[dev] clean code of s2anet (#3733) (#3778)

* clean code of s2anet

* fix for paddle==2.1.0

* remove debug info

* update comment

configs/dota/_base_/s2anet.yml

 architecture: S2ANet
-pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
 weights: output/s2anet_r50_fpn_1x_dota/model_final.pdparams
 
 
@@ -12,6 +12,7 @@ S2ANet:
 
 ResNet:
   depth: 50
+  variant: d
   norm_type: bn
   return_idx: [1,2,3]
   num_stages: 4
@@ -33,11 +34,9 @@ S2ANetHead:
   feat_in: 256
   feat_out: 256
   num_classes: 15
-  align_conv_type: 'Conv'  # AlignConv Conv
+  align_conv_type: 'AlignConv'  # AlignConv Conv
   align_conv_size: 3
   use_sigmoid_cls: True
-  reg_loss_weight: [ 1.0, 1.0, 1.0, 1.0, 1.1 ]
-  cls_loss_weight: [ 1.1, 1.05 ]
 
 RBoxAssigner:
   pos_iou_thr: 0.5
@@ -54,3 +53,4 @@ S2ANetBBoxPostProcess:
     score_threshold: 0.05
     nms_threshold: 0.1
     normalized: False
+    #background_label: -1

ppdet/modeling/heads/s2anet_head.py

@@ -23,20 +23,16 @@ from ppdet.modeling.proposal_generator.target_layer import RBoxAssigner
 import numpy as np
 
 
-class S2ANetAnchorGenerator(object):
+class S2ANetAnchorGenerator(nn.Layer):
     """
-    S2ANetAnchorGenerator by np
+    AnchorGenerator by paddle
     """
 
-    def __init__(self,
-                 base_size=8,
-                 scales=1.0,
-                 ratios=1.0,
-                 scale_major=True,
-                 ctr=None):
+    def __init__(self, base_size, scales, ratios, scale_major=True, ctr=None):
+        super(S2ANetAnchorGenerator, self).__init__()
         self.base_size = base_size
-        self.scales = scales
-        self.ratios = ratios
+        self.scales = paddle.to_tensor(scales)
+        self.ratios = paddle.to_tensor(ratios)
         self.scale_major = scale_major
         self.ctr = ctr
         self.base_anchors = self.gen_base_anchors()
@@ -54,7 +50,7 @@ class S2ANetAnchorGenerator(object):
         else:
             x_ctr, y_ctr = self.ctr
 
-        h_ratios = np.sqrt(self.ratios)
+        h_ratios = paddle.sqrt(self.ratios)
         w_ratios = 1 / h_ratios
         if self.scale_major:
             ws = (w * w_ratios[:] * self.scales[:]).reshape([-1])
@@ -63,53 +59,51 @@ class S2ANetAnchorGenerator(object):
             ws = (w * self.scales[:] * w_ratios[:]).reshape([-1])
             hs = (h * self.scales[:] * h_ratios[:]).reshape([-1])
 
-        # yapf: disable
-        base_anchors = np.stack(
+        base_anchors = paddle.stack(
             [
                 x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1),
                 x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1)
             ],
             axis=-1)
-        base_anchors = np.round(base_anchors)
-        # yapf: enable
-
+        base_anchors = paddle.round(base_anchors)
         return base_anchors
 
     def _meshgrid(self, x, y, row_major=True):
-        xx, yy = np.meshgrid(x, y)
-        xx = xx.reshape(-1)
-        yy = yy.reshape(-1)
+        yy, xx = paddle.meshgrid(x, y)
+        yy = yy.reshape([-1])
+        xx = xx.reshape([-1])
         if row_major:
             return xx, yy
         else:
             return yy, xx
 
