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提交 c63ba922 编写于 作者: L liuqi
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Add anonymous namespace for local variables and functions.

上级 13b9e55c
隐藏空白更改
内联 并排
Showing with 414 addition and 207 deletion
mace/core/arg_helper.cc
浏览文件 @ c63ba922
......@@ -28,7 +28,7 @@ ArgumentHelper::ArgumentHelper(const NetDef &netdef) {
}
}
bool ArgumentHelper::HasArgument(const string &name) const {
bool ArgumentHelper::HasArgument(const std::string &name) const {
return arg_map_.count(name);
}
......@@ -44,7 +44,7 @@ bool SupportsLosslessConversion(const InputType &value) {
#define INSTANTIATE_GET_SINGLE_ARGUMENT(T, fieldname, \
enforce_lossless_conversion) \
template <> \
T ArgumentHelper::GetSingleArgument<T>(const string &name, \
T ArgumentHelper::GetSingleArgument<T>(const std::string &name, \
const T &default_value) const { \
if (arg_map_.count(name) == 0) { \
VLOG(3) << "Using default parameter value " << default_value \
......@@ -63,7 +63,8 @@ bool SupportsLosslessConversion(const InputType &value) {
return value; \
} \
template <> \
bool ArgumentHelper::HasSingleArgumentOfType<T>(const string &name) const { \
bool ArgumentHelper::HasSingleArgumentOfType<T>( \
const std::string &name) const { \
if (arg_map_.count(name) == 0) { \
return false; \
} \
......@@ -80,28 +81,28 @@ INSTANTIATE_GET_SINGLE_ARGUMENT(int64_t, i, true)
INSTANTIATE_GET_SINGLE_ARGUMENT(uint8_t, i, true)
INSTANTIATE_GET_SINGLE_ARGUMENT(uint16_t, i, true)
INSTANTIATE_GET_SINGLE_ARGUMENT(size_t, i, true)
INSTANTIATE_GET_SINGLE_ARGUMENT(string, s, false)
INSTANTIATE_GET_SINGLE_ARGUMENT(std::string, s, false)
#undef INSTANTIATE_GET_SINGLE_ARGUMENT
#define INSTANTIATE_GET_REPEATED_ARGUMENT(T, fieldname, \
enforce_lossless_conversion) \
template <> \
std::vector<T> ArgumentHelper::GetRepeatedArgument<T>( \
const string &name, const std::vector<T> &default_value) const { \
if (arg_map_.count(name) == 0) { \
return default_value; \
} \
std::vector<T> values; \
for (const auto &v : arg_map_.at(name).fieldname()) { \
if (enforce_lossless_conversion) { \
auto supportsConversion = \
SupportsLosslessConversion<decltype(v), T>(v); \
MACE_CHECK(supportsConversion, "Value", v, " of argument ", name, \
"cannot be represented correctly in a target type"); \
} \
values.push_back(v); \
} \
return values; \
#define INSTANTIATE_GET_REPEATED_ARGUMENT(T, fieldname, \
enforce_lossless_conversion) \
template <> \
std::vector<T> ArgumentHelper::GetRepeatedArgument<T>( \
const std::string &name, const std::vector<T> &default_value) const { \
if (arg_map_.count(name) == 0) { \
return default_value; \
} \
std::vector<T> values; \
for (const auto &v : arg_map_.at(name).fieldname()) { \
if (enforce_lossless_conversion) { \
auto supportsConversion = \
SupportsLosslessConversion<decltype(v), T>(v); \
MACE_CHECK(supportsConversion, "Value", v, " of argument ", name, \
"cannot be represented correctly in a target type"); \
} \
values.push_back(v); \
} \
return values; \
}
INSTANTIATE_GET_REPEATED_ARGUMENT(float, floats, false)
......@@ -114,7 +115,7 @@ INSTANTIATE_GET_REPEATED_ARGUMENT(int64_t, ints, true)
INSTANTIATE_GET_REPEATED_ARGUMENT(uint8_t, ints, true)
INSTANTIATE_GET_REPEATED_ARGUMENT(uint16_t, ints, true)
INSTANTIATE_GET_REPEATED_ARGUMENT(size_t, ints, true)
INSTANTIATE_GET_REPEATED_ARGUMENT(string, strings, false)
INSTANTIATE_GET_REPEATED_ARGUMENT(std::string, strings, false)
#undef INSTANTIATE_GET_REPEATED_ARGUMENT
} // namespace mace
mace/core/arg_helper.h
浏览文件 @ c63ba922
......@@ -14,8 +14,6 @@
namespace mace {
using std::string;
/**
* @brief A helper class to index into arguments.
*
......@@ -27,45 +25,45 @@ using std::string;
class ArgumentHelper {
public:
template <typename Def>
static bool HasArgument(const Def &def, const string &name) {
static bool HasArgument(const Def &def, const std::string &name) {
return ArgumentHelper(def).HasArgument(name);
}
template <typename Def, typename T>
static T GetSingleArgument(const Def &def,
const string &name,
const std::string &name,
const T &default_value) {
return ArgumentHelper(def).GetSingleArgument<T>(name, default_value);
}
template <typename Def, typename T>
static bool HasSingleArgumentOfType(const Def &def, const string &name) {
static bool HasSingleArgumentOfType(const Def &def, const std::string &name) {
return ArgumentHelper(def).HasSingleArgumentOfType<T>(name);
}
template <typename Def, typename T>
static std::vector<T> GetRepeatedArgument(
const Def &def,
const string &name,
const std::string &name,
const std::vector<T> &default_value = std::vector<T>()) {
return ArgumentHelper(def).GetRepeatedArgument<T>(name, default_value);
}
explicit ArgumentHelper(const OperatorDef &def);
explicit ArgumentHelper(const NetDef &netdef);
bool HasArgument(const string &name) const;
bool HasArgument(const std::string &name) const;
template <typename T>
T GetSingleArgument(const string &name, const T &default_value) const;
T GetSingleArgument(const std::string &name, const T &default_value) const;
template <typename T>
bool HasSingleArgumentOfType(const string &name) const;
bool HasSingleArgumentOfType(const std::string &name) const;
template <typename T>
std::vector<T> GetRepeatedArgument(
const string &name,
const std::string &name,
const std::vector<T> &default_value = std::vector<T>()) const;
private:
std::map<string, Argument> arg_map_;
std::map<std::string, Argument> arg_map_;
};
} // namespace mace
......
mace/core/net.h
浏览文件 @ c63ba922
......@@ -28,10 +28,10 @@ class NetBase {
virtual bool Run(RunMetadata *run_metadata = nullptr) = 0;
const string &Name() const { return name_; }
const std::string &Name() const { return name_; }
protected:
string name_;
std::string name_;
const std::shared_ptr<const OperatorRegistry> op_registry_;
DISABLE_COPY_AND_ASSIGN(NetBase);
......
mace/core/operator.h
浏览文件 @ c63ba922
......@@ -25,25 +25,26 @@ class OperatorBase {
explicit OperatorBase(const OperatorDef &operator_def, Workspace *ws);
virtual ~OperatorBase() noexcept {}
inline bool HasArgument(const string &name) const {
inline bool HasArgument(const std::string &name) const {
MACE_CHECK(operator_def_, "operator_def was null!");
return ArgumentHelper::HasArgument(*operator_def_, name);
}
template <typename T>
inline T GetSingleArgument(const string &name, const T &default_value) const {
inline T GetSingleArgument(const std::string &name,
const T &default_value) const {
MACE_CHECK(operator_def_, "operator_def was null!");
return ArgumentHelper::GetSingleArgument<OperatorDef, T>(
*operator_def_, name, default_value);
}
template <typename T>
inline bool HasSingleArgumentOfType(const string &name) const {
inline bool HasSingleArgumentOfType(const std::string &name) const {
MACE_CHECK(operator_def_, "operator_def was null!");
return ArgumentHelper::HasSingleArgumentOfType<OperatorDef, T>(
*operator_def_, name);
}
template <typename T>
inline std::vector<T> GetRepeatedArgument(
const string &name, const std::vector<T> &default_value = {}) const {
const std::string &name, const std::vector<T> &default_value = {}) const {
MACE_CHECK(operator_def_, "operator_def was null!");
return ArgumentHelper::GetRepeatedArgument<OperatorDef, T>(
*operator_def_, name, default_value);
......@@ -90,14 +91,14 @@ class Operator : public OperatorBase {
public:
explicit Operator(const OperatorDef &operator_def, Workspace *ws)
: OperatorBase(operator_def, ws) {
for (const string &input_str : operator_def.input()) {
for (const std::string &input_str : operator_def.input()) {
const Tensor *tensor = ws->GetTensor(input_str);
MACE_CHECK(tensor != nullptr, "op ", operator_def.type(),
": Encountered a non-existing input tensor: ", input_str);
inputs_.push_back(tensor);
}
for (const string &output_str : operator_def.output()) {
for (const std::string &output_str : operator_def.output()) {
if (ws->HasTensor(output_str)) {
outputs_.push_back(ws->GetTensor(output_str));
} else {
......
mace/kernels/addn.h
浏览文件 @ c63ba922
......@@ -19,7 +19,7 @@
namespace mace {
namespace kernels {
constexpr int kCostPerGroup = 1024;
static constexpr int kCostPerGroup = 1024;
template <DeviceType D, typename T>
struct AddNFunctor {
......
