resolve conflicts and adjust gemm code style

src/common/types.cpp

@@ -106,6 +106,11 @@ const char *G_OP_TYPE_SEQUENCE_EXPAND = "sequence_expand";
 const char *G_OP_TYPE_SEQUENCE_POOL = "sequence_pool";
 const char *G_OP_TYPE_SEQUENCE_SOFTMAX = "sequence_softmax";
 
+const char *G_OP_TYPE_SLICE = "slice";
+const char *G_OP_TYPE_ANCHOR_GENERATOR = "anchor_generator";
+const char *G_OP_TYPE_GENERATE_PROPOSALS = "generate_proposals";
+const char *G_OP_TYPE_PSROI_POOL = "psroi_pool";
+
 std::unordered_map<
     std::string, std::pair<std::vector<std::string>, std::vector<std::string>>>
     op_input_output_key = {
@@ -197,5 +202,11 @@ std::unordered_map<
         {G_OP_TYPE_WRITE_TO_ARRAY, {{"X", "I"}, {"Out"}}},
         {G_OP_TYPE_READ_FROM_ARRAY, {{"X", "I"}, {"Out"}}},
         {G_OP_TYPE_IS_EMPTY, {{"X"}, {"Out"}}},
-        {G_OP_TYPE_INCREMENT, {{"X"}, {"Out"}}}};
+        {G_OP_TYPE_INCREMENT, {{"X"}, {"Out"}}},
+        {G_OP_TYPE_SLICE, {{"Input"}, {"Out"}}},
+        {G_OP_TYPE_ANCHOR_GENERATOR, {{"Input"}, {"Anchors", "Variances"}}},
+        {G_OP_TYPE_GENERATE_PROPOSALS,
+         {{"Scores", "BboxDeltas", "ImInfo", "Anchors", "Variances"},
+          {"RpnRois", "RpnRoiProbs"}}},
+        {G_OP_TYPE_PSROI_POOL, {{"X", "ROIs"}, {"Out"}}}};
 }  // namespace paddle_mobile

src/common/types.h

@@ -194,6 +194,11 @@ extern const char *G_OP_TYPE_SEQUENCE_EXPAND;
 extern const char *G_OP_TYPE_SEQUENCE_POOL;
 extern const char *G_OP_TYPE_SEQUENCE_SOFTMAX;
 
+extern const char *G_OP_TYPE_SLICE;
+extern const char *G_OP_TYPE_ANCHOR_GENERATOR;
+extern const char *G_OP_TYPE_GENERATE_PROPOSALS;
+extern const char *G_OP_TYPE_PSROI_POOL;
+
 extern std::unordered_map<
     std::string, std::pair<std::vector<std::string>, std::vector<std::string>>>
     op_input_output_key;

src/framework/load_ops.h

@@ -312,3 +312,12 @@ LOAD_OP1(is_empty, CPU);
 #ifdef INCREMENT_OP
 LOAD_OP1(increment, CPU);
 #endif
+#ifdef ANCHOR_GENERATOR_OP
+LOAD_OP1(anchor_generator, CPU);
+#endif
+#ifdef PROPOSAL_OP
+LOAD_OP1(generate_proposals, CPU);
+#endif
+#ifdef PSROI_POOL_OP
+LOAD_OP1(psroi_pool, CPU);
+#endif

