solve conflicts

mace/kernels/opencl/cl/space_to_depth.cl

+#include <common.h>
+
+__kernel void space_to_depth(__read_only image2d_t input,
+                      __private const int block_size,
+                      __private const int input_depth,
+                      __write_only image2d_t output) {
+  const int d = get_global_id(0);
+  const int w = get_global_id(1);
+  const int h = get_global_id(2);
+  const int input_width = get_global_size(1);
+  const int in_pos = mad24(d, input_width, w); 
+  const int output_width = input_width / block_size;
+ 
+  const int out_h = h / block_size; 
+  const int offset_h = h % block_size;
+  const int out_w = w / block_size;
+  const int offset_w = w % block_size;
+  const int offset_d = (offset_h * block_size + offset_w) * input_depth;
+  const int out_d = d + offset_d;
+  const int out_pos = mad24(out_d, output_width, out_w);
+ 
+  DATA_TYPE4 in_data = READ_IMAGET(input, SAMPLER, (int2)(in_pos, h));
+  
+  WRITE_IMAGET(output, (int2)(out_pos, out_h), in_data);
+}

mace/kernels/opencl/space_to_depth_opencl.cc

+//
+// Copyright (c) 2018 XiaoMi All rights reserved.
+//
+
+#include "mace/kernels/space_to_depth.h"
+#include "mace/core/runtime/opencl/cl2_header.h"
+#include "mace/core/runtime/opencl/opencl_runtime.h"
+#include "mace/kernels/opencl/helper.h"
+#include "mace/utils/utils.h"
+#include "mace/utils/tuner.h"
+
+namespace mace {
+namespace kernels {
+
+template <typename T>
+void SpaceToDepthOpFunctor<DeviceType::OPENCL, T>::operator()(
+    const Tensor *input,
+    Tensor *output,
+    StatsFuture *future) {
+  const index_t batch_size = input->dim(0);
+  const index_t input_height = input->dim(1);
+  const index_t input_width = input->dim(2);
+  const index_t input_depth = input->dim(3);
+  
+  const index_t output_height = input_height / block_size_;
+  const index_t output_width = input_width / block_size_;
+  const index_t output_depth = input_depth * block_size_ * block_size_;
+  
+  std::vector<index_t> output_shape = {batch_size, output_height, output_width, output_depth};  
+  
+  std::vector<size_t> image_shape;
+  CalImage2DShape(output_shape, BufferType::IN_OUT_CHANNEL, &image_shape);  
+  output->ResizeImage(output_shape, image_shape);
+  
+  const int input_depth_blocks = RoundUpDiv4(input_depth); 
+
+  if (kernel_.get() == nullptr) {
+    auto runtime = OpenCLRuntime::Global();
+    std::set<std::string> built_options;
+    std::string kernel_name = MACE_OBFUSCATE_SYMBOL("space_to_depth");
+    built_options.emplace("-Dspace_to_depth=" + kernel_name);
+    auto dt = DataTypeToEnum<T>::value;
+    built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(dt));
+    built_options.emplace("-DCMD_DATA_TYPE=" + DtToUpstreamCLCMDDt(dt));
+    kernel_ = runtime->BuildKernel("space_to_depth", kernel_name,
+                                   built_options);
+  }
+  if (!IsVecEqual(input_shape_, input->shape())) {
+    uint32_t idx = 0;
+    kernel_.setArg(idx++, *(input->opencl_image()));
+    kernel_.setArg(idx++, block_size_);
+    kernel_.setArg(idx++, input_depth_blocks);
+    kernel_.setArg(idx++, *(output->opencl_image()));
+    
+    input_shape_ = input->shape();
+  }
+  
+  const uint32_t gws[3] = {static_cast<uint32_t>(input_depth_blocks),
+                           static_cast<uint32_t>(input_width),
+                           static_cast<uint32_t>(input_height * batch_size)};
+  const std::vector<uint32_t> lws = {8, 16, 8, 1};
+  std::stringstream ss;
+  ss << "space_to_depth_opencl_kernel_"
+     << input->dim(0) << "_"
+     << input->dim(1) << "_"
+     << input->dim(2) << "_"
+     << input->dim(3);
+  TuningOrRun3DKernel(kernel_, ss.str(), gws, lws, future);
+}
+
+template
+struct SpaceToDepthOpFunctor<DeviceType::OPENCL, float>;
+template
+struct SpaceToDepthOpFunctor<DeviceType::OPENCL, half>;
+
+}  // namespace kernels
+}  // namespace mace

