Update generate_proposal_labels_op to support CascadeRCNN. (#17200)

* Update generate_proposal_labels_op to support CascadeRCNN.

paddle/fluid/API.spec

@@ -351,7 +351,7 @@ paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits',
 paddle.fluid.layers.retinanet_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'gt_labels', 'is_crowd', 'im_info', 'num_classes', 'positive_overlap', 'negative_overlap'], varargs=None, keywords=None, defaults=(1, 0.5, 0.4)), ('document', 'fa1d1c9d5e0111684c0db705f86a2595'))
 paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', '82b2aefeeb1b706bc4afec70928a259a'))
 paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'd1ddc75629fedee46f82e631e22c79dc'))
-paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)), ('document', '9c601df88b251f22e9311c52939948cd'))
+paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random', 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', 'c0d00acf724691ff3480d4207036a722'))
 paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', 'b7d707822b6af2a586bce608040235b1'))
 paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef'))
 paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '72fca4a39ccf82d5c746ae62d1868a99'))

paddle/fluid/operators/detection/generate_proposal_labels_op.cc

@@ -109,17 +109,18 @@ std::vector<std::vector<int>> SampleFgBgGt(
     const platform::CPUDeviceContext& context, Tensor* iou,
     const Tensor& is_crowd, const int batch_size_per_im,
     const float fg_fraction, const float fg_thresh, const float bg_thresh_hi,
-    const float bg_thresh_lo, std::minstd_rand engine, const bool use_random) {
+    const float bg_thresh_lo, std::minstd_rand engine, const bool use_random,
+    const bool is_cascade_rcnn, const Tensor& rpn_rois) {
   std::vector<int> fg_inds;
   std::vector<int> bg_inds;
-  std::vector<int> gt_inds;
+  std::vector<int> mapped_gt_inds;
   int64_t gt_num = is_crowd.numel();
   const int* crowd_data = is_crowd.data<int>();
   T* proposal_to_gt_overlaps = iou->data<T>();
   int64_t row = iou->dims()[0];
   int64_t col = iou->dims()[1];
   float epsilon = 0.00001;
-
+  const T* rpn_rois_dt = rpn_rois.data<T>();
   // Follow the Faster RCNN's implementation
   for (int64_t i = 0; i < row; ++i) {
     const T* v = proposal_to_gt_overlaps + i * col;
@@ -127,24 +128,37 @@ std::vector<std::vector<int>> SampleFgBgGt(
     if ((i < gt_num) && (crowd_data[i])) {
       max_overlap = -1.0;
     }
-    if (max_overlap > fg_thresh) {
+    if (is_cascade_rcnn &&
+        ((rpn_rois_dt[i * 4 + 2] - rpn_rois_dt[i * 4 + 0] + 1) <= 0 ||
+         (rpn_rois_dt[i * 4 + 3] - rpn_rois_dt[i * 4 + 1] + 1) <= 0)) {
+      continue;
+    }
+    if (max_overlap >= fg_thresh) {
+      // fg mapped gt label index
       for (int64_t j = 0; j < col; ++j) {
         T val = proposal_to_gt_overlaps[i * col + j];
         auto diff = std::abs(max_overlap - val);
         if (diff < epsilon) {
           fg_inds.emplace_back(i);
-          gt_inds.emplace_back(j);
+          mapped_gt_inds.emplace_back(j);
           break;
         }
       }
-    } else {
-      if ((max_overlap >= bg_thresh_lo) && (max_overlap < bg_thresh_hi)) {
+    } else if ((max_overlap >= bg_thresh_lo) && (max_overlap < bg_thresh_hi)) {
       bg_inds.emplace_back(i);
-      }
+    } else {
+      continue;
     }
   }
 
