def test_modeling_hook_runner(self):
"""Create model with modeling hook from runner"""
runner = default_runner.Detectron2GoRunner()
cfg = runner.get_default_cfg()
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.META_ARCHITECTURE = "TestArch"
cfg.MODEL.MODELING_HOOKS = ["PlusOneHook", "TimesTwoHook"]
model = runner.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)
default_runner._close_all_tbx_writers()
def test_d2go_build_evaluator(self):
for rotated, evaluator in [
(True, RotatedCOCOEvaluator),
(False, COCOEvaluator),
]:
with tempfile.TemporaryDirectory() as tmp_dir:
ds_name = create_local_dataset(tmp_dir,
5,
10,
10,
is_rotated=rotated)
runner = default_runner.Detectron2GoRunner()
cfg = _get_cfg(runner, tmp_dir, ds_name)
ds_evaluators = runner.get_evaluator(cfg, ds_name, tmp_dir)
self.assertTrue(
isinstance(ds_evaluators._evaluators[0], evaluator))
def test_d2go_runner_trainer_hooks(self):
counts = 0
@TRAINER_HOOKS_REGISTRY.register()
def _check_hook_func(hooks):
nonlocal counts
counts = len(hooks)
print(hooks)
with tempfile.TemporaryDirectory() as tmp_dir:
ds_name = create_local_dataset(tmp_dir, 5, 10, 10)
runner = default_runner.Detectron2GoRunner()
cfg = _get_cfg(runner, tmp_dir, ds_name)
model = runner.build_model(cfg)
runner.do_train(cfg, model, resume=True)
default_runner._close_all_tbx_writers()
self.assertGreater(counts, 0)
def get_optimizer_cfg(
lr,
weight_decay=None,
weight_decay_norm=None,
weight_decay_bias=None,
lr_mult=None,
):
runner = default_runner.Detectron2GoRunner()
cfg = runner.get_default_cfg()
if lr is not None:
cfg.SOLVER.BASE_LR = lr
if weight_decay is not None:
cfg.SOLVER.WEIGHT_DECAY = weight_decay
if weight_decay_norm is not None:
cfg.SOLVER.WEIGHT_DECAY_NORM = weight_decay_norm
if weight_decay_bias is not None:
cfg.SOLVER.WEIGHT_DECAY_BIAS = weight_decay_bias
if lr_mult is not None:
cfg.SOLVER.LR_MULTIPLIER_OVERWRITE = [lr_mult]
return cfg
def test_dict_based_dataloader_visualizer_wrapper(self, tmp_dir: str):
image_dir, json_file = create_test_images_and_dataset_json(
tmp_dir, 60, 60)
# Create config data
runner = default_runner.Detectron2GoRunner()
cfg = runner.get_default_cfg()
cfg.merge_from_list([
"D2GO_DATA.DATASETS.COCO_INJECTION.NAMES",
str(["inj_ds3"]),
"D2GO_DATA.DATASETS.COCO_INJECTION.IM_DIRS",
str([image_dir]),
"D2GO_DATA.DATASETS.COCO_INJECTION.JSON_FILES",
str([json_file]),
"DATASETS.TRAIN",
str(["inj_ds3"]),
"MODEL.META_ARCHITECTURE",
"DummyMetaArch",
])
# Register configs
runner.register(cfg)
DatasetCatalog.get("inj_ds3")
with EventStorage():
# Create mock storage for writer
mock_tbx_writer = MockTbxWriter()
# Create a wrapper around an iterable object and run once
input_dict = create_dummy_input_dict(60, 60, [[1, 1, 2, 2]])
dl_wrapper = DataLoaderVisWrapper(cfg, mock_tbx_writer,
[[input_dict], [input_dict]])
for _ in dl_wrapper:
break
# Check data has been written to buffer
self.assertTrue(len(mock_tbx_writer._writer.write_buffer) == 2)
self.assertTrue("train_loader_batch_0/default" in
mock_tbx_writer._writer.write_buffer[0]["tag"])
self.assertTrue("train_loader_batch_0/secondary" in
mock_tbx_writer._writer.write_buffer[1]["tag"])
def _get_cfg(self, tmp_dir: str) -> CfgNode:
runner = default_runner.Detectron2GoRunner()
cfg = mah.create_detection_cfg(runner, tmp_dir)
cfg.TEST.EVAL_PERIOD = cfg.SOLVER.MAX_ITER
return cfg