fix distribute fpn proposals, test=develop

paddle/fluid/operators/detection/bbox_util.h

@@ -15,11 +15,37 @@ limitations under the License. */
 #pragma once
 #include <algorithm>
 #include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/tensor.h"
 
 namespace paddle {
 namespace operators {
 
+struct RangeInitFunctor {
+  int start_;
+  int delta_;
+  int* out_;
+  HOSTDEVICE void operator()(size_t i) { out_[i] = start_ + i * delta_; }
+};
+
+template <typename T>
+inline HOSTDEVICE T RoIArea(const T* box, bool normalized) {
+  if (box[2] < box[0] || box[3] < box[1]) {
+    // If coordinate values are is invalid
+    // (e.g. xmax < xmin or ymax < ymin), return 0.
+    return static_cast<T>(0.);
+  } else {
+    const T w = box[2] - box[0];
+    const T h = box[3] - box[1];
+    if (normalized) {
+      return w * h;
+    } else {
+      // If coordinate values are not within range [0, 1].
+      return (w + 1) * (h + 1);
+    }
+  }
+}
+
 /*
  * transform that computes target bounding-box regression deltas
  * given proposal boxes and ground-truth boxes.

paddle/fluid/operators/detection/distribute_fpn_proposals_op.cc

@@ -40,14 +40,14 @@ class DistributeFpnProposalsOp : public framework::OperatorWithKernel {
       outs_dims.push_back(out_dim);
     }
     ctx->SetOutputsDim("MultiFpnRois", outs_dims);
-    ctx->SetOutputDim("RestoreIndex", {1, -1});
+    ctx->SetOutputDim("RestoreIndex", {-1, 1});
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("FpnRois"));
-    return framework::OpKernelType(data_type, platform::CPUPlace());
+    return framework::OpKernelType(data_type, ctx.device_context());
   }
 };
 

paddle/fluid/operators/detection/distribute_fpn_proposals_op.cu

@@ -15,8 +15,10 @@ limitations under the License. */
 #include <paddle/fluid/memory/allocation/allocator.h>
 #include "cub/cub.cuh"
 #include "paddle/fluid/memory/memcpy.h"
+#include "paddle/fluid/operators/detection/bbox_util.h"
 #include "paddle/fluid/operators/detection/distribute_fpn_proposals_op.h"
 #include "paddle/fluid/operators/gather.cu.h"
+#include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
 #include "paddle/fluid/platform/for_range.h"
 
@@ -26,7 +28,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 
-static constexpr int kNumCUDAThreads = 512;
+static constexpr int kNumCUDAThreads = 64;
 static constexpr int kNumMaxinumNumBlocks = 4096;
 
 #define CUDA_1D_KERNEL_LOOP(i, n)                              \
@@ -35,47 +37,13 @@ static constexpr int kNumMaxinumNumBlocks = 4096;
 
 int const BBoxSize = 4;
 
-struct RangeInitFunctor {
-  int start_;
-  int delta_;
-  int* out_;
-  __device__ void operator()(size_t i) { out_[i] = start_ + i * delta_; }
-};
-
 static inline int NumBlocks(const int N) {
   return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
                   kNumMaxinumNumBlocks);
 }
 
-static inline void TransLoD(const int* length_lod, const int lod_size,
-                            int* offset_lod) {
-  int offset = 0;
-  for (int i = 0; i < lod_size; ++i) {
-    offset_lod[i] = offset;
-    offset += length_lod[i];
-  }
-}
-
-template <typename T>
-static __device__ inline T RoIArea(const T* box, bool normalized) {
-  if (box[2] < box[0] || box[3] < box[1]) {
-    // If coordinate values are is invalid
-    // (e.g. xmax < xmin or ymax < ymin), return 0.
-    return static_cast<T>(0.);
-  } else {
-    const T w = box[2] - box[0];
-    const T h = box[3] - box[1];
-    if (normalized) {
-      return w * h;
-    } else {
-      // If coordinate values are not within range [0, 1].
-      return (w + 1) * (h + 1);
-    }
-  }
-}
-
 template <class T>
-static __global__ void GPUDistFpnProposalsHelper(
+__global__ void GPUDistFpnProposalsHelper(
     const int nthreads, const T* rois, const int lod_size,
     const int refer_level, const int refer_scale, const int max_level,
     const int min_level, int* roi_batch_id_data, int* sub_lod_list,
@@ -86,12 +54,13 @@ static __global__ void GPUDistFpnProposalsHelper(
     // get the target level of current rois
     T roi_area = RoIArea(offset_roi, false);
     T roi_scale = sqrt(roi_area);
-    int tgt_lvl = floor(log2(roi_scale / refer_scale) + refer_level);
+    int tgt_lvl = floor(
+        log2(roi_scale / static_cast<T>(refer_scale) + (T)1e-6) + refer_level);
     tgt_lvl = min(max_level, max(tgt_lvl, min_level));
     target_lvls[i] = tgt_lvl;
     // compute number of rois in the same batch and same target level
-    platform::CudaAtomicAdd(sub_lod_list + tgt_lvl * lod_size + roi_batch_ind,
-                            1);
+    platform::CudaAtomicAdd(
+        sub_lod_list + (tgt_lvl - min_level) * lod_size + roi_batch_ind, 1);
   }
 }
 
@@ -138,18 +107,22 @@ class GPUDistributeFpnProposalsOpKernel : public framework::OpKernel<T> {
     Tensor sub_lod_list;
     sub_lod_list.Resize({num_level, lod_size});
     int* sub_lod_list_data = sub_lod_list.mutable_data<int>(dev_ctx.GetPlace());
+    math::SetConstant<platform::CUDADeviceContext, int> set_zero;
+    set_zero(dev_ctx, &sub_lod_list, static_cast<int>(0));
+
     Tensor target_lvls;
     target_lvls.Resize({roi_num});
     int* target_lvls_data = target_lvls.mutable_data<int>(dev_ctx.GetPlace());
 
-    int blocks = NumBlocks(roi_num);
+    int dist_blocks = NumBlocks(roi_num);
     int threads = kNumCUDAThreads;
-
     // get target levels and sub_lod list
-    GPUDistFpnProposalsHelper<T><<<blocks, threads>>>(
+    GPUDistFpnProposalsHelper<T><<<dist_blocks, threads>>>(
         roi_num, fpn_rois->data<T>(), lod_size, refer_level, refer_scale,
         max_level, min_level, roi_batch_id_list_gpu.data<int>(),
         sub_lod_list_data, target_lvls_data);
+    dev_ctx.Wait();
+    auto place = boost::get<platform::CUDAPlace>(dev_ctx.GetPlace());
 
     Tensor index_in_t;
     int* idx_in = index_in_t.mutable_data<int>({roi_num}, dev_ctx.GetPlace());
@@ -163,46 +136,54 @@ class GPUDistributeFpnProposalsOpKernel : public framework::OpKernel<T> {
 
     // Determine temporary device storage requirements
     size_t temp_storage_bytes = 0;
-    cub::DeviceRadixSort::SortPairsDescending<int, int>(
-        nullptr, temp_storage_bytes, target_lvls_data, keys_out, idx_in,
-        idx_out, roi_num);
+    cub::DeviceRadixSort::SortPairs<int, int>(nullptr, temp_storage_bytes,
+                                              target_lvls_data, keys_out,
+                                              idx_in, idx_out, roi_num);
     // Allocate temporary storage
-    auto place = boost::get<platform::CUDAPlace>(dev_ctx.GetPlace());
     auto d_temp_storage = memory::Alloc(place, temp_storage_bytes,
                                         memory::Allocator::kScratchpad);
 
     // Run sorting operation
     // sort target level to get corresponding index
-    cub::DeviceRadixSort::SortPairsDescending<int, int>(
+    cub::DeviceRadixSort::SortPairs<int, int>(
         d_temp_storage->ptr(), temp_storage_bytes, target_lvls_data, keys_out,
         idx_in, idx_out, roi_num);
 
     int* restore_idx_data =
         restore_index->mutable_data<int>({roi_num, 1}, dev_ctx.GetPlace());
     // sort current index to get restore index
-    cub::DeviceRadixSort::SortPairsDescending<int, int>(
+    cub::DeviceRadixSort::SortPairs<int, int>(
         d_temp_storage->ptr(), temp_storage_bytes, idx_out, keys_out, idx_in,
         restore_idx_data, roi_num);
 
-    Tensor offset_lod;
-    int* offset_lod_data =
-        offset_lod.mutable_data<int>({lod_size + 1}, dev_ctx.GetPlace());
+    int start = 0;
     for (int i = 0; i < num_level; ++i) {
       Tensor sub_lod = sub_lod_list.Slice(i, i + 1);
       int* sub_lod_data = sub_lod.data<int>();
       // transfer length-based lod to offset-based lod
-      TransLoD(sub_lod_data, lod_size + 1, offset_lod_data);
-      int sub_rois_num = offset_lod_data[lod_size];
-      Tensor sub_idx = index_out_t.Slice(0, sub_rois_num);
-
-      multi_fpn_rois[i]->mutable_data<T>({sub_rois_num, kBoxDim},
-                                         dev_ctx.GetPlace());
+      std::vector<size_t> offset(1, 0);
+      std::vector<int> sub_lod_cpu(lod_size);
+      memory::Copy(platform::CPUPlace(), sub_lod_cpu.data(), place,
+                   sub_lod_data, sizeof(int) * lod_size, dev_ctx.stream());
+      dev_ctx.Wait();
+      for (int j = 0; j < lod_size; ++j) {
+        offset.emplace_back(offset.back() + sub_lod_cpu[j]);
+      }
 
-      GPUGather<T>(dev_ctx, *fpn_rois, sub_idx, multi_fpn_rois[i]);
+      int sub_rois_num = offset.back();
+
+      int end = start + sub_rois_num;
+      if (end > start) {
+        Tensor sub_idx = index_out_t.Slice(start, end);
+        start = end;
+        multi_fpn_rois[i]->mutable_data<T>({sub_rois_num, kBoxDim},
+                                           dev_ctx.GetPlace());
+        GPUGather<T>(dev_ctx, *fpn_rois, sub_idx, multi_fpn_rois[i]);
+      } else {
+        multi_fpn_rois[i]->mutable_data<T>({sub_rois_num, kBoxDim},
+                                           dev_ctx.GetPlace());
+      }
       framework::LoD lod;
-      std::vector<size_t> offset;
-      memory::Copy(platform::CPUPlace(), offset.data(), place, offset_lod_data,
-                   sizeof(int) * (lod_size + 1), 0);
       lod.emplace_back(offset);
       multi_fpn_rois[i]->set_lod(lod);
     }

paddle/fluid/operators/detection/distribute_fpn_proposals_op.h

@@ -83,8 +83,8 @@ class DistributeFpnProposalsOpKernel : public framework::OpKernel<T> {
       for (int j = 0; j < fpn_rois_slice.dims()[0]; ++j) {
         // get the target level of current rois
         T roi_scale = std::sqrt(BBoxArea(rois_data, false));
-        int tgt_lvl =
-            std::floor(std::log2(roi_scale / refer_scale) + refer_level);
+        int tgt_lvl = std::floor(std::log2(roi_scale / refer_scale + (T)1e-6) +
+                                 refer_level);
         tgt_lvl = std::min(max_level, std::max(tgt_lvl, min_level));
         target_level.push_back(tgt_lvl);
         num_rois_level[tgt_lvl - min_level]++;
@@ -107,7 +107,7 @@ class DistributeFpnProposalsOpKernel : public framework::OpKernel<T> {
       num_rois_level_integral[i + 1] =
           num_rois_level_integral[i] + num_rois_level[i];
     }
-    restore_index->mutable_data<int>({1, fpn_rois_num}, context.GetPlace());
+    restore_index->mutable_data<int>({fpn_rois_num, 1}, context.GetPlace());
     int* restore_index_data = restore_index->data<int>();
     std::vector<int> restore_index_inter(fpn_rois_num, -1);
     // distribute the rois into different fpn level by target level

python/paddle/fluid/layers/detection.py

@@ -2375,7 +2375,7 @@ def distribute_fpn_proposals(fpn_rois,
     """
 
     helper = LayerHelper('distribute_fpn_proposals', **locals())
-    dtype = helper.input_dtype()
+    dtype = helper.input_dtype('fpn_rois')
     num_lvl = max_level - min_level + 1
     multi_rois = [
         helper.create_variable_for_type_inference(dtype) for i in range(num_lvl)

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

@@ -37,7 +37,7 @@ def set_data(self):
                   for i in range(len(self.rois_fpn))]
         self.outputs = {
             'MultiFpnRois': output,
-            'RestoreIndex': self.rois_idx_restore
+            'RestoreIndex': self.rois_idx_restore.reshape(-1, 1)
         }
 
     def init_test_case(self):
@@ -63,10 +63,10 @@ def map_rois_to_fpn_levels(self, rois, lvl_min, lvl_max):
         return target_lvls
 
     def get_sub_lod(self, sub_lvl):
-        sub_lod = []
+        sub_lod = [0, 0]
         max_batch_id = sub_lvl[-1]
         for i in range(max_batch_id.astype(np.int32) + 1):
-            sub_lod.append(np.where(sub_lvl == i)[0].size)
+            sub_lod[i] = np.where(sub_lvl == i)[0].size
         return sub_lod
 
     def add_multilevel_roi(self, rois, target_lvls, lvl_min, lvl_max):
@@ -115,3 +115,7 @@ def setUp(self):
 
     def test_check_output(self):
         self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()