[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

auto3dseg/README.md

@@ -60,12 +60,12 @@ To further demonstrate the capabilities of **Auto3DSeg**, [here](./tasks/instanc
 
 To run Auto3DSeg on your own dataset, you need to build a `datalist.json` file, and pass it to the AutoRunner.
 
-The datalist format is based on the datasets released by the [Medical Segmentation Decathlon](http://medicaldecathlon.com). 
+The datalist format is based on the datasets released by the [Medical Segmentation Decathlon](http://medicaldecathlon.com).
 See the function `load_decathlon_datalist` in `monai/data/decathlon_datalist.py` for a description of the format.
 
 For the AutoRunner, we only need the `training` list in the JSON, it does not use any other fields.
-The `fold` key for each image is not required, as the AutoRunner will automatically create cross-validation folds (the number of folds is hard-coded to 5).  
-If you do add the cross-validation folds beforehand, the AutoRunner will use these by default.  
+The `fold` key for each image is not required, as the AutoRunner will automatically create cross-validation folds (the number of folds is hard-coded to 5).
+If you do add the cross-validation folds beforehand, the AutoRunner will use these by default.
 You can also choose to include a `validation` list in the JSON file, in which case the AutoRunner will disable cross-validation and use the specified validation set.
 Any other metadata, such as `modality`, `numTraining`, `name`, etc. will not be used by the AutoRunner, but we do recommend using metadata fields to keep track of names and versions of your dataset. If you are using multi-modal scans, it is possible to enter lists of image paths for both the `image` and `label` keys; MONAI will stack them into channels.
 In short, your `datalist.json` file should look like this:

auto3dseg/docs/run_with_minimal_input.md

@@ -33,8 +33,8 @@ For the AutoRunner, you only need the `training` field with its list of training
 }
 
 ```
-In each training item, you can add a `fold` field (with an integer starting at 0) to pre-specify the cross-validation folds, otherwise the AutoRunner will generate its own folds (always 5). All trained algorithms will use the same generated or pre-specified folds, the file can be found in the `work_dir` folder that the AutoRunner generates.  
-If you have a validation set, you can include it under a `validation` key with the same format as the `training` list. This will disable cross-validation.  
+In each training item, you can add a `fold` field (with an integer starting at 0) to pre-specify the cross-validation folds, otherwise the AutoRunner will generate its own folds (always 5). All trained algorithms will use the same generated or pre-specified folds, the file can be found in the `work_dir` folder that the AutoRunner generates.
+If you have a validation set, you can include it under a `validation` key with the same format as the `training` list. This will disable cross-validation.
 It is recommended to add a `name` field and any other metadata fields that allow you to track which version of your dataset the models are trained on.
 
 Save the file to `./datalist.json`.