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import argparse
import logging
from pathlib import Path
import torch
import yaml
import train_models
import evaluate_models
from src.commons import set_seed
LOGGER = logging.getLogger()
LOGGER.setLevel(logging.INFO)
ch = logging.StreamHandler()
formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
LOGGER.addHandler(ch)
def parse_args():
parser = argparse.ArgumentParser()
ASVSPOOF_DATASET_PATH = "../datasets/ASVspoof2021/DF"
IN_THE_WILD_DATASET_PATH = "../datasets/release_in_the_wild"
parser.add_argument(
"--asv_path",
type=str,
default=ASVSPOOF_DATASET_PATH,
help="Path to ASVspoof2021 dataset directory",
)
parser.add_argument(
"--in_the_wild_path",
type=str,
default=IN_THE_WILD_DATASET_PATH,
help="Path to In The Wild dataset directory",
)
default_model_config = "config.yaml"
parser.add_argument(
"--config",
help="Model config file path (default: config.yaml)",
type=str,
default=default_model_config,
)
default_train_amount = None
parser.add_argument(
"--train_amount",
"-a",
help=f"Amount of files to load for training.",
type=int,
default=default_train_amount,
)
default_valid_amount = None
parser.add_argument(
"--valid_amount",
"-va",
help=f"Amount of files to load for testing.",
type=int,
default=default_valid_amount,
)
default_test_amount = None
parser.add_argument(
"--test_amount",
"-ta",
help=f"Amount of files to load for testing.",
type=int,
default=default_test_amount,
)
default_batch_size = 8
parser.add_argument(
"--batch_size",
"-b",
help=f"Batch size (default: {default_batch_size}).",
type=int,
default=default_batch_size,
)
default_epochs = 10 # it was 5 originally
parser.add_argument(
"--epochs",
"-e",
help=f"Epochs (default: {default_epochs}).",
type=int,
default=default_epochs,
)
default_model_dir = "trained_models"
parser.add_argument(
"--ckpt",
help=f"Checkpoint directory (default: {default_model_dir}).",
type=str,
default=default_model_dir,
)
parser.add_argument("--cpu", "-c", help="Force using cpu?", action="store_true")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
# TRAIN MODEL
with open(args.config, "r") as f:
config = yaml.safe_load(f)
seed = config["data"].get("seed", 42)
# fix all seeds
set_seed(seed)
if not args.cpu and torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
model_dir = Path(args.ckpt)
model_dir.mkdir(parents=True, exist_ok=True)
evaluation_config_path, model_path = train_models.train_nn(
datasets_paths=[
args.asv_path,
],
device=device,
amount_to_use=(args.train_amount, args.valid_amount),
batch_size=args.batch_size,
epochs=args.epochs,
model_dir=model_dir,
config=config,
)
with open(evaluation_config_path, "r") as f:
config = yaml.safe_load(f)
evaluate_models.evaluate_nn(
model_paths=config["checkpoint"].get("path", []),
batch_size=args.batch_size,
datasets_paths=[args.in_the_wild_path],
model_config=config["model"],
amount_to_use=args.test_amount,
device=device,
)
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