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Mace
提交 3ba07e0b 编写于 作者: L liuqi
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Change DeviceType::OPENCL to DeviceType::GPU and fix some bugs.

上级 d98386d6
展开全部 隐藏空白更改
内联 并排
Showing with 1053 addition and 2077 deletion
docs/developer/adding_a_new_op.md
浏览文件 @ 3ba07e0b
...@@ -24,16 +24,16 @@ void Register_Custom_Op(OperatorRegistry *op_registry) { ...@@ -24,16 +24,16 @@ void Register_Custom_Op(OperatorRegistry *op_registry) {
Custom_Op<DeviceType::CPU, float>); Custom_Op<DeviceType::CPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("op_name") REGISTER_OPERATOR(op_registry, OpKeyBuilder("op_name")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
Custom_Op<DeviceType::OPENCL, float>); Custom_Op<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("op_name") REGISTER_OPERATOR(op_registry, OpKeyBuilder("op_name")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
Custom_Op<DeviceType::OPENCL, half>); Custom_Op<DeviceType::GPU, half>);
} }
} // namespace ops } // namespace ops
......
docs/user/introduction.md
浏览文件 @ 3ba07e0b
...@@ -316,7 +316,7 @@ unsigned char *model_data = mace::MACE_MODEL_TAG::LoadModelData(FLAGS_model_data ...@@ -316,7 +316,7 @@ unsigned char *model_data = mace::MACE_MODEL_TAG::LoadModelData(FLAGS_model_data
NetDef net_def = mace::MACE_MODEL_TAG::CreateNet(model_data); NetDef net_def = mace::MACE_MODEL_TAG::CreateNet(model_data);
//3. 声明设备类型 //3. 声明设备类型
DeviceType device_type = DeviceType::OPENCL; DeviceType device_type = DeviceType::GPU;
//4. 定义输入输出名称数组 //4. 定义输入输出名称数组
std::vector<std::string> input_names = {...}; std::vector<std::string> input_names = {...};
...@@ -350,8 +350,8 @@ for (size_t i = 0; i < output_count; ++i) { ...@@ -350,8 +350,8 @@ for (size_t i = 0; i < output_count; ++i) {
//6. 创建MaceEngine对象 //6. 创建MaceEngine对象
mace::MaceEngine engine(&net_def, device_type, input_names, output_names); mace::MaceEngine engine(&net_def, device_type, input_names, output_names);
//7. 如果设备类型是OPENCL或HEXAGON，可以在此释放model_data //7. 如果设备类型是GPU或者HEXAGON，可以在此释放model_data
if (device_type == DeviceType::OPENCL || device_type == DeviceType::HEXAGON) { if (device_type == DeviceType::GPU || device_type == DeviceType::HEXAGON) {
mace::MACE_MODEL_TAG::UnloadModelData(model_data); mace::MACE_MODEL_TAG::UnloadModelData(model_data);
} }
......
mace/benchmark/benchmark_model.cc
浏览文件 @ 3ba07e0b
...@@ -108,10 +108,8 @@ inline int64_t NowMicros() { ...@@ -108,10 +108,8 @@ inline int64_t NowMicros() {
DeviceType ParseDeviceType(const std::string &device_str) { DeviceType ParseDeviceType(const std::string &device_str) {
if (device_str.compare("CPU") == 0) { if (device_str.compare("CPU") == 0) {
return DeviceType::CPU; return DeviceType::CPU;
} else if (device_str.compare("NEON") == 0) { } else if (device_str.compare("GPU") == 0) {
return DeviceType::NEON; return DeviceType::GPU;
} else if (device_str.compare("OPENCL") == 0) {
return DeviceType::OPENCL;
} else if (device_str.compare("HEXAGON") == 0) { } else if (device_str.compare("HEXAGON") == 0) {
return DeviceType::HEXAGON; return DeviceType::HEXAGON;
} else { } else {
...@@ -198,7 +196,7 @@ bool Run(MaceEngine *engine, ...@@ -198,7 +196,7 @@ bool Run(MaceEngine *engine,
return true; return true;
} }
DEFINE_string(device, "CPU", "Device [CPU|NEON|OPENCL]"); DEFINE_string(device, "CPU", "Device [CPU|GPU|DSP]");
DEFINE_string(input_node, "input_node0,input_node1", DEFINE_string(input_node, "input_node0,input_node1",
"input nodes, separated by comma"); "input nodes, separated by comma");
DEFINE_string(output_node, "output_node0,output_node1", DEFINE_string(output_node, "output_node0,output_node1",
...@@ -279,7 +277,7 @@ int Main(int argc, char **argv) { ...@@ -279,7 +277,7 @@ int Main(int argc, char **argv) {
mace::SetOpenMPThreadPolicy( mace::SetOpenMPThreadPolicy(
FLAGS_omp_num_threads, FLAGS_omp_num_threads,
static_cast<CPUAffinityPolicy >(FLAGS_cpu_affinity_policy)); static_cast<CPUAffinityPolicy >(FLAGS_cpu_affinity_policy));
if (device_type == DeviceType::OPENCL) { if (device_type == DeviceType::GPU) {
mace::SetGPUHints( mace::SetGPUHints(
static_cast<GPUPerfHint>(FLAGS_gpu_perf_hint), static_cast<GPUPerfHint>(FLAGS_gpu_perf_hint),
static_cast<GPUPriorityHint>(FLAGS_gpu_priority_hint)); static_cast<GPUPriorityHint>(FLAGS_gpu_priority_hint));
...@@ -347,7 +345,7 @@ int Main(int argc, char **argv) { ...@@ -347,7 +345,7 @@ int Main(int argc, char **argv) {
LOG(INFO) << "Run init"; LOG(INFO) << "Run init";
std::unique_ptr<mace::MaceEngine> engine_ptr( std::unique_ptr<mace::MaceEngine> engine_ptr(
new mace::MaceEngine(&net_def, device_type, input_names, output_names)); new mace::MaceEngine(&net_def, device_type, input_names, output_names));
if (device_type == DeviceType::OPENCL || device_type == DeviceType::HEXAGON) { if (device_type == DeviceType::GPU || device_type == DeviceType::HEXAGON) {
mace::MACE_MODEL_TAG::UnloadModelData(model_data); mace::MACE_MODEL_TAG::UnloadModelData(model_data);
} }
......
mace/benchmark/model_throughput_test.cc
浏览文件 @ 3ba07e0b
...@@ -141,10 +141,8 @@ std::string FormatName(const std::string input) { ...@@ -141,10 +141,8 @@ std::string FormatName(const std::string input) {
DeviceType ParseDeviceType(const std::string &device_str) { DeviceType ParseDeviceType(const std::string &device_str) {
if (device_str.compare("CPU") == 0) { if (device_str.compare("CPU") == 0) {
return DeviceType::CPU; return DeviceType::CPU;
} else if (device_str.compare("NEON") == 0) { } else if (device_str.compare("GPU") == 0) {
return DeviceType::NEON; return DeviceType::GPU;
} else if (device_str.compare("OPENCL") == 0) {
return DeviceType::OPENCL;
} else if (device_str.compare("HEXAGON") == 0) { } else if (device_str.compare("HEXAGON") == 0) {
return DeviceType::HEXAGON; return DeviceType::HEXAGON;
} else { } else {
...@@ -277,7 +275,7 @@ int Main(int argc, char **argv) { ...@@ -277,7 +275,7 @@ int Main(int argc, char **argv) {
FLAGS_gpu_model_data_file.c_str()); FLAGS_gpu_model_data_file.c_str());
NetDef gpu_net_def = mace::MACE_GPU_MODEL_TAG::CreateNet(gpu_model_data); NetDef gpu_net_def = mace::MACE_GPU_MODEL_TAG::CreateNet(gpu_model_data);
mace::MaceEngine gpu_engine(&gpu_net_def, DeviceType::OPENCL, input_names, mace::MaceEngine gpu_engine(&gpu_net_def, DeviceType::GPU, input_names,
output_names); output_names);
mace::MACE_GPU_MODEL_TAG::UnloadModelData(gpu_model_data); mace::MACE_GPU_MODEL_TAG::UnloadModelData(gpu_model_data);
......
mace/core/allocator.cc
浏览文件 @ 3ba07e0b
...@@ -34,9 +34,8 @@ Allocator *GetDeviceAllocator(DeviceType type) { ...@@ -34,9 +34,8 @@ Allocator *GetDeviceAllocator(DeviceType type) {
} }
MACE_REGISTER_ALLOCATOR(DeviceType::CPU, new CPUAllocator()); MACE_REGISTER_ALLOCATOR(DeviceType::CPU, new CPUAllocator());
MACE_REGISTER_ALLOCATOR(DeviceType::NEON, new CPUAllocator());
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
MACE_REGISTER_ALLOCATOR(DeviceType::OPENCL, new OpenCLAllocator()); MACE_REGISTER_ALLOCATOR(DeviceType::GPU, new OpenCLAllocator());
#endif #endif
MACE_REGISTER_ALLOCATOR(DeviceType::HEXAGON, new CPUAllocator()); MACE_REGISTER_ALLOCATOR(DeviceType::HEXAGON, new CPUAllocator());
......
mace/core/buffer.h
浏览文件 @ 3ba07e0b
...@@ -189,7 +189,7 @@ class Image : public BufferBase { ...@@ -189,7 +189,7 @@ class Image : public BufferBase {
public: public:
Image() Image()
: BufferBase(0), : BufferBase(0),
allocator_(GetDeviceAllocator(OPENCL)), allocator_(GetDeviceAllocator(GPU)),
buf_(nullptr), buf_(nullptr),
mapped_buf_(nullptr) {} mapped_buf_(nullptr) {}
...@@ -198,7 +198,7 @@ class Image : public BufferBase { ...@@ -198,7 +198,7 @@ class Image : public BufferBase {
std::accumulate( std::accumulate(
shape.begin(), shape.end(), 1, std::multiplies<index_t>()) * shape.begin(), shape.end(), 1, std::multiplies<index_t>()) *
GetEnumTypeSize(data_type)), GetEnumTypeSize(data_type)),
allocator_(GetDeviceAllocator(OPENCL)), allocator_(GetDeviceAllocator(GPU)),
mapped_buf_(nullptr) { mapped_buf_(nullptr) {
shape_ = shape; shape_ = shape;
data_type_ = data_type; data_type_ = data_type;
......
mace/core/mace.cc
浏览文件 @ 3ba07e0b
...@@ -193,7 +193,7 @@ MaceStatus MaceEngine::Impl::Run( ...@@ -193,7 +193,7 @@ MaceStatus MaceEngine::Impl::Run(
input_tensors.push_back(input_tensor); input_tensors.push_back(input_tensor);
} }
for (auto &output : *outputs) { for (auto &output : *outputs) {
if (device_type_ == DeviceType::OPENCL) { if (device_type_ == DeviceType::GPU) {
MACE_CHECK(output.second.shape().size() == 4, MACE_CHECK(output.second.shape().size() == 4,
"The outputs' shape must be 4-dimension with NHWC format," "The outputs' shape must be 4-dimension with NHWC format,"
" please use 1 to fill missing dimensions"); " please use 1 to fill missing dimensions");
...@@ -217,7 +217,7 @@ MaceStatus MaceEngine::Impl::Run( ...@@ -217,7 +217,7 @@ MaceStatus MaceEngine::Impl::Run(
#endif #endif
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
if (device_type_ == OPENCL) { if (device_type_ == GPU) {
OpenCLRuntime::Global()->SaveBuiltCLProgram(); OpenCLRuntime::Global()->SaveBuiltCLProgram();
} }
#endif #endif
......
mace/core/net.cc
浏览文件 @ 3ba07e0b
...@@ -54,7 +54,7 @@ bool SerialNet::Run(RunMetadata *run_metadata) { ...@@ -54,7 +54,7 @@ bool SerialNet::Run(RunMetadata *run_metadata) {
auto &op = *iter; auto &op = *iter;
MACE_LATENCY_LOGGER(2, "Running operator ", op->debug_def().name(), "(", MACE_LATENCY_LOGGER(2, "Running operator ", op->debug_def().name(), "(",
op->debug_def().type(), ")"); op->debug_def().type(), ")");
bool future_wait = (device_type_ == DeviceType::OPENCL && bool future_wait = (device_type_ == DeviceType::GPU &&
(run_metadata != nullptr || (run_metadata != nullptr ||
std::distance(iter, operators_.end()) == 1)); std::distance(iter, operators_.end()) == 1));
......
mace/core/operator.cc
浏览文件 @ 3ba07e0b
...@@ -88,7 +88,6 @@ extern void Register_Dequantize(OperatorRegistry *op_registry); ...@@ -88,7 +88,6 @@ extern void Register_Dequantize(OperatorRegistry *op_registry);
extern void Register_Eltwise(OperatorRegistry *op_registry); extern void Register_Eltwise(OperatorRegistry *op_registry);
extern void Register_FoldedBatchNorm(OperatorRegistry *op_registry); extern void Register_FoldedBatchNorm(OperatorRegistry *op_registry);
extern void Register_FullyConnected(OperatorRegistry *op_registry); extern void Register_FullyConnected(OperatorRegistry *op_registry);
extern void Register_FusedConv2D(OperatorRegistry *op_registry);
extern void Register_LocalResponseNorm(OperatorRegistry *op_registry); extern void Register_LocalResponseNorm(OperatorRegistry *op_registry);
extern void Register_MatMul(OperatorRegistry *op_registry); extern void Register_MatMul(OperatorRegistry *op_registry);
extern void Register_Pad(OperatorRegistry *op_registry); extern void Register_Pad(OperatorRegistry *op_registry);
...@@ -96,7 +95,6 @@ extern void Register_Pooling(OperatorRegistry *op_registry); ...@@ -96,7 +95,6 @@ extern void Register_Pooling(OperatorRegistry *op_registry);
extern void Register_Proposal(OperatorRegistry *op_registry); extern void Register_Proposal(OperatorRegistry *op_registry);
extern void Register_PSROIAlign(OperatorRegistry *op_registry); extern void Register_PSROIAlign(OperatorRegistry *op_registry);
extern void Register_Quantize(OperatorRegistry *op_registry); extern void Register_Quantize(OperatorRegistry *op_registry);
extern void Register_ReOrganize(OperatorRegistry *op_registry);
extern void Register_Requantize(OperatorRegistry *op_registry); extern void Register_Requantize(OperatorRegistry *op_registry);
extern void Register_Reshape(OperatorRegistry *op_registry); extern void Register_Reshape(OperatorRegistry *op_registry);
extern void Register_ResizeBilinear(OperatorRegistry *op_registry); extern void Register_ResizeBilinear(OperatorRegistry *op_registry);
...@@ -130,7 +128,6 @@ OperatorRegistry::OperatorRegistry() { ...@@ -130,7 +128,6 @@ OperatorRegistry::OperatorRegistry() {
ops::Register_Eltwise(this); ops::Register_Eltwise(this);
ops::Register_FoldedBatchNorm(this); ops::Register_FoldedBatchNorm(this);
ops::Register_FullyConnected(this); ops::Register_FullyConnected(this);
ops::Register_FusedConv2D(this);
ops::Register_LocalResponseNorm(this); ops::Register_LocalResponseNorm(this);
ops::Register_MatMul(this); ops::Register_MatMul(this);
ops::Register_Pad(this); ops::Register_Pad(this);
...@@ -139,7 +136,6 @@ OperatorRegistry::OperatorRegistry() { ...@@ -139,7 +136,6 @@ OperatorRegistry::OperatorRegistry() {
ops::Register_PSROIAlign(this); ops::Register_PSROIAlign(this);
ops::Register_Quantize(this); ops::Register_Quantize(this);
ops::Register_Requantize(this); ops::Register_Requantize(this);
ops::Register_ReOrganize(this);
ops::Register_Reshape(this); ops::Register_Reshape(this);
ops::Register_ResizeBilinear(this); ops::Register_ResizeBilinear(this);
ops::Register_Slice(this); ops::Register_Slice(this);
......
mace/core/workspace.cc
浏览文件 @ 3ba07e0b
...@@ -82,7 +82,7 @@ void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) { ...@@ -82,7 +82,7 @@ void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) {
VLOG(3) << "Model data size: " << model_data_size; VLOG(3) << "Model data size: " << model_data_size;
if (model_data_size > 0) { if (model_data_size > 0) {
if (type == DeviceType::CPU || type == DeviceType::NEON) { if (type == DeviceType::CPU) {
tensor_buffer_ = std::unique_ptr<Buffer>( tensor_buffer_ = std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(type), new Buffer(GetDeviceAllocator(type),
model_data_ptr, model_data_ptr,
...@@ -119,7 +119,7 @@ void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) { ...@@ -119,7 +119,7 @@ void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) {
tensor_map_[const_tensor.name()] = std::move(tensor); tensor_map_[const_tensor.name()] = std::move(tensor);
} }
if (type == DeviceType::CPU || type == DeviceType::OPENCL) { if (type == DeviceType::CPU || type == DeviceType::GPU) {
CreateOutputTensorBuffer(net_def, type); CreateOutputTensorBuffer(net_def, type);
} }
} }
...@@ -149,7 +149,7 @@ void Workspace::CreateOutputTensorBuffer(const NetDef &net_def, ...@@ -149,7 +149,7 @@ void Workspace::CreateOutputTensorBuffer(const NetDef &net_def,
} }
MACE_CHECK(dtype != DataType::DT_INVALID, "data type is invalid."); MACE_CHECK(dtype != DataType::DT_INVALID, "data type is invalid.");
for (auto &mem_block : net_def.mem_arena().mem_block()) { for (auto &mem_block : net_def.mem_arena().mem_block()) {
if (device_type == DeviceType::OPENCL) { if (device_type == DeviceType::GPU) {
std::unique_ptr<BufferBase> image_buf( std::unique_ptr<BufferBase> image_buf(
new Image({mem_block.x(), mem_block.y()}, dtype)); new Image({mem_block.x(), mem_block.y()}, dtype));
preallocated_allocator_.SetBuffer(mem_block.mem_id(), preallocated_allocator_.SetBuffer(mem_block.mem_id(),
...@@ -170,7 +170,7 @@ void Workspace::CreateOutputTensorBuffer(const NetDef &net_def, ...@@ -170,7 +170,7 @@ void Workspace::CreateOutputTensorBuffer(const NetDef &net_def,
std::unique_ptr<Tensor> tensor std::unique_ptr<Tensor> tensor
(new Tensor(preallocated_allocator_.GetBuffer(mem_ids[i]), dtype)); (new Tensor(preallocated_allocator_.GetBuffer(mem_ids[i]), dtype));
tensor->SetSourceOpName(op.name()); tensor->SetSourceOpName(op.name());
if (device_type == DeviceType::OPENCL) { if (device_type == DeviceType::GPU) {
VLOG(3) << "Tensor: " << op.name() << "(" << op.type() << ")" VLOG(3) << "Tensor: " << op.name() << "(" << op.type() << ")"
<< " Mem: " << mem_ids[i] << " Mem: " << mem_ids[i]
<< " Image shape: " << " Image shape: "
...@@ -191,7 +191,7 @@ void Workspace::CreateOutputTensorBuffer(const NetDef &net_def, ...@@ -191,7 +191,7 @@ void Workspace::CreateOutputTensorBuffer(const NetDef &net_def,
} }
ScratchBuffer *Workspace::GetScratchBuffer(DeviceType device_type) { ScratchBuffer *Workspace::GetScratchBuffer(DeviceType device_type) {
if (device_type == CPU || device_type == NEON) { if (device_type == CPU) {
return host_scratch_buffer_.get(); return host_scratch_buffer_.get();
} else { } else {
return nullptr; return nullptr;
......
mace/examples/example.cc
浏览文件 @ 3ba07e0b
...@@ -22,7 +22,7 @@ ...@@ -22,7 +22,7 @@
* --input_file=input_data \ * --input_file=input_data \
* --output_file=mace.out \ * --output_file=mace.out \
* --model_data_file=model_data.data \ * --model_data_file=model_data.data \
* --device=OPENCL * --device=GPU
*/ */
#include <malloc.h> #include <malloc.h>
#include <stdint.h> #include <stdint.h>
...@@ -102,10 +102,8 @@ std::string FormatName(const std::string input) { ...@@ -102,10 +102,8 @@ std::string FormatName(const std::string input) {
DeviceType ParseDeviceType(const std::string &device_str) { DeviceType ParseDeviceType(const std::string &device_str) {
if (device_str.compare("CPU") == 0) { if (device_str.compare("CPU") == 0) {
return DeviceType::CPU; return DeviceType::CPU;
} else if (device_str.compare("NEON") == 0) { } else if (device_str.compare("GPU") == 0) {
return DeviceType::NEON; return DeviceType::GPU;
} else if (device_str.compare("OPENCL") == 0) {
return DeviceType::OPENCL;
} else if (device_str.compare("HEXAGON") == 0) { } else if (device_str.compare("HEXAGON") == 0) {
return DeviceType::HEXAGON; return DeviceType::HEXAGON;
} else { } else {
...@@ -135,7 +133,7 @@ DEFINE_string(output_file, ...@@ -135,7 +133,7 @@ DEFINE_string(output_file,
DEFINE_string(model_data_file, DEFINE_string(model_data_file,
"", "",
"model data file name, used when EMBED_MODEL_DATA set to 0"); "model data file name, used when EMBED_MODEL_DATA set to 0");
DEFINE_string(device, "OPENCL", "CPU/NEON/OPENCL/HEXAGON"); DEFINE_string(device, "GPU", "CPU/GPU/HEXAGON");
DEFINE_int32(round, 1, "round"); DEFINE_int32(round, 1, "round");
DEFINE_int32(restart_round, 1, "restart round"); DEFINE_int32(restart_round, 1, "restart round");
DEFINE_int32(malloc_check_cycle, -1, "malloc debug check cycle, -1 to disable"); DEFINE_int32(malloc_check_cycle, -1, "malloc debug check cycle, -1 to disable");
...@@ -160,7 +158,7 @@ bool RunModel(const std::vector<std::string> &input_names, ...@@ -160,7 +158,7 @@ bool RunModel(const std::vector<std::string> &input_names,
MaceStatus res = mace::SetOpenMPThreadPolicy( MaceStatus res = mace::SetOpenMPThreadPolicy(
FLAGS_omp_num_threads, FLAGS_omp_num_threads,
static_cast<CPUAffinityPolicy >(FLAGS_cpu_affinity_policy)); static_cast<CPUAffinityPolicy >(FLAGS_cpu_affinity_policy));
if (device_type == DeviceType::OPENCL) { if (device_type == DeviceType::GPU) {
mace::SetGPUHints( mace::SetGPUHints(
static_cast<GPUPerfHint>(FLAGS_gpu_perf_hint), static_cast<GPUPerfHint>(FLAGS_gpu_perf_hint),
static_cast<GPUPriorityHint>(FLAGS_gpu_priority_hint)); static_cast<GPUPriorityHint>(FLAGS_gpu_priority_hint));
...@@ -178,7 +176,7 @@ bool RunModel(const std::vector<std::string> &input_names, ...@@ -178,7 +176,7 @@ bool RunModel(const std::vector<std::string> &input_names,
// Init model // Init model
mace::MaceEngine engine(&net_def, device_type, input_names, mace::MaceEngine engine(&net_def, device_type, input_names,
output_names); output_names);
if (device_type == DeviceType::OPENCL || device_type == DeviceType::HEXAGON) { if (device_type == DeviceType::GPU || device_type == DeviceType::HEXAGON) {
mace::MACE_MODEL_TAG::UnloadModelData(model_data); mace::MACE_MODEL_TAG::UnloadModelData(model_data);
} }
......
mace/kernels/activation.h
浏览文件 @ 3ba07e0b
...@@ -162,7 +162,7 @@ class ActivationFunctor<DeviceType::CPU, float> { ...@@ -162,7 +162,7 @@ class ActivationFunctor<DeviceType::CPU, float> {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
class ActivationFunctor<DeviceType::OPENCL, T> { class ActivationFunctor<DeviceType::GPU, T> {
public: public:
ActivationFunctor(ActivationType type, T relux_max_limit) ActivationFunctor(ActivationType type, T relux_max_limit)
: activation_(type), relux_max_limit_(static_cast<T>(relux_max_limit)) {} : activation_(type), relux_max_limit_(static_cast<T>(relux_max_limit)) {}
......
mace/kernels/addn.h
浏览文件 @ 3ba07e0b
...@@ -93,7 +93,7 @@ struct AddNFunctor { ...@@ -93,7 +93,7 @@ struct AddNFunctor {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct AddNFunctor<DeviceType::OPENCL, T> { struct AddNFunctor<DeviceType::GPU, T> {
void operator()(const std::vector<const Tensor *> &input_tensors, void operator()(const std::vector<const Tensor *> &input_tensors,
Tensor *output_tensor, Tensor *output_tensor,
StatsFuture *future); StatsFuture *future);
......
mace/kernels/batch_norm.h
浏览文件 @ 3ba07e0b
...@@ -128,7 +128,7 @@ struct BatchNormFunctor<DeviceType::CPU, float> : BatchNormFunctorBase { ...@@ -128,7 +128,7 @@ struct BatchNormFunctor<DeviceType::CPU, float> : BatchNormFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct BatchNormFunctor<DeviceType::OPENCL, T> : BatchNormFunctorBase { struct BatchNormFunctor<DeviceType::GPU, T> : BatchNormFunctorBase {
BatchNormFunctor(const bool folded_constant, BatchNormFunctor(const bool folded_constant,
const ActivationType activation, const ActivationType activation,
const float relux_max_limit) const float relux_max_limit)
......
mace/kernels/bias_add.h
浏览文件 @ 3ba07e0b
...@@ -65,7 +65,7 @@ struct BiasAddFunctor<DeviceType::CPU, float> { ...@@ -65,7 +65,7 @@ struct BiasAddFunctor<DeviceType::CPU, float> {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct BiasAddFunctor<DeviceType::OPENCL, T> { struct BiasAddFunctor<DeviceType::GPU, T> {
void operator()(const Tensor *input, void operator()(const Tensor *input,
const Tensor *bias, const Tensor *bias,
Tensor *output, Tensor *output,
......
mace/kernels/buffer_to_image.h
浏览文件 @ 3ba07e0b
...@@ -44,7 +44,7 @@ struct BufferToImageFunctor : BufferToImageFunctorBase { ...@@ -44,7 +44,7 @@ struct BufferToImageFunctor : BufferToImageFunctorBase {
}; };
template <typename T> template <typename T>
struct BufferToImageFunctor<DeviceType::OPENCL, T> : BufferToImageFunctorBase { struct BufferToImageFunctor<DeviceType::GPU, T> : BufferToImageFunctorBase {
explicit BufferToImageFunctor(bool i2b = false) explicit BufferToImageFunctor(bool i2b = false)
: BufferToImageFunctorBase(i2b) {} : BufferToImageFunctorBase(i2b) {}
void operator()(Tensor *input, void operator()(Tensor *input,
......
mace/kernels/channel_shuffle.h
浏览文件 @ 3ba07e0b
...@@ -67,7 +67,7 @@ struct ChannelShuffleFunctor { ...@@ -67,7 +67,7 @@ struct ChannelShuffleFunctor {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct ChannelShuffleFunctor<DeviceType::OPENCL, T> { struct ChannelShuffleFunctor<DeviceType::GPU, T> {
explicit ChannelShuffleFunctor(const int groups) : groups_(groups) {} explicit ChannelShuffleFunctor(const int groups) : groups_(groups) {}
void operator()(const Tensor *input, Tensor *output, StatsFuture *future); void operator()(const Tensor *input, Tensor *output, StatsFuture *future);
......
mace/kernels/concat.h
浏览文件 @ 3ba07e0b
...@@ -93,7 +93,7 @@ struct ConcatFunctor : ConcatFunctorBase { ...@@ -93,7 +93,7 @@ struct ConcatFunctor : ConcatFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct ConcatFunctor<DeviceType::OPENCL, T> : ConcatFunctorBase { struct ConcatFunctor<DeviceType::GPU, T> : ConcatFunctorBase {
explicit ConcatFunctor(const int32_t axis) : ConcatFunctorBase(axis) {} explicit ConcatFunctor(const int32_t axis) : ConcatFunctorBase(axis) {}
void operator()(const std::vector<const Tensor *> &input_list, void operator()(const std::vector<const Tensor *> &input_list,
......
mace/kernels/conv_2d.h
浏览文件 @ 3ba07e0b
...@@ -615,7 +615,7 @@ struct Conv2dFunctor<DeviceType::CPU, float> : Conv2dFunctorBase { ...@@ -615,7 +615,7 @@ struct Conv2dFunctor<DeviceType::CPU, float> : Conv2dFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct Conv2dFunctor<DeviceType::OPENCL, T> : Conv2dFunctorBase { struct Conv2dFunctor<DeviceType::GPU, T> : Conv2dFunctorBase {
Conv2dFunctor(const int *strides, Conv2dFunctor(const int *strides,
const Padding &padding_type, const Padding &padding_type,
const std::vector<int> &paddings, const std::vector<int> &paddings,
......
mace/kernels/depth_to_space.h
浏览文件 @ 3ba07e0b
...@@ -117,7 +117,7 @@ struct DepthToSpaceOpFunctor { ...@@ -117,7 +117,7 @@ struct DepthToSpaceOpFunctor {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct DepthToSpaceOpFunctor<DeviceType::OPENCL, T> { struct DepthToSpaceOpFunctor<DeviceType::GPU, T> {
DepthToSpaceOpFunctor(const int block_size, bool d2s) DepthToSpaceOpFunctor(const int block_size, bool d2s)
: block_size_(block_size), d2s_(d2s) {} : block_size_(block_size), d2s_(d2s) {}
void operator()(const Tensor *input, Tensor *output, StatsFuture *future); void operator()(const Tensor *input, Tensor *output, StatsFuture *future);
......
mace/kernels/depthwise_conv2d.h
浏览文件 @ 3ba07e0b
...@@ -297,7 +297,7 @@ struct DepthwiseConv2dFunctor<DeviceType::CPU, float> ...@@ -297,7 +297,7 @@ struct DepthwiseConv2dFunctor<DeviceType::CPU, float>
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct DepthwiseConv2dFunctor<DeviceType::OPENCL, T> struct DepthwiseConv2dFunctor<DeviceType::GPU, T>
: DepthwiseConv2dFunctorBase { : DepthwiseConv2dFunctorBase {
DepthwiseConv2dFunctor(const int *strides, DepthwiseConv2dFunctor(const int *strides,
const Padding padding_type, const Padding padding_type,
......
mace/kernels/eltwise.h
浏览文件 @ 3ba07e0b
...@@ -363,7 +363,7 @@ struct EltwiseFunctor<DeviceType::CPU, float>: EltwiseFunctorBase { ...@@ -363,7 +363,7 @@ struct EltwiseFunctor<DeviceType::CPU, float>: EltwiseFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct EltwiseFunctor<DeviceType::OPENCL, T> : EltwiseFunctorBase { struct EltwiseFunctor<DeviceType::GPU, T> : EltwiseFunctorBase {
EltwiseFunctor(const EltwiseType type, EltwiseFunctor(const EltwiseType type,
const std::vector<float> &coeff, const std::vector<float> &coeff,
const float value) const float value)
......
mace/kernels/fully_connected.h
浏览文件 @ 3ba07e0b
...@@ -88,7 +88,7 @@ struct FullyConnectedFunctor<DeviceType::CPU, float>: FullyConnectedBase { ...@@ -88,7 +88,7 @@ struct FullyConnectedFunctor<DeviceType::CPU, float>: FullyConnectedBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct FullyConnectedFunctor<DeviceType::OPENCL, T> : FullyConnectedBase { struct FullyConnectedFunctor<DeviceType::GPU, T> : FullyConnectedBase {
FullyConnectedFunctor(const int /*BufferType*/ weight_type, FullyConnectedFunctor(const int /*BufferType*/ weight_type,
const ActivationType activation, const ActivationType activation,
const float relux_max_limit) const float relux_max_limit)
......
mace/kernels/matmul.h
浏览文件 @ 3ba07e0b
...@@ -75,7 +75,7 @@ struct MatMulFunctor { ...@@ -75,7 +75,7 @@ struct MatMulFunctor {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct MatMulFunctor<DeviceType::OPENCL, T> { struct MatMulFunctor<DeviceType::GPU, T> {
void operator()(const Tensor *A, void operator()(const Tensor *A,
const Tensor *B, const Tensor *B,
Tensor *C, Tensor *C,
......
mace/kernels/opencl/activation_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -23,7 +23,7 @@ namespace mace { ...@@ -23,7 +23,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void ActivationFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, void ActivationFunctor<DeviceType::GPU, T>::operator()(const Tensor *input,
const Tensor *alpha, const Tensor *alpha,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
...@@ -46,7 +46,7 @@ void ActivationFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -46,7 +46,7 @@ void ActivationFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -124,7 +124,7 @@ void ActivationFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -124,7 +124,7 @@ void ActivationFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
} }
} }
template struct ActivationFunctor<DeviceType::OPENCL, float>; template struct ActivationFunctor<DeviceType::GPU, float>;
template struct ActivationFunctor<DeviceType::OPENCL, half>; template struct ActivationFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/addn.cc
浏览文件 @ 3ba07e0b
...@@ -22,7 +22,7 @@ namespace mace { ...@@ -22,7 +22,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void AddNFunctor<DeviceType::OPENCL, T>::operator()( void AddNFunctor<DeviceType::GPU, T>::operator()(
const std::vector<const Tensor *> &input_tensors, const std::vector<const Tensor *> &input_tensors,
Tensor *output_tensor, Tensor *output_tensor,
StatsFuture *future) { StatsFuture *future) {
...@@ -58,7 +58,7 @@ void AddNFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -58,7 +58,7 @@ void AddNFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -119,9 +119,9 @@ void AddNFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -119,9 +119,9 @@ void AddNFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct AddNFunctor<DeviceType::OPENCL, float>; template struct AddNFunctor<DeviceType::GPU, float>;
template struct AddNFunctor<DeviceType::OPENCL, half>; template struct AddNFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/batch_norm_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -23,7 +23,7 @@ namespace mace { ...@@ -23,7 +23,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, void BatchNormFunctor<DeviceType::GPU, T>::operator()(const Tensor *input,
const Tensor *scale, const Tensor *scale,
const Tensor *offset, const Tensor *offset,
const Tensor *mean, const Tensor *mean,
...@@ -56,7 +56,7 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -56,7 +56,7 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -130,7 +130,7 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -130,7 +130,7 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
} }
} }
template struct BatchNormFunctor<DeviceType::OPENCL, float>; template struct BatchNormFunctor<DeviceType::GPU, float>;
template struct BatchNormFunctor<DeviceType::OPENCL, half>; template struct BatchNormFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/bias_add_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -22,7 +22,7 @@ namespace mace { ...@@ -22,7 +22,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void BiasAddFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, void BiasAddFunctor<DeviceType::GPU, T>::operator()(const Tensor *input,
const Tensor *bias, const Tensor *bias,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
...@@ -49,7 +49,7 @@ void BiasAddFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -49,7 +49,7 @@ void BiasAddFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -115,7 +115,7 @@ void BiasAddFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -115,7 +115,7 @@ void BiasAddFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
} }
} }
template struct BiasAddFunctor<DeviceType::OPENCL, float>; template struct BiasAddFunctor<DeviceType::GPU, float>;
template struct BiasAddFunctor<DeviceType::OPENCL, half>; template struct BiasAddFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/buffer_to_image.cc
浏览文件 @ 3ba07e0b
...@@ -20,7 +20,7 @@ namespace mace { ...@@ -20,7 +20,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void BufferToImageFunctor<DeviceType::OPENCL, T>::operator()( void BufferToImageFunctor<DeviceType::GPU, T>::operator()(
Tensor *buffer, const BufferType type, Tensor *image, StatsFuture *future) { Tensor *buffer, const BufferType type, Tensor *image, StatsFuture *future) {
std::vector<size_t> image_shape; std::vector<size_t> image_shape;
...@@ -95,7 +95,7 @@ void BufferToImageFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -95,7 +95,7 @@ void BufferToImageFunctor<DeviceType::OPENCL, T>::operator()(
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
if (!kernel_error_) { if (!kernel_error_) {
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -177,8 +177,8 @@ void BufferToImageFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -177,8 +177,8 @@ void BufferToImageFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct BufferToImageFunctor<DeviceType::OPENCL, float>; template struct BufferToImageFunctor<DeviceType::GPU, float>;
template struct BufferToImageFunctor<DeviceType::OPENCL, half>; template struct BufferToImageFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/channel_shuffle.cc
浏览文件 @ 3ba07e0b
...@@ -23,7 +23,7 @@ namespace mace { ...@@ -23,7 +23,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void ChannelShuffleFunctor<DeviceType::OPENCL, T>::operator()( void ChannelShuffleFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input, const Tensor *input,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
...@@ -56,7 +56,7 @@ void ChannelShuffleFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -56,7 +56,7 @@ void ChannelShuffleFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -108,8 +108,8 @@ void ChannelShuffleFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -108,8 +108,8 @@ void ChannelShuffleFunctor<DeviceType::OPENCL, T>::operator()(
} }
template template
struct ChannelShuffleFunctor<DeviceType::OPENCL, float>; struct ChannelShuffleFunctor<DeviceType::GPU, float>;
template template
struct ChannelShuffleFunctor<DeviceType::OPENCL, half>; struct ChannelShuffleFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/concat.cc
浏览文件 @ 3ba07e0b
...@@ -50,7 +50,7 @@ static void Concat2(cl::Kernel *kernel, ...@@ -50,7 +50,7 @@ static void Concat2(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
...@@ -132,7 +132,7 @@ static void ConcatN(cl::Kernel *kernel, ...@@ -132,7 +132,7 @@ static void ConcatN(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
...@@ -216,7 +216,7 @@ static void ConcatN(cl::Kernel *kernel, ...@@ -216,7 +216,7 @@ static void ConcatN(cl::Kernel *kernel,
} }
template <typename T> template <typename T>
void ConcatFunctor<DeviceType::OPENCL, T>::operator()( void ConcatFunctor<DeviceType::GPU, T>::operator()(
const std::vector<const Tensor *> &input_list, const std::vector<const Tensor *> &input_list,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
...@@ -264,8 +264,8 @@ void ConcatFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -264,8 +264,8 @@ void ConcatFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct ConcatFunctor<DeviceType::OPENCL, float>; template struct ConcatFunctor<DeviceType::GPU, float>;
template struct ConcatFunctor<DeviceType::OPENCL, half>; template struct ConcatFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/conv_2d_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -67,7 +67,7 @@ extern void Conv2dOpencl(cl::Kernel *kernel, ...@@ -67,7 +67,7 @@ extern void Conv2dOpencl(cl::Kernel *kernel,
std::unique_ptr<BufferBase> *kernel_error); std::unique_ptr<BufferBase> *kernel_error);
template <typename T> template <typename T>
void Conv2dFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, void Conv2dFunctor<DeviceType::GPU, T>::operator()(const Tensor *input,
const Tensor *filter, const Tensor *filter,
const Tensor *bias, const Tensor *bias,
Tensor *output, Tensor *output,
...@@ -128,8 +128,8 @@ void Conv2dFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -128,8 +128,8 @@ void Conv2dFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
} }
} }
template struct Conv2dFunctor<DeviceType::OPENCL, float>; template struct Conv2dFunctor<DeviceType::GPU, float>;
template struct Conv2dFunctor<DeviceType::OPENCL, half>; template struct Conv2dFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/conv_2d_opencl_1x1.cc
浏览文件 @ 3ba07e0b
...@@ -61,7 +61,7 @@ extern void Conv2dOpenclK1x1(cl::Kernel *kernel, ...@@ -61,7 +61,7 @@ extern void Conv2dOpenclK1x1(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
......