-    def grid_anchors(self, featmap_size, stride=16):
+    def forward(self, featmap_size, stride=16):
         # featmap_size*stride project it to original area
-        base_anchors = self.base_anchors
-        feat_h, feat_w = featmap_size
-        shift_x = np.arange(0, feat_w, 1, 'int32') * stride
-        shift_y = np.arange(0, feat_h, 1, 'int32') * stride
+
+        feat_h = featmap_size[0]
+        feat_w = featmap_size[1]
+        shift_x = paddle.arange(0, feat_w, 1, 'int32') * stride
+        shift_y = paddle.arange(0, feat_h, 1, 'int32') * stride
         shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
-        shifts = np.stack([shift_xx, shift_yy, shift_xx, shift_yy], axis=-1)
+        shifts = paddle.stack([shift_xx, shift_yy, shift_xx, shift_yy], axis=-1)
 
-        all_anchors = base_anchors[None, :, :] + shifts[:, None, :]
+        all_anchors = self.base_anchors[:, :] + shifts[:, :]
+        all_anchors = all_anchors.reshape([feat_h * feat_w, 4])
         return all_anchors
 
     def valid_flags(self, featmap_size, valid_size):
         feat_h, feat_w = featmap_size
         valid_h, valid_w = valid_size
         assert valid_h <= feat_h and valid_w <= feat_w
-        valid_x = np.zeros([feat_w], dtype='uint8')
-        valid_y = np.zeros([feat_h], dtype='uint8')
+        valid_x = paddle.zeros([feat_w], dtype='int32')
+        valid_y = paddle.zeros([feat_h], dtype='int32')
         valid_x[:valid_w] = 1
         valid_y[:valid_h] = 1
         valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
         valid = valid_xx & valid_yy
-        valid = valid.reshape([-1])
-
-        # valid = valid[:, None].expand(
-        #    [valid.size(0), self.num_base_anchors]).reshape([-1])
+        valid = paddle.reshape(valid, [-1, 1])
+        valid = paddle.expand(valid,
+            [-1, self.num_base_anchors]).reshape([-1])
         return valid
 
 
@@ -138,7 +132,8 @@ class AlignConv(nn.Layer):
         """
         anchors = paddle.reshape(anchors, [-1, 5])  # (NA,5)
         dtype = anchors.dtype
-        feat_h, feat_w = featmap_size
+        feat_h = featmap_size[0]
+        feat_w = featmap_size[1]
         pad = (self.kernel_size - 1) // 2
         idx = paddle.arange(-pad, pad + 1, dtype=dtype)
 
@@ -164,11 +159,11 @@ class AlignConv(nn.Layer):
         h = anchors[:, 3]
         a = anchors[:, 4]
 
-        x_ctr = paddle.reshape(x_ctr, [x_ctr.shape[0], 1])
-        y_ctr = paddle.reshape(y_ctr, [y_ctr.shape[0], 1])
-        w = paddle.reshape(w, [w.shape[0], 1])
-        h = paddle.reshape(h, [h.shape[0], 1])
-        a = paddle.reshape(a, [a.shape[0], 1])
+        x_ctr = paddle.reshape(x_ctr, [-1, 1])
+        y_ctr = paddle.reshape(y_ctr, [-1, 1])
+        w = paddle.reshape(w, [-1, 1])
+        h = paddle.reshape(h, [-1, 1])
+        a = paddle.reshape(a, [-1, 1])
 
         x_ctr = x_ctr / stride
         y_ctr = y_ctr / stride
@@ -183,20 +178,13 @@ class AlignConv(nn.Layer):
         # get offset filed
         offset_x = x_anchor - x_conv
         offset_y = y_anchor - y_conv
-        # x, y in anchors is opposite in image coordinates,
-        # so we stack them with y, x other than x, y
         offset = paddle.stack([offset_y, offset_x], axis=-1)
-        # NA,ks*ks*2
-        # [NA, ks, ks, 2] --> [NA, ks*ks*2]
-        offset = paddle.reshape(offset, [offset.shape[0], -1])
-        # [NA, ks*ks*2] --> [ks*ks*2, NA]
+        offset = paddle.reshape(offset, [feat_h * feat_w, self.kernel_size * self.kernel_size * 2])                    
         offset = paddle.transpose(offset, [1, 0])
-        # [NA, ks*ks*2] --> [1, ks*ks*2, H, W]
-        offset = paddle.reshape(offset, [1, -1, feat_h, feat_w])
+        offset = paddle.reshape(offset, [1, self.kernel_size * self.kernel_size * 2, feat_h, feat_w]) 
         return offset
 