mace/kernels/opencl/depthwise_conv_opencl.cc
浏览文件 @ c63ba922
......@@ -11,21 +11,21 @@
namespace mace {
namespace kernels {
void DepthwiseConv2d(cl::Kernel *kernel,
const Tensor *input, // NHWC
const Tensor *filter, // HWIM
const Tensor *bias,
const int stride,
const int *paddings,
const int *dilations,
const ActivationType activation,
const float relux_max_limit,
const DataType dt,
std::vector<index_t> *prev_input_shape,
Tensor *output,
StatsFuture *future,
uint32_t *kwg_size,
std::unique_ptr<BufferBase> *kernel_error) {
static void DepthwiseConv2d(cl::Kernel *kernel,
const Tensor *input, // NHWC
const Tensor *filter, // HWIM
const Tensor *bias,
const int stride,
const int *paddings,
const int *dilations,
const ActivationType activation,
const float relux_max_limit,
const DataType dt,
std::vector<index_t> *prev_input_shape,
Tensor *output,
StatsFuture *future,
uint32_t *kwg_size,
std::unique_ptr<BufferBase> *kernel_error) {
const index_t batch = output->dim(0);
const index_t height = output->dim(1);
const index_t width = output->dim(2);
......
mace/kernels/opencl/fully_connected_opencl.cc
浏览文件 @ c63ba922
......@@ -8,6 +8,7 @@
namespace mace {
namespace kernels {
namespace {
template <typename T>
void FCWXKernel(cl::Kernel *kernel,
const Tensor *input,
......@@ -268,6 +269,7 @@ void FCWTXKernel(cl::Kernel *kernel,
(*kernel_error)->UnMap();
}
}
} // namespace
template <typename T>
void FullyConnectedFunctor<DeviceType::OPENCL, T>::operator()(
......
mace/kernels/opencl/helper.cc
浏览文件 @ c63ba922
......@@ -14,6 +14,7 @@
namespace mace {
namespace kernels {
namespace {
// [(C + 3) / 4 * W, N * H]
void CalInOutputImageShape(const std::vector<index_t> &shape, /* NHWC */
std::vector<size_t> *image_shape) {
......@@ -97,6 +98,7 @@ void CalWeightWidthImageShape(const std::vector<index_t> &shape, /* HW */
(*image_shape)[0] = RoundUpDiv4(shape[1]);
(*image_shape)[1] = shape[0];
}
} // namespace
void CalImage2DShape(const std::vector<index_t> &shape, /* NHWC */
const BufferType type,
......
mace/kernels/proposal.h
浏览文件 @ c63ba922
......@@ -15,7 +15,7 @@
namespace mace {
namespace kernels {
static std::vector<float> WHCenters(const std::vector<float> &anchor) {
static inline std::vector<float> WHCenters(const std::vector<float> &anchor) {
// width, height, width_center, height_center
std::vector<float> window(4);
window[0] = anchor[2] - anchor[0] + 1;
......@@ -25,7 +25,7 @@ static std::vector<float> WHCenters(const std::vector<float> &anchor) {
return window;
}
std::vector<std::vector<float>> GenerateAnchors(
static inline std::vector<std::vector<float>> GenerateAnchors(
const std::vector<int> &scales,
const std::vector<float> &ratios,
const int base_size) {
......@@ -65,10 +65,10 @@ std::vector<std::vector<float>> GenerateAnchors(
return anchors;
}
std::vector<int> nms(const float *bboxes_ptr,
const index_t num_bboxes,
const float thresh,
const int post_nms_top_n) {
static inline std::vector<int> nms(const float *bboxes_ptr,
const index_t num_bboxes,
const float thresh,
const int post_nms_top_n) {
std::vector<int> keep;
std::vector<int> suppressed(num_bboxes, 0);
......
mace/kernels/resize_bilinear.h
浏览文件 @ c63ba922
......@@ -23,18 +23,19 @@ struct CachedInterpolation {
float lerp;
};
inline float CalculateResizeScale(index_t in_size,
index_t out_size,
bool align_corners) {
static inline float CalculateResizeScale(index_t in_size,
index_t out_size,
bool align_corners) {
return (align_corners && out_size > 1)
? (in_size - 1) / static_cast<float>(out_size - 1)
: in_size / static_cast<float>(out_size);
}
inline void ComputeInterpolationWeights(const index_t out_size,
const index_t in_size,
const float scale,
CachedInterpolation *interpolation) {
static inline void ComputeInterpolationWeights(
const index_t out_size,
const index_t in_size,
const float scale,
CachedInterpolation *interpolation) {
interpolation[out_size].lower = 0;
interpolation[out_size].upper = 0;
for (index_t i = out_size - 1; i >= 0; --i) {
......@@ -45,12 +46,12 @@ inline void ComputeInterpolationWeights(const index_t out_size,
}
}
inline float ComputeLerp(const float top_left,
const float top_right,
const float bottom_left,
const float bottom_right,
const float x_lerp,
const float y_lerp) {
static inline float ComputeLerp(const float top_left,
const float top_right,
const float bottom_left,
const float bottom_right,
const float x_lerp,
const float y_lerp) {
const float top = top_left + (top_right - top_left) * x_lerp;
const float bottom = bottom_left + (bottom_right - bottom_left) * x_lerp;
return top + (bottom - top) * y_lerp;
......
mace/ops/activation_benchmark.cc
浏览文件 @ c63ba922
......@@ -12,8 +12,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void ReluBenchmark(
void ReluBenchmark(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -51,6 +52,7 @@ static void ReluBenchmark(
}
net.Sync();
}
} // namespace
#define BM_RELU_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_RELU_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE(int iters) { \
......@@ -72,8 +74,9 @@ BM_RELU(1, 3, 512, 512);
BM_RELU(1, 32, 112, 112);
BM_RELU(1, 64, 256, 256);
namespace {
template <DeviceType D, typename T>
static void ReluxBenchmark(
void ReluxBenchmark(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -113,6 +116,7 @@ static void ReluxBenchmark(
}
net.Sync();
}
} // namespace
#define BM_RELUX_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_RELUX_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE(int iters) { \
......@@ -134,8 +138,9 @@ BM_RELUX(1, 3, 512, 512);
BM_RELUX(1, 32, 112, 112);
BM_RELUX(1, 64, 256, 256);
namespace {
template <DeviceType D, typename T>
static void PreluBenchmark(
void PreluBenchmark(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -178,6 +183,7 @@ static void PreluBenchmark(
}
net.Sync();
}
} // namespace
#define BM_PRELU_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_PRELU_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE(int iters) { \
......@@ -199,8 +205,9 @@ BM_PRELU(1, 3, 512, 512);
BM_PRELU(1, 32, 112, 112);
BM_PRELU(1, 64, 256, 256);
namespace {
template <DeviceType D, typename T>
static void TanhBenchmark(
void TanhBenchmark(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -238,6 +245,7 @@ static void TanhBenchmark(
}
net.Sync();
}
} // namespace
#define BM_TANH_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_TANH_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE(int iters) { \
......@@ -259,8 +267,9 @@ BM_TANH(1, 3, 512, 512);
BM_TANH(1, 32, 112, 112);
BM_TANH(1, 64, 256, 256);
namespace {
template <DeviceType D, typename T>
static void SigmoidBenchmark(
void SigmoidBenchmark(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -298,6 +307,7 @@ static void SigmoidBenchmark(
}
net.Sync();
}
} // namespace
#define BM_SIGMOID_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_SIGMOID_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE( \
......
mace/ops/activation_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class ActivationOpTest : public OpsTestBase {};
namespace {
template <DeviceType D>
void TestSimpleRelu() {
OpsTestNet net;
......@@ -52,6 +53,7 @@ void TestSimpleRelu() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(ActivationOpTest, CPUSimpleRelu) { TestSimpleRelu<DeviceType::CPU>(); }
......@@ -59,6 +61,7 @@ TEST_F(ActivationOpTest, OPENCLSimpleRelu) {
TestSimpleRelu<DeviceType::OPENCL>();
}
namespace {
template <DeviceType D>
void TestUnalignedSimpleRelu() {
OpsTestNet net;
......@@ -97,6 +100,7 @@ void TestUnalignedSimpleRelu() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(ActivationOpTest, CPUUnalignedSimpleRelu) {
TestUnalignedSimpleRelu<DeviceType::CPU>();
......@@ -106,6 +110,8 @@ TEST_F(ActivationOpTest, OPENCLUnalignedSimpleRelu) {
TestUnalignedSimpleRelu<DeviceType::OPENCL>();
}
namespace {
template <DeviceType D>
void TestSimpleRelux() {
OpsTestNet net;
......@@ -149,6 +155,7 @@ void TestSimpleRelux() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(ActivationOpTest, CPUSimple) { TestSimpleRelux<DeviceType::CPU>(); }
......@@ -156,6 +163,7 @@ TEST_F(ActivationOpTest, OPENCLSimple) {
TestSimpleRelux<DeviceType::OPENCL>();
}
namespace {
template <DeviceType D>
void TestSimpleReluRelux() {
OpsTestNet net;
......@@ -199,6 +207,7 @@ void TestSimpleReluRelux() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(ActivationOpTest, CPUSimpleRelux) {
TestSimpleReluRelux<DeviceType::CPU>();
......@@ -208,6 +217,7 @@ TEST_F(ActivationOpTest, OPENCLSimpleRelux) {
TestSimpleReluRelux<DeviceType::OPENCL>();
}
namespace {
template <DeviceType D>
void TestSimplePrelu() {
OpsTestNet net;
......@@ -261,6 +271,7 @@ void TestSimplePrelu() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
}
} // namespace
TEST_F(ActivationOpTest, CPUSimplePrelu) {
TestSimplePrelu<DeviceType::CPU>();
......@@ -274,6 +285,7 @@ TEST_F(ActivationOpTest, OPENCLSimplePrelu) {
TestSimplePrelu<DeviceType::OPENCL>();
}
namespace {
template <DeviceType D>
void TestSimpleTanh() {
OpsTestNet net;
......@@ -318,6 +330,7 @@ void TestSimpleTanh() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(ActivationOpTest, CPUSimpleTanh) { TestSimpleTanh<DeviceType::CPU>(); }
......@@ -325,6 +338,7 @@ TEST_F(ActivationOpTest, OPENCLSimpleTanh) {
TestSimpleTanh<DeviceType::OPENCL>();
}
namespace {
template <DeviceType D>
void TestSimpleSigmoid() {
OpsTestNet net;
......@@ -370,6 +384,7 @@ void TestSimpleSigmoid() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(ActivationOpTest, CPUSimpleSigmoid) {
TestSimpleSigmoid<DeviceType::CPU>();
......
mace/ops/addn_benchmark.cc
浏览文件 @ c63ba922
......@@ -12,8 +12,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) {
void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -57,6 +58,7 @@ static void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) {
net.Sync();
}
}
} // namespace
#define BM_ADDN_MACRO(INPUTS, N, H, W, C, TYPE, DEVICE) \
static void BM_ADDN_##INPUTS##_##N##_##H##_##W##_##C##_##TYPE##_##DEVICE( \
......
mace/ops/addn_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class AddnOpTest : public OpsTestBase {};
namespace {
template <DeviceType D>
void SimpleAdd2() {
// Construct graph
......@@ -32,9 +33,11 @@ void SimpleAdd2() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(AddnOpTest, CPUSimpleAdd2) { SimpleAdd2<DeviceType::CPU>(); }
namespace {
template <DeviceType D>
void SimpleAdd3() {
// Construct graph
......@@ -58,9 +61,11 @@ void SimpleAdd3() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(AddnOpTest, CPUSimpleAdd3) { SimpleAdd3<DeviceType::CPU>(); }
namespace {
template <DeviceType D>
void RandomTest() {
testing::internal::LogToStderr();
......@@ -116,6 +121,7 @@ void RandomTest() {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 0.1);
}
}
} // namespace
TEST_F(AddnOpTest, OPENCLRandom) { RandomTest<DeviceType::OPENCL>(); }
......
mace/ops/batch_norm_benchmark.cc
浏览文件 @ c63ba922
......@@ -11,8 +11,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void BatchNorm(
void BatchNorm(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -74,6 +75,7 @@ static void BatchNorm(
}
net.Sync();
}
} // namespace
#define BM_BATCH_NORM_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_BATCH_NORM_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE( \
......
mace/ops/batch_norm_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class BatchNormOpTest : public OpsTestBase {};
namespace {
template<DeviceType D>
void Simple() {
OpsTestNet net;
......@@ -71,6 +72,7 @@ void Simple() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-2);
}
} // namespace
TEST_F(BatchNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); }
......
mace/ops/batch_to_space_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,8 +10,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void BMBatchToSpace(
void BMBatchToSpace(
int iters, int batch, int channels, int height, int width, int arg) {
mace::testing::StopTiming();
......@@ -38,6 +39,7 @@ static void BMBatchToSpace(
}
net.Sync();
}
} // namespace
#define BM_BATCH_TO_SPACE_MACRO(N, H, W, C, ARG, TYPE, DEVICE) \
static void \
......
mace/ops/bias_add_benchmark.cc
浏览文件 @ c63ba922
......@@ -11,8 +11,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void BiasAdd(int iters, int batch, int channels, int height, int width) {
void BiasAdd(int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -51,6 +52,7 @@ static void BiasAdd(int iters, int batch, int channels, int height, int width) {
}
net.Sync();
}
} // namespace
#define BM_BIAS_ADD_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_BIAS_ADD_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE( \
......
mace/ops/bias_add_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class BiasAddOpTest : public OpsTestBase {};
namespace {
template <DeviceType D>
void BiasAddSimple() {
OpsTestNet net;
......@@ -54,6 +55,7 @@ void BiasAddSimple() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-2);
}
} // namespace
TEST_F(BiasAddOpTest, BiasAddSimpleCPU) { BiasAddSimple<DeviceType::CPU>(); }
......
mace/ops/buffer_to_image_test.cc
浏览文件 @ c63ba922
......@@ -9,6 +9,7 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
void TestBidirectionTransform(const int type,
const std::vector<index_t> &input_shape) {
......@@ -40,6 +41,7 @@ void TestBidirectionTransform(const int type,
ExpectTensorNear<T>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"),
1e-5);
}
} // namespace
TEST(BufferToImageTest, ArgSmall) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::ARGUMENT, {1});
......@@ -112,6 +114,7 @@ TEST(BufferToImageTest, Filter3x3Large) {
{3, 3, 128, 256});
}
namespace {
template <DeviceType D, typename T>
void TestDiffTypeBidirectionTransform(const int type,
const std::vector<index_t> &input_shape) {
......@@ -142,12 +145,14 @@ void TestDiffTypeBidirectionTransform(const int type,
ExpectTensorNear<float>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"),
1e-2);
}
} // namespace
TEST(BufferToImageTest, ArgFloatToHalfSmall) {
TestDiffTypeBidirectionTransform<DeviceType::OPENCL, half>(kernels::ARGUMENT,
{11});
}
namespace {
template <DeviceType D, typename T>
void TestStringHalfBidirectionTransform(const int type,
const std::vector<index_t> &input_shape,
......@@ -182,6 +187,7 @@ void TestStringHalfBidirectionTransform(const int type,
ExpectTensorNear<half>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"),
1e-2);
}
} // namespace
TEST(BufferToImageTest, ArgStringHalfToHalfSmall) {
const unsigned char input_data[] = {
......
mace/ops/channel_shuffle_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,8 +10,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void ChannelShuffle(
void ChannelShuffle(
int iters, int batch, int channels, int height, int width, int group) {
mace::testing::StopTiming();
......@@ -48,6 +49,7 @@ static void ChannelShuffle(
}
net.Sync();
}
} // namespace
#define BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, TYPE, DEVICE) \
static void \
......
mace/ops/concat_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,8 +10,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void ConcatHelper(int iters, int concat_dim, int dim1) {
void ConcatHelper(int iters, int concat_dim, int dim1) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -39,6 +40,7 @@ static void ConcatHelper(int iters, int concat_dim, int dim1) {
net.RunOp(D);
}
}
} // namespace
#define BM_CONCAT_CPU_MACRO(DIM0, DIM1) \
static void BM_CONCAT_CPU_##DIM0##_##DIM1(int iters) { \
......@@ -51,11 +53,12 @@ BM_CONCAT_CPU_MACRO(0, 100000);
BM_CONCAT_CPU_MACRO(1, 1000);
BM_CONCAT_CPU_MACRO(1, 100000);
namespace {
template <typename T>
static void OpenclConcatHelper(int iters,
const std::vector<index_t> &shape0,
const std::vector<index_t> &shape1,
int concat_dim) {
void OpenclConcatHelper(int iters,
const std::vector<index_t> &shape0,
const std::vector<index_t> &shape1,
int concat_dim) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -91,6 +94,7 @@ static void OpenclConcatHelper(int iters,
net.RunOp(DeviceType::OPENCL);
}
}
} // namespace
#define BM_CONCAT_OPENCL_MACRO(N, H, W, C, TYPE) \
static void BM_CONCAT_OPENCL_##N##_##H##_##W##_##C##_##TYPE(int iters) { \
......
mace/ops/concat_test.cc
浏览文件 @ c63ba922
......@@ -144,6 +144,7 @@ TEST_F(ConcatOpTest, CPURandom) {
}
}
namespace {
template <typename T>
void OpenclRandomTest(const std::vector<std::vector<index_t>> &shapes,
const int axis) {
......@@ -208,6 +209,7 @@ void OpenclRandomTest(const std::vector<std::vector<index_t>> &shapes,
k++;
}
}
} // namespace
TEST_F(ConcatOpTest, OPENCLAligned) {
OpenclRandomTest<float>({{3, 32, 32, 32}, {3, 32, 32, 64}}, 3);
......
mace/ops/conv_2d_benchmark.cc
浏览文件 @ c63ba922
......@@ -13,18 +13,19 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void Conv2d(int iters,
int batch,
int channels,
int height,
int width,
int kernel_h,
int kernel_w,
int stride,
int dilation,
Padding padding,
int output_channels) {
void Conv2d(int iters,
int batch,
int channels,
int height,
int width,
int kernel_h,
int kernel_w,
int stride,
int dilation,
Padding padding,
int output_channels) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -88,6 +89,7 @@ static void Conv2d(int iters,
net.Sync();
}
}
} // namespace
// In common network, there are usually more than 1 layers, this is used to
// approximate the amortized latency. The OpenCL runtime for Mali/Adreno is
......
mace/ops/conv_2d_test.cc
浏览文件 @ c63ba922
......@@ -14,6 +14,7 @@ namespace test {
class Conv2dOpTest : public OpsTestBase {};
namespace {
template<DeviceType D, typename T>
void TestNHWCSimple3x3VALID() {
OpsTestNet net;
......@@ -129,6 +130,7 @@ void TestNHWCSimple3x3SAME() {
ExpectTensorNear<float, T>(*expected, *net.GetOutput("Output"), 0.01);
}
} // namespace
TEST_F(Conv2dOpTest, CPUSimple) {
TestNHWCSimple3x3VALID<DeviceType::CPU, float>();
......@@ -140,6 +142,7 @@ TEST_F(Conv2dOpTest, OPENCLSimple) {
TestNHWCSimple3x3SAME<DeviceType::OPENCL, float>();
}
namespace {
template<DeviceType D, typename T>
void TestNHWCSimple3x3WithoutBias() {
OpsTestNet net;
......@@ -193,6 +196,7 @@ void TestNHWCSimple3x3WithoutBias() {
ExpectTensorNear<float, T>(*expected, *net.GetOutput("Output"), 0.01);
}
} // namespace
TEST_F(Conv2dOpTest, CPUWithoutBias) {
TestNHWCSimple3x3WithoutBias<DeviceType::CPU, float>();
......@@ -202,8 +206,9 @@ TEST_F(Conv2dOpTest, OPENCLWithoutBias) {
TestNHWCSimple3x3WithoutBias<DeviceType::OPENCL, float>();
}
namespace {
template<DeviceType D, typename T>
static void TestNHWCCombined3x3() {
void TestNHWCCombined3x3() {
// Construct graph
OpsTestNet net;
......@@ -263,6 +268,7 @@ static void TestNHWCCombined3x3() {
9.2f, 12.1f, 6.2f, 8.1f, 4.2f, 12.1f, 6.2f, 8.1f, 4.2f});
ExpectTensorNear<float, T>(*expected, *net.GetOutput("Output"), 0.01);
}
} // namespace
TEST_F(Conv2dOpTest, CPUStride2) {
TestNHWCCombined3x3<DeviceType::CPU, float>();
......@@ -272,6 +278,7 @@ TEST_F(Conv2dOpTest, OPENCLStride2) {
TestNHWCCombined3x3<DeviceType::OPENCL, float>();
}
namespace {
template<DeviceType D>
void TestConv1x1() {
// Construct graph
......@@ -340,14 +347,16 @@ void TestConv1x1() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
} // namespace
TEST_F(Conv2dOpTest, CPUConv1x1) { TestConv1x1<DeviceType::CPU>(); }
TEST_F(Conv2dOpTest, OPENCLConv1x1) { TestConv1x1<DeviceType::OPENCL>(); }
namespace {
template<DeviceType D, typename T>
static void TestComplexConvNxNS12(const std::vector<index_t> &shape,
const int stride) {
void TestComplexConvNxNS12(const std::vector<index_t> &shape,
const int stride) {
testing::internal::LogToStderr();
auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
Padding type) {
......@@ -414,6 +423,7 @@ static void TestComplexConvNxNS12(const std::vector<index_t> &shape,
func(kernel_size, kernel_size, stride, stride, SAME);
}
}
} // namespace
TEST_F(Conv2dOpTest, OPENCLAlignedConvNxNS12) {
TestComplexConvNxNS12<DeviceType::OPENCL, float>({32, 16, 16, 32}, 1);
......@@ -430,10 +440,11 @@ TEST_F(Conv2dOpTest, OPENCLUnalignedConvNxNS34) {
TestComplexConvNxNS12<DeviceType::OPENCL, float>({32, 32, 13, 17}, 4);
}
namespace {
template<DeviceType D>
static void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
const std::vector<index_t> &filter_shape,
const std::vector<int> &dilations) {
void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
const std::vector<index_t> &filter_shape,
const std::vector<int> &dilations) {
testing::internal::LogToStderr();
srand(time(NULL));
......@@ -515,6 +526,7 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
func(2, 2, SAME);
}
}
} // namespace
TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv1x1S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32}, {1, 1, 32, 64},
......@@ -566,9 +578,10 @@ TEST_F(Conv2dOpTest, OPENCLHalfConv7x7Dilation4) {
{4, 4});
}
namespace {
template<DeviceType D, typename T>
static void TestDilationConvNxN(const std::vector<index_t> &shape,
const int dilation_rate) {
void TestDilationConvNxN(const std::vector<index_t> &shape,
const int dilation_rate) {
testing::internal::LogToStderr();
auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
Padding type) {
......@@ -638,6 +651,7 @@ static void TestDilationConvNxN(const std::vector<index_t> &shape,
}
}
}
} // namespace
TEST_F(Conv2dOpTest, OPENCLAlignedDilation2) {
TestDilationConvNxN<DeviceType::OPENCL, float>({32, 32, 32, 64}, 2);
......@@ -651,9 +665,10 @@ TEST_F(Conv2dOpTest, OPENCLUnalignedDilation4) {
TestDilationConvNxN<DeviceType::OPENCL, float>({107, 113, 5, 7}, 4);
}
namespace {
template<DeviceType D, typename T>
static void TestArbitraryPadConvNxN(const std::vector<index_t> &shape,
const std::vector<int> &paddings) {
void TestArbitraryPadConvNxN(const std::vector<index_t> &shape,
const std::vector<int> &paddings) {
testing::internal::LogToStderr();
auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w) {
srand(time(NULL));
......@@ -719,6 +734,7 @@ static void TestArbitraryPadConvNxN(const std::vector<index_t> &shape,
}
}
}
} // namespace
TEST_F(Conv2dOpTest, OPENCLAlignedPad1) {
TestArbitraryPadConvNxN<DeviceType::OPENCL, float>({32, 32, 32, 64}, {1, 1});
......
mace/ops/cwise_benchmark.cc
浏览文件 @ c63ba922
......@@ -11,9 +11,10 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void CWise(int iters, int batch, int channels,
int height, int width, float x, int type) {
void CWise(int iters, int batch, int channels,
int height, int width, float x, int type) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -51,6 +52,7 @@ static void CWise(int iters, int batch, int channels,
}
net.Sync();
}
} // namespace
#define BM_CWISE_MACRO(N, C, H, W, X, G, TYPE, DEVICE) \
static void \
......
mace/ops/cwise_test.cc
浏览文件 @ c63ba922
......@@ -12,7 +12,7 @@ namespace test {
class CWiseOpTest : public OpsTestBase {};
namespace {
template <DeviceType D>
void Simple(const kernels::CWiseType type,
const std::vector<index_t> &shape,
......@@ -56,6 +56,7 @@ void Simple(const kernels::CWiseType type,
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-3);
}
} // namespace
TEST_F(CWiseOpTest, CPUSimple) {
Simple<DeviceType::CPU>(kernels::CWiseType::MUL, {1, 1, 2, 3},
......@@ -97,6 +98,7 @@ TEST_F(CWiseOpTest, GPUSimple) {
{1, -2, -0.0001, 4, 5, 6}, 2.0, {1, 2, 0.0001, 4, 5, 6});
}
namespace {
template <DeviceType D, typename T>
void RandomTest(const kernels::CWiseType type,
const std::vector<index_t> &shape) {
......@@ -144,6 +146,7 @@ void RandomTest(const kernels::CWiseType type,
*net.GetOutput("OPENCLOutput"), 1e-1);
}
}
} // namespace
TEST_F(CWiseOpTest, OPENCLRandomFloat) {
RandomTest<DeviceType::OPENCL, float>(kernels::CWiseType::MUL,
......
mace/ops/depth_to_space_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,8 +10,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void DepthToSpace(
void DepthToSpace(
int iters, int batch, int channels, int height, int width, int block_size) {
mace::testing::StopTiming();
......@@ -48,6 +49,7 @@ static void DepthToSpace(
}
net.Sync();
}
} // namespace
#define BM_DEPTH_TO_SPACE_MACRO(N, C, H, W, G, TYPE, DEVICE) \
static void \
......
mace/ops/depth_to_space_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D>
void RunDepthToSpace(const bool d2s,
const std::vector<index_t> &input_shape,
......@@ -49,6 +50,7 @@ void RunDepthToSpace(const bool d2s,
auto expected = CreateTensor<float>(expected_shape, expected_data);
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
} // namespace
class SpaceToDepthOpTest : public OpsTestBase {};
......@@ -149,6 +151,7 @@ TEST_F(DepthToSpaceOpTest, InputLarger_B2_OPENCL) {
}
namespace {
template <DeviceType D, typename T>
void RandomTest(const bool d2s, const int block_size,
const std::vector<index_t> &shape) {
......@@ -197,6 +200,7 @@ void RandomTest(const bool d2s, const int block_size,
*net.GetOutput("OPENCLOutput"), 1e-1);
}
}
} // namespace
TEST_F(DepthToSpaceOpTest, OPENCLRandomFloat) {
RandomTest<DeviceType::OPENCL, float>(true, 2, {1, 192, 192, 128});
......
mace/ops/depthwise_conv2d_benchmark.cc
浏览文件 @ c63ba922
......@@ -13,17 +13,18 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void DepthwiseConv2d(int iters,
int batch,
int input_channels,
int height,
int width,
int kernel_h,
int kernel_w,
int stride,
Padding padding,
int multiplier) {
void DepthwiseConv2d(int iters,
int batch,
int input_channels,
int height,
int width,
int kernel_h,
int kernel_w,
int stride,
Padding padding,
int multiplier) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -87,6 +88,7 @@ static void DepthwiseConv2d(int iters,
net.Sync();
}
}
} // namespace
#define BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, STRIDE, P, M, TYPE, \
DEVICE) \
......
