Add annotations to vision/fair/detectron2/projects/DensePose

Reviewed By: shannonzhu

Differential Revision: D33970334

fbshipit-source-id: 629c0b28fb1886bbbee4b30ee33273a644522d73

projects/DensePose/densepose/config.py

@@ -5,7 +5,7 @@
 from detectron2.config import CfgNode as CN
 
 
-def add_dataset_category_config(cfg: CN):
+def add_dataset_category_config(cfg: CN) -> None:
     """
     Add config for additional category-related dataset options
      - category whitelisting
@@ -18,7 +18,7 @@ def add_dataset_category_config(cfg: CN):
     _C.DATASETS.CLASS_TO_MESH_NAME_MAPPING = CN(new_allowed=True)
 
 
-def add_evaluation_config(cfg: CN):
+def add_evaluation_config(cfg: CN) -> None:
     _C = cfg
     _C.DENSEPOSE_EVALUATION = CN()
     # evaluator type, possible values:
@@ -47,7 +47,7 @@ def add_evaluation_config(cfg: CN):
     _C.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES = []
 
 
-def add_bootstrap_config(cfg: CN):
+def add_bootstrap_config(cfg: CN) -> None:
     """ """
     _C = cfg
     _C.BOOTSTRAP_DATASETS = []
@@ -85,7 +85,7 @@ def get_bootstrap_dataset_config() -> CN:
     return _C
 
 
-def load_bootstrap_config(cfg: CN):
+def load_bootstrap_config(cfg: CN) -> None:
     """
     Bootstrap datasets are given as a list of `dict` that are not automatically
     converted into CfgNode. This method processes all bootstrap dataset entries
@@ -102,7 +102,7 @@ def load_bootstrap_config(cfg: CN):
     cfg.BOOTSTRAP_DATASETS = bootstrap_datasets_cfgnodes
 
 
-def add_densepose_head_cse_config(cfg: CN):
+def add_densepose_head_cse_config(cfg: CN) -> None:
     """
     Add configuration options for Continuous Surface Embeddings (CSE)
     """
@@ -155,7 +155,7 @@ def add_densepose_head_cse_config(cfg: CN):
     _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL = 0.05
 
 
-def add_densepose_head_config(cfg: CN):
+def add_densepose_head_config(cfg: CN) -> None:
     """
     Add config for densepose head.
     """
@@ -234,7 +234,7 @@ def add_densepose_head_config(cfg: CN):
     add_densepose_head_cse_config(cfg)
 
 
-def add_hrnet_config(cfg: CN):
+def add_hrnet_config(cfg: CN) -> None:
     """
     Add config for HRNet backbone.
     """
@@ -269,7 +269,7 @@ def add_hrnet_config(cfg: CN):
     _C.MODEL.HRNET.HRFPN.OUT_CHANNELS = 256
 
 
-def add_densepose_config(cfg: CN):
+def add_densepose_config(cfg: CN) -> None:
     add_densepose_head_config(cfg)
     add_hrnet_config(cfg)
     add_bootstrap_config(cfg)

projects/DensePose/densepose/data/build.py

@@ -56,7 +56,7 @@
 InstancePredicate = Callable[[Instance], bool]
 
 
-def _compute_num_images_per_worker(cfg: CfgNode):
+def _compute_num_images_per_worker(cfg: CfgNode) -> int:
     num_workers = get_world_size()
     images_per_batch = cfg.SOLVER.IMS_PER_BATCH
     assert (
@@ -73,7 +73,7 @@ def _compute_num_images_per_worker(cfg: CfgNode):
     return images_per_worker
 
 
-def _map_category_id_to_contiguous_id(dataset_name: str, dataset_dicts: Iterable[Instance]):
+def _map_category_id_to_contiguous_id(dataset_name: str, dataset_dicts: Iterable[Instance]) -> None:
     meta = MetadataCatalog.get(dataset_name)
     for dataset_dict in dataset_dicts:
         for ann in dataset_dict["annotations"]:
@@ -109,7 +109,7 @@ class _DatasetCategory:
 _MergedCategoriesT = Dict[int, List[_DatasetCategory]]
 