-    def forward(self, x, refine_anchors, stride):
-        featmap_size = (x.shape[2], x.shape[3])
+    def forward(self, x, refine_anchors, featmap_size, stride):
         offset = self.get_offset(refine_anchors, featmap_size, stride)
         x = F.relu(self.align_conv(x, offset))
         return x
@@ -232,14 +220,15 @@ class S2ANetHead(nn.Layer):
                  anchor_strides=[8, 16, 32, 64, 128],
                  anchor_scales=[4],
                  anchor_ratios=[1.0],
-                 target_means=(.0, .0, .0, .0, .0),
-                 target_stds=(1.0, 1.0, 1.0, 1.0, 1.0),
+                 target_means=0.0,
+                 target_stds=1.0,
                  align_conv_type='AlignConv',
                  align_conv_size=3,
                  use_sigmoid_cls=True,
                  anchor_assign=RBoxAssigner().__dict__,
                  reg_loss_weight=[1.0, 1.0, 1.0, 1.0, 1.0],
-                 cls_loss_weight=[1.0, 1.0]):
+                 cls_loss_weight=[1.0, 1.0],
+                 reg_loss_type='l1'):
         super(S2ANetHead, self).__init__()
         self.stacked_convs = stacked_convs
         self.feat_in = feat_in
@@ -248,9 +237,10 @@ class S2ANetHead(nn.Layer):
         self.anchor_scales = anchor_scales
         self.anchor_ratios = anchor_ratios
         self.anchor_strides = anchor_strides
+        self.anchor_strides = paddle.to_tensor(anchor_strides)
         self.anchor_base_sizes = list(anchor_strides)
-        self.target_means = target_means
-        self.target_stds = target_stds
+        self.means = paddle.ones(shape=[5]) * target_means
+        self.stds = paddle.ones(shape=[5]) * target_stds
         assert align_conv_type in ['AlignConv', 'Conv', 'DCN']
         self.align_conv_type = align_conv_type
         self.align_conv_size = align_conv_size
@@ -261,16 +251,20 @@ class S2ANetHead(nn.Layer):
         self.anchor_assign = anchor_assign
         self.reg_loss_weight = reg_loss_weight
         self.cls_loss_weight = cls_loss_weight
+        self.alpha = 1.0
+        self.beta = 1.0
+        self.reg_loss_type = reg_loss_type
 
         self.s2anet_head_out = None
 
         # anchor
         self.anchor_generators = []
         for anchor_base in self.anchor_base_sizes:
-            self.anchor_generators.append(
-                S2ANetAnchorGenerator(anchor_base, anchor_scales,
-                                      anchor_ratios))
+                self.anchor_generators.append(
+                    S2ANetAnchorGenerator(anchor_base, anchor_scales,
+                                          anchor_ratios))
 
+        self.anchor_generators = nn.LayerList(self.anchor_generators)
         self.fam_cls_convs = nn.Sequential()
         self.fam_reg_convs = nn.Sequential()
 
@@ -404,9 +398,8 @@ class S2ANetHead(nn.Layer):
             weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
             bias_attr=ParamAttr(initializer=Constant(0)))
 
-        self.base_anchors = dict()
-        self.featmap_sizes = dict()
-        self.base_anchors = dict()
+        self.featmap_sizes = []
+        self.base_anchors_list = []
         self.refine_anchor_list = []
 
     def forward(self, feats):
@@ -416,11 +409,12 @@ class S2ANetHead(nn.Layer):
         odm_reg_branch_list = []
         odm_cls_branch_list = []
 
-        self.featmap_sizes = dict()
-        self.base_anchors = dict()
+        self.featmap_sizes_list = []
+        self.base_anchors_list = []
         self.refine_anchor_list = []
 
-        for i, feat in enumerate(feats):
+        for feat_idx in range(len(feats)):
+            feat = feats[feat_idx]
             fam_cls_feat = self.fam_cls_convs(feat)
 
             fam_cls = self.fam_cls(fam_cls_feat)
@@ -439,26 +433,30 @@ class S2ANetHead(nn.Layer):
             fam_reg_branch_list.append(fam_reg_reshape)
 
