def run(
dataset,
data_dir,
result_dir,
config_id,
num_gpus,
total_kimg,
gamma,
mirror_augment,
metrics,
resume_pkl,
D_global_size=3,
C_global_size=0, # Global C_latents.
sb_C_global_size=4,
C_local_hfeat_size=0, # Local heatmap*features learned C_latents.
C_local_heat_size=0, # Local heatmap learned C_latents.
n_samples_per=10,
module_list=None,
single_const=True,
model_type='spatial_biased'):
# print('module_list:', module_list)
train = EasyDict(
run_func_name='training.training_loop_dsp.training_loop_dsp'
) # Options for training loop.
if model_type == 'spatial_biased':
G = EasyDict(
func_name=
'training.spatial_biased_networks.G_main_spatial_biased_dsp',
mapping_fmaps=128,
fmap_max=128,
latent_size=D_global_size + sb_C_global_size,
dlatent_size=D_global_size + sb_C_global_size,
D_global_size=D_global_size,
sb_C_global_size=sb_C_global_size
) # Options for generator network.
desc = 'spatial_biased_net'
elif model_type == 'sb_general':
G = EasyDict(
func_name=
'training.spatial_biased_networks.G_main_spatial_biased_dsp',
synthesis_func='G_synthesis_sb_general_dsp',
mapping_fmaps=128,
fmap_max=128,
latent_size=D_global_size + C_global_size + sb_C_global_size +
C_local_hfeat_size + C_local_heat_size,
dlatent_size=D_global_size + C_global_size + sb_C_global_size +
C_local_hfeat_size + C_local_heat_size,
D_global_size=D_global_size,
C_global_size=C_global_size,
sb_C_global_size=sb_C_global_size,
C_local_hfeat_size=C_local_hfeat_size,
C_local_heat_size=C_local_heat_size,
use_noise=False) # Options for generator network.
desc = 'sb_general_net'
elif model_type == 'sb_modular':
module_list = _str_to_list(module_list)
key_ls, size_ls, count_dlatent_size, _ = split_module_names(
module_list)
for i, key in enumerate(key_ls):
if key.startswith('D_global'):
D_global_size = size_ls[i]
break
print('D_global_size:', D_global_size)
G = EasyDict(
func_name=
'training.spatial_biased_networks.G_main_spatial_biased_dsp',
synthesis_func='G_synthesis_sb_modular',
mapping_fmaps=128,
fmap_max=128,
latent_size=count_dlatent_size,
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
module_list=module_list,
single_const=single_const,
use_noise=False) # Options for generator network.
desc = 'sb_modular_net'
elif model_type == 'sb_singlelayer_modi':
G = EasyDict(func_name='training.simple_networks.G_main_simple_dsp',
synthesis_func='G_synthesis_sb_singlelayer_modi_dsp',
mapping_fmaps=128,
fmap_max=128,
latent_size=D_global_size + sb_C_global_size,
dlatent_size=D_global_size + sb_C_global_size,
D_global_size=D_global_size,
sb_C_global_size=sb_C_global_size
) # Options for generator network.
desc = 'sb_singlelayer_net'
elif model_type == 'stylegan2':
G = EasyDict(
func_name=
'training.spatial_biased_networks.G_main_spatial_biased_dsp',
dlatent_avg_beta=None,
mapping_fmaps=128,
fmap_max=128,
latent_size=12,
D_global_size=D_global_size,
sb_C_global_size=sb_C_global_size
) # Options for generator network.
desc = 'stylegan2_net'
elif model_type == 'simple':
G = EasyDict(func_name='training.simple_networks.G_main_simple_dsp',
latent_size=D_global_size + sb_C_global_size,
dlatent_size=D_global_size + sb_C_global_size,
D_global_size=D_global_size,
sb_C_global_size=sb_C_global_size
) # Options for generator network.
else:
raise ValueError('Not supported model tyle: ' + model_type)
if model_type == 'simple':
D = EasyDict(func_name='training.simple_networks.D_simple_dsp'
) # Options for discriminator network.
else:
D = EasyDict(func_name='training.networks_stylegan2.D_stylegan2',
fmap_max=128) # Options for discriminator network.
# D = EasyDict(func_name='training.spatial_biased_networks.D_with_discrete_dsp', fmap_max=128) # 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.
G_loss = EasyDict(
func_name='training.loss.G_logistic_ns_dsp',
D_global_size=D_global_size) # Options for generator loss.
D_loss = EasyDict(
func_name='training.loss.D_logistic_r1_dsp',
D_global_size=D_global_size) # Options for discriminator loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='1080p',
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().