src/operators/detection_ops.cpp

+/* 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 "operators/detection_ops.h"
+#include <vector>
+
+namespace paddle_mobile {
+namespace operators {
+
+#ifdef ANCHOR_GENERATOR_OP
+template <typename DeviceType, typename T>
+void AnchorGeneratorOp<DeviceType, T>::InferShape() const {
+  const auto &input_dims = this->param_.input_->dims();
+  PADDLE_MOBILE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
+  const auto &anchor_sizes = this->param_.anchor_sizes_;
+  const auto &aspect_ratios = this->param_.aspect_ratios_;
+
+  size_t num_anchors = aspect_ratios.size() * anchor_sizes.size();
+  std::vector<int64_t> dim_vec(4);
+  dim_vec[0] = input_dims[2];
+  dim_vec[1] = input_dims[3];
+  dim_vec[2] = num_anchors;
+  dim_vec[3] = 4;
+
+  this->param_.output_anchors_->Resize(framework::make_ddim(dim_vec));
+  this->param_.output_variances_->Resize(framework::make_ddim(dim_vec));
+}
+#endif
+
+#ifdef PROPOSAL_OP
+template <typename DeviceType, typename T>
+void ProposalOp<DeviceType, T>::InferShape() const {
+  this->param_.rpn_rois_->Resize(framework::make_ddim({-1, 4}));
+  this->param_.rpn_probs_->Resize(framework::make_ddim({-1, 1}));
+}
+#endif
+
+#ifdef PSROI_POOL_OP
+template <typename DeviceType, typename T>
+void PSRoiPoolOp<DeviceType, T>::InferShape() const {
+  const auto &rois_dims = this->param_.input_rois_->dims();
+  const int pooled_height = this->param_.pooled_height_;
+  const int pooled_width = this->param_.pooled_width_;
+  const int output_channels = this->param_.output_channels_;
+
+  auto out_dims = this->param_.input_x_->dims();
+  out_dims[0] = rois_dims[0];
+  out_dims[1] =
+      output_channels;  // input_dims[1] / (pooled_height * pooled_width);
+  out_dims[2] = pooled_height;
+  out_dims[3] = pooled_width;
+  this->param_.output_->Resize(out_dims);
+}
+#endif
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+namespace ops = paddle_mobile::operators;
+#ifdef PADDLE_MOBILE_CPU
+#ifdef ANCHOR_GENERATOR_OP
+REGISTER_OPERATOR_CPU(anchor_generator, ops::AnchorGeneratorOp);
+#endif
+#ifdef PROPOSAL_OP
+REGISTER_OPERATOR_CPU(generate_proposals, ops::ProposalOp);
+#endif
+#ifdef PSROI_POOL_OP
+REGISTER_OPERATOR_CPU(psroi_pool, ops::PSRoiPoolOp);
+#endif
+#endif

src/operators/detection_ops.h

+/* 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. */
+
+#pragma once
+
+#include <string>
+#include "framework/operator.h"
+#include "operators/kernel/detection_kernel.h"
+#include "operators/op_param.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+#ifdef ANCHOR_GENERATOR_OP
+DECLARE_OPERATOR(AnchorGenerator, AnchorGeneratorParam, AnchorGeneratorKernel);
+#endif
+
+#ifdef PROPOSAL_OP
+DECLARE_OPERATOR(Proposal, ProposalParam, ProposalKernel);
+#endif
+
+#ifdef PSROI_POOL_OP
+DECLARE_OPERATOR(PSRoiPool, PSRoiPoolParam, PSRoiPoolKernel);
+#endif
+
+}  // namespace operators
+}  // namespace paddle_mobile

src/operators/kernel/arm/anchor_generator_kernel.cpp

+/* 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. */
+
+#ifdef ANCHOR_GENERATOR_OP
+
+#include <vector>
+#include "operators/kernel/detection_kernel.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <>
+bool AnchorGeneratorKernel<CPU, float>::Init(AnchorGeneratorParam<CPU> *param) {
+  return true;
+}
+
+template <>
+void AnchorGeneratorKernel<CPU, float>::Compute(
+    const AnchorGeneratorParam<CPU> &param) {
+  // TODO(hjchen2)
+}
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif  // ANCHOR_GENERATOR_OP

