update rcnn fit for paddle 2.2 (#7707)

ppdet/modeling/heads/roi_extractor.py

@@ -88,21 +88,17 @@ class RoIAlign(object):
             k_min = self.start_level + offset
             k_max = self.end_level + offset
             if hasattr(paddle.vision.ops, "distribute_fpn_proposals"):
-                rois_dist, restore_index, rois_num_dist = paddle.vision.ops.distribute_fpn_proposals(
-                    roi,
-                    k_min,
-                    k_max,
-                    self.canconical_level,
-                    self.canonical_size,
-                    rois_num=rois_num)
+                distribute_fpn_proposals = getattr(paddle.vision.ops,
+                                                   "distribute_fpn_proposals")
             else:
-                rois_dist, restore_index, rois_num_dist = ops.distribute_fpn_proposals(
-                    roi,
-                    k_min,
-                    k_max,
-                    self.canconical_level,
-                    self.canonical_size,
-                    rois_num=rois_num)
+                distribute_fpn_proposals = ops.distribute_fpn_proposals
+            rois_dist, restore_index, rois_num_dist = distribute_fpn_proposals(
+                roi,
+                k_min,
+                k_max,
+                self.canconical_level,
+                self.canonical_size,
+                rois_num=rois_num)
 
             rois_feat_list = []
             for lvl in range(self.start_level, self.end_level + 1):

ppdet/modeling/proposal_generator/proposal_generator.py

@@ -63,30 +63,21 @@ class ProposalGenerator(object):
         top_n = self.pre_nms_top_n if self.topk_after_collect else self.post_nms_top_n
         variances = paddle.ones_like(anchors)
         if hasattr(paddle.vision.ops, "generate_proposals"):
-            rpn_rois, rpn_rois_prob, rpn_rois_num = paddle.vision.ops.generate_proposals(
-                scores,
-                bbox_deltas,
-                im_shape,
-                anchors,
-                variances,
-                pre_nms_top_n=self.pre_nms_top_n,
-                post_nms_top_n=top_n,
-                nms_thresh=self.nms_thresh,
-                min_size=self.min_size,
-                eta=self.eta,
-                return_rois_num=True)
+            generate_proposals = getattr(paddle.vision.ops,
+                                         "generate_proposals")
         else:
-            rpn_rois, rpn_rois_prob, rpn_rois_num = ops.generate_proposals(
-                scores,
-                bbox_deltas,
-                im_shape,
-                anchors,
-                variances,
-                pre_nms_top_n=self.pre_nms_top_n,
-                post_nms_top_n=top_n,
-                nms_thresh=self.nms_thresh,
-                min_size=self.min_size,
-                eta=self.eta,
-                return_rois_num=True)
+            generate_proposals = ops.generate_proposals
+        rpn_rois, rpn_rois_prob, rpn_rois_num = generate_proposals(
+            scores,
+            bbox_deltas,
+            im_shape,
+            anchors,
+            variances,
+            pre_nms_top_n=self.pre_nms_top_n,
+            post_nms_top_n=top_n,
+            nms_thresh=self.nms_thresh,
+            min_size=self.min_size,
+            eta=self.eta,
+            return_rois_num=True)
 
         return rpn_rois, rpn_rois_prob, rpn_rois_num, self.post_nms_top_n