def test_load(self):
cfg = LazyConfig.load(self.root_filename)
self.assertEqual(cfg.dir1a_dict.a, "modified")
self.assertEqual(cfg.dir1b_dict.a, 1)
self.assertEqual(cfg.lazyobj.x, "base_a_1")
cfg.lazyobj.x = "new_x"
# reload
cfg = LazyConfig.load(self.root_filename)
self.assertEqual(cfg.lazyobj.x, "base_a_1")
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)
def test_to_py(self):
cfg = LazyConfig.load(self.root_filename)
cfg.lazyobj.x = {
"a": 1,
"b": 2,
"c": L(count)(x={
"r": "a",
"s": 2.4,
"t": [1, 2, 3, "z"]
})
}
cfg.list = ["a", 1, "b", 3.2]
py_str = LazyConfig.to_py(cfg)
expected = """cfg.dir1a_dict.a = "modified"
cfg.dir1a_dict.b = 2
cfg.dir1b_dict.a = 1
cfg.dir1b_dict.b = 2
cfg.lazyobj = itertools.count(
x={
"a": 1,
"b": 2,
"c": itertools.count(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]}),
},
y="base_a_1_from_b",
)
cfg.list = ["a", 1, "b", 3.2]
"""
self.assertEqual(py_str, expected)
def get_config(config_path, trained: bool = False):
"""
Returns a config object for a model in model zoo.
Args:
config_path (str): config file name relative to detectron2's "configs/"
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights.
If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used
instead; this will typically (though not always) initialize a subset of weights using
an ImageNet pre-trained model, while randomly initializing the other weights.
Returns:
CfgNode or omegaconf.DictConfig: a config object
"""
cfg_file = get_config_file(config_path)
if cfg_file.endswith(".yaml"):
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
if trained:
cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path)
return cfg
elif cfg_file.endswith(".py"):
cfg = LazyConfig.load(cfg_file)
if trained:
url = get_checkpoint_url(config_path)
if "train" in cfg and "init_checkpoint" in cfg.train:
cfg.train.init_checkpoint = url
else:
raise NotImplementedError
return cfg
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 setup(args):
if args.config_file.endswith(".yaml"):
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.SOLVER.BASE_LR = 0.001 # Avoid NaNs. Not useful in this script anyway.
cfg.merge_from_list(args.opts)
cfg.freeze()
else:
cfg = LazyConfig.load(args.config_file)
cfg = LazyConfig.apply_overrides(cfg, args.opts)
setup_logger(distributed_rank=comm.get_rank())
return cfg
def main(args):
cfg = LazyConfig.load(args.config_file)
cfg = LazyConfig.apply_overrides(cfg, args.opts)
default_setup(cfg, args)
if args.eval_only:
model = instantiate(cfg.model)
model = create_ddp_model(model)
DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
print(do_test(cfg, model))
else:
do_train(args, cfg)
def setup(args):
if args.config_file.endswith(".yaml"):
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.DATALOADER.NUM_WORKERS = 0
cfg.merge_from_list(args.opts)
cfg.freeze()
else:
cfg = LazyConfig.load(args.config_file)
cfg = LazyConfig.apply_overrides(cfg, args.opts)
setup_logger(name="fvcore")
setup_logger()
return cfg
def create_lazy_cfg(lazy_path, max_iter=100):
print(f"Creating {lazy_path}...\n")
cfg = LazyConfig.load(model_zoo.get_config_file(lazy_path))
default_setup(cfg, [])
model = cfg.model
model.backbone.bottom_up.stem.norm = \
model.backbone.bottom_up.stages.norm = \
model.backbone.norm = "FrozenBN"
cfg.model = model
cfg.train.init_checkpoint = 'model_final_bb69de.pkl'
cfg.model.roi_heads.batch_size_per_image = 64
cfg.dataloader.train.total_batch_size = 8
cfg.optimizer.lr = 0.003
cfg.train.amp.enabled = True
cfg.train.output_dir = '../logs/mask_rcnn'
cfg.train.max_iter = max_iter
cfg.model.roi_heads.num_classes = 13
cfg.dataloader.train.dataset.names = 'modanet_instance_segmentation_train'
cfg.dataloader.test.dataset.names = 'modanet_instance_segmentation_test'
return cfg
def test_invalid_overrides(self):
cfg = LazyConfig.load(self.root_filename)
with self.assertRaises(KeyError):
LazyConfig.apply_overrides(cfg, ["lazyobj.x.xxx=123"])
def test_overrides(self):
cfg = LazyConfig.load(self.root_filename)
LazyConfig.apply_overrides(cfg,
["lazyobj.x=123", 'dir1b_dict.a="123"'])
self.assertEqual(cfg.dir1b_dict.a, "123")
self.assertEqual(cfg.lazyobj.x, 123)