def train(config, inputs, args):
gan = setup_gan(config, inputs, args)
trainable_gan = hg.TrainableGAN(gan,
save_file=save_file,
devices=args.devices,
backend_name=args.backend)
sampler = TrainingVideoFrameSampler(gan)
gan.selected_sampler = ""
samples = 0
#metrics = [batch_accuracy(gan.inputs.x, gan.uniform_sample), batch_diversity(gan.uniform_sample)]
#sum_metrics = [0 for metric in metrics]
for i in range(args.steps):
trainable_gan.step()
if args.action == 'train' and i % args.save_every == 0 and i > 0:
print("saving " + save_file)
trainable_gan.save()
if i % args.sample_every == 0:
sample_file = "samples/" + args.config + "/%06d.png" % (samples)
os.makedirs(os.path.expanduser(os.path.dirname(sample_file)),
exist_ok=True)
samples += 1
sampler.sample(sample_file, args.save_samples)
#if i > args.steps * 9.0/10:
# for k, metric in enumerate(gan.session.run(metrics)):
# print("Metric "+str(k)+" "+str(metric))
# sum_metrics[k] += metric
return [] #sum_metrics
def train(config, inputs, args):
gan = setup_gan(config, inputs, args)
trainable_gan = hg.TrainableGAN(gan,
save_file=save_file,
devices=args.devices,
backend_name=args.backend)
trainers = []
x_0 = gan.inputs.next()
z_0 = gan.latent.sample()
ax_sum = 0
ag_sum = 0
diversity = 0.00001
dlog = 0
last_i = 0
steps = 0
while (True):
steps += 1
if steps > args.steps and args.steps != -1:
break
trainable_gan.step()
if args.action == 'train' and steps % args.save_every == 0 and steps > 0:
print("saving " + save_file)
trainable_gan.save()
if steps % args.sample_every == 0:
x_val = gan.inputs.next()
q, a = torch.split(x_val, x_val[0].shape[-1] // 2, dim=-1)
g = gan.generator.forward(gan.latent.sample(),
context={"q": q})
print("Query:")
print(
inputs.textdata.sample_output(q[0].cpu().detach().numpy()))
print("X answer:")
print(
inputs.textdata.sample_output(a[0].cpu().detach().numpy()))
print("G answer:")
g_output = inputs.textdata.sample_output(
g[0].cpu().detach().numpy())
g_output = re.sub(r'[\x00-\x1f\x7f-\x9f]', '�', g_output)
print(g_output)
if args.config is None:
with open("sequence-results-10k.csv", "a") as myfile:
myfile.write(config_name + "," + str(ax_sum) + "," +
str(ag_sum) + "," + str(ax_sum + ag_sum) + "," +
str(ax_sum * ag_sum) + "," + str(dlog) + "," +
str(diversity) + "," + str(ax_sum * ag_sum *
(1 / diversity)) +
"," + str(last_i) + "\n")
def sample_forever(self):
self.gan.inputs.next()
self.lazy_create()
self.trainable_gan = hg.TrainableGAN(self.gan, save_file = self.save_file, devices = self.devices, backend_name = self.args.backend)
if self.trainable_gan.load():
print("Model loaded")
else:
print("Could not load save")
return
steps = 0
while not self.gan.destroy and (steps <= self.args.steps or self.args.steps == -1):
self.trainable_gan.sample(self.sampler, self.sample_path)
steps += 1
def train(self):
i=0
if(self.args.ipython):
import fcntl
fd = sys.stdin.fileno()
fl = fcntl.fcntl(fd, fcntl.F_GETFL)
fcntl.fcntl(fd, fcntl.F_SETFL, fl | os.O_NONBLOCK)
self.gan = hg.GAN(config=self.gan_config, inputs=self.create_input(), device=self.args.parameter_server_device)
self.gan.cli = self #TODO remove this link
self.gan.inputs.next()
self.lazy_create()
self.trainable_gan = hg.TrainableGAN(self.gan, save_file = self.save_file, devices = self.devices, backend_name = self.args.backend)
if self.trainable_gan.load():
print("Model loaded")
else:
print("Initializing new model")
self.trainable_gan.sample(self.sampler, self.sample_path)
while((self.steps < self.total_steps or self.total_steps == -1) and not self.gan.destroy):
self.step()
if self.should_sample:
self.should_sample = False
self.sample(False)
if (self.args.save_every != None and
self.args.save_every != -1 and
self.args.save_every > 0 and
self.steps % self.args.save_every == 0):
print(" |= Saving network")
self.trainable_gan.save()
self.create_path(self.advSavePath+'advSave.txt')
if os.path.isfile(self.advSavePath+'advSave.txt'):
with open(self.advSavePath+'advSave.txt', 'w') as the_file:
the_file.write(str(self.samples)+"\n")
if self.args.ipython:
self.check_stdin()
print("Done training model. Saving")
self.trainable_gan.save()
print("============================")
print("HyperGAN model trained")
print("============================")
def train(config, args):
gan = setup_gan(config, inputs, args)
trainable_gan = hg.TrainableGAN(gan, save_file = save_file, devices = args.devices, backend_name = args.backend)
test_batches = []
accuracy = 0
for i in range(args.steps):
trainable_gan.step()
if i == args.steps-1 or i % args.sample_every == 0 and i > 0:
correct_prediction = 0
total = 0
for (x,y) in gan.inputs.testdata():
prediction = gan.generator(x)
correct_prediction += (torch.argmax(prediction,1) == torch.argmax(y,1)).sum()
total += y.shape[0]
accuracy = (float(correct_prediction) / total)*100
print(config_name)
print("accuracy: ", accuracy)
return accuracy
def train(config, inputs, args):
gan = setup_gan(config, inputs, args)
gan.name = config_name
trainable_gan = hg.TrainableGAN(gan,
save_file=save_file,
devices=args.devices,
backend_name=args.backend)
gan.selected_sampler = ""
sampler = Sampler(gan)
samples = 0
for i in range(args.steps):
trainable_gan.step()
if args.action == 'train' and i % args.save_every == 0 and i > 0:
print("saving " + save_file)
trainable_gan.save()
if i % args.sample_every == 0:
sample_file = "samples/" + config_name + "/%06d.png" % (samples)
os.makedirs(os.path.expanduser(os.path.dirname(sample_file)),
exist_ok=True)
samples += 1
sampler.sample(sample_file, args.save_samples)
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}))
trainable_gan = hg.TrainableGAN(gan,
devices=args.devices,
backend_name=args.backend)
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:
if gan.config.ali:
accuracy_x_to_g = lambda: distribution_accuracy(
gan.inputs.next(1),
gan.generator(gan.encoder(gan.inputs.next())))
accuracy_g_to_x = lambda: distribution_accuracy(
gan.generator(gan.encoder(gan.inputs.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
trainable_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