Merge branch 'develop' of https://github.com/codeWorm2015/paddle-mobile into develop

CMakeLists.txt

@@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 3.0)
 project(paddle-mobile)
 
 option(DEBUGING "enable debug mode" ON)
-option(USE_OPENMP "openmp support" OFF)
+option(USE_OPENMP "openmp support" ON)
 option(USE_EXCEPTION "use std exception" ON)
 option(LOG_PROFILE "log profile" ON)
 # select the platform to build

src/io/executor.cpp

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "io/executor.h"
+#include <operators/math/gemm.h>
 #include <algorithm>
 #include <vector>
 #include "common/enforce.h"
@@ -25,6 +26,9 @@ limitations under the License. */
 #include "framework/program/var_desc.h"
 #include "framework/scope.h"
 #include "framework/tensor.h"
+#ifdef _OPENMP
+#include <omp.h>
+#endif  // _OPENMP
 #ifdef PADDLE_EXECUTOR_MULTITHREAD
 #include <queue>
 #include <utility>
@@ -403,6 +407,14 @@ std::vector<typename Executor<Dtype, P>::Ptype> Executor<Dtype, P>::Predict(
   return result_vector;
 }
 
+template <typename Dtype, Precision P>
+void Executor<Dtype, P>::SetThreadNum(int num) {
+#ifdef _OPENMP
+  //  omp_set_dynamic(0);
+  omp_set_num_threads(num);
+#endif
+}
+
 template class Executor<CPU, Precision::FP32>;
 template class Executor<FPGA, Precision::FP32>;
 template class Executor<GPU_MALI, Precision::FP32>;

src/io/executor.h

@@ -58,6 +58,8 @@ class Executor {
   std::vector<Ptype> Predict(const std::vector<Ptype> &input,
                              const std::vector<int64_t> &dims);
 
+  void SetThreadNum(int num);
+
  protected:
   Executor() = default;
   void InitMemory();

src/operators/kernel/lrn_kernel.h

@@ -13,7 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #ifdef LRN_OP
-
+#ifdef _OPENMP
+#include <omp.h>
+#endif
 #include "framework/operator.h"
 #include "operators/op_param.h"
 
@@ -47,6 +49,7 @@ struct LRNFunctor {
     std::fill(sqr_buffer_ptr, sqr_buffer_ptr + sqr_buffer.numel(), 0.0);
 
     for (int a = 0; a < N; a++) {
+#pragma parallel for
       for (int b = 0; b < C; b++) {
         for (int index = start; index < end; index++) {
           int channel = b + index;

src/operators/math/gemm.cpp

@@ -1209,12 +1209,12 @@ void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc) {
 // C = A * B, batchnorm(C)
 void WriteWithBn(int mc, int nc, float *c, float *C, int ldc, float *scale,
                  float *bias) {
-  int nc1 = nc / 16;
+  int volatile nc1 = nc / 16;
   int _nc1 = nc % 16;
-  int nc2 = _nc1 / 4;
-  int nc3 = 16 - 4 * (_nc1 % 4);
-  int step = 4 * (ldc - nc);
-  int step1 = 4 * (NC - nc);
+  int volatile nc2 = _nc1 / 4;
+  int volatile nc3 = 16 - 4 * (_nc1 % 4);
+  int volatile step = 4 * (ldc - nc);
+  int volatile step1 = 4 * (NC - nc);
 
   asm volatile(
       "subs       %[mc], %[mc], #1        \n\t"

src/operators/math/pool_3x3.cpp

@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #ifdef POOL_OP
-#include "operators/math/pool_3x3.h"
+#ifdef _OPENMP
+#include <omp.h>
+#endif
 #include "framework/tensor.h"
+#include "pool_3x3.h"
 #if __ARM_NEON
 #include <arm_neon.h>
 #endif  // __ARM_NEON
@@ -40,46 +43,52 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
   const int w_out = output->dims()[3];
   const int outputdata_channel_stride = h_out * w_out;
   const int inputdata_channel_stride = h_in * w_in;
+  const int input_batch_stride = output_channels * inputdata_channel_stride;
+  const int output_batch_stride = output_channels * outputdata_channel_stride;
   float *out_data = output->data<float>();
   const float *input_data = input->data<float>();
+
   const float coef = 1.0 / 9.0;
   for (int k = 0; k < batch_size; ++k) {
+#pragma omp parallel for
     for (int c = 0; c < output_channels; ++c) {
+      const float *input_seg = input_data + c * inputdata_channel_stride;
+      float *output_seg = out_data + c * outputdata_channel_stride;
       // four corner point
-      out_data[0] = (input_data[0] + input_data[1] + input_data[w_in] +
-                     input_data[w_in + 1]) *
-                    coef;
-      out_data[w_out - 1] =
-          (input_data[w_in - 2] + input_data[w_in - 1] +
-           input_data[w_in * 2 - 2] + input_data[2 * w_in - 1]) *
+      output_seg[0] = (input_seg[0] + input_seg[1] + input_seg[w_in] +
+                       input_seg[w_in + 1]) *
+                      coef;
+      output_seg[w_out - 1] =
+          (input_seg[w_in - 2] + input_seg[w_in - 1] + input_seg[w_in * 2 - 2] +
+           input_seg[2 * w_in - 1]) *
           coef;
-      out_data[(h_out - 1) * w_out] =
-          (input_data[(h_in - 2) * w_in] + input_data[(h_in - 2) * w_in + 1] +
-           input_data[(h_in - 1) * w_in] + input_data[(h_in - 1) * w_in + 1]) *
+      output_seg[(h_out - 1) * w_out] =
+          (input_seg[(h_in - 2) * w_in] + input_seg[(h_in - 2) * w_in + 1] +
+           input_seg[(h_in - 1) * w_in] + input_seg[(h_in - 1) * w_in + 1]) *
           coef;
-      out_data[h_out * w_out - 1] =
-          (input_data[h_in * w_in - 1] + input_data[h_in * w_in - 2] +
-           input_data[(h_in - 1) * w_in - 1] +
-           input_data[(h_in - 1) * w_in - 2]) *
+      output_seg[h_out * w_out - 1] =
+          (input_seg[h_in * w_in - 1] + input_seg[h_in * w_in - 2] +
+           input_seg[(h_in - 1) * w_in - 1] +
+           input_seg[(h_in - 1) * w_in - 2]) *
           coef;
       // left side & right side
       for (int i = 1; i < h_in - 1; ++i) {
-        out_data[i * w_out] =
-            (input_data[i * w_in - w_in] + input_data[i * w_in - w_in + 1] +
-             input_data[i * w_in] + input_data[i * w_in + 1] +
-             input_data[i * w_in + w_in] + input_data[i * w_in + w_in + 1]) *
+        output_seg[i * w_out] =
+            (input_seg[i * w_in - w_in] + input_seg[i * w_in - w_in + 1] +
+             input_seg[i * w_in] + input_seg[i * w_in + 1] +
+             input_seg[i * w_in + w_in] + input_seg[i * w_in + w_in + 1]) *
             coef;
-        out_data[i * w_out + w_out - 1] =
-            (input_data[i * w_in - w_in + w_in - 2] +
-             input_data[i * w_in - w_in + 1 + w_in - 2] +
-             input_data[i * w_in + w_in - 2] +
-             input_data[i * w_in + 1 + w_in - 2] +
-             input_data[i * w_in + w_in + w_in - 2] +
-             input_data[i * w_in + w_in + 1 + w_in - 2]) *
+        output_seg[i * w_out + w_out - 1] =
+            (input_seg[i * w_in - w_in + w_in - 2] +
+             input_seg[i * w_in - w_in + 1 + w_in - 2] +
+             input_seg[i * w_in + w_in - 2] +
+             input_seg[i * w_in + 1 + w_in - 2] +
+             input_seg[i * w_in + w_in + w_in - 2] +
+             input_seg[i * w_in + w_in + 1 + w_in - 2]) *
             coef;
       }
       // top 1 row & bottom 1 row
-      const float *input_tmp = input_data;
+      const float *input_tmp = input_seg;
 
