ESRGAN_FineTune (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6BPxh_VmVVIu"
   },
   "source": [
    "# 1. Preparations\n",
    "Before start, make sure that you choose\n",
    "* Runtime Type = Python 3\n",
    "* Hardware Accelerator = GPU\n",
    "\n",
    "in the **Runtime** menu -> **Change runtime type**\n",
    "\n",
    "Then, we clone the repository, set up the envrironment, and download the pre-trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "GnpnrLfMV2jU",
    "outputId": "de22b0b3-8af5-460c-d243-71224947e27d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'Real-ESRGAN'...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: filterpy in f:\\anaconda\\lib\\site-packages (from facexlib>=0.2.0.3->-r requirements.txt (line 2)) (1.4.5)\n",
      "Requirement already satisfied: typing-extensions in f:\\anaconda\\lib\\site-packages (from torch>=1.7->-r requirements.txt (line 7)) (3.7.4.3)\n",
      "Requirement already satisfied: matplotlib in f:\\anaconda\\lib\\site-packages (from filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (3.3.4)\n",
      "Requirement already satisfied: cycler>=0.10 in f:\\anaconda\\lib\\site-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in f:\\anaconda\\lib\\site-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in f:\\anaconda\\lib\\site-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in f:\\anaconda\\lib\\site-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (2.8.1)\n",
      "Requirement already satisfied: six in f:\\anaconda\\lib\\site-packages (from cycler>=0.10->matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (1.15.0)\n",
      "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in f:\\anaconda\\lib\\site-packages (from numba->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (0.36.0)\n",
      "Requirement already satisfied: setuptools in f:\\anaconda\\lib\\site-packages (from numba->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (52.0.0.post20210125)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in f:\\anaconda\\lib\\site-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.26.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in f:\\anaconda\\lib\\site-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in f:\\anaconda\\lib\\site-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (4.0.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in f:\\anaconda\\lib\\site-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2020.12.5)\n",
      "Requirement already satisfied: networkx>=2.0 in f:\\anaconda\\lib\\site-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.5)\n",
      "Requirement already satisfied: imageio>=2.3.0 in f:\\anaconda\\lib\\site-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.9.0)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in f:\\anaconda\\lib\\site-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2021.4.8)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in f:\\anaconda\\lib\\site-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.1.1)\n",
      "Requirement already satisfied: decorator>=4.3.0 in f:\\anaconda\\lib\\site-packages (from networkx>=2.0->scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (5.0.6)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.6.1)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.35.0)\n",
      "Requirement already satisfied: absl-py>=0.4 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.14.0)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.0.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.4.6)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.8.0)\n",
      "Requirement already satisfied: wheel>=0.26 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.36.2)\n",
      "Requirement already satisfied: markdown>=2.6.8 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.3.4)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.18.0)\n",
      "Requirement already satisfied: grpcio>=1.24.3 in f:\\anaconda\\lib\\site-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.41.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in f:\\anaconda\\lib\\site-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (4.7.2)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in f:\\anaconda\\lib\\site-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (4.2.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.3.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-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)) (3.1.1)\n",
      "running develop\n",
      "running egg_info\n",
      "creating realesrgan.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",
      "writing manifest file 'realesrgan.egg-info\\SOURCES.txt'\n",
      "reading manifest file 'realesrgan.egg-info\\SOURCES.txt'\n",
      "reading manifest template 'MANIFEST.in'\n",
      "writing manifest file 'realesrgan.egg-info\\SOURCES.txt'\n",
      "running build_ext\n",
      "Creating f:\\anaconda\\lib\\site-packages\\realesrgan.egg-link (link to .)\n",
      "realesrgan 0.2.5.0 is already the active version in easy-install.pth\n",
      "\n",
      "Installed c:\\users\\aman bhansali\\downloads\\real-esrgan\n",
      "Processing dependencies for realesrgan==0.2.5.0\n",
      "Searching for tqdm==4.59.0\n",
      "Best match: tqdm 4.59.0\n",
      "Adding tqdm 4.59.0 to easy-install.pth file\n",
      "Installing tqdm-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing tqdm.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for torchvision==0.12.0\n",
      "Best match: torchvision 0.12.0\n",
      "Adding torchvision 0.12.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for torch==1.11.0\n",
      "Best match: torch 1.11.0\n",
      "Adding torch 1.11.0 to easy-install.pth file\n",
      "Installing convert-caffe2-to-onnx-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing convert-caffe2-to-onnx.exe script to F:\\Anaconda\\Scripts\n",
      "Installing convert-onnx-to-caffe2-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing convert-onnx-to-caffe2.exe script to F:\\Anaconda\\Scripts\n",
      "Installing torchrun-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing torchrun.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for Pillow==8.2.0\n",
