fix ARM kernel of pool2d op for armv7/v8

paddle/fluid/lite/arm/math/pooling.h

@@ -25,7 +25,7 @@ namespace arm {
 namespace math {
 
 // !pooling fp32 Op
-void pooling_basic(const void* din, void* dout, int num, int chout, int hout,
+void pooling_basic(const float* din, float* dout, int num, int chout, int hout,
                    int wout, int chin, int hin, int win,
                    const std::vector<int>& ksize,
                    const std::vector<int>& strides,
@@ -33,77 +33,39 @@ void pooling_basic(const void* din, void* dout, int num, int chout, int hout,
                    bool exclusive, bool adaptive, bool ceil_mode,
                    bool use_quantizer, const std::string& pooling_type);
 
-void pooling_global(const void* din, void* dout, int num, int chout, int hout,
-                    int wout, int chin, int hin, int win,
-                    const std::vector<int>& ksize,
-                    const std::vector<int>& strides,
-                    const std::vector<int>& paddings, bool global_pooling,
-                    bool exclusive, bool adaptive, bool ceil_mode,
-                    bool use_quantizer, const std::string& pooling_type);
+void pooling_global_max(const float* din, float* dout, int num, int chout,
+                        int hout, int wout, int chin, int hin, int win);
 
-void pooling2x2s2_max(const void* din, void* dout, int num, int chout, int hout,
-                      int wout, int chin, int hin, int win,
-                      const std::vector<int>& ksize,
-                      const std::vector<int>& strides,
-                      const std::vector<int>& paddings, bool global_pooling,
-                      bool exclusive, bool adaptive, bool ceil_mode,
-                      bool use_quantizer, const std::string& pooling_type);
+void pooling_global_avg(const float* din, float* dout, int num, int chout,
+                        int hout, int wout, int chin, int hin, int win);
 
-void pooling2x2s2_ave(const void* din, void* dout, int num, int chout, int hout,
-                      int wout, int chin, int hin, int win,
-                      const std::vector<int>& ksize,
-                      const std::vector<int>& strides,
-                      const std::vector<int>& paddings, bool global_pooling,
-                      bool exclusive, bool adaptive, bool ceil_mode,
-                      bool use_quantizer, const std::string& pooling_type);
+void pooling2x2s2_max(const float* din, float* dout, int num, int chout,
+                      int hout, int wout, int chin, int hin, int win);
 
-void pooling3x3s1p1_max(const void* din, void* dout, int num, int chout,
+void pooling2x2s2_avg(const float* din, float* dout, int num, int chout,
                       int hout, int wout, int chin, int hin, int win,
-                        const std::vector<int>& ksize,
-                        const std::vector<int>& strides,
-                        const std::vector<int>& paddings, bool global_pooling,
-                        bool exclusive, bool adaptive, bool ceil_mode,
-                        bool use_quantizer, const std::string& pooling_type);
+                      bool exclusive);
 
-void pooling3x3s1p1_ave(const void* din, void* dout, int num, int chout,
-                        int hout, int wout, int chin, int hin, int win,
-                        const std::vector<int>& ksize,
-                        const std::vector<int>& strides,
-                        const std::vector<int>& paddings, bool global_pooling,
-                        bool exclusive, bool adaptive, bool ceil_mode,
-                        bool use_quantizer, const std::string& pooling_type);
+void pooling3x3s1p1_max(const float* din, float* dout, int num, int chout,
+                        int hout, int wout, int chin, int hin, int win);
 
-void pooling3x3s2p1_max(const void* din, void* dout, int num, int chout,
+void pooling3x3s1p1_avg(const float* din, float* dout, int num, int chout,
                         int hout, int wout, int chin, int hin, int win,
-                        const std::vector<int>& ksize,
-                        const std::vector<int>& strides,
-                        const std::vector<int>& paddings, bool global_pooling,
-                        bool exclusive, bool adaptive, bool ceil_mode,
-                        bool use_quantizer, const std::string& pooling_type);
+                        bool exclusive);
 
