StyleGAN2_Colab_Train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "StyleGAN2-Colab-Train.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "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/dvschultz/ai/blob/master/StyleGAN2_Colab_Train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEx9IOnF1hKO",
        "colab_type": "text"
      },
      "source": [
        "#Training StyleGAN2 on Colab\n",
        "Y’all won’t stop asking me about this so here ya go 😂\n",
        "\n",
        "If it were me I’d sign up for Colab Pro ($10/month) to get a couple extra hours of training time in per session. But you crazy kids can do whatever you want."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcDHP4h-11Ii",
        "colab_type": "text"
      },
      "source": [
        "##Mounting Google Drive\n",
        "So I’m actually gonna install my entire repo directly into Google Drive. This will make the setup a little easier, but its a little strange I admit.\n",
        "\n",
        "First, connect your Drive to Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJazuNYurryY",
        "colab_type": "code",
        "outputId": "b2a7eaea-ad32-494f-9c4b-14b03e5e227c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aadrbMyR2F00",
        "colab_type": "text"
      },
      "source": [
        "##Install the repo\n",
        "**Only do this for the first time ever setting this up!**\n",
        "\n",
        "If this is your first time ever running this notebook, you’ll want to install my fork of StyleGAN2 to your Drive account. Make sure you have ample space on your Drive (I’d say at least 50GB). This will install the repo and add some dependecies (like transferring from FFHQ the first time).\n",
        "\n",
        "Every time after your first use of this notebook you’ll want to skip this cell and run the cell after this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoTNQ3Gyr0ih",
        "colab_type": "code",
        "outputId": "e7c57f4b-5b7f-4a52-8bed-8ee7daec622d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "#SKIP this if you already have a stylegan2 folder in your google drive\n",
        "%cd /content/drive/My\\ Drive/\n",
        "!mkdir stylegan2-colab\n",
        "%cd stylegan2-colab/\n",
        "!git clone https://github.com/dvschultz/stylegan2\n",
        "%cd stylegan2\n",
        "!mkdir pkl\n",
        "%cd pkl\n",
        "!gdown --id 1JLqXE5bGZnlu2BkbLPD5_ZxoO3Nz-AvF #inception: https://drive.google.com/open?id=1JLqXE5bGZnlu2BkbLPD5_ZxoO3Nz-AvF\n",
        "%cd ../\n",
        "!mkdir results\n",
        "!mkdir results/00001-pretrained\n",
        "%cd results/00001-pretrained\n",
        "!gdown --id 1UlDmJVLLnBD9SnLSMXeiZRO6g-OMQCA_\n",
        "!mv stylegan2-ffhq-config-f.pkl network-snapshot-10000.pkl\n",
        "%cd ../../\n",
        "%mkdir datasets"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive\n",
            "/content/drive/My Drive/stylegan2-colab-test\n",
            "Cloning into 'stylegan2'...\n",
            "remote: Enumerating objects: 4, done.\u001b[K\n",
            "remote: Counting objects: 100% (4/4), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 261 (delta 0), reused 1 (delta 0), pack-reused 257\u001b[K\n",
            "Receiving objects: 100% (261/261), 15.25 MiB | 14.95 MiB/s, done.\n",
            "Resolving deltas: 100% (137/137), done.\n",
            "/content/drive/My Drive/stylegan2-colab-test/stylegan2\n",
            "/content/drive/My Drive/stylegan2-colab-test/stylegan2/pkl\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1JLqXE5bGZnlu2BkbLPD5_ZxoO3Nz-AvF\n",
            "To: /content/drive/My Drive/stylegan2-colab-test/stylegan2/pkl/inception_v3_features.pkl\n",
            "87.3MB [00:01, 68.7MB/s]\n",
            "/content/drive/My Drive/stylegan2-colab-test/stylegan2\n",
            "/content/drive/My Drive/stylegan2-colab-test/stylegan2/results/00001-pretrained\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1UlDmJVLLnBD9SnLSMXeiZRO6g-OMQCA_\n",
            "To: /content/drive/My Drive/stylegan2-colab-test/stylegan2/results/00001-pretrained/stylegan2-ffhq-config-f.pkl\n",
            "382MB [00:06, 61.8MB/s]\n",
            "/content/drive/My Drive/stylegan2-colab-test/stylegan2\n",
            "mkdir: cannot create directory ‘results’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN2K68CE27py",
        "colab_type": "text"
      },
      "source": [
        "##Picking up from a previous session\n",
        "If you already have the StyleGAN2 repo installed in Drive skip the above cell and run the following. This will make sure you have the latest version in case I make updates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8WgjhRFsFJ0",
