[rename] - preprocess-batch -> preprocess, preprocess_preds -> postprocess (#42)

ultralytics/yolo/engine/validator.py

@@ -54,7 +54,7 @@ class BaseValidator:
                 self.batch_i = batch_i
                 # pre-process
                 with dt[0]:
-                    batch = self.preprocess_batch(batch)
+                    batch = self.preprocess(batch)
 
                 # inference
                 with dt[1]:
@@ -69,7 +69,7 @@ class BaseValidator:
 
                 # pre-process predictions
                 with dt[3]:
-                    preds = self.preprocess_preds(preds)
+                    preds = self.postprocess(preds)
 
                 self.update_metrics(preds, batch)
 
@@ -89,10 +89,10 @@ class BaseValidator:
 
         return stats
 
-    def preprocess_batch(self, batch):
+    def preprocess(self, batch):
         return batch
 
-    def preprocess_preds(self, preds):
+    def postprocess(self, preds):
         return preds
 
     def init_metrics(self):

ultralytics/yolo/v8/classify/val.py

@@ -8,7 +8,7 @@ class ClassificationValidator(BaseValidator):
     def init_metrics(self, model):
         self.correct = torch.tensor([])
 
-    def preprocess_batch(self, batch):
+    def preprocess(self, batch):
         batch["img"] = batch["img"].to(self.device)
         batch["cls"] = batch["cls"].to(self.device)
         return batch

ultralytics/yolo/v8/segment/val.py

@@ -28,7 +28,7 @@ class SegmentationValidator(BaseValidator):
         self.class_map = None
         self.targets = None
 
-    def preprocess_batch(self, batch):
+    def preprocess(self, batch):
         batch["img"] = batch["img"].to(self.device, non_blocking=True)
         batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 225
         batch["bboxes"] = batch["bboxes"].to(self.device)
@@ -66,7 +66,7 @@ class SegmentationValidator(BaseValidator):
         return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P",
                                          "R", "mAP50", "mAP50-95)")
 
-    def preprocess_preds(self, preds):
+    def postprocess(self, preds):
         p = ops.non_max_suppression(preds[0],
                                     self.args.conf_thres,
                                     self.args.iou_thres,