-void pooling3x3s2p0_max(const void* din, void* dout, int num, int chout,
-                        int hout, int wout, int chin, int hin, int win,
-                        const std::vector<int>& ksize,
-                        const std::vector<int>& strides,
-                        const std::vector<int>& paddings, bool global_pooling,
-                        bool exclusive, bool adaptive, bool ceil_mode,
-                        bool use_quantizer, const std::string& pooling_type);
+void pooling3x3s2p1_max(const float* din, float* dout, int num, int chout,
+                        int hout, int wout, int chin, int hin, int win);
 
-void pooling3x3s2p1_ave(const void* din, void* dout, int num, int chout,
+void pooling3x3s2p1_avg(const float* din, float* dout, int num, int chout,
                         int hout, int wout, int chin, int hin, int win,
-                        const std::vector<int>& ksize,
-                        const std::vector<int>& strides,
-                        const std::vector<int>& paddings, bool global_pooling,
-                        bool exclusive, bool adaptive, bool ceil_mode,
-                        bool use_quantizer, const std::string& pooling_type);
+                        bool exclusive);
 
-void pooling3x3s2p0_ave(const void* din, void* dout, int num, int chout,
+void pooling3x3s2p0_max(const float* din, float* dout, int num, int chout,
+                        int hout, int wout, int chin, int hin, int win);
+
+void pooling3x3s2p0_avg(const float* din, float* dout, int num, int chout,
                         int hout, int wout, int chin, int hin, int win,
-                        const std::vector<int>& ksize,
-                        const std::vector<int>& strides,
-                        const std::vector<int>& paddings, bool global_pooling,
-                        bool exclusive, bool adaptive, bool ceil_mode,
-                        bool use_quantizer, const std::string& pooling_type);
+                        bool exclusive);
 
 }  // namespace math
 }  // namespace arm

paddle/fluid/lite/kernels/arm/pool_compute.cc

@@ -48,120 +48,96 @@ void PoolCompute::Run() {
   bool use_quantizer = param.use_quantizer;
   std::string& data_format = param.data_format;
 
