2228 lines
926 KiB
Plaintext
2228 lines
926 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "tDybPQiEFQuJ"
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},
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"source": [
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"In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "k0GqvYgB9JLC"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
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"/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",
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" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
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"/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",
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" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
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"/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",
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" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
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"/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",
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" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
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"/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",
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" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
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"/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",
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" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
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"/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",
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" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
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"/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",
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" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
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"/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",
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" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
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"/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",
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" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
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"/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",
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" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
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]
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}
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],
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"source": [
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"# import the required libraries\n",
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"import numpy as np\n",
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"import time\n",
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"import random\n",
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"import pickle\n",
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"import codecs\n",
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"import collections\n",
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"import os\n",
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"import math\n",
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"import json\n",
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"import tensorflow as tf\n",
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"from six.moves import xrange"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "UI4ZC__4FQuL"
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},
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"outputs": [],
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"source": [
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"# libraries required for visualisation:\n",
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"from IPython.display import SVG, display\n",
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"import PIL\n",
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"from PIL import Image\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# set numpy output to something sensible\n",
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"np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "D7ObpAUh9jrk"
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},
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"outputs": [],
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"source": [
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"# !pip install -qU svgwrite"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "4xYY-TUd9aiD"
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},
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"outputs": [],
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"source": [
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"import svgwrite # conda install -c omnia svgwrite=1.1.6"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"colab_type": "code",
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"id": "NzPSD-XRFQuP",
