brownbear2polarbear_cyclegan.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "brownbear2polarbear_cyclegan.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "I9cPytYsCBuM",
        "colab_type": "code",
        "outputId": "3b868198-01d9-4665-9fe2-d4f2c1e4c563",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_zwRdQtUKWhF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K4utdpLjKqZS",
        "colab_type": "code",
        "outputId": "716da591-93b5-4b02-9e0a-e0db8da668cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        }
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pytorch-CycleGAN-and-pix2pix'...\n",
            "remote: Enumerating objects: 2, done.\u001b[K\n",
            "remote: Counting objects:  50% (1/2)   \u001b[K\rremote: Counting objects: 100% (2/2)   \u001b[K\rremote: Counting objects: 100% (2/2), done.\u001b[K\n",
            "remote: Compressing objects:  50% (1/2)   \u001b[K\rremote: Compressing objects: 100% (2/2)   \u001b[K\rremote: Compressing objects: 100% (2/2), done.\u001b[K\n",
            "Receiving objects:   0% (1/1989)   \rReceiving objects:   1% (20/1989)   \rReceiving objects:   2% (40/1989)   \rReceiving objects:   3% (60/1989)   \rReceiving objects:   4% (80/1989)   \rReceiving objects:   5% (100/1989)   \rReceiving objects:   6% (120/1989)   \rReceiving objects:   7% (140/1989)   \rReceiving objects:   8% (160/1989)   \rReceiving objects:   9% (180/1989)   \rReceiving objects:  10% (199/1989)   \rReceiving objects:  11% (219/1989)   \rReceiving objects:  12% (239/1989)   \rReceiving objects:  13% (259/1989)   \rReceiving objects:  14% (279/1989)   \rReceiving objects:  15% (299/1989)   \rReceiving objects:  16% (319/1989)   \rReceiving objects:  17% (339/1989)   \rReceiving objects:  18% (359/1989)   \rReceiving objects:  19% (378/1989)   \rReceiving objects:  20% (398/1989)   \rReceiving objects:  21% (418/1989)   \rReceiving objects:  22% (438/1989)   \rReceiving objects:  23% (458/1989)   \rReceiving objects:  24% (478/1989)   \rReceiving objects:  25% (498/1989)   \rReceiving objects:  26% (518/1989)   \rReceiving objects:  27% (538/1989)   \rReceiving objects:  28% (557/1989)   \rReceiving objects:  29% (577/1989)   \rReceiving objects:  30% (597/1989)   \rReceiving objects:  31% (617/1989)   \rReceiving objects:  32% (637/1989)   \rReceiving objects:  33% (657/1989)   \rReceiving objects:  34% (677/1989)   \rReceiving objects:  35% (697/1989)   \rReceiving objects:  36% (717/1989)   \rReceiving objects:  37% (736/1989)   \rReceiving objects:  38% (756/1989)   \rReceiving objects:  39% (776/1989)   \rReceiving objects:  40% (796/1989)   \rReceiving objects:  41% (816/1989)   \rReceiving objects:  42% (836/1989)   \rReceiving objects:  43% (856/1989)   \rReceiving objects:  44% (876/1989)   \rReceiving objects:  45% (896/1989)   \rReceiving objects:  46% (915/1989)   \rReceiving objects:  47% (935/1989)   \rReceiving objects:  48% (955/1989)   \rReceiving objects:  49% (975/1989)   \rReceiving objects:  50% (995/1989)   \rReceiving objects:  51% (1015/1989)   \rReceiving objects:  52% (1035/1989)   \rReceiving objects:  53% (1055/1989)   \rReceiving objects:  54% (1075/1989)   \rReceiving objects:  55% (1094/1989)   \rReceiving objects:  56% (1114/1989)   \rReceiving objects:  57% (1134/1989)   \rReceiving objects:  58% (1154/1989)   \rReceiving objects:  59% (1174/1989)   \rReceiving objects:  60% (1194/1989)   \rReceiving objects:  61% (1214/1989)   \rReceiving objects:  62% (1234/1989)   \rReceiving objects:  63% (1254/1989)   \rReceiving objects:  64% (1273/1989)   \rReceiving objects:  65% (1293/1989)   \rReceiving objects:  66% (1313/1989)   \rReceiving objects:  67% (1333/1989)   \rReceiving objects:  68% (1353/1989)   \rReceiving objects:  69% (1373/1989)   \rReceiving objects:  70% (1393/1989)   \rReceiving objects:  71% (1413/1989)   \rReceiving objects:  72% (1433/1989)   \rReceiving objects:  73% (1452/1989)   \rReceiving objects:  74% (1472/1989)   \rReceiving objects:  75% (1492/1989)   \rReceiving objects:  76% (1512/1989)   \rReceiving objects:  77% (1532/1989)   \rReceiving objects:  78% (1552/1989)   \rReceiving objects:  79% (1572/1989)   \rReceiving objects:  80% (1592/1989)   \rReceiving objects:  81% (1612/1989)   \rReceiving objects:  82% (1631/1989)   \rReceiving objects:  83% (1651/1989)   \rReceiving objects:  84% (1671/1989)   \rReceiving objects:  85% (1691/1989)   \rReceiving objects:  86% (1711/1989)   \rReceiving objects:  87% (1731/1989)   \rReceiving objects:  88% (1751/1989)   \rReceiving objects:  89% (1771/1989)   \rReceiving objects:  90% (1791/1989)   \rReceiving objects:  91% (1810/1989)   \rReceiving objects:  92% (1830/1989)   \rReceiving objects:  93% (1850/1989)   \rremote: Total 1989 (delta 0), reused 0 (delta 0), pack-reused 1987\u001b[K\n",
