STYLE: Ensure line breaks at the end of all files (#717)

* Add line breaks at the end of all files

* Handle last line in VS Code settings

.github/workflows/codeql-analysis.yml

@@ -54,4 +54,3 @@ jobs:
 
  - name: Perform CodeQL Analysis
  uses: github/codeql-action/analyze@v1
-

.vscode/settings.json

@@ -5,5 +5,7 @@
  ],
  "python.testing.unittestEnabled": false,
  "python.testing.pytestEnabled": true,
- "files.trimTrailingWhitespace": true
+ "files.trimTrailingWhitespace": true,
+ "files.trimFinalNewlines": true,
+ "files.insertFinalNewline": true
 }

GeoPol.xml

@@ -26,4 +26,4 @@
  <ExcludeFolder>GeoPol.xml</ExcludeFolder>
  <ExcludeFolder>*.nii.gz</ExcludeFolder>
  </Component>
-</GeoPol_Folders>
+</GeoPol_Folders>

InnerEye-DataQuality/README.md

@@ -1 +1 @@
-The InnerEye-DataQuality code has been moved here: [https://github.com/microsoft/InnerEye-DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality](https://github.com/microsoft/InnerEye-DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality)
+The InnerEye-DataQuality code has been moved here: [https://github.com/microsoft/InnerEye-DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality](https://github.com/microsoft/InnerEye-DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality)

InnerEye/ML/configs/classification/DummyClassification.py

@@ -49,4 +49,3 @@ def create_model(self) -> Any:
  # Use a local import so that we don't need to import pytorch when creating configs in the runner
  from Tests.ML.models.architectures.DummyScalarModel import DummyScalarModel
  return DummyScalarModel(self.expected_image_size_zyx)
-

InnerEye/ML/models/architectures/classification/bit.py

@@ -165,4 +165,3 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: # type: ignore
  x = self.conv_stack(x)
  x = self.linear(x)
  return x
-

InnerEye/ML/reports/classification_crossval_report.ipynb

@@ -280,4 +280,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

InnerEye/ML/reports/classification_multilabel_report.ipynb

@@ -163,4 +163,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

InnerEye/ML/reports/segmentation_report.ipynb

@@ -162,4 +162,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

RegressionTestResults/PR_BasicModel2Epochs/AZUREML_OUTPUT/final_model/model_inference_config.json

@@ -1 +1 @@
-{"model_name": "BasicModel2Epochs", "checkpoint_paths": ["checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.segmentation.BasicModel2Epochs"}
+{"model_name": "BasicModel2Epochs", "checkpoint_paths": ["checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.segmentation.BasicModel2Epochs"}

RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Test/BaselineComparisonWilcoxonSignedRankTestResults.txt

@@ -1 +1 @@
-There were not enough data points for any statistically meaningful comparisons.
+There were not enough data points for any statistically meaningful comparisons.

RegressionTestResults/PR_GlaucomaCV/AZUREML_PARENT_OUTPUT/CrossValResults/0/Train/metrics.csv

@@ -998,4 +998,4 @@ epoch,subject,prediction_target,model_output,label,data_split,cross_validation_s
 1,003469-OD,Default,0.482178,0,Train,0
 1,001578-OS,Default,0.487305,1,Train,0
 1,003327-OD,Default,0.477539,1,Train,0
-1,002457-OS,Default,0.466064,0,Train,0
+1,002457-OS,Default,0.466064,0,Train,0

RegressionTestResults/PR_GlaucomaCV/AZUREML_PARENT_OUTPUT/CrossValResults/0/Val/metrics.csv

@@ -998,4 +998,4 @@ epoch,subject,prediction_target,model_output,label,data_split,cross_validation_s
 1,003423-OS,Default,0.330811,1,Val,0
 1,003476-OD,Default,0.313477,1,Val,0
 1,003505-OS,Default,0.322266,1,Val,0
-1,003516-OD,Default,0.295898,1,Val,0
+1,003516-OD,Default,0.295898,1,Val,0

