Refactor conv2d NEON implementations

mace/core/tensor.h

@@ -288,7 +288,8 @@ class Tensor {
       }
       CASES(dtype_, (os << (this->data<T>()[i]) << ", "));
     }
-    LOG(INFO) << os.str();
+    LOG(INFO) << "Tensor size: [" << dim(0) << ", " << dim(1) << ", "
+              << dim(2) << ", " << dim(3) << "], content:\n" << os.str();
   }
 
   inline size_t SizeOfType() const {

mace/kernels/BUILD

@@ -15,15 +15,14 @@ cc_library(
         "*.cc",
         "opencl/*.cc",
     ]) + if_neon_enabled(glob([
-        "neon/*.cc",
+        "neon/addn_neon.cc",
+        "neon/batch_norm_neon.cc",
     ])),
     hdrs = glob([
         "*.h",
         "opencl/*.h",
-    ]) + if_neon_enabled(glob([
-        "neon/*.h",
-    ])),
-    copts = if_openmp_enabled(["-fopenmp"]),
+    ]),
+    copts = if_openmp_enabled(["-fopenmp"]) + if_neon_enabled(["-DMACE_ENABLE_NEON"]),
     linkopts = if_android(["-lm"]),
     deps = [
         "//mace/core",

mace/kernels/addn.h

@@ -5,6 +5,10 @@
 #ifndef MACE_KERNELS_ADDN_H_
 #define MACE_KERNELS_ADDN_H_
 
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+#include <arm_neon.h>
+#endif
+
 #include "mace/core/future.h"
 #include "mace/core/tensor.h"
 #include "mace/core/runtime/opencl/cl2_header.h"
@@ -12,7 +16,6 @@
 namespace mace {
 namespace kernels {
 
-
 template <DeviceType D, typename T>
 struct AddNFunctor {
   void operator()(const std::vector<const Tensor *> &input_tensors,
@@ -47,7 +50,7 @@ struct AddNFunctor<DeviceType::OPENCL, T> {
   cl::Kernel kernel_;
 };
 
-}  //  namespace kernels
-}  //  namespace mace
+}  // namespace kernels
+}  // namespace mace
 
 #endif  // MACE_KERNELS_ADDN_H_

mace/kernels/batch_norm.h

@@ -136,7 +136,7 @@ struct BatchNormFunctor<DeviceType::OPENCL, T> : BatchNormFunctorBase {
   cl::Kernel kernel_;
 };
 
-}  //  namepsace kernels
-}  //  namespace mace
+}  // namepsace kernels
+}  // namespace mace
 
-#endif  //  MACE_KERNELS_BATCH_NORM_H_
+#endif  // MACE_KERNELS_BATCH_NORM_H_

mace/kernels/conv_2d.h

@@ -5,14 +5,176 @@
 #ifndef MACE_KERNELS_CONV_2D_H_
 #define MACE_KERNELS_CONV_2D_H_
 
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+#include <arm_neon.h>
+#endif
+
 #include "mace/core/future.h"
+#include "mace/core/runtime/opencl/cl2_header.h"
 #include "mace/core/tensor.h"
 #include "mace/kernels/activation.h"
 #include "mace/kernels/conv_pool_2d_util.h"
-#include "mace/core/runtime/opencl/cl2_header.h"
+#include "mace/utils/utils.h"
 
 namespace mace {
 namespace kernels {
+namespace {
+
+template <typename T,
+          int inc_tile_size,
+          int c_count,
+          int h_count,
+          int w_count>
+void Conv2dKernelFunc(const T *input_ptr,  // batch start
+                      const T *filter_ptr,
+                      const T *bias_ptr,
+                      T *output_ptr,  // batch start
+                      const int h_offset,
+                      const int w_offset,
+                      const int c_offset,
+                      const int kernel_h,
+                      const int kernel_w,
+                      const int stride_h,
+                      const int stride_w,
+                      const int dilation_h,
+                      const int dilation_w,
+                      const int channels,
+                      const int input_channels,
+                      const int width,
+                      const int padded_width) {
+  T sum[h_count * w_count * c_count] = {0.0f};
+  if (bias_ptr != nullptr) {
+    for (int hi = 0; hi < h_count; ++hi) {
+      for (int wi = 0; wi < w_count; ++wi) {
+        for (int ci = 0; ci < c_count; ++ci) {
+          const int sum_idx = (hi * w_count + wi) * c_count + ci;
+          sum[sum_idx] = bias_ptr[c_offset + ci];
+        }
+      }
+    }
+  }
+
+  for (int kh = 0; kh < kernel_h; ++kh) {
+    for (int kw = 0; kw < kernel_w; ++kw) {
+      int inc = 0;
+      for (; inc + inc_tile_size <= input_channels; inc += inc_tile_size) {
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+        // AArch64 NEON has 32 128-bit general purpose registers
+        static_assert(inc_tile_size == 4, "input channels tile size must be 4");
+        float32x4_t in[h_count * w_count];
+#else
+        T in[h_count * w_count * inc_tile_size];
+#endif
+        for (int hi = 0; hi < h_count; ++hi) {
+          for (int wi = 0; wi < w_count; ++wi) {
+            const int in_idx = hi * w_count + wi;
+            const int inh = (h_offset + hi) * stride_h + kh * dilation_h;
+            const int inw = (w_offset + wi) * stride_w + kw * dilation_w;
+            const int in_offset =
+                (inh * padded_width + inw) * input_channels + inc;
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+            static_assert(inc_tile_size == 4,
+                          "input channels tile size must be 4");
+            in[in_idx] = vld1q_f32(input_ptr + in_offset);
+#else
+            for (int inci = 0; inci < inc_tile_size; ++inci) {
+              in[in_idx * inc_tile_size + inci] = input_ptr[in_offset + inci];
+            }
+#endif
+          }
+        }
+
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+        static_assert(inc_tile_size == 4, "input channels tile size must be 4");
+        float32x4_t weights[c_count];
+#else
+        T weights[c_count * inc_tile_size];
+#endif
+        for (int ci = 0; ci < c_count; ++ci) {
+          const int weights_idx = ci;
+          const int filter_offset =
+              ((kh * kernel_w + kw) * channels + c_offset + ci) *
+                  input_channels +
+              inc;
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+          weights[weights_idx] = vld1q_f32(filter_ptr + filter_offset);
+#else
+          for (int inci = 0; inci < inc_tile_size; ++inci) {
+            weights[weights_idx * inc_tile_size + inci] =
+                filter_ptr[filter_offset + inci];
+          }
+#endif
+        }
+        for (int hi = 0; hi < h_count; ++hi) {
+          for (int wi = 0; wi < w_count; ++wi) {
+            for (int ci = 0; ci < c_count; ++ci) {
+              const int weights_idx = ci;
+              const int in_idx = hi * w_count + wi;
+              const int sum_idx = (hi * w_count + wi) * c_count + ci;
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+              float32x4_t tmp = vmulq_f32(in[in_idx], weights[weights_idx]);
+              sum[sum_idx] += vaddvq_f32(tmp);
+#else
+              for (int inci = 0; inci < inc_tile_size; ++inci) {
+                sum[sum_idx] +=
+                    in[in_idx * inc_tile_size + inci] *
+                    weights[weights_idx * inc_tile_size + inci];
+              }
+#endif
+            }
+          }
+        }
+      }
+      // handling the remaining input channels
+      for (; inc < input_channels; ++inc) {
+        T in[h_count * w_count];
+        for (int hi = 0; hi < h_count; ++hi) {
+          for (int wi = 0; wi < w_count; ++wi) {
+            const int in_idx = hi * w_count + wi;
+            const int inh = (h_offset + hi) * stride_h + kh * dilation_h;
+            const int inw = (w_offset + wi) * stride_w + kw * dilation_w;
+            const int in_offset =
+                (inh * padded_width + inw) * input_channels + inc;
+            in[in_idx] = input_ptr[in_offset];
+          }
+        }
+
+        T weights[c_count];
+        for (int ci = 0; ci < c_count; ++ci) {
+          const int weights_idx = ci;
+          const int filter_offset =
+              ((kh * kernel_w + kw) * channels + c_offset + ci) *
+                  input_channels +
+              inc;
+          weights[weights_idx] = filter_ptr[filter_offset];
+        }
+        for (int hi = 0; hi < h_count; ++hi) {
+          for (int wi = 0; wi < w_count; ++wi) {
+            for (int ci = 0; ci < c_count; ++ci) {
+              const int weights_idx = ci;
+              const int in_idx = hi * w_count + wi;
+              const int sum_idx = (hi * w_count + wi) * c_count + ci;
+              sum[sum_idx] += in[in_idx] * weights[weights_idx];
+            }
+          }
+        }
+      }
+    }
+  }
+  // save output
+  for (int hi = 0; hi < h_count; ++hi) {
+    for (int wi = 0; wi < w_count; ++wi) {
+      for (int ci = 0; ci < c_count; ++ci) {
+        const int out_offset =
+            ((h_offset + hi) * width + w_offset + wi) * channels + c_offset +
+            ci;
+        const int sum_idx = (hi * w_count + wi) * c_count + ci;
+        output_ptr[out_offset] = sum[sum_idx];
+      }
+    }
+  }
+}
+};  // namespace
 
 struct Conv2dFunctorBase {
   Conv2dFunctorBase(const int *strides,
@@ -28,7 +190,7 @@ struct Conv2dFunctorBase {
         relux_max_limit_(relux_max_limit),
         prelu_alpha_(prelu_alpha) {}
 
-  const int *strides_;    // [stride_h, stride_w]
+  const int *strides_;  // [stride_h, stride_w]
   const Padding paddings_;
   const int *dilations_;  // [dilation_h, dilation_w]
   const ActivationType activation_;
@@ -51,8 +213,8 @@ struct Conv2dFunctor : Conv2dFunctorBase {
                           relux_max_limit,
                           prelu_alpha) {}
 
-  void operator()(const Tensor *input,  // NHWC
-                  const Tensor *filter, // HWIO
+  void operator()(const Tensor *input,   // NHWC
+                  const Tensor *filter,  // HWOI
                   const Tensor *bias,
                   Tensor *output,
                   StatsFuture *future) {
@@ -67,18 +229,21 @@ struct Conv2dFunctor : Conv2dFunctorBase {
         paddings_, output_shape.data(), paddings.data());
     output->Resize(output_shape);
 
-    index_t batch = output->dim(0);
-    index_t height = output->dim(1);
-    index_t width = output->dim(2);
-    index_t channels = output->dim(3);
+    int batch = output->dim(0);
+    int height = output->dim(1);
+    int width = output->dim(2);
+    int channels = output->dim(3);
 
-    index_t input_batch = input->dim(0);
-    index_t input_height = input->dim(1);
-    index_t input_width = input->dim(2);
-    index_t input_channels = input->dim(3);
+    int input_batch = input->dim(0);
+    int input_height = input->dim(1);
+    int input_width = input->dim(2);
+    int input_channels = input->dim(3);
 
-    index_t kernel_h = filter->dim(0);
-    index_t kernel_w = filter->dim(1);
+    int kernel_h = filter->dim(0);
+    int kernel_w = filter->dim(1);
+    MACE_CHECK(filter->dim(2) == channels, filter->dim(2), " != ", channels);
+    MACE_CHECK(filter->dim(3) == input_channels, filter->dim(3), " != ",
+               input_channels);
 
     int stride_h = strides_[0];
     int stride_w = strides_[1];
@@ -88,11 +253,17 @@ struct Conv2dFunctor : Conv2dFunctorBase {
 
     MACE_CHECK(batch == input_batch, "Input/Output batch size mismatch");
 
-    // The left-upper most offset of the padded input
-    int padded_h_start = 0 - paddings[0] / 2;
-    int padded_w_start = 0 - paddings[1] / 2;
-    index_t padded_h_stop = input_height + paddings[0] - paddings[0] / 2;
-    index_t padded_w_stop = input_width + paddings[1] - paddings[1] / 2;
+    int padded_height = input_height + paddings[0];
+    int padded_width = input_width + paddings[1];
+
+    Tensor padded_input;
+    // Keep this alive during kernel execution
+    if (paddings[0] > 0 || paddings[1] > 0) {
+      ConstructNHWCInputWithPadding(input, paddings.data(), &padded_input);
+      input = &padded_input;
+    }
+
+    // padded_input.DebugPrint();
 
     Tensor::MappingGuard input_mapper(input);
     Tensor::MappingGuard filter_mapper(filter);
@@ -103,40 +274,338 @@ struct Conv2dFunctor : Conv2dFunctorBase {
     auto bias_data = bias == nullptr ? nullptr : bias->data<T>();
     auto output_data = output->mutable_data<T>();
 
+    constexpr int inc_tile_size = 4;
+// TODO Auto tuning these parameters
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+    const int c_tile_size = 4;
+    const int h_tile_size = 2;
+    const int w_tile_size = 2;
+#else
+    const int c_tile_size = 4;
+    const int h_tile_size = 1;
+    const int w_tile_size = 2;
+#endif
+
+    const int c_tiles = RoundUpDiv(channels, c_tile_size);
+    const int h_tiles = RoundUpDiv(height, h_tile_size);
+    const int w_tiles = RoundUpDiv(width, w_tile_size);
+
 #pragma omp parallel for collapse(4)
     for (int n = 0; n < batch; ++n) {
-      for (int h = 0; h < height; ++h) {
-        for (int w = 0; w < width; ++w) {
-          for (int c = 0; c < channels; ++c) {
-            const int out_idx = ((n * height + h) * width + w) * channels + c;
-            T bias_channel = 0.0f;
-            if (bias) bias_channel = bias_data[c];
-            output_data[out_idx] = bias_channel;
-            T sum = 0.0f;
-            const T *filter_ptr = filter_data + c;
-            for (int kh = 0; kh < kernel_h; ++kh) {
-              for (int kw = 0; kw < kernel_w; ++kw) {
-                for (int inc = 0; inc < input_channels; ++inc) {
-                  int inh = padded_h_start + h * stride_h + dilation_h * kh;
-                  int inw = padded_w_start + w * stride_w + dilation_w * kw;
-                  if (inh < 0 || inh >= input_height || inw < 0 ||
-                      inw >= input_width) {
-                    MACE_CHECK(inh >= padded_h_start && inh < padded_h_stop &&
-                                   inw >= padded_w_start && inw < padded_w_stop,
-                               "Out of range read from input: ", inh, ", ",
-                               inw);
-                  } else {
-                    index_t input_offset =
-                        n * input_height * input_width * input_channels +
-                        inh * input_width * input_channels +
-                        inw * input_channels + inc;
-                    sum += input_data[input_offset] * *filter_ptr;
-                  }
-                  filter_ptr += channels;
+      for (int cb = 0; cb < c_tiles; ++cb) {
+        for (int hb = 0; hb < h_tiles; ++hb) {
+          for (int wb = 0; wb < w_tiles; ++wb) {
+            const T *input_ptr =
+                input_data + n * padded_height * padded_width * input_channels;
+            T *output_ptr = output_data + n * height * width * channels;
+            const int h_offset = hb * h_tile_size;
+            const int w_offset = wb * w_tile_size;
+            const int c_offset = cb * c_tile_size;
+
+            const int h_count = std::min(h_tile_size, height - h_offset);
+            const int w_count = std::min(w_tile_size, width - w_offset);
+            const int c_count = std::min(c_tile_size, channels - c_offset);
+
+            switch (c_count) {
+              case 1:
+                switch (h_count) {
+                  case 1:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 1, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 1, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 1, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 1, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  case 2:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 2, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 2, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 2, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 1, 2, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  default:
+                    LOG(FATAL) << "Unsupported height tile: " << h_count;
                 }
-              }
+                break;
+              case 2:
+                switch (h_count) {
+                  case 1:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 1, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 1, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 1, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 1, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  case 2:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 2, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 2, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 2, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 2, 2, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  default:
+                    LOG(FATAL) << "Unsupported height tile: " << h_count;
+                }
+                break;
+              case 3:
+                switch (h_count) {
+                  case 1:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 1, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 1, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 1, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 1, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  case 2:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 2, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 2, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 2, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 3, 2, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  default:
+                    LOG(FATAL) << "Unsupported height tile: " << h_count;
+                }
+                break;
+              case 4:
+                switch (h_count) {
+                  case 1:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 1, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 1, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 1, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 1, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  case 2:
+                    switch (w_count) {
+                      case 1:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 2, 1>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 2:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 2, 2>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 3:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 2, 3>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      case 4:
+                        Conv2dKernelFunc<T, inc_tile_size, 4, 2, 4>(
+                            input_ptr, filter_data, bias_data, output_ptr,
+                            h_offset, w_offset, c_offset, kernel_h, kernel_w,
+                            stride_h, stride_w, dilation_h, dilation_w,
+                            channels, input_channels, width, padded_width);
+                        break;
+                      default:
+                        LOG(FATAL) << "Unsupported width tile: " << w_count;
+                    }
+                    break;
+                  default:
+                    LOG(FATAL) << "Unsupported height tile: " << h_count;
+                }
+                break;
+              default:
+                LOG(FATAL) << "Unsupported channel tile: " << c_count;
             }
-            output_data[out_idx] += sum;
           }
         }
       }

mace/kernels/conv_pool_2d_util.cc

@@ -17,7 +17,7 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,   // NCHW
   MACE_CHECK(dilations[0] > 0 && dilations[1] > 0,
              "Invalid dilations, must >= 1");
   MACE_CHECK((dilations[0] == 1 || strides[0] == 1) &&
-      (dilations[1] == 1 || strides[1] == 1),
+                 (dilations[1] == 1 || strides[1] == 1),
              "If dilations > 1, strides should be 1");
   MACE_CHECK_NOTNULL(output_shape);
   MACE_CHECK_NOTNULL(padding_size);
@@ -51,7 +51,8 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,   // NCHW
       output_height = (input_shape[2] + k_extent_height - 2) / strides[0] + 1;
       output_width = (input_shape[3] + k_extent_width - 2) / strides[1] + 1;
       break;
-    default:MACE_CHECK(false, "Unsupported padding type: ", padding);
+    default:
+      MACE_CHECK(false, "Unsupported padding type: ", padding);
   }
 
   // Note: TensorFlow may padded one more on the right/bottom side
@@ -59,12 +60,10 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,   // NCHW
   // utilize the more centered features. We need to benchmark
   // based on the model accuracy.
 