-  if (param.global_pooling) {
+  bool kps_equal = (ksize[0] == ksize[1]) && (strides[0] == strides[1]) &&
+                   (paddings[0] == paddings[1]);
+
+  if (global_pooling) {
     for (size_t i = 0; i < ksize.size(); ++i) {
       paddings[i] = 0;
       ksize[i] = static_cast<int>(in_dims[i + 2]);
     }
+    if (pooling_type == "max") {
+      lite::arm::math::pooling_global_max(din, dout, out_dims[0], out_dims[1],
+                                          out_dims[2], out_dims[3], in_dims[1],
+                                          in_dims[2], in_dims[3]);
+      VLOG(3) << "invoking pooling_global_max";
+      return;
+    } else if (pooling_type == "avg") {
+      lite::arm::math::pooling_global_avg(din, dout, out_dims[0], out_dims[1],
+                                          out_dims[2], out_dims[3], in_dims[1],
+                                          in_dims[2], in_dims[3]);
+      VLOG(3) << "invoking pooling_global_ave";
+      return;
     }
-
-#if 0
-  for (int i = 0; i < in_dims.size(); ++i) {
-    LOG(INFO) << "in_dims[" << i << "]:" << in_dims[i];
-  }
-  for (int i = 0; i < out_dims.size(); ++i) {
-    LOG(INFO) << "out_dims[" << i << "]:" << out_dims[i];
-  }
-  for (int i = 0; i < ksize.size(); ++i) {
-    LOG(INFO) << "ksize[" << i << "]:" << ksize[i];
-  }
-  for (int i = 0; i < strides.size(); ++i) {
-    LOG(INFO) << "strides[" << i << "]:" << strides[i];
-  }
-  for (int i = 0; i < paddings.size(); ++i) {
-    LOG(INFO) << "paddings[" << i << "]:" << paddings[i];
-  }
-  LOG(INFO) << "global_pooling:" << global_pooling;
-  LOG(INFO) << "exclusive:" << exclusive;
-  LOG(INFO) << "adaptive:" << adaptive;
-  LOG(INFO) << "ceil_mode:" << ceil_mode;
-  LOG(INFO) << "use_quantizer:" << use_quantizer;
-  LOG(INFO) << "data_format:" << data_format;
-  LOG(INFO) << "din:" << din;
-  LOG(INFO) << "dout:" << dout;
-#endif
-
-  // global
-  if (global_pooling == true) {
-    lite::arm::math::pooling_global(
-        din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-        in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-        global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-        pooling_type);
-  } else if (ksize[0] == 2 && ksize[0] == ksize[1] && strides[0] == 2 &&
-             strides[0] == strides[1]) {
+  } else {
+    if (ksize[0] == 2 && strides[0] == 2 && paddings[0] == 0 && kps_equal) {
       if (pooling_type == "max") {
-      lite::arm::math::pooling2x2s2_max(
-          din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+        lite::arm::math::pooling2x2s2_max(din, dout, out_dims[0], out_dims[1],
+                                          out_dims[2], out_dims[3], in_dims[1],
+                                          in_dims[2], in_dims[3]);
+        VLOG(3) << "invoking pooling2x2s2_max";
+        return;
       } else if (pooling_type == "avg") {
-      lite::arm::math::pooling2x2s2_ave(
-          din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+        lite::arm::math::pooling2x2s2_avg(din, dout, out_dims[0], out_dims[1],
+                                          out_dims[2], out_dims[3], in_dims[1],
+                                          in_dims[2], in_dims[3], exclusive);
+        VLOG(3) << "invoking pooling2x2s2_avg";
+        return;
       }
-  } else if (ksize[0] == 3 && ksize[0] == ksize[1] && strides[0] == 1 &&
-             strides[0] == strides[1] && paddings[0] == 1) {
+    } else if (ksize[0] == 3 && strides[0] == 1 && paddings[0] == 1 &&
+               kps_equal) {
       if (pooling_type == "max") {
