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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn
Step3:  define the path of the model you want to load, and also the path of the dataset
Step4:  We define two convenience functions to encode a stroke into a latent vector, and decode from latent vector to stroke.
Step5:  Let's try to encode the sample stroke into latent vector $z$
Step6:  Create generated grid at various temperatures from 0.1 to 1.0
Step7:  Latent Space Interpolation Example between $z_0$ and $z_1$
Step8:  Now we interpolate between sheep $z_0$ and sheep $z_1$
Step9:  Let's load the Flamingo Model, and try Unconditional (Decoder-Only) Generation
Step10:  Let's load the owl model, and generate two sketches using two random IID gaussian latent vectors
Step11:  Let's interpolate between the two owls $z_0$ and $z_1$
Step12:  Let's load the model trained on both cats and buses!  catbus!
Step13:  Let's interpolate between a cat and a bus!!!
Step14:  Why stop here? Let's load the model trained on both elephants and pigs!!!
Step15:  Tribute to an episode of South Park
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Python Code:
# import the required libraries
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 PIL
from PIL import Image
import matplotlib.pyplot as plt
# set numpy output to something sensible
np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
!pip install -qU svgwrite
import svgwrite # conda install -c omnia svgwrite=1.1.6
tf.logging.info("TensorFlow Version: %s", tf.__version__)
!pip install -q magenta
# import our command line tools
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 *
# little function that displays vector images and saves them to .svg
def draw_strokes(data, factor=0.2, svg_filename = '/tmp/sketch_rnn/svg/sample.svg'):
  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()
  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)
Explanation: In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn
End of explanation
data_dir = 'http://github.com/hardmaru/sketch-rnn-datasets/raw/master/aaron_sheep/'
models_root_dir = '/tmp/sketch_rnn/models'
model_dir = '/tmp/sketch_rnn/models/aaron_sheep/layer_norm'
download_pretrained_models(models_root_dir=models_root_dir)
def load_env_compatible(data_dir, model_dir):
  Loads environment for inference mode, used in jupyter notebook.
  # modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py
  # to work with depreciated tf.HParams functionality
  model_params = sketch_rnn_model.get_default_hparams()
  with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
    data = json.load(f)
  fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']
  for fix in fix_list:
    data[fix] = (data[fix] == 1)
  model_params.parse_json(json.dumps(data))
  return load_dataset(data_dir, model_params, inference_mode=True)
def load_model_compatible(model_dir):
  Loads model for inference mode, used in jupyter notebook.
  # modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py
  # to work with depreciated tf.HParams functionality
  model_params = sketch_rnn_model.get_default_hparams()
  with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
    data = json.load(f)
  fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']
  for fix in fix_list:
    data[fix] = (data[fix] == 1)
  model_params.parse_json(json.dumps(data))
  model_params.batch_size = 1  # only sample one at a time
  eval_model_params = sketch_rnn_model.copy_hparams(model_params)
  eval_model_params.use_input_dropout = 0
  eval_model_params.use_recurrent_dropout = 0
  eval_model_params.use_output_dropout = 0
  eval_model_params.is_training = 0
  sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)
  sample_model_params.max_seq_len = 1  # sample one point at a time
  return [model_params, eval_model_params, sample_model_params]
[train_set, valid_set, test_set, hps_model, eval_hps_model, sample_hps_model] = load_env_compatible(data_dir, model_dir)
# construct the sketch-rnn model here:
reset_graph()
model = Model(hps_model)
eval_model = Model(eval_hps_model, reuse=True)
sample_model = Model(sample_hps_model, reuse=True)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# loads the weights from checkpoint into our model
load_checkpoint(sess, model_dir)
Explanation: define the path of the model you want to load, and also the path of the dataset
End of explanation
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]
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
# get a sample drawing from the test set, and render it to .svg
stroke = test_set.random_sample()
draw_strokes(stroke)
Explanation: We define two convenience functions to encode a stroke into a latent vector, and decode from latent vector to stroke.