-  padding_size[0] =
-      std::max<int>(0, (output_height - 1) * strides[0]
-          + k_extent_height - input_shape[2]);
-  padding_size[1] =
-      std::max<int>(0, (output_width - 1) * strides[1]
-          + k_extent_width - input_shape[3]);
+  padding_size[0] = std::max<int>(
+      0, (output_height - 1) * strides[0] + k_extent_height - input_shape[2]);
+  padding_size[1] = std::max<int>(
+      0, (output_width - 1) * strides[1] + k_extent_width - input_shape[3]);
 
   output_shape[0] = input_shape[0];
   output_shape[1] = output_channels;
@@ -73,7 +72,7 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,   // NCHW
 }
 
 void CalcNHWCPaddingAndOutputSize(const index_t *input_shape,   // NHWC
-                                  const index_t *filter_shape,  // HWIO
+                                  const index_t *filter_shape,  // HWOI
                                   const int *dilations,
                                   const int *strides,
                                   Padding padding,
@@ -82,7 +81,7 @@ void CalcNHWCPaddingAndOutputSize(const index_t *input_shape,   // NHWC
   MACE_CHECK(dilations[0] > 0 && dilations[1] > 0,
              "Invalid dilations, must >= 1");
   MACE_CHECK((dilations[0] == 1 || strides[0] == 1) &&
-      (dilations[1] == 1 || strides[1] == 1),
+                 (dilations[1] == 1 || strides[1] == 1),
              "If dilations > 1, strides should be 1");
   MACE_CHECK_NOTNULL(output_shape);
   MACE_CHECK_NOTNULL(padding_size);
@@ -98,7 +97,7 @@ void CalcNHWCPaddingAndOutputSize(const index_t *input_shape,   // NHWC
   index_t output_height = 0, output_width = 0;
   index_t kernel_height = filter_shape[0];
   index_t kernel_width = filter_shape[1];
-  index_t output_channels = filter_shape[3];
+  index_t output_channels = filter_shape[2];
 
   index_t k_extent_height = (kernel_height - 1) * dilations[0] + 1;
   index_t k_extent_width = (kernel_width - 1) * dilations[1] + 1;
@@ -116,7 +115,8 @@ void CalcNHWCPaddingAndOutputSize(const index_t *input_shape,   // NHWC
       output_height = (input_shape[1] + k_extent_height - 2) / strides[0] + 1;
       output_width = (input_shape[2] + k_extent_width - 2) / strides[1] + 1;
       break;
-    default:MACE_CHECK(false, "Unsupported padding type: ", padding);
+    default:
+      MACE_CHECK(false, "Unsupported padding type: ", padding);
   }
 
   // Note: TensorFlow may padded one more on the right/bottom side
@@ -124,12 +124,10 @@ void CalcNHWCPaddingAndOutputSize(const index_t *input_shape,   // NHWC
   // utilize the more centered features. We need to benchmark
   // based on the model accuracy.
 
-  padding_size[0] =
-      std::max<int>(0, (output_height - 1) * strides[0]
-          + k_extent_height - input_shape[1]);
-  padding_size[1] =
-      std::max<int>(0, (output_width - 1) * strides[1]
-          + k_extent_width - input_shape[2]);
+  padding_size[0] = std::max<int>(
+      0, (output_height - 1) * strides[0] + k_extent_height - input_shape[1]);
+  padding_size[1] = std::max<int>(
+      0, (output_width - 1) * strides[1] + k_extent_width - input_shape[2]);
 
   output_shape[0] = input_shape[0];
   output_shape[1] = output_height;
@@ -146,7 +144,7 @@ void CalPaddingSize(const index_t *input_shape,   // NCHW
   MACE_CHECK(dilations[0] > 0 && dilations[1] > 0,
              "Invalid dilations, must >= 1");
   MACE_CHECK((dilations[0] == 1 || strides[0] == 1) &&
-      (dilations[1] == 1 || strides[1] == 1),
+                 (dilations[1] == 1 || strides[1] == 1),
              "If dilations > 1, strides should be 1");
   MACE_CHECK_NOTNULL(padding_size);
 
@@ -167,19 +165,18 @@ void CalPaddingSize(const index_t *input_shape,   // NCHW
       output_height = (input_shape[2] + k_extent_height - 2) / strides[0] + 1;
       output_width = (input_shape[3] + k_extent_width - 2) / strides[1] + 1;
       break;
-    default:MACE_CHECK(false, "Unsupported padding type: ", padding);
+    default:
+      MACE_CHECK(false, "Unsupported padding type: ", padding);
   }
 
   // Note: TensorFlow may padded one more on the right/bottom side
   // TODO may be it's better to also truncate the left/top to
   // utilize the more centered features. We need to benchmark
   // based on the model accuracy.
-  padding_size[0] =
-      std::max<int>(0, (output_height - 1) * strides[0]
-          + k_extent_height - input_shape[2]);
-  padding_size[1] =
-      std::max<int>(0, (output_width - 1) * strides[1]
-          + k_extent_width - input_shape[3]);
+  padding_size[0] = std::max<int>(
+      0, (output_height - 1) * strides[0] + k_extent_height - input_shape[2]);
+  padding_size[1] = std::max<int>(
+      0, (output_width - 1) * strides[1] + k_extent_width - input_shape[3]);
 }
 
 void ConstructInputWithPadding(const Tensor *input_tensor,
@@ -206,18 +203,18 @@ void ConstructInputWithPadding(const Tensor *input_tensor,
   output_tensor->Resize(output_shape);
 
   Tensor::MappingGuard padded_output_mapper(output_tensor);
-  float *output_ptr = output_tensor->mutable_data<float>();
-  memset(output_ptr, 0, output_tensor->size() * sizeof(float));
+  float *output_data = output_tensor->mutable_data<float>();
+  memset(output_data, 0, output_tensor->size() * sizeof(float));
 
   // Skip the padded top rows
   if (padding_same_value) {
-#define COPY_INPUT            \
-    std::fill(output_ptr, output_ptr+padded_left, input[0]);           \
-    output_ptr += padded_left;                                         \
-    memcpy(output_ptr, input, width * sizeof(float));                  \
-    output_ptr += width;                                               \
-    std::fill(output_ptr , output_ptr + padded_right, input[width-1]); \
-    output_ptr += padded_right;
+#define COPY_INPUT                                                      \
+  std::fill(output_data, output_data + padded_left, input[0]);          \
+  output_data += padded_left;                                           \
+  memcpy(output_data, input, width * sizeof(float));                    \
+  output_data += width;                                                 \
+  std::fill(output_data, output_data + padded_right, input[width - 1]); \
+  output_data += padded_right;
 
     const int padded_bottom = paddings[0] - padded_top;
     const int padded_right = paddings[1] - padded_left;
@@ -239,19 +236,69 @@ void ConstructInputWithPadding(const Tensor *input_tensor,
     }
 #undef COPY_INPUT
   } else {
-    output_ptr += padded_top * output_width;
+    output_data += padded_top * output_width;
     for (int i = 0; i < batch; ++i) {
       for (int j = 0; j < channels; ++j) {
         for (int k = 0; k < height; ++k) {
-          memcpy(output_ptr + padded_left, input, width * sizeof(float));
+          memcpy(output_data + padded_left, input, width * sizeof(float));
           input += width;
-          output_ptr += output_width;
+          output_data += output_width;
         }
         // Skip the padded bottom in this channel and top in the next channel
-        output_ptr += paddings[0] * output_width;
+        output_data += paddings[0] * output_width;
       }
     }
   }
 }
-}  //  namespace kernels
-}  //  namespace mace
+
+void ConstructNHWCInputWithPadding(const Tensor *input_tensor,
+                                   const int *paddings,
+                                   Tensor *output_tensor,
+                                   bool padding_same_value) {
+  VLOG(1) << "input: " << input_tensor->NumElements();
+  Tensor::MappingGuard input_mapper(input_tensor);
+  const float *input = input_tensor->data<float>();
+  const index_t *input_shape = input_tensor->shape().data();
+
+  index_t batch = input_shape[0];
+  index_t height = input_shape[1];
+  index_t width = input_shape[2];
+  index_t channels = input_shape[3];
+
+  std::vector<index_t> output_shape(
+      {batch, paddings[0] + height, paddings[1] + width, channels});
+
+  const int output_height = output_shape[1];
+  const int output_width = output_shape[2];
+  const int padded_top = paddings[0] / 2;
+  const int padded_left = paddings[1] / 2;
+
+  output_tensor->Resize(output_shape);
+
+  Tensor::MappingGuard padded_output_mapper(output_tensor);
+  float *output_data = output_tensor->mutable_data<float>();
+  memset(output_data, 0, output_tensor->size() * sizeof(float));
+
+  // Skip the padded top rows
+  if (padding_same_value) {
+    LOG(FATAL) << "Not implemented";
+  } else {
+#pragma omp parallel for collapse(3)
+    for (int n = 0; n < batch; ++n) {
+      for (int h = 0; h < height; ++h) {
+        for (int w = 0; w < width; ++w) {
+          const float *input_ptr =
+              input + ((n * height + h) * width + w) * channels;
+          float *output_ptr =
+              output_data +
+              ((n * output_height + h + padded_top) * output_width + w +
+               padded_left) *
+                  channels;
+          memcpy(output_ptr, input_ptr, channels * sizeof(float));
+        }
+      }
+    }
+  }
+}
+}  // namespace kernels
+}  // namespace mace

mace/kernels/conv_pool_2d_util.h

@@ -44,6 +44,12 @@ void ConstructInputWithPadding(const Tensor *input,
                                const int *paddings,
                                Tensor *output_tensor,
                                bool padding_same_value = false);
+
+void ConstructNHWCInputWithPadding(const Tensor *input,
+                                   const int *paddings,
+                                   Tensor *output_tensor,
+                                   bool padding_same_value = false);
+
 }  //  namespace kernels
 }  //  namespace mace
 

mace/kernels/depthwise_conv2d.h

@@ -64,8 +64,8 @@ struct DepthwiseConv2dFunctor : public DepthwiseConv2dFunctorBase {
     std::vector<index_t> fake_filter_shape(4);
     fake_filter_shape[0] = filter->shape()[0];
     fake_filter_shape[1] = filter->shape()[1];
-    fake_filter_shape[3] = filter->shape()[2] * filter->shape()[3];
-    fake_filter_shape[2] = 1;
+    fake_filter_shape[2] = filter->shape()[2] * filter->shape()[3];
+    fake_filter_shape[3] = 1;
 
     std::vector<index_t> output_shape(4);
     std::vector<int> paddings(2);

mace/kernels/neon/addn_neon.cc

@@ -10,9 +10,11 @@ namespace kernels {
 
 template <>
 void AddNFunctor<DeviceType::NEON, float>::operator()(
-    const std::vector<const Tensor *> &input_tensors, Tensor *output_tensor,
+    const std::vector<const Tensor *> &input_tensors,
+    Tensor *output_tensor,
     StatsFuture *future) {
   // TODO: neon mem copy
+  output_tensor->ResizeLike(input_tensors[0]);
   index_t size = output_tensor->size();
   float *output_ptr = output_tensor->mutable_data<float>();
   memset(output_ptr, 0, size * sizeof(float));

mace/kernels/neon/avg_pooling_neon_3x3.cc

-//
-// Copyright (c) 2017 XiaoMi All rights reserved.
-//
-
-#include <arm_neon.h>
-#include <float.h>
-#include <limits>
-
-#include "mace/core/common.h"
-
-namespace mace {
-namespace kernels {
-
-void PoolingAvgNeonK3x3S2x2(const float *input,
-                            const index_t *in_shape,
-                            float *output,
-                            const index_t *out_shape,
-                            const int *paddings) {
-  index_t batch = in_shape[0];
-  index_t channels = in_shape[1];
-  index_t in_height = in_shape[2];
-  index_t in_width = in_shape[3];
-
-  index_t out_height = out_shape[2];
-  index_t out_width = out_shape[3];
-
-  int padding_top = paddings[0] / 2;
-  int padding_bottom = paddings[0] - padding_top;
-  int padding_left = paddings[1] / 2;
-  int padding_right = paddings[1] - padding_left;
-
-  int in_image_size = in_height * in_width;
-  int out_image_size = out_height * out_width;
-  index_t input_offset = 0;
-  index_t output_offset = 0;
-  float avg_factors[4] = {1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0};
-
-#pragma omp parallel for collapse(2)
-  for (int b = 0; b < batch; ++b) {
-    for (int c = 0; c < channels; ++c) {
-      float *outptr = output + output_offset;
-
-      for (int h = 0; h < out_height; ++h) {
-        int w = 0;
-        int num_vectors = 0;
-        const float *r0, *r1, *r2;
-        if (!((h == 0 && padding_top > 0) ||
-              (h == out_height - 1 && padding_bottom > 0))) {
-          r0 = input + input_offset + (h * 2 - padding_top) * in_width;
-          r1 = r0 + in_width;
-          r2 = r1 + in_width;
-
-          if (padding_left > 0) {
-            if (padding_left == 1) {
-              float sum0 = std::max(r0[0], r0[1]);
-              float sum1 = std::max(r1[0], r1[1]);
-              float max2 = std::max(r2[0], r2[1]);
-              *outptr = (r0[0] + r0[1] + r1[0] + r1[1] + r2[0] + r2[1]) / 9.0;
-              ++r0;
-              ++r1;
-            } else {  // padding_left == 2
-              *outptr = (r0[0] + r1[0] + r2[0]) / 9.0;
-            }
-            ++outptr;
-            ++w;
-          }
-          if (padding_right > 0) {
-            num_vectors = (out_width - w - 1) >> 2;
-          } else {
-            num_vectors = (out_width - w) >> 2;
-          }
-        }
-
-        w += num_vectors << 2;
-        float32x4_t factors = vld1q_f32(avg_factors);
-        float32x4x2_t row0 = vld2q_f32(r0);
-        float32x4x2_t row1 = vld2q_f32(r1);
-        float32x4x2_t row2 = vld2q_f32(r2);
-        for (; num_vectors > 0; --num_vectors) {
-          float32x4x2_t row0_next = vld2q_f32(r0 + 8);
-          float32x4x2_t row1_next = vld2q_f32(r1 + 8);
-          float32x4x2_t row2_next = vld2q_f32(r2 + 8);
-
-          float32x4_t sum0 = vaddq_f32(row0.val[0], row0.val[1]);
-          float32x4_t sum1 = vaddq_f32(row1.val[0], row1.val[1]);
-          float32x4_t sum2 = vaddq_f32(row2.val[0], row2.val[1]);
-
-          float32x4_t row02 = vextq_f32(row0.val[0], row0_next.val[0], 1);
-          float32x4_t row12 = vextq_f32(row1.val[0], row1_next.val[0], 1);
-          float32x4_t row22 = vextq_f32(row2.val[0], row2_next.val[0], 1);
-
-          sum0 = vaddq_f32(sum0, row02);
-          sum1 = vaddq_f32(sum1, row12);
-          sum2 = vaddq_f32(sum2, row22);
-
-          float32x4_t sum_result = vaddq_f32(vaddq_f32(sum0, sum1), sum2);
-          float32x4_t avg_result = vmulq_f32(sum_result, factors);
-
-          vst1q_f32(outptr, avg_result);
-
-          row0 = row0_next;
-          row1 = row1_next;
-          row2 = row2_next;
-
-          r0 += 8;
-          r1 += 8;
-          r2 += 8;
-          outptr += 4;
-        }
-
-        for (; w < out_width; ++w) {
-          float sum = 0.0;
-          for (int kh = 0; kh < 3; ++kh) {
-            for (int kw = 0; kw < 3; ++kw) {
-              int inh = h * 2 - padding_top + kh;
-              int inw = w * 2 - padding_left + kw;
-              if (inh >= 0 && inh < in_height && inw >= 0 && inw < in_width) {
-                sum += input[input_offset + inh * in_width + inw];
-              }
-            }
-          }
-
-          *outptr = sum / 9.0;
-          ++outptr;
-        }
-      }
-      input_offset += in_image_size;
-      output_offset += out_image_size;
-    }
-  }
-}
-
-// assume the input has already been padded
-void PoolingAvgNeonK3x3S2x2Padded(const float *input,
-                                  const index_t *in_shape,
-                                  float *output,
-                                  const index_t *out_shape) {
-  index_t batch = in_shape[0];
-  index_t channels = in_shape[1];
-  index_t in_height = in_shape[2];
-  index_t in_width = in_shape[3];
-
-  index_t out_height = out_shape[2];
-  index_t out_width = out_shape[3];
-
-  int in_image_size = in_height * in_width;
-  int out_image_size = out_height * out_width;
-  index_t input_offset = 0;
-  index_t output_offset = 0;
-  float avg_factors[4] = {1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0};
-
-#pragma omp parallel for collapse(2)
-  for (int b = 0; b < batch; ++b) {
-    for (int c = 0; c < channels; ++c) {
-      const float *img0 = input + input_offset;
-      float *outptr = output + output_offset;
-
-      const float *r0 = img0;
-      const float *r1 = r0 + in_width;
-      const float *r2 = r1 + in_width;
-
-      for (int h = 0; h < out_height; h++) {
-        int num_vectors = out_width >> 2;
-        int remain = out_width - (num_vectors << 2);
-
-        float32x4_t factors = vld1q_f32(avg_factors);
-        float32x4x2_t row0 = vld2q_f32(r0);
-        float32x4x2_t row1 = vld2q_f32(r1);
-        float32x4x2_t row2 = vld2q_f32(r2);
-        for (; num_vectors > 0; --num_vectors) {
-          float32x4x2_t row0_next = vld2q_f32(r0 + 8);
-          float32x4x2_t row1_next = vld2q_f32(r1 + 8);
-          float32x4x2_t row2_next = vld2q_f32(r2 + 8);
-
-          float32x4_t sum0 = vaddq_f32(row0.val[0], row0.val[1]);
-          float32x4_t sum1 = vaddq_f32(row1.val[0], row1.val[1]);
-          float32x4_t sum2 = vaddq_f32(row2.val[0], row2.val[1]);
-
-          float32x4_t row02 = vextq_f32(row0.val[0], row0_next.val[0], 1);
-          float32x4_t row12 = vextq_f32(row1.val[0], row1_next.val[0], 1);
-          float32x4_t row22 = vextq_f32(row2.val[0], row2_next.val[0], 1);
-
-          sum0 = vaddq_f32(sum0, row02);
-          sum1 = vaddq_f32(sum1, row12);
-          sum2 = vaddq_f32(sum2, row22);
-
-          float32x4_t sum_result = vaddq_f32(vaddq_f32(sum0, sum1), sum2);
-          float32x4_t avg_result = vmulq_f32(sum_result, factors);
-
-          vst1q_f32(outptr, avg_result);
-
-          row0 = row0_next;
-          row1 = row1_next;
-          row2 = row2_next;
-
-          r0 += 8;
-          r1 += 8;
-          r2 += 8;
-          outptr += 4;
-        }
-
-        for (; remain > 0; remain--) {
-          *outptr = (r0[0] + r0[1] + r0[2] + r1[0] + r1[1] + r1[2] + r2[0] +
-                     r2[1] + r2[2]) /
-                    9.0;
-
-          r0 += 2;
-          r1 += 2;
-          r2 += 2;
-          outptr++;
-        }
-
-        r0 += 1 + in_width;
-        r1 += 1 + in_width;
-        r2 += 1 + in_width;
-      }
-      input_offset += in_image_size;
-      output_offset += out_image_size;
-    }
-  }
-}
-
-}  // namespace kernels
-}  // namespace mace

mace/kernels/neon/batch_norm_neon.cc

@@ -15,6 +15,7 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()(
     const Tensor *offset,
     const Tensor *mean,
     const Tensor *var,
+    const float epsilon,
     Tensor *output,
     StatsFuture *future) {
   // Batch normalization in the paper https://arxiv.org/abs/1502.03167 .
@@ -26,8 +27,8 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()(
   // new_offset = \offset - mean * common_val;
   // Y = new_scale * X + new_offset;
   const index_t n = input->dim(0);
-  const index_t channel = input->dim(1);
-  const index_t sample_size = input->dim(2) * input->dim(3);
+  const index_t sample_size = input->dim(1) * input->dim(2);
+  const index_t channel = input->dim(3);
 
   const float *input_ptr = input->data<float>();
   const float *scale_ptr = scale->data<float>();
@@ -36,36 +37,47 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()(
   const float *var_ptr = var->data<float>();
   float *output_ptr = output->mutable_data<float>();
 
-  index_t count = sample_size >> 2;
-  index_t remain_count = sample_size - (count << 2);
+  const index_t ch_blks = channel >> 2;
+  const index_t remain_chs = channel - (ch_blks << 2);
+
+  std::vector<float> new_scale(channel);
+  std::vector<float> new_offset(channel);
+
 #pragma omp parallel for
   for (index_t c = 0; c < channel; ++c) {
-    float new_scale = scale_ptr[c] / std::sqrt(var_ptr[c] + epsilon_);
-    float new_offset = offset_ptr[c] - mean_ptr[c] * new_scale;
-    index_t pos = c * sample_size;
-
-    float32x4_t new_scale_f = vdupq_n_f32(new_scale);
-    float32x4_t new_offset_f = vdupq_n_f32(new_offset);
-    for (index_t i = 0; i < n; ++i) {
-      const float *input_sample_ptr = input_ptr + pos;
-      float *output_sample_ptr = output_ptr + pos;
+    new_scale[c] = scale_ptr[c] / std::sqrt(var_ptr[c] + epsilon);
+    new_offset[c] = offset_ptr[c] - mean_ptr[c] * new_scale[c];
+  }
 
-      for (index_t j = 0; j < count; ++j) {
+#pragma omp parallel for collapse(2)
+  for (index_t i = 0; i < n; ++i) {
+    for (index_t j = 0; j < sample_size; ++j) {
+      const float *input_sample_ptr = input_ptr + (i * sample_size + j) * channel;
+      float *output_sample_ptr = output_ptr + (i * sample_size + j) * channel;
+      const float *new_scale_ptr = new_scale.data();
+      const float *new_offset_ptr = new_offset.data();
+      for (index_t cb = 0; cb < ch_blks; ++cb) {
+        float32x4_t new_scale_f = vld1q_f32(new_scale_ptr);
+        float32x4_t new_offset_f = vld1q_f32(new_offset_ptr);
         float32x4_t input_f = vld1q_f32(input_sample_ptr);
         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;
+        new_scale_ptr += 4;
+        new_offset_ptr += 4;
       }
-      for (index_t j = 0; j < remain_count; ++j) {
-        *output_sample_ptr = new_scale * *input_sample_ptr + new_offset;
+      for (index_t c = (ch_blks << 2); c < channel; ++c) {
+        *output_sample_ptr = new_scale[c] * *input_sample_ptr + new_offset[c];
         ++output_sample_ptr;
         ++input_sample_ptr;
+        ++new_scale_ptr;
+        ++new_offset_ptr;
       }
-      pos += channel * sample_size;
     }
   }
 };
 
 }  // namespace kernels
-}  //  namespace mace
+}  // namespace mace

mace/kernels/neon/global_avg_pooling_neon.cc

-//
-// Copyright (c) 2017 XiaoMi All rights reserved.
-//
-
-#include "mace/kernels/global_avg_pooling.h"
-#include <arm_neon.h>
-
-namespace mace {
-namespace kernels {
-
-template <>
-void GlobalAvgPoolingFunctor<DeviceType::NEON, float>::operator()(
-    const float *input, const index_t *input_shape,
-    float *output, StatsFuture *future) {
-  index_t batch = input_shape[0];
-  index_t channels = input_shape[1];
-  index_t height = input_shape[2];
-  index_t width = input_shape[3];
-
-  index_t image_size = height * width;
-  index_t input_offset = 0;
-  index_t total_channels = batch * channels;
-
-#pragma omp parallel for
-  for (int c = 0; c < total_channels; ++c) {
-    const float *inptr = input + c * image_size;
-    float sum = 0.0;
-
-    int num_vectors = image_size >> 2;
-    int remain = image_size - (num_vectors << 2);
-
-    if (num_vectors > 0) {
-      float sum_out[4] = {0.0, 0.0, 0.0, 0.0};
-
-      float32x4_t sum_vector = vld1q_f32(inptr);
-      inptr += 4;
-      for (int n = 1; n < num_vectors; ++n) {
-        float32x4_t vector = vld1q_f32(inptr);
-        sum_vector = vaddq_f32(sum_vector, vector);
-        inptr += 4;
-      }
-      vst1q_f32(sum_out, sum_vector);
-
-      sum = sum_out[0] + sum_out[1] + sum_out[2] + sum_out[3];
-    }
-
-    for (int i = 0; i < remain; ++i) {
-      sum += *inptr;
-      ++inptr;
-    }
-    output[c] = sum / image_size;
-  }
-};
-
-}  // namespace kernels
-}  // namespace mace

mace/kernels/neon/max_pooling_neon_2x2.cc

-//
-// Copyright (c) 2017 XiaoMi All rights reserved.
-//
-
-#include <arm_neon.h>
-#include <float.h>
-#include <limits>
-
-#include "mace/core/common.h"
-
-namespace mace {
-namespace kernels {
-
-void PoolingMaxNeonK2x2S2x2(const float *input,
-                            const index_t *in_shape,
-                            float *output,
-                            const index_t *out_shape,
-                            const int *paddings) {
-  index_t batch = in_shape[0];
-  index_t channels = in_shape[1];
-  index_t in_height = in_shape[2];
-  index_t in_width = in_shape[3];
-
-  index_t out_height = out_shape[2];
-  index_t out_width = out_shape[3];
-
-  int padding_top = paddings[0] / 2;
-  int padding_bottom = paddings[0] - padding_top;
-  int padding_left = paddings[1] / 2;
-  int padding_right = paddings[1] - padding_left;
-
-  int in_image_size = in_height * in_width;
-  int out_image_size = out_height * out_width;
-  index_t input_offset = 0;
-  index_t output_offset = 0;
-
-#pragma omp parallel for collapse(2)
-  for (int b = 0; b < batch; ++b) {
-    for (int c = 0; c < channels; ++c) {
-      float *outptr = output + output_offset;
-      const float *r0, *r1;
-
-      for (int h = 0; h < out_height; ++h) {
-        int w = 0;
-        int num_vectors = 0;
-        if (!((h == 0 && padding_top > 0) ||
-              (h == out_height - 1 && padding_bottom > 0))) {
-          r0 = input + input_offset + (h * 2 - padding_top) * in_width;
-          r1 = r0 + in_width;
-          if (padding_left > 0) {
-            *outptr = std::max(r0[0], r1[0]);
-            ++r0;
-            ++r1;
-            ++outptr;
-            ++w;
-          }
-          if (padding_right > 0) {
-            num_vectors = (out_width - w - 1) >> 2;
-          } else {
-            num_vectors = (out_width - w) >> 2;
-          }
-        }
-
-        w += num_vectors << 2;
-
-        for (; num_vectors > 0; --num_vectors) {
-          float32x4_t r00 = vld1q_f32(r0);
-          float32x4_t r10 = vld1q_f32(r1);
-          float32x4_t r01 = vld1q_f32(r0 + 4);
-          float32x4_t r11 = vld1q_f32(r1 + 4);
-
-          float32x4_t max0 = vmaxq_f32(r00, r10);
-          float32x4_t max1 = vmaxq_f32(r01, r11);
-
-          float32x4_t max_result = vpmaxq_f32(max0, max1);
-
-          vst1q_f32(outptr, max_result);
-
-          r0 += 8;
-          r1 += 8;
-          outptr += 4;
-        }
-
-        for (; w < out_width; ++w) {
-          float max = std::numeric_limits<float>::lowest();
-          for (int kh = 0; kh < 2; ++kh) {
-            for (int kw = 0; kw < 2; ++kw) {
-              int inh = h * 2 - padding_top + kh;
-              int inw = w * 2 - padding_left + kw;
-              if (inh >= 0 && inh < in_height && inw >= 0 && inw < in_width) {
-                max = std::max(max, input[input_offset + inh * in_width + inw]);
-              }
-            }
-          }
-
-          *outptr = max;
-          ++outptr;
-        }
-      }
-      input_offset += in_image_size;
-      output_offset += out_image_size;
-    }
-  }
-}
-
-// assume the input has already been padded
-void PoolingMaxNeonK2x2S2x2Padded(const float *input,
-                                  const index_t *in_shape,
-                                  float *output,
-                                  const index_t *out_shape) {
-  index_t batch = in_shape[0];
-  index_t channels = in_shape[1];
-  index_t in_height = in_shape[2];
-  index_t in_width = in_shape[3];
-
-  index_t out_height = out_shape[2];
-  index_t out_width = out_shape[3];
-
-  int in_image_size = in_height * in_width;
-  int out_image_size = out_height * out_width;
-  index_t input_offset = 0;
-  index_t output_offset = 0;
-
-#pragma omp parallel for collapse(2)
-  for (int b = 0; b < batch; ++b) {
-    for (int c = 0; c < channels; ++c) {
-      const float *img0 = input + input_offset;
-      float *outptr = output + output_offset;
-
-      const float *r0 = img0;
-      const float *r1 = img0 + in_width;
-
-      for (int h = 0; h < out_height; ++h) {
-        int num_vectors = out_width >> 2;
-        int remain = out_width - (num_vectors << 2);
-
-        for (; num_vectors > 0; --num_vectors) {
-          float32x4_t r00 = vld1q_f32(r0);
-          float32x4_t r10 = vld1q_f32(r1);
-          float32x4_t r01 = vld1q_f32(r0 + 4);
-          float32x4_t r11 = vld1q_f32(r1 + 4);
-
-          float32x4_t max0 = vmaxq_f32(r00, r10);
-          float32x4_t max1 = vmaxq_f32(r01, r11);
-
-          float32x4_t max_result = vpmaxq_f32(max0, max1);
-
-          vst1q_f32(outptr, max_result);
-          r0 += 8;
-          r1 += 8;
-          outptr += 4;
-        }
-
-        for (; remain > 0; --remain) {
-          float max0 = std::max(r0[0], r0[1]);
-          float max1 = std::max(r1[0], r1[1]);
-          *outptr = std::max(max0, max1);
-
-          r0 += 2;
-          r1 += 2;
-          outptr++;
-        }
-        r0 += in_width;
-        r1 += in_width;
-      }
-      input_offset += in_image_size;
-      output_offset += out_image_size;
-    }
-  }
-}
-
-}  // namespace kernels
-}  // namespace mace

