.9869e-01 out_d_real: 8.8144e-01 l_d_fake: 4.0499e-01 out_d_fake: -1.3534e+00 \n",
            "2022-06-03 14:30:44,694 INFO: [train..][epoch: 14, iter: 247,400, lr:(1.000e-04,)] [eta: 0:18:36, time (data): 0.365 (0.003)] l_g_pix: 1.9718e-02 l_g_percep: 5.4581e+00 l_g_gan: 1.8881e-01 l_d_real: 3.4414e-01 out_d_real: 1.8344e+00 l_d_fake: 3.5584e-01 out_d_fake: -1.5302e+00 \n",
            "2022-06-03 14:31:21,891 INFO: [train..][epoch: 14, iter: 247,500, lr:(1.000e-04,)] [eta: 0:17:48, time (data): 0.373 (0.003)] l_g_pix: 1.5340e-02 l_g_percep: 4.7547e+00 l_g_gan: 2.1317e-01 l_d_real: 3.8613e-01 out_d_real: 1.3659e+00 l_d_fake: 2.5429e-01 out_d_fake: -1.8769e+00 \n",
            "2022-06-03 14:31:58,462 INFO: [train..][epoch: 14, iter: 247,600, lr:(1.000e-04,)] [eta: 0:16:59, time (data): 0.369 (0.003)] l_g_pix: 1.3429e-02 l_g_percep: 3.3919e+00 l_g_gan: 1.8275e-01 l_d_real: 2.2913e-01 out_d_real: 2.2124e+00 l_d_fake: 4.7144e-01 out_d_fake: -1.3547e+00 \n",
            "2022-06-03 14:32:35,793 INFO: [train..][epoch: 14, iter: 247,700, lr:(1.000e-04,)] [eta: 0:16:12, time (data): 0.373 (0.003)] l_g_pix: 1.7105e-02 l_g_percep: 4.6875e+00 l_g_gan: 1.6775e-01 l_d_real: 2.9628e-01 out_d_real: 1.9312e+00 l_d_fake: 5.3629e-01 out_d_fake: -1.1408e+00 \n",
            "2022-06-03 14:33:12,539 INFO: [train..][epoch: 14, iter: 247,800, lr:(1.000e-04,)] [eta: 0:15:26, time (data): 0.370 (0.003)] l_g_pix: 1.3145e-02 l_g_percep: 3.5695e+00 l_g_gan: 3.4080e-01 l_d_real: 2.9390e-01 out_d_real: 1.8298e+00 l_d_fake: 1.0908e-01 out_d_fake: -3.2973e+00 \n",
            "2022-06-03 14:33:49,263 INFO: [train..][epoch: 14, iter: 247,900, lr:(1.000e-04,)] [eta: 0:14:40, time (data): 0.367 (0.003)] l_g_pix: 1.4786e-02 l_g_percep: 3.8773e+00 l_g_gan: 2.4250e-01 l_d_real: 5.6500e-01 out_d_real: 1.0286e+00 l_d_fake: 3.3970e-01 out_d_fake: -2.0849e+00 \n",
            "2022-06-03 14:34:25,946 INFO: [train..][epoch: 14, iter: 248,000, lr:(1.000e-04,)] [eta: 0:13:54, time (data): 0.367 (0.003)] l_g_pix: 1.5738e-02 l_g_percep: 3.7638e+00 l_g_gan: 2.1016e-01 l_d_real: 3.4671e-01 out_d_real: 1.7387e+00 l_d_fake: 3.6595e-01 out_d_fake: -1.7351e+00 \n",
            "2022-06-03 14:35:02,711 INFO: [train..][epoch: 15, iter: 248,100, lr:(1.000e-04,)] [eta: 0:13:09, time (data): 0.368 (0.005)] l_g_pix: 2.2266e-02 l_g_percep: 4.8800e+00 l_g_gan: 2.0489e-01 l_d_real: 2.8322e-01 out_d_real: 2.2299e+00 l_d_fake: 3.5925e-01 out_d_fake: -1.6867e+00 \n",
            "2022-06-03 14:35:39,318 INFO: [train..][epoch: 15, iter: 248,200, lr:(1.000e-04,)] [eta: 0:12:25, time (data): 0.367 (0.004)] l_g_pix: 1.6871e-02 l_g_percep: 4.1621e+00 l_g_gan: 1.4467e-01 l_d_real: 5.2941e-01 out_d_real: 8.5259e-01 l_d_fake: 5.3716e-01 out_d_fake: -9.0897e-01 \n",
            "2022-06-03 14:36:16,585 INFO: [train..][epoch: 15, iter: 248,300, lr:(1.000e-04,)] [eta: 0:11:41, time (data): 0.374 (0.003)] l_g_pix: 1.8036e-02 l_g_percep: 4.1698e+00 l_g_gan: 1.8750e-01 l_d_real: 3.6236e-01 out_d_real: 1.5765e+00 l_d_fake: 3.5103e-01 out_d_fake: -1.5230e+00 \n",
