def run(network_pkl,
metrics,
dataset,
data_dir,
mirror_augment,
include_I=False,
is_vae=False,
mapping_nodup=False,
avg_mv_for_I=False):
print('Evaluating metrics "%s" for "%s"...' %
(','.join(metrics), network_pkl))
tflib.init_tf()
network_pkl = pretrained_networks.get_path_or_url(network_pkl)
dataset_args = dnnlib.EasyDict(tfrecord_dir=dataset, shuffle_mb=0)
num_gpus = dnnlib.submit_config.num_gpus
metric_group = metric_base.MetricGroup(
[metric_defaults[metric] for metric in metrics])
metric_group.run(network_pkl,
data_dir=data_dir,
dataset_args=dataset_args,
mirror_augment=mirror_augment,
num_gpus=num_gpus,
include_I=include_I,
is_vae=is_vae,
mapping_nodup=mapping_nodup,
avg_mv_for_I=avg_mv_for_I)
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, total_samples = dataset_tool.create_from_images(dataset, resolution)
print('Evaluating metrics "%s" for "%s"...' % (','.join(metrics), resume))
tflib.init_tf()
resume = pretrained_networks.get_path_or_url(resume)
dataset_args = dnnlib.EasyDict(tfrecord_dir=dataset, num_samples=num_samples or total_samples, resolution=resolution, 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(network_pkl, metrics, dataset, data_dir, mirror_augment, rho_steps):
print('Evaluating metrics "%s" for "%s"...' % (','.join(metrics), network_pkl))
tflib.init_tf()
network_pkl = pretrained_networks.get_path_or_url(network_pkl)
dataset_args = dnnlib.EasyDict(tfrecord_dir=dataset, shuffle_mb=0)
num_gpus = dnnlib.submit_config.num_gpus
metric_group = metric_base.MetricGroup([metric_defaults[metric] for metric in metrics])
if rho_steps > 1:
rho_sweep = np.linspace(0, 1, rho_steps)
else:
rho_sweep = [1]
for rho in rho_sweep:
print(rho)
metric_group.run(network_pkl, data_dir=data_dir, dataset_args=dataset_args, mirror_augment=mirror_augment, num_gpus=num_gpus, rho=rho)
def run(network_pkl, metrics, dataset, data_dir, mirror_augment):
print('Evaluating metrics "%s" for "%s"...' %
(','.join(metrics), network_pkl))
tflib.init_tf()
network_pkl = pretrained_networks.get_path_or_url(network_pkl)
dataset_args = dnnlib.EasyDict(tfrecord_dir=dataset,
shuffle_mb=0,
max_label_size="full")
num_gpus = dnnlib.submit_config.num_gpus
metric_group = metric_base.MetricGroup(
[metric_defaults[metric] for metric in metrics])
metric_group.run(network_pkl,
data_dir=data_dir,
dataset_args=dataset_args,
mirror_augment=mirror_augment,
num_gpus=num_gpus)
def run(network_pkl, metrics, dataset, data_dir, mirror_augment, paths):
print("Evaluating metrics %s for %s..." % (",".join(metrics), network_pkl))
tflib.init_tf()
network_pkl = pretrained_networks.get_path_or_url(network_pkl)
dataset_args = dnnlib.EasyDict(tfrecord_dir = dataset, shuffle_mb = 0)
num_gpus = dnnlib.submit_config.num_gpus
metric_group = metric_base.MetricGroup([metric_defaults[metric] for metric in metrics])
tf_config = {
"rnd.np_random_seed": 1000,
"allow_soft_placement": True,
"gpu_options.per_process_gpu_memory_fraction": 1.0
}
metric_group.run(network_pkl, data_dir = data_dir, dataset_args = dataset_args, tf_config = tf_config,
mirror_augment = mirror_augment, num_gpus = num_gpus, paths = paths)
def run(network_pkl, metrics, dataset, data_dir, mirror_augment):
print('Evaluating metrics "%s" for "%s"...' %
(','.join(metrics), network_pkl))
tflib.init_tf()
pkls = [
v for v in os.listdir(network_pkl)
if v.startswith('network') and v.endswith('.pkl')
]
pkls.sort()
for pkl in pkls:
net_pkl = pretrained_networks.get_path_or_url(
os.path.join(network_pkl, pkl))
print('Process pkl %s' % pkl)
dataset_args = dnnlib.EasyDict(tfrecord_dir=dataset, shuffle_mb=0)
num_gpus = dnnlib.submit_config.num_gpus
metric_group = metric_base.MetricGroup(
[metric_defaults[metric] for metric in metrics])
metric_group.run(net_pkl,
data_dir=data_dir,
dataset_args=dataset_args,
mirror_augment=mirror_augment,
num_gpus=num_gpus)
metric_group.update_autosummaries()