def _reset_dataset_evaluators(self):
"""reset validation dataset evaluator to be run in EVAL_PERIOD steps"""
assert (not self.trainer.distributed_backend
or self.trainer.distributed_backend.lower() in [
"ddp",
"ddp_cpu",
]), ("Only DDP and DDP_CPU distributed backend are supported")
def _get_inference_dir_name(base_dir, inference_type, dataset_name,
model_tag: ModelTag):
next_eval_iter = self.trainer.global_step + self.cfg.TEST.EVAL_PERIOD
if self.trainer.global_step == 0:
next_eval_iter -= 1
return os.path.join(
base_dir,
inference_type,
model_tag,
str(next_eval_iter),
dataset_name,
)
for tag, dataset_evaluators in self.dataset_evaluators.items():
dataset_evaluators.clear()
assert self.cfg.OUTPUT_DIR, "Expect output_dir to be specified in config"
for dataset_name in self.cfg.DATASETS.TEST:
# setup evaluator for each dataset
output_folder = _get_inference_dir_name(
self.cfg.OUTPUT_DIR, "inference", dataset_name, tag)
evaluator = Detectron2GoRunner.get_evaluator(
self.cfg, dataset_name, output_folder=output_folder)
evaluator.reset()
dataset_evaluators.append(evaluator)
def _evaluation_dataloader(self):
# TODO: Support subsample n images
assert len(self.cfg.DATASETS.TEST)
dataloaders = []
for dataset_name in self.cfg.DATASETS.TEST:
dataloaders.append(
Detectron2GoRunner.build_detection_test_loader(self.cfg, dataset_name)
)
self._reset_dataset_evaluators()
return dataloaders
def get_default_cfg(cls):
return Detectron2GoRunner.get_default_cfg()
def train_dataloader(self):
return Detectron2GoRunner.build_detection_train_loader(self.cfg)
def build_detection_test_loader(cfg, dataset_name, mapper=None):
return Detectron2GoRunner.build_detection_test_loader(
cfg, dataset_name, mapper)
def build_detection_train_loader(cfg, *args, mapper=None, **kwargs):
return Detectron2GoRunner.build_detection_train_loader(
cfg, *args, **kwargs)
def get_mapper(cfg, is_train):
return Detectron2GoRunner.get_mapper(cfg, is_train)
def get_evaluator(cfg: CfgNode, dataset_name: str, output_folder: str):
return Detectron2GoRunner.get_evaluator(cfg=cfg,
dataset_name=dataset_name,
output_folder=output_folder)
def get_visualization_evaluator(
) -> Optional[Type[VisualizationEvaluator]]:
return Detectron2GoRunner.get_visualization_evaluator()
