Add pooling2x2 neon implementation

src/operators/kernel/arm/pool_kernel.cpp

@@ -15,7 +15,8 @@ limitations under the License. */
 #ifdef POOL_OP
 
 #include "operators/kernel/pool_kernel.h"
-#include "../central-arm-func/pool_arm_func.h"
+#include "operators/kernel/central-arm-func/pool_arm_func.h"
+
 namespace paddle_mobile {
 namespace operators {
 
@@ -28,7 +29,8 @@ template <>
 void PoolKernel<CPU, float>::Compute(const PoolParam<CPU> &param) {
   PoolCompute<float>(param);
 }
+
 }  // namespace operators
 }  // namespace paddle_mobile
 
-#endif
+#endif  // POOL_OP

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

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #ifdef POOL_OP
+
 #pragma once
 
 #include <string>
@@ -54,8 +55,24 @@ void PoolCompute(const PoolParam<CPU> &param) {
       } else {
         math::Pooling<AVG>()(*input, ksize, strides, paddings, output);
       }
-    } else {
-      // Others
+    }
+  } else if (ksize[0] == 2 && ksize[0] == ksize[1]) {
+    if (pooling_type == "max" && strides[0] == strides[1]) {
+      if (strides[0] == 1) {
+        math::Pooling2x2<MAX, 1>()(*input, paddings, output);
+      } else if (strides[0] == 2) {
+        math::Pooling2x2<MAX, 2>()(*input, paddings, output);
+      } else {
+        math::Pooling<MAX>()(*input, ksize, strides, paddings, output);
+      }
+    } else if (pooling_type == "avg" && strides[0] == strides[1]) {
+      if (strides[0] == 1) {
+        math::Pooling2x2<AVG, 1>()(*input, paddings, output);
+      } else if (strides[0] == 2) {
+        math::Pooling2x2<AVG, 2>()(*input, paddings, output);
+      } else {
+        math::Pooling<AVG>()(*input, ksize, strides, paddings, output);
+      }
     }
   } else {
     if (pooling_type == "max") {

src/operators/math/depthwise_conv5x5.cpp

@@ -12,8 +12,6 @@ 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
-
 #if defined(__ARM_NEON__) && !defined(__aarch64__)
 
 #include "operators/math/depthwise_conv5x5.h"
@@ -81,7 +79,6 @@ inline void DepthwiseConv5x5NormalRow(const float *input, const float *filter,
 
   int valid_w_start = (padding_w + Stride_w - 1) / Stride_w;
   int valid_w_end = output_w - valid_w_start;
-
   float *output_ptr = output + h_output * output_w;
   // border left
   DEPTHWISE_CONV_NORMAL_BORDER(0, valid_w_start)
@@ -111,6 +108,8 @@ inline void DepthwiseConv5x5NormalRow(const float *input, const float *filter,
     _sum = vdupq_n_f32(0.f);
     int remain_start = valid_w_start + (output_tiles << 2);
     int input_w_offset = remain_start * Stride_w - padding_w;
+    float *output_ptr0 = output_ptr + remain_start;
+
     for (int h_in = h_start; h_in < h_end; ++h_in) {
       int index = h_in - h_in_start;
       Depth5x5NormalRowLoadInput<Stride_w>(
@@ -123,14 +122,14 @@ inline void DepthwiseConv5x5NormalRow(const float *input, const float *filter,
     }
     switch (remain) {
       case 1:
-        vst1_lane_f32(output_ptr + remain_start, vget_low_f32(_sum), 0);
+        vst1_lane_f32(output_ptr0, vget_low_f32(_sum), 0);
         break;
       case 2:
-        vst1_f32(output_ptr + remain_start, vget_low_f32(_sum));
+        vst1_f32(output_ptr0, vget_low_f32(_sum));
         break;
       case 3:
-        vst1_f32(output_ptr + remain_start, vget_low_f32(_sum));
-        vst1_lane_f32(output_ptr + remain_start + 2, vget_high_f32(_sum), 0);
+        vst1_f32(output_ptr0, vget_low_f32(_sum));
+        vst1_lane_f32(output_ptr0 + 2, vget_high_f32(_sum), 0);
         break;
     }
   }

test/operators/test_pool_op.cpp

@@ -169,28 +169,55 @@ int main(int argc, char *argv[]) {
       << "float, pooling_type=avg, kernel=3, pad=5, stride=2";
   paddle_mobile::TestPoolOp<1, 3, 5, 2>(in_channels, in_height, in_width);
 
-  //  // kernel = 5, pad = 0, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, ceil_mode=false, pooling_type=avg, kernel=5, pad=0,
-  //      stride=1";
-  //  paddle_mobile::TestPoolOp<float, 0, 1, 5, 0, 1>(in_channels, in_height,
-  //                                                  in_width);
-  //  // kernel = 5, pad = 0, stride = 2
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, ceil_mode=false, pooling_type=avg, kernel=5, pad=0,
-  //      stride=1";
-  //  paddle_mobile::TestPoolOp<float, 0, 1, 5, 0, 2>(in_channels, in_height,
-  //                                                  in_width);
-  //  // kernel = 7, pad = 0, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, ceil_mode=false, pooling_type=avg, kernel=7, pad=0,
-  //      stride=1";
-  //  paddle_mobile::TestPoolOp<float, 0, 1, 7, 0, 1>(in_channels, in_height,
-  //                                                  in_width);
-  //  // kernel = 7, pad = 0, stride = 4
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, ceil_mode=false, pooling_type=avg, kernel=7, pad=0,
-  //      stride=4";
-  //  paddle_mobile::TestPoolOp<float, 0, 1, 7, 0, 4>(in_channels, in_height,
-  //                                                  in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=0, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 0, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=1, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 1, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=2, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 2, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=5, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 5, 1>(in_channels, in_height, in_width);
+
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=0, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 0, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=1, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 1, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=2, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 2, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=5, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 5, 1>(in_channels, in_height, in_width);
+
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=0, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 0, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=1, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 1, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=2, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 2, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=5, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 5, 2>(in_channels, in_height, in_width);
+
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=0, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 0, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=1, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 1, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=2, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 2, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=5, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 5, 2>(in_channels, in_height, in_width);
 }