-      lite::arm::math::pooling3x3s1p1_max(
-          din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+        lite::arm::math::pooling3x3s1p1_max(din, dout, out_dims[0], out_dims[1],
+                                            out_dims[2], out_dims[3],
+                                            in_dims[1], in_dims[2], in_dims[3]);
+        VLOG(3) << "invokingpooling3x3s1p1_max";
+        return;
       } else if (pooling_type == "avg") {
-      lite::arm::math::pooling3x3s1p1_ave(
+        lite::arm::math::pooling3x3s1p1_avg(
             din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+            in_dims[1], in_dims[2], in_dims[3], exclusive);
+        VLOG(3) << "invoking pooling3x3s1p1_avg";
+        return;
       }
-  } else if (ksize[0] == 3 && ksize[0] == ksize[1] && strides[0] == 2 &&
-             strides[0] == strides[1] && paddings[0] == 0) {
+    } else if (ksize[0] == 3 && strides[0] == 2 && paddings[0] == 0 &&
+               kps_equal) {
       if (pooling_type == "max") {
-      lite::arm::math::pooling3x3s2p0_max(
-          din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+        lite::arm::math::pooling3x3s2p0_max(din, dout, out_dims[0], out_dims[1],
+                                            out_dims[2], out_dims[3],
+                                            in_dims[1], in_dims[2], in_dims[3]);
+        VLOG(3) << "pooling3x3s2p0_max";
+        return;
       } else if (pooling_type == "avg") {
-      lite::arm::math::pooling3x3s2p0_ave(
+        lite::arm::math::pooling3x3s2p0_avg(
             din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+            in_dims[1], in_dims[2], in_dims[3], exclusive);
+        VLOG(3) << "invoking pooling3x3s2p0_avg";
+        return;
       }
-  } else if (ksize[0] == 3 && ksize[0] == ksize[1] && strides[0] == 2 &&
-             strides[0] == strides[1] && paddings[0] == 1) {
+    } else if (ksize[0] == 3 && strides[0] == 2 && paddings[0] == 1 &&
+               kps_equal) {
       if (pooling_type == "max") {
-      lite::arm::math::pooling3x3s2p1_max(
-          din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+        lite::arm::math::pooling3x3s2p1_max(din, dout, out_dims[0], out_dims[1],
+                                            out_dims[2], out_dims[3],
+                                            in_dims[1], in_dims[2], in_dims[3]);
+        VLOG(3) << "invoking pooling3x3s2p1_max";
+        return;
       } else if (pooling_type == "avg") {
-      lite::arm::math::pooling3x3s2p1_ave(
+        lite::arm::math::pooling3x3s2p1_avg(
             din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-          in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-          global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-          pooling_type);
+            in_dims[1], in_dims[2], in_dims[3], exclusive);
+        VLOG(3) << "invoking pooling3x3s2p1_avg";
+        return;
       }
-  } else {
-    lite::arm::math::pooling_basic(
-        din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3],
-        in_dims[1], in_dims[2], in_dims[3], ksize, strides, paddings,
-        global_pooling, exclusive, adaptive, ceil_mode, use_quantizer,
-        pooling_type);
     }
-  return;
+  }
+  lite::arm::math::pooling_basic(
+      din, dout, out_dims[0], out_dims[1], out_dims[2], out_dims[3], in_dims[1],
+      in_dims[2], in_dims[3], ksize, strides, paddings, global_pooling,
+      exclusive, adaptive, ceil_mode, use_quantizer, pooling_type);
+  VLOG(3) << "invoking pooling_basic";
 }
 
-TargetType PoolCompute::target() const { return TARGET(kARM); }
-
-PrecisionType PoolCompute::precision() const { return PRECISION(kFloat); }
-
 }  // namespace arm
 }  // namespace kernels
 }  // namespace lite

paddle/fluid/lite/kernels/arm/pool_compute.h

@@ -29,9 +29,6 @@ class PoolCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
   void PrepareForRun() override;
   void Run() override;
 
-  TargetType target() const override;
-  PrecisionType precision() const override;
-
   virtual ~PoolCompute() = default;
 };
 

paddle/fluid/lite/kernels/arm/pool_compute_test.cc

@@ -101,94 +101,65 @@ void pool_compute_ref(const operators::PoolParam& param) {
   int pad_w = paddings[1];
 
   if (global_pooling == true) {
-    ksize[0] = in_h;
-    ksize[1] = in_w;
-
     for (int n = 0; n < in_n; ++n) {
       for (int c = 0; c < in_c; ++c) {
-        const float* src = src_ptr + n * in_c * in_h * in_w + c * in_h * in_w;
+        const float* src = src_ptr + n * size_in_n + c * size_in_c;
         float res = src[0];
         if (pooling_type == "max") {
-          for (int i = 1; i < in_h * in_w; ++i) {
+          for (int i = 1; i < size_in_c; ++i) {
             float cur_val = src[i];
             res = cur_val > res ? cur_val : res;
           }
         } else if (pooling_type == "avg") {
-          for (int i = 1; i < in_h * in_w; ++i) {
+          for (int i = 1; i < size_in_c; ++i) {
             float cur_val = src[i];
             res += cur_val;
           }
-          res /= (in_h * in_w);
-        }
-        dst_ptr[n * in_c * out_h * out_w + c] = res;
+          res /= size_in_c;
         }
+        dst_ptr[n * size_out_n + c] = res;
       }
-    return;
     }
-
-  for (int ind_n = 0; ind_n < in_n; ++ind_n) {
-    for (int ind_c = 0; ind_c < in_c; ++ind_c) {
-      for (int ind_h = 0; ind_h < out_h; ++ind_h) {
-        int sh = ind_h * stride_h;
+  } else {
+    for (int n = 0; n < in_n; ++n) {
+      for (int c = 0; c < in_c; ++c) {
+        for (int h = 0; h < out_h; ++h) {
+          int sh = h * stride_h;
           int eh = sh + window_h;
           sh = (sh - pad_h) < 0 ? 0 : sh - pad_h;
           eh = (eh - pad_h) > in_h ? in_h : eh - pad_h;
-
-        for (int ind_w = 0; ind_w < out_w; ++ind_w) {
-          int sw = ind_w * stride_w;
+          for (int w = 0; w < out_w; ++w) {
+            int sw = w * stride_w;
             int ew = sw + window_w;
             sw = (sw - pad_w) < 0 ? 0 : sw - pad_w;
             ew = (ew - pad_w) > in_w ? in_w : ew - pad_w;
-
-          float result = static_cast<float>(0);
-
-          int dst_ind =
-              ind_n * size_out_n + ind_c * size_out_c + ind_h * out_w + ind_w;
-
+            int pooling_size = (ew - sw) * (eh - sh);
+            if (pooling_size == 0) continue;
+            float res = 0.f;
             for (int kh = sh; kh < eh; ++kh) {
               for (int kw = sw; kw < ew; ++kw) {
-              int src_ind =
-                  ind_n * size_in_n + ind_c * size_in_c + kh * in_w + kw;
-
+                int src_idx = n * size_in_n + c * size_in_c + kh * in_w + kw;
                 if (kh == sh && kw == sw) {
-                result = src_ptr[src_ind];
+                  res = src_ptr[src_idx];
                 } else {
                   if (pooling_type == "max") {
-                  result =
-                      result >= src_ptr[src_ind] ? result : src_ptr[src_ind];
-                }
-                if (pooling_type == "avg" && exclusive == false) {
-                  // Pooling_average_include_padding
-                  result += src_ptr[src_ind];
+                    res = res >= src_ptr[src_idx] ? res : src_ptr[src_idx];
                   }
-                if (pooling_type == "avg" && exclusive == true) {
-                  // Pooling_average_include_padding
-                  result += src_ptr[src_ind];
+                  if (pooling_type == "avg") {
+                    res += src_ptr[src_idx];
                   }
                 }
               }
             }
-          if (pooling_type == "avg" && exclusive == false) {
-            // Pooling_average_include_padding
-            // result /= param.window_h * param.window_w;
-            // LOG(ERROR)<<"cpu"<<param.window_h * param.window_w;
-            int bh = window_h;
-            int bw = window_w;
-            if (ew == in_w) {
-              bw = sw + window_w >= in_w + pad_w ? in_w + pad_w : sw + window_w;
-              bw -= sw;
-            }
-            if (eh == in_h) {
-              bh = sh + window_h >= in_h + pad_h ? in_h + pad_h : sh + window_h;
-              bh -= sh;
+            if (pooling_type == "avg") {
+              if (exclusive) {
+                res /= pooling_size;
+              } else {
+                res /= window_h * window_w;
               }
-            result /= bh * bw;
             }
-          if (pooling_type == "avg" && exclusive == true) {
-            // Pooling_average_exclude_padding
-            result /= (ew - sw) * (eh - sh);
+            dst_ptr[n * size_out_n + c * size_out_c + h * out_w + w] = res;
           }
-          dst_ptr[dst_ind] = result;
         }
       }
     }
@@ -209,41 +180,54 @@ TEST(pool_arm, compute) {
   lite::Tensor output;
   lite::Tensor output_ref;
 
-  for (auto pooling_type : {"avg", "max"}) {
-    for (auto global_pooling : {true}) {
-      // for (auto ksize: {3}) { // TODO(yuanshuai): ksize enable 2, 3
+  // speedup for ci
+  for (auto pooling_type : {"max", "avg"}) {
+    for (auto ceil_mode : {true, false}) {
+      for (auto global_pooling : {true, false}) {
+        for (auto exclusive : {true, false}) {
+          for (auto ksize : {2, 3}) {
             for (auto stride : {1, 2}) {
               for (auto pad : {0, 1}) {
-          for (auto n : {1, 3, 4, 11}) {
-            for (auto c : {1, 3, 11 /* ,1024 */}) {  // speedup for ci
+                for (auto n : {1, 2}) {
+                  for (auto c : {1, 3}) {
+#if 1
                     for (auto h : {2, 3, 4, 11}) {
                       for (auto w : {2, 3, 4, 11}) {
-                  LOG(INFO) << "n:" << n << " c:" << c << " h:" << h
-                            << " w:" << w  // << " ksize:" << ksize
+#else
+                    for (int h = 2; h < 25; h++) {
+                      for (int w = 2; w < 25; w++) {
+#endif
+                        VLOG(3) << "n:" << n << " c:" << c << " h:" << h
+                                << " w:" << w << " ksize:" << ksize
                                 << " stride:" << stride << " pad:" << pad
-                            << " pooling_type:" << pooling_type
-                            << " global_pooling:" << global_pooling;
+                                << " exclusive:" << exclusive
+                                << " global_pooling:" << global_pooling
+                                << " ceil_mode: " << ceil_mode
+                                << " pooling_type:" << pooling_type;
 