mace/kernels/opencl/conv_2d_opencl_3x3.cc
浏览文件 @ 3ba07e0b
...@@ -58,7 +58,7 @@ extern void Conv2dOpenclK3x3(cl::Kernel *kernel, ...@@ -58,7 +58,7 @@ extern void Conv2dOpenclK3x3(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
......
mace/kernels/opencl/conv_2d_opencl_general.cc
浏览文件 @ 3ba07e0b
...@@ -58,7 +58,7 @@ extern void Conv2dOpencl(cl::Kernel *kernel, ...@@ -58,7 +58,7 @@ extern void Conv2dOpencl(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
......
mace/kernels/opencl/depth_to_space_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -23,7 +23,7 @@ namespace mace { ...@@ -23,7 +23,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void DepthToSpaceOpFunctor<DeviceType::OPENCL, T>::operator()( void DepthToSpaceOpFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input, Tensor *output, StatsFuture *future) { const Tensor *input, Tensor *output, StatsFuture *future) {
const index_t batch = input->dim(0); const index_t batch = input->dim(0);
const index_t input_height = input->dim(1); const index_t input_height = input->dim(1);
...@@ -86,7 +86,7 @@ void DepthToSpaceOpFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -86,7 +86,7 @@ void DepthToSpaceOpFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -145,8 +145,8 @@ void DepthToSpaceOpFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -145,8 +145,8 @@ void DepthToSpaceOpFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct DepthToSpaceOpFunctor<DeviceType::OPENCL, float>; template struct DepthToSpaceOpFunctor<DeviceType::GPU, float>;
template struct DepthToSpaceOpFunctor<DeviceType::OPENCL, half>; template struct DepthToSpaceOpFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/depthwise_conv_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -66,7 +66,7 @@ static void DepthwiseConv2d(cl::Kernel *kernel, ...@@ -66,7 +66,7 @@ static void DepthwiseConv2d(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
...@@ -163,7 +163,7 @@ static void DepthwiseConv2d(cl::Kernel *kernel, ...@@ -163,7 +163,7 @@ static void DepthwiseConv2d(cl::Kernel *kernel,
} }
template <typename T> template <typename T>
void DepthwiseConv2dFunctor<DeviceType::OPENCL, T>::operator()( void DepthwiseConv2dFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input, const Tensor *input,
const Tensor *filter, const Tensor *filter,
const Tensor *bias, const Tensor *bias,
...@@ -215,8 +215,8 @@ void DepthwiseConv2dFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -215,8 +215,8 @@ void DepthwiseConv2dFunctor<DeviceType::OPENCL, T>::operator()(
&kwg_size_, &kernel_error_); &kwg_size_, &kernel_error_);
} }
template struct DepthwiseConv2dFunctor<DeviceType::OPENCL, float>; template struct DepthwiseConv2dFunctor<DeviceType::GPU, float>;
template struct DepthwiseConv2dFunctor<DeviceType::OPENCL, half>; template struct DepthwiseConv2dFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/eltwise_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -21,7 +21,7 @@ namespace mace { ...@@ -21,7 +21,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0, void EltwiseFunctor<DeviceType::GPU, T>::operator()(const Tensor *input0,
const Tensor *input1, const Tensor *input1,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
...@@ -74,7 +74,7 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0, ...@@ -74,7 +74,7 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -129,7 +129,7 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0, ...@@ -129,7 +129,7 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0,
} }
} }
template struct EltwiseFunctor<DeviceType::OPENCL, float>; template struct EltwiseFunctor<DeviceType::GPU, float>;
template struct EltwiseFunctor<DeviceType::OPENCL, half>; template struct EltwiseFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/fully_connected_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -76,7 +76,7 @@ void FCWXKernel(cl::Kernel *kernel, ...@@ -76,7 +76,7 @@ void FCWXKernel(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
...@@ -202,7 +202,7 @@ void FCWTXKernel(cl::Kernel *kernel, ...@@ -202,7 +202,7 @@ void FCWTXKernel(cl::Kernel *kernel,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
*kernel_error = std::move(std::unique_ptr<Buffer>( *kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
(*kernel_error)->Map(nullptr); (*kernel_error)->Map(nullptr);
*((*kernel_error)->mutable_data<char>()) = 0; *((*kernel_error)->mutable_data<char>()) = 0;
(*kernel_error)->UnMap(); (*kernel_error)->UnMap();
...@@ -282,7 +282,7 @@ void FCWTXKernel(cl::Kernel *kernel, ...@@ -282,7 +282,7 @@ void FCWTXKernel(cl::Kernel *kernel,
} // namespace } // namespace
template <typename T> template <typename T>
void FullyConnectedFunctor<DeviceType::OPENCL, T>::operator()( void FullyConnectedFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input, const Tensor *input,
const Tensor *weight, const Tensor *weight,
const Tensor *bias, const Tensor *bias,
...@@ -305,9 +305,9 @@ void FullyConnectedFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -305,9 +305,9 @@ void FullyConnectedFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct FullyConnectedFunctor<DeviceType::OPENCL, float>; template struct FullyConnectedFunctor<DeviceType::GPU, float>;
template struct FullyConnectedFunctor<DeviceType::OPENCL, half>; template struct FullyConnectedFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/matmul.cc
浏览文件 @ 3ba07e0b
...@@ -21,7 +21,7 @@ namespace mace { ...@@ -21,7 +21,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void MatMulFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *A, void MatMulFunctor<DeviceType::GPU, T>::operator()(const Tensor *A,
const Tensor *B, const Tensor *B,
Tensor *C, Tensor *C,
StatsFuture *future) { StatsFuture *future) {
...@@ -53,7 +53,7 @@ void MatMulFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *A, ...@@ -53,7 +53,7 @@ void MatMulFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *A,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -98,9 +98,9 @@ void MatMulFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *A, ...@@ -98,9 +98,9 @@ void MatMulFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *A,
} }
} }
template struct MatMulFunctor<DeviceType::OPENCL, float>; template struct MatMulFunctor<DeviceType::GPU, float>;
template struct MatMulFunctor<DeviceType::OPENCL, half>; template struct MatMulFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/out_of_range_check_test.cc
浏览文件 @ 3ba07e0b
...@@ -56,7 +56,7 @@ const bool BufferToImageOpImpl(Tensor *buffer, ...@@ -56,7 +56,7 @@ const bool BufferToImageOpImpl(Tensor *buffer,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error = std::move(std::unique_ptr<Buffer>( kernel_error = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error->Map(nullptr); kernel_error->Map(nullptr);
*(kernel_error->mutable_data<char>()) = 0; *(kernel_error->mutable_data<char>()) = 0;
kernel_error->UnMap(); kernel_error->UnMap();
...@@ -136,13 +136,13 @@ TEST(OutOfRangeCheckTest, RandomTest) { ...@@ -136,13 +136,13 @@ TEST(OutOfRangeCheckTest, RandomTest) {
std::vector<index_t> buffer_shape = {batch, height, width, channels}; std::vector<index_t> buffer_shape = {batch, height, width, channels};
Workspace ws; Workspace ws;
Tensor *buffer = ws.CreateTensor("Buffer", Tensor *buffer = ws.CreateTensor("Buffer",
GetDeviceAllocator(DeviceType::OPENCL), GetDeviceAllocator(DeviceType::GPU),
DataTypeToEnum<float>::v()); DataTypeToEnum<float>::v());
buffer->Resize(buffer_shape); buffer->Resize(buffer_shape);
std::vector<size_t> image_shape; std::vector<size_t> image_shape;
Tensor *image = ws.CreateTensor("Image", Tensor *image = ws.CreateTensor("Image",
GetDeviceAllocator(DeviceType::OPENCL), GetDeviceAllocator(DeviceType::GPU),
DataTypeToEnum<float>::v()); DataTypeToEnum<float>::v());
CalImage2DShape(buffer->shape(), IN_OUT_CHANNEL, &image_shape); CalImage2DShape(buffer->shape(), IN_OUT_CHANNEL, &image_shape);
image->ResizeImage(buffer->shape(), image_shape); image->ResizeImage(buffer->shape(), image_shape);
......
mace/kernels/opencl/pad.cc
浏览文件 @ 3ba07e0b
...@@ -21,7 +21,7 @@ namespace mace { ...@@ -21,7 +21,7 @@ namespace mace {
namespace kernels { namespace kernels {
template<typename T> template<typename T>
void PadFunctor<DeviceType::OPENCL, T>::operator()( void PadFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input, const Tensor *input,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
...@@ -59,7 +59,7 @@ void PadFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -59,7 +59,7 @@ void PadFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -115,9 +115,9 @@ void PadFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -115,9 +115,9 @@ void PadFunctor<DeviceType::OPENCL, T>::operator()(
} }
template template
struct PadFunctor<DeviceType::OPENCL, float>; struct PadFunctor<DeviceType::GPU, float>;
template template
struct PadFunctor<DeviceType::OPENCL, half>; struct PadFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/pooling_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -22,7 +22,7 @@ namespace mace { ...@@ -22,7 +22,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void PoolingFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, void PoolingFunctor<DeviceType::GPU, T>::operator()(const Tensor *input,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
MACE_CHECK(dilations_[0] == 1 && dilations_[1] == 1) MACE_CHECK(dilations_[0] == 1 && dilations_[1] == 1)
...@@ -50,7 +50,7 @@ void PoolingFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -50,7 +50,7 @@ void PoolingFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -148,7 +148,7 @@ void PoolingFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input, ...@@ -148,7 +148,7 @@ void PoolingFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
} }
} }
template struct PoolingFunctor<DeviceType::OPENCL, float>; template struct PoolingFunctor<DeviceType::GPU, float>;
template struct PoolingFunctor<DeviceType::OPENCL, half>; template struct PoolingFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/resize_bilinear_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -23,7 +23,7 @@ namespace mace { ...@@ -23,7 +23,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()( void ResizeBilinearFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input, Tensor *output, StatsFuture *future) { const Tensor *input, Tensor *output, StatsFuture *future) {
const index_t batch = input->dim(0); const index_t batch = input->dim(0);
const index_t in_height = input->dim(1); const index_t in_height = input->dim(1);
...@@ -50,7 +50,7 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -50,7 +50,7 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -113,8 +113,8 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -113,8 +113,8 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct ResizeBilinearFunctor<DeviceType::OPENCL, float>; template struct ResizeBilinearFunctor<DeviceType::GPU, float>;
template struct ResizeBilinearFunctor<DeviceType::OPENCL, half>; template struct ResizeBilinearFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/slice.cc
浏览文件 @ 3ba07e0b
...@@ -21,7 +21,7 @@ namespace mace { ...@@ -21,7 +21,7 @@ namespace mace {
namespace kernels { namespace kernels {
template<typename T> template<typename T>
void SliceFunctor<DeviceType::OPENCL, T>::operator()( void SliceFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input, const Tensor *input,
const std::vector<Tensor *> &output_list, const std::vector<Tensor *> &output_list,
StatsFuture *future) { StatsFuture *future) {
...@@ -51,7 +51,7 @@ void SliceFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -51,7 +51,7 @@ void SliceFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -133,9 +133,9 @@ void SliceFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -133,9 +133,9 @@ void SliceFunctor<DeviceType::OPENCL, T>::operator()(
} }
template template
struct SliceFunctor<DeviceType::OPENCL, float>; struct SliceFunctor<DeviceType::GPU, float>;
template template
struct SliceFunctor<DeviceType::OPENCL, half>; struct SliceFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/softmax_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -23,7 +23,7 @@ namespace mace { ...@@ -23,7 +23,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void SoftmaxFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *logits, void SoftmaxFunctor<DeviceType::GPU, T>::operator()(const Tensor *logits,
Tensor *output, Tensor *output,
StatsFuture *future) { StatsFuture *future) {
const index_t batch = logits->dim(0); const index_t batch = logits->dim(0);
...@@ -49,7 +49,7 @@ void SoftmaxFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *logits, ...@@ -49,7 +49,7 @@ void SoftmaxFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *logits,
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -95,7 +95,7 @@ void SoftmaxFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *logits, ...@@ -95,7 +95,7 @@ void SoftmaxFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *logits,
} }
} }
template struct SoftmaxFunctor<DeviceType::OPENCL, float>; template struct SoftmaxFunctor<DeviceType::GPU, float>;
template struct SoftmaxFunctor<DeviceType::OPENCL, half>; template struct SoftmaxFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/space_to_batch_opencl.cc
浏览文件 @ 3ba07e0b
...@@ -25,7 +25,7 @@ namespace mace { ...@@ -25,7 +25,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void SpaceToBatchFunctor<DeviceType::OPENCL, T>::operator()( void SpaceToBatchFunctor<DeviceType::GPU, T>::operator()(
Tensor *space_tensor, Tensor *space_tensor,
const std::vector<index_t> &output_shape, const std::vector<index_t> &output_shape,
Tensor *batch_tensor, Tensor *batch_tensor,
...@@ -60,7 +60,7 @@ void SpaceToBatchFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -60,7 +60,7 @@ void SpaceToBatchFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -120,8 +120,8 @@ void SpaceToBatchFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -120,8 +120,8 @@ void SpaceToBatchFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct SpaceToBatchFunctor<DeviceType::OPENCL, float>; template struct SpaceToBatchFunctor<DeviceType::GPU, float>;
template struct SpaceToBatchFunctor<DeviceType::OPENCL, half>; template struct SpaceToBatchFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
......
mace/kernels/opencl/winograd_transform.cc
浏览文件 @ 3ba07e0b
...@@ -22,7 +22,7 @@ namespace mace { ...@@ -22,7 +22,7 @@ namespace mace {
namespace kernels { namespace kernels {
template <typename T> template <typename T>
void WinogradTransformFunctor<DeviceType::OPENCL, T>::operator()( void WinogradTransformFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input_tensor, Tensor *output_tensor, StatsFuture *future) { const Tensor *input_tensor, Tensor *output_tensor, StatsFuture *future) {
auto runtime = OpenCLRuntime::Global(); auto runtime = OpenCLRuntime::Global();
...@@ -39,7 +39,7 @@ void WinogradTransformFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -39,7 +39,7 @@ void WinogradTransformFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -117,7 +117,7 @@ void WinogradTransformFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -117,7 +117,7 @@ void WinogradTransformFunctor<DeviceType::OPENCL, T>::operator()(
} }
template <typename T> template <typename T>
void WinogradInverseTransformFunctor<DeviceType::OPENCL, T>::operator()( void WinogradInverseTransformFunctor<DeviceType::GPU, T>::operator()(
const Tensor *input_tensor, const Tensor *input_tensor,
const Tensor *bias, const Tensor *bias,
Tensor *output_tensor, Tensor *output_tensor,
...@@ -138,7 +138,7 @@ void WinogradInverseTransformFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -138,7 +138,7 @@ void WinogradInverseTransformFunctor<DeviceType::OPENCL, T>::operator()(
if (runtime->IsOutOfRangeCheckEnabled()) { if (runtime->IsOutOfRangeCheckEnabled()) {
built_options.emplace("-DOUT_OF_RANGE_CHECK"); built_options.emplace("-DOUT_OF_RANGE_CHECK");
kernel_error_ = std::move(std::unique_ptr<Buffer>( kernel_error_ = std::move(std::unique_ptr<Buffer>(
new Buffer(GetDeviceAllocator(DeviceType::OPENCL), 1))); new Buffer(GetDeviceAllocator(DeviceType::GPU), 1)));
kernel_error_->Map(nullptr); kernel_error_->Map(nullptr);
*(kernel_error_->mutable_data<char>()) = 0; *(kernel_error_->mutable_data<char>()) = 0;
kernel_error_->UnMap(); kernel_error_->UnMap();
...@@ -231,11 +231,11 @@ void WinogradInverseTransformFunctor<DeviceType::OPENCL, T>::operator()( ...@@ -231,11 +231,11 @@ void WinogradInverseTransformFunctor<DeviceType::OPENCL, T>::operator()(
} }
} }
template struct WinogradTransformFunctor<DeviceType::OPENCL, float>; template struct WinogradTransformFunctor<DeviceType::GPU, float>;
template struct WinogradTransformFunctor<DeviceType::OPENCL, half>; template struct WinogradTransformFunctor<DeviceType::GPU, half>;
template struct WinogradInverseTransformFunctor<DeviceType::OPENCL, float>; template struct WinogradInverseTransformFunctor<DeviceType::GPU, float>;
template struct WinogradInverseTransformFunctor<DeviceType::OPENCL, half>; template struct WinogradInverseTransformFunctor<DeviceType::GPU, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/pad.h
浏览文件 @ 3ba07e0b
...@@ -61,21 +61,21 @@ struct PadFunctor : public PadFunctorBase { ...@@ -61,21 +61,21 @@ struct PadFunctor : public PadFunctorBase {
std::fill(output_ptr, output_ptr + output->size(), this->constant_value_); std::fill(output_ptr, output_ptr + output->size(), this->constant_value_);
const index_t batch = input->dim(0); const index_t batch = input->dim(0);
const index_t height = input->dim(1); const index_t channel = input->dim(1);
const index_t width = input->dim(2); const index_t height = input->dim(2);
const index_t channel = input->dim(3); const index_t width = input->dim(3);
#pragma omp parallel for collapse(3) #pragma omp parallel for collapse(3)
for (index_t b = 0; b < batch; ++b) { for (index_t b = 0; b < batch; ++b) {
for (index_t h = 0; h < height; ++h) { for (index_t c = 0; c < channel; ++c) {
for (index_t w = 0; w < width; ++w) { for (index_t h = 0; h < height; ++h) {
const index_t in_offset = (((b * height + h) * width) + w) * channel; const index_t in_offset = (((b * channel + c) * height) + h) * width;
const index_t out_offset = (((b + this->paddings_[0]) * output->dim(1) const index_t out_offset = (((b + this->paddings_[0]) * output->dim(1)
+ (h + this->paddings_[2])) * output->dim(2) + (c + this->paddings_[2])) * output->dim(2)
+ (w + this->paddings_[4])) * output->dim(3) + (h + this->paddings_[4])) * output->dim(3)
+ this->paddings_[6]; + this->paddings_[6];
memcpy(output_ptr + out_offset, memcpy(output_ptr + out_offset,
input_ptr + in_offset, input_ptr + in_offset,
channel * sizeof(T)); width * sizeof(T));
} }
} }
} }
...@@ -84,7 +84,7 @@ struct PadFunctor : public PadFunctorBase { ...@@ -84,7 +84,7 @@ struct PadFunctor : public PadFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct PadFunctor<DeviceType::OPENCL, T> : PadFunctorBase { struct PadFunctor<DeviceType::GPU, T> : PadFunctorBase {
PadFunctor(const std::vector<int> &paddings, PadFunctor(const std::vector<int> &paddings,
const float constant_value) const float constant_value)
: PadFunctorBase(paddings, constant_value) {} : PadFunctorBase(paddings, constant_value) {}
......
mace/kernels/pooling.h
浏览文件 @ 3ba07e0b
...@@ -261,7 +261,7 @@ struct PoolingFunctor<DeviceType::CPU, float>: PoolingFunctorBase { ...@@ -261,7 +261,7 @@ struct PoolingFunctor<DeviceType::CPU, float>: PoolingFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct PoolingFunctor<DeviceType::OPENCL, T> : PoolingFunctorBase { struct PoolingFunctor<DeviceType::GPU, T> : PoolingFunctorBase {
PoolingFunctor(const PoolingType pooling_type, PoolingFunctor(const PoolingType pooling_type,
const int *kernels, const int *kernels,
const int *strides, const int *strides,
......