       float32x4_t in0, in1, in2, in3, in4, in5, in6, in7, tmp0, tmp1, tmp2,
           tmp3, tmp4, tmp5, sum, out0;
@@ -90,7 +99,7 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
       in4 = vld1q_f32(input_tmp_end);
       in6 = vld1q_f32(input_tmp_end + w_in);
       int c_mid = w_out - 2;
-      auto output_ptr = out_data + 1;
+      auto output_ptr = output_seg + 1;
       for (; c_mid > 3; c_mid -= 4) {
         in1 = vld1q_f32(input_tmp + 4);
         in3 = vld1q_f32(input_tmp + w_in + 4);
@@ -135,8 +144,8 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
         in6 = in7;
       }
       // top right remain
-      float32x4_t pad0 = vdupq_n_f32(input_data[w_in - 1]);
-      float32x4_t pad1 = vdupq_n_f32(input_data[2 * w_in - 1]);
+      float32x4_t pad0 = vdupq_n_f32(input_seg[w_in - 1]);
+      float32x4_t pad1 = vdupq_n_f32(input_seg[2 * w_in - 1]);
 
       tmp0 = vextq_f32(in0, pad0, 1);
       tmp1 = vextq_f32(in0, pad0, 2);
@@ -163,8 +172,8 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
       }
 
       // bottom_right remain
-      float32x4_t pad2 = vdupq_n_f32(input_data[(h_in - 1) * w_in - 1]);
-      float32x4_t pad3 = vdupq_n_f32(input_data[h_in * w_in - 1]);
+      float32x4_t pad2 = vdupq_n_f32(input_seg[(h_in - 1) * w_in - 1]);
+      float32x4_t pad3 = vdupq_n_f32(input_seg[h_in * w_in - 1]);
 
       tmp0 = vextq_f32(in4, pad2, 1);
       tmp1 = vextq_f32(in4, pad2, 2);
@@ -191,8 +200,8 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
       }
       // mid
       for (int j = 0; j < h_out - 2; ++j) {
-        output_ptr = out_data + w_out * (j + 1) + 1;
-        input_tmp = input_data + j * w_in;
+        output_ptr = output_seg + w_out * (j + 1) + 1;
+        input_tmp = input_seg + j * w_in;
 
         in0 = vld1q_f32(input_tmp);
         in2 = vld1q_f32(input_tmp + w_in);
@@ -228,9 +237,9 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
           in4 = in5;
         }
         // mid remain
-        float32x4_t pad0 = vdupq_n_f32(input_data[(j + 1) * w_in - 1]);
-        float32x4_t pad1 = vdupq_n_f32(input_data[(j + 2) * w_in - 1]);
-        float32x4_t pad2 = vdupq_n_f32(input_data[(j + 2) * w_in - 1]);
+        float32x4_t pad0 = vdupq_n_f32(input_seg[(j + 1) * w_in - 1]);
+        float32x4_t pad1 = vdupq_n_f32(input_seg[(j + 2) * w_in - 1]);
+        float32x4_t pad2 = vdupq_n_f32(input_seg[(j + 2) * w_in - 1]);
 
         tmp0 = vextq_f32(in0, pad0, 1);
         tmp1 = vextq_f32(in0, pad0, 2);
@@ -261,9 +270,11 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
           }
         }
       }
-      input_data += inputdata_channel_stride;
-      out_data += outputdata_channel_stride;
+      //      input_data += inputdata_channel_stride;
+      //      out_data += outputdata_channel_stride;
     }
+    input_data += input_batch_stride;
+    out_data += output_batch_stride;
   }
 #endif
 }
@@ -282,44 +293,50 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
   const int w_out = output->dims()[3];
   const int outputdata_channel_stride = h_out * w_out;
   const int inputdata_channel_stride = h_in * w_in;
+  const int input_batch_stride = output_channels * inputdata_channel_stride;
+  const int output_batch_stride = output_channels * outputdata_channel_stride;
   float *out_data = output->data<float>();
   const float *input_data = input->data<float>();
   for (int k = 0; k < batch_size; ++k) {
+#pragma omp parallel for
     for (int c = 0; c < output_channels; ++c) {
+      const float *input_seg = input_data + c * inputdata_channel_stride;
+      float *output_seg = out_data + c * outputdata_channel_stride;
       // four corner point
-      out_data[0] = std::max(std::max(input_data[0], input_data[1]),
-                             std::max(input_data[w_in], input_data[w_in + 1]));
-      out_data[w_out - 1] = std::max(
-          std::max(input_data[w_in - 2], input_data[w_in - 1]),
-          std::max(input_data[w_in * 2 - 2], input_data[2 * w_in - 1]));
-      out_data[(h_out - 1) * w_out] =
-          std::max(std::max(input_data[(h_in - 2) * w_in],
-                            input_data[(h_in - 2) * w_in + 1]),
-                   std::max(input_data[(h_in - 1) * w_in],
-                            input_data[(h_in - 1) * w_in + 1]));
-      out_data[h_out * w_out - 1] = std::max(
-          std::max(input_data[(h_in - 1) * w_in - 1],
-                   input_data[(h_in - 1) * w_in - 2]),
-          std::max(input_data[h_in * w_in - 1], input_data[h_in * w_in - 2]));
+      output_seg[0] = std::max(std::max(input_seg[0], input_seg[1]),
+                               std::max(input_seg[w_in], input_seg[w_in + 1]));
+      output_seg[w_out - 1] =
+          std::max(std::max(input_seg[w_in - 2], input_seg[w_in - 1]),
+                   std::max(input_seg[w_in * 2 - 2], input_seg[2 * w_in - 1]));
+      output_seg[(h_out - 1) * w_out] =
+          std::max(std::max(input_seg[(h_in - 2) * w_in],
+                            input_seg[(h_in - 2) * w_in + 1]),
+                   std::max(input_seg[(h_in - 1) * w_in],
+                            input_seg[(h_in - 1) * w_in + 1]));
+      output_seg[h_out * w_out - 1] = std::max(
+          std::max(input_seg[(h_in - 1) * w_in - 1],
+                   input_seg[(h_in - 1) * w_in - 2]),
+          std::max(input_seg[h_in * w_in - 1], input_seg[h_in * w_in - 2]));
       // left side & right side
       for (int i = 1; i < h_in - 1; ++i) {
-        float max1 = std::max(input_data[i * w_in - w_in],
-                              input_data[i * w_in - w_in + 1]);
-        float max2 = std::max(input_data[i * w_in], input_data[i * w_in + 1]);
-        float max3 = std::max(input_data[i * w_in + w_in],
-                              input_data[i * w_in + w_in + 1]);
-        out_data[i * w_out] = std::max(std::max(max1, max2), max3);
-
-        max1 = std::max(input_data[i * w_in - w_in + w_in - 2],
-                        input_data[i * w_in - w_in + 1 + w_in - 2]);
-        max2 = std::max(input_data[i * w_in + w_in - 2],
-                        input_data[i * w_in + 1 + w_in - 2]);
-        max3 = std::max(input_data[i * w_in + w_in + w_in - 2],
-                        input_data[i * w_in + w_in + 1 + w_in - 2]);
-        out_data[i * w_out + w_out - 1] = std::max(std::max(max1, max2), max3);
+        float max1 = std::max(input_seg[i * w_in - w_in],
+                              input_seg[i * w_in - w_in + 1]);
+        float max2 = std::max(input_seg[i * w_in], input_seg[i * w_in + 1]);
+        float max3 = std::max(input_seg[i * w_in + w_in],
+                              input_seg[i * w_in + w_in + 1]);
+        output_seg[i * w_out] = std::max(std::max(max1, max2), max3);
+
+        max1 = std::max(input_seg[i * w_in - w_in + w_in - 2],
+                        input_seg[i * w_in - w_in + 1 + w_in - 2]);
+        max2 = std::max(input_seg[i * w_in + w_in - 2],
+                        input_seg[i * w_in + 1 + w_in - 2]);
+        max3 = std::max(input_seg[i * w_in + w_in + w_in - 2],
+                        input_seg[i * w_in + w_in + 1 + w_in - 2]);
+        output_seg[i * w_out + w_out - 1] =
+            std::max(std::max(max1, max2), max3);
       }
       // top 1 row & bottom 1 row
-      const float *input_tmp = input_data;
+      const float *input_tmp = input_seg;
 