      "Best match: Pillow 8.2.0\n",
      "Adding Pillow 8.2.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for opencv-python==4.5.3.56\n",
      "Best match: opencv-python 4.5.3.56\n",
      "Adding opencv-python 4.5.3.56 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for numpy==1.19.5\n",
      "Best match: numpy 1.19.5\n",
      "Adding numpy 1.19.5 to easy-install.pth file\n",
      "Installing f2py-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing f2py.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages\n",
      "Searching for requests==2.25.1\n",
      "Best match: requests 2.25.1\n",
      "Adding requests 2.25.1 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for typing-extensions==3.7.4.3\n",
      "Best match: typing-extensions 3.7.4.3\n",
      "Adding typing-extensions 3.7.4.3 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for lmdb==1.3.0\n",
      "Best match: lmdb 1.3.0\n",
      "Adding lmdb 1.3.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for tb-nightly==2.10.0a20220528\n",
      "Best match: tb-nightly 2.10.0a20220528\n",
      "Adding tb-nightly 2.10.0a20220528 to easy-install.pth file\n",
      "Installing tensorboard-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing tensorboard.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for yapf==0.31.0\n",
      "Best match: yapf 0.31.0\n",
      "Adding yapf 0.31.0 to easy-install.pth file\n",
      "Installing yapf-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing yapf.exe script to F:\\Anaconda\\Scripts\n",
      "Installing yapf-diff-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing yapf-diff.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for scipy==1.6.2\n",
      "Best match: scipy 1.6.2\n",
      "Adding scipy 1.6.2 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for PyYAML==5.4.1\n",
      "Best match: PyYAML 5.4.1\n",
      "Adding PyYAML 5.4.1 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for numba==0.53.1\n",
      "Best match: numba 0.53.1\n",
      "Adding numba 0.53.1 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages\n",
      "Searching for future==0.18.2\n",
      "Best match: future 0.18.2\n",
      "Adding future 0.18.2 to easy-install.pth file\n",
      "Installing futurize-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing futurize.exe script to F:\\Anaconda\\Scripts\n",
      "Installing pasteurize-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing pasteurize.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for scikit-image==0.18.1\n",
      "Best match: scikit-image 0.18.1\n",
      "Adding scikit-image 0.18.1 to easy-install.pth file\n",
      "Installing skivi-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing skivi.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for certifi==2020.12.5\n",
      "Best match: certifi 2020.12.5\n",
      "Adding certifi 2020.12.5 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages\n",
      "Searching for chardet==4.0.0\n",
      "Best match: chardet 4.0.0\n",
      "Adding chardet 4.0.0 to easy-install.pth file\n",
      "Installing chardetect-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing chardetect.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for urllib3==1.26.4\n",
      "Best match: urllib3 1.26.4\n",
      "Adding urllib3 1.26.4 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-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.py script to F:\\Anaconda\\Scripts\n",
      "Installing google-oauthlib-tool.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for setuptools==52.0.0.post20210125\n",
      "Best match: setuptools 52.0.0.post20210125\n",
      "Adding setuptools 52.0.0.post20210125 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for grpcio==1.41.0\n",
      "Best match: grpcio 1.41.0\n",
      "Adding grpcio 1.41.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for wheel==0.37.1\n",
      "Best match: wheel 0.37.1\n",
      "Processing wheel-0.37.1-py3.8.egg\n",
      "wheel 0.37.1 is already the active version in easy-install.pth\n",
      "Installing wheel-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing wheel.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\\wheel-0.37.1-py3.8.egg\n",
      "Searching for Markdown==3.3.4\n",
      "Best match: Markdown 3.3.4\n",
      "Adding Markdown 3.3.4 to easy-install.pth file\n",
      "Installing markdown_py-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing markdown_py.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages\n",
      "Searching for tensorboard-plugin-wit==1.8.0\n",
      "Best match: tensorboard-plugin-wit 1.8.0\n",
      "Adding tensorboard-plugin-wit 1.8.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for protobuf==3.18.0\n",
      "Best match: protobuf 3.18.0\n",
      "Adding protobuf 3.18.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for absl-py==0.14.0\n",
      "Best match: absl-py 0.14.0\n",
      "Adding absl-py 0.14.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for llvmlite==0.36.0\n",
      "Best match: llvmlite 0.36.0\n",
      "Adding llvmlite 0.36.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for matplotlib==3.3.4\n",
      "Best match: matplotlib 3.3.4\n",
      "Adding matplotlib 3.3.4 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for PyWavelets==1.1.1\n",
      "Best match: PyWavelets 1.1.1\n",
      "Adding PyWavelets 1.1.1 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for tifffile==2021.4.8\n",
      "Best match: tifffile 2021.4.8\n",
      "Adding tifffile 2021.4.8 to easy-install.pth file\n",
      "Installing lsm2bin-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing lsm2bin.exe script to F:\\Anaconda\\Scripts\n",
      "Installing tiff2fsspec-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing tiff2fsspec.exe script to F:\\Anaconda\\Scripts\n",
      "Installing tiffcomment-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing tiffcomment.exe script to F:\\Anaconda\\Scripts\n",
      "Installing tifffile-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing tifffile.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for imageio==2.9.0\n",
