fix one_hot error (#5532)

ppdet/modeling/assigners/atss_assigner.py

@@ -199,7 +199,11 @@ class ATSSAssigner(nn.Layer):
             gt_bboxes.reshape([-1, 4]), assigned_gt_index.flatten(), axis=0)
         assigned_bboxes = assigned_bboxes.reshape([batch_size, num_anchors, 4])
 
-        assigned_scores = F.one_hot(assigned_labels, self.num_classes)
+        assigned_scores = F.one_hot(assigned_labels, self.num_classes + 1)
+        ind = list(range(self.num_classes + 1))
+        ind.remove(bg_index)
+        assigned_scores = paddle.index_select(
+            assigned_scores, paddle.to_tensor(ind), axis=-1)
         if pred_bboxes is not None:
             # assigned iou
             ious = batch_iou_similarity(gt_bboxes, pred_bboxes) * mask_positive

ppdet/modeling/assigners/task_aligned_assigner.py

@@ -143,7 +143,11 @@ class TaskAlignedAssigner(nn.Layer):
             gt_bboxes.reshape([-1, 4]), assigned_gt_index.flatten(), axis=0)
         assigned_bboxes = assigned_bboxes.reshape([batch_size, num_anchors, 4])
 
-        assigned_scores = F.one_hot(assigned_labels, num_classes)
+        assigned_scores = F.one_hot(assigned_labels, num_classes + 1)
+        ind = list(range(num_classes + 1))
+        ind.remove(bg_index)
+        assigned_scores = paddle.index_select(
+            assigned_scores, paddle.to_tensor(ind), axis=-1)
         # rescale alignment metrics
         alignment_metrics *= mask_positive
         max_metrics_per_instance = alignment_metrics.max(axis=-1, keepdim=True)

ppdet/modeling/heads/ppyoloe_head.py

@@ -331,7 +331,8 @@ class PPYOLOEHead(nn.Layer):
         assigned_bboxes /= stride_tensor
         # cls loss
         if self.use_varifocal_loss:
-            one_hot_label = F.one_hot(assigned_labels, self.num_classes)
+            one_hot_label = F.one_hot(assigned_labels,
+                                      self.num_classes + 1)[..., :-1]
             loss_cls = self._varifocal_loss(pred_scores, assigned_scores,
                                             one_hot_label)
         else:

ppdet/modeling/losses/detr_loss.py

@@ -80,7 +80,7 @@ class DETRLoss(nn.Layer):
             target_label = target_label.reshape([bs, num_query_objects])
         if self.use_focal_loss:
             target_label = F.one_hot(target_label,
-                                     self.num_classes + 1)[:, :, :-1]
+                                     self.num_classes + 1)[..., :-1]
         return {
             'loss_class': self.loss_coeff['class'] * sigmoid_focal_loss(
                 logits, target_label, num_gts / num_query_objects)