mace/ops/depthwise_conv2d_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class DepthwiseConv2dOpTest : public OpsTestBase {};
namespace {
template<DeviceType D, typename T>
void SimpleValidTest() {
testing::internal::LogToStderr();
......@@ -69,6 +70,7 @@ void SimpleValidTest() {
ExpectTensorNear<T>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(DepthwiseConv2dOpTest, SimpleCPU) {
SimpleValidTest<DeviceType::CPU, float>();
......@@ -82,6 +84,7 @@ TEST_F(DepthwiseConv2dOpTest, SimpleOpenCLHalf) {
SimpleValidTest<DeviceType::OPENCL, half>();
}
namespace {
template<DeviceType D, typename T>
void ComplexValidTest() {
testing::internal::LogToStderr();
......@@ -188,6 +191,7 @@ void ComplexValidTest() {
ExpectTensorNear<T>(*expected, *net.GetOutput("Output"), 0.2);
}
} // namespace
TEST_F(DepthwiseConv2dOpTest, ComplexCPU) {
ComplexValidTest<DeviceType::CPU, float>();
......@@ -201,6 +205,7 @@ TEST_F(DepthwiseConv2dOpTest, ComplexOpenCLHalf) {
ComplexValidTest<DeviceType::OPENCL, half>();
}
namespace {
template<DeviceType D, typename T>
void TestNxNS12(const index_t height, const index_t width) {
testing::internal::LogToStderr();
......@@ -287,6 +292,7 @@ void TestNxNS12(const index_t height, const index_t width) {
}
}
}
} // namespace
TEST_F(DepthwiseConv2dOpTest, OpenCLSimpleNxNS12) {
TestNxNS12<DeviceType::OPENCL, float>(4, 4);
......@@ -314,6 +320,7 @@ TEST_F(DepthwiseConv2dOpTest, OpenCLUnalignedNxNS12Half) {
TestNxNS12<DeviceType::OPENCL, half>(107, 113);
}
namespace {
void TestNEONNxNS12(const index_t height,
const index_t width,
const index_t input_channels,
......@@ -385,6 +392,7 @@ void TestNEONNxNS12(const index_t height,
}
}
}
} // namespace
TEST_F(DepthwiseConv2dOpTest, NEONTest) {
TestNEONNxNS12(4, 4, 32, 1);
......
mace/ops/eltwise_benchmark.cc
浏览文件 @ c63ba922
......@@ -13,8 +13,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void EltwiseBenchmark(
void EltwiseBenchmark(
int iters, kernels::EltwiseType type, int n, int h, int w, int c) {
mace::testing::StopTiming();
......@@ -59,6 +60,7 @@ static void EltwiseBenchmark(
net.Sync();
}
}
} // namespace
#define BM_ELTWISE_MACRO(ELT_TYPE, N, H, W, C, TYPE, DEVICE) \
static void \
......
mace/ops/eltwise_test.cc
浏览文件 @ c63ba922
......@@ -12,6 +12,7 @@ namespace test {
class EltwiseOpTest : public OpsTestBase {};
namespace {
template <DeviceType D>
void Simple(const kernels::EltwiseType type,
const std::vector<index_t> &shape,
......@@ -61,6 +62,7 @@ void Simple(const kernels::EltwiseType type,
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-3);
}
} // namespace
TEST_F(EltwiseOpTest, CPUSimple) {
Simple<DeviceType::CPU>(kernels::EltwiseType::PROD, {1, 1, 2, 3},
......@@ -98,6 +100,7 @@ TEST_F(EltwiseOpTest, GPUSimple) {
{1, 1, 3, 3, 5, 6});
}
namespace {
template <DeviceType D, typename T>
void RandomTest(const kernels::EltwiseType type,
const std::vector<index_t> &shape) {
......@@ -149,6 +152,7 @@ void RandomTest(const kernels::EltwiseType type,
*net.GetOutput("OPENCLOutput"), 1e-1);
}
}
} // namespace
TEST_F(EltwiseOpTest, OPENCLRandomFloat) {
RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::PROD,
......
mace/ops/folded_batch_norm_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class FoldedBatchNormOpTest : public OpsTestBase {};
namespace {
void CalculateScaleOffset(const std::vector<float> &gamma,
const std::vector<float> &beta,
const std::vector<float> &mean,
......@@ -21,7 +22,7 @@ void CalculateScaleOffset(const std::vector<float> &gamma,
size_t size = gamma.size();
for (int i = 0; i < size; ++i) {
(*scale)[i] = gamma[i] / std::sqrt(var[i] + epsilon);
(*offset)[i] = (*offset)[i] - mean[i] * (*scale)[i];
(*offset)[i] = beta[i] - mean[i] * (*scale)[i];
}
}
......@@ -76,6 +77,7 @@ void Simple() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-2);
}
} // namespace
TEST_F(FoldedBatchNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); }
......
mace/ops/fully_connected_benchmark.cc
浏览文件 @ c63ba922
......@@ -12,8 +12,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void FCBenchmark(
void FCBenchmark(
int iters, int batch, int height, int width, int channel, int out_channel) {
mace::testing::StopTiming();
......@@ -64,6 +65,7 @@ static void FCBenchmark(
}
net.Sync();
}
} // namespace
#define BM_FC_MACRO(N, H, W, C, OC, TYPE, DEVICE) \
static void BM_FC_##N##_##H##_##W##_##C##_##OC##_##TYPE##_##DEVICE( \
......
mace/ops/fully_connected_test.cc
浏览文件 @ c63ba922
......@@ -13,6 +13,7 @@ namespace test {
class FullyConnectedOpTest : public OpsTestBase {};
namespace {
template<DeviceType D>
void Simple(const std::vector<index_t> &input_shape,
const std::vector<float> &input_value,
......@@ -66,6 +67,7 @@ void Simple(const std::vector<index_t> &input_shape,
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(FullyConnectedOpTest, SimpleCPU) {
Simple<DeviceType::CPU>({1, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 8},
......@@ -107,6 +109,7 @@ TEST_F(FullyConnectedOpTest, SimpleGPUWithBatch) {
{1, 2, 3, 4}, {1}, {2}, {2, 1, 1, 1}, {32, 72});
}
namespace {
template<typename T>
void Complex(const index_t batch,
const index_t height,
......@@ -166,6 +169,7 @@ void Complex(const index_t batch,
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-3);
}
}
} // namespace
TEST_F(FullyConnectedOpTest, OPENCLAlignedWithoutBatch) {
Complex<float>(1, 16, 16, 32, 16);
......@@ -189,6 +193,7 @@ TEST_F(FullyConnectedOpTest, OPENCLHalfUnAlignedWithBatch) {
Complex<half>(31, 21, 11, 23, 103);
}
namespace {
template<typename T>
void TestWXFormat(const index_t batch,
const index_t height,
......@@ -247,6 +252,7 @@ void TestWXFormat(const index_t batch,
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2);
}
}
} // namespace
TEST_F(FullyConnectedOpTest, OPENCLWidthFormatAligned) {
TestWXFormat<float>(1, 7, 7, 32, 16);
......@@ -266,11 +272,12 @@ TEST_F(FullyConnectedOpTest, OPENCLHalfWidthFormatAligned) {
TestWXFormat<half>(1, 16, 32, 32, 32);
}
namespace {
void FullyConnectedTestNEON(const index_t batch,
const index_t height,
const index_t width,
const index_t channels,
const index_t out_channel) {
const index_t height,
const index_t width,
const index_t channels,
const index_t out_channel) {
srand(time(NULL));
// Construct graph
......@@ -310,6 +317,7 @@ void FullyConnectedTestNEON(const index_t batch,
*net.GetOutput("OutputNeon"),
0.01);
}
} // namespace
TEST_F(FullyConnectedOpTest, TestNEON) {
FullyConnectedTestNEON(1, 7, 7, 32, 16);
......
