CycleGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CycleGAN",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/justin95214/CycleGAN-GTA-to-Real-Image/blob/main/CycleGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1-rJhxT6hJ7",
        "outputId": "41780fe5-5272-4356-b5fd-a51ca53b5927",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VIGyIus8Vr7"
      },
      "source": [
        "Take a look at the [repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) for more information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wNjDKdQy35h"
      },
      "source": [
        "# Install"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRm-USlsHgEV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c48da1d-d97f-42b9-8dc2-e9c9b29d900a"
      },
      "source": [
        "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pytorch-CycleGAN-and-pix2pix'...\n",
            "remote: Enumerating objects: 2337, done.\u001b[K\n",
            "remote: Total 2337 (delta 0), reused 0 (delta 0), pack-reused 2337\u001b[K\n",
            "Receiving objects: 100% (2337/2337), 8.09 MiB | 35.41 MiB/s, done.\n",
            "Resolving deltas: 100% (1499/1499), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pt3igws3eiVp"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/pytorch-CycleGAN-and-pix2pix/')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1EySlOXwwoa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9492aabf-ff2e-444f-fb24-45b1ca7e1bc8"
      },
      "source": [
        "!pip install -r requirements.txt"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.9.0+cu102)\n",
            "Requirement already satisfied: torchvision>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (0.10.0+cu102)\n",
            "Requirement already satisfied: dominate>=2.4.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (2.6.0)\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: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4.0->-r requirements.txt (line 1)) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=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: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->-r requirements.txt (line 2)) (1.19.5)\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)) (1.1.0)\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: scipy in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.4.1)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (2.23.0)\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: 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: 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: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (2021.5.30)\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)) (2.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoSa8VZA6-qQ"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwgxYRM77Cd4",
        "outputId": "53244fe7-dcc0-4179-d79e-ed4c5fb82092",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!unzip frame30.zip "
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  frame30.zip\n",
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            "   creating: trainA/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9DWq-JI-qR0"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/pytorch-CycleGAN-and-pix2pix')\n"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8daqlgVhw29P"
      },
      "source": [
        "# Datasets\n",
        "\n",
        "Download one of the official datasets with:\n",
        "\n",
        "-   `bash ./datasets/download_cyclegan_dataset.sh [apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower]`\n",
        "\n",
        "Or use your own dataset by creating the appropriate folders and adding in the images.\n",
        "\n",
        "-   Create a dataset folder under `/dataset` for your dataset.\n",
