jupyter-notebooks/Sketch_RNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sketch_RNN.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 2",
      "language": "python",
      "name": "python2"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "tDybPQiEFQuJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn"
      ]
    },
    {
      "metadata": {
        "id": "k0GqvYgB9JLC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# import the required libraries\n",
        "import numpy as np\n",
        "import time\n",
        "import random\n",
        "import cPickle\n",
        "import codecs\n",
        "import collections\n",
        "import os\n",
        "import math\n",
        "import json\n",
        "import tensorflow as tf\n",
        "from six.moves import xrange"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UI4ZC__4FQuL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# libraries required for visualisation:\n",
        "from IPython.display import SVG, display\n",
        "import PIL\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# set numpy output to something sensible\n",
        "np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D7ObpAUh9jrk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -qU svgwrite"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4xYY-TUd9aiD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import svgwrite # conda install -c omnia svgwrite=1.1.6"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NzPSD-XRFQuP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "daa0dd33-6d59-4d15-f437-d8ec787c8884"
      },
      "cell_type": "code",
      "source": [
        "tf.logging.info(\"TensorFlow Version: %s\", tf.__version__)\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:TensorFlow Version: 1.8.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LebxcF4p90OR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -q magenta"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NkFS0E1zFQuU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# import our command line tools\n",
        "from magenta.models.sketch_rnn.sketch_rnn_train import *\n",
        "from magenta.models.sketch_rnn.model import *\n",
        "from magenta.models.sketch_rnn.utils import *\n",
        "from magenta.models.sketch_rnn.rnn import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GBde4xkEFQuX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# little function that displays vector images and saves them to .svg\n",
        "def draw_strokes(data, factor=0.2, svg_filename = '/tmp/sketch_rnn/svg/sample.svg'):\n",
        "  tf.gfile.MakeDirs(os.path.dirname(svg_filename))\n",
        "  min_x, max_x, min_y, max_y = get_bounds(data, factor)\n",
        "  dims = (50 + max_x - min_x, 50 + max_y - min_y)\n",
        "  dwg = svgwrite.Drawing(svg_filename, size=dims)\n",
        "  dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\n",
        "  lift_pen = 1\n",
        "  abs_x = 25 - min_x \n",
        "  abs_y = 25 - min_y\n",
        "  p = \"M%s,%s \" % (abs_x, abs_y)\n",
        "  command = \"m\"\n",
        "  for i in xrange(len(data)):\n",
        "    if (lift_pen == 1):\n",
        "      command = \"m\"\n",
        "    elif (command != \"l\"):\n",
        "      command = \"l\"\n",
        "    else:\n",
        "      command = \"\"\n",
        "    x = float(data[i,0])/factor\n",
        "    y = float(data[i,1])/factor\n",
        "    lift_pen = data[i, 2]\n",
        "    p += command+str(x)+\",\"+str(y)+\" \"\n",
        "  the_color = \"black\"\n",
        "  stroke_width = 1\n",
        "  dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\"none\"))\n",
        "  dwg.save()\n",
        "  display(SVG(dwg.tostring()))\n",
        "\n",
        "# generate a 2D grid of many vector drawings\n",
        "def make_grid_svg(s_list, grid_space=10.0, grid_space_x=16.0):\n",
        "  def get_start_and_end(x):\n",
        "    x = np.array(x)\n",
        "    x = x[:, 0:2]\n",
        "    x_start = x[0]\n",
        "    x_end = x.sum(axis=0)\n",
        "    x = x.cumsum(axis=0)\n",
        "    x_max = x.max(axis=0)\n",
        "    x_min = x.min(axis=0)\n",
        "    center_loc = (x_max+x_min)*0.5\n",
        "    return x_start-center_loc, x_end\n",
        "  x_pos = 0.0\n",
        "  y_pos = 0.0\n",
        "  result = [[x_pos, y_pos, 1]]\n",
        "  for sample in s_list:\n",
        "    s = sample[0]\n",
        "    grid_loc = sample[1]\n",
        "    grid_y = grid_loc[0]*grid_space+grid_space*0.5\n",
        "    grid_x = grid_loc[1]*grid_space_x+grid_space_x*0.5\n",
        "    start_loc, delta_pos = get_start_and_end(s)\n",
        "\n",
        "    loc_x = start_loc[0]\n",
        "    loc_y = start_loc[1]\n",
        "    new_x_pos = grid_x+loc_x\n",
        "    new_y_pos = grid_y+loc_y\n",
        "    result.append([new_x_pos-x_pos, new_y_pos-y_pos, 0])\n",
        "\n",
        "    result += s.tolist()\n",
        "    result[-1][2] = 1\n",
        "    x_pos = new_x_pos+delta_pos[0]\n",
        "    y_pos = new_y_pos+delta_pos[1]\n",
        "  return np.array(result)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "if7-UyxzFQuY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "define the path of the model you want to load, and also the path of the dataset"
      ]
    },
    {
      "metadata": {
        "id": "Dipv1EbsFQuZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_dir = 'http://github.com/hardmaru/sketch-rnn-datasets/raw/master/aaron_sheep/'\n",
        "models_root_dir = '/tmp/sketch_rnn/models'\n",