            "Receiving objects:  94% (1870/1989)   \rReceiving objects:  95% (1890/1989)   \rReceiving objects:  96% (1910/1989)   \rReceiving objects:  97% (1930/1989)   \rReceiving objects:  98% (1950/1989)   \rReceiving objects:  99% (1970/1989)   \rReceiving objects: 100% (1989/1989)   \rReceiving objects: 100% (1989/1989), 7.92 MiB | 29.82 MiB/s, done.\n",
            "Resolving deltas:   0% (0/1309)   \rResolving deltas:   1% (18/1309)   \rResolving deltas:   2% (32/1309)   \rResolving deltas:   3% (42/1309)   \rResolving deltas:   4% (53/1309)   \rResolving deltas:   5% (67/1309)   \rResolving deltas:   6% (84/1309)   \rResolving deltas:   7% (104/1309)   \rResolving deltas:   8% (116/1309)   \rResolving deltas:   9% (118/1309)   \rResolving deltas:  10% (131/1309)   \rResolving deltas:  11% (148/1309)   \rResolving deltas:  12% (158/1309)   \rResolving deltas:  13% (182/1309)   \rResolving deltas:  14% (184/1309)   \rResolving deltas:  21% (284/1309)   \rResolving deltas:  22% (289/1309)   \rResolving deltas:  23% (312/1309)   \rResolving deltas:  24% (316/1309)   \rResolving deltas:  26% (349/1309)   \rResolving deltas:  27% (357/1309)   \rResolving deltas:  28% (376/1309)   \rResolving deltas:  31% (412/1309)   \rResolving deltas:  32% (421/1309)   \rResolving deltas:  35% (471/1309)   \rResolving deltas:  36% (474/1309)   \rResolving deltas:  37% (486/1309)   \rResolving deltas:  38% (504/1309)   \rResolving deltas:  39% (512/1309)   \rResolving deltas:  40% (530/1309)   \rResolving deltas:  42% (553/1309)   \rResolving deltas:  43% (563/1309)   \rResolving deltas:  44% (582/1309)   \rResolving deltas:  45% (595/1309)   \rResolving deltas:  46% (604/1309)   \rResolving deltas:  49% (644/1309)   \rResolving deltas:  50% (658/1309)   \rResolving deltas:  52% (684/1309)   \rResolving deltas:  53% (695/1309)   \rResolving deltas:  54% (711/1309)   \rResolving deltas:  55% (726/1309)   \rResolving deltas:  56% (741/1309)   \rResolving deltas:  57% (754/1309)   \rResolving deltas:  58% (772/1309)   \rResolving deltas:  61% (809/1309)   \rResolving deltas:  62% (812/1309)   \rResolving deltas:  63% (825/1309)   \rResolving deltas:  64% (838/1309)   \rResolving deltas:  65% (851/1309)   \rResolving deltas:  66% (867/1309)   \rResolving deltas:  67% (882/1309)   \rResolving deltas:  69% (904/1309)   \rResolving deltas:  70% (921/1309)   \rResolving deltas:  71% (931/1309)   \rResolving deltas:  72% (948/1309)   \rResolving deltas:  73% (957/1309)   \rResolving deltas:  74% (969/1309)   \rResolving deltas:  75% (984/1309)   \rResolving deltas:  76% (1005/1309)   \rResolving deltas:  77% (1008/1309)   \rResolving deltas:  78% (1022/1309)   \rResolving deltas:  79% (1042/1309)   \rResolving deltas:  80% (1050/1309)   \rResolving deltas:  81% (1065/1309)   \rResolving deltas:  82% (1076/1309)   \rResolving deltas:  83% (1087/1309)   \rResolving deltas:  84% (1100/1309)   \rResolving deltas:  85% (1114/1309)   \rResolving deltas:  86% (1129/1309)   \rResolving deltas:  88% (1154/1309)   \rResolving deltas:  89% (1172/1309)   \rResolving deltas:  90% (1179/1309)   \rResolving deltas:  91% (1201/1309)   \rResolving deltas:  92% (1206/1309)   \rResolving deltas:  93% (1222/1309)   \rResolving deltas:  94% (1236/1309)   \rResolving deltas:  95% (1248/1309)   \rResolving deltas:  98% (1292/1309)   \rResolving deltas:  99% (1300/1309)   \rResolving deltas: 100% (1309/1309)   \rResolving deltas: 100% (1309/1309), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1gr7DtzLMJdJ",
        "colab_type": "code",
        "outputId": "e7d3c7d3-405c-472d-b186-3c34b9c397b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        }
      },
      "cell_type": "code",
      "source": [
        "#!pip3 install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl \n",
        "!pip3 install -r pytorch-CycleGAN-and-pix2pix/requirements.txt\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from -r pytorch-CycleGAN-and-pix2pix/requirements.txt (line 1)) (1.0.1.post2)\n",
            "Requirement already satisfied: torchvision>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from -r pytorch-CycleGAN-and-pix2pix/requirements.txt (line 2)) (0.2.1)\n",
            "Collecting dominate>=2.3.1 (from -r pytorch-CycleGAN-and-pix2pix/requirements.txt (line 3))\n",
            "  Downloading https://files.pythonhosted.org/packages/1a/54/50ec03a4b4114e2c02d049cc26fe6ad8f0653ba5d0cd55d7c9bf9a260434/dominate-2.3.5-py2.py3-none-any.whl\n",