RegressionTestResults/PR_GlaucomaCV/AZUREML_PARENT_OUTPUT/CrossValResults/1/Train/metrics.csv

@@ -998,4 +998,4 @@ epoch,subject,prediction_target,model_output,label,data_split,cross_validation_s
 1,001436-OD,Default,0.553223,1,Train,1
 1,001255-OS,Default,0.553223,1,Train,1
 1,003059-OS,Default,0.539062,1,Train,1
-1,002407-OD,Default,0.532715,0,Train,1
+1,002407-OD,Default,0.532715,0,Train,1

RegressionTestResults/PR_GlaucomaCV/AZUREML_PARENT_OUTPUT/CrossValResults/1/Val/metrics.csv

@@ -998,4 +998,4 @@ epoch,subject,prediction_target,model_output,label,data_split,cross_validation_s
 1,003423-OD,Default,0.422363,1,Val,1
 1,003476-OS,Default,0.388184,1,Val,1
 1,003485-OS,Default,0.418945,1,Val,1
-1,003502-OS,Default,0.399170,1,Val,1
+1,003502-OS,Default,0.399170,1,Val,1

RegressionTestResults/PR_GlaucomaCV/AZUREML_PARENT_OUTPUT/final_ensemble_model/model_inference_config.json

@@ -1 +1 @@
-{"model_name": "GlaucomaPublic", "checkpoint_paths": ["checkpoints/OTHER_RUNS/1/last.ckpt", "checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.classification.GlaucomaPublic"}
+{"model_name": "GlaucomaPublic", "checkpoint_paths": ["checkpoints/OTHER_RUNS/1/last.ckpt", "checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.classification.GlaucomaPublic"}

RegressionTestResults/PR_HelloContainer/AZUREML_OUTPUT/final_model/model_inference_config.json

@@ -1 +1 @@
-{"model_name": "HelloContainer", "checkpoint_paths": ["checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.other.HelloContainer"}
+{"model_name": "HelloContainer", "checkpoint_paths": ["checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.other.HelloContainer"}

RegressionTestResults/PR_HelloContainer/OUTPUT/test_mse.txt

@@ -1 +1 @@
-0.012701375409960747
+0.012701375409960747

RegressionTestResults/PR_Train2Nodes/OUTPUT/best_validation_epoch/Test/BaselineComparisonWilcoxonSignedRankTestResults.txt

@@ -1 +1 @@
-There were not enough data points for any statistically meaningful comparisons.
+There were not enough data points for any statistically meaningful comparisons.

RegressionTestResults/PR_TrainEnsemble/AZUREML_PARENT_OUTPUT/CrossValResults/CrossValidationMannWhitneyTestResults.txt

@@ -1 +1 @@
-There were not enough data points for any statistically meaningful comparisons.
+There were not enough data points for any statistically meaningful comparisons.

RegressionTestResults/PR_TrainEnsemble/AZUREML_PARENT_OUTPUT/CrossValResults/CrossValidationWilcoxonSignedRankTestResults.txt

@@ -1 +1 @@
-There were not enough data points for any statistically meaningful comparisons.
+There were not enough data points for any statistically meaningful comparisons.

RegressionTestResults/PR_TrainEnsemble/AZUREML_PARENT_OUTPUT/CrossValResults/Test_outliers.txt

@@ -6,4 +6,4 @@ No outliers found
 
 === METRIC: HausdorffDistance_mm ===
 
-No outliers found
+No outliers found

RegressionTestResults/PR_TrainEnsemble/AZUREML_PARENT_OUTPUT/final_ensemble_model/model_inference_config.json

@@ -1 +1 @@
-{"model_name": "BasicModelForEnsembleTest", "checkpoint_paths": ["checkpoints/OTHER_RUNS/1/last.ckpt", "checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.segmentation.BasicModel2Epochs"}
+{"model_name": "BasicModelForEnsembleTest", "checkpoint_paths": ["checkpoints/OTHER_RUNS/1/last.ckpt", "checkpoints/last.ckpt"], "model_configs_namespace": "InnerEye.ML.configs.segmentation.BasicModel2Epochs"}

SECURITY.md

@@ -38,4 +38,4 @@ We prefer all communications to be in English.
 
 Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
 
-<!-- END MICROSOFT SECURITY.MD BLOCK -->
+<!-- END MICROSOFT SECURITY.MD BLOCK -->

Tests/ML/test_data/Val_outliers.txt

@@ -55,4 +55,4 @@ Patient Structure seriesId
  prostate 6fa04f324339c532a7c06877d1129bfacd7504d9c0bf6277a393915e73e41d66 5a6ba8fe-65bc-43ec-b1fc-682c8c37e40c 1.0 0.0 0.0 0.0
  seminalvesicles 6fa04f324339c532a7c06877d1129bfacd7504d9c0bf6277a393915e73e41d66 5a6ba8fe-65bc-43ec-b1fc-682c8c37e40c 1.0 0.0 0.0 0.0
 
-SELECT * FROM ROOT as r WHERE true AND (r.InstitutionId = "641eda02-90c3-45ed-b8b1-2651b6a5da6c" OR r.InstitutionId = "5a6ba8fe-65bc-43ec-b1fc-682c8c37e40c") AND (STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"9e84c7f5b030cc48e867430e762e1bb7e756051e27468b4fa6a0472292bb59eb") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"4af0d32eb2c7b4d253b98530b8c6f5aac10602d51ff4097ab3c932331cf0d2a5") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"d41cb61517793f87f97d5d6c0cee9f91df94169c29701c10190723ff8ede419a") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"5f1573dbbf6a8335bcf8bf5eed74e9157d4e7ee9dae8d679d4d4d7118510d741") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"991648f92b04f05e316fe7c99b1aa748c2d556b1e873591ae9b9bbd2c319254a") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"1deef6a1145f76617016a3e26288b009028e411ae4a236043afe7aa8cfbff6b4") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"f040ac05a1eeee9a064662adda21f703d7ba969b7dfc2787248b2c596ccf59a7") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"4b6610b5b249fbc149eb0a47abe97d08df6b26b76ffcf59431271d057f722d14") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"776d594c55725ad79bf59df2e3e9b6e6ded64cd37a1df45b03847c1e54828246") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"6fa04f324339c532a7c06877d1129bfacd7504d9c0bf6277a393915e73e41d66"))
+SELECT * FROM ROOT as r WHERE true AND (r.InstitutionId = "641eda02-90c3-45ed-b8b1-2651b6a5da6c" OR r.InstitutionId = "5a6ba8fe-65bc-43ec-b1fc-682c8c37e40c") AND (STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"9e84c7f5b030cc48e867430e762e1bb7e756051e27468b4fa6a0472292bb59eb") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"4af0d32eb2c7b4d253b98530b8c6f5aac10602d51ff4097ab3c932331cf0d2a5") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"d41cb61517793f87f97d5d6c0cee9f91df94169c29701c10190723ff8ede419a") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"5f1573dbbf6a8335bcf8bf5eed74e9157d4e7ee9dae8d679d4d4d7118510d741") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"991648f92b04f05e316fe7c99b1aa748c2d556b1e873591ae9b9bbd2c319254a") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"1deef6a1145f76617016a3e26288b009028e411ae4a236043afe7aa8cfbff6b4") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"f040ac05a1eeee9a064662adda21f703d7ba969b7dfc2787248b2c596ccf59a7") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"4b6610b5b249fbc149eb0a47abe97d08df6b26b76ffcf59431271d057f722d14") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"776d594c55725ad79bf59df2e3e9b6e6ded64cd37a1df45b03847c1e54828246") OR STARTSWITH(r.VersionedDicomImageSeries.Latest.Series.InstanceUID,"6fa04f324339c532a7c06877d1129bfacd7504d9c0bf6277a393915e73e41d66"))