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = train.network_snapshot_ticks = 10
sched.G_lrate_base = sched.D_lrate_base = 0.002
sched.minibatch_size_base = 32
sched.minibatch_gpu_base = 4
D_loss.gamma = 10
metrics = [metric_defaults[x] for x in metrics]
desc += '-' + dataset
dataset_args = EasyDict(tfrecord_dir=dataset, max_label_size='full')
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
assert config_id in _valid_configs
desc += '-' + config_id
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id != 'config-f':
G.fmap_base = D.fmap_base = 8 << 10
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith('config-e'):
D_loss.gamma = 100
if 'Gorig' in config_id: G.architecture = 'orig'
if 'Gskip' in config_id: G.architecture = 'skip' # (default)
if 'Gresnet' in config_id: G.architecture = 'resnet'
if 'Dorig' in config_id: D.architecture = 'orig'
if 'Dskip' in config_id: D.architecture = 'skip'
if 'Dresnet' in config_id: D.architecture = 'resnet' # (default)
if gamma is not None:
D_loss.gamma = gamma
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,
G_loss_args=G_loss,
D_loss_args=D_loss,
traversal_grid=True)
if model_type == 'sb_modular':
n_continuous = 0
for i, key in enumerate(key_ls):
m_name = key.split('-')[0]
if (m_name in LATENT_MODULES) and (not m_name == 'D_global'):
n_continuous += size_ls[i]
else:
n_continuous = C_global_size + sb_C_global_size + \
C_local_hfeat_size + C_local_heat_size
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config,
resume_pkl=resume_pkl,
n_discrete=D_global_size,
n_continuous=n_continuous,
n_samples_per=n_samples_per)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(dataset, data_dir, result_dir, config_id, num_gpus, total_kimg, gamma,
mirror_augment, metrics):
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.
G_loss = EasyDict(func_name='training.loss.G_logistic_ns_pathreg'
) # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_logistic_r1'
) # Options for discriminator loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='8k', 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().
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = train.network_snapshot_ticks = 10
sched.G_lrate_base = float(
os.environ['G_LR']) if 'G_LR' in os.environ else 0.002
sched.D_lrate_base = float(
os.environ['D_LR']) if 'D_LR' in os.environ else 0.002
sched.G_lrate_base *= float(
os.environ['G_LR_MULT']) if 'G_LR_MULT' in os.environ else 1.0
sched.D_lrate_base *= float(
os.environ['D_LR_MULT']) if 'D_LR_MULT' in os.environ else 1.0
G_opt.beta2 = float(
os.environ['G_BETA2']) if 'G_BETA2' in os.environ else 0.99
D_opt.beta2 = float(
os.environ['D_BETA2']) if 'D_BETA2' in os.environ else 0.99
print('G_lrate: %f' % sched.G_lrate_base)
print('D_lrate: %f' % sched.D_lrate_base)
print('G_beta2: %f' % G_opt.beta2)
print('D_beta2: %f' % D_opt.beta2)
sched.minibatch_size_base = int(
os.environ['BATCH_SIZE']) if 'BATCH_SIZE' in os.environ else num_gpus
sched.minibatch_gpu_base = int(
os.environ['BATCH_PER']) if 'BATCH_PER' in os.environ else 1
D_loss.gamma = 10
metrics = [metric_defaults[x] for x in metrics]
desc = 'stylegan2'
desc += '-' + dataset
resolution = int(
os.environ['RESOLUTION']) if 'RESOLUTION' in os.environ else 64
dataset_args = EasyDict(tfrecord_dir=dataset, resolution=resolution)
assert num_gpus in [
1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192
]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
assert config_id in _valid_configs
desc += '-' + config_id
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id != 'config-f':
G.fmap_base = D.fmap_base = 8 << 10
if 'FMAP_BASE' in os.environ:
G.fmap_base = D.fmap_base = int(os.environ['FMAP_BASE']) << 10
else:
G.fmap_base = D.fmap_base = 16 << 10 # default
print('G_fmap_base: %d' % G.fmap_base)
print('D_fmap_base: %d' % D.fmap_base)
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith('config-e'):
D_loss.gamma = 100
if 'Gorig' in config_id: G.architecture = 'orig'
if 'Gskip' in config_id: G.architecture = 'skip' # (default)
if 'Gresnet' in config_id: G.architecture = 'resnet'
if 'Dorig' in config_id: D.architecture = 'orig'
if 'Dskip' in config_id: D.architecture = 'skip'
if 'Dresnet' in config_id: D.architecture = 'resnet' # (default)
# Configs A-D: Enable progressive growing and switch to networks that support it.
if config_id in ['config-a', 'config-b', 'config-c', 'config-d']:
sched.lod_initial_resolution = 8
sched.G_lrate_base = sched.D_lrate_base = 0.001
sched.G_lrate_dict = sched.D_lrate_dict = {
128: 0.0015,
256: 0.002,
512: 0.003,
1024: 0.003
}
sched.minibatch_size_base = 32 # (default)
sched.minibatch_size_dict = {8: 256, 16: 128, 32: 64, 64: 32}
sched.minibatch_gpu_base = 4 # (default)
sched.minibatch_gpu_dict = {8: 32, 16: 16, 32: 8, 64: 4}
G.synthesis_func = 'G_synthesis_stylegan_revised'
D.func_name = 'training.networks_stylegan2.D_stylegan'
# Configs A-C: Disable path length regularization.
if config_id in ['config-a', 'config-b', 'config-c']:
G_loss = EasyDict(func_name='training.loss.G_logistic_ns')
# Configs A-B: Disable lazy regularization.
if config_id in ['config-a', 'config-b']:
train.lazy_regularization = False
# Config A: Switch to original StyleGAN networks.
if config_id == 'config-a':
G = EasyDict(func_name='training.networks_stylegan.G_style')
D = EasyDict(func_name='training.networks_stylegan.D_basic')
if gamma is not None:
D_loss.gamma = gamma
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,
G_loss_args=G_loss,
D_loss_args=D_loss)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(dataset, data_dir, result_dir, config_id, num_gpus, total_kimg, gamma,
mirror_augment, metrics, image_snapshot_ticks, network_snapshot_ticks,
resume_pkl):
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.
G_loss = EasyDict(func_name='training.loss.G_logistic_ns_pathreg'
) # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_logistic_r1'
) # Options for discriminator loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='8k', 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().
try:
pkl, kimg = misc.locate_latest_pkl(result_dir)
train.resume_pkl = pkl
train.resume_kimg = kimg
except:
print('Couldn\'t find valid snapshot, starting over')
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = image_snapshot_ticks
train.network_snapshot_ticks = network_snapshot_ticks
train.resume_pkl = resume_pkl
sched.G_lrate_base = sched.D_lrate_base = 0.002
sched.minibatch_size_base = 32
sched.minibatch_gpu_base = 4
D_loss.gamma = 10
metrics = [metric_defaults[x] for x in metrics]
desc = 'stylegan2'
desc += '-' + dataset
dataset_args = EasyDict(tfrecord_dir=dataset)
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
assert config_id in _valid_configs
desc += '-' + config_id
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id != 'config-f':
G.fmap_base = D.fmap_base = 8 << 10
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith('config-e'):
D_loss.gamma = 100
if 'Gorig' in config_id: G.architecture = 'orig'
if 'Gskip' in config_id: G.architecture = 'skip' # (default)
if 'Gresnet' in config_id: G.architecture = 'resnet'
if 'Dorig' in config_id: D.architecture = 'orig'
if 'Dskip' in config_id: D.architecture = 'skip'
if 'Dresnet' in config_id: D.architecture = 'resnet' # (default)
# Configs A-D: Enable progressive growing and switch to networks that support it.
if config_id in ['config-a', 'config-b', 'config-c', 'config-d']:
sched.lod_initial_resolution = 8
sched.G_lrate_base = sched.D_lrate_base = 0.001
sched.G_lrate_dict = sched.D_lrate_dict = {
128: 0.0015,
256: 0.002,
512: 0.003,
1024: 0.003
}
sched.minibatch_size_base = 32 # (default)
sched.minibatch_size_dict = {8: 256, 16: 128, 32: 64, 64: 32}
sched.minibatch_gpu_base = 4 # (default)
sched.minibatch_gpu_dict = {8: 32, 16: 16, 32: 8, 64: 4}
G.synthesis_func = 'G_synthesis_stylegan_revised'
D.func_name = 'training.networks_stylegan2.D_stylegan'
# Configs A-C: Disable path length regularization.
if config_id in ['config-a', 'config-b', 'config-c']:
G_loss = EasyDict(func_name='training.loss.G_logistic_ns')
# Configs A-B: Disable lazy regularization.
if config_id in ['config-a', 'config-b']:
