def test_diff_cfg_no_new_allowed(self):
"""check that if new_allowed is False, new keys cause key error"""
# create base config
cfg1 = CfgNode()
cfg1.A = CfgNode()
cfg1.A.set_new_allowed(False)
cfg1.A.Y = 2
# case 2: new allowed not set, new config has new keys
cfg2 = cfg1.clone()
cfg2.A.X = 2
self.assertRaises(KeyError, get_diff_cfg, cfg1, cfg2)
def test_diff_cfg_with_new_allowed(self):
"""diff config with new keys and new_allowed set to True"""
# create base config
cfg1 = CfgNode()
cfg1.A = CfgNode()
cfg1.A.set_new_allowed(True)
cfg1.A.Y = 2
# case 3: new allowed set, new config has new keys
cfg2 = cfg1.clone()
cfg2.A.X = 2
gt = CfgNode()
gt.A = CfgNode()
gt.A.X = 2
self.assertEqual(gt, get_diff_cfg(cfg1, cfg2))
def test_get_diff_cfg(self):
"""check config that is diff from default config, no new keys"""
# create base config
cfg1 = CfgNode()
cfg1.A = CfgNode()
cfg1.A.Y = 2
# case 1: new allowed not set, new config has only old keys
cfg2 = cfg1.clone()
cfg2.set_new_allowed(False)
cfg2.A.Y = 3
gt = CfgNode()
gt.A = CfgNode()
gt.A.Y = 3
self.assertEqual(gt, get_diff_cfg(cfg1, cfg2))
def create_cfg_from_cli_args(args, default_cfg):
"""
Instead of loading from defaults.py, this binary only includes necessary
configs building from scratch, and overrides them from args. There're two
levels of config:
_C: the config system used by this binary, which is a sub-set of training
config, override by configurable_cfg. It can also be override by
args.opts for convinience.
configurable_cfg: common configs that user should explicitly specify
in the args.
"""
_C = CN()
_C.INPUT = default_cfg.INPUT
_C.DATASETS = default_cfg.DATASETS
_C.DATALOADER = default_cfg.DATALOADER
_C.TEST = default_cfg.TEST
if hasattr(default_cfg, "D2GO_DATA"):
_C.D2GO_DATA = default_cfg.D2GO_DATA
if hasattr(default_cfg, "TENSORBOARD"):
_C.TENSORBOARD = default_cfg.TENSORBOARD
# NOTE configs below might not be necessary, but must add to make code work
_C.MODEL = CN()
_C.MODEL.META_ARCHITECTURE = default_cfg.MODEL.META_ARCHITECTURE
_C.MODEL.MASK_ON = default_cfg.MODEL.MASK_ON
_C.MODEL.KEYPOINT_ON = default_cfg.MODEL.KEYPOINT_ON
_C.MODEL.LOAD_PROPOSALS = default_cfg.MODEL.LOAD_PROPOSALS
assert _C.MODEL.LOAD_PROPOSALS is False, "caffe2 model doesn't support"
_C.OUTPUT_DIR = args.output_dir
configurable_cfg = [
"DATASETS.TEST",
args.datasets,
"INPUT.MIN_SIZE_TEST",
args.min_size,
"INPUT.MAX_SIZE_TEST",
args.max_size,
]
cfg = _C.clone()
cfg.merge_from_list(configurable_cfg)
cfg.merge_from_list(args.opts)
return cfg
def do_train(cfg: CfgNode, trainer: pl.Trainer,
task: GeneralizedRCNNTask) -> Dict[str, str]:
"""Runs the training loop with given trainer and task.
Args:
cfg: The normalized ConfigNode for this D2Go Task.
trainer: PyTorch Lightning trainer.
task: Lightning module instance.
Returns:
A map of model name to trained model config path.
"""
with EventStorage() as storage:
task.storage = storage
trainer.fit(task)
final_ckpt = os.path.join(cfg.OUTPUT_DIR, FINAL_MODEL_CKPT)
trainer.save_checkpoint(final_ckpt) # for validation monitor
trained_cfg = cfg.clone()
with temp_defrost(trained_cfg):
trained_cfg.MODEL.WEIGHTS = final_ckpt
model_configs = dump_trained_model_configs(
cfg.OUTPUT_DIR, {"model_final": trained_cfg})
return model_configs