            "Collecting visdom>=0.1.8.3 (from -r pytorch-CycleGAN-and-pix2pix/requirements.txt (line 4))\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/97/c4/5f5356fd57ae3c269e0e31601ea6487e0622fedc6756a591e4a5fd66cc7a/visdom-0.1.8.8.tar.gz (1.4MB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision>=0.2.1->-r pytorch-CycleGAN-and-pix2pix/requirements.txt (line 2)) (1.14.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision>=0.2.1->-r pytorch-CycleGAN-and-pix2pix/requirements.txt (line 2)) (1.11.0)\n",
            "Collecting pillow>=4.1.1 (from torchvision>=0.2.1->-r pytorch-CycleGAN-and-pix2pix/requirements.txt (line 2))\n",
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            "  Found existing installation: Pillow 4.0.0\n",
            "    Uninstalling Pillow-4.0.0:\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-oi3dJxIW4fM",
        "colab_type": "code",
        "outputId": "95f8bbab-f947-4fdf-dffd-890c82ab2494",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        }
      },
      "cell_type": "code",
      "source": [
        "!mkdir brownbear2polarbear\n",
        "#!mkdir brownbear2polarbear/trainA\n",
        "#!mkdir brownbear2polarbear/trainB\n",
        "!ls\n",
        "%cd brownbear2polarbear\n",
        "!ls\n",
        "!ls \"/content/gdrive/My Drive/Colab Notebooks/brownbear2polarbear/\"\n",
        "!unzip -o -qq \"/content/gdrive/My Drive/Colab Notebooks/brownbear2polarbear/trainA.zip\"\n",
        "#%cd ..\n",
        "#%cd trainB\n",
        "!unzip -o -qq \"/content/gdrive/My Drive/Colab Notebooks/brownbear2polarbear/trainB.zip\"\n",
        "%cd .."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "brownbear2polarbear  gdrive  pytorch-CycleGAN-and-pix2pix  sample_data\n",
            "/content/brownbear2polarbear\n",
            "testA  testA.zip  testB  testB.zip  trainA  trainA.zip\ttrainB\ttrainB.zip\n",
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fKtJwSoM_Vln",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# randomly shuffle the names of the files, as CycleGAN reads the files using os.walk in the default order, we need to shuffle to generalize the training set when using small dataset sizes\n",
        "import os\n",
        "import random\n",
        "seq = list(range(len(os.listdir(\"/content/brownbear2polarbear/trainA\"))))\n",
        "random.shuffle(seq)\n",
        "path = \"/content/brownbear2polarbear/trainA\"\n",
        "for image in os.listdir(\"/content/brownbear2polarbear/trainA\"):\n",
        "  os.rename(os.path.join(path, image), os.path.join(path, \"%s.jpg\" % seq.pop(0)))\n",
        "  \n",
        "  \n",
        "seq = list(range(len(os.listdir(\"/content/brownbear2polarbear/trainB\"))))\n",
        "random.shuffle(seq)\n",
        "path = \"/content/brownbear2polarbear/trainB\"\n",
        "for image in os.listdir(\"/content/brownbear2polarbear/trainB\"):\n",
        "  os.rename(os.path.join(path, image), os.path.join(path, \"%s.jpg\" % seq.pop(0)))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aKwXCHzQ3RCG",
        "colab_type": "code",
        "outputId": "80f29e77-91e0-4c96-ac44-dd09f7015b11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "os.environ[\"output_directory\"] = \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_checkpoint_%s/\" % datetime.datetime.now()\n",
        "os.environ[\"directory_to_be_loaded\"] = \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_checkpoint/\"\n",
        "!echo saving checkpoint in $output_directory\n",
        "!echo will copy checkpoint to $directory_to_be_loaded"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "saving checkpoint in gdrive/My Drive/Colab Notebooks/brownbear2polarbear_checkpoint_2019-02-15 14:45:25.542304/\n",
            "will copy checkpoint to gdrive/My Drive/Colab Notebooks/brownbear2polarbear_checkpoint/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NyX5GOulKrTL",
        "colab_type": "code",
        "outputId": "fd2d3df3-656c-4699-c9d7-3987373d7b9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5196
        }
      },
      "cell_type": "code",
      "source": [
        "#!ln -s \"gdrive/My Drive/Colab Notebooks/checkpoints\" checkpoints\n",
        "#data_path = \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear/\"\n",
        "checkpoint_path = \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_checkpoint/opt.txt\"\n",
        "if os.path.exists(checkpoint_path):\n",
        "  if not os.path.exists(\"/content/checkpoints\"):\n",
        "    !mkdir /content/checkpoints\n",
        "  if not os.path.exists(\"/content/checkpoints/brownbear2polarbear_cyclegan\"):\n",
        "    !mkdir /content/checkpoints/brownbear2polarbear_cyclegan\n",