mace/kernels/reorganize.h 已删除 100644 → 0
浏览文件 @ d98386d6
// Copyright 2018 Xiaomi, Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef MACE_KERNELS_REORGANIZE_H_
#define MACE_KERNELS_REORGANIZE_H_
#include <vector>
#include "mace/core/future.h"
#include "mace/core/tensor.h"
#ifdef MACE_ENABLE_OPENCL
#include "mace/core/runtime/opencl/cl2_header.h"
#endif // MACE_ENABLE_OPENCL
namespace mace {
namespace kernels {
template <DeviceType D, typename T>
struct ReOrganizeFunctor {
void operator()(const Tensor *input,
const std::vector<index_t> &out_shape,
Tensor *output,
StatsFuture *future) {
const bool w2c = out_shape[3] > input->dim(3);
const index_t height = input->dim(1);
const index_t input_width = input->dim(2);
const index_t input_chan = input->dim(3);
const index_t output_width = output->dim(2);
const index_t output_chan = output->dim(3);
const T *input_ptr = input->data<T>();
T *output_ptr = output->mutable_data<T>();
if (w2c) {
MACE_CHECK((out_shape[3] % input->dim(3)) == 0);
const index_t multiplier = out_shape[3] / input->dim(3);
#pragma omp parallel for collapse(4)
for (index_t n = 0; n < out_shape[0]; ++n) {
for (index_t h = 0; h < out_shape[1]; ++h) {
for (index_t w = 0; w < out_shape[2]; ++w) {
for (index_t c = 0; c < out_shape[3]; ++c) {
const index_t out_offset =
((n * height + h) * output_width + w)
* output_chan + c;
const index_t in_w_idx = w + (c % multiplier) * output_width;
const index_t in_chan_idx = c / multiplier;
const index_t in_offset =
((n * height + h) * input_width + in_w_idx)
* input_chan + in_chan_idx;
output_ptr[out_offset] = input_ptr[in_offset];
}
}
}
}
} else {
MACE_CHECK((input->dim(3) % out_shape[3]) == 0);
const index_t multiplier = input->dim(3) / out_shape[3];
#pragma omp parallel for collapse(4)
for (index_t n = 0; n < out_shape[0]; ++n) {
for (index_t h = 0; h < out_shape[1]; ++h) {
for (index_t w = 0; w < out_shape[2]; ++w) {
for (index_t c = 0; c < out_shape[3]; ++c) {
const index_t out_offset =
((n * height + h) * output_width + w)
* output_chan + c;
const index_t in_w_idx = w % input_width;
const index_t in_chan_idx = w / input_width + c * multiplier;
const index_t in_offset =
((n * height + h) * input_width + in_w_idx)
* input_chan + in_chan_idx;
output_ptr[out_offset] = input_ptr[in_offset];
}
}
}
}
}
}
};
} // namespace kernels
} // namespace mace
#endif // MACE_KERNELS_REORGANIZE_H_
mace/kernels/resize_bilinear.h
浏览文件 @ 3ba07e0b
...@@ -179,7 +179,7 @@ struct ResizeBilinearFunctor<DeviceType::CPU, float> ...@@ -179,7 +179,7 @@ struct ResizeBilinearFunctor<DeviceType::CPU, float>
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct ResizeBilinearFunctor<DeviceType::OPENCL, T> struct ResizeBilinearFunctor<DeviceType::GPU, T>
: ResizeBilinearFunctorBase { : ResizeBilinearFunctorBase {
ResizeBilinearFunctor(const std::vector<index_t> &size, bool align_corners) ResizeBilinearFunctor(const std::vector<index_t> &size, bool align_corners)
: ResizeBilinearFunctorBase(size, align_corners) {} : ResizeBilinearFunctorBase(size, align_corners) {}
......
mace/kernels/slice.h
浏览文件 @ 3ba07e0b
...@@ -86,7 +86,7 @@ struct SliceFunctor : SliceFunctorBase { ...@@ -86,7 +86,7 @@ struct SliceFunctor : SliceFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct SliceFunctor<DeviceType::OPENCL, T> : SliceFunctorBase { struct SliceFunctor<DeviceType::GPU, T> : SliceFunctorBase {
explicit SliceFunctor(const int32_t axis) : SliceFunctorBase(axis) {} explicit SliceFunctor(const int32_t axis) : SliceFunctorBase(axis) {}
void operator()(const Tensor *input, void operator()(const Tensor *input,
......
mace/kernels/softmax.h
浏览文件 @ 3ba07e0b
...@@ -94,7 +94,7 @@ struct SoftmaxFunctor<DeviceType::CPU, float> { ...@@ -94,7 +94,7 @@ struct SoftmaxFunctor<DeviceType::CPU, float> {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template<typename T> template<typename T>
struct SoftmaxFunctor<DeviceType::OPENCL, T> { struct SoftmaxFunctor<DeviceType::GPU, T> {
void operator()(const Tensor *logits, Tensor *output, StatsFuture *future); void operator()(const Tensor *logits, Tensor *output, StatsFuture *future);
cl::Kernel kernel_; cl::Kernel kernel_;
......
mace/kernels/space_to_batch.h
浏览文件 @ 3ba07e0b
...@@ -59,7 +59,7 @@ struct SpaceToBatchFunctor : SpaceToBatchFunctorBase { ...@@ -59,7 +59,7 @@ struct SpaceToBatchFunctor : SpaceToBatchFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct SpaceToBatchFunctor<DeviceType::OPENCL, T> : SpaceToBatchFunctorBase { struct SpaceToBatchFunctor<DeviceType::GPU, T> : SpaceToBatchFunctorBase {
SpaceToBatchFunctor(const std::vector<int> &paddings, SpaceToBatchFunctor(const std::vector<int> &paddings,
const std::vector<int> &block_shape, const std::vector<int> &block_shape,
bool b2s) bool b2s)
......
mace/kernels/winograd_transform.h
浏览文件 @ 3ba07e0b
...@@ -57,7 +57,7 @@ struct WinogradTransformFunctor : WinogradTransformFunctorBase { ...@@ -57,7 +57,7 @@ struct WinogradTransformFunctor : WinogradTransformFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct WinogradTransformFunctor<DeviceType::OPENCL, T> struct WinogradTransformFunctor<DeviceType::GPU, T>
: WinogradTransformFunctorBase { : WinogradTransformFunctorBase {
WinogradTransformFunctor(const Padding &padding_type, WinogradTransformFunctor(const Padding &padding_type,
const std::vector<int> &paddings) const std::vector<int> &paddings)
...@@ -111,7 +111,7 @@ struct WinogradInverseTransformFunctor : WinogradInverseTransformFunctorBase { ...@@ -111,7 +111,7 @@ struct WinogradInverseTransformFunctor : WinogradInverseTransformFunctorBase {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
template <typename T> template <typename T>
struct WinogradInverseTransformFunctor<DeviceType::OPENCL, T> struct WinogradInverseTransformFunctor<DeviceType::GPU, T>
: WinogradInverseTransformFunctorBase { : WinogradInverseTransformFunctorBase {
WinogradInverseTransformFunctor(const int batch, WinogradInverseTransformFunctor(const int batch,
const int height, const int height,
......
mace/ops/activation.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_Activation(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_Activation(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Activation") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Activation")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
ActivationOp<DeviceType::OPENCL, float>); ActivationOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Activation") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Activation")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
ActivationOp<DeviceType::OPENCL, half>); ActivationOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/activation_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -33,7 +33,7 @@ void ReluBenchmark( ...@@ -33,7 +33,7 @@ void ReluBenchmark(
// Add input data // Add input data
if (D == DeviceType::CPU) { if (D == DeviceType::CPU) {
net.AddRandomInput<D, float>("Input", {batch, channels, height, width}); net.AddRandomInput<D, float>("Input", {batch, channels, height, width});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
} else { } else {
MACE_NOT_IMPLEMENTED; MACE_NOT_IMPLEMENTED;
...@@ -45,7 +45,7 @@ void ReluBenchmark( ...@@ -45,7 +45,7 @@ void ReluBenchmark(
.Output("Output") .Output("Output")
.AddStringArg("activation", "RELU") .AddStringArg("activation", "RELU")
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -83,8 +83,8 @@ void ReluBenchmark( ...@@ -83,8 +83,8 @@ void ReluBenchmark(
#define BM_RELU(N, C, H, W) \ #define BM_RELU(N, C, H, W) \
BM_RELU_MACRO(N, C, H, W, float, CPU); \ BM_RELU_MACRO(N, C, H, W, float, CPU); \
BM_RELU_MACRO(N, C, H, W, float, OPENCL); \ BM_RELU_MACRO(N, C, H, W, float, GPU); \
BM_RELU_MACRO(N, C, H, W, half, OPENCL); BM_RELU_MACRO(N, C, H, W, half, GPU);
BM_RELU(1, 1, 512, 512); BM_RELU(1, 1, 512, 512);
BM_RELU(1, 3, 128, 128); BM_RELU(1, 3, 128, 128);
...@@ -107,7 +107,7 @@ void ReluxBenchmark( ...@@ -107,7 +107,7 @@ void ReluxBenchmark(
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
} }
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -151,8 +151,8 @@ void ReluxBenchmark( ...@@ -151,8 +151,8 @@ void ReluxBenchmark(
#define BM_RELUX(N, C, H, W) \ #define BM_RELUX(N, C, H, W) \
BM_RELUX_MACRO(N, C, H, W, float, CPU); \ BM_RELUX_MACRO(N, C, H, W, float, CPU); \
BM_RELUX_MACRO(N, C, H, W, float, OPENCL); \ BM_RELUX_MACRO(N, C, H, W, float, GPU); \
BM_RELUX_MACRO(N, C, H, W, half, OPENCL); BM_RELUX_MACRO(N, C, H, W, half, GPU);
BM_RELUX(1, 1, 512, 512); BM_RELUX(1, 1, 512, 512);
BM_RELUX(1, 3, 128, 128); BM_RELUX(1, 3, 128, 128);
...@@ -171,7 +171,7 @@ void PreluBenchmark( ...@@ -171,7 +171,7 @@ void PreluBenchmark(
// Add input data // Add input data
if (D == DeviceType::CPU) { if (D == DeviceType::CPU) {
net.AddRandomInput<D, float>("Input", {batch, channels, height, width}); net.AddRandomInput<D, float>("Input", {batch, channels, height, width});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
} else { } else {
MACE_NOT_IMPLEMENTED; MACE_NOT_IMPLEMENTED;
...@@ -185,7 +185,7 @@ void PreluBenchmark( ...@@ -185,7 +185,7 @@ void PreluBenchmark(
.Output("Output") .Output("Output")
.AddStringArg("activation", "PRELU") .AddStringArg("activation", "PRELU")
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Alpha", "AlphaImage", BufferToImage<D, float>(&net, "Alpha", "AlphaImage",
...@@ -226,8 +226,8 @@ void PreluBenchmark( ...@@ -226,8 +226,8 @@ void PreluBenchmark(
#define BM_PRELU(N, C, H, W) \ #define BM_PRELU(N, C, H, W) \
BM_PRELU_MACRO(N, C, H, W, float, CPU); \ BM_PRELU_MACRO(N, C, H, W, float, CPU); \
BM_PRELU_MACRO(N, C, H, W, float, OPENCL); \ BM_PRELU_MACRO(N, C, H, W, float, GPU); \
BM_PRELU_MACRO(N, C, H, W, half, OPENCL); BM_PRELU_MACRO(N, C, H, W, half, GPU);
BM_PRELU(1, 1, 512, 512); BM_PRELU(1, 1, 512, 512);
BM_PRELU(1, 3, 128, 128); BM_PRELU(1, 3, 128, 128);
...@@ -250,7 +250,7 @@ void TanhBenchmark( ...@@ -250,7 +250,7 @@ void TanhBenchmark(
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
} }
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -292,8 +292,8 @@ void TanhBenchmark( ...@@ -292,8 +292,8 @@ void TanhBenchmark(
#define BM_TANH(N, C, H, W) \ #define BM_TANH(N, C, H, W) \
BM_TANH_MACRO(N, C, H, W, float, CPU); \ BM_TANH_MACRO(N, C, H, W, float, CPU); \
BM_TANH_MACRO(N, C, H, W, float, OPENCL); \ BM_TANH_MACRO(N, C, H, W, float, GPU); \
BM_TANH_MACRO(N, C, H, W, half, OPENCL); BM_TANH_MACRO(N, C, H, W, half, GPU);
BM_TANH(1, 1, 512, 512); BM_TANH(1, 1, 512, 512);
BM_TANH(1, 3, 128, 128); BM_TANH(1, 3, 128, 128);
...@@ -316,7 +316,7 @@ void SigmoidBenchmark( ...@@ -316,7 +316,7 @@ void SigmoidBenchmark(
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
} }
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -359,8 +359,8 @@ void SigmoidBenchmark( ...@@ -359,8 +359,8 @@ void SigmoidBenchmark(
#define BM_SIGMOID(N, C, H, W) \ #define BM_SIGMOID(N, C, H, W) \
BM_SIGMOID_MACRO(N, C, H, W, float, CPU); \ 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, float, GPU); \
BM_SIGMOID_MACRO(N, C, H, W, half, OPENCL); BM_SIGMOID_MACRO(N, C, H, W, half, GPU);
BM_SIGMOID(1, 1, 512, 512); BM_SIGMOID(1, 1, 512, 512);
BM_SIGMOID(1, 3, 128, 128); BM_SIGMOID(1, 3, 128, 128);
......
mace/ops/activation_test.cc
浏览文件 @ 3ba07e0b
...@@ -31,7 +31,7 @@ void TestSimpleRelu() { ...@@ -31,7 +31,7 @@ void TestSimpleRelu() {
"Input", {2, 2, 2, 2}, "Input", {2, 2, 2, 2},
{-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0}); {-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -68,7 +68,7 @@ void TestSimpleRelu() { ...@@ -68,7 +68,7 @@ void TestSimpleRelu() {
TEST_F(ActivationOpTest, CPUSimpleRelu) { TestSimpleRelu<DeviceType::CPU>(); } TEST_F(ActivationOpTest, CPUSimpleRelu) { TestSimpleRelu<DeviceType::CPU>(); }
TEST_F(ActivationOpTest, OPENCLSimpleRelu) { TEST_F(ActivationOpTest, OPENCLSimpleRelu) {
TestSimpleRelu<DeviceType::OPENCL>(); TestSimpleRelu<DeviceType::GPU>();
} }
namespace { namespace {
...@@ -79,7 +79,7 @@ void TestUnalignedSimpleRelu() { ...@@ -79,7 +79,7 @@ void TestUnalignedSimpleRelu() {
// Add input data // Add input data
net.AddInputFromArray<D, float>("Input", {1, 3, 2, 1}, {-7, 7, -6, 6, -5, 5}); net.AddInputFromArray<D, float>("Input", {1, 3, 2, 1}, {-7, 7, -6, 6, -5, 5});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -117,7 +117,7 @@ TEST_F(ActivationOpTest, CPUUnalignedSimpleRelu) { ...@@ -117,7 +117,7 @@ TEST_F(ActivationOpTest, CPUUnalignedSimpleRelu) {
} }
TEST_F(ActivationOpTest, OPENCLUnalignedSimpleRelu) { TEST_F(ActivationOpTest, OPENCLUnalignedSimpleRelu) {
TestUnalignedSimpleRelu<DeviceType::OPENCL>(); TestUnalignedSimpleRelu<DeviceType::GPU>();
} }
...@@ -131,7 +131,7 @@ void TestSimpleRelux() { ...@@ -131,7 +131,7 @@ void TestSimpleRelux() {
"Input", {2, 2, 2, 2}, "Input", {2, 2, 2, 2},
{-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0}); {-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -170,7 +170,7 @@ void TestSimpleRelux() { ...@@ -170,7 +170,7 @@ void TestSimpleRelux() {
TEST_F(ActivationOpTest, CPUSimple) { TestSimpleRelux<DeviceType::CPU>(); } TEST_F(ActivationOpTest, CPUSimple) { TestSimpleRelux<DeviceType::CPU>(); }
TEST_F(ActivationOpTest, OPENCLSimple) { TEST_F(ActivationOpTest, OPENCLSimple) {
TestSimpleRelux<DeviceType::OPENCL>(); TestSimpleRelux<DeviceType::GPU>();
} }
namespace { namespace {
...@@ -183,7 +183,7 @@ void TestSimpleReluRelux() { ...@@ -183,7 +183,7 @@ void TestSimpleReluRelux() {
"Input", {2, 2, 2, 2}, "Input", {2, 2, 2, 2},
{-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0}); {-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -224,7 +224,7 @@ TEST_F(ActivationOpTest, CPUSimpleRelux) { ...@@ -224,7 +224,7 @@ TEST_F(ActivationOpTest, CPUSimpleRelux) {
} }
TEST_F(ActivationOpTest, OPENCLSimpleRelux) { TEST_F(ActivationOpTest, OPENCLSimpleRelux) {
TestSimpleReluRelux<DeviceType::OPENCL>(); TestSimpleReluRelux<DeviceType::GPU>();
} }
namespace { namespace {
...@@ -238,7 +238,7 @@ void TestSimplePrelu() { ...@@ -238,7 +238,7 @@ void TestSimplePrelu() {
{-7, 7, -6, 6, -5, -5, -4, -4, -3, 3, -2, 2, -1, -1, 0, 0}); {-7, 7, -6, 6, -5, -5, -4, -4, -3, 3, -2, 2, -1, -1, 0, 0});
net.AddInputFromArray<D, float>("Alpha", {2}, {2.0, 3.0}); net.AddInputFromArray<D, float>("Alpha", {2}, {2.0, 3.0});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Alpha", "AlphaImage", BufferToImage<D, float>(&net, "Alpha", "AlphaImage",
...@@ -283,7 +283,7 @@ TEST_F(ActivationOpTest, CPUSimplePrelu) { ...@@ -283,7 +283,7 @@ TEST_F(ActivationOpTest, CPUSimplePrelu) {
} }
TEST_F(ActivationOpTest, OPENCLSimplePrelu) { TEST_F(ActivationOpTest, OPENCLSimplePrelu) {
TestSimplePrelu<DeviceType::OPENCL>(); TestSimplePrelu<DeviceType::GPU>();
} }
namespace { namespace {
...@@ -296,7 +296,7 @@ void TestSimpleTanh() { ...@@ -296,7 +296,7 @@ void TestSimpleTanh() {
"Input", {2, 2, 2, 2}, "Input", {2, 2, 2, 2},
{-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0}); {-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -336,7 +336,7 @@ void TestSimpleTanh() { ...@@ -336,7 +336,7 @@ void TestSimpleTanh() {
TEST_F(ActivationOpTest, CPUSimpleTanh) { TestSimpleTanh<DeviceType::CPU>(); } TEST_F(ActivationOpTest, CPUSimpleTanh) { TestSimpleTanh<DeviceType::CPU>(); }
TEST_F(ActivationOpTest, OPENCLSimpleTanh) { TEST_F(ActivationOpTest, OPENCLSimpleTanh) {
TestSimpleTanh<DeviceType::OPENCL>(); TestSimpleTanh<DeviceType::GPU>();
} }
namespace { namespace {
...@@ -349,7 +349,7 @@ void TestSimpleSigmoid() { ...@@ -349,7 +349,7 @@ void TestSimpleSigmoid() {
"Input", {2, 2, 2, 2}, "Input", {2, 2, 2, 2},
{-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0}); {-7, 7, -6, 6, -5, 5, -4, 4, -3, 3, -2, 2, -1, 1, 0, 0});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -392,7 +392,7 @@ TEST_F(ActivationOpTest, CPUSimpleSigmoid) { ...@@ -392,7 +392,7 @@ TEST_F(ActivationOpTest, CPUSimpleSigmoid) {
} }
TEST_F(ActivationOpTest, OPENCLSimpleSigmoid) { TEST_F(ActivationOpTest, OPENCLSimpleSigmoid) {
TestSimpleSigmoid<DeviceType::OPENCL>(); TestSimpleSigmoid<DeviceType::GPU>();
} }
} // namespace test } // namespace test
......