        "-   Create subfolders `testA`, `testB`, `trainA`, and `trainB` under your dataset's folder. Place any images you want to transform from a to b (cat2dog) in the `testA` folder, images you want to transform from b to a (dog2cat) in the `testB` folder, and do the same for the `trainA` and `trainB` folders."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrdOettJxaCc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51319c04-798d-40db-eb75-6b85f5e88116"
      },
      "source": [
        "!bash ./datasets/download_cyclegan_dataset.sh horse2zebra"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Specified [horse2zebra]\n",
            "WARNING: timestamping does nothing in combination with -O. See the manual\n",
            "for details.\n",
            "\n",
            "--2021-07-08 17:18:58--  https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip\n",
            "Resolving people.eecs.berkeley.edu (people.eecs.berkeley.edu)... 128.32.244.190\n",
            "Connecting to people.eecs.berkeley.edu (people.eecs.berkeley.edu)|128.32.244.190|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 116867962 (111M) [application/zip]\n",
            "Saving to: ‘./datasets/horse2zebra.zip’\n",
            "\n",
            "./datasets/horse2ze 100%[===================>] 111.45M  4.73MB/s    in 47s     \n",
            "\n",
            "2021-07-08 17:19:46 (2.38 MB/s) - ‘./datasets/horse2zebra.zip’ saved [116867962/116867962]\n",
            "\n",
            "Archive:  ./datasets/horse2zebra.zip\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdUz4116xhpm"
      },
      "source": [
        "# Pretrained models\n",
        "\n",
        "Download one of the official pretrained models with:\n",
        "\n",
        "-   `bash ./scripts/download_cyclegan_model.sh [apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower]`\n",
        "\n",
        "Or add your own pretrained model to `./checkpoints/{NAME}_pretrained/latest_net_G.pt`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B75UqtKhxznS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ca3ca5e-796e-43bb-b9a3-9ac1b24228ce"
      },
      "source": [
        "!bash ./scripts/download_cyclegan_model.sh horse2zebra"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Note: available models are apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower\n",
            "Specified [horse2zebra]\n",
            "WARNING: timestamping does nothing in combination with -O. See the manual\n",
            "for details.\n",
            "\n",
            "--2021-07-08 17:20:23--  http://efrosgans.eecs.berkeley.edu/cyclegan/pretrained_models/horse2zebra.pth\n",
            "Resolving efrosgans.eecs.berkeley.edu (efrosgans.eecs.berkeley.edu)... 128.32.244.190\n",
            "Connecting to efrosgans.eecs.berkeley.edu (efrosgans.eecs.berkeley.edu)|128.32.244.190|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 45575747 (43M)\n",
            "Saving to: ‘./checkpoints/horse2zebra_pretrained/latest_net_G.pth’\n",
            "\n",
            "./checkpoints/horse 100%[===================>]  43.46M  2.76MB/s    in 17s     \n",
            "\n",
            "2021-07-08 17:20:40 (2.51 MB/s) - ‘./checkpoints/horse2zebra_pretrained/latest_net_G.pth’ saved [45575747/45575747]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFw1kDQBx3LN"
      },
      "source": [
        "# Training\n",
        "\n",
        "-   `python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan`\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. I've found that a batch size of 16 fits onto 4 V100s and can finish training an epoch in ~90s.\n",
        "\n",
        "Once your model has trained, copy over the last checkpoint to a format that the testing model can automatically detect:\n",
        "\n",