        "model_dir = '/tmp/sketch_rnn/models/aaron_sheep/layer_norm'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eaSqI0fIFQub",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "06df45a6-cc86-4f50-802e-25ae185037f7"
      },
      "cell_type": "code",
      "source": [
        "download_pretrained_models(models_root_dir=models_root_dir)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:/tmp/sketch_rnn/models/sketch_rnn.zip already exists, using cached copy\n",
            "INFO:tensorflow:Unzipping /tmp/sketch_rnn/models/sketch_rnn.zip...\n",
            "INFO:tensorflow:Unzipping complete.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "G4sRuxyn_1aO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_env_compatible(data_dir, model_dir):\n",
        "  \"\"\"Loads environment for inference mode, used in jupyter notebook.\"\"\"\n",
        "  # modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py\n",
        "  # to work with depreciated tf.HParams functionality\n",
        "  model_params = sketch_rnn_model.get_default_hparams()\n",
        "  with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:\n",
        "    data = json.load(f)\n",
        "  fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']\n",
        "  for fix in fix_list:\n",
        "    data[fix] = (data[fix] == 1)\n",
        "  model_params.parse_json(json.dumps(data))\n",
        "  return load_dataset(data_dir, model_params, inference_mode=True)\n",
        "\n",
        "def load_model_compatible(model_dir):\n",
        "  \"\"\"Loads model for inference mode, used in jupyter notebook.\"\"\"\n",
        "  # modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py\n",
        "  # to work with depreciated tf.HParams functionality\n",
        "  model_params = sketch_rnn_model.get_default_hparams()\n",
        "  with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:\n",
        "    data = json.load(f)\n",
        "  fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']\n",
        "  for fix in fix_list:\n",
        "    data[fix] = (data[fix] == 1)\n",
        "  model_params.parse_json(json.dumps(data))\n",
        "\n",
        "  model_params.batch_size = 1  # only sample one at a time\n",
        "  eval_model_params = sketch_rnn_model.copy_hparams(model_params)\n",
        "  eval_model_params.use_input_dropout = 0\n",
        "  eval_model_params.use_recurrent_dropout = 0\n",
        "  eval_model_params.use_output_dropout = 0\n",
        "  eval_model_params.is_training = 0\n",
        "  sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)\n",
        "  sample_model_params.max_seq_len = 1  # sample one point at a time\n",
        "  return [model_params, eval_model_params, sample_model_params]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9m-jSAb3FQuf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "debc045d-d15a-4b30-f747-fa4bcbd069fd"
      },
      "cell_type": "code",
      "source": [
        "[train_set, valid_set, test_set, hps_model, eval_hps_model, sample_hps_model] = load_env_compatible(data_dir, model_dir)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Downloading http://github.com/hardmaru/sketch-rnn-datasets/raw/master/aaron_sheep/aaron_sheep.npz\n",
            "INFO:tensorflow:Loaded 7400/300/300 from aaron_sheep.npz\n",
            "INFO:tensorflow:Dataset combined: 8000 (7400/300/300), avg len 125\n",
            "INFO:tensorflow:model_params.max_seq_len 250.\n",
            "total images <= max_seq_len is 7400\n",
            "total images <= max_seq_len is 300\n",
            "total images <= max_seq_len is 300\n",
            "INFO:tensorflow:normalizing_scale_factor 18.5198.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1pHS8TSgFQui",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "50b0e14d-ff0f-43bf-d996-90e9e6a1491e"
      },
      "cell_type": "code",
      "source": [
        "# construct the sketch-rnn model here:\n",
        "reset_graph()\n",
        "model = Model(hps_model)\n",
        "eval_model = Model(eval_hps_model, reuse=True)\n",
        "sample_model = Model(sample_hps_model, reuse=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = False.\n",
            "INFO:tensorflow:Output dropout mode = False.\n",
            "INFO:tensorflow:Recurrent dropout mode = True.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/sketch_rnn/rnn.py:137: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "keep_dims is deprecated, use keepdims instead\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/sketch_rnn/model.py:287: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "keep_dims is deprecated, use keepdims instead\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/sketch_rnn/model.py:297: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
            "\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1gxYLPTQFQuk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sess = tf.InteractiveSession()\n",
        "sess.run(tf.global_variables_initializer())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bVlDyfN_FQum",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fb41ce20-4c7f-4991-e9f6-559ea9b34a31"
      },
      "cell_type": "code",
      "source": [
        "# loads the weights from checkpoint into our model\n",
        "load_checkpoint(sess, model_dir)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Loading model /tmp/sketch_rnn/models/aaron_sheep/layer_norm/vector.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/sketch_rnn/models/aaron_sheep/layer_norm/vector\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EOblwpFeFQuq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We define two convenience functions to encode a stroke into a latent vector, and decode from latent vector to stroke."