      "Best match: imageio 2.9.0\n",
      "Adding imageio 2.9.0 to easy-install.pth file\n",
      "Installing imageio_download_bin-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing imageio_download_bin.exe script to F:\\Anaconda\\Scripts\n",
      "Installing imageio_remove_bin-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing imageio_remove_bin.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for networkx==2.5\n",
      "Best match: networkx 2.5\n",
      "Adding networkx 2.5 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for cachetools==4.2.2\n",
      "Best match: cachetools 4.2.2\n",
      "Adding cachetools 4.2.2 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages\n",
      "Searching for rsa==4.7.2\n",
      "Best match: rsa 4.7.2\n",
      "Adding rsa 4.7.2 to easy-install.pth file\n",
      "Installing pyrsa-decrypt-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-decrypt.exe script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-encrypt-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-encrypt.exe script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-keygen-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-keygen.exe script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-priv2pub-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-priv2pub.exe script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-sign-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-sign.exe script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-verify-script.py script to F:\\Anaconda\\Scripts\n",
      "Installing pyrsa-verify.exe script to F:\\Anaconda\\Scripts\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages\n",
      "Searching for requests-oauthlib==1.3.0\n",
      "Best match: requests-oauthlib 1.3.0\n",
      "Adding requests-oauthlib 1.3.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for kiwisolver==1.3.1\n",
      "Best match: kiwisolver 1.3.1\n",
      "Adding kiwisolver 1.3.1 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for pyparsing==2.4.7\n",
      "Best match: pyparsing 2.4.7\n",
      "Adding pyparsing 2.4.7 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for python-dateutil==2.8.1\n",
      "Best match: python-dateutil 2.8.1\n",
      "Adding python-dateutil 2.8.1 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for cycler==0.10.0\n",
      "Best match: cycler 0.10.0\n",
      "Adding cycler 0.10.0 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Searching for decorator==5.0.6\n",
      "Best match: decorator 5.0.6\n",
      "Adding decorator 5.0.6 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-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 f:\\anaconda\\lib\\site-packages\n",
      "Searching for oauthlib==3.1.1\n",
      "Best match: oauthlib 3.1.1\n",
      "Adding oauthlib 3.1.1 to easy-install.pth file\n",
      "\n",
      "Using f:\\anaconda\\lib\\site-packages\n",
      "Finished processing dependencies for realesrgan==0.2.5.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-07-23 11:52:27--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\n",
      "Resolving github.com (github.com)... 13.234.176.102\n",
      "Connecting to github.com (github.com)|13.234.176.102|:443... connected.\n",
      "ERROR: cannot verify github.com's certificate, issued by 'CN=DigiCert TLS Hybrid ECC SHA384 2020 CA1,O=DigiCert Inc,C=US':\n",
      "  Unable to locally verify the issuer's authority.\n",
      "To connect to github.com insecurely, use `--no-check-certificate'.\n"
     ]
    }
   ],
   "source": [
    "# Clone Real-ESRGAN and enter the Real-ESRGAN\n",
    "!git clone https://github.com/xinntao/Real-ESRGAN.git\n",
    "%cd Real-ESRGAN\n",
    "# Set up the environment\n",
    "!pip install basicsr\n",
    "!pip install facexlib\n",
    "!pip install gfpgan\n",
    "!pip install -r requirements.txt\n",
    "!python setup.py develop\n",
    "# Download the pre-trained model\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": 2,
   "metadata": {
    "id": "071mNWTPotjd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\t0.75\n",
      "\t0.50"
     ]
    }
   ],
   "source": [
    "!python scripts/generate_multiscale_DF2K.py --input datasets/DF2K/DF2K_HR --output datasets/DF2K/DF2K_multiscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "9qvcwFlrptCW",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "DF2K_multiscale\\SOB_B_F-14-14134-100-029T0.png\n",
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      "DF2K_multiscale\\SOB_B_F-14-14134-100-029T2.png\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-100-029T3.png\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-100-030T0.png\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-100-030T1.png\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-100-030T2.png\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-100-030T3.png\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-100-031T0.png\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-100-031T1.png\n",
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      "DF2K_multiscale\\SOB_B_F-14-14134-100-031T3.png"
     ]
    }
   ],
   "source": [
    "! python scripts/generate_meta_info.py --input datasets/DF2K/DF2K_HR, datasets/DF2K/DF2K_multiscale --root datasets/DF2K, datasets/DF2K --meta_info datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DF2K_multiscale\\SOB_B_F-14-14134-200-001T0.png\n",
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      "DF2K_multiscale\\SOB_M_PC-14-9146-400-021T0.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-021T1.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-021T2.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-021T3.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-022T0.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-022T1.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-022T2.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-022T3.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-023T0.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-023T1.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-023T2.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-023T3.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-024T0.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-024T1.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-024T2.png\n",