       float32x4_t in0, in1, in2, in3, in4, in5, in6, in7, tmp0, tmp1, tmp2,
           tmp3, tmp4, tmp5, max;
@@ -329,7 +346,7 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
       in4 = vld1q_f32(input_tmp_end);
       in6 = vld1q_f32(input_tmp_end + w_in);
       int c_mid = w_out - 2;
-      auto output_ptr = out_data + 1;
+      auto output_ptr = output_seg + 1;
       for (; c_mid > 3; c_mid -= 4) {
         in1 = vld1q_f32(input_tmp + 4);
         in3 = vld1q_f32(input_tmp + w_in + 4);
@@ -373,8 +390,8 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
         in6 = in7;
       }
       // top right remain
-      float32x4_t pad0 = vdupq_n_f32(input_data[w_in - 1]);
-      float32x4_t pad1 = vdupq_n_f32(input_data[2 * w_in - 1]);
+      float32x4_t pad0 = vdupq_n_f32(input_seg[w_in - 1]);
+      float32x4_t pad1 = vdupq_n_f32(input_seg[2 * w_in - 1]);
 
       tmp0 = vextq_f32(in0, pad0, 1);
       tmp1 = vextq_f32(in0, pad0, 2);
@@ -400,8 +417,8 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
       }
 
       // bottom_right remain
-      float32x4_t pad2 = vdupq_n_f32(input_data[(h_in - 1) * w_in - 1]);
-      float32x4_t pad3 = vdupq_n_f32(input_data[h_in * w_in - 1]);
+      float32x4_t pad2 = vdupq_n_f32(input_seg[(h_in - 1) * w_in - 1]);
+      float32x4_t pad3 = vdupq_n_f32(input_seg[h_in * w_in - 1]);
 
       tmp0 = vextq_f32(in4, pad2, 1);
       tmp1 = vextq_f32(in4, pad2, 2);
@@ -427,8 +444,8 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
       }
       // mid
       for (int j = 0; j < h_out - 2; ++j) {
-        output_ptr = out_data + (j + 1) * w_out + 1;
-        input_tmp = input_data + j * w_in;
+        output_ptr = output_seg + (j + 1) * w_out + 1;
+        input_tmp = input_seg + j * w_in;
 
         in0 = vld1q_f32(input_tmp);
         in2 = vld1q_f32(input_tmp + w_in);
@@ -463,9 +480,9 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
           in4 = in5;
         }
         // mid remain
-        float32x4_t pad0 = vdupq_n_f32(input_data[(j + 1) * w_in - 1]);
-        float32x4_t pad1 = vdupq_n_f32(input_data[(j + 2) * w_in - 1]);
-        float32x4_t pad2 = vdupq_n_f32(input_data[(j + 3) * w_in - 1]);
+        float32x4_t pad0 = vdupq_n_f32(input_seg[(j + 1) * w_in - 1]);
+        float32x4_t pad1 = vdupq_n_f32(input_seg[(j + 2) * w_in - 1]);
+        float32x4_t pad2 = vdupq_n_f32(input_seg[(j + 3) * w_in - 1]);
 
         tmp0 = vextq_f32(in0, pad0, 1);
         tmp1 = vextq_f32(in0, pad0, 2);
@@ -495,9 +512,11 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
           }
         }
       }
-      input_data += inputdata_channel_stride;
-      out_data += outputdata_channel_stride;
+      //      input_data += inputdata_channel_stride;
+      //      out_data += outputdata_channel_stride;
     }
+    input_data += input_batch_stride;
+    out_data += output_batch_stride;
   }
 #endif
 }
@@ -515,11 +534,11 @@ void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
 
   const int output_height = output->dims()[2];
   const int output_width = output->dims()[3];
-  const int _kernel_size = 3;
-  const int stride_height = strides[0];
-  const int stride_width = strides[1];
-  const int padding_height = paddings[0];
-  const int padding_width = paddings[1];
+  //  const int _kernel_size = 3;
+  const int stride = strides[0];
+  //  const int stride_width = strides[1];
+  const int padding = paddings[0];
+  //  const int padding_width = paddings[1];
   const float negative_max = -INT_MAX;
   const int input_channel_stride = input_height * input_width;
   const int output_channel_stride = output_height * output_width;
@@ -529,36 +548,39 @@ void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
 
   const int input_batch_stride = output_channels * input_channel_stride;
   const int output_batch_stride = output_channels * output_channel_stride;
-  const float *pos1, *pos2, *pos3, *output_ptr;
+  const float *pos1, *output_ptr;
   int hstart, wstart, hend, wend;
   for (int i = 0; i < batch_size; ++i) {
+#pragma omp parallel for
     for (int c = 0; c < output_channels; ++c) {
+      const float *input_seg = input_data + c * input_channel_stride;
+      float *output_seg = output_data + c * output_channel_stride;
       for (int ph = 0; ph < output_height; ph++) {
         for (int pw = 0; pw < output_width; pw++) {
-          hstart = ph * stride_height - padding_height;
-          wstart = pw * stride_width - padding_width;
-          hend = min(hstart + _kernel_size, input_height + padding_height);
-          wend = min(wstart + _kernel_size, input_width + padding_width);
+          int hstart = ph * stride - padding;
+          int wstart = pw * stride - padding;
+          int hend = min(hstart + 3, input_height + padding);
+          int wend = min(wstart + 3, input_width + padding);
           hstart = max(hstart, 0);
           wstart = max(wstart, 0);
           hend = min(hend, input_height);
           wend = min(wend, input_width);
-          pos1 = input_data + hstart * input_width + wstart;
-          pos2 = input_data + (hstart + 1) * input_width + wstart;
-          pos3 = input_data + (hstart + 2) * input_width + wstart;
-          output_ptr = output_data + ph * output_width + pw;
+          const float *pos1 = input_seg + hstart * input_width + wstart;
+          const float *pos2 = input_seg + (hstart + 1) * input_width + wstart;
+          const float *pos3 = input_seg + (hstart + 2) * input_width + wstart;
+          output_ptr = output_seg + ph * output_width + pw;
 
