Merge branch 'develop' into quantize_transpiler_update

paddle/fluid/API.spec

@@ -302,6 +302,7 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
 paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
 paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.contrib.op_freq_statistic ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)

paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc

@@ -257,6 +257,22 @@ std::unique_ptr<ir::Graph> AttentionLSTMFusePass::ApplyImpl(
     std::unique_ptr<ir::Graph> graph) const {
   PDPattern external_pattern, subblock_pattern;
 
+  // Use the following variables to tell whether this model is RNN1.
+  // This fuse can only works on the RNN1 model.
+  std::unordered_set<std::string> specified_vars({"data_lod_attention",
+                                                  "cell_init", "hidden_init",
+                                                  "data", "week", "minute"});
+  int count = 0;
+  for (auto* node : graph->Nodes()) {
+    if (node->IsVar() && specified_vars.count(node->Name())) {
+      ++count;
+    }
+  }
+  if (count < specified_vars.size()) {
+    return graph;
+  }
+
+  // Continue to fuse.
   FindWhileOp(graph.get());
   return graph;
 }

paddle/fluid/inference/api/paddle_inference_api.h

@@ -212,10 +212,11 @@ struct AnalysisConfig : public NativeConfig {
     kExclude   // Specify the disabled passes in `ir_passes`.
   };
 
+  // Determine whether to perform graph optimization.
   bool enable_ir_optim = true;
+  // Manually determine the IR passes to run.
   IrPassMode ir_mode{IrPassMode::kExclude};
-  // attention lstm fuse works only on some specific models, disable as default.
-  std::vector<std::string> ir_passes{"attention_lstm_fuse_pass"};
+  std::vector<std::string> ir_passes;
 
   // NOTE this is just for internal development, please not use it.
   bool _use_mkldnn{false};

paddle/fluid/operators/detection/CMakeLists.txt

@@ -30,7 +30,13 @@ detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc
 polygon_box_transform_op.cu)
 detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
 detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
-detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
+
+if(WITH_GPU)
+  detection_library(generate_proposals_op SRCS generate_proposals_op.cc generate_proposals_op.cu DEPS memory cub)
+else()
+  detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
+endif()
+
 detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op.cc roi_perspective_transform_op.cu)
 #Export local libraries to parent
 set(DETECTION_LIBRARY ${LOCAL_DETECTION_LIBS} PARENT_SCOPE)

paddle/fluid/operators/detection/generate_proposals_op.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/operators/gather.h"
 #include "paddle/fluid/operators/math/math_function.h"
 
@@ -69,7 +70,7 @@ class GenerateProposalsOp : public framework::OperatorWithKernel {
       const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Anchors")->type()),
-        platform::CPUPlace());
+        ctx.device_context());
   }
 };
 
@@ -162,7 +163,7 @@ void FilterBoxes(const platform::DeviceContext &ctx, Tensor *boxes,
   const T *im_info_data = im_info.data<T>();
   T *boxes_data = boxes->mutable_data<T>(ctx.GetPlace());
   T im_scale = im_info_data[2];
-  keep->Resize({boxes->dims()[0], 1});
+  keep->Resize({boxes->dims()[0]});
   min_size = std::max(min_size, 1.0f);
   int *keep_data = keep->mutable_data<int>(ctx.GetPlace());
 
@@ -463,7 +464,7 @@ class GenerateProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<int>("post_nms_topN", "post_nms_topN");
     AddAttr<float>("nms_thresh", "nms_thres");
     AddAttr<float>("min_size", "min size");
-    AddAttr<float>("eta", "eta");
+    AddAttr<float>("eta", "The parameter for adaptive NMS.");
     AddComment(R"DOC(
 Generate Proposals OP
 

