diff --git a/ppdet/modeling/architectures/picodet.py b/ppdet/modeling/architectures/picodet.py
index 7e9382b7c0b83b102e90dc5f1cfa0d490f738248..9c67005b0aee1bddf27b0a8cda1c3155a64f3c25 100644
--- a/ppdet/modeling/architectures/picodet.py
+++ b/ppdet/modeling/architectures/picodet.py
@@ -64,7 +64,7 @@ class PicoDet(BaseArch):
         fpn_feats = self.neck(body_feats)
         head_outs = self.head(fpn_feats, self.deploy)
         if self.training or self.deploy:
-            return head_outs
+            return head_outs, None
         else:
             im_shape = self.inputs['im_shape']
             scale_factor = self.inputs['scale_factor']
diff --git a/ppdet/modeling/assigners/simota_assigner.py b/ppdet/modeling/assigners/simota_assigner.py
index 34e26eee6f25e7211c845c747743f8c6fa35550a..eacd23022f1c95b05fad17c742932e8d80987324 100644
--- a/ppdet/modeling/assigners/simota_assigner.py
+++ b/ppdet/modeling/assigners/simota_assigner.py
@@ -172,16 +172,11 @@ class SimOTAAssigner(object):
             (num_bboxes, ), 0, dtype=paddle.int64).numpy()
         if num_gt == 0 or num_bboxes == 0:
             # No ground truth or boxes, return empty assignment
-            max_overlaps = decoded_bboxes.new_zeros((num_bboxes, ))
-            if num_gt == 0:
-                # No truth, assign everything to background
-                assigned_gt_inds[:] = 0
-            if gt_labels is None:
-                assigned_labels = None
-            else:
-                assigned_labels = paddle.full(
-                    (num_bboxes, ), -1, dtype=paddle.int64)
-            return
+            priors = priors.numpy()
+            labels = np.ones([num_bboxes], dtype=np.int64) * self.num_classes
+            label_weights = np.ones([num_bboxes], dtype=np.float32)
+            bbox_targets = np.zeros_like(priors)
+            return priors, labels, label_weights, bbox_targets, 0
 
         valid_mask, is_in_boxes_and_center = self.get_in_gt_and_in_center_info(
             priors, gt_bboxes)
@@ -238,8 +233,6 @@ class SimOTAAssigner(object):
         gt_bboxes = gt_bboxes.numpy()
 
         assigned_gt_inds[valid_mask] = matched_gt_inds + 1
-        assigned_labels = np.full((num_bboxes, ), self.num_classes)
-        assigned_labels[valid_mask] = gt_labels.squeeze(-1)[matched_gt_inds]
 
         pos_inds, neg_inds, pos_gt_bboxes, pos_assigned_gt_inds \
             = self.get_sample(assigned_gt_inds, gt_bboxes)
diff --git a/ppdet/modeling/heads/simota_head.py b/ppdet/modeling/heads/simota_head.py
index e312726dab72476eee7946fa73a8f4dce2495366..2a870dc13c7570ebd78899d47bbfe7eaaf3101cd 100644
--- a/ppdet/modeling/heads/simota_head.py
+++ b/ppdet/modeling/heads/simota_head.py
@@ -127,11 +127,6 @@ class OTAHead(GFLHead):
                            gt_labels):
         """Compute targets for priors in a single image.
         """
-        num_gts = gt_labels.shape[0]
-        # No target
-        if num_gts == 0:
-            pass
-
         centors, labels, label_weights, bbox_targets, pos_num = self.assigner(
             F.sigmoid(cls_preds), centors, decoded_bboxes, gt_bboxes, gt_labels)