def default_setup(cfg, args):
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
PathManager.mkdirs(output_dir)
rank = comm.get_rank()
setup_logger(output_dir, distributed_rank=rank, name="fvcore")
logger = setup_logger(output_dir, distributed_rank=rank)
logger.info("Rank of current process: {}. World size: {}".format(
rank, comm.get_world_size()))
logger.info("Environment info:\n" + collect_env_info())
logger.info("Command line arguments: " + str(args))
if hasattr(args, "config") and args.config != "":
logger.info("Contents of args.config={}:\n{}".format(
args.config,
PathManager.open(args.config, "r").read()))
logger.info("Running with full config:\n{}".format(cfg))
seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + rank)
if not (hasattr(args, "eval_only") and args.eval_only):
torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
def evaluate(self):
comm.synchronize()
if comm.get_rank() > 0:
return
import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
self._logger.info(f"Evaluating results under {self._temp_dir} ...".format())
cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
cityscapes_eval.args.predictionWalk = None
cityscapes_eval.args.JSONOutput = False
cityscapes_eval.args.colorized = False
cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
assert len(groundTruthImgList), \
"Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
cityscapes_eval.args.groundTruthSearch
)
predictionImgList = []
for gt in groundTruthImgList:
predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
results = cityscapes_eval.evaluateImgLists(
predictionImgList, groundTruthImgList, cityscapes_eval.args
)["averages"]
ret = OrderedDict()
ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
self._working_dir.cleanup()
return ret
def __init__(self, repeat_factors, *, shuffle=True, seed=None):
self._shuffle = shuffle
if seed is None:
seed = comm.shared_random_seed()
self._seed = int(seed)
self._rank = comm.get_rank()
self._world_size = comm.get_world_size()
self._int_part = torch.trunc(repeat_factors)
self._frac_part = repeat_factors - self._int_part
def __init__(self, size: int):
assert size > 0
self._size = size
self._rank = comm.get_rank()
self._world_size = comm.get_world_size()
shard_size = (self._size - 1) // self._world_size + 1
begin = shard_size * self._rank
end = min(shard_size * (self._rank + 1), self._size)
self._local_indices = range(begin, end)
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
ignore_label=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
output_dir=output_folder,
)
)
if evaluator_type in ["coco", "coco_panoptic_seg"]:
evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
if evaluator_type == "coco_panoptic_seg":
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
if evaluator_type == "cityscapes_instance":
assert torch.cuda.device_count() >= comm.get_rank(), \
"CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert torch.cuda.device_count() >= comm.get_rank(), \
"CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
elif evaluator_type == "pascal_voc":
return PascalVOCDetectionEvaluator(dataset_name)
elif evaluator_type == "lvis":
return LVISEvaluator(dataset_name, cfg, True, output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
f"no Evaluator for the dataset {dataset_name} with the type {evaluator_type}"
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
def __init__(self,
size: int,
shuffle: bool = True,
seed: Optional[int] = None):
self._size = size
assert size > 0
self._shuffle = shuffle
if seed is None:
seed = comm.shared_random_seed()
self._seed = int(seed)
self._rank = comm.get_rank()
self._world_size = comm.get_world_size()
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, cfg, True, output_folder)
if evaluator_type == "coco":
return COCOEvaluator(dataset_name, cfg, True, output_folder)
if evaluator_type == "cityscapes":
assert torch.cuda.device_count() >= comm.get_rank(), \
"CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if len(evaluator_list) == 0:
raise NotImplementedError(
f"no Evaluator for the dataset {dataset_name} with the type {evaluator_type}"
)
if len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
def evaluate(self):
comm.synchronize()
if comm.get_rank() > 0:
return
import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
cityscapes_eval.args.predictionWalk = None
cityscapes_eval.args.JSONOutput = False
cityscapes_eval.args.colorized = False
gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
assert len(groundTruthImgList), \
"Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
cityscapes_eval.args.groundTruthSearch
)
predictionImgList = []
for gt in groundTruthImgList:
predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
results = cityscapes_eval.evaluateImgLists(
predictionImgList,
groundTruthImgList,
cityscapes_eval.args
)
ret = OrderedDict()
ret["sem_seg"] = {
"IoU": 100.0 * results["averageScoreClasses"],
"iIoU": 100.0 * results["averageScoreInstClasses"],
"IoU_sup": 100.0 * results["averageScoreCategories"],
"iIoU_sup": 100.0 * results["averageScoreInstCategories"],
}
self._working_dir.cleanup()
return ret