      ]
    },
    {
      "metadata": {
        "id": "tMFlV487FQur",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def encode(input_strokes):\n",
        "  strokes = to_big_strokes(input_strokes).tolist()\n",
        "  strokes.insert(0, [0, 0, 1, 0, 0])\n",
        "  seq_len = [len(input_strokes)]\n",
        "  draw_strokes(to_normal_strokes(np.array(strokes)))\n",
        "  return sess.run(eval_model.batch_z, feed_dict={eval_model.input_data: [strokes], eval_model.sequence_lengths: seq_len})[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1D5CV7ZlFQut",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def decode(z_input=None, draw_mode=True, temperature=0.1, factor=0.2):\n",
        "  z = None\n",
        "  if z_input is not None:\n",
        "    z = [z_input]\n",
        "  sample_strokes, m = sample(sess, sample_model, seq_len=eval_model.hps.max_seq_len, temperature=temperature, z=z)\n",
        "  strokes = to_normal_strokes(sample_strokes)\n",
        "  if draw_mode:\n",
        "    draw_strokes(strokes, factor)\n",
        "  return strokes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fUOAvRQtFQuw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "c8e9a1c3-28db-4263-ac67-62ffece1e1e0"
      },
      "cell_type": "code",
      "source": [
        "# get a sample drawing from the test set, and render it to .svg\n",
        "stroke = test_set.random_sample()\n",
        "draw_strokes(stroke)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "j114Re2JFQuz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's try to encode the sample stroke into latent vector $z$"
      ]
    },
    {
      "metadata": {
        "id": "DBRjPBo-FQu0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "e089dc78-88e3-44c6-ed7e-f1844471f47f"
      },
      "cell_type": "code",
      "source": [
        "z = encode(stroke)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg baseProfile=\"full\" height=\"102.106330395\" version=\"1.1\" width=\"134.774027355\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs/><rect fill=\"white\" height=\"102.106330395\" width=\"134.774027355\" x=\"0\" y=\"0\"/><path d=\"M46.3284978271,44.1686502844 m0.0,0.0 l-0.269980989397,-10.2592778206 1.07992395759,-4.31969583035 l3.5097527504,-1.61988586187 5.66960036755,0.0 l2.42982879281,1.61988586187 0.269980989397,0.809942930937 l2.69980967045,-1.61988586187 0.539961978793,-1.61988586187 l1.07992395759,-0.539961978793 10.7992386818,-1.61988586187 l8.09942960739,0.0 2.15984791517,0.809942930937 l1.07992395759,3.23977172375 0.809942930937,4.58967655897 l9.98929619789,-2.42982879281 6.20956242085,-0.269980989397 l1.07992395759,6.74952447414 5.9395813942,0.539961978793 l0.269980989397,8.63939166069 -1.07992395759,4.58967655897 l-1.07992395759,1.34990483522 -3.77973377705,0.269980989397 l-1.34990483522,4.31969583035 -2.9697906971,5.9395813942 l-1.34990483522,1.34990483522 -4.85965758562,-0.539961978793 l-1.07992395759,-4.0497148037 -3.77973377705,4.58967655897 l-17.8187441826,3.23977172375 -1.61988586187,-0.269980989397 l-2.15984791517,-2.15984791517 -0.809942930937,-1.34990483522 l0.0,-1.07992395759 -7.28948652744,0.269980989397 l-6.47954344749,1.34990483522 -2.15984791517,-1.61988586187 l-0.539961978793,-1.61988586187 -0.539961978793,-5.3996193409 l-7.55946755409,-0.269980989397 1.34990483522,-7.82944858074 l2.42982879281,-2.69980967045 m-7.55946755409,-12.1491444111 l0.809942930937,-2.9697906971 0.269980989397,0.269980989397 m2.69980967045,3.77973377705 l1.88986688852,-2.9697906971 m-6.74952447414,3.23977172375 l-4.85965758562,0.269980989397 -5.3996193409,5.12963891029 l-1.34990483522,2.69980967045 0.0,2.9697906971 l2.15984791517,3.23977172375 2.69980967045,0.0 l4.0497148037,-0.809942930937 m-4.85965758562,2.15984791517 l0.269980989397,0.539961978793 7.55946755409,1.07992395759 l3.5097527504,0.0 0.539961978793,-0.269980989397 l0.0,-0.539961978793 m-7.01950550079,-11.3392007351 l1.07992395759,0.0 m23.2183647156,24.0283083916 l-0.269980989397,3.23977172375 -2.69980967045,7.55946755409 l1.34990483522,-1.07992395759 m7.55946755409,-7.01950550079 l-0.269980989397,1.88986688852 -2.15984791517,4.31969583035 l-0.269980989397,1.61988586187 m19.1686499119,-7.55946755409 l1.07992395759,5.9395813942 0.0,4.58967655897 m3.23977172375,-12.1491444111 l2.15984791517,5.3996193409 3.5097527504,5.3996193409 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-37v6eZLFQu5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "5ddac2f2-5b3b-4cd7-b81f-7a8fa374aa6b"
      },
      "cell_type": "code",
      "source": [
        "_ = decode(z, temperature=0.8) # convert z back to drawing at temperature of 0.8"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg baseProfile=\"full\" height=\"103.441761217\" version=\"1.1\" width=\"122.975787192\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs/><rect fill=\"white\" height=\"103.441761217\" width=\"122.975787192\" x=\"0\" y=\"0\"/><path d=\"M38.2935093547,43.1177271903 m-0.340951383114,-9.55003380775 l2.20347970724,-3.26370030642 1.45378634334,-0.861101225019 l5.35445392132,-1.95433869958 2.85991221666,-0.426612757146 l3.23420614004,0.61924610287 1.76099523902,1.17085948586 l-0.000224313243962,2.8007131815 1.62095278502,-1.93868651986 l7.70326256752,-3.35318088531 5.08853077888,-1.3608917594 l6.57757937908,0.000288826431643 6.3368922472,1.7680811882 l1.8955771625,5.07947385311 8.86431097984,3.68271976709 l2.44708895683,1.14726290107 1.33326768875,2.15126395226 l1.2891574204,4.82484698296 -2.93176651001,0.191802792251 l-1.01530641317,0.369459055364 -3.72778713703,0.490534305573 l1.92548766732,8.07218194008 -0.636532381177,3.88820767403 l-10.0819528103,3.1732109189 -12.5187420845,-2.19246834517 l-7.21166193485,-3.72750967741 -3.59191596508,-1.58369138837 l-1.61233827472,3.50915759802 -1.63391545415,1.96250066161 l-8.23710858822,2.57059127092 -3.92850697041,-0.0894535146654 l-5.39045572281,-3.61232995987 -1.41789451241,-3.1729221344 l0.000157722988661,-0.189666822553 0.767163261771,-0.44281270355 l0.000143757897604,-1.46493420005 m-11.7328548431,-9.84380543232 l4.56918567419,0.274272467941 1.39962747693,6.07070614933e-05 l-0.000191727031051,-0.00354142626747 0.118541941047,0.397051982582 l5.49370406588e-05,1.10314272344 m-3.87571752071,-0.000533753554919 l-0.000263419005933,0.872648358345 2.12115556002,-0.000199357964448 l-1.79571181536,0.699473321438 m-0.64867913723,-2.49047070742 l-0.153107512742,-0.0678024441004 0.593399144709,-1.15580543876 l0.782524943352,-0.21773930639 0.209943745285,0.896871387959 m4.61298912764,-1.09024040401 l0.000133186440507,0.0945561006665 -1.10620357096,0.290551614016 l-1.61601245403,-0.89173167944 1.09008438885,0.000581867607252 l-0.000276200280496,1.61469995975 -0.341594591737,-3.26650621219e-06 m10.2935230732,16.7232847214 l-2.47041195631,7.12780237198 -6.05746209621,8.1383305788 l-2.34212562442,2.54901111126 m18.4888255596,-13.7993752956 l0.278686545789,12.296475172 0.774371773005,2.02842161059 m18.0001282692,-15.3073883057 l2.6828032732,13.0685150623 1.7698790133,3.72885197401 m5.57813405991,-17.1058559418 l5.43337762356,10.6714761257 1.84081166983,4.72313702106 m-20.4520964622,-33.4079837799 l1.07226662338,-0.0129359017592 4.18061882257,-1.2942481041 l8.89779388905,0.31430978328 5.08294522762,-6.81180199535e-05 l0.693141296506,-0.624739639461 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "M5ft6IEBFQu9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Create generated grid at various temperatures from 0.1 to 1.0"
      ]
    },
    {
      "metadata": {
        "id": "BuhaZI0aFQu9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "outputId": "d87d4b00-30c2-4302-bec8-46566ef26922"
      },
      "cell_type": "code",
      "source": [
        "stroke_list = []\n",
        "for i in range(10):\n",
        "  stroke_list.append([decode(z, draw_mode=False, temperature=0.1*i+0.1), [0, i]])\n",
        "stroke_grid = make_grid_svg(stroke_list)\n",
        "draw_strokes(stroke_grid)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4xiwp3_DFQvB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Latent Space Interpolation Example between $z_0$ and $z_1$"
      ]
    },
    {
      "metadata": {
        "id": "WSX0uvZTFQvD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "cd67af4e-5ae6-4327-876e-e1385dadbafc"
      },
      "cell_type": "code",
      "source": [
        "# get a sample drawing from the test set, and render it to .svg\n",
        "z0 = z\n",
        "_ = decode(z0)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jQf99TxOFQvH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "4265bd5f-8c66-494e-b26e-d3ac874d69bb"
      },
      "cell_type": "code",
      "source": [
        "stroke = test_set.random_sample()\n",
        "z1 = encode(stroke)\n",
        "_ = decode(z1)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg baseProfile=\"full\" height=\"85.6374906376\" version=\"1.1\" width=\"106.426024958\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs/><rect fill=\"white\" height=\"85.6374906376\" width=\"106.426024958\" x=\"0\" y=\"0\"/><path d=\"M44.4386315346,25.5399619788 m0.0,0.0 l5.9395813942,-0.539961978793 9.17935311794,2.42982879281 l0.809942930937,2.42982879281 2.69980967045,-2.15984791517 l5.3996193409,-1.07992395759 2.42982879281,0.0 l0.269980989397,1.07992395759 3.77973377705,0.539961978793 l2.69980967045,3.5097527504 2.42982879281,1.61988586187 l1.07992395759,1.88986688852 0.269980989397,2.9697906971 l-8.90937209129,4.85965758562 -7.82944858074,1.34990483522 l-12.6891064644,0.0 -4.58967655897,-0.539961978793 l-3.23977172375,-2.42982879281 -8.36941063404,-4.58967655897 l-3.23977172375,-6.74952447414 m10.5292582512,6.74952447414 l0.539961978793,0.539961978793 0.0,9.44933414459 l-1.61988586187,7.28948652744 m15.9288775921,-13.4990489483 l2.15984791517,0.269980989397 3.77973377705,17.2787833214 m-14.3089914322,-17.8187441826 l0.0,15.1189351082 m17.2787833214,-13.4990489483 l1.07992395759,0.809942930937 1.61988586187,3.23977172375 l4.85965758562,14.5789730549 m-37.2573757172,-31.8577551842 l0.0,-0.539961978793 -0.809942930937,-0.539961978793 l-4.0497148037,-1.61988586187 -5.12963891029,-0.539961978793 l-1.34990483522,0.809942930937 -0.809942930937,1.61988586187 l0.269980989397,3.23977172375 1.07992395759,0.809942930937 l3.77973377705,0.539961978793 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
