PixBike.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PixBike.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "I am putting this here to not disturb unattended run."
      ],
      "metadata": {
        "id": "k8lL1CB1litU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mounting Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc8I9I7Ms6n_",
        "outputId": "76ff70a8-ff86-4039-9768-a4647978b857"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PixBike Notebook\n",
        "Authors: Ozan Karaali, H. Yildiz Basol\n",
        "\n",
        "References (as Bibtex format): \n",
        "\n",
        "@article{Szell_Growing_urban_bicycle_2022,\n",
        "author = {Szell, Michael and Mimar, Sayat and Perlman, Tyler and Ghoshal, Gourab and Sinatra, Roberta},\n",
        "doi = {10.1038/s41598-022-10783-y},\n",
        "journal = {Scientific Reports},\n",
        "number = {6765},\n",
        "pages = {1--14},\n",
        "title = {{Growing urban bicycle networks}},\n",
        "volume = {12},\n",
        "year = {2022}\n",
        "}\n",
        "\n",
        "@inproceedings{isola2017image,\n",
        "  title={Image-to-Image Translation with Conditional Adversarial Networks},\n",
        "  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},\n",
        "  booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},\n",
        "  year={2017}\n",
        "}"
      ],
      "metadata": {
        "id": "mIU2ZGGWpyjA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Collecting"
      ],
      "metadata": {
        "id": "J1J9PpeWqszn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMx6bUMkpwoL",
        "outputId": "8d6456ac-7355-47fc-f174-da384b70234e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: sniffio, outcome, h11, async-generator, wsproto, urllib3, trio, trio-websocket, selenium\n",
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            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "requests 2.23.0 requires urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1, but you have urllib3 1.26.11 which is incompatible.\u001b[0m\n",
            "Successfully installed async-generator-1.10 h11-0.13.0 outcome-1.2.0 selenium-4.4.3 sniffio-1.2.0 trio-0.21.0 trio-websocket-0.9.2 urllib3-1.26.11 wsproto-1.1.0\n",
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            "Building dependency tree       \n",
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            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  chromium-browser chromium-browser-l10n chromium-codecs-ffmpeg-extra\n",
            "Suggested packages:\n",
            "  webaccounts-chromium-extension unity-chromium-extension\n",
            "The following NEW packages will be installed:\n",
            "  chromium-browser chromium-browser-l10n chromium-chromedriver\n",
            "  chromium-codecs-ffmpeg-extra\n",
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            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/x-www-browser (x-www-browser) in auto mode\n",
            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/gnome-www-browser (gnome-www-browser) in auto mode\n",
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            "cp: '/usr/lib/chromium-browser/chromedriver' and '/usr/bin/chromedriver' are the same file\n"
          ]
        }
      ],
      "source": [
        "!pip install selenium\n",
        "!apt-get update\n",
        "!apt install chromium-chromedriver\n",
        "!cp /usr/lib/chromium-browser/chromedriver /usr/bin\n",
        "import sys\n",
        "sys.path.insert(0,'/usr/lib/chromium-browser/chromedriver')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/')\n",
        "!rm -rf ./nc # if there exists any remaining files in this folder, delete\n",
        "!mkdir nc"
      ],
      "metadata": {
        "id": "dkIWILPdq00G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://ozankaraali.com/growbike_data.json -O data.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lkh_q3vHsI2p",
        "outputId": "b547696f-5715-4897-96ee-f108d5b09178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-20 21:17:09--  https://ozankaraali.com/growbike_data.json\n",
            "Resolving ozankaraali.com (ozankaraali.com)... 185.199.111.153, 185.199.110.153, 185.199.108.153, ...\n",
            "Connecting to ozankaraali.com (ozankaraali.com)|185.199.111.153|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12104 (12K) [application/json]\n",
            "Saving to: ‘data.json’\n",
            "\n",
            "\rdata.json             0%[                    ]       0  --.-KB/s               \rdata.json           100%[===================>]  11.82K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-08-20 21:17:09 (35.9 MB/s) - ‘data.json’ saved [12104/12104]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Selenium connect to https://growbike.net/ and get screenshot of the page\n",
        "import os\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "\n",
        "from selenium import webdriver\n",
        "import time\n",
        "import uuid\n",
        "import cv2\n",
        "import json\n",
        "\n",
        "from selenium import webdriver\n",
        "chrome_options = webdriver.ChromeOptions()\n",
        "chrome_options.add_argument('--headless')\n",
        "chrome_options.add_argument('--no-sandbox')\n",
        "chrome_options.add_argument('--use-gl=egl')\n",
        "# chrome_options.add_argument('--disable-dev-shm-usage')\n",
        "chrome_options.add_argument(\"window-size=1920,1080\")\n",
        "\n",
        "def screenshot():\n",
        "    slider = driver.find_element(\"xpath\", '/html/body/div/div/div/main/div[2]/div[2]/div[1]/input')\n",
        "\n",
        "    slider.click()  # puts 20 on the slider\n",
        "    for i in range(22):\n",
        "        slider.send_keys(u'\\ue012')  # arrow left\n",
        "\n",
        "    canvas.screenshot(\"./testpure.png\")\n",
        "\n",
        "    slider.click()  # puts 20 on the slider\n",
        "    for i in range(22):\n",
        "        slider.send_keys(u'\\ue014')  # arrow right\n",
        "    canvas.screenshot(\"./testroads.png\")\n",
        "\n",
        "    img1 = Image.open('./testpure.png')\n",
        "    img2 = Image.open('./testroads.png')\n",
        "    new_width = 600\n",
        "    new_height = 600\n",
        "\n",
        "    width, height = img1.size  # Get dimensions\n",
        "    left = (width - new_width) / 2\n",
        "    top = (height - new_height) / 2\n",
        "    right = (width + new_width) / 2\n",
        "    bottom = (height + new_height) / 2\n",
        "\n",
        "    # Crop the center of the image\n",
        "    img1 = img1.crop((left, top, right, bottom))\n",
        "    img2 = img2.crop((left, top, right, bottom))\n",
        "\n",
        "    img1 = np.array(img1)\n",
        "    img2 = np.array(img2)\n",
        "\n",
        "    # set red range\n",
        "    lowcolor = (58, 139, 161, 255)\n",
        "    highcolor = (90, 190, 220, 255)\n",