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"outputId": "daa0dd33-6d59-4d15-f437-d8ec787c8884"
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},
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"outputs": [],
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"source": [
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"tf.logging.info(\"TensorFlow Version: %s\", tf.__version__)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "LebxcF4p90OR"
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},
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"outputs": [],
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"source": [
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"# !pip install -q magenta"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "NkFS0E1zFQuU"
|
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|
},
|
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"outputs": [
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{
|
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING: Logging before flag parsing goes to stderr.\n",
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"W0825 15:58:50.188074 140470926227264 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",
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"/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",
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" warnings.warn(msg)\n",
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"W0825 15:58:50.811670 140470926227264 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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"W0825 15:58:51.114169 140470926227264 lazy_loader.py:50] \n",
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"The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
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"For more information, please see:\n",
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" * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
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" * https://github.com/tensorflow/addons\n",
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" * https://github.com/tensorflow/io (for I/O related ops)\n",
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"If you depend on functionality not listed there, please file an issue.\n",
|
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"\n",
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"W0825 15:58:51.115457 140470926227264 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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|
"W0825 15:58:51.116125 140470926227264 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"
|
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|
]
|
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|
}
|
||
|
],
|
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"source": [
|
||
|
"# import our command line tools\n",
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"from magenta.models.sketch_rnn.sketch_rnn_train import *\n",
|
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"from magenta.models.sketch_rnn.model import *\n",
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"from magenta.models.sketch_rnn.utils import *\n",
|
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"from magenta.models.sketch_rnn.rnn import *"
|
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
|
||
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"colab": {},
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"colab_type": "code",
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"id": "GBde4xkEFQuX"
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},
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"outputs": [],
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"source": [
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"# little function that displays vector images and saves them to .svg\n",
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"def draw_strokes(data, factor=0.2, svg_filename = '/tmp/sketch_rnn/svg/sample.svg'):\n",
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" tf.gfile.MakeDirs(os.path.dirname(svg_filename))\n",
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" min_x, max_x, min_y, max_y = get_bounds(data, factor)\n",
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" dims = (50 + max_x - min_x, 50 + max_y - min_y)\n",
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" dwg = svgwrite.Drawing(svg_filename, size=dims)\n",
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" dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\n",
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" lift_pen = 1\n",
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" abs_x = 25 - min_x \n",
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" abs_y = 25 - min_y\n",
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" p = \"M%s,%s \" % (abs_x, abs_y)\n",
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" command = \"m\"\n",
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" for i in xrange(len(data)):\n",
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" if (lift_pen == 1):\n",
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" command = \"m\"\n",
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" elif (command != \"l\"):\n",
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" command = \"l\"\n",
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" else:\n",
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" command = \"\"\n",
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" x = float(data[i,0])/factor\n",