paddle/fluid/operators/detection/generate_proposals_op.cu

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <stdio.h>
+#include <string>
+#include <vector>
+#include "cub/cub.cuh"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/memory/memory.h"
+#include "paddle/fluid/operators/gather.cu.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+namespace {
+
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+#define CUDA_1D_KERNEL_LOOP(i, n)                              \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+int const kThreadsPerBlock = sizeof(uint64_t) * 8;
+
+template <typename T>
+__global__ void RangeInitKernel(const T start, const T delta, const int size,
+                                T *out) {
+  CUDA_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; }
+}
+
+template <typename T>
+void SortDescending(const platform::CUDADeviceContext &ctx, const Tensor &value,
+                    Tensor *value_out, Tensor *index_out) {
+  int num = value.numel();
+  Tensor index_in_t;
+  int *idx_in = index_in_t.mutable_data<int>({num}, ctx.GetPlace());
+  int block = 512;
+  auto stream = ctx.stream();
+  RangeInitKernel<<<DIVUP(num, block), block, 0, stream>>>(0, 1, num, idx_in);
+  int *idx_out = index_out->mutable_data<int>({num}, ctx.GetPlace());
+
+  const T *keys_in = value.data<T>();
+  T *keys_out = value_out->mutable_data<T>({num}, ctx.GetPlace());
+
+  // Determine temporary device storage requirements
+  void *d_temp_storage = NULL;
+  size_t temp_storage_bytes = 0;
+  cub::DeviceRadixSort::SortPairsDescending<T, int>(
+      d_temp_storage, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out,
+      num);
+
+  // Allocate temporary storage
+  auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+  d_temp_storage = memory::Alloc(place, temp_storage_bytes);
+
+  // Run sorting operation
+  cub::DeviceRadixSort::SortPairsDescending<T, int>(
+      d_temp_storage, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out,
+      num);
+
+  memory::Free(place, d_temp_storage);
+}
+
+template <typename T>
+__device__ __forceinline__ T Min(T x, T y) {
+  return x < y ? x : y;
+}
+
+template <typename T>
+__device__ __forceinline__ T Max(T x, T y) {
+  return x > y ? x : y;
+}
+
+template <typename T>
+__global__ void BoxDecodeAndClipKernel(const T *anchor, const T *deltas,
+                                       const T *var, const int *index,
+                                       const T *im_info, const int num,
+                                       T *proposals) {
+  T kBBoxClipDefault = log(1000.0 / 16.0);
+  CUDA_1D_KERNEL_LOOP(i, num) {
+    int k = index[i] * 4;
+    T axmin = anchor[k];
+    T aymin = anchor[k + 1];
+    T axmax = anchor[k + 2];
+    T aymax = anchor[k + 3];
+
+    T w = axmax - axmin + 1.0;
+    T h = aymax - aymin + 1.0;
+    T cx = axmin + 0.5 * w;
+    T cy = aymin + 0.5 * h;
+
+    T dxmin = deltas[k];
+    T dymin = deltas[k + 1];
+    T dxmax = deltas[k + 2];
+    T dymax = deltas[k + 3];
+
+    T d_cx = 0., d_cy = 0., d_w = 0., d_h = 0.;
+    if (var) {
+      d_cx = cx + dxmin * w * var[k];
+      d_cy = cy + dymin * h * var[k + 1];