        "  !cp \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_checkpoint/.\" /content/checkpoints/brownbear2polarbear_cyclegan/ -r\n",
        "  #!rm -rf /content/checkpoints/brownbear2polarbear_cyclegan/web/ -r\n",
        "  !time python pytorch-CycleGAN-and-pix2pix/train.py --dataroot /content/brownbear2polarbear --name brownbear2polarbear_cyclegan --model cycle_gan --continue_train --max_dataset_size 20 --save_epoch_freq 50\n",
        "else:\n",
        "  !time python pytorch-CycleGAN-and-pix2pix/train.py --dataroot /content/brownbear2polarbear --name brownbear2polarbear_cyclegan --model cycle_gan --max_dataset_size 20 --save_epoch_freq 50\n",
        "#!rm -rf \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_checkpoint/\"\n",
        "\n",
        "!mkdir \"$output_directory\"\n",
        "!cp /content/checkpoints/brownbear2polarbear_cyclegan/* \"$output_directory\" -r\n",
        "!rm -rf \"$directory_to_be_loaded\"\n",
        "!cp -r \"$output_directory\" \"$directory_to_be_loaded\"\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------- Options ---------------\n",
            "               batch_size: 1                             \n",
            "                    beta1: 0.5                           \n",
            "          checkpoints_dir: ./checkpoints                 \n",
            "           continue_train: True                          \t[default: False]\n",
            "                crop_size: 256                           \n",
            "                 dataroot: /content/brownbear2polarbear  \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: 20                            \t[default: inf]\n",
            "                    model: cycle_gan                     \n",
            "               n_layers_D: 3                             \n",
            "                     name: brownbear2polarbear_cyclegan  \t[default: experiment_name]\n",
            "                      ndf: 64                            \n",
            "                     netD: basic                         \n",
            "                     netG: resnet_9blocks                \n",
            "                      ngf: 64                            \n",
            "                    niter: 100                           \n",
            "              niter_decay: 100                           \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: 50                            \t[default: 5]\n",
            "         save_latest_freq: 5000                          \n",
            "           serial_batches: False                         \n",
            "                   suffix:                               \n",
            "         update_html_freq: 1000                          \n",
            "                  verbose: False                         \n",
            "----------------- End -------------------\n",
            "dataset [UnalignedDataset] was created\n",
            "The number of training images = 20\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",
            "loading the model from ./checkpoints/brownbear2polarbear_cyclegan/latest_net_G_A.pth\n",
            "loading the model from ./checkpoints/brownbear2polarbear_cyclegan/latest_net_G_B.pth\n",
            "loading the model from ./checkpoints/brownbear2polarbear_cyclegan/latest_net_D_A.pth\n",
            "loading the model from ./checkpoints/brownbear2polarbear_cyclegan/latest_net_D_B.pth\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",
            "WARNING:root:Setting up a new session...\n",
            "Exception in user code:\n",
            "------------------------------------------------------------\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/connection.py\", line 141, in _new_conn\n",
            "    (self.host, self.port), self.timeout, **extra_kw)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/util/connection.py\", line 83, in create_connection\n",
            "    raise err\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/util/connection.py\", line 73, 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/python3.6/dist-packages/urllib3/connectionpool.py\", line 601, in urlopen\n",
            "    chunked=chunked)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py\", line 357, in _make_request\n",
            "    conn.request(method, url, **httplib_request_kw)\n",
            "  File \"/usr/lib/python3.6/http/client.py\", line 1239, in request\n",
            "    self._send_request(method, url, body, headers, encode_chunked)\n",
            "  File \"/usr/lib/python3.6/http/client.py\", line 1285, in _send_request\n",
            "    self.endheaders(body, encode_chunked=encode_chunked)\n",