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            "image/svg+xml": "<svg baseProfile=\"full\" height=\"83.0564136058\" version=\"1.1\" width=\"93.7295763449\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs/><rect fill=\"white\" height=\"83.0564136058\" width=\"93.7295763449\" x=\"0\" y=\"0\"/><path d=\"M29.7001612084,25.492028445 m6.12457633018,-0.492028445005 l8.83016347885,1.8819282949 2.64706224203,1.26904651523 l2.73448884487,1.89952537417 2.48898088932,2.15412139893 l4.31182414293,2.03947201371 5.9728372097,1.81413322687 l3.41846108437,2.09005489945 2.50078171492,3.16048324108 l0.000239199325733,4.14908617735 -2.05914735794,3.12297672033 l-4.39917087555,2.27648496628 -10.0312280655,0.553998202085 l-5.18458724022,-1.24503679574 -8.12663495541,-3.6948376894 l-3.65948140621,-2.44298309088 -2.6890322566,-2.95634448528 l-2.10033684969,-3.72734755278 -0.519202388823,-7.05223739147 l0.59235367924,-1.256929636 2.58313268423,-1.88882425427 m29.5964217186,15.4435396194 l2.82048881054,11.0222280025 0.000133922794703,4.44387584925 m-15.5658245087,-9.26815748215 l-5.14172506882e-05,8.00958454609 -4.64057683945,0.478398874402 m-4.6766269207,-14.1202735901 l-7.85498832556e-05,7.89820194244 -1.58904016018,6.39098823071 m-10.0955557823,-20.9512782097 l-1.61133436904e-05,4.93145227432 -1.85778081417,7.03386187553 l-0.00016616017092,2.28863850236 -1.21746793389,-8.9298328021e-05 l-0.910100564361,-0.915008336306 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "tDqJR8_eFQvK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we interpolate between sheep $z_0$ and sheep $z_1$"
      ]
    },
    {
      "metadata": {
        "id": "_YkPNL5SFQvL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z_list = [] # interpolate spherically between z0 and z1\n",
        "N = 10\n",
        "for t in np.linspace(0, 1, N):\n",
        "  z_list.append(slerp(z0, z1, t))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UoM-W1tQFQvM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# for every latent vector in z_list, sample a vector image\n",
        "reconstructions = []\n",
        "for i in range(N):\n",
        "  reconstructions.append([decode(z_list[i], draw_mode=False), [0, i]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mTqmlL6GFQvQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "062e015f-29c6-4e77-c6db-e403d5cabd59"
      },
      "cell_type": "code",
      "source": [
        "stroke_grid = make_grid_svg(reconstructions)\n",
        "draw_strokes(stroke_grid)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vFwPna6uFQvS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's load the Flamingo Model, and try Unconditional (Decoder-Only) Generation"
      ]
    },
    {
      "metadata": {
        "id": "HH-YclgNFQvT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_dir = '/tmp/sketch_rnn/models/flamingo/lstm_uncond'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-Znvy3KxFQvU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cqDNK1cYFQvZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "d346d57c-f51a-4286-ba55-705bc27d4d0d"
      },
      "cell_type": "code",
      "source": [
        "# construct the sketch-rnn model here:\n",
        "reset_graph()\n",
        "model = Model(hps_model)\n",
        "eval_model = Model(eval_hps_model, reuse=True)\n",
        "sample_model = Model(sample_hps_model, reuse=True)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = False.\n",
            "INFO:tensorflow:Output dropout mode = False.\n",
            "INFO:tensorflow:Recurrent dropout mode = False.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7wzerSI6FQvd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sess = tf.InteractiveSession()\n",
        "sess.run(tf.global_variables_initializer())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6mzk8KjOFQvf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c450a6c6-22ee-4a58-8451-443462b42d58"
      },
      "cell_type": "code",
      "source": [
        "# loads the weights from checkpoint into our model\n",
        "load_checkpoint(sess, model_dir)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Loading model /tmp/sketch_rnn/models/flamingo/lstm_uncond/vector.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/sketch_rnn/models/flamingo/lstm_uncond/vector\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "X88CgcyuFQvh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# randomly unconditionally generate 10 examples\n",
        "N = 10\n",
        "reconstructions = []\n",
        "for i in range(N):\n",
        "  reconstructions.append([decode(temperature=0.5, draw_mode=False), [0, i]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "k57REtd_FQvj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "outputId": "8bd69652-9d1d-475e-fc64-f205cf6b9ed1"
      },
      "cell_type": "code",
      "source": [
        "stroke_grid = make_grid_svg(reconstructions)\n",
        "draw_strokes(stroke_grid)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "L-rJ0iUQFQvl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's load the owl model, and generate two sketches using two random IID gaussian latent vectors"
      ]
    },
    {
      "metadata": {
        "id": "of4SWwGdFQvm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_dir = '/tmp/sketch_rnn/models/owl/lstm'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jJiSZFQeFQvp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "f84360ca-c2be-482f-db57-41b5ecc05768"
      },
      "cell_type": "code",
      "source": [
        "[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)\n",