           if (hend - hstart != 3 || wend - wstart != 3) {
             float max_value = -INT_MAX;
             for (int h = hstart; h < hend; h++) {
               for (int w = wstart; w < wend; w++) {
-                float value = input_data[h * input_width + w];
+                float value = input_seg[h * input_width + w];
                 if (value > max_value) {
                   max_value = value;
                 }
               }
             }
-            output_data[ph * output_width + pw] = max_value;
+            output_seg[ph * output_width + pw] = max_value;
           } else {
 #if defined(ARMV7)
             asm volatile(
@@ -572,27 +594,25 @@ void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
                 "vpmax.f32  d7, d6, d6             \n\t"
                 "vst1.32 {d7[0]},[%[output_ptr]]    \n\t"
                 :
-                : [input_data] "r"(input_data), [pos1] "r"(pos1),
+                : [input_seg] "r"(input_seg), [pos1] "r"(pos1),
                   [pos2] "r"(pos2), [pos3] "r"(pos3),
                   [output_ptr] "r"(output_ptr), [negative_max] "r"(negative_max)
                 : "memory", "q1", "q2", "q3", "q4");
 #else
             const float32x4_t data1 = vld1q_f32(pos1);
-            const float32x4_t data2 = vld1q_f32(pos2);
-            const float32x4_t data3 = vld1q_f32(pos3);
+            const float32x4_t data2 = vld1q_f32(pos1 + input_width);
+            const float32x4_t data3 = vld1q_f32(pos1 + 2 * input_width);
             const float32x4_t max_data =
-                vmaxq_f32(vmaxq_f32(data1, data3), data2);
+                vmaxq_f32(vmaxq_f32(data1, data2), data3);
             float32x2_t res =
                 vpmax_f32(vget_high_f32(vsetq_lane_f32(-INT_MAX, max_data, 3)),
                           vget_low_f32(max_data));
             res = vpmax_f32(res, res);
-            output_data[ph * output_width + pw] = vget_lane_f32(res, 0);
+            output_seg[ph * output_width + pw] = vget_lane_f32(res, 0);
 #endif
           }
         }
       }
-      input_data += input_channel_stride;
-      output_data += output_channel_stride;
     }
     input_data += input_batch_stride;
     output_data += output_batch_stride;
@@ -613,11 +633,8 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
 
   const int output_height = output->dims()[2];
   const int output_width = output->dims()[3];
-  const int _kernel_size = 3;
-  const int stride_height = strides[0];
-  const int stride_width = strides[1];
-  const int padding_height = paddings[0];
-  const int padding_width = paddings[1];
+  const int stride = strides[0];
+  const int padding = paddings[0];
 
   const int input_channel_stride = input_height * input_width;
   const int output_channel_stride = output_height * output_width;
@@ -631,30 +648,33 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
   const int input_batch_stride = output_channels * input_channel_stride;
   const int output_batch_stride = output_channels * output_channel_stride;
   for (int i = 0; i < batch_size; ++i) {
+#pragma omp parallel for
     for (int c = 0; c < output_channels; ++c) {
+      const float *input_seg = input_data + c * input_channel_stride;
+      float *output_seg = output_data + c * output_channel_stride;
       for (int ph = 0; ph < output_height; ph++) {
         for (int pw = 0; pw < output_width; pw++) {
-          int hstart = ph * stride_height - padding_height;
-          int wstart = pw * stride_width - padding_width;
-          int hend = min(hstart + _kernel_size, input_height + padding_height);
-          int wend = min(wstart + _kernel_size, input_width + padding_width);
+          int hstart = ph * stride - padding;
+          int wstart = pw * stride - padding;
+          int hend = min(hstart + 3, input_height + padding);
+          int wend = min(wstart + 3, input_width + padding);
           hstart = max(hstart, 0);
           wstart = max(wstart, 0);
           hend = min(hend, input_height);
           wend = min(wend, input_width);
-          const float *pos1 = input_data + hstart * input_width + wstart;
-          const float *pos2 = input_data + (hstart + 1) * input_width + wstart;
-          const float *pos3 = input_data + (hstart + 2) * input_width + wstart;
-          const float *output_ptr = output_data + ph * output_width + pw;
+          const float *pos1 = input_seg + hstart * input_width + wstart;
+          const float *pos2 = input_seg + (hstart + 1) * input_width + wstart;
+          const float *pos3 = input_seg + (hstart + 2) * input_width + wstart;
+          float *output_ptr = output_seg + ph * output_width + pw;
 
           if (hend - hstart != 3 || wend - wstart != 3) {
             float sum = 0;
             for (int h = hstart; h < hend; h++) {
               for (int w = wstart; w < wend; w++) {
-                sum += input_data[h * input_width + w];
+                sum += input_seg[h * input_width + w];
               }
             }
-            output_data[ph * output_width + pw] = sum / 9.0;
+            output_seg[ph * output_width + pw] = sum / 9.0;
           } else {
 #if defined(ARMV7)
 