src/operators/kernel/arm/proposal_kernel.cpp

+/* 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. */
+
+#ifdef PROPOSAL_OP
+
+#include <vector>
+#include "operators/kernel/detection_kernel.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <>
+bool ProposalKernel<CPU, float>::Init(ProposalParam<CPU> *param) {
+  return true;
+}
+
+template <>
+void ProposalKernel<CPU, float>::Compute(const ProposalParam<CPU> &param) {
+  // TODO(hjchen2)
+}
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif  // PROPOSAL_OP

src/operators/kernel/arm/psroi_pool_kernel.cpp

+/* 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. */
+
+#ifdef PSROI_POOL_OP
+
+#include <vector>
+#include "operators/kernel/detection_kernel.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <>
+bool PSRoiPoolKernel<CPU, float>::Init(PSRoiPoolParam<CPU> *param) {
+  return true;
+}
+
+template <>
+void PSRoiPoolKernel<CPU, float>::Compute(const PSRoiPoolParam<CPU> &param) {
+  // TODO(hjchen2)
+}
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif  // PSROI_POOL_OP

src/operators/kernel/central-arm-func/conv_add_add_prelu_arm_func.h

@@ -32,11 +32,11 @@ void ConvAddAddPReluCompute(const FusionConvAddAddPReluParam<CPU> &param) {
   Tensor filter = *param.Filter();
   Tensor bias = *param.Bias();
   Tensor bias1 = *param.Bias1();
-  int axis = param.Axis();
   Tensor *output = param.Output();
-
+  output->mutable_data<float>();
   float *biase_data = bias.data<float>();
 
+  int axis = param.Axis();
   int groups = param.Groups();
   std::vector<int> strides = param.Strides();
   std::vector<int> paddings = param.Paddings();

src/operators/kernel/central-arm-func/conv_add_arm_func.h

@@ -30,10 +30,11 @@ void ConvAddBasic(const FusionConvAddParam<CPU> &param) {
   const Tensor *input = param.Input();
   Tensor filter = *param.Filter();
   Tensor bias = *param.Bias();
-  int axis = param.Axis();
   Tensor *output = param.Output();
+  output->mutable_data<float>();
   float *biase_data = bias.data<float>();
 
+  int axis = param.Axis();
   int groups = param.Groups();
   std::vector<int> strides = param.Strides();
   std::vector<int> paddings = param.Paddings();

src/operators/kernel/central-arm-func/conv_add_bn_relu_arm_func.h

@@ -32,6 +32,8 @@ void ConvAddBNReluBasic(const FusionConvAddBNReluParam<CPU> &param) {
   Tensor new_bias = *param.NewBias();
   Tensor new_scale = *param.NewScale();
   Tensor *output = param.Output();
+  output->mutable_data<float>();
+
   int groups = param.Groups();
   std::vector<int> strides = param.Strides();
   std::vector<int> paddings = param.Paddings();
@@ -115,8 +117,6 @@ void ConvAddBNReluBasic(const FusionConvAddBNReluParam<CPU> &param) {
 
 template <typename P>
 void ConvAddBNReluCompute(const FusionConvAddBNReluParam<CPU> &param) {
-  Tensor Bias;
-  Bias.mutable_data<float>({param.Groups()});
   if (param.Groups() == param.Input()->dims()[1] &&
       param.Input()->dims()[1] == param.Output()->dims()[1] &&
       param.Filter()->dims()[2] == param.Filter()->dims()[3] &&

src/operators/kernel/central-arm-func/conv_add_prelu_arm_func.h

@@ -31,10 +31,11 @@ void ConvAddPReluCompute(const FusionConvAddPReluParam<CPU> &param) {
   const Tensor *input = param.Input();
   Tensor filter = *param.Filter();
   Tensor bias = *param.Bias();
-  int axis = param.Axis();
   Tensor *output = param.Output();
+  output->mutable_data<float>();
   float *biase_data = bias.data<float>();
 
