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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import torch
from models.vc.Noro.noro_trainer import NoroTrainer
from utils.util import load_config
def build_trainer(args, cfg):
supported_trainer = {
"VC": NoroTrainer,
}
trainer_class = supported_trainer[cfg.model_type]
trainer = trainer_class(args, cfg)
return trainer
def cuda_relevant(deterministic=False):
torch.cuda.empty_cache()
# TF32 on Ampere and above
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.enabled = True
torch.backends.cudnn.allow_tf32 = True
# Deterministic
torch.backends.cudnn.deterministic = deterministic
torch.backends.cudnn.benchmark = not deterministic
torch.use_deterministic_algorithms(deterministic)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--config",
default="config.json",
help="json files for configurations.",
required=True,
)
parser.add_argument(
"--exp_name",
type=str,
default="exp_name",
help="A specific name to note the experiment",
required=True,
)
parser.add_argument(
"--resume", action="store_true", help="The model name to restore"
)
parser.add_argument(
"--log_level", default="warning", help="logging level (debug, info, warning)"
)
parser.add_argument(
"--resume_type",
type=str,
default="resume",
help="Resume training or finetuning.",
)
parser.add_argument(
"--checkpoint_path",
type=str,
default=None,
help="Checkpoint for resume training or finetuning.",
)
args = parser.parse_args()
cfg = load_config(args.config)
print("experiment name: ", args.exp_name)
# # CUDA settings
cuda_relevant()
# Build trainer
print(f"Building {cfg.model_type} trainer")
trainer = build_trainer(args, cfg)
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
print(f"Start training {cfg.model_type} model")
trainer.train_loop()
if __name__ == "__main__":
main()
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