diff --git a/ppdet/data/transform/batch_operator.py b/ppdet/data/transform/batch_operator.py
index 870f1ad3b052abf6937f509476010f1c38580909..fa4c9e9ca580716898a564cb3bc32fcb743c0823 100644
--- a/ppdet/data/transform/batch_operator.py
+++ b/ppdet/data/transform/batch_operator.py
@@ -94,7 +94,8 @@ class PadBatchOp(BaseOperator):
 
         if self.pad_gt:
             gt_num = []
-            if data['gt_poly'] is not None and len(data['gt_poly']) > 0:
+            if 'gt_poly' in data and data['gt_poly'] is not None and len(data[
+                    'gt_poly']) > 0:
                 pad_mask = True
             else:
                 pad_mask = False
@@ -112,18 +113,20 @@ class PadBatchOp(BaseOperator):
                         for p_p in poly:
                             point_num.append(int(len(p_p) / 2))
             gt_num_max = max(gt_num)
-            gt_box_data = np.zeros([gt_num_max, 4])
-            gt_class_data = np.zeros([gt_num_max])
-            is_crowd_data = np.ones([gt_num_max])
-
-            if pad_mask:
-                poly_num_max = max(poly_num)
-                poly_part_num_max = max(poly_part_num)
-                point_num_max = max(point_num)
-                gt_masks_data = -np.ones(
-                    [poly_num_max, poly_part_num_max, point_num_max, 2])
 
             for i, data in enumerate(samples):
+                gt_box_data = np.zeros([gt_num_max, 4], dtype=np.float32)
+                gt_class_data = np.zeros([gt_num_max], dtype=np.int32)
+                is_crowd_data = np.ones([gt_num_max], dtype=np.int32)
+
+                if pad_mask:
+                    poly_num_max = max(poly_num)
+                    poly_part_num_max = max(poly_part_num)
+                    point_num_max = max(point_num)
+                    gt_masks_data = -np.ones(
+                        [poly_num_max, poly_part_num_max, point_num_max, 2],
+                        dtype=np.float32)
+
                 gt_num = data['gt_bbox'].shape[0]
                 gt_box_data[0:gt_num, :] = data['gt_bbox']
                 gt_class_data[0:gt_num] = np.squeeze(data['gt_class'])
diff --git a/ppdet/data/transform/batch_operators.py b/ppdet/data/transform/batch_operators.py
index d4c1c81d8a40303749ba381fe0aae92126c22916..dbb640496a8be6bb128ed74ccb61e3e08a853fad 100644
--- a/ppdet/data/transform/batch_operators.py
+++ b/ppdet/data/transform/batch_operators.py
@@ -85,7 +85,8 @@ class PadBatch(BaseOperator):
                 data['im_info'][:2] = max_shape[1:3]
         if self.pad_gt:
             gt_num = []
-            if data['gt_poly'] is not None and len(data['gt_poly']) > 0:
+            if 'gt_poly' in data and data['gt_poly'] is not None and len(data[
+                    'gt_poly']) > 0:
                 pad_mask = True
             else:
                 pad_mask = False
@@ -103,18 +104,19 @@ class PadBatch(BaseOperator):
                         for p_p in poly:
                             point_num.append(int(len(p_p) / 2))
             gt_num_max = max(gt_num)
-            gt_box_data = np.zeros([gt_num_max, 4])
-            gt_class_data = np.zeros([gt_num_max])
-            is_crowd_data = np.ones([gt_num_max])
-
-            if pad_mask:
-                poly_num_max = max(poly_num)
-                poly_part_num_max = max(poly_part_num)
-                point_num_max = max(point_num)
-                gt_masks_data = -np.ones(
-                    [poly_num_max, poly_part_num_max, point_num_max, 2])
 
             for i, data in enumerate(samples):
+                gt_box_data = np.zeros([gt_num_max, 4], dtype=np.float32)
+                gt_class_data = np.zeros([gt_num_max], dtype=np.int32)
+                is_crowd_data = np.ones([gt_num_max], dtype=np.int32)
+                if pad_mask:
+                    poly_num_max = max(poly_num)
+                    poly_part_num_max = max(poly_part_num)
+                    point_num_max = max(point_num)
+                    gt_masks_data = -np.ones(
+                        [poly_num_max, poly_part_num_max, point_num_max, 2],
+                        dtype=np.float32)
+
                 gt_num = data['gt_bbox'].shape[0]
                 gt_box_data[0:gt_num, :] = data['gt_bbox']
                 gt_class_data[0:gt_num] = np.squeeze(data['gt_class'])