Cherry pick fix generate proposals labels (#28165)

* fix generate_proposal_labels in cascade-rcnn series model, test=develop

* fix example code & unittest, test=develop

* update code from review comments, test=develop

paddle/fluid/operators/detection/bbox_util.h

@@ -149,5 +149,20 @@ void ClipTiledBoxes(const platform::DeviceContext& ctx,
   }
 }
 
+// Calculate max IoU between each box and ground-truth and
+// each row represents one box
+template <typename T>
+void MaxIoU(const framework::Tensor& iou, framework::Tensor* max_iou) {
+  const T* iou_data = iou.data<T>();
+  int row = iou.dims()[0];
+  int col = iou.dims()[1];
+  T* max_iou_data = max_iou->data<T>();
+  for (int i = 0; i < row; ++i) {
+    const T* v = iou_data + i * col;
+    T max_v = *std::max_element(v, v + col);
+    max_iou_data[i] = max_v;
+  }
+}
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detection/generate_proposal_labels_op.cc

@@ -33,6 +33,28 @@ void AppendRois(LoDTensor* out, int64_t offset, Tensor* to_add) {
   memcpy(out_data + offset, to_add_data, to_add->numel() * sizeof(T));
 }
 
+// Filter the ground-truth in RoIs and the RoIs with non-positive area.
+// The ground-truth has max overlap with itself so the max_overlap is 1
+// and the corresponding RoI will be removed.
+template <typename T>
+void FilterRoIs(const platform::DeviceContext& ctx, const Tensor& rpn_rois,
+                const Tensor& max_overlap, Tensor* keep) {
+  const T* rpn_rois_dt = rpn_rois.data<T>();
+  const T* max_overlap_dt = max_overlap.data<T>();
+  int rois_num = max_overlap.numel();
+  keep->Resize({rois_num});
+  int* keep_data = keep->mutable_data<int>(ctx.GetPlace());
+  int keep_len = 0;
+  for (int i = 0; i < rois_num; ++i) {
+    if ((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 &&
+        max_overlap_dt[i] < 1.) {
+      keep_data[keep_len++] = i;
+    }
+  }
+  keep->Resize({keep_len});
+}
+
 class GenerateProposalLabelsOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -98,12 +120,21 @@ class GenerateProposalLabelsOp : public framework::OperatorWithKernel {
                           im_info_dims.size(), im_info_dims));
 
     int class_nums = ctx->Attrs().Get<int>("class_nums");
+    bool is_cascade_rcnn = ctx->Attrs().Get<bool>("is_cascade_rcnn");
+    if (is_cascade_rcnn) {
+      PADDLE_ENFORCE_EQ(
+          ctx->HasInput("MaxOverlap"), true,
+          platform::errors::NotFound(
+              "Input(MaxOverlap) of GenerateProposalLabelsOp "
+              "should not be null when is_cascade_rcnn is True."));
+    }
 
     ctx->SetOutputDim("Rois", {-1, 4});
     ctx->SetOutputDim("LabelsInt32", {-1, 1});
     ctx->SetOutputDim("BboxTargets", {-1, 4 * class_nums});
     ctx->SetOutputDim("BboxInsideWeights", {-1, 4 * class_nums});
     ctx->SetOutputDim("BboxOutsideWeights", {-1, 4 * class_nums});
+    ctx->SetOutputDim("MaxOverlapWithGT", {-1});
   }
 
  protected:
@@ -142,7 +173,6 @@ std::vector<std::vector<int>> SampleFgBgGt(
   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;
@@ -151,11 +181,6 @@ std::vector<std::vector<int>> SampleFgBgGt(
     if ((i < gt_num) && (crowd_data[i])) {
       max_overlap = -1.0;
     }
-    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) {
@@ -232,12 +257,13 @@ std::vector<std::vector<int>> SampleFgBgGt(
 