mace/kernels/neon/max_pooling_neon_3x3.cc

-//
-// Copyright (c) 2017 XiaoMi All rights reserved.
-//
-
-#include <arm_neon.h>
-#include <limits>
-
-#include "mace/core/common.h"
-
-namespace mace {
-namespace kernels {
-
-void PoolingMaxNeonK3x3S2x2(const float *input,
-                            const index_t *in_shape,
-                            float *output,
-                            const index_t *out_shape,
-                            const int *paddings) {
-  index_t batch = in_shape[0];
-  index_t channels = in_shape[1];
-  index_t in_height = in_shape[2];
-  index_t in_width = in_shape[3];
-
-  index_t out_height = out_shape[2];
-  index_t out_width = out_shape[3];
-
-  int padding_top = paddings[0] / 2;
-  int padding_bottom = paddings[0] - padding_top;
-  int padding_left = paddings[1] / 2;
-  int padding_right = paddings[1] - padding_left;
-
-  int in_image_size = in_height * in_width;
-  int out_image_size = out_height * out_width;
-  index_t input_offset = 0;
-  index_t output_offset = 0;
-
-#pragma omp parallel for collapse(2)
-  for (int b = 0; b < batch; ++b) {
-    for (int c = 0; c < channels; ++c) {
-      float *outptr = output + output_offset;
-
-      for (int h = 0; h < out_height; ++h) {
-        int w = 0;
-        int num_vectors = 0;
-        const float *r0, *r1, *r2;
-        if (!((h == 0 && padding_top > 0) ||
-              (h == out_height - 1 && padding_bottom > 0))) {
-          r0 = input + input_offset + (h * 2 - padding_top) * in_width;
-          r1 = r0 + in_width;
-          r2 = r1 + in_width;
-
-          if (padding_left > 0) {
-            if (padding_left == 1) {
-              float max0 = std::max(r0[0], r0[1]);
-              float max1 = std::max(r1[0], r1[1]);
-              float max2 = std::max(r2[0], r2[1]);
-              *outptr = std::max(std::max(max0, max1), max2);
-              ++r0;
-              ++r1;
-            } else {  // padding_left == 2
-              float max_tmp = std::max(r0[0], r1[0]);
-              *outptr = std::max(max_tmp, r2[0]);
-            }
-            ++outptr;
-            ++w;
-          }
-          if (padding_right > 0) {
-            num_vectors = (out_width - w - 1) >> 2;
-          } else {
-            num_vectors = (out_width - w) >> 2;
-          }
-        }
-
-        w += num_vectors << 2;
-        float32x4x2_t row0 = vld2q_f32(r0);
-        float32x4x2_t row1 = vld2q_f32(r1);
-        float32x4x2_t row2 = vld2q_f32(r2);
-        for (; num_vectors > 0; --num_vectors) {
-          float32x4x2_t row0_next = vld2q_f32(r0 + 8);
-          float32x4x2_t row1_next = vld2q_f32(r1 + 8);
-          float32x4x2_t row2_next = vld2q_f32(r2 + 8);
-
-          float32x4_t max0 = vmaxq_f32(row0.val[0], row0.val[1]);
-          float32x4_t max1 = vmaxq_f32(row1.val[0], row1.val[1]);
-          float32x4_t max2 = vmaxq_f32(row2.val[0], row2.val[1]);
-
-          float32x4_t row02 = vextq_f32(row0.val[0], row0_next.val[0], 1);
-          float32x4_t row12 = vextq_f32(row1.val[0], row1_next.val[0], 1);
-          float32x4_t row22 = vextq_f32(row2.val[0], row2_next.val[0], 1);
-
-          max0 = vmaxq_f32(max0, row02);
-          max1 = vmaxq_f32(max1, row12);
-          max2 = vmaxq_f32(max2, row22);
-
-          float32x4_t max_result = vmaxq_f32(vmaxq_f32(max0, max1), max2);
-
-          vst1q_f32(outptr, max_result);
-
-          row0 = row0_next;
-          row1 = row1_next;
-          row2 = row2_next;
-
-          r0 += 8;
-          r1 += 8;
-          r2 += 8;
-          outptr += 4;
-        }
-
-        for (; w < out_width; ++w) {
-          float max = std::numeric_limits<float>::lowest();
-          for (int kh = 0; kh < 3; ++kh) {
-            for (int kw = 0; kw < 3; ++kw) {
-              int inh = h * 2 - padding_top + kh;
-              int inw = w * 2 - padding_left + kw;
-              if (inh >= 0 && inh < in_height && inw >= 0 && inw < in_width) {
-                max = std::max(max, input[input_offset + inh * in_width + inw]);
-              }
-            }
-          }
-
-          *outptr = max;
-          ++outptr;
-        }
-      }
-      input_offset += in_image_size;
-      output_offset += out_image_size;
-    }
-  }
-}
-
-// assume the input has already been padded
-void PoolingMaxNeonK3x3S2x2Padded(const float *input,
-                                  const index_t *in_shape,
-                                  float *output,
-                                  const index_t *out_shape) {
-  index_t batch = in_shape[0];
-  index_t channels = in_shape[1];
-  index_t in_height = in_shape[2];
-  index_t in_width = in_shape[3];
-
-  index_t out_height = out_shape[2];
-  index_t out_width = out_shape[3];
-
-  int in_image_size = in_height * in_width;
-  int out_image_size = out_height * out_width;
-  index_t input_offset = 0;
-  index_t output_offset = 0;
-
-#pragma omp parallel for collapse(2)
-  for (int b = 0; b < batch; ++b) {
-    for (int c = 0; c < channels; ++c) {
-      const float *img0 = input + input_offset;
-      float *outptr = output + output_offset;
-
-      const float *r0 = img0;
-      const float *r1 = r0 + in_width;
-      const float *r2 = r1 + in_width;
-
-      for (int h = 0; h < out_height; h++) {
-        int num_vectors = out_width >> 2;
-        int remain = out_width - (num_vectors << 2);
-
-        float32x4x2_t row0 = vld2q_f32(r0);
-        float32x4x2_t row1 = vld2q_f32(r1);
-        float32x4x2_t row2 = vld2q_f32(r2);
-        for (; num_vectors > 0; num_vectors--) {
-          float32x4x2_t row0_next = vld2q_f32(r0 + 8);
-          float32x4x2_t row1_next = vld2q_f32(r1 + 8);
-          float32x4x2_t row2_next = vld2q_f32(r2 + 8);
-
-          float32x4_t max0 = vmaxq_f32(row0.val[0], row0.val[1]);
-          float32x4_t max1 = vmaxq_f32(row1.val[0], row1.val[1]);
-          float32x4_t max2 = vmaxq_f32(row2.val[0], row2.val[1]);
-
-          float32x4_t row02 = vextq_f32(row0.val[0], row0_next.val[0], 1);
-          float32x4_t row12 = vextq_f32(row1.val[0], row1_next.val[0], 1);
-          float32x4_t row22 = vextq_f32(row2.val[0], row2_next.val[0], 1);
-
-          max0 = vmaxq_f32(max0, row02);
-          max1 = vmaxq_f32(max1, row12);
-          max2 = vmaxq_f32(max2, row22);
-
-          float32x4_t max_result = vmaxq_f32(vmaxq_f32(max0, max1), max2);
-
-          vst1q_f32(outptr, max_result);
-
-          row0 = row0_next;
-          row1 = row1_next;
-          row2 = row2_next;
-
-          r0 += 8;
-          r1 += 8;
-          r2 += 8;
-          outptr += 4;
-        }
-
-        for (; remain > 0; remain--) {
-          float max0 = std::max(std::max(r0[0], r0[1]), r0[2]);
-          float max1 = std::max(std::max(r1[0], r1[1]), r1[2]);
-          float max2 = std::max(std::max(r2[0], r2[1]), r2[2]);
-
-          *outptr = std::max(std::max(max0, max1), max2);
-
-          r0 += 2;
-          r1 += 2;
-          r2 += 2;
-          outptr++;
-        }
-
-        r0 += 1 + in_width;
-        r1 += 1 + in_width;
-        r2 += 1 + in_width;
-      }
-      input_offset += in_image_size;
-      output_offset += out_image_size;
-    }
-  }
-}
-
-}  // namespace kernels
-}  // namespace mace

mace/kernels/neon/pooling_neon.cc

-//
-// Copyright (c) 2017 XiaoMi All rights reserved.
-//
-
-#include "mace/kernels/pooling.h"
-
-namespace mace {
-namespace kernels {
-
-extern void PoolingMaxNeonK2x2S2x2(const float *input,
-                                   const index_t *in_shape,
-                                   float *output,
-                                   const index_t *out_shape,
-                                   const int *paddings);
-
-extern void PoolingAvgNeonK2x2S2x2(const float *input,
-                                   const index_t *in_shape,
-                                   float *output,
-                                   const index_t *out_shape,
-                                   const int *paddings);
-
-extern void PoolingMaxNeonK3x3S2x2(const float *input,
-                                   const index_t *in_shape,
-                                   float *output,
-                                   const index_t *out_shape,
-                                   const int *paddings);
-
-extern void PoolingAvgNeonK3x3S2x2(const float *input,
-                                   const index_t *in_shape,
-                                   float *output,
-                                   const index_t *out_shape,
-                                   const int *paddings);
-
-#ifdef __COPY_MAKE_PADDING
-extern void PoolingMaxNeonK2x2S2x2Padded(const float *input,
-                                         const index_t *in_shape,
-                                         float *output,
-                                         const index_t *out_shape);
-
-extern void PoolingAvgNeonK2x2S2x2Padded(const float *input,
-                                         const index_t *in_shape,
-                                         float *output,
-                                         const index_t *out_shape);
-
-extern void PoolingMaxNeonK3x3S2x2Padded(const float *input,
-                                         const index_t *in_shape,
-                                         float *output,
-                                         const index_t *out_shape);
-
-extern void PoolingAvgNeonK3x3S2x2Padded(const float *input,
-                                         const index_t *in_shape,
-                                         float *output,
-                                         const index_t *out_shape);
-#endif
-
-template <>
-void PoolingFunctor<DeviceType::NEON, float>::operator()(
-    const Tensor *input_tensor,
-    Tensor *output_tensor,
-    StatsFuture *future) {
-
-  std::vector<index_t> output_shape(4);
-  std::vector<int> paddings(2);
-  std::vector<index_t> filter_shape(4);
-  filter_shape[0] = input_tensor->shape()[1];
-  filter_shape[1] = input_tensor->shape()[1];
-  filter_shape[2] = kernels_[0];
-  filter_shape[3] = kernels_[1];
-
-  kernels::CalcPaddingAndOutputSize(
-      input_tensor->shape().data(), filter_shape.data(), this->dilations_,
-      strides_, this->padding_, output_shape.data(),
-      paddings.data());
-  output_tensor->Resize(output_shape);
-
-  const float *input = input_tensor->data<float>();
-  float *output = output_tensor->mutable_data<float>();
-  const index_t *input_shape = input_tensor->shape().data();
-
-#ifdef __COPY_MAKE_PADDING
-  Tensor padded_input;
-  ConstructInputWithPadding(input_tensor, paddings.data(), &padded_input);
-  input = padded_input.data<float>();
-  input_shape = padded_input.shape().data();
-#endif
-
-  if (kernels_[0] == 2 && kernels_[1] == 2 && strides_[0] == 2 &&
-      strides_[1] == 2) {
-    // kernel_size: 2x2, strides: 2x2
-    if (pooling_type_ == MAX) {  // MAX_POOL_2x2s2x2
-#ifdef __COPY_MAKE_PADDING
-      PoolingMaxNeonK2x2S2x2Padded(input, input_shape, output, output_shape.data());
-#else
-      PoolingMaxNeonK2x2S2x2(input, input_shape, output, output_shape.data(),
-                             paddings.data());
-#endif
-    } else {  // AVG_POOL_2x2s2x2
-#ifdef __COPY_MAKE_PADDING
-      PoolingAvgNeonK2x2S2x2Padded(input, input_shape, output, output_shape.data());
-#else
-      PoolingAvgNeonK2x2S2x2(input, input_shape, output, output_shape.data(),
-                             paddings.data());
-#endif
-    }
-  } else if (kernels_[0] == 3 && kernels_[1] == 3 && strides_[0] == 2 &&
-             strides_[1] == 2) {
-    // kernel_size: 3x3, strides: 2x2
-    if (pooling_type_ == MAX) {  // MAX_POOL_3x3s2x2
-#ifdef __COPY_MAKE_PADDING
-      PoolingMaxNeonK3x3S2x2Padded(input, input_shape, output, output_shape.data());
-#else
-      PoolingMaxNeonK3x3S2x2(input, input_shape, output, output_shape.data(),
-                             paddings.data());
-#endif
-    } else {  // AVG_POOL_3x3s2x2
-#ifdef __COPY_MAKE_PADDING
-      PoolingAvgNeonK3x3S2x2Padded(input, input_shape, output, output_shape.data());
-#else
-      PoolingAvgNeonK3x3S2x2(input, input_shape, output, output_shape.data(),
-                             paddings.data());
-#endif
-    }
-  } else {  // not implement yet
-    PoolingFunctor<DeviceType::CPU, float>(pooling_type_, kernels_, strides_,
-                                           padding_, dilations_)(
-        input_tensor, output_tensor, future);
-  }
-}
-
-}  //  namespace kernels
-}  //  namespace mace

mace/kernels/neon/relu_neon.cc

-//
-// Copyright (c) 2017 XiaoMi All rights reserved.
-//
-
-#include "mace/kernels/relu.h"
-#include <arm_neon.h>
-
-namespace mace {
-namespace kernels {
-
-template <>
-void ActivationFunctor<DeviceType::NEON, float>::operator()(const Tensor *input_tensor,
-                                                      Tensor *output_tensor,
-                                                      StatsFuture *future) {
-  const float *input = input_tensor->data<float>();
-  float *output = output_tensor->mutable_data<float>();
-  index_t size = input_tensor->size();
-  if (max_limit_ < 0) {
-#pragma omp parallel for
-    for (int64_t i = 0; i < size; i += kCostPerGroup) {
-      int64_t count = std::min(static_cast<int64_t>(kCostPerGroup), size - i);
-      int block = count >> 2;
-      int remain = count - (block << 2);
-      const float *inptr = input + i;
-      float *outptr = output + i;
-      float32x4_t zero = vdupq_n_f32(0.f);
-      for (; block > 0; --block) {
-        float32x4_t in = vld1q_f32(inptr);
-        float32x4_t out = vmaxq_f32(in, zero);
-        vst1q_f32(outptr, out);
-
-        inptr += 4;
-        outptr += 4;
-      }
-      for (; remain > 0; --remain) {
-        *outptr = std::max(*inptr, 0.f);
-        ++inptr;
-        ++outptr;
-      }
-    }
-  } else {
-#pragma omp parallel for
-    for (int64_t i = 0; i < size; i += kCostPerGroup) {
-      int64_t count = std::min(static_cast<int64_t>(kCostPerGroup), size - i);
-      int block = count >> 2;
-      int remain = count - (block << 2);
-      const float *inptr = input + i;
-      float *outptr = output + i;
-      float32x4_t zero = vdupq_n_f32(0.f);
-      float32x4_t vmax = vdupq_n_f32(max_limit_);
-      for (; block > 0; --block) {
-        float32x4_t in = vld1q_f32(inptr);
-        float32x4_t out = vmaxq_f32(in, zero);
-        out = vminq_f32(out, vmax);
-        vst1q_f32(outptr, out);
-
-        inptr += 4;
-        outptr += 4;
-      }
-      for (; remain > 0; --remain) {
-        *outptr = std::min(std::max(*inptr, 0.f), max_limit_);
-        ++inptr;
-        ++outptr;
-      }
-    }
-  }
-};
-
-}  // namespace kernels
-}  // namespace mace

mace/kernels/opencl/cl/buffer_to_image.cl

 #include <common.h>
 
-__kernel void filter_buffer_to_image(__global const DATA_TYPE *input, /* h, w, ic, oc */
+__kernel void filter_buffer_to_image(__global const DATA_TYPE *input, /* h, w, oc, ic */
                                      __private const int filter_w,
-                                     __private const int in_channel,
                                      __private const int out_channel,
+                                     __private const int in_channel,
                                      __write_only image2d_t output) {
   int w = get_global_id(0);
   int h = get_global_id(1);
@@ -13,23 +13,26 @@ __kernel void filter_buffer_to_image(__global const DATA_TYPE *input, /* h, w, i
   const int in_channel_idx = w % rounded_in_channel;
   const int h_idx = hw_idx / filter_w;
   const int w_idx = hw_idx % filter_w;
-  const int offset = ((h_idx * filter_w + w_idx) * in_channel + in_channel_idx) * out_channel
-                           + out_channel_idx;
+  const int offset = ((h_idx * filter_w + w_idx) * out_channel + out_channel_idx) * in_channel
+                           + in_channel_idx;
 
-  const int size = out_channel - out_channel_idx;
   VEC_DATA_TYPE(DATA_TYPE, 4) values = 0;
-  if (in_channel_idx < in_channel) {
+  if (out_channel_idx < out_channel) {
+    const int size = out_channel - out_channel_idx;
     if (size < 4) {
-      switch(size) {
+      switch (size) {
         case 3:
-          values.z = *(input + offset + 2);
+          values.z = *(input + offset + 2 * in_channel);
         case 2:
-          values.y = *(input + offset + 1);
+          values.y = *(input + offset + 1 * in_channel);
         case 1:
           values.x = *(input + offset);
       }
     } else {
-      values = vload4(0, input + offset);
+      values.w = *(input + offset + 3 * in_channel);
+      values.z = *(input + offset + 2 * in_channel);
+      values.y = *(input + offset + 1 * in_channel);
+      values.x = *(input + offset);
     }
   }
 
@@ -37,10 +40,10 @@ __kernel void filter_buffer_to_image(__global const DATA_TYPE *input, /* h, w, i
   CMD_TYPE(write_image, CMD_DATA_TYPE)(output, coord, values);
 }
 
-__kernel void filter_image_to_buffer(__global DATA_TYPE *output, /* h, w, ic, oc */
+__kernel void filter_image_to_buffer(__global DATA_TYPE *output, /* h, w, oc, ic */
                                      __private const int filter_w,
-                                     __private const int in_channel,
                                      __private const int out_channel,
+                                     __private const int in_channel,
                                      __read_only image2d_t input) {
   int w = get_global_id(0);
   int h = get_global_id(1);
@@ -50,29 +53,31 @@ __kernel void filter_image_to_buffer(__global DATA_TYPE *output, /* h, w, ic, oc
   const int in_channel_idx = w % rounded_in_channel;
   const int h_idx = hw_idx / filter_w;
   const int w_idx = hw_idx % filter_w;
-  const int offset = ((h_idx * filter_w + w_idx) * in_channel + in_channel_idx) * out_channel
-                           + out_channel_idx;
+  const int offset = ((h_idx * filter_w + w_idx) * out_channel + out_channel_idx) * in_channel
+                           + in_channel_idx;
 
-  if (in_channel_idx < in_channel) {
+  if (out_channel_idx < out_channel) {
     int2 coord = (int2)(w, h);
     VEC_DATA_TYPE(DATA_TYPE, 4) values = CMD_TYPE(read_image, CMD_DATA_TYPE)(input, SAMPLER, coord);
     const int size = (out_channel - out_channel_idx);
     if (size < 4) {
       switch (size) {
         case 3:
-          output[offset+2] = values.s2;
+          output[offset + 2 * in_channel] = values.z;
         case 2:
-          output[offset+1] = values.s1;
+          output[offset + 1 * in_channel] = values.y;
         case 1:
-          output[offset] = values.s0;
+          output[offset] = values.x;
       }
     } else {
-      vstore4(values, 0, output + offset);
+      output[offset + 3 * in_channel] = values.w;
+      output[offset + 2 * in_channel] = values.z;
+      output[offset + 1 * in_channel] = values.y;
+      output[offset] = values.x;
     }
   }
 }
 
-
 __kernel void dw_filter_buffer_to_image(__global const DATA_TYPE *input, /* h, w, ic, m */
                                         __private const int filter_w,
                                         __private const int in_channel,

mace/kernels/opencl/depthwise_conv_opencl.cc

@@ -149,8 +149,8 @@ void DepthwiseConv2dFunctor<DeviceType::OPENCL, T>::operator()(
   std::vector<index_t> fake_filter_shape(4);
   fake_filter_shape[0] = filter->shape()[0];
   fake_filter_shape[1] = filter->shape()[1];
-  fake_filter_shape[3] = filter->shape()[2] * filter->shape()[3];
-  fake_filter_shape[2] = 1;
+  fake_filter_shape[2] = filter->shape()[2] * filter->shape()[3];
+  fake_filter_shape[3] = 1;
 
   std::vector<index_t> output_shape(4);
   std::vector<int> paddings(2);

mace/kernels/opencl/helper.cc

@@ -19,12 +19,12 @@ void CalInOutputImageShape(const std::vector<index_t> &shape, /* NHWC */
 }
 
 // [RoundUp<4>(Ic) * H * W, (Oc + 3) / 4]
-void CalConv2dFilterImageShape(const std::vector<index_t> &shape, /* HWIO */
+void CalConv2dFilterImageShape(const std::vector<index_t> &shape, /* HWOI */
                                std::vector<size_t> &image_shape) {
   MACE_CHECK(shape.size() == 4);
   image_shape.resize(2);
-  image_shape[0] = shape[0] * shape[1] * RoundUp<index_t>(shape[2], 4);
-  image_shape[1] = RoundUpDiv4(shape[3]);
+  image_shape[0] = shape[0] * shape[1] * RoundUp<index_t>(shape[3], 4);
+  image_shape[1] = RoundUpDiv4(shape[2]);
 }
 
 // [H * W * M, (Ic + 3) / 4]
@@ -179,6 +179,7 @@ void TuningOrRun3DKernel(cl::Kernel &kernel,
     local_ws[2] = std::min<uint32_t>(gws[2],
                                      kwg_size / (local_ws[0] * local_ws[1]));
     return {
+      // TODO tuning these magic numbers
         {local_ws[0], local_ws[1], local_ws[2], 1},
         {kwg_size / 16, 4, 4, 1},
         {kwg_size / 32, 4, 8, 1},
@@ -200,7 +201,7 @@ void TuningOrRun3DKernel(cl::Kernel &kernel,
         {9, 7, 15, 1},
         {15, 7, 9, 1},
         {1, kwg_size, 1, 1},
-        {4, 15, 8, 1},  // SNPE size
+        {4, 15, 8, 1},
     };
   };
   cl::Event event;

mace/ops/activation.cc

@@ -13,14 +13,6 @@ void Register_Activation(OperatorRegistry *op_registry) {
                                      .Build(),
                     ActivationOp<DeviceType::CPU, float>);
 
-#if MACE_ENABLE_NEON
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("Activation")
-                                     .Device(DeviceType::NEON)
-                                     .TypeConstraint<float>("T")
-                                     .Build(),
-                    ActivationOp<DeviceType::NEON, float>);
-#endif  // MACE_ENABLE_NEON
-
   REGISTER_OPERATOR(op_registry, OpKeyBuilder("Activation")
                                      .Device(DeviceType::OPENCL)
                                      .TypeConstraint<float>("T")

mace/ops/activation_benchmark.cc

@@ -298,14 +298,15 @@ static void SigmoidBenchmark(
   }                                                                  \
   BENCHMARK(BM_SIGMOID_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE)
 
-#define BM_SIGMOID(N, C, H, W, TYPE)       \
-  BM_SIGMOID_MACRO(N, C, H, W, TYPE, CPU); \
-  BM_SIGMOID_MACRO(N, C, H, W, TYPE, OPENCL);
-
-BM_SIGMOID(1, 1, 512, 512, float);
-BM_SIGMOID(1, 3, 128, 128, float);
-BM_SIGMOID(1, 3, 512, 512, float);
-BM_SIGMOID(1, 32, 112, 112, float);
-BM_SIGMOID(1, 64, 256, 256, float);
+#define BM_SIGMOID(N, C, H, W)                 \
+  BM_SIGMOID_MACRO(N, C, H, W, float, CPU);    \
+  BM_SIGMOID_MACRO(N, C, H, W, float, OPENCL); \
+  BM_SIGMOID_MACRO(N, C, H, W, half, OPENCL);
+
+BM_SIGMOID(1, 1, 512, 512);
+BM_SIGMOID(1, 3, 128, 128);
+BM_SIGMOID(1, 3, 512, 512);
+BM_SIGMOID(1, 32, 112, 112);
+BM_SIGMOID(1, 64, 256, 256);
 
 }  // namespace mace

mace/ops/activation_test.cc

@@ -53,10 +53,6 @@ void TestSimpleRelu() {
 
 TEST_F(ActivationOpTest, CPUSimpleRelu) { TestSimpleRelu<DeviceType::CPU>(); }
 
-#if __ARM_NEON
-TEST_F(ActivationOpTest, NEONSimpleRelu) { TestSimpleRelu<DeviceType::NEON>(); }
-#endif
-
 TEST_F(ActivationOpTest, OPENCLSimpleRelu) {
   TestSimpleRelu<DeviceType::OPENCL>();
 }
@@ -104,12 +100,6 @@ TEST_F(ActivationOpTest, CPUUnalignedSimpleRelu) {
   TestUnalignedSimpleRelu<DeviceType::CPU>();
 }
 
-#if __ARM_NEON
-TEST_F(ActivationOpTest, NEONUnalignedSimpleRelu) {
-  TestUnalignedSimpleRelu<DeviceType::NEON>();
-}
-#endif
-
 TEST_F(ActivationOpTest, OPENCLUnalignedSimpleRelu) {
   TestUnalignedSimpleRelu<DeviceType::OPENCL>();
 }
@@ -160,10 +150,6 @@ void TestSimpleRelux() {
 
 TEST_F(ActivationOpTest, CPUSimple) { TestSimpleRelux<DeviceType::CPU>(); }
 
-#if __ARM_NEON
-TEST_F(ActivationOpTest, NEONSimple) { TestSimpleRelux<DeviceType::NEON>(); }
-#endif
-
 TEST_F(ActivationOpTest, OPENCLSimple) {
   TestSimpleRelux<DeviceType::OPENCL>();
 }
@@ -216,12 +202,6 @@ TEST_F(ActivationOpTest, CPUSimpleRelux) {
   TestSimpleReluRelux<DeviceType::CPU>();
 }
 
-#if __ARM_NEON
-TEST_F(ActivationOpTest, NEONSimpleRelux) {
-  TestSimpleReluRelux<DeviceType::NEON>();
-}
-#endif
-
 TEST_F(ActivationOpTest, OPENCLSimpleRelux) {
   TestSimpleReluRelux<DeviceType::OPENCL>();
 }
@@ -272,12 +252,6 @@ void TestSimplePrelu() {
 
 TEST_F(ActivationOpTest, CPUSimplePrelu) { TestSimplePrelu<DeviceType::CPU>(); }
 
-#if __ARM_NEON
-TEST_F(ActivationOpTest, NEONSimplePrelu) {
-  TestSimplePrelu<DeviceType::NEON>();
-}
-#endif
-
 TEST_F(ActivationOpTest, OPENCLSimplePrelu) {
   TestSimplePrelu<DeviceType::OPENCL>();
 }
@@ -329,10 +303,6 @@ void TestSimpleTanh() {
 
 TEST_F(ActivationOpTest, CPUSimpleTanh) { TestSimpleTanh<DeviceType::CPU>(); }
 
-#if __ARM_NEON
-TEST_F(ActivationOpTest, NEONSimpleTanh) { TestSimpleTanh<DeviceType::NEON>(); }
-#endif
-
 TEST_F(ActivationOpTest, OPENCLSimpleTanh) {
   TestSimpleTanh<DeviceType::OPENCL>();
 }
@@ -387,12 +357,6 @@ TEST_F(ActivationOpTest, CPUSimpleSigmoid) {
   TestSimpleSigmoid<DeviceType::CPU>();
 }
 
-#if __ARM_NEON
-TEST_F(ActivationOpTest, NEONSimpleSigmoid) {
-  TestSimpleSigmoid<DeviceType::NEON>();
-}
-#endif
-
 TEST_F(ActivationOpTest, OPENCLSimpleSigmoid) {
   TestSimpleSigmoid<DeviceType::OPENCL>();
 }

mace/ops/addn_benchmark.cc

@@ -65,16 +65,16 @@ static void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) {
   }                                                                         \
   BENCHMARK(BM_ADDN_##INPUTS##_##N##_##H##_##W##_##C##_##TYPE##_##DEVICE)
 
-#define BM_ADDN(INPUTS, N, H, W, C, TYPE)       \
-  BM_ADDN_MACRO(INPUTS, N, H, W, C, TYPE, CPU); \
-  BM_ADDN_MACRO(INPUTS, N, H, W, C, TYPE, OPENCL);
+#define BM_ADDN(INPUTS, N, H, W, C)       \
+  BM_ADDN_MACRO(INPUTS, N, H, W, C, float, CPU); \
+  BM_ADDN_MACRO(INPUTS, N, H, W, C, float, NEON); \
+  BM_ADDN_MACRO(INPUTS, N, H, W, C, float, OPENCL); \
+  BM_ADDN_MACRO(INPUTS, N, H, W, C, half, OPENCL);
 
-BM_ADDN(2, 1, 256, 256, 32, float);
-BM_ADDN(2, 1, 128, 128, 32, float);
-// BM_ADDN(2, 1, 240, 240, 256, half);
-BM_ADDN(4, 1, 128, 128, 3, float);
-BM_ADDN(2, 1, 256, 256, 3, float);
-BM_ADDN(2, 1, 512, 512, 3, float);
-// BM_ADDN(4, 1, 240, 240, 256, half);
+BM_ADDN(2, 1, 256, 256, 32);
+BM_ADDN(2, 1, 128, 128, 32);
+BM_ADDN(4, 1, 128, 128, 3);
+BM_ADDN(2, 1, 256, 256, 3);
+BM_ADDN(2, 1, 512, 512, 3);
 
-}  //  namespace mace
+}  // namespace mace

mace/ops/addn_test.cc

@@ -33,12 +33,8 @@ void SimpleAdd2() {
 
 TEST_F(AddnOpTest, CPUSimpleAdd2) { SimpleAdd2<DeviceType::CPU>(); }
 
-/*
 TEST_F(AddnOpTest, NEONSimpleAdd2) { SimpleAdd2<DeviceType::NEON>(); }
 
-TEST_F(AddnOpTest, OPENCLSimpleAdd2) { SimpleAdd2<DeviceType::OPENCL>(); }
-*/
-
 template <DeviceType D>
 void SimpleAdd3() {
   // Construct graph
@@ -65,9 +61,7 @@ void SimpleAdd3() {
 
 TEST_F(AddnOpTest, CPUSimpleAdd3) { SimpleAdd3<DeviceType::CPU>(); }
 
-/*
 TEST_F(AddnOpTest, NEONSimpleAdd3) { SimpleAdd3<DeviceType::NEON>(); }
-*/
 
 template <DeviceType D>
 void RandomTest() {

mace/ops/batch_norm_benchmark.cc

@@ -82,21 +82,24 @@ static void BatchNorm(
   }                                                                    \
   BENCHMARK(BM_BATCH_NORM_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE)
 
-#define BM_BATCH_NORM(N, C, H, W, TYPE)        \
-  BM_BATCH_NORM_MACRO(N, C, H, W, TYPE, CPU);  \
-  BM_BATCH_NORM_MACRO(N, C, H, W, TYPE, OPENCL);
+#define BM_BATCH_NORM(N, C, H, W)                 \
+  BM_BATCH_NORM_MACRO(N, C, H, W, float, CPU);    \
+  BM_BATCH_NORM_MACRO(N, C, H, W, float, NEON);   \
+  BM_BATCH_NORM_MACRO(N, C, H, W, float, OPENCL); \
+  BM_BATCH_NORM_MACRO(N, C, H, W, half, OPENCL);
 
-BM_BATCH_NORM(1, 1, 512, 512, float);
-BM_BATCH_NORM(1, 3, 128, 128, float);
-BM_BATCH_NORM(1, 3, 512, 512, float);
-BM_BATCH_NORM(1, 32, 112, 112, float);
-BM_BATCH_NORM(1, 64, 256, 256, float);
-BM_BATCH_NORM(1, 64, 512, 512, float);
-BM_BATCH_NORM(1, 128, 56, 56, float);
-BM_BATCH_NORM(1, 128, 256, 256, float);
-BM_BATCH_NORM(1, 256, 14, 14, float);
-BM_BATCH_NORM(1, 512, 14, 14, float);
-BM_BATCH_NORM(1, 1024, 7, 7, float);
-BM_BATCH_NORM(32, 1, 256, 256, float);
-BM_BATCH_NORM(32, 3, 256, 256, float);
-}  //  namespace mace
+BM_BATCH_NORM(1, 1, 512, 512);
+BM_BATCH_NORM(1, 3, 128, 128);
+BM_BATCH_NORM(1, 3, 512, 512);
+BM_BATCH_NORM(1, 32, 112, 112);
+BM_BATCH_NORM(1, 64, 256, 256);
+BM_BATCH_NORM(1, 64, 512, 512);
+BM_BATCH_NORM(1, 128, 56, 56);
+BM_BATCH_NORM(1, 128, 256, 256);
+BM_BATCH_NORM(1, 256, 14, 14);
+BM_BATCH_NORM(1, 512, 14, 14);
+BM_BATCH_NORM(1, 1024, 7, 7);
+BM_BATCH_NORM(32, 1, 256, 256);
+BM_BATCH_NORM(32, 3, 256, 256);
+
+}  // namespace mace

mace/ops/batch_norm_test.cc

@@ -72,23 +72,18 @@ void Simple() {
 
 TEST_F(BatchNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); }
 
-/*
-TEST_F(BatchNormOpTest, SimpleNEON) {
-  Simple<DeviceType::NEON>();
-}
-*/
+TEST_F(BatchNormOpTest, SimpleNEON) { Simple<DeviceType::NEON>(); }
 