             # prepare anchor
-            featmap_size = feat.shape[-2:]
-            self.featmap_sizes[i] = featmap_size
-            init_anchors = self.anchor_generators[i].grid_anchors(
-                featmap_size, self.anchor_strides[i])
-
-            init_anchors = bbox_utils.rect2rbox(init_anchors)
-            self.base_anchors[(i, featmap_size[0])] = init_anchors
-
-            #fam_reg1 = fam_reg
-            #fam_reg1.stop_gradient = True
-            refine_anchor = bbox_utils.bbox_decode(
-                fam_reg.detach(), init_anchors, self.target_means,
-                self.target_stds)
+            featmap_size = (paddle.shape(feat)[2], paddle.shape(feat)[3])
+            self.featmap_sizes_list.append(featmap_size)
+            init_anchors = self.anchor_generators[feat_idx](
+                featmap_size, self.anchor_strides[feat_idx])
+
+            init_anchors = paddle.to_tensor(init_anchors, dtype='float32')
+            NA = featmap_size[0] * featmap_size[1]
+            init_anchors = paddle.reshape(
+                init_anchors, [NA, 4])
+            init_anchors = self.rect2rbox(init_anchors)
+            self.base_anchors_list.append(init_anchors)
+
+            fam_reg1 = fam_reg
+            fam_reg1.stop_gradient = True
+            refine_anchor = self.bbox_decode(fam_reg1, init_anchors)
+            #refine_anchor = self.bbox_decode(fam_reg.detach(), init_anchors)
 
             self.refine_anchor_list.append(refine_anchor)
 
             if self.align_conv_type == 'AlignConv':
                 align_feat = self.align_conv(feat,
                                              refine_anchor.clone(),
-                                             self.anchor_strides[i])
+                                             featmap_size,
+                                             self.anchor_strides[feat_idx])
             elif self.align_conv_type == 'DCN':
                 align_offset = self.align_conv_offset(feat)
                 align_feat = self.align_conv(feat, align_offset)
@@ -475,9 +473,10 @@ class S2ANetHead(nn.Layer):
             odm_cls_score = self.odm_cls(odm_cls_feat)
             # [N, CLS, H, W] --> [N, H, W, CLS]
             odm_cls_score = odm_cls_score.transpose([0, 2, 3, 1])
+            odm_cls_score_shape = odm_cls_score.shape
             odm_cls_score_reshape = paddle.reshape(
                 odm_cls_score,
-                [odm_cls_score.shape[0], -1, self.cls_out_channels])
+                [odm_cls_score_shape[0], odm_cls_score_shape[1] * odm_cls_score_shape[2], self.cls_out_channels])
 
             odm_cls_branch_list.append(odm_cls_score_reshape)
 
@@ -485,23 +484,27 @@ class S2ANetHead(nn.Layer):
             # [N, 5, H, W] --> [N, H, W, 5]
             odm_bbox_pred = odm_bbox_pred.transpose([0, 2, 3, 1])
             odm_bbox_pred_reshape = paddle.reshape(
-                odm_bbox_pred, [odm_bbox_pred.shape[0], -1, 5])
+                odm_bbox_pred, [-1, 5])
+            odm_bbox_pred_reshape = paddle.unsqueeze(odm_bbox_pred_reshape, axis=0)
             odm_reg_branch_list.append(odm_bbox_pred_reshape)
 
         self.s2anet_head_out = (fam_cls_branch_list, fam_reg_branch_list,
                                 odm_cls_branch_list, odm_reg_branch_list)
         return self.s2anet_head_out
 