 template <typename T>
 void GatherBoxesLabels(const platform::CPUDeviceContext& context,
-                       const Tensor& boxes, const Tensor& gt_boxes,
-                       const Tensor& gt_classes,
+                       const Tensor& boxes, const Tensor& max_overlap,
+                       const Tensor& gt_boxes, const Tensor& gt_classes,
                        const std::vector<int>& fg_inds,
                        const std::vector<int>& bg_inds,
                        const std::vector<int>& gt_inds, Tensor* sampled_boxes,
-                       Tensor* sampled_labels, Tensor* sampled_gts) {
+                       Tensor* sampled_labels, Tensor* sampled_gts,
+                       Tensor* sampled_max_overlap) {
   int fg_num = fg_inds.size();
   int bg_num = bg_inds.size();
   Tensor fg_inds_t, bg_inds_t, gt_box_inds_t, gt_label_inds_t;
@@ -264,6 +290,13 @@ void GatherBoxesLabels(const platform::CPUDeviceContext& context,
   bg_labels.mutable_data<int>({bg_num}, context.GetPlace());
   math::set_constant(context, &bg_labels, 0);
   Concat<int>(context, fg_labels, bg_labels, sampled_labels);
+
+  Tensor fg_max_overlap, bg_max_overlap;
+  fg_max_overlap.mutable_data<T>({fg_num}, context.GetPlace());
+  CPUGather<T>(context, max_overlap, fg_inds_t, &fg_max_overlap);
+  bg_max_overlap.mutable_data<T>({bg_num}, context.GetPlace());
+  CPUGather<T>(context, max_overlap, bg_inds_t, &bg_max_overlap);
+  Concat<T>(context, fg_max_overlap, bg_max_overlap, sampled_max_overlap);
 }
 
 template <typename T>
@@ -274,43 +307,58 @@ std::vector<Tensor> SampleRoisForOneImage(
     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, bool is_cascade_rcnn,
-    bool is_cls_agnostic) {
+    bool is_cls_agnostic, const Tensor& max_overlap) {
   // 1.1 map to original image
   auto im_scale = im_info.data<T>()[2];
-
   Tensor rpn_rois;
   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>();
-  int gt_num = gt_boxes.dims()[0] * 4;
+
   for (int i = 0; i < rpn_rois.numel(); ++i) {
-    if (i < gt_num && is_cascade_rcnn) {
-      rpn_rois_dt[i] = rpn_rois_in_dt[i];
+    rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale;
+  }
+
+  int proposals_num = 1;
+
+  if (is_cascade_rcnn) {
+    Tensor keep;
+    FilterRoIs<T>(context, rpn_rois, max_overlap, &keep);
+    Tensor roi_filter;
+    // Tensor box_filter;
+    if (keep.numel() == 0) {
+      math::SetConstant<platform::CPUDeviceContext, T> set_zero;
+      roi_filter.mutable_data<T>({proposals_num, kBoxDim}, context.GetPlace());
+      set_zero(context, &roi_filter, static_cast<T>(0));
     } else {
-      rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale;
+      proposals_num = keep.numel();
+      roi_filter.mutable_data<T>({proposals_num, kBoxDim}, context.GetPlace());
+      CPUGather<T>(context, rpn_rois, keep, &roi_filter);
     }
+    T* roi_filter_dt = roi_filter.data<T>();
+    memcpy(rpn_rois_dt, roi_filter_dt, roi_filter.numel() * sizeof(T));
+    rpn_rois.Resize(roi_filter.dims());
+  } else {
+    proposals_num = rpn_rois.dims()[0];
   }
-
   // 1.2 compute overlaps
-  int proposals_num = rpn_rois.dims()[0];
-  if (!is_cascade_rcnn) {
-    proposals_num += gt_boxes.dims()[0];
-  }
+  proposals_num += gt_boxes.dims()[0];
+
   Tensor proposal_to_gt_overlaps;
   proposal_to_gt_overlaps.mutable_data<T>({proposals_num, gt_boxes.dims()[0]},
                                           context.GetPlace());
 
   Tensor boxes;
   boxes.mutable_data<T>({proposals_num, kBoxDim}, context.GetPlace());
-  if (!is_cascade_rcnn) {
-    Concat<T>(context, gt_boxes, rpn_rois, &boxes);
-  } else {
-    T* boxes_dt = boxes.data<T>();
-    for (int i = 0; i < boxes.numel(); ++i) {
-      boxes_dt[i] = rpn_rois_dt[i];
-    }
-  }
+  Concat<T>(context, gt_boxes, rpn_rois, &boxes);
   BboxOverlaps<T>(boxes, gt_boxes, &proposal_to_gt_overlaps);
+
+  Tensor proposal_with_max_overlap;
+  proposal_with_max_overlap.mutable_data<T>({proposals_num},
+                                            context.GetPlace());
+
+  MaxIoU<T>(proposal_to_gt_overlaps, &proposal_with_max_overlap);
+
   // Generate proposal index
   std::vector<std::vector<int>> fg_bg_gt =
       SampleFgBgGt<T>(context, &proposal_to_gt_overlaps, is_crowd,
@@ -321,7 +369,7 @@ std::vector<Tensor> SampleRoisForOneImage(
   std::vector<int> mapped_gt_inds = fg_bg_gt[2];  // mapped_gt_labels
 
