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 _add_rcnn_default_config(_C):
_C.EXPORT_CAFFE2 = CfgNode()
_C.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT = False
# Options about how to export the model
_C.RCNN_EXPORT = CfgNode()
# whether or not to include the postprocess (GeneralizedRCNN._postprocess) step
# inside the exported model
_C.RCNN_EXPORT.INCLUDE_POSTPROCESS = False
_C.RCNN_PREPARE_FOR_EXPORT = "default_rcnn_prepare_for_export"
_C.RCNN_PREPARE_FOR_QUANT = "default_rcnn_prepare_for_quant"
_C.RCNN_PREPARE_FOR_QUANT_CONVERT = "default_rcnn_prepare_for_quant_convert"
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 test_modeling_hook_cfg(self):
"""Create model with modeling hook using build_model"""
cfg = CfgNode()
cfg.MODEL = CfgNode()
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.META_ARCHITECTURE = "TestArch"
cfg.MODEL.MODELING_HOOKS = ["PlusOneHook", "TimesTwoHook"]
model = build_model(cfg)
self.assertEqual(model(2), 10)
self.assertTrue(hasattr(model, "_modeling_hooks"))
self.assertTrue(hasattr(model, "unapply_modeling_hooks"))
orig_model = model.unapply_modeling_hooks()
self.assertIsInstance(orig_model, TestArch)
self.assertEqual(orig_model(2), 4)
def cast_from_other_class(cls, other_cfg):
"""Cast an instance of other CfgNode to D2Go's CfgNode (or its subclass)"""
new_cfg = CfgNode(other_cfg)
# copy all fields inside __dict__, this will preserve fields like __deprecated_keys__
for k, v in other_cfg.__dict__.items():
new_cfg.__dict__[k] = v
return new_cfg
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 = CfgNode()
_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 = CfgNode()
_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 test_modeling_hook_copy(self):
"""Create model with modeling hook, the model could be copied"""
cfg = CfgNode()
cfg.MODEL = CfgNode()
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.META_ARCHITECTURE = "TestArch"
cfg.MODEL.MODELING_HOOKS = ["PlusOneHook", "TimesTwoHook"]
model = build_model(cfg)
self.assertEqual(model(2), 10)
model_copy = copy.deepcopy(model)
orig_model = model.unapply_modeling_hooks()
self.assertIsInstance(orig_model, TestArch)
self.assertEqual(orig_model(2), 4)
orig_model_copy = model_copy.unapply_modeling_hooks()
self.assertEqual(orig_model_copy(2), 4)
def get_default_cfg():
_C = super(GeneralizedRCNNRunner, GeneralizedRCNNRunner).get_default_cfg()
_C.EXPORT_CAFFE2 = CfgNode()
_C.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT = False
_C.RCNN_PREPARE_FOR_EXPORT = "default_rcnn_prepare_for_export"
_C.RCNN_PREPARE_FOR_QUANT = "default_rcnn_prepare_for_quant"
_C.RCNN_PREPARE_FOR_QUANT_CONVERT = "default_rcnn_prepare_for_quant_convert"
return _C
def test_merge_from_list_with_new_allowed(self):
"""
YACS's merge_from_list doesn't take new_allowed into account, D2Go override its behavior, and this test covers it.
"""
# new_allowed is not set
cfg = CfgNode()
cfg.A = CfgNode()
cfg.A.X = 1
self.assertRaises(Exception, cfg.merge_from_list, ["A.Y", "2"])
# new_allowed is set for sub key
cfg = CfgNode()
cfg.A = CfgNode(new_allowed=True)
cfg.A.X = 1
cfg.merge_from_list(["A.Y", "2"])
self.assertEqual(cfg.A.Y,
2) # note that the string will be converted to number
# however new_allowed is not set for root key
self.assertRaises(Exception, cfg.merge_from_list, ["B", "3"])
def run_with_cmdline_args(args):
cfg, output_dir, runner = prepare_for_launch(args)
inference_config = None
if args.inference_config_file:
inference_config = CfgNode(
CfgNode.load_yaml_with_base(args.inference_config_file))
return main(
cfg,
output_dir,
runner,
# binary specific optional arguments
predictor_types=args.predictor_types,
compare_accuracy=args.compare_accuracy,
skip_if_fail=args.skip_if_fail,
inference_config=inference_config,
)
def _get_default_config():
cfg = CfgNode()
cfg.INPUT = CfgNode()
cfg.INPUT.CROP = CfgNode()
cfg.INPUT.CROP.ENABLED = False
cfg.INPUT.CROP.SIZE = (0.9, 0.9)
cfg.INPUT.CROP.TYPE = "relative_range"
cfg.MODEL = CfgNode()
cfg.MODEL.MIN_DIM_SIZE = 360
cfg.INFERENCE_SDK = CfgNode()
cfg.INFERENCE_SDK.MODEL = CfgNode()
cfg.INFERENCE_SDK.MODEL.SCORE_THRESHOLD = 0.8
cfg.INFERENCE_SDK.IOU_TRACKER = CfgNode()
cfg.INFERENCE_SDK.IOU_TRACKER.IOU_THRESHOLD = 0.15
cfg.INFERENCE_SDK.ENABLE_ID_TRACKING = True
return cfg
def get_default_config():
cfg = CfgNode()
cfg.D2GO_DATA = CfgNode()
cfg.D2GO_DATA.AUG_OPS = CfgNode()
return cfg
def _test2(cfg):
cfg.TEST2 = CfgNode()
cfg.TEST2.Y = 2
return cfg
def _test1(cfg):
cfg.TEST1 = CfgNode()
cfg.TEST1.X = 1
return cfg