Add input channels consideration refered from ncnn

CMakeLists.txt

 cmake_minimum_required(VERSION 3.0.0)
 
 option(USE_OPENMP "openmp support" ON)
-option(DEBUGING "enable debug mode" ON)
-option(USE_EXCEPTION "use std exception" ON)
+option(DEBUGING "enable debug mode" OFF)
+option(USE_EXCEPTION "use std exception" OFF)
 option(SYMBOL_HIDDEN "symbol hidden" OFF) # on when use jni or ios io
 option(LOG_PROFILE "log profile" OFF)
 # select the platform to build
@@ -247,6 +247,5 @@ elseif(FPGA)
     add_subdirectory(test)
 endif()
 
-
-
+add_subdirectory(test)
 

src/framework/executor.cpp

@@ -30,7 +30,6 @@ limitations under the License. */
 
 #ifdef PADDLE_EXECUTOR_MULTITHREAD
 #include <queue>
-#include <utility>
 #include "common/threadpool.h"
 #endif
 
@@ -96,13 +95,12 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
 }
 
 template <typename Dtype>
-static void LoadMemInternal(void **data, framework::LoDTensor *tensor,
-                            bool quant_uint8 = false) {
+void LoadMemInternal(void **data, framework::LoDTensor *tensor) {
   char **data_buf = reinterpret_cast<char **>(data);
   int64_t size = tensor->numel();
   Dtype *tensor_data = tensor->mutable_data<Dtype>();
-  if (quant_uint8) {
-    // should be moved into operator init function
+  if (0) {
+    // TODO(hjchen2) should be moved into operator init function
     float min_value;
     float max_value;
     memory::Copy(&min_value, data_buf, sizeof(float));
@@ -158,8 +156,7 @@ void Executor<Dtype, P>::LoadMemory(
   // parse tensor from stream
   switch (tensor_desc.DataType()) {
     case framework::VARTYPE_TYPE_FP32:
-      LoadMemInternal<float>(reinterpret_cast<void **>(data_buf), tensor,
-                             program_.quantification);
+      LoadMemInternal<float>(reinterpret_cast<void **>(data_buf), tensor);
       break;
     case framework::VARTYPE_TYPE_INT8:
       LoadMemInternal<int8_t>(reinterpret_cast<void **>(data_buf), tensor);
@@ -266,6 +263,7 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
   framework::Variable *g_feed_value = program_.scope->Var("feed");
   framework::Tensor *feed_tensor =
       g_feed_value->GetMutable<framework::LoDTensor>();
+  DLOG << "feed_tensor dim: " << feed_tensor->dims();
   feed_tensor->Resize(t.dims());
   feed_tensor->ShareDataWith(t);
   std::shared_ptr<framework::BlockDesc> to_predict_block =
@@ -300,8 +298,16 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
   for (int i = 0; i < profile.size(); i++) {
     const auto &pInfo = profile[i];
     uint64_t timeCost = pInfo.runEnd - pInfo.runBegin;
+    if (ops[i]->Type() == "conv2d") {
+      auto inputs = ops[i]->Inputs();
+      auto *filter = framework::GetVarValue<framework::LoDTensor>(
+          "Filter", inputs, *(program_.scope));
+      int kernel_size = filter->dims()[2];
+      _tp[ops[i]->Type() + "_" + std::to_string(kernel_size)] += timeCost;
+    } else {
       _tp[ops[i]->Type()] += timeCost;
     }
+  }
   printf("====================[ profile ]======================\n");
   using prof_t = std::pair<std::string, uint64_t>;
   std::vector<prof_t> _tv(_tp.begin(), _tp.end());
@@ -370,6 +376,14 @@ std::shared_ptr<framework::LoDTensor> Executor<Dtype, P>::PredictLod(
   for (int i = 0; i < profile.size(); i++) {
     const auto &pInfo = profile[i];
     uint64_t timeCost = pInfo.runEnd - pInfo.runBegin;
+    if (ops[i]->Type() == "conv2d") {
+      auto inputs = ops[i]->Inputs();
+      auto input_keys = ops[i]->GetInputKeys();
+      auto *filter = framework::GetVarValue<framework::LoDTensor>(
+          input_keys[1], inputs, *(program_.scope));
+      int kernel_size = filter->dims()[2];
+      printf("kernel size: %d\n", kernel_size);
+    }
     _tp[ops[i]->Type()] += timeCost;
   }
   printf("====================[ profile ]======================\n");

src/operators/kernel/arm/conv_kernel.cpp

@@ -40,7 +40,8 @@ bool ConvKernel<CPU, float>::Init(ConvParam<CPU> *param) {
                param->Dilations()[0] == param->Dilations()[1] &&
                param->Filter()->dims()[2] == 3 && param->Strides()[0] == 1 &&
                param->Dilations()[0] == 1 && param->Output()->dims()[1] >= 16 &&
-               param->Input()->dims()[2] >= 16) {
+               param->Input()->dims()[1] >= 16 &&
+               param->Input()->dims()[2] <= 140 /* refered from ncnn */) {
       param->ExecMode() = ConvParam<CPU>::EXEC_WINOGRAD3X3_FLOAT;
       // transform weight
       framework::Tensor *transformed_weight = new framework::Tensor;

tools/toolchains/arm-android-neon.cmake

@@ -3,3 +3,4 @@ set(ANDROID_PIE TRUE)
 set(ANDROID_STL "c++_static")
 set(ANDROID_PLATFORM "android-22")
 include("${CMAKE_CURRENT_LIST_DIR}/../android-cmake/android.toolchain.cmake")
+#include("/Users/chenhoujiang/Project/android-ndk-r16b/build/cmake/android.toolchain.cmake")