StyleGAN2_Projection.ipynb

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        "<a href=\"https://colab.research.google.com/github/dvschultz/stylegan2/blob/master/StyleGAN2_Projection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "# StyleGAN2\n",
        "\n",
        "This notebook demonstrates how to run NVIDIA's StyleGAN2 on Google Colab.\n",
        "Make sure to specify a GPU runtime.\n",
        "\n",
        "This notebook mainly adds a few convenience functions. \n",
        "\n",
        "For information on StyleGAN2, see:\n",
        "\n",
        "Paper: https://arxiv.org/abs/1812.04948\n",
        "\n",
        "Video: https://youtu.be/kSLJriaOumA\n",
        "\n",
        "Code: https://github.com/NVlabs/stylegan\n",
        "\n",
        "FFHQ: https://github.com/NVlabs/ffhq-dataset\n",
        "\n",
        "/Mikael Christensen, 2019.\n"
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "\n",
        "# Download the code\n",
        "!git clone https://github.com/dvschultz/stylegan2\n",
        "%cd stylegan2\n",
        "!nvcc test_nvcc.cu -o test_nvcc -run\n",
        "\n",
        "print('Tensorflow version: {}'.format(tf.__version__) )\n",
        "!nvidia-smi -L\n",
        "print('GPU Identified at: {}'.format(tf.test.gpu_device_name()))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'stylegan2'...\n",
            "remote: Enumerating objects: 15, done.\u001b[K\n",
            "remote: Counting objects: 100% (15/15), done.\u001b[K\n",
            "remote: Compressing objects: 100% (15/15), done.\u001b[K\n",
            "remote: Total 239 (delta 6), reused 0 (delta 0), pack-reused 224\u001b[K\n",
            "Receiving objects: 100% (239/239), 15.23 MiB | 6.93 MiB/s, done.\n",
            "Resolving deltas: 100% (132/132), done.\n",
            "/content/stylegan2\n",
            "CPU says hello.\n",
            "GPU says hello.\n",
            "Tensorflow version: 1.15.0\n",
            "GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-889e7a7f-d169-40c6-6889-6021e8b8f4f9)\n",
            "GPU Identified at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        "id": "cwVXBFaSuoIU",
        "colab_type": "code",
        "outputId": "25ca2fc7-3165-473b-a1b0-581f5eab5f2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# Download the model of choice\n",
        "import argparse\n",
        "import numpy as np\n",
        "import PIL.Image\n",
        "import dnnlib\n",
        "import dnnlib.tflib as tflib\n",
        "import re\n",
        "import sys\n",
        "from io import BytesIO\n",
        "import IPython.display\n",
        "import numpy as np\n",
        "from math import ceil\n",
        "from PIL import Image, ImageDraw\n",
        "import imageio\n",
        "\n",
        "import pretrained_networks\n",
        "\n",
        "# Choose between these pretrained models - I think 'f' is the best choice:\n",
        "\n",
        "# 1024×1024 faces\n",
        "# stylegan2-ffhq-config-a.pkl\n",
        "# stylegan2-ffhq-config-b.pkl\n",
        "# stylegan2-ffhq-config-c.pkl\n",
        "# stylegan2-ffhq-config-d.pkl\n",
        "# stylegan2-ffhq-config-e.pkl\n",
        "# stylegan2-ffhq-config-f.pkl\n",
        "\n",
        "# 512×384 cars\n",
        "# stylegan2-car-config-a.pkl\n",
        "# stylegan2-car-config-b.pkl\n",
        "# stylegan2-car-config-c.pkl\n",
        "# stylegan2-car-config-d.pkl\n",
        "# stylegan2-car-config-e.pkl\n",
        "# stylegan2-car-config-f.pkl\n",
        "\n",
        "# 256x256 horses\n",
        "# stylegan2-horse-config-a.pkl\n",
        "# stylegan2-horse-config-f.pkl\n",
        "\n",
        "# 256x256 churches\n",
        "# stylegan2-church-config-a.pkl\n",
        "# stylegan2-church-config-f.pkl\n",
        "\n",
        "# 256x256 cats\n",
        "# stylegan2-cat-config-f.pkl\n",
        "# stylegan2-cat-config-a.pkl\n",
        "network_pkl = \"gdrive:networks/stylegan2-ffhq-config-f.pkl\"\n",
        "\n",
        "# If downloads fails, due to 'Google Drive download quota exceeded' you can try downloading manually from your own Google Drive account\n",
        "# network_pkl = \"/content/drive/My Drive/GAN/stylegan2-ffhq-config-f.pkl\"\n",
        "\n",
        "print('Loading networks from \"%s\"...' % network_pkl)\n",
        "_G, _D, Gs = pretrained_networks.load_networks(network_pkl)\n",
        "noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading networks from \"gdrive:networks/stylegan2-ffhq-config-f.pkl\"...\n",