+  std::vector<std::vector<int>> res;
+  if (is_cascade_rcnn) {
+    res.emplace_back(fg_inds);
+    res.emplace_back(bg_inds);
+    res.emplace_back(mapped_gt_inds);
+  } else {
     // Reservoir Sampling
+    // sampling fg
     std::uniform_real_distribution<float> uniform(0, 1);
     int fg_rois_per_im = std::floor(batch_size_per_im * fg_fraction);
     int fg_rois_this_image = fg_inds.size();
@@ -156,19 +170,22 @@ std::vector<std::vector<int>> SampleFgBgGt(
           int rng_ind = std::floor(uniform(engine) * i);
           if (rng_ind < fg_rois_per_this_image) {
             std::iter_swap(fg_inds.begin() + rng_ind, fg_inds.begin() + i);
-          std::iter_swap(gt_inds.begin() + rng_ind, gt_inds.begin() + i);
+            std::iter_swap(mapped_gt_inds.begin() + rng_ind,
+                           mapped_gt_inds.begin() + i);
           }
         }
       }
     }
     std::vector<int> new_fg_inds(fg_inds.begin(),
                                  fg_inds.begin() + fg_rois_per_this_image);
-  std::vector<int> new_gt_inds(gt_inds.begin(),
-                               gt_inds.begin() + fg_rois_per_this_image);
-
+    std::vector<int> new_gt_inds(
+        mapped_gt_inds.begin(),
+        mapped_gt_inds.begin() + fg_rois_per_this_image);
+    // sampling bg
     int bg_rois_per_image = batch_size_per_im - fg_rois_per_this_image;
     int bg_rois_this_image = bg_inds.size();
-  int bg_rois_per_this_image = std::min(bg_rois_per_image, bg_rois_this_image);
+    int bg_rois_per_this_image =
+        std::min(bg_rois_per_image, bg_rois_this_image);
     if (use_random) {
       const int64_t bg_size = static_cast<int64_t>(bg_inds.size());
       if (bg_size > bg_rois_per_this_image) {
@@ -181,10 +198,12 @@ std::vector<std::vector<int>> SampleFgBgGt(
     }
     std::vector<int> new_bg_inds(bg_inds.begin(),
                                  bg_inds.begin() + bg_rois_per_this_image);
-  std::vector<std::vector<int>> res;
+    //
     res.emplace_back(new_fg_inds);
     res.emplace_back(new_bg_inds);
     res.emplace_back(new_gt_inds);
+  }
+
   return res;
 }
 
@@ -231,35 +250,50 @@ std::vector<Tensor> SampleRoisForOneImage(
     const Tensor& im_info, const int batch_size_per_im, const float fg_fraction,
     const float fg_thresh, const float bg_thresh_hi, const float bg_thresh_lo,
     const std::vector<float>& bbox_reg_weights, const int class_nums,
-    std::minstd_rand engine, bool use_random) {
+    std::minstd_rand engine, bool use_random, bool is_cascade_rcnn,
+    bool is_cls_agnostic) {
+  // 1.1 map to original image
   auto im_scale = im_info.data<T>()[2];
-
+  Tensor rpn_rois_slice;
   Tensor rpn_rois;
-  rpn_rois.mutable_data<T>(rpn_rois_in.dims(), context.GetPlace());
+
+  if (is_cascade_rcnn) {
+    // slice rpn_rois from gt_box_num refer to detectron
+    rpn_rois_slice =
+        rpn_rois_in.Slice(gt_boxes.dims()[0], rpn_rois_in.dims()[0]);
+    rpn_rois.mutable_data<T>(rpn_rois_slice.dims(), context.GetPlace());
+    const T* rpn_rois_in_dt = rpn_rois_slice.data<T>();
     T* rpn_rois_dt = rpn_rois.data<T>();
+    for (int i = 0; i < rpn_rois.numel(); ++i) {
+      rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale;
+    }
+  } else {
+    rpn_rois.mutable_data<T>(rpn_rois_in.dims(), context.GetPlace());
     const T* rpn_rois_in_dt = rpn_rois_in.data<T>();
+    T* rpn_rois_dt = rpn_rois.data<T>();
     for (int i = 0; i < rpn_rois.numel(); ++i) {
       rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale;
     }
+  }
 
-  Tensor boxes;
+  // 1.2 compute overlaps
   int proposals_num = gt_boxes.dims()[0] + rpn_rois.dims()[0];
+  Tensor boxes;
   boxes.mutable_data<T>({proposals_num, kBoxDim}, context.GetPlace());
   Concat<T>(context, gt_boxes, rpn_rois, &boxes);
-
-  // Overlaps
   Tensor proposal_to_gt_overlaps;
   proposal_to_gt_overlaps.mutable_data<T>({proposals_num, gt_boxes.dims()[0]},
                                           context.GetPlace());
   BboxOverlaps<T>(boxes, gt_boxes, &proposal_to_gt_overlaps);
 
   // Generate proposal index
-  std::vector<std::vector<int>> fg_bg_gt = SampleFgBgGt<T>(
-      context, &proposal_to_gt_overlaps, is_crowd, batch_size_per_im,
-      fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, engine, use_random);
+  std::vector<std::vector<int>> fg_bg_gt =
+      SampleFgBgGt<T>(context, &proposal_to_gt_overlaps, is_crowd,
+                      batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi,
+                      bg_thresh_lo, engine, use_random, is_cascade_rcnn, boxes);
   std::vector<int> fg_inds = fg_bg_gt[0];
   std::vector<int> bg_inds = fg_bg_gt[1];
-  std::vector<int> gt_inds = fg_bg_gt[2];
+  std::vector<int> mapped_gt_inds = fg_bg_gt[2];  // mapped_gt_labels
 
   // Gather boxes and labels
   Tensor sampled_boxes, sampled_labels, sampled_gts;
@@ -271,7 +305,8 @@ std::vector<Tensor> SampleRoisForOneImage(
   sampled_labels.mutable_data<int>({boxes_num}, context.GetPlace());
   sampled_gts.mutable_data<T>({fg_num, kBoxDim}, context.GetPlace());
   GatherBoxesLabels<T>(context, boxes, gt_boxes, gt_classes, fg_inds, bg_inds,
-                       gt_inds, &sampled_boxes, &sampled_labels, &sampled_gts);
+                       mapped_gt_inds, &sampled_boxes, &sampled_labels,
+                       &sampled_gts);
 
   // Compute targets
   Tensor bbox_targets_single;
@@ -305,6 +340,9 @@ std::vector<Tensor> SampleRoisForOneImage(
   for (int64_t i = 0; i < boxes_num; ++i) {
     int label = sampled_labels_data[i];
     if (label > 0) {
+      if (is_cls_agnostic) {
+        label = 1;
+      }
       int dst_idx = i * width + kBoxDim * label;
       int src_idx = kBoxDim * i;
       bbox_targets_data[dst_idx] = bbox_targets_single_data[src_idx];
@@ -356,7 +394,8 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
         context.Attr<std::vector<float>>("bbox_reg_weights");
     int class_nums = context.Attr<int>("class_nums");
     bool use_random = context.Attr<bool>("use_random");
-
+    bool is_cascade_rcnn = context.Attr<bool>("is_cascade_rcnn");
+    bool is_cls_agnostic = context.Attr<bool>("is_cls_agnostic");
     PADDLE_ENFORCE_EQ(rpn_rois->lod().size(), 1UL,
                       "GenerateProposalLabelsOp rpn_rois needs 1 level of LoD");
     PADDLE_ENFORCE_EQ(
@@ -411,7 +450,7 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
           dev_ctx, rpn_rois_slice, gt_classes_slice, is_crowd_slice,
           gt_boxes_slice, im_info_slice, batch_size_per_im, fg_fraction,
           fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums,
-          engine, use_random);
+          engine, use_random, is_cascade_rcnn, is_cls_agnostic);
       Tensor sampled_rois = tensor_output[0];
       Tensor sampled_labels_int32 = tensor_output[1];
       Tensor sampled_bbox_targets = tensor_output[2];
@@ -513,6 +552,13 @@ class GenerateProposalLabelsOpMaker : public framework::OpProtoAndCheckerMaker {
         "use_random",
         "Use random sampling to choose foreground and background boxes.")
         .SetDefault(true);
+    AddAttr<bool>("is_cascade_rcnn",
+                  "cascade rcnn sampling policy changed from stage 2.")
+        .SetDefault(false);
+    AddAttr<bool>(
+        "is_cls_agnostic",
+        "the box regress will only include fg and bg locations if set true ")
+        .SetDefault(false);
 
