except ImportError:
pass
hparams = create_hparams(args.hparams)
torch.backends.cudnn.enabled = hparams.cudnn_enabled
torch.backends.cudnn.benchmark = hparams.cudnn_benchmark
print("FP16 Run:", hparams.fp16_run)
print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)
print("Distributed Run:", hparams.distributed_run)
print("cuDNN Enabled:", hparams.cudnn_enabled)
print("cuDNN Benchmark:", hparams.cudnn_benchmark)
meta_folder = os.path.join(args.output_directory, 'metadata')
os.makedirs(meta_folder, exist_ok=True)
path = os.path.join(meta_folder, "args.json")
obj = args.__dict__
json_dump(obj, path)
print('{}\nargs:'.format('-' * 50))
print(yaml.dump(args.__dict__))
print('{}\nhparams:'.format('-' * 50))
print(yaml.dump({k: v for k, v in hparams.items()}))
train(args.input_directory, args.output_directory, args.log_directory,
args.checkpoint_path, args.warm_start, args.n_gpus, args.rank,
args.group_name, hparams)
setproctitle('zhrtvc-mellotron-train')
except ImportError:
pass
hparams = create_hparams(args.hparams_json, level=args.hparams_level)
torch.backends.cudnn.enabled = hparams.cudnn_enabled
torch.backends.cudnn.benchmark = hparams.cudnn_benchmark
print("FP16 Run:", hparams.fp16_run)
print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)
print("Distributed Run:", hparams.distributed_run)
print("cuDNN Enabled:", hparams.cudnn_enabled)
print("cuDNN Benchmark:", hparams.cudnn_benchmark)
meta_folder = os.path.join(args.output_directory, 'metadata')
os.makedirs(meta_folder, exist_ok=True)
stem_path = os.path.join(meta_folder, "args")
obj = args.__dict__
json_dump(obj, f'{stem_path}.json')
yaml_dump(obj, f'{stem_path}.yml')
print('{}\nargs:'.format('-' * 50))
print(yaml.dump(args.__dict__))
print('{}\nhparams:'.format('-' * 50))
print(yaml.dump({k: v for k, v in hparams.items()}))
train(hparams=hparams, **args.__dict__)