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" y = float(data[i,1])/factor\n",
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" lift_pen = data[i, 2]\n",
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" p += command+str(x)+\",\"+str(y)+\" \"\n",
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" the_color = \"black\"\n",
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" stroke_width = 1\n",
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" dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\"none\"))\n",
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" dwg.save()\n",
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" display(SVG(dwg.tostring()))\n",
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"\n",
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"# generate a 2D grid of many vector drawings\n",
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"def make_grid_svg(s_list, grid_space=10.0, grid_space_x=16.0):\n",
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" def get_start_and_end(x):\n",
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" x = np.array(x)\n",
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" x = x[:, 0:2]\n",
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" x_start = x[0]\n",
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" x_end = x.sum(axis=0)\n",
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" x = x.cumsum(axis=0)\n",
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" x_max = x.max(axis=0)\n",
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" x_min = x.min(axis=0)\n",
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" center_loc = (x_max+x_min)*0.5\n",
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" return x_start-center_loc, x_end\n",
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" x_pos = 0.0\n",
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" y_pos = 0.0\n",
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" result = [[x_pos, y_pos, 1]]\n",
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" for sample in s_list:\n",
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" s = sample[0]\n",
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" grid_loc = sample[1]\n",
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" grid_y = grid_loc[0]*grid_space+grid_space*0.5\n",
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" grid_x = grid_loc[1]*grid_space_x+grid_space_x*0.5\n",
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" start_loc, delta_pos = get_start_and_end(s)\n",
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"\n",
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" loc_x = start_loc[0]\n",
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" loc_y = start_loc[1]\n",
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" new_x_pos = grid_x+loc_x\n",
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" new_y_pos = grid_y+loc_y\n",
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" result.append([new_x_pos-x_pos, new_y_pos-y_pos, 0])\n",
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"\n",
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" result += s.tolist()\n",
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" result[-1][2] = 1\n",
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" x_pos = new_x_pos+delta_pos[0]\n",
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" y_pos = new_y_pos+delta_pos[1]\n",
|
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" return np.array(result)"
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]
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},
|
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{
|
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"cell_type": "markdown",
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||
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"metadata": {
|
||
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"colab_type": "text",
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||
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"id": "if7-UyxzFQuY"
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},
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"source": [
|
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"define the path of the model you want to load, and also the path of the dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 139,
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"metadata": {
|
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"colab": {},
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"colab_type": "code",
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"id": "Dipv1EbsFQuZ"
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},
|
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"outputs": [],
|
||
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"source": [
|
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"data_dir = 'datasets/naam4'\n",
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"model_dir = 'models/naam4'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 140,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 68
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},
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"colab_type": "code",
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||
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"id": "eaSqI0fIFQub",
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||