        "colab_type": "code",
        "outputId": "fe11f1ee-26e1-455d-c56f-3b3660c7e425",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#USE this if you already have a stylegan2 folder in google drive\n",
        "%cd /content/drive/My\\ Drive/stylegan2-colab/stylegan2\n",
        "!git pull"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "HEAD is now at cdc8c0e set latest\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSbEY2pT3TOb",
        "colab_type": "text"
      },
      "source": [
        "##Make sure Tensorflow 1.15 is set\n",
        "Colab now defaults to Tensorflow 2. Make sure you run this cell to reset it to TF1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMdpKY1XODz2",
        "colab_type": "code",
        "outputId": "e6e36d84-88c4-4bdc-81dc-267030739285",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1pIBZGzZxSA",
        "colab_type": "text"
      },
      "source": [
        "##Converting your dataset\n",
        "StyleGAN requires you to convert your standard jpg or png images into a new format (.tfrecords). \n",
        "\n",
        "I’ve seen some recommendations to run this command every time you restart your Colab machine. I think if you ahve a small-ish dataset (< 2000 images) that’s probably unnecessary.\n",
        "\n",
        "I recommend you upload your dataset to Google Drive and copy its path from the Drive folder in Colab and paste its path in the below cell.\n",
        "\n",
        "After the `create_from_images` argument you need to pass in two paths. The first path is where the .tfrecords files should be output (just edit the last part to have a unique name). The second path is to the directory of your images.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ixjcx2-cbTDm",
        "colab_type": "code",
        "outputId": "2cab51ea-6932-4352-8ed2-d121e681f301",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#2nd argument is where to put your tfrecords files\n",
        "#3rd should point at your image dataset\n",
        "!python dataset_tool.py create_from_images ./datasets/birdaus /content/drive/My\\ Drive/Datasets/birdsaus/sq-1024"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading images from \"/content/drive/My Drive/Datasets/birdsaus/sq-1024\"\n",
            "100% 597/597 [06:33<00:00,  1.52it/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAfUNG60aRD1",
        "colab_type": "text"
      },
      "source": [
        "##Training\n",
        "Note: this will require you to restart your Colab machine every 8–16 hours. You’ve been warned!\n",
        "\n",
        "This library is set up to automatically find your latest .pkl file so it should keep re-training and making progress.\n",
        "\n",
        "##Training Options\n",
        "`--dataset`\n",
        "\n",
        "This should be the name you used in the first path when converting your dataset.\n",
        "\n",
        "`--mirror-augment`\n",
        "\n",
        "Using this option loads some images at random mirrored horizontally (left-to-right). This might help if you have a very small dataset.\n",
        "\n",
        "`--metrics`\n",
        "\n",
        "METRICS DON’T MATTER. It’s art! Use your eyes. Set `--metrics=None` and live your life.\n",
        "\n",
        "If you must use metrics, you have a few options. `fid50k`, the default, uses Frechet Inception Distance score. It’s what was used in StyleGAN1 and what most people know. It’s fine for images of animals and things, but it’s not great. `ppl_wend` is what StyleGAN2 prefers and claims to be more accurate. There are a bunch of other options but I’d recommend you stick with those. Note that both of these take 30–45minutes to run every time it runs so that cuts into your training time in Colab.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QrOVqEHaipA",
        "colab_type": "code",
        "outputId": "d330f643-1c88-4df0-c4ec-fa8e032f9c7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!python run_training.py --num-gpus=1 --data-dir=./datasets --config=config-f --dataset=birdaus --mirror-augment=true --metrics=None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Local submit - run_dir: results/00002-stylegan2-birdaus-1gpu-config-f\n",
            "dnnlib: Running training.training_loop.training_loop() on localhost...\n",
            "Streaming data using training.dataset.TFRecordDataset...\n",
