Merge branch 'conv2d-padding' into 'master'

Change conv2d functor to member variable.

See merge request !52

mace/kernels/conv_2d.h

@@ -6,23 +6,39 @@
 #define MACE_KERNELS_CONV_2D_H_
 
 #include "mace/core/tensor.h"
+#include "mace/kernels/conv_pool_2d_util.h"
 
 namespace mace {
 namespace kernels {
 
-template <DeviceType D, typename T>
+template<DeviceType D, typename T>
 class Conv2dFunctor {
  public:
-  Conv2dFunctor(const int* strides, const int* paddings, const int* dilations)
-      : strides_(strides), paddings_(paddings), dilations_(dilations) {}
-
-  void operator()(const T* input,  // NCHW
-                  const index_t* input_shape,
-                  const T* filter,  // c_out, c_in, kernel_h, kernel_w
-                  const index_t* filter_shape,
-                  const T* bias,  // c_out
-                  T* output,      // NCHW
-                  const index_t* output_shape) {
+  Conv2dFunctor(const index_t *input_shape,
+                const index_t *filter_shape,
+                const int *strides,
+                const Padding padding,
+                const int *dilations) :
+      strides_(strides),
+      paddings_(2, 0),
+      dilations_(dilations) {
+    CalPaddingSize(input_shape, filter_shape, dilations_, strides_, padding, paddings_.data());
+  }
+
+  Conv2dFunctor(const int *strides,
+                const std::vector<int> &paddings,
+                const int *dilations) :
+      strides_(strides),
+      paddings_(paddings),
+      dilations_(dilations) {}
+
+  void operator()(const T *input,  // NCHW
+                  const index_t *input_shape,
+                  const T *filter,  // c_out, c_in, kernel_h, kernel_w
+                  const index_t *filter_shape,
+                  const T *bias,  // c_out
+                  T *output,      // NCHW
+                  const index_t *output_shape) {
     MACE_CHECK_NOTNULL(output);
 
     index_t batch = output_shape[0];
@@ -62,7 +78,7 @@ class Conv2dFunctor {
             index_t offset = n * channels * height * width +
                 c * height * width + h * width + w;
             T sum = 0;
-            const T* filter_ptr = filter + c * kernel_size;
+            const T *filter_ptr = filter + c * kernel_size;
             for (int inc = 0; inc < input_channels; ++inc) {
               for (int kh = 0; kh < kernel_h; ++kh) {
                 for (int kw = 0; kw < kernel_w; ++kw) {
@@ -95,20 +111,20 @@ class Conv2dFunctor {
   }
 
  private:
-  const int* strides_;    // [stride_h, stride_w]
-  const int* paddings_;   // [padding_h, padding_w]
-  const int* dilations_;  // [dilation_h, dilation_w]
+  const int *strides_;    // [stride_h, stride_w]
+  std::vector<int> paddings_;   // [padding_h, padding_w]
+  const int *dilations_;  // [dilation_h, dilation_w]
 };
 
-template <>
+template<>
 void Conv2dFunctor<DeviceType::NEON, float>::operator()(
-    const float* input,
-    const index_t* input_shape,
-    const float* filter,
-    const index_t* filter_shape,
-    const float* bias,
-    float* output,
-    const index_t* output_shape);
+    const float *input,
+    const index_t *input_shape,
+    const float *filter,
+    const index_t *filter_shape,
+    const float *bias,
+    float *output,
+    const index_t *output_shape);
 
 }  //  namespace kernels
 }  //  namespace mace

mace/kernels/neon/batch_norm_neon.cc

@@ -44,8 +44,7 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()(
 
       for (index_t j = 0; j < count; ++j) {
         float32x4_t input_f = vld1q_f32(input_sample_ptr);
-        float32x4_t output_f = new_offset_f;
-        output_f = vfmaq_f32(output_f, input_f, new_scale_f);
+        float32x4_t output_f = vfmaq_f32(new_offset_f, input_f, new_scale_f);
         vst1q_f32(output_sample_ptr, output_f);
         input_sample_ptr += 4;
         output_sample_ptr += 4;

mace/kernels/neon/conv_2d_neon.cc

@@ -81,7 +81,7 @@ void Conv2dFunctor<DeviceType::NEON, float>::operator()(const float *input,
   // Keep this alive during kernel execution
   Tensor padded_input;
   if (paddings_[0] > 0 || paddings_[1] > 0) {
-    ConstructInputWithPadding(input, input_shape, paddings_, &padded_input);
+    ConstructInputWithPadding(input, input_shape, paddings_.data(), &padded_input);
     input = padded_input.data<float>();
     input_shape = padded_input.shape().data();
   }

mace/ops/conv_2d.h

@@ -17,7 +17,12 @@ template<DeviceType D, typename T>
 class Conv2dOp : public ConvPool2dOpBase<D, T> {
  public:
   Conv2dOp(const OperatorDef &op_def, Workspace *ws)
-      : ConvPool2dOpBase<D, T>(op_def, ws) {};
+      : ConvPool2dOpBase<D, T>(op_def, ws),
+        functor_(this->Input(INPUT)->shape().data(),
+                 this->Input(FILTER)->shape().data(),
+                 this->strides_.data(),
+                 this->padding_,
+                 this->dilations_.data()) {}
 
   bool Run() override {
     const Tensor *input = this->Input(INPUT);
@@ -27,21 +32,19 @@ class Conv2dOp : public ConvPool2dOpBase<D, T> {
 
     std::vector<index_t> output_shape(4);
     std::vector<int> paddings(2);
-    kernels::CalcPaddingAndOutputSize(
-        input->shape().data(), filter->shape().data(), this->dilations_.data(),
-        this->strides_.data(), this->padding_, output_shape.data(),
-        paddings.data());
+    this->CalOutputSize(input->shape().data(), filter->shape().data(), output_shape.data());
     output->Resize(output_shape);
 
-    auto conv2d = kernels::Conv2dFunctor<D, T>(
-        this->strides_.data(), paddings.data(), this->dilations_.data());
-    conv2d(input->data<T>(), input->shape().data(), filter->data<T>(),
+    functor_(input->data<T>(), input->shape().data(), filter->data<T>(),
              filter->shape().data(), bias->data<T>(), output->mutable_data<T>(),
              output->shape().data());
 
     return true;
   }
 
+ private:
+  kernels::Conv2dFunctor<D, T> functor_;
+
  protected:
   OP_INPUT_TAGS(INPUT, FILTER, BIAS);
   OP_OUTPUT_TAGS(OUTPUT);