   // Gather boxes and labels
-  Tensor sampled_boxes, sampled_labels, sampled_gts;
+  Tensor sampled_boxes, sampled_labels, sampled_gts, sampled_max_overlap;
   int fg_num = fg_inds.size();
   int bg_num = bg_inds.size();
   int boxes_num = fg_num + bg_num;
@@ -329,9 +377,11 @@ std::vector<Tensor> SampleRoisForOneImage(
   sampled_boxes.mutable_data<T>(bbox_dim, context.GetPlace());
   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,
-                       mapped_gt_inds, &sampled_boxes, &sampled_labels,
-                       &sampled_gts);
+  sampled_max_overlap.mutable_data<T>({boxes_num}, context.GetPlace());
+  GatherBoxesLabels<T>(context, boxes, proposal_with_max_overlap, gt_boxes,
+                       gt_classes, fg_inds, bg_inds, mapped_gt_inds,
+                       &sampled_boxes, &sampled_labels, &sampled_gts,
+                       &sampled_max_overlap);
 
   // Compute targets
   Tensor bbox_targets_single;
@@ -390,6 +440,7 @@ std::vector<Tensor> SampleRoisForOneImage(
   res.emplace_back(bbox_targets);
   res.emplace_back(bbox_inside_weights);
   res.emplace_back(bbox_outside_weights);
+  res.emplace_back(sampled_max_overlap);
   return res;
 }
 
@@ -409,6 +460,7 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
     auto* bbox_inside_weights = context.Output<LoDTensor>("BboxInsideWeights");
     auto* bbox_outside_weights =
         context.Output<LoDTensor>("BboxOutsideWeights");
+    auto* max_overlap_with_gt = context.Output<LoDTensor>("MaxOverlapWithGT");
 
     int batch_size_per_im = context.Attr<int>("batch_size_per_im");
     float fg_fraction = context.Attr<float>("fg_fraction");
@@ -446,16 +498,21 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
             "received level of LoD is [%d], LoD is [%s].",
             gt_boxes->lod().size(), gt_boxes->lod()));
     int64_t n = static_cast<int64_t>(rpn_rois->lod().back().size() - 1);
-
-    rois->mutable_data<T>({n * batch_size_per_im, kBoxDim}, context.GetPlace());
-    labels_int32->mutable_data<int>({n * batch_size_per_im, 1},
-                                    context.GetPlace());
-    bbox_targets->mutable_data<T>({n * batch_size_per_im, kBoxDim * class_nums},
+    int64_t rois_num = rpn_rois->dims()[0];
+    int64_t gts_num = gt_boxes->dims()[0];
+    int64_t init_num =
+        is_cascade_rcnn ? rois_num + gts_num : n * batch_size_per_im;
+
+    rois->mutable_data<T>({init_num, kBoxDim}, context.GetPlace());
+    labels_int32->mutable_data<int>({init_num, 1}, context.GetPlace());
+    bbox_targets->mutable_data<T>({init_num, kBoxDim * class_nums},
                                   context.GetPlace());
-    bbox_inside_weights->mutable_data<T>(
-        {n * batch_size_per_im, kBoxDim * class_nums}, context.GetPlace());
-    bbox_outside_weights->mutable_data<T>(
-        {n * batch_size_per_im, kBoxDim * class_nums}, context.GetPlace());
+    bbox_inside_weights->mutable_data<T>({init_num, kBoxDim * class_nums},
+                                         context.GetPlace());
+    bbox_outside_weights->mutable_data<T>({init_num, kBoxDim * class_nums},
+                                          context.GetPlace());
+    max_overlap_with_gt->Resize({init_num});
+    max_overlap_with_gt->mutable_data<T>(context.GetPlace());
 
     std::random_device rnd;
     std::minstd_rand engine;
@@ -486,25 +543,36 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
       Tensor gt_boxes_slice =
           gt_boxes->Slice(gt_boxes_lod[i], gt_boxes_lod[i + 1]);
       Tensor im_info_slice = im_info->Slice(i, i + 1);
+      Tensor max_overlap_slice;
+      if (is_cascade_rcnn) {
+        auto* max_overlap = context.Input<Tensor>("MaxOverlap");
+        max_overlap_slice =
+            max_overlap->Slice(rpn_rois_lod[i], rpn_rois_lod[i + 1]);
+      } else {
+        max_overlap_slice.mutable_data<T>({rpn_rois_slice.dims()[0]},
+                                          context.GetPlace());
+      }
       std::vector<Tensor> tensor_output = SampleRoisForOneImage<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, is_cascade_rcnn, is_cls_agnostic);
+          engine, use_random, is_cascade_rcnn, is_cls_agnostic,
+          max_overlap_slice);
       Tensor sampled_rois = tensor_output[0];
       Tensor sampled_labels_int32 = tensor_output[1];
       Tensor sampled_bbox_targets = tensor_output[2];
       Tensor sampled_bbox_inside_weights = tensor_output[3];
       Tensor sampled_bbox_outside_weights = tensor_output[4];
+      Tensor sampled_max_overlap = tensor_output[5];
 
       AppendRois<T>(rois, kBoxDim * num_rois, &sampled_rois);
       AppendRois<int>(labels_int32, num_rois, &sampled_labels_int32);
-      AppendRois<T>(bbox_targets, kBoxDim * num_rois * class_nums,
-                    &sampled_bbox_targets);
-      AppendRois<T>(bbox_inside_weights, kBoxDim * num_rois * class_nums,
-                    &sampled_bbox_inside_weights);
-      AppendRois<T>(bbox_outside_weights, kBoxDim * num_rois * class_nums,
+      int64_t offset = kBoxDim * num_rois * class_nums;
+      AppendRois<T>(bbox_targets, offset, &sampled_bbox_targets);
+      AppendRois<T>(bbox_inside_weights, offset, &sampled_bbox_inside_weights);
+      AppendRois<T>(bbox_outside_weights, offset,
                     &sampled_bbox_outside_weights);
+      AppendRois<T>(max_overlap_with_gt, num_rois, &sampled_max_overlap);
 
       num_rois += sampled_rois.dims()[0];
       lod0.emplace_back(num_rois);
@@ -521,6 +589,8 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
     bbox_targets->Resize({num_rois, kBoxDim * class_nums});
     bbox_inside_weights->Resize({num_rois, kBoxDim * class_nums});
     bbox_outside_weights->Resize({num_rois, kBoxDim * class_nums});
+    max_overlap_with_gt->Resize({num_rois});
+    max_overlap_with_gt->set_lod(lod);
   }
 };
 
@@ -550,6 +620,12 @@ class GenerateProposalLabelsOpMaker : public framework::OpProtoAndCheckerMaker {
              "(Tensor), This input is a 2D Tensor with shape [B, 3]. "
              "B is the number of input images, "
              "each element consists of im_height, im_width, im_scale.");
+    AddInput("MaxOverlap",
+             "(LoDTensor), This input is a 1D LoDTensor with shape [N]."
+             "N is the number of Input(RpnRois), "
+             "each element is the maximum overlap between "
+             "the proposal RoI and ground-truth.")
+        .AsDispensable();
 
     AddOutput(
         "Rois",
@@ -573,6 +649,12 @@ class GenerateProposalLabelsOpMaker : public framework::OpProtoAndCheckerMaker {
         "(LoDTensor), This output is a 2D LoDTensor with shape [P, 4 * "
         "class_nums], "
         "each element indicates whether a box should contribute to loss.");
+    AddOutput("MaxOverlapWithGT",
+              "(LoDTensor), This output is a 1D LoDTensor with shape [P], "
+              "each element indicates the maxoverlap "
+              "between output RoIs and ground-truth. "
+              "The output RoIs may include ground-truth "
+              "and the output maxoverlap may contain 1.");
 
     AddAttr<int>("batch_size_per_im", "Batch size of rois per images.");
     AddAttr<float>("fg_fraction",

python/paddle/fluid/layers/detection.py

@@ -2601,7 +2601,9 @@ def generate_proposal_labels(rpn_rois,
                              class_nums=None,
                              use_random=True,
                              is_cls_agnostic=False,
-                             is_cascade_rcnn=False):
+                             is_cascade_rcnn=False,
+                             max_overlap=None,
+                             return_max_overlap=False):
     """
 
     **Generate Proposal Labels of Faster-RCNN**
@@ -2638,25 +2640,29 @@ def generate_proposal_labels(rpn_rois,
         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.
+        max_overlap(Variable): Maximum overlap between each proposal box and ground-truth.
+        return_max_overlap(bool): Whether return the maximum overlap between each sampled RoI and ground-truth.
 
     Returns:
         tuple:
-        A tuple with format``(rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights)``.
+        A tuple with format``(rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, max_overlap)``.
 
         - **rois**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4]``. The data type is the same as ``rpn_rois``.
         - **labels_int32**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 1]``. The data type must be int32.
         - **bbox_targets**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The regression targets of all RoIs. The data type is the same as ``rpn_rois``.
         - **bbox_inside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of foreground boxes' regression loss. The data type is the same as ``rpn_rois``.
         - **bbox_outside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of regression loss. The data type is the same as ``rpn_rois``.
-
+        - **max_overlap**: 1-D LoDTensor with shape ``[P]``. P is the number of output ``rois``. The maximum overlap between each sampled RoI and ground-truth.
 