-    def get_prediction(self, nms_pre):
+    def get_prediction(self, nms_pre=2000):
         refine_anchors = self.refine_anchor_list
-        fam_cls_branch_list, fam_reg_branch_list, odm_cls_branch_list, odm_reg_branch_list = self.s2anet_head_out
+        fam_cls_branch_list = self.s2anet_head_out[0]
+        fam_reg_branch_list = self.s2anet_head_out[1]
+        odm_cls_branch_list = self.s2anet_head_out[2]
+        odm_reg_branch_list = self.s2anet_head_out[3]
         pred_scores, pred_bboxes = self.get_bboxes(
             odm_cls_branch_list,
             odm_reg_branch_list,
             refine_anchors,
             nms_pre,
-            cls_out_channels=self.cls_out_channels,
-            use_sigmoid_cls=self.use_sigmoid_cls)
+            self.cls_out_channels,
+            self.use_sigmoid_cls)
         return pred_scores, pred_bboxes
 
     def smooth_l1_loss(self, pred, label, delta=1.0 / 9.0):
@@ -519,7 +522,7 @@ class S2ANetHead(nn.Layer):
                             diff - 0.5 * delta)
         return loss
 
-    def get_fam_loss(self, fam_target, s2anet_head_out):
+    def get_fam_loss(self, fam_target, s2anet_head_out, reg_loss_type='gwd'):
         (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
          neg_inds) = fam_target
         fam_cls_branch_list, fam_reg_branch_list, odm_cls_branch_list, odm_reg_branch_list = s2anet_head_out
@@ -527,12 +530,11 @@ class S2ANetHead(nn.Layer):
         fam_cls_losses = []
         fam_bbox_losses = []
         st_idx = 0
-        featmap_sizes = [self.featmap_sizes[e] for e in self.featmap_sizes]
         num_total_samples = len(pos_inds) + len(
             neg_inds) if self.sampling else len(pos_inds)
         num_total_samples = max(1, num_total_samples)
 
-        for idx, feat_size in enumerate(featmap_sizes):
+        for idx, feat_size in enumerate(self.featmap_sizes_list):
             feat_anchor_num = feat_size[0] * feat_size[1]
 
             # step1:  get data
@@ -587,13 +589,17 @@ class S2ANetHead(nn.Layer):
             fam_bbox_pred = paddle.squeeze(fam_bbox_pred, axis=0)
             fam_bbox_pred = paddle.reshape(fam_bbox_pred, [-1, 5])
             fam_bbox = self.smooth_l1_loss(fam_bbox_pred, feat_bbox_targets)
-            loss_weight = paddle.to_tensor(
-                self.reg_loss_weight, dtype='float32', stop_gradient=True)
-            fam_bbox = paddle.multiply(fam_bbox, loss_weight)
-            feat_bbox_weights = paddle.to_tensor(
-                feat_bbox_weights, stop_gradient=True)
-            fam_bbox = fam_bbox * feat_bbox_weights
-            fam_bbox_total = paddle.sum(fam_bbox) / num_total_samples
+
+            # iou_factor
+            if reg_loss_type == 'l1':
+                fam_bbox = self.smooth_l1_loss(fam_bbox_pred, feat_bbox_targets)
+                loss_weight = paddle.to_tensor(
+                    self.reg_loss_weight, dtype='float32', stop_gradient=True)
+                fam_bbox = paddle.multiply(fam_bbox, loss_weight)
+                feat_bbox_weights = paddle.to_tensor(
+                    feat_bbox_weights, stop_gradient=True)
+                fam_bbox = fam_bbox * feat_bbox_weights
+                fam_bbox_total = paddle.sum(fam_bbox) / num_total_samples
 
             fam_bbox_losses.append(fam_bbox_total)
 
@@ -604,7 +610,7 @@ class S2ANetHead(nn.Layer):
         fam_reg_loss = paddle.add_n(fam_bbox_losses)
         return fam_cls_loss, fam_reg_loss
 
-    def get_odm_loss(self, odm_target, s2anet_head_out):
+    def get_odm_loss(self, odm_target, s2anet_head_out, reg_loss_type='gwd'):
         (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
          neg_inds) = odm_target
         fam_cls_branch_list, fam_reg_branch_list, odm_cls_branch_list, odm_reg_branch_list = s2anet_head_out
@@ -612,11 +618,11 @@ class S2ANetHead(nn.Layer):
         odm_cls_losses = []
         odm_bbox_losses = []
         st_idx = 0
-        featmap_sizes = [self.featmap_sizes[e] for e in self.featmap_sizes]
         num_total_samples = len(pos_inds) + len(
             neg_inds) if self.sampling else len(pos_inds)
         num_total_samples = max(1, num_total_samples)
-        for idx, feat_size in enumerate(featmap_sizes):
+        
+        for idx, feat_size in enumerate(self.featmap_sizes_list):
             feat_anchor_num = feat_size[0] * feat_size[1]
 
