def test_failed_save(self):
cfg = DictConfig({"x": lambda: 3}, flags={"allow_objects": True})
with tempfile.TemporaryDirectory(prefix="detectron2") as d:
fname = os.path.join(d, "test_config.yaml")
LazyConfig.save(cfg, fname)
self.assertTrue(os.path.exists(fname))
self.assertTrue(os.path.exists(fname + ".pkl"))
def default_setup(cfg, args):
"""
Perform some basic common setups at the beginning of a job, including:
1. Set up the detectron2 logger
2. Log basic information about environment, cmdline arguments, and config
3. Backup the config to the output directory
Args:
cfg (CfgNode or omegaconf.DictConfig): the full config to be used
args (argparse.NameSpace): the command line arguments to be logged
"""
output_dir = _try_get_key(cfg, "OUTPUT_DIR", "output_dir",
"train.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="fvcore")
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,
_highlight(
PathManager.open(args.config_file, "r").read(),
args.config_file),
))
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")
if isinstance(cfg, CfgNode):
logger.info("Running with full config:\n{}".format(
_highlight(cfg.dump(), ".yaml")))
with PathManager.open(path, "w") as f:
f.write(cfg.dump())
else:
LazyConfig.save(cfg, path)
logger.info("Full config saved to {}".format(path))
# make sure each worker has a different, yet deterministic seed if specified
seed = _try_get_key(cfg, "SEED", "train.seed", default=-1)
seed_all_rng(None if seed < 0 else 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 = _try_get_key(cfg,
"CUDNN_BENCHMARK",
"train.cudnn_benchmark",
default=False)
def reload_lazy_config(cfg):
"""
Save an object by LazyConfig.save and load it back.
This is used to test that a config still works the same after
serialization/deserialization.
"""
with tempfile.TemporaryDirectory(prefix="detectron2") as d:
fname = os.path.join(d, "d2_cfg_test.yaml")
LazyConfig.save(cfg, fname)
return LazyConfig.load(fname)
def test_save_load(self):
cfg = LazyConfig.load(self.root_filename)
with tempfile.TemporaryDirectory(prefix="detectron2") as d:
fname = os.path.join(d, "test_config.yaml")
LazyConfig.save(cfg, fname)
cfg2 = LazyConfig.load(fname)
self.assertEqual(cfg2.lazyobj._target_, "itertools.count")
self.assertEqual(cfg.lazyobj._target_, count)
cfg2.lazyobj.pop("_target_")
cfg.lazyobj.pop("_target_")
# the rest are equal
self.assertEqual(cfg, cfg2)