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remove deprecated mentioning #736

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Merged
merged 1 commit into from
May 27, 2022
Merged

remove deprecated mentioning #736

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2 changes: 1 addition & 1 deletion acceleration/distributed_training/unet_evaluation_workflows.py
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Expand Up @@ -31,7 +31,7 @@
- Wrap the model with `DistributedDataParallel` after moving to expected device.
- Put model file on every node, then load and map to expected GPU device in every process.
- Wrap Dataset with `DistributedSampler`, disable the `shuffle` in sampler and DataLoader.
- Add `StatsHandler` and `SegmentationSaver` to the master process which is `dist.get_rank() == 0`.
- Add `StatsHandler` to the master process which is `dist.get_rank() == 0`.
- ignite can automatically reduce metrics for distributed evaluation, refer to:
https://github.com/pytorch/ignite/blob/v0.4.2/ignite/metrics/metric.py#L507

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2 changes: 1 addition & 1 deletion modules/dynunet_pipeline/evaluator.py
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Expand Up @@ -42,7 +42,7 @@ class DynUNetEvaluator(SupervisedEvaluator):
checkpoint into files.
additional_metrics: more Ignite metrics that also attach to Ignite Engine.
val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:
CheckpointHandler, StatsHandler, SegmentationSaver, etc.
CheckpointHandler, StatsHandler, etc.
amp: whether to enable auto-mixed-precision evaluation, default is False.
tta_val: whether to do the 8 flips (8 = 2 ** 3, where 3 represents the three dimensions)
test time augmentation, default is False.
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