     AddComment(R"DOC(
 This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth,

python/paddle/fluid/layers/detection.py

@@ -2075,9 +2075,13 @@ def generate_proposal_labels(rpn_rois,
                              bg_thresh_lo=0.0,
                              bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
                              class_nums=None,
-                             use_random=True):
+                             use_random=True,
+                             is_cls_agnostic=False,
+                             is_cascade_rcnn=False):
     """
+
     ** Generate Proposal Labels of Faster-RCNN **
+
     This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth,
     to sample foreground boxes and background boxes, and compute loss target.
 
@@ -2108,6 +2112,8 @@ def generate_proposal_labels(rpn_rois,
         bbox_reg_weights(list|tuple): Box regression weights.
         class_nums(int): Class number.
         use_random(bool): Use random sampling to choose foreground and background boxes.
+        is_cls_agnostic(bool): bbox regression use class agnostic simply which only represent fg and bg boxes.
+        is_cascade_rcnn(bool): it will filter some bbox crossing the image's boundary when setting True.
 
     Examples:
         .. code-block:: python
@@ -2166,7 +2172,9 @@ def generate_proposal_labels(rpn_rois,
             'bg_thresh_lo': bg_thresh_lo,
             'bbox_reg_weights': bbox_reg_weights,
             'class_nums': class_nums,
-            'use_random': use_random
+            'use_random': use_random,
+            'is_cls_agnostic': is_cls_agnostic,
+            'is_cascade_rcnn': is_cascade_rcnn
         })
 
     rois.stop_gradient = True

python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py

@@ -22,10 +22,10 @@ import paddle.fluid as fluid
 from op_test import OpTest
 
 
-def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes,
-                                       im_info, batch_size_per_im, fg_fraction,
-                                       fg_thresh, bg_thresh_hi, bg_thresh_lo,
-                                       bbox_reg_weights, class_nums):
+def generate_proposal_labels_in_python(
+        rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, batch_size_per_im,
+        fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights,
+        class_nums, is_cls_agnostic, is_cascade_rcnn):
     rois = []
     labels_int32 = []
     bbox_targets = []
@@ -36,13 +36,12 @@ def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes,
         im_info), 'batch size of rpn_rois and ground_truth is not matched'
 
     for im_i in range(len(im_info)):
-        frcn_blobs = _sample_rois(
-            rpn_rois[im_i], gt_classes[im_i], is_crowd[im_i], gt_boxes[im_i],
-            im_info[im_i], batch_size_per_im, fg_fraction, fg_thresh,
-            bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums)
-
+        frcn_blobs = _sample_rois(rpn_rois[im_i], gt_classes[im_i],
+                                  is_crowd[im_i], gt_boxes[im_i], im_info[im_i],
+                                  batch_size_per_im, fg_fraction, fg_thresh,
+                                  bg_thresh_hi, bg_thresh_lo, bbox_reg_weights,
+                                  class_nums, is_cls_agnostic, is_cascade_rcnn)
         lod.append(frcn_blobs['rois'].shape[0])
-
         rois.append(frcn_blobs['rois'])
         labels_int32.append(frcn_blobs['labels_int32'])
         bbox_targets.append(frcn_blobs['bbox_targets'])
@@ -54,7 +53,8 @@ def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes,
 
 def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
                  batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi,
-                 bg_thresh_lo, bbox_reg_weights, class_nums):
+                 bg_thresh_lo, bbox_reg_weights, class_nums, is_cls_agnostic,
+                 is_cascade_rcnn):
     rois_per_image = int(batch_size_per_im)
     fg_rois_per_im = int(np.round(fg_fraction * rois_per_image))
 
