def register_coco_instances(name, metadata, json_file, image_root):
"""
Register a dataset in COCO's json annotation format for
instance detection, instance segmentation and keypoint detection.
(i.e., Type 1 and 2 in http://cocodataset.org/#format-data.
`instances*.json` and `person_keypoints*.json` in the dataset).
This is an example of how to register a new dataset.
You can do something similar to this function, to register new datasets.
Args:
name (str): the name that identifies a dataset, e.g. "coco_2014_train".
metadata (dict): extra metadata associated with this dataset. You can
leave it as an empty dict.
json_file (str): path to the json instance annotation file.
image_root (str or path-like): directory which contains all the images.
"""
# 1. register a function which returns dicts
DatasetCatalog.register(
name, lambda: load_coco_json(json_file, image_root, name))
# 2. Optionally, add metadata about this dataset,
# since they might be useful in evaluation, visualization or logging
MetadataCatalog.get(name).set(json_file=json_file,
image_root=image_root,
evaluator_type="coco",
**metadata)
def register_coco_panoptic_separated(name, metadata, image_root, panoptic_root,
panoptic_json, sem_seg_root,
instances_json):
"""
Register a COCO panoptic segmentation dataset named `name`.
The annotations in this registered dataset will contain both instance annotations and
semantic annotations, each with its own contiguous ids. Hence it's called "separated".
It follows the setting used by the PanopticFPN paper:
1. The instance annotations directly come from polygons in the COCO
instances annotation task, rather than from the masks in the COCO panoptic annotations.
The two format have small differences:
Polygons in the instance annotations may have overlaps.
The mask annotations are produced by labeling the overlapped polygons
with depth ordering.
2. The semantic annotations are converted from panoptic annotations, where
all "things" are assigned a semantic id of 0.
All semantic categories will therefore have ids in contiguous
range [1, #stuff_categories].
This function will also register a pure semantic segmentation dataset
named ``name + '_stuffonly'``.
Args:
name (str): the name that identifies a dataset,
e.g. "coco_2017_train_panoptic"
metadata (dict): extra metadata associated with this dataset.
image_root (str): directory which contains all the images
panoptic_root (str): directory which contains panoptic annotation images
panoptic_json (str): path to the json panoptic annotation file
sem_seg_root (str): directory which contains all the ground truth segmentation annotations.
instances_json (str): path to the json instance annotation file
"""
panoptic_name = name + "_separated"
DatasetCatalog.register(
panoptic_name,
lambda: merge_to_panoptic(
load_coco_json(instances_json, image_root, panoptic_name),
load_sem_seg(sem_seg_root, image_root),
),
)
MetadataCatalog.get(panoptic_name).set(
panoptic_root=panoptic_root,
image_root=image_root,
panoptic_json=panoptic_json,
sem_seg_root=sem_seg_root,
json_file=instances_json, # TODO rename
evaluator_type="coco_panoptic_seg",
**metadata)
semantic_name = name + "_stuffonly"
DatasetCatalog.register(semantic_name,
lambda: load_sem_seg(sem_seg_root, image_root))
MetadataCatalog.get(semantic_name).set(sem_seg_root=sem_seg_root,
image_root=image_root,
evaluator_type="sem_seg",
**metadata)
def register_lvis_instances(name, metadata, json_file, image_root):
"""
Register a dataset in LVIS's json annotation format for instance detection and segmentation.
Args:
name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train".
metadata (dict): extra metadata associated with this dataset. It can be an empty dict.
json_file (str): path to the json instance annotation file.
image_root (str or path-like): directory which contains all the images.
"""
DatasetCatalog.register(name, lambda: load_lvis_json(json_file, image_root, name))
MetadataCatalog.get(name).set(
json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata
)
def __init__(self, dataset_name, cfg, distributed, output_dir=None):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have the following corresponding metadata:
"json_file": the path to the LVIS format annotation
cfg (CfgNode): config instance
distributed (True): if True, will collect results from all ranks for evaluation.
Otherwise, will evaluate the results in the current process.
output_dir (str): optional, an output directory to dump results.