            "Downloading https://drive.google.com/uc?id=1Mgh-jglZjgksupF0XLl0KzuOqd1LXcoE .... done\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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zxbhe4uLvF_a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Useful utility functions...\n",
        "\n",
        "# Generates a list of images, based on a list of latent vectors (Z), and a list (or a single constant) of truncation_psi's.\n",
        "def generate_images_in_w_space(dlatents, truncation_psi):\n",
        "    Gs_kwargs = dnnlib.EasyDict()\n",
        "    Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)\n",
        "    Gs_kwargs.randomize_noise = False\n",
        "    Gs_kwargs.truncation_psi = truncation_psi\n",
        "    dlatent_avg = Gs.get_var('dlatent_avg') # [component]\n",
        "\n",
        "    imgs = []\n",
        "    for row, dlatent in log_progress(enumerate(dlatents), name = \"Generating images\"):\n",
        "        #row_dlatents = (dlatent[np.newaxis] - dlatent_avg) * np.reshape(truncation_psi, [-1, 1, 1]) + dlatent_avg\n",
        "        dl = (dlatent-dlatent_avg)*truncation_psi   + dlatent_avg\n",
        "        row_images = Gs.components.synthesis.run(dlatent,  **Gs_kwargs)\n",
        "        imgs.append(PIL.Image.fromarray(row_images[0], 'RGB'))\n",
        "    return imgs       \n",
        "\n",
        "def generate_images(zs, truncation_psi):\n",
        "    Gs_kwargs = dnnlib.EasyDict()\n",
        "    Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)\n",
        "    Gs_kwargs.randomize_noise = False\n",
        "    if not isinstance(truncation_psi, list):\n",
        "        truncation_psi = [truncation_psi] * len(zs)\n",
        "        \n",
        "    imgs = []\n",
        "    for z_idx, z in log_progress(enumerate(zs), size = len(zs), name = \"Generating images\"):\n",
        "        Gs_kwargs.truncation_psi = truncation_psi[z_idx]\n",
        "        noise_rnd = np.random.RandomState(1) # fix noise\n",
        "        tflib.set_vars({var: noise_rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]\n",
        "        print(noise_rnd.shape)\n",
        "        images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]\n",
        "        imgs.append(PIL.Image.fromarray(images[0], 'RGB'))\n",
        "    return imgs\n",
        "\n",
        "def generate_zs_from_seeds(seeds):\n",
        "    zs = []\n",
        "    for seed_idx, seed in enumerate(seeds):\n",
        "        rnd = np.random.RandomState(seed)\n",
        "        z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]\n",
        "        zs.append(z)\n",
        "    return zs\n",
        "\n",
        "# Generates a list of images, based on a list of seed for latent vectors (Z), and a list (or a single constant) of truncation_psi's.\n",
        "def generate_images_from_seeds(seeds, truncation_psi):\n",
        "    return generate_images(generate_zs_from_seeds(seeds), truncation_psi)\n",
        "\n",
        "def saveImgs(imgs, location):\n",
        "  for idx, img in log_progress(enumerate(imgs), size = len(imgs), name=\"Saving images\"):\n",
        "    file = location+ str(idx) + \".png\"\n",
        "    img.save(file)\n",
        "\n",
        "def imshow(a, format='png', jpeg_fallback=True):\n",
        "  a = np.asarray(a, dtype=np.uint8)\n",
        "  str_file = BytesIO()\n",
        "  PIL.Image.fromarray(a).save(str_file, format)\n",
        "  im_data = str_file.getvalue()\n",
        "  try:\n",
        "    disp = IPython.display.display(IPython.display.Image(im_data))\n",
        "  except IOError:\n",
        "    if jpeg_fallback and format != 'jpeg':\n",
        "      print ('Warning: image was too large to display in format \"{}\"; '\n",
        "             'trying jpeg instead.').format(format)\n",
        "      return imshow(a, format='jpeg')\n",
        "    else:\n",
        "      raise\n",
        "  return disp\n",
        "\n",
        "def showarray(a, fmt='png'):\n",
        "    a = np.uint8(a)\n",
        "    f = StringIO()\n",
        "    PIL.Image.fromarray(a).save(f, fmt)\n",
        "    IPython.display.display(IPython.display.Image(data=f.getvalue()))\n",
        "\n",
        "        \n",
        "def clamp(x, minimum, maximum):\n",
        "    return max(minimum, min(x, maximum))\n",
        "    \n",
