Fix potential bugs in rtmdet multi-scale training (#439)

* Distinguish variables when training and testing

* beauty

mmyolo/models/dense_heads/rtmdet_head.py

@@ -260,6 +260,9 @@ class RTMDetHead(YOLOv5Head):
             else:
                 self.sampler = PseudoSampler(context=self)
 
+            self.featmap_sizes_train = None
+            self.flatten_priors_train = None
+
     def forward(self, x: Tuple[Tensor]) -> Tuple[List]:
         """Forward features from the upstream network.
 
@@ -312,12 +315,12 @@ class RTMDetHead(YOLOv5Head):
         device = cls_scores[0].device
 
         # If the shape does not equal, generate new one
-        if featmap_sizes != self.featmap_sizes:
-            self.featmap_sizes = featmap_sizes
-            mlvl_priors = self.prior_generator.grid_priors(
+        if featmap_sizes != self.featmap_sizes_train:
+            self.featmap_sizes_train = featmap_sizes
+            mlvl_priors_with_stride = self.prior_generator.grid_priors(
                 featmap_sizes, device=device, with_stride=True)
-            self.flatten_priors = torch.cat(mlvl_priors, dim=0)
-            self.mlvl_priors = [mlvl[:, :2] for mlvl in mlvl_priors]
+            self.flatten_priors_train = torch.cat(
+                mlvl_priors_with_stride, dim=0)
 
         flatten_cls_scores = torch.cat([
             cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
@@ -329,13 +332,14 @@ class RTMDetHead(YOLOv5Head):
             bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
             for bbox_pred in bbox_preds
         ], 1)
-        flatten_bboxes = flatten_bboxes * self.flatten_priors[..., -1, None]
-        flatten_bboxes = distance2bbox(self.flatten_priors[..., :2],
+        flatten_bboxes = flatten_bboxes * self.flatten_priors_train[..., -1,
+                                                                    None]
+        flatten_bboxes = distance2bbox(self.flatten_priors_train[..., :2],
                                        flatten_bboxes)
 
         assigned_result = self.assigner(flatten_bboxes.detach(),
                                         flatten_cls_scores.detach(),
-                                        self.flatten_priors, gt_labels,
+                                        self.flatten_priors_train, gt_labels,
                                         gt_bboxes, pad_bbox_flag)
 
         labels = assigned_result['assigned_labels'].reshape(-1)

projects/assigner_visualization/assigner_visualization.py

@@ -111,6 +111,7 @@ def main():
 
     # make output dir
     os.makedirs(args.output_dir, exist_ok=True)
+    print('Results will save to ', args.output_dir)
 
     # init visualization image number
     assert args.show_number > 0