mace/ops/fused_conv_2d_test.cc
浏览文件 @ c63ba922
......@@ -13,6 +13,7 @@ namespace test {
class FusedConv2dOpTest : public OpsTestBase {};
namespace {
template<DeviceType D, typename T>
void TestNHWCSimple3x3VALID() {
OpsTestNet net;
......@@ -21,7 +22,7 @@ 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});
......@@ -42,6 +43,7 @@ void TestNHWCSimple3x3VALID() {
.AddIntArg("padding", Padding::VALID)
.AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddStringArg("activation", "RELU")
.Finalize(net.NewOperatorDef());
net.RunOp(D);
......@@ -60,6 +62,7 @@ void TestNHWCSimple3x3VALID() {
.AddIntArg("padding", Padding::VALID)
.AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddStringArg("activation", "RELU")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
......@@ -78,7 +81,7 @@ 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});
......@@ -99,6 +102,7 @@ void TestNHWCSimple3x3SAME() {
.AddIntArg("padding", Padding::SAME)
.AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddStringArg("activation", "RELU")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
......@@ -117,6 +121,7 @@ void TestNHWCSimple3x3SAME() {
.AddIntArg("padding", Padding::SAME)
.AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddStringArg("activation", "RELU")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
......@@ -127,6 +132,7 @@ void TestNHWCSimple3x3SAME() {
ExpectTensorNear<float, T>(*expected, *net.GetOutput("Output"), 0.01);
}
} // namespace
TEST_F(FusedConv2dOpTest, CPUSimple) {
TestNHWCSimple3x3VALID<DeviceType::CPU, float>();
......@@ -138,6 +144,7 @@ TEST_F(FusedConv2dOpTest, OPENCLSimple) {
TestNHWCSimple3x3SAME<DeviceType::OPENCL, float>();
}
namespace {
template<DeviceType D, typename T>
void TestNHWCSimple3x3WithoutBias() {
OpsTestNet net;
......@@ -147,7 +154,7 @@ 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});
......@@ -165,6 +172,7 @@ void TestNHWCSimple3x3WithoutBias() {
.AddIntArg("padding", Padding::VALID)
.AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddStringArg("activation", "RELU")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
......@@ -180,6 +188,7 @@ void TestNHWCSimple3x3WithoutBias() {
.AddIntArg("padding", Padding::VALID)
.AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddStringArg("activation", "RELU")
.Finalize(net.NewOperatorDef());
// Run
......@@ -191,6 +200,7 @@ void TestNHWCSimple3x3WithoutBias() {
ExpectTensorNear<float, T>(*expected, *net.GetOutput("Output"), 0.01);
}
} // namespace
TEST_F(FusedConv2dOpTest, CPUWithoutBias) {
TestNHWCSimple3x3WithoutBias<DeviceType::CPU, float>();
......@@ -200,6 +210,7 @@ TEST_F(FusedConv2dOpTest, OPENCLWithoutBias) {
TestNHWCSimple3x3WithoutBias<DeviceType::OPENCL, float>();
}
namespace {
template<DeviceType D>
void TestConv1x1() {
// Construct graph
......@@ -216,8 +227,8 @@ 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) {
......@@ -268,13 +279,15 @@ void TestConv1x1() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
} // namespace
TEST_F(FusedConv2dOpTest, CPUConv1x1) { TestConv1x1<DeviceType::CPU>(); }
TEST_F(FusedConv2dOpTest, OPENCLConv1x1) { TestConv1x1<DeviceType::OPENCL>(); }
namespace {
template<DeviceType D, typename T>
static void TestComplexConvNxNS12(const std::vector<index_t> &shape) {
void TestComplexConvNxNS12(const std::vector<index_t> &shape) {
testing::internal::LogToStderr();
auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
Padding type) {
......@@ -343,13 +356,15 @@ static void TestComplexConvNxNS12(const std::vector<index_t> &shape) {
}
}
}
} // namespace
TEST_F(FusedConv2dOpTest, OPENCLUnalignedConvNxNS12) {
TestComplexConvNxNS12<DeviceType::OPENCL, float>({107, 113, 5, 7});
}
namespace {
template<DeviceType D>
static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
testing::internal::LogToStderr();
auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
Padding type) {
......@@ -428,14 +443,16 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
}
}
}
} // namespace
TEST_F(FusedConv2dOpTest, OPENCLHalfAlignedConvNxNS12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32, 32, 64});
}
namespace {
template<DeviceType D, typename T>
static void TestGeneralConvNxNS12(const std::vector<index_t> &image_shape,
const std::vector<index_t> &filter_shape) {
void TestGeneralConvNxNS12(const std::vector<index_t> &image_shape,
const std::vector<index_t> &filter_shape) {
testing::internal::LogToStderr();
auto func = [&](int stride_h, int stride_w, Padding type) {
srand(time(NULL));
......@@ -444,10 +461,10 @@ static void TestGeneralConvNxNS12(const std::vector<index_t> &image_shape,
index_t batch = 1;
index_t height = image_shape[0];
index_t width = image_shape[1];
index_t input_channels = filter_shape[2];
index_t output_channels = filter_shape[3];
index_t kernel_h = filter_shape[0];
index_t kernel_w = filter_shape[1];
index_t output_channels = filter_shape[2];
index_t input_channels = filter_shape[3];
// Construct graph
OpsTestNet net;
OpDefBuilder("FusedConv2D", "FusedConv2dTest")
......@@ -504,18 +521,20 @@ static void TestGeneralConvNxNS12(const std::vector<index_t> &image_shape,
func(stride, stride, SAME);
}
}
} // namespace
TEST_F(FusedConv2dOpTest, OPENCL7X7ConvNxNS12) {
TestGeneralConvNxNS12<DeviceType::OPENCL, float>({32, 32}, {7, 7, 3, 64});
TestGeneralConvNxNS12<DeviceType::OPENCL, float>({32, 32}, {7, 7, 64, 3});
}
TEST_F(FusedConv2dOpTest, OPENCL15X1ConvNxNS12) {
TestGeneralConvNxNS12<DeviceType::OPENCL, float>({40, 40}, {15, 1, 32, 64});
TestGeneralConvNxNS12<DeviceType::OPENCL, float>({40, 40}, {15, 1, 64, 32});
}
namespace {
template<DeviceType D, typename T>
static void TestAtrousConvNxN(const std::vector<index_t> &shape,
const int dilation) {
void TestAtrousConvNxN(const std::vector<index_t> &shape,
const int dilation) {
testing::internal::LogToStderr();
auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
Padding type) {
......@@ -525,8 +544,8 @@ static void TestAtrousConvNxN(const std::vector<index_t> &shape,
index_t batch = 1;
index_t height = shape[0];
index_t width = shape[1];
index_t input_channels = shape[2];
index_t output_channels = shape[3];
index_t output_channels = shape[2];
index_t input_channels = shape[3];
// Construct graph
OpsTestNet net;
OpDefBuilder("FusedConv2D", "FusedConv2dTest")
......@@ -585,6 +604,7 @@ static void TestAtrousConvNxN(const std::vector<index_t> &shape,
}
}
}
} // namespace
TEST_F(FusedConv2dOpTest, OPENCLalignedAtrousConvNxN2) {
TestAtrousConvNxN<DeviceType::OPENCL, float>({128, 128, 16, 16}, 2);
......@@ -598,10 +618,11 @@ TEST_F(FusedConv2dOpTest, OPENCLUnalignedAtrousConvNxN) {
TestAtrousConvNxN<DeviceType::OPENCL, float>({107, 113, 5, 7}, 2);
}
namespace {
template<DeviceType D>
static void TestGeneralHalfAtrousConv(const std::vector<index_t> &image_shape,
const std::vector<index_t> &filter_shape,
const std::vector<int> &dilations) {
void TestGeneralHalfAtrousConv(const std::vector<index_t> &image_shape,
const std::vector<index_t> &filter_shape,
const std::vector<int> &dilations) {
testing::internal::LogToStderr();
auto func = [&](int stride_h, int stride_w, Padding type) {
srand(time(NULL));
......@@ -610,10 +631,10 @@ static void TestGeneralHalfAtrousConv(const std::vector<index_t> &image_shape,
index_t batch = 1;
index_t height = image_shape[0];
index_t width = image_shape[1];
index_t input_channels = filter_shape[2];
index_t output_channels = filter_shape[3];
index_t kernel_h = filter_shape[0];
index_t kernel_w = filter_shape[1];
index_t output_channels = filter_shape[2];
index_t input_channels = filter_shape[3];
// Construct graph
OpsTestNet net;
OpDefBuilder("FusedConv2D", "FusedConv2dTest")
......@@ -668,9 +689,10 @@ static void TestGeneralHalfAtrousConv(const std::vector<index_t> &image_shape,
func(1, 1, VALID);
func(1, 1, SAME);
}
} // namespace
TEST_F(FusedConv2dOpTest, OPENCL7X7AtrousConvD2) {
TestGeneralHalfAtrousConv<DeviceType::OPENCL>({32, 32}, {7, 7, 3, 16},
TestGeneralHalfAtrousConv<DeviceType::OPENCL>({32, 32}, {7, 7, 16, 3},
{2, 2});
}
......@@ -679,7 +701,8 @@ TEST_F(FusedConv2dOpTest, OPENCL15X15AtrousConvD4) {
{2, 2});
}
static void TestNEONGeneralConvNxNS12(
namespace {
void TestNEONGeneralConvNxNS12(
const std::vector<index_t> &image_shape,
const std::vector<index_t> &filter_shape) {
testing::internal::LogToStderr();
......@@ -690,10 +713,10 @@ static void TestNEONGeneralConvNxNS12(
index_t batch = 1;
index_t height = image_shape[0];
index_t width = image_shape[1];
index_t input_channels = filter_shape[2];
index_t output_channels = filter_shape[3];
index_t kernel_h = filter_shape[0];
index_t kernel_w = filter_shape[1];
index_t output_channels = filter_shape[2];
index_t input_channels = filter_shape[3];
// Construct graph
OpsTestNet net;
OpDefBuilder("FusedConv2D", "FusedConv2dTest")
......@@ -748,9 +771,10 @@ static void TestNEONGeneralConvNxNS12(
func(stride, stride, SAME);
}
}
} // namespace
TEST_F(FusedConv2dOpTest, NEONTest) {
TestNEONGeneralConvNxNS12({32, 32}, {7, 7, 3, 64});
TestNEONGeneralConvNxNS12({32, 32}, {7, 7, 64, 3});
}
} // namespace test
} // namespace ops
......
mace/ops/global_avg_pooling_benchmark.cc
浏览文件 @ c63ba922
......@@ -11,8 +11,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D>
static void GlobalAvgPooling(
void GlobalAvgPooling(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -36,6 +37,7 @@ static void GlobalAvgPooling(
net.RunOp(D);
}
}
} // namespace
#define BM_GLOBAL_AVG_POOLING_MACRO(N, C, H, W, DEVICE) \
static void BM_GLOBAL_AVG_POOLING_##N##_##C##_##H##_##W##_##DEVICE( \
......