             # step1:  get data
@@ -670,13 +676,18 @@ class S2ANetHead(nn.Layer):
             odm_bbox_pred = paddle.squeeze(odm_bbox_pred, axis=0)
             odm_bbox_pred = paddle.reshape(odm_bbox_pred, [-1, 5])
             odm_bbox = self.smooth_l1_loss(odm_bbox_pred, feat_bbox_targets)
-            loss_weight = paddle.to_tensor(
-                self.reg_loss_weight, dtype='float32', stop_gradient=True)
-            odm_bbox = paddle.multiply(odm_bbox, loss_weight)
-            feat_bbox_weights = paddle.to_tensor(
-                feat_bbox_weights, stop_gradient=True)
-            odm_bbox = odm_bbox * feat_bbox_weights
-            odm_bbox_total = paddle.sum(odm_bbox) / num_total_samples
+            
+            # iou_factor odm not use_iou                             
+            if reg_loss_type == 'l1':
+                odm_bbox = self.smooth_l1_loss(odm_bbox_pred, feat_bbox_targets)
+                loss_weight = paddle.to_tensor(
+                    self.reg_loss_weight, dtype='float32', stop_gradient=True)
+                odm_bbox = paddle.multiply(odm_bbox, loss_weight)
+                feat_bbox_weights = paddle.to_tensor(
+                    feat_bbox_weights, stop_gradient=True)
+                odm_bbox = odm_bbox * feat_bbox_weights
+                odm_bbox_total = paddle.sum(odm_bbox) / num_total_samples
+            
             odm_bbox_losses.append(odm_bbox_total)
 
         odm_cls_loss = paddle.add_n(odm_cls_losses)
@@ -706,15 +717,7 @@ class S2ANetHead(nn.Layer):
             gt_labels = gt_labels + 1
 
             # featmap_sizes
-            featmap_sizes = [self.featmap_sizes[e] for e in self.featmap_sizes]
-            anchors_list, valid_flag_list = self.get_init_anchors(featmap_sizes,
-                                                                  np_im_shape)
-            anchors_list_all = []
-            for ii, anchor in enumerate(anchors_list):
-                anchor = anchor.reshape(-1, 4)
-                anchor = bbox_utils.rect2rbox(anchor)
-                anchors_list_all.extend(anchor)
-            anchors_list_all = np.array(anchors_list_all)
+            anchors_list_all = np.concatenate(self.base_anchors_list)
 
             # get im_feat
             fam_cls_feats_list = [e[im_id] for e in self.s2anet_head_out[0]]
@@ -729,20 +732,20 @@ class S2ANetHead(nn.Layer):
                                                gt_labels, is_crowd)
             if im_fam_target is not None:
                 im_fam_cls_loss, im_fam_reg_loss = self.get_fam_loss(
-                    im_fam_target, im_s2anet_head_out)
+                    im_fam_target, im_s2anet_head_out, self.reg_loss_type)
                 fam_cls_loss_lst.append(im_fam_cls_loss)
                 fam_reg_loss_lst.append(im_fam_reg_loss)
 
             # ODM
-            refine_anchors_list, valid_flag_list = self.get_refine_anchors(
-                featmap_sizes, image_shape=np_im_shape)
-            refine_anchors_list = np.array(refine_anchors_list)
-            im_odm_target = self.anchor_assign(refine_anchors_list, gt_bboxes,
+            np_refine_anchors_list  = paddle.concat(self.refine_anchor_list).numpy()
+            np_refine_anchors_list = np.concatenate(np_refine_anchors_list)
+            np_refine_anchors_list = np_refine_anchors_list.reshape(-1, 5)
+            im_odm_target = self.anchor_assign(np_refine_anchors_list, gt_bboxes,
                                                gt_labels, is_crowd)
 
             if im_odm_target is not None:
                 im_odm_cls_loss, im_odm_reg_loss = self.get_odm_loss(
-                    im_odm_target, im_s2anet_head_out)
+                    im_odm_target, im_s2anet_head_out, self.reg_loss_type)
                 odm_cls_loss_lst.append(im_odm_cls_loss)
                 odm_reg_loss_lst.append(im_odm_reg_loss)
         fam_cls_loss = paddle.add_n(fam_cls_loss_lst)
@@ -756,65 +759,6 @@ class S2ANetHead(nn.Layer):
             'odm_reg_loss': odm_reg_loss
         }
 