            "tcmalloc: large alloc 4294967296 bytes == 0x76da000 @  0x7f3057e80001 0x7f305490c765 0x7f3054970dc0 0x7f3054972c5f 0x7f3054a09238 0x50ac25 0x50d390 0x508245 0x509642 0x595311 0x54a6ff 0x551b81 0x5a067e 0x50d966 0x508245 0x58958c 0x5a067e 0x50d966 0x508245 0x58958c 0x5a067e 0x50d966 0x509d48 0x50aa7d 0x50c5b9 0x509d48 0x50aa7d 0x50c5b9 0x508245 0x58958c 0x5a067e\n",
            "tcmalloc: large alloc 4294967296 bytes == 0x7f2e8e2c6000 @  0x7f3057e7e1e7 0x7f305490c5e1 0x7f3054970e88 0x7f3054971147 0x7f3054a09118 0x50ac25 0x50d390 0x508245 0x50a080 0x50aa7d 0x50d390 0x508245 0x50a080 0x50aa7d 0x50d390 0x508245 0x50a080 0x50aa7d 0x50d390 0x509d48 0x50aa7d 0x50c5b9 0x508245 0x50a080 0x50aa7d 0x50c5b9 0x508245 0x50a080 0x50aa7d 0x50d390 0x508245\n",
            "tcmalloc: large alloc 4294967296 bytes == 0x7f2d8d2c4000 @  0x7f3057e7e1e7 0x7f305490c5e1 0x7f3054970e88 0x7f3054971147 0x7f3019942f05 0x7f30192c6742 0x7f30192c6cf2 0x7f301927fa7e 0x50a8af 0x50c5b9 0x509d48 0x50aa7d 0x50c5b9 0x508245 0x5893bb 0x5a067e 0x50d966 0x508245 0x50a080 0x50aa7d 0x50c5b9 0x508245 0x50a080 0x50aa7d 0x50c5b9 0x509d48 0x50aa7d 0x50c5b9 0x508245 0x509642 0x595311\n",
            "Dataset shape = [3, 1024, 1024]\n",
            "Dynamic range = [0, 255]\n",
            "Label size    = 0\n",
            "Loading networks from \"results/00001-pretrained/network-snapshot-10000.pkl\"...\n",
            "Setting up TensorFlow plugin \"fused_bias_act.cu\": Preprocessing... Loading... Done.\n",
            "Setting up TensorFlow plugin \"upfirdn_2d.cu\": Preprocessing... Loading... Done.\n",
            "\n",
            "G                               Params    OutputShape          WeightShape     \n",
            "---                             ---       ---                  ---             \n",
            "latents_in                      -         (?, 512)             -               \n",
            "labels_in                       -         (?, 0)               -               \n",
            "lod                             -         ()                   -               \n",
            "dlatent_avg                     -         (512,)               -               \n",
            "G_mapping/latents_in            -         (?, 512)             -               \n",
            "G_mapping/labels_in             -         (?, 0)               -               \n",
            "G_mapping/Normalize             -         (?, 512)             -               \n",
            "G_mapping/Dense0                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Dense1                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Dense2                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Dense3                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Dense4                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Dense5                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Dense6                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Dense7                262656    (?, 512)             (512, 512)      \n",
            "G_mapping/Broadcast             -         (?, 18, 512)         -               \n",
            "G_mapping/dlatents_out          -         (?, 18, 512)         -               \n",
            "Truncation/Lerp                 -         (?, 18, 512)         -               \n",
            "G_synthesis/dlatents_in         -         (?, 18, 512)         -               \n",
            "G_synthesis/4x4/Const           8192      (?, 512, 4, 4)       (1, 512, 4, 4)  \n",
            "G_synthesis/4x4/Conv            2622465   (?, 512, 4, 4)       (3, 3, 512, 512)\n",
            "G_synthesis/4x4/ToRGB           264195    (?, 3, 4, 4)         (1, 1, 512, 3)  \n",
            "G_synthesis/8x8/Conv0_up        2622465   (?, 512, 8, 8)       (3, 3, 512, 512)\n",
            "G_synthesis/8x8/Conv1           2622465   (?, 512, 8, 8)       (3, 3, 512, 512)\n",
            "G_synthesis/8x8/Upsample        -         (?, 3, 8, 8)         -               \n",
            "G_synthesis/8x8/ToRGB           264195    (?, 3, 8, 8)         (1, 1, 512, 3)  \n",
            "G_synthesis/16x16/Conv0_up      2622465   (?, 512, 16, 16)     (3, 3, 512, 512)\n",
            "G_synthesis/16x16/Conv1         2622465   (?, 512, 16, 16)     (3, 3, 512, 512)\n",
            "G_synthesis/16x16/Upsample      -         (?, 3, 16, 16)       -               \n",
            "G_synthesis/16x16/ToRGB         264195    (?, 3, 16, 16)       (1, 1, 512, 3)  \n",
            "G_synthesis/32x32/Conv0_up      2622465   (?, 512, 32, 32)     (3, 3, 512, 512)\n",
            "G_synthesis/32x32/Conv1         2622465   (?, 512, 32, 32)     (3, 3, 512, 512)\n",
