diff --git a/configs/dota/s2anet_1x_spine.yml b/configs/dota/s2anet_1x_spine.yml
index 6ebe668c8c23298b3a2870716a2e16e62faa5ea2..5cf215b54d7633d75225608b0fd4ab45ecd1edd0 100644
--- a/configs/dota/s2anet_1x_spine.yml
+++ b/configs/dota/s2anet_1x_spine.yml
@@ -27,4 +27,3 @@ S2ANetHead:
   reg_loss_weight: [1.0, 1.0, 1.0, 1.0, 1.05]
   cls_loss_weight: [1.05, 1.0]
   reg_loss_type: 'l1'
-  is_training: True
diff --git a/configs/dota/s2anet_conv_2x_dota.yml b/configs/dota/s2anet_conv_2x_dota.yml
index eaca36207fe3f9652a745cca017b514a5f42d406..795ba242b116b01489924a687490492799875d07 100644
--- a/configs/dota/s2anet_conv_2x_dota.yml
+++ b/configs/dota/s2anet_conv_2x_dota.yml
@@ -6,6 +6,7 @@ _BASE_: [
   '_base_/s2anet_reader.yml',
 ]
 weights: output/s2anet_conv_1x_dota/model_final
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
 
 ResNet:
   depth: 50
diff --git a/ppdet/ext_op/rbox_iou_op.cc b/ppdet/ext_op/rbox_iou_op.cc
index ce27e8e919e80bd2cc2a59f2087b41151b880a50..6031953d20e6302759621ac80b7a3e6ca35928db 100644
--- a/ppdet/ext_op/rbox_iou_op.cc
+++ b/ppdet/ext_op/rbox_iou_op.cc
@@ -45,8 +45,7 @@ std::vector<paddle::Tensor> RboxIouCPUForward(const paddle::Tensor& rbox1, const
     auto rbox1_num = rbox1.shape()[0];
     auto rbox2_num = rbox2.shape()[0];
 
-    auto output = paddle::Tensor(paddle::PlaceType::kCPU);
-    output.reshape({rbox1_num, rbox2_num});
+    auto output = paddle::Tensor(paddle::PlaceType::kCPU, {rbox1_num, rbox2_num});
 
     PD_DISPATCH_FLOATING_TYPES(
         rbox1.type(),
diff --git a/ppdet/ext_op/rbox_iou_op.cu b/ppdet/ext_op/rbox_iou_op.cu
index 72cac238922bdad50999e9ae7116181cf303c212..8ec43e54b4a813ef5829ba3120cc4a2be4d5d9b9 100644
--- a/ppdet/ext_op/rbox_iou_op.cu
+++ b/ppdet/ext_op/rbox_iou_op.cu
@@ -94,8 +94,7 @@ std::vector<paddle::Tensor> RboxIouCUDAForward(const paddle::Tensor& rbox1, cons
     auto rbox1_num = rbox1.shape()[0];
     auto rbox2_num = rbox2.shape()[0];
 
-    auto output = paddle::Tensor(paddle::PlaceType::kGPU);
-    output.reshape({rbox1_num, rbox2_num});
+    auto output = paddle::Tensor(paddle::PlaceType::kGPU, {rbox1_num, rbox2_num});
 
     const int blocks_x = CeilDiv(rbox1_num, BLOCK_DIM_X);
     const int blocks_y = CeilDiv(rbox2_num, BLOCK_DIM_Y);
diff --git a/ppdet/modeling/bbox_utils.py b/ppdet/modeling/bbox_utils.py
index 789d064674cf7031e31fa3035d1d9224f935f739..e040ba69b755fdc5c329413343edaba463ba7925 100644
--- a/ppdet/modeling/bbox_utils.py
+++ b/ppdet/modeling/bbox_utils.py
@@ -529,18 +529,18 @@ def poly2rbox(polys):
         rbox_angle = 0
         if edge1 > edge2:
             rbox_angle = np.arctan2(
-                np.float(pt2[1] - pt1[1]), np.float(pt2[0] - pt1[0]))
+                float(pt2[1] - pt1[1]), float(pt2[0] - pt1[0]))
         elif edge2 >= edge1:
             rbox_angle = np.arctan2(
-                np.float(pt4[1] - pt1[1]), np.float(pt4[0] - pt1[0]))
+                float(pt4[1] - pt1[1]), float(pt4[0] - pt1[0]))
 
         def norm_angle(angle, range=[-np.pi / 4, np.pi]):
             return (angle - range[0]) % range[1] + range[0]
 
         rbox_angle = norm_angle(rbox_angle)
 
-        x_ctr = np.float(pt1[0] + pt3[0]) / 2
-        y_ctr = np.float(pt1[1] + pt3[1]) / 2
+        x_ctr = float(pt1[0] + pt3[0]) / 2
+        y_ctr = float(pt1[1] + pt3[1]) / 2
         rotated_box = np.array([x_ctr, y_ctr, width, height, rbox_angle])
         rotated_boxes.append(rotated_box)
     ret_rotated_boxes = np.array(rotated_boxes)