def run():
# Determine path to configuration file.
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'novelty_config')
# Note that we provide the custom stagnation class to the Config constructor.
config = neatfast.Config(neatfast.DefaultGenome,
neatfast.DefaultReproduction,
neatfast.DefaultSpeciesSet,
neatfast.DefaultStagnation, config_path)
ne = NoveltyEvaluator(4, 'color')
if ne.scheme == 'color':
config.output_nodes = 3
else:
config.output_nodes = 1
pop = neatfast.Population(config)
# Add a stdout reporter to show progress in the terminal.
pop.add_reporter(neatfast.StdOutReporter(True))
stats = neatfast.StatisticsReporter()
pop.add_reporter(stats)
while 1:
pop.run(ne.evaluate, 1)
winner = stats.best_genome()
if ne.scheme == 'gray':
image = eval_gray_image(winner, config, full_scale * width,
full_scale * height)
elif ne.scheme == 'color':
image = eval_color_image(winner, config, full_scale * width,
full_scale * height)
elif ne.scheme == 'mono':
image = eval_mono_image(winner, config, full_scale * width,
full_scale * height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(ne.scheme))
im = np.clip(np.array(image), 0, 255).astype(np.uint8)
im = ne.image_from_array(im)
im.save('winning-novelty-{0:06d}.png'.format(pop.generation))
if ne.scheme == 'gray':
image = eval_gray_image(winner, config, width, height)
elif ne.scheme == 'color':
image = eval_color_image(winner, config, width, height)
elif ne.scheme == 'mono':
image = eval_mono_image(winner, config, width, height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(ne.scheme))
float_image = np.array(image, dtype=np.float32) / 255.0
ne.archive.append(float_image)
def make_high_resolution(self, genome, config):
genome_id, genome = genome
# Make sure the output directory exists.
if not os.path.isdir('rendered'):
os.mkdir('rendered')
if self.scheme == 'gray':
image_data = eval_gray_image(genome, config, self.full_width,
self.full_height)
elif self.scheme == 'color':
image_data = eval_color_image(genome, config, self.full_width,
self.full_height)
else:
image_data = eval_mono_image(genome, config, self.full_width,
self.full_height)
image = self.make_image_from_data(image_data)
pygame.image.save(
image,
"rendered/rendered-{}-{}.png".format(os.getpid(), genome_id))
with open("rendered/genome-{}-{}.bin".format(os.getpid(), genome_id),
"wb") as f:
pickle.dump(genome, f, 2)
def evaluate_lowres(genome, config, scheme):
if scheme == 'gray':
return eval_gray_image(genome, config, width, height)
elif scheme == 'color':
return eval_color_image(genome, config, width, height)
elif scheme == 'mono':
return eval_mono_image(genome, config, width, height)
raise Exception('Unexpected scheme: {0!r}'.format(scheme))
def evaluate(genome, scheme):
if scheme == 'gray':
return eval_gray_image(genome, width, height)
elif scheme == 'color':
return eval_color_image(genome, width, height)
elif scheme == 'mono':
return eval_mono_image(genome, width, height)
raise Exception('Unexpected scheme: {0!r}'.format(scheme))
def run():
cfg = config.Config('novelty_config')
cfg.pop_size = 100
cfg.max_fitness_threshold = 1e38
ne = NoveltyEvaluator(4, 'gray')
if ne.scheme == 'color':
cfg.output_nodes = 3
else:
cfg.output_nodes = 1
pop = population.Population(cfg)
while 1:
pop.run(ne.evaluate, 1)
winner = pop.statistics.best_genome()
if ne.scheme == 'gray':
image = eval_gray_image(winner, full_scale * width,
full_scale * height)
elif ne.scheme == 'color':
image = eval_color_image(winner, full_scale * width,
full_scale * height)
elif ne.scheme == 'mono':
image = eval_mono_image(winner, full_scale * width,
full_scale * height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(ne.scheme))
im = np.clip(np.array(image), 0, 255).astype(np.uint8)
im = ne.image_from_array(im)
im.save('winning-novelty-{0:06d}.png'.format(pop.generation))
if ne.scheme == 'gray':
image = eval_gray_image(winner, width, height)
elif ne.scheme == 'color':
image = eval_color_image(winner, width, height)
elif ne.scheme == 'mono':
image = eval_mono_image(winner, width, height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(ne.scheme))
float_image = np.array(image, dtype=np.float32) / 255.0
ne.archive.append(float_image)
def run():
cfg = config.Config('config')
cfg.pop_size = 100
cfg.max_fitness_threshold = 1e38
ne = NoveltyEvaluator(4, 'gray')
if ne.scheme == 'color':
cfg.output_nodes = 3
else:
cfg.output_nodes = 1
pop = population.Population(cfg)
while 1:
pop.run(ne.evaluate, 1)
winner = pop.statistics.best_genome()
if ne.scheme == 'gray':
image = eval_gray_image(winner, full_scale * width, full_scale * height)
elif ne.scheme == 'color':
image = eval_color_image(winner, full_scale * width, full_scale * height)
elif ne.scheme == 'mono':
image = eval_mono_image(winner, full_scale * width, full_scale * height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(ne.scheme))
im = np.clip(np.array(image), 0, 255).astype(np.uint8)
im = ne.image_from_array(im)
im.save('winning-novelty-{0:06d}.png'.format(pop.generation))
if ne.scheme == 'gray':
image = eval_gray_image(winner, width, height)
elif ne.scheme == 'color':
image = eval_color_image(winner, width, height)
elif ne.scheme == 'mono':
image = eval_mono_image(winner, width, height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(ne.scheme))
float_image = np.array(image, dtype=np.float32) / 255.0
ne.archive.append(float_image)
def make_high_resolution(self, genome):
# Make sure the output directory exists.
if not os.path.isdir('rendered'):
os.mkdir('rendered')
if self.scheme == 'gray':
image_data = eval_gray_image(genome, self.full_width, self.full_height)
elif self.scheme == 'color':
image_data = eval_color_image(genome, self.full_width, self.full_height)
else:
image_data = eval_mono_image(genome, self.full_width, self.full_height)
image = self.make_image_from_data(image_data)
pygame.image.save(image, "rendered/rendered-{}-{}.png".format(os.getpid(), genome.ID))
with open("rendered/genome-{}-{}.bin".format(os.getpid(), genome.ID), "wb") as f:
pickle.dump(genome, f, 2)
def evaluate(self, genomes, config):
jobs = []
for genome_id, genome in genomes:
jobs.append(
self.pool.apply_async(evaluate_lowres,
(genome, config, self.scheme)))
new_archive_entries = []
for (genome_id, genome), j in zip(genomes, jobs):
image = np.clip(np.array(j.get()), 0, 255).astype(np.uint8)
float_image = image.astype(np.float32) / 255.0
genome.fitness = (width * height)**0.5
for a in self.archive:
adist = np.linalg.norm(float_image.ravel() - a.ravel())
genome.fitness = min(genome.fitness, adist)
if random.random() < 0.02:
new_archive_entries.append(float_image)
#im = self.image_from_array(image)
#im.save("novelty-{0:06d}.png".format(self.out_index))
if self.scheme == 'gray':
image = eval_gray_image(genome, config, full_scale * width,
full_scale * height)
elif self.scheme == 'color':
image = eval_color_image(genome, config,
full_scale * width,
full_scale * height)
elif self.scheme == 'mono':
image = eval_mono_image(genome, config, full_scale * width,
full_scale * height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(
self.scheme))
im = np.clip(np.array(image), 0, 255).astype(np.uint8)
im = self.image_from_array(im)
im.save('novelty-{0:06d}.png'.format(self.out_index))
self.out_index += 1
self.archive.extend(new_archive_entries)
print('{0} archive entries'.format(len(self.archive)))
def evaluate(self, genomes):
jobs = []
for genome in genomes:
jobs.append(self.pool.apply_async(evaluate, (genome, self.scheme)))
new_archive_entries = []
for g, j in zip(genomes, jobs):
image = np.clip(np.array(j.get()), 0, 255).astype(np.uint8)
float_image = image.astype(np.float32) / 255.0
g.fitness = (width * height) ** 0.5
for a in self.archive:
adist = np.linalg.norm(float_image.ravel() - a.ravel())
g.fitness = min(g.fitness, adist)
if random.random() < 0.02:
new_archive_entries.append(float_image)
#im = self.image_from_array(image)
#im.save("novelty-{0:06d}.png".format(self.out_index))
if self.scheme == 'gray':
image = eval_gray_image(g, full_scale * width, full_scale * height)
elif self.scheme == 'color':
image = eval_color_image(g, full_scale * width, full_scale * height)
elif self.scheme == 'mono':
image = eval_mono_image(g, full_scale * width, full_scale * height)
else:
raise Exception('Unexpected scheme: {0!r}'.format(self.scheme))
im = np.clip(np.array(image), 0, 255).astype(np.uint8)
im = self.image_from_array(im)
im.save('novelty-{0:06d}.png'.format(self.out_index))
self.out_index += 1
self.archive.extend(new_archive_entries)
print('{0} archive entries'.format(len(self.archive)))