+      d_w = exp(Min<T>(dxmax * var[k + 2], kBBoxClipDefault)) * w;
+      d_h = exp(Min<T>(dymax * var[k + 3], kBBoxClipDefault)) * h;
+    } else {
+      d_cx = cx + dxmin * w;
+      d_cy = cy + dymin * h;
+      d_w = exp(Min<T>(dxmax, kBBoxClipDefault)) * w;
+      d_h = exp(Min<T>(dymax, kBBoxClipDefault)) * h;
+    }
+
+    T oxmin = d_cx - d_w * 0.5;
+    T oymin = d_cy - d_h * 0.5;
+    T oxmax = d_cx + d_w * 0.5 - 1.;
+    T oymax = d_cy + d_h * 0.5 - 1.;
+
+    proposals[i * 4] = Max<T>(Min<T>(oxmin, im_info[1] - 1.), 0.);
+    proposals[i * 4 + 1] = Max<T>(Min<T>(oymin, im_info[0] - 1.), 0.);
+    proposals[i * 4 + 2] = Max<T>(Min<T>(oxmax, im_info[1] - 1.), 0.);
+    proposals[i * 4 + 3] = Max<T>(Min<T>(oymax, im_info[0] - 1.), 0.);
+  }
+}
+
+template <typename T, int BlockSize>
+__global__ void FilterBBoxes(const T *bboxes, const T *im_info,
+                             const T min_size, const int num, int *keep_num,
+                             int *keep) {
+  T im_h = im_info[0];
+  T im_w = im_info[1];
+  T im_scale = im_info[2];
+
+  int cnt = 0;
+  __shared__ int keep_index[BlockSize];
+
+  CUDA_1D_KERNEL_LOOP(i, num) {
+    keep_index[threadIdx.x] = -1;
+    __syncthreads();
+
+    int k = i * 4;
+    T xmin = bboxes[k];
+    T ymin = bboxes[k + 1];
+    T xmax = bboxes[k + 2];
+    T ymax = bboxes[k + 3];
+
+    T w = xmax - xmin + 1.0;
+    T h = ymax - ymin + 1.0;
+    T cx = xmin + w / 2.;
+    T cy = ymin + h / 2.;
+
+    T w_s = (xmax - xmin) / im_scale + 1.;
+    T h_s = (ymax - ymin) / im_scale + 1.;
+
+    if (w_s >= min_size && h_s >= min_size && cx <= im_w && cy <= im_h) {
+      keep_index[threadIdx.x] = i;
+    }
+    __syncthreads();
+    if (threadIdx.x == 0) {
+      int size = (num - i) < BlockSize ? num - i : BlockSize;
+      for (int j = 0; j < size; ++j) {
+        if (keep_index[j] > -1) {
+          keep[cnt++] = keep_index[j];
+        }
+      }
+    }
+    __syncthreads();
+  }
+  if (threadIdx.x == 0) {
+    keep_num[0] = cnt;
+  }
+}
+
+__device__ inline float IoU(const float *a, const float *b) {
+  float left = max(a[0], b[0]), right = min(a[2], b[2]);
+  float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
+  float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
+  float inter_s = width * height;
+  float s_a = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
+  float s_b = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
+  return inter_s / (s_a + s_b - inter_s);
+}
+
+__global__ void NMSKernel(const int n_boxes, const float nms_overlap_thresh,
+                          const float *dev_boxes, uint64_t *dev_mask) {
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  const int row_size =
+      min(n_boxes - row_start * kThreadsPerBlock, kThreadsPerBlock);
+  const int col_size =
+      min(n_boxes - col_start * kThreadsPerBlock, kThreadsPerBlock);
+
+  __shared__ float block_boxes[kThreadsPerBlock * 4];
+  if (threadIdx.x < col_size) {
+    block_boxes[threadIdx.x * 4 + 0] =
+        dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 0];
+    block_boxes[threadIdx.x * 4 + 1] =
