update numpy.bool8 to numpy.bool_

export_waymo_to_json.py

@@ -70,12 +70,12 @@ if __name__ == "__main__":
         decoded = json.load(f)
             
     x_c = torch.from_numpy(numpy.array(decoded["x"]).astype(numpy.float32))
-    mask_x_c = torch.from_numpy(numpy.array(decoded["mask_x"]).astype(numpy.bool8))
+    mask_x_c = torch.from_numpy(numpy.array(decoded["mask_x"]).astype(numpy.bool_))
     y_c = torch.from_numpy(numpy.array(decoded["y"]).astype(numpy.float32))
-    mask_y_c = torch.from_numpy(numpy.array(decoded["mask_y"]).astype(numpy.bool8))
-    mask_loss_c = torch.from_numpy( numpy.array(decoded["mask_loss"]).astype(numpy.bool8))
+    mask_y_c = torch.from_numpy(numpy.array(decoded["mask_y"]).astype(numpy.bool_))
+    mask_loss_c = torch.from_numpy( numpy.array(decoded["mask_loss"]).astype(numpy.bool_))
     map_data_c = torch.from_numpy(numpy.array(decoded["map_data"]).astype(numpy.float32))
-    mask_map_c = torch.from_numpy(numpy.array(decoded["mask_map"]).astype(numpy.bool8))
+    mask_map_c = torch.from_numpy(numpy.array(decoded["mask_map"]).astype(numpy.bool_))
     offset_c = torch.from_numpy(numpy.array(decoded["offset"]).astype(numpy.float32))
     x_ego_c = torch.from_numpy(numpy.array(decoded["x_ego"]).astype(numpy.float32))
     y_ego_c = torch.from_numpy(numpy.array(decoded["y_ego"]).astype(numpy.float32))

import_dataset_from_huggingface.py

@@ -30,12 +30,12 @@ sample_dataloader = dataloaders.sample_dataloader()
 dataset = load_dataset("jmercat/risk_biased_dataset", split="test")
 
 x_c = torch.from_numpy(numpy.array(dataset["x"]).astype(numpy.float32))
-mask_x_c = torch.from_numpy(numpy.array(dataset["mask_x"]).astype(numpy.bool8))
+mask_x_c = torch.from_numpy(numpy.array(dataset["mask_x"]).astype(numpy.bool_))
 y_c = torch.from_numpy(numpy.array(dataset["y"]).astype(numpy.float32))
-mask_y_c = torch.from_numpy(numpy.array(dataset["mask_y"]).astype(numpy.bool8))
-mask_loss_c = torch.from_numpy( numpy.array(dataset["mask_loss"]).astype(numpy.bool8))
+mask_y_c = torch.from_numpy(numpy.array(dataset["mask_y"]).astype(numpy.bool_))
+mask_loss_c = torch.from_numpy( numpy.array(dataset["mask_loss"]).astype(numpy.bool_))
 map_data_c = torch.from_numpy(numpy.array(dataset["map_data"]).astype(numpy.float32))
-mask_map_c = torch.from_numpy(numpy.array(dataset["mask_map"]).astype(numpy.bool8))
+mask_map_c = torch.from_numpy(numpy.array(dataset["mask_map"]).astype(numpy.bool_))
 offset_c = torch.from_numpy(numpy.array(dataset["offset"]).astype(numpy.float32))
 x_ego_c = torch.from_numpy(numpy.array(dataset["x_ego"]).astype(numpy.float32))
 y_ego_c = torch.from_numpy(numpy.array(dataset["y_ego"]).astype(numpy.float32))

scripts/scripts_utils/plotly_interface.py

@@ -56,12 +56,12 @@ def configuration_paths() -> Iterable[os.PathLike]:
 
 def load_item(index: int, dataset: Dataset, device: str = "cpu") -> Tuple:
     x = torch.from_numpy(numpy.array(dataset[index]["x"]).astype(numpy.float32)).to(device)
-    mask_x = torch.from_numpy(numpy.array(dataset[index]["mask_x"]).astype(numpy.bool8)).to(device)
+    mask_x = torch.from_numpy(numpy.array(dataset[index]["mask_x"]).astype(numpy.bool_)).to(device)
     y = torch.from_numpy(numpy.array(dataset[index]["y"]).astype(numpy.float32)).to(device)
-    mask_y = torch.from_numpy(numpy.array(dataset[index]["mask_y"]).astype(numpy.bool8)).to(device)
-    mask_loss = torch.from_numpy( numpy.array(dataset[index]["mask_loss"]).astype(numpy.bool8)).to(device)
+    mask_y = torch.from_numpy(numpy.array(dataset[index]["mask_y"]).astype(numpy.bool_)).to(device)
+    mask_loss = torch.from_numpy( numpy.array(dataset[index]["mask_loss"]).astype(numpy.bool_)).to(device)
     map_data = torch.from_numpy(numpy.array(dataset[index]["map_data"]).astype(numpy.float32)).to(device)
-    mask_map = torch.from_numpy(numpy.array(dataset[index]["mask_map"]).astype(numpy.bool8)).to(device)
+    mask_map = torch.from_numpy(numpy.array(dataset[index]["mask_map"]).astype(numpy.bool_)).to(device)
     offset = torch.from_numpy(numpy.array(dataset[index]["offset"]).astype(numpy.float32)).to(device)
     x_ego = torch.from_numpy(numpy.array(dataset[index]["x_ego"]).astype(numpy.float32)).to(device)
     y_ego = torch.from_numpy(numpy.array(dataset[index]["y_ego"]).astype(numpy.float32)).to(device)