pytorch · vfdev-5 · Oct 29, 2021 · Oct 29, 2021 · Oct 29, 2021 · Oct 29, 2021
diff --git a/ignite/handlers/checkpoint.py b/ignite/handlers/checkpoint.py
@@ -507,14 +507,14 @@ def _check_objects(objs: Mapping, attr: str) -> None:
                 raise TypeError(f"Object {type(obj)} should have `{attr}` method")
 
     @staticmethod
-    def load_objects(to_load: Mapping, checkpoint: Mapping, **kwargs: Any) -> None:
+    def load_objects(to_load: Mapping, checkpoint: Union[str, Mapping], **kwargs: Any) -> None:
         """Helper method to apply ``load_state_dict`` on the objects from ``to_load`` using states from ``checkpoint``.
 
         Args:
             to_load: a dictionary with objects, e.g. `{"model": model, "optimizer": optimizer, ...}`
-            checkpoint: a dictionary with state_dicts to load, e.g. `{"model": model_state_dict,
-                "optimizer": opt_state_dict}`. If `to_load` contains a single key, then checkpoint can contain directly
-                corresponding state_dict.
+            checkpoint: a string filepath or a dictionary with state_dicts to load, e.g. `{"model": model_state_dict,
+                "optimizer": opt_state_dict}`. If `to_load` contains a single key, then checkpoint can contain
+                directly corresponding state_dict.
             kwargs: Keyword arguments accepted for `nn.Module.load_state_dict()`. Passing `strict=False` enables
                 the user to load part of the pretrained model (useful for example, in Transfer Learning)
 
@@ -537,18 +537,29 @@ def load_objects(to_load: Mapping, checkpoint: Mapping, **kwargs: Any) -> None:
                 checkpoint = torch.load(checkpoint_fp)
                Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint)
 
+                # or using a string for checkpoint filepath
+
+                to_load = to_save
+                checkpoint_fp = "/tmp/models/myprefix_checkpoint_40.pth"
+                Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint_fp)
+
         Note:
             If ``to_load`` contains objects of type torch `DistributedDataParallel`_ or
             `DataParallel`_, method ``load_state_dict`` will applied to their internal wrapped model (``obj.module``).
 
         .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/
             torch.nn.parallel.DistributedDataParallel.html
         .. _DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html
-
         """
+
+        if isinstance(checkpoint, str):
+            checkpoint_obj = torch.load(checkpoint)
+        else:
+            checkpoint_obj = checkpoint
+
         Checkpoint._check_objects(to_load, "load_state_dict")
-        if not isinstance(checkpoint, collections.Mapping):
-            raise TypeError(f"Argument checkpoint should be a dictionary, but given {type(checkpoint)}")
+        if not isinstance(checkpoint, (collections.Mapping, str)):
+            raise TypeError(f"Argument checkpoint should be a string or a dictionary, but given {type(checkpoint)}")
 
         if len(kwargs) > 1 or any(k for k in kwargs if k not in ["strict"]):
             warnings.warn("kwargs contains keys other than strict and these will be ignored")
@@ -557,22 +568,22 @@ def load_objects(to_load: Mapping, checkpoint: Mapping, **kwargs: Any) -> None:
         if len(to_load) == 1:
             # single object and checkpoint is directly a state_dict
             key, obj = list(to_load.items())[0]
-            if key not in checkpoint:
+            if key not in checkpoint_obj:
                 if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
                     obj = obj.module
-                obj.load_state_dict(checkpoint, strict=is_state_dict_strict)
+                obj.load_state_dict(checkpoint_obj, strict=is_state_dict_strict)
                 return
 
         # multiple objects to load
         for k, obj in to_load.items():
-            if k not in checkpoint:
+            if k not in checkpoint_obj:
                 raise ValueError(f"Object labeled by '{k}' from `to_load` is not found in the checkpoint")
             if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
                 obj = obj.module
             if isinstance(obj, torch.nn.Module):
-                obj.load_state_dict(checkpoint[k], strict=is_state_dict_strict)
+                obj.load_state_dict(checkpoint_obj[k], strict=is_state_dict_strict)
             else:
-                obj.load_state_dict(checkpoint[k])
+                obj.load_state_dict(checkpoint_obj[k])
 
     def state_dict(self) -> "OrderedDict[str, List[Tuple[int, str]]]":
         """Method returns state dict with saved items: list of ``(priority, filename)`` pairs.

diff --git a/tests/ignite/handlers/test_checkpoint.py b/tests/ignite/handlers/test_checkpoint.py
@@ -1070,7 +1070,7 @@ def test_save_model_optimizer_lr_scheduler_with_validation(dirname):
 
 def test_checkpoint_load_objects():
 
-    with pytest.raises(TypeError, match=r"Argument checkpoint should be a dictionary"):
+    with pytest.raises(TypeError, match=r"Argument checkpoint should be a string or a dictionary"):
         Checkpoint.load_objects({}, [])
 
     with pytest.raises(TypeError, match=r"should have `load_state_dict` method"):
@@ -1107,6 +1107,17 @@ def _get_multiple_objs_to_save():
     trainer = Engine(lambda e, b: None)
     trainer.state = State(epoch=0, iteration=0)
 
+    # case: load from filepath
+    handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1)
+    to_save = _get_multiple_objs_to_save()
+    handler(trainer, to_save)
+    fname = handler.last_checkpoint
+    assert isinstance(fname, str)
+    assert os.path.join(dirname, _PREFIX) in fname
+    assert os.path.exists(fname)
+    Checkpoint.load_objects(to_save, fname)
+    os.remove(fname)
+
     # case: multiple objects
     handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1)
     to_save = _get_multiple_objs_to_save()
@@ -1142,6 +1153,7 @@ def _get_multiple_objs_to_save():
     assert os.path.exists(fname)
     loaded_objects = torch.load(fname)
     Checkpoint.load_objects(to_save, loaded_objects)
+    os.remove(fname)
 
 
 def test_load_checkpoint_with_different_num_classes(dirname):