        "Use `cp ./checkpoints/horse2zebra/latest_net_G_A.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class A to class B and `cp ./checkpoints/horse2zebra/latest_net_G_B.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class B to class A.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sp7TCT2x9dB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76823d4d-098e-458e-f996-7cef21ad8ae2"
      },
      "source": [
        "!python train.py --dataroot ./datasets/frame --name horse2zebra --model cycle_gan"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------- Options ---------------\n",
            "               batch_size: 1                             \n",
            "                    beta1: 0.5                           \n",
            "          checkpoints_dir: ./checkpoints                 \n",
            "           continue_train: False                         \n",
            "                crop_size: 256                           \n",
            "                 dataroot: ./datasets/frame              \t[default: None]\n",
            "             dataset_mode: unaligned                     \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: lsgan                         \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_A: 10.0                          \n",
            "                 lambda_B: 10.0                          \n",
            "          lambda_identity: 0.5                           \n",
            "                load_iter: 0                             \t[default: 0]\n",
            "                load_size: 286                           \n",
            "                       lr: 0.0002                        \n",
            "           lr_decay_iters: 50                            \n",
            "                lr_policy: linear                        \n",
            "         max_dataset_size: inf                           \n",
            "                    model: cycle_gan                     \n",
            "                 n_epochs: 100                           \n",
            "           n_epochs_decay: 100                           \n",
            "               n_layers_D: 3                             \n",
            "                     name: horse2zebra                   \t[default: experiment_name]\n",
            "                      ndf: 64                            \n",
            "                     netD: basic                         \n",
            "                     netG: resnet_9blocks                \n",
            "                      ngf: 64                            \n",
            "               no_dropout: True                          \n",
            "                  no_flip: False                         \n",
            "                  no_html: False                         \n",
            "                     norm: instance                      \n",
            "              num_threads: 4                             \n",
            "                output_nc: 3                             \n",
            "                    phase: train                         \n",
            "                pool_size: 50                            \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",
            "                  verbose: False                         \n",
            "----------------- End -------------------\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py:281: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n",
            "  \"Argument interpolation should be of type InterpolationMode instead of int. \"\n",
            "dataset [UnalignedDataset] was created\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "The number of training images = 622\n",
            "initialize network with normal\n",
            "initialize network with normal\n",
            "initialize network with normal\n",
            "initialize network with normal\n",
            "model [CycleGANModel] was created\n",
            "---------- Networks initialized -------------\n",
            "[Network G_A] Total number of parameters : 11.378 M\n",