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
             rpn_rois = fluid.data(name='rpn_rois', shape=[None, 4], dtype='float32')
-            gt_classes = fluid.data(name='gt_classes', shape=[None, 1], dtype='float32')
-            is_crowd = fluid.data(name='is_crowd', shape=[None, 1], dtype='float32')
+            gt_classes = fluid.data(name='gt_classes', shape=[None, 1], dtype='int32')
+            is_crowd = fluid.data(name='is_crowd', shape=[None, 1], dtype='int32')
             gt_boxes = fluid.data(name='gt_boxes', shape=[None, 4], dtype='float32')
             im_info = fluid.data(name='im_info', shape=[None, 3], dtype='float32')
             rois, labels, bbox, inside_weights, outside_weights = fluid.layers.generate_proposal_labels(
@@ -2673,6 +2679,8 @@ def generate_proposal_labels(rpn_rois,
                              'generate_proposal_labels')
     check_variable_and_dtype(is_crowd, 'is_crowd', ['int32'],
                              'generate_proposal_labels')
+    if is_cascade_rcnn:
+        assert max_overlap is not None, "Input max_overlap of generate_proposal_labels should not be None if is_cascade_rcnn is True"
 
     rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
     labels_int32 = helper.create_variable_for_type_inference(
@@ -2683,22 +2691,28 @@ def generate_proposal_labels(rpn_rois,
         dtype=rpn_rois.dtype)
     bbox_outside_weights = helper.create_variable_for_type_inference(
         dtype=rpn_rois.dtype)
+    max_overlap_with_gt = helper.create_variable_for_type_inference(
+        dtype=rpn_rois.dtype)
 
+    inputs = {
+        'RpnRois': rpn_rois,
+        'GtClasses': gt_classes,
+        'IsCrowd': is_crowd,
+        'GtBoxes': gt_boxes,
+        'ImInfo': im_info,
+    }
+    if max_overlap is not None:
+        inputs['MaxOverlap'] = max_overlap
     helper.append_op(
         type="generate_proposal_labels",
-        inputs={
-            'RpnRois': rpn_rois,
-            'GtClasses': gt_classes,
-            'IsCrowd': is_crowd,
-            'GtBoxes': gt_boxes,
-            'ImInfo': im_info
-        },
+        inputs=inputs,
         outputs={
             'Rois': rois,
             'LabelsInt32': labels_int32,
             'BboxTargets': bbox_targets,
             'BboxInsideWeights': bbox_inside_weights,
-            'BboxOutsideWeights': bbox_outside_weights
+            'BboxOutsideWeights': bbox_outside_weights,
+            'MaxOverlapWithGT': max_overlap_with_gt
         },
         attrs={
             'batch_size_per_im': batch_size_per_im,
@@ -2718,7 +2732,10 @@ def generate_proposal_labels(rpn_rois,
     bbox_targets.stop_gradient = True
     bbox_inside_weights.stop_gradient = True
     bbox_outside_weights.stop_gradient = True
+    max_overlap_with_gt.stop_gradient = True
 
+    if return_max_overlap:
+        return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, max_overlap_with_gt
     return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights
 
 

python/paddle/fluid/tests/test_detection.py

@@ -289,40 +289,39 @@ class TestAnchorGenerator(unittest.TestCase):
 
