undo no-op diffs

Author: leeclemnet

inference_models/inference_models/models/deep_lab_v3_plus/deep_lab_v3_plus_segmentation_onnx.py

@@ -311,9 +311,10 @@ def post_process(
             conf = result.confidence.clone()
             seg_map[below] = self._background_class_id
             conf[below] = 0.0
-            result = SemanticSegmentationResult(
-                segmentation_map=seg_map,
-                confidence=conf,
+            results.append(
+                SemanticSegmentationResult(
+                    segmentation_map=seg_map,
+                    confidence=conf,
+                )
             )
-            results.append(result)
         return results

inference_models/inference_models/models/deep_lab_v3_plus/deep_lab_v3_plus_segmentation_torch.py

@@ -286,9 +286,10 @@ def post_process(
             conf = result.confidence.clone()
             seg_map[below] = self._background_class_id
             conf[below] = 0.0
-            result = SemanticSegmentationResult(
-                segmentation_map=seg_map,
-                confidence=conf,
+            results.append(
+                SemanticSegmentationResult(
+                    segmentation_map=seg_map,
+                    confidence=conf,
+                )
             )
-            results.append(result)
         return results

inference_models/inference_models/models/deep_lab_v3_plus/deep_lab_v3_plus_segmentation_trt.py

@@ -376,11 +376,12 @@ def post_process(
                 conf = result.confidence.clone()
                 seg_map[below] = self._background_class_id
                 conf[below] = 0.0
-                result = SemanticSegmentationResult(
-                    segmentation_map=seg_map,
-                    confidence=conf,
+                results.append(
+                    SemanticSegmentationResult(
+                        segmentation_map=seg_map,
+                        confidence=conf,
+                    )
                 )
-                results.append(result)
         self._post_process_stream.synchronize()
         return results
 

inference_models/inference_models/models/dinov3/dinov3_classification_onnx.py

@@ -380,9 +380,10 @@ def post_process(
             predicted_classes = torch.argwhere(
                 batch_element_confidence >= thresholds
             ).squeeze(dim=-1)
-            prediction = MultiLabelClassificationPrediction(
-                class_ids=predicted_classes,
-                confidence=batch_element_confidence,
+            results.append(
+                MultiLabelClassificationPrediction(
+                    class_ids=predicted_classes,
+                    confidence=batch_element_confidence,
+                )
             )
-            results.append(prediction)
         return results

inference_models/inference_models/models/dinov3/dinov3_classification_torch.py

@@ -358,9 +358,10 @@ def post_process(
             predicted_classes = torch.argwhere(
                 batch_element_confidence >= thresholds
             ).squeeze(dim=-1)
-            prediction = MultiLabelClassificationPrediction(
-                class_ids=predicted_classes,
-                confidence=batch_element_confidence,
+            results.append(
+                MultiLabelClassificationPrediction(
+                    class_ids=predicted_classes,
+                    confidence=batch_element_confidence,
+                )
             )
-            results.append(prediction)
         return results

inference_models/inference_models/models/resnet/resnet_classification_onnx.py

@@ -364,9 +364,10 @@ def post_process(
             predicted_classes = torch.argwhere(
                 batch_element_confidence >= thresholds
             ).squeeze(dim=-1)
-            prediction = MultiLabelClassificationPrediction(
-                class_ids=predicted_classes,
-                confidence=batch_element_confidence,
+            results.append(
+                MultiLabelClassificationPrediction(
+                    class_ids=predicted_classes,
+                    confidence=batch_element_confidence,
+                )
             )
-            results.append(prediction)
         return results

inference_models/inference_models/models/resnet/resnet_classification_torch.py

@@ -342,9 +342,10 @@ def post_process(
             predicted_classes = torch.argwhere(
                 batch_element_confidence >= thresholds
             ).squeeze(dim=-1)
-            prediction = MultiLabelClassificationPrediction(
-                class_ids=predicted_classes,
-                confidence=batch_element_confidence,
+            results.append(
+                MultiLabelClassificationPrediction(
+                    class_ids=predicted_classes,
+                    confidence=batch_element_confidence,
+                )
             )
-            results.append(prediction)
         return results

inference_models/inference_models/models/resnet/resnet_classification_trt.py

@@ -474,11 +474,12 @@ def post_process(
                 predicted_classes = torch.argwhere(
                     batch_element_confidence >= thresholds
                 ).squeeze(dim=-1)
-                prediction = MultiLabelClassificationPrediction(
-                    class_ids=predicted_classes,
-                    confidence=batch_element_confidence,
+                results.append(
+                    MultiLabelClassificationPrediction(
+                        class_ids=predicted_classes,
+                        confidence=batch_element_confidence,
+                    )
                 )
-                results.append(prediction)
         self._post_process_stream.synchronize()
         return results
 

inference_models/inference_models/models/rfdetr/rfdetr_object_detection_onnx.py

@@ -275,10 +275,11 @@ def post_process(
                 image_detections=selected_boxes_xyxy,
                 image_metadata=image_meta,
             )
-            detections = Detections(
-                xyxy=selected_boxes_xyxy.round().int(),
-                confidence=predicted_confidence,
-                class_id=top_classes.int(),
+            results.append(
+                Detections(
+                    xyxy=selected_boxes_xyxy.round().int(),
+                    confidence=predicted_confidence,
+                    class_id=top_classes.int(),
+                )
             )
-            results.append(detections)
         return results

inference_models/inference_models/models/rfdetr/rfdetr_object_detection_pytorch.py

@@ -533,10 +533,11 @@ def post_process(
                     device=boxes.device,
                 )
                 boxes[:, :4].add_(static_crop_offsets)
-            detections = Detections(
-                xyxy=boxes.round().int(),
-                confidence=scores,
-                class_id=labels.int(),
+            detections_list.append(
+                Detections(
+                    xyxy=boxes.round().int(),
+                    confidence=scores,
+                    class_id=labels.int(),
+                )
             )
-            detections_list.append(detections)
         return detections_list