def default_setup(cfg, args):
"""
Perform some basic common setups at the beginning of a job, including:
1. Set up the cvpods logger
2. Log basic information about environment, cmdline arguments, and config
3. Backup the config to the output directory
Args:
cfg (BaseConfig): the full config to be used
args (argparse.NameSpace): the command line arguments to be logged
"""
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
PathManager.mkdirs(output_dir)
rank = comm.get_rank()
# setup_logger(output_dir, distributed_rank=rank, name="cvpods")
logger = setup_logger(output_dir, distributed_rank=rank)
logger.info("Rank of current process: {}. World size: {}".format(
rank, comm.get_world_size()))
logger.info("Environment info:\n" + collect_env_info())
logger.info("Command line arguments: " + str(args))
if hasattr(args, "config_file") and args.config_file != "":
logger.info("Contents of args.config_file={}:\n{}".format(
args.config_file,
PathManager.open(args.config_file, "r").read()))
adjust_config(cfg)
logger.info("Running with full config:\n{}".format(cfg))
base_config = cfg.__class__.__base__()
logger.info("different config with base class:\n{}".format(
cfg.diff(base_config)))
# if comm.is_main_process() and output_dir:
# # Note: some of our scripts may expect the existence of
# # config.yaml in output directory
# path = os.path.join(output_dir, "config.yaml")
# with PathManager.open(path, "w") as f:
# f.write(cfg.dump())
# logger.info("Full config saved to {}".format(os.path.abspath(path)))
# make sure each worker has a different, yet deterministic seed if specified
seed = seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + rank)
# save seed to config for dump
cfg.SEED = seed
# cudnn benchmark has large overhead. It shouldn't be used considering the small size of
# typical validation set.
if not (hasattr(args, "eval_only") and args.eval_only):
torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
return cfg, logger
def default_setup(cfg, args):
"""
Perform some basic common setups at the beginning of a job, including:
1. Set up the cvpods logger
2. Log basic information about environment, cmdline arguments, and config
3. Backup the config to the output directory
Args:
cfg (BaseConfig): the full config to be used
args (argparse.NameSpace): the command line arguments to be logged
"""
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
ensure_dir(output_dir)
rank = comm.get_rank()
# setup_logger(output_dir, distributed_rank=rank, name="cvpods")
setup_logger(output_dir, distributed_rank=rank)
logger.info("Rank of current process: {}. World size: {}".format(
rank, comm.get_world_size()))
logger.info("Environment info:\n" + collect_env_info())
logger.info("Command line arguments: " + str(args))
if hasattr(args, "config_file") and args.config_file != "":
logger.info("Contents of args.config_file={}:\n{}".format(
args.config_file,
megfile.smart_open(args.config_file, "r").read()))
adjust_config(cfg)
# make sure each worker has a different, yet deterministic seed if specified
seed = seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + rank)
# save seed to config for dump
cfg.SEED = seed
# cudnn benchmark has large overhead. It shouldn't be used considering the small size of
# typical validation set.
if not (hasattr(args, "eval_only") and args.eval_only):
torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
return cfg
def worker_init_reset_seed(worker_id):
seed_all_rng(np.random.randint(2**31) + worker_id)
def default_setup(cfg, args):
"""
Perform some basic common setups at the beginning of a job, including:
1. Set up the cvpods logger
2. Log basic information about environment, cmdline arguments, and config
3. Backup the config to the output directory
Args:
cfg (BaseConfig): the full config to be used
args (argparse.NameSpace): the command line arguments to be logged
"""
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
PathManager.mkdirs(output_dir)
rank = comm.get_rank()
# setup_logger(output_dir, distributed_rank=rank, name="cvpods")
logger = setup_logger(output_dir, distributed_rank=rank)
logger.info("Rank of current process: {}. World size: {}".format(
rank, comm.get_world_size()))
logger.info("Environment info:\n" + collect_env_info())
logger.info("Command line arguments: " + str(args))
if hasattr(args, "config_file") and args.config_file != "":
logger.info("Contents of args.config_file={}:\n{}".format(
args.config_file,
PathManager.open(args.config_file, "r").read()))
logger.info("Running with full config:\n{}".format(cfg))
base_config = cfg.__class__.__base__()
logger.info("different config with base class:\n{}".format(
cfg.show_diff(base_config)))
# if comm.is_main_process() and output_dir:
# # Note: some of our scripts may expect the existence of
# # config.yaml in output directory
# path = os.path.join(output_dir, "config.yaml")
# with PathManager.open(path, "w") as f:
# f.write(cfg.dump())
# logger.info("Full config saved to {}".format(os.path.abspath(path)))
# make sure each worker has a different, yet deterministic seed if specified
seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + rank)
# cudnn benchmark has large overhead. It shouldn't be used considering the small size of
# typical validation set.
if not (hasattr(args, "eval_only") and args.eval_only):
torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
# dynamic adjust batch_size, steps according to world size
base_world_size = int(cfg.SOLVER.IMS_PER_BATCH / cfg.SOLVER.IMS_PER_DEVICE)
world_size = comm.get_world_size()
ratio = world_size / base_world_size
cfg.SOLVER.IMS_PER_BATCH = int(ratio * cfg.SOLVER.IMS_PER_BATCH)
cfg.SOLVER.LR_SCHEDULER.MAX_ITER = int(cfg.SOLVER.LR_SCHEDULER.MAX_ITER /
ratio)
# Divided by scale ratio when using iterations rather than epochs
if cfg.SOLVER.LR_SCHEDULER.MAX_EPOCH is None:
cfg.SOLVER.LR_SCHEDULER.STEPS = list(
(int(step / ratio) for step in cfg.SOLVER.LR_SCHEDULER.STEPS))
cfg.SOLVER.CHECKPOINT_PERIOD = int(cfg.SOLVER.CHECKPOINT_PERIOD /
ratio)
cfg.TEST.EVAL_PERIOD = int(cfg.TEST.EVAL_PERIOD / ratio)
cfg.SOLVER.OPTIMIZER.BASE_LR = ratio * cfg.SOLVER.OPTIMIZER.BASE_LR
assert cfg.SOLVER.IMS_PER_BATCH / cfg.SOLVER.IMS_PER_DEVICE == world_size
return cfg, logger