            "2022-06-03 14:36:53,350 INFO: [train..][epoch: 15, iter: 248,400, lr:(1.000e-04,)] [eta: 0:10:58, time (data): 0.370 (0.003)] l_g_pix: 1.6556e-02 l_g_percep: 4.9629e+00 l_g_gan: 1.3701e-01 l_d_real: 2.1899e-01 out_d_real: 2.4771e+00 l_d_fake: 6.4943e-01 out_d_fake: -7.2003e-01 \n",
            "2022-06-03 14:37:30,077 INFO: [train..][epoch: 15, iter: 248,500, lr:(1.000e-04,)] [eta: 0:10:15, time (data): 0.367 (0.003)] l_g_pix: 1.5866e-02 l_g_percep: 4.8345e+00 l_g_gan: 1.7656e-01 l_d_real: 3.3085e-01 out_d_real: 1.7640e+00 l_d_fake: 3.9359e-01 out_d_fake: -1.3713e+00 \n",
            "2022-06-03 14:38:06,854 INFO: [train..][epoch: 15, iter: 248,600, lr:(1.000e-04,)] [eta: 0:09:32, time (data): 0.367 (0.003)] l_g_pix: 2.0486e-02 l_g_percep: 4.8819e+00 l_g_gan: 2.7265e-01 l_d_real: 4.2648e-01 out_d_real: 1.4329e+00 l_d_fake: 2.0279e-01 out_d_fake: -2.5216e+00 \n",
            "2022-06-03 14:38:43,406 INFO: [train..][epoch: 15, iter: 248,700, lr:(1.000e-04,)] [eta: 0:08:50, time (data): 0.366 (0.003)] l_g_pix: 1.6960e-02 l_g_percep: 4.8740e+00 l_g_gan: 2.1149e-01 l_d_real: 4.2125e-01 out_d_real: 1.4526e+00 l_d_fake: 3.3430e-01 out_d_fake: -1.7792e+00 \n",
            "2022-06-03 14:39:20,043 INFO: [train..][epoch: 15, iter: 248,800, lr:(1.000e-04,)] [eta: 0:08:08, time (data): 0.366 (0.003)] l_g_pix: 1.5333e-02 l_g_percep: 4.4196e+00 l_g_gan: 1.4111e-01 l_d_real: 3.8662e-01 out_d_real: 1.6401e+00 l_d_fake: 6.2524e-01 out_d_fake: -7.8576e-01 \n",
            "2022-06-03 14:39:56,464 INFO: [train..][epoch: 15, iter: 248,900, lr:(1.000e-04,)] [eta: 0:07:26, time (data): 0.364 (0.003)] l_g_pix: 1.6217e-02 l_g_percep: 4.4685e+00 l_g_gan: 1.8651e-01 l_d_real: 3.0948e-01 out_d_real: 1.8425e+00 l_d_fake: 3.6209e-01 out_d_fake: -1.5015e+00 \n",
            "2022-06-03 14:40:33,088 INFO: [train..][epoch: 15, iter: 249,000, lr:(1.000e-04,)] [eta: 0:06:44, time (data): 0.365 (0.003)] l_g_pix: 1.9874e-02 l_g_percep: 4.8372e+00 l_g_gan: 2.5499e-01 l_d_real: 4.0607e-01 out_d_real: 1.3137e+00 l_d_fake: 2.7420e-01 out_d_fake: -2.2749e+00 \n",
            "2022-06-03 14:41:10,296 INFO: [train..][epoch: 15, iter: 249,100, lr:(1.000e-04,)] [eta: 0:06:03, time (data): 0.374 (0.003)] l_g_pix: 1.7539e-02 l_g_percep: 4.5652e+00 l_g_gan: 1.4964e-01 l_d_real: 2.0248e-01 out_d_real: 2.5072e+00 l_d_fake: 5.0925e-01 out_d_fake: -9.8680e-01 \n",
            "2022-06-03 14:41:46,781 INFO: [train..][epoch: 15, iter: 249,200, lr:(1.000e-04,)] [eta: 0:05:22, time (data): 0.369 (0.003)] l_g_pix: 1.4967e-02 l_g_percep: 5.4749e+00 l_g_gan: 2.1436e-01 l_d_real: 9.2860e-01 out_d_real: 9.5635e-02 l_d_fake: 2.8260e-01 out_d_fake: -1.8601e+00 \n",
            "2022-06-03 14:42:23,220 INFO: [train..][epoch: 15, iter: 249,300, lr:(1.000e-04,)] [eta: 0:04:41, time (data): 0.364 (0.003)] l_g_pix: 1.8262e-02 l_g_percep: 4.1739e+00 l_g_gan: 1.9932e-01 l_d_real: 6.0459e-01 out_d_real: 7.8411e-01 l_d_fake: 3.4989e-01 out_d_fake: -1.6424e+00 \n",