mace/ops/matmul_benchmark.cc
浏览文件 @ c63ba922
......@@ -12,8 +12,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void MatMulBenchmark(
void MatMulBenchmark(
int iters, int batch, int height, int channels, int out_width) {
mace::testing::StopTiming();
......@@ -54,6 +55,7 @@ static void MatMulBenchmark(
}
net.Sync();
}
} // namespace
#define BM_MATMUL_MACRO(N, H, C, W, TYPE, DEVICE) \
static void BM_MATMUL_##N##_##H##_##C##_##W##_##TYPE##_##DEVICE(int iters) { \
......
mace/ops/matmul_test.cc
浏览文件 @ c63ba922
......@@ -13,6 +13,7 @@ namespace test {
class MatMulOpTest : public OpsTestBase {};
namespace {
template <DeviceType D>
void Simple(const std::vector<index_t> &A_shape,
const std::vector<float> &A_value,
......@@ -58,6 +59,7 @@ void Simple(const std::vector<index_t> &A_shape,
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
} // namespace
TEST_F(MatMulOpTest, SimpleCPU) {
Simple<DeviceType::CPU>({1, 2, 3, 1}, {1, 2, 3, 4, 5, 6}, {1, 3, 2, 1},
......@@ -98,6 +100,7 @@ TEST_F(MatMulOpTest, SimpleGPUWithBatch) {
{2, 2, 2, 1}, {22, 28, 49, 64, 22, 28, 49, 64});
}
namespace {
template <typename T>
void Complex(const index_t batch,
const index_t height,
......@@ -150,6 +153,7 @@ void Complex(const index_t batch,
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-4);
}
}
} // namespace
TEST_F(MatMulOpTest, OPENCLAlignedWithoutBatch) {
Complex<float>(1, 64, 128, 32);
......
mace/ops/pad_benchmark.cc
浏览文件 @ c63ba922
......@@ -11,9 +11,10 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void Pad(int iters, int batch, int height,
int width, int channels, int pad) {
void Pad(int iters, int batch, int height,
int width, int channels, int pad) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -52,6 +53,7 @@ static void Pad(int iters, int batch, int height,
}
net.Sync();
}
} // namespace
#define BM_PAD_MACRO(N, H, W, C, PAD, TYPE, DEVICE) \
static void BM_PAD_##N##_##H##_##W##_##C##_##PAD##_##TYPE##_##DEVICE( \
......
mace/ops/pad_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class PadTest : public OpsTestBase {};
namespace {
template <DeviceType D>
void Simple() {
// Construct graph
......@@ -57,6 +58,7 @@ void Simple() {
});
ExpectTensorNear<float>(*expected, *output, 1e-5);
}
} // namespace
TEST_F(PadTest, SimpleCPU) {
Simple<DeviceType::CPU>();
......@@ -94,6 +96,7 @@ TEST_F(PadTest, ComplexCPU) {
ExpectTensorNear<float>(*expected, *output, 1e-5);
}
namespace {
template <typename T>
void Complex(const std::vector<index_t> &input_shape,
const std::vector<int> &paddings) {
......@@ -139,6 +142,7 @@ void Complex(const std::vector<index_t> &input_shape,
ExpectTensorNear<float>(expected, *output, 1e-5);
}
}
} // namespace
TEST_F(PadTest, ComplexFloat) {
Complex<float>({1, 32, 32, 4}, {0, 0, 2, 2, 1, 1, 0, 0});
......
mace/ops/pooling_benchmark.cc
浏览文件 @ c63ba922
......@@ -12,16 +12,17 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D>
static void Pooling(int iters,
int batch,
int channels,
int height,
int width,
int kernel,
int stride,
Padding padding,
PoolingType pooling_type) {
void Pooling(int iters,
int batch,
int channels,
int height,
int width,
int kernel,
int stride,
Padding padding,
PoolingType pooling_type) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -49,6 +50,7 @@ static void Pooling(int iters,
net.RunOp(D);
}
}
} // namespace
#define BM_POOLING_MACRO(N, C, H, W, KE, STRIDE, PA, PO, DEVICE) \
static void \
......
mace/ops/pooling_test.cc
浏览文件 @ c63ba922
......@@ -123,8 +123,9 @@ TEST_F(PoolingOpTest, MAX_k2x2s2x2) {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
namespace {
template<DeviceType D>
static void SimpleMaxPooling3S2() {
void SimpleMaxPooling3S2() {
// Construct graph
OpsTestNet net;
......@@ -168,6 +169,7 @@ static void SimpleMaxPooling3S2() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
} // namespace
TEST_F(PoolingOpTest, CPUSimpleMaxPooling3S2) { SimpleMaxPooling3S2<CPU>(); }
......@@ -175,10 +177,11 @@ TEST_F(PoolingOpTest, OPENCLSimpleMaxPooling3S2) {
SimpleMaxPooling3S2<OPENCL>();
}
namespace {
template<DeviceType D, typename T>
static void MaxPooling3S2(const std::vector<index_t> &input_shape,
const std::vector<int> strides,
Padding padding) {
void MaxPooling3S2(const std::vector<index_t> &input_shape,
const std::vector<int> strides,
Padding padding) {
// Construct graph
OpsTestNet net;
OpDefBuilder("Pooling", "PoolingTest")
......@@ -218,6 +221,7 @@ static void MaxPooling3S2(const std::vector<index_t> &input_shape,
ExpectTensorNear<T>(expected, *net.GetOutput("OPENCLOutput"), 0.001);
}
} // namespace
// TODO(chenghui) : there is a bug.
// TEST_F(PoolingOpTest, NEONAlignedMaxPooling3S2) {
......@@ -275,8 +279,9 @@ TEST_F(PoolingOpTest, AVG_VALID) {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
namespace {
template<DeviceType D>
static void SimpleAvgPoolingTest() {
void SimpleAvgPoolingTest() {
// Construct graph
OpsTestNet net;
......@@ -306,16 +311,18 @@ static void SimpleAvgPoolingTest() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
} // namespace
TEST_F(PoolingOpTest, OPENCLSimpleAvgPooling) {
SimpleAvgPoolingTest<OPENCL>();
}
namespace {
template<DeviceType D, typename T>
static void AvgPoolingTest(const std::vector<index_t> &shape,
const std::vector<int> &kernels,
const std::vector<int> &strides,
Padding padding) {
void AvgPoolingTest(const std::vector<index_t> &shape,
const std::vector<int> &kernels,
const std::vector<int> &strides,
Padding padding) {
// Construct graph
OpsTestNet net;
OpDefBuilder("Pooling", "PoolingTest")
......@@ -354,6 +361,7 @@ static void AvgPoolingTest(const std::vector<index_t> &shape,
ExpectTensorNear<float, T>(expected, *net.GetOutput("OPENCLOutput"), 0.01);
}
} // namespace
TEST_F(PoolingOpTest, OPENCLAlignedAvgPooling) {
AvgPoolingTest<OPENCL, float>({3, 15, 15, 128}, {4, 4}, {4, 4},
......@@ -396,11 +404,12 @@ TEST_F(PoolingOpTest, OPENCLUnAlignedLargeKernelAvgPooling) {
Padding::SAME);
}
static void AvgPoolingNEONTest(const std::vector<index_t> &shape,
const std::vector<int> &kernels,
const std::vector<int> &strides,
Padding padding,
PoolingType pooling_type) {
namespace {
void AvgPoolingNEONTest(const std::vector<index_t> &shape,
const std::vector<int> &kernels,
const std::vector<int> &strides,
Padding padding,
PoolingType pooling_type) {
// Construct graph
OpsTestNet net;
OpDefBuilder("Pooling", "PoolingTest")
......@@ -441,6 +450,7 @@ static void AvgPoolingNEONTest(const std::vector<index_t> &shape,
*net.GetOutput("OutputNeon"),
0.01);
}
} // namespace
TEST_F(PoolingOpTest, NEONTest) {
AvgPoolingNEONTest({3, 31, 37, 128}, {8, 8}, {8, 8},
......
mace/ops/reorganize_test.cc
浏览文件 @ c63ba922
......@@ -12,6 +12,7 @@ namespace test {
class ReOrganizeTest : public OpsTestBase {};
namespace {
void TestReOrganize(const std::vector<index_t> &input_shape,
const std::vector<float> &input_data,
const std::vector<index_t> &output_shape,
......@@ -69,6 +70,7 @@ void TestReOrganize(const std::vector<index_t> &input_shape,
ASSERT_EQ(input_data[i], output_ptr[i]) << "With Index " << i;
}
}
} // namespace
TEST_F(ReOrganizeTest, Simple) {
TestReOrganize({1, 1, 4, 6},
......
mace/ops/reshape_test.cc
浏览文件 @ c63ba922
......@@ -12,6 +12,7 @@ namespace test {
class ReshapeTest : public OpsTestBase {};
namespace {
void TestReshape(const std::vector<index_t> &org_shape,
const std::vector<int> &output_shape,
const std::vector<index_t> &res_shape) {
......@@ -41,6 +42,7 @@ void TestReshape(const std::vector<index_t> &org_shape,
ASSERT_EQ(input_ptr[i], output_ptr[i]);
}
}
} // namespace
TEST_F(ReshapeTest, Simple) {
TestReshape({1, 2, 3, 4}, {1, 2, -1, 4}, {1, 2, 3, 4});
......
mace/ops/resize_bilinear_benchmark.cc
浏览文件 @ c63ba922
......@@ -11,14 +11,15 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void ResizeBilinearBenchmark(int iters,
int batch,
int channels,
int input_height,
int input_width,
int output_height,
int output_width) {
void ResizeBilinearBenchmark(int iters,
int batch,
int channels,
int input_height,
int input_width,
int output_height,
int output_width) {
mace::testing::StopTiming();
OpsTestNet net;
......@@ -59,6 +60,7 @@ static void ResizeBilinearBenchmark(int iters,
}
net.Sync();
}
} // namespace
#define BM_RESIZE_BILINEAR_MACRO(N, C, H0, W0, H1, W1, TYPE, DEVICE) \
static void \
......