 TEST_F(BatchNormOpTest, SimpleOPENCL) { Simple<DeviceType::OPENCL>(); }
 
-/*
 TEST_F(BatchNormOpTest, SimpleRandomNeon) {
   srand(time(NULL));
 
   // generate random input
   index_t batch = 1 + rand() % 10;
-  index_t channels = 3 + rand() % 50;
   index_t height = 64;
   index_t width = 64;
+  index_t channels = 3 + rand() % 50;
   // Construct graph
   OpsTestNet net;
   OpDefBuilder("BatchNorm", "BatchNormTest")
@@ -97,18 +92,17 @@ TEST_F(BatchNormOpTest, SimpleRandomNeon) {
       .Input("Offset")
       .Input("Mean")
       .Input("Var")
-      .Input("Epsilon")
+      .AddFloatArg("epsilon", 1e-3)
       .Output("Output")
       .Finalize(net.NewOperatorDef());
 
   // Add input data
-  net.AddRandomInput<DeviceType::CPU, float>("Input", {batch, channels, height,
-width});
+  net.AddRandomInput<DeviceType::CPU, float>("Input",
+                                             {batch, height, width, channels});
   net.AddRandomInput<DeviceType::CPU, float>("Scale", {channels});
   net.AddRandomInput<DeviceType::CPU, float>("Offset", {channels});
   net.AddRandomInput<DeviceType::CPU, float>("Mean", {channels});
   net.AddRandomInput<DeviceType::CPU, float>("Var", {channels}, true);
-  net.AddInputFromArray<DeviceType::CPU, float>("Epsilon", {}, {1e-3});
 
   // run cpu
   net.RunOp();
@@ -139,18 +133,17 @@ TEST_F(BatchNormOpTest, ComplexRandomNeon) {
       .Input("Offset")
       .Input("Mean")
       .Input("Var")
-      .Input("Epsilon")
+      .AddFloatArg("epsilon", 1e-3)
       .Output("Output")
       .Finalize(net.NewOperatorDef());
 
   // Add input data
-  net.AddRandomInput<DeviceType::CPU, float>("Input", {batch, channels, height,
-width});
+  net.AddRandomInput<DeviceType::CPU, float>("Input",
+                                             {batch, height, width, channels});
   net.AddRandomInput<DeviceType::CPU, float>("Scale", {channels});
   net.AddRandomInput<DeviceType::CPU, float>("Offset", {channels});
   net.AddRandomInput<DeviceType::CPU, float>("Mean", {channels});
   net.AddRandomInput<DeviceType::CPU, float>("Var", {channels}, true);
-  net.AddInputFromArray<DeviceType::CPU, float>("Epsilon", {}, {1e-3});
 
   // run cpu
   net.RunOp();
@@ -164,7 +157,6 @@ width});
 
   ExpectTensorNear<float>(expected, *net.GetOutput("Output"), 1e-2);
 }
-*/
 
 TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
   srand(time(NULL));

mace/ops/batch_to_space_benchmark.cc

@@ -47,10 +47,10 @@ static void BMBatchToSpace(
   }                                                                        \
   BENCHMARK(BM_BATCH_TO_SPACE_##N##_##H##_##W##_##C##_##ARG##_##TYPE##_##DEVICE)
 
-#define BM_BATCH_TO_SPACE(N, H, W, C, ARG, TYPE) \
-  BM_BATCH_TO_SPACE_MACRO(N, H, W, C, ARG, TYPE, OPENCL);
+#define BM_BATCH_TO_SPACE(N, H, W, C, ARG) \
+  BM_BATCH_TO_SPACE_MACRO(N, H, W, C, ARG, float, OPENCL);
 
-BM_BATCH_TO_SPACE(128, 8, 8, 128, 2, float);
-BM_BATCH_TO_SPACE(4, 128, 128, 32, 2, float);
-BM_BATCH_TO_SPACE(16, 64, 64, 32, 4, float);
-}  //  namespace mace
\ No newline at end of file
+BM_BATCH_TO_SPACE(128, 8, 8, 128, 2);
+BM_BATCH_TO_SPACE(4, 128, 128, 32, 2);
+BM_BATCH_TO_SPACE(16, 64, 64, 32, 4);
+}  // namespace mace

mace/ops/bias_add.cc

@@ -13,16 +13,6 @@ void Register_BiasAdd(OperatorRegistry *op_registry) {
                                      .Build(),
                     BiasAddOp<DeviceType::CPU, float>);
 
-  /*
-  #if __ARM_NEON
-  REGISTER_OPERATOR(op_registry,OpKeyBuilder("BiasAdd")
-                               .Device(DeviceType::NEON)
-                               .TypeConstraint<float>("T")
-                               .Build(),
-                         BiasAddOp<DeviceType::NEON, float>);
-  #endif  // __ARM_NEON
-  */
-
   REGISTER_OPERATOR(op_registry, OpKeyBuilder("BiasAdd")
                                      .Device(DeviceType::OPENCL)
                                      .TypeConstraint<float>("T")

mace/ops/bias_add_benchmark.cc

@@ -59,21 +59,22 @@ static void BiasAdd(int iters, int batch, int channels, int height, int width) {
   }                                                                  \
   BENCHMARK(BM_BIAS_ADD_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE)
 
-#define BM_BIAS_ADD(N, C, H, W, TYPE)       \
-  BM_BIAS_ADD_MACRO(N, C, H, W, TYPE, CPU); \
-  BM_BIAS_ADD_MACRO(N, C, H, W, TYPE, OPENCL);
+#define BM_BIAS_ADD(N, C, H, W)                 \
+  BM_BIAS_ADD_MACRO(N, C, H, W, float, CPU);    \
+  BM_BIAS_ADD_MACRO(N, C, H, W, float, OPENCL); \
+  BM_BIAS_ADD_MACRO(N, C, H, W, half, OPENCL);
 
-BM_BIAS_ADD(1, 1, 512, 512, float);
-BM_BIAS_ADD(1, 3, 128, 128, float);
-BM_BIAS_ADD(1, 3, 512, 512, float);
-BM_BIAS_ADD(1, 32, 112, 112, float);
-BM_BIAS_ADD(1, 64, 256, 256, float);
-BM_BIAS_ADD(1, 64, 512, 512, float);
-BM_BIAS_ADD(1, 128, 56, 56, float);
-BM_BIAS_ADD(1, 128, 256, 256, float);
-BM_BIAS_ADD(1, 256, 14, 14, float);
-BM_BIAS_ADD(1, 512, 14, 14, float);
-BM_BIAS_ADD(1, 1024, 7, 7, float);
-BM_BIAS_ADD(32, 1, 256, 256, float);
-BM_BIAS_ADD(32, 3, 256, 256, float);
-}  //  namespace mace
+BM_BIAS_ADD(1, 1, 512, 512);
+BM_BIAS_ADD(1, 3, 128, 128);
+BM_BIAS_ADD(1, 3, 512, 512);
+BM_BIAS_ADD(1, 32, 112, 112);
+BM_BIAS_ADD(1, 64, 256, 256);
+BM_BIAS_ADD(1, 64, 512, 512);
+BM_BIAS_ADD(1, 128, 56, 56);
+BM_BIAS_ADD(1, 128, 256, 256);
+BM_BIAS_ADD(1, 256, 14, 14);
+BM_BIAS_ADD(1, 512, 14, 14);
+BM_BIAS_ADD(1, 1024, 7, 7);
+BM_BIAS_ADD(32, 1, 256, 256);
+BM_BIAS_ADD(32, 3, 256, 256);
+}  // namespace mace

mace/ops/buffer_to_image.cc

@@ -20,4 +20,4 @@ void Register_BufferToImage(OperatorRegistry *op_registry) {
                     BufferToImageOp<DeviceType::OPENCL, half>);
 }
 
-}  //  namespace mace
+}  // namespace mace

mace/ops/buffer_to_image.h

@@ -35,5 +35,5 @@ class BufferToImageOp: public Operator<D, T> {
   OP_OUTPUT_TAGS(OUTPUT);
 };
 
-} //  namespace mace
-#endif //  MACE_OPS_BUFFER_TO_IMAGE_H_
+}  // namespace mace
+#endif  // MACE_OPS_BUFFER_TO_IMAGE_H_

mace/ops/conv_2d.cc

@@ -13,20 +13,6 @@ void Register_Conv2D(OperatorRegistry *op_registry) {
                                      .Build(),
                     Conv2dOp<DeviceType::CPU, float>);
 
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("Conv2D")
-                                     .Device(DeviceType::CPU)
-                                     .TypeConstraint<half>("T")
-                                     .Build(),
-                    Conv2dOp<DeviceType::CPU, half>);
-
-#if MACE_ENABLE_NEON
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("Conv2D")
-                                     .Device(DeviceType::NEON)
-                                     .TypeConstraint<float>("T")
-                                     .Build(),
-                    Conv2dOp<DeviceType::NEON, float>);
-#endif  // MACE_ENABLE_NEON
-
   REGISTER_OPERATOR(op_registry, OpKeyBuilder("Conv2D")
                                      .Device(DeviceType::OPENCL)
                                      .TypeConstraint<float>("T")

mace/ops/conv_2d_benchmark.cc

@@ -29,7 +29,7 @@ static void Conv2d(int iters,
   // Add input data
   net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
   net.AddRandomInput<D, float>("Filter",
-                               {kernel_h, kernel_w, channels, output_channels});
+                               {kernel_h, kernel_w, output_channels, channels});
   net.AddRandomInput<D, float>("Bias", {output_channels});
 
   if (D == DeviceType::OPENCL) {
@@ -92,50 +92,46 @@ static void Conv2d(int iters,
   BENCHMARK(                                                                                       \
       BM_CONV_2D_##N##_##C##_##H##_##W##_K##KH##x##KW##S##STRIDE##_##P##_##OC##_##TYPE##_##DEVICE)
 
-#define BM_CONV_2D(N, C, H, W, KH, KW, S, P, OC, TYPE) \
-  BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, TYPE, OPENCL);
-
-// ICNet
-BM_CONV_2D(1, 512, 15, 15, 1, 1, 1, VALID, 1024, half);
-//// SNPE GPU ExecutionDuration = 448us, % ALU Utilization = 105
-BM_CONV_2D(1, 64, 60, 60, 1, 1, 1, VALID, 128, half);
-//// SNPE GPU ExecutionDuration = 258us, % ALU Utilization = 108
-BM_CONV_2D(1, 32, 60, 60, 1, 1, 1, VALID, 128, half);
-
-BM_CONV_2D(1, 128, 60, 60, 3, 3, 1, VALID, 128, half);
-//// SNPE GPU ExecutionDuration = 506us, % ALU Utilization = 106.8
-BM_CONV_2D(1, 32, 60, 60, 3, 3, 1, SAME, 32, half);
-BM_CONV_2D(1, 3, 512, 512, 7, 7, 2, SAME, 64, half);
-BM_CONV_2D(1, 512, 64, 64, 1, 1, 1, SAME, 256, half);
-
-BM_CONV_2D(1, 128, 16, 16, 3, 3, 1, VALID, 32, half);
-BM_CONV_2D(1, 128, 64, 64, 3, 3, 1, VALID, 32, half);
-BM_CONV_2D(1, 128, 128, 128, 3, 3, 1, VALID, 32, half);
-
-// Test RGB <-> YUV
-// BM_CONV_2D(1, 3, 2160, 1080, 1, 1, 1, VALID, 3, float);
-// BM_CONV_2D(1, 3, 480, 480, 1, 1, 1, VALID, 3, float);
-//
-// BM_CONV_2D(1, 64, 32, 32, 1, 1, 1, VALID, 128, float);
-// BM_CONV_2D(1, 64, 33, 31, 1, 1, 1, VALID, 128, float);  // Test bad
-// alignments
-// BM_CONV_2D(1, 3, 512, 512, 1, 1, 1, VALID, 3, float);
-// BM_CONV_2D(1, 32, 112, 112, 1, 1, 1, VALID, 64, float);
-// BM_CONV_2D(1, 64, 56, 56, 1, 1, 1, VALID, 128, float);
-// BM_CONV_2D(1, 256, 28, 28, 1, 1, 1, VALID, 256, float);
-// BM_CONV_2D(1, 1024, 7, 7, 1, 1, 1, VALID, 1024, float);
-// BM_CONV_2D(1, 64, 32, 32, 3, 3, 1, VALID, 128, float);
-// BM_CONV_2D(1, 64, 33, 31, 3, 3, 1, VALID, 128, float);
-// BM_CONV_2D(1, 3, 512, 512, 3, 3, 1, VALID, 3, float);
-// BM_CONV_2D(1, 64, 32, 32, 3, 3, 1, SAME, 128, float);
-// BM_CONV_2D(1, 64, 33, 31, 3, 3, 1, SAME, 128, float);
-// BM_CONV_2D(1, 64, 32, 32, 3, 3, 2, VALID, 128, float);
-// BM_CONV_2D(1, 3, 512, 512, 3, 3, 2, VALID, 3, float);
-// BM_CONV_2D(1, 64, 33, 31, 3, 3, 2, VALID, 128, float);
-// BM_CONV_2D(1, 64, 32, 32, 3, 3, 2, SAME, 128, float);
-// BM_CONV_2D(1, 64, 33, 31, 3, 3, 2, SAME, 128, float);
-// BM_CONV_2D(1, 64, 32, 32, 5, 5, 1, VALID, 128, float);
-// BM_CONV_2D(1, 64, 32, 31, 5, 5, 1, VALID, 128, float);
-// BM_CONV_2D(1, 64, 32, 32, 5, 5, 1, SAME, 128, float);
-// BM_CONV_2D(1, 64, 32, 31, 5, 5, 1, SAME, 128, float);
-}  //  namespace mace
+#define BM_CONV_2D(N, C, H, W, KH, KW, S, P, OC)                 \
+  BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, float, CPU);    \
+  BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, float, OPENCL); \
+  BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, half, OPENCL);
+
+BM_CONV_2D(1, 512, 15, 15, 1, 1, 1, VALID, 1024);
+BM_CONV_2D(1, 64, 60, 60, 1, 1, 1, VALID, 128);
+BM_CONV_2D(1, 32, 60, 60, 1, 1, 1, VALID, 128);
+
+BM_CONV_2D(1, 128, 60, 60, 3, 3, 1, VALID, 128);
+BM_CONV_2D(1, 32, 60, 60, 3, 3, 1, SAME, 32);
+BM_CONV_2D(1, 3, 512, 512, 7, 7, 2, SAME, 64);
+BM_CONV_2D(1, 512, 64, 64, 1, 1, 1, SAME, 256);
+
+BM_CONV_2D(1, 128, 16, 16, 3, 3, 1, VALID, 32);
+BM_CONV_2D(1, 128, 64, 64, 3, 3, 1, VALID, 32);
+BM_CONV_2D(1, 128, 128, 128, 3, 3, 1, VALID, 32);
+
+BM_CONV_2D(1, 3, 480, 480, 1, 1, 1, VALID, 3);
+
+BM_CONV_2D(1, 64, 32, 32, 1, 1, 1, VALID, 128);
+BM_CONV_2D(1, 64, 33, 31, 1, 1, 1, VALID, 128);  // Test bad alignments
+BM_CONV_2D(1, 3, 512, 512, 1, 1, 1, VALID, 3);
+BM_CONV_2D(1, 32, 112, 112, 1, 1, 1, VALID, 64);
+BM_CONV_2D(1, 64, 56, 56, 1, 1, 1, VALID, 128);
+BM_CONV_2D(1, 256, 28, 28, 1, 1, 1, VALID, 256);
+BM_CONV_2D(1, 1024, 7, 7, 1, 1, 1, VALID, 1024);
+BM_CONV_2D(1, 64, 32, 32, 3, 3, 1, VALID, 128);
+BM_CONV_2D(1, 64, 33, 31, 3, 3, 1, VALID, 128);
+BM_CONV_2D(1, 3, 512, 512, 3, 3, 1, VALID, 3);
+BM_CONV_2D(1, 64, 32, 32, 3, 3, 1, SAME, 128);
+BM_CONV_2D(1, 64, 33, 31, 3, 3, 1, SAME, 128);
+BM_CONV_2D(1, 64, 32, 32, 3, 3, 2, VALID, 128);
+BM_CONV_2D(1, 3, 512, 512, 3, 3, 2, VALID, 3);
+BM_CONV_2D(1, 64, 33, 31, 3, 3, 2, VALID, 128);
+BM_CONV_2D(1, 64, 32, 32, 3, 3, 2, SAME, 128);
+BM_CONV_2D(1, 64, 33, 31, 3, 3, 2, SAME, 128);
+BM_CONV_2D(1, 64, 32, 32, 5, 5, 1, VALID, 128);
+BM_CONV_2D(1, 64, 32, 31, 5, 5, 1, VALID, 128);
+BM_CONV_2D(1, 64, 32, 32, 5, 5, 1, SAME, 128);
+BM_CONV_2D(1, 64, 32, 31, 5, 5, 1, SAME, 128);
+
+}  // namespace mace

mace/ops/conv_2d_test.cc

@@ -10,81 +10,6 @@ using namespace mace;
 
 class Conv2dOpTest : public OpsTestBase {};
 