-    def get_init_anchors(self, featmap_sizes, image_shape):
-        """Get anchors according to feature map sizes.
-
-        Args:
-            featmap_sizes (list[tuple]): Multi-level feature map sizes.
-            image_shape (list[dict]): Image meta info.
-        Returns:
-            tuple: anchors of each image, valid flags of each image
-        """
-        num_levels = len(featmap_sizes)
-
-        # since feature map sizes of all images are the same, we only compute
-        # anchors for one time
-        anchor_list = []
-        for i in range(num_levels):
-            anchors = self.anchor_generators[i].grid_anchors(
-                featmap_sizes[i], self.anchor_strides[i])
-            anchor_list.append(anchors)
-
-        # for each image, we compute valid flags of multi level anchors
-        valid_flag_list = []
-        for i in range(num_levels):
-            anchor_stride = self.anchor_strides[i]
-            feat_h, feat_w = featmap_sizes[i]
-            h, w = image_shape
-            valid_feat_h = min(int(np.ceil(h / anchor_stride)), feat_h)
-            valid_feat_w = min(int(np.ceil(w / anchor_stride)), feat_w)
-            flags = self.anchor_generators[i].valid_flags(
-                (feat_h, feat_w), (valid_feat_h, valid_feat_w))
-            valid_flag_list.append(flags)
-
-        return anchor_list, valid_flag_list
-
-    def get_refine_anchors(self, featmap_sizes, image_shape):
-        num_levels = len(featmap_sizes)
-
-        refine_anchors_list = []
-        for i in range(num_levels):
-            refine_anchor = self.refine_anchor_list[i]
-            refine_anchor = paddle.squeeze(refine_anchor, axis=0)
-            refine_anchor = refine_anchor.numpy()
-            refine_anchor = np.reshape(refine_anchor,
-                                       [-1, refine_anchor.shape[-1]])
-            refine_anchors_list.extend(refine_anchor)
-
-        # for each image, we compute valid flags of multi level anchors
-        valid_flag_list = []
-        for i in range(num_levels):
-            anchor_stride = self.anchor_strides[i]
-        feat_h, feat_w = featmap_sizes[i]
-        h, w = image_shape
-        valid_feat_h = min(int(np.ceil(h / anchor_stride)), feat_h)
-        valid_feat_w = min(int(np.ceil(w / anchor_stride)), feat_w)
-        flags = self.anchor_generators[i].valid_flags(
-            (feat_h, feat_w), (valid_feat_h, valid_feat_w))
-        valid_flag_list.append(flags)
-
-        return refine_anchors_list, valid_flag_list
-
     def get_bboxes(self, cls_score_list, bbox_pred_list, mlvl_anchors, nms_pre,
                    cls_out_channels, use_sigmoid_cls):
         assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
@@ -848,10 +792,8 @@ class S2ANetHead(nn.Layer):
                 bbox_pred = paddle.gather(bbox_pred, topk_inds)
                 scores = paddle.gather(scores, topk_inds)
 
-            target_means = (.0, .0, .0, .0, .0)
-            target_stds = (1.0, 1.0, 1.0, 1.0, 1.0)
-            bboxes = bbox_utils.delta2rbox(anchors, bbox_pred, target_means,
-                                           target_stds)
+            bbox_delta = paddle.reshape(bbox_pred, [-1, 5])
+            bboxes = self.delta2rbox(anchors, bbox_delta)
             mlvl_bboxes.append(bboxes)
             mlvl_scores.append(scores)
 
@@ -861,3 +803,86 @@ class S2ANetHead(nn.Layer):
         mlvl_scores = paddle.concat(mlvl_scores)
 
         return mlvl_scores, mlvl_bboxes
+
+    def rect2rbox(self, bboxes):
+        """
+        :param bboxes: shape (n, 4) (xmin, ymin, xmax, ymax)
+        :return: dbboxes: shape (n, 5) (x_ctr, y_ctr, w, h, angle)
+        """
+        bboxes = paddle.reshape(bboxes, [-1, 4])
+        num_boxes = paddle.shape(bboxes)[0]
+        x_ctr = (bboxes[:, 2] + bboxes[:, 0]) / 2.0
+        y_ctr = (bboxes[:, 3] + bboxes[:, 1]) / 2.0
+        edges1 = paddle.abs(bboxes[:, 2] - bboxes[:, 0])
+        edges2 = paddle.abs(bboxes[:, 3] - bboxes[:, 1])
+
+        rbox_w = paddle.maximum(edges1, edges2)
+        rbox_h = paddle.minimum(edges1, edges2)
+
+        # set angle
+        inds = edges1 < edges2
+        inds = paddle.cast(inds, 'int32')
+        rboxes_angle = inds * np.pi / 2.0
+
+        rboxes = paddle.stack(
+            (x_ctr, y_ctr, rbox_w, rbox_h, rboxes_angle), axis=1)
+        return rboxes
+
+    # deltas to rbox
+    def delta2rbox(self, rrois, deltas, wh_ratio_clip=1e-6):
+        """
+        :param rrois: (cx, cy, w, h, theta)
+        :param deltas: (dx, dy, dw, dh, dtheta)
+        :param means: means of anchor
+        :param stds: stds of anchor
+        :param wh_ratio_clip: clip threshold of wh_ratio
+        :return:
+        """
+        deltas = paddle.reshape(deltas, [-1, 5])
+        rrois = paddle.reshape(rrois, [-1, 5])
+        # fix dy2st bug denorm_deltas = deltas * self.stds + self.means
+        denorm_deltas = paddle.add(paddle.multiply(deltas, self.stds), self.means)
+
+        dx = denorm_deltas[:, 0]
+        dy = denorm_deltas[:, 1]
+        dw = denorm_deltas[:, 2]
+        dh = denorm_deltas[:, 3]
+        dangle = denorm_deltas[:, 4]
+        max_ratio = np.abs(np.log(wh_ratio_clip))
+        dw = paddle.clip(dw, min=-max_ratio, max=max_ratio)
+        dh = paddle.clip(dh, min=-max_ratio, max=max_ratio)
+
+        rroi_x = rrois[:, 0]
+        rroi_y = rrois[:, 1]
+        rroi_w = rrois[:, 2]
+        rroi_h = rrois[:, 3]
+        rroi_angle = rrois[:, 4]
+
+        gx = dx * rroi_w * paddle.cos(rroi_angle) - dy * rroi_h * paddle.sin(
+            rroi_angle) + rroi_x
+        gy = dx * rroi_w * paddle.sin(rroi_angle) + dy * rroi_h * paddle.cos(
+            rroi_angle) + rroi_y
+        gw = rroi_w * dw.exp()
+        gh = rroi_h * dh.exp()
+        ga = np.pi * dangle + rroi_angle
+        ga = (ga + np.pi / 4) % np.pi - np.pi / 4
+        ga = paddle.to_tensor(ga)
+        gw = paddle.to_tensor(gw, dtype='float32')
+        gh = paddle.to_tensor(gh, dtype='float32')
+        bboxes = paddle.stack([gx, gy, gw, gh, ga], axis=-1)
+        return bboxes
+
+    def bbox_decode(self,
+                bbox_preds,
+                anchors):
+        """decode bbox from deltas
+        Args:
+            bbox_preds: [N,H,W,5]
+            anchors: [H*W,5]
+        return:
+            bboxes: [N,H,W,5]
+        """
+        num_imgs, H, W, _ = bbox_preds.shape
+        bbox_delta = paddle.reshape(bbox_preds, [-1, 5])
+        bboxes = self.delta2rbox(anchors, bbox_delta)
+        return bboxes