            "2022-06-03 14:42:59,583 INFO: [train..][epoch: 15, iter: 249,400, lr:(1.000e-04,)] [eta: 0:04:00, time (data): 0.364 (0.003)] l_g_pix: 1.5008e-02 l_g_percep: 4.7588e+00 l_g_gan: 2.3979e-01 l_d_real: 6.2060e-01 out_d_real: 8.7386e-01 l_d_fake: 2.4214e-01 out_d_fake: -2.1549e+00 \n",
            "2022-06-03 14:43:36,043 INFO: [train..][epoch: 15, iter: 249,500, lr:(1.000e-04,)] [eta: 0:03:19, time (data): 0.365 (0.003)] l_g_pix: 1.3834e-02 l_g_percep: 5.3110e+00 l_g_gan: 2.6011e-01 l_d_real: 6.8856e-01 out_d_real: 9.6184e-01 l_d_fake: 2.8697e-01 out_d_fake: -2.3108e+00 \n",
            "2022-06-03 14:44:12,371 INFO: [train..][epoch: 15, iter: 249,600, lr:(1.000e-04,)] [eta: 0:02:39, time (data): 0.364 (0.003)] l_g_pix: 2.1392e-02 l_g_percep: 4.7203e+00 l_g_gan: 1.7899e-01 l_d_real: 3.3482e-01 out_d_real: 1.6123e+00 l_d_fake: 4.0796e-01 out_d_fake: -1.3808e+00 \n",
            "2022-06-03 14:44:48,859 INFO: [train..][epoch: 16, iter: 249,700, lr:(1.000e-04,)] [eta: 0:01:59, time (data): 0.366 (0.005)] l_g_pix: 1.5583e-02 l_g_percep: 4.2267e+00 l_g_gan: 1.4550e-01 l_d_real: 1.7488e-01 out_d_real: 2.7350e+00 l_d_fake: 6.0619e-01 out_d_fake: -8.4753e-01 \n",
            "2022-06-03 14:45:25,771 INFO: [train..][epoch: 16, iter: 249,800, lr:(1.000e-04,)] [eta: 0:01:19, time (data): 0.368 (0.004)] l_g_pix: 2.0148e-02 l_g_percep: 4.7508e+00 l_g_gan: 1.5997e-01 l_d_real: 1.9125e-01 out_d_real: 2.5946e+00 l_d_fake: 5.2796e-01 out_d_fake: -1.0710e+00 \n",
            "2022-06-03 14:46:02,131 INFO: [train..][epoch: 16, iter: 249,900, lr:(1.000e-04,)] [eta: 0:00:39, time (data): 0.364 (0.003)] l_g_pix: 1.8331e-02 l_g_percep: 4.5367e+00 l_g_gan: 2.5689e-01 l_d_real: 5.2641e-01 out_d_real: 9.7153e-01 l_d_fake: 1.7989e-01 out_d_fake: -2.3880e+00 \n",
            "2022-06-03 14:46:38,481 INFO: [train..][epoch: 16, iter: 250,000, lr:(1.000e-04,)] [eta: 0:00:00, time (data): 0.364 (0.003)] l_g_pix: 2.2153e-02 l_g_percep: 4.6286e+00 l_g_gan: 1.0748e-01 l_d_real: 3.3723e-01 out_d_real: 2.2102e+00 l_d_fake: 9.6357e-01 out_d_fake: -1.1046e-01 \n",
            "2022-06-03 14:46:38,481 INFO: Saving models and training states.\n",
            "2022-06-03 14:47:22,330 INFO: End of training. Time consumed: 0:33:49\n",
            "2022-06-03 14:47:22,331 INFO: Save the latest model.\n"
          ]
        }
      ],
      "source": [
        "#starting training\n",
        "!python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --auto_resume"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zl26_bEZJ3QM"
      },
      "outputs": [],
      "source": [
        "#testing \n",
        "model_path = 'experiments/train_RealESRGAN_in_ou_1_fft_new/models/net_g_250000.pth'\n",
        "\n",
        "tile = 0\n",
        "tile_pad = 10\n",
        "pre_pad = 0\n",
        "#location of the folder with test images in numpy array format\n",
        "input = 'inputs/BRATS_FFT_First'\n",
        "\n",
        "netscale = 4\n",
        "outscale = 4\n",
        "block = 23\n",
        "\n",
        "model = RRDBNet(num_in_ch=1, num_out_ch=1, num_feat=64, num_block=block, num_grow_ch=32, scale=4) \n",