        "def drawLatent(image,latents,x,y,x2,y2, color=(255,0,0,100)):\n",
        "  buffer = PIL.Image.new('RGBA', image.size, (0,0,0,0))\n",
        "   \n",
        "  draw = ImageDraw.Draw(buffer)\n",
        "  cy = (y+y2)/2\n",
        "  draw.rectangle([x,y,x2,y2],fill=(255,255,255,180), outline=(0,0,0,180))\n",
        "  for i in range(len(latents)):\n",
        "    mx = x + (x2-x)*(float(i)/len(latents))\n",
        "    h = (y2-y)*latents[i]*0.1\n",
        "    h = clamp(h,cy-y2,y2-cy)\n",
        "    draw.line((mx,cy,mx,cy+h),fill=color)\n",
        "  return PIL.Image.alpha_composite(image,buffer)\n",
        "             \n",
        "  \n",
        "def createImageGrid(images, scale=0.25, rows=1):\n",
        "   w,h = images[0].size\n",
        "   w = int(w*scale)\n",
        "   h = int(h*scale)\n",
        "   height = rows*h\n",
        "   cols = ceil(len(images) / rows)\n",
        "   width = cols*w\n",
        "   canvas = PIL.Image.new('RGBA', (width,height), 'white')\n",
        "   for i,img in enumerate(images):\n",
        "     img = img.resize((w,h), PIL.Image.ANTIALIAS)\n",
        "     canvas.paste(img, (w*(i % cols), h*(i // cols))) \n",
        "   return canvas\n",
        "\n",
        "def convertZtoW(latent, truncation_psi=0.7, truncation_cutoff=9):\n",
        "  dlatent = Gs.components.mapping.run(latent, None) # [seed, layer, component]\n",
        "  dlatent_avg = Gs.get_var('dlatent_avg') # [component]\n",
        "  for i in range(truncation_cutoff):\n",
        "    dlatent[0][i] = (dlatent[0][i]-dlatent_avg)*truncation_psi + dlatent_avg\n",
        "    \n",
        "  return dlatent\n",
        "\n",
        "def interpolate(zs, steps):\n",
        "   out = []\n",
        "   for i in range(len(zs)-1):\n",
        "    for index in range(steps):\n",
        "     fraction = index/float(steps) \n",
        "     out.append(zs[i+1]*fraction + zs[i]*(1-fraction))\n",
        "   return out\n",
        "\n",
        "# Taken from https://github.com/alexanderkuk/log-progress\n",
        "def log_progress(sequence, every=1, size=None, name='Items'):\n",
        "    from ipywidgets import IntProgress, HTML, VBox\n",
        "    from IPython.display import display\n",
        "\n",
        "    is_iterator = False\n",
        "    if size is None:\n",
        "        try:\n",
        "            size = len(sequence)\n",
        "        except TypeError:\n",
        "            is_iterator = True\n",
        "    if size is not None:\n",
        "        if every is None:\n",
        "            if size <= 200:\n",
        "                every = 1\n",
        "            else:\n",
        "                every = int(size / 200)     # every 0.5%\n",
        "    else:\n",
        "        assert every is not None, 'sequence is iterator, set every'\n",
        "\n",
        "    if is_iterator:\n",
        "        progress = IntProgress(min=0, max=1, value=1)\n",
        "        progress.bar_style = 'info'\n",
        "    else:\n",
        "        progress = IntProgress(min=0, max=size, value=0)\n",
        "    label = HTML()\n",
        "    box = VBox(children=[label, progress])\n",
        "    display(box)\n",
        "\n",
        "    index = 0\n",
        "    try:\n",
        "        for index, record in enumerate(sequence, 1):\n",
        "            if index == 1 or index % every == 0:\n",
        "                if is_iterator:\n",
        "                    label.value = '{name}: {index} / ?'.format(\n",
        "                        name=name,\n",
        "                        index=index\n",
        "                    )\n",
        "                else:\n",
        "                    progress.value = index\n",
        "                    label.value = u'{name}: {index} / {size}'.format(\n",
        "                        name=name,\n",
        "                        index=index,\n",
        "                        size=size\n",
        "                    )\n",
        "            yield record\n",
        "    except:\n",
        "        progress.bar_style = 'danger'\n",
        "        raise\n",
        "    else:\n",
        "        progress.bar_style = 'success'\n",
        "        progress.value = index\n",
        "        label.value = \"{name}: {index}\".format(\n",
        "            name=name,\n",
        "            index=str(index or '?')\n",
        "        )\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYdsgv4i6YPl",