      "DF2K_multiscale\\SOB_M_PC-14-9146-400-024T3.png\n",
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "conda install -c menpo wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-07-22 19:51:00--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\n",
      "Resolving github.com (github.com)... 13.234.210.38\n",
      "Connecting to github.com (github.com)|13.234.210.38|:443... connected.\n",
      "ERROR: cannot verify github.com's certificate, issued by 'CN=DigiCert TLS Hybrid ECC SHA384 2020 CA1,O=DigiCert Inc,C=US':\n",
      "  Unable to locally verify the issuer's authority.\n",
      "To connect to github.com insecurely, use `--no-check-certificate'.\n"
     ]
    }
   ],
   "source": [
    "!wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "KFZ3UrJGfoyW"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-07-22 19:51:14--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth\n",
      "Resolving github.com (github.com)... 13.234.210.38\n",
      "Connecting to github.com (github.com)|13.234.210.38|:443... connected.\n",
      "ERROR: cannot verify github.com's certificate, issued by 'CN=DigiCert TLS Hybrid ECC SHA384 2020 CA1,O=DigiCert Inc,C=US':\n",
      "  Unable to locally verify the issuer's authority.\n",
      "To connect to github.com insecurely, use `--no-check-certificate'.\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": 6,
   "metadata": {
    "id": "AP9SB8hVX5Js"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-05-29 11:17:54--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth\n",
      "Resolving github.com (github.com)... 13.234.176.102\n",
      "Connecting to github.com (github.com)|13.234.176.102|:443... connected.\n",
      "ERROR: cannot verify github.com's certificate, issued by 'CN=DigiCert TLS Hybrid ECC SHA384 2020 CA1,O=DigiCert Inc,C=US':\n",
      "  Unable to locally verify the issuer's authority.\n",
      "To connect to github.com insecurely, use `--no-check-certificate'.\n"
     ]
    }
   ],
   "source": [
    "!wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "PxhstA-qbMN5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-07-22 19:51:24--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth\n",
      "Resolving github.com (github.com)... 13.234.210.38\n",
      "Connecting to github.com (github.com)|13.234.210.38|:443... connected.\n",
      "ERROR: cannot verify github.com's certificate, issued by 'CN=DigiCert TLS Hybrid ECC SHA384 2020 CA1,O=DigiCert Inc,C=US':\n",
      "  Unable to locally verify the issuer's authority.\n",
      "To connect to github.com insecurely, use `--no-check-certificate'.\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": 7,
   "metadata": {
    "id": "MBGUhoGDhJot"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disable distributed."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-23 14:03:39,712 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+cpu\n",
      "\tTorchVision: 0.12.0+cpu\n",
      "2022-07-23 14:03:39,713 INFO: \n",
      "  name: finetune_RealESRGANx4plus_400k\n",
      "  model_type: RealESRGANModel\n",
      "  scale: 4\n",
      "  num_gpu: 0\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: DF2K+OST\n",
      "      type: RealESRGANDataset\n",
      "      dataroot_gt: datasets/DF2K\n",
      "      meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 5\n",
      "      batch_size_per_gpu: 12\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: 3\n",
      "    num_out_ch: 3\n",
      "    num_feat: 64\n",
      "    num_block: 23\n",
      "    num_grow_ch: 32\n",
      "  ]\n",
      "  network_d:[\n",
      "    type: UNetDiscriminatorSN\n",
      "    num_in_ch: 3\n",
      "    num_feat: 64\n",
      "    skip_connection: True\n",
      "  ]\n",
      "  path:[\n",
      "    pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth\n",
      "    param_key_g: params_ema\n",
      "    strict_load_g: True\n",
      "    pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth\n",
      "    param_key_d: params\n",
      "    strict_load_d: True\n",
      "    resume_state: None\n",
      "    experiments_root: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\finetune_RealESRGANx4plus_400k\n",
      "    models: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\finetune_RealESRGANx4plus_400k\\models\n",
      "    training_states: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\finetune_RealESRGANx4plus_400k\\training_states\n",
      "    log: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\finetune_RealESRGANx4plus_400k\n",
      "    visualization: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\finetune_RealESRGANx4plus_400k\\visualization\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: 400000\n",
      "    warmup_iter: -1\n",
      "    pixel_opt:["
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Path already exists. Rename it to C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\finetune_RealESRGANx4plus_400k_archived_20220723_140339\n",
      "Path already exists. Rename it to C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\tb_logger\\finetune_RealESRGANx4plus_400k_archived_20220723_140339\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\n",
      "\n",
      "2022-07-23 14:03:56.615094: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2022-07-23 14:03:56.615841: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2022-07-23 14:04:37,844 INFO: Dataset [RealESRGANDataset] - DF2K+OST is built.\n",
      "2022-07-23 14:04:37,848 INFO: Training statistics:\n",
      "\tNumber of train images: 6996\n",
      "\tDataset enlarge ratio: 1\n",
      "\tBatch size per gpu: 12\n",
      "\tWorld size (gpu number): 1\n",
      "\tRequire iter number per epoch: 583\n",
      "\tTotal epochs: 687; iters: 400000.\n",
      "2022-07-23 14:04:38,513 INFO: Network [RRDBNet] is created.\n",
      "2022-07-23 14:04:38,567 INFO: Network: RRDBNet, with parameters: 16,697,987\n",
      "2022-07-23 14:04:38,568 INFO: RRDBNet(\n",