+        dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 1];
+    block_boxes[threadIdx.x * 4 + 2] =
+        dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 2];
+    block_boxes[threadIdx.x * 4 + 3] =
+        dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 3];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size) {
+    const int cur_box_idx = kThreadsPerBlock * row_start + threadIdx.x;
+    const float *cur_box = dev_boxes + cur_box_idx * 4;
+    int i = 0;
+    uint64_t t = 0;
+    int start = 0;
+    if (row_start == col_start) {
+      start = threadIdx.x + 1;
+    }
+    for (i = start; i < col_size; i++) {
+      if (IoU(cur_box, block_boxes + i * 4) > nms_overlap_thresh) {
+        t |= 1ULL << i;
+      }
+    }
+    const int col_blocks = DIVUP(n_boxes, kThreadsPerBlock);
+    dev_mask[cur_box_idx * col_blocks + col_start] = t;
+  }
+}
+
+template <typename T>
+void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
+         const Tensor &sorted_indices, const T nms_threshold,
+         Tensor *keep_out) {
+  int boxes_num = proposals.dims()[0];
+  PADDLE_ENFORCE_EQ(boxes_num, sorted_indices.dims()[0]);
+
+  const int col_blocks = DIVUP(boxes_num, kThreadsPerBlock);
+  dim3 blocks(DIVUP(boxes_num, kThreadsPerBlock),
+              DIVUP(boxes_num, kThreadsPerBlock));
+  dim3 threads(kThreadsPerBlock);
+
+  const T *boxes = proposals.data<T>();
+  auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+  int size_bytes = boxes_num * col_blocks * sizeof(uint64_t);
+  uint64_t *d_mask =
+      reinterpret_cast<uint64_t *>(memory::Alloc(place, size_bytes));
+  NMSKernel<<<blocks, threads>>>(boxes_num, nms_threshold, boxes, d_mask);
+  uint64_t *h_mask = reinterpret_cast<uint64_t *>(
+      memory::Alloc(platform::CPUPlace(), size_bytes));
+  memory::Copy(platform::CPUPlace(), h_mask, place, d_mask, size_bytes, 0);
+
+  std::vector<uint64_t> remv(col_blocks);
+  memset(&remv[0], 0, sizeof(uint64_t) * col_blocks);
+
+  std::vector<int> keep_vec;
+  int num_to_keep = 0;
+  for (int i = 0; i < boxes_num; i++) {
+    int nblock = i / kThreadsPerBlock;
+    int inblock = i % kThreadsPerBlock;
+
+    if (!(remv[nblock] & (1ULL << inblock))) {
+      ++num_to_keep;
+      keep_vec.push_back(i);
+      uint64_t *p = &h_mask[0] + i * col_blocks;
+      for (int j = nblock; j < col_blocks; j++) {
+        remv[j] |= p[j];
+      }
+    }
+  }
+  int *keep = keep_out->mutable_data<int>({num_to_keep}, ctx.GetPlace());
+  memory::Copy(place, keep, platform::CPUPlace(), keep_vec.data(),
+               sizeof(int) * num_to_keep, 0);
+  memory::Free(place, d_mask);
+  memory::Free(platform::CPUPlace(), h_mask);
+}
+
+template <typename T>
+std::pair<Tensor, Tensor> ProposalForOneImage(
+    const platform::CUDADeviceContext &ctx, const Tensor &im_info,
+    const Tensor &anchors, const Tensor &variances,
+    const Tensor &bbox_deltas,  // [M, 4]
+    const Tensor &scores,       // [N, 1]
+    int pre_nms_top_n, int post_nms_top_n, float nms_thresh, float min_size,
+    float eta) {
+  // 1. pre nms
+  Tensor scores_sort, index_sort;
+  SortDescending<T>(ctx, scores, &scores_sort, &index_sort);