        "colab_type": "text"
      },
      "source": [
        "# Projecting images onto the generatable manifold\n",
        "\n",
        "StyleGAN2 comes with a projector that finds the closest generatable image based on any input image. This allows you to get a feeling for the diversity of the portrait manifold."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urzy8lw76j_r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir projection\n",
        "!mkdir projection/imgs\n",
        "!mkdir projection/out\n",
        "\n",
        "# Now upload a single image to 'stylegan2/projection/imgs' (use the Files side panel). Image should be color PNG, with a size of 1024x1024."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bi2mA9XaQxWb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3eba3ee5-72af-4967-ee86-07eec5efccd6"
      },
      "source": [
        "# Convert uploaded images to TFRecords\n",
        "import dataset_tool\n",
        "dataset_tool.create_from_images(\"./projection/records/\", \"./projection/imgs/\", True)\n",
        "\n",
        "# Run the projector\n",
        "import run_projector\n",
        "import projector\n",
        "import training.dataset\n",
        "import training.misc\n",
        "import os\n",
        "\n",
        "global proj\n",
        "\n",
        "def project_image_run(proj, targets, png_prefix, num_snapshots):\n",
        "    snapshot_steps = set(proj.num_steps - np.linspace(0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))\n",
        "    training.misc.save_image_grid(targets, png_prefix + 'target.png', drange=[-1,1])\n",
        "    proj.start(targets)\n",
        "    while proj.get_cur_step() < proj.num_steps:\n",
        "        print('\\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps), end='', flush=True)\n",
        "        proj.step()\n",
        "        if proj.get_cur_step() in snapshot_steps:\n",
        "            training.misc.save_image_grid(proj.get_images(), png_prefix + 'step%04d.png' % proj.get_cur_step(), drange=[-1,1])\n",
        "    print('\\r%-30s\\r' % '', end='', flush=True)\n",
        "    np.save(\"./projection/latent.npy\", proj.get_dlatents())\n",
        "    np.save(\"./projection/noise.npy\", proj.get_noises())\n",
        "\n",
        "def project_real_images(dataset_name, data_dir, num_images, num_snapshots):\n",
        "    proj = projector.Projector()\n",
        "    proj.set_network(Gs)\n",
        "\n",
        "    print('Loading images from \"%s\"...' % dataset_name)\n",
        "    dataset_obj = training.dataset.load_dataset(data_dir=data_dir, tfrecord_dir=dataset_name, max_label_size=0, verbose=True, repeat=False, shuffle_mb=0)\n",
        "    assert dataset_obj.shape == Gs.output_shape[1:]\n",
        "\n",
        "    for image_idx in range(num_images):\n",
        "        print('Projecting image %d/%d ...' % (image_idx, num_images))\n",
        "        images, _labels = dataset_obj.get_minibatch_np(1)\n",
        "        images = training.misc.adjust_dynamic_range(images, [0, 255], [-1, 1])\n",
        "        project_image_run(proj, targets=images, png_prefix=dnnlib.make_run_dir_path('projection/out/image%04d-' % image_idx), num_snapshots=num_snapshots)\n",
        "\n",
        "project_real_images(\"records\",\"./projection\",1,100)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/1 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loading images from \"./projection/imgs/\"\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r100%|██████████| 1/1 [00:00<00:00,  3.21it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Projector: Finding W midpoint and stddev using 10000 samples...\n",
            "Projector: std = 10.0057\n",
            "Projector: Setting up noise inputs...\n",
