def test_d2go_runner_test(self):
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)
results = runner.do_test(cfg, model)
self.assertEqual(results["default"][ds_name]["bbox"]["AP"], 10.0)
default_runner._close_all_tbx_writers()
def setup(tmp_dir):
ds_name = create_local_dataset(tmp_dir, 5, 10, 10)
runner = default_runner.Detectron2GoRunner()
cfg = _get_cfg(runner, tmp_dir, ds_name)
cfg.merge_from_list(
(["MODEL.META_ARCHITECTURE", "MetaArchForTestQAT"] +
["QUANTIZATION.QAT.ENABLED", "True"] +
["QUANTIZATION.QAT.START_ITER", "0"] +
["QUANTIZATION.QAT.ENABLE_OBSERVER_ITER", "0"]))
return runner, cfg
def test_d2go_runner_train(self):
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)
final_model_path = os.path.join(tmp_dir, "model_final.pth")
self.assertTrue(os.path.isfile(final_model_path))
default_runner._close_all_tbx_writers()
def test_d2go_runner_build_model(self):
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)
dl = runner.build_detection_train_loader(cfg)
batch = next(iter(dl))
output = model(batch)
self.assertIsInstance(output, dict)
model.eval()
output = model(batch)
self.assertIsInstance(output, list)
default_runner._close_all_tbx_writers()
def test_d2go_runner_ema(self):
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)
cfg.MODEL.META_ARCHITECTURE = "MetaArchForTestSingleValue"
cfg.MODEL_EMA.ENABLED = True
cfg.MODEL_EMA.DECAY = 0.9
def _run_train(cfg):
cfg = copy.deepcopy(cfg)
model = runner.build_model(cfg)
model = DistributedDataParallel(model, broadcast_buffers=False)
runner.do_train(cfg, model, True)
final_model_path = os.path.join(tmp_dir, "model_final.pth")
trained_weights = torch.load(final_model_path)
self.assertIn("ema_state", trained_weights)
default_runner._close_all_tbx_writers()
return final_model_path, model.module.ema_state
def _run_test(cfg, final_path, gt_ema):
cfg = copy.deepcopy(cfg)
cfg.MODEL.WEIGHTS = final_path
model = runner.build_model(cfg, eval_only=True)
self.assertGreater(len(model.ema_state.state), 0)
self.assertEqual(len(model.ema_state.state), len(gt_ema.state))
self.assertTrue(
_compare_state_dict(model.ema_state.state_dict(),
gt_ema.state_dict()))
results = runner.do_test(cfg, model)
self.assertEqual(results["default"][ds_name]["bbox"]["AP"],
3.0)
self.assertEqual(results["ema"][ds_name]["bbox"]["AP"], 9.0)
default_runner._close_all_tbx_writers()
def _run_build_model_with_ema_weight(cfg, final_path, gt_ema):
cfg = copy.deepcopy(cfg)
cfg.MODEL.WEIGHTS = final_path
cfg.MODEL_EMA.USE_EMA_WEIGHTS_FOR_EVAL_ONLY = True
model = runner.build_model(cfg, eval_only=True)
self.assertTrue(
_compare_state_dict(model.state_dict(),
gt_ema.state_dict()))
final_model_path, gt_ema = _run_train(cfg)
_run_test(cfg, final_model_path, gt_ema)
_run_build_model_with_ema_weight(cfg, final_model_path, gt_ema)
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 create_detection_cfg(runner, output_dir):
ds_name = create_local_dataset(output_dir, 5, 10, 10)
cfg = runner.get_default_cfg()
return get_det_meta_arch_cfg(cfg, ds_name, output_dir)