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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import argparse
import json
import logging
import torch
import torch.distributed as dist
from monai.utils import RankFilter
def setup_logging(logger_name: str = "") -> logging.Logger:
"""
Setup the logging configuration.
Args:
logger_name (str): logger name.
Returns:
logging.Logger: Configured logger.
"""
logger = logging.getLogger(logger_name)
if dist.is_initialized():
logger.addFilter(RankFilter())
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s.%(msecs)03d][%(levelname)5s](%(name)s) - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
return logger
def load_config(env_config_path: str, model_config_path: str, model_def_path: str) -> argparse.Namespace:
"""
Load configuration from JSON files.
Args:
env_config_path (str): Path to the environment configuration file.
model_config_path (str): Path to the model configuration file.
model_def_path (str): Path to the model definition file.
Returns:
argparse.Namespace: Loaded configuration.
"""
args = argparse.Namespace()
with open(env_config_path, "r") as f:
env_config = json.load(f)
for k, v in env_config.items():
setattr(args, k, v)
with open(model_config_path, "r") as f:
model_config = json.load(f)
for k, v in model_config.items():
setattr(args, k, v)
with open(model_def_path, "r") as f:
model_def = json.load(f)
for k, v in model_def.items():
setattr(args, k, v)
return args
def initialize_distributed(num_gpus: int) -> tuple:
"""
Initialize distributed training.
Returns:
tuple: local_rank, world_size, and device.
"""
if torch.cuda.is_available() and num_gpus > 1:
dist.init_process_group(backend="nccl", init_method="env://")
local_rank = dist.get_rank()
world_size = dist.get_world_size()
else:
local_rank = 0
world_size = 1
device = torch.device("cuda", local_rank)
torch.cuda.set_device(device)
return local_rank, world_size, device
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