            "Projector: G_synthesis/noise0 <tf.Variable 'G_synthesis_2/noise0:0' shape=(1, 1, 4, 4) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise1 <tf.Variable 'G_synthesis_2/noise1:0' shape=(1, 1, 8, 8) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise2 <tf.Variable 'G_synthesis_2/noise2:0' shape=(1, 1, 8, 8) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise3 <tf.Variable 'G_synthesis_2/noise3:0' shape=(1, 1, 16, 16) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise4 <tf.Variable 'G_synthesis_2/noise4:0' shape=(1, 1, 16, 16) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise5 <tf.Variable 'G_synthesis_2/noise5:0' shape=(1, 1, 32, 32) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise6 <tf.Variable 'G_synthesis_2/noise6:0' shape=(1, 1, 32, 32) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise7 <tf.Variable 'G_synthesis_2/noise7:0' shape=(1, 1, 64, 64) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise8 <tf.Variable 'G_synthesis_2/noise8:0' shape=(1, 1, 64, 64) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise9 <tf.Variable 'G_synthesis_2/noise9:0' shape=(1, 1, 128, 128) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise10 <tf.Variable 'G_synthesis_2/noise10:0' shape=(1, 1, 128, 128) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise11 <tf.Variable 'G_synthesis_2/noise11:0' shape=(1, 1, 256, 256) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise12 <tf.Variable 'G_synthesis_2/noise12:0' shape=(1, 1, 256, 256) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise13 <tf.Variable 'G_synthesis_2/noise13:0' shape=(1, 1, 512, 512) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise14 <tf.Variable 'G_synthesis_2/noise14:0' shape=(1, 1, 512, 512) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise15 <tf.Variable 'G_synthesis_2/noise15:0' shape=(1, 1, 1024, 1024) dtype=float32_ref>\n",
            "Projector: G_synthesis/noise16 <tf.Variable 'G_synthesis_2/noise16:0' shape=(1, 1, 1024, 1024) dtype=float32_ref>\n",
            "Projector: Building image output graph...\n",
            "Projector: Building loss graph...\n",
            "Downloading https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2 ... done\n",
            "Projector: Building noise regularization graph...\n",
            "Projector: Setting up optimizer...\n",
            "Loading images from \"records\"...\n",
            "Streaming data using training.dataset.TFRecordDataset...\n",
            "Dataset shape = [3, 1024, 1024]\n",
            "Dynamic range = [0, 255]\n",
            "Label size    = 0\n",
            "Projecting image 0/1 ...\n",
            "Projector: Preparing target images...\n",
            "Projector: Initializing optimization state...\n",
            "0 / 1000 ... Projector: Running...\n",
            "9 / 1000 ... Projector: 10      0.50737     4462.11     \n",
            "19 / 1000 ... Projector: 20      0.49182     1216        \n",
            "29 / 1000 ... Projector: 30      0.437515    662.71      \n",
            "39 / 1000 ... Projector: 40      0.427343    103.587     \n",
            "49 / 1000 ... Projector: 50      0.430409    124.715     \n",
            "59 / 1000 ... Projector: 60      0.399688    28.5336     \n",
            "69 / 1000 ... Projector: 70      0.407623    28.4535     \n",
            "79 / 1000 ... Projector: 80      0.393681    14.015      \n",
            "89 / 1000 ... Projector: 90      0.379206    8.05402     \n",
            "99 / 1000 ... Projector: 100     0.369578    8.51495     \n",
            "109 / 1000 ... Projector: 110     0.36702     4.9342      \n",
            "119 / 1000 ... Projector: 120     0.3647      8.88957     \n",
            "129 / 1000 ... Projector: 130     0.352689    7.77005     \n",
            "139 / 1000 ... Projector: 140     0.366161    7.40459     \n",
            "149 / 1000 ... Projector: 150     0.342366    6.88291     \n",
            "159 / 1000 ... Projector: 160     0.335139    18.8409     \n",
            "169 / 1000 ... Projector: 170     0.354045    5.55035     \n",
            "179 / 1000 ... Projector: 180     0.351004    3.92602     \n",
            "189 / 1000 ... Projector: 190     0.337143    2.22259     \n",
            "199 / 1000 ... Projector: 200     0.337931    21.0605     \n",
            "209 / 1000 ... Projector: 210     0.320178    6.21294     \n",
            "219 / 1000 ... Projector: 220     0.327786    12.3962     \n",
            "229 / 1000 ... Projector: 230     0.338249    9.3317      \n",
            "239 / 1000 ... Projector: 240     0.324118    5.37894     \n",
            "249 / 1000 ... Projector: 250     0.315708    10.172      \n",
            "259 / 1000 ... Projector: 260     0.313333    3.8412      \n",
            "269 / 1000 ... Projector: 270     0.318681    6.77849     \n",
            "279 / 1000 ... Projector: 280     0.316176    4.96442     \n",
            "289 / 1000 ... Projector: 290     0.305131    4.40333     \n",
            "299 / 1000 ... Projector: 300     0.308211    4.04372     \n",
            "309 / 1000 ... Projector: 310     0.308095    36.0909     \n",