"""
from lvis import LVIS
self._tasks = self._tasks_from_config(cfg)
self._distributed = distributed
self._output_dir = output_dir
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
self._metadata = MetadataCatalog.get(dataset_name)
json_file = PathManager.get_local_path(self._metadata.json_file)
self._lvis_api = LVIS(json_file)
# Test set json files do not contain annotations (evaluation must be
# performed using the LVIS evaluation server).
self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0
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, 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 CityscapesEvaluator(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 __init__(self, cfg, is_train=True):
self.tfm_gens = utils.build_transform_gen(cfg, is_train)
# fmt: off
self.img_format = cfg.INPUT.FORMAT
self.mask_on = cfg.MODEL.MASK_ON
self.keypoint_on = cfg.MODEL.KEYPOINT_ON
self.densepose_on = cfg.MODEL.DENSEPOSE_ON
assert not cfg.MODEL.LOAD_PROPOSALS, "not supported yet"
# fmt: on
if self.keypoint_on and is_train:
# Flip only makes sense in training
self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(
cfg.DATASETS.TRAIN)
else:
self.keypoint_hflip_indices = None
if self.densepose_on:
densepose_transform_srcs = [
MetadataCatalog.get(ds).densepose_transform_src
for ds in cfg.DATASETS.TRAIN + cfg.DATASETS.TEST
]
assert len(densepose_transform_srcs) > 0
# TODO: check that DensePose transformation data is the same for
# all the datasets. Otherwise one would have to pass DB ID with
# each entry to select proper transformation data. For now, since
# all DensePose annotated data uses the same data semantics, we
# omit this check.
densepose_transform_data_fpath = PathManager.get_local_path(
densepose_transform_srcs[0])
self.densepose_transform_data = DensePoseTransformData.load(
densepose_transform_data_fpath)
self.is_train = is_train
def register_all_coco(root="datasets"):
for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items():
for key, (image_root, json_file) in splits_per_dataset.items():
# Assume pre-defined datasets live in `./datasets`.
register_coco_instances(
key,
_get_builtin_metadata(dataset_name),
os.path.join(root, json_file) if "://" not in json_file else json_file,
os.path.join(root, image_root),
)
for (
prefix,
(panoptic_root, panoptic_json, semantic_root),
) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
prefix_instances = prefix[: -len("_panoptic")]
instances_meta = MetadataCatalog.get(prefix_instances)
image_root, instances_json = instances_meta.image_root, instances_meta.json_file
register_coco_panoptic_separated(
prefix,
_get_builtin_metadata("coco_panoptic_separated"),
image_root,
os.path.join(root, panoptic_root),
os.path.join(root, panoptic_json),
os.path.join(root, semantic_root),
instances_json,
)
def register_all_human_protein(root="datasets"):
SPLITS = [
("protein_trainval", "train", "train"),
]
for name, dirname, split in SPLITS:
# register_human_protein(name, os.path.join(root, dirname), split)
register_human_protein(name, os.path.join(root, dirname), split)
MetadataCatalog.get(name).evaluator_type = "pascal_voc"
def __init__(self, dataset_name, distributed, output_dir=None):
self._distributed = distributed
self._output_dir = output_dir
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
self._metadata = MetadataCatalog.get(dataset_name)
with contextlib.redirect_stdout(io.StringIO()):
self._coco_api = COCO(self._metadata.json_file)
def __init__(self, dataset_name):
"""
Args:
dataset_name (str): the name of the dataset.
It must have the following metadata associated with it:
"thing_classes", "gt_dir".
"""
self._metadata = MetadataCatalog.get(dataset_name)
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
def test_draw_no_metadata(self):
img, boxes, _, _, masks = self._random_data()
num_inst = len(boxes)
inst = Instances((img.shape[0], img.shape[1]))
inst.pred_classes = torch.randint(0, 80, size=(num_inst, ))
inst.scores = torch.rand(num_inst)
inst.pred_boxes = torch.from_numpy(boxes)
inst.pred_masks = torch.from_numpy(np.asarray(masks))
v = Visualizer(img, MetadataCatalog.get("asdfasdf"))
v.draw_instance_predictions(inst)
def register_all_pascal_voc(root="datasets"):
SPLITS = [
("voc_2007_trainval", "VOC2007", "trainval"),
("voc_2007_train", "VOC2007", "train"),
("voc_2007_val", "VOC2007", "val"),
("voc_2007_test", "VOC2007", "test"),
("voc_2012_trainval", "VOC2012", "trainval"),
("voc_2012_train", "VOC2012", "train"),
("voc_2012_val", "VOC2012", "val"),
]
for name, dirname, split in SPLITS:
year = 2007 if "2007" in name else 2012
register_pascal_voc(name, os.path.join(root, dirname), split, year)
MetadataCatalog.get(name).evaluator_type = "pascal_voc"
def register_all_cityscapes(root="datasets"):
for key, (image_dir, gt_dir) in _RAW_CITYSCAPES_SPLITS.items():
meta = _get_builtin_metadata("cityscapes")
image_dir = os.path.join(root, image_dir)
gt_dir = os.path.join(root, gt_dir)
inst_key = key.format(task="instance_seg")
DatasetCatalog.register(
inst_key,
lambda x=image_dir, y=gt_dir: load_cityscapes_instances(
x, y, from_json=True, to_polygons=True
),
)
MetadataCatalog.get(inst_key).set(
image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes", **meta
)
sem_key = key.format(task="sem_seg")
DatasetCatalog.register(
sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y)
)
MetadataCatalog.get(sem_key).set(
image_dir=image_dir, gt_dir=gt_dir, evaluator_type="sem_seg", **meta
)
def __init__(self, cfg):
self.cfg = cfg.clone() # cfg can be modified by model
self.model = build_model(self.cfg)
self.model.eval()
self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])
checkpointer = DetectionCheckpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
self.transform_gen = T.ResizeShortestEdge(
[cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],
cfg.INPUT.MAX_SIZE_TEST)
self.input_format = cfg.INPUT.FORMAT
assert self.input_format in ["RGB", "BGR"], self.input_format
def __init__(self, dataset_name, output_dir):
"""
Args:
dataset_name (str): name of the dataset
output_dir (str): output directory to save results for evaluation
"""
self._metadata = MetadataCatalog.get(dataset_name)
self._thing_contiguous_id_to_dataset_id = {
v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
}
self._stuff_contiguous_id_to_dataset_id = {
v: k for k, v in self._metadata.stuff_dataset_id_to_contiguous_id.items()
}
self._predictions_json = os.path.join(output_dir, "predictions.json")
def __init__(self, dataset_name, cfg, distributed, output_dir=None):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have either the following corresponding metadata:
"json_file": the path to the COCO format annotation
Or it must be in mydl's standard dataset format
so it can be converted to COCO format automatically.
cfg (CfgNode): config instance
distributed (True): if True, will collect results from all ranks and run evaluation
in the main process.
Otherwise, will evaluate the results in the current process.
output_dir (str): optional, an output directory to dump all
results predicted on the dataset. The dump contains two files:
1. "instance_predictions.pth" a file in torch serialization
format that contains all the raw original predictions.
2. "coco_instances_results.json" a json file in COCO's result
format.
"""
self._tasks = self._tasks_from_config(cfg)
self._distributed = distributed
self._output_dir = output_dir
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
self._metadata = MetadataCatalog.get(dataset_name)
if not hasattr(self._metadata, "json_file"):
self._logger.warning(
f"json_file was not found in MetaDataCatalog for '{dataset_name}'."
" Trying to convert it to COCO format ...")
cache_path = os.path.join(output_dir,
f"{dataset_name}_coco_format.json")
self._metadata.json_file = cache_path
convert_to_coco_json(dataset_name, cache_path)
json_file = PathManager.get_local_path(self._metadata.json_file)
with contextlib.redirect_stdout(io.StringIO()):
self._coco_api = COCO(json_file)
self._kpt_oks_sigmas = cfg.TEST.KEYPOINT_OKS_SIGMAS
# Test set json files do not contain annotations (evaluation must be
# performed using the COCO evaluation server).
self._do_evaluation = "annotations" in self._coco_api.dataset
def __init__(self, dataset_name):
"""
Args:
dataset_name (str): name of the dataset, e.g., "voc_2007_test"
"""
self._dataset_name = dataset_name
meta = MetadataCatalog.get(dataset_name)
self._anno_file_template = os.path.join(meta.dirname, "Annotations",
"{}.xml")
self._image_set_path = os.path.join(meta.dirname, "ImageSets", "Main",
meta.split + ".txt")
self._class_names = meta.thing_classes
assert meta.year in [2007, 2012], meta.year
self._is_2007 = meta.year == 2007
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
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))
elif evaluator_type == "cityscapes":
assert (
torch.cuda.device_count() >= comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesEvaluator(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 __init__(self,
dataset_name,
distributed,
num_classes,
ignore_label=255,
output_dir=None):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
distributed (True): if True, will collect results from all ranks for evaluation.
Otherwise, will evaluate the results in the current process.
num_classes (int): number of classes
ignore_label (int): value in semantic segmentation ground truth. Predictions for the
corresponding pixels should be ignored.
output_dir (str): an output directory to dump results.
"""
self._dataset_name = dataset_name
self._distributed = distributed
self._output_dir = output_dir
self._num_classes = num_classes
self._ignore_label = ignore_label
self._N = num_classes + 1
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
self.input_file_to_gt_file = {
dataset_record["file_name"]: dataset_record["sem_seg_file_name"]
for dataset_record in DatasetCatalog.get(dataset_name)
}
meta = MetadataCatalog.get(dataset_name)
# Dict that maps contiguous training ids to COCO category ids
try:
c2d = meta.stuff_dataset_id_to_contiguous_id
self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()}
except AttributeError:
self._contiguous_id_to_dataset_id = None
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
return parser.parse_args(in_args)
if __name__ == "__main__":
args = parse_args()
logger = setup_logger()
logger.info("Arguments: " + str(args))
cfg = setup(args)
dirname = args.output_dir
os.makedirs(dirname, exist_ok=True)
metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0])
def output(vis, fname):
if args.show:
print(fname)
cv2.imshow("window", vis.get_image()[:, :, ::-1])
cv2.waitKey()
else:
filepath = os.path.join(dirname, fname)
print("Saving to {} ...".format(filepath))
vis.save(filepath)
scale = 2.0 if args.show else 1.0
if args.source == "dataloader":
train_data_loader = build_detection_train_loader(cfg)
for batch in train_data_loader:
if __name__ == "__main__":
"""
Test the LVIS json dataset loader.
Usage:
python -m mydl.data.datasets.lvis \
path/to/json path/to/image_root dataset_name vis_limit
"""
import sys
import numpy as np
from mydl.utils.logger import setup_logger
from PIL import Image
import mydl.data.datasets # noqa # add pre-defined metadata
from mydl.utils.visualizer import Visualizer
logger = setup_logger(name=__name__)
meta = MetadataCatalog.get(sys.argv[3])
dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3])
logger.info("Done loading {} samples.".format(len(dicts)))
dirname = "lvis-data-vis"
os.makedirs(dirname, exist_ok=True)
for d in dicts[: int(sys.argv[4])]:
img = np.array(Image.open(d["file_name"]))
visualizer = Visualizer(img, metadata=meta)
vis = visualizer.draw_dataset_dict(d)
fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
vis.save(fpath)
def load_lvis_json(json_file, image_root, dataset_name=None):
"""
Load a json file in LVIS's annotation format.
Args:
json_file (str): full path to the LVIS json annotation file.
image_root (str): the directory where the images in this json file exists.
dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train").
If provided, this function will put "thing_classes" into the metadata
associated with this dataset.
Returns:
list[dict]: a list of dicts in mydl standard format. (See
`Using Custom Datasets </tutorials/datasets.html>`_ )
Notes:
1. This function does not read the image files.
The results do not have the "image" field.
"""
from lvis import LVIS
json_file = PathManager.get_local_path(json_file)
timer = Timer()
lvis_api = LVIS(json_file)
if timer.seconds() > 1:
logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
if dataset_name is not None:
meta = get_lvis_instances_meta(dataset_name)
MetadataCatalog.get(dataset_name).set(**meta)
# sort indices for reproducible results
img_ids = sorted(lvis_api.imgs.keys())
# imgs is a list of dicts, each looks something like:
# {'license': 4,
# 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
# 'file_name': 'COCO_val2014_000000001268.jpg',
# 'height': 427,
# 'width': 640,
# 'date_captured': '2013-11-17 05:57:24',
# 'id': 1268}
imgs = lvis_api.load_imgs(img_ids)
# anns is a list[list[dict]], where each dict is an annotation
# record for an object. The inner list enumerates the objects in an image
# and the outer list enumerates over images. Example of anns[0]:
# [{'segmentation': [[192.81,
# 247.09,
# ...
# 219.03,
# 249.06]],
# 'area': 1035.749,
# 'image_id': 1268,
# 'bbox': [192.81, 224.8, 74.73, 33.43],
# 'category_id': 16,
# 'id': 42986},
# ...]
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
# Sanity check that each annotation has a unique id
ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format(
json_file
)
imgs_anns = list(zip(imgs, anns))
logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file))
dataset_dicts = []
for (img_dict, anno_dict_list) in imgs_anns:
record = {}
file_name = img_dict["file_name"]
if img_dict["file_name"].startswith("COCO"):
# Convert form the COCO 2014 file naming convention of
# COCO_[train/val/test]2014_000000000000.jpg to the 2017 naming convention of
# 000000000000.jpg (LVIS v1 will fix this naming issue)
file_name = file_name[-16:]
record["file_name"] = os.path.join(image_root, file_name)
record["height"] = img_dict["height"]
record["width"] = img_dict["width"]
record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", [])
record["neg_category_ids"] = img_dict.get("neg_category_ids", [])
image_id = record["image_id"] = img_dict["id"]
objs = []
for anno in anno_dict_list:
# Check that the image_id in this annotation is the same as
# the image_id we're looking at.
# This fails only when the data parsing logic or the annotation file is buggy.
assert anno["image_id"] == image_id
obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed
segm = anno["segmentation"] # list[list[float]]
# filter out invalid polygons (< 3 points)
valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
assert len(segm) == len(
valid_segm
), "Annotation contains an invalid polygon with < 3 points"
assert len(segm) > 0
obj["segmentation"] = segm
objs.append(obj)
record["annotations"] = objs
dataset_dicts.append(record)
return dataset_dicts
("coco/train2014", "coco/annotations/densepose_train2014.json"),
"densepose_coco_2014_minival":
("coco/val2014", "coco/annotations/densepose_minival2014.json"),
"densepose_coco_2014_minival_100": (
"coco/val2014",
"coco/annotations/densepose_minival2014_100.json",
),
"densepose_coco_2014_valminusminival": (
"coco/val2014",
"coco/annotations/densepose_valminusminival2014.json",
),
}
DENSEPOSE_KEYS = ["dp_x", "dp_y", "dp_I", "dp_U", "dp_V", "dp_masks"]
for key, (image_root, json_file) in SPLITS.items():
# Assume pre-defined datasets live in `./datasets`.
json_file = os.path.join("datasets", json_file)
image_root = os.path.join("datasets", image_root)
DatasetCatalog.register(
key,
lambda key=key, json_file=json_file, image_root=image_root:
load_coco_json(
json_file, image_root, key, extra_annotation_keys=DENSEPOSE_KEYS),
)
MetadataCatalog.get(key).set(json_file=json_file,
image_root=image_root,
**get_densepose_metadata())
default=0.5,
type=float,
help="confidence threshold")
args = parser.parse_args()
logger = setup_logger()
with PathManager.open(args.input, "r") as f:
predictions = json.load(f)
pred_by_image = defaultdict(list)
for p in predictions:
pred_by_image[p["image_id"]].append(p)
dicts = list(DatasetCatalog.get(args.dataset))
metadata = MetadataCatalog.get(args.dataset)
if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
def dataset_id_map(ds_id):
return metadata.thing_dataset_id_to_contiguous_id[ds_id]
elif "lvis" in args.dataset:
# LVIS results are in the same format as COCO results, but have a different
# mapping from dataset category id to contiguous category id in [0, #categories - 1]
def dataset_id_map(ds_id):
return ds_id - 1
else:
raise ValueError("Unsupported dataset: {}".format(args.dataset))
os.makedirs(args.output, exist_ok=True)
def metadata(self):
return MetadataCatalog.get("coco_2017_train")
def register_pascal_voc(name, dirname, split, year):
DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split))
MetadataCatalog.get(name).set(thing_classes=CLASS_NAMES,
dirname=dirname,
year=year,
split=split)
def setUp(self):
json_file = MetadataCatalog.get("coco_2017_val_100").json_file
if not os.path.isfile(json_file):
raise unittest.SkipTest("{} not found".format(json_file))
with contextlib.redirect_stdout(io.StringIO()):
self.coco = COCO(json_file)