            "G_synthesis/32x32/Upsample      -         (?, 3, 32, 32)       -               \n",
            "G_synthesis/32x32/ToRGB         264195    (?, 3, 32, 32)       (1, 1, 512, 3)  \n",
            "G_synthesis/64x64/Conv0_up      2622465   (?, 512, 64, 64)     (3, 3, 512, 512)\n",
            "G_synthesis/64x64/Conv1         2622465   (?, 512, 64, 64)     (3, 3, 512, 512)\n",
            "G_synthesis/64x64/Upsample      -         (?, 3, 64, 64)       -               \n",
            "G_synthesis/64x64/ToRGB         264195    (?, 3, 64, 64)       (1, 1, 512, 3)  \n",
            "G_synthesis/128x128/Conv0_up    1442561   (?, 256, 128, 128)   (3, 3, 512, 256)\n",
            "G_synthesis/128x128/Conv1       721409    (?, 256, 128, 128)   (3, 3, 256, 256)\n",
            "G_synthesis/128x128/Upsample    -         (?, 3, 128, 128)     -               \n",
            "G_synthesis/128x128/ToRGB       132099    (?, 3, 128, 128)     (1, 1, 256, 3)  \n",
            "G_synthesis/256x256/Conv0_up    426369    (?, 128, 256, 256)   (3, 3, 256, 128)\n",
            "G_synthesis/256x256/Conv1       213249    (?, 128, 256, 256)   (3, 3, 128, 128)\n",
            "G_synthesis/256x256/Upsample    -         (?, 3, 256, 256)     -               \n",
            "G_synthesis/256x256/ToRGB       66051     (?, 3, 256, 256)     (1, 1, 128, 3)  \n",
            "G_synthesis/512x512/Conv0_up    139457    (?, 64, 512, 512)    (3, 3, 128, 64) \n",
            "G_synthesis/512x512/Conv1       69761     (?, 64, 512, 512)    (3, 3, 64, 64)  \n",
            "G_synthesis/512x512/Upsample    -         (?, 3, 512, 512)     -               \n",
            "G_synthesis/512x512/ToRGB       33027     (?, 3, 512, 512)     (1, 1, 64, 3)   \n",
            "G_synthesis/1024x1024/Conv0_up  51297     (?, 32, 1024, 1024)  (3, 3, 64, 32)  \n",
            "G_synthesis/1024x1024/Conv1     25665     (?, 32, 1024, 1024)  (3, 3, 32, 32)  \n",
            "G_synthesis/1024x1024/Upsample  -         (?, 3, 1024, 1024)   -               \n",
            "G_synthesis/1024x1024/ToRGB     16515     (?, 3, 1024, 1024)   (1, 1, 32, 3)   \n",
            "G_synthesis/images_out          -         (?, 3, 1024, 1024)   -               \n",
            "G_synthesis/noise0              -         (1, 1, 4, 4)         -               \n",
            "G_synthesis/noise1              -         (1, 1, 8, 8)         -               \n",
            "G_synthesis/noise2              -         (1, 1, 8, 8)         -               \n",
            "G_synthesis/noise3              -         (1, 1, 16, 16)       -               \n",
            "G_synthesis/noise4              -         (1, 1, 16, 16)       -               \n",
            "G_synthesis/noise5              -         (1, 1, 32, 32)       -               \n",
            "G_synthesis/noise6              -         (1, 1, 32, 32)       -               \n",
            "G_synthesis/noise7              -         (1, 1, 64, 64)       -               \n",
            "G_synthesis/noise8              -         (1, 1, 64, 64)       -               \n",
            "G_synthesis/noise9              -         (1, 1, 128, 128)     -               \n",
            "G_synthesis/noise10             -         (1, 1, 128, 128)     -               \n",
            "G_synthesis/noise11             -         (1, 1, 256, 256)     -               \n",
            "G_synthesis/noise12             -         (1, 1, 256, 256)     -               \n",
            "G_synthesis/noise13             -         (1, 1, 512, 512)     -               \n",
            "G_synthesis/noise14             -         (1, 1, 512, 512)     -               \n",
            "G_synthesis/noise15             -         (1, 1, 1024, 1024)   -               \n",
            "G_synthesis/noise16             -         (1, 1, 1024, 1024)   -               \n",
            "images_out                      -         (?, 3, 1024, 1024)   -               \n",
            "---                             ---       ---                  ---             \n",
            "Total                           30370060                                       \n",
            "\n",
            "\n",
            "D                     Params    OutputShape          WeightShape     \n",
            "---                   ---       ---                  ---             \n",
            "images_in             -         (?, 3, 1024, 1024)   -               \n",
            "labels_in             -         (?, 0)               -               \n",
