adapter new type promotion rule for Paddle 2.6

ppdet/modeling/assigners/atss_assigner.py

@@ -192,7 +192,7 @@ def forward(self,
         # assigned target
         batch_ind = paddle.arange(
             end=batch_size, dtype=gt_labels.dtype).unsqueeze(-1)
-        assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
+        assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
         assigned_labels = paddle.gather(
             gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
         assigned_labels = assigned_labels.reshape([batch_size, num_anchors])

ppdet/modeling/assigners/fcosr_assigner.py

@@ -193,7 +193,7 @@ def forward(self,
         # assigned target
         batch_ind = paddle.arange(
             end=batch_size, dtype=gt_labels.dtype).unsqueeze(-1)
-        assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
+        assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
         assigned_labels = paddle.gather(
             gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
         assigned_labels = assigned_labels.reshape([batch_size, num_anchors])
@@ -224,4 +224,4 @@ def forward(self,
                     (-1, 5))).reshape((b, l, 1))
             assigned_scores = assigned_scores * iou_score
 
-        return assigned_labels, assigned_rboxes, assigned_scores
+        return assigned_labels, assigned_rboxes, assigned_scores

ppdet/modeling/assigners/rotated_task_aligned_assigner.py

@@ -113,7 +113,7 @@ def forward(self,
         # select topk largest alignment metrics pred bbox as candidates
         # for each gt, [B, n, L]
         is_in_topk = gather_topk_anchors(
-            alignment_metrics * is_in_gts, self.topk, topk_mask=pad_gt_mask)
+            alignment_metrics * is_in_gts.astype(alignment_metrics.dtype), self.topk, topk_mask=pad_gt_mask)
 
         # select positive sample, [B, n, L]
         mask_positive = is_in_topk * is_in_gts * pad_gt_mask

ppdet/modeling/assigners/task_aligned_assigner.py

@@ -163,7 +163,7 @@ def forward(self,
         assigned_gt_index = mask_positive.argmax(axis=-2)
 
         # assigned target
-        assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
+        assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
         assigned_labels = paddle.gather(
             gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
         assigned_labels = assigned_labels.reshape([batch_size, num_anchors])

ppdet/modeling/assigners/task_aligned_assigner_cr.py

@@ -151,7 +151,7 @@ def forward(self,
         assigned_gt_index = mask_positive.argmax(axis=-2)
 
         # assigned target
-        assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
+        assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
         assigned_labels = paddle.gather(
             gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
         assigned_labels = assigned_labels.reshape([batch_size, num_anchors])

ppdet/modeling/heads/ttf_head.py

@@ -281,7 +281,7 @@ def get_loss(self, pred_hm, pred_wh, target_hm, box_target, target_weight):
         base_loc.stop_gradient = True
 
         pred_boxes = paddle.concat(
-            [0 - pred_wh[:, 0:2, :, :] + base_loc, pred_wh[:, 2:4] + base_loc],
+            [0 - pred_wh[:, 0:2, :, :] + base_loc.astype(pred_wh.dtype), pred_wh[:, 2:4] + base_loc.astype(pred_wh.dtype)],
             axis=1)
         pred_boxes = paddle.transpose(pred_boxes, [0, 2, 3, 1])
         boxes = paddle.transpose(box_target, [0, 2, 3, 1])

ppdet/modeling/losses/gfocal_loss.py

@@ -75,7 +75,7 @@ def quality_focal_loss(pred, target, beta=2.0, use_sigmoid=True):
 
     loss_pos = func(
         pred, score, reduction='none') * scale_factor_new.abs().pow(beta)
-    loss = loss * paddle.logical_not(pos_mask) + loss_pos * pos_mask
+    loss = loss * paddle.logical_not(pos_mask).astype(loss.dtype) + loss_pos * pos_mask
     loss = loss.sum(axis=1)
     return loss
 

ppdet/modeling/losses/iou_loss.py

@@ -184,7 +184,7 @@ def __call__(self, pbox, gbox, iou_weight=1.):
 
         intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
         intsctk = intsctk * paddle.greater_than(
-            xkis2, xkis1) * paddle.greater_than(ykis2, ykis1)
+            xkis2, xkis1).astype(intsctk.dtype) * paddle.greater_than(ykis2, ykis1).astype(intsctk.dtype)
         unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g
                                                         ) - intsctk + self.eps
         iouk = intsctk / unionk

ppdet/modeling/losses/ssd_loss.py

@@ -124,7 +124,7 @@ def _mine_hard_example(self,
                 [pos.shape[1] * mine_neg_ratio])
             num_negs.append(num_neg)
         num_negs = paddle.stack(num_negs).expand_as(idx_rank)
-        neg_mask = (idx_rank < num_negs).astype(conf_loss.dtype)
+        neg_mask = (idx_rank.astype(num_negs.dtype) < num_negs).astype(conf_loss.dtype)
 
         return (neg_mask + pos).astype('bool')
 

ppdet/modeling/proposal_generator/anchor_generator.py

@@ -187,11 +187,11 @@ def gen_base_anchors(self):
         h_ratios = paddle.sqrt(self.ratios)
         w_ratios = 1 / h_ratios
         if self.scale_major:
-            ws = (w * w_ratios[:] * self.scales[:]).reshape([-1])
-            hs = (h * h_ratios[:] * self.scales[:]).reshape([-1])
+            ws = (w * w_ratios[:] * self.scales[:].astype(w_ratios.dtype)).reshape([-1])
+            hs = (h * h_ratios[:] * self.scales[:].astype(h_ratios.dtype)).reshape([-1])
         else:
-            ws = (w * self.scales[:] * w_ratios[:]).reshape([-1])
-            hs = (h * self.scales[:] * h_ratios[:]).reshape([-1])
+            ws = (w * self.scales[:].astype(w_ratios.dtype) * w_ratios[:]).reshape([-1])
+            hs = (h * self.scales[:].astype(h_ratios.dtype) * h_ratios[:]).reshape([-1])
 
         base_anchors = paddle.stack(
             [