Tests/ML/test_data/Val_outliers_pruned.txt

@@ -53,4 +53,4 @@ Patient Structure
 411 bladder 1.0 0.0 0.0 0.0
  femur_r 1.0 0.0 0.0 0.0
  prostate 1.0 0.0 0.0 0.0
- seminalvesicles 1.0 0.0 0.0 0.0
+ seminalvesicles 1.0 0.0 0.0 0.0

Tests/ML/test_data/classification_data_multiclass/dataset.csv

@@ -4,4 +4,4 @@ S1,green,1_green.png,
 S2,blue,1_blue.png,2|3
 S2,green,1_green.png,
 S3,blue,1_blue.png,3
-S3,green,1_green.png,
+S3,green,1_green.png,

Tests/ML/test_data/sequence_data_for_classification/dataset.csv

@@ -25,4 +25,4 @@
 3250.00005,VST 7,25,Bar,category_1,category_B,233,0,0,84,img_8,5,0
 3250.00005,VST 8,25,Bar,category_1,category_B,218,0,0,84,img_9,6,0
 3250.00005,VST 9,25,Bar,category_1,category_B,221,0,0,84,img_10,7,0
-3250.00005,VST 10,25,Bar,category_1,category_B,238,0,0.02,84,img_11,8,0
+3250.00005,VST 10,25,Bar,category_1,category_B,238,0,0.02,84,img_11,8,0

Tests/ML/test_data/test_dataset_parameters.csv

@@ -1,4 +1,3 @@
 1,2,crop_sizes
 [ 37 37 37],[ 37 37 37],"[55, 55, 55]"
 [ 37 37 37],[ 37 37 37],"[55, 55, 55]"
-

Tests/ML/utils/test_temperature_scaling.py

@@ -42,4 +42,3 @@ def criterion_fn(_logits: torch.Tensor, _labels: torch.Tensor) -> Tuple[torch.Te
  assert after_ece.item() < before_ece.item()
  assert np.isclose(optimal_temperature, 1.44, rtol=0.1)
  assert model.temperature.requires_grad is False
-

docs/bring_your_own_model.md

@@ -252,4 +252,3 @@ class Container2(LightningContainer):
  # These arguments will be passed through to the Lightning trainer.
  return {"gradient_clip_val": 1, "limit_train_batches": 10}
 ```
-

docs/move_model.md

@@ -25,4 +25,4 @@ workspace specified in the parameters.
 The model is expected to be in the format above, i.e. contained in a subfolder called `name_version` of the path in `--path` parameter. The model will be automatically given the next available `id` once uploaded to the workspace. For example, if `my_model:100` already exists then it will be called `my_model:101`.
 
 Once in place you may want to run inference on the model with partial test data, i.e. test data from patients for whom
-some of the labels are missing. Normally inference on partial test data would raise an exception. To allow inference to continue over partial test data add the flag `--allow_incomplete_labels` to your inference call, for example `python InnerEye/ML/runner.py --allow_incomplete_labels --no-train --azureml --model=<model_name> --run_recovery_id=<recovery_id>`
+some of the labels are missing. Normally inference on partial test data would raise an exception. To allow inference to continue over partial test data add the flag `--allow_incomplete_labels` to your inference call, for example `python InnerEye/ML/runner.py --allow_incomplete_labels --no-train --azureml --model=<model_name> --run_recovery_id=<recovery_id>`

docs/self_supervised_models.md

@@ -196,4 +196,4 @@ or for a local run
 
 ```
 python ML/runner.py --model=CXRImageClassifier --local_weights_path={LOCAL_PATH_TO_YOUR_SSL_CHECKPOINT}
-```
+```