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import argparse
import datetime
import json
from pathlib import Path
from helpers import save_useful_info
from train import train
def train_zeggs():
# Setting parser
parser = argparse.ArgumentParser(description="Train ZEGGS Network.")
# Hparams
parser.add_argument(
"-o",
"--options",
type=str,
help="Options filename",
)
parser.add_argument('-n', '--name', type=str, help="Name", required=False)
args = parser.parse_args()
with open(args.options, "r") as f:
options = json.load(f)
if args.name:
options["name"] = args.name
train_options = options["train_opt"]
network_options = options["net_opt"]
paths = options["paths"]
base_path = Path(paths["base_path"])
path_processed_data = base_path / paths["path_processed_data"] / "processed_data.npz"
path_data_definition = base_path / paths["path_processed_data"] / "data_definition.json"
# Output directory
if paths["output_dir"] is None:
output_dir = (base_path / "outputs") / datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
output_dir.mkdir(exist_ok=True, parents=True)
paths["output_dir"] = str(output_dir)
else:
output_dir = Path(paths["output_dir"])
# Path to models
if paths["models_dir"] is None and not train_options["resume"]:
models_dir = output_dir / "saved_models"
models_dir.mkdir(exist_ok=True)
paths["models_dir"] = str(models_dir)
else:
models_dir = Path(paths["models_dir"])
# Log directory
logs_dir = output_dir / "logs"
logs_dir.mkdir(exist_ok=True)
options["paths"] = paths
with open(output_dir / 'options.json', 'w') as fp:
json.dump(options, fp, indent=4)
save_useful_info(output_dir)
train(
models_dir=models_dir,
logs_dir=logs_dir,
path_processed_data=path_processed_data,
path_data_definition=path_data_definition,
train_options=train_options,
network_options=network_options,
)
if __name__ == "__main__":
train_zeggs()
# python .\main.py -o "../configs/configs.json" -n "test"
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