train.lazy_regularization = False
# Config A: Switch to original StyleGAN networks.
if config_id == 'config-a':
G = EasyDict(func_name='training.networks_stylegan.G_style')
D = EasyDict(func_name='training.networks_stylegan.D_basic')
if gamma is not None:
D_loss.gamma = gamma
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,
G_loss_args=G_loss,
D_loss_args=D_loss)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(dataset, data_dir, result_dir, config_id, num_gpus, total_kimg, gamma,
mirror_augment, metrics):
train = EasyDict(
run_func_name=
'training.training_loop.training_loop_mirror_v6.training_loop')
G = EasyDict(func_name='training.networks.networks_stylegan2.G_main')
D = EasyDict(
func_name=
'training.networks.networks_stylegan2_discriminator_new_rotation.D_stylegan2_new_rotaion'
) # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8)
D_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8)
G_loss = EasyDict(
func_name=
'training.loss.loss_G_new_rotation_squared_euclidean_10_interpolate_50_percent_uniform_dist.G_logistic_ns_pathreg'
)
D_loss = EasyDict(
func_name=
'training.loss.loss_D_logistic_r1_new_rotation_euclidean.D_logistic_r1_new_rotation'
)
sched = EasyDict()
grid = EasyDict(size='1080p', layout='random')
sc = dnnlib.SubmitConfig()
tf_config = {'rnd.np_random_seed': 1000}
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = train.network_snapshot_ticks = 10
sched.G_lrate_base = sched.D_lrate_base = 0.002
sched.minibatch_size_base = 32
sched.minibatch_gpu_base = 4
D_loss.gamma = 10
metrics = [metric_defaults[x] for x in metrics]
desc = 'stylegan2'
G.style_mixing_prob = None
desc += '-' + dataset
dataset_args = EasyDict(tfrecord_dir=dataset)
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
assert config_id in _valid_configs
desc += '-' + config_id
desc += '-squared_euclidean_10_interpolate_50_percent'
desc += '-v7-256'
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id != 'config-f':
G.fmap_base = D.fmap_base = 8 << 10
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith('config-e'):
D_loss.gamma = 100
if 'Gorig' in config_id: G.architecture = 'orig'
if 'Gskip' in config_id: G.architecture = 'skip' # (default)
if 'Gresnet' in config_id: G.architecture = 'resnet'
if 'Dorig' in config_id: D.architecture = 'orig'
if 'Dskip' in config_id: D.architecture = 'skip'
if 'Dresnet' in config_id: D.architecture = 'resnet' # (default)
# Configs A-D: Enable progressive growing and switch to networks that support it.
if config_id in ['config-a', 'config-b', 'config-c', 'config-d']:
sched.lod_initial_resolution = 8
sched.G_lrate_base = sched.D_lrate_base = 0.001
sched.G_lrate_dict = sched.D_lrate_dict = {
128: 0.0015,
256: 0.002,
512: 0.003,
1024: 0.003
}
sched.minibatch_size_base = 32 # (default)
sched.minibatch_size_dict = {8: 256, 16: 128, 32: 64, 64: 32}
sched.minibatch_gpu_base = 4 # (default)
sched.minibatch_gpu_dict = {8: 32, 16: 16, 32: 8, 64: 4}
G.synthesis_func = 'G_synthesis_stylegan_revised'
D.func_name = 'training.networks_stylegan2.D_stylegan'
# Configs A-C: Disable path length regularization.
if config_id in ['config-a', 'config-b', 'config-c']:
G_loss = EasyDict(func_name='training.loss.G_logistic_ns')
# Configs A-B: Disable lazy regularization.
if config_id in ['config-a', 'config-b']:
train.lazy_regularization = False
# Config A: Switch to original StyleGAN networks.
if config_id == 'config-a':
G = EasyDict(func_name='training.networks_stylegan.G_style')
D = EasyDict(func_name='training.networks_stylegan.D_basic')
if gamma is not None:
D_loss.gamma = gamma
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,
G_loss_args=G_loss,
D_loss_args=D_loss)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(dataset,
data_dir,
result_dir,
config_id,
num_gpus,
total_kimg,
gamma,
mirror_augment,
metrics,
resume_pkl,
fmap_decay=0.15,
D_lambda=1,
C_lambda=1,
MI_lambda=1,
cls_alpha=0,
n_samples_per=10,
module_list=None,
single_const=True,
model_type='spatial_biased',
phi_blurry=0.5,
latent_type='uniform'):
train = EasyDict(
run_func_name='training.training_loop_vid.training_loop_vid'
) # Options for training loop.
D_global_size = 0
module_list = _str_to_list(module_list)
key_ls, size_ls, count_dlatent_size, _ = split_module_names(module_list)
for i, key in enumerate(key_ls):
if key.startswith('D_global'):
D_global_size += size_ls[i]
break
print('D_global_size:', D_global_size)
print('key_ls:', key_ls)
print('size_ls:', size_ls)
print('count_dlatent_size:', count_dlatent_size)
if model_type == 'vid_model':
G = EasyDict(func_name='training.vid_networks.G_main_vid',
synthesis_func='G_synthesis_vid_modular',
fmap_min=16,
fmap_max=512,
fmap_decay=fmap_decay,
latent_size=count_dlatent_size,
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
module_list=module_list,
single_const=single_const,
use_noise=True) # Options for generator network.
I = EasyDict(func_name='training.vid_networks.vid_head',
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
fmap_max=512)
D = EasyDict(func_name='training.networks_stylegan2.D_stylegan2',
fmap_max=512) # Options for discriminator network.
I_info = EasyDict()
desc = model_type
elif model_type == 'vid_with_cls':
G = EasyDict(func_name='training.vid_networks.G_main_vid',
synthesis_func='G_synthesis_vid_modular',
fmap_min=16,
fmap_max=512,
fmap_decay=fmap_decay,
latent_size=count_dlatent_size,
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
module_list=module_list,
single_const=single_const,
use_noise=True) # Options for generator network.
I = EasyDict(func_name='training.vid_networks.vid_head',
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
fmap_max=512)
I_info = EasyDict(
func_name='training.info_gan_networks.info_gan_head_cls',
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
fmap_decay=fmap_decay,
fmap_min=16,
fmap_max=512)
D = EasyDict(
func_name='training.info_gan_networks.D_info_gan_stylegan2',
fmap_max=512) # Options for discriminator network.
desc = model_type
elif model_type == 'vid_naive_cluster_model':
G = EasyDict(func_name='training.vid_networks.G_main_vid',
synthesis_func='G_synthesis_vid_modular',
fmap_min=16,
fmap_max=512,
fmap_decay=fmap_decay,
latent_size=count_dlatent_size,
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
module_list=module_list,
single_const=single_const,
use_noise=True) # Options for generator network.
I = EasyDict(func_name='training.vid_networks.vid_naive_cluster_head',
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
fmap_max=512) # Options for estimator network.
D = EasyDict(func_name='training.networks_stylegan2.D_stylegan2',
fmap_max=512) # Options for discriminator network.
I_info = EasyDict()
desc = model_type
elif model_type == 'vid_blurry_model':
G = EasyDict(func_name='training.vid_networks.G_main_vid',
synthesis_func='G_synthesis_vid_modular',
fmap_min=16,
fmap_max=512,
fmap_decay=fmap_decay,
latent_size=count_dlatent_size,
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
module_list=module_list,
single_const=single_const,
use_noise=True) # Options for generator network.
I = EasyDict(func_name='training.vid_networks.vid_naive_cluster_head',
dlatent_size=count_dlatent_size,
D_global_size=D_global_size,
fmap_max=512) # Options for estimator network.
D = EasyDict(func_name='training.networks_stylegan2.D_stylegan2',
fmap_max=512) # Options for discriminator network.
I_info = EasyDict()
desc = model_type
else:
raise ValueError('Not supported model tyle: ' + model_type)
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.
I_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for discriminator optimizer.
if model_type == 'vid_model':
G_loss = EasyDict(
func_name='training.loss_vid.G_logistic_ns_vid',
D_global_size=D_global_size,
C_lambda=C_lambda,
latent_type=latent_type) # Options for generator loss.
D_loss = EasyDict(
func_name='training.loss_vid.D_logistic_r1_vid',
D_global_size=D_global_size,
latent_type=latent_type) # Options for discriminator loss.
I_loss = EasyDict(func_name='training.loss_vid.I_vid',
D_global_size=D_global_size,
latent_type=latent_type,
C_lambda=C_lambda,
MI_lambda=MI_lambda) # Options for estimator loss.
elif model_type == 'vid_with_cls':
G_loss = EasyDict(
func_name='training.loss_vid.G_logistic_ns_vid',
D_global_size=D_global_size,
C_lambda=C_lambda,
cls_alpha=cls_alpha,
latent_type=latent_type) # Options for generator loss.
D_loss = EasyDict(
func_name='training.loss_vid.D_logistic_r1_info_gan_vid',
D_global_size=D_global_size,
latent_type=latent_type) # Options for discriminator loss.
I_loss = EasyDict(func_name='training.loss_vid.I_vid',
D_global_size=D_global_size,
latent_type=latent_type,
C_lambda=C_lambda,
MI_lambda=MI_lambda) # Options for estimator loss.
elif model_type == 'vid_naive_cluster_model':
G_loss = EasyDict(
func_name='training.loss_vid.G_logistic_ns_vid_naive_cluster',
D_global_size=D_global_size,
C_lambda=C_lambda,
latent_type=latent_type) # Options for generator loss.