 
-def _add_category_id_to_contiguous_id_maps_to_metadata(merged_categories: _MergedCategoriesT):
+def _add_category_id_to_contiguous_id_maps_to_metadata(merged_categories: _MergedCategoriesT) -> None:
     merged_categories_per_dataset = {}
     for contiguous_cat_id, cat_id in enumerate(sorted(merged_categories.keys())):
         for cat in merged_categories[cat_id]:
@@ -266,7 +266,7 @@ def _maybe_filter_and_map_categories(
     return filtered_dataset_dicts
 
 
-def _add_category_whitelists_to_metadata(cfg: CfgNode):
+def _add_category_whitelists_to_metadata(cfg: CfgNode) -> None:
     for dataset_name, whitelisted_cat_ids in cfg.DATASETS.WHITELISTED_CATEGORIES.items():
         meta = MetadataCatalog.get(dataset_name)
         meta.whitelisted_categories = whitelisted_cat_ids
@@ -278,7 +278,7 @@ def _add_category_whitelists_to_metadata(cfg: CfgNode):
         )
 
 
-def _add_category_maps_to_metadata(cfg: CfgNode):
+def _add_category_maps_to_metadata(cfg: CfgNode) -> None:
     for dataset_name, category_map in cfg.DATASETS.CATEGORY_MAPS.items():
         category_map = {
             int(cat_id_src): int(cat_id_dst) for cat_id_src, cat_id_dst in category_map.items()
@@ -289,7 +289,7 @@ def _add_category_maps_to_metadata(cfg: CfgNode):
         logger.info("Category maps for dataset {}: {}".format(dataset_name, meta.category_map))
 
 
-def _add_category_info_to_bootstrapping_metadata(dataset_name: str, dataset_cfg: CfgNode):
+def _add_category_info_to_bootstrapping_metadata(dataset_name: str, dataset_cfg: CfgNode) -> None:
     meta = MetadataCatalog.get(dataset_name)
     meta.category_to_class_mapping = get_category_to_class_mapping(dataset_cfg)
     meta.categories = dataset_cfg.CATEGORIES
@@ -302,7 +302,7 @@ def _add_category_info_to_bootstrapping_metadata(dataset_name: str, dataset_cfg:
     )
 
 
-def _maybe_add_class_to_mesh_name_map_to_metadata(dataset_names: List[str], cfg: CfgNode):
+def _maybe_add_class_to_mesh_name_map_to_metadata(dataset_names: List[str], cfg: CfgNode) -> None:
     for dataset_name in dataset_names:
         meta = MetadataCatalog.get(dataset_name)
         if not hasattr(meta, "class_to_mesh_name"):
@@ -348,7 +348,7 @@ def _merge_categories(dataset_names: Collection[str]) -> _MergedCategoriesT:
     return merged_categories
 
 
-def _warn_if_merged_different_categories(merged_categories: _MergedCategoriesT):
+def _warn_if_merged_different_categories(merged_categories: _MergedCategoriesT) -> None:
     logger = logging.getLogger(__name__)
     for cat_id in merged_categories:
         merged_categories_i = merged_categories[cat_id]

projects/DensePose/densepose/data/datasets/chimpnsee.py

@@ -10,7 +10,7 @@
 CHIMPNSEE_DATASET_NAME = "chimpnsee"
 
 
-def register_dataset(datasets_root: Optional[str] = None):
+def register_dataset(datasets_root: Optional[str] = None) -> None:
     def empty_load_callback():
         pass
 

projects/DensePose/densepose/data/datasets/lvis.py

@@ -68,7 +68,7 @@ def _load_lvis_annotations(json_file: str):
     return lvis_api
 
 
-def _add_categories_metadata(dataset_name: str):
+def _add_categories_metadata(dataset_name: str) -> None:
     metadict = get_lvis_instances_meta(dataset_name)
     categories = metadict["thing_classes"]
     metadata = MetadataCatalog.get(dataset_name)
@@ -77,21 +77,21 @@ def _add_categories_metadata(dataset_name: str):
     logger.info(f"Dataset {dataset_name} has {len(categories)} categories")
 
 
-def _verify_annotations_have_unique_ids(json_file: str, anns: List[List[Dict[str, Any]]]):
+def _verify_annotations_have_unique_ids(json_file: str, anns: List[List[Dict[str, Any]]]) -> None:
     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
     )
 
 
-def _maybe_add_bbox(obj: Dict[str, Any], ann_dict: Dict[str, Any]):
+def _maybe_add_bbox(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
     if "bbox" not in ann_dict:
         return
     obj["bbox"] = ann_dict["bbox"]
     obj["bbox_mode"] = BoxMode.XYWH_ABS
 
 
-def _maybe_add_segm(obj: Dict[str, Any], ann_dict: Dict[str, Any]):
+def _maybe_add_segm(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
     if "segmentation" not in ann_dict:
         return
     segm = ann_dict["segmentation"]
@@ -103,7 +103,7 @@ def _maybe_add_segm(obj: Dict[str, Any], ann_dict: Dict[str, Any]):
     obj["segmentation"] = segm
 
 
-def _maybe_add_keypoints(obj: Dict[str, Any], ann_dict: Dict[str, Any]):
+def _maybe_add_keypoints(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
     if "keypoints" not in ann_dict:
         return
     keypts = ann_dict["keypoints"]  # list[int]
@@ -117,7 +117,7 @@ def _maybe_add_keypoints(obj: Dict[str, Any], ann_dict: Dict[str, Any]):
     obj["keypoints"] = keypts
 
 
-def _maybe_add_densepose(obj: Dict[str, Any], ann_dict: Dict[str, Any]):
+def _maybe_add_densepose(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
     for key in DENSEPOSE_ALL_POSSIBLE_KEYS:
         if key in ann_dict:
             obj[key] = ann_dict[key]
@@ -212,7 +212,7 @@ def load_lvis_json(annotations_json_file: str, image_root: str, dataset_name: st
     return dataset_records
 
 
-def register_dataset(dataset_data: CocoDatasetInfo, datasets_root: Optional[str] = None):
+def register_dataset(dataset_data: CocoDatasetInfo, datasets_root: Optional[str] = None) -> None:
     """
     Registers provided LVIS DensePose dataset
 
@@ -243,7 +243,7 @@ def load_annotations():
 
 def register_datasets(
     datasets_data: Iterable[CocoDatasetInfo], datasets_root: Optional[str] = None
-):
+) -> None:
     """
     Registers provided LVIS DensePose datasets
 

projects/DensePose/densepose/data/meshes/catalog.py

@@ -49,7 +49,7 @@ def get_mesh_name(self, mesh_id: int) -> str:
 MeshCatalog = _MeshCatalog()
 
 
-def register_mesh(mesh_info: MeshInfo, base_path: Optional[str]):
+def register_mesh(mesh_info: MeshInfo, base_path: Optional[str]) -> None:
     geodists, symmetry, texcoords = mesh_info.geodists, mesh_info.symmetry, mesh_info.texcoords
     if geodists:
         geodists = maybe_prepend_base_path(base_path, geodists)
@@ -66,6 +66,6 @@ def register_mesh(mesh_info: MeshInfo, base_path: Optional[str]):
     )
 
 
-def register_meshes(mesh_infos: Iterable[MeshInfo], base_path: Optional[str]):
+def register_meshes(mesh_infos: Iterable[MeshInfo], base_path: Optional[str]) -> None:
     for mesh_info in mesh_infos:
         register_mesh(mesh_info, base_path)

projects/DensePose/densepose/data/utils.py

@@ -6,7 +6,7 @@
 from detectron2.config import CfgNode
 
 
-def is_relative_local_path(path: str):
+def is_relative_local_path(path: str) -> bool:
     path_str = os.fsdecode(path)
     return ("://" not in path_str) and not os.path.isabs(path)
 

projects/DensePose/densepose/evaluation/d2_evaluator_adapter.py

@@ -9,7 +9,7 @@
 )
 
 
-def _maybe_add_iscrowd_annotations(cocoapi):
+def _maybe_add_iscrowd_annotations(cocoapi) -> None:
     for ann in cocoapi.dataset["annotations"]:
         if "iscrowd" not in ann:
             ann["iscrowd"] = 0

projects/DensePose/densepose/evaluation/evaluator.py

@@ -291,7 +291,7 @@ def _evaluate_predictions_on_coco(
     multi_storage=None,
     embedder=None,
     class_names=None,
-    min_threshold=0.5,
+    min_threshold: float=0.5,
     img_ids=None,
 ):
     logger = logging.getLogger(__name__)
@@ -319,7 +319,7 @@ def _evaluate_predictions_on_coco(
     return results
 
 
-def _get_densepose_metrics(min_threshold=0.5):
+def _get_densepose_metrics(min_threshold: float=0.5):
     metrics = ["AP"]
     if min_threshold <= 0.201:
         metrics += ["AP20"]
@@ -332,7 +332,7 @@ def _get_densepose_metrics(min_threshold=0.5):
 
 
 def _derive_results_from_coco_eval(
-    coco_eval, eval_mode_name, metrics, class_names, min_threshold, img_ids
+    coco_eval, eval_mode_name, metrics, class_names, min_threshold: float, img_ids
 ):
     if img_ids is not None:
         coco_eval.params.imgIds = img_ids

projects/DensePose/densepose/modeling/hrfpn.py

@@ -162,7 +162,7 @@ def forward(self, inputs):
 
 
 @BACKBONE_REGISTRY.register()
-def build_hrfpn_backbone(cfg, input_shape: ShapeSpec):
+def build_hrfpn_backbone(cfg, input_shape: ShapeSpec) -> HRFPN:
 
     in_channels = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS
     in_features = ["p%d" % (i + 1) for i in range(cfg.MODEL.HRNET.STAGE4.NUM_BRANCHES)]

projects/DensePose/densepose/modeling/inference.py

@@ -6,7 +6,7 @@
 from detectron2.structures import Instances
 
 
-def densepose_inference(densepose_predictor_output: Any, detections: List[Instances]):
+def densepose_inference(densepose_predictor_output: Any, detections: List[Instances]) -> None:
     """
     Splits DensePose predictor outputs into chunks, each chunk corresponds to
     detections on one image. Predictor output chunks are stored in `pred_densepose`

projects/DensePose/densepose/modeling/losses/utils.py

@@ -193,7 +193,7 @@ def extract_at_points(
 
 
 def resample_data(
-    z, bbox_xywh_src, bbox_xywh_dst, wout, hout, mode="nearest", padding_mode="zeros"
+    z, bbox_xywh_src, bbox_xywh_dst, wout, hout, mode: str="nearest", padding_mode: str="zeros"
 ):
     """
     Args:

projects/DensePose/densepose/modeling/utils.py

@@ -3,7 +3,7 @@
 from torch import nn
 
 
-def initialize_module_params(module: nn.Module):
+def initialize_module_params(module: nn.Module) -> None:
     for name, param in module.named_parameters():
         if "bias" in name:
             nn.init.constant_(param, 0)

projects/DensePose/densepose/structures/mesh.py

@@ -166,5 +166,5 @@ def load_mesh_symmetry(
 
 
 @lru_cache()
-def create_mesh(mesh_name: str, device: Optional[torch.device] = None):
+def create_mesh(mesh_name: str, device: Optional[torch.device] = None) -> Mesh:
     return Mesh(mesh_info=MeshCatalog[mesh_name], device=device)

projects/DensePose/densepose/utils/logger.py

@@ -2,7 +2,7 @@
 import logging
 
 
-def verbosity_to_level(verbosity):
+def verbosity_to_level(verbosity) -> int:
     if verbosity is not None:
         if verbosity == 0:
             return logging.WARNING