def run_eval(dataset, resolution, result_dir, DiffAugment, num_gpus, batch_size, total_kimg, ema_kimg, num_samples, gamma, fmap_base, fmap_max, latent_size, mirror_augment, impl, metrics, resume, resume_kimg, num_repeats, eval):
dataset = dataset_tool.create_dataset(dataset, resolution)
print('Evaluating metrics "%s" for "%s"...' % (','.join(metrics), resume))
tflib.init_tf()
dataset_args = dnnlib.EasyDict(tfrecord_dir=dataset, shuffle_mb=0, from_tfrecords=True)
metric_group = metric_base.MetricGroup([metric_defaults[metric] for metric in metrics], num_repeats=num_repeats)
metric_group.run(resume, dataset_args=dataset_args, num_gpus=num_gpus)
def run_eval(dataset, resolution, num_gpus, metrics, resume, num_repeats, **kwargs):
dataset = dataset_tool.create_dataset(dataset, resolution)
print('Evaluating metrics "%s" for "%s"...' % (','.join(metrics), resume))
tflib.init_tf()
dataset_args = dnnlib.EasyDict(tfrecord_dir=dataset, shuffle_mb=0, from_tfrecords=True)
metric_group = metric_base.MetricGroup([metric_defaults[metric] for metric in metrics], num_repeats=num_repeats)
metric_group.run(resume, dataset_args=dataset_args, num_gpus=num_gpus)
def run(dataset, resolution, result_dir, DiffAugment, num_gpus, batch_size,
total_kimg, ema_kimg, num_samples, gamma, fmap_base, fmap_max,
latent_size, mirror_augment, impl, metrics, resume, resume_kimg,
num_repeats, eval):
train = EasyDict(run_func_name='training.training_loop.training_loop'
) # Options for training loop.
G = EasyDict(func_name='training.networks_stylegan2.G_main'
) # Options for generator network.
D = EasyDict(func_name='training.networks_stylegan2.D_stylegan2'
) # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for discriminator optimizer.
loss_args = EasyDict(
func_name='training.loss.ns_DiffAugment_r1') # Options for loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='4k', layout='random') # Options for setup_snapshot_image_grid().
sc = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
tf_config = {'rnd.np_random_seed': 1000} # Options for tflib.init_tf().
# preprocess dataset into tfrecords
dataset = dataset_tool.create_dataset(dataset, resolution)
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
metrics = [metric_defaults[x] for x in metrics]
metric_args = EasyDict(cache_dir=dataset, num_repeats=num_repeats)
desc = 'DiffAugment-stylegan2' if DiffAugment else 'stylegan2'
dataset_args = EasyDict(tfrecord_dir=dataset,
resolution=resolution,
from_tfrecords=True)
desc += '-' + os.path.basename(dataset)
if resolution is not None:
desc += '-{}'.format(resolution)
if num_samples is not None:
dataset_args.num_samples = num_samples
desc += '-{}samples'.format(num_samples)
if batch_size is not None:
desc += '-batch{}'.format(batch_size)
else:
batch_size = 32
assert batch_size % num_gpus == 0
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
sched.minibatch_size_base = batch_size
sched.minibatch_gpu_base = batch_size // num_gpus
G.impl = D.impl = impl
if fmap_base is not None:
G.fmap_base = D.fmap_base = fmap_base
desc += '-fmap{}'.format(fmap_base)
if fmap_max is not None:
G.fmap_max = D.fmap_max = fmap_max
desc += '-fmax{}'.format(fmap_max)
if latent_size is not None:
G.latent_size = G.mapping_fmaps = G.dlatent_size = latent_size
desc += '-latent{}'.format(latent_size)
if gamma is not None:
loss_args.gamma = gamma
desc += '-gamma{}'.format(gamma)
if DiffAugment:
loss_args.policy = DiffAugment
desc += '-' + DiffAugment.replace(',', '-')
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
kwargs = EasyDict(train)
kwargs.update(G_args=G,
D_args=D,
G_opt_args=G_opt,
D_opt_args=D_opt,
loss_args=loss_args)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config)
kwargs.update(resume_pkl=resume,
resume_kimg=resume_kimg,
resume_with_new_nets=True)
kwargs.update(metric_args=metric_args)
if ema_kimg is not None:
kwargs.update(G_ema_kimg=ema_kimg)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)