@@ -62,7 +62,8 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
     im_scale = im_info[2]
     inv_im_scale = 1. / im_scale
     rpn_rois = rpn_rois * inv_im_scale
-
+    if is_cascade_rcnn:
+        rpn_rois = rpn_rois[gt_boxes.shape[0]:, :]
     boxes = np.vstack([gt_boxes, rpn_rois])
     gt_overlaps = np.zeros((boxes.shape[0], class_nums))
     box_to_gt_ind_map = np.zeros((boxes.shape[0]), dtype=np.int32)
@@ -87,15 +88,26 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
     max_overlaps = gt_overlaps.max(axis=1)
     max_classes = gt_overlaps.argmax(axis=1)
 
+    # Cascade RCNN Decode Filter
+    if is_cascade_rcnn:
+        ws = boxes[:, 2] - boxes[:, 0] + 1
+        hs = boxes[:, 3] - boxes[:, 1] + 1
+        keep = np.where((ws > 0) & (hs > 0))[0]
+        boxes = boxes[keep]
+        fg_inds = np.where(max_overlaps >= fg_thresh)[0]
+        bg_inds = np.where((max_overlaps < bg_thresh_hi) & (max_overlaps >=
+                                                            bg_thresh_lo))[0]
+        fg_rois_per_this_image = fg_inds.shape[0]
+        bg_rois_per_this_image = bg_inds.shape[0]
+    else:
         # Foreground
         fg_inds = np.where(max_overlaps >= fg_thresh)[0]
         fg_rois_per_this_image = np.minimum(fg_rois_per_im, fg_inds.shape[0])
         # Sample foreground if there are too many
-    # if fg_inds.shape[0] > fg_rois_per_this_image:
-    #     fg_inds = np.random.choice(
-    #         fg_inds, size=fg_rois_per_this_image, replace=False)
+        if fg_inds.shape[0] > fg_rois_per_this_image:
+            fg_inds = np.random.choice(
+                fg_inds, size=fg_rois_per_this_image, replace=False)
         fg_inds = fg_inds[:fg_rois_per_this_image]
-
         # Background
         bg_inds = np.where((max_overlaps < bg_thresh_hi) & (max_overlaps >=
                                                             bg_thresh_lo))[0]
@@ -103,9 +115,9 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
         bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,
                                             bg_inds.shape[0])
         # Sample background if there are too many
-    # if bg_inds.shape[0] > bg_rois_per_this_image:
-    #     bg_inds = np.random.choice(
-    #         bg_inds, size=bg_rois_per_this_image, replace=False)
+        if bg_inds.shape[0] > bg_rois_per_this_image:
+            bg_inds = np.random.choice(
+                bg_inds, size=bg_rois_per_this_image, replace=False)
         bg_inds = bg_inds[:bg_rois_per_this_image]
 
     keep_inds = np.append(fg_inds, bg_inds)
@@ -114,14 +126,12 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
     sampled_boxes = boxes[keep_inds]
     sampled_gts = gt_boxes[box_to_gt_ind_map[keep_inds]]
     sampled_gts[fg_rois_per_this_image:, :] = gt_boxes[0]
-
     bbox_label_targets = _compute_targets(sampled_boxes, sampled_gts,
                                           sampled_labels, bbox_reg_weights)
-    bbox_targets, bbox_inside_weights = _expand_bbox_targets(bbox_label_targets,
-                                                             class_nums)
+    bbox_targets, bbox_inside_weights = _expand_bbox_targets(
+        bbox_label_targets, class_nums, is_cls_agnostic)
     bbox_outside_weights = np.array(
         bbox_inside_weights > 0, dtype=bbox_inside_weights.dtype)
-
     # Scale rois
     sampled_rois = sampled_boxes * im_scale
 