        "upsampler = RealESRGANer(\n",
        "    scale=4,\n",
        "    model_path=model_path,\n",
        "    model=model,\n",
        "    tile=tile,\n",
        "    tile_pad=tile_pad,\n",
        "    pre_pad=pre_pad)\n",
        "    #half=half) \n",
        "\n",
        "\n",
        "if os.path.isfile(input):\n",
        "    paths = [input]\n",
        "else:\n",
        "    paths = sorted(glob.glob(os.path.join(input, '*')))  \n",
        " \n",
        "generated_realesrgan = []\n",
        "for idx, path in enumerate(paths):\n",
        "    imgname, extension = os.path.splitext(os.path.basename(path))\n",
        "    print('Testing', idx, imgname)  \n",
        "    img = np.load(path)\n",
        "\n",
        "\n",
        "    h, w = img.shape[0:2]\n",
        "    if max(h, w) > 1000 and netscale == 4:\n",
        "        import warnings\n",
        "        warnings.warn('The input image is large, try X2 model for better performance.')\n",
        "    if max(h, w) < 500 and netscale == 2:\n",
        "        import warnings\n",
        "        warnings.warn('The input image is small, try X4 model for better performance.')  \n",
        "\n",
        "\n",
        "    output = upsampler.enhance(img, outscale=outscale)\n",
        "   \n",
        "    generated_realesrgan.append(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting the list to numpy array containing the SR images\n",
        "generated_realesrgan = np.array(generated_realesrgan)\n",
        "print(generated_realesrgan.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4pm5Im2VTsw",
        "outputId": "0ec9f18d-7694-4f49-f5fa-02a983df3fe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7861, 256, 256)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qQdijCf8fuYp"
      },
      "outputs": [],
      "source": [
        "#load the LR and HR images\n",
        "lq_image = []\n",
        "input = 'inputs/Brats_FFT_First/'\n",
        "paths = sorted(glob.glob(os.path.join(input, '*')))  \n",
        "for path in tqdm(paths):\n",
        "    img = np.load(path)\n",
        "    lq_image.append(img)\n",
        "\n",
        "ground_truth = []\n",
        "input = 'results/Brats_FFT_Original/'\n",
        "all_files = sorted(glob.glob(os.path.join(input, '*'))) \n",
        "for path in tqdm(all_files):\n",
        "    img = np.load(path)\n",
        "    ground_truth.append(img)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lq_image = np.array(lq_image)\n",
        "ground_truth = np.array(ground_truth)"
      ],
      "metadata": {
        "id": "uJo0DVq8Jn_e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sewar\n",
        "\n",
        "from sewar.full_ref import mse, rmse, psnr, uqi, ssim, ergas, scc, rase, sam, msssim, vifp"
      ],
      "metadata": {
        "id": "dZjb5xu1yMM2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe7f2134-9c3a-4e7d-b01f-8010efff8b40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sewar\n",
            "  Downloading sewar-0.4.5.tar.gz (11 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sewar) (1.20.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sewar) (1.4.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from sewar) (7.1.2)\n",
            "Building wheels for collected packages: sewar\n",
            "  Building wheel for sewar (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sewar: filename=sewar-0.4.5-py3-none-any.whl size=10608 sha256=e99b868010fe6ce6c6437447f7a497d29dcd8c16977e1a097676f4b7612b671e\n",
            "  Stored in directory: /root/.cache/pip/wheels/7a/38/6b/d066cfcb2b1c1c9b059b9ce1bdc4803b469359214e8dad4e3e\n",
            "Successfully built sewar\n",
            "Installing collected packages: sewar\n",
            "Successfully installed sewar-0.4.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "from sklearn.metrics import mean_absolute_error as mae\n",
        "from skimage.metrics import mean_squared_error as mse\n",
        "from skimage.metrics import normalized_root_mse as nrm\n",
        "def rmsdiff(im1, im2):\n",
        "    \"\"\"Calculates the root mean square error (RSME) between two images\"\"\"\n",
        "    return math.sqrt(mse(im1, im2))\n",
        "def nrmdiff(im1, im2):\n",
        "    \"\"\"Calculates the root mean square error (RSME) between two images\"\"\"\n",
        "    return nrm(im1, im2, normalization='mean')\n",
        "def mean_abs_err(im1, im2):\n",
        "    return mae(im1, im2)"
      ],
      "metadata": {
        "id": "9HfbvqLdnjM5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IBdig5JDQIyb"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "from tqdm import tqdm\n",
        "\n",
        "from skimage.metrics import structural_similarity as ssim\n",
        "from skimage.metrics import peak_signal_noise_ratio as psnr\n",
        "from skimage.transform import rescale\n",
        "import matplotlib\n",
        "#slce_ds = rescale(slce, scale=(0.25, 0.25)) #skimage rescale\n",
        "\n",
        "dim = (256, 256)\n",
        "\n",
        "array_psnr = []\n",
        "array_ssim = []\n",
        "array_nrm = []\n",
        "array_mae = []\n",
        "array_vif = []\n",
        "\n",
        "array_psnr_linear = []\n",
        "array_ssim_linear = []\n",
        "array_nrm_linear = []\n",
        "array_mae_linear = []\n",
        "array_vif_linear = []\n",
        "\n",
        "array_psnr_cubic = []\n",
        "array_ssim_cubic = []\n",
        "array_nrm_cubic = []\n",
        "array_mae_cubic = []\n",
        "array_vif_cubic = []\n",
        "k = 0\n",
        "\n",
        "for i in tqdm(range(len(ground_truth))):\n",
        "\n",
        "  data_orig = ground_truth[i][:,:,0]\n",
        "  data_lr = lq_image[i][:,:,0]\n",
        "  #The order of interpolation. The order has to be in the range 0-5:\n",
        "  #       - 0: Nearest-neighbor\n",
        "  #       - 1: Bi-linear (default)\n",
        "  #       - 2: Bi-quadratic\n",
        "  #       - 3: Bi-cubic\n",
        "  #       - 4: Bi-quartic\n",
        "  #       - 5: Bi-quintic\n",
        "  x_temp_linear = rescale(data_lr, scale=(4, 4), order = 1)\n",
        "  x_temp_linear = x_temp_linear.astype(np.float32)\n",
        "  x_temp_linear = (x_temp_linear-np.min(x_temp_linear))/(np.max(x_temp_linear)-np.min(x_temp_linear))\n",
        "\n",
        "\n",
        "  val_psnr_linear = psnr(data_orig, x_temp_linear, data_range = x_temp_linear.max() - x_temp_linear.min())\n",
        "  val_ssim_linear = ssim(data_orig, x_temp_linear, data_range = x_temp_linear.max() - x_temp_linear.min())\n",
        "\n",
        "  val_mae_linear = mean_abs_err(data_orig, x_temp_linear)\n",
        "  val_nrm_linear = nrmdiff(data_orig, x_temp_linear)\n",
        "  val_vif_linear = vifp(data_orig, x_temp_linear)\n",
        "\n",
        "  x_temp_cubic = rescale(data_lr, scale=(4, 4), order = 3)\n",
        "  x_temp_cubic = x_temp_cubic.astype(np.float32)\n",
        "  x_temp_cubic = (x_temp_cubic-np.min(x_temp_cubic))/(np.max(x_temp_cubic)-np.min(x_temp_cubic))\n",
        "\n",
        "  val_psnr_cubic = psnr(data_orig, x_temp_cubic, data_range = x_temp_cubic.max() - x_temp_cubic.min())\n",
        "  val_ssim_cubic = ssim(data_orig, x_temp_cubic, data_range = x_temp_cubic.max() - x_temp_cubic.min())\n",
        "  val_mae_cubic = mean_abs_err(data_orig, x_temp_cubic)\n",
        "  val_nrm_cubic = nrmdiff(data_orig, x_temp_cubic)\n",
        "  val_vif_cubic = vifp(data_orig, x_temp_cubic)\n",
        "\n",
        "  data_gen = generated_realesrgan[i,:,:] \n",
        "  data_gen = data_gen.astype(np.float32)\n",
        "  data_gen = (data_gen-np.min(data_gen))/(np.max(data_gen)-np.min(data_gen))\n",
        "\n",
        "  val_psnr = psnr(data_orig, data_gen, data_range = data_gen.max() - data_gen.min())\n",
        "  val_ssim = ssim(data_orig, data_gen, data_range = data_gen.max() - data_gen.min())\n",
        "  val_mae = mean_abs_err(data_orig, data_gen)\n",
        "  val_nrm = nrmdiff(data_orig, data_gen)\n",
        "  val_vif = vifp(data_orig, data_gen)\n",
        "\n",
        "  array_psnr_linear.append(val_psnr_linear)\n",
        "  array_ssim_linear.append(val_ssim_linear)\n",
        "  array_nrm_linear.append(val_nrm_linear)\n",
        "  array_mae_linear.append(val_mae_linear)\n",
        "  array_vif_linear.append(val_vif_linear)\n",
        "\n",
        "  array_psnr_cubic.append(val_psnr_cubic)\n",
        "  array_ssim_cubic.append(val_ssim_cubic)\n",
        "  array_nrm_cubic.append(val_nrm_cubic)\n",
        "  array_mae_cubic.append(val_mae_cubic)\n",
        "  array_vif_cubic.append(val_vif_cubic)\n",
        "\n",
        "  array_psnr.append(val_psnr)\n",
        "  array_ssim.append(val_ssim)\n",
        "  array_nrm.append(val_nrm)\n",
        "  array_mae.append(val_mae)\n",
        "  array_vif.append(val_vif)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YXr-rqTKWDrz"
      },
      "outputs": [],
      "source": [
