From 358c2485e551bf392a8f5a5fd78504044c96a2c9 Mon Sep 17 00:00:00 2001
From: HAOCHENYE <21724054@zju.edu.cn>
Date: Sat, 25 Oct 2025 18:40:12 +0000
Subject: [PATCH] [Fix] Load checkpoint with `weights_only=Flase`

---
 mmengine/runner/checkpoint.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mmengine/runner/checkpoint.py b/mmengine/runner/checkpoint.py
index 2bf5f50f7c..f5556ed236 100644
--- a/mmengine/runner/checkpoint.py
+++ b/mmengine/runner/checkpoint.py
@@ -344,7 +344,7 @@ def load_from_local(filename, map_location):
     filename = osp.expanduser(filename)
     if not osp.isfile(filename):
         raise FileNotFoundError(f'{filename} can not be found.')
-    checkpoint = torch.load(filename, map_location=map_location)
+    checkpoint = torch.load(filename, map_location=map_location, weights_only=False)
     return checkpoint
 
 
@@ -412,7 +412,7 @@ def load_from_pavi(filename, map_location=None):
     with TemporaryDirectory() as tmp_dir:
         downloaded_file = osp.join(tmp_dir, model.name)
         model.download(downloaded_file)
-        checkpoint = torch.load(downloaded_file, map_location=map_location)
+        checkpoint = torch.load(downloaded_file, map_location=map_location, weights_only=False)
     return checkpoint
 
 
@@ -435,7 +435,7 @@ def load_from_ceph(filename, map_location=None, backend='petrel'):
     file_backend = get_file_backend(
         filename, backend_args={'backend': backend})
     with io.BytesIO(file_backend.get(filename)) as buffer:
-        checkpoint = torch.load(buffer, map_location=map_location)
+        checkpoint = torch.load(buffer, map_location=map_location, weights_only=False)
     return checkpoint
 
 
@@ -504,7 +504,7 @@ def load_from_openmmlab(filename, map_location=None):
         filename = osp.join(_get_mmengine_home(), model_url)
         if not osp.isfile(filename):
             raise FileNotFoundError(f'{filename} can not be found.')
-        checkpoint = torch.load(filename, map_location=map_location)
+        checkpoint = torch.load(filename, map_location=map_location, weights_only=False)
     return checkpoint