Fix a bug in implementation of DetectionRGB2BGR.get_equivalent_preprocessing

src/super_gradients/training/transforms/transforms.py

@@ -979,7 +979,7 @@ def __call__(self, sample: dict) -> dict:
     def get_equivalent_preprocessing(self) -> List:
         if self.prob < 1:
             raise RuntimeError("Cannot set preprocessing pipeline with randomness. Set prob to 1.")
-        return [{Processings.ReverseImageChannels}]
+        return [{Processings.ReverseImageChannels: {}}]
 
 
 @register_transform(Transforms.DetectionHSV)

tests/unit_tests/preprocessing_unit_test.py

@@ -2,12 +2,23 @@
 import unittest
 from pathlib import Path
 
+import numpy as np
+
 from super_gradients import Trainer
+from super_gradients.common.factories.list_factory import ListFactory
+from super_gradients.common.factories.processing_factory import ProcessingFactory
 from super_gradients.training import models
 from super_gradients.training.datasets import COCODetectionDataset
 from super_gradients.training.metrics import DetectionMetrics
 from super_gradients.training.models import YoloXPostPredictionCallback
-from super_gradients.training.processing import ReverseImageChannels, DetectionLongestMaxSizeRescale, DetectionBottomRightPadding, ImagePermute
+from super_gradients.training.processing import (
+    ReverseImageChannels,
+    DetectionLongestMaxSizeRescale,
+    DetectionBottomRightPadding,
+    ImagePermute,
+    ComposeProcessing,
+)
+from super_gradients.training.transforms import DetectionPaddedRescale, DetectionRGB2BGR
 from super_gradients.training.utils.detection_utils import DetectionCollateFN, CrowdDetectionCollateFN
 from super_gradients.training import dataloaders
 
@@ -176,6 +187,18 @@ def test_setting_preprocessing_params_from_checkpoint(self):
         self.assertEqual(model._default_nms_iou, 0.65)
         self.assertEqual(model._default_nms_conf, 0.5)
 
+    def test_processings_from_dataset_params(self):
+        transforms = [DetectionRGB2BGR(prob=1), DetectionPaddedRescale(input_dim=(512, 512))]
+
+        processings = []
+        for t in transforms:
+            processings += t.get_equivalent_preprocessing()
+
+        instantiated_processing = ListFactory(ProcessingFactory()).get(processings)
+        processing_pipeline = ComposeProcessing(instantiated_processing)
+        result = processing_pipeline.preprocess_image(np.zeros((480, 640, 3)))
+        print(result)
+
 
 if __name__ == "__main__":
     unittest.main()