        "array_psnr_linear = np.array(array_psnr_linear)\n",
        "array_ssim_linear = np.array(array_ssim_linear)\n",
        "array_nrm_linear = np.array(array_nrm_linear)\n",
        "array_mae_linear = np.array(array_mae_linear)\n",
        "array_vif_linear = np.array(array_vif_linear)\n",
        "\n",
        "array_psnr_cubic = np.array(array_psnr_cubic)\n",
        "array_ssim_cubic = np.array(array_ssim_cubic)\n",
        "array_nrm_cubic = np.array(array_nrm_cubic)\n",
        "array_mae_cubic = np.array(array_mae_cubic)\n",
        "array_vif_cubic = np.array(array_vif_cubic)\n",
        "\n",
        "array_psnr = np.array(array_psnr)\n",
        "array_ssim = np.array(array_ssim)\n",
        "array_nrm = np.array(array_nrm)\n",
        "array_mae = np.array(array_mae)\n",
        "array_vif = np.array(array_vif)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#PSNR, SSIM, RMSE, MAE and VIF values after testing 7861 images from BRATS dataset\n",
        "\n",
        "print(\"The mean PSNR of model is \" + str(array_psnr.mean()))\n",
        "print(\"The mean SSIM of model is \" + str(array_ssim.mean()))\n",
        "print(\"The mean root mean square error of the model is \" + str(array_nrm.mean()))\n",
        "print(\"The mean absolute error of the model is \" + str(array_mae.mean()))\n",
        "print(\"The mean Visual Information Fidelity of the model is \" + str(array_vif.mean()))"
      ],
      "metadata": {
        "id": "rxsngmMAdOWZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "230109d0-074d-4d57-deea-baa25951cb19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The mean PSNR of model is 30.622941901565277\n",
            "The mean SSIM of model is 0.9492087749576471\n",
            "The mean root mean square error of the model is 1.7666398264507335\n",
            "The mean absolute error of the model is 0.0076687858\n",
            "The mean Visual Information Fidelity of the model is 0.7251137657932014\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Showing one of the HR images, its LR version, output from model, bilinear and bicubic interpolation\n",
        "from skimage.transform import rescale\n",
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(1,5, figsize=(15,15), constrained_layout=True)\n",
        "x = 250\n",
        "ax[0].imshow(ground_truth[x,:,:,0],cmap='gray')\n",
        "ax[1].imshow(lq_image[x,:,:,0],cmap='gray')\n",
        "ax[2].imshow(generated_realesrgan[x],cmap='gray',vmin=np.min(lq_image[x,:,:,0]), vmax= np.max(lq_image[x,:,:,0]))\n",
        "ax[3].imshow(rescale(lq_image[x,:,:,0], scale=(4, 4), order = 3),cmap='gray')\n",
        "ax[4].imshow(rescale(lq_image[x,:,:,0], scale=(4, 4), order = 1),cmap='gray')\n",
        "\n",
        "for axes in ax.flatten():\n",
        "  axes.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "vH8pUPSMRWrS",
        "outputId": "ae8f3e6c-2eb2-4a26-e579-33cd7a48efc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "real-esrgan_notebook.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}