def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each builtin dataset.
For your own dataset, you can simply create an evaluator manually in your
script and do not have to worry about the hacky if-else logic here.
"""
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(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type))
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each builtin dataset.
For your own dataset, you can simply create an evaluator manually in your
script and do not have to worry about the hacky if-else logic here.
"""
if cfg.MODEL.PANOPTIC_DEEPLAB.BENCHMARK_NETWORK_SPEED:
return 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 ["cityscapes_panoptic_seg", "coco_panoptic_seg"]:
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
if evaluator_type == "cityscapes_panoptic_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
if evaluator_type == "coco_panoptic_seg":
# `thing_classes` in COCO panoptic metadata includes both thing and
# stuff classes for visualization. COCOEvaluator requires metadata
# which only contains thing classes, thus we map the name of
# panoptic datasets to their corresponding instance datasets.
dataset_name_mapper = {
"coco_2017_val_panoptic": "coco_2017_val",
"coco_2017_val_100_panoptic": "coco_2017_val_100",
}
evaluator_list.append(
COCOEvaluator(dataset_name_mapper[dataset_name], output_dir=output_folder)
)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
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(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type))
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each builtin dataset.
For your own dataset, you can simply create an evaluator manually in your
script and do not have to worry about the hacky if-else logic here.
"""
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, output_dir=output_folder)
if evaluator_type == "coco":
return COCOEvaluator(dataset_name, output_dir=output_folder)
if evaluator_type == "sem_seg":
return SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=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)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
if len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
def build_evaluator(cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each builtin dataset.
For your own dataset, you can simply create an evaluator manually in your
script and do not have to worry about the hacky if-else logic here.
"""
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,
output_dir=output_folder,
))
if evaluator_type in ["coco", "coco_panoptic_seg"]:
evaluator_list.append(
COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg":
evaluator_list.append(
COCOPanopticEvaluator(dataset_name, output_folder))
if evaluator_type == "cityscapes_instance":
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
return CityscapesSemSegEvaluator(dataset_name)
elif evaluator_type == "pascal_voc":
return PascalVOCDetectionEvaluator(dataset_name)
elif evaluator_type == "lvis":
return LVISEvaluator(dataset_name, output_dir=output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type))
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