 
 class TestGenerateProposalLabels(unittest.TestCase):
+    def check_out(self, outs):
+        rois = outs[0]
+        labels_int32 = outs[1]
+        bbox_targets = outs[2]
+        bbox_inside_weights = outs[3]
+        bbox_outside_weights = outs[4]
+        assert rois.shape[1] == 4
+        assert rois.shape[0] == labels_int32.shape[0]
+        assert rois.shape[0] == bbox_targets.shape[0]
+        assert rois.shape[0] == bbox_inside_weights.shape[0]
+        assert rois.shape[0] == bbox_outside_weights.shape[0]
+        assert bbox_targets.shape[1] == 4 * self.class_nums
+        assert bbox_inside_weights.shape[1] == 4 * self.class_nums
+        assert bbox_outside_weights.shape[1] == 4 * self.class_nums
+        if len(outs) == 6:
+            max_overlap_with_gt = outs[5]
+            assert max_overlap_with_gt.shape[0] == rois.shape[0]
+
     def test_generate_proposal_labels(self):
         program = Program()
         with program_guard(program):
-            rpn_rois = layers.data(
-                name='rpn_rois',
-                shape=[4, 4],
-                dtype='float32',
-                lod_level=1,
-                append_batch_size=False)
-            gt_classes = layers.data(
-                name='gt_classes',
-                shape=[6],
-                dtype='int32',
-                lod_level=1,
-                append_batch_size=False)
-            is_crowd = layers.data(
-                name='is_crowd',
-                shape=[6],
-                dtype='int32',
-                lod_level=1,
-                append_batch_size=False)
-            gt_boxes = layers.data(
-                name='gt_boxes',
-                shape=[6, 4],
-                dtype='float32',
-                lod_level=1,
-                append_batch_size=False)
-            im_info = layers.data(
-                name='im_info',
-                shape=[1, 3],
-                dtype='float32',
-                lod_level=1,
-                append_batch_size=False)
-            class_nums = 5
+            rpn_rois = fluid.data(
+                name='rpn_rois', shape=[4, 4], dtype='float32', lod_level=1)
+            gt_classes = fluid.data(
+                name='gt_classes', shape=[6], dtype='int32', lod_level=1)
+            is_crowd = fluid.data(
+                name='is_crowd', shape=[6], dtype='int32', lod_level=1)
+            gt_boxes = fluid.data(
+                name='gt_boxes', shape=[6, 4], dtype='float32', lod_level=1)
+            im_info = fluid.data(name='im_info', shape=[1, 3], dtype='float32')
+            max_overlap = fluid.data(
+                name='max_overlap', shape=[4], dtype='float32', lod_level=1)
+            self.class_nums = 5
             outs = fluid.layers.generate_proposal_labels(
                 rpn_rois=rpn_rois,
                 gt_classes=gt_classes,
@@ -335,20 +334,27 @@ class TestGenerateProposalLabels(unittest.TestCase):
                 bg_thresh_hi=0.5,
                 bg_thresh_lo=0.0,
                 bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
-                class_nums=class_nums)
+                class_nums=self.class_nums)
+            outs_1 = fluid.layers.generate_proposal_labels(
+                rpn_rois=rpn_rois,
+                gt_classes=gt_classes,
+                is_crowd=is_crowd,
+                gt_boxes=gt_boxes,
+                im_info=im_info,
+                batch_size_per_im=2,
+                fg_fraction=0.5,
+                fg_thresh=0.5,
+                bg_thresh_hi=0.5,
+                bg_thresh_lo=0.0,
+                bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
+                class_nums=self.class_nums,
+                is_cascade_rcnn=True,
+                max_overlap=max_overlap,
+                return_max_overlap=True)
+
+            self.check_out(outs)
+            self.check_out(outs_1)
             rois = outs[0]
-            labels_int32 = outs[1]
-            bbox_targets = outs[2]
-            bbox_inside_weights = outs[3]
-            bbox_outside_weights = outs[4]
-            assert rois.shape[1] == 4
-            assert rois.shape[0] == labels_int32.shape[0]
-            assert rois.shape[0] == bbox_targets.shape[0]
-            assert rois.shape[0] == bbox_inside_weights.shape[0]
-            assert rois.shape[0] == bbox_outside_weights.shape[0]
-            assert bbox_targets.shape[1] == 4 * class_nums
-            assert bbox_inside_weights.shape[1] == 4 * class_nums
-            assert bbox_outside_weights.shape[1] == 4 * class_nums
 
 
 class TestGenerateMaskLabels(unittest.TestCase):

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

@@ -22,66 +22,91 @@ 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, use_random, is_cls_agnostic, is_cascade_rcnn):
+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,
+                                       use_random,
+                                       is_cls_agnostic,
+                                       is_cascade_rcnn,
+                                       max_overlaps=None):
     rois = []
     labels_int32 = []
     bbox_targets = []
     bbox_inside_weights = []
     bbox_outside_weights = []
+    max_overlap_with_gt = []
     lod = []
     assert len(rpn_rois) == len(
         im_info), 'batch size of rpn_rois and ground_truth is not matched'
 
     for im_i in range(len(im_info)):
+        max_overlap = max_overlaps[im_i] if is_cascade_rcnn else None
         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,
-            use_random, is_cls_agnostic, is_cascade_rcnn)
+            use_random, is_cls_agnostic, is_cascade_rcnn, max_overlap)
         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'])
         bbox_inside_weights.append(frcn_blobs['bbox_inside_weights'])
         bbox_outside_weights.append(frcn_blobs['bbox_outside_weights'])
+        max_overlap_with_gt.append(frcn_blobs['max_overlap'])
 
