209 lines
6.9 KiB
Python
209 lines
6.9 KiB
Python
# import the required libraries
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import numpy as np
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import time
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import random
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import pickle
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import codecs
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import collections
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import os
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import math
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import json
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import tensorflow as tf
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from six.moves import xrange
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import argparse
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import logging
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from tqdm import tqdm
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# libraries required for visualisation:
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from IPython.display import SVG, display
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import PIL
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from PIL import Image
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import matplotlib.pyplot as plt
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# set numpy output to something sensible
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np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
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import svgwrite # conda install -c omnia svgwrite=1.1.6
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger('dataset')
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argParser = argparse.ArgumentParser(description='Create dataset from SVG. We do not mind overfitting, so training==validation==test')
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argParser.add_argument(
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'--dataset_dir',
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type=str,
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default='./datasets/naam',
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)
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argParser.add_argument(
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'--model_dir',
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type=str,
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default='./models/naam',
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)
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argParser.add_argument(
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'--generated_dir',
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type=str,
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default='./generated/naam',
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)
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argParser.add_argument(
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'--verbose',
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'-v',
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action='store_true',
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help='Debug logging'
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)
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args = argParser.parse_args()
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if args.verbose:
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logger.setLevel(logging.DEBUG)
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data_dir = args.dataset_dir
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model_dir = args.model_dir
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tf.logging.info("TensorFlow Version: %s", tf.__version__)
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# import our command line tools
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from magenta.models.sketch_rnn.sketch_rnn_train import *
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from magenta.models.sketch_rnn.model import *
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from magenta.models.sketch_rnn.utils import *
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from magenta.models.sketch_rnn.rnn import *
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# little function that displays vector images and saves them to .svg
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def draw_strokes(data, factor=0.2, svg_filename = '/tmp/sketch_rnn/svg/sample.svg'):
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tf.gfile.MakeDirs(os.path.dirname(svg_filename))
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min_x, max_x, min_y, max_y = get_bounds(data, factor)
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dims = (50 + max_x - min_x, 50 + max_y - min_y)
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dwg = svgwrite.Drawing(svg_filename, size=dims)
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# dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))
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lift_pen = 1
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abs_x = 25 - min_x
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abs_y = 25 - min_y
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p = "M%s,%s " % (abs_x, abs_y)
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command = "m"
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for i in xrange(len(data)):
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if (lift_pen == 1):
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command = "m"
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elif (command != "l"):
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command = "l"
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else:
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command = ""
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x = float(data[i,0])/factor
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y = float(data[i,1])/factor
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lift_pen = data[i, 2]
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p += command+str(x)+","+str(y)+" "
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the_color = "black"
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stroke_width = 1
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dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill("none"))
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dwg.save()
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# display(SVG(dwg.tostring()))
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# generate a 2D grid of many vector drawings
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def make_grid_svg(s_list, grid_space=10.0, grid_space_x=16.0):
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def get_start_and_end(x):
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x = np.array(x)
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x = x[:, 0:2]
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x_start = x[0]
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x_end = x.sum(axis=0)
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x = x.cumsum(axis=0)
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x_max = x.max(axis=0)
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x_min = x.min(axis=0)
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center_loc = (x_max+x_min)*0.5
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return x_start-center_loc, x_end
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x_pos = 0.0
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y_pos = 0.0
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result = [[x_pos, y_pos, 1]]
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for sample in s_list:
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s = sample[0]
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grid_loc = sample[1]
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grid_y = grid_loc[0]*grid_space+grid_space*0.5
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grid_x = grid_loc[1]*grid_space_x+grid_space_x*0.5
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start_loc, delta_pos = get_start_and_end(s)
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loc_x = start_loc[0]
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loc_y = start_loc[1]
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new_x_pos = grid_x+loc_x
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new_y_pos = grid_y+loc_y
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result.append([new_x_pos-x_pos, new_y_pos-y_pos, 0])
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result += s.tolist()
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result[-1][2] = 1
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x_pos = new_x_pos+delta_pos[0]
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y_pos = new_y_pos+delta_pos[1]
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return np.array(result)
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def load_env_compatible(data_dir, model_dir):
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"""Loads environment for inference mode, used in jupyter notebook."""
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# modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py
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# to work with depreciated tf.HParams functionality
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model_params = sketch_rnn_model.get_default_hparams()
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with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
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data = json.load(f)
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fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']
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for fix in fix_list:
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data[fix] = (data[fix] == 1)
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model_params.parse_json(json.dumps(data))
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return load_dataset(data_dir, model_params, inference_mode=True)
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def load_model_compatible(model_dir):
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"""Loads model for inference mode, used in jupyter notebook."""
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# modified https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/sketch_rnn_train.py
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# to work with depreciated tf.HParams functionality
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model_params = sketch_rnn_model.get_default_hparams()
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with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
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data = json.load(f)
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fix_list = ['conditional', 'is_training', 'use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']
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for fix in fix_list:
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data[fix] = (data[fix] == 1)
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model_params.parse_json(json.dumps(data))
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model_params.batch_size = 1 # only sample one at a time
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eval_model_params = sketch_rnn_model.copy_hparams(model_params)
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eval_model_params.use_input_dropout = 0
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eval_model_params.use_recurrent_dropout = 0
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eval_model_params.use_output_dropout = 0
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eval_model_params.is_training = 0
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sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)
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sample_model_params.max_seq_len = 1 # sample one point at a time
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return [model_params, eval_model_params, sample_model_params]
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[train_set, valid_set, test_set, hps_model, eval_hps_model, sample_hps_model] = load_env_compatible(data_dir, model_dir)
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# construct the sketch-rnn model here:
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reset_graph()
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model = Model(hps_model)
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eval_model = Model(eval_hps_model, reuse=True)
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sample_model = Model(sample_hps_model, reuse=True)
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sess = tf.InteractiveSession()
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sess.run(tf.global_variables_initializer())
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# loads the weights from checkpoint into our model
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load_checkpoint(sess, args.model_dir)
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# We define two convenience functions to encode a stroke into a latent vector, and decode from latent vector to stroke.
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def encode(input_strokes):
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strokes = to_big_strokes(input_strokes).tolist()
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strokes.insert(0, [0, 0, 1, 0, 0])
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seq_len = [len(input_strokes)]
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draw_strokes(to_normal_strokes(np.array(strokes)))
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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, filename=None):
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z = None
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if z_input is not None:
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z = [z_input]
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sample_strokes, m = sample(sess, sample_model, seq_len=eval_model.hps.max_seq_len, temperature=temperature, z=z)
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strokes = to_normal_strokes(sample_strokes)
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if draw_mode:
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draw_strokes(strokes, factor, svg_filename = filename)
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return strokes
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with tqdm(total=10*50) as pbar:
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for i in range(10):
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temperature = float(i+1) / 10.
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for j in range(50):
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filename = os.path.join(args.generated_dir, f"generated{temperature}-{j:03d}.svg")
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_ = decode(temperature=temperature, filename=filename)
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pbar.update()
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