            "319 / 1000 ... Projector: 320     0.305979    24.1286     \n",
            "329 / 1000 ... Projector: 330     0.310991    27.9949     \n",
            "339 / 1000 ... Projector: 340     0.295145    15.7513     \n",
            "349 / 1000 ... Projector: 350     0.310234    10.0267     \n",
            "359 / 1000 ... Projector: 360     0.302474    8.39803     \n",
            "369 / 1000 ... Projector: 370     0.294995    3.04594     \n",
            "379 / 1000 ... Projector: 380     0.293491    6.36605     \n",
            "389 / 1000 ... Projector: 390     0.28776     10.9444     \n",
            "399 / 1000 ... Projector: 400     0.290816    12.1212     \n",
            "409 / 1000 ... Projector: 410     0.28268     1.85276     \n",
            "419 / 1000 ... Projector: 420     0.286597    1.50085     \n",
            "429 / 1000 ... Projector: 430     0.286818    1.98922     \n",
            "439 / 1000 ... Projector: 440     0.279904    12.6662     \n",
            "449 / 1000 ... Projector: 450     0.28145     1.02328     \n",
            "459 / 1000 ... Projector: 460     0.277614    6.53975     \n",
            "469 / 1000 ... Projector: 470     0.276968    19.3241     \n",
            "479 / 1000 ... Projector: 480     0.275036    2.10258     \n",
            "489 / 1000 ... Projector: 490     0.271709    9.2647      \n",
            "499 / 1000 ... Projector: 500     0.270244    2.43833     \n",
            "509 / 1000 ... Projector: 510     0.272885    29.3278     \n",
            "519 / 1000 ... Projector: 520     0.273735    11.961      \n",
            "529 / 1000 ... Projector: 530     0.270944    24.9014     \n",
            "539 / 1000 ... Projector: 540     0.267844    18.7696     \n",
            "549 / 1000 ... Projector: 550     0.266629    33.1113     \n",
            "559 / 1000 ... Projector: 560     0.262496    6.68128     \n",
            "569 / 1000 ... Projector: 570     0.26487     2.68871     \n",
            "579 / 1000 ... Projector: 580     0.266357    31.9682     \n",
            "589 / 1000 ... Projector: 590     0.26144     2.21434     \n",
            "599 / 1000 ... Projector: 600     0.266181    8.32217     \n",
            "609 / 1000 ... Projector: 610     0.257773    2.96618     \n",
            "619 / 1000 ... Projector: 620     0.262144    9.18803     \n",
            "629 / 1000 ... Projector: 630     0.259725    1.32222     \n",
            "639 / 1000 ... Projector: 640     0.259469    0.338235    \n",
            "649 / 1000 ... Projector: 650     0.259213    7.40648     \n",
            "659 / 1000 ... Projector: 660     0.256848    6.45258     \n",
            "669 / 1000 ... Projector: 670     0.256128    7.04302     \n",
            "679 / 1000 ... Projector: 680     0.253629    7.5228      \n",
            "689 / 1000 ... Projector: 690     0.261583    3.95695     \n",
            "699 / 1000 ... Projector: 700     0.253976    12.5902     \n",
            "709 / 1000 ... Projector: 710     0.250788    1.65929     \n",
            "719 / 1000 ... Projector: 720     0.256059    34.5121     \n",
            "729 / 1000 ... Projector: 730     0.250886    28.3069     \n",
            "739 / 1000 ... Projector: 740     0.255565    33.0718     \n",
            "749 / 1000 ... Projector: 750     0.254305    35.8355     \n",
            "759 / 1000 ... Projector: 760     0.254406    19.8557     \n",
            "769 / 1000 ... Projector: 770     0.253087    24.3587     \n",
            "779 / 1000 ... Projector: 780     0.248588    9.94346     \n",
            "789 / 1000 ... Projector: 790     0.247518    2.68934     \n",
            "799 / 1000 ... Projector: 800     0.248274    1.58034     \n",
            "809 / 1000 ... Projector: 810     0.245431    0.432876    \n",
            "819 / 1000 ... Projector: 820     0.247122    0.335979    \n",
            "829 / 1000 ... Projector: 830     0.245688    0.273941    \n",
            "839 / 1000 ... Projector: 840     0.246536    0.257178    \n",
            "849 / 1000 ... Projector: 850     0.24546     0.247132    \n",
            "859 / 1000 ... Projector: 860     0.24571     0.246441    \n",
            "869 / 1000 ... Projector: 870     0.243707    0.243802    \n",
            "879 / 1000 ... Projector: 880     0.242774    0.242879    \n",
            "889 / 1000 ... Projector: 890     0.242238    0.242244    \n",
            "899 / 1000 ... Projector: 900     0.241588    0.241595    \n",
            "909 / 1000 ... Projector: 910     0.241257    0.241259    \n",
            "919 / 1000 ... Projector: 920     0.240844    0.240845    \n",
            "929 / 1000 ... Projector: 930     0.240346    0.240347    \n",
            "939 / 1000 ... Projector: 940     0.240124    0.240124    \n",
            "949 / 1000 ... Projector: 950     0.239978    0.239978    \n",
            "959 / 1000 ... Projector: 960     0.239871    0.239871    \n",
            "969 / 1000 ... Projector: 970     0.23982     0.23982     \n",
            "979 / 1000 ... Projector: 980     0.239792    0.239792    \n",
            "989 / 1000 ... Projector: 990     0.239782    0.239782    \n",
            "999 / 1000 ... Projector: 1000    0.23978     0.23978     \n",
            "Projector: Done.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-a63fb2ac8e0e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mproject_image_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpng_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdnnlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_run_dir_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'projection/out/image%04d-'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mimage_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_snapshots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_snapshots\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mproject_real_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"records\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"./projection\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-5-a63fb2ac8e0e>\u001b[0m in \u001b[0;36mproject_real_images\u001b[0;34m(dataset_name, data_dir, num_images, num_snapshots)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_minibatch_np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madjust_dynamic_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m255\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m         \u001b[0mproject_image_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpng_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdnnlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_run_dir_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'projection/out/image%04d-'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mimage_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_snapshots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_snapshots\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0mproject_real_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"records\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"./projection\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-5-a63fb2ac8e0e>\u001b[0m in \u001b[0;36mproject_image_run\u001b[0;34m(proj, targets, png_prefix, num_snapshots)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\r%-30s\\r'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflush\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./projection/latent.npy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dlatents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./projection/noise.npy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_noises\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproject_real_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_snapshots\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36msave\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(file, arr, allow_pickle, fix_imports)\u001b[0m\n\u001b[1;32m    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    541\u001b[0m         format.write_array(fid, arr, allow_pickle=allow_pickle,\n\u001b[1;32m    542\u001b[0m                            pickle_kwargs=pickle_kwargs)\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \"\"\"\n\u001b[0;32m--> 138\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (4,4) into shape (1,1)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmjPpjFU6yq3",
        "colab_type": "code",
        "outputId": "bff6ea57-df58-459a-c7ff-7d484b432fb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "17daf463bf6d481cb55e23f2f0b450ad",
            "55930d292ce64b6eae6dd0e56b849e80",
            "43b18cb6fe5443e69da153c2dc2090a1",
            "22ce98fcf87e48c08ba495f446a2eba4",
            "bfd6297ac4a243a186d3f8068efcaf40",
            "adb5d2fb55af40b5b408885e12477375",
            "b3a460871c6340e7acba56bbd7f19c56",
            "b747f6b31a204a31ab83880067943850"
          ]
        }
      },
      "source": [
        "# Create video \n",
        "\n",
        "import glob\n",
        "\n",
        "imgs = sorted(glob.glob(\"projection/out/*step*.png\"))\n",
        "\n",
        "target_imgs = sorted(glob.glob(\"projection/out/*target*.png\"))\n",
        "assert len(target_imgs) == 1, \"More than one target found?\"\n",
        "target_img = imageio.imread(target_imgs[0])\n",
        "\n",
        "movieName = \"projection/movie.mp4\"\n",
        "with imageio.get_writer(movieName, mode='I') as writer:\n",
        "    for filename in log_progress(imgs, name = \"Creating animation\"):\n",
        "        image = imageio.imread(filename)\n",
        "\n",
        "        # Concatenate images with original target image\n",
        "        w,h = image.shape[0:2]\n",
        "        canvas = PIL.Image.new('RGBA', (w*2,h), 'white')\n",
        "        canvas.paste(Image.fromarray(target_img), (0, 0))\n",
        "        canvas.paste(Image.fromarray(image), (w, 0))\n",
        "\n",
        "        writer.append_data(np.array(canvas))  "
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17daf463bf6d481cb55e23f2f0b450ad",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(HTML(value=''), IntProgress(value=0)))"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aZvophLZQOw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415,
          "referenced_widgets": [
            "f2ec0bc1b0b84cdca9db764ac1c05504",
            "15bb78ec7c1749a68c7514e35a1ed69d",
            "f8e73d9cd3e8497781d0bb53c98f71fa",
            "73ff5c816a7244eaad887477f73fd3ba",
            "6c0891c628e24e97a536f28ee107541d",
            "1de36827cf944b93a336124a921ed8d9",
            "55331f5e7f6844dfbdc6bc23d7b96099",
            "aa4c59b95df1488184734aa7eeabf7c4"
          ]
        },
        "outputId": "cd7936ec-1824-4861-b552-493cad28f0dd"
      },
      "source": [
        "latent1 = np.load('/content/stylegan2/projection/latent.npy')\n",
        "\n",
        "imgs = generate_images(latent1,None)\n",
        "number_of_images = len(imgs)\n",
        "imshow(createImageGrid(imgs, 0.4 , 3))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2ec0bc1b0b84cdca9db764ac1c05504",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(HTML(value=''), IntProgress(value=0, max=1)))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-3bf1b7a839d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlatent1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/stylegan2/projection/latent.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mimgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlatent1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mnumber_of_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcreateImageGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-f33a67c328c4>\u001b[0m in \u001b[0;36mgenerate_images\u001b[0;34m(zs, truncation_psi)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mnoise_rnd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRandomState\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fix noise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0mtflib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnoise_rnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnoise_vars\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# [height, width]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise_rnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mGs_kwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# [minibatch, height, width, channel]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0mimgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPIL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'RGB'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.random.mtrand.RandomState' object has no attribute 'shape'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGVarLre63dL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Now you can download the video (find it in the Files side panel under 'stylegan2/projection')\n",
        "\n",
        "# To cleanup\n",
        "!rm projection/out/*.*\n",
        "#!rm projection/records/*.*\n",
        "#!rm projection/imgs/*.*"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mGDRao-wrDx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git pull"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RPLWN_M_kLv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}