+  int num = scores.numel();
+  int pre_nms_num = (pre_nms_top_n <= 0 || pre_nms_top_n > num) ? scores.numel()
+                                                                : pre_nms_top_n;
+  scores_sort.Resize({pre_nms_num, 1});
+  index_sort.Resize({pre_nms_num, 1});
+
+  // 2. box decode and clipping
+  Tensor proposals;
+  proposals.mutable_data<T>({pre_nms_num, 4}, ctx.GetPlace());
+  int block = 512;
+  auto stream = ctx.stream();
+  BoxDecodeAndClipKernel<T><<<DIVUP(pre_nms_num, block), block, 0, stream>>>(
+      anchors.data<T>(), bbox_deltas.data<T>(), variances.data<T>(),
+      index_sort.data<int>(), im_info.data<T>(), pre_nms_num,
+      proposals.data<T>());
+
+  // 3. filter
+  Tensor keep_index, keep_num_t;
+  keep_index.mutable_data<int>({pre_nms_num}, ctx.GetPlace());
+  keep_num_t.mutable_data<int>({1}, ctx.GetPlace());
+  min_size = std::max(min_size, 1.0f);
+  FilterBBoxes<T, 512><<<1, 512, 0, stream>>>(
+      proposals.data<T>(), im_info.data<T>(), min_size, pre_nms_num,
+      keep_num_t.data<int>(), keep_index.data<int>());
+  int keep_num;
+  const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+  memory::Copy(platform::CPUPlace(), &keep_num, gpu_place,
+               keep_num_t.data<int>(), sizeof(int), 0);
+  keep_index.Resize({keep_num});
+
+  Tensor scores_filter, proposals_filter;
+  proposals_filter.mutable_data<T>({keep_num, 4}, ctx.GetPlace());
+  scores_filter.mutable_data<T>({keep_num, 1}, ctx.GetPlace());
+  GPUGather<T>(ctx, proposals, keep_index, &proposals_filter);
+  GPUGather<T>(ctx, scores_sort, keep_index, &scores_filter);
+
+  if (nms_thresh <= 0) {
+    return std::make_pair(proposals_filter, scores_filter);
+  }
+
+  // 4. nms
+  Tensor keep_nms;
+  NMS<T>(ctx, proposals_filter, keep_index, nms_thresh, &keep_nms);
+  if (post_nms_top_n > 0 && post_nms_top_n < keep_nms.numel()) {
+    keep_nms.Resize({post_nms_top_n});
+  }
+
+  Tensor scores_nms, proposals_nms;
+  proposals_nms.mutable_data<T>({keep_nms.numel(), 4}, ctx.GetPlace());
+  scores_nms.mutable_data<T>({keep_nms.numel(), 1}, ctx.GetPlace());
+  GPUGather<T>(ctx, proposals_filter, keep_nms, &proposals_nms);
+  GPUGather<T>(ctx, scores_filter, keep_nms, &scores_nms);
+
+  return std::make_pair(proposals_nms, scores_nms);
+}
+}  // namespace
+
+template <typename DeviceContext, typename T>
+class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto *scores = context.Input<Tensor>("Scores");
+    auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
+    auto *im_info = context.Input<Tensor>("ImInfo");
+    auto *anchors = context.Input<Tensor>("Anchors");
+    auto *variances = context.Input<Tensor>("Variances");
+
+    auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
+    auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");
+
+    int pre_nms_top_n = context.Attr<int>("pre_nms_topN");
+    int post_nms_top_n = context.Attr<int>("post_nms_topN");
+    float nms_thresh = context.Attr<float>("nms_thresh");
+    float min_size = context.Attr<float>("min_size");
+    float eta = context.Attr<float>("eta");