@@ -192,19 +202,22 @@ def _box_to_delta(ex_boxes, gt_boxes, weights):
     return targets
 
 
-def _expand_bbox_targets(bbox_targets_input, class_nums):
+def _expand_bbox_targets(bbox_targets_input, class_nums, is_cls_agnostic):
     class_labels = bbox_targets_input[:, 0]
     fg_inds = np.where(class_labels > 0)[0]
-
-    bbox_targets = np.zeros((class_labels.shape[0], 4 * class_nums))
+    #if is_cls_agnostic:
+    #	class_labels = [1 if ll > 0 else 0 for ll in class_labels]
+    #    class_labels = np.array(class_labels, dtype=np.int32)
+    #	class_nums = 2
+    bbox_targets = np.zeros((class_labels.shape[0], 4 * class_nums
+                             if not is_cls_agnostic else 4 * 2))
     bbox_inside_weights = np.zeros(bbox_targets.shape)
     for ind in fg_inds:
-        class_label = int(class_labels[ind])
+        class_label = int(class_labels[ind]) if not is_cls_agnostic else 1
         start_ind = class_label * 4
         end_ind = class_label * 4 + 4
         bbox_targets[ind, start_ind:end_ind] = bbox_targets_input[ind, 1:]
         bbox_inside_weights[ind, start_ind:end_ind] = (1.0, 1.0, 1.0, 1.0)
-
     return bbox_targets, bbox_inside_weights
 
 
@@ -228,7 +241,9 @@ class TestGenerateProposalLabelsOp(OpTest):
             'bg_thresh_lo': self.bg_thresh_lo,
             'bbox_reg_weights': self.bbox_reg_weights,
             'class_nums': self.class_nums,
-            'use_random': False
+            'use_random': False,
+            'is_cls_agnostic': self.is_cls_agnostic,
+            'is_cascade_rcnn': self.is_cascade_rcnn
         }
         self.outputs = {
             'Rois': (self.rois, [self.lod]),
@@ -252,12 +267,15 @@ class TestGenerateProposalLabelsOp(OpTest):
         self.bg_thresh_hi = 0.5
         self.bg_thresh_lo = 0.0
         self.bbox_reg_weights = [0.1, 0.1, 0.2, 0.2]
-        self.class_nums = 81
+        #self.class_nums = 81
+        self.is_cls_agnostic = False  #True
+        self.is_cascade_rcnn = True
+        self.class_nums = 2 if self.is_cls_agnostic else 81
 
     def init_test_input(self):
         np.random.seed(0)
         gt_nums = 6  # Keep same with batch_size_per_im for unittest
-        proposal_nums = 2000  #self.batch_size_per_im - gt_nums
+        proposal_nums = 2000 if not self.is_cascade_rcnn else 512  #self.batch_size_per_im - gt_nums
         images_shape = [[64, 64]]
         self.im_info = np.ones((len(images_shape), 3)).astype(np.float32)
         for i in range(len(images_shape)):
@@ -280,7 +298,8 @@ class TestGenerateProposalLabelsOp(OpTest):
                 self.rpn_rois, self.gt_classes, self.is_crowd, self.gt_boxes, self.im_info,
                 self.batch_size_per_im, self.fg_fraction,
                 self.fg_thresh, self.bg_thresh_hi, self.bg_thresh_lo,
-                self.bbox_reg_weights, self.class_nums
+                self.bbox_reg_weights, self.class_nums,
+                self.is_cls_agnostic, self.is_cascade_rcnn
             )
         self.rois = np.vstack(self.rois)
         self.labels_int32 = np.hstack(self.labels_int32)