      "  (conv_first): Conv2d(3, 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, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      ")\n",
      "Traceback (most recent call last):\n",
      "  File \"realesrgan/train.py\", line 11, in <module>\n",
      "    train_pipeline(root_path)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\train.py\", line 124, in train_pipeline\n",
      "    model = build_model(opt)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\__init__.py\", line 27, in build_model\n",
      "    model = MODEL_REGISTRY.get(opt['model_type'])(opt)\n",
      "  File \"c:\\users\\aman bhansali\\downloads\\real-esrgan\\realesrgan\\models\\realesrgan_model.py\", line 24, in __init__\n",
      "    super(RealESRGANModel, self).__init__(opt)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\sr_model.py\", line 30, in __init__\n",
      "    self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\base_model.py\", line 290, in load_network\n",
      "    load_net = torch.load(load_path, map_location=lambda storage, loc: storage)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\serialization.py\", line 699, in load\n",
      "    with _open_file_like(f, 'rb') as opened_file:\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\serialization.py\", line 231, in _open_file_like\n",
      "    return _open_file(name_or_buffer, mode)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\serialization.py\", line 212, in __init__\n",
      "    super(_open_file, self).__init__(open(name, mode))\n",
      "FileNotFoundError: [Errno 2] No such file or directory: 'experiments/pretrained_models/RealESRNet_x4plus.pth'\n"
     ]
    }
   ],
   "source": [
    "!python realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --auto_resume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "4Z0tG9FE6Gde"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        (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, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      ")\n",
      "2022-05-29 12:17:11,376 INFO: Loading RRDBNet model from experiments/pretrained_models/RealESRNet_x4plus.pth, with param key: [params_ema].\n",
      "2022-05-29 12:17:11,701 INFO: Use Exponential Moving Average with decay: 0.999\n",
      "2022-05-29 12:17:12,088 INFO: Network [RRDBNet] is created.\n",
      "2022-05-29 12:17:12,254 INFO: Loading RRDBNet model from experiments/pretrained_models/RealESRNet_x4plus.pth, with param key: [params_ema].\n",
      "2022-05-29 12:17:12,609 INFO: Network [UNetDiscriminatorSN] is created.\n",
      "2022-05-29 12:17:12,611 INFO: Network: UNetDiscriminatorSN, with parameters: 4,376,897\n",
      "2022-05-29 12:17:12,611 INFO: UNetDiscriminatorSN(\n",
      "  (conv0): Conv2d(3, 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",
      "Traceback (most recent call last):\n",
      "  File \"realesrgan/train.py\", line 11, in <module>\n",
      "    train_pipeline(root_path)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\train.py\", line 124, in train_pipeline\n",
      "    model = build_model(opt)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\__init__.py\", line 27, in build_model\n",
      "    model = MODEL_REGISTRY.get(opt['model_type'])(opt)\n",
      "  File \"c:\\users\\aman bhansali\\downloads\\real-esrgan\\realesrgan\\models\\realesrgan_model.py\", line 24, in __init__\n",
      "    super(RealESRGANModel, self).__init__(opt)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\sr_model.py\", line 33, in __init__\n",
      "    self.init_training_settings()\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\srgan_model.py\", line 43, in init_training_settings\n",
      "    self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\base_model.py\", line 290, in load_network\n",
      "    load_net = torch.load(load_path, map_location=lambda storage, loc: storage)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\serialization.py\", line 699, in load\n",
      "    with _open_file_like(f, 'rb') as opened_file:\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\serialization.py\", line 231, in _open_file_like\n",
      "    return _open_file(name_or_buffer, mode)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\serialization.py\", line 212, in __init__\n",
      "    super(_open_file, self).__init__(open(name, mode))\n",
      "FileNotFoundError: [Errno 2] No such file or directory: 'experiments/pretrained_models/RealESRGAN_x4plus_netD.pth'\n",
      "2022-05-29 12:17:27,515 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+cpu\n",
      "\tTorchVision: 0.12.0+cpu\n",
      "2022-05-29 12:17:27,516 INFO: \n",
      "  name: debug_train_RealESRNetx4plus_1000k_B12G4\n",
      "  model_type: RealESRNetModel\n",
      "  scale: 4\n",
      "  num_gpu: 0\n",
      "  manual_seed: 0\n",
      "  gt_usm: True\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: DF2K+OST\n",
      "      type: RealESRGANDataset\n",
      "      dataroot_gt: datasets/DF2K\n",
      "      meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 5\n",
      "      batch_size_per_gpu: 12\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: 3\n",
      "    num_out_ch: 3\n",
      "    num_feat: 64\n",
      "    num_block: 23\n",
      "    num_grow_ch: 32\n",
      "  ]\n",
      "  path:[\n",
      "    pretrain_network_g: experiments/pretrained_models/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth\n",
      "    param_key_g: params_ema\n",
      "    strict_load_g: True\n",
      "    resume_state: None\n",
      "    experiments_root: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\debug_train_RealESRNetx4plus_1000k_B12G4\n",
      "    models: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\debug_train_RealESRNetx4plus_1000k_B12G4\\models\n",
      "    training_states: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\debug_train_RealESRNetx4plus_1000k_B12G4\\training_states\n",
      "    log: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\debug_train_RealESRNetx4plus_1000k_B12G4\n",
      "    visualization: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\debug_train_RealESRNetx4plus_1000k_B12G4\\visualization\n",
      "  ]\n",
      "  train:[\n",
      "    ema_decay: 0.999\n",
      "    optim_g:[\n",
      "      type: Adam\n",
      "      lr: 0.0002\n",
      "      weight_decay: 0\n",
      "      betas: [0.9, 0.99]\n",
      "    ]\n",
      "    scheduler:[\n",
      "      type: MultiStepLR\n",
      "      milestones: [1000000]\n",
      "      gamma: 0.5\n",
      "    ]\n",
      "    total_iter: 1000000\n",
      "    warmup_iter: -1\n",
      "    pixel_opt:[\n",
      "      type: L1Loss\n",
      "      loss_weight: 1.0\n",
      "      reduction: mean\n",
      "    ]\n",
      "  ]\n",
      "  logger:[\n",
      "    print_freq: 1\n",
      "    save_checkpoint_freq: 8\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: False\n",
      "  is_train: True\n",
      "  root_path: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\n",
      "\n",
      "2022-05-29 12:17:27,542 INFO: Dataset [RealESRGANDataset] - DF2K+OST is built.\n",
      "2022-05-29 12:17:27,542 INFO: Training statistics:\n",
      "\tNumber of train images: 5152\n",
      "\tDataset enlarge ratio: 1\n",
      "\tBatch size per gpu: 12\n",
      "\tWorld size (gpu number): 1\n",
      "\tRequire iter number per epoch: 430\n",
      "\tTotal epochs: 2326; iters: 1000000.\n",
      "2022-05-29 12:17:27,933 INFO: Network [RRDBNet] is created.\n",
      "2022-05-29 12:17:27,951 INFO: Network: RRDBNet, with parameters: 16,697,987\n",
      "2022-05-29 12:17:27,951 INFO: RRDBNet(\n",
      "  (conv_first): Conv2d(3, 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disable distributed.\n",
      "Path already exists. Rename it to C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\debug_train_RealESRNetx4plus_1000k_B12G4_archived_20220529_121727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        (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, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      ")\n",
      "2022-05-29 12:17:28,086 INFO: Loading: params_ema does not exist, use params.\n",
      "2022-05-29 12:17:28,086 INFO: Loading RRDBNet model from experiments/pretrained_models/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth, with param key: [params].\n",
      "2022-05-29 12:17:28,325 INFO: Use Exponential Moving Average with decay: 0.999\n",
      "2022-05-29 12:17:28,693 INFO: Network [RRDBNet] is created.\n",
      "2022-05-29 12:17:28,791 INFO: Loading: params_ema does not exist, use params.\n",
      "2022-05-29 12:17:28,791 INFO: Loading RRDBNet model from experiments/pretrained_models/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth, with param key: [params].\n",
      "2022-05-29 12:17:29,037 INFO: Loss [L1Loss] is created.\n",
      "Traceback (most recent call last):\n",
      "  File \"realesrgan/train.py\", line 11, in <module>\n",
      "    train_pipeline(root_path)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\train.py\", line 124, in train_pipeline\n",
      "    model = build_model(opt)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\__init__.py\", line 27, in build_model\n",
      "    model = MODEL_REGISTRY.get(opt['model_type'])(opt)\n",
      "  File \"c:\\users\\aman bhansali\\downloads\\real-esrgan\\realesrgan\\models\\realesrnet_model.py\", line 25, in __init__\n",
      "    self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 688, in cuda\n",
      "    return self._apply(lambda t: t.cuda(device))\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 578, in _apply\n",
      "    module._apply(fn)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 578, in _apply\n",
      "    module._apply(fn)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 578, in _apply\n",
      "    module._apply(fn)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 601, in _apply\n",
      "    param_applied = fn(param)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 688, in <lambda>\n",
      "    return self._apply(lambda t: t.cuda(device))\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\cuda\\__init__.py\", line 210, in _lazy_init\n",
      "    raise AssertionError(\"Torch not compiled with CUDA enabled\")\n",
      "AssertionError: Torch not compiled with CUDA enabled\n"
     ]
    }
   ],
   "source": [
    "!python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "p1of202KtVSz",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disable distributed.\n",
      "Path already exists. Rename it to C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\train_RealESRNetx4plus_1000k_B12G4_archived_20220529_121733\n",
      "Path already exists. Rename it to C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\tb_logger\\train_RealESRNetx4plus_1000k_B12G4_archived_20220529_121733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-29 12:17:33,184 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+cpu\n",
      "\tTorchVision: 0.12.0+cpu\n",
      "2022-05-29 12:17:33,184 INFO: \n",
      "  name: train_RealESRNetx4plus_1000k_B12G4\n",
      "  model_type: RealESRNetModel\n",
      "  scale: 4\n",
      "  num_gpu: 0\n",
      "  manual_seed: 0\n",
      "  gt_usm: True\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: DF2K+OST\n",
      "      type: RealESRGANDataset\n",
      "      dataroot_gt: datasets/DF2K\n",
      "      meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 5\n",
      "      batch_size_per_gpu: 12\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: 3\n",
      "    num_out_ch: 3\n",
      "    num_feat: 64\n",
      "    num_block: 23\n",
      "    num_grow_ch: 32\n",
      "  ]\n",
      "  path:[\n",
      "    pretrain_network_g: experiments/pretrained_models/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth\n",
      "    param_key_g: params_ema\n",
      "    strict_load_g: True\n",
      "    resume_state: None\n",
      "    experiments_root: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\train_RealESRNetx4plus_1000k_B12G4\n",
      "    models: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\train_RealESRNetx4plus_1000k_B12G4\\models\n",
      "    training_states: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\train_RealESRNetx4plus_1000k_B12G4\\training_states\n",
      "    log: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\train_RealESRNetx4plus_1000k_B12G4\n",
