release infer and demo

matanyone2/inference/object_manager.py

@@ -0,0 +1,149 @@
+from typing import Union, List, Dict
+
+import torch
+from matanyone2.inference.object_info import ObjectInfo
+
+
+class ObjectManager:
+    """
+    Object IDs are immutable. The same ID always represent the same object.
+    Temporary IDs are the positions of each object in the tensor. It changes as objects get removed.
+    Temporary IDs start from 1.
+    """
+
+    def __init__(self):
+        self.obj_to_tmp_id: Dict[ObjectInfo, int] = {}
+        self.tmp_id_to_obj: Dict[int, ObjectInfo] = {}
+        self.obj_id_to_obj: Dict[int, ObjectInfo] = {}
+
+        self.all_historical_object_ids: List[int] = []
+
+    def _recompute_obj_id_to_obj_mapping(self) -> None:
+        self.obj_id_to_obj = {obj.id: obj for obj in self.obj_to_tmp_id}
+
+    def add_new_objects(
+            self, objects: Union[List[ObjectInfo], ObjectInfo,
+                                 List[int]]) -> (List[int], List[int]):
+        if not isinstance(objects, list):
+            objects = [objects]
+
+        corresponding_tmp_ids = []
+        corresponding_obj_ids = []
+        for obj in objects:
+            if isinstance(obj, int):
+                obj = ObjectInfo(id=obj)
+
+            if obj in self.obj_to_tmp_id:
+                # old object
+                corresponding_tmp_ids.append(self.obj_to_tmp_id[obj])
+                corresponding_obj_ids.append(obj.id)
+            else:
+                # new object
+                new_obj = ObjectInfo(id=obj.id)
+
+                # new object
+                new_tmp_id = len(self.obj_to_tmp_id) + 1
+                self.obj_to_tmp_id[new_obj] = new_tmp_id
+                self.tmp_id_to_obj[new_tmp_id] = new_obj
+                self.all_historical_object_ids.append(new_obj.id)
+                corresponding_tmp_ids.append(new_tmp_id)
+                corresponding_obj_ids.append(new_obj.id)
+
+        self._recompute_obj_id_to_obj_mapping()
+        assert corresponding_tmp_ids == sorted(corresponding_tmp_ids)
+        return corresponding_tmp_ids, corresponding_obj_ids
+
+    def delete_objects(self, obj_ids_to_remove: Union[int, List[int]]) -> None:
+        # delete an object or a list of objects
+        # re-sort the tmp ids
+        if isinstance(obj_ids_to_remove, int):
+            obj_ids_to_remove = [obj_ids_to_remove]
+
+        new_tmp_id = 1
+        total_num_id = len(self.obj_to_tmp_id)
+
+        local_obj_to_tmp_id = {}
+        local_tmp_to_obj_id = {}
+
+        for tmp_iter in range(1, total_num_id + 1):
+            obj = self.tmp_id_to_obj[tmp_iter]
+            if obj.id not in obj_ids_to_remove:
+                local_obj_to_tmp_id[obj] = new_tmp_id
+                local_tmp_to_obj_id[new_tmp_id] = obj
+                new_tmp_id += 1
+
+        self.obj_to_tmp_id = local_obj_to_tmp_id
+        self.tmp_id_to_obj = local_tmp_to_obj_id
+        self._recompute_obj_id_to_obj_mapping()
+
+    def purge_inactive_objects(self,
+                               max_missed_detection_count: int) -> (bool, List[int], List[int]):
+        # remove tmp ids of objects that are removed
+        obj_id_to_be_deleted = []
+        tmp_id_to_be_deleted = []
+        tmp_id_to_keep = []
+        obj_id_to_keep = []
+
+        for obj in self.obj_to_tmp_id:
+            if obj.poke_count > max_missed_detection_count:
+                obj_id_to_be_deleted.append(obj.id)
+                tmp_id_to_be_deleted.append(self.obj_to_tmp_id[obj])
+            else:
+                tmp_id_to_keep.append(self.obj_to_tmp_id[obj])
+                obj_id_to_keep.append(obj.id)
+
+        purge_activated = len(obj_id_to_be_deleted) > 0
+        if purge_activated:
+            self.delete_objects(obj_id_to_be_deleted)
+        return purge_activated, tmp_id_to_keep, obj_id_to_keep
+
+    def tmp_to_obj_cls(self, mask) -> torch.Tensor:
+        # remap tmp id cls representation to the true object id representation
+        new_mask = torch.zeros_like(mask)
+        for tmp_id, obj in self.tmp_id_to_obj.items():
+            new_mask[mask == tmp_id] = obj.id
+        return new_mask
+
+    def get_tmp_to_obj_mapping(self) -> Dict[int, ObjectInfo]:
+        # returns the mapping in a dict format for saving it with pickle
+        return {obj.id: tmp_id for obj, tmp_id in self.tmp_id_to_obj.items()}
+
+    def realize_dict(self, obj_dict, dim=1) -> torch.Tensor:
+        # turns a dict indexed by obj id into a tensor, ordered by tmp IDs
+        output = []
+        for _, obj in self.tmp_id_to_obj.items():
+            if obj.id not in obj_dict:
+                raise NotImplementedError
+            output.append(obj_dict[obj.id])
+        output = torch.stack(output, dim=dim)
+        return output
+
+    def make_one_hot(self, cls_mask) -> torch.Tensor:
+        output = []
+        for _, obj in self.tmp_id_to_obj.items():
+            output.append(cls_mask == obj.id)
+        if len(output) == 0:
+            output = torch.zeros((0, *cls_mask.shape), dtype=torch.bool, device=cls_mask.device)
+        else:
+            output = torch.stack(output, dim=0)
+        return output
+
+    @property
+    def all_obj_ids(self) -> List[int]:
+        return [k.id for k in self.obj_to_tmp_id]
+
+    @property
+    def num_obj(self) -> int:
+        return len(self.obj_to_tmp_id)
+
+    def has_all(self, objects: List[int]) -> bool:
+        for obj in objects:
+            if obj not in self.obj_to_tmp_id:
+                return False
+        return True
+
+    def find_object_by_id(self, obj_id) -> ObjectInfo:
+        return self.obj_id_to_obj[obj_id]
+
+    def find_tmp_by_id(self, obj_id) -> int:
+        return self.obj_to_tmp_id[self.obj_id_to_obj[obj_id]]