mace/ops/resize_bilinear_test.cc
浏览文件 @ c63ba922
......@@ -63,6 +63,7 @@ TEST_F(ResizeBilinearTest, ResizeBilinearWAlignCorners) {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
}
namespace {
template <DeviceType D>
void TestRandomResizeBilinear() {
testing::internal::LogToStderr();
......@@ -115,6 +116,7 @@ void TestRandomResizeBilinear() {
ExpectTensorNear<float>(expected, *net.GetOutput("DeviceOutput"), 0.001);
}
}
} // namespace
/*
TEST_F(ResizeBilinearTest, NEONRandomResizeBilinear) {
......
mace/ops/slice_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,10 +10,11 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template<DeviceType D, typename T>
static void BMSliceHelper(int iters,
const std::vector<index_t> &input_shape,
const index_t num_outputs) {
void BMSliceHelper(int iters,
const std::vector<index_t> &input_shape,
const index_t num_outputs) {
mace::testing::StopTiming();
// Construct graph
......@@ -60,6 +61,7 @@ static void BMSliceHelper(int iters,
net.Sync();
}
}
} // namespace
#define BM_SLICE_MACRO(N, H, W, C, NO, TYPE, DEVICE) \
static void \
......
mace/ops/slice_test.cc
浏览文件 @ c63ba922
......@@ -15,6 +15,7 @@ namespace test {
class SliceOpTest : public OpsTestBase {};
namespace {
template<DeviceType D, typename T>
void RandomTest(const int num_outputs, const int axis) {
static unsigned int seed = time(NULL);
......@@ -104,6 +105,7 @@ void RandomTest(const int num_outputs, const int axis) {
}
}
}
} // namespace
TEST_F(SliceOpTest, CPU) {
RandomTest<DeviceType::CPU, float>(2, 3);
......
mace/ops/softmax_benchmark.cc
浏览文件 @ c63ba922
......@@ -12,8 +12,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void SoftmaxBenchmark(
void SoftmaxBenchmark(
int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming();
......@@ -49,6 +50,7 @@ static void SoftmaxBenchmark(
}
net.Sync();
}
} // namespace
#define BM_SOFTMAX_MACRO(N, C, H, W, TYPE, DEVICE) \
static void BM_SOFTMAX_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE( \
......
mace/ops/softmax_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class SoftmaxOpTest : public OpsTestBase {};
namespace {
template<DeviceType D>
void Simple() {
// Construct graph
......@@ -50,10 +51,12 @@ void Simple() {
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-7);
}
} // namespace
TEST_F(SoftmaxOpTest, CPUSimple) { Simple<DeviceType::CPU>(); }
TEST_F(SoftmaxOpTest, OPENCLSimple) { Simple<DeviceType::OPENCL>(); }
namespace {
template<DeviceType D>
void Complex(const std::vector<index_t> &logits_shape) {
// Construct graph
......@@ -88,6 +91,7 @@ void Complex(const std::vector<index_t> &logits_shape) {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5);
}
} // namespace
TEST_F(SoftmaxOpTest, OPENCLAligned) {
Complex<DeviceType::OPENCL>({1, 256, 256, 3});
......@@ -104,6 +108,7 @@ TEST_F(SoftmaxOpTest, OPENCLUnAligned) {
Complex<DeviceType::OPENCL>({5, 211, 107, 1});
}
namespace {
void SoftMaxNEONTest(const std::vector<index_t> &logits_shape) {
// Construct graph
OpsTestNet net;
......@@ -135,6 +140,7 @@ void SoftMaxNEONTest(const std::vector<index_t> &logits_shape) {
*net.GetOutput("OutputNeon"),
0.01);
}
} // namespace
TEST_F(SoftmaxOpTest, NEONTest) {
SoftMaxNEONTest({5, 64, 64, 3});
......
mace/ops/space_to_batch_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,8 +10,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void BMSpaceToBatch(
void BMSpaceToBatch(
int iters, int batch, int height, int width, int channels, int shape) {
mace::testing::StopTiming();
......@@ -39,6 +40,7 @@ static void BMSpaceToBatch(
}
net.Sync();
}
} // namespace
#define BM_SPACE_TO_BATCH_MACRO(N, H, W, C, SHAPE, TYPE, DEVICE) \
static void \
......
mace/ops/space_to_batch_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D>
void RunSpaceToBatch(const std::vector<index_t> &input_shape,
const std::vector<float> &input_data,
......@@ -101,6 +102,7 @@ void TestBidirectionalTransform(const std::vector<index_t> &space_shape,
RunBatchToSpace<DeviceType::OPENCL>(batch_shape, batch_data, block_data,
padding_data, space_tensor.get());
}
} // namespace
TEST(SpaceToBatchTest, SmallData) {
TestBidirectionalTransform<float>({1, 2, 2, 1}, {1, 2, 3, 4}, {2, 2},
......
mace/ops/space_to_depth_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,8 +10,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void SpaceToDepth(
void SpaceToDepth(
int iters, int batch, int channels, int height, int width, int block_size) {
mace::testing::StopTiming();
......@@ -48,6 +49,7 @@ static void SpaceToDepth(
}
net.Sync();
}
} // namespace
#define BM_SPACE_TO_DEPTH_MACRO(N, C, H, W, G, TYPE, DEVICE) \
static void \
......
mace/ops/transpose_test.cc
浏览文件 @ c63ba922
......@@ -11,6 +11,7 @@ namespace test {
class TransposeOpTest : public OpsTestBase {};
namespace {
void TransposeNCHWTest(const std::vector<index_t> &input_shape) {
// Construct graph
OpsTestNet net;
......@@ -32,6 +33,7 @@ void TransposeNCHWTest(const std::vector<index_t> &input_shape) {
*net.GetOutput("Output"),
0.01);
}
} // namespace
TEST_F(TransposeOpTest, NCHW) {
TransposeNCHWTest({3, 64, 64, 128});
......
mace/ops/winograd_convolution_test.cc
浏览文件 @ c63ba922
......@@ -14,6 +14,7 @@ namespace test {
class WinogradConvlutionTest : public OpsTestBase {};
namespace {
void TransposeFilter(const std::vector<float> &input,
const std::vector<index_t> &input_shape,
std::vector<float> *output) {
......@@ -131,6 +132,7 @@ void WinogradConvolution(const index_t batch,
ExpectTensorNear<float>(expected, *net.GetOutput("WinoOutput"), 1e-4);
}
}
} // namespace
TEST_F(WinogradConvlutionTest, AlignedConvolution) {
WinogradConvolution<DeviceType::OPENCL, float>(1, 32, 32, 32, 16,
......@@ -153,6 +155,7 @@ TEST_F(WinogradConvlutionTest, BatchConvolution) {
Padding::SAME);
}
namespace {
template <DeviceType D, typename T>
void WinogradConvolutionWithPad(const index_t batch,
const index_t height,
......@@ -248,6 +251,7 @@ void WinogradConvolutionWithPad(const index_t batch,
ExpectTensorNear<float>(expected, *net.GetOutput("WinoOutput"), 1e-3);
}
}
} // namespace
} // namespace test
} // namespace ops
......
mace/ops/winograd_transform_benchmark.cc
浏览文件 @ c63ba922
......@@ -10,8 +10,9 @@ namespace mace {
namespace ops {
namespace test {
namespace {
template <DeviceType D, typename T>
static void BMWinogradTransform(
void BMWinogradTransform(
int iters, int batch, int height, int width, int channels) {
mace::testing::StopTiming();
......@@ -38,6 +39,7 @@ static void BMWinogradTransform(
}
net.Sync();
}
} // namespace
#define BM_WINOGRAD_TRANSFORM_MACRO(N, H, W, C, TYPE, DEVICE) \
static void BM_WINOGRAD_TRANSFORM_##N##_##H##_##W##_##C##_##TYPE##_##DEVICE( \
......@@ -56,8 +58,9 @@ BM_WINOGRAD_TRANSFORM(1, 16, 16, 128);
BM_WINOGRAD_TRANSFORM(1, 64, 64, 128);
BM_WINOGRAD_TRANSFORM(1, 128, 128, 128);
namespace {
template <DeviceType D, typename T>
static void BMWinogradInverseTransform(
void BMWinogradInverseTransform(
int iters, int batch, int height, int width, int channels) {
mace::testing::StopTiming();
......@@ -88,6 +91,7 @@ static void BMWinogradInverseTransform(
}
net.Sync();
}
} // namespace
#define BM_WINOGRAD_INVERSE_TRANSFORM_MACRO(N, H, W, C, TYPE, DEVICE) \
static void \
......
mace/tools/validation/mace_run.cc
浏览文件 @ c63ba922
......@@ -45,7 +45,8 @@ extern const std::string ModelChecksum();
} // namespace mace
namespace mace {
namespace examples {
namespace tools {
namespace validation {
namespace str_util {
......@@ -384,7 +385,8 @@ int Main(int argc, char **argv) {
}
}
} // namespace examples
} // namespace validation
} // namespace tools
} // namespace mace
int main(int argc, char **argv) { mace::examples::Main(argc, argv); }
int main(int argc, char **argv) { mace::tools::validation::Main(argc, argv); }
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