      "    visualization: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\\experiments\\train_RealESRNetx4plus_1000k_B12G4\\visualization\n",
      "  ]\n",
      "  train:[\n",
      "    ema_decay: 0.999\n",
      "    optim_g:[\n",
      "      type: Adam\n",
      "      lr: 0.0002\n",
      "      weight_decay: 0\n",
      "      betas: [0.9, 0.99]\n",
      "    ]\n",
      "    scheduler:[\n",
      "      type: MultiStepLR\n",
      "      milestones: [1000000]\n",
      "      gamma: 0.5\n",
      "    ]\n",
      "    total_iter: 1000000\n",
      "    warmup_iter: -1\n",
      "    pixel_opt:[\n",
      "      type: L1Loss\n",
      "      loss_weight: 1.0\n",
      "      reduction: mean\n",
      "    ]\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: C:\\Users\\Aman Bhansali\\Downloads\\Real-ESRGAN\n",
      "\n",
      "2022-05-29 12:17:36.566499: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2022-05-29 12:17:36.566541: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2022-05-29 12:17:46,268 INFO: Dataset [RealESRGANDataset] - DF2K+OST is built.\n",
      "2022-05-29 12:17:46,268 INFO: Training statistics:\n",
      "\tNumber of train images: 5152\n",
      "\tDataset enlarge ratio: 1\n",
      "\tBatch size per gpu: 12\n",
      "\tWorld size (gpu number): 1\n",
      "\tRequire iter number per epoch: 430\n",
      "\tTotal epochs: 2326; iters: 1000000.\n",
      "2022-05-29 12:17:46,647 INFO: Network [RRDBNet] is created.\n",
      "2022-05-29 12:17:46,663 INFO: Network: RRDBNet, with parameters: 16,697,987\n",
      "2022-05-29 12:17:46,663 INFO: RRDBNet(\n",
      "  (conv_first): Conv2d(3, 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, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      ")\n",
      "2022-05-29 12:17:46,758 INFO: Loading: params_ema does not exist, use params.\n",
      "2022-05-29 12:17:46,758 INFO: Loading RRDBNet model from experiments/pretrained_models/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth, with param key: [params].\n",
      "2022-05-29 12:17:47,012 INFO: Use Exponential Moving Average with decay: 0.999\n",
      "2022-05-29 12:17:47,396 INFO: Network [RRDBNet] is created.\n",
      "2022-05-29 12:17:47,526 INFO: Loading: params_ema does not exist, use params.\n",
      "2022-05-29 12:17:47,527 INFO: Loading RRDBNet model from experiments/pretrained_models/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth, with param key: [params].\n",
      "2022-05-29 12:17:47,817 INFO: Loss [L1Loss] is created.\n",
      "Traceback (most recent call last):\n",
      "  File \"realesrgan/train.py\", line 11, in <module>\n",
      "    train_pipeline(root_path)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\train.py\", line 124, in train_pipeline\n",
      "    model = build_model(opt)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\basicsr\\models\\__init__.py\", line 27, in build_model\n",
      "    model = MODEL_REGISTRY.get(opt['model_type'])(opt)\n",
      "  File \"c:\\users\\aman bhansali\\downloads\\real-esrgan\\realesrgan\\models\\realesrnet_model.py\", line 25, in __init__\n",
      "    self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 688, in cuda\n",
      "    return self._apply(lambda t: t.cuda(device))\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 578, in _apply\n",
      "    module._apply(fn)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 578, in _apply\n",
      "    module._apply(fn)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 578, in _apply\n",
      "    module._apply(fn)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 601, in _apply\n",
      "    param_applied = fn(param)\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 688, in <lambda>\n",
      "    return self._apply(lambda t: t.cuda(device))\n",
      "  File \"F:\\Anaconda\\lib\\site-packages\\torch\\cuda\\__init__.py\", line 210, in _lazy_init\n",
      "    raise AssertionError(\"Torch not compiled with CUDA enabled\")\n",
      "AssertionError: Torch not compiled with CUDA enabled\n"
     ]
    }
   ],
   "source": [
    "! python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --auto_resume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_list = []\n",
    "path = r\"F:\\BreaKHis_v1\\Degraded_TestData\\*.*\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-16184CD-200-002.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-16184CD-200-003.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-16184CD-200-004.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-16184CD-40-001.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-16184CD-40-002.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-16184CD-40-003.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-21978AB-200-012.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-21978AB-200-013.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-21978AB-200-014.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-21978AB-200-015.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-21978AB-200-016.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-21978AB-200-017.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-001.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-002.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-003.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-004.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-005.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-006.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-007.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-008.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-009.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-010.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-011.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-012.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-013.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-014.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-015.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-016.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-100-017.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-001.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-002.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-003.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-004.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-005.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-006.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-007.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-008.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-009.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-010.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-011.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-012.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-013.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-014.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-015.png\n",
      "F:\\BreaKHis_v1\\Degraded_TestData\\SOB_B_TA-14-3411F-40-016.png\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import cv2\n",
    "for file in glob.glob(path):\n",
    "    print(file)\n",
    "    img = cv2.imread(file, 0)\n",
    "    images_list.append(img)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "images_list = np.array(images_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-05-29 12:06:41--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\n",
      "Resolving github.com (github.com)... 13.234.176.102\n",
      "Connecting to github.com (github.com)|13.234.176.102|:443... connected.\n",
      "ERROR: cannot verify github.com's certificate, issued by 'CN=DigiCert TLS Hybrid ECC SHA384 2020 CA1,O=DigiCert Inc,C=US':\n",
      "  Unable to locally verify the issuer's authority.\n",
      "To connect to github.com insecurely, use `--no-check-certificate'.\n"
     ]
    }
   ],
   "source": [
    "!wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rqiEtaOYXnrt"
   },
   "source": [
    "# 3. Inference\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "XTVL4m_zXqBU",
    "outputId": "6417baec-a66f-47f9-9a9a-fc52824f808f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"inference_realesrgan.py\", line 128, in <module>\n",
      "    main()\n",
      "  File \"inference_realesrgan.py\", line 67, in main\n",
      "    raise ValueError(f'Model {args.model_name} does not exist.')\n",
      "ValueError: Model train_finetune_RealESRGANx4plus_400k_20220723_140339 does not exist.\n"
     ]
    }
   ],
   "source": [
    "# if it is out of memory, try to use the `--tile` option\n",
    "# We upsample the image with the scale factor X3.5\n",
    "!python inference_realesrgan.py -n RealESRGAN_x4plus -i upload --outscale 3.5 --fp32 --face_enhance\n",
    "# Arguments\n",
    "# -n, --model_name: Model names\n",
    "# -i, --input: input folder or image\n",
    "# --outscale: Output scale, can be arbitrary scale factore. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dUJ_0fQ5YYm2"
   },
   "source": [
    "# 4. Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "collapsed": true,
    "id": "7IMD5vhOYp68",
    "outputId": "c9839d74-5dd3-4e34-f153-43c871cf02a8"
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-60-9c16ab0f0ae2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minput_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_path\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m   \u001b[0mimg_input\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m   \u001b[0mimg_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-60-9c16ab0f0ae2>\u001b[0m in \u001b[0;36mimread\u001b[1;34m(img_path)\u001b[0m\n\u001b[0;32m     13\u001b[0m   \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOLOR_BGR2RGB\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# utils for visualization\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "def display(img1, img2):\n",
    "  fig = plt.figure(figsize=(25, 10))\n",
    "  ax1 = fig.add_subplot(1, 2, 1) \n",
    "  plt.title('Input image', fontsize=16)\n",
    "  ax1.axis('off')\n",
    "  ax2 = fig.add_subplot(1, 2, 2)\n",
    "  plt.title('Real-ESRGAN output', fontsize=16)\n",
    "  ax2.axis('off')\n",
    "  ax1.imshow(img1)\n",
    "  ax2.imshow(img2)\n",
    "def imread(img_path):\n",
    "  img = cv2.imread(img_path)\n",
    "  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "  return img\n",
    "\n",
    "# display each image in the upload folder\n",
    "import os\n",
    "import glob\n",
    "\n",
    "input_folder = 'upload'\n",
    "result_folder = 'results'\n",
    "input_list = sorted(glob.glob(os.path.join(input_folder, '*')))\n",
    "output_list = sorted(glob.glob(os.path.join(result_folder, '*')))\n",
    "for input_path, output_path in zip(input_list, output_list):\n",
    "  img_input = imread(input_path)\n",
    "  img_output = imread(output_path)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nKH0syu9ZAwV"
   },
   "source": [
    "# 5. Download Results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "collapsed": true,
    "id": "lHNHoP8PZJQ7",
    "outputId": "00a7c26b-e79f-4a7d-f500-b14361e35606"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'r' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-39bda9942c11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m   \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip_filename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"zip -r -j {zip_filename} results/*\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mfiles\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"F:\\BreaKHis_v1\\Finetune dataset\\Result\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mfiles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip_filename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'r' is not defined"
     ]
    }
   ],
   "source": [
    "# Download the results\n",
    "zip_filename = 'Real-ESRGAN_result.zip'\n",
    "if os.path.exists(zip_filename):\n",
    "  os.remove(zip_filename)\n",
    "os.system(f\"zip -r -j {zip_filename} results/*\")\n",
    "files = \"F:\\BreaKHis_v1\\Finetune dataset\\Result\"\n",
    "files.download(zip_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "collapsed_sections": [],
   "name": "ESRGAN_FineTune.ipynb",
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  "kernelspec": {
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