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"outputId": "06df45a6-cc86-4f50-802e-25ae185037f7"
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},
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"outputs": [],
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"source": [
|
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"# download_pretrained_models(models_root_dir=models_root_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 153,
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"metadata": {
|
||
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"colab": {},
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||
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"colab_type": "code",
|
||
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"id": "G4sRuxyn_1aO"
|
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},
|
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"outputs": [],
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"source": [
|
||
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"def load_env_compatible(data_dir, model_dir):\n",
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" \"\"\"Loads environment for inference mode, used in jupyter notebook.\"\"\"\n",
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" # modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py\n",
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" # to work with depreciated tf.HParams functionality\n",
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" model_params = sketch_rnn_model.get_default_hparams()\n",
|
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" with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:\n",
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" data = json.load(f)\n",
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" fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']\n",
|
||
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" for fix in fix_list:\n",
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" data[fix] = (data[fix] == 1)\n",
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||
|
" 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": 154,
|
||
|
"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": [
|
||
|
"I0825 17:10:28.492715 140470926227264 sketch_rnn_train.py:142] Loaded 161/161/161 from diede.npz\n",
|
||
|
"I0825 17:10:28.666448 140470926227264 sketch_rnn_train.py:142] Loaded 100/100/100 from lijn.npz\n",
|
||
|
"I0825 17:10:29.123052 140470926227264 sketch_rnn_train.py:142] Loaded 100/100/100 from blokletters.npz\n",
|
||
|
"I0825 17:10:29.189439 140470926227264 sketch_rnn_train.py:159] Dataset combined: 1083 (361/361/361), avg len 234\n",
|
||
|
"I0825 17:10:29.190502 140470926227264 sketch_rnn_train.py:166] model_params.max_seq_len 614.\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"total images <= max_seq_len is 361\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"name": "stderr",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"I0825 17:10:29.655039 140470926227264 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"
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"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": 155,
|
||
|
"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": [
|
||
|
"I0825 17:10:34.129225 140470926227264 model.py:87] Model using gpu.\n",
|
||
|
"I0825 17:10:34.139610 140470926227264 model.py:175] Input dropout mode = False.\n",
|
||
|
"I0825 17:10:34.141004 140470926227264 model.py:176] Output dropout mode = False.\n",
|
||
|
"I0825 17:10:34.143371 140470926227264 model.py:177] Recurrent dropout mode = False.\n",
|
||
|
"I0825 17:10:42.551079 140470926227264 model.py:87] Model using gpu.\n",
|
||
|
"I0825 17:10:42.553035 140470926227264 model.py:175] Input dropout mode = 0.\n",
|
||
|
"I0825 17:10:42.554712 140470926227264 model.py:176] Output dropout mode = 0.\n",
|
||
|
"I0825 17:10:42.556115 140470926227264 model.py:177] Recurrent dropout mode = 0.\n",
|
||
|
"I0825 17:10:43.679191 140470926227264 model.py:87] Model using gpu.\n",
|
||
|
"I0825 17:10:43.681183 140470926227264 model.py:175] Input dropout mode = 0.\n",
|
||
|
"I0825 17:10:43.682615 140470926227264 model.py:176] Output dropout mode = 0.\n",
|
||
|
"I0825 17:10:43.683944 140470926227264 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": 156,
|
||
|
"metadata": {
|
||
|
"colab": {},
|
||
|
"colab_type": "code",
|
||
|
"id": "1gxYLPTQFQuk"
|
||
|
},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"sess = tf.InteractiveSession()\n",
|
||
|
"sess.run(tf.global_variables_initializer())"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 199,
|
||
|
"metadata": {
|
||
|
"colab": {
|
||
|
"base_uri": "https://localhost:8080/",
|
||
|
"height": 51
|
||
|
},
|
||
|
"colab_type": "code",
|
||
|
"id": "bVlDyfN_FQum",
|
||
|
"outputId": "fb41ce20-4c7f-4991-e9f6-559ea9b34a31"
|
||
|
},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"48.0\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"ename": "ValueError",
|
||
|
"evalue": "The passed save_path is not a valid checkpoint: models/naam4/vector-4100",
|
||
|
"output_type": "error",
|
||
|
"traceback": [
|
||
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
|
"\u001b[0;32m<ipython-input-199-787fc7b1ddc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mckpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_checkpoint_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# tf.logging.info('Loading model %s.', ckpt.model_checkpoint_path)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"models/naam4/vector-4100\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||
