birthcard/Sketch_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tDybPQiEFQuJ"
   },
   "source": [
    "In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k0GqvYgB9JLC"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/ruben/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "# import the required libraries\n",
    "import numpy as np\n",
    "import time\n",
    "import random\n",
    "import pickle\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UI4ZC__4FQuL"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D7ObpAUh9jrk"
   },
   "outputs": [],
   "source": [
    "# !pip install -qU svgwrite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4xYY-TUd9aiD"
   },
   "outputs": [],
   "source": [
    "import svgwrite # conda install -c omnia svgwrite=1.1.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "NzPSD-XRFQuP",
    "outputId": "daa0dd33-6d59-4d15-f437-d8ec787c8884"
   },
   "outputs": [],
   "source": [
    "tf.logging.info(\"TensorFlow Version: %s\", tf.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LebxcF4p90OR"
   },
   "outputs": [],
   "source": [
    "# !pip install -q magenta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NkFS0E1zFQuU"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1030 17:41:34.144723 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/pipelines/statistics.py:132: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
      "\n",
      "/home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/numba/errors.py:131: UserWarning: Insufficiently recent colorama version found. Numba requires colorama >= 0.3.9\n",
      "  warnings.warn(msg)\n",
      "W1030 17:41:35.331515 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/music/note_sequence_io.py:60: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
      "\n",
      "W1030 17:41:36.170687 139942895679296 lazy_loader.py:50] \n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "W1030 17:41:36.172011 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:34: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
      "\n",
      "W1030 17:41:36.172474 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:34: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.\n",
      "\n"
     ]
    }
   ],
   "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 *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GBde4xkEFQuX"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "if7-UyxzFQuY"
   },
   "source": [
    "define the path of the model you want to load, and also the path of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dipv1EbsFQuZ"
   },
   "outputs": [],
   "source": [
    "data_dir = 'datasets/naam4'\n",
    "model_dir = 'models/naam4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "eaSqI0fIFQub",
    "outputId": "06df45a6-cc86-4f50-802e-25ae185037f7"
   },
   "outputs": [],
   "source": [
    "# download_pretrained_models(models_root_dir=models_root_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G4sRuxyn_1aO"
   },
   "outputs": [],
   "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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "9m-jSAb3FQuf",
    "outputId": "debc045d-d15a-4b30-f747-fa4bcbd069fd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I1030 17:41:40.760584 139942895679296 sketch_rnn_train.py:142] Loaded 161/161/161 from diede.npz\n",
      "I1030 17:41:40.985831 139942895679296 sketch_rnn_train.py:142] Loaded 100/100/100 from lijn.npz\n",
      "I1030 17:41:41.029351 139942895679296 sketch_rnn_train.py:142] Loaded 100/100/100 from blokletters.npz\n",
      "I1030 17:41:41.234873 139942895679296 sketch_rnn_train.py:159] Dataset combined: 1083 (361/361/361), avg len 234\n",
      "I1030 17:41:41.235846 139942895679296 sketch_rnn_train.py:166] model_params.max_seq_len 614.\n",
      "I1030 17:41:41.412048 139942895679296 sketch_rnn_train.py:209] normalizing_scale_factor 55.2581.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total images <= max_seq_len is 361\n",
      "total images <= max_seq_len is 361\n",
      "total images <= max_seq_len is 361\n"
     ]
    }
   ],
   "source": [
    "[train_set, valid_set, test_set, hps_model, eval_hps_model, sample_hps_model] = load_env_compatible(data_dir, model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 479
    },
    "colab_type": "code",
    "id": "1pHS8TSgFQui",
    "outputId": "50b0e14d-ff0f-43bf-d996-90e9e6a1491e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1030 17:41:48.243727 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:62: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W1030 17:41:48.244626 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:65: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n",
      "W1030 17:41:48.245686 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:81: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "I1030 17:41:48.254927 139942895679296 model.py:87] Model using gpu.\n",
      "I1030 17:41:48.262013 139942895679296 model.py:175] Input dropout mode = False.\n",
      "I1030 17:41:48.263224 139942895679296 model.py:176] Output dropout mode = False.\n",
      "I1030 17:41:48.263903 139942895679296 model.py:177] Recurrent dropout mode = False.\n",
      "W1030 17:41:48.264627 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:190: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W1030 17:41:48.274967 139942895679296 deprecation.py:323] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:100: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API\n",
      "W1030 17:41:48.275926 139942895679296 deprecation.py:323] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/ops/rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "W1030 17:41:48.364203 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/rnn.py:288: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "W1030 17:41:48.365810 139942895679296 deprecation.py:506] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W1030 17:41:48.573339 139942895679296 deprecation.py:323] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/ops/rnn.py:244: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "W1030 17:41:49.201451 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:256: The name tf.nn.xw_plus_b is deprecated. Please use tf.compat.v1.nn.xw_plus_b instead.\n",
      "\n",
      "W1030 17:41:49.216303 139942895679296 deprecation.py:323] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:266: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "W1030 17:41:49.229308 139942895679296 deprecation.py:506] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:285: calling reduce_sum_v1 (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",
      "W1030 17:41:49.238550 139942895679296 deprecation.py:323] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:295: 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",
      "W1030 17:41:49.286651 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/model.py:351: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n",
      "I1030 17:41:51.179355 139942895679296 model.py:87] Model using gpu.\n",
      "I1030 17:41:51.179930 139942895679296 model.py:175] Input dropout mode = 0.\n",
      "I1030 17:41:51.180556 139942895679296 model.py:176] Output dropout mode = 0.\n",
      "I1030 17:41:51.181033 139942895679296 model.py:177] Recurrent dropout mode = 0.\n",
      "I1030 17:41:51.579374 139942895679296 model.py:87] Model using gpu.\n",
      "I1030 17:41:51.579939 139942895679296 model.py:175] Input dropout mode = 0.\n",
      "I1030 17:41:51.580369 139942895679296 model.py:176] Output dropout mode = 0.\n",
      "I1030 17:41:51.580825 139942895679296 model.py:177] Recurrent dropout mode = 0.\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1gxYLPTQFQuk"
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "bVlDyfN_FQum",
    "outputId": "fb41ce20-4c7f-4991-e9f6-559ea9b34a31"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1030 17:42:02.262253 139942895679296 deprecation_wrapper.py:119] From /home/ruben/Documents/Geboortekaartje/sketch_rnn/venv/lib/python3.7/site-packages/magenta/models/sketch_rnn/sketch_rnn_train.py:239: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
      "\n",
      "I1030 17:42:02.329423 139942895679296 sketch_rnn_train.py:241] Loading model models/naam4/vector-4800.\n",
      "W1030 17:42:02.330948 139942895679296 deprecation.py:323] From /home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "I1030 17:42:02.334095 139942895679296 saver.py:1280] Restoring parameters from models/naam4/vector-4800\n"
     ]
    }
   ],
   "source": [
    "# loads the weights from checkpoint into our model\n",
    "load_checkpoint(sess, model_dir)\n",
    "# saver = tf.train.Saver(tf.global_variables())\n",
    "# ckpt = tf.train.get_checkpoint_state(model_dir)\n",
    "# print(int(ckpt.model_checkpoint_path.split(\"-\")[-1])/100)\n",
    "# # tf.logging.info('Loading model %s.', ckpt.model_checkpoint_path)\n",
    "# saver.restore(sess, \"models/naam4/vector-4100\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EOblwpFeFQuq"
   },
   "source": [
    "We define two convenience functions to encode a stroke into a latent vector, and decode from latent vector to stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tMFlV487FQur"
   },
   "outputs": [],
   "source": [
    "def encode(input_strokes):\n",
    "  strokes = to_big_strokes(input_strokes, 614).tolist()\n",
    "  strokes.insert(0, [0, 0, 1, 0, 0])\n",
    "  seq_len = [len(input_strokes)]\n",
    "  print(seq_len)\n",
    "  draw_strokes(to_normal_strokes(np.array(strokes)))\n",
    "  print(np.array([strokes]).shape)\n",
    "  return sess.run(eval_model.batch_z, feed_dict={eval_model.input_data: [strokes], eval_model.sequence_lengths: seq_len})[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1D5CV7ZlFQut"
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "fUOAvRQtFQuw",
    "outputId": "c8e9a1c3-28db-4263-ac67-62ffece1e1e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162\n"
     ]
    },
    {
     "data": {
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       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get a sample drawing from the test set, and render it to .svg\n",
    "i = random.randint(101,360)\n",
    "#i=260\n",
    "print(i)\n",
    "stroke = test_set.random_sample()\n",
    "stroke=test_set.strokes[i]\n",
    "draw_strokes(stroke)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j114Re2JFQuz"
   },
   "source": [
    "Let's try to encode the sample stroke into latent vector $z$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method random_sample in module magenta.models.sketch_rnn.utils:\n",
      "\n",
      "random_sample() method of magenta.models.sketch_rnn.utils.DataLoader instance\n",
      "    Return a random sample, in stroke-3 format as used by draw_strokes.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(test_set.random_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "DBRjPBo-FQu0",
    "outputId": "e089dc78-88e3-44c6-ed7e-f1844471f47f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[181]\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 615, 5)\n"
     ]
    }
   ],
   "source": [
    "z = encode(stroke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "_ = decode(z,temperature=1) # convert z back to drawing at temperature of 0.8\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "M5ft6IEBFQu9"
   },
   "source": [
    "Create generated grid at various temperatures from 0.1 to 1.0"
   ]
  },
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   "execution_count": 29,
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     "text": [
      "0.1\n"
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   ],
   "source": [
    "for i in range(10):\n",
    "    temp = .1 + i*.1\n",
    "    print(temp)\n",
    "    for j in range(5):\n",
    "        stroke = decode(draw_mode=False, temperature=temp)\n",
    "        draw_strokes(stroke)\n",
    "        \n",
    "    \n",
    "# stroke_grid = make_grid_svg(stroke_list)\n",
    "# draw_strokes(stroke_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4xiwp3_DFQvB"
   },
   "source": [
    "Latent Space Interpolation Example between $z_0$ and $z_1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "WSX0uvZTFQvD",
    "outputId": "cd67af4e-5ae6-4327-876e-e1385dadbafc"
   },
   "outputs": [
    {
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       "<IPython.core.display.SVG object>"
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     "metadata": {},
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   ],
   "source": [
    "# get a sample drawing from the test set, and render it to .svg\n",
    "z0 = z\n",
    "_ = decode(z0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "colab_type": "code",
    "id": "jQf99TxOFQvH",
    "outputId": "4265bd5f-8c66-494e-b26e-d3ac874d69bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[425]\n"
     ]
    },
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      "(1, 615, 5)\n"
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   "source": [
    "stroke = test_set.random_sample()\n",
    "z1 = encode(stroke)\n",
    "_ = decode(z1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tDqJR8_eFQvK"
   },
   "source": [
    "Now we interpolate between sheep $z_0$ and sheep $z_1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_YkPNL5SFQvL"
   },
   "outputs": [],
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UoM-W1tQFQvM"
   },
   "outputs": [
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   ],
   "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=True), [0, i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "mTqmlL6GFQvQ",
    "outputId": "062e015f-29c6-4e77-c6db-e403d5cabd59"
   },
   "outputs": [],
   "source": [
    "stroke_grid = make_grid_svg(reconstructions)\n",
    "draw_strokes(stroke_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vFwPna6uFQvS"
   },
   "source": [
    "Let's load the Flamingo Model, and try Unconditional (Decoder-Only) Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HH-YclgNFQvT"
   },
   "outputs": [],
   "source": [
    "model_dir = '/tmp/sketch_rnn/models/flamingo/lstm_uncond'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-Znvy3KxFQvU"
   },
   "outputs": [],
   "source": [
    "[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "cqDNK1cYFQvZ",
    "outputId": "d346d57c-f51a-4286-ba55-705bc27d4d0d"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7wzerSI6FQvd"
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "6mzk8KjOFQvf",
    "outputId": "c450a6c6-22ee-4a58-8451-443462b42d58"
   },
   "outputs": [],
   "source": [
    "# loads the weights from checkpoint into our model\n",
    "load_checkpoint(sess, model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X88CgcyuFQvh"
   },
   "outputs": [],
   "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]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 149
    },
    "colab_type": "code",
    "id": "k57REtd_FQvj",
    "outputId": "8bd69652-9d1d-475e-fc64-f205cf6b9ed1"
   },
   "outputs": [],
   "source": [
    "stroke_grid = make_grid_svg(reconstructions)\n",
    "draw_strokes(stroke_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L-rJ0iUQFQvl"
   },
   "source": [
    "Let's load the owl model, and generate two sketches using two random IID gaussian latent vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "of4SWwGdFQvm"
   },
   "outputs": [],
   "source": [
    "model_dir = '/tmp/sketch_rnn/models/owl/lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "jJiSZFQeFQvp",
    "outputId": "f84360ca-c2be-482f-db57-41b5ecc05768"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "colab_type": "code",
    "id": "vR4TDoi5FQvr",
    "outputId": "db08cb2c-952c-4949-d2b0-94c11351264b"
   },
   "outputs": [],
   "source": [
    "z_0 = np.random.randn(eval_model.hps.z_size)\n",
    "_ = decode(z_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 124
    },
    "colab_type": "code",
    "id": "ZX23lTnpFQvt",
    "outputId": "247052f2-a0f3-4046-83d6-d08e0429fafb"
   },
   "outputs": [],
   "source": [
    "z_1 = np.random.randn(eval_model.hps.z_size)\n",
    "_ = decode(z_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7FjQsF_2FQvv"
   },
   "source": [
    "Let's interpolate between the two owls $z_0$ and $z_1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u6G37E8_FQvw"
   },
   "outputs": [],
   "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]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 149
    },
    "colab_type": "code",
    "id": "OULjMktmFQvx",
    "outputId": "94b7b68e-9c57-4a1b-b216-83770fa4be81"
   },
   "outputs": [],
   "source": [
    "stroke_grid = make_grid_svg(reconstructions)\n",
    "draw_strokes(stroke_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OiXNC-YsFQv0"
   },
   "source": [
    "Let's load the model trained on both cats and buses!  catbus!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SL7WpDDQFQv0"
   },
   "outputs": [],
   "source": [
    "model_dir = '/tmp/sketch_rnn/models/catbus/lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "Cvk5WOqHFQv2",
    "outputId": "8081d53d-52d6-4d18-f973-a9dd44c897f2"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "icvlBPVkFQv5",
    "outputId": "f7b415fe-4d65-4b00-c0eb-fb592597dba2"
   },
   "outputs": [],
   "source": [
    "z_1 = np.random.randn(eval_model.hps.z_size)\n",
    "z_1 = z\n",
    "_ = decode(z_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "uaNxd0LuFQv-",
    "outputId": "4de5ee9a-cf14-49f4-e5f5-399a0d0b8215"
   },
   "outputs": [],
   "source": [
    "z_0 = np.random.randn(eval_model.hps.z_size)\n",
    "_ = decode(z_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VtSYkS6mFQwC"
   },
   "source": [
    "Let's interpolate between a cat and a bus!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qIDYUxBEFQwD"
   },
   "outputs": [],
   "source": [
    "z_list = [] # interpolate spherically between z_1 and z_0\n",
    "N = 50\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]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "colab_type": "code",
    "id": "ZHmnSjSaFQwH",
    "outputId": "38fe3c7e-698b-4b19-8851-e7f3ff037744"
   },
   "outputs": [],
   "source": [
    "stroke_grid = make_grid_svg(reconstructions)\n",
    "draw_strokes(stroke_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "flZ_OgzCFQwJ"
   },
   "source": [
    "Why stop here? Let's load the model trained on both elephants and pigs!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S8WwK8FPFQwK"
   },
   "outputs": [],
   "source": [
    "model_dir = '/tmp/sketch_rnn/models/elephantpig/lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "meOH4AFXFQwM",
    "outputId": "764938a7-bbdc-4732-e688-a8a278ab3089"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "foZiiYPdFQwO",
    "outputId": "a09fc4fb-110f-4280-8515-c9b673cb6b90"
   },
   "outputs": [],
   "source": [
    "z_0 = np.random.randn(eval_model.hps.z_size)\n",
    "_ = decode(z_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163
    },
    "colab_type": "code",
    "id": "6Gaz3QG1FQwS",
    "outputId": "0cfc279c-1c59-419f-86d4-ed74d5e38a26"
   },
   "outputs": [],
   "source": [
    "z_1 = np.random.randn(eval_model.hps.z_size)\n",
    "_ = decode(z_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oVtr7NnGFQwU"
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lJs9JbROFQwU"
   },
   "outputs": [],
   "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]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0FOuNfJMFQwW"
   },
   "outputs": [],
   "source": [
    "stroke_grid = make_grid_svg(reconstructions, grid_space_x=25.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 130
    },
    "colab_type": "code",
    "id": "bZ6zpdiMFQwX",
    "outputId": "70679bd1-4dba-4c08-b39f-bbde81d22019"
   },
   "outputs": [],
   "source": [
    "draw_strokes(stroke_grid, factor=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KUgVRGnSFQwa"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Sketch_RNN.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "sketchrnn",
   "language": "python",
   "name": "sketchrnn"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
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}