mace/ops/addn.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_AddN(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_AddN(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("AddN") REGISTER_OPERATOR(op_registry, OpKeyBuilder("AddN")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
AddNOp<DeviceType::OPENCL, float>); AddNOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("AddN") REGISTER_OPERATOR(op_registry, OpKeyBuilder("AddN")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
AddNOp<DeviceType::OPENCL, half>); AddNOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/addn_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -33,7 +33,7 @@ void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) { ...@@ -33,7 +33,7 @@ void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) {
net.AddRandomInput<D, float>(MakeString("Input", i).c_str(), {n, h, w, c}); net.AddRandomInput<D, float>(MakeString("Input", i).c_str(), {n, h, w, c});
} }
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
for (int i = 0; i < inputs; ++i) { for (int i = 0; i < inputs; ++i) {
BufferToImage<D, T>(&net, MakeString("Input", i).c_str(), BufferToImage<D, T>(&net, MakeString("Input", i).c_str(),
MakeString("InputImage", i).c_str(), MakeString("InputImage", i).c_str(),
...@@ -82,8 +82,8 @@ void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) { ...@@ -82,8 +82,8 @@ void AddNBenchmark(int iters, int inputs, int n, int h, int w, int c) {
#define BM_ADDN(INPUTS, N, H, W, C) \ #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, CPU); \
BM_ADDN_MACRO(INPUTS, N, H, W, C, float, OPENCL); \ BM_ADDN_MACRO(INPUTS, N, H, W, C, float, GPU); \
BM_ADDN_MACRO(INPUTS, N, H, W, C, half, OPENCL); BM_ADDN_MACRO(INPUTS, N, H, W, C, half, GPU);
BM_ADDN(2, 1, 256, 256, 32); BM_ADDN(2, 1, 256, 256, 32);
BM_ADDN(2, 1, 128, 128, 32); BM_ADDN(2, 1, 128, 128, 32);
......
mace/ops/addn_test.cc
浏览文件 @ 3ba07e0b
...@@ -64,7 +64,7 @@ void SimpleAdd3() { ...@@ -64,7 +64,7 @@ void SimpleAdd3() {
{-0.1582, 2, 3, 4, 5, 6}); {-0.1582, 2, 3, 4, 5, 6});
const int input_num = 4; const int input_num = 4;
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
// run on gpu // run on gpu
for (int i = 0; i < input_num; ++i) { for (int i = 0; i < input_num; ++i) {
BufferToImage<D, half>(&net, MakeString("Input", i), BufferToImage<D, half>(&net, MakeString("Input", i),
...@@ -105,7 +105,7 @@ void SimpleAdd3() { ...@@ -105,7 +105,7 @@ void SimpleAdd3() {
} // namespace } // namespace
TEST_F(AddnOpTest, CPUSimpleAdd3) { SimpleAdd3<DeviceType::CPU>(); } TEST_F(AddnOpTest, CPUSimpleAdd3) { SimpleAdd3<DeviceType::CPU>(); }
TEST_F(AddnOpTest, GPUSimpleAdd3) { SimpleAdd3<DeviceType::OPENCL>(); } TEST_F(AddnOpTest, GPUSimpleAdd3) { SimpleAdd3<DeviceType::GPU>(); }
namespace { namespace {
template <DeviceType D> template <DeviceType D>
...@@ -166,7 +166,7 @@ void RandomTest() { ...@@ -166,7 +166,7 @@ void RandomTest() {
} }
} // namespace } // namespace
TEST_F(AddnOpTest, OPENCLRandom) { RandomTest<DeviceType::OPENCL>(); } TEST_F(AddnOpTest, OPENCLRandom) { RandomTest<DeviceType::GPU>(); }
} // namespace test } // namespace test
} // namespace ops } // namespace ops
......
mace/ops/batch_norm.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_BatchNorm(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_BatchNorm(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchNorm") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchNorm")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
BatchNormOp<DeviceType::OPENCL, float>); BatchNormOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchNorm") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchNorm")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
BatchNormOp<DeviceType::OPENCL, half>); BatchNormOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/batch_norm_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -32,7 +32,7 @@ void BatchNorm( ...@@ -32,7 +32,7 @@ void BatchNorm(
// Add input data // Add input data
if (D == DeviceType::CPU) { if (D == DeviceType::CPU) {
net.AddRandomInput<D, T>("Input", {batch, channels, height, width}); net.AddRandomInput<D, T>("Input", {batch, channels, height, width});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, T>("Input", {batch, height, width, channels}); net.AddRandomInput<D, T>("Input", {batch, height, width, channels});
} else { } else {
MACE_NOT_IMPLEMENTED; MACE_NOT_IMPLEMENTED;
...@@ -52,7 +52,7 @@ void BatchNorm( ...@@ -52,7 +52,7 @@ void BatchNorm(
.AddFloatArg("epsilon", 1e-3) .AddFloatArg("epsilon", 1e-3)
.Output("Output") .Output("Output")
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Scale", "ScaleImage", BufferToImage<D, float>(&net, "Scale", "ScaleImage",
...@@ -107,8 +107,8 @@ void BatchNorm( ...@@ -107,8 +107,8 @@ void BatchNorm(
#define BM_BATCH_NORM(N, C, H, W) \ #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, CPU); \
BM_BATCH_NORM_MACRO(N, C, H, W, float, OPENCL); \ BM_BATCH_NORM_MACRO(N, C, H, W, float, GPU); \
BM_BATCH_NORM_MACRO(N, C, H, W, half, OPENCL); BM_BATCH_NORM_MACRO(N, C, H, W, half, GPU);
BM_BATCH_NORM(1, 1, 512, 512); BM_BATCH_NORM(1, 1, 512, 512);
BM_BATCH_NORM(1, 3, 128, 128); BM_BATCH_NORM(1, 3, 128, 128);
......
mace/ops/batch_norm_test.cc
浏览文件 @ 3ba07e0b
...@@ -49,7 +49,7 @@ void Simple() { ...@@ -49,7 +49,7 @@ void Simple() {
net.RunOp(D); net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC); net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Scale", "ScaleImage", BufferToImage<D, float>(&net, "Scale", "ScaleImage",
...@@ -90,7 +90,7 @@ void Simple() { ...@@ -90,7 +90,7 @@ void Simple() {
TEST_F(BatchNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); } TEST_F(BatchNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); }
TEST_F(BatchNormOpTest, SimpleOPENCL) { Simple<DeviceType::OPENCL>(); } TEST_F(BatchNormOpTest, SimpleOPENCL) { Simple<DeviceType::GPU>(); }
TEST_F(BatchNormOpTest, SimpleRandomOPENCL) { TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
// generate random input // generate random input
...@@ -103,12 +103,12 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) { ...@@ -103,12 +103,12 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Var", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -139,15 +139,15 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) { ...@@ -139,15 +139,15 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, float>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, float>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, float>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Mean", "MeanImage", BufferToImage<DeviceType::GPU, float>(&net, "Mean", "MeanImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Var", "VarImage", BufferToImage<DeviceType::GPU, float>(&net, "Var", "VarImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("BatchNorm", "BatchNormTest") OpDefBuilder("BatchNorm", "BatchNormTest")
...@@ -162,14 +162,14 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) { ...@@ -162,14 +162,14 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
// Tuning // Tuning
setenv("MACE_TUNING", "1", 1); setenv("MACE_TUNING", "1", 1);
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
unsetenv("MACE_TUNING"); unsetenv("MACE_TUNING");
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4);
} }
...@@ -186,12 +186,12 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) { ...@@ -186,12 +186,12 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Var", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -221,15 +221,15 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) { ...@@ -221,15 +221,15 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, half>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, half>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, half>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, half>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, half>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Mean", "MeanImage", BufferToImage<DeviceType::GPU, half>(&net, "Mean", "MeanImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Var", "VarImage", BufferToImage<DeviceType::GPU, half>(&net, "Var", "VarImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("BatchNorm", "BatchNormTest") OpDefBuilder("BatchNorm", "BatchNormTest")
...@@ -245,14 +245,14 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) { ...@@ -245,14 +245,14 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) {
// Tuning // Tuning
setenv("MACE_TUNING", "1", 1); setenv("MACE_TUNING", "1", 1);
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
unsetenv("MACE_TUNING"); unsetenv("MACE_TUNING");
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-1, 1e-2); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-1, 1e-2);
} }
...@@ -269,12 +269,12 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) { ...@@ -269,12 +269,12 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Var", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -304,15 +304,15 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) { ...@@ -304,15 +304,15 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, float>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, float>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, float>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Mean", "MeanImage", BufferToImage<DeviceType::GPU, float>(&net, "Mean", "MeanImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Var", "VarImage", BufferToImage<DeviceType::GPU, float>(&net, "Var", "VarImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("BatchNorm", "BatchNormTest") OpDefBuilder("BatchNorm", "BatchNormTest")
...@@ -327,14 +327,14 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) { ...@@ -327,14 +327,14 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
// tuning // tuning
setenv("MACE_TUNING", "1", 1); setenv("MACE_TUNING", "1", 1);
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
unsetenv("MACE_TUNING"); unsetenv("MACE_TUNING");
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4);
} }
...@@ -351,12 +351,12 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) { ...@@ -351,12 +351,12 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Var", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -386,15 +386,15 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) { ...@@ -386,15 +386,15 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, half>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, half>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, half>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, half>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, half>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Mean", "MeanImage", BufferToImage<DeviceType::GPU, half>(&net, "Mean", "MeanImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Var", "VarImage", BufferToImage<DeviceType::GPU, half>(&net, "Var", "VarImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("BatchNorm", "BatchNormTest") OpDefBuilder("BatchNorm", "BatchNormTest")
...@@ -410,14 +410,14 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) { ...@@ -410,14 +410,14 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) {
// tuning // tuning
setenv("MACE_TUNING", "1", 1); setenv("MACE_TUNING", "1", 1);
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
unsetenv("MACE_TUNING"); unsetenv("MACE_TUNING");
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-1, 1e-2); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-1, 1e-2);
} }
......
mace/ops/batch_to_space.cc
浏览文件 @ 3ba07e0b
...@@ -20,15 +20,15 @@ namespace ops { ...@@ -20,15 +20,15 @@ namespace ops {
void Register_BatchToSpaceND(OperatorRegistry *op_registry) { void Register_BatchToSpaceND(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchToSpaceND") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchToSpaceND")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
BatchToSpaceNDOp<DeviceType::OPENCL, float>); BatchToSpaceNDOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchToSpaceND") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BatchToSpaceND")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
BatchToSpaceNDOp<DeviceType::OPENCL, half>); BatchToSpaceNDOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/batch_to_space_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -63,7 +63,7 @@ void BMBatchToSpace( ...@@ -63,7 +63,7 @@ void BMBatchToSpace(
BENCHMARK(BM_BATCH_TO_SPACE_##N##_##H##_##W##_##C##_##ARG##_##TYPE##_##DEVICE) BENCHMARK(BM_BATCH_TO_SPACE_##N##_##H##_##W##_##C##_##ARG##_##TYPE##_##DEVICE)
#define BM_BATCH_TO_SPACE(N, H, W, C, ARG) \ #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_MACRO(N, H, W, C, ARG, float, GPU);
BM_BATCH_TO_SPACE(128, 8, 8, 128, 2); BM_BATCH_TO_SPACE(128, 8, 8, 128, 2);
BM_BATCH_TO_SPACE(4, 128, 128, 32, 2); BM_BATCH_TO_SPACE(4, 128, 128, 32, 2);
......
mace/ops/bias_add.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_BiasAdd(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_BiasAdd(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BiasAdd") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BiasAdd")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
BiasAddOp<DeviceType::OPENCL, float>); BiasAddOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BiasAdd") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BiasAdd")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
BiasAddOp<DeviceType::OPENCL, half>); BiasAddOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/bias_add_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -31,7 +31,7 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) { ...@@ -31,7 +31,7 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) {
// Add input data // Add input data
if (D == DeviceType::CPU) { if (D == DeviceType::CPU) {
net.AddRandomInput<D, T>("Input", {batch, channels, height, width}); net.AddRandomInput<D, T>("Input", {batch, channels, height, width});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, T>("Input", {batch, height, width, channels}); net.AddRandomInput<D, T>("Input", {batch, height, width, channels});
} else { } else {
MACE_NOT_IMPLEMENTED; MACE_NOT_IMPLEMENTED;
...@@ -44,7 +44,7 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) { ...@@ -44,7 +44,7 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) {
.Input("Bias") .Input("Bias")
.Output("Output") .Output("Output")
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Bias", "BiasImage", BufferToImage<D, T>(&net, "Bias", "BiasImage",
...@@ -84,8 +84,8 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) { ...@@ -84,8 +84,8 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) {
#define BM_BIAS_ADD(N, C, H, W) \ #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, CPU); \
BM_BIAS_ADD_MACRO(N, C, H, W, float, OPENCL); \ BM_BIAS_ADD_MACRO(N, C, H, W, float, GPU); \
BM_BIAS_ADD_MACRO(N, C, H, W, half, OPENCL); BM_BIAS_ADD_MACRO(N, C, H, W, half, GPU);
BM_BIAS_ADD(1, 1, 512, 512); BM_BIAS_ADD(1, 1, 512, 512);
BM_BIAS_ADD(1, 3, 128, 128); BM_BIAS_ADD(1, 3, 128, 128);
......
mace/ops/bias_add_test.cc
浏览文件 @ 3ba07e0b
...@@ -47,7 +47,7 @@ void BiasAddSimple() { ...@@ -47,7 +47,7 @@ void BiasAddSimple() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Bias", "BiasImage", BufferToImage<D, float>(&net, "Bias", "BiasImage",
...@@ -80,7 +80,7 @@ void BiasAddSimple() { ...@@ -80,7 +80,7 @@ void BiasAddSimple() {
TEST_F(BiasAddOpTest, BiasAddSimpleCPU) { BiasAddSimple<DeviceType::CPU>(); } TEST_F(BiasAddOpTest, BiasAddSimpleCPU) { BiasAddSimple<DeviceType::CPU>(); }
TEST_F(BiasAddOpTest, BiasAddSimpleOPENCL) { TEST_F(BiasAddOpTest, BiasAddSimpleOPENCL) {
BiasAddSimple<DeviceType::OPENCL>(); BiasAddSimple<DeviceType::GPU>();
} }
TEST_F(BiasAddOpTest, SimpleRandomOPENCL) { TEST_F(BiasAddOpTest, SimpleRandomOPENCL) {
...@@ -94,9 +94,9 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) { ...@@ -94,9 +94,9 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Bias", {channels}, true); net.AddRandomInput<DeviceType::GPU, float>("Bias", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -123,9 +123,9 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) { ...@@ -123,9 +123,9 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, float>(&net, "Bias", "BiasImage", BufferToImage<DeviceType::GPU, float>(&net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("BiasAdd", "BiasAddTest") OpDefBuilder("BiasAdd", "BiasAddTest")
...@@ -135,10 +135,10 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) { ...@@ -135,10 +135,10 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5);
} }
...@@ -154,9 +154,9 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) { ...@@ -154,9 +154,9 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Bias", {channels}, true); net.AddRandomInput<DeviceType::GPU, float>("Bias", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -182,9 +182,9 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) { ...@@ -182,9 +182,9 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, float>(&net, "Bias", "BiasImage", BufferToImage<DeviceType::GPU, float>(&net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("BiasAdd", "BiasAddTest") OpDefBuilder("BiasAdd", "BiasAddTest")
...@@ -194,10 +194,10 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) { ...@@ -194,10 +194,10 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5);
} }
......
mace/ops/buffer_to_image.cc
浏览文件 @ 3ba07e0b
...@@ -19,16 +19,16 @@ namespace ops { ...@@ -19,16 +19,16 @@ namespace ops {
void Register_BufferToImage(OperatorRegistry *op_registry) { void Register_BufferToImage(OperatorRegistry *op_registry) {
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BufferToImage") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BufferToImage")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
BufferToImageOp<DeviceType::OPENCL, float>); BufferToImageOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("BufferToImage") REGISTER_OPERATOR(op_registry, OpKeyBuilder("BufferToImage")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
BufferToImageOp<DeviceType::OPENCL, half>); BufferToImageOp<DeviceType::GPU, half>);
} }
} // namespace ops } // namespace ops
......
mace/ops/buffer_to_image_test.cc
浏览文件 @ 3ba07e0b
...@@ -54,73 +54,73 @@ void TestBidirectionTransform(const int type, ...@@ -54,73 +54,73 @@ void TestBidirectionTransform(const int type,
} // namespace } // namespace
TEST(BufferToImageTest, ArgSmall) { TEST(BufferToImageTest, ArgSmall) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::ARGUMENT, {1}); TestBidirectionTransform<DeviceType::GPU, float>(kernels::ARGUMENT, {1});
} }
TEST(BufferToImageTest, ArgHalfSmall) { TEST(BufferToImageTest, ArgHalfSmall) {
TestBidirectionTransform<DeviceType::OPENCL, half>(kernels::ARGUMENT, {11}); TestBidirectionTransform<DeviceType::GPU, half>(kernels::ARGUMENT, {11});
} }
TEST(BufferToImageTest, ArgMedia) { TEST(BufferToImageTest, ArgMedia) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::ARGUMENT, {11}); TestBidirectionTransform<DeviceType::GPU, float>(kernels::ARGUMENT, {11});
} }
TEST(BufferToImageTest, ArgLarge) { TEST(BufferToImageTest, ArgLarge) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::ARGUMENT, {256}); TestBidirectionTransform<DeviceType::GPU, float>(kernels::ARGUMENT, {256});
} }
TEST(BufferToImageTest, InputSmallSingleChannel) { TEST(BufferToImageTest, InputSmallSingleChannel) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::IN_OUT_CHANNEL, TestBidirectionTransform<DeviceType::GPU, float>(kernels::IN_OUT_CHANNEL,
{1, 2, 3, 1}); {1, 2, 3, 1});
} }
TEST(BufferToImageTest, InputSmallMultipleChannel) { TEST(BufferToImageTest, InputSmallMultipleChannel) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::IN_OUT_CHANNEL, TestBidirectionTransform<DeviceType::GPU, float>(kernels::IN_OUT_CHANNEL,
{1, 2, 3, 3}); {1, 2, 3, 3});
} }
TEST(BufferToImageTest, InputSmallMultipleBatchAndChannel) { TEST(BufferToImageTest, InputSmallMultipleBatchAndChannel) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::IN_OUT_CHANNEL, TestBidirectionTransform<DeviceType::GPU, float>(kernels::IN_OUT_CHANNEL,
{3, 2, 3, 3}); {3, 2, 3, 3});
} }
TEST(BufferToImageTest, InputMedia) { TEST(BufferToImageTest, InputMedia) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::IN_OUT_CHANNEL, TestBidirectionTransform<DeviceType::GPU, float>(kernels::IN_OUT_CHANNEL,
{3, 13, 17, 128}); {3, 13, 17, 128});
} }
TEST(BufferToImageTest, InputLarge) { TEST(BufferToImageTest, InputLarge) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::IN_OUT_CHANNEL, TestBidirectionTransform<DeviceType::GPU, float>(kernels::IN_OUT_CHANNEL,
{3, 64, 64, 256}); {3, 64, 64, 256});
} }
TEST(BufferToImageTest, Filter1x1Small) { TEST(BufferToImageTest, Filter1x1Small) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::CONV2D_FILTER, TestBidirectionTransform<DeviceType::GPU, float>(kernels::CONV2D_FILTER,
{1, 1, 3, 5}); {1, 1, 3, 5});
} }
TEST(BufferToImageTest, Filter1x1Media) { TEST(BufferToImageTest, Filter1x1Media) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::CONV2D_FILTER, TestBidirectionTransform<DeviceType::GPU, float>(kernels::CONV2D_FILTER,
{1, 1, 13, 17}); {1, 1, 13, 17});
} }
TEST(BufferToImageTest, Filter1x1Large) { TEST(BufferToImageTest, Filter1x1Large) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::CONV2D_FILTER, TestBidirectionTransform<DeviceType::GPU, float>(kernels::CONV2D_FILTER,
{1, 1, 128, 512}); {1, 1, 128, 512});
} }
TEST(BufferToImageTest, Filter3x3Small) { TEST(BufferToImageTest, Filter3x3Small) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::CONV2D_FILTER, TestBidirectionTransform<DeviceType::GPU, float>(kernels::CONV2D_FILTER,
{3, 3, 3, 5}); {3, 3, 3, 5});
} }
TEST(BufferToImageTest, Filter3x3Meida) { TEST(BufferToImageTest, Filter3x3Meida) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::CONV2D_FILTER, TestBidirectionTransform<DeviceType::GPU, float>(kernels::CONV2D_FILTER,
{3, 3, 13, 17}); {3, 3, 13, 17});
} }
TEST(BufferToImageTest, Filter3x3Large) { TEST(BufferToImageTest, Filter3x3Large) {
TestBidirectionTransform<DeviceType::OPENCL, float>(kernels::CONV2D_FILTER, TestBidirectionTransform<DeviceType::GPU, float>(kernels::CONV2D_FILTER,
{3, 3, 128, 256}); {3, 3, 128, 256});
} }
...@@ -158,7 +158,7 @@ void TestDiffTypeBidirectionTransform(const int type, ...@@ -158,7 +158,7 @@ void TestDiffTypeBidirectionTransform(const int type,
} // namespace } // namespace
TEST(BufferToImageTest, ArgFloatToHalfSmall) { TEST(BufferToImageTest, ArgFloatToHalfSmall) {
TestDiffTypeBidirectionTransform<DeviceType::OPENCL, half>(kernels::ARGUMENT, TestDiffTypeBidirectionTransform<DeviceType::GPU, half>(kernels::ARGUMENT,
{11}); {11});
} }
...@@ -203,7 +203,7 @@ TEST(BufferToImageTest, ArgStringHalfToHalfSmall) { ...@@ -203,7 +203,7 @@ TEST(BufferToImageTest, ArgStringHalfToHalfSmall) {
const unsigned char input_data[] = { const unsigned char input_data[] = {
0xCD, 0x3C, 0x33, 0x40, 0xCD, 0x3C, 0x33, 0x40,
}; };
TestStringHalfBidirectionTransform<DeviceType::OPENCL, half>( TestStringHalfBidirectionTransform<DeviceType::GPU, half>(
kernels::ARGUMENT, {2}, input_data); kernels::ARGUMENT, {2}, input_data);
} }
......