D_loss = EasyDict(
func_name='training.loss_vid.D_logistic_r1_vid',
D_global_size=D_global_size,
latent_type=latent_type) # Options for discriminator loss.
I_loss = EasyDict() # Options for estimator loss.
I_opt = EasyDict()
elif model_type == 'vid_blurry_model':
G_loss = EasyDict(
func_name='training.loss_vid.G_logistic_ns_vid_naive_cluster',
D_global_size=D_global_size,
C_lambda=C_lambda,
latent_type=latent_type) # Options for generator loss.
D_loss = EasyDict(
func_name='training.loss_vid.D_logistic_r1_vid',
D_global_size=D_global_size,
latent_type=latent_type) # Options for discriminator loss.
I_loss = EasyDict(func_name='training.loss_vid.I_vid_blurry',
D_global_size=D_global_size,
latent_type=latent_type,
C_lambda=C_lambda,
MI_lambda=MI_lambda,
phi=phi_blurry) # Options for estimator loss.
else:
raise ValueError('Not supported loss tyle: ' + model_type)
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='1080p',
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().
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = train.network_snapshot_ticks = 10
sched.G_lrate_base = sched.D_lrate_base = sched.I_lrate_base = 0.002
sched.minibatch_size_base = 16
sched.minibatch_gpu_base = 8
D_loss.gamma = 10
metrics = [metric_defaults[x] for x in metrics]
desc += '-' + dataset
dataset_args = EasyDict(tfrecord_dir=dataset, max_label_size='full')
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
assert config_id in _valid_configs
desc += '-' + config_id
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id != 'config-f':
# I.fmap_base = G.fmap_base = D.fmap_base = 8 << 10
I.fmap_base = G.fmap_base = D.fmap_base = 2 << 8
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith('config-e'):
D_loss.gamma = 100
if 'Gorig' in config_id: G.architecture = 'orig'
if 'Gskip' in config_id: G.architecture = 'skip' # (default)
if 'Gresnet' in config_id: G.architecture = 'resnet'
if 'Dorig' in config_id: D.architecture = 'orig'
if 'Dskip' in config_id: D.architecture = 'skip'
if 'Dresnet' in config_id: D.architecture = 'resnet' # (default)
if gamma is not None:
D_loss.gamma = gamma
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,
I_args=I,
I_info_args=I_info,
G_opt_args=G_opt,
D_opt_args=D_opt,
I_opt_args=I_opt,
G_loss_args=G_loss,
D_loss_args=D_loss,
I_loss_args=I_loss,
use_vid_head=(model_type == 'vid_model'),
use_vid_head_with_cls=(model_type == 'vid_with_cls'),
use_vid_naive_cluster=(model_type == 'vid_naive_cluster_model'),
use_vid_blurry=(model_type == 'vid_blurry_model'),
traversal_grid=True)
n_continuous = 0
for i, key in enumerate(key_ls):
m_name = key.split('-')[0]
if (m_name in LATENT_MODULES) and (not m_name == 'D_global'):
n_continuous += size_ls[i]
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config,
resume_pkl=resume_pkl,
n_discrete=D_global_size,
n_continuous=n_continuous,
n_samples_per=n_samples_per,
C_lambda=C_lambda,
MI_lambda=MI_lambda)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(dataset, data_dir, result_dir, config_id, num_gpus, total_kimg, gamma,
mirror_augment, metrics):
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.
G_loss = EasyDict(func_name='training.loss.G_logistic_ns_pathreg'
) # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_logistic_r1'
) # Options for discriminator loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='8k', 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().
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = train.network_snapshot_ticks = 10
sched.G_lrate_base = sched.D_lrate_base = 0.002
sched.minibatch_size_base = 32
sched.minibatch_gpu_base = 4
D_loss.gamma = 10
metrics = [metric_defaults[x] for x in metrics]
desc = 'stylegan2'
desc += '-' + dataset
dataset_args = EasyDict(tfrecord_dir=dataset)
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
assert config_id in _valid_configs
desc += '-' + config_id
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id not in ['config-f', 'config-l']:
G.fmap_base = D.fmap_base = 8 << 10
# Config L: Generator training only
if config_id == 'config-l':
# Use labels as latent vector input
dataset_args.max_label_size = "full"
# Deactivate methods specific for GAN training
G.truncation_psi = None
G.randomize_noise = False
G.style_mixing_prob = None
G.dlatent_avg_beta = None
G.conditional_labels = False
# Refinement training
G_loss.func_name = 'training.loss.G_reconstruction'
train.run_func_name = 'training.training_loop.training_loop_refinement'
# G.freeze_layers = ["mapping", "noise"]#, "4x4", "8x8", "16x16", "32x32"]
# Network for refinement
train.resume_pkl = "nets/stylegan2-ffhq-config-f.pkl" # TODO init net
train.resume_with_new_nets = True
# Maintenance tasks
sched.tick_kimg_base = 1 # 1 tick = 5000 images (metric update)
sched.tick_kimg_dict = {}
train.image_snapshot_ticks = 5 # Save every 5000 images
train.network_snapshot_ticks = 10 # Save every 10000 images
# Training parameters
sched.G_lrate_base = 1e-4
train.G_smoothing_kimg = 0.0
sched.minibatch_size_base = sched.minibatch_gpu_base * num_gpus # 4 per GPU
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith('config-e'):
D_loss.gamma = 100
if 'Gorig' in config_id: G.architecture = 'orig'
if 'Gskip' in config_id: G.architecture = 'skip' # (default)
if 'Gresnet' in config_id: G.architecture = 'resnet'
if 'Dorig' in config_id: D.architecture = 'orig'
if 'Dskip' in config_id: D.architecture = 'skip'
if 'Dresnet' in config_id: D.architecture = 'resnet' # (default)
# Configs A-D: Enable progressive growing and switch to networks that support it.
if config_id in ['config-a', 'config-b', 'config-c', 'config-d']:
sched.lod_initial_resolution = 8
sched.G_lrate_base = sched.D_lrate_base = 0.001
sched.G_lrate_dict = sched.D_lrate_dict = {
128: 0.0015,
256: 0.002,
512: 0.003,
1024: 0.003
}
sched.minibatch_size_base = 32 # (default)
sched.minibatch_size_dict = {8: 256, 16: 128, 32: 64, 64: 32}
sched.minibatch_gpu_base = 4 # (default)
sched.minibatch_gpu_dict = {8: 32, 16: 16, 32: 8, 64: 4}
G.synthesis_func = 'G_synthesis_stylegan_revised'
D.func_name = 'training.networks_stylegan2.D_stylegan'
# Configs A-C: Disable path length regularization.
if config_id in ['config-a', 'config-b', 'config-c']:
G_loss = EasyDict(func_name='training.loss.G_logistic_ns')
# Configs A-B: Disable lazy regularization.
if config_id in ['config-a', 'config-b']:
train.lazy_regularization = False
# Config A: Switch to original StyleGAN networks.
if config_id == 'config-a':
G = EasyDict(func_name='training.networks_stylegan.G_style')
D = EasyDict(func_name='training.networks_stylegan.D_basic')
if gamma is not None:
D_loss.gamma = gamma
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,
G_loss_args=G_loss,
D_loss_args=D_loss)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(
dataset,
data_dir,
result_dir,
config_id,
num_gpus,
total_kimg,
gamma,
mirror_augment,
metrics,
resume_pkl=None,
resume_kimg=None,
):
train = EasyDict(
run_func_name="training.training_loop.training_loop",
# training resume options:
resume_pkl=
resume_pkl, # Network pickle to resume training from, None = train from scratch.
resume_kimg=
resume_kimg, # Assumed training progress at the beginning. Affects reporting and training schedule.
) # 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.
G_loss = EasyDict(func_name="training.loss.G_logistic_ns_pathreg"
) # Options for generator loss.
D_loss = EasyDict(func_name="training.loss.D_logistic_r1"
) # Options for discriminator loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size="8k", 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().
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = train.network_snapshot_ticks = 10
sched.G_lrate_base = sched.D_lrate_base = 0.002
sched.minibatch_size_base = 32
sched.minibatch_gpu_base = 4
D_loss.gamma = 10
metrics = [metric_defaults[x] for x in metrics]
desc = "stylegan2"
desc += "-" + dataset
dataset_args = EasyDict(tfrecord_dir=dataset)
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += "-%dgpu" % num_gpus
assert config_id in _valid_configs
desc += "-" + config_id
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id != "config-f":
G.fmap_base = D.fmap_base = 8 << 10
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith("config-e"):
D_loss.gamma = 100
if "Gorig" in config_id:
G.architecture = "orig"
if "Gskip" in config_id:
G.architecture = "skip" # (default)
if "Gresnet" in config_id:
G.architecture = "resnet"
if "Dorig" in config_id:
D.architecture = "orig"
if "Dskip" in config_id:
D.architecture = "skip"
if "Dresnet" in config_id:
D.architecture = "resnet" # (default)