        "\n",
        "    # threshold\n",
        "    thresh = cv2.inRange(img2, lowcolor, highcolor)\n",
        "\n",
        "    # Method 1: count number of white pixels and test if zero\n",
        "    count = np.sum(np.nonzero(thresh))\n",
        "    found = False\n",
        "    # print(count)\n",
        "    if count < 10000:\n",
        "        pass\n",
        "    else:\n",
        "        img3 = np.concatenate((img1, img2), axis=1)\n",
        "        img3 = Image.fromarray(img3)\n",
        "        img3.save('./nc/' + str(uuid.uuid4()) + '.png')\n",
        "        found = True\n",
        "    return found\n",
        "\n",
        "\n",
        "def move(position):\n",
        "    if position == 'left':\n",
        "        for i in range(10):\n",
        "            canvas.send_keys(u'\\ue012')  # arrow left\n",
        "    elif position == 'right':\n",
        "        for i in range(10):\n",
        "            canvas.send_keys(u'\\ue014')\n",
        "    elif position == 'up':\n",
        "        for i in range(10):\n",
        "            canvas.send_keys(u'\\ue013')\n",
        "    elif position == 'down':\n",
        "        for i in range(10):\n",
        "            canvas.send_keys(u'\\ue015')\n",
        "\n",
        "# read json file\n",
        "data = json.load(open('data.json'))\n",
        "x = list(data.keys())\n",
        "\n",
        "for i in x:\n",
        "    try:\n",
        "      url = 'https://growbike.net/city/' + i\n",
        "\n",
        "      driver = webdriver.Chrome('chromedriver', options=chrome_options)\n",
        "      # driver.set_page_load_timeout(10)\n",
        "      driver.get(url)\n",
        "\n",
        "      time.sleep(7)\n",
        "\n",
        "      # driver.set_window_size(1920, 1080)  # the trick\n",
        "\n",
        "      driver.find_element(\"xpath\", '//*[@id=\"__next\"]/div/div/main/div[2]/div[3]/div[1]//*[local-name()=\"svg\"]').click()\n",
        "\n",
        "      canvas = driver.find_element(\"xpath\", '//canvas[@class=\"mapboxgl-canvas\"]')\n",
        "      canvas.click()\n",
        "\n",
        "      # canvas send + to zoom in\n",
        "\n",
        "      time.sleep(1)\n",
        "      for i in range(12 - data[i]['zoom']):\n",
        "          canvas.send_keys(\"+\")\n",
        "          time.sleep(1)\n",
        "\n",
        "      #keep like 8x8 grid, four up and four left to get starting point\n",
        "\n",
        "      for i in range(10):\n",
        "          move('left')\n",
        "          if screenshot() is False:\n",
        "              break\n",
        "      for i in range(10):\n",
        "          move('up')\n",
        "          if screenshot() is False:\n",
        "              break\n",
        "\n",
        "      # then for each for four right, send a down and four left\n",
        "      for j in range(10):\n",
        "          for i in range(10):\n",
        "              move('right')  # arrow right\n",
        "              if screenshot() is False:\n",
        "                  break\n",
        "\n",
        "          move('down')  # arrow down\n",
        "          if screenshot() is False:\n",
        "              break\n",
        "\n",
        "          for i in range(10):\n",
        "              canvas.send_keys(u'\\ue012')  # arrow left\n",
        "              if screenshot() is False:\n",
        "                  break\n",
        "\n",
        "          move('down')  # arrow down\n",
        "          if screenshot() is False:\n",
        "              break\n",
        "\n",
        "      # time.sleep(2)\n",
        "      driver.quit()\n",
        "    except:\n",
        "      pass"
      ],
      "metadata": {
        "id": "IFMGs_Kxq9Ev"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Currently we should have around ~1000 images in /content/nc folder."
      ],
      "metadata": {
        "id": "kDED37czsomz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I will put here the codes for saving and preloading the data I have, to create a shortcut and then I will comment out, you can turn these on optionally."
      ],
      "metadata": {
        "id": "OaG_g_GItFys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# To store data in Google Drive\n",
        "!cp -R /content/nc /content/drive/MyDrive/nc20aug"
      ],
      "metadata": {
        "id": "AF7v4JYgtEE6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To load back data from Google Drive\n",
        "# import os\n",
        "# os.chdir('/content/')\n",
        "# !rm -rf ./nc # if there exists any remaining files in this folder, delete\n",
        "# !mkdir nc\n",
        "# !cp -R /content/drive/MyDrive/nc20aug/* /content/nc"
      ],
      "metadata": {
        "id": "jE19e0ttslJz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike\n",
        "!rm -rf /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split"
      ],
      "metadata": {
        "id": "jJFaShw1l2ha"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using Pix2Pix"
      ],
      "metadata": {
        "id": "3J_Q4yHlt12w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# If you download from Google Drive\n",
        "# import os\n",
        "# os.chdir('/content/')\n",
        "# !rm -rf ./pytorch-CycleGAN-and-pix2pix # if there exists any remaining files in this folder, delete\n",
        "# !mkdir pytorch-CycleGAN-and-pix2pix\n",
        "# !cp -R /content/drive/MyDrive/pix2pix1/* /content/pytorch-CycleGAN-and-pix2pix"
      ],
      "metadata": {
        "id": "3d6W40ZxS4A1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKm2BS6Nt77C",
        "outputId": "df218eb6-90f2-4239-8949-07d6e1a9d568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'pytorch-CycleGAN-and-pix2pix'...\n",
            "remote: Enumerating objects: 2452, done.\u001b[K\n",
            "remote: Total 2452 (delta 0), reused 0 (delta 0), pack-reused 2452\u001b[K\n",
            "Receiving objects: 100% (2452/2452), 8.18 MiB | 14.80 MiB/s, done.\n",
            "Resolving deltas: 100% (1538/1538), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('pytorch-CycleGAN-and-pix2pix/')"
      ],
      "metadata": {
        "id": "dDhdB3dJuIrJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYksR9MkuD1R",
        "outputId": "f456f265-cf57-4e0a-d74e-18ffefd3dd56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.12.1+cu113)\n",
            "Requirement already satisfied: torchvision>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (0.13.1+cu113)\n",
            "Requirement already satisfied: dominate>=2.4.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (2.7.0)\n",
            "Requirement already satisfied: visdom>=0.1.8.8 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (0.1.8.9)\n",
            "Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (0.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4.0->-r requirements.txt (line 1)) (4.1.1)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->-r requirements.txt (line 2)) (7.1.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->-r requirements.txt (line 2)) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->-r requirements.txt (line 2)) (1.21.6)\n",
            "Requirement already satisfied: tornado in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (5.1.1)\n",
            "Requirement already satisfied: pyzmq in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (23.2.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.7.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.15.0)\n",
            "Requirement already satisfied: jsonpatch in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.32)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.3.3)\n",
            "Requirement already satisfied: torchfile in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (0.1.0)\n",
            "Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (7.1.2)\n",
            "Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (3.1.27)\n",
            "Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (1.0.9)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (6.0)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (0.1.2)\n",
            "Requirement already satisfied: sentry-sdk>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (1.9.0)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (5.4.8)\n",
            "Requirement already satisfied: protobuf<4.0dev,>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (3.17.3)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (0.4.0)\n",
            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (1.3.2)\n",
            "Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (2.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from wandb->-r requirements.txt (line 5)) (57.4.0)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb->-r requirements.txt (line 5)) (4.0.9)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb->-r requirements.txt (line 5)) (5.0.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->-r requirements.txt (line 2)) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->-r requirements.txt (line 2)) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->-r requirements.txt (line 2)) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->-r requirements.txt (line 2)) (2022.6.15)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.7/dist-packages (from jsonpatch->visdom>=0.1.8.8->-r requirements.txt (line 4)) (2.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install split-folders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKsPavbju6rl",
        "outputId": "ed7c24bf-341d-406e-b955-52e6a6b10f58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: split-folders in /usr/local/lib/python3.7/dist-packages (0.5.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rm -rf /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike/"
      ],
      "metadata": {
        "id": "32GacFOIwaHm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike/"
      ],
      "metadata": {
        "id": "hyKdMnEIw3cW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -R /content/nc /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike"
      ],
      "metadata": {
        "id": "xarVMbhpujFf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import splitfolders\n",
        "os.chdir('/content/pytorch-CycleGAN-and-pix2pix/datasets/')\n",
        "splitfolders.ratio('pixbike', output=\"pixbike-split\", seed=1337, ratio=(.8, 0.1,0.1)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHWFaFFbww9Q",
        "outputId": "df377468-cee0-4184-cfc2-6f69d64c1dc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Copying files: 1278 files [00:00, 1419.64 files/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/test/nc/* /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/test\n",
        "!mv /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/train/nc/* /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/train\n",
        "!mv /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/val/nc/* /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/val"
      ],
      "metadata": {
        "id": "BbYPmBt6xbku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/test/nc/\n",
        "!rm -rf /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/train/nc/\n",
        "!rm -rf /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/val/nc/"
      ],
      "metadata": {
        "id": "DT8faHKExvU_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# If we want to save it in Google Drive\n",
        "# !cp -R /content/pytorch-CycleGAN-and-pix2pix/datasets/pixbike-split/ /content/drive/MyDrive/pixbike-split"
      ],
      "metadata": {
        "id": "QMEmEkOFx8vS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('/content/pytorch-CycleGAN-and-pix2pix/')"
      ],
      "metadata": {
        "id": "2VbXvVnVyDy1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training\n",
        "\n",
        "-   `python train.py --dataroot ./datasets/pixbike-split --name pixbike --model pixbike --direction AtoB --use_wandb`\n",
        "\n",
        "Change the `--dataroot` and `--name` to your own dataset's path and model's name. Use `--gpu_ids 0,1,..` to train on multiple GPUs and `--batch_size` to change the batch size. Add `--direction BtoA` if you want to train a model to transfrom from class B to A."
      ],
      "metadata": {
        "id": "vqwt2NfT0bw2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py --dataroot ./datasets/pixbike-split --name pixbike --model pix2pix --direction AtoB --use_wandb --batch_size 32 --lr 0.001"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NVz0S_qyL_B",
        "outputId": "d0b4a4c7-3b96-4d1a-d7fe-bdb44f1ebb1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------- Options ---------------\n",
            "               batch_size: 32                            \t[default: 1]\n",
            "                    beta1: 0.5                           \n",
            "          checkpoints_dir: ./checkpoints                 \n",
            "           continue_train: False                         \n",
            "                crop_size: 256                           \n",
            "                 dataroot: ./datasets/pixbike-split      \t[default: None]\n",
            "             dataset_mode: aligned                       \n",
            "                direction: AtoB                          \n",
            "              display_env: main                          \n",
            "             display_freq: 400                           \n",
            "               display_id: 1                             \n",
            "            display_ncols: 4                             \n",
            "             display_port: 8097                          \n",
            "           display_server: http://localhost              \n",
            "          display_winsize: 256                           \n",
            "                    epoch: latest                        \n",