        "# construct the sketch-rnn model here:\n",
        "reset_graph()\n",
        "model = Model(hps_model)\n",
        "eval_model = Model(eval_hps_model, reuse=True)\n",
        "sample_model = Model(sample_hps_model, reuse=True)\n",
        "sess = tf.InteractiveSession()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "# loads the weights from checkpoint into our model\n",
        "load_checkpoint(sess, model_dir)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = False.\n",
            "INFO:tensorflow:Output dropout mode = False.\n",
            "INFO:tensorflow:Recurrent dropout mode = False.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Loading model /tmp/sketch_rnn/models/owl/lstm/vector.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/sketch_rnn/models/owl/lstm/vector\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vR4TDoi5FQvr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "db08cb2c-952c-4949-d2b0-94c11351264b"
      },
      "cell_type": "code",
      "source": [
        "z_0 = np.random.randn(eval_model.hps.z_size)\n",
        "_ = decode(z_0)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg baseProfile=\"full\" height=\"120.184078347\" version=\"1.1\" width=\"98.8725474746\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs/><rect fill=\"white\" height=\"120.184078347\" width=\"98.8725474746\" x=\"0\" y=\"0\"/><path d=\"M41.5331514304,47.8121304913 m-0.132065089419,-0.818965733051 l-1.78446769714,-4.37339872122 -0.449359863997,-1.68071240187 l0.000442053933511,-3.18809241056 0.000388614498661,-3.89637112617 l0.346569791436,-1.25295981765 1.10776923597,-1.88584446907 l1.73459753394,-1.50131702423 2.03410193324,-0.757536664605 l1.99948683381,-0.00102532932942 0.786211192608,0.968269556761 l0.787319988012,2.56595700979 0.000546700066479,4.06032085419 l-0.822296440601,3.35661351681 -1.45263329148,2.40721821785 l-1.8565261364,1.62552013993 -2.165376544,0.877661034465 l-1.34748280048,0.000444044126198 m15.3083527088,-0.802424028516 l-1.47368788719,-1.27479910851 -1.06197156012,-2.65573889017 l0.000208061137528,-4.3788933754 0.000424580211984,-4.40278887749 l0.612830072641,-1.24189786613 0.911226272583,-0.867653787136 l2.1902307868,-0.0012513724505 2.43894606829,-4.11187374993e-05 l1.87056452036,0.885151997209 1.50832056999,1.92514464259 l0.857977494597,2.64217227697 0.00038065478293,4.15196985006 l-0.435804836452,1.60096615553 -1.34428799152,2.02306777239 l-1.8200494349,1.43815994263 -2.15610936284,0.813227519393 l-1.99906826019,-0.00274521386018 -0.792735591531,-0.654452368617 m-5.3206551075,-0.570695027709 l0.650407522917,3.24591726065 0.431762933731,3.05901706219 l1.05495944619,0.348732098937 1.48608103395,-3.38784664869 l0.99411688745,-2.4404284358 m-12.5001680851,-17.9557824135 l-1.74334436655,-0.494269132614 -2.9695045948,-0.319730080664 l-1.87135264277,0.192461144179 -3.49551528692,1.5555383265 l-2.48406752944,2.06320300698 -1.80969551206,2.53702431917 l-1.31562590599,3.0613130331 -1.043523103,5.44697701931 l0.000178788177436,6.89047813416 0.852816924453,4.29370284081 l2.60408937931,6.39090538025 2.51832842827,4.09500092268 l5.35060226917,7.79427945614 2.07839220762,4.61394906044 l1.2106475234,4.54003036022 0.788104310632,4.97067660093 l0.452739298344,5.35718619823 0.114500746131,3.70851904154 l-0.181541219354,2.67283350229 m2.99266695976,-69.4676494598 l3.46948355436,-0.592494606972 4.990683496,-0.0154769234359 l7.8266787529,0.000340488804795 3.68951737881,0.682502835989 l3.12869757414,1.26171424985 2.70443201065,1.93808957934 l2.12300464511,2.56196171045 1.39556929469,3.11388999224 l0.762749612331,3.80722731352 0.000205539708986,6.73621177673 l-0.92931471765,4.54613059759 -1.62916317582,4.4717541337 l-4.26714062691,10.0283360481 -1.95177778602,5.89771330357 l-1.39518469572,6.74194931984 -0.816034302115,6.8567699194 l0.000380763995054,5.70140123367 0.916537940502,3.6203455925 m-24.9645543098,-40.7061433792 l-0.676299929619,1.41931325197 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZX23lTnpFQvt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "247052f2-a0f3-4046-83d6-d08e0429fafb"
      },
      "cell_type": "code",
      "source": [
        "z_1 = np.random.randn(eval_model.hps.z_size)\n",
        "_ = decode(z_1)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7FjQsF_2FQvv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's interpolate between the two owls $z_0$ and $z_1$"
      ]
    },
    {
      "metadata": {
        "id": "u6G37E8_FQvw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z_list = [] # interpolate spherically between z_0 and z_1\n",
        "N = 10\n",
        "for t in np.linspace(0, 1, N):\n",
        "  z_list.append(slerp(z_0, z_1, t))\n",
        "# for every latent vector in z_list, sample a vector image\n",
        "reconstructions = []\n",
        "for i in range(N):\n",
        "  reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.1), [0, i]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OULjMktmFQvx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "outputId": "94b7b68e-9c57-4a1b-b216-83770fa4be81"
      },
      "cell_type": "code",
      "source": [
        "stroke_grid = make_grid_svg(reconstructions)\n",
        "draw_strokes(stroke_grid)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's load the model trained on both cats and buses!  catbus!"
      ]
    },
    {
      "metadata": {
        "id": "SL7WpDDQFQv0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_dir = '/tmp/sketch_rnn/models/catbus/lstm'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Cvk5WOqHFQv2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "8081d53d-52d6-4d18-f973-a9dd44c897f2"
      },
      "cell_type": "code",
      "source": [
        "[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)\n",
        "# construct the sketch-rnn model here:\n",
        "reset_graph()\n",
        "model = Model(hps_model)\n",
        "eval_model = Model(eval_hps_model, reuse=True)\n",
        "sample_model = Model(sample_hps_model, reuse=True)\n",
        "sess = tf.InteractiveSession()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "# loads the weights from checkpoint into our model\n",
        "load_checkpoint(sess, model_dir)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = False.\n",
            "INFO:tensorflow:Output dropout mode = False.\n",
            "INFO:tensorflow:Recurrent dropout mode = False.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Loading model /tmp/sketch_rnn/models/catbus/lstm/vector.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/sketch_rnn/models/catbus/lstm/vector\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "icvlBPVkFQv5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "f7b415fe-4d65-4b00-c0eb-fb592597dba2"
      },
      "cell_type": "code",
      "source": [
        "z_1 = np.random.randn(eval_model.hps.z_size)\n",
        "_ = decode(z_1)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
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            "image/svg+xml": "<svg baseProfile=\"full\" height=\"85.0024624268\" version=\"1.1\" width=\"97.0106323063\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs/><rect fill=\"white\" height=\"85.0024624268\" width=\"97.0106323063\" x=\"0\" y=\"0\"/><path d=\"M46.3545013685,33.7004358135 m-1.67252272367,0.394050441682 l-2.03154876828,1.45254462957 -1.77578791976,1.80640622973 l-1.6109457612,2.1960131824 -1.18691310287,2.34753742814 l-0.678012892604,2.30114400387 -0.20122455433,2.21701934934 l0.332671366632,2.12932109833 1.03898189962,1.93650469184 l1.74471467733,1.76480427384 2.42952331901,1.4399163425 l3.00780057907,0.961375236511 3.41435223818,0.406128205359 l3.47424894571,-0.119643304497 3.25872182846,-0.736866593361 l2.82667756081,-1.2964771688 2.19879046082,-1.72045439482 l1.47952780128,-2.00524181128 0.781586319208,-2.26519107819 l0.0863485690206,-2.36544474959 -0.624918863177,-2.38882064819 l-1.35048046708,-2.27941468358 -2.01366201043,-2.13800251484 l-2.45773822069,-1.7978258431 -2.69231259823,-1.3554097712 l-2.68344372511,-0.840844660997 -2.60668128729,-0.349534451962 l-2.29395762086,0.0881606899202 -1.97751566768,0.484185256064 m-4.22170132399,5.59232115746 l-0.858224183321,-0.992628484964 -1.42562314868,-2.25309178233 l-1.4492110908,-2.57754087448 -2.78117597103,-5.97079396248 l-0.8167950809,-2.6043471694 1.32482737303,-0.160294082016 l1.46054670215,0.706794261932 5.88625133038,3.87950181961 l3.83870124817,3.03945600986 1.23776055872,0.720141157508 m9.16391193867,1.79712742567 l1.0277723521,-0.794012546539 4.92988169193,-3.58641952276 l7.03148722649,-4.49979126453 1.72269210219,-0.779656022787 l0.18622552976,1.22162371874 -1.75484806299,3.27743798494 l-3.89630943537,6.59069061279 -1.24373376369,2.57274150848 l-0.521678514779,1.66077747941 m-15.3570437431,4.34784531593 l-0.703595504165,-0.366752743721 -2.21099600196,-0.000145574467751 l-5.26128053665,0.468756966293 -6.46269977093,1.18766956031 l-6.91953063011,2.10482001305 -1.49639204144,0.736465454102 m21.9713330269,-1.93512454629 l-2.08371579647,0.242639295757 -2.40464165807,0.793681591749 l-4.48679834604,2.26089000702 -3.41465473175,2.18791306019 l-2.46110990644,1.95080041885 m16.475225687,-5.77536463737 l-1.67380392551,0.825476571918 -5.39076983929,4.59509968758 l-2.86009788513,3.01156193018 -1.92672237754,2.55981743336 m18.4251654148,-15.2193069458 l1.54628425837,-0.425299890339 5.11826336384,-1.03468291461 l4.404476583,-0.45712724328 5.77219843864,0.0480564497411 m-16.6457545757,5.08838534355 l4.32074040174,0.242472905666 5.61843752861,1.09646350145 l3.88478636742,1.24357588589 2.69302010536,1.29176437855 m-16.8987333775,-1.01418435574 l3.28390866518,1.62312328815 4.95268970728,2.7775490284 l4.96877580881,3.17819237709 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uaNxd0LuFQv-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "4de5ee9a-cf14-49f4-e5f5-399a0d0b8215"
      },
      "cell_type": "code",
      "source": [
        "z_0 = np.random.randn(eval_model.hps.z_size)\n",
        "_ = decode(z_0)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
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          },
          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "metadata": {
        "id": "VtSYkS6mFQwC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's interpolate between a cat and a bus!!!"
      ]
    },
    {
      "metadata": {
        "id": "qIDYUxBEFQwD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z_list = [] # interpolate spherically between z_1 and z_0\n",
        "N = 10\n",
        "for t in np.linspace(0, 1, N):\n",
        "  z_list.append(slerp(z_1, z_0, t))\n",
        "# for every latent vector in z_list, sample a vector image\n",
        "reconstructions = []\n",
        "for i in range(N):\n",
        "  reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.15), [0, i]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZHmnSjSaFQwH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "38fe3c7e-698b-4b19-8851-e7f3ff037744"
      },
      "cell_type": "code",
      "source": [
        "stroke_grid = make_grid_svg(reconstructions)\n",
        "draw_strokes(stroke_grid)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "flZ_OgzCFQwJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Why stop here? Let's load the model trained on both elephants and pigs!!!"
      ]
    },
    {
      "metadata": {
        "id": "S8WwK8FPFQwK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_dir = '/tmp/sketch_rnn/models/elephantpig/lstm'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "meOH4AFXFQwM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "764938a7-bbdc-4732-e688-a8a278ab3089"
      },
      "cell_type": "code",
      "source": [
        "[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)\n",
        "# construct the sketch-rnn model here:\n",
        "reset_graph()\n",
        "model = Model(hps_model)\n",
        "eval_model = Model(eval_hps_model, reuse=True)\n",
        "sample_model = Model(sample_hps_model, reuse=True)\n",
        "sess = tf.InteractiveSession()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "# loads the weights from checkpoint into our model\n",
        "load_checkpoint(sess, model_dir)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = False.\n",
            "INFO:tensorflow:Output dropout mode = False.\n",
            "INFO:tensorflow:Recurrent dropout mode = False.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:Input dropout mode = 0.\n",
            "INFO:tensorflow:Output dropout mode = 0.\n",
            "INFO:tensorflow:Recurrent dropout mode = 0.\n",
            "INFO:tensorflow:Loading model /tmp/sketch_rnn/models/elephantpig/lstm/vector.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/sketch_rnn/models/elephantpig/lstm/vector\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "foZiiYPdFQwO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "a09fc4fb-110f-4280-8515-c9b673cb6b90"
      },
      "cell_type": "code",
      "source": [
        "z_0 = np.random.randn(eval_model.hps.z_size)\n",
        "_ = decode(z_0)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          "metadata": {
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    },
    {
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        "colab_type": "code",
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      "cell_type": "code",
      "source": [
        "z_1 = np.random.randn(eval_model.hps.z_size)\n",
        "_ = decode(z_1)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oVtr7NnGFQwU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Tribute to an episode of [South Park](https://en.wikipedia.org/wiki/An_Elephant_Makes_Love_to_a_Pig): The interpolation between an Elephant and a Pig"
      ]
    },
    {
      "metadata": {
        "id": "lJs9JbROFQwU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z_list = [] # interpolate spherically between z_1 and z_0\n",
        "N = 10\n",
        "for t in np.linspace(0, 1, N):\n",
        "  z_list.append(slerp(z_0, z_1, t))\n",
        "# for every latent vector in z_list, sample a vector image\n",
        "reconstructions = []\n",
        "for i in range(N):\n",
        "  reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.15), [0, i]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0FOuNfJMFQwW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "stroke_grid = make_grid_svg(reconstructions, grid_space_x=25.0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bZ6zpdiMFQwX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "outputId": "70679bd1-4dba-4c08-b39f-bbde81d22019"
      },
      "cell_type": "code",
      "source": [
        "draw_strokes(stroke_grid, factor=0.3)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "metadata": {
        "id": "KUgVRGnSFQwa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}