fix conflict

lite/backends/arm/math/conv3x3s2p01_depthwise_fp32.cc

@@ -15,6 +15,7 @@
 #include <arm_neon.h>
 #include "lite/backends/arm/math/conv_block_utils.h"
 #include "lite/backends/arm/math/conv_depthwise.h"
+#inlcude "lite/backends/arm/math/conv3x3s2p01_depthwise_fp32_relu.cc"
 
 namespace paddle {
 namespace lite {

lite/kernels/arm/pool_compute.cc

@@ -47,13 +47,6 @@ void PoolCompute::Run() {
   bool use_quantizer = param.use_quantizer;
   std::string& data_format = param.data_format;
 
-<<<<<<< HEAD
-  bool pads_equal =
-      (paddings[0] == paddings[1]) && (paddings[2] == paddings[3]);
-
-  bool kps_equal = (ksize[0] == ksize[1]) && (strides[0] == strides[1]) &&
-                   (paddings[0] == paddings[2]);
-=======
   bool pads_less =
       (paddings[0] == paddings[2]) && (paddings[1] < 2) && (paddings[3] < 2);
 
@@ -65,7 +58,7 @@ void PoolCompute::Run() {
   bool global_pooling = (paddings[0] == 0) && (ksize[0] == in_dims[2]) &&
                         (ksize[1] == in_dims[3]) && kps_equal && pads_equal;
   global_pooling = param.global_pooling || global_pooling;
->>>>>>> PaddlePaddle-develop1
+
   if (global_pooling) {
     for (size_t i = 0; i < ksize.size(); ++i) {
       paddings[2 * i] = 0;
@@ -96,10 +89,6 @@ void PoolCompute::Run() {
       return;
     }
   } else {
-<<<<<<< HEAD
-    if (ksize[0] == 2 && strides[0] == 2 && paddings[0] == 0 && pads_equal &&
-        kps_equal) {
-=======
     if (ksize[0] == 1 && strides[0] == 2 && paddings[0] == 0 && kps_equal) {
       auto& ctx = this->ctx_->template As<ARMContext>();
       if (pooling_type == "max") {
@@ -118,7 +107,6 @@ void PoolCompute::Run() {
       }
     } else if (ksize[0] == 2 && strides[0] == 2 && paddings[0] == 0 &&
                kps_equal) {
->>>>>>> PaddlePaddle-develop1
       if (pooling_type == "max") {
         lite::arm::math::pooling2x2s2_max(din,
                                           dout,

lite/kernels/cuda/pool_compute_test.cc

@@ -27,8 +27,6 @@ namespace cuda {
 using Tensor = lite::Tensor;
 using DDim = lite::DDim;
 
-<<<<<<< HEAD
-=======
 #define IN(n, c, h, w)                                 \
   input_data[w + h * input_w + c * input_h * input_w + \
              n * input_c * input_h * input_w]
@@ -94,7 +92,6 @@ void nhwc2nchw_ref(lite::Tensor* input, lite::Tensor* output) {
   }
 }
 
->>>>>>> PaddlePaddle-develop1
 static int PoolOutputSize(int input_size,
                           int filter_size,
                           int pad_left,

lite/kernels/npu/bridges/pool_op.cc

@@ -64,7 +64,6 @@ int PoolConverter(void* ctx, OpLite* op, KernelBase* kernel) {
     return FAILED;
   }
 
-<<<<<<< HEAD
   auto padding = op_info->GetAttr<std::vector<int>>("paddings");
   bool pads_equal = (padding[0] == padding[1]) && (padding[2] == padding[3]);
   if (!pads_equal) {
@@ -78,7 +77,7 @@ int PoolConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   int npu_ceil_mode = 0;
   if (op_info->HasAttr("ceil_mode")) {
     npu_ceil_mode = op_info->GetAttr<bool>("ceil_mode") ? 1 : 0;
-=======
+
   // pad mode
   int pad_mode = 0;
   std::string padding_algorithm("");
@@ -89,7 +88,6 @@ int PoolConverter(void* ctx, OpLite* op, KernelBase* kernel) {
     pad_mode = 6;
   } else if (padding_algorithm == "VALID") {
     pad_mode = 5;
->>>>>>> PaddlePaddle-develop1
   }
 
   // paddings and strides

lite/operators/pool_op.cc

@@ -41,7 +41,6 @@ bool PoolOpLite::CheckShape() const {
   return true;
 }
 
-<<<<<<< HEAD
 inline void UpdatePadding(std::vector<int>* paddings,
                           const bool global_pooling,
                           const bool adaptive,
@@ -75,8 +74,6 @@ inline void UpdatePadding(std::vector<int>* paddings,
   }
 }
 
-=======
->>>>>>> PaddlePaddle-develop1
 int PoolOutputSize(int input_size,
                    int filter_size,
                    int pad_left,

lite/operators/pool_op.h

@@ -14,10 +14,7 @@
 
 #pragma once
 
-<<<<<<< HEAD
-=======
 #include <algorithm>
->>>>>>> PaddlePaddle-develop1
 #include <memory>
 #include <string>
 #include <vector>