End of explanation
z = encode(stroke)
_ = decode(z, temperature=0.8) # convert z back to drawing at temperature of 0.8
Explanation: Let's try to encode the sample stroke into latent vector $z$
End of explanation
stroke_list = []
for i in range(10):
  stroke_list.append([decode(z, draw_mode=False, temperature=0.1*i+0.1), [0, i]])
stroke_grid = make_grid_svg(stroke_list)
draw_strokes(stroke_grid)
Explanation: Create generated grid at various temperatures from 0.1 to 1.0
End of explanation
# get a sample drawing from the test set, and render it to .svg
z0 = z
_ = decode(z0)
stroke = test_set.random_sample()
z1 = encode(stroke)
_ = decode(z1)
Explanation: Latent Space Interpolation Example between $z_0$ and $z_1$
End of explanation
z_list = [] # interpolate spherically between z0 and z1
N = 10
for t in np.linspace(0, 1, N):
  z_list.append(slerp(z0, z1, t))
# for every latent vector in z_list, sample a vector image
reconstructions = []
for i in range(N):
  reconstructions.append([decode(z_list[i], draw_mode=False), [0, i]])
stroke_grid = make_grid_svg(reconstructions)
draw_strokes(stroke_grid)
Explanation: Now we interpolate between sheep $z_0$ and sheep $z_1$
End of explanation
model_dir = '/tmp/sketch_rnn/models/flamingo/lstm_uncond'
[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)
# construct the sketch-rnn model here:
reset_graph()
model = Model(hps_model)
eval_model = Model(eval_hps_model, reuse=True)
sample_model = Model(sample_hps_model, reuse=True)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# loads the weights from checkpoint into our model
load_checkpoint(sess, model_dir)
# randomly unconditionally generate 10 examples
N = 10
reconstructions = []
for i in range(N):
  reconstructions.append([decode(temperature=0.5, draw_mode=False), [0, i]])
stroke_grid = make_grid_svg(reconstructions)
draw_strokes(stroke_grid)
Explanation: Let's load the Flamingo Model, and try Unconditional (Decoder-Only) Generation
End of explanation
model_dir = '/tmp/sketch_rnn/models/owl/lstm'
[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)
# construct the sketch-rnn model here:
reset_graph()
model = Model(hps_model)
eval_model = Model(eval_hps_model, reuse=True)
sample_model = Model(sample_hps_model, reuse=True)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# loads the weights from checkpoint into our model
load_checkpoint(sess, model_dir)
z_0 = np.random.randn(eval_model.hps.z_size)
_ = decode(z_0)
z_1 = np.random.randn(eval_model.hps.z_size)
_ = decode(z_1)
Explanation: Let's load the owl model, and generate two sketches using two random IID gaussian latent vectors
End of explanation
z_list = [] # interpolate spherically between z_0 and z_1
N = 10
for t in np.linspace(0, 1, N):
  z_list.append(slerp(z_0, z_1, t))
# for every latent vector in z_list, sample a vector image
reconstructions = []
for i in range(N):
  reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.1), [0, i]])
stroke_grid = make_grid_svg(reconstructions)
draw_strokes(stroke_grid)
Explanation: Let's interpolate between the two owls $z_0$ and $z_1$
End of explanation
model_dir = '/tmp/sketch_rnn/models/catbus/lstm'
[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)
# construct the sketch-rnn model here:
reset_graph()
model = Model(hps_model)
eval_model = Model(eval_hps_model, reuse=True)
sample_model = Model(sample_hps_model, reuse=True)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# loads the weights from checkpoint into our model
load_checkpoint(sess, model_dir)
z_1 = np.random.randn(eval_model.hps.z_size)
_ = decode(z_1)
z_0 = np.random.randn(eval_model.hps.z_size)
_ = decode(z_0)
Explanation: Let's load the model trained on both cats and buses!  catbus!
End of explanation
z_list = [] # interpolate spherically between z_1 and z_0
N = 10
for t in np.linspace(0, 1, N):
  z_list.append(slerp(z_1, z_0, t))
# for every latent vector in z_list, sample a vector image
reconstructions = []
for i in range(N):
  reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.15), [0, i]])
stroke_grid = make_grid_svg(reconstructions)
draw_strokes(stroke_grid)
Explanation: Let's interpolate between a cat and a bus!!!
End of explanation
model_dir = '/tmp/sketch_rnn/models/elephantpig/lstm'
[hps_model, eval_hps_model, sample_hps_model] = load_model_compatible(model_dir)
# construct the sketch-rnn model here:
reset_graph()
model = Model(hps_model)
eval_model = Model(eval_hps_model, reuse=True)
sample_model = Model(sample_hps_model, reuse=True)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# loads the weights from checkpoint into our model
load_checkpoint(sess, model_dir)
z_0 = np.random.randn(eval_model.hps.z_size)
_ = decode(z_0)
z_1 = np.random.randn(eval_model.hps.z_size)
_ = decode(z_1)
Explanation: Why stop here? Let's load the model trained on both elephants and pigs!!!
End of explanation
z_list = [] # interpolate spherically between z_1 and z_0
N = 10
for t in np.linspace(0, 1, N):
  z_list.append(slerp(z_0, z_1, t))
# for every latent vector in z_list, sample a vector image
reconstructions = []
for i in range(N):
  reconstructions.append([decode(z_list[i], draw_mode=False, temperature=0.15), [0, i]])
stroke_grid = make_grid_svg(reconstructions, grid_space_x=25.0)
draw_strokes(stroke_grid, factor=0.3)
Explanation: Tribute to an episode of South Park: The interpolation between an Elephant and a Pig
End of explanation
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