            "[Network G_B] Total number of parameters : 11.378 M\n",
            "[Network D_A] Total number of parameters : 2.765 M\n",
            "[Network D_B] Total number of parameters : 2.765 M\n",
            "-----------------------------------------------\n",
            "Setting up a new session...\n",
            "Exception in user code:\n",
            "------------------------------------------------------------\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/connection.py\", line 159, in _new_conn\n",
            "    (self._dns_host, self.port), self.timeout, **extra_kw)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/util/connection.py\", line 80, in create_connection\n",
            "    raise err\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/util/connection.py\", line 70, in create_connection\n",
            "    sock.connect(sa)\n",
            "ConnectionRefusedError: [Errno 111] Connection refused\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py\", line 600, in urlopen\n",
            "    chunked=chunked)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py\", line 354, in _make_request\n",
            "    conn.request(method, url, **httplib_request_kw)\n",
            "  File \"/usr/lib/python3.7/http/client.py\", line 1277, in request\n",
            "    self._send_request(method, url, body, headers, encode_chunked)\n",
            "  File \"/usr/lib/python3.7/http/client.py\", line 1323, in _send_request\n",
            "    self.endheaders(body, encode_chunked=encode_chunked)\n",
            "  File \"/usr/lib/python3.7/http/client.py\", line 1272, in endheaders\n",
            "    self._send_output(message_body, encode_chunked=encode_chunked)\n",
            "  File \"/usr/lib/python3.7/http/client.py\", line 1032, in _send_output\n",
            "    self.send(msg)\n",
            "  File \"/usr/lib/python3.7/http/client.py\", line 972, in send\n",
            "    self.connect()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/connection.py\", line 181, in connect\n",
            "    conn = self._new_conn()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/connection.py\", line 168, in _new_conn\n",
            "    self, \"Failed to establish a new connection: %s\" % e)\n",
            "urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7f3756abbf50>: Failed to establish a new connection: [Errno 111] Connection refused\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/adapters.py\", line 449, in send\n",
            "    timeout=timeout\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py\", line 638, in urlopen\n",
            "    _stacktrace=sys.exc_info()[2])\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/urllib3/util/retry.py\", line 399, in increment\n",
            "    raise MaxRetryError(_pool, url, error or ResponseError(cause))\n",
            "urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=8097): Max retries exceeded with url: /env/main (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f3756abbf50>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/visdom/__init__.py\", line 711, in _send\n",
            "    data=json.dumps(msg),\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/visdom/__init__.py\", line 677, in _handle_post\n",
            "    r = self.session.post(url, data=data)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 578, in post\n",
            "    return self.request('POST', url, data=data, json=json, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 530, in request\n",
            "    resp = self.send(prep, **send_kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 643, in send\n",
            "    r = adapter.send(request, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/adapters.py\", line 516, in send\n",
            "    raise ConnectionError(e, request=request)\n",