+  int axis = param.Axis();
   int groups = param.Groups();
   std::vector<int> strides = param.Strides();
   std::vector<int> paddings = param.Paddings();

src/operators/kernel/central-arm-func/conv_bn_add_relu_arm_func.h

@@ -30,11 +30,11 @@ void ConvBNAddReluBasic(const FusionConvBNAddReluParam<CPU> &param) {
   Tensor filter = *param.Filter();
   Tensor new_bias = *param.NewBias();
   Tensor new_scale = *param.NewScale();
-  Tensor *output = param.Output();
   Tensor *bias1 = param.Bias();
+  Tensor *output = param.Output();
+  output->mutable_data<float>();
+
   int groups = param.Groups();
-  DLOG << "yangfei2";
-  DLOG << bias1->dims();
   std::vector<int> strides = param.Strides();
   std::vector<int> paddings = param.Paddings();
   std::vector<int> dilations = param.Dilations();

src/operators/kernel/central-arm-func/conv_transpose_arm_func.h

@@ -31,6 +31,7 @@ void ConvTransposeCompute(const ConvTransposeParam<CPU> &param) {
   const Tensor *input = param.Input();
   Tensor filter = *param.Filter();
   Tensor *output = param.Output();
+  output->mutable_data<P>();
 
   auto strides = param.Strides();
   auto paddings = param.Paddings();
@@ -76,8 +77,6 @@ void ConvTransposeCompute(const ConvTransposeParam<CPU> &param) {
   framework::DDim filter_matrix_shape = {input->dims()[1], col_matrix_shape[0]};
   filter.Resize(filter_matrix_shape);
 
-  output->mutable_data<P>();
-
   int in_step = static_cast<int>(input->dims()[1]) / groups;
   int out_step = static_cast<int>(output->dims()[1]) / groups;
 

src/operators/kernel/detection_kernel.h

+/* 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. */
+
+#pragma once
+
+#include <vector>
+#include "framework/operator.h"
+#include "operators/op_param.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+#ifdef ANCHOR_GENERATOR_OP
+template <typename Dtype>
+class AnchorGeneratorParam : public OpParam {
+ public:
+  AnchorGeneratorParam(const VariableNameMap &inputs,
+                       const VariableNameMap &outputs,
+                       const AttributeMap &attrs, const Scope &scope) {
+    input_ = OpParam::GetVarValue<framework::Tensor>("Input", inputs, scope);
+    output_anchors_ =
+        OpParam::GetVarValue<framework::Tensor>("Anchors", outputs, scope);
+    output_variances_ =
+        OpParam::GetVarValue<framework::Tensor>("Variances", outputs, scope);
+
+    anchor_sizes_ = OpParam::GetAttr<std::vector<float>>("anchor_sizes", attrs);
+    aspect_ratios_ =
+        OpParam::GetAttr<std::vector<float>>("aspect_ratios", attrs);
+    variances_ = OpParam::GetAttr<std::vector<float>>("variances", attrs);
+    stride_ = OpParam::GetAttr<std::vector<float>>("stride", attrs);
+    offset_ = OpParam::GetAttr<float>("offset", attrs);
+  }
+
+ public:
+  // input
+  framework::Tensor *input_;
+  // outputs
+  framework::Tensor *output_anchors_;
+  framework::Tensor *output_variances_;
+
+  std::vector<float> anchor_sizes_;
+  std::vector<float> aspect_ratios_;
+  std::vector<float> variances_;
+  std::vector<float> stride_;
+  float offset_;
+};
+
+DECLARE_KERNEL(AnchorGenerator, AnchorGeneratorParam);
+#endif
+
+#ifdef PROPOSAL_OP
+template <typename Dtype>
+class ProposalParam : public OpParam {
+ public:
+  ProposalParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
+                const AttributeMap &attrs, const Scope &scope) {
+    scores_ = OpParam::GetVarValue<framework::Tensor>("Scores", inputs, scope);
+    bbox_deltas_ =
+        OpParam::GetVarValue<framework::Tensor>("BboxDeltas", inputs, scope);
+    im_info_ = OpParam::GetVarValue<framework::Tensor>("ImInfo", inputs, scope);
+    anchors_ =
+        OpParam::GetVarValue<framework::Tensor>("Anchors", inputs, scope);
+    variances_ =
+        OpParam::GetVarValue<framework::Tensor>("Variances", inputs, scope);
+
+    rpn_rois_ =
+        OpParam::GetVarValue<framework::LoDTensor>("RpnRois", outputs, scope);
+    rpn_probs_ = OpParam::GetVarValue<framework::LoDTensor>("RpnRoiProbs",
+                                                            outputs, scope);
+
+    pre_nms_topn_ = OpParam::GetAttr<int>("pre_nms_topN", attrs);
+    post_nms_topn_ = OpParam::GetAttr<int>("post_nms_topN", attrs);
+    nms_thresh_ = OpParam::GetAttr<float>("nms_thresh", attrs);
+    min_size_ = OpParam::GetAttr<float>("min_size", attrs);
+    eta_ = OpParam::GetAttr<float>("eta", attrs);
+  }
+
+ public:
+  framework::Tensor *scores_;
+  framework::Tensor *bbox_deltas_;
+  framework::Tensor *im_info_;
+  framework::Tensor *anchors_;
+  framework::Tensor *variances_;
+
+  framework::LoDTensor *rpn_rois_;
+  framework::LoDTensor *rpn_probs_;
+
+  int pre_nms_topn_;
+  int post_nms_topn_;
+  float nms_thresh_;
+  float min_size_;
+  float eta_;
+};
+
+DECLARE_KERNEL(Proposal, ProposalParam);
+#endif
+
+#ifdef PSROI_POOL_OP
+template <typename Dtype>
+class PSRoiPoolParam : public OpParam {
+ public:
+  PSRoiPoolParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
+                 const AttributeMap &attrs, const Scope &scope) {
+    input_x_ = OpParam::GetVarValue<framework::Tensor>("X", inputs, scope);
+    input_rois_ =
+        OpParam::GetVarValue<framework::LoDTensor>("ROIs", inputs, scope);
+    output_ = OpParam::GetVarValue<framework::Tensor>("Out", outputs, scope);
+
+    output_channels_ = OpParam::GetAttr<int>("output_channels", attrs);
+    pooled_height_ = OpParam::GetAttr<int>("pooled_height", attrs);
+    pooled_width_ = OpParam::GetAttr<int>("pooled_width", attrs);
+    spatial_scale_ = OpParam::GetAttr<float>("spatial_scale", attrs);
+  }
+
+ public:
+  framework::Tensor *input_x_;
+  framework::LoDTensor *input_rois_;
+  framework::Tensor *output_;
+  int output_channels_;
+  int pooled_height_;
+  int pooled_width_;
+  float spatial_scale_;
+};
+
+DECLARE_KERNEL(PSRoiPool, PSRoiPoolParam);
+#endif
+
+}  // namespace operators
+}  // namespace paddle_mobile

src/operators/math/gemm.h

@@ -46,15 +46,6 @@ namespace math {
 
 class Gemm {
  public:
-  /*
-// 将 A 矩阵分块复制到连续内存(ColMajor)
-void PackMatrixA(int m, int k, int m_tail, const float *A, int lda,
-           float *buffer);
-
-// 将 B 矩阵分块复制到连续内存(ColMajor)
-void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
-           float *buffer);
-*/
   typedef void (Gemm::*FnPack)(int, int, int, const float *, int, float *);
   typedef void (Gemm::*FnAddDot)(int, const float *, const float *, float *,
                                  int);
@@ -62,31 +53,31 @@ void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
   FnPack procPackB;
   FnAddDot procAddDot;
 
-  // 将 A 矩阵分块复制到连续内存(RowMajor)
+  // 将 A\B 矩阵分块复制到连续内存(RowMajor)
   void PackMatrixA_4r(int m, int k, int m_tail, const float *A, int lda,
                       float *buffer);
   void PackMatrixA_6r(int m, int k, int m_tail, const float *A, int lda,
                       float *buffer);
-  void PackMatrixA_8r(int m, int k, int m_tail, const float *A, int lda,
-                      float *buffer);
   void PackMatrixA_omp_6r(int m, int k, int m_tail, const float *A, int lda,
                           float *buffer);
+  void PackMatrixA_8r(int m, int k, int m_tail, const float *A, int lda,
+                      float *buffer);
   void PackMatrixA_omp_8r(int m, int k, int m_tail, const float *A, int lda,
                           float *buffer);
-
-  // 将 B 矩阵分块复制到连续内存(RowMajor)
   void PackMatrixB_8c(int k, int n, int n_tail, const float *B, int ldb,
                       float *buffer);
-  void PackMatrixB_12c(int k, int n, int n_tail, const float *B, int ldb,
-                       float *buffer);
-  void PackMatrixB_16c(int k, int n, int n_tail, const float *B, int ldb,
-                       float *buffer);
   void PackMatrixB_omp_8c(int k, int n, int n_tail, const float *B, int ldb,
                           float *buffer);
+#if __aarch64__
+  void PackMatrixB_12c(int k, int n, int n_tail, const float *B, int ldb,
+                       float *buffer);
   void PackMatrixB_omp_12c(int k, int n, int n_tail, const float *B, int ldb,
                            float *buffer);
+  void PackMatrixB_16c(int k, int n, int n_tail, const float *B, int ldb,
+                       float *buffer);
   void PackMatrixB_omp_16c(int k, int n, int n_tail, const float *B, int ldb,
                            float *buffer);
+#endif
 
   // 分块矩阵乘法
   void InnerKernel(int mc, int nc, float alpha, const float *a, const float *b,
@@ -106,22 +97,16 @@ void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
                             float *c, float *C, int ldc, float *p,
                             std::string mode, float *bias, float *bias1);
 
-  // 向量矩阵乘法 (M = 1)
-  void VectorKernel(int m, int n, int k, float alpha, const float *A, int lda,
-                    const float *B, int ldb, float beta, float *C, int ldc,
-                    bool relu);
-  /*
-    void VectorKernelWithBn(int m, int n, int k, float alpha, const float *A,
-                            int lda, const float *B, int ldb, float beta, float
-    *C, int ldc, bool relu, float *new_scale, float *new_bias);
-    */
-
   // 计算一个更小的 C 矩阵分块
-  void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc);
-  void AddDot4x8(int k, const float *a, const float *b, float *c, int ldc);
+#if __aarch64__
   void AddDot6x8(int k, const float *a, const float *b, float *c, int ldc);
   void AddDot8x12(int k, const float *a, const float *b, float *c, int ldc);
   void AddDot6x16(int k, const float *a, const float *b, float *c, int ldc);
+#else
+  void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc);
+  void AddDot4x8(int k, const float *a, const float *b, float *c, int ldc);
+  void AddDot6x8(int k, const float *a, const float *b, float *c, int ldc);
+#endif
 
   // 分块矩阵乘法结果回写
   // C = A * B
@@ -149,6 +134,18 @@ void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
   void WriteWithBnAddRelu(int mc, int nc, float *c, float *C, int ldc,
                           float *new_scale, float *new_bias, float *bias1);
 
+  // 向量矩阵乘法 (M = 1)
+#if __aarch64__
+#else
+  void VectorKernel(int m, int n, int k, float alpha, const float *A, int lda,
+                    const float *B, int ldb, float beta, float *C, int ldc,
+                    bool relu);
+
+  void VectorKernelWithBn(int m, int n, int k, float alpha, const float *A,
+                          int lda, const float *B, int ldb, float beta,
+                          float *C, int ldc, bool relu, float *new_scale,
+                          float *new_bias);
+
   // 向量矩阵乘法结果回写
   // C = A * B
   void VecWriteBasic(int n, float *c, float *C, int ldc);
@@ -158,14 +155,13 @@ void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
   void VecWriteWithAdd(int n, float *c, float *C, int ldc);
   // C = A * B + C, relu(C)
   void VecWriteWithAddRelu(int n, float *c, float *C, int ldc);
-  /*
-    // C = A * B, batchnorm(C)
-    void VecWriteWithBn(int n, float *c, float *C, int ldc, float *new_scale,
-                        float *new_bias);
-    // C = A * B, batchnorm(C), relu(C)
-    void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float
-    *new_scale, float *new_bias);
-    */
+  // C = A * B, batchnorm(C)
+  void VecWriteWithBn(int n, float *c, float *C, int ldc, float *new_scale,
+                      float *new_bias);
+  // C = A * B, batchnorm(C), relu(C)
+  void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float *new_scale,
+                          float *new_bias);
+#endif
 
   // 32位 float 矩阵乘法
   void Sgemm(int m, int n, int k, float alpha, const float *A, int lda,

src/operators/op_param.h

@@ -1521,33 +1521,20 @@ class SliceParam : public OpParam {
  public:
   SliceParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
              const AttributeMap &attrs, const Scope &scope) {
-    input_x_ = InputXFrom<GType>(inputs, scope);
-    input_shape_ = InputShapeFrom<GType>(inputs, scope);
-    out_ = OutFrom<GType>(outputs, scope);
-    axis_ = GetAttr<int>("axis", attrs);
-    slice_points_ = GetAttr<vector<int>>("slice_points", attrs);
-    inplace_ = GetAttr<bool>("inplace", attrs);
-  }
-
-  const RType *InputX() const { return input_x_; }
-
-  const RType *InputShape() const { return input_shape_; }
-
-  RType *Out() const { return out_; }
-
-  const int &Axis() const { return axis_; }
-
-  const vector<int> &SlicePoints() const { return slice_points_; }
+    input_ = InputFrom<GType>(inputs, scope);
+    output_ = OutFrom<GType>(outputs, scope);
 
-  const bool &Inplace() const { return inplace_; }
+    axes_ = GetAttr<std::vector<int>>("axes", attrs);
+    starts_ = GetAttr<std::vector<int>>("starts", attrs);
+    ends_ = GetAttr<std::vector<int>>("ends", attrs);
+  }
 
- private:
-  RType *input_x_;
-  RType *input_shape_;
-  RType *out_;
-  int axis_;
-  vector<int> slice_points_;
-  bool inplace_;
+ public:
+  GType *input_;
+  GType *output_;
+  std::vector<int> axes_;
+  std::vector<int> starts_;
+  std::vector<int> ends_;
 };
 #endif
 

tools/op.cmake

@@ -290,6 +290,9 @@ if(NOT FOUND_MATCH)
   set(READ_FROM_ARRAY_OP ON)
   set(IS_EMPTY_OP ON)
   set(INCREMENT_OP ON)
+  set(ANCHOR_GENERATOR_OP ON)
+  set(PROPOSAL_OP ON)
+  set(PSROI_POOL_OP ON)
 endif()
 
   # option(BATCHNORM_OP "" ON)
@@ -579,4 +582,14 @@ if (IS_EMPTY_OP)
 endif()
 if (INCREMENT_OP)
   add_definitions(-DINCREMENT_OP)
-endif()
+endif()
\ No newline at end of file
+
+if (ANCHOR_GENERATOR_OP)
+  add_definitions(-DANCHOR_GENERATOR_OP)
+endif()
+if (PROPOSAL_OP)
+  add_definitions(-DPROPOSAL_OP)
+endif()
+if (PSROI_POOL_OP)
+  add_definitions(-DPSROI_POOL_OP)
+endif()