fix training error brought by 0-d getitem (#8140)

* fix training error brought by 0-d getitem

* fix other model

ppdet/modeling/architectures/faster_rcnn.py

@@ -86,15 +86,16 @@ class FasterRCNN(BaseArch):
             preds, _ = self.bbox_head(body_feats, rois, rois_num, None)
             im_shape = self.inputs['im_shape']
             scale_factor = self.inputs['scale_factor']
-            bbox, bbox_num, nms_keep_idx = self.bbox_post_process(preds, (rois, rois_num),
-                                                    im_shape, scale_factor)
+            bbox, bbox_num, nms_keep_idx = self.bbox_post_process(
+                preds, (rois, rois_num), im_shape, scale_factor)
 
             # rescale the prediction back to origin image
             bboxes, bbox_pred, bbox_num = self.bbox_post_process.get_pred(
                 bbox, bbox_num, im_shape, scale_factor)
 
             if self.use_extra_data:
-                extra_data = {}  # record the bbox output before nms, such like scores and nms_keep_idx
+                extra_data = {
+                }  # record the bbox output before nms, such like scores and nms_keep_idx
                 """extra_data:{
                             'scores': predict scores,
                             'nms_keep_idx': bbox index before nms,
@@ -102,12 +103,12 @@ class FasterRCNN(BaseArch):
                 """
                 extra_data['scores'] = preds[1]  # predict scores (probability)
                 # Todo: get logits output
-                extra_data['nms_keep_idx'] = nms_keep_idx  # bbox index before nms
+                extra_data[
+                    'nms_keep_idx'] = nms_keep_idx  # bbox index before nms
                 return bbox_pred, bbox_num, extra_data
             else:
                 return bbox_pred, bbox_num
 
-
     def get_loss(self, ):
         rpn_loss, bbox_loss = self._forward()
         loss = {}
@@ -120,7 +121,11 @@ class FasterRCNN(BaseArch):
     def get_pred(self):
         if self.use_extra_data:
             bbox_pred, bbox_num, extra_data = self._forward()
-            output = {'bbox': bbox_pred, 'bbox_num': bbox_num, 'extra_data': extra_data}
+            output = {
+                'bbox': bbox_pred,
+                'bbox_num': bbox_num,
+                'extra_data': extra_data
+            }
         else:
             bbox_pred, bbox_num = self._forward()
             output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
@@ -131,7 +136,7 @@ class FasterRCNN(BaseArch):
         if self.neck is not None:
             body_feats = self.neck(body_feats)
         rois = [roi for roi in data['gt_bbox']]
-        rois_num = paddle.concat([paddle.shape(roi)[0] for roi in rois])
+        rois_num = paddle.concat([paddle.shape(roi)[0:1] for roi in rois])
 
         preds, _ = self.bbox_head(body_feats, rois, rois_num, None, cot=True)
         return preds
@@ -142,13 +147,13 @@ class FasterRCNN(BaseArch):
         label_list = []
 
         for step_id, data in enumerate(loader):
-            _, bbox_prob = self.target_bbox_forward(data)      
+            _, bbox_prob = self.target_bbox_forward(data)
             batch_size = data['im_id'].shape[0]
             for i in range(batch_size):
-                num_bbox = data['gt_class'][i].shape[0]           
+                num_bbox = data['gt_class'][i].shape[0]
                 train_labels = data['gt_class'][i]
                 train_labels_list.append(train_labels.numpy().squeeze(1))
-            base_labels = bbox_prob.detach().numpy()[:,:-1]
+            base_labels = bbox_prob.detach().numpy()[:, :-1]
             label_list.append(base_labels)
 
         labels = np.concatenate(train_labels_list, 0)
@@ -159,4 +164,4 @@ class FasterRCNN(BaseArch):
             this_class = probabilities[labels == i]
             average = np.mean(this_class, axis=0, keepdims=True)
             conditional.append(average)
-        return np.concatenate(conditional)
\ No newline at end of file
+        return np.concatenate(conditional)

ppdet/modeling/heads/cascade_head.py

@@ -301,7 +301,7 @@ class CascadeHead(BBoxHead):
                     keep = paddle.zeros([1], dtype='int32')
                 clip_box = paddle.gather(clip_box, keep)
             rois.append(clip_box)
-        rois_num = paddle.concat([paddle.shape(r)[0] for r in rois])
+        rois_num = paddle.concat([paddle.shape(r)[0:1] for r in rois])
         return rois, rois_num
 
     def _get_pred_bbox(self, deltas, proposals, weights):

ppdet/modeling/heads/s2anet_head.py

@@ -360,7 +360,7 @@ class S2ANetHead(nn.Layer):
         for i in range(bbox_num.shape[0]):
             expand_shape = paddle.expand(origin_shape[i:i + 1, :],
                                          [bbox_num[i], 2])
-            scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
+            scale_y, scale_x = scale_factor[i, 0:1], scale_factor[i, 1:2]
             scale = paddle.concat([
                 scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x,
                 scale_y

ppdet/modeling/heads/tood_head.py

@@ -86,7 +86,10 @@ class TaskDecomposition(nn.Layer):
         normal_(self.la_conv2.weight, std=0.001)
 
     def forward(self, feat, avg_feat):
-        b, _, h, w = get_static_shape(feat)
+        feat_shape = get_static_shape(feat)
+        b = feat_shape[0:1]
+        h = feat_shape[2:3]
+        w = feat_shape[3:4]
         weight = F.relu(self.la_conv1(avg_feat))
         weight = F.sigmoid(self.la_conv2(weight)).unsqueeze(-1)
         feat = paddle.reshape(
@@ -204,7 +207,10 @@ class TOODHead(nn.Layer):
             constant_(self.reg_offset_conv2.bias)
 
     def _reg_grid_sample(self, feat, offset, anchor_points):
-        b, _, h, w = get_static_shape(feat)
+        feat_shape = get_static_shape(feat)
+        b = feat_shape[0:1]
+        h = feat_shape[2:3]
+        w = feat_shape[3:4]
         feat = paddle.reshape(feat, [-1, 1, h, w])
         offset = paddle.reshape(offset, [-1, 2, h, w]).transpose([0, 2, 3, 1])
         grid_shape = paddle.concat([w, h]).astype('float32')

ppdet/modeling/proposal_generator/target.py

@@ -237,7 +237,7 @@ def generate_proposal_target(rpn_rois,
         tgt_bboxes.append(sampled_bbox)
         rois_with_gt.append(rois_per_image)
         tgt_gt_inds.append(sampled_gt_ind)
-        new_rois_num.append(paddle.shape(sampled_inds)[0])
+        new_rois_num.append(paddle.shape(sampled_inds)[0:1])
     new_rois_num = paddle.concat(new_rois_num)
     return rois_with_gt, tgt_labels, tgt_bboxes, tgt_gt_inds, new_rois_num
 
@@ -380,7 +380,7 @@ def generate_mask_target(gt_segms, rois, labels_int32, sampled_gt_inds,
 
         mask_index.append(fg_inds)
         mask_rois.append(fg_rois)
-        mask_rois_num.append(paddle.shape(fg_rois)[0])
+        mask_rois_num.append(paddle.shape(fg_rois)[0:1])
         tgt_classes.append(fg_classes)
         tgt_masks.append(tgt_mask)
         tgt_weights.append(weight)
@@ -672,7 +672,7 @@ def libra_generate_proposal_target(rpn_rois,
         rois_with_gt.append(rois_per_image)
         sampled_max_overlaps.append(sampled_overlap)
         tgt_gt_inds.append(sampled_gt_ind)
-        new_rois_num.append(paddle.shape(sampled_inds)[0])
+        new_rois_num.append(paddle.shape(sampled_inds)[0:1])
     new_rois_num = paddle.concat(new_rois_num)
     # rois_with_gt, tgt_labels, tgt_bboxes, tgt_gt_inds, new_rois_num
     return rois_with_gt, tgt_labels, tgt_bboxes, tgt_gt_inds, new_rois_num

ppdet/modeling/transformers/deformable_transformer.py

@@ -486,7 +486,10 @@ class DeformableTransformer(nn.Layer):
         spatial_shapes = []
         valid_ratios = []
         for level, src in enumerate(srcs):
-            bs, _, h, w = paddle.shape(src)
+            src_shape = paddle.shape(src)
+            bs = src_shape[0:1]
+            h = src_shape[2:3]
+            w = src_shape[3:4]
             spatial_shapes.append(paddle.concat([h, w]))
             src = src.flatten(2).transpose([0, 2, 1])
             src_flatten.append(src)