            "              epoch_count: 1                             \n",
            "                 gan_mode: vanilla                       \n",
            "                  gpu_ids: 0                             \n",
            "                init_gain: 0.02                          \n",
            "                init_type: normal                        \n",
            "                 input_nc: 3                             \n",
            "                  isTrain: True                          \t[default: None]\n",
            "                lambda_L1: 100.0                         \n",
            "                load_iter: 0                             \t[default: 0]\n",
            "                load_size: 286                           \n",
            "                       lr: 0.001                         \t[default: 0.0002]\n",
            "           lr_decay_iters: 50                            \n",
            "                lr_policy: linear                        \n",
            "         max_dataset_size: inf                           \n",
            "                    model: pix2pix                       \t[default: cycle_gan]\n",
            "                 n_epochs: 100                           \n",
            "           n_epochs_decay: 100                           \n",
            "               n_layers_D: 3                             \n",
            "                     name: pixbike                       \t[default: experiment_name]\n",
            "                      ndf: 64                            \n",
            "                     netD: basic                         \n",
            "                     netG: unet_256                      \n",
            "                      ngf: 64                            \n",
            "               no_dropout: False                         \n",
            "                  no_flip: False                         \n",
            "                  no_html: False                         \n",
            "                     norm: batch                         \n",
            "              num_threads: 4                             \n",
            "                output_nc: 3                             \n",
            "                    phase: train                         \n",
            "                pool_size: 0                             \n",
            "               preprocess: resize_and_crop               \n",
            "               print_freq: 100                           \n",
            "             save_by_iter: False                         \n",
            "          save_epoch_freq: 5                             \n",
            "         save_latest_freq: 5000                          \n",
            "           serial_batches: False                         \n",
            "                   suffix:                               \n",
            "         update_html_freq: 1000                          \n",
            "                use_wandb: True                          \t[default: False]\n",
            "                  verbose: False                         \n",
            "----------------- End -------------------\n",
            "dataset [AlignedDataset] was created\n",
            "The number of training images = 1022\n",
            "initialize network with normal\n",
            "initialize network with normal\n",
            "model [Pix2PixModel] was created\n",
            "---------- Networks initialized -------------\n",
            "[Network G] Total number of parameters : 54.414 M\n",
            "[Network D] Total number of parameters : 2.769 M\n",
            "-----------------------------------------------\n",
            "Setting up a new session...\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mozankaraali\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.13.1\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/pytorch-CycleGAN-and-pix2pix/wandb/run-20220821_082940-2fks0a12\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mpixbike\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/ozankaraali/CycleGAN-and-pix2pix\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/ozankaraali/CycleGAN-and-pix2pix/runs/2fks0a12\u001b[0m\n",
            "create web directory ./checkpoints/pixbike/web...\n",
            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 1, iters: 800, time: 0.059, data: 2.048) G_GAN: 1.615 G_L1: 5.742 D_real: 0.429 D_fake: 0.218 \n",
            "End of epoch 1 / 200 \t Time Taken: 24 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 2, iters: 576, time: 0.043, data: 0.040) G_GAN: 2.359 G_L1: 5.950 D_real: 0.116 D_fake: 0.822 \n",
            "End of epoch 2 / 200 \t Time Taken: 18 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 3, iters: 352, time: 0.044, data: 0.023) G_GAN: 2.587 G_L1: 6.099 D_real: 0.009 D_fake: 2.505 \n",
            "End of epoch 3 / 200 \t Time Taken: 18 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 4, iters: 128, time: 0.047, data: 0.015) G_GAN: 2.996 G_L1: 6.563 D_real: 0.161 D_fake: 0.220 \n",
            "(epoch: 4, iters: 928, time: 0.021, data: 0.030) G_GAN: 2.814 G_L1: 6.081 D_real: 0.274 D_fake: 0.128 \n",
            "End of epoch 4 / 200 \t Time Taken: 18 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 5, iters: 704, time: 0.036, data: 0.008) G_GAN: 2.329 G_L1: 6.832 D_real: 0.255 D_fake: 0.103 \n",
            "saving the model at the end of epoch 5, iters 5120\n",
            "End of epoch 5 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 6, iters: 480, time: 0.046, data: 0.020) G_GAN: 2.691 G_L1: 6.215 D_real: 0.028 D_fake: 0.989 \n",
            "End of epoch 6 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 7, iters: 256, time: 0.048, data: 0.012) G_GAN: 4.075 G_L1: 6.870 D_real: 0.047 D_fake: 1.073 \n",
            "End of epoch 7 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 8, iters: 32, time: 0.036, data: 0.028) G_GAN: 3.706 G_L1: 6.566 D_real: 0.060 D_fake: 0.888 \n",
            "(epoch: 8, iters: 832, time: 0.040, data: 0.023) G_GAN: 1.521 G_L1: 6.186 D_real: 1.283 D_fake: 0.044 \n",
            "End of epoch 8 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 9, iters: 608, time: 0.043, data: 0.012) G_GAN: 2.631 G_L1: 6.096 D_real: 0.400 D_fake: 0.029 \n",
            "End of epoch 9 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 10, iters: 384, time: 0.050, data: 0.012) G_GAN: 2.679 G_L1: 7.155 D_real: 0.050 D_fake: 0.532 \n",
            "saving the model at the end of epoch 10, iters 10240\n",
            "End of epoch 10 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 11, iters: 160, time: 0.042, data: 0.021) G_GAN: 1.129 G_L1: 6.149 D_real: 0.366 D_fake: 0.441 \n",
            "(epoch: 11, iters: 960, time: 0.021, data: 0.014) G_GAN: 2.148 G_L1: 5.677 D_real: 0.161 D_fake: 0.482 \n",
            "End of epoch 11 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 12, iters: 736, time: 0.045, data: 0.007) G_GAN: 1.617 G_L1: 5.942 D_real: 1.116 D_fake: 0.558 \n",
