def generic_test(self, dataset_info, n_inst, loader_fun):
datasets_root = DENSEPOSE_ANNOTATIONS_DIR
annotations_fpath = maybe_prepend_base_path(
datasets_root, dataset_info.annotations_fpath)
images_root = maybe_prepend_base_path(datasets_root,
dataset_info.images_root)
image_annotation_dicts = loader_fun(
annotations_json_file=annotations_fpath,
image_root=images_root,
dataset_name=dataset_info.name,
)
num_valid = sum(1 for image_annotation_dict in image_annotation_dicts
for ann in image_annotation_dict["annotations"]
if DensePoseDataRelative.validate_annotation(ann)[0])
self.assertEqual(num_valid, n_inst)
def _extract_data_for_visualizers_from_entry(
cls: type, vis_specs: List[str], entry: Dict[str, Any]
):
dp_list = []
bbox_list = []
for annotation in entry["annotations"]:
is_valid, _ = DensePoseDataRelative.validate_annotation(annotation)
if not is_valid:
continue
bbox = torch.as_tensor(annotation["bbox"])
bbox_list.append(bbox)
dp_data = DensePoseDataRelative(annotation)
dp_list.append(dp_data)
datas = []
for vis_spec in vis_specs:
datas.append(bbox_list if "bbox" == vis_spec else (bbox_list, dp_list))
return datas
def _transform_densepose(self, annotation, transforms):
if not self.densepose_on:
return annotation
# Handle densepose annotations
is_valid, reason_not_valid = DensePoseDataRelative.validate_annotation(
annotation)
if is_valid:
densepose_data = DensePoseDataRelative(annotation, cleanup=True)
densepose_data.apply_transform(transforms,
self.densepose_transform_data)
annotation["densepose"] = densepose_data
else:
# logger = logging.getLogger(__name__)
# logger.debug("Could not load DensePose annotation: {}".format(reason_not_valid))
DensePoseDataRelative.cleanup_annotation(annotation)
# NOTE: annotations for certain instances may be unavailable.
# 'None' is accepted by the DensePostList data structure.
annotation["densepose"] = None
return annotation