-    return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, lod
+    return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, max_overlap_with_gt, lod
+
+
+def filter_roi(rois, max_overlap):
+    ws = rois[:, 2] - rois[:, 0] + 1
+    hs = rois[:, 3] - rois[:, 1] + 1
+    keep = np.where((ws > 0) & (hs > 0) & (max_overlap < 1.0))[0]
+    if len(keep) > 0:
+        return rois[keep, :]
+    return np.zeros((1, 4)).astype('float32')
 
 
 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, use_random,
-                 is_cls_agnostic, is_cascade_rcnn):
+                 is_cls_agnostic, is_cascade_rcnn, max_overlap):
     rois_per_image = int(batch_size_per_im)
     fg_rois_per_im = int(np.round(fg_fraction * rois_per_image))
 
     # Roidb
     im_scale = im_info[2]
     inv_im_scale = 1. / im_scale
-    if is_cascade_rcnn:
-        rpn_rois = rpn_rois[len(gt_boxes):, :]
     rpn_rois = rpn_rois * inv_im_scale
+
+    if is_cascade_rcnn:
+        rpn_rois = filter_roi(rpn_rois, max_overlap)
+
     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)
-    if len(gt_boxes) > 0:
-        proposal_to_gt_overlaps = _bbox_overlaps(boxes, gt_boxes)
-
-        overlaps_argmax = proposal_to_gt_overlaps.argmax(axis=1)
-        overlaps_max = proposal_to_gt_overlaps.max(axis=1)
-        # Boxes which with non-zero overlap with gt boxes
-        overlapped_boxes_ind = np.where(overlaps_max > 0)[0]
-        overlapped_boxes_gt_classes = gt_classes[overlaps_argmax[
-            overlapped_boxes_ind]]
-        gt_overlaps[overlapped_boxes_ind,
-                    overlapped_boxes_gt_classes] = overlaps_max[
-                        overlapped_boxes_ind]
-        box_to_gt_ind_map[overlapped_boxes_ind] = overlaps_argmax[
-            overlapped_boxes_ind]
+    proposal_to_gt_overlaps = _bbox_overlaps(boxes, gt_boxes)
+
+    overlaps_argmax = proposal_to_gt_overlaps.argmax(axis=1)
+    overlaps_max = proposal_to_gt_overlaps.max(axis=1)
+    # Boxes which with non-zero overlap with gt boxes
+    overlapped_boxes_ind = np.where(overlaps_max > 0)[0]
+    overlapped_boxes_gt_classes = gt_classes[overlaps_argmax[
+        overlapped_boxes_ind]]
+    gt_overlaps[overlapped_boxes_ind,
+                overlapped_boxes_gt_classes] = overlaps_max[
+                    overlapped_boxes_ind]
+    box_to_gt_ind_map[overlapped_boxes_ind] = overlaps_argmax[
+        overlapped_boxes_ind]
 
     crowd_ind = np.where(is_crowd)[0]
     gt_overlaps[crowd_ind] = -1.0
@@ -90,11 +115,6 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
 
     if is_cascade_rcnn:
         # Cascade RCNN Decode Filter
-        ws = boxes[:, 2] - boxes[:, 0] + 1
-        hs = boxes[:, 3] - boxes[:, 1] + 1
-        keep = np.where((ws > 0) & (hs > 0))[0]
-        boxes = boxes[keep]
-        max_overlaps = max_overlaps[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]
@@ -125,6 +145,7 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
     sampled_labels = max_classes[keep_inds]
     sampled_labels[fg_rois_per_this_image:] = 0
     sampled_boxes = boxes[keep_inds]
+    sampled_max_overlap = max_overlaps[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,
@@ -142,7 +163,8 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
         labels_int32=sampled_labels,
         bbox_targets=bbox_targets,
         bbox_inside_weights=bbox_inside_weights,
-        bbox_outside_weights=bbox_outside_weights)
+        bbox_outside_weights=bbox_outside_weights,
+        max_overlap=sampled_max_overlap)
     return frcn_blobs
 
 
@@ -226,9 +248,9 @@ class TestGenerateProposalLabelsOp(OpTest):
     def set_data(self):
         #self.use_random = False
         self.init_use_random()
-        self.init_test_cascade()
         self.init_test_params()
         self.init_test_input()
+        self.init_test_cascade()
         self.init_test_output()
 