            "  File \"/usr/lib/python3.6/http/client.py\", line 1234, in endheaders\n",
            "    self._send_output(message_body, encode_chunked=encode_chunked)\n",
            "  File \"/usr/lib/python3.6/http/client.py\", line 1026, in _send_output\n",
            "    self.send(msg)\n",
            "  File \"/usr/lib/python3.6/http/client.py\", line 964, in send\n",
            "    self.connect()\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/connection.py\", line 166, in connect\n",
            "    conn = self._new_conn()\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/connection.py\", line 150, in _new_conn\n",
            "    self, \"Failed to establish a new connection: %s\" % e)\n",
            "urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7f550c7ba208>: 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/python3.6/dist-packages/requests/adapters.py\", line 440, in send\n",
            "    timeout=timeout\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py\", line 639, in urlopen\n",
            "    _stacktrace=sys.exc_info()[2])\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/urllib3/util/retry.py\", line 388, 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 0x7f550c7ba208>: 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/python3.6/dist-packages/visdom/__init__.py\", line 548, in _send\n",
            "    data=json.dumps(msg),\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/requests/sessions.py\", line 555, in post\n",
            "    return self.request('POST', url, data=data, json=json, **kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/requests/sessions.py\", line 508, in request\n",
            "    resp = self.send(prep, **send_kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/requests/sessions.py\", line 618, in send\n",
            "    r = adapter.send(request, **kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/requests/adapters.py\", line 508, 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 0x7f550c7ba208>: Failed to establish a new connection: [Errno 111] Connection refused',))\n",
            "ERROR:visdom:[Errno 111] Connection refused\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/brownbear2polarbear_cyclegan/web...\n",
            "End of epoch 1 / 200 \t Time Taken: 32 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 2 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 3 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 4 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 5, iters: 20, time: 1.350, data: 0.250) D_A: 0.142 G_A: 0.522 cycle_A: 1.131 idt_A: 0.249 D_B: 0.113 G_B: 0.506 cycle_B: 0.687 idt_B: 0.495 \n",
            "End of epoch 5 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 6 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 7 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 8 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 9 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 10, iters: 20, time: 1.356, data: 0.300) D_A: 0.092 G_A: 0.761 cycle_A: 0.516 idt_A: 0.236 D_B: 0.115 G_B: 0.522 cycle_B: 0.603 idt_B: 0.215 \n",
            "End of epoch 10 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 11 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 12 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 13 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 14 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 15, iters: 20, time: 1.362, data: 0.298) D_A: 0.169 G_A: 0.520 cycle_A: 0.533 idt_A: 0.979 D_B: 0.083 G_B: 0.450 cycle_B: 1.209 idt_B: 0.208 \n",
            "End of epoch 15 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 16 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 17 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 18 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 19 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 20, iters: 20, time: 1.928, data: 0.252) D_A: 0.076 G_A: 0.611 cycle_A: 0.543 idt_A: 0.244 D_B: 0.088 G_B: 0.810 cycle_B: 0.748 idt_B: 0.222 \n",
            "End of epoch 20 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 21 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 22 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 23 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 24 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 25, iters: 20, time: 1.356, data: 0.209) D_A: 0.132 G_A: 0.379 cycle_A: 0.526 idt_A: 0.210 D_B: 0.149 G_B: 0.483 cycle_B: 0.445 idt_B: 0.211 \n",
            "End of epoch 25 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 26 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 27 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 28 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 29 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 30, iters: 20, time: 1.348, data: 0.193) D_A: 0.060 G_A: 0.667 cycle_A: 0.622 idt_A: 0.177 D_B: 0.196 G_B: 0.444 cycle_B: 0.576 idt_B: 0.211 \n",
            "End of epoch 30 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 31 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 32 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 33 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 34 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 35, iters: 20, time: 1.355, data: 0.219) D_A: 0.138 G_A: 0.344 cycle_A: 0.557 idt_A: 0.271 D_B: 0.185 G_B: 0.545 cycle_B: 0.469 idt_B: 0.224 \n",
            "End of epoch 35 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 36 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 37 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 38 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 39 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 40, iters: 20, time: 2.061, data: 0.265) D_A: 0.177 G_A: 0.897 cycle_A: 0.631 idt_A: 0.226 D_B: 0.229 G_B: 0.276 cycle_B: 0.689 idt_B: 0.230 \n",
            "End of epoch 40 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 41 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 42 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 43 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 44 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 45, iters: 20, time: 1.355, data: 0.210) D_A: 0.154 G_A: 0.348 cycle_A: 0.629 idt_A: 0.304 D_B: 0.088 G_B: 0.398 cycle_B: 0.630 idt_B: 0.226 \n",
            "End of epoch 45 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 46 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 47 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 48 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 49 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 50, iters: 20, time: 1.351, data: 0.233) D_A: 0.212 G_A: 0.746 cycle_A: 0.723 idt_A: 0.235 D_B: 0.141 G_B: 0.646 cycle_B: 1.743 idt_B: 0.268 \n",
            "saving the model at the end of epoch 50, iters 1000\n",
            "End of epoch 50 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 51 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 52 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 53 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 54 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "(epoch: 55, iters: 20, time: 1.363, data: 0.276) D_A: 0.197 G_A: 0.229 cycle_A: 0.574 idt_A: 0.158 D_B: 0.155 G_B: 0.854 cycle_B: 0.522 idt_B: 0.204 \n",
            "End of epoch 55 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 56 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n",
            "End of epoch 57 / 200 \t Time Taken: 27 sec\n",
            "learning rate = 0.0002000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "14ieELR1_QEp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d6aNELt0LI_s",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!ls /content/checkpoints/brownbear2polarbear_cyclegan/web/images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vzM9je5VgqBX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "path = \"/content/checkpoints/brownbear2polarbear_cyclegan/web/images\"\n",
        "for image in os.listdir(path):\n",
        "  plt.imshow(plt.imread(os.path.join(path, image)))\n",
        "  plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "W3aejs8ONsk5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%cd brownbear2polarbear\n",
        "!ls\n",
        "!ls \"/content/gdrive/My Drive/Colab Notebooks/brownbear2polarbear/\"\n",
        "!unzip -o -qq \"/content/gdrive/My Drive/Colab Notebooks/brownbear2polarbear/testA.zip\"\n",
        "!unzip -o -qq \"/content/gdrive/My Drive/Colab Notebooks/brownbear2polarbear/testB.zip\"\n",
        "%cd .."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7QJKEEkELJvn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!time python pytorch-CycleGAN-and-pix2pix/test.py --dataroot /content/brownbear2polarbear --name brownbear2polarbear_cyclegan --model cycle_gan --num_test 155"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6utfjedbN9pX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!rm -rf \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_results/\"\n",
        "!mkdir \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_results/\"\n",
        "!cp -r /content/results/brownbear2polarbear_cyclegan/. \"gdrive/My Drive/Colab Notebooks/brownbear2polarbear_results/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SS7AAc3kVbw4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}