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{
"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",
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"W0827 17:43:31.091431 140714736174912 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",
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"\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",
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"W0827 17:43:31.889853 140714736174912 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",
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"\n",
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"W0827 17:43:32.257223 140714736174912 lazy_loader.py:50] \n",
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"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",
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"W0827 17:43:32.258507 140714736174912 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",
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"\n",
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"W0827 17:43:32.259193 140714736174912 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",
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"\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",
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"execution_count": 20,
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "Dipv1EbsFQuZ"
},
"outputs": [],
"source": [
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"data_dir = 'datasets/naam4'\n",
"model_dir = 'models/naam4'"
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]
},
{
"cell_type": "code",
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"execution_count": 21,
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"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",
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"execution_count": 22,
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"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",
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"execution_count": 23,
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"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": [
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"I0827 17:54:57.952397 140714736174912 sketch_rnn_train.py:142] Loaded 161/161/161 from diede.npz\n",
"I0827 17:54:58.126416 140714736174912 sketch_rnn_train.py:142] Loaded 100/100/100 from lijn.npz\n",
"I0827 17:54:58.167372 140714736174912 sketch_rnn_train.py:142] Loaded 100/100/100 from blokletters.npz\n",
"I0827 17:54:58.205663 140714736174912 sketch_rnn_train.py:159] Dataset combined: 1083 (361/361/361), avg len 234\n",
"I0827 17:54:58.208060 140714736174912 sketch_rnn_train.py:166] model_params.max_seq_len 614.\n",
"I0827 17:54:58.410045 140714736174912 sketch_rnn_train.py:209] normalizing_scale_factor 55.2581.\n"
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"total images <= max_seq_len is 361\n",
"total images <= max_seq_len is 361\n",
"total images <= max_seq_len is 361\n"
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]
}
],
"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",
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"execution_count": 24,
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"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": [
2019-08-27 20:41:06 +02:00
"I0827 17:54:58.430566 140714736174912 model.py:87] Model using gpu.\n",
"I0827 17:54:58.436558 140714736174912 model.py:175] Input dropout mode = False.\n",
"I0827 17:54:58.437577 140714736174912 model.py:176] Output dropout mode = False.\n",
"I0827 17:54:58.438252 140714736174912 model.py:177] Recurrent dropout mode = False.\n",
"W0827 17:54:58.446333 140714736174912 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",
2019-08-26 13:45:04 +02:00
"Instructions for updating:\n",
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"Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API\n",
"W0827 17:54:58.743351 140714736174912 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",
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"Instructions for updating:\n",
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"Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
"I0827 17:55:01.114828 140714736174912 model.py:87] Model using gpu.\n",
"I0827 17:55:01.115479 140714736174912 model.py:175] Input dropout mode = 0.\n",
"I0827 17:55:01.116087 140714736174912 model.py:176] Output dropout mode = 0.\n",
"I0827 17:55:01.116714 140714736174912 model.py:177] Recurrent dropout mode = 0.\n",
"I0827 17:55:01.471915 140714736174912 model.py:87] Model using gpu.\n",
"I0827 17:55:01.472840 140714736174912 model.py:175] Input dropout mode = 0.\n",
"I0827 17:55:01.473559 140714736174912 model.py:176] Output dropout mode = 0.\n",
"I0827 17:55:01.474178 140714736174912 model.py:177] Recurrent dropout mode = 0.\n"
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]
}
],
"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",
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"execution_count": 25,
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "1gxYLPTQFQuk"
},
"outputs": [],
"source": [
"sess = tf.InteractiveSession()\n",
"sess.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"colab_type": "code",
"id": "bVlDyfN_FQum",
"outputId": "fb41ce20-4c7f-4991-e9f6-559ea9b34a31"
},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
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"I0827 17:55:02.746215 140714736174912 sketch_rnn_train.py:241] Loading model models/naam4/vector-4800.\n",
"I0827 17:55:02.749120 140714736174912 saver.py:1280] Restoring parameters from models/naam4/vector-4800\n"
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]
}
],
"source": [
"# loads the weights from checkpoint into our model\n",
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"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\")"
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]
},
{
"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",
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"execution_count": 27,
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"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",
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"execution_count": 28,
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"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",
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"execution_count": 69,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 123
},
"colab_type": "code",
"id": "fUOAvRQtFQuw",
"outputId": "c8e9a1c3-28db-4263-ac67-62ffece1e1e0"
},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"260\n"
]
},
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{
"data": {
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"<svg baseProfile=\"full\" height=\"95.42318388819695\" version=\"1.1\" width=\"164.91522688418627\" 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=\"95.42318388819695\" width=\"164.91522688418627\" x=\"0\" y=\"0\"/><path d=\"M44.27318353205919,51.60242237150669 m-1.5382352471351624,-0.3619377315044403 l-0.18096886575222015,-0.3619377315044403 -0.9048443287611008,0.0 l-0.8143598586320877,0.0 -0.5429065972566605,0.3619377315044403 l-0.7238754630088806,0.0 -0.8143598586320877,0.27145329862833023 l-0.7238754630088806,0.0 -0.8143598586320877,0.27145329862833023 l-0.8143598586320877,0.0 -1.266781985759735,0.27145329862833023 l-0.7238754630088806,0.18096886575222015 -1.3572664558887482,0.5429065972566605 l-1.8096886575222015,0.8143598586320877 -0.4524221643805504,0.3619377315044403 l-1.5382352471351624,0.8143598586320877 -1.9001729786396027,2.443079650402069 l-0.8143598586320877,1.4477509260177612 -0.5429065972566605,0.7238754630088806 l-0.6333909928798676,0.9953287243843079 0.0,0.8143598586320877 l-0.18096886575222015,1.4477509260177612 -0.18096886575222015,0.9953287243843079 l0.0,1.5382352471351624 0.3619377315044403,0.8143598586320877 l0.0,1.266781985759735 1.4477509260177612,1.6287197172641754 l0.5429065972566605,0.9048443287611008 0.9048443287611008,0.3619377315044403 l0.9048443287611008,0.27145329862833023 1.9906574487686157,0.0 l2.352595180273056,0.0 4.433737099170685,-2.081141918897629 l1.266781985759735,-0.6333909928798676 1.8096886575222015,-1.4477509260177612 l1.4477509260177612,-1.4477509260177612 3.619377315044403,-3.257439434528351 l1.9001729786396027,-3.619377315044403 0.9953287243843079,-1.5382352471351624 l0.8143598586320877,-1.9001729786396027 1.176297590136528,-2.171626389026642 l0.8143598586320877,-1.9001729786396027 0.9953287243843079,-1.7192041873931885 l0.6333909928798676,-2.262110710144043 0.4524221643805504,-2.624048590660095 l0.7238754630088806,-2.262110710144043 0.9048443287611008,-5.610034465789795 l0.5429065972566605,-4.343252778053284 0.0,-2.8955018520355225 l0.0,-3.0764704942703247 -0.09048443287611008,-0.6333909928798676 l0.0,0.3619377315044403 -0.3619377315044403,0.09048443287611008 l-0.18096886575222015,0.5429065972566605 -0.6333909928798676,0.6333909928798676 l-1.9001729786396027,4.795674681663513 -0.8143598586320877,2.352595180273056 l-0.7238754630088806,1.176297590136528 -1.085813194513321,4.433737099170685 l-1.085813194513321,2.8050172328948975 -0.5429065972566605,1.3572664558887482 l-0.6333909928798676,3.1669551134109497 -0.3619377315044403,1.9906574487686157 l0.0,1.266781985759735 -1.5382352471351624,6.786332130432129 l0.0,5.791003704071045 0.0,2.53356397151947 l0.3619377315044403,0.9048443287611008 0.0,2.53356397151947 l0.3619377315044403,0.9048443287611008 0.0,0.7238754630088806 l0.5429065972566605,0.27145329862833023 0.9953287243843079,-0.8143598586320877 l0.3619377315044403,-0.5429065972566605 0.27145329862833023,-0.9048443287611008 m16.287198066711426,-15.563322305679321 l-1.4477509260177612,1.9906574487686157 -1.7192041873931885,3.709861636161804 l-1.9001729786396027,4.252768158912659 -0.3619377315044403,0.8143598586320877 l0.0,1.4477509260177612 0.0,1.8096886575222015 l0.3619377315044403,0.9953287243843079 0.27145329862833023,0.27145329862833023 l0.27145329862833023,0.0 0.7238754630088806,0.09048443287611008 l1.266781985759735,0.6333909928798676 1.6287197172641754,0.0 l2.7145329117774963,-0.8143598586320877 1.5382352471351624,-0.6333909928798676 m1.5382352471351624,-25.154671669006348 l-1.176297590136528,-0.6333909928798676 -0.3619377315044403,-0.09048443287611008 l0.0,0.0 0.0,0.27145329862833023 l0.18096886575222015,0.27145329862833023 0.3619377315044403,0.27145329862833023 l0.09048443287611008,0.18096886575222015 0.0,0.18096886575222015 l0.09048443287611008,0.09048443287611008 0.18096886575222015,0.0 l0.18096886575222015,0.09048443287611008 0.18096886575222015,0.27145329862833023 m-1.9906574487686157,-3.1669551134109497 l-0.9953287243843079,-0.271453298628
2019-08-25 17:19:27 +02:00
],
"text/plain": [
"<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",
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"i = random.randint(101,360)\n",
"i=260\n",
"print(i)\n",
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"stroke = test_set.random_sample()\n",
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"stroke=test_set.strokes[i]\n",
"draw_strokes(stroke)\n"
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]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "j114Re2JFQuz"
},
"source": [
"Let's try to encode the sample stroke into latent vector $z$"
]
},
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{
"cell_type": "code",
"execution_count": 19,
"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)"
]
},
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{
"cell_type": "code",
"execution_count": 190,
"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": [
"[244]\n"
]
},
{
"data": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 615, 5)\n"
]
}
],
"source": [
"z = encode(stroke)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 124
},
"colab_type": "code",
"id": "-37v6eZLFQu5",
"outputId": "5ddac2f2-5b3b-4cd7-b81f-7a8fa374aa6b"
},
"outputs": [
{
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"_ = decode(z,temperature=1) # convert z back to drawing at temperature of 0.8\n"
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"Create generated grid at various temperatures from 0.1 to 1.0"
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"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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"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"
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"source": [
"Now we interpolate between sheep $z_0$ and sheep $z_1$"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
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"id": "_YkPNL5SFQvL"
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"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))"
]
},
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"execution_count": 85,
"metadata": {
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],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