            "requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=8097): Max retries exceeded with url: /env/main (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f3756abbf50>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "[Errno 99] Cannot assign requested address\n",
            "[Errno 99] Cannot assign requested address\n",
            "[Errno 99] Cannot assign requested address\n",
            "Visdom python client failed to establish socket to get messages from the server. This feature is optional and can be disabled by initializing Visdom with `use_incoming_socket=False`, which will prevent waiting for this request to timeout.\n",
            "\n",
            "\n",
            "Could not connect to Visdom server. \n",
            " Trying to start a server....\n",
            "Command: /usr/bin/python3 -m visdom.server -p 8097 &>/dev/null &\n",
            "create web directory ./checkpoints/horse2zebra/web...\n",
            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: 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 0.0002000 -> 0.0002000\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "[Errno 99] Cannot assign requested address\n",
            "(epoch: 1, iters: 100, time: 0.508, data: 1.865) D_A: 0.258 G_A: 0.425 cycle_A: 1.727 idt_A: 0.979 D_B: 0.519 G_B: 0.623 cycle_B: 2.088 idt_B: 0.706 \n",
            "(epoch: 1, iters: 200, time: 0.510, data: 0.003) D_A: 0.277 G_A: 0.236 cycle_A: 2.569 idt_A: 1.010 D_B: 0.320 G_B: 0.373 cycle_B: 2.498 idt_B: 0.933 \n",
            "(epoch: 1, iters: 300, time: 0.532, data: 0.005) D_A: 0.349 G_A: 0.052 cycle_A: 1.542 idt_A: 0.869 D_B: 0.254 G_B: 0.377 cycle_B: 2.049 idt_B: 0.663 \n",
            "(epoch: 1, iters: 400, time: 1.063, data: 0.009) D_A: 0.247 G_A: 0.285 cycle_A: 1.628 idt_A: 0.614 D_B: 0.278 G_B: 0.385 cycle_B: 1.133 idt_B: 0.812 \n",
            "(epoch: 1, iters: 500, time: 0.543, data: 0.002) D_A: 0.226 G_A: 0.342 cycle_A: 1.072 idt_A: 1.189 D_B: 0.338 G_B: 0.770 cycle_B: 2.768 idt_B: 0.341 \n",
            "(epoch: 1, iters: 600, time: 0.544, data: 0.002) D_A: 0.279 G_A: 0.350 cycle_A: 3.180 idt_A: 0.902 D_B: 0.277 G_B: 0.218 cycle_B: 1.952 idt_B: 0.975 \n",
            "End of epoch 1 / 200 \t Time Taken: 312 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 2, iters: 78, time: 0.546, data: 0.010) D_A: 0.353 G_A: 0.515 cycle_A: 1.793 idt_A: 0.698 D_B: 0.358 G_B: 0.659 cycle_B: 1.799 idt_B: 0.886 \n",
            "(epoch: 2, iters: 178, time: 1.037, data: 0.002) D_A: 0.261 G_A: 0.182 cycle_A: 1.333 idt_A: 0.531 D_B: 0.413 G_B: 0.466 cycle_B: 1.404 idt_B: 0.614 \n",
            "(epoch: 2, iters: 278, time: 0.531, data: 0.002) D_A: 0.187 G_A: 0.526 cycle_A: 0.933 idt_A: 1.201 D_B: 0.135 G_B: 0.345 cycle_B: 2.284 idt_B: 0.454 \n",
            "(epoch: 2, iters: 378, time: 0.548, data: 0.002) D_A: 0.230 G_A: 0.436 cycle_A: 0.468 idt_A: 0.535 D_B: 0.768 G_B: 1.022 cycle_B: 1.139 idt_B: 0.200 \n",
            "(epoch: 2, iters: 478, time: 0.544, data: 0.001) D_A: 0.315 G_A: 0.419 cycle_A: 0.641 idt_A: 0.484 D_B: 0.273 G_B: 0.174 cycle_B: 1.021 idt_B: 0.299 \n",
            "(epoch: 2, iters: 578, time: 0.813, data: 0.003) D_A: 0.277 G_A: 0.815 cycle_A: 2.840 idt_A: 0.496 D_B: 0.197 G_B: 0.366 cycle_B: 1.194 idt_B: 1.182 \n",
            "End of epoch 2 / 200 \t Time Taken: 321 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 3, iters: 56, time: 0.548, data: 0.002) D_A: 0.225 G_A: 0.822 cycle_A: 0.376 idt_A: 0.723 D_B: 0.154 G_B: 0.421 cycle_B: 1.470 idt_B: 0.213 \n",
            "(epoch: 3, iters: 156, time: 0.550, data: 0.006) D_A: 0.090 G_A: 0.322 cycle_A: 1.138 idt_A: 0.731 D_B: 0.314 G_B: 0.326 cycle_B: 1.545 idt_B: 0.407 \n",
            "(epoch: 3, iters: 256, time: 0.547, data: 0.002) D_A: 0.123 G_A: 0.360 cycle_A: 0.804 idt_A: 0.492 D_B: 0.247 G_B: 0.307 cycle_B: 1.005 idt_B: 0.393 \n",
            "(epoch: 3, iters: 356, time: 1.013, data: 0.002) D_A: 0.550 G_A: 0.459 cycle_A: 1.351 idt_A: 0.619 D_B: 0.286 G_B: 0.205 cycle_B: 1.267 idt_B: 0.601 \n",
            "(epoch: 3, iters: 456, time: 0.542, data: 0.002) D_A: 0.255 G_A: 0.229 cycle_A: 1.266 idt_A: 0.554 D_B: 0.207 G_B: 0.303 cycle_B: 1.722 idt_B: 0.536 \n",
            "(epoch: 3, iters: 556, time: 0.546, data: 0.002) D_A: 0.202 G_A: 0.318 cycle_A: 2.005 idt_A: 0.646 D_B: 0.334 G_B: 0.510 cycle_B: 1.809 idt_B: 0.683 \n",
            "End of epoch 3 / 200 \t Time Taken: 320 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 4, iters: 34, time: 0.545, data: 0.002) D_A: 0.378 G_A: 0.187 cycle_A: 1.463 idt_A: 0.510 D_B: 0.243 G_B: 0.412 cycle_B: 0.690 idt_B: 0.602 \n",
            "(epoch: 4, iters: 134, time: 1.042, data: 0.002) D_A: 0.247 G_A: 0.216 cycle_A: 2.016 idt_A: 0.486 D_B: 0.348 G_B: 0.270 cycle_B: 1.086 idt_B: 0.911 \n",
            "(epoch: 4, iters: 234, time: 0.533, data: 0.002) D_A: 0.367 G_A: 0.331 cycle_A: 0.293 idt_A: 0.396 D_B: 0.259 G_B: 0.138 cycle_B: 0.920 idt_B: 0.133 \n",
            "(epoch: 4, iters: 334, time: 0.545, data: 0.004) D_A: 0.178 G_A: 0.390 cycle_A: 0.449 idt_A: 0.543 D_B: 0.240 G_B: 0.340 cycle_B: 1.300 idt_B: 0.264 \n",
            "(epoch: 4, iters: 434, time: 0.546, data: 0.002) D_A: 0.161 G_A: 0.090 cycle_A: 1.310 idt_A: 0.269 D_B: 0.425 G_B: 0.353 cycle_B: 0.747 idt_B: 0.707 \n",
            "(epoch: 4, iters: 534, time: 0.835, data: 0.002) D_A: 0.471 G_A: 0.472 cycle_A: 0.804 idt_A: 0.454 D_B: 0.303 G_B: 0.355 cycle_B: 0.999 idt_B: 0.343 \n",
            "End of epoch 4 / 200 \t Time Taken: 320 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 5, iters: 12, time: 0.546, data: 0.002) D_A: 0.316 G_A: 0.489 cycle_A: 0.886 idt_A: 0.538 D_B: 0.256 G_B: 0.391 cycle_B: 1.130 idt_B: 0.357 \n",
            "(epoch: 5, iters: 112, time: 0.547, data: 0.002) D_A: 0.194 G_A: 0.351 cycle_A: 0.580 idt_A: 0.416 D_B: 0.171 G_B: 0.687 cycle_B: 1.606 idt_B: 0.258 \n",
            "(epoch: 5, iters: 212, time: 0.543, data: 0.002) D_A: 0.191 G_A: 0.268 cycle_A: 0.742 idt_A: 0.317 D_B: 0.212 G_B: 0.483 cycle_B: 0.787 idt_B: 0.324 \n",
            "(epoch: 5, iters: 312, time: 1.082, data: 0.013) D_A: 0.269 G_A: 1.068 cycle_A: 0.706 idt_A: 0.345 D_B: 0.302 G_B: 0.214 cycle_B: 1.025 idt_B: 0.319 \n",
            "(epoch: 5, iters: 412, time: 0.546, data: 0.002) D_A: 0.416 G_A: 0.741 cycle_A: 0.801 idt_A: 0.484 D_B: 0.299 G_B: 0.299 cycle_B: 1.079 idt_B: 0.316 \n",
            "(epoch: 5, iters: 512, time: 0.544, data: 0.002) D_A: 0.162 G_A: 0.472 cycle_A: 1.091 idt_A: 0.565 D_B: 0.165 G_B: 0.453 cycle_B: 1.013 idt_B: 0.466 \n",
            "(epoch: 5, iters: 612, time: 0.548, data: 0.002) D_A: 0.114 G_A: 0.267 cycle_A: 1.064 idt_A: 0.397 D_B: 0.234 G_B: 0.250 cycle_B: 0.884 idt_B: 0.443 \n",
            "saving the model at the end of epoch 5, iters 3110\n",
            "End of epoch 5 / 200 \t Time Taken: 321 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 6, iters: 90, time: 1.188, data: 0.002) D_A: 0.242 G_A: 0.800 cycle_A: 0.360 idt_A: 0.280 D_B: 0.188 G_B: 0.554 cycle_B: 0.763 idt_B: 0.175 \n",
            "(epoch: 6, iters: 190, time: 0.547, data: 0.002) D_A: 0.287 G_A: 0.083 cycle_A: 0.612 idt_A: 0.320 D_B: 0.197 G_B: 0.554 cycle_B: 0.628 idt_B: 0.225 \n",
            "(epoch: 6, iters: 290, time: 0.546, data: 0.010) D_A: 0.168 G_A: 0.408 cycle_A: 0.612 idt_A: 0.319 D_B: 0.258 G_B: 0.264 cycle_B: 0.824 idt_B: 0.233 \n",
            "(epoch: 6, iters: 390, time: 0.543, data: 0.002) D_A: 0.073 G_A: 0.597 cycle_A: 1.230 idt_A: 1.010 D_B: 0.233 G_B: 0.271 cycle_B: 1.649 idt_B: 0.380 \n",
            "(epoch: 6, iters: 490, time: 0.795, data: 0.002) D_A: 0.331 G_A: 0.133 cycle_A: 1.461 idt_A: 0.388 D_B: 0.244 G_B: 0.342 cycle_B: 0.719 idt_B: 0.643 \n",
            "(epoch: 6, iters: 590, time: 0.548, data: 0.002) D_A: 0.186 G_A: 0.214 cycle_A: 0.885 idt_A: 0.608 D_B: 0.257 G_B: 0.428 cycle_B: 1.501 idt_B: 0.516 \n",
            "End of epoch 6 / 200 \t Time Taken: 321 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 7, iters: 68, time: 0.543, data: 0.003) D_A: 0.581 G_A: 0.021 cycle_A: 0.409 idt_A: 0.398 D_B: 0.251 G_B: 0.365 cycle_B: 1.043 idt_B: 0.163 \n",
            "(epoch: 7, iters: 168, time: 0.546, data: 0.002) D_A: 0.093 G_A: 0.635 cycle_A: 1.638 idt_A: 0.650 D_B: 0.324 G_B: 0.341 cycle_B: 1.316 idt_B: 0.576 \n",
            "(epoch: 7, iters: 268, time: 1.164, data: 0.002) D_A: 0.164 G_A: 0.364 cycle_A: 2.034 idt_A: 0.238 D_B: 0.292 G_B: 0.314 cycle_B: 0.718 idt_B: 0.728 \n",
            "(epoch: 7, iters: 368, time: 0.547, data: 0.002) D_A: 0.558 G_A: 0.747 cycle_A: 0.688 idt_A: 0.318 D_B: 0.290 G_B: 0.261 cycle_B: 0.750 idt_B: 0.327 \n",
            "(epoch: 7, iters: 468, time: 0.546, data: 0.002) D_A: 0.180 G_A: 0.394 cycle_A: 0.978 idt_A: 0.279 D_B: 0.296 G_B: 0.330 cycle_B: 0.514 idt_B: 0.374 \n",
            "(epoch: 7, iters: 568, time: 0.545, data: 0.002) D_A: 0.255 G_A: 0.108 cycle_A: 0.866 idt_A: 0.660 D_B: 0.242 G_B: 0.350 cycle_B: 1.865 idt_B: 0.264 \n",
            "End of epoch 7 / 200 \t Time Taken: 319 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 8, iters: 46, time: 1.090, data: 0.002) D_A: 0.216 G_A: 0.378 cycle_A: 0.263 idt_A: 0.570 D_B: 0.201 G_B: 0.381 cycle_B: 1.613 idt_B: 0.113 \n",
            "(epoch: 8, iters: 146, time: 0.546, data: 0.002) D_A: 0.119 G_A: 0.243 cycle_A: 0.574 idt_A: 0.397 D_B: 0.315 G_B: 0.513 cycle_B: 0.895 idt_B: 0.206 \n",
            "(epoch: 8, iters: 246, time: 0.547, data: 0.002) D_A: 0.192 G_A: 0.713 cycle_A: 0.464 idt_A: 0.466 D_B: 0.282 G_B: 0.225 cycle_B: 0.804 idt_B: 0.166 \n",
            "(epoch: 8, iters: 346, time: 0.548, data: 0.002) D_A: 0.181 G_A: 0.220 cycle_A: 0.855 idt_A: 0.402 D_B: 0.281 G_B: 0.244 cycle_B: 1.203 idt_B: 0.428 \n",
            "(epoch: 8, iters: 446, time: 0.818, data: 0.002) D_A: 0.253 G_A: 0.088 cycle_A: 0.380 idt_A: 0.210 D_B: 0.222 G_B: 0.288 cycle_B: 0.415 idt_B: 0.226 \n",
            "(epoch: 8, iters: 546, time: 0.544, data: 0.002) D_A: 0.233 G_A: 0.997 cycle_A: 0.429 idt_A: 0.316 D_B: 0.197 G_B: 0.425 cycle_B: 0.857 idt_B: 0.201 \n",
            "End of epoch 8 / 200 \t Time Taken: 319 sec\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 9, iters: 24, time: 0.547, data: 0.002) D_A: 0.138 G_A: 0.441 cycle_A: 0.844 idt_A: 0.320 D_B: 0.263 G_B: 0.334 cycle_B: 0.852 idt_B: 0.252 \n",
            "saving the latest model (epoch 9, total_iters 5000)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGQ5QDOSB-TB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwIJxShPGtBj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xTEzbixI1tq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UkcaFZiyASl"
      },
      "source": [
        "# Testing\n",
        "\n",
        "-   `python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout`\n",
        "\n",
        "Change the `--dataroot` and `--name` to be consistent with your trained model's configuration.\n",
        "\n",
        "> from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix:\n",
        "> The option --model test is used for generating results of CycleGAN only for one side. This option will automatically set --dataset_mode single, which only loads the images from one set. On the contrary, using --model cycle_gan requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.\n",
        "\n",
        "> For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCsKkEq0yGh0"
      },
      "source": [
        "!python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OzSKIPUByfiN"
      },
      "source": [
        "# Visualize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Mgg8raPyizq"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_fake.png')\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0G3oVH9DyqLQ"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_real.png')\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}