DatEff_StyleGAN2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DatEff-StyleGAN2.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyMJi6TUj8tlVW1ChEA6b9sl",
      "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/DatEff_StyleGAN2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "if2ibnHt3mzw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "10ad05f4-9f27-44a9-bb6e-8f17ab44c4da"
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CiEN5aik2CPy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "6e008a86-25d7-4b11-c87e-4c48518997d1"
      },
      "source": [
        "!git clone https://github.com/dvschultz/data-efficient-gans"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'data-efficient-gans'...\n",
            "remote: Enumerating objects: 247, done.\u001b[K\n",
            "remote: Counting objects: 100% (247/247), done.\u001b[K\n",
            "remote: Compressing objects: 100% (161/161), done.\u001b[K\n",
            "remote: Total 247 (delta 136), reused 189 (delta 84), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (247/247), 1.04 MiB | 3.69 MiB/s, done.\n",
            "Resolving deltas: 100% (136/136), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwdEpFte2pS1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "99cd9acf-689d-4fe3-9644-5d6ce90bfa0b"
      },
      "source": [
        "%cd data-efficient-gans/DiffAugment-stylegan2/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/data-efficient-gans/DiffAugment-stylegan2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqbmIM5n2vMB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1491c322-453f-4f99-eacd-d23b060a6dd4"
      },
      "source": [
        "!python run_few_shot.py --dataset=100-shot-panda --num-gpus=1 --DiffAugment=color,translation,cutout --gamma=1 --total-kimg=600 --batch-size=16 --gpu-batch=8"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://hanlab.mit.edu/projects/data-efficient-gans/datasets/100-shot-panda.zip ... done\n",
            "Loading images from \"datasets/100-shot-panda\"\n",
            "Creating dataset \"datasets/100-shot-panda\"\n",
            "Added 100 images.\n",
            "Local submit - run_dir: results/00000-DiffAugment-stylegan2-100-shot-panda-256-batch16-1gpu-gamma1.0-color-translation-cutout\n",
            "dnnlib: Running training.training_loop.training_loop() on localhost...\n",
            "Streaming data using training.dataset.TFRecordDataset...\n",
            "tcmalloc: large alloc 4294967296 bytes == 0x76e8000 @  0x7f24949d1001 0x7f249141d765 0x7f2491481bb0 0x7f2491483a4f 0x7f249151a048 0x50a635 0x50cd96 0x507d64 0x509042 0x594931 0x549e5f 0x5513d1 0x59fc4e 0x50d356 0x507d64 0x588e5c 0x59fc4e 0x50d356 0x507d64 0x588e5c 0x59fc4e 0x50d356 0x509758 0x50a48d 0x50bfb4 0x509758 0x50a48d 0x50bfb4 0x507d64 0x588e5c 0x59fc4e\n",
            "Dataset shape = [3, 256, 256]\n",
            "Dynamic range = [0, 255]\n",
            "Label size    = 0\n",
            "Constructing networks...\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",
            "\n",
            "G                             Params    OutputShape         WeightShape     \n",
            "---                           ---       ---                 ---             \n",
            "latents_in                    -         (?, 512)            -               \n",
            "labels_in                     -         (?,)                -               \n",
            "lod                           -         ()                  -               \n",
            "dlatent_avg                   -         (512,)              -               \n",
            "G_mapping/latents_in          -         (?, 512)            -               \n",
            "G_mapping/labels_in           -         (?,)                -               \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           -         (?, 14, 512)        -               \n",
            "G_mapping/dlatents_out        -         (?, 14, 512)        -               \n",
            "G_synthesis/dlatents_in       -         (?, 14, 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/images_out        -         (?, 3, 256, 256)    -               \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",
            "images_out                    -         (?, 3, 256, 256)    -               \n",
            "---                           ---       ---                 ---             \n",
            "Total                         30034338                                      \n",
            "\n",
            "\n",
            "D                    Params    OutputShape         WeightShape     \n",
            "---                  ---       ---                 ---             \n",
            "images_in            -         (?, 3, 256, 256)    -               \n",
            "Pad                  -         (?, 3, 256, 256)    -               \n",
            "256x256/FromRGB      512       (?, 128, 256, 256)  (1, 1, 3, 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       (?,)                (512, 1)        \n",
            "scores_out           -         (?,)                -               \n",
            "---                  ---       ---                 ---             \n",
            "Total                28864129                                      \n",
            "\n",
            "Building TensorFlow graph...\n",
            "Initializing logs...\n",
            "Training for 600 kimg...\n",
            "\n",
            "tick 0     kimg 0.1      lod 0.00  minibatch 16   time 21s          sec/tick 21.2    sec/kimg 331.97  maintenance 0.0    gpumem 8.8\n",
            "tcmalloc: large alloc 4294967296 bytes == 0x7f1e41dd4000 @  0x7f24949d1001 0x7f249141d765 0x7f2491481bb0 0x7f2491483a4f 0x7f249151a048 0x50a635 0x50cd96 0x507d64 0x509042 0x594931 0x549e5f 0x5513d1 0x59fc4e 0x50d356 0x507d64 0x588e5c 0x59fc4e 0x50d356 0x509758 0x50a48d 0x50bfb4 0x507d64 0x509042 0x594931 0x59fc4e 0x50d356 0x507d64 0x509a90 0x50a48d 0x50cd96 0x507d64\n",
            "Downloading http://d36zk2xti64re0.cloudfront.net/stylegan1/networks/metrics/inception_v3_features.pkl ... done\n",
            "network-snapshot-000000        time 2m 35s       fid5k-train 352.7956\n",
            "tick 1     kimg 10.1     lod 0.00  minibatch 16   time 36m 25s      sec/tick 1996.4  sec/kimg 198.68  maintenance 167.6  gpumem 8.8\n",
            "tick 2     kimg 20.2     lod 0.00  minibatch 16   time 1h 09m 41s   sec/tick 1996.2  sec/kimg 198.67  maintenance 0.0    gpumem 8.8\n",
            "tick 3     kimg 30.2     lod 0.00  minibatch 16   time 1h 42m 57s   sec/tick 1995.8  sec/kimg 198.63  maintenance 0.0    gpumem 8.8\n",
            "tick 4     kimg 40.3     lod 0.00  minibatch 16   time 2h 16m 13s   sec/tick 1995.9  sec/kimg 198.64  maintenance 0.0    gpumem 8.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZy9zNRY3jnD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "d431be53-fd5c-445c-81e8-37451bc53b91"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun Jul  5 21:28:21 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 450.36.06    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P0    36W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCTC-_Io4S37",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}