+    PADDLE_ENFORCE_GE(eta, 1., "Not support adaptive NMS.");
+
+    auto &dev_ctx = context.template device_context<DeviceContext>();
+
+    auto scores_dim = scores->dims();
+    int64_t num = scores_dim[0];
+    int64_t c_score = scores_dim[1];
+    int64_t h_score = scores_dim[2];
+    int64_t w_score = scores_dim[3];
+
+    auto bbox_dim = bbox_deltas->dims();
+    int64_t c_bbox = bbox_dim[1];
+    int64_t h_bbox = bbox_dim[2];
+    int64_t w_bbox = bbox_dim[3];
+
+    Tensor bbox_deltas_swap, scores_swap;
+    bbox_deltas_swap.mutable_data<T>({num, h_bbox, w_bbox, c_bbox},
+                                     dev_ctx.GetPlace());
+    scores_swap.mutable_data<T>({num, h_score, w_score, c_score},
+                                dev_ctx.GetPlace());
+
+    math::Transpose<DeviceContext, T, 4> trans;
+    std::vector<int> axis = {0, 2, 3, 1};
+    trans(dev_ctx, *bbox_deltas, &bbox_deltas_swap, axis);
+    trans(dev_ctx, *scores, &scores_swap, axis);
+
+    Tensor *anchor = const_cast<framework::Tensor *>(anchors);
+    anchor->Resize({anchors->numel() / 4, 4});
+    Tensor *var = const_cast<framework::Tensor *>(variances);
+    var->Resize({var->numel() / 4, 4});
+
+    rpn_rois->mutable_data<T>({bbox_deltas->numel() / 4, 4},
+                              context.GetPlace());
+    rpn_roi_probs->mutable_data<T>({scores->numel(), 1}, context.GetPlace());
+
+    T *rpn_rois_data = rpn_rois->data<T>();
+    T *rpn_roi_probs_data = rpn_roi_probs->data<T>();
+
+    auto place = boost::get<platform::CUDAPlace>(dev_ctx.GetPlace());
+
+    int64_t num_proposals = 0;
+    std::vector<size_t> offset(1, 0);
+    for (int64_t i = 0; i < num; ++i) {
+      Tensor im_info_slice = im_info->Slice(i, i + 1);
+      Tensor bbox_deltas_slice = bbox_deltas_swap.Slice(i, i + 1);
+      Tensor scores_slice = scores_swap.Slice(i, i + 1);
+
+      bbox_deltas_slice.Resize({h_bbox * w_bbox * c_bbox / 4, 4});
+      scores_slice.Resize({h_score * w_score * c_score, 1});
+
+      std::pair<Tensor, Tensor> box_score_pair =
+          ProposalForOneImage<T>(dev_ctx, im_info_slice, *anchor, *var,
+                                 bbox_deltas_slice, scores_slice, pre_nms_top_n,
+                                 post_nms_top_n, nms_thresh, min_size, eta);
+
+      Tensor proposals = box_score_pair.first;
+      Tensor scores = box_score_pair.second;
+
+      memory::Copy(place, rpn_rois_data + num_proposals * 4, place,
+                   proposals.data<T>(), sizeof(T) * proposals.numel(), 0);
+      memory::Copy(place, rpn_roi_probs_data + num_proposals, place,
+                   scores.data<T>(), sizeof(T) * scores.numel(), 0);
+      num_proposals += proposals.dims()[0];
+      offset.emplace_back(num_proposals);
+    }
+    framework::LoD lod;
+    lod.emplace_back(offset);
+    rpn_rois->set_lod(lod);
+    rpn_roi_probs->set_lod(lod);
+    rpn_rois->Resize({num_proposals, 4});
+    rpn_roi_probs->Resize({num_proposals, 1});
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(generate_proposals,
+                        ops::CUDAGenerateProposalsKernel<
+                            paddle::platform::CUDADeviceContext, float>);