            "End of epoch 12 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 13, iters: 512, time: 0.049, data: 0.012) G_GAN: 1.808 G_L1: 5.880 D_real: 0.235 D_fake: 0.642 \n",
            "End of epoch 13 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 14, iters: 288, time: 0.036, data: 0.020) G_GAN: 0.525 G_L1: 5.184 D_real: 2.095 D_fake: 0.049 \n",
            "End of epoch 14 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 15, iters: 64, time: 0.048, data: 0.018) G_GAN: 2.917 G_L1: 6.196 D_real: 0.097 D_fake: 0.559 \n",
            "(epoch: 15, iters: 864, time: 0.036, data: 0.026) G_GAN: 1.709 G_L1: 5.691 D_real: 0.033 D_fake: 1.279 \n",
            "saving the model at the end of epoch 15, iters 15360\n",
            "End of epoch 15 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 16, iters: 640, time: 0.051, data: 0.018) G_GAN: 1.323 G_L1: 5.630 D_real: 0.219 D_fake: 0.587 \n",
            "End of epoch 16 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 17, iters: 416, time: 0.040, data: 0.120) G_GAN: 0.385 G_L1: 5.597 D_real: 1.007 D_fake: 0.076 \n",
            "End of epoch 17 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 18, iters: 192, time: 0.041, data: 0.036) G_GAN: 1.846 G_L1: 5.411 D_real: 0.211 D_fake: 0.380 \n",
            "(epoch: 18, iters: 992, time: 0.032, data: 0.028) G_GAN: 1.706 G_L1: 5.466 D_real: 0.317 D_fake: 0.177 \n",
            "End of epoch 18 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 19, iters: 768, time: 0.048, data: 0.008) G_GAN: 1.935 G_L1: 4.815 D_real: 1.244 D_fake: 0.530 \n",
            "End of epoch 19 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 20, iters: 544, time: 0.041, data: 0.052) G_GAN: 0.965 G_L1: 6.351 D_real: 0.632 D_fake: 0.214 \n",
            "saving the latest model (epoch 20, total_iters 20000)\n",
            "saving the model at the end of epoch 20, iters 20480\n",
            "End of epoch 20 / 200 \t Time Taken: 23 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 21, iters: 320, time: 0.052, data: 0.008) G_GAN: 0.462 G_L1: 5.844 D_real: 0.524 D_fake: 0.173 \n",
            "End of epoch 21 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 22, iters: 96, time: 0.050, data: 0.039) G_GAN: 2.022 G_L1: 5.128 D_real: 0.423 D_fake: 0.450 \n",
            "(epoch: 22, iters: 896, time: 0.036, data: 0.035) G_GAN: 0.608 G_L1: 5.968 D_real: 0.841 D_fake: 0.153 \n",
            "End of epoch 22 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 23, iters: 672, time: 0.038, data: 0.219) G_GAN: 2.520 G_L1: 5.215 D_real: 0.098 D_fake: 0.588 \n",
            "End of epoch 23 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 24, iters: 448, time: 0.051, data: 0.013) G_GAN: 0.530 G_L1: 5.855 D_real: 2.076 D_fake: 0.033 \n",
            "End of epoch 24 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 25, iters: 224, time: 0.048, data: 0.022) G_GAN: 2.979 G_L1: 5.141 D_real: 0.160 D_fake: 1.069 \n",
            "(epoch: 25, iters: 1024, time: 0.031, data: 0.025) G_GAN: 2.419 G_L1: 5.649 D_real: 0.196 D_fake: 0.514 \n",
            "saving the model at the end of epoch 25, iters 25600\n",
            "End of epoch 25 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 26, iters: 800, time: 0.038, data: 2.473) G_GAN: 3.468 G_L1: 6.646 D_real: 0.047 D_fake: 0.473 \n",
            "End of epoch 26 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 27, iters: 576, time: 0.040, data: 0.022) G_GAN: 4.807 G_L1: 6.095 D_real: 0.033 D_fake: 1.347 \n",
            "End of epoch 27 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 28, iters: 352, time: 0.049, data: 0.012) G_GAN: 2.683 G_L1: 6.232 D_real: 0.178 D_fake: 0.475 \n",
            "End of epoch 28 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 29, iters: 128, time: 0.054, data: 0.039) G_GAN: 0.990 G_L1: 5.161 D_real: 0.562 D_fake: 0.618 \n",
            "(epoch: 29, iters: 928, time: 0.022, data: 0.015) G_GAN: 0.815 G_L1: 5.296 D_real: 0.575 D_fake: 0.258 \n",
            "End of epoch 29 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 30, iters: 704, time: 0.056, data: 0.224) G_GAN: 0.792 G_L1: 4.423 D_real: 0.525 D_fake: 0.464 \n",
            "saving the model at the end of epoch 30, iters 30720\n",
            "End of epoch 30 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 31, iters: 480, time: 0.055, data: 0.013) G_GAN: 2.073 G_L1: 5.272 D_real: 0.153 D_fake: 0.467 \n",
            "End of epoch 31 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 32, iters: 256, time: 0.051, data: 0.019) G_GAN: 1.121 G_L1: 5.067 D_real: 0.505 D_fake: 0.051 \n",
            "End of epoch 32 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 33, iters: 32, time: 0.037, data: 0.015) G_GAN: 1.985 G_L1: 5.220 D_real: 0.433 D_fake: 0.099 \n",
            "(epoch: 33, iters: 832, time: 0.024, data: 0.046) G_GAN: 3.495 G_L1: 5.665 D_real: 0.037 D_fake: 0.262 \n",
            "End of epoch 33 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 34, iters: 608, time: 0.051, data: 0.029) G_GAN: 0.866 G_L1: 4.414 D_real: 0.535 D_fake: 0.919 \n",
            "End of epoch 34 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 35, iters: 384, time: 0.063, data: 0.032) G_GAN: 0.825 G_L1: 4.463 D_real: 0.748 D_fake: 0.892 \n",
            "saving the model at the end of epoch 35, iters 35840\n",
            "End of epoch 35 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 36, iters: 160, time: 0.058, data: 0.231) G_GAN: 0.390 G_L1: 4.428 D_real: 0.926 D_fake: 0.591 \n",
            "(epoch: 36, iters: 960, time: 0.032, data: 0.013) G_GAN: 0.925 G_L1: 4.763 D_real: 0.529 D_fake: 0.850 \n",
            "End of epoch 36 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 37, iters: 736, time: 0.064, data: 0.007) G_GAN: 0.538 G_L1: 4.027 D_real: 1.363 D_fake: 0.319 \n",
            "End of epoch 37 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 38, iters: 512, time: 0.073, data: 0.013) G_GAN: 0.371 G_L1: 6.063 D_real: 1.743 D_fake: 0.274 \n",
            "End of epoch 38 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 39, iters: 288, time: 0.060, data: 0.037) G_GAN: 0.245 G_L1: 3.934 D_real: 1.277 D_fake: 0.231 \n",
            "End of epoch 39 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 40, iters: 64, time: 0.076, data: 0.015) G_GAN: 1.137 G_L1: 4.825 D_real: 0.354 D_fake: 0.911 \n",
            "saving the latest model (epoch 40, total_iters 40000)\n",
            "(epoch: 40, iters: 864, time: 0.031, data: 0.008) G_GAN: 2.262 G_L1: 4.735 D_real: 0.148 D_fake: 1.445 \n",
            "saving the model at the end of epoch 40, iters 40960\n",
            "End of epoch 40 / 200 \t Time Taken: 24 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 41, iters: 640, time: 0.074, data: 0.030) G_GAN: 1.226 G_L1: 4.684 D_real: 0.423 D_fake: 0.731 \n",
            "End of epoch 41 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 42, iters: 416, time: 0.060, data: 0.025) G_GAN: 1.273 G_L1: 4.003 D_real: 0.356 D_fake: 0.987 \n",
            "End of epoch 42 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 43, iters: 192, time: 0.075, data: 0.032) G_GAN: 0.699 G_L1: 4.193 D_real: 0.605 D_fake: 0.417 \n",
            "(epoch: 43, iters: 992, time: 0.045, data: 0.014) G_GAN: 0.944 G_L1: 4.351 D_real: 0.781 D_fake: 0.474 \n",
            "End of epoch 43 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 44, iters: 768, time: 0.071, data: 0.008) G_GAN: 0.670 G_L1: 4.473 D_real: 0.869 D_fake: 0.393 \n",
            "End of epoch 44 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 45, iters: 544, time: 0.067, data: 0.023) G_GAN: 1.200 G_L1: 4.487 D_real: 0.286 D_fake: 1.327 \n",
            "saving the model at the end of epoch 45, iters 46080\n",
            "End of epoch 45 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 46, iters: 320, time: 0.090, data: 0.019) G_GAN: 0.605 G_L1: 5.625 D_real: 1.348 D_fake: 0.235 \n",
            "End of epoch 46 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 47, iters: 96, time: 0.053, data: 0.013) G_GAN: 1.585 G_L1: 4.556 D_real: 0.306 D_fake: 0.688 \n",
            "(epoch: 47, iters: 896, time: 0.039, data: 0.020) G_GAN: 2.080 G_L1: 4.762 D_real: 0.290 D_fake: 0.630 \n",
            "End of epoch 47 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 48, iters: 672, time: 0.048, data: 0.014) G_GAN: 2.001 G_L1: 4.590 D_real: 0.250 D_fake: 0.587 \n",
            "End of epoch 48 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 49, iters: 448, time: 0.054, data: 0.018) G_GAN: 1.511 G_L1: 4.247 D_real: 0.928 D_fake: 0.155 \n",
            "End of epoch 49 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 50, iters: 224, time: 0.055, data: 0.029) G_GAN: 1.701 G_L1: 4.778 D_real: 0.292 D_fake: 0.252 \n",
            "(epoch: 50, iters: 1024, time: 0.031, data: 0.035) G_GAN: 1.270 G_L1: 4.305 D_real: 2.112 D_fake: 1.304 \n",
            "saving the model at the end of epoch 50, iters 51200\n",
            "End of epoch 50 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 51, iters: 800, time: 0.042, data: 2.264) G_GAN: 1.813 G_L1: 4.900 D_real: 0.160 D_fake: 1.298 \n",
            "End of epoch 51 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 52, iters: 576, time: 0.040, data: 0.012) G_GAN: 0.844 G_L1: 4.049 D_real: 0.363 D_fake: 0.724 \n",
            "End of epoch 52 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 53, iters: 352, time: 0.054, data: 0.032) G_GAN: 0.555 G_L1: 4.307 D_real: 1.421 D_fake: 0.175 \n",
            "End of epoch 53 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 54, iters: 128, time: 0.056, data: 0.019) G_GAN: 0.609 G_L1: 4.727 D_real: 0.702 D_fake: 0.163 \n",
            "(epoch: 54, iters: 928, time: 0.018, data: 0.026) G_GAN: 1.445 G_L1: 4.455 D_real: 0.193 D_fake: 0.686 \n",
            "End of epoch 54 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 55, iters: 704, time: 0.058, data: 0.017) G_GAN: 1.418 G_L1: 4.614 D_real: 0.617 D_fake: 0.818 \n",
            "saving the model at the end of epoch 55, iters 56320\n",
            "End of epoch 55 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 56, iters: 480, time: 0.055, data: 0.013) G_GAN: 3.063 G_L1: 5.010 D_real: 0.195 D_fake: 0.822 \n",
            "End of epoch 56 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 57, iters: 256, time: 0.070, data: 0.029) G_GAN: 2.558 G_L1: 4.444 D_real: 0.201 D_fake: 0.156 \n",
            "End of epoch 57 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 58, iters: 32, time: 0.043, data: 0.026) G_GAN: 2.451 G_L1: 5.445 D_real: 0.537 D_fake: 0.040 \n",
            "(epoch: 58, iters: 832, time: 0.033, data: 0.029) G_GAN: 3.792 G_L1: 5.137 D_real: 0.064 D_fake: 0.047 \n",
            "End of epoch 58 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 59, iters: 608, time: 0.055, data: 0.033) G_GAN: 0.939 G_L1: 4.984 D_real: 0.620 D_fake: 0.426 \n",
            "saving the latest model (epoch 59, total_iters 60000)\n",
            "End of epoch 59 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 60, iters: 384, time: 0.062, data: 0.013) G_GAN: 1.878 G_L1: 4.451 D_real: 0.245 D_fake: 0.606 \n",
            "saving the model at the end of epoch 60, iters 61440\n",
            "End of epoch 60 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 61, iters: 160, time: 0.047, data: 0.020) G_GAN: 4.277 G_L1: 4.969 D_real: 0.056 D_fake: 0.354 \n",
            "(epoch: 61, iters: 960, time: 0.031, data: 0.029) G_GAN: 0.223 G_L1: 5.096 D_real: 1.781 D_fake: 0.206 \n",
            "End of epoch 61 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 62, iters: 736, time: 0.050, data: 0.015) G_GAN: 1.509 G_L1: 4.799 D_real: 0.341 D_fake: 0.068 \n",
            "End of epoch 62 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 63, iters: 512, time: 0.067, data: 0.041) G_GAN: 3.570 G_L1: 5.220 D_real: 0.043 D_fake: 0.066 \n",
            "End of epoch 63 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 64, iters: 288, time: 0.055, data: 0.038) G_GAN: 1.039 G_L1: 4.441 D_real: 0.457 D_fake: 0.694 \n",
            "End of epoch 64 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 65, iters: 64, time: 0.055, data: 0.012) G_GAN: 1.075 G_L1: 4.624 D_real: 0.684 D_fake: 0.232 \n",
            "(epoch: 65, iters: 864, time: 0.033, data: 0.022) G_GAN: 3.800 G_L1: 5.613 D_real: 0.057 D_fake: 0.030 \n",
            "saving the model at the end of epoch 65, iters 66560\n",
            "End of epoch 65 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 66, iters: 640, time: 0.060, data: 0.016) G_GAN: 4.065 G_L1: 5.889 D_real: 0.021 D_fake: 0.086 \n",
            "End of epoch 66 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 67, iters: 416, time: 0.047, data: 0.033) G_GAN: 5.388 G_L1: 6.097 D_real: 0.007 D_fake: 0.100 \n",
            "End of epoch 67 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 68, iters: 192, time: 0.062, data: 0.020) G_GAN: 2.704 G_L1: 5.312 D_real: 0.126 D_fake: 0.086 \n",
            "(epoch: 68, iters: 992, time: 0.032, data: 0.017) G_GAN: 1.293 G_L1: 5.106 D_real: 0.663 D_fake: 0.650 \n",
            "End of epoch 68 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 69, iters: 768, time: 0.055, data: 0.007) G_GAN: 3.172 G_L1: 4.860 D_real: 0.054 D_fake: 0.142 \n",
            "End of epoch 69 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 70, iters: 544, time: 0.054, data: 0.024) G_GAN: 1.061 G_L1: 4.678 D_real: 0.668 D_fake: 0.303 \n",
            "saving the model at the end of epoch 70, iters 71680\n",
            "End of epoch 70 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 71, iters: 320, time: 0.053, data: 0.032) G_GAN: 2.202 G_L1: 4.758 D_real: 0.161 D_fake: 0.157 \n",
            "End of epoch 71 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 72, iters: 96, time: 0.061, data: 0.022) G_GAN: 3.425 G_L1: 5.559 D_real: 0.075 D_fake: 0.050 \n",
            "(epoch: 72, iters: 896, time: 0.031, data: 0.015) G_GAN: 4.285 G_L1: 4.976 D_real: 0.015 D_fake: 0.038 \n",