         self.inputs = {
@@ -236,8 +258,12 @@ class TestGenerateProposalLabelsOp(OpTest):
             'GtClasses': (self.gt_classes[0], self.gts_lod),
             'IsCrowd': (self.is_crowd[0], self.gts_lod),
             'GtBoxes': (self.gt_boxes[0], self.gts_lod),
-            'ImInfo': self.im_info
+            'ImInfo': self.im_info,
         }
+        if self.max_overlaps is not None:
+            self.inputs['MaxOverlap'] = (self.max_overlaps[0],
+                                         self.rpn_rois_lod)
+
         self.attrs = {
             'batch_size_per_im': self.batch_size_per_im,
             'fg_fraction': self.fg_fraction,
@@ -256,6 +282,7 @@ class TestGenerateProposalLabelsOp(OpTest):
             'BboxTargets': (self.bbox_targets, [self.lod]),
             'BboxInsideWeights': (self.bbox_inside_weights, [self.lod]),
             'BboxOutsideWeights': (self.bbox_outside_weights, [self.lod]),
+            'MaxOverlapWithGT': (self.max_overlap_with_gt, [self.lod]),
         }
 
     def test_check_output(self):
@@ -267,12 +294,13 @@ class TestGenerateProposalLabelsOp(OpTest):
 
     def init_test_cascade(self, ):
         self.is_cascade_rcnn = False
+        self.max_overlaps = None
 
     def init_use_random(self):
         self.use_random = False
 
     def init_test_params(self):
-        self.batch_size_per_im = 512
+        self.batch_size_per_im = 100
         self.fg_fraction = 0.25
         self.fg_thresh = 0.5
         self.bg_thresh_hi = 0.5
@@ -284,7 +312,7 @@ class TestGenerateProposalLabelsOp(OpTest):
     def init_test_input(self):
         np.random.seed(0)
         gt_nums = 6  # Keep same with batch_size_per_im for unittest
-        proposal_nums = 2000 if not self.is_cascade_rcnn else 512  #self.batch_size_per_im - gt_nums
+        proposal_nums = 200
         images_shape = [[64, 64]]
         self.im_info = np.ones((len(images_shape), 3)).astype(np.float32)
         for i in range(len(images_shape)):
@@ -301,24 +329,16 @@ class TestGenerateProposalLabelsOp(OpTest):
         self.gt_boxes = [gt['boxes'] for gt in ground_truth]
         self.is_crowd = [gt['is_crowd'] for gt in ground_truth]
 
-        if self.is_cascade_rcnn:
-            rpn_rois_new = []
-            for im_i in range(len(self.im_info)):
-                gt_boxes = self.gt_boxes[im_i]
-                rpn_rois = np.vstack(
-                    [gt_boxes, self.rpn_rois[im_i][len(gt_boxes):, :]])
-                rpn_rois_new.append(rpn_rois)
-            self.rpn_rois = rpn_rois_new
-
     def init_test_output(self):
         self.rois, self.labels_int32, self.bbox_targets, \
         self.bbox_inside_weights, self.bbox_outside_weights, \
+        self.max_overlap_with_gt, \
         self.lod = generate_proposal_labels_in_python(
                 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.use_random,
-                self.is_cls_agnostic, self.is_cascade_rcnn
+                self.is_cls_agnostic, self.is_cascade_rcnn, self.max_overlaps
             )
         self.rois = np.vstack(self.rois)
         self.labels_int32 = np.hstack(self.labels_int32)
@@ -326,11 +346,18 @@ class TestGenerateProposalLabelsOp(OpTest):
         self.bbox_targets = np.vstack(self.bbox_targets)
         self.bbox_inside_weights = np.vstack(self.bbox_inside_weights)
         self.bbox_outside_weights = np.vstack(self.bbox_outside_weights)
+        self.max_overlap_with_gt = np.vstack(self.max_overlap_with_gt)
 
 
 class TestCascade(TestGenerateProposalLabelsOp):
     def init_test_cascade(self):
         self.is_cascade_rcnn = True
+        roi_num = len(self.rpn_rois[0])
+        self.max_overlaps = []
+        max_overlap = np.random.rand(roi_num).astype('float32')
+        # Make GT samples with overlap = 1
+        max_overlap[max_overlap > 0.9] = 1.
+        self.max_overlaps.append(max_overlap)
 
 
 class TestUseRandom(TestGenerateProposalLabelsOp):
@@ -389,6 +416,15 @@ class TestOnlyGT(TestCascade):
         self.rpn_rois_lod = self.gts_lod
 
 
+class TestOnlyGT2(TestCascade):
+    def init_test_cascade(self):
+        self.is_cascade_rcnn = True
+        roi_num = len(self.rpn_rois[0])
+        self.max_overlaps = []
+        max_overlap = np.ones(roi_num).astype('float32')
+        self.max_overlaps.append(max_overlap)
+
+
 def _generate_proposals(images_shape, proposal_nums):
     rpn_rois = []
     rpn_rois_lod = []