Merge pull request #211 from agimus-project/fix/deprecation_warnings

Fix/deprecation warnings

Author: MedericFourmy

happypose/pose_estimators/cosypose/cosypose/evaluation/evaluation.py

@@ -60,7 +60,9 @@ def load_detector(run_id, ds_name):
     cfg = check_update_config_detector(cfg)
     label_to_category_id = cfg.label_to_category_id
     model = create_model_detector(cfg, len(label_to_category_id))
-    ckpt = torch.load(run_dir / "checkpoint.pth.tar", map_location=device)
+    ckpt = torch.load(
+        run_dir / "checkpoint.pth.tar", map_location=device, weights_only=True
+    )
     ckpt = ckpt["state_dict"]
     model.load_state_dict(ckpt)
     model = model.to(device).eval()

happypose/pose_estimators/cosypose/cosypose/evaluation/meters/base.py

@@ -38,7 +38,7 @@ def gather_distributed(self, tmp_dir):
             all_datas = self.datas
             for n in range(1, world_size):
                 tmp_file = tmp_file_template.format(rank=n)
-                datas = torch.load(tmp_file)
+                datas = torch.load(tmp_file, weights_only=True)
                 for k in all_datas.keys():
                     all_datas[k].extend(datas.get(k, []))
                 Path(tmp_file).unlink()

happypose/pose_estimators/cosypose/cosypose/multiview/ransac.py

@@ -133,7 +133,7 @@ def make_obj_infos(matched_candidates):
     scene_infos = matched_candidates.infos.loc[:, ["obj_id", "score", "label"]].copy()
     gb = scene_infos.groupby("obj_id")
     scene_infos["n_cand"] = gb["score"].transform(len).astype(int)
-    scene_infos["score"] = gb["score"].transform(np.sum)
+    scene_infos["score"] = gb["score"].transform("sum")
     scene_infos = gb.first().reset_index(drop=False)
     return scene_infos
 

happypose/pose_estimators/cosypose/cosypose/scripts/run_bop_inference.py

@@ -83,7 +83,7 @@ def load_detector(run_id):
     cfg = check_update_config_detector(cfg)
     label_to_category_id = cfg.label_to_category_id
     model = create_model_detector(cfg, len(label_to_category_id))
-    ckpt = torch.load(run_dir / "checkpoint.pth.tar")
+    ckpt = torch.load(run_dir / "checkpoint.pth.tar", weights_only=True)
     ckpt = ckpt["state_dict"]
     model.load_state_dict(ckpt)
     model = model.cuda().eval()

happypose/pose_estimators/cosypose/cosypose/scripts/run_detection_eval.py

@@ -55,7 +55,7 @@ def load_detector(run_id):
     cfg = check_update_config(cfg)
     label_to_category_id = cfg.label_to_category_id
     model = create_model_detector(cfg, len(label_to_category_id))
-    ckpt = torch.load(run_dir / "checkpoint.pth.tar")
+    ckpt = torch.load(run_dir / "checkpoint.pth.tar", weights_only=True)
     ckpt = ckpt["state_dict"]
     model.load_state_dict(ckpt)
     model = model.cuda().eval()

happypose/pose_estimators/cosypose/cosypose/training/evaluation.py

@@ -69,7 +69,9 @@ def load_detector(run_id, ds_name):
     cfg = check_update_config_detector(cfg)
     label_to_category_id = cfg.label_to_category_id
     model = create_model_detector(cfg, len(label_to_category_id))
-    ckpt = torch.load(run_dir / "checkpoint.pth.tar", map_location=device)
+    ckpt = torch.load(
+        run_dir / "checkpoint.pth.tar", map_location=device, weights_only=True
+    )
     ckpt = ckpt["state_dict"]
     model.load_state_dict(ckpt)
     model = model.to(device).eval()

happypose/pose_estimators/cosypose/cosypose/training/pose_models_cfg.py

@@ -65,7 +65,9 @@ def load_model_cosypose(
     cfg = yaml.load((run_dir / "config.yaml").read_text(), Loader=yaml.UnsafeLoader)
     cfg = check_update_config(cfg)
     model = create_pose_model_cosypose(cfg, renderer=renderer, mesh_db=mesh_db_batched)
-    ckpt = torch.load(run_dir / "checkpoint.pth.tar", map_location=device)
+    ckpt = torch.load(
+        run_dir / "checkpoint.pth.tar", map_location=device, weights_only=True
+    )
     ckpt = ckpt["state_dict"]
     model.load_state_dict(ckpt)
     model = model.to(device).eval()

happypose/pose_estimators/cosypose/cosypose/training/train_detector.py

@@ -218,7 +218,7 @@ def make_datasets(dataset_names):
         resume_dir = EXP_DIR / args.resume_run_id
         path = resume_dir / "checkpoint.pth.tar"
         logger.info(f"Loading checkpoing from {path}")
-        save = torch.load(path)
+        save = torch.load(path, weights_only=True)
         state_dict = save["state_dict"]
         model.load_state_dict(state_dict)
         start_epoch = save["epoch"] + 1

happypose/pose_estimators/cosypose/cosypose/training/train_pose.py

@@ -349,7 +349,7 @@ def make_datasets(dataset_names):
         resume_dir = EXP_DIR / args.resume_run_id
         path = resume_dir / "checkpoint.pth.tar"
         logger.info(f"Loading checkpoing from {path}")
-        save = torch.load(path)
+        save = torch.load(path, weights_only=True)
         state_dict = save["state_dict"]
         model.load_state_dict(state_dict)
         start_epoch = save["epoch"] + 1

happypose/toolbox/inference/utils.py

@@ -64,7 +64,11 @@ def load_detector(run_id: str, device="cpu") -> torch.nn.Module:
     cfg = check_update_config_detector(cfg)
     label_to_category_id = cfg.label_to_category_id
     model = create_model_detector(cfg, len(label_to_category_id))
-    ckpt = torch.load(run_dir / "checkpoint.pth.tar", map_location=torch.device(device))
+    ckpt = torch.load(
+        run_dir / "checkpoint.pth.tar",
+        map_location=torch.device(device),
+        weights_only=True,
+    )
     ckpt = ckpt["state_dict"]
     model.load_state_dict(ckpt)
     model = model.to(device).eval()