Template Fix (Project-MONAI#530)

### Description
A minor fix to the segmentation template to clarify usage.

### Status
**Ready**

### Please ensure all the checkboxes:
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Codeformat tests passed locally by running `./runtests.sh
--codeformat`.
- [ ] In-line docstrings updated.
- [ ] Update `version` and `changelog` in `metadata.json` if changing an
existing bundle.
- [ ] Please ensure the naming rules in config files meet our
requirements (please refer to: `CONTRIBUTING.md`).
- [ ] Ensure versions of packages such as `monai`, `pytorch` and `numpy`
are correct in `metadata.json`.
- [ ] Descriptions should be consistent with the content, such as
`eval_metrics` of the provided weights and TorchScript modules.
- [ ] Files larger than 25MB are excluded and replaced by providing
download links in `large_file.yml`.
- [ ] Avoid using path that contains personal information within config
files (such as use `/home/your_name/` for `"bundle_root"`).

---------

Signed-off-by: Eric Kerfoot <[email protected]>
Signed-off-by: Eric Kerfoot <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: YunLiu <[email protected]>

models/segmentation_template/configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.0.1",
+    "version": "0.0.2",
     "changelog": {
+        "0.0.2": "Minor train.yaml clarifications",
         "0.0.1": "Initial version"
     },
     "monai_version": "1.2.0",

models/segmentation_template/configs/train.yaml

@@ -128,8 +128,8 @@ val_dataloader:
 lossfn:
   _target_: DiceLoss
   include_background: true  # if your segmentations are relatively small it might help for this to be false
-  to_onehot_y: true
-  softmax: true
+  to_onehot_y: true  # convert ground truth to one-hot for training
+  softmax: true  # softmax applied to prediction
 
 # hyperparameters could be added for other arguments of this class
 optimizer:
@@ -141,7 +141,7 @@ optimizer:
 inferer:
   _target_: SimpleInferer
 
-# transform to apply to data from network to be suitable for loss function and validation
+# transform to apply to data from network to be suitable for validation
 postprocessing:
   _target_: Compose
   transforms:
@@ -235,7 +235,7 @@ trainer:
   inferer: '@inferer'  # unnecessary since SimpleInferer is the default if this isn't provided
   loss_function: '@lossfn'
   optimizer: '@optimizer'
-  postprocessing: '@postprocessing'
+  # postprocessing: '@postprocessing'  # uncomment if you have train metrics that need post-processing
   key_train_metric: null
   train_handlers: '@handlers'