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.conditional.v5_baseline.training_loop') # Options for training loop.
G = EasyDict(func_name='training.networks.conditional.baseline.G_main') # Options for generator network.
D = EasyDict(func_name='training.networks.conditional.baseline.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.conditional.label_dropout.G_logistic_ns_pathreg') # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.conditional.label_dropout.D_logistic_r1') # 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.resume_pkl = './../results/00326-conditional_label_dropout_25/network-snapshot-000887.pkl'
# train.resume_kimg = 887.0
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 = 'conditional_label_dropout_25'
G_loss.label_dropout_prob = 0.5
D_loss.label_dropout_prob = 0.5
dataset_args = EasyDict(tfrecord_dir=dataset)
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
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)