                         // init x, output
                         x.Resize(DDim(std::vector<int64_t>({n, c, h, w})));
                         auto* x_data = x.mutable_data<float>();
                         for (int i = 0; i < x.dims().production(); ++i) {
-                    x_data[i] = i;
+                          float sign = i % 3 == 0 ? -0.03 : 0.05f;
+                          x_data[i] = sign * (i % 128);
                         }
 
                         // fill param
                         param.x = &x;
                         param.output = &output;
                         param.pooling_type = pooling_type;
-                  // param.ksize = {ksize, ksize}; //TODO(yuanshuai): ksize
-                  // enable
+                        if (global_pooling) {
                           param.ksize = {h, w};
+                        } else {
+                          param.ksize = {ksize, ksize};
+                        }
                         param.global_pooling = global_pooling;
                         param.strides = {stride, stride};
                         param.paddings = {pad, pad};
-                  param.exclusive = true;
+                        param.exclusive = exclusive;
+                        param.ceil_mode = ceil_mode;
                         param.adaptive = false;
-                  param.ceil_mode = false;
                         param.use_quantizer = false;
 
                         const std::vector<int64_t>& output_shape =
@@ -251,6 +235,14 @@ TEST(pool_arm, compute) {
                         output.Resize(DDim(output_shape));
                         output_ref.Resize(DDim(output_shape));
 
+                        auto* output_data = output.mutable_data<float>();
+                        auto* output_ref_data =
+                            output_ref.mutable_data<float>();
+                        for (int i = 0; i < output.dims().production(); ++i) {
+                          output_data[i] = -2;
+                          output_ref_data[i] = -2;
+                        }
+
                         // compute
                         pool.SetParam(param);
                         pool.Run();
@@ -260,41 +252,24 @@ TEST(pool_arm, compute) {
                         pool_compute_ref(param);
 
                         // compare
-                  auto* output_data = output.mutable_data<float>();
-                  auto* output_ref_data = output_ref.mutable_data<float>();
                         for (int i = 0; i < output.dims().production(); i++) {
-                    EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
-                    float tmp = output_data[i] - output_ref_data[i];
-                    tmp = tmp < 0 ? -tmp : tmp;
-                    if (tmp > 1e-5) {
-                      std::cout << "output_data[0]:" << output_data[0]
-                                << std::endl;
-                      std::cout << "output_ref_data[0]:" << output_ref_data[0]
-                                << std::endl;
-                      std::cout
-                          << "x.dims().production():" << x.dims().production()
-                          << std::endl;
-                      for (int ii = 0; ii < x.dims().production(); ++ii) {
-                        std::cout << x_data[ii] << " ";
-                      }
-                      std::cout;
-                      exit(0);
-                    }
+                          EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-4);
                         }
-
                         VLOG(3) << "compare pass";
                       }
                     }
                   }
                 }
-        }  // pad
-      }    // stride
-           //} // ksize TODO(yuanshuai): ksize enable
-    }      // global_pooling
-  }        // pooling_type
+              }
+            }
+          }
+        }
+      }
+    }
+  }
 }
 
-TEST(pool, retrive_op) {
+TEST(pool_arm, retrive_op) {
   auto pool = KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>(
       "pool2d");
   ASSERT_FALSE(pool.empty());