            "End of epoch 72 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 73, iters: 672, time: 0.052, data: 0.015) G_GAN: 4.724 G_L1: 6.321 D_real: 0.035 D_fake: 0.011 \n",
            "End of epoch 73 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 74, iters: 448, time: 0.068, data: 0.030) G_GAN: 4.713 G_L1: 5.672 D_real: 0.013 D_fake: 0.022 \n",
            "End of epoch 74 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 75, iters: 224, time: 0.061, data: 0.049) G_GAN: 6.172 G_L1: 5.018 D_real: 0.015 D_fake: 0.002 \n",
            "(epoch: 75, iters: 1024, time: 0.029, data: 0.021) G_GAN: 5.567 G_L1: 4.451 D_real: 0.004 D_fake: 0.006 \n",
            "saving the model at the end of epoch 75, iters 76800\n",
            "End of epoch 75 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 76, iters: 800, time: 0.051, data: 2.314) G_GAN: 6.379 G_L1: 4.428 D_real: 0.005 D_fake: 0.004 \n",
            "End of epoch 76 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 77, iters: 576, time: 0.059, data: 0.047) G_GAN: 6.243 G_L1: 4.485 D_real: 0.003 D_fake: 0.003 \n",
            "End of epoch 77 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 78, iters: 352, time: 0.055, data: 0.040) G_GAN: 6.780 G_L1: 5.434 D_real: 0.068 D_fake: 0.003 \n",
            "End of epoch 78 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 79, iters: 128, time: 0.059, data: 0.023) G_GAN: 4.135 G_L1: 6.164 D_real: 0.025 D_fake: 0.126 \n",
            "saving the latest model (epoch 79, total_iters 80000)\n",
            "(epoch: 79, iters: 928, time: 0.020, data: 0.041) G_GAN: 5.352 G_L1: 4.683 D_real: 0.081 D_fake: 0.007 \n",
            "End of epoch 79 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 80, iters: 704, time: 0.071, data: 0.017) G_GAN: 4.895 G_L1: 5.619 D_real: 0.086 D_fake: 0.008 \n",
            "saving the model at the end of epoch 80, iters 81920\n",
            "End of epoch 80 / 200 \t Time Taken: 22 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 81, iters: 480, time: 0.067, data: 0.017) G_GAN: 5.888 G_L1: 6.141 D_real: 0.005 D_fake: 0.109 \n",
            "End of epoch 81 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 82, iters: 256, time: 0.061, data: 0.013) G_GAN: 4.633 G_L1: 6.908 D_real: 0.004 D_fake: 0.054 \n",
            "End of epoch 82 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 83, iters: 32, time: 0.051, data: 0.036) G_GAN: 6.725 G_L1: 6.694 D_real: 0.026 D_fake: 0.019 \n",
            "(epoch: 83, iters: 832, time: 0.036, data: 0.015) G_GAN: 6.567 G_L1: 6.312 D_real: 0.047 D_fake: 0.006 \n",
            "End of epoch 83 / 200 \t Time Taken: 20 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 84, iters: 608, time: 0.060, data: 0.014) G_GAN: 6.132 G_L1: 6.537 D_real: 0.058 D_fake: 0.002 \n",
            "End of epoch 84 / 200 \t Time Taken: 19 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 85, iters: 384, time: 0.060, data: 0.014) G_GAN: 5.662 G_L1: 7.258 D_real: 0.011 D_fake: 0.054 \n",
            "saving the model at the end of epoch 85, iters 87040\n",
            "End of epoch 85 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 86, iters: 160, time: 0.053, data: 0.036) G_GAN: 2.059 G_L1: 5.701 D_real: 0.744 D_fake: 1.000 \n",
            "(epoch: 86, iters: 960, time: 0.046, data: 0.025) G_GAN: 1.104 G_L1: 5.378 D_real: 1.289 D_fake: 0.069 \n",
            "End of epoch 86 / 200 \t Time Taken: 21 sec\n",
            "learning rate 0.0010000 -> 0.0010000\n",
            "(epoch: 87, iters: 736, time: 0.062, data: 0.010) G_GAN: 2.374 G_L1: 5.619 D_real: 0.389 D_fake: 0.484 \n",
            "Traceback (most recent call last):\n",
            "  File \"train.py\", line 51, in <module>\n",
            "    model.set_input(data)         # unpack data from dataset and apply preprocessing\n",
            "  File \"/content/pytorch-CycleGAN-and-pix2pix/models/pix2pix_model.py\", line 82, in set_input\n",
            "    self.real_A = input['A' if AtoB else 'B'].to(self.device)\n",
            "KeyboardInterrupt\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[31m(failed 255).\u001b[0m Press Control-C to abort syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: D_fake ▂█▁▃▂▃▁▃▁▁▃▂▅▂▁▄▃▂▅▂▅▃▂▅▁▃▁▂▁▂▁▁▁▁▁▁▁▁▁▂\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: D_real ▂▁▂▁▁▅█▂▂▃▁▂▁▃▃▃▃▇▁▃▂▂▂▂▃▂▁▁▂▃▁▁▁▁▁▁▁▁▁▂\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:  G_GAN ▂▃▃▅▄▂▁▂▂▁▃▃▆▁▂▂▂▁▃▁▂▃▂▃▁▄▅▅▂▂▆▄▄▆▇█▆█▇▃\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   G_L1 ▅▅▇▆█▅▃▄▄▅▃▄▅▄▃▂▂▅▂▁▂▂▂▃▂▃▃▃▂▂▅▃▄▄▂▄▄▇█▄\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: D_fake 0.48447\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: D_real 0.38856\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:  G_GAN 2.37432\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   G_L1 5.61901\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mpixbike\u001b[0m: \u001b[34m\u001b[4mhttps://wandb.ai/ozankaraali/CycleGAN-and-pix2pix/runs/2fks0a12\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 420 media file(s), 348 artifact file(s) and 0 other file(s)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20220821_082940-2fks0a12/logs\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing\n",
        "\n",
        "-   `python test.py --dataroot ./datasets/pixbike-split --direction AtoB --model pixbike --name pixbike`\n",
        "\n",
        "Change the `--dataroot`, `--name`, and `--direction` to be consistent with your trained model's configuration and how you want to transform images.\n",
        "\n",
        "> from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix:\n",
        "> Note that we specified --direction BtoA as Facades dataset's A to B direction is photos to labels.\n",
        "\n",
        "> If you would like to apply a pre-trained model to a collection of input images (rather than image pairs), please use --model test option. See ./scripts/test_single.sh for how to apply a model to Facade label maps (stored in the directory facades/testB).\n",
        "\n",
        "> See a list of currently available models at ./scripts/download_pix2pix_model.sh"
      ],
      "metadata": {
        "id": "p0B-4HlW0p8W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python test.py --dataroot ./datasets/pixbike-split --direction AtoB --model pix2pix --name pixbike --use_wandb --epoch 55"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbIStu9uHnLN",
        "outputId": "9d970dba-1f52-40c3-b448-95c42911eedc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------- Options ---------------\n",
            "             aspect_ratio: 1.0                           \n",
            "               batch_size: 1                             \n",
            "          checkpoints_dir: ./checkpoints                 \n",
            "                crop_size: 256                           \n",
            "                 dataroot: ./datasets/pixbike-split      \t[default: None]\n",
            "             dataset_mode: aligned                       \n",
            "                direction: AtoB                          \n",
            "          display_winsize: 256                           \n",
            "                    epoch: 55                            \t[default: latest]\n",
            "                     eval: False                         \n",
            "                  gpu_ids: 0                             \n",
            "                init_gain: 0.02                          \n",
            "                init_type: normal                        \n",
            "                 input_nc: 3                             \n",
            "                  isTrain: False                         \t[default: None]\n",
            "                load_iter: 0                             \t[default: 0]\n",
            "                load_size: 256                           \n",
            "         max_dataset_size: inf                           \n",
            "                    model: pix2pix                       \t[default: test]\n",
            "               n_layers_D: 3                             \n",
            "                     name: pixbike                       \t[default: experiment_name]\n",
            "                      ndf: 64                            \n",
            "                     netD: basic                         \n",
            "                     netG: unet_256                      \n",
            "                      ngf: 64                            \n",
            "               no_dropout: False                         \n",
            "                  no_flip: False                         \n",
            "                     norm: batch                         \n",
            "                 num_test: 50                            \n",
            "              num_threads: 4                             \n",
            "                output_nc: 3                             \n",
            "                    phase: test                          \n",
            "               preprocess: resize_and_crop               \n",
            "              results_dir: ./results/                    \n",
            "           serial_batches: False                         \n",
            "                   suffix:                               \n",
            "                use_wandb: True                          \t[default: False]\n",
            "                  verbose: False                         \n",
            "----------------- End -------------------\n",
            "dataset [AlignedDataset] was created\n",
            "initialize network with normal\n",
            "model [Pix2PixModel] was created\n",
            "loading the model from ./checkpoints/pixbike/55_net_G.pth\n",
            "---------- Networks initialized -------------\n",
            "[Network G] Total number of parameters : 54.414 M\n",
            "-----------------------------------------------\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mozankaraali\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.13.1\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/pytorch-CycleGAN-and-pix2pix/wandb/run-20220821_090803-2jalit4v\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mpixbike\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/ozankaraali/CycleGAN-and-pix2pix\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/ozankaraali/CycleGAN-and-pix2pix/runs/2jalit4v\u001b[0m\n",
            "creating web directory ./results/pixbike/test_55\n",
            "processing (0000)-th image... ['./datasets/pixbike-split/test/046cce94-43ec-4b03-8589-f5137fef9936.png']\n",
            "processing (0005)-th image... ['./datasets/pixbike-split/test/0ea0e14a-001e-4b95-85d8-ab196641235e.png']\n",
            "processing (0010)-th image... ['./datasets/pixbike-split/test/1442e077-dc8f-4b62-ab36-49997437cb06.png']\n",
            "processing (0015)-th image... ['./datasets/pixbike-split/test/19b26a11-6aeb-4b5f-95ff-f6012ee8aee0.png']\n",
            "processing (0020)-th image... ['./datasets/pixbike-split/test/2b7c6064-4cdc-461f-b745-008262afa6ab.png']\n",
            "processing (0025)-th image... ['./datasets/pixbike-split/test/40c642b0-9fbb-4efa-a0ef-3f2ebfcace4c.png']\n",
            "processing (0030)-th image... ['./datasets/pixbike-split/test/477353c2-8a30-40b7-ac32-8dd62b18dde9.png']\n",
            "processing (0035)-th image... ['./datasets/pixbike-split/test/4fb80a1d-2379-4c7f-a67a-9ccd570d4c49.png']\n",
            "processing (0040)-th image... ['./datasets/pixbike-split/test/5511f953-24a2-4086-9909-decbf03158e4.png']\n",
            "processing (0045)-th image... ['./datasets/pixbike-split/test/662b4299-ff92-4db0-94eb-2c015b29cba7.png']\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mpixbike\u001b[0m: \u001b[34m\u001b[4mhttps://wandb.ai/ozankaraali/CycleGAN-and-pix2pix/runs/2jalit4v\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 150 media file(s), 0 artifact file(s) and 0 other file(s)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20220821_090803-2jalit4v/logs\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization\n",
        "More can be found in W&B data but let's render locally:"
      ],
      "metadata": {
        "id": "pJ0Bu_jS1o64"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# These images might not be in your run since UUIDs won't match if you didn't preload, please refer to the\n",
        "# ./results/pixbike/test_latest/images/ folder\n",
        "img = plt.imread('./results/pixbike/test_55/images/046cce94-43ec-4b03-8589-f5137fef9936_fake_B.png')\n",
        "plt.imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "8DIrHOAj1u_J",
        "outputId": "e277043a-5bab-43d6-8a22-65028b1e9011"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdc47351f50>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = plt.imread('./results/pixbike/test_60/images/046cce94-43ec-4b03-8589-f5137fef9936_real_B.png')\n",
        "plt.imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "0XRcCHuj1xwe",
        "outputId": "ea5e1527-8b7c-4429-997e-ab35dc5764c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdc47764b50>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = plt.imread('./results/pixbike/test_60/images/046cce94-43ec-4b03-8589-f5137fef9936_real_A.png')\n",
        "plt.imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "2Iw5rgXl11AB",
        "outputId": "0d9fba70-8a8b-402d-86a6-b16d8efd5dfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdc476da790>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can save all the folder in Google Drive\n",
        "# !cp -R /content/pytorch-CycleGAN-and-pix2pix/ /content/drive/MyDrive/pixbike-project"
      ],
      "metadata": {
        "id": "69wTxxMl14dX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We can save all the folder in Google Drive\n",
        "!cp -R /content/pytorch-CycleGAN-and-pix2pix/ /content/drive/MyDrive/pixbike-project-21aug-save-2"
      ],
      "metadata": {
        "id": "vYX2A2Gu_RN1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}