python/paddle/fluid/contrib/__init__.py

@@ -18,8 +18,13 @@ from . import decoder
 from .decoder import *
 from . import memory_usage_calc
 from .memory_usage_calc import *
+from . import op_frequence
+from .op_frequence import *
 from . import quantize
 from .quantize import *
 
-__all__ = decoder.__all__ + memory_usage_calc.__all__
+__all__ = []
+__all__ += decoder.__all__
+__all__ += memory_usage_calc.__all__
+__all__ += op_frequence.__all__
 __all__ += quantize.__all__
\ No newline at end of file

python/paddle/fluid/contrib/op_frequence.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+from collections import OrderedDict
+
+from ..framework import Program
+
+__all__ = ['op_freq_statistic']
+
+
+def op_freq_statistic(program):
+    """
+    Statistics of Op frequency.
+
+    Args:
+        program(Program): The current Program.
+
+    Returns:
+        uni_op_freq(dict): the single op frequency.
+        adj_2_op_freq(dict): the two adjacent ops frequency.
+
+    Examples:
+
+        >>> import paddle.fluid as fluid
+        >>> uni_op_freq, adj_2_op_freq = fluid.contrib.op_freq_statistic(
+        >>>        fluid.default_main_program())
+        >>> for op_type, op_num in uni_op_freq:
+        >>>     print("%s  \t  %d" % (op_type, op_num))
+        >>> for op_type, op_num in adj_2_op_freq:
+        >>>     print("%s  \t  %d" % (op_type, op_num))
+
+    """
+
+    if not isinstance(program, Program):
+        raise TypeError("The input type should be Porgram."
+                        "But you passed in %s" % (type(program)))
+
+    uni_op_freq = OrderedDict()
+    adj_2_op_freq = OrderedDict()
+    op_in_ops = OrderedDict()
+
+    parameters = [p.name for p in program.blocks[0].all_parameters()]
+
+    # get uni_op_freq
+    for op in program.global_block().ops:
+        had_recorded = False
+        for var_name in op.output_arg_names:
+            if var_name in parameters:
+                continue
+            if not had_recorded and uni_op_freq.has_key(op.type):
+                uni_op_freq[op.type] += 1
+                had_recorded = True
+            elif not had_recorded:
+                uni_op_freq[op.type] = 1
+                had_recorded = True
+
+    # get adj_2_op_freq
+    var_gen_op = {}
+    for op in program.global_block().ops:
+        for var_name in op.input_arg_names:
+            if var_name in parameters:
+                continue
+            if var_gen_op.has_key(var_name):
+                assert len(var_gen_op[var_name]) > 0
+                if op_in_ops.has_key(op.type):
+                    op_in_ops[op.type].append(var_gen_op[var_name][-1])
+                else:
+                    op_in_ops[op.type] = [var_gen_op[var_name][-1]]
+            else:
+                print("Var's generate op is not found,%s, %s" %
+                      (var_name, op.type))
+
+        for var_name in op.output_arg_names:
+            if var_gen_op.has_key(var_name):
+                var_gen_op[var_name].append(op.type)
+            else:
+                var_gen_op[var_name] = [op.type]
+
+    for op, in_ops in op_in_ops.iteritems():
+        for in_op in in_ops:
+            op_op = in_op + "->" + op
+            if adj_2_op_freq.has_key(op_op):
+                adj_2_op_freq[op_op] += 1
+            else:
+                adj_2_op_freq[op_op] = 1
+
+    uni_op_freq = sorted(
+        uni_op_freq.items(), key=lambda item: item[1], reverse=True)
+    adj_2_op_freq = sorted(
+        adj_2_op_freq.items(), key=lambda item: item[1], reverse=True)
+
+    return uni_op_freq, adj_2_op_freq

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

@@ -277,7 +277,6 @@ class TestGenerateProposalsOp(OpTest):
             'eta': self.eta
         }
 
-        print("lod = ", self.lod)
         self.outputs = {
             'RpnRois': (self.rpn_rois[0], [self.lod]),
             'RpnRoiProbs': (self.rpn_roi_probs[0], [self.lod])
@@ -295,7 +294,7 @@ class TestGenerateProposalsOp(OpTest):
         self.post_nms_topN = 5000  # train 6000, test 1000
         self.nms_thresh = 0.7
         self.min_size = 3.0
-        self.eta = 0.8
+        self.eta = 1.
 
     def init_test_input(self):
         batch_size = 1

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -470,7 +470,10 @@ class DistributeTranspiler(object):
         """
         # remove optimize ops and add a send op to main_program
         # FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay?
+        lr_ops = self._get_lr_ops()
         delete_ops(self.origin_program.global_block(), self.optimize_ops)
+        delete_ops(self.origin_program.global_block(), lr_ops)
+
         self.origin_program.__str__()
 
         if wait_port: