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.rotation.v5_int_reg.training_loop'
)
G = EasyDict(func_name='training.networks.rotation.v5_int_reg.G_main')
D = EasyDict(func_name='training.networks.rotation.v5_int_reg.D_stylegan2')
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.rotation.v5_int_reg.G_logistic_ns_pathreg')
D_loss = EasyDict(
func_name='training.loss.rotation.v5_int_reg.D_logistic_r1')
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 = 'rotation-v5-int-reg_256'
G_loss.int_reg_clip = 5.0
G_loss.rotation_step_size = 0.08 / 2
dataset_args = EasyDict(tfrecord_dir=dataset)
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
assert config_id in _valid_configs
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)