-template <DeviceType D>
-void TestSimple3x3VALID() {
-  OpsTestNet net;
-  OpDefBuilder("Conv2D", "Conv2dTest")
-      .Input("Input")
-      .Input("Filter")
-      .Input("Bias")
-      .Output("Output")
-      .AddIntsArg("strides", {1, 1})
-      .AddIntArg("padding", Padding::VALID)
-      .AddIntsArg("dilations", {1, 1})
-      .Finalize(net.NewOperatorDef());
-
-  // Add args
-
-  // Add input data
-  net.AddInputFromArray<D, float>(
-      "Input", {1, 2, 3, 3},
-      {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
-  net.AddInputFromArray<D, float>(
-      "Filter", {1, 2, 3, 3},
-      {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-       1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
-  net.AddInputFromArray<D, float>("Bias", {1}, {0.1f});
-
-  // Run
-  net.RunOp(D);
-
-  // Check
-  auto expected = CreateTensor<float>({1, 1, 1, 1}, {18.1f});
-
-  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
-}
-
-template <DeviceType D>
-void TestSimple3x3SAME() {
-  OpsTestNet net;
-  OpDefBuilder("Conv2D", "Conv2dTest")
-      .Input("Input")
-      .Input("Filter")
-      .Input("Bias")
-      .Output("Output")
-      .AddIntsArg("strides", {1, 1})
-      .AddIntArg("padding", Padding::SAME)
-      .AddIntsArg("dilations", {1, 1})
-      .Finalize(net.NewOperatorDef());
-
-  // Add input data
-  net.AddInputFromArray<D, float>(
-      "Input", {1, 2, 3, 3},
-      {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
-  net.AddInputFromArray<D, float>(
-      "Filter", {1, 2, 3, 3},
-      {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-       1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
-  net.AddInputFromArray<D, float>("Bias", {1}, {0.1f});
-
-  // Run
-  net.RunOp(D);
-
-  // Check
-  auto expected = CreateTensor<float>(
-      {1, 1, 3, 3},
-      {8.1f, 12.1f, 8.1f, 12.1f, 18.1f, 12.1f, 8.1f, 12.1f, 8.1f});
-
-  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
-}
-
-#if __ARM_NEON
-TEST_F(Conv2dOpTest, NEONSimple) {
-  TestSimple3x3VALID<DeviceType::NEON>();
-  TestSimple3x3SAME<DeviceType::NEON>();
-}
-#endif
-
 template <DeviceType D, typename T>
 void TestNHWCSimple3x3VALID() {
   OpsTestNet net;
@@ -93,7 +18,7 @@ void TestNHWCSimple3x3VALID() {
       "Input", {1, 3, 3, 2},
       {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
   net.AddInputFromArray<D, T>(
-      "Filter", {3, 3, 2, 1},
+      "Filter", {3, 3, 1, 2},
       {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
        1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
   net.AddInputFromArray<D, T>("Bias", {1}, {0.1f});
@@ -150,7 +75,7 @@ void TestNHWCSimple3x3SAME() {
       "Input", {1, 3, 3, 2},
       {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
   net.AddInputFromArray<D, T>(
-      "Filter", {3, 3, 2, 1},
+      "Filter", {3, 3, 1, 2},
       {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
        1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
   net.AddInputFromArray<D, T>("Bias", {1}, {0.1f});
@@ -211,42 +136,6 @@ TEST_F(Conv2dOpTest, OPENCLSimple) {
   TestNHWCSimple3x3SAME<DeviceType::OPENCL, float>();
 }
 
-template <DeviceType D>
-void TestSimple3x3WithoutBias() {
-  OpsTestNet net;
-  OpDefBuilder("Conv2D", "Conv2dTest")
-      .Input("Input")
-      .Input("Filter")
-      .Output("Output")
-      .AddIntsArg("strides", {1, 1})
-      .AddIntArg("padding", Padding::VALID)
-      .AddIntsArg("dilations", {1, 1})
-      .Finalize(net.NewOperatorDef());
-
-  // Add input data
-  net.AddInputFromArray<D, float>(
-      "Input", {1, 2, 3, 3},
-      {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
-  net.AddInputFromArray<D, float>(
-      "Filter", {1, 2, 3, 3},
-      {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-       1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
-
-  // Run
-  net.RunOp(D);
-
-  // Check
-  auto expected = CreateTensor<float>({1, 1, 1, 1}, {18.0f});
-
-  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
-}
-
-#ifdef __ARM_NEON
-TEST_F(Conv2dOpTest, NEONWithouBias) {
-  TestSimple3x3WithoutBias<DeviceType::NEON>();
-}
-#endif
-
 template <DeviceType D, typename T>
 void TestNHWCSimple3x3WithoutBias() {
   OpsTestNet net;
@@ -256,7 +145,7 @@ void TestNHWCSimple3x3WithoutBias() {
       "Input", {1, 3, 3, 2},
       {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
   net.AddInputFromArray<D, T>(
-      "Filter", {3, 3, 2, 1},
+      "Filter", {3, 3, 1, 2},
       {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
        1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
 
@@ -309,47 +198,6 @@ TEST_F(Conv2dOpTest, OPENCLWithoutBias) {
   TestNHWCSimple3x3WithoutBias<DeviceType::OPENCL, float>();
 }
 
-template <DeviceType D>
-static void TestCombined3x3() {
-  // Construct graph
-  OpsTestNet net;
-  OpDefBuilder("Conv2D", "Conv2DTest")
-      .Input("Input")
-      .Input("Filter")
-      .Input("Bias")
-      .Output("Output")
-      .AddIntsArg("strides", {2, 2})
-      .AddIntArg("padding", Padding::SAME)
-      .AddIntsArg("dilations", {1, 1})
-      .Finalize(net.NewOperatorDef());
-
-  // Add input data
-  net.AddInputFromArray<D, float>(
-      "Input", {1, 2, 5, 5}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-                              1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-                              1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
-  net.AddInputFromArray<D, float>(
-      "Filter", {2, 2, 3, 3},
-      {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-       1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-       0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f});
-  net.AddInputFromArray<D, float>("Bias", {2}, {0.1f, 0.2f});
-
-  // Run
-  net.RunOp(D);
-
-  // Check
-  auto expected = CreateTensor<float>(
-      {1, 2, 3, 3}, {8.1f, 12.1f, 8.1f, 12.1f, 18.1f, 12.1f, 8.1f, 12.1f, 8.1f,
-                     4.2f, 6.2f, 4.2f, 6.2f, 9.2f, 6.2f, 4.2f, 6.2f, 4.2f});
-
-  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
-}
-
-#ifdef __ARM_NEON
-TEST_F(Conv2dOpTest, NEONCombined) { TestCombined3x3<DeviceType::NEON>(); }
-#endif
-
 template <DeviceType D, typename T>
 static void TestNHWCCombined3x3() {
   // Construct graph
@@ -362,9 +210,9 @@ static void TestNHWCCombined3x3() {
                               1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
   net.AddInputFromArray<D, T>(
       "Filter", {3, 3, 2, 2},
-      {1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f,
-       1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f,
-       1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f, 1.0f, 0.5f});
+      {1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f,
+       1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f,
+       1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f});
   net.AddInputFromArray<D, T>("Bias", {2}, {0.1f, 0.2f});
 
   if (D == DeviceType::OPENCL) {
@@ -436,8 +284,8 @@ void TestConv1x1() {
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
   net.AddInputFromArray<D, float>(
-      "Filter", {1, 1, 5, 2},
-      {1.0f, 2.0f, 1.0f, 2.0f, 1.0f, 2.0f, 1.0f, 2.0f, 1.0f, 2.0f});
+      "Filter", {1, 1, 2, 5},
+      {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f});
   net.AddInputFromArray<D, float>("Bias", {2}, {0.1f, 0.2f});
 
   if (D == DeviceType::OPENCL) {
@@ -522,7 +370,7 @@ static void TestComplexConvNxNS12(const std::vector<index_t> &shape) {
     // Add input data
     net.AddRandomInput<D, T>("Input", {batch, height, width, input_channels});
     net.AddRandomInput<D, T>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels});
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels});
     net.AddRandomInput<D, T>("Bias", {output_channels});
 
     // run on cpu
@@ -606,7 +454,7 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
                                float_input_data);
     std::vector<float> float_filter_data;
     GenerateRandomRealTypeData(
-        {kernel_h, kernel_w, input_channels, output_channels},
+        {kernel_h, kernel_w, output_channels, input_channels},
         float_filter_data);
     std::vector<float> float_bias_data;
     GenerateRandomRealTypeData({output_channels}, float_bias_data);
@@ -614,7 +462,7 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
     net.AddInputFromArray<D, float>(
         "Input", {batch, height, width, input_channels}, float_input_data);
     net.AddInputFromArray<D, float>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels},
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels},
         float_filter_data);
     net.AddInputFromArray<D, float>("Bias", {output_channels}, float_bias_data);
 
@@ -748,7 +596,7 @@ static void TestDilationConvNxN(const std::vector<index_t> &shape, const int dil
     // Add input data
     net.AddRandomInput<D, T>("Input", {batch, height, width, input_channels});
     net.AddRandomInput<D, T>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels});
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels});
     net.AddRandomInput<D, T>("Bias", {output_channels});
 
     // run on cpu

mace/ops/depthwise_conv2d.cc

@@ -13,14 +13,6 @@ void Register_DepthwiseConv2d(OperatorRegistry *op_registry) {
                                      .Build(),
                     DepthwiseConv2dOp<DeviceType::CPU, float>);
 
-#if MACE_ENABLE_NEON
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d")
-                                     .Device(DeviceType::NEON)
-                                     .TypeConstraint<float>("T")
-                                     .Build(),
-                    DepthwiseConv2dOp<DeviceType::NEON, float>);
-#endif  // MACE_ENABLE_NEON
-
   REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d")
                                      .Device(DeviceType::OPENCL)
                                      .TypeConstraint<float>("T")

mace/ops/depthwise_conv2d_test.cc

@@ -288,12 +288,6 @@ void TestNxNS12(const index_t height, const index_t width) {
   }
 }
 
-#if __ARM_NEON
-TEST_F(DepthwiseConv2dOpTest, NeonSimpleNxNS12) {
-  TestNxNS12<DeviceType::NEON, float>(4, 4);
-}
-#endif
-
 TEST_F(DepthwiseConv2dOpTest, OpenCLSimpleNxNS12) {
   TestNxNS12<DeviceType::OPENCL, float>(4, 4);
 }
@@ -302,13 +296,6 @@ TEST_F(DepthwiseConv2dOpTest, OpenCLSimpleNxNS12Half) {
   TestNxNS12<DeviceType::OPENCL, half>(4, 4);
 }
 
-#if __ARM_NEON
-TEST_F(DepthwiseConv2dOpTest, NeonAlignedNxNS12) {
-  TestNxNS12<DeviceType::NEON, float>(64, 64);
-  TestNxNS12<DeviceType::NEON, float>(128, 128);
-}
-#endif
-
 TEST_F(DepthwiseConv2dOpTest, OpenCLAlignedNxNS12) {
   TestNxNS12<DeviceType::OPENCL, float>(64, 64);
   TestNxNS12<DeviceType::OPENCL, float>(128, 128);
@@ -319,12 +306,6 @@ TEST_F(DepthwiseConv2dOpTest, OpenCLAlignedNxNS12Half) {
   TestNxNS12<DeviceType::OPENCL, half>(128, 128);
 }
 
-#if __ARM_NEON
-TEST_F(DepthwiseConv2dOpTest, NeonUnalignedNxNS12) {
-  TestNxNS12<DeviceType::NEON, float>(107, 113);
-}
-#endif
-
 TEST_F(DepthwiseConv2dOpTest, OpenCLUnalignedNxNS12) {
   TestNxNS12<DeviceType::OPENCL, float>(107, 113);
 }

mace/ops/depthwise_conv_2d_benchmark.cc

@@ -89,21 +89,22 @@ static void DepthwiseConv2d(int iters,
   BENCHMARK(                                                                                            \
       BM_DEPTHWISE_2D_##N##_##C##_##H##_##W##_K##KH##x##KW##S##STRIDE##_##P##_##OC##_##TYPE##_##DEVICE)
 
-#define BM_DEPTHWISE_CONV_2D(N, C, H, W, KH, KW, S, P, OC, TYPE)       \
-  BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, TYPE, CPU); \
-  BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, TYPE, OPENCL);
+#define BM_DEPTHWISE_CONV_2D(N, C, H, W, KH, KW, S, P, OC)                 \
+  BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, float, CPU);    \
+  BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, float, OPENCL); \
+  BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, OC, half, OPENCL);
 
-BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 1, VALID, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 1, VALID, 1, float);
-BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 1, SAME, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 1, SAME, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 1, VALID, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 1, SAME, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 2, VALID, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 2, VALID, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 2, SAME, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 2, SAME, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 2, VALID, 1, float);
-//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 2, SAME, 1, float);
+BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 1, VALID, 1);
+//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 1, VALID, 1);
+BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 1, SAME, 1);
+//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 1, SAME, 1);
+//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 1, VALID, 1);
+//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 1, SAME, 1);
+//BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 2, VALID, 1);
+//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 2, VALID, 1);
+//BM_DEPTHWISE_CONV_2D(1, 64, 32, 32, 3, 3, 2, SAME, 1);
+//BM_DEPTHWISE_CONV_2D(1, 64, 33, 31, 3, 3, 2, SAME, 1);
+//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 2, VALID, 1);
+//BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 2, SAME, 1);
 
 }  //  namespace mace

mace/ops/folded_batch_norm.cc

@@ -14,14 +14,6 @@ void Register_FoldedBatchNorm(OperatorRegistry *op_registry) {
                         .Build(),
                     FoldedBatchNormOp<DeviceType::CPU, float>);
 
-#if MACE_ENABLE_NEON
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("FoldedBatchNorm")
-                                     .Device(DeviceType::NEON)
-                                     .TypeConstraint<float>("T")
-                                     .Build(),
-                    FoldedBatchNormOp<DeviceType::NEON, float>);
-#endif  // MACE_ENABLE_NEON
-
   REGISTER_OPERATOR(op_registry,
                     OpKeyBuilder("FoldedBatchNorm")
                         .Device(DeviceType::OPENCL)

mace/ops/fused_conv_2d.cc

@@ -13,12 +13,6 @@ void Register_FusedConv2D(OperatorRegistry *op_registry) {
                                      .Build(),
                     FusedConv2dOp<DeviceType::CPU, float>);
 
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("FusedConv2D")
-                                     .Device(DeviceType::CPU)
-                                     .TypeConstraint<half>("T")
-                                     .Build(),
-                    FusedConv2dOp<DeviceType::CPU, half>);
-
   REGISTER_OPERATOR(op_registry, OpKeyBuilder("FusedConv2D")
                                      .Device(DeviceType::OPENCL)
                                      .TypeConstraint<float>("T")

mace/ops/fused_conv_2d_test.cc

@@ -298,7 +298,7 @@ static void TestComplexConvNxNS12(const std::vector<index_t> &shape) {
     // Add input data
     net.AddRandomInput<D, T>("Input", {batch, height, width, input_channels});
     net.AddRandomInput<D, T>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels});
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels});
     net.AddRandomInput<D, T>("Bias", {output_channels});
 