|
"\u001b[0;32m/home/ruben/.local/lib/python3.7/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, sess, save_path)\u001b[0m\n\u001b[1;32m 1276\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheckpoint_management\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckpoint_exists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1277\u001b[0m raise ValueError(\"The passed save_path is not a valid checkpoint: \" +\n\u001b[0;32m-> 1278\u001b[0;31m compat.as_text(save_path))\n\u001b[0m\u001b[1;32m 1279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1280\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Restoring parameters from %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
"\u001b[0;31mValueError\u001b[0m: The passed save_path is not a valid checkpoint: models/naam4/vector-4100"
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"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": 159,
|
||
|
"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": 160,
|
||
|
"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": 189,
|
||
|
"metadata": {
|
||
|
"colab": {
|
||
|
"base_uri": "https://localhost:8080/",
|
||
|
"height": 123
|
||
|
},
|
||
|
"colab_type": "code",
|
||
|
"id": "fUOAvRQtFQuw",
|
||
|
"outputId": "c8e9a1c3-28db-4263-ac67-62ffece1e1e0"
|
||
|
},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"image/svg+xml": [
|
||
|
"<svg baseProfile=\"full\" height=\"94.69930820167065\" version=\"1.1\" width=\"169.4394477829337\" 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=\"94.69930820167065\" width=\"169.4394477829337\" x=\"0\" y=\"0\"/><path d=\"M47.168685272336006,54.04550187289715 m-1.7192041873931885,0.3619377315044403 l-2.9859861731529236,0.0 -0.8143598586320877,-0.3619377315044403 l-1.266781985759735,0.0 -0.8143598586320877,-0.3619377315044403 l-2.9859861731529236,-0.3619377315044403 -3.438408374786377,2.081141918897629 l-0.8143598586320877,0.8143598586320877 -1.266781985759735,0.3619377315044403 l-2.081141918897629,2.081141918897629 -0.8143598586320877,0.3619377315044403 l-0.3619377315044403,0.8143598586320877 -1.6287197172641754,1.6287197172641754 l0.0,0.8143598586320877 -0.8143598586320877,1.266781985759735 l0.0,1.266781985759735 -0.3619377315044403,0.8143598586320877 l0.3619377315044403,0.3619377315044403 0.0,0.8143598586320877 l2.081141918897629,1.7192041873931885 0.8143598586320877,0.0 l2.081141918897629,0.8143598586320877 0.8143598586320877,0.0 l0.3619377315044403,0.3619377315044403 1.7192041873931885,0.0 l0.8143598586320877,-0.3619377315044403 0.8143598586320877,0.0 l2.9859861731529236,-1.266781985759735 0.8143598586320877,-0.8143598586320877 l1.7192041873931885,-0.8143598586320877 2.53356397151947,-2.53356397151947 l0.8143598586320877,-0.3619377315044403 0.3619377315044403,-0.8143598586320877 l1.7192041873931885,-1.7192041873931885 2.53356397151947,-3.438408374786377 l0.3619377315044403,-1.266781985759735 0.8143598586320877,-0.8143598586320877 l0.3619377315044403,-1.266781985759735 1.7192041873931885,-2.53356397151947 l0.3619377315044403,-1.7192041873931885 0.8143598586320877,-1.266781985759735 l1.9001729786396027,-5.06712794303894 1.9906574487686157,-4.343252778053284 l1.266781985759735,-2.081141918897629 0.0,-0.8143598586320877 l0.8143598586320877,-1.266781985759735 0.0,-1.7192041873931885 l0.3619377315044403,-0.3619377315044403 0.0,-1.266781985759735 l0.3619377315044403,0.0 0.0,-0.3619377315044403 l0.3619377315044403,-0.3619377315044403 0.0,-0.8143598586320877 l0.8143598586320877,-1.266781985759735 0.0,-0.8143598586320877 l0.3619377315044403,-1.266781985759735 0.3619377315044403,-0.3619377315044403 l-0.3619377315044403,0.0 0.0,0.8143598586320877 l-0.8143598586320877,2.081141918897629 0.0,0.8143598586320877 l-1.266781985759735,2.9859861731529236 0.0,0.8143598586320877 l-2.53356397151947,6.424394845962524 -0.8143598586320877,0.8143598586320877 l-0.8143598586320877,2.081141918897629 -2.171626389026642,3.438408374786377 l-0.8143598586320877,2.081141918897629 -0.8143598586320877,0.8143598586320877 l-1.7192041873931885,4.252768158912659 0.0,0.8143598586320877 l-0.8143598586320877,2.081141918897629 0.0,1.266781985759735 l-0.3619377315044403,0.8143598586320877 0.0,2.081141918897629 l0.3619377315044403,0.3619377315044403 0.3619377315044403,1.266781985759735 l1.266781985759735,1.266781985759735 0.3619377315044403,0.0 l0.3619377315044403,0.3619377315044403 3.8908305764198303,0.0 l0.3619377315044403,-0.3619377315044403 1.266781985759735,0.0 l3.8908305764198303,-2.171626389026642 2.53356397151947,-1.7192041873931885 l0.4524221643805504,-0.8143598586320877 3.8003459572792053,-4.252768158912659 l0.0,-0.3619377315044403 0.3619377315044403,0.0 l0.0,-0.3619377315044403 1.266781985759735,-1.266781985759735 l0.0,-0.3619377315044403 0.3619377315044403,0.0 l0.0,-0.3619377315044403 0.8143598586320877,-0.8143598586320877 l0.0,-0.8143598586320877 0.3619377315044403,0.0 l0.0,-0.8143598586320877 0.3619377315044403,0.0 l-0.3619377315044403,-0.3619377315044403 0.0,0.8143598586320877 l-0.3619377315044403,0.0 -0.3619377315044403,1.266781985759735 l-0.3619377315044403,0.0 0.0,0.3619377315044403 l-1.6287197172641754,1.6287197172641754 0.0,0.3619377315044403 l-0.8143598586320877,0.8143598586320877 0.0,0.8143598586320877 l-1.266781985759735,2.081141918897629 -0.3619377315044403,1.266781985759735 l0.0,2.9859861731529236 1.7192041873931885
|
||
|
],
|
||
|
"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",
|
||
|
"stroke = test_set.random_sample()\n",
|
||
|
"stroke=test_set.strokes[202]\n",
|
||
|
"draw_strokes(stroke)"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"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": 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": {
|
||
|
"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": 197,
|
||
|
"metadata": {
|
||
|
"colab": {
|
||
|
"base_uri": "https://localhost:8080/",
|
||
|
"height": 124
|
||
|
},
|
||
|
"colab_type": "code",
|
||
|
"id": "-37v6eZLFQu5",
|
||
|
"outputId": "5ddac2f2-5b3b-4cd7-b81f-7a8fa374aa6b"
|
||
|
},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"image/svg+xml": [
|
||
|
"<svg baseProfile=\"full\" height=\"98.60003169000265\" version=\"1.1\" width=\"152.20113983656802\" 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=\"98.60003169000265\" width=\"152.20113983656802\" x=\"0\" y=\"0\"/><path d=\"M42.42648744198959,43.05125633138232 m0.1862548291683197,-1.9295993447303772 l-1.191716268658638,-0.12015363201498985 -0.46020425856113434,0.32373636960983276 l-0.9181787073612213,0.06575166247785091 -0.8480583131313324,0.009919659933075309 l-1.0575512796640396,0.2869098260998726 -1.0493094474077225,0.09308814071118832 l-0.7265898585319519,0.3001134656369686 -0.7810255885124207,0.4221324622631073 l-1.205417811870575,0.08514165878295898 -0.8341624587774277,0.39647310972213745 l-0.5819345638155937,0.5273960903286934 -1.1446667462587357,0.46101629734039307 l-0.45249637216329575,0.03863557009026408 -1.7732638120651245,2.3012886941432953 l-0.7310863584280014,0.8512885868549347 -0.4262998700141907,0.6674831360578537 l-2.054581046104431,3.159855902194977 -0.6272304058074951,1.9465045630931854 l-0.34248169511556625,1.199522539973259 -0.0026846787659451365,2.707573175430298 l-0.4038027301430702,0.9078937023878098 0.003878803108818829,2.0480501651763916 l0.51289401948452,0.8101581037044525 0.29287245124578476,0.4627874121069908 l0.13180269859731197,0.3076687268912792 0.6197455897927284,0.8134213835000992 l1.3128812611103058,-0.021080365404486656 2.825867533683777,-0.7779388874769211 l1.068887785077095,-1.0222355276346207 -0.0934797152876854,0.18439093604683876 l-0.22443119436502457,0.1051363255828619 0.8497701585292816,-0.059685707092285156 l0.672295019030571,-0.5298596993088722 2.056182473897934,-2.003650963306427 l0.4568270221352577,-0.24280007928609848 0.6457137316465378,-0.758010596036911 l0.4958105832338333,-0.6950593739748001 0.7725908607244492,-0.5841373652219772 l0.07648885250091553,0.09800276719033718 1.3690243661403656,-2.0735442638397217 l1.3249605894088745,-2.5180408358573914 1.5033236145973206,-3.8412049412727356 l0.5461340397596359,-1.4265176653862 0.8776359260082245,-2.395220249891281 l0.23161949589848518,-0.91923788189888 0.5715756490826607,-1.1421089619398117 l-0.14863894321024418,-1.3522228598594666 0.1065175049006939,0.5072471871972084 l-0.13954687863588333,-0.24326099082827568 0.13169826939702034,0.24211140349507332 l-0.06206361576914787,-0.2392142452299595 0.19028086215257645,-0.31894903630018234 l-0.10499604977667332,-0.3496748208999634 -0.10198510251939297,0.32620251178741455 l-0.12519176118075848,0.23153908550739288 0.22708913311362267,-0.32581087201833725 l-0.08003687486052513,0.4116949811577797 -0.0945097953081131,0.4653310775756836 l-0.05257121752947569,-0.1459981221705675 0.16099771484732628,-0.6907115131616592 l0.40358442813158035,0.13079974800348282 0.10490933433175087,-0.22778570652008057 l0.23102451115846634,-0.2989422157406807 0.04118245095014572,-0.2553596533834934 l0.3809770941734314,-0.10748535394668579 0.7352985441684723,-0.776011273264885 l0.23585768416523933,-0.5388379469513893 0.27585437521338463,-0.4162510111927986 l0.26114800944924355,-0.6598640233278275 1.061323806643486,-2.0901118218898773 l1.072045937180519,-1.8917810916900635 0.4348094016313553,-0.9272965788841248 l0.6718036532402039,-0.7385149598121643 0.47442901879549026,-0.8120592683553696 l0.7490034401416779,-1.189127340912819 1.0271857678890228,-1.7251388728618622 l0.47172289341688156,-1.145540252327919 0.8402421325445175,-0.7179567962884903 l-0.4963887110352516,-0.7035297900438309 -0.15822481364011765,0.4361773282289505 l-0.22392980754375458,0.6608793884515762 -1.0916312783956528,0.9337881207466125 l0.06046489346772432,0.6692387163639069 -1.3392147421836853,1.813783049583435 l-0.07611645851284266,1.0222074389457703 -0.27916068211197853,0.167036522179842 l-1.3155271112918854,3.2446837425231934 -0.2689936198294163,1.1645165085792542 l-0.13193137012422085,1.1834697425365448 -1.37436643242836,3.42643141746521 l-0.29770102351903915,1.4979633688926697 -0.3149104490876198,1.4045056700706482 l
|
||
|
],
|
||
|
"text/plain": [
|
||
|
"<IPython.core.display.SVG object>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"_ = decode(z,temperature=1) # convert z back to drawing at temperature of 0.8\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "markdown",
|
||
|
"metadata": {
|
||
|
"colab_type": "text",
|
||
|
"id": "M5ft6IEBFQu9"
|
||
|
},
|
||
|
"source": [
|
||
|
"Create generated grid at various temperatures from 0.1 to 1.0"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 102,
|
||
|
"metadata": {
|
||
|
"colab": {
|
||
|
"base_uri": "https://localhost:8080/",
|
||
|
"height": 130
|
||
|
},
|
||
|
"colab_type": "code",
|
||
|
"id": "BuhaZI0aFQu9",
|
||
|
"outputId": "d87d4b00-30c2-4302-bec8-46566ef26922",
|
||
|
"scrolled": false
|
||
|
},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"0.1\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/svg+xml": [
|
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],
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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},
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|
"metadata": {},
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||
|
"output_type": "display_data"
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||
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},
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||
|
{
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||
|
"data": {
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||
|
"image/svg+xml": [
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|
||
|
],
|
||
|
"text/plain": [
|
||
|
"<IPython.core.display.SVG object>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"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": [
|
||
|
{
|
||
|
"data": {
|
||
|
"image/svg+xml": [
|
||
|
"<svg baseProfile=\"full\" height=\"90.11853251140565\" version=\"1.1\" width=\"183.5966605367139\" 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=\"90.11853251140565\" width=\"183.5966605367139\" x=\"0\" y=\"0\"/><path d=\"M38.487485870718956,65.11853251140565 m-6.107860207557678,-2.870575487613678 l-4.996210336685181,-0.7894060015678406 -1.8128877878189087,-2.911415696144104 l-0.5705275386571884,-2.8740668296813965 0.05646933801472187,0.24372059851884842 l0.06725526880472898,0.4540058597922325 0.6718767434358597,0.1635473407804966 l-0.4683074727654457,-1.1790595948696136 0.3687518462538719,0.0568343885242939 l1.102830395102501,-1.7055943608283997 0.4945341870188713,-1.4653514325618744 l0.6107145920395851,0.11514725163578987 1.0401514172554016,-0.9861967712640762 l0.7465182989835739,-2.2748970985412598 0.7058843970298767,-0.06860395427793264 l0.8488832414150238,-3.825756013393402 0.23598186671733856,-0.858948603272438 l0.492764413356781,-4.196976721286774 1.2618504464626312,-5.524801015853882 l1.49126335978508,0.46297427266836166 0.5710553377866745,-0.5914181470870972 l1.324111521244049,-2.869957983493805 1.1529235541820526,-0.7602452486753464 l0.9423540532588959,-0.7763708382844925 1.5857017040252686,-1.7225421965122223 l1.190985068678856,-0.7728572189807892 1.0245110094547272,-0.8566032350063324 l1.3762655854225159,-0.5740072950720787 1.9652363657951355,-0.682358518242836 l2.5619062781333923,-0.2922399342060089 3.602866530418396,-0.18451202660799026 l4.601471722126007,0.33862102776765823 5.727468132972717,0.890490785241127 l6.5982043743133545,1.508084237575531 7.389050126075745,2.2215652465820312 l8.343808054924011,2.5292623043060303 9.627784490585327,3.1690260767936707 l10.69189190864563,3.1827539205551147 12.598700523376465,3.4215247631073 l6.384940147399902,-0.000957229858613573 13.277490139007568,2.1324726939201355 l6.945451498031616,-0.29052194207906723 14.38506007194519,0.9609673917293549 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>"
|
||
|
],
|
||
|
"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",
|
||
|
"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"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/svg+xml": [
|
||
|
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],
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(1, 615, 5)\n"
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|
]
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},
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{
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"data": {
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],
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||
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"text/plain": [
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||
|
"<IPython.core.display.SVG object>"
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||
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]
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||
|
},
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||
|
"metadata": {},
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||
|
"output_type": "display_data"
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||
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}
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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": {
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||
|
"colab": {},
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||
|
"colab_type": "code",
|
||
|
"id": "UoM-W1tQFQvM"
|
||
|
},
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||
|
"outputs": [
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||
|
{
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|
"data": {
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"image/svg+xml": [
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],
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"text/plain": [
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||
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"<IPython.core.display.SVG object>"
|
||
|
]
|
||
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},
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||
|
"metadata": {},
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||
|
"output_type": "display_data"
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||
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},
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|
{
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||
|
"data": {
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|
"image/svg+xml": [
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|
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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
|
||
|
}
|