def GetAllTrainingParams():
all_params = set(["architecture"])
for gan_type in SUPPORTED_GANS:
for _ in ["mnist", "fashion-mnist", "cifar10", "celeba"]:
p = params.GetParameters(gan_type, "wide")
all_params.update(p.keys())
logging.info("All training parameter exported: %s", sorted(all_params))
return sorted(all_params)
def CreateCrossProductAndAddDefaultParams(config):
tasks = task_utils.CrossProduct(config)
# Add GAN and dataset specific hyperparams.
for task in tasks:
defaults = GetDefaultParams(
params.GetParameters(task["gan_type"], "wide"))
defaults.update(task)
task.update(defaults)
return tasks
def RepeatExp(gan_type, num_repeat):
"""Experiment where 1 fixed gan is trained with num_repeat seeds."""
config = {
"gan_type": [gan_type],
"dataset": ["mnist", "fashion-mnist"],
"training_steps": [20 * 60000 // BATCH_SIZE],
"save_checkpoint_steps": [5 * 60000 // BATCH_SIZE],
"batch_size": [BATCH_SIZE],
"z_dim": [64],
"tf_seed": range(num_repeat)
}
tasks = task_utils.CrossProduct(config)
# Add GAN and dataset specific hyperparams.
for task in tasks:
task.update(
GetDefaultParams(
params.GetParameters(task["gan_type"], task["dataset"])))
return tasks
def TestExp():
"""Run for one epoch over all tested GANs."""
config = {
"gan_type": [
"GAN", "GAN_MINMAX", "WGAN", "WGAN_GP", "DRAGAN", "VAE", "LSGAN",
"BEGAN"
],
"dataset": ["mnist"],
"training_steps": [60000 // BATCH_SIZE],
"save_checkpoint_steps": [10000],
"batch_size": [BATCH_SIZE],
"tf_seed": [42],
"z_dim": [64],
}
tasks = task_utils.CrossProduct(config)
# Add GAN and dataset specific hyperparams.
for task in tasks:
task.update(
GetDefaultParams(
params.GetParameters(task["gan_type"], task["dataset"])))
return tasks
def TuneParams(gan_types,
dataset_name,
num_samples,
num_repeat,
range_type="wide"):
"""Create a set of tasks for optimizing model score."""
all_tasks = []
for gan_type in gan_types:
gan_params = params.GetParameters(gan_type, dataset_name, range_type)
# Always include default params.
samples = [GetDefaultParams(gan_params)]
for _ in range(num_samples - 1):
samples.append(GetSample(gan_params))
for idx, sample in enumerate(samples):
for i in range(num_repeat):
s = copy.deepcopy(sample)
s["gan_type"] = gan_type
s["sample_id"] = idx # For joins in dremel.
s["dataset"] = dataset_name
s["tf_seed"] = i
if dataset_name == "cifar10":
# 100 epochs for CIFAR100
epoch = 50000
s["training_steps"] = 100 * epoch // BATCH_SIZE
elif dataset_name == "celeba":
# 40 epochs for CelebA
epoch = 162000
s["training_steps"] = 40 * epoch // BATCH_SIZE
else:
# 20 epochs for MNIST/FASHION-MNIST
epoch = 50000
s["training_steps"] = 20 * epoch // BATCH_SIZE
s["save_checkpoint_steps"] = 5 * epoch // BATCH_SIZE
all_tasks.append(s)
all_tasks = [collections.OrderedDict(sorted(x.items())) for x in all_tasks]
return all_tasks
def testParameterRanges(self):
training_parameters = params.GetParameters("WGAN", "wide")
self.assertEqual(len(list(training_parameters.keys())), 5)
training_parameters = params.GetParameters("BEGAN", "wide")
self.assertEqual(len(list(training_parameters.keys())), 6)