Moving nii.gz from git lfs to git to simplify the HelloWorld test (#632)

2022-01-11 16:19:17 +00:00 · 2022-01-11 16:19:17 +00:00 · 015e9e4829
--- a/.gitattributes
+++ b/.gitattributes
@ -1,18 +1,22 @@
 *.tar filter=lfs diff=lfs merge=lfs -text
 *.nii filter=lfs diff=lfs merge=lfs -text
-*.nii.gz filter=lfs diff=lfs merge=lfs -text
 *.pth.tar filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.npy filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.dcm filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
 * text=auto
 *.tar binary
 *.nii binary
 *.nii.gz binary
 *.pth.tar binary
 *.npy binary
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.dcm filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
+*.npz binary
+*.dcm binary
+*.zip binary
+*.jpg binary
+*.tiff binary
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -72,6 +72,7 @@ gets uploaded to AzureML, by skipping all test folders.
 - ([#596](https://github.com/microsoft/InnerEye-DeepLearning/pull/596)) Add `cudatoolkit=11.1` specification to environment.yml.
 - ([#615](https://github.com/microsoft/InnerEye-DeepLearning/pull/615)) Minor changes to checkpoint download from AzureML.
 - ([#605](https://github.com/microsoft/InnerEye-DeepLearning/pull/605)) Make build jobs deterministic for regression testing.
+- ([#632](https://github.com/microsoft/InnerEye-DeepLearning/pull/632)) Nifti test data is no longer stored in Git LFS

 ### Fixed
 - ([#606](https://github.com/microsoft/InnerEye-DeepLearning/pull/606)) Bug fix: registered models do not include the hi-ml submodule
--- a/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Test/005/background.nii.gz
+++ b/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Test/005/background.nii.gz
--- a/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Test/005/posterior_lung_l.nii.gz
+++ b/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Test/005/posterior_lung_l.nii.gz
--- a/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Val/002/background.nii.gz
+++ b/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Val/002/background.nii.gz
--- a/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Val/002/posterior_lung_r.nii.gz
+++ b/RegressionTestResults/PR_BasicModel2Epochs/OUTPUT/best_validation_epoch/Val/002/posterior_lung_r.nii.gz
--- a/RegressionTestResults/PR_TrainEnsemble/AZUREML_PARENT_OUTPUT/best_validation_epoch/Test/005/posterior_lung_r.nii.gz
+++ b/RegressionTestResults/PR_TrainEnsemble/AZUREML_PARENT_OUTPUT/best_validation_epoch/Test/005/posterior_lung_r.nii.gz
--- a/Tests/ML/test_data/classification_data/id1_channel1.nii.gz
+++ b/Tests/ML/test_data/classification_data/id1_channel1.nii.gz
--- a/Tests/ML/test_data/dicom_series_data/hnsegmentation.nii.gz
+++ b/Tests/ML/test_data/dicom_series_data/hnsegmentation.nii.gz
--- a/Tests/ML/test_data/full_header_csv/ptv.nii.gz
+++ b/Tests/ML/test_data/full_header_csv/ptv.nii.gz
--- a/Tests/ML/test_data/full_header_csv/rectum.nii.gz
+++ b/Tests/ML/test_data/full_header_csv/rectum.nii.gz
--- a/Tests/ML/test_data/patch_sampling/sampled_center.nii.gz
+++ b/Tests/ML/test_data/patch_sampling/sampled_center.nii.gz
--- a/Tests/ML/test_data/patch_sampling/sampled_to_boundary.nii.gz
+++ b/Tests/ML/test_data/patch_sampling/sampled_to_boundary.nii.gz
--- a/Tests/ML/test_data/patch_sampling/scan_small.nii.gz
+++ b/Tests/ML/test_data/patch_sampling/scan_small.nii.gz
--- a/Tests/ML/test_data/posterior_bladder.nii.gz
+++ b/Tests/ML/test_data/posterior_bladder.nii.gz
--- a/Tests/ML/test_data/scale_and_unscale_image.nii.gz
+++ b/Tests/ML/test_data/scale_and_unscale_image.nii.gz
--- a/Tests/ML/test_data/smoothed_posterior_bladder.nii.gz
+++ b/Tests/ML/test_data/smoothed_posterior_bladder.nii.gz
--- a/Tests/ML/test_data/test_good.nii.gz
+++ b/Tests/ML/test_data/test_good.nii.gz
--- a/Tests/ML/test_data/test_img.nii.gz
+++ b/Tests/ML/test_data/test_img.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id1_channel1.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id1_channel1.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id1_channel2.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id1_channel2.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id1_mask.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id1_mask.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id1_region.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id1_region.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id2_channel1.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id2_channel1.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id2_channel2.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id2_channel2.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id2_mask.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id2_mask.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/id2_region.nii.gz
+++ b/Tests/ML/test_data/train_and_test_data/id2_region.nii.gz
--- a/Tests/ML/test_data/train_and_test_data/scalar_epoch_metrics.csv
+++ b/Tests/ML/test_data/train_and_test_data/scalar_epoch_metrics.csv
@ -1,5 +1,5 @@
-loss,cross_entropy,accuracy_at_threshold_05,seconds_per_batch,seconds_per_epoch,learning_rate,area_under_roc_curve,area_under_pr_curve,accuracy_at_optimal_threshold,false_positive_rate_at_optimal_threshold,false_negative_rate_at_optimal_threshold,optimal_threshold,subject_count,epoch,cross_validation_split_index
-0.7016490995883942,0.7016490697860718,0.375,0.02346169948577881,0.06398129463195801,0.0001,0.8125,0.85,0.6666666865348816,0.0,0.5,0.5281670689582825,6.0,1,-1
-0.702895998954773,0.7028960287570953,0.375,0.019975781440734863,0.05499887466430664,9.999712322065557e-05,0.5625,0.6458333333333333,0.6666666865348816,0.0,0.5,0.5280245542526245,6.0,2,-1
-0.7125596106052399,0.712559700012207,0.25,0.02925896644592285,0.07402157783508301,9.999306876841536e-05,0.875,0.875,0.6666666865348816,0.0,0.5,0.5277201533317566,6.0,3,-1
-0.7119755446910858,0.711975485086441,0.25,0.025499820709228516,0.0679938793182373,9.998613801725043e-05,0.875,0.875,0.6666666865348816,0.0,0.5,0.5274553894996643,6.0,4,-1
+loss,cross_entropy,accuracy_at_threshold_05,seconds_per_batch,seconds_per_epoch,learning_rate,area_under_roc_curve,area_under_pr_curve,accuracy_at_optimal_threshold,false_positive_rate_at_optimal_threshold,false_negative_rate_at_optimal_threshold,optimal_threshold,subject_count,epoch,cross_validation_split_index
+0.7016490995883942,0.7016490697860718,0.375,0.02346169948577881,0.06398129463195801,0.0001,0.8125,0.85,0.6666666865348816,0.0,0.5,0.5281670689582825,6.0,1,-1
+0.702895998954773,0.7028960287570953,0.375,0.019975781440734863,0.05499887466430664,9.999712322065557e-05,0.5625,0.6458333333333333,0.6666666865348816,0.0,0.5,0.5280245542526245,6.0,2,-1
+0.7125596106052399,0.712559700012207,0.25,0.02925896644592285,0.07402157783508301,9.999306876841536e-05,0.875,0.875,0.6666666865348816,0.0,0.5,0.5277201533317566,6.0,3,-1
+0.7119755446910858,0.711975485086441,0.25,0.025499820709228516,0.0679938793182373,9.998613801725043e-05,0.875,0.875,0.6666666865348816,0.0,0.5,0.5274553894996643,6.0,4,-1
--- a/Tests/ML/test_data/train_and_test_data/scalar_prediction_target_metrics.csv
+++ b/Tests/ML/test_data/train_and_test_data/scalar_prediction_target_metrics.csv
@ -1,25 +1,25 @@
-prediction_target,epoch,subject,model_output,label,cross_validation_split_index,data_split
-Default,1,S4,0.5216594338417053,0.0,-1,Train
-Default,1,S5,0.5216594338417053,1.0,-1,Train
-Default,1,S6,0.5216594338417053,0.0,-1,Train
-Default,1,S7,0.5216594338417053,0.0,-1,Train
-Default,1,S8,0.5213258266448975,0.0,-1,Train
-Default,1,S1,0.5281670689582825,1.0,-1,Train
-Default,2,S1,0.5280245542526245,1.0,-1,Train
-Default,2,S4,0.521056056022644,0.0,-1,Train
-Default,2,S7,0.521056056022644,0.0,-1,Train
-Default,2,S6,0.521056056022644,0.0,-1,Train
-Default,2,S8,0.5207627415657043,0.0,-1,Train
-Default,2,S5,0.5207627415657043,1.0,-1,Train
-Default,3,S5,0.5205143690109253,1.0,-1,Train
-Default,3,S8,0.5205143690109253,0.0,-1,Train
-Default,3,S1,0.5277201533317566,1.0,-1,Train
-Default,3,S7,0.5205143690109253,0.0,-1,Train
-Default,3,S4,0.5202868580818176,0.0,-1,Train
-Default,3,S6,0.5202868580818176,0.0,-1,Train
-Default,4,S1,0.5274553894996643,1.0,-1,Train
-Default,4,S8,0.520030677318573,0.0,-1,Train
-Default,4,S5,0.520030677318573,1.0,-1,Train
-Default,4,S4,0.520030677318573,0.0,-1,Train
-Default,4,S7,0.5197962522506714,0.0,-1,Train
-Default,4,S6,0.5197962522506714,0.0,-1,Train
+prediction_target,epoch,subject,model_output,label,cross_validation_split_index,data_split
+Default,1,S4,0.5216594338417053,0.0,-1,Train
+Default,1,S5,0.5216594338417053,1.0,-1,Train
+Default,1,S6,0.5216594338417053,0.0,-1,Train
+Default,1,S7,0.5216594338417053,0.0,-1,Train
+Default,1,S8,0.5213258266448975,0.0,-1,Train
+Default,1,S1,0.5281670689582825,1.0,-1,Train
+Default,2,S1,0.5280245542526245,1.0,-1,Train
+Default,2,S4,0.521056056022644,0.0,-1,Train
+Default,2,S7,0.521056056022644,0.0,-1,Train
+Default,2,S6,0.521056056022644,0.0,-1,Train
+Default,2,S8,0.5207627415657043,0.0,-1,Train
+Default,2,S5,0.5207627415657043,1.0,-1,Train
+Default,3,S5,0.5205143690109253,1.0,-1,Train
+Default,3,S8,0.5205143690109253,0.0,-1,Train
+Default,3,S1,0.5277201533317566,1.0,-1,Train
+Default,3,S7,0.5205143690109253,0.0,-1,Train
+Default,3,S4,0.5202868580818176,0.0,-1,Train
+Default,3,S6,0.5202868580818176,0.0,-1,Train
+Default,4,S1,0.5274553894996643,1.0,-1,Train
+Default,4,S8,0.520030677318573,0.0,-1,Train
+Default,4,S5,0.520030677318573,1.0,-1,Train
+Default,4,S4,0.520030677318573,0.0,-1,Train
+Default,4,S7,0.5197962522506714,0.0,-1,Train
+Default,4,S6,0.5197962522506714,0.0,-1,Train