@@ -671,7 +691,7 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
                 "vmul.f32 d6,d7                     \n\t"
                 "vst1.32 {d6[0]},[%[output_ptr]]    \n\t"
                 :
-                : [input_data] "r"(input_data), [pos1] "r"(pos1),
+                : [input_seg] "r"(input_seg), [pos1] "r"(pos1),
                   [pos2] "r"(pos2), [pos3] "r"(pos3),
                   [output_ptr] "r"(output_ptr), [zero] "r"(zero),
                   [nine_ptr] "r"(nine_ptr)
@@ -686,13 +706,11 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
                 vpadd_f32(vget_high_f32(vsetq_lane_f32(0, sum_data, 3)),
                           vget_low_f32(sum_data));
             res = vpadd_f32(res, res);
-            output_data[ph * output_width + pw] = vget_lane_f32(res, 0) / 9.0;
+            output_seg[ph * output_width + pw] = vget_lane_f32(res, 0) / 9.0;
 #endif
           }
         }
       }
-      input_data += input_channel_stride;
-      output_data += output_channel_stride;
     }
     input_data += input_batch_stride;
     output_data += output_batch_stride;

src/operators/math/pool_3x3.h

@@ -15,10 +15,13 @@ limitations under the License. */
 #ifdef POOL_OP
 
 #pragma once
+#ifdef _OPENMP
+#include <omp.h>
+#endif
 #include <algorithm>
 #include <vector>
 #include "framework/tensor.h"
-#ifdef __ARM_NEON
+#if __ARM_NEON
 #include <arm_neon.h>
 #endif  // __ARM_NEON
 

src/operators/math/pooling.cpp

@@ -14,10 +14,11 @@ limitations under the License. */
 
 #ifdef POOL_OP
 
-#include "operators/math/pooling.h"
-#include <algorithm>
-#include <vector>
+#include "pooling.h"
 #include "common/types.h"
+#ifdef _OPENMP
+#include <omp.h>
+#endif
 
 namespace paddle_mobile {
 namespace operators {
@@ -59,7 +60,7 @@ class PoolFunctor<CPU, PoolProcess, T> {
     T *output_data = output->mutable_data<T>();
 
     for (int i = 0; i < batch_size; i++) {
-      //  #pragma omp parallel for
+#pragma omp parallel for
       for (int c = 0; c < output_channels; ++c) {
         for (int ph = 0; ph < output_height; ++ph) {
           int hstart = ph * stride_height - padding_height;

test/net/test_googlenet.cpp

@@ -26,16 +26,17 @@ int main() {
   auto time2 = time();
   DLOG << "load cost :" << time_diff(time1, time2) << "ms\n";
   paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, optimize);
+  executor.SetThreadNum(4);
   std::vector<float> input;
   std::vector<int64_t> dims{1, 3, 224, 224};
   GetInput<float>(g_test_image_1x3x224x224, &input, dims);
   auto time3 = time();
-
-  for (int i = 0; i < 10; ++i) {
+  int count = 1;
+  for (int i = 0; i < count; ++i) {
     executor.Predict(input, dims);
   }
 
   auto time4 = time();
-  DLOG << "predict cost :" << time_diff(time3, time4) << "ms\n";
+  DLOG << "predict cost :" << time_diff(time3, time4) / count << "ms\n";
   return 0;
 }