STYLE: Trim trailing whitespaces with pre-commit (#715)

Author: fepegar

.github/workflows/linting_and_hello_world.yml

@@ -21,7 +21,7 @@ jobs:
           pip install flake8
           python -m flake8
         shell: bash
-        if: always()        
+        if: always()
 
       # This script also does "conda init" for all shells. For bash, this modifies .bashrc.
       # However, the default "bash" in a github workflow does not execute bashrc. Hence, all

.pre-commit-config.yaml

@@ -2,28 +2,29 @@
 # See https://pre-commit.com/hooks.html for more hooks
 # See https://github.com/pre-commit/pre-commit-hooks/blob/master/.pre-commit-config.yaml for an example with more hooks
 
-exclude: '^excluded_files_regex$'
 repos:
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
-    hooks:
-    - id: trailing-whitespace
-    - id: end-of-file-fixer
-    - id: check-yaml
-    - id: check-added-large-files
-    - id: check-ast
-    - id: check-merge-conflict
-    - id: debug-statements
-    - id: mixed-line-ending
-      args: [--fix=lf]
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.0.1
+  hooks:
+  - id: trailing-whitespace
+  - id: end-of-file-fixer
+  - id: check-yaml
+  - id: check-added-large-files
+  - id: check-ast
+  - id: check-merge-conflict
+  - id: debug-statements
+  - id: mixed-line-ending
+    args:
+    - --fix=lf
 
--   repo: https://github.com/PyCQA/flake8
-    rev: 3.9.2
-    hooks:
-    -   id: flake8
-        additional_dependencies: [flake8-typing-imports==1.7.0]
+- repo: https://github.com/PyCQA/flake8
+  rev: 3.9.2
+  hooks:
+  - id: flake8
+    additional_dependencies:
+    - flake8-typing-imports==1.7.0
 
--   repo: https://github.com/pre-commit/mirrors-autopep8
-    rev: v1.5.7
-    hooks:
-    - id: autopep8
+- repo: https://github.com/pre-commit/mirrors-autopep8
+  rev: v1.5.7
+  hooks:
+  - id: autopep8

.vscode/settings.json

@@ -4,5 +4,6 @@
         "Tests"
     ],
     "python.testing.unittestEnabled": false,
-    "python.testing.pytestEnabled": true
-}
+    "python.testing.pytestEnabled": true,
+    "files.trimTrailingWhitespace": true
+}

InnerEye/ML/SSL/utils.py

@@ -39,7 +39,7 @@ def create_ssl_encoder(encoder_name: str, use_7x7_first_conv_in_resnet: bool = T
     """
     Creates SSL encoder.
     :param encoder_name: available choices: resnet18, resnet50, resnet101 and densenet121.
-    :param use_7x7_first_conv_in_resnet: If True, use a 7x7 kernel (default) in the first layer of resnet. 
+    :param use_7x7_first_conv_in_resnet: If True, use a 7x7 kernel (default) in the first layer of resnet.
     If False, replace first layer by a 3x3 kernel. This is required for small CIFAR 32x32 images to not shrink them.
     """
     from pl_bolts.models.self_supervised.resnets import resnet18, resnet50, resnet101

InnerEye/ML/configs/classification/DummyClassification.py

@@ -12,7 +12,7 @@
 
 
 class DummyClassification(ScalarModelBase):
-    "A config file for dummy image classification model for debugging purposes" 
+    "A config file for dummy image classification model for debugging purposes"
 
     def __init__(self) -> None:
         num_epochs = 4

InnerEye/ML/configs/other/HelloContainer.py

@@ -39,7 +39,7 @@ def __init__(self, raw_data: List[List[float]]) -> None:
         must be numeric data which can be converted into a tensor. See the static method
         from_path_and_indexes for an example call.
         """
-        super().__init__()      
+        super().__init__()
         self.data = torch.tensor(raw_data, dtype=torch.float)
 
     def __len__(self) -> int:
@@ -253,9 +253,9 @@ def create_model(self) -> LightningModule:
         return HelloRegression()
 
     # This method must be overridden by any subclass of LightningContainer. It returns a data module, which
-    # in turn contains 3 data loaders for training, validation, and test set. 
-    # 
-    # If the container is used for cross validation then this method must handle the cross validation splits. 
+    # in turn contains 3 data loaders for training, validation, and test set.
+    #
+    # If the container is used for cross validation then this method must handle the cross validation splits.
     # Because this deals with data loaders, not loaded data, we cannot check automatically that cross validation is
     # handled correctly within the LightningContainer base class, i.e. if you forget to do the cross validation split
     # in your subclass nothing will fail, but each child run will be identical since they will each be given the full

InnerEye/ML/configs/segmentation/HelloWorld.py

@@ -28,7 +28,7 @@ class HelloWorld(SegmentationModelBase):
 
     * This model can be trained from the commandline: python InnerEye/runner.py --model=HelloWorld
 
-    * If you want to test that your AzureML workspace is working, please follow the instructions in 
+    * If you want to test that your AzureML workspace is working, please follow the instructions in
     <repo_root>/docs/hello_world_model.md.
 
     In this example, the model is trained on 2 input image channels channel1 and channel2, and

InnerEye/ML/dataset/scalar_dataset.py

@@ -498,7 +498,7 @@ def _load_single_data_source(_rows: pd.DataFrame,
                 is_classification_dataset=self.is_classification_dataset,
                 num_classes=self.num_classes,
                 sequence_position_numeric=_sequence_position_numeric
-            )       
+            )
         if len(self.expected_channels) > 0:
             missing_channels = self.expected_channels - set(rows[self.channel_column])
             if len(missing_channels) > 0:

InnerEye/ML/lightning_container.py

@@ -295,7 +295,7 @@ def get_cross_validation_hyperdrive_config(self, run_config: ScriptRunConfig) ->
         """
         Returns a configuration for AzureML Hyperdrive that varies the cross validation split index.
         Because this adds a val/Loss metric it is important that when subclassing LightningContainer
-        your implementeation of LightningModule logs val/Loss. There is an example of this in 
+        your implementation of LightningModule logs val/Loss. There is an example of this in
         HelloRegression's validation_step method.
         :param run_config: The AzureML run configuration object that training for an individual model.
         :return: A hyperdrive configuration object.

SECURITY.md

@@ -14,7 +14,7 @@ Instead, please report them to the Microsoft Security Response Center (MSRC) at
 
 If you prefer to submit without logging in, send email to [[email protected]](mailto:[email protected]).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc).
 
-You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 
+You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc).
 
 Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: