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Fixed issue when loading a single non-nn.Module object #2487

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Merged
vfdev-5 merged 5 commits into from
Feb 20, 2022
Merged

Fixed issue when loading a single non-nn.Module object #2487

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4f0733b
Fixed issue when loading a single non-nn.Module object
vfdev-5 Feb 20, 2022
fd51114
Merge branch 'master' into fix-2479-checkpoint-load-optim
vfdev-5 Feb 20, 2022
afffae3
Merge branch 'master' of github.com:pytorch/ignite into fix-2479-chec…
vfdev-5 Feb 20, 2022
1464373
Fixed mypy issue and removed a todo
vfdev-5 Feb 20, 2022
83ce0d2
Merge branch 'fix-2479-checkpoint-load-optim' of github.com:vfdev-5/i…
vfdev-5 Feb 20, 2022
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20 changes: 11 additions & 9 deletions ignite/handlers/checkpoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -573,25 +573,27 @@ def load_objects(to_load: Mapping, checkpoint: Union[str, Mapping], **kwargs: An
warnings.warn("kwargs contains keys other than strict and these will be ignored")

is_state_dict_strict = kwargs.get("strict", True)

def _load_object(obj: Any, chkpt_obj: Any) -> None:
if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
obj = obj.module
if isinstance(obj, torch.nn.Module):
obj.load_state_dict(chkpt_obj, strict=is_state_dict_strict)
else:
obj.load_state_dict(chkpt_obj)

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_obj:
if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
obj = obj.module
obj.load_state_dict(checkpoint_obj, strict=is_state_dict_strict)
_load_object(obj, checkpoint_obj)
return

# multiple objects to load
for k, obj in to_load.items():
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_obj[k], strict=is_state_dict_strict)
else:
obj.load_state_dict(checkpoint_obj[k])
_load_object(obj, 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.
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19 changes: 19 additions & 0 deletions tests/ignite/handlers/test_checkpoint.py
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Expand Up @@ -1671,3 +1671,22 @@ def test_get_default_score_fn():
score_fn = Checkpoint.get_default_score_fn("loss", -1)
score = score_fn(engine)
assert score == -0.123


@pytest.mark.parametrize("obj_to_save", ["optim", "trainer"])
def test_load_single_object(obj_to_save, dirname):
# Checks https://github.com/pytorch/ignite/issues/2479

trainer = Engine(lambda e, b: None)
if obj_to_save == "optim":
t = torch.tensor(0.0)
optim = torch.optim.SGD([t], lr=0.1)
to_save = {"optim": optim}
elif obj_to_save == "trainer":
to_save = {"trainer": trainer}

c = Checkpoint(to_save, save_handler=dirname)
c(trainer)

checkpoint_fp = dirname / c.last_checkpoint
Checkpoint.load_objects(to_load=to_save, checkpoint=str(checkpoint_fp))