In [20]:

    

!pip install svgwrite==1.1.6;
!pip uninstall -y tensorflow;
!pip uninstall -y magenta;
!pip install tensorflow==1.2.1 magenta;

import numpy as np
import time
import random
import cPickle
import codecs
import collections
import os
import math
import json
import tensorflow as tf
from six.moves import xrange

# libraries required for visualisation:
from IPython.display import SVG, display
import svgwrite # conda install -c omnia svgwrite=1.1.6
import PIL
from PIL import Image
import matplotlib.pyplot as plt


from google.colab import files


# set numpy output to something sensible
np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)


    

    




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tf.logging.info("TensorFlow Version: %s", tf.__version__)


    

    




INFO:tensorflow:TensorFlow Version: 1.2.1

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#!kill -9 -1


    

In [0]:

    

# import our command line tools
#!pip uninstall -y magenta;
#!pip install magenta;

from magenta.models.sketch_rnn.sketch_rnn_train import *
from magenta.models.sketch_rnn.model import *
from magenta.models.sketch_rnn.utils import *
from magenta.models.sketch_rnn.rnn import *


    

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tf.logging.info("TensorFlow Version: %s", tf.__version__)


    

    




INFO:tensorflow:TensorFlow Version: 1.2.1

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# little function that displays vector images and saves them to .svg
count = 0
def draw_strokes(data, factor=0.2, svg_filename = '/tmp/sketch_rnn/svg/sample.svg'):
  global count
  tf.gfile.MakeDirs(os.path.dirname(svg_filename))
  min_x, max_x, min_y, max_y = get_bounds(data, factor)
  dims = (50 + max_x - min_x, 50 + max_y - min_y)
  dwg = svgwrite.Drawing(svg_filename, size=dims)
  dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))
  lift_pen = 1
  abs_x = 25 - min_x 
  abs_y = 25 - min_y
  p = "M%s,%s " % (abs_x, abs_y)
  command = "m"
  for i in xrange(len(data)):
    if (lift_pen == 1):
      command = "m"
    elif (command != "l"):
      command = "l"
    else:
      command = ""
    x = float(data[i,0])/factor
    y = float(data[i,1])/factor
    lift_pen = data[i, 2]
    p += command+str(x)+","+str(y)+" "
  the_color = "black"
  stroke_width = 1
  dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill("none"))
  dwg.save()
  #dwg.write(f)
  #f.write(SVG(dwg.tostring()))
  dwg.save()
  files.download(svg_filename)
  count += 1
  display(SVG(dwg.tostring()))

# generate a 2D grid of many vector drawings
def make_grid_svg(s_list, grid_space=10.0, grid_space_x=16.0):
  def get_start_and_end(x):
    x = np.array(x)
    x = x[:, 0:2]
    x_start = x[0]
    x_end = x.sum(axis=0)
    x = x.cumsum(axis=0)
    x_max = x.max(axis=0)
    x_min = x.min(axis=0)
    center_loc = (x_max+x_min)*0.5
    return x_start-center_loc, x_end
  x_pos = 0.0
  y_pos = 0.0
  result = [[x_pos, y_pos, 1]]
  for sample in s_list:
    s = sample[0]
    grid_loc = sample[1]
    grid_y = grid_loc[0]*grid_space+grid_space*0.5
    grid_x = grid_loc[1]*grid_space_x+grid_space_x*0.5
    start_loc, delta_pos = get_start_and_end(s)

    loc_x = start_loc[0]
    loc_y = start_loc[1]
    new_x_pos = grid_x+loc_x
    new_y_pos = grid_y+loc_y
    result.append([new_x_pos-x_pos, new_y_pos-y_pos, 0])

    result += s.tolist()
    result[-1][2] = 1
    x_pos = new_x_pos+delta_pos[0]
    y_pos = new_y_pos+delta_pos[1]
  return np.array(result)


    

In [41]:

    

!mkdir sketch_rnn;
!mkdir sketch_rnn/models;
!mkdir sketch_rnn/models/aaron_sheep;
!mkdir sketch_rnn/models/aaron_sheep/layer_norm;


data_dir = 'http://github.com/hardmaru/sketch-rnn-datasets/raw/master/aaron_sheep/'
models_root_dir = './sketch_rnn/models'
model_dir = './sketch_rnn/models/aaron_sheep/layer_norm'


    

    




mkdir: cannot create directory ‘sketch_rnn’: File exists
mkdir: cannot create directory ‘sketch_rnn/models’: File exists
mkdir: cannot create directory ‘sketch_rnn/models/aaron_sheep’: File exists
mkdir: cannot create directory ‘sketch_rnn/models/aaron_sheep/layer_norm’: File exists

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download_pretrained_models(models_root_dir=models_root_dir)


    

    




INFO:tensorflow:./sketch_rnn/models/sketch_rnn.zip already exists, using cached copy
INFO:tensorflow:Unzipping ./sketch_rnn/models/sketch_rnn.zip...
INFO:tensorflow:Unzipping complete.

In [43]:

    

[train_set, valid_set, test_set, hps_model, eval_hps_model, sample_hps_model] = load_env(data_dir, model_dir)


    

    




INFO:tensorflow:Downloading http://github.com/hardmaru/sketch-rnn-datasets/raw/master/aaron_sheep/aaron_sheep.npz
INFO:tensorflow:Loaded 7400/300/300 from aaron_sheep.npz
INFO:tensorflow:Dataset combined: 8000 (7400/300/300), avg len 125
INFO:tensorflow:model_params.max_seq_len 250.
total images <= max_seq_len is 7400
total images <= max_seq_len is 300
total images <= max_seq_len is 300
INFO:tensorflow:normalizing_scale_factor 18.5198.

In [44]:

    

reset_graph()
model = Model(hps_model)
eval_model = Model(eval_hps_model, reuse=True)
sample_model = Model(sample_hps_model, reuse=True)


    

    




INFO:tensorflow:Model using gpu.
INFO:tensorflow:Input dropout mode = 0.
INFO:tensorflow:Output dropout mode = 0.
INFO:tensorflow:Recurrent dropout mode = 1.
INFO:tensorflow:Model using gpu.
INFO:tensorflow:Input dropout mode = 0.
INFO:tensorflow:Output dropout mode = 0.
INFO:tensorflow:Recurrent dropout mode = 0.
INFO:tensorflow:Model using gpu.
INFO:tensorflow:Input dropout mode = 0.
INFO:tensorflow:Output dropout mode = 0.
INFO:tensorflow:Recurrent dropout mode = 0.

In [0]:

    

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())


    

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load_checkpoint(sess, model_dir)


    

    




INFO:tensorflow:Loading model ./sketch_rnn/models/aaron_sheep/layer_norm/vector.
INFO:tensorflow:Restoring parameters from ./sketch_rnn/models/aaron_sheep/layer_norm/vector

In [0]:

    

def encode(input_strokes):
  strokes = to_big_strokes(input_strokes).tolist()
  strokes.insert(0, [0, 0, 1, 0, 0])
  seq_len = [len(input_strokes)]
  draw_strokes(to_normal_strokes(np.array(strokes)))
  return sess.run(eval_model.batch_z, feed_dict={eval_model.input_data: [strokes], eval_model.sequence_lengths: seq_len})[0]


    

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def decode(z_input=None, draw_mode=True, temperature=0.1, factor=0.2):
  z = None
  if z_input is not None:
    z = [z_input]
  sample_strokes, m = sample(sess, sample_model, seq_len=eval_model.hps.max_seq_len, temperature=temperature, z=z)
  strokes = to_normal_strokes(sample_strokes)
  if draw_mode:
    draw_strokes(strokes, factor)
  return strokes


    

In [49]:

    

stroke = test_set.random_sample()
draw_strokes(stroke)


    

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z = encode(stroke)


    

In [52]:

    

_ = decode(z, temperature=0.9)


    

In [54]:

    

stroke_list = []
for i in range(10):
  stroke_list.append([decode(z, draw_mode=False, temperature=0.05*i+0.1), [0, i]])
stroke_grid = make_grid_svg(stroke_list)
draw_strokes(stroke_grid)


    

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