def plot(self, image, filename, save_sample, regularize=True):
""" Plot an image."""
if regularize:
image = np.minimum(image, 1)
image = np.maximum(image, -1)
image = np.squeeze(image)
if np.shape(image)[2] == 4:
fmt = "RGBA"
else:
fmt = "RGB"
# Scale to 0..255.
imin, imax = -1.0, 1.0
image = (image - imin) * 255. / (imax - imin) + .5
image = image.astype(np.uint8)
if save_sample:
try:
Image.fromarray(image, fmt).save(filename)
except Exception as e:
print(
"Warning: could not sample to ", filename,
". Please check permissions and make sure the path exists"
)
print(e)
GlobalViewer.update(self.gan, image)
return image
def train(config, args):
title = "[hypergan] 2d-test " + config_filename
GlobalViewer.set_options(enabled=args.viewer, title=title, viewer_size=1)
print("ARGS", args)
gan = hg.GAN(config,
inputs=Custom2DInputDistribution(
{"batch_size": args.batch_size}))
gan.name = config_filename
if gan.config.use_latent:
accuracy_x_to_g = lambda: distribution_accuracy(
gan.inputs.next(1), gan.generator(gan.latent.next()))
accuracy_g_to_x = lambda: distribution_accuracy(
gan.generator(gan.latent.next()), gan.inputs.next(1))
else:
accuracy_x_to_g = lambda: distribution_accuracy(
gan.inputs.next(1), gan.generator(gan.inputs.next()))
accuracy_g_to_x = lambda: distribution_accuracy(
gan.generator(gan.inputs.next()), gan.inputs.next(1))
sampler = Custom2DSampler(gan)
gan.selected_sampler = sampler
samples = 0
steps = args.steps
sample_file = "samples/" + config_filename + "/000000.png"
os.makedirs(os.path.expanduser(os.path.dirname(sample_file)),
exist_ok=True)
sampler.sample(sample_file, args.save_samples)
metrics = [accuracy_x_to_g, accuracy_g_to_x]
sum_metrics = [0 for metric in metrics]
broken = False
for i in range(steps):
if broken:
break
gan.step()
if args.viewer and i % args.sample_every == 0:
samples += 1
print("Sampling " + str(samples))
sample_file = "samples/" + config_filename + "/%06d.png" % (
samples)
sampler.sample(sample_file, args.save_samples)
if i % 100 == 0:
for k, metric in enumerate(metrics):
_metric = metric().cpu().detach().numpy()
sum_metrics[k] += _metric
if not np.isfinite(_metric):
broken = True
break
return sum_metrics
def setup_gan(config, inputs, args):
gan = AutoencoderGAN(config=config, inputs=inputs)
gan.create()
if (args.action != 'search' and os.path.isfile(save_file + ".meta")):
gan.load(save_file)
tf.train.start_queue_runners(sess=gan.session)
GlobalViewer.enable()
config_name = args.config
title = "[hypergan] colorizer " + config_name
GlobalViewer.window.set_title(title)
return gan
def plot(self, image, filename, save_sample):
""" Plot an image."""
image = np.minimum(image, 1)
image = np.maximum(image, -1)
image = np.squeeze(image)
# Scale to 0..255.
imin, imax = image.min(), image.max()
image = (image - imin) * 255. / (imax - imin) + .5
image = image.astype(np.uint8)
if save_sample:
try:
Image.fromarray(image).save(filename)
except Exception as e:
print("Warning: could not sample to ", filename, ". Please check permissions and make sure the path exists")
print(e)
GlobalViewer.update(self.gan, image)
def plot(self, image, filename, save_sample):
""" Plot an image."""
image = np.minimum(image, 1)
image = np.maximum(image, -1)
image = np.squeeze(image)
# Scale to 0..255.
imin, imax = image.min(), image.max()
image = (image - imin) * 255. / (imax - imin) + .5
image = image.astype(np.uint8)
if save_sample:
try:
Image.fromarray(image).save(filename)
except Exception as e:
print("Warning: could not sample to ", filename, ". Please check permissions and make sure the path exists")
print(e)
GlobalViewer.update(self.gan, image)
def train(config, args):
if (args.viewer):
GlobalViewer.enable()
title = "[hypergan] 2d-test " + args.config
GlobalViewer.window.set_title(title)
with tf.device(args.device):
config.generator['end_features'] = 2
gan = hg.GAN(config, inputs=Custom2DInputDistribution(args))
gan.discriminator = Custom2DDiscriminator(gan, config.discriminator)
gan.generator = Custom2DGenerator(gan, config.generator)
gan.encoder = gan.create_component(gan.config.encoder)
gan.encoder.create()
gan.generator.create()
gan.discriminator.create()
gan.create()
accuracy_x_to_g = batch_accuracy(gan.inputs.x, gan.generator.sample)
accuracy_g_to_x = batch_accuracy(gan.generator.sample, gan.inputs.x)
sampler = Custom2DSampler(gan)
tf.train.start_queue_runners(sess=gan.session)
samples = 0
steps = args.steps
sampler.sample("samples/000000.png", args.save_samples)
metrics = [accuracy_x_to_g, accuracy_g_to_x]
sum_metrics = [0 for metric in metrics]
for i in range(steps):
gan.step()
if args.viewer and i % args.sample_every == 0:
samples += 1
print("Sampling " + str(samples), args.save_samples)
sample_file = "samples/%06d.png" % (samples)
sampler.sample(sample_file, args.save_samples)
if i > steps * 9.0 / 10:
for k, metric in enumerate(gan.session.run(metrics)):
sum_metrics[k] += metric
tf.reset_default_graph()
gan.session.close()
return sum_metrics
def plot_image(self, image, filename, save_sample, regularize=True):
""" Plot an image from an external source."""
if np.shape(image)[2] == 4:
fmt = "RGBA"
else:
fmt = "RGB"
if save_sample:
try:
Image.fromarray(image, fmt).save(filename)
except Exception as e:
print(
"Warning: could not sample to ", filename,
". Please check permissions and make sure the path exists"
)
print(e)
GlobalViewer.update(self.gan, image)
return image
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import torchvision
import uuid
arg_parser = ArgumentParser("render next frame")
parser = arg_parser.add_image_arguments()
parser.add_argument('--frames',
type=int,
default=4,
help='Number of frames to embed.')
args = arg_parser.parse_args()
GlobalViewer.set_options(enabled=args.viewer,
title="[hypergan] next-frame " + args.config,
viewer_size=args.zoom)
width, height, channels = [int(x) for x in args.size.split('x')]
input_config = hc.Config({
"batch_size": args.batch_size,
"directories": [args.directory],
"channels": channels,
"crop": args.crop,
"height": height,
"random_crop": False,
"resize": args.resize,
"shuffle": args.action == "train",
"width": width
})
for k, metric in enumerate(metrics):
_metric = metric().cpu().detach().numpy()
sum_metrics[k] += _metric
if not np.isfinite(_metric):
broken = True
break
return sum_metrics
if args.action == 'train':
metrics = train(config, args)
print("Resulting metrics:", metrics)
elif args.action == 'search':
metric_sum = train(config, args)
if 'search_output' in args:
search_output = args.search_output
else:
search_output = "2d-test-results.csv"
hc.Selector().save(config_filename, config)
with open(search_output, "a") as myfile:
total = sum(metric_sum)
myfile.write(config_filename + "," +
",".join([str(x)
for x in metric_sum]) + "," + str(total) + "\n")
else:
print("Unknown action: " + args.action)
GlobalViewer.close()