# Configs A-D: Enable progressive growing and switch to networks that support it.
if config_id in ["config-a", "config-b", "config-c", "config-d"]:
sched.lod_initial_resolution = 8
sched.G_lrate_base = sched.D_lrate_base = 0.001
sched.G_lrate_dict = sched.D_lrate_dict = {
128: 0.0015,
256: 0.002,
512: 0.003,
1024: 0.003,
}
sched.minibatch_size_base = 32 # (default)
sched.minibatch_size_dict = {8: 256, 16: 128, 32: 64, 64: 32}
sched.minibatch_gpu_base = 4 # (default)
sched.minibatch_gpu_dict = {8: 32, 16: 16, 32: 8, 64: 4}
G.synthesis_func = "G_synthesis_stylegan_revised"
D.func_name = "training.networks_stylegan2.D_stylegan"
# Configs A-C: Disable path length regularization.
if config_id in ["config-a", "config-b", "config-c"]:
G_loss = EasyDict(func_name="training.loss.G_logistic_ns")
# Configs A-B: Disable lazy regularization.
if config_id in ["config-a", "config-b"]:
train.lazy_regularization = False
# Config A: Switch to original StyleGAN networks.
if config_id == "config-a":
G = EasyDict(func_name="training.networks_stylegan.G_style")
D = EasyDict(func_name="training.networks_stylegan.D_basic")
if gamma is not None:
D_loss.gamma = gamma
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,
G_loss_args=G_loss,
D_loss_args=D_loss,
)
kwargs.update(
dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
metric_arg_list=metrics,
tf_config=tf_config,
)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(config_id, gamma, height, width, cond):
train = EasyDict(run_func_name='training.diagnostic.create_initial_pkl'
) # 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.
G_loss = EasyDict(func_name='training.loss.G_logistic_ns_pathreg'
) # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_logistic_r1'
) # Options for discriminator loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='8k', 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().
sched.minibatch_size_base = 192
sched.minibatch_gpu_base = 3
D_loss.gamma = 10
desc = 'stylegan2'
dataset_args = EasyDict() # (tfrecord_dir=dataset)
if cond:
desc += '-cond'
dataset_args.max_label_size = 'full' # conditioned on full label
desc += '-' + config_id
# Configs A-E: Shrink networks to match original StyleGAN.
if config_id != 'config-f':
G.fmap_base = D.fmap_base = 8 << 10
# Config E: Set gamma to 100 and override G & D architecture.
if config_id.startswith('config-e'):
D_loss.gamma = 100
if 'Gorig' in config_id: G.architecture = 'orig'
if 'Gskip' in config_id: G.architecture = 'skip' # (default)
if 'Gresnet' in config_id: G.architecture = 'resnet'
if 'Dorig' in config_id: D.architecture = 'orig'
if 'Dskip' in config_id: D.architecture = 'skip'
if 'Dresnet' in config_id: D.architecture = 'resnet' # (default)
# Configs A-D: Enable progressive growing and switch to networks that support it.
if config_id in ['config-a', 'config-b', 'config-c', 'config-d']:
sched.lod_initial_resolution = 8
sched.G_lrate_base = sched.D_lrate_base = 0.001
sched.G_lrate_dict = sched.D_lrate_dict = {
128: 0.0015,
256: 0.002,
512: 0.003,
1024: 0.003
}
sched.minibatch_size_base = 32 # (default)
sched.minibatch_size_dict = {8: 256, 16: 128, 32: 64, 64: 32}
sched.minibatch_gpu_base = 4 # (default)
sched.minibatch_gpu_dict = {8: 32, 16: 16, 32: 8, 64: 4}
G.synthesis_func = 'G_synthesis_stylegan_revised'
D.func_name = 'training.networks_stylegan2.D_stylegan'
# Configs A-C: Disable path length regularization.
if config_id in ['config-a', 'config-b', 'config-c']:
G_loss = EasyDict(func_name='training.loss.G_logistic_ns')
# Configs A-B: Disable lazy regularization.
if config_id in ['config-a', 'config-b']:
train.lazy_regularization = False
# Config A: Switch to original StyleGAN networks.
if config_id == 'config-a':
G = EasyDict(func_name='training.networks_stylegan.G_style')
D = EasyDict(func_name='training.networks_stylegan.D_basic')
if gamma is not None:
D_loss.gamma = gamma
G.update(resolution_h=height)
G.update(resolution_w=width)
D.update(resolution_h=height)
D.update(resolution_w=width)
sc.submit_target = dnnlib.SubmitTarget.DIAGNOSTIC
sc.local.do_not_copy_source_files = True
kwargs = EasyDict(train)
kwargs.update(G_args=G,
D_args=D,
tf_config=tf_config,
config_id=config_id,
resolution_h=height,
resolution_w=width)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_desc = desc
# dnnlib.submit_run(**kwargs)
dnnlib.submit_diagnostic(**kwargs)