     // run on cpu
@@ -375,7 +375,7 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
                                float_input_data);
     std::vector<float> float_filter_data;
     GenerateRandomRealTypeData(
-        {kernel_h, kernel_w, input_channels, output_channels},
+        {kernel_h, kernel_w, output_channels, input_channels},
         float_filter_data);
     std::vector<float> float_bias_data;
     GenerateRandomRealTypeData({output_channels}, float_bias_data);
@@ -383,7 +383,7 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
     net.AddInputFromArray<D, float>(
         "Input", {batch, height, width, input_channels}, float_input_data);
     net.AddInputFromArray<D, float>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels},
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels},
         float_filter_data);
     net.AddInputFromArray<D, float>("Bias", {output_channels}, float_bias_data);
 
@@ -462,7 +462,7 @@ static void TestGeneralConvNxNS12(const std::vector<index_t> &image_shape,
     // Add input data
     net.AddRandomInput<D, T>("Input", {batch, height, width, input_channels});
     net.AddRandomInput<D, T>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels});
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels});
     net.AddRandomInput<D, T>("Bias", {output_channels});
 
     // run on cpu
@@ -540,7 +540,7 @@ static void TestAtrousConvNxN(const std::vector<index_t> &shape, const int dilat
     // Add input data
     net.AddRandomInput<D, T>("Input", {batch, height, width, input_channels});
     net.AddRandomInput<D, T>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels});
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels});
     net.AddRandomInput<D, T>("Bias", {output_channels});
 
     // run on cpu
@@ -622,7 +622,7 @@ static void TestGeneralHalfAtrousConv(const std::vector<index_t> &image_shape,
     // Add input data
     net.AddRandomInput<D, float>("Input", {batch, height, width, input_channels});
     net.AddRandomInput<D, float>(
-        "Filter", {kernel_h, kernel_w, input_channels, output_channels});
+        "Filter", {kernel_h, kernel_w, output_channels, input_channels});
     net.AddRandomInput<D, float>("Bias", {output_channels});
 
     // run on cpu

mace/ops/global_avg_pooling.cc

@@ -12,14 +12,6 @@ void Register_GlobalAvgPooling(OperatorRegistry *op_registry) {
                                      .TypeConstraint<float>("T")
                                      .Build(),
                     GlobalAvgPoolingOp<DeviceType::CPU, float>);
-
-#if MACE_ENABLE_NEON
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("GlobalAvgPooling")
-                                     .Device(DeviceType::NEON)
-                                     .TypeConstraint<float>("T")
-                                     .Build(),
-                    GlobalAvgPoolingOp<DeviceType::NEON, float>);
-#endif  // MACE_ENABLE_NEON
 }
 
 }  //  namespace mace

mace/ops/global_avg_pooling_test.cc

@@ -31,29 +31,3 @@ TEST_F(GlobalAvgPoolingOpTest, 3x7x7_CPU) {
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
 }
-
-#if __ARM_NEON
-TEST_F(GlobalAvgPoolingOpTest, 3x7x7_NEON) {
-  // Construct graph
-  OpsTestNet net;
-  OpDefBuilder("GlobalAvgPooling", "GlobalAvgPoolingTest")
-      .Input("Input")
-      .Output("Output")
-      .Finalize(net.NewOperatorDef());
-
-  // Add input data
-  std::vector<float> input(147);
-  for (int i = 0; i < 147; ++i) {
-    input[i] = i / 49 + 1;
-  }
-  net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 3, 7, 7}, input);
-
-  // Run
-  net.RunOp(DeviceType::NEON);
-
-  // Check
-  auto expected = CreateTensor<float>({1, 3, 1, 1}, {1, 2, 3});
-
-  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
-}
-#endif

mace/ops/matmul_benchmark.cc

@@ -61,10 +61,10 @@ static void MatMulBenchmark(
   }                                                                  \
   BENCHMARK(BM_MATMUL_##N##_##H##_##C##_##W##_##TYPE##_##DEVICE)
 
-#define BM_MATMUL(N, H, C, W, TYPE)        \
-  BM_MATMUL_MACRO(N, H, C, W, TYPE, OPENCL);
+#define BM_MATMUL(N, H, C, W) \
+  BM_MATMUL_MACRO(N, H, C, W, half, OPENCL);
 
-BM_MATMUL(16, 32, 128, 49, half);
-BM_MATMUL(16, 32, 128, 961, half);
-BM_MATMUL(16, 32, 128, 3969, half);
-}  //  namespace mace
+BM_MATMUL(16, 32, 128, 49);
+BM_MATMUL(16, 32, 128, 961);
+BM_MATMUL(16, 32, 128, 3969);
+}  // namespace mace

mace/ops/pooling.cc

@@ -18,14 +18,6 @@ void Register_Pooling(OperatorRegistry *op_registry) {
                                      .Build(),
                     PoolingOp<DeviceType::CPU, half>);
 
-#if MACE_ENABLE_NEON
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("Pooling")
-                                     .Device(DeviceType::NEON)
-                                     .TypeConstraint<float>("T")
-                                     .Build(),
-                    PoolingOp<DeviceType::NEON, float>);
-#endif  // MACE_ENABLE_NEON
-
   REGISTER_OPERATOR(op_registry, OpKeyBuilder("Pooling")
                                      .Device(DeviceType::OPENCL)
                                      .TypeConstraint<float>("T")

mace/ops/resize_bilinear.cc

@@ -13,14 +13,6 @@ void Register_ResizeBilinear(OperatorRegistry *op_registry) {
                                      .Build(),
                     ResizeBilinearOp<DeviceType::CPU, float>);
 
-#if MACE_ENABLE_NEON
-  REGISTER_OPERATOR(op_registry, OpKeyBuilder("ResizeBilinear")
-                                     .Device(DeviceType::NEON)
-                                     .TypeConstraint<float>("T")
-                                     .Build(),
-                    ResizeBilinearOp<DeviceType::NEON, float>);
-#endif  // MACE_ENABLE_NEON
-
   REGISTER_OPERATOR(op_registry, OpKeyBuilder("ResizeBilinear")
                                      .Device(DeviceType::OPENCL)
                                      .TypeConstraint<float>("T")

mace/ops/resize_bilinear_benchmark.cc

@@ -69,18 +69,18 @@ static void ResizeBilinearBenchmark(int iters,
   BENCHMARK(                                                                        \
       BM_RESIZE_BILINEAR_##N##_##C##_##H0##_##W0##_##H1##_##W1##_##TYPE##_##DEVICE)
 
-#define BM_RESIZE_BILINEAR(N, C, H0, W0, H1, W1, TYPE)       \
-  BM_RESIZE_BILINEAR_MACRO(N, C, H0, W0, H1, W1, TYPE, CPU); \
-  BM_RESIZE_BILINEAR_MACRO(N, C, H0, W0, H1, W1, TYPE, OPENCL);
+#define BM_RESIZE_BILINEAR(N, C, H0, W0, H1, W1)                 \
+  BM_RESIZE_BILINEAR_MACRO(N, C, H0, W0, H1, W1, float, CPU);    \
+  BM_RESIZE_BILINEAR_MACRO(N, C, H0, W0, H1, W1, float, OPENCL); \
+  BM_RESIZE_BILINEAR_MACRO(N, C, H0, W0, H1, W1, half, OPENCL);
 
-// SNPE 835 GPU: 6870us
-BM_RESIZE_BILINEAR(1, 128, 120, 120, 480, 480, float);
+BM_RESIZE_BILINEAR(1, 128, 120, 120, 480, 480);
 
-BM_RESIZE_BILINEAR(1, 256, 7, 7, 15, 15, float);
-BM_RESIZE_BILINEAR(1, 256, 15, 15, 30, 30, float);
-BM_RESIZE_BILINEAR(1, 128, 30, 30, 60, 60, float);
-BM_RESIZE_BILINEAR(1, 128, 240, 240, 480, 480, float);
-BM_RESIZE_BILINEAR(1, 3, 4032, 3016, 480, 480, float);
-BM_RESIZE_BILINEAR(1, 3, 480, 480, 4032, 3016, float);
+BM_RESIZE_BILINEAR(1, 256, 7, 7, 15, 15);
+BM_RESIZE_BILINEAR(1, 256, 15, 15, 30, 30);
+BM_RESIZE_BILINEAR(1, 128, 30, 30, 60, 60);
+BM_RESIZE_BILINEAR(1, 128, 240, 240, 480, 480);
+BM_RESIZE_BILINEAR(1, 3, 4032, 3016, 480, 480);
+BM_RESIZE_BILINEAR(1, 3, 480, 480, 4032, 3016);
 
-}  //  namespace mace
+}  // namespace mace

mace/ops/softmax_benchmark.cc

@@ -55,13 +55,14 @@ static void SoftmaxBenchmark(
   }                                                                  \
   BENCHMARK(BM_SOFTMAX_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE)
 
-#define BM_SOFTMAX(N, C, H, W, TYPE)        \
-  BM_SOFTMAX_MACRO(N, C, H, W, TYPE, CPU);  \
-  BM_SOFTMAX_MACRO(N, C, H, W, TYPE, OPENCL);
+#define BM_SOFTMAX(N, C, H, W)                 \
+  BM_SOFTMAX_MACRO(N, C, H, W, float, CPU);    \
+  BM_SOFTMAX_MACRO(N, C, H, W, float, OPENCL); \
+  BM_SOFTMAX_MACRO(N, C, H, W, half, OPENCL);
 
-BM_SOFTMAX(1, 1, 512, 512, float);
-BM_SOFTMAX(1, 3, 128, 128, float);
-BM_SOFTMAX(1, 3, 512, 512, float);
-BM_SOFTMAX(1, 32, 112, 112, float);
-BM_SOFTMAX(1, 64, 256, 256, float);
+BM_SOFTMAX(1, 1, 512, 512);
+BM_SOFTMAX(1, 3, 128, 128);
+BM_SOFTMAX(1, 3, 512, 512);
+BM_SOFTMAX(1, 32, 112, 112);
+BM_SOFTMAX(1, 64, 256, 256);
 }  //  namespace mace

mace/ops/space_to_batch_benchmark.cc

@@ -49,10 +49,10 @@ static void BMSpaceToBatch(
   BENCHMARK(                                                                 \
       BM_SPACE_TO_BATCH_##N##_##H##_##W##_##C##_##SHAPE##_##TYPE##_##DEVICE)
 
-#define BM_SPACE_TO_BATCH(N, H, W, C, SHAPE, TYPE) \
-  BM_SPACE_TO_BATCH_MACRO(N, H, W, C, SHAPE, TYPE, OPENCL);
+#define BM_SPACE_TO_BATCH(N, H, W, C, SHAPE) \
+  BM_SPACE_TO_BATCH_MACRO(N, H, W, C, SHAPE, float, OPENCL);
 
-BM_SPACE_TO_BATCH(128, 16, 16, 128, 2, float);
-BM_SPACE_TO_BATCH(1, 256, 256, 32, 2, float);
-BM_SPACE_TO_BATCH(1, 256, 256, 32, 4, float);
-}  //  namespace mace
\ No newline at end of file
+BM_SPACE_TO_BATCH(128, 16, 16, 128, 2);
+BM_SPACE_TO_BATCH(1, 256, 256, 32, 2);
+BM_SPACE_TO_BATCH(1, 256, 256, 32, 4);
+}  // namespace mace

mace/ops/winograd_convolution_test.cc

@@ -19,9 +19,9 @@ void TransposeFilter(const std::vector<float> &input,
   const float *input_ptr = input.data();
   for (index_t h = 0; h < input_shape[0]; ++h) {
     for (index_t w = 0; w < input_shape[1]; ++w) {
-      for (index_t ic = 0; ic < input_shape[2]; ++ic) {
-        for (index_t oc = 0; oc < input_shape[3]; ++oc) {
-          int offset = ((oc * input_shape[2] + ic) * input_shape[0] + h) * input_shape[1] + w;
+      for (index_t oc = 0; oc < input_shape[2]; ++oc) {
+        for (index_t ic = 0; ic < input_shape[3]; ++ic) {
+          int offset = ((oc * input_shape[3] + ic) * input_shape[0] + h) * input_shape[1] + w;
           output[offset] = *input_ptr;
           ++input_ptr;
         }
@@ -43,7 +43,7 @@ void WinogradConvolution(const index_t batch,
   OpsTestNet net;
   // Add input data
   std::vector<float> filter_data;
-  std::vector<index_t> filter_shape = {3, 3, in_channels, out_channels};
+  std::vector<index_t> filter_shape = {3, 3, out_channels, in_channels};
   GenerateRandomRealTypeData<float>(filter_shape, filter_data);
   net.AddRandomInput<D, float>("Input", {batch, height, width, in_channels});
   net.AddInputFromArray<D, float>("Filter", filter_shape, filter_data);

mace/ops/winograd_transform_benchmark.cc

@@ -48,12 +48,12 @@ static void BMWinogradTransform(
   BENCHMARK(                                                          \
       BM_WINOGRAD_TRANSFORM_##N##_##H##_##W##_##C##_##TYPE##_##DEVICE)
 
-#define BM_WINOGRAD_TRANSFORM(N, H, W, C, TYPE) \
-  BM_WINOGRAD_TRANSFORM_MACRO(N, H, W, C, TYPE, OPENCL);
+#define BM_WINOGRAD_TRANSFORM(N, H, W, C) \
+  BM_WINOGRAD_TRANSFORM_MACRO(N, H, W, C, half, OPENCL);
 
-BM_WINOGRAD_TRANSFORM(1, 16, 16, 128, half);
-BM_WINOGRAD_TRANSFORM(1, 64, 64, 128, half);
-BM_WINOGRAD_TRANSFORM(1, 128, 128, 128, half);
+BM_WINOGRAD_TRANSFORM(1, 16, 16, 128);
+BM_WINOGRAD_TRANSFORM(1, 64, 64, 128);
+BM_WINOGRAD_TRANSFORM(1, 128, 128, 128);
 
 template <DeviceType D, typename T>
 static void BMWinogradInverseTransform(
@@ -100,11 +100,11 @@ static void BMWinogradInverseTransform(
   BENCHMARK(                                                          \
       BM_WINOGRAD_INVERSE_TRANSFORM_##N##_##H##_##W##_##C##_##TYPE##_##DEVICE)
 
-#define BM_WINOGRAD_INVERSE_TRANSFORM(N, H, W, C, TYPE) \
-  BM_WINOGRAD_INVERSE_TRANSFORM_MACRO(N, H, W, C, TYPE, OPENCL);
+#define BM_WINOGRAD_INVERSE_TRANSFORM(N, H, W, C) \
+  BM_WINOGRAD_INVERSE_TRANSFORM_MACRO(N, H, W, C, half, OPENCL);
 
-BM_WINOGRAD_INVERSE_TRANSFORM(1, 14, 14, 32, half);
-BM_WINOGRAD_INVERSE_TRANSFORM(1, 62, 62, 32, half);
-BM_WINOGRAD_INVERSE_TRANSFORM(1, 126, 126, 32, half);
+BM_WINOGRAD_INVERSE_TRANSFORM(1, 14, 14, 32);
+BM_WINOGRAD_INVERSE_TRANSFORM(1, 62, 62, 32);
+BM_WINOGRAD_INVERSE_TRANSFORM(1, 126, 126, 32);
 
-}  //  namespace mace
\ No newline at end of file
+}  // namespace mace

mace/utils/utils.h

@@ -30,6 +30,11 @@ Integer RoundUpDiv8(Integer i) {
   return (i + 7) >> 3;
 }
 
+template <typename Integer>
+Integer RoundUpDiv(Integer i, Integer factor) {
+  return (i + factor - 1) / factor;
+}
+
 template <typename Integer>
 Integer CeilQuotient(Integer a, Integer b) {
   return (a + b - 1) / b;

tools/bazel-adb-run.sh

@@ -18,8 +18,8 @@ BAZEL_BIN_PATH=${BAZEL_BIN_PATH#//}
 BAZEL_BIN_PATH=bazel-bin/$BAZEL_BIN_PATH
 BIN_NAME=`echo $BAZEL_TARGET | cut -d: -f2`
 
-ANDROID_ABI=arm64-v8a
 ANDROID_ABI=armeabi-v7a
+ANDROID_ABI=arm64-v8a
 STRIP="--strip always"
 VLOG_LEVEL=0
 PROFILING="1"
@@ -43,7 +43,8 @@ bazel build -c opt $STRIP --verbose_failures $BAZEL_TARGET \
    --copt="-D_GLIBCXX_USE_C99_MATH_TR1" \
    --copt="-DMACE_DISABLE_NO_TUNING_WARNING" \
    --copt="-Werror=return-type" \
-   --define neon=false \
+   --copt="-O3" \
+   --define neon=true \
    --define openmp=true
 
 if [ $? -ne 0 ]; then