            "1024x1024/FromRGB     128       (?, 32, 1024, 1024)  (1, 1, 3, 32)   \n",
            "1024x1024/Conv0       9248      (?, 32, 1024, 1024)  (3, 3, 32, 32)  \n",
            "1024x1024/Conv1_down  18496     (?, 64, 512, 512)    (3, 3, 32, 64)  \n",
            "1024x1024/Skip        2048      (?, 64, 512, 512)    (1, 1, 32, 64)  \n",
            "512x512/Conv0         36928     (?, 64, 512, 512)    (3, 3, 64, 64)  \n",
            "512x512/Conv1_down    73856     (?, 128, 256, 256)   (3, 3, 64, 128) \n",
            "512x512/Skip          8192      (?, 128, 256, 256)   (1, 1, 64, 128) \n",
            "256x256/Conv0         147584    (?, 128, 256, 256)   (3, 3, 128, 128)\n",
            "256x256/Conv1_down    295168    (?, 256, 128, 128)   (3, 3, 128, 256)\n",
            "256x256/Skip          32768     (?, 256, 128, 128)   (1, 1, 128, 256)\n",
            "128x128/Conv0         590080    (?, 256, 128, 128)   (3, 3, 256, 256)\n",
            "128x128/Conv1_down    1180160   (?, 512, 64, 64)     (3, 3, 256, 512)\n",
            "128x128/Skip          131072    (?, 512, 64, 64)     (1, 1, 256, 512)\n",
            "64x64/Conv0           2359808   (?, 512, 64, 64)     (3, 3, 512, 512)\n",
            "64x64/Conv1_down      2359808   (?, 512, 32, 32)     (3, 3, 512, 512)\n",
            "64x64/Skip            262144    (?, 512, 32, 32)     (1, 1, 512, 512)\n",
            "32x32/Conv0           2359808   (?, 512, 32, 32)     (3, 3, 512, 512)\n",
            "32x32/Conv1_down      2359808   (?, 512, 16, 16)     (3, 3, 512, 512)\n",
            "32x32/Skip            262144    (?, 512, 16, 16)     (1, 1, 512, 512)\n",
            "16x16/Conv0           2359808   (?, 512, 16, 16)     (3, 3, 512, 512)\n",
            "16x16/Conv1_down      2359808   (?, 512, 8, 8)       (3, 3, 512, 512)\n",
            "16x16/Skip            262144    (?, 512, 8, 8)       (1, 1, 512, 512)\n",
            "8x8/Conv0             2359808   (?, 512, 8, 8)       (3, 3, 512, 512)\n",
            "8x8/Conv1_down        2359808   (?, 512, 4, 4)       (3, 3, 512, 512)\n",
            "8x8/Skip              262144    (?, 512, 4, 4)       (1, 1, 512, 512)\n",
            "4x4/MinibatchStddev   -         (?, 513, 4, 4)       -               \n",
            "4x4/Conv              2364416   (?, 512, 4, 4)       (3, 3, 513, 512)\n",
            "4x4/Dense0            4194816   (?, 512)             (8192, 512)     \n",
            "Output                513       (?, 1)               (512, 1)        \n",
            "scores_out            -         (?, 1)               -               \n",
            "---                   ---       ---                  ---             \n",
            "Total                 29012513                                       \n",
            "\n",
            "Building TensorFlow graph...\n",
            "Initializing logs...\n",
            "Training for 25000 kimg...\n",
            "\n",
            "tick 0     kimg 10000.1  lod 0.00  minibatch 32   time 1m 20s       sec/tick 79.8    sec/kimg 623.19  maintenance 0.0    gpumem 13.2\n",
            "Traceback (most recent call last):\n",
            "  File \"run_training.py\", line 198, in <module>\n",
            "    main()\n",
            "  File \"run_training.py\", line 193, in main\n",
            "    run(**vars(args))\n",
            "  File \"run_training.py\", line 126, in run\n",
            "    dnnlib.submit_run(**kwargs)\n",
            "  File \"/content/drive/My Drive/stylegan2-colab-test/stylegan2/dnnlib/submission/submit.py\", line 343, in submit_run\n",
            "    return farm.submit(submit_config, host_run_dir)\n",
            "  File \"/content/drive/My Drive/stylegan2-colab-test/stylegan2/dnnlib/submission/internal/local.py\", line 22, in submit\n",
            "    return run_wrapper(submit_config)\n",
            "  File \"/content/drive/My Drive/stylegan2-colab-test/stylegan2/dnnlib/submission/submit.py\", line 280, in run_wrapper\n",
            "    run_func_obj(**submit_config.run_func_kwargs)\n",
            "  File \"/content/drive/My Drive/stylegan2-colab-test/stylegan2/training/training_loop.py\", line 309, in training_loop\n",
            "    tflib.run(D_train_op, feed_dict)\n",
            "  File \"/content/drive/My Drive/stylegan2-colab-test/stylegan2/dnnlib/tflib/tfutil.py\", line 31, in run\n",
            "    return tf.get_default_session().run(*args, **kwargs)\n",
            "  File \"/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\", line 956, in run\n",
            "    run_metadata_ptr)\n",
            "  File \"/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\", line 1180, in _run\n",
            "    feed_dict_tensor, options, run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\", line 1359, in _do_run\n",
            "    run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\", line 1365, in _do_call\n",
            "    return fn(*args)\n",
            "  File \"/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\", line 1350, in _run_fn\n",
            "    target_list, run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\", line 1443, in _call_tf_sessionrun\n",
            "    run_metadata)\n",
            "KeyboardInterrupt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQ7U1ftuj_Dc",
        "colab_type": "text"
      },
      "source": [
        "Once running, your training files will show up in the results folder."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9vCDt9LRtXl",
        "colab_type": "text"
      },
      "source": [
        "#Testing the model (generating images)\n",
        "The following command will generate 55 sample images from the model.\n",
        "\n",
        "##Options\n",
        "`--network`\n",
        "\n",
        "Make sure the `--network` argument points to your .pkl file. (My preferred method is to right click on the file in the Files pane to your left and choose `Copy Path`, then paste that into the argument after the `=` sign).\n",
        "\n",
        "`--seeds`\n",
        "\n",
        "This allows you to choose random seeds from the model. Remember that our input to StyleGAN is a 512-dimensional array. These seeds will generate those 512 values. Each seed will generate a different, random array. The same seed value will also always generate the same random array, so we can later use it for other purposes like interpolation.\n",
        "\n",
        "`--truncation-psi`\n",
        "\n",
        "Truncation is a special argument of StyleGAN. Essentially values that are closer to 0 will be more real than numbers further away from 0. I generally recommend a value between `0.5` and `1.0`. `0.5` will give you pretty \"realistic\" results, while `1.0` is likely to give you \"weirder\" results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3MhXEAMOMXH",
        "colab_type": "code",
        "outputId": "537f4e6e-4150-4a73-efe8-9a593bda6de9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "!python run_generator.py generate-images --network=/content/ladiesfloralcrop-network-snapshot-010237.pkl --seeds=3875451-3876000 --truncation-psi=0.7"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Local submit - run_dir: results/00000-generate-images\n",
            "dnnlib: Running run_generator.generate_images() on localhost...\n",
            "Loading networks from \"/content/ladiesfloralcrop-network-snapshot-010237.pkl\"...\n",
            "Setting up TensorFlow plugin \"fused_bias_act.cu\": Preprocessing... Compiling... Loading... Done.\n",
            "Setting up TensorFlow plugin \"upfirdn_2d.cu\": Preprocessing... Compiling... Loading... Done.\n",
            "Generating image for seed 1 (0/25) ...\n",
            "Generating image for seed 2 (1/25) ...\n",
            "Generating image for seed 3 (2/25) ...\n",
            "Generating image for seed 4 (3/25) ...\n",
            "Generating image for seed 5 (4/25) ...\n",
            "Generating image for seed 6 (5/25) ...\n",
            "Generating image for seed 7 (6/25) ...\n",
            "Generating image for seed 8 (7/25) ...\n",
            "Generating image for seed 9 (8/25) ...\n",
            "Generating image for seed 10 (9/25) ...\n",
            "Generating image for seed 11 (10/25) ...\n",
            "Generating image for seed 12 (11/25) ...\n",
            "Generating image for seed 13 (12/25) ...\n",
            "Generating image for seed 14 (13/25) ...\n",
            "Generating image for seed 15 (14/25) ...\n",
            "Generating image for seed 16 (15/25) ...\n",
            "Generating image for seed 17 (16/25) ...\n",
            "Generating image for seed 18 (17/25) ...\n",
            "Generating image for seed 19 (18/25) ...\n",
            "Generating image for seed 20 (19/25) ...\n",
            "Generating image for seed 21 (20/25) ...\n",
            "Generating image for seed 22 (21/25) ...\n",
            "Generating image for seed 23 (22/25) ...\n",
            "Generating image for seed 24 (23/25) ...\n",
            "Generating image for seed 25 (24/25) ...\n",
            "dnnlib: Finished run_generator.generate_images() in 1m 19s.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMiqkA3IReZB",
        "colab_type": "text"
      },
      "source": [
        "Let’s zip the generated files and download them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tp8O01O3PlFx",
        "colab_type": "code",
        "outputId": "e9e84d6d-1523-4283-af1e-11299a014c6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "!zip -r generated-0.7.zip /content/stylegan2/results/00000-generate-images"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/stylegan2/results/00000-generate-images/ (stored 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0025.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0014.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0007.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0018.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0017.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0010.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0015.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0024.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0021.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0004.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/log.txt (deflated 74%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0012.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0009.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0022.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0013.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0011.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0008.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0002.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0001.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/submit_config.txt (deflated 53%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/submit_config.pkl (deflated 43%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0019.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0016.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/run.txt (deflated 34%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0006.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0005.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/_finished.txt (stored 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0023.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0020.png (deflated 0%)\n",
            "  adding: content/stylegan2/results/00000-generate-images/seed0003.png (deflated 0%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTwJjmCrlfAc",
        "colab_type": "text"
      },
      "source": [
        "##Interpolation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQ2rYIC4TdaJ",
        "colab_type": "code",
        "outputId": "889a6ced-d1bc-421f-8d71-a6afedd5310c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!python run_generator.py generate-latent-walk --network=/content/ladiesfloralcrop-network-snapshot-010237.pkl --seeds=3,11,17,25,3 --frames 200 --truncation-psi=0.7"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Local submit - run_dir: results/00002-generate-latent-walk\n",
            "dnnlib: Running run_generator.generate_latent_walk() on localhost...\n",
            "Loading networks from \"/content/ladiesfloralcrop-network-snapshot-010237.pkl\"...\n",
            "Setting up TensorFlow plugin \"fused_bias_act.cu\": Preprocessing... Loading... Done.\n",
            "Setting up TensorFlow plugin \"upfirdn_2d.cu\": Preprocessing... Loading... Done.\n",
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            "Generating image for step 192/204 ...\n",
            "Generating image for step 193/204 ...\n",
            "Generating image for step 194/204 ...\n",
            "Generating image for step 195/204 ...\n",
            "Generating image for step 196/204 ...\n",
            "Generating image for step 197/204 ...\n",
            "Generating image for step 198/204 ...\n",
            "Generating image for step 199/204 ...\n",
            "Generating image for step 200/204 ...\n",
            "Generating image for step 201/204 ...\n",
            "Generating image for step 202/204 ...\n",
            "Generating image for step 203/204 ...\n",
            "dnnlib: Finished run_generator.generate_latent_walk() in 2m 20s.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dceBSxTsmW1H",
        "colab_type": "code",
        "outputId": "3e7f602e-8ec4-461d-ab6c-5aee753fff83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        }
      },
      "source": [
        "#convert to video \n",
        "!ffmpeg -r 24 -i ./results/00001-generate-latent-walk/step%05d.png -vcodec libx264 -pix_fmt yuv420p latent-walk-v2.mp4"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ffmpeg version 3.4.6-0ubuntu0.18.04.1 Copyright (c) 2000-2019 the FFmpeg developers\n",
            "  built with gcc 7 (Ubuntu 7.3.0-16ubuntu3)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.18.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
            "  libavutil      55. 78.100 / 55. 78.100\n",
            "  libavcodec     57.107.100 / 57.107.100\n",
            "  libavformat    57. 83.100 / 57. 83.100\n",
            "  libavdevice    57. 10.100 / 57. 10.100\n",
            "  libavfilter     6.107.100 /  6.107.100\n",
            "  libavresample   3.  7.  0 /  3.  7.  0\n",
            "  libswscale      4.  8.100 /  4.  8.100\n",
            "  libswresample   2.  9.100 /  2.  9.100\n",
            "  libpostproc    54.  7.100 / 54.  7.100\n",
            "Input #0, image2, from './results/00001-generate-latent-walk/step%05d.png':\n",
            "  Duration: 00:00:08.16, start: 0.000000, bitrate: N/A\n",
            "    Stream #0:0: Video: png, rgb24(pc), 1024x1024, 25 fps, 25 tbr, 25 tbn, 25 tbc\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n",
            "Press [q] to stop, [?] for help\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mprofile High, level 3.2\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0m264 - core 152 r2854 e9a5903 - H.264/MPEG-4 AVC codec - Copyleft 2003-2017 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=3 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=24 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n",
            "Output #0, mp4, to 'latent-walk-v2.mp4':\n",
            "  Metadata:\n",
            "    encoder         : Lavf57.83.100\n",
            "    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 1024x1024, q=-1--1, 24 fps, 12288 tbn, 24 tbc\n",
            "    Metadata:\n",
            "      encoder         : Lavc57.107.100 libx264\n",
            "    Side data:\n",
            "      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1\n",
            "frame=  204 fps=7.7 q=-1.0 Lsize=   12124kB time=00:00:08.37 bitrate=11859.2kbits/s speed=0.315x    \n",
            "video:12121kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.024653%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mframe I:1     Avg QP:27.07  size:147205\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mframe P:93    Avg QP:27.89  size: 81048\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mframe B:110   Avg QP:30.42  size: 42971\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mconsecutive B-frames: 27.5%  2.0%  0.0% 70.6%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mmb I  I16..4:  8.6% 57.3% 34.2%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mmb P  I16..4:  0.6% 15.3%  8.5%  P16..4: 32.1% 22.5% 15.4%  0.0%  0.0%    skip: 5.6%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mmb B  I16..4:  0.1%  2.7%  2.8%  B16..8: 27.2% 16.2%  8.5%  direct:17.4%  skip:25.2%  L0:29.0% L1:34.5% BI:36.5%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0m8x8 transform intra:59.7% inter:56.9%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mcoded y,uvDC,uvAC intra: 88.8% 83.9% 51.5% inter: 53.3% 28.1% 1.7%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mi16 v,h,dc,p: 43% 22% 21% 14%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 11% 12%  8% 11% 12% 10% 11% 10%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 12% 13%  7% 13% 12% 11%  9%  7%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mi8c dc,h,v,p: 52% 18% 21%  9%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mWeighted P-Frames: Y:19.4% UV:14.0%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mref P L0: 64.4% 26.9%  6.1%  2.0%  0.5%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mref B L0: 95.6%  3.3%  1.1%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mref B L1: 99.1%  0.9%\n",
            "\u001b[1;36m[libx264 @ 0x55a800235e00] \u001b[0mkb/s:11681.39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7A7jRRGmzhU",
        "colab_type": "code",
        "outputId": "7cdce8b6-3ee4-459a-cff8-c787bd016269",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "rm -r /content/drive/My Drive/stylegan2-colab-test/stylegan2/results/00002-stylegan2-birdaus-1gpu-config-f"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove '/content/drive/My': No such file or directory\n",
            "rm: cannot remove 'Drive/stylegan2-colab-test/stylegan2/results/00002-stylegan2-birdaus-1gpu-config-f': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9UpP_fVdql9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}