mace/ops/channel_shuffle.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_ChannelShuffle(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_ChannelShuffle(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("ChannelShuffle") REGISTER_OPERATOR(op_registry, OpKeyBuilder("ChannelShuffle")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
ChannelShuffleOp<DeviceType::OPENCL, float>); ChannelShuffleOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("ChannelShuffle") REGISTER_OPERATOR(op_registry, OpKeyBuilder("ChannelShuffle")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
ChannelShuffleOp<DeviceType::OPENCL, half>); ChannelShuffleOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/channel_shuffle.h
浏览文件 @ 3ba07e0b
...@@ -35,7 +35,7 @@ class ChannelShuffleOp : public Operator<D, T> { ...@@ -35,7 +35,7 @@ class ChannelShuffleOp : public Operator<D, T> {
const Tensor *input = this->Input(INPUT); const Tensor *input = this->Input(INPUT);
Tensor *output = this->Output(OUTPUT); Tensor *output = this->Output(OUTPUT);
int channels; int channels;
if (D == OPENCL) { if (D == GPU) {
channels = input->dim(3); channels = input->dim(3);
} else if (D == CPU) { } else if (D == CPU) {
channels = input->dim(1); channels = input->dim(1);
......
mace/ops/channel_shuffle_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -31,7 +31,7 @@ void ChannelShuffle( ...@@ -31,7 +31,7 @@ void ChannelShuffle(
// Add input data // Add input data
if (D == DeviceType::CPU) { if (D == DeviceType::CPU) {
net.AddRandomInput<D, float>("Input", {batch, height, channels, width}); net.AddRandomInput<D, float>("Input", {batch, height, channels, width});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
} else { } else {
MACE_NOT_IMPLEMENTED; MACE_NOT_IMPLEMENTED;
...@@ -42,7 +42,7 @@ void ChannelShuffle( ...@@ -42,7 +42,7 @@ void ChannelShuffle(
.Input("Input") .Input("Input")
.Output("Output") .Output("Output")
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -82,8 +82,8 @@ void ChannelShuffle( ...@@ -82,8 +82,8 @@ void ChannelShuffle(
#define BM_CHANNEL_SHUFFLE(N, C, H, W, G) \ #define BM_CHANNEL_SHUFFLE(N, C, H, W, G) \
BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, float, CPU); \ BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, float, CPU); \
BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, float, OPENCL); \ BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, float, GPU); \
BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, half, OPENCL); BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, half, GPU);
BM_CHANNEL_SHUFFLE(1, 64, 64, 64, 8); BM_CHANNEL_SHUFFLE(1, 64, 64, 64, 8);
BM_CHANNEL_SHUFFLE(1, 64, 128, 128, 8); BM_CHANNEL_SHUFFLE(1, 64, 128, 128, 8);
......
mace/ops/channel_shuffle_test.cc
浏览文件 @ 3ba07e0b
...@@ -60,11 +60,11 @@ TEST_F(ChannelShuffleOpTest, C16G4_OPENCL) { ...@@ -60,11 +60,11 @@ TEST_F(ChannelShuffleOpTest, C16G4_OPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddInputFromArray<DeviceType::OPENCL, float>( net.AddInputFromArray<DeviceType::GPU, float>(
"Input", {1, 1, 2, 16}, "Input", {1, 1, 2, 16},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, {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}); 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31});
BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("ChannelShuffle", "ChannelShuffleTest") OpDefBuilder("ChannelShuffle", "ChannelShuffleTest")
...@@ -74,10 +74,10 @@ TEST_F(ChannelShuffleOpTest, C16G4_OPENCL) { ...@@ -74,10 +74,10 @@ TEST_F(ChannelShuffleOpTest, C16G4_OPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run // Run
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
// Transfer output // Transfer output
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "Output", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "Output",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
// Check // Check
......
mace/ops/concat.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_Concat(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_Concat(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Concat") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Concat")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
ConcatOp<DeviceType::OPENCL, float>); ConcatOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Concat") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Concat")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
ConcatOp<DeviceType::OPENCL, half>); ConcatOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/concat_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -74,12 +74,12 @@ void OpenclConcatHelper(int iters, ...@@ -74,12 +74,12 @@ void OpenclConcatHelper(int iters,
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>("Input0", shape0); net.AddRandomInput<DeviceType::GPU, float>("Input0", shape0);
net.AddRandomInput<DeviceType::OPENCL, float>("Input1", shape1); net.AddRandomInput<DeviceType::GPU, float>("Input1", shape1);
BufferToImage<DeviceType::OPENCL, T>(&net, "Input0", "InputImage0", BufferToImage<DeviceType::GPU, T>(&net, "Input0", "InputImage0",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, T>(&net, "Input1", "InputImage1", BufferToImage<DeviceType::GPU, T>(&net, "Input1", "InputImage1",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("Concat", "ConcatBM") OpDefBuilder("Concat", "ConcatBM")
.Input("InputImage0") .Input("InputImage0")
...@@ -91,7 +91,7 @@ void OpenclConcatHelper(int iters, ...@@ -91,7 +91,7 @@ void OpenclConcatHelper(int iters,
// Warm-up // Warm-up
for (int i = 0; i < 5; ++i) { for (int i = 0; i < 5; ++i) {
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
} }
const int64_t tot = const int64_t tot =
...@@ -101,7 +101,7 @@ void OpenclConcatHelper(int iters, ...@@ -101,7 +101,7 @@ void OpenclConcatHelper(int iters,
testing::BytesProcessed(tot * sizeof(T)); testing::BytesProcessed(tot * sizeof(T));
mace::testing::StartTiming(); mace::testing::StartTiming();
while (iters--) { while (iters--) {
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
} }
} }
} // namespace } // namespace
......
mace/ops/concat_test.cc
浏览文件 @ 3ba07e0b
...@@ -171,9 +171,9 @@ void OpenclRandomTest(const std::vector<std::vector<index_t>> &shapes, ...@@ -171,9 +171,9 @@ void OpenclRandomTest(const std::vector<std::vector<index_t>> &shapes,
concat_axis_size += shapes[i][axis]; concat_axis_size += shapes[i][axis];
GenerateRandomRealTypeData(shapes[i], &inputs[i]); GenerateRandomRealTypeData(shapes[i], &inputs[i]);
input_ptrs[i] = inputs[i].data(); input_ptrs[i] = inputs[i].data();
net.AddInputFromArray<DeviceType::OPENCL, float>(input_name, net.AddInputFromArray<DeviceType::GPU, float>(input_name,
shapes[i], inputs[i]); shapes[i], inputs[i]);
BufferToImage<DeviceType::OPENCL, T>(&net, input_name, image_name, BufferToImage<DeviceType::GPU, T>(&net, input_name, image_name,
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
} }
...@@ -188,9 +188,9 @@ void OpenclRandomTest(const std::vector<std::vector<index_t>> &shapes, ...@@ -188,9 +188,9 @@ void OpenclRandomTest(const std::vector<std::vector<index_t>> &shapes,
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run // Run
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "Output", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "Output",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
// Check // Check
......
mace/ops/conv_2d.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_Conv2D(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_Conv2D(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Conv2D") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Conv2D")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
Conv2dOp<DeviceType::OPENCL, float>); Conv2dOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Conv2D") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Conv2D")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
Conv2dOp<DeviceType::OPENCL, half>); Conv2dOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/conv_2d.h
浏览文件 @ 3ba07e0b
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
#define MACE_OPS_CONV_2D_H_ #define MACE_OPS_CONV_2D_H_
#include <memory> #include <memory>
#include <string>
#include "mace/core/operator.h" #include "mace/core/operator.h"
#include "mace/kernels/conv_2d.h" #include "mace/kernels/conv_2d.h"
...@@ -33,8 +34,10 @@ class Conv2dOp : public ConvPool2dOpBase<D, T> { ...@@ -33,8 +34,10 @@ class Conv2dOp : public ConvPool2dOpBase<D, T> {
this->padding_type_, this->padding_type_,
this->paddings_, this->paddings_,
this->dilations_.data(), this->dilations_.data(),
kernels::ActivationType::NOOP, kernels::StringToActivationType(
0.0f, OperatorBase::GetSingleArgument<std::string>("activation",
"NOOP")),
OperatorBase::GetSingleArgument<float>("max_limit", 0.0f),
static_cast<bool>(OperatorBase::GetSingleArgument<int>( static_cast<bool>(OperatorBase::GetSingleArgument<int>(
"is_filter_transformed", false)), "is_filter_transformed", false)),
ws->GetScratchBuffer(D)) {} ws->GetScratchBuffer(D)) {}
......
mace/ops/conv_2d_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -47,7 +47,7 @@ void Conv2d(int iters, ...@@ -47,7 +47,7 @@ void Conv2d(int iters,
{output_channels, channels, kernel_h, {output_channels, channels, kernel_h,
kernel_w}); kernel_w});
net.AddRandomInput<D, float>("Bias", {output_channels}); net.AddRandomInput<D, float>("Bias", {output_channels});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
net.AddRandomInput<D, float>("Filter", net.AddRandomInput<D, float>("Filter",
{kernel_h, kernel_w, output_channels, {kernel_h, kernel_w, output_channels,
...@@ -68,7 +68,7 @@ void Conv2d(int iters, ...@@ -68,7 +68,7 @@ void Conv2d(int iters,
.AddIntsArg("dilations", {dilation, dilation}) .AddIntsArg("dilations", {dilation, dilation})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value)) .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -138,8 +138,8 @@ void Conv2d(int iters, ...@@ -138,8 +138,8 @@ void Conv2d(int iters,
#define BM_CONV_2D(N, C, H, W, KH, KW, S, D, P, OC) \ #define BM_CONV_2D(N, C, H, W, KH, KW, S, D, P, OC) \
BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, D, P, OC, float, CPU); \ BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, D, P, OC, float, CPU); \
BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, D, P, OC, float, OPENCL); \ BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, D, P, OC, float, GPU); \
BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, D, P, OC, half, OPENCL); BM_CONV_2D_MACRO(N, C, H, W, KH, KW, S, D, P, OC, half, GPU);
......
mace/ops/conv_2d_test.cc
浏览文件 @ 3ba07e0b
...@@ -63,7 +63,7 @@ void TestNHWCSimple3x3VALID() { ...@@ -63,7 +63,7 @@ void TestNHWCSimple3x3VALID() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -134,7 +134,7 @@ void TestNHWCSimple3x3SAME() { ...@@ -134,7 +134,7 @@ void TestNHWCSimple3x3SAME() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -176,8 +176,8 @@ TEST_F(Conv2dOpTest, CPUSimple) { ...@@ -176,8 +176,8 @@ TEST_F(Conv2dOpTest, CPUSimple) {
} }
TEST_F(Conv2dOpTest, OPENCLSimple) { TEST_F(Conv2dOpTest, OPENCLSimple) {
TestNHWCSimple3x3VALID<DeviceType::OPENCL, float>(); TestNHWCSimple3x3VALID<DeviceType::GPU, float>();
TestNHWCSimple3x3SAME<DeviceType::OPENCL, float>(); TestNHWCSimple3x3SAME<DeviceType::GPU, float>();
} }
namespace { namespace {
...@@ -219,7 +219,7 @@ void TestNHWCSimple3x3WithoutBias() { ...@@ -219,7 +219,7 @@ void TestNHWCSimple3x3WithoutBias() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -255,7 +255,7 @@ TEST_F(Conv2dOpTest, CPUWithoutBias) { ...@@ -255,7 +255,7 @@ TEST_F(Conv2dOpTest, CPUWithoutBias) {
} }
TEST_F(Conv2dOpTest, OPENCLWithoutBias) { TEST_F(Conv2dOpTest, OPENCLWithoutBias) {
TestNHWCSimple3x3WithoutBias<DeviceType::OPENCL, float>(); TestNHWCSimple3x3WithoutBias<DeviceType::GPU, float>();
} }
namespace { namespace {
...@@ -301,7 +301,7 @@ void TestNHWCCombined3x3() { ...@@ -301,7 +301,7 @@ void TestNHWCCombined3x3() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -341,9 +341,164 @@ TEST_F(Conv2dOpTest, CPUStride2) { ...@@ -341,9 +341,164 @@ TEST_F(Conv2dOpTest, CPUStride2) {
} }
TEST_F(Conv2dOpTest, OPENCLStride2) { TEST_F(Conv2dOpTest, OPENCLStride2) {
TestNHWCCombined3x3<DeviceType::OPENCL, float>(); TestNHWCCombined3x3<DeviceType::GPU, float>();
} }
namespace {
template<DeviceType D, typename T>
void TestFusedNHWCSimple3x3VALID() {
OpsTestNet net;
// Add input data
net.AddInputFromArray<D, float>(
"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, float>(
"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, float>("Bias", {1}, {-0.1f});
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC,
"InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Filter",
HWOI,
"FilterOIHW",
OIHW);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
.Input("FilterOIHW")
.Input("Bias")
.Output("OutputNCHW")
.AddIntsArg("strides", {1, 1})
.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);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW",
NCHW,
"Output",
NHWC);
} else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage",
kernels::BufferType::CONV2D_FILTER);
BufferToImage<D, T>(&net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT);
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("InputImage")
.Input("FilterImage")
.Input("BiasImage")
.Output("OutputImage")
.AddIntsArg("strides", {1, 1})
.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);
// Transfer output
ImageToBuffer<D, float>(&net, "OutputImage", "Output",
kernels::BufferType::IN_OUT_CHANNEL);
} else {
MACE_NOT_IMPLEMENTED;
}
auto expected = CreateTensor<float>({1, 1, 1, 1}, {0.0f});
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"));
}
template<DeviceType D, typename T>
void TestFusedNHWCSimple3x3WithoutBias() {
OpsTestNet net;
// Add input data
net.AddInputFromArray<D, float>(
"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, float>(
"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});
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC,
"InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Filter",
HWOI,
"FilterOIHW",
OIHW);
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("InputNCHW")
.Input("FilterOIHW")
.Output("OutputNCHW")
.AddIntsArg("strides", {1, 1})
.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);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW",
NCHW,
"Output",
NHWC);
} else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage",
kernels::BufferType::CONV2D_FILTER);
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("InputImage")
.Input("FilterImage")
.Output("OutputImage")
.AddIntsArg("strides", {1, 1})
.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);
// Transfer output
ImageToBuffer<D, float>(&net, "OutputImage", "Output",
kernels::BufferType::IN_OUT_CHANNEL);
} else {
MACE_NOT_IMPLEMENTED;
}
// Check
auto expected = CreateTensor<float>({1, 1, 1, 1}, {0.0f});
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"));
}
} // namespace
TEST_F(Conv2dOpTest, FusedCPUSimple) {
TestFusedNHWCSimple3x3VALID<DeviceType::CPU, float>();
TestFusedNHWCSimple3x3WithoutBias<DeviceType::CPU, float>();
}
TEST_F(Conv2dOpTest, FusedOPENCLSimple) {
TestFusedNHWCSimple3x3VALID<DeviceType::GPU, float>();
TestFusedNHWCSimple3x3WithoutBias<DeviceType::GPU, float>();
}
namespace { namespace {
template<DeviceType D> template<DeviceType D>
void TestConv1x1() { void TestConv1x1() {
...@@ -389,7 +544,7 @@ void TestConv1x1() { ...@@ -389,7 +544,7 @@ void TestConv1x1() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Filter", "FilterImage", BufferToImage<D, float>(&net, "Filter", "FilterImage",
...@@ -431,7 +586,7 @@ void TestConv1x1() { ...@@ -431,7 +586,7 @@ void TestConv1x1() {
TEST_F(Conv2dOpTest, CPUConv1x1) { TestConv1x1<DeviceType::CPU>(); } TEST_F(Conv2dOpTest, CPUConv1x1) { TestConv1x1<DeviceType::CPU>(); }
TEST_F(Conv2dOpTest, OPENCLConv1x1) { TestConv1x1<DeviceType::OPENCL>(); } TEST_F(Conv2dOpTest, OPENCLConv1x1) { TestConv1x1<DeviceType::GPU>(); }
namespace { namespace {
template<DeviceType D, typename T> template<DeviceType D, typename T>
...@@ -524,18 +679,18 @@ void TestComplexConvNxNS12(const std::vector<index_t> &shape, ...@@ -524,18 +679,18 @@ void TestComplexConvNxNS12(const std::vector<index_t> &shape,
} // namespace } // namespace
TEST_F(Conv2dOpTest, OPENCLAlignedConvNxNS12) { TEST_F(Conv2dOpTest, OPENCLAlignedConvNxNS12) {
TestComplexConvNxNS12<DeviceType::OPENCL, float>({32, 16, 16, 32}, 1); TestComplexConvNxNS12<DeviceType::GPU, float>({32, 16, 16, 32}, 1);
TestComplexConvNxNS12<DeviceType::OPENCL, float>({32, 16, 16, 32}, 2); TestComplexConvNxNS12<DeviceType::GPU, float>({32, 16, 16, 32}, 2);
} }
TEST_F(Conv2dOpTest, OPENCLUnalignedConvNxNS12) { TEST_F(Conv2dOpTest, OPENCLUnalignedConvNxNS12) {
TestComplexConvNxNS12<DeviceType::OPENCL, float>({17, 113, 5, 7}, 1); TestComplexConvNxNS12<DeviceType::GPU, float>({17, 113, 5, 7}, 1);
TestComplexConvNxNS12<DeviceType::OPENCL, float>({17, 113, 5, 7}, 2); TestComplexConvNxNS12<DeviceType::GPU, float>({17, 113, 5, 7}, 2);
} }
TEST_F(Conv2dOpTest, OPENCLUnalignedConvNxNS34) { TEST_F(Conv2dOpTest, OPENCLUnalignedConvNxNS34) {
TestComplexConvNxNS12<DeviceType::OPENCL, float>({31, 113, 13, 17}, 3); TestComplexConvNxNS12<DeviceType::GPU, float>({31, 113, 13, 17}, 3);
TestComplexConvNxNS12<DeviceType::OPENCL, float>({32, 32, 13, 17}, 4); TestComplexConvNxNS12<DeviceType::GPU, float>({32, 32, 13, 17}, 4);
} }
namespace { namespace {
...@@ -644,52 +799,52 @@ void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape, ...@@ -644,52 +799,52 @@ void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
} // namespace } // namespace
TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv1x1S12) { TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv1x1S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32}, {1, 1, 32, 64}, TestHalfComplexConvNxNS12<DeviceType::GPU>({32, 32}, {1, 1, 32, 64},
{1, 1}); {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv3x3S12) { TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv3x3S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32}, {3, 3, 32, 64}, TestHalfComplexConvNxNS12<DeviceType::GPU>({32, 32}, {3, 3, 32, 64},
{1, 1}); {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv15x1S12) { TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv15x1S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32}, {15, 1, 256, 2}, TestHalfComplexConvNxNS12<DeviceType::GPU>({32, 32}, {15, 1, 256, 2},
{1, 1}); {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv1x15S12) { TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv1x15S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32}, {1, 15, 256, 2}, TestHalfComplexConvNxNS12<DeviceType::GPU>({32, 32}, {1, 15, 256, 2},
{1, 1}); {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv7x75S12) { TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv7x75S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32}, {7, 7, 3, 64}, TestHalfComplexConvNxNS12<DeviceType::GPU>({32, 32}, {7, 7, 3, 64},
{1, 1}); {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLHalfUnalignedConv1x1S12) { TEST_F(Conv2dOpTest, OPENCLHalfUnalignedConv1x1S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({107, 113}, {1, 1, 5, 7}, TestHalfComplexConvNxNS12<DeviceType::GPU>({107, 113}, {1, 1, 5, 7},
{1, 1}); {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLHalfUnalignedConv3x3S12) { TEST_F(Conv2dOpTest, OPENCLHalfUnalignedConv3x3S12) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({107, 113}, {3, 3, 5, 7}, TestHalfComplexConvNxNS12<DeviceType::GPU>({107, 113}, {3, 3, 5, 7},
{1, 1}); {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLHalfConv5x5Dilation2) { TEST_F(Conv2dOpTest, OPENCLHalfConv5x5Dilation2) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({64, 64}, {5, 5, 16, 16}, TestHalfComplexConvNxNS12<DeviceType::GPU>({64, 64}, {5, 5, 16, 16},
{2, 2}); {2, 2});
} }
TEST_F(Conv2dOpTest, OPENCLHalfConv7x7Dilation2) { TEST_F(Conv2dOpTest, OPENCLHalfConv7x7Dilation2) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({64, 64}, {7, 7, 16, 16}, TestHalfComplexConvNxNS12<DeviceType::GPU>({64, 64}, {7, 7, 16, 16},
{2, 2}); {2, 2});
} }
TEST_F(Conv2dOpTest, OPENCLHalfConv7x7Dilation4) { TEST_F(Conv2dOpTest, OPENCLHalfConv7x7Dilation4) {
TestHalfComplexConvNxNS12<DeviceType::OPENCL>({63, 67}, {7, 7, 16, 16}, TestHalfComplexConvNxNS12<DeviceType::GPU>({63, 67}, {7, 7, 16, 16},
{4, 4}); {4, 4});
} }
...@@ -787,15 +942,115 @@ void TestDilationConvNxN(const std::vector<index_t> &shape, ...@@ -787,15 +942,115 @@ void TestDilationConvNxN(const std::vector<index_t> &shape,
} // namespace } // namespace
TEST_F(Conv2dOpTest, OPENCLAlignedDilation2) { TEST_F(Conv2dOpTest, OPENCLAlignedDilation2) {
TestDilationConvNxN<DeviceType::OPENCL, float>({32, 32, 32, 64}, 2); TestDilationConvNxN<DeviceType::GPU, float>({32, 32, 32, 64}, 2);
} }
TEST_F(Conv2dOpTest, OPENCLAligned2Dilation4) { TEST_F(Conv2dOpTest, OPENCLAligned2Dilation4) {
TestDilationConvNxN<DeviceType::OPENCL, float>({128, 128, 16, 16}, 4); TestDilationConvNxN<DeviceType::GPU, float>({128, 128, 16, 16}, 4);
} }
TEST_F(Conv2dOpTest, OPENCLUnalignedDilation4) { TEST_F(Conv2dOpTest, OPENCLUnalignedDilation4) {
TestDilationConvNxN<DeviceType::OPENCL, float>({107, 113, 5, 7}, 4); TestDilationConvNxN<DeviceType::GPU, float>({107, 113, 5, 7}, 4);
}
namespace {
template<DeviceType D>
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));
// generate random input
index_t batch = 1;
index_t height = image_shape[0];
index_t width = image_shape[1];
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];
OpsTestNet net;
// Add input data
net.AddRandomInput<D, float>("Input",
{batch, height, width, input_channels});
net.AddRandomInput<D, float>(
"Filter", {kernel_h, kernel_w, output_channels, input_channels});
net.AddRandomInput<D, float>("Bias", {output_channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC,
"InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Filter",
HWOI,
"FilterOIHW",
OIHW);
// Construct graph
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
.Input("FilterOIHW")
.Input("Bias")
.Output("OutputNCHW")
.AddIntsArg("strides", {stride_h, stride_w})
.AddIntArg("padding", type)
.AddIntsArg("dilations", dilations)
.Finalize(net.NewOperatorDef());
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW",
NCHW,
"Output",
NHWC);
// Check
Tensor expected;
expected.Copy(*net.GetOutput("Output"));
// run on gpu
BufferToImage<D, half>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, half>(&net, "Filter", "FilterImage",
kernels::BufferType::CONV2D_FILTER);
BufferToImage<D, half>(&net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputImage")
.Input("FilterImage")
.Input("BiasImage")
.Output("OutputImage")
.AddIntsArg("strides", {stride_h, stride_w})
.AddIntArg("padding", type)
.AddIntsArg("dilations", dilations)
.AddIntArg("T", static_cast<int>(DataTypeToEnum<half>::value))
.Finalize(net.NewOperatorDef());
// Run on device
net.RunOp(D);
ImageToBuffer<D, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"),
1e-2, 1e-1);
};
func(1, 1, VALID);
func(1, 1, SAME);
}
} // namespace
TEST_F(Conv2dOpTest, OPENCLHalf7X7AtrousConvD2) {
TestGeneralHalfAtrousConv<DeviceType::GPU>({32, 32}, {7, 7, 16, 3},
{2, 2});
}
TEST_F(Conv2dOpTest, OPENCLHalf15X15AtrousConvD4) {
TestGeneralHalfAtrousConv<DeviceType::GPU>({63, 71}, {15, 15, 16, 16},
{2, 2});
} }
namespace { namespace {
...@@ -887,16 +1142,16 @@ void TestArbitraryPadConvNxN(const std::vector<index_t> &shape, ...@@ -887,16 +1142,16 @@ void TestArbitraryPadConvNxN(const std::vector<index_t> &shape,
} // namespace } // namespace
TEST_F(Conv2dOpTest, OPENCLAlignedPad1) { TEST_F(Conv2dOpTest, OPENCLAlignedPad1) {
TestArbitraryPadConvNxN<DeviceType::OPENCL, float>({32, 32, 32, 64}, {1, 1}); TestArbitraryPadConvNxN<DeviceType::GPU, float>({32, 32, 32, 64}, {1, 1});
} }
TEST_F(Conv2dOpTest, OPENCLAlignedPad2) { TEST_F(Conv2dOpTest, OPENCLAlignedPad2) {
TestArbitraryPadConvNxN<DeviceType::OPENCL, float>({128, 128, 16, 16}, TestArbitraryPadConvNxN<DeviceType::GPU, float>({128, 128, 16, 16},
{2, 2}); {2, 2});
} }
TEST_F(Conv2dOpTest, OPENCLUnalignedPad4) { TEST_F(Conv2dOpTest, OPENCLUnalignedPad4) {
TestArbitraryPadConvNxN<DeviceType::OPENCL, float>({107, 113, 5, 7}, {4, 4}); TestArbitraryPadConvNxN<DeviceType::GPU, float>({107, 113, 5, 7}, {4, 4});
} }
} // namespace test } // namespace test
......
mace/ops/core_test.cc
浏览文件 @ 3ba07e0b
...@@ -32,7 +32,7 @@ TEST(CoreTest, INIT_MODE) { ...@@ -32,7 +32,7 @@ TEST(CoreTest, INIT_MODE) {
.Finalize(&op_defs[op_defs.size() - 1]); .Finalize(&op_defs[op_defs.size() - 1]);
Tensor *input = Tensor *input =
ws.CreateTensor("Input", GetDeviceAllocator(DeviceType::OPENCL), ws.CreateTensor("Input", GetDeviceAllocator(DeviceType::GPU),
DataTypeToEnum<float>::v()); DataTypeToEnum<float>::v());
input->Resize({1, 3, 3, 3}); input->Resize({1, 3, 3, 3});
{ {
...@@ -54,13 +54,13 @@ TEST(CoreTest, INIT_MODE) { ...@@ -54,13 +54,13 @@ TEST(CoreTest, INIT_MODE) {
} }
std::shared_ptr<OperatorRegistry> op_registry(new OperatorRegistry()); std::shared_ptr<OperatorRegistry> op_registry(new OperatorRegistry());
auto net = auto net =
CreateNet(op_registry, net_def, &ws, DeviceType::OPENCL, NetMode::INIT); CreateNet(op_registry, net_def, &ws, DeviceType::GPU, NetMode::INIT);
net->Run(); net->Run();
EXPECT_TRUE(ws.GetTensor("B2IOutput") != nullptr); EXPECT_TRUE(ws.GetTensor("B2IOutput") != nullptr);
EXPECT_TRUE(ws.GetTensor("Output") == nullptr); EXPECT_TRUE(ws.GetTensor("Output") == nullptr);
net = CreateNet(op_registry, net_def, &ws, DeviceType::OPENCL); net = CreateNet(op_registry, net_def, &ws, DeviceType::GPU);
net->Run(); net->Run();
EXPECT_TRUE(ws.GetTensor("Output") != nullptr); EXPECT_TRUE(ws.GetTensor("Output") != nullptr);
......
mace/ops/depth_to_space.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_DepthToSpace(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_DepthToSpace(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthToSpace") REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthToSpace")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
DepthToSpaceOp<DeviceType::OPENCL, float>); DepthToSpaceOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthToSpace") REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthToSpace")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
DepthToSpaceOp<DeviceType::OPENCL, half>); DepthToSpaceOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/depth_to_space.h
浏览文件 @ 3ba07e0b
...@@ -40,7 +40,7 @@ class DepthToSpaceOp : public Operator<D, T> { ...@@ -40,7 +40,7 @@ class DepthToSpaceOp : public Operator<D, T> {
int input_depth; int input_depth;
if (D == CPU) { if (D == CPU) {
input_depth = input->dim(1); input_depth = input->dim(1);
} else if (D == OPENCL) { } else if (D == GPU) {
input_depth = input->dim(3); input_depth = input->dim(3);
} else { } else {
MACE_NOT_IMPLEMENTED; MACE_NOT_IMPLEMENTED;
......
mace/ops/depth_to_space_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -31,7 +31,7 @@ void DepthToSpace( ...@@ -31,7 +31,7 @@ void DepthToSpace(
// Add input data // Add input data
if (D == DeviceType::CPU) { if (D == DeviceType::CPU) {
net.AddRandomInput<D, float>("Input", {batch, channels, height, width}); net.AddRandomInput<D, float>("Input", {batch, channels, height, width});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, float>("Input", {batch, height, width, channels}); net.AddRandomInput<D, float>("Input", {batch, height, width, channels});
} else { } else {
MACE_NOT_IMPLEMENTED; MACE_NOT_IMPLEMENTED;
...@@ -42,7 +42,7 @@ void DepthToSpace( ...@@ -42,7 +42,7 @@ void DepthToSpace(
.Input("Input") .Input("Input")
.Output("Output") .Output("Output")
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
...@@ -82,8 +82,8 @@ void DepthToSpace( ...@@ -82,8 +82,8 @@ void DepthToSpace(
#define BM_DEPTH_TO_SPACE(N, C, H, W, G) \ #define BM_DEPTH_TO_SPACE(N, C, H, W, G) \
BM_DEPTH_TO_SPACE_MACRO(N, C, H, W, G, float, CPU); \ BM_DEPTH_TO_SPACE_MACRO(N, C, H, W, G, float, CPU); \
BM_DEPTH_TO_SPACE_MACRO(N, C, H, W, G, float, OPENCL); \ BM_DEPTH_TO_SPACE_MACRO(N, C, H, W, G, float, GPU); \
BM_DEPTH_TO_SPACE_MACRO(N, C, H, W, G, half, OPENCL); BM_DEPTH_TO_SPACE_MACRO(N, C, H, W, G, half, GPU);
BM_DEPTH_TO_SPACE(1, 64, 64, 64, 4); BM_DEPTH_TO_SPACE(1, 64, 64, 64, 4);
BM_DEPTH_TO_SPACE(1, 64, 128, 128, 4); BM_DEPTH_TO_SPACE(1, 64, 128, 128, 4);
......
mace/ops/depth_to_space_test.cc
浏览文件 @ 3ba07e0b
...@@ -65,8 +65,8 @@ void RunDepthToSpace(const bool d2s, ...@@ -65,8 +65,8 @@ void RunDepthToSpace(const bool d2s,
} }
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "Output", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "Output",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
} }
auto expected = CreateTensor<float>(expected_shape, expected_data); auto expected = CreateTensor<float>(expected_shape, expected_data);
...@@ -88,7 +88,7 @@ TEST_F(SpaceToDepthOpTest, Input2x4x4_B2_CPU) { ...@@ -88,7 +88,7 @@ TEST_F(SpaceToDepthOpTest, Input2x4x4_B2_CPU) {
} }
TEST_F(SpaceToDepthOpTest, Input2x4x4_B2_OPENCL) { TEST_F(SpaceToDepthOpTest, Input2x4x4_B2_OPENCL) {
RunDepthToSpace<DeviceType::OPENCL>(false, {1, 2, 4, 4}, RunDepthToSpace<DeviceType::GPU>(false, {1, 2, 4, 4},
{0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23, {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}, 8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31},
2, 2,
...@@ -110,7 +110,7 @@ TEST_F(SpaceToDepthOpTest, Input2x2x4_B2_CPU) { ...@@ -110,7 +110,7 @@ TEST_F(SpaceToDepthOpTest, Input2x2x4_B2_CPU) {
} }
TEST_F(SpaceToDepthOpTest, Input4x4x1_B2_OPENCL) { TEST_F(SpaceToDepthOpTest, Input4x4x1_B2_OPENCL) {
RunDepthToSpace<DeviceType::OPENCL>(false, {1, 2, 2, 4}, RunDepthToSpace<DeviceType::GPU>(false, {1, 2, 2, 4},
{1, 2, 3, 4, 5, 6, 7, 8, {1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16}, 9, 10, 11, 12, 13, 14, 15, 16},
2, 2,
...@@ -132,7 +132,7 @@ TEST_F(DepthToSpaceOpTest, Input1x2x16_B2_CPU) { ...@@ -132,7 +132,7 @@ TEST_F(DepthToSpaceOpTest, Input1x2x16_B2_CPU) {
} }
TEST_F(DepthToSpaceOpTest, Input1x2x16_B2_OPENCL) { TEST_F(DepthToSpaceOpTest, Input1x2x16_B2_OPENCL) {
RunDepthToSpace<DeviceType::OPENCL>(true, {1, 1, 2, 16}, RunDepthToSpace<DeviceType::GPU>(true, {1, 1, 2, 16},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, {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}, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31},
2, 2,
...@@ -152,7 +152,7 @@ TEST_F(DepthToSpaceOpTest, Input1x1x16_B2_CPU) { ...@@ -152,7 +152,7 @@ TEST_F(DepthToSpaceOpTest, Input1x1x16_B2_CPU) {
} }
TEST_F(DepthToSpaceOpTest, Input1x1x16_B2_OPENCL) { TEST_F(DepthToSpaceOpTest, Input1x1x16_B2_OPENCL) {
RunDepthToSpace<DeviceType::OPENCL>(true, {1, 1, 1, 16}, RunDepthToSpace<DeviceType::GPU>(true, {1, 1, 1, 16},
{1, 2, 3, 4, 5, 6, 7, 8, {1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16}, 9, 10, 11, 12, 13, 14, 15, 16},
2, 2,
...@@ -165,7 +165,7 @@ TEST_F(DepthToSpaceOpTest, Input1x1x16_B2_OPENCL) { ...@@ -165,7 +165,7 @@ TEST_F(DepthToSpaceOpTest, Input1x1x16_B2_OPENCL) {
TEST_F(DepthToSpaceOpTest, InputLarger_B2_OPENCL) { TEST_F(DepthToSpaceOpTest, InputLarger_B2_OPENCL) {
const std::vector<float > in = std::vector<float >(192 * 192 *128, 1.0); const std::vector<float > in = std::vector<float >(192 * 192 *128, 1.0);
RunDepthToSpace<DeviceType::OPENCL>(true, {1, 192, 192, 128}, RunDepthToSpace<DeviceType::GPU>(true, {1, 192, 192, 128},
in, in,
2, 2,
{1, 384, 384, 32}, {1, 384, 384, 32},
...@@ -234,19 +234,19 @@ void RandomTest(const bool d2s, const int block_size, ...@@ -234,19 +234,19 @@ void RandomTest(const bool d2s, const int block_size,
} // namespace } // namespace
TEST_F(DepthToSpaceOpTest, OPENCLRandomFloat) { TEST_F(DepthToSpaceOpTest, OPENCLRandomFloat) {
RandomTest<DeviceType::OPENCL, float>(true, 2, {1, 192, 192, 128}); RandomTest<DeviceType::GPU, float>(true, 2, {1, 192, 192, 128});
} }
TEST_F(DepthToSpaceOpTest, OPENCLRandomHalf) { TEST_F(DepthToSpaceOpTest, OPENCLRandomHalf) {
RandomTest<DeviceType::OPENCL, half>(true, 2, {1, 192, 192, 128}); RandomTest<DeviceType::GPU, half>(true, 2, {1, 192, 192, 128});
} }
TEST_F(SpaceToDepthOpTest, OPENCLRandomFloat) { TEST_F(SpaceToDepthOpTest, OPENCLRandomFloat) {
RandomTest<DeviceType::OPENCL, float>(false, 2, {1, 384, 384, 32}); RandomTest<DeviceType::GPU, float>(false, 2, {1, 384, 384, 32});
} }
TEST_F(SpaceToDepthOpTest, OPENCLRandomHalf) { TEST_F(SpaceToDepthOpTest, OPENCLRandomHalf) {
RandomTest<DeviceType::OPENCL, half>(false, 2, {1, 384, 384, 32}); RandomTest<DeviceType::GPU, half>(false, 2, {1, 384, 384, 32});
} }
} // namespace test } // namespace test
......
mace/ops/depthwise_conv2d.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_DepthwiseConv2d(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_DepthwiseConv2d(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d") REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
DepthwiseConv2dOp<DeviceType::OPENCL, float>); DepthwiseConv2dOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d") REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
DepthwiseConv2dOp<DeviceType::OPENCL, half>); DepthwiseConv2dOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/depthwise_conv2d_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -46,7 +46,7 @@ void DepthwiseConv2d(int iters, ...@@ -46,7 +46,7 @@ void DepthwiseConv2d(int iters,
net.AddRandomInput<D, float>( net.AddRandomInput<D, float>(
"Filter", {multiplier, input_channels, kernel_h, kernel_w}); "Filter", {multiplier, input_channels, kernel_h, kernel_w});
net.AddRandomInput<D, float>("Bias", {input_channels * multiplier}); net.AddRandomInput<D, float>("Bias", {input_channels * multiplier});
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
net.AddRandomInput<D, float>("Input", net.AddRandomInput<D, float>("Input",
{batch, height, width, input_channels}); {batch, height, width, input_channels});
net.AddRandomInput<D, float>( net.AddRandomInput<D, float>(
...@@ -67,7 +67,7 @@ void DepthwiseConv2d(int iters, ...@@ -67,7 +67,7 @@ void DepthwiseConv2d(int iters,
.AddIntsArg("dilations", {1, 1}) .AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value)) .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -134,8 +134,8 @@ void DepthwiseConv2d(int iters, ...@@ -134,8 +134,8 @@ void DepthwiseConv2d(int iters,
#define BM_DEPTHWISE_CONV_2D(N, C, H, W, KH, KW, S, P, M) \ #define BM_DEPTHWISE_CONV_2D(N, C, H, W, KH, KW, S, P, M) \
BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, float, CPU); \ BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, float, CPU); \
BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, float, OPENCL); \ BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, float, GPU); \
BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, half, OPENCL); BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, half, GPU);
BM_DEPTHWISE_CONV_2D(1, 32, 112, 112, 3, 3, 1, SAME, 1); BM_DEPTHWISE_CONV_2D(1, 32, 112, 112, 3, 3, 1, SAME, 1);
BM_DEPTHWISE_CONV_2D(1, 32, 56, 56, 3, 3, 2, VALID, 1); BM_DEPTHWISE_CONV_2D(1, 32, 56, 56, 3, 3, 2, VALID, 1);
......
mace/ops/depthwise_conv2d_test.cc
浏览文件 @ 3ba07e0b
...@@ -59,7 +59,7 @@ void SimpleValidTest() { ...@@ -59,7 +59,7 @@ void SimpleValidTest() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -105,11 +105,11 @@ TEST_F(DepthwiseConv2dOpTest, SimpleCPU) { ...@@ -105,11 +105,11 @@ TEST_F(DepthwiseConv2dOpTest, SimpleCPU) {
} }
TEST_F(DepthwiseConv2dOpTest, SimpleOpenCL) { TEST_F(DepthwiseConv2dOpTest, SimpleOpenCL) {
SimpleValidTest<DeviceType::OPENCL, float>(); SimpleValidTest<DeviceType::GPU, float>();
} }
TEST_F(DepthwiseConv2dOpTest, SimpleOpenCLHalf) { TEST_F(DepthwiseConv2dOpTest, SimpleOpenCLHalf) {
SimpleValidTest<DeviceType::OPENCL, half>(); SimpleValidTest<DeviceType::GPU, half>();
} }
namespace { namespace {
...@@ -184,7 +184,7 @@ void ComplexValidTest() { ...@@ -184,7 +184,7 @@ void ComplexValidTest() {
NCHW, NCHW,
"Output", "Output",
NHWC); NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input", "InputImage", BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Filter", "FilterImage", BufferToImage<D, T>(&net, "Filter", "FilterImage",
...@@ -245,11 +245,11 @@ TEST_F(DepthwiseConv2dOpTest, ComplexCPU) { ...@@ -245,11 +245,11 @@ TEST_F(DepthwiseConv2dOpTest, ComplexCPU) {
} }
TEST_F(DepthwiseConv2dOpTest, ComplexOpenCL) { TEST_F(DepthwiseConv2dOpTest, ComplexOpenCL) {
ComplexValidTest<DeviceType::OPENCL, float>(); ComplexValidTest<DeviceType::GPU, float>();
} }
TEST_F(DepthwiseConv2dOpTest, ComplexOpenCLHalf) { TEST_F(DepthwiseConv2dOpTest, ComplexOpenCLHalf) {
ComplexValidTest<DeviceType::OPENCL, half>(); ComplexValidTest<DeviceType::GPU, half>();
} }
namespace { namespace {
...@@ -267,12 +267,12 @@ void TestNxNS12(const index_t height, const index_t width) { ...@@ -267,12 +267,12 @@ void TestNxNS12(const index_t height, const index_t width) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>("Input", net.AddRandomInput<DeviceType::GPU, float>("Input",
{batch, height, width, {batch, height, width,
input_channels}); input_channels});
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Filter", {kernel_h, kernel_w, input_channels, multiplier}); "Filter", {kernel_h, kernel_w, input_channels, multiplier});
net.AddRandomInput<DeviceType::OPENCL, float>("Bias", net.AddRandomInput<DeviceType::GPU, float>("Bias",
{multiplier {multiplier
* input_channels}); * input_channels});
...@@ -307,11 +307,11 @@ void TestNxNS12(const index_t height, const index_t width) { ...@@ -307,11 +307,11 @@ void TestNxNS12(const index_t height, const index_t width) {
Tensor expected; Tensor expected;
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
BufferToImage<DeviceType::OPENCL, T>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, T>(&net, "Filter", "FilterImage", BufferToImage<DeviceType::GPU, T>(&net, "Filter", "FilterImage",
kernels::BufferType::DW_CONV2D_FILTER); kernels::BufferType::DW_CONV2D_FILTER);
BufferToImage<DeviceType::OPENCL, T>(&net, "Bias", "BiasImage", BufferToImage<DeviceType::GPU, T>(&net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest") OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("InputImage") .Input("InputImage")
...@@ -324,10 +324,10 @@ void TestNxNS12(const index_t height, const index_t width) { ...@@ -324,10 +324,10 @@ void TestNxNS12(const index_t height, const index_t width) {
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value)) .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
// Transfer output // Transfer output
ImageToBuffer<DeviceType::OPENCL, float>(&net, ImageToBuffer<DeviceType::GPU, float>(&net,
"OutputImage", "OutputImage",
"DeviceOutput", "DeviceOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
......
mace/ops/eltwise.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_Eltwise(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_Eltwise(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Eltwise") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Eltwise")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
EltwiseOp<DeviceType::OPENCL, float>); EltwiseOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("Eltwise") REGISTER_OPERATOR(op_registry, OpKeyBuilder("Eltwise")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
EltwiseOp<DeviceType::OPENCL, half>); EltwiseOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/eltwise_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -34,11 +34,11 @@ void EltwiseBenchmark( ...@@ -34,11 +34,11 @@ void EltwiseBenchmark(
net.AddRandomInput<D, T>("Input0", {n, h, w, c}); net.AddRandomInput<D, T>("Input0", {n, h, w, c});
net.AddRandomInput<D, T>("Input1", {n, h, w, c}); net.AddRandomInput<D, T>("Input1", {n, h, w, c});
if (D == DeviceType::OPENCL) { if (D == DeviceType::GPU) {
BufferToImage<D, T>(&net, "Input0", "InputImg0", BufferToImage<D, half>(&net, "Input0", "InputImg0",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(&net, "Input1", "InputImg1", BufferToImage<D, half>(&net, "Input1", "InputImg1",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("Eltwise", "EltwiseTest") OpDefBuilder("Eltwise", "EltwiseTest")
.Input("InputImg0") .Input("InputImg0")
.Input("InputImg1") .Input("InputImg1")
...@@ -90,8 +90,8 @@ void EltwiseBenchmark( ...@@ -90,8 +90,8 @@ void EltwiseBenchmark(
#define BM_ELTWISE(ELT_TYPE, N, H, W, C) \ #define BM_ELTWISE(ELT_TYPE, N, H, W, C) \
BM_ELTWISE_MACRO(ELT_TYPE, N, H, W, C, float, CPU); \ BM_ELTWISE_MACRO(ELT_TYPE, N, H, W, C, float, CPU); \
BM_ELTWISE_MACRO(ELT_TYPE, N, H, W, C, float, OPENCL); \ BM_ELTWISE_MACRO(ELT_TYPE, N, H, W, C, float, GPU); \
BM_ELTWISE_MACRO(ELT_TYPE, N, H, W, C, half, OPENCL); BM_ELTWISE_MACRO(ELT_TYPE, N, H, W, C, half, GPU);
BM_ELTWISE(2, 1, 128, 128, 32); BM_ELTWISE(2, 1, 128, 128, 32);
BM_ELTWISE(2, 1, 240, 240, 256); BM_ELTWISE(2, 1, 240, 240, 256);
......
mace/ops/eltwise_test.cc
浏览文件 @ 3ba07e0b
...@@ -183,49 +183,49 @@ TEST_F(EltwiseOpTest, CPUSimpleTensorScalar) { ...@@ -183,49 +183,49 @@ TEST_F(EltwiseOpTest, CPUSimpleTensorScalar) {
} }
TEST_F(EltwiseOpTest, GPUSimpleTensorScalar) { TEST_F(EltwiseOpTest, GPUSimpleTensorScalar) {
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::SUM, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::SUM,
{1, 1, 1, 1}, {1}, 1, {1, 1, 1, 1}, {1}, 1,
{2}); {2});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::SUB, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::SUB,
{1, 1, 2, 3}, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
1, 1,
{0, 1, 2, 3, 4, 5}); {0, 1, 2, 3, 4, 5});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::PROD, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::PROD,
{1, 1, 2, 3}, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
2, 2,
{2, 4, 6, 8, 10, 12}); {2, 4, 6, 8, 10, 12});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::DIV, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::DIV,
{1, 1, 2, 3}, {1, 1, 2, 3},
{2, 4, 6, 8, 10, 12}, {2, 4, 6, 8, 10, 12},
2, 2,
{1, 2, 3, 4, 5, 6}); {1, 2, 3, 4, 5, 6});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::MIN, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::MIN,
{1, 1, 2, 3}, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
1, 1,
{1, 1, 1, 1, 1, 1}); {1, 1, 1, 1, 1, 1});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::MAX, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::MAX,
{1, 1, 2, 3}, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
3, 3,
{3, 3, 3, 4, 5, 6}); {3, 3, 3, 4, 5, 6});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::NEG, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::NEG,
{1, 1, 2, 3}, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
3, 3,
{-1, -2, -3, -4, -5, -6}); {-1, -2, -3, -4, -5, -6});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::ABS, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::ABS,
{1, 1, 2, 3}, {1, 1, 2, 3},
{-1, -2, -3, -4, -5, -6}, {-1, -2, -3, -4, -5, -6},
3, 3,
{1, 2, 3, 4, 5, 6}); {1, 2, 3, 4, 5, 6});
SimpleTensorScalar<DeviceType::OPENCL, float>(kernels::EltwiseType::SQR_DIFF, SimpleTensorScalar<DeviceType::GPU, float>(kernels::EltwiseType::SQR_DIFF,
{1, 1, 2, 3}, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
1, 1,
{0, 1, 4, 9, 16, 25}); {0, 1, 4, 9, 16, 25});
} }
TEST_F(EltwiseOpTest, CPUSimpleTensorVector) { TEST_F(EltwiseOpTest, CPUSimpleTensorVector) {
...@@ -277,49 +277,49 @@ TEST_F(EltwiseOpTest, CPUSimpleTensorVector) { ...@@ -277,49 +277,49 @@ TEST_F(EltwiseOpTest, CPUSimpleTensorVector) {
} }
TEST_F(EltwiseOpTest, GPUSimpleTensorVector) { TEST_F(EltwiseOpTest, GPUSimpleTensorVector) {
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SUM, kernels::EltwiseType::SUM,
{1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6},
{1, 1, 1, 3}, {1, 2, 3}, {1, 1, 1, 3}, {1, 2, 3},
{2, 4, 6, 5, 7, 9}); {2, 4, 6, 5, 7, 9});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SUB, kernels::EltwiseType::SUB,
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
{1, 1, 1, 5}, {1, 2, 3, 4, 5}, {1, 1, 1, 5}, {1, 2, 3, 4, 5},
{0, 0, 0, 0, 0, 5, 5, 5, 5, 5}); {0, 0, 0, 0, 0, 5, 5, 5, 5, 5});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SUB, kernels::EltwiseType::SUB,
{1, 1, 1, 5}, {1, 2, 3, 4, 5}, {1, 1, 1, 5}, {1, 2, 3, 4, 5},
{1, 2, 1, 5}, {1, 2, 1, 5},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
{0, 0, 0, 0, 0, -5, -5, -5, -5, -5}); {0, 0, 0, 0, 0, -5, -5, -5, -5, -5});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::PROD, kernels::EltwiseType::PROD,
{1, 1, 1, 3}, {1, 2, 3}, {1, 1, 1, 3}, {1, 2, 3},
{1, 2, 1, 3}, {1, 2, 3, 4, 5, 6}, {1, 2, 1, 3}, {1, 2, 3, 4, 5, 6},
{1, 4, 9, 4, 10, 18}); {1, 4, 9, 4, 10, 18});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::DIV, kernels::EltwiseType::DIV,
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
{1, 1, 1, 5}, {1, 1, 1, 1, 5}, {1, 1, 1, 5}, {1, 1, 1, 1, 5},
{1, 2, 3, 4, 1, 6, 7, 8, 9, 2}); {1, 2, 3, 4, 1, 6, 7, 8, 9, 2});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::DIV, kernels::EltwiseType::DIV,
{1, 1, 1, 5}, {1, 1, 1, 2, 4}, {1, 1, 1, 5}, {1, 1, 1, 2, 4},
{1, 2, 1, 5}, {1, 2, 1, 5},
{1, 1, 1, 2, 2, 1, 1, 1, 1, 1}, {1, 1, 1, 2, 2, 1, 1, 1, 1, 1},
{1, 1, 1, 1, 2, 1, 1, 1, 2, 4}); {1, 1, 1, 1, 2, 1, 1, 1, 2, 4});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::MIN, kernels::EltwiseType::MIN,
{1, 1, 1, 5}, {1, 2, 3, 4, 5}, {1, 1, 1, 5}, {1, 2, 3, 4, 5},
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
{1, 2, 3, 4, 5, 1, 2, 3, 4, 5}); {1, 2, 3, 4, 5, 1, 2, 3, 4, 5});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::MAX, kernels::EltwiseType::MAX,
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
{1, 1, 1, 5}, {1, 2, 3, 4, 5}, {1, 1, 1, 5}, {1, 2, 3, 4, 5},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); {1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SQR_DIFF, kernels::EltwiseType::SQR_DIFF,
{1, 1, 1, 5}, {1, 2, 3, 4, 5}, {1, 1, 1, 5}, {1, 2, 3, 4, 5},
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
...@@ -369,43 +369,43 @@ TEST_F(EltwiseOpTest, CPUSimpleTensorTensor) { ...@@ -369,43 +369,43 @@ TEST_F(EltwiseOpTest, CPUSimpleTensorTensor) {
25}); 25});
} }
TEST_F(EltwiseOpTest, GPUSimpleTensorTensor) { TEST_F(EltwiseOpTest, GPUSimpleTensorTensor) {
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SUM, kernels::EltwiseType::SUM,
{1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6},
{1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6},
{2, 4, 6, 8, 10, 12}); {2, 4, 6, 8, 10, 12});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SUM, kernels::EltwiseType::SUM,
{1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6},
{1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6},
{0.2, 0.4, 0.6, 0.8, 1, 1.2}, {0.2, 0.4, 0.6, 0.8, 1, 1.2},
{0.1, 0.1}); {0.1, 0.1});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SUB, kernels::EltwiseType::SUB,
{1, 1, 1, 5}, {1, 2, 3, 4, 5}, {1, 1, 1, 5}, {1, 2, 3, 4, 5},
{1, 1, 1, 5}, {1, 2, 3, 4, 5}, {1, 1, 1, 5}, {1, 2, 3, 4, 5},
{0, 0, 0, 0, 0}); {0, 0, 0, 0, 0});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::PROD, kernels::EltwiseType::PROD,
{1, 2, 1, 3}, {1, 2, 3, 4, 5, 6}, {1, 2, 1, 3}, {1, 2, 3, 4, 5, 6},
{1, 2, 1, 3}, {1, 2, 3, 4, 5, 6}, {1, 2, 1, 3}, {1, 2, 3, 4, 5, 6},
{1, 4, 9, 16, 25, 36}); {1, 4, 9, 16, 25, 36});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::DIV, kernels::EltwiseType::DIV,
{1, 2, 1, 3}, {1, 2, 3, 4, 5, 6}, {1, 2, 1, 3}, {1, 2, 3, 4, 5, 6},
{1, 2, 1, 3}, {1, 2, 3, 4, 5, 6}, {1, 2, 1, 3}, {1, 2, 3, 4, 5, 6},
{1, 1, 1, 1, 1, 1}); {1, 1, 1, 1, 1, 1});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::MIN, kernels::EltwiseType::MIN,
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5},
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
{1, 2, 3, 4, 5, 1, 2, 3, 4, 5}); {1, 2, 3, 4, 5, 1, 2, 3, 4, 5});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::MAX, kernels::EltwiseType::MAX,
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); {1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
SimpleTensorEltwise<DeviceType::OPENCL, float>( SimpleTensorEltwise<DeviceType::GPU, float>(
kernels::EltwiseType::SQR_DIFF, kernels::EltwiseType::SQR_DIFF,
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5},
{1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 1, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
...@@ -420,7 +420,7 @@ void RandomTensorScalar(const kernels::EltwiseType type, ...@@ -420,7 +420,7 @@ void RandomTensorScalar(const kernels::EltwiseType type,
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>("Input", shape, true, true); net.AddRandomInput<DeviceType::GPU, float>("Input", shape, true, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -441,7 +441,7 @@ void RandomTensorScalar(const kernels::EltwiseType type, ...@@ -441,7 +441,7 @@ void RandomTensorScalar(const kernels::EltwiseType type,
Tensor expected; Tensor expected;
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
BufferToImage<DeviceType::OPENCL, T>(&net, "Input", "InputImg", BufferToImage<DeviceType::GPU, T>(&net, "Input", "InputImg",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("Eltwise", "EltwiseTest") OpDefBuilder("Eltwise", "EltwiseTest")
.Input("InputImg") .Input("InputImg")
...@@ -452,15 +452,15 @@ void RandomTensorScalar(const kernels::EltwiseType type, ...@@ -452,15 +452,15 @@ void RandomTensorScalar(const kernels::EltwiseType type,
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run // Run
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImg", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImg", "GPUOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
if (DataTypeToEnum<T>::value == DT_FLOAT) { if (DataTypeToEnum<T>::value == DT_FLOAT) {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5); ExpectTensorNear<float>(expected, *net.GetOutput("GPUOutput"), 1e-5);
} else { } else {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2, ExpectTensorNear<float>(expected, *net.GetOutput("GPUOutput"), 1e-2,
1e-2); 1e-2);
} }
} }
...@@ -474,8 +474,8 @@ void RandomTensorEltwise(const kernels::EltwiseType type, ...@@ -474,8 +474,8 @@ void RandomTensorEltwise(const kernels::EltwiseType type,
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>("Input0", shape0, true, true); net.AddRