fix PadBatch (#1800)

* fix PadBatch

* fix PadBatchOp

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])
+
+            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])
+                        [poly_num_max, poly_part_num_max, point_num_max, 2],
+                        dtype=np.float32)
 
-            for i, data in enumerate(samples):
                 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'])

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])
 
+            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])
+                        [poly_num_max, poly_part_num_max, point_num_max, 2],
+                        dtype=np.float32)
 
-            for i, data in enumerate(samples):
                 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'])