Forams_latent.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "Use our trained StyleGAN2 model to generate a movie of foram latent space.\n",
        "Taken from the last section of the demo notebook here:\n",
        "\n",
        "https://github.com/metazool/stylegan-art/blob/master/styleganportraits.ipynb\n"
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "home = '/content/drive/My Drive/Forams'\n",
        "os.chdir(home)\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 62
        },
        "outputId": "40ed79cc-a5d1-48cc-9ea3-bd575348d380"
      },
      "source": [
        "import sys, os\n",
        "home = '/content/drive/My Drive/Forams'\n",
        "sys.path.append(os.path.join(home, 'stylegan2'))\n",
        "os.chdir(home)\n",
        "import os\n",
        "import pickle\n",
        "import numpy as np\n",
        "import math\n",
        "import random\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "\n",
        "import dnnlib.tflib as tflib\n",
        "\n",
        "tflib.init_tf()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1TXwaSs5_m1F",
        "colab_type": "text"
      },
      "source": [
        "Load the model whose latents we want to visualise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_P_opvZNcXS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "8d578d36-4dc1-4bae-e753-8fedbc849da0"
      },
      "source": [
        "\n",
        "model = os.path.join(home, 'results/00026-stylegan2-tfrecords-1gpu-config-f/network-snapshot-000048.pkl')\n",
        "\n",
        "truncation = 0.7\n",
        "\n",
        "with open(model, 'rb') as f:\n",
        "    _G, _D, Gs = pickle.load(f)\n",
        "\n",
        "fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)\n",
        "#synthesis_kwargs = dict(output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True), minibatch_size=8)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setting up TensorFlow plugin \"fused_bias_act.cu\": Preprocessing... Loading... Done.\n",
            "Setting up TensorFlow plugin \"upfirdn_2d.cu\": Preprocessing... Loading... Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLaq9EG7B654",
        "colab_type": "text"
      },
      "source": [
        "Functions to sample the model and create series of intermediate images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTPUTBOXNpil",
        "colab_type": "code",
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      "source": [
        "def generate_images(latents, labels):\n",
        "    batch_size = 8\n",
        "    num_frames = latents.shape[0]\n",
        "    num_batches = int(np.ceil(num_frames/batch_size))\n",
        "    images = []\n",
        "    for b in tqdm(range(num_batches)):\n",
        "        new_images = Gs.run(latents[b*batch_size:min((b+1)*batch_size, num_frames-1), :], None, truncation_psi=truncation, randomize_noise=False, output_transform=fmt)\n",
        "        for img in new_images:\n",
        "            images.append(img)\n",
        "    return images\n",
        "\n",
        "def make_movie(images, out_dir, out_name):\n",
        "    temp_dir = 'frames%06d'%int(1000000*random.random())\n",
        "    os.system('mkdir %s'%temp_dir)\n",
        "    for idx in tqdm(range(len(images))):\n",
        "        skimage.io.imsave(f'{temp_dir}/frame{idx:05}.png', images[idx])\n",
        "    # This doesn't throw errors on Google Colab / Drive, but doesn't generate the file either.\n",
        "    # cmd = f'ffmpeg -i {temp_dir}/frame%05d.png -c:v libx264 -pix_fmt yuv420p {home}/{out_name}.mp4' \n",
        "    # print(cmd)\n",
        "    # os.system(cmd)\n",
        "    # os.system('rm -rf %s'%temp_dir)\n",
        "\n",
        "def random_sample(num_images, scale, truncation=0.5, seed=None):\n",
        "    seed = seed if seed is not None else 1000*random.random()\n",
        "    latents = np.random.RandomState(int(seed)).randn(num_images, *Gs.input_shapes[0][1:])\n",
        "    print(latents.shape)\n",
        "    labels = np.zeros([latents.shape[0]])\n",
        "    images = Gs.run(latents, None, truncation_psi=truncation, randomize_noise=False, output_transform=fmt)\n",
        "    print(images.shape)\n",
        "    images_ct = np.concatenate([img for img in images], axis=1)\n",
        "    print(images_ct.shape)\n",
        "    return images, latents\n",
        "\n",
        "def get_latent_interpolation(endpoints, num_frames_per, mode, shuffle):\n",
        "    if shuffle:\n",
        "        random.shuffle(endpoints)\n",
        "    num_endpoints, dim = len(endpoints), len(endpoints[0])\n",
        "    num_frames = num_frames_per * num_endpoints\n",
        "    endpoints = np.array(endpoints)\n",
        "    latents = np.zeros((num_frames, dim))\n",
        "    for e in range(num_endpoints):\n",
        "        e1, e2 = e, (e+1)%num_endpoints\n",
        "        for t in range(num_frames_per):\n",
        "            frame = e * num_frames_per + t\n",
        "            r = 0.5 - 0.5 * np.cos(np.pi*t/(num_frames_per-1)) if mode == 'ease' else float(t) / num_frames_per\n",
        "            latents[frame, :] = (1.0-r) * endpoints[e1,:] + r * endpoints[e2,:]\n",
        "    return latents"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCYaFUaMN7rG",
        "colab_type": "code",
        "outputId": "dd247b3e-5863-46c0-a225-ecdab5bb1d8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "images, latents = random_sample(60, scale=4, truncation=truncation)\n",
        "import skimage.io\n",
        "\n",
        "\n",
        "latents = get_latent_interpolation(latents, 30, 'linear', False)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60, 512)\n",
            "(60, 256, 256, 1)\n",
            "(256, 15360, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MiljnR8_OKx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114,
          "referenced_widgets": [
            "8a430a30a5ca47bd8aa45f81ffeda3a0",
            "018bdfbb870846fbaa8da8bc866ab924",
            "0521817fe0d84aa684c71f1582323f09",
            "f8ebb214275c425d8cc4b10e766b38a3",
            "032fd6d636014a8a9e3eb9cd155810a5",
            "0750df5858f94481b74d655c453d7b0d",
            "31e3df87801743d4a99ec44633dabd8b",
            "706874f23b3f44ccaee64efb4203412e",
            "114bf8726b694ae98bdb3e41b3a76784",
            "2eb5a56e86aa4a33839f3525fb99252c",
            "d3a903212eae4b09b845619391777360",
            "d2a727a7df374e5885ff5a65cfa8be98",
            "c889c9c166f14239be3bda6e12559861",
            "c394557d30224547a59cec139e8536c4",
            "c6ff7e6ab3e94e96aadfb62537544cf8",
            "9aede80504df42eda4857b5d22508969"
          ]
        },
        "outputId": "d45fee69-f503-4dcd-ac37-20a5d4b50d08"
      },
      "source": [
        "labels = np.zeros([latents.shape[0]] + [256,256, 1])\n",
        "\n",
        "images = generate_images(latents, labels)    \n",
        "\n",
        "make_movie(images, '.', 'latent_forams')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a430a30a5ca47bd8aa45f81ffeda3a0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=225), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "114bf8726b694ae98bdb3e41b3a76784",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=1799), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFxp7bDzAeQy",
        "colab_type": "text"
      },
      "source": [
        "As Drive doesn't want to persist our .mp4 we download the resulting output directory ({home}/framesXXXXXX) and do something like this locally:\n",
        "```\n",
        "mkdir tmp\n",
        "mogrify -path tmp -border 128 -bordercolor grey *.png\n",
        "cd tmp\n",
        "ffmpeg -i frame%05d.png -c:v libx264 -pix_fmt yuv420p latent_forams.mp4\n",
        "ffmpeg -t 2.5 -i latent_forams.mp4 -f gif latent_forams.gif\n",
        "```\n",
        "\n"
      ]
    }
  ]
}