fix a bug np.int

models/detectors/yolovx/loss.py

@@ -41,10 +41,7 @@ class Criterion(object):
 
     def loss_bboxes(self, pred_box, gt_box):
         # regression loss
-        ious = get_ious(pred_box,
-                        gt_box,
-                        box_mode="xyxy",
-                        iou_type='giou')
+        ious = get_ious(pred_box, gt_box, "xyxy", 'giou')
         loss_box = 1.0 - ious
 
         return loss_box
@@ -123,16 +120,16 @@ class Criterion(object):
             torch.distributed.all_reduce(num_fgs)
         num_fgs = (num_fgs / get_world_size()).clamp(1.0)
 
-        # obj loss
+        # ------------------ objecntness loss ------------------
         loss_obj = self.loss_objectness(obj_preds.view(-1, 1), obj_targets.float())
         loss_obj = loss_obj.sum() / num_fgs
         
-        # cls loss
+        # ------------------ classification loss ------------------
         cls_preds_pos = cls_preds.view(-1, self.num_classes)[fg_masks]
         loss_cls = self.loss_classes(cls_preds_pos, cls_targets)
         loss_cls = loss_cls.sum() / num_fgs
 
-        # regression loss
+        # ------------------ regression loss ------------------
         box_preds_pos = box_preds.view(-1, 4)[fg_masks]
         loss_box = self.loss_bboxes(box_preds_pos, box_targets)
         loss_box = loss_box.sum() / num_fgs

utils/misc.py

@@ -303,7 +303,7 @@ def multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh):
 ## class-aware NMS 
 def multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes):
     # nms
-    keep = np.zeros(len(bboxes), dtype=np.int)
+    keep = np.zeros(len(bboxes), dtype=np.int32)
     for i in range(num_classes):
         inds = np.where(labels == i)[0]
         if len(inds) == 0: