Merge branch 'add_op_lrn' into 'master'

add op : local response norm

See merge request !394

mace/core/operator.cc

@@ -92,6 +92,7 @@ extern void Register_FullyConnected(OperatorRegistry *op_registry);
 extern void Register_FusedConv2D(OperatorRegistry *op_registry);
 extern void Register_GlobalAvgPooling(OperatorRegistry *op_registry);
 extern void Register_ImageToBuffer(OperatorRegistry *op_registry);
+extern void Register_LocalResponseNorm(OperatorRegistry *op_registry);
 extern void Register_MatMul(OperatorRegistry *op_registry);
 extern void Register_Pad(OperatorRegistry *op_registry);
 extern void Register_Pooling(OperatorRegistry *op_registry);
@@ -129,6 +130,7 @@ OperatorRegistry::OperatorRegistry() {
   ops::Register_FusedConv2D(this);
   ops::Register_GlobalAvgPooling(this);
   ops::Register_ImageToBuffer(this);
+  ops::Register_LocalResponseNorm(this);
   ops::Register_MatMul(this);
   ops::Register_Pad(this);
   ops::Register_Pooling(this);

mace/kernels/arm/local_response_norm.cc

+//
+// Copyright (c) 2018 XiaoMi All rights reserved.
+//
+
+#include "mace/kernels/local_response_norm.h"
+
+namespace mace {
+namespace kernels {
+
+void LocalResponseNormFunctor<DeviceType::NEON, float>::operator()(
+  const Tensor *input,
+  int depth_radius,
+  float bias,
+  float alpha,
+  float beta,
+  Tensor *output,
+  StatsFuture *future) {
+  const index_t batch = input->dim(0);
+  const index_t channels = input->dim(1);
+  const index_t height = input->dim(2);
+  const index_t width = input->dim(3);
+
+  const float *input_ptr = input->data<float>();
+  float *output_ptr = output->mutable_data<float>();
+
+  index_t image_size = height * width;
+  index_t batch_size = channels * image_size;
+
+#pragma omp parallel for collapse(2)
+  for (index_t b = 0; b < batch; ++b) {
+    for (index_t c = 0; c < channels; ++c) {
+      const int begin_input_c = std::max(static_cast<index_t>(0),
+                                         c - depth_radius);
+      const int end_input_c = std::min(channels, c + depth_radius + 1);
+
+      index_t pos = b * batch_size;
+      for (index_t hw = 0; hw < height * width; ++hw, ++pos) {
+        float accum = 0.f;
+        for (int input_c = begin_input_c; input_c < end_input_c; ++input_c) {
+          const float input_val = input_ptr[pos + input_c * image_size];
+          accum += input_val * input_val;
+        }
+        const float multiplier = std::pow(bias + alpha * accum, -beta);
+        output_ptr[pos + c * image_size] =
+            input_ptr[pos + c * image_size] * multiplier;
+      }
+    }
+  }
+}
+
+}  // namespace kernels
+}  // namespace mace

mace/kernels/local_response_norm.h

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#ifndef MACE_KERNELS_LOCAL_RESPONSE_NORM_H_
+#define MACE_KERNELS_LOCAL_RESPONSE_NORM_H_
+
+#include <algorithm>
+#include <memory>
+#include <vector>
+
+#include "mace/core/future.h"
+#include "mace/core/runtime/opencl/cl2_header.h"
+#include "mace/core/tensor.h"
+#include "mace/public/mace.h"
+
+namespace mace {
+namespace kernels {
+
+template <DeviceType D, typename T>
+struct LocalResponseNormFunctor {
+  void operator()(const Tensor *input,
+                  int depth_radius,
+                  float bias,
+                  float alpha,
+                  float beta,
+                  Tensor *output,
+                  StatsFuture *future) {
+    const index_t batch = input->dim(0);
+    const index_t height = input->dim(1);
+    const index_t width = input->dim(2);
+    const index_t channels = input->dim(3);
+
+    Tensor::MappingGuard input_mapper(input);
+    Tensor::MappingGuard output_mapper(output);
+
+    const T *input_ptr = input->data<T>();
+    T *output_ptr = output->mutable_data<T>();
+
+    const int elements = batch * height * width;
+
+#pragma omp parallel for collapse(2)
+    for (index_t i = 0; i < elements; ++i) {
+      for (index_t c = 0; c < channels; ++c) {
+        const int begin_input_c = std::max(static_cast<index_t>(0),
+                                           c - depth_radius);
+        const int end_input_c = std::min(channels, c + depth_radius + 1);
+        index_t pos = i * channels;
+        float accum = 0.f;
+        for (int input_c = begin_input_c; input_c < end_input_c; ++input_c) {
+          const float input_val = input_ptr[pos + input_c];
+          accum += input_val * input_val;
+        }
+        const float multiplier = std::pow(bias + alpha * accum, -beta);
+        output_ptr[pos + c] = input_ptr[pos + c] * multiplier;
+      }
+    }
+  }
+};
+
+template <>
+struct LocalResponseNormFunctor<DeviceType::NEON, float> {
+  void operator()(const Tensor *input,
+                  int depth_radius,
+                  float bias,
+                  float alpha,
+                  float beta,
+                  Tensor *output,
+                  StatsFuture *future);
+};
+
+}  // namespace kernels
+}  // namespace mace
+
+#endif  // MACE_KERNELS_LOCAL_RESPONSE_NORM_H_

mace/ops/local_response_norm.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/ops/local_response_norm.h"
+
+namespace mace {
+namespace ops {
+
+void Register_LocalResponseNorm(OperatorRegistry *op_registry) {
+  REGISTER_OPERATOR(op_registry, OpKeyBuilder("LocalResponseNorm")
+                                     .Device(DeviceType::CPU)
+                                     .TypeConstraint<float>("T")
+                                     .Build(),
+                    LocalResponseNormOp<DeviceType::CPU, float>);
+
+  REGISTER_OPERATOR(op_registry, OpKeyBuilder("LocalResponseNorm")
+                                     .Device(DeviceType::NEON)
+                                     .TypeConstraint<float>("T")
+                                     .Build(),
+                    LocalResponseNormOp<DeviceType::NEON, float>);
+}
+
+}  // namespace ops
+}  // namespace mace

mace/ops/local_response_norm.h

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#ifndef MACE_OPS_LOCAL_RESPONSE_NORM_H_
+#define MACE_OPS_LOCAL_RESPONSE_NORM_H_
+
+#include "mace/core/operator.h"
+#include "mace/kernels/local_response_norm.h"
+
+namespace mace {
+namespace ops {
+
+template <DeviceType D, class T>
+class LocalResponseNormOp : public Operator<D, T> {
+ public:
+  LocalResponseNormOp(const OperatorDef &operator_def, Workspace *ws)
+      : Operator<D, T>(operator_def, ws),
+        functor_() {
+    depth_radius_ = OperatorBase::GetSingleArgument<int>("depth_radius", 5);
+    bias_ = OperatorBase::GetSingleArgument<float>("bias", 1.0f);
+    alpha_ = OperatorBase::GetSingleArgument<float>("alpha", 1.0f);
+    beta_ = OperatorBase::GetSingleArgument<float>("beta", 0.5f);
+  }
+
+  bool Run(StatsFuture *future) override {
+    const Tensor *input = this->Input(INPUT);
+
+    MACE_CHECK(input->dim_size() == 4, "input must be 4-dimensional. ",
+               input->dim_size());
+
+    Tensor *output = this->Output(OUTPUT);
+    output->ResizeLike(input);
+
+    functor_(input, depth_radius_, bias_, alpha_, beta_, output, future);
+    return true;
+  }
+
+ private:
+  int depth_radius_;
+  float bias_;
+  float alpha_;
+  float beta_;
+  kernels::LocalResponseNormFunctor<D, T> functor_;
+
+ protected:
+  OP_INPUT_TAGS(INPUT);
+  OP_OUTPUT_TAGS(OUTPUT);
+};
+
+}  // namespace ops
+}  // namespace mace
+
+#endif  // MACE_OPS_LOCAL_RESPONSE_NORM_H_

mace/ops/local_response_norm_benchmark.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/core/operator.h"
+#include "mace/core/testing/test_benchmark.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+template <DeviceType D, typename T>
+static void LocalResponseNorm(
+    int iters, int batch, int channels, int height, int width) {
+  mace::testing::StopTiming();
+
+  OpsTestNet net;
+
+  // Add input data
+  net.AddRandomInput<D, T>("Input", {batch, height, width, channels});
+
+  OpDefBuilder("LocalResponseNorm", "LocalResponseNormBM")
+      .Input("Input")
+      .Output("Output")
+      .Finalize(net.NewOperatorDef());
+
+  // tuning
+  setenv("MACE_TUNING", "1", 1);
+  net.RunOp(D);
+  unsetenv("MACE_TUNING");
+
+  // Warm-up
+  for (int i = 0; i < 5; ++i) {
+    net.RunOp(D);
+  }
+  net.Sync();
+
+  mace::testing::StartTiming();
+  while (iters--) {
+    net.RunOp(D);
+  }
+  net.Sync();
+}
+
+#define BM_LOCAL_RESPONSE_NORM_MACRO(N, C, H, W, TYPE, DEVICE)                 \
+  static void BM_LOCAL_RESPONSE_NORM_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE(\
+      int iters) {                                                             \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;           \
+    mace::testing::MaccProcessed(tot);                                         \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                        \
+    LocalResponseNorm<DEVICE, TYPE>(iters, N, C, H, W);                        \
+  }                                                                            \
+  BENCHMARK(BM_LOCAL_RESPONSE_NORM_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE)
+
+#define BM_LOCAL_RESPONSE_NORM(N, C, H, W)                 \
+  BM_LOCAL_RESPONSE_NORM_MACRO(N, C, H, W, float, CPU);    \
+  BM_LOCAL_RESPONSE_NORM_MACRO(N, C, H, W, float, NEON);
+
+BM_LOCAL_RESPONSE_NORM(1, 1, 512, 512);
+BM_LOCAL_RESPONSE_NORM(1, 3, 128, 128);
+BM_LOCAL_RESPONSE_NORM(1, 3, 512, 512);
+BM_LOCAL_RESPONSE_NORM(1, 32, 112, 112);
+BM_LOCAL_RESPONSE_NORM(1, 64, 256, 256);
+BM_LOCAL_RESPONSE_NORM(1, 64, 512, 512);
+BM_LOCAL_RESPONSE_NORM(1, 128, 56, 56);
+BM_LOCAL_RESPONSE_NORM(1, 128, 256, 256);
+BM_LOCAL_RESPONSE_NORM(1, 256, 14, 14);
+BM_LOCAL_RESPONSE_NORM(1, 512, 14, 14);
+BM_LOCAL_RESPONSE_NORM(1, 1024, 7, 7);
+BM_LOCAL_RESPONSE_NORM(32, 1, 256, 256);
+BM_LOCAL_RESPONSE_NORM(32, 3, 256, 256);
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace

mace/ops/local_response_norm_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 LocalResponseNormOpTest : public OpsTestBase {};
+
+template<DeviceType D>
+void Simple() {
+  OpsTestNet net;
+
+  // Add input data
+  net.AddInputFromArray<D, float>("Input", {1, 1, 2, 6},
+                                  {5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15, 15});
+
+  OpDefBuilder("LocalResponseNorm", "LocalResponseNormTest")
+      .Input("Input")
+      .AddIntArg("depth_radius", 5)
+      .AddFloatArg("bias", 1.0f)
+      .AddFloatArg("alpha", 1.0f)
+      .AddFloatArg("beta", 0.5f)
+      .Output("Output")
+      .Finalize(net.NewOperatorDef());
+  // Run
+  net.RunOp(D);
+
+  // Check
+  auto expected =
+    CreateTensor<float>({1, 1, 2, 6}, {0.28, 0.28, 0.39, 0.39, 0.51, 0.51,
+                                       0.34, 0.34, 0.40, 0.40, 0.47, 0.47});
+
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0, 1e-2);
+}
+
+TEST_F(LocalResponseNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); }
+
+TEST_F(LocalResponseNormOpTest, NEONTest) {
+  srand(time(NULL));
+  unsigned int seed;
+
+  // generate random input
+  index_t batch = 1 + rand_r(&seed) % 10;
+  index_t channels = 3 + rand_r(&seed) % 50;
+  index_t height = 64;
+  index_t width = 64;
+
+  // Construct graph
+  OpsTestNet net;
+  OpDefBuilder("LocalResponseNorm", "LocalResponseNormTest")
+      .Input("Input")
+      .AddIntArg("depth_radius", 5)
+      .AddFloatArg("bias", 1.0f)
+      .AddFloatArg("alpha", 1.0f)
+      .AddFloatArg("beta", 0.5f)
+      .Output("Output")
+      .Finalize(net.NewOperatorDef());
+
+  // Add input data
+  net.AddRandomInput<DeviceType::CPU, float>(
+    "Input", {batch, height, width, channels});
+
+  // run cpu
+  net.RunOp();
+
+  OpDefBuilder("LocalResponseNorm", "LocalResponseNormTest")
+      .Input("InputNeon")
+      .AddIntArg("depth_radius", 5)
+      .AddFloatArg("bias", 1.0f)
+      .AddFloatArg("alpha", 1.0f)
+      .AddFloatArg("beta", 0.5f)
+      .Output("OutputNeon")
+      .Finalize(net.NewOperatorDef());
+
+  net.FillNHWCInputToNCHWInput<DeviceType::CPU, float>("InputNeon", "Input");
+
+  // Run on neon
+  net.RunOp(DeviceType::NEON);
+  net.Sync();
+
+  net.FillNHWCInputToNCHWInput<DeviceType::CPU, float>("OutputExpected",
+                                                       "Output");
+
+  ExpectTensorNear<float>(*net.GetOutput("OutputExpected"),
+                          *net.GetOutput("OutputNeon"),
+                          0, 0.001);
+}
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace