fix dynamic shape of reshape op when export model (#7229)

ppdet/modeling/heads/gfl_head.py

@@ -258,7 +258,7 @@ class GFLHead(nn.Layer):
                 center_points = paddle.stack([x, y], axis=-1)
                 cls_score = cls_score.reshape([b, -1, self.cls_out_channels])
                 bbox_pred = self.distribution_project(bbox_pred) * stride
-                bbox_pred = bbox_pred.reshape([b, cell_h * cell_w, 4])
+                bbox_pred = bbox_pred.reshape([-1, cell_h * cell_w, 4])
 
                 # NOTE: If keep_ratio=False and image shape value that
                 # multiples of 32, distance2bbox not set max_shapes parameter

ppdet/modeling/heads/pico_head.py

@@ -353,13 +353,13 @@ class PicoHead(OTAVFLHead):
                 bbox_pred = bbox_pred.reshape([1, (self.reg_max + 1) * 4,
                                                -1]).transpose([0, 2, 1])
             else:
-                b, _, h, w = fpn_feat.shape
+                _, _, h, w = fpn_feat.shape
                 l = h * w
                 cls_score_out = F.sigmoid(
-                    cls_score.reshape([b, self.cls_out_channels, l]))
+                    cls_score.reshape([-1, self.cls_out_channels, l]))
                 bbox_pred = bbox_pred.transpose([0, 2, 3, 1])
                 bbox_pred = self.distribution_project(bbox_pred)
-                bbox_pred = bbox_pred.reshape([b, l, 4])
+                bbox_pred = bbox_pred.reshape([-1, l, 4])
 
             cls_logits_list.append(cls_score_out)
             bboxes_reg_list.append(bbox_pred)
@@ -597,7 +597,7 @@ class PicoHeadV2(GFLHead):
             anchor_points, stride_tensor = self._generate_anchors(fpn_feats)
         cls_score_list, box_list = [], []
         for i, (fpn_feat, stride) in enumerate(zip(fpn_feats, self.fpn_stride)):
-            b, _, h, w = fpn_feat.shape
+            _, _, h, w = fpn_feat.shape
             # task decomposition
             conv_cls_feat, se_feat = self.conv_feat(fpn_feat, i)
             cls_logit = self.head_cls_list[i](se_feat)
@@ -620,10 +620,11 @@ class PicoHeadV2(GFLHead):
                         [0, 2, 1]))
             else:
                 l = h * w
-                cls_score_out = cls_score.reshape([b, self.cls_out_channels, l])
+                cls_score_out = cls_score.reshape(
+                    [-1, self.cls_out_channels, l])
                 bbox_pred = reg_pred.transpose([0, 2, 3, 1])
                 bbox_pred = self.distribution_project(bbox_pred)
-                bbox_pred = bbox_pred.reshape([b, l, 4])
+                bbox_pred = bbox_pred.reshape([-1, l, 4])
                 cls_score_list.append(cls_score_out)
                 box_list.append(bbox_pred)
 

ppdet/modeling/heads/ppyoloe_head.py

@@ -192,7 +192,7 @@ class PPYOLOEHead(nn.Layer):
             anchor_points, stride_tensor = self._generate_anchors(feats)
         cls_score_list, reg_dist_list = [], []
         for i, feat in enumerate(feats):
-            b, _, h, w = feat.shape
+            _, _, h, w = feat.shape
             l = h * w
             avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
             cls_logit = self.pred_cls[i](self.stem_cls[i](feat, avg_feat) +
@@ -203,7 +203,7 @@ class PPYOLOEHead(nn.Layer):
             reg_dist = self.proj_conv(F.softmax(reg_dist, axis=1)).squeeze(1)
             # cls and reg
             cls_score = F.sigmoid(cls_logit)
-            cls_score_list.append(cls_score.reshape([b, self.num_classes, l]))
+            cls_score_list.append(cls_score.reshape([-1, self.num_classes, l]))
             reg_dist_list.append(reg_dist)
 
         cls_score_list = paddle.concat(cls_score_list, axis=-1)
@@ -238,8 +238,8 @@ class PPYOLOEHead(nn.Layer):
         return loss
 
     def _bbox_decode(self, anchor_points, pred_dist):
-        b, l, _ = get_static_shape(pred_dist)
-        pred_dist = F.softmax(pred_dist.reshape([b, l, 4, self.reg_max + 1]))
+        _, l, _ = get_static_shape(pred_dist)
+        pred_dist = F.softmax(pred_dist.reshape([-1, l, 4, self.reg_max + 1]))
         pred_dist = self.proj_conv(pred_dist.transpose([0, 3, 1, 2])).squeeze(1)
         return batch_distance2bbox(anchor_points, pred_dist)