mace/kernels/space_to_depth.h

+//
+// Created by liutuo on 18-3-20.
+//
+
+#ifndef MACE_KERNELS_SPACE_TO_DEPTH_H
+#define MACE_KERNELS_SPACE_TO_DEPTH_H
+
+#include "mace/core/future.h"
+#include "mace/core/tensor.h"
+
+namespace mace {
+namespace kernels {
+
+template <DeviceType D, typename T>
+struct SpaceToDepthOpFunctor {
+  explicit SpaceToDepthOpFunctor(const int block_size) : block_size_(block_size) {}
+  void operator()(const Tensor *input,
+                  Tensor *output,
+                  StatsFuture *future) {
+    
+    const int batch_size = input->dim(0);
+    const int input_height = input->dim(1);
+    const int input_width = input->dim(2);
+    const int input_depth = input->dim(3);
+    
+    const index_t output_depth = input_depth * block_size_ * block_size_;
+    const index_t output_width = input_width / block_size_;
+    const index_t output_height = input_height / block_size_;
+    
+    std::vector<index_t> output_shape = {batch_size, output_height, output_width, output_depth};
+    
+    output->Resize(output_shape);
+
+    Tensor::MappingGuard logits_guard(input);
+    Tensor::MappingGuard output_guard(output);
+    const T *input_ptr = input->data<T>();
+    T *output_ptr = output->mutable_data<T>();
+
+#pragma omp parallel for
+  for (int b = 0; b < batch_size; ++b) {
+	  for (int h = 0; h < input_height; ++h) {
+		  const int out_h = h / block_size_;
+		  const int offset_h = (h % block_size_);
+		  for (int w = 0; w < input_width; ++w) {
+			  const int out_w = w/ block_size_;
+			  const int offset_w = (w % block_size_);
+			  const int offset_d = (offset_h * block_size_ + offset_w) * input_depth;
+			  for (int d = 0; d < input_depth; ++d) {
+				  const int out_d = d + offset_d;
+				  const int o_index = ((b * output_height + out_h) * output_width + out_w) * output_depth + out_d;
+			      const int i_index = ((b * input_height + h) * input_width + w) * input_depth + d;
+				  output_ptr[o_index] = input_ptr[i_index];
+			  }
+		  }
+	  }
+  }
+
+  }
+  const int block_size_;
+};
+
+template <typename T>
+struct SpaceToDepthOpFunctor<DeviceType::OPENCL, T> {
+
+  SpaceToDepthOpFunctor(const int block_size) : block_size_(block_size) {}
+  void operator()(const Tensor *input, Tensor *output, StatsFuture *future);
+
+  cl::Kernel kernel_;
+  const int block_size_;
+  std::vector<index_t> input_shape_;
+};
+
+}  // namespace kernels
+}  // namespace mace
+
+#endif //MACE_KERNELS_SPACE_TO_DEPTH_H

mace/ops/space_to_depth_test.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/core/operator.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+class SpaceToDepthOpTest : public OpsTestBase {};
+
+TEST_F(SpaceToDepthOpTest, C8G4_CPU) {
+  // Construct graph
+  OpsTestNet net;
+  OpDefBuilder("SpaceToDepth", "SpaceToDepthTest")
+      .Input("Input")
+      .Output("Output")
+      .AddIntArg("block_size", 2)
+      .Finalize(net.NewOperatorDef());
+
+  // Add input data
+  net.AddInputFromArray<DeviceType::CPU, float>(
+      "Input", {1, 2, 4, 4},
+       {0,  1,  2,  3,  4,  5,  6,  7,  16, 17, 18, 19, 20, 21, 22, 23,
+	   8,  9,  10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31});
+
+  // Run
+  net.RunOp();
+
+  // Check
+  auto expected = CreateTensor<float>(
+      {1, 1, 2, 16},
+      {0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
+       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31});
+
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
+}
+
+TEST_F(SpaceToDepthOpTest, C16G4_OPENCL) {
+  // Construct graph
+  OpsTestNet net;
+
+  // Add input data
+  net.AddInputFromArray<DeviceType::OPENCL, float>(
+      "Input", {1, 2, 4, 4},
+       {0,  1,  2,  3,  4,  5,  6,  7,  16, 17, 18, 19, 20, 21, 22, 23,
+	   8,  9,  10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31});
+  BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage",
+                                           kernels::BufferType::IN_OUT_CHANNEL);
+
+  OpDefBuilder("SpaceToDepth", "SpaceToDepthTest")
+      .Input("InputImage")
+      .Output("OutputImage")
+      .AddIntArg("block_size", 2)
+      .Finalize(net.NewOperatorDef());
+
+  // Run
+  net.RunOp(DeviceType::OPENCL);
+
+  // Transfer output
+  ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "Output",
+                                           kernels::BufferType::IN_OUT_CHANNEL);
+
+  // Check
+  auto expected = CreateTensor<float>(
+      {1, 1, 2, 16},
+      {0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
+       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31});
+
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
+}
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace