Fix indentation in log_training_progress() (#4126)

utils/wandb_logging/wandb_utils.py

@@ -293,26 +293,26 @@ class WandbLogger():
         return artifact
 
     def log_training_progress(self, predn, path, names):
-            class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
-            box_data = []
-            total_conf = 0
-            for *xyxy, conf, cls in predn.tolist():
-                if conf >= 0.25:
-                    box_data.append(
-                        {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
-                         "class_id": int(cls),
-                         "box_caption": "%s %.3f" % (names[cls], conf),
-                         "scores": {"class_score": conf},
-                         "domain": "pixel"})
-                    total_conf = total_conf + conf
-            boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-            id = self.val_table_path_map[Path(path).name]
-            self.result_table.add_data(self.current_epoch,
-                                       id,
-                                       self.val_table.data[id][1],
-                                       wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
-                                       total_conf / max(1, len(box_data))
-                                       )
+        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
+        box_data = []
+        total_conf = 0
+        for *xyxy, conf, cls in predn.tolist():
+            if conf >= 0.25:
+                box_data.append(
+                    {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
+                     "class_id": int(cls),
+                     "box_caption": "%s %.3f" % (names[cls], conf),
+                     "scores": {"class_score": conf},
+                     "domain": "pixel"})
+                total_conf = total_conf + conf
+        boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
+        id = self.val_table_path_map[Path(path).name]
+        self.result_table.add_data(self.current_epoch,
+                                   id,
+                                   self.val_table.data[id][1],
+                                   wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
+                                   total_conf / max(1, len(box_data))
+                                   )
 
     def val_one_image(self, pred, predn, path, names, im):
         if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact