def run_auto(dataset, data_dir, result_dir, config_id, num_gpus, resolution,
total_kimg, gamma, mirror_augment, metrics, train_auto):
train = EasyDict(run_func_name='training.training_loop.training_auto_loop'
) # Options for training loop.
Enc = EasyDict(func_name='training.networks_stylegan2.Encoder'
) # Options for encoder network.
Dec = EasyDict(func_name='training.networks_stylegan2.Decoder'
) # Options for decoder network.
opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for autoencoder optimizer.
loss = EasyDict(
func_name='training.loss.auto_l1') # Options for autoencoder 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.image_snapshot_ticks = 10
train.network_snapshot_ticks = 125
sched.lrate = 0.003
sched.minibatch_size = 64
sched.minibatch_gpu = 64
desc = 'stylegan2-hrae'
desc += '-' + dataset
dataset_args = EasyDict(tfrecord_dir=dataset)
dataset_args.resolution = resolution
dataset_args.num_threads = 4
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
desc += '-auto'
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
kwargs = EasyDict(train)
kwargs.update(Enc_args=Enc, Dec_args=Dec, opt_args=opt, loss_args=loss)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
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(result_dir,
num_gpus,
total_kimg,
mirror_augment,
metrics,
resume_pkl,
G_pkl,
I_fmap_base=8,
fmap_decay=0.15,
n_samples_per=10,
module_list=None,
latent_type='uniform',
batch_size=32,
batch_per_gpu=16,
random_seed=1000,
fmap_min=16,
fmap_max=512,
dlatent_size=10,
I_nf_scale=4,
arch='resnet'):
print('module_list:', module_list)
train = EasyDict(
run_func_name='training.training_loop_infernet.training_loop_infernet'
) # Options for training loop.
module_list = _str_to_list(module_list)
I = EasyDict(func_name='training.vc_networks2.infer_modular',
dlatent_size=dlatent_size,
fmap_min=fmap_min,
fmap_max=fmap_max,
module_list=module_list,
I_nf_scale=I_nf_scale)
desc = 'inference_net'
I_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for discriminator optimizer.
loss = EasyDict(func_name='training.loss_inference.I_loss',
latent_type=latent_type,
dlatent_size=dlatent_size) # Options for generator loss.
sched = EasyDict() # Options for TrainingSchedule.
sc = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
# tf_config = {'rnd.np_random_seed': 1000} # Options for tflib.init_tf().
tf_config = {
'rnd.np_random_seed': random_seed
} # Options for tflib.init_tf().
train.total_kimg = total_kimg
sched.lrate = 0.002
sched.tick_kimg = 1
sched.minibatch_size = batch_size
sched.minibatch_gpu = batch_per_gpu
metrics = [metric_defaults[x] for x in metrics]
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
# Configs A-E: Shrink networks to match original StyleGAN.
I.fmap_base = 2 << I_fmap_base
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
kwargs = EasyDict(train)
kwargs.update(I_args=I, I_opt_args=I_opt, loss_args=loss)
kwargs.update(sched_args=sched,
metric_arg_list=metrics,
tf_config=tf_config,
resume_pkl=resume_pkl,
G_pkl=G_pkl,
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)
