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1570 Fix distributed issue in ClassificationSaver #1571

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Merged
wyli merged 5 commits into from
Feb 9, 2021
Merged

1570 Fix distributed issue in ClassificationSaver #1571

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42a45e0
Merge pull request #19 from Project-MONAI/master
Nic-Ma Feb 1, 2021
5f5ddc2
Merge pull request #26 from Project-MONAI/master
Nic-Ma Feb 9, 2021
f66b2e4
[DLMED] fix classification saver issue
Nic-Ma Feb 9, 2021
3afea2c
[DLMED] enhance distributed tests
Nic-Ma Feb 9, 2021
b51b3b2
[MONAI] python code formatting
monai-bot Feb 9, 2021
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4 changes: 2 additions & 2 deletions monai/handlers/classification_saver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,8 +88,8 @@ def __call__(self, engine: Engine) -> None:
"""
_meta_data = self.batch_transform(engine.state.batch)
if Key.FILENAME_OR_OBJ in _meta_data:
# all gather filenames across ranks
_meta_data[Key.FILENAME_OR_OBJ] = string_list_all_gather(_meta_data[Key.FILENAME_OR_OBJ])
# all gather filenames across ranks, only filenames are necessary
_meta_data = {Key.FILENAME_OR_OBJ: string_list_all_gather(_meta_data[Key.FILENAME_OR_OBJ])}
# all gather predictions across ranks
_engine_output = evenly_divisible_all_gather(self.output_transform(engine.state.output))

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2 changes: 1 addition & 1 deletion monai/handlers/utils.py
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Expand Up @@ -103,7 +103,7 @@ def string_list_all_gather(strings: List[str], delimiter: str = "\t") -> List[st
# all gather across all ranks
_joined = delimiter.join(idist.all_gather(_joined))
else:
raise RuntimeError("MetricsSaver can not save metric details in distributed mode with PyTorch < 1.7.0.")
raise RuntimeError("string all_gather can not be supported in PyTorch < 1.7.0.")

return _joined.split(delimiter)

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7 changes: 6 additions & 1 deletion tests/test_handler_classification_saver_dist.py
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Expand Up @@ -41,7 +41,12 @@ def _train_func(engine, batch):
saver.attach(engine)

# rank 0 has 8 images, rank 1 has 10 images
data = [{"filename_or_obj": ["testfile" + str(i) for i in range(8 * rank, (8 + rank) * (rank + 1))]}]
data = [
{
"filename_or_obj": ["testfile" + str(i) for i in range(8 * rank, (8 + rank) * (rank + 1))],
"data_shape": [(1, 1) for _ in range(8 * rank, (8 + rank) * (rank + 1))],
}
]
engine.run(data, max_epochs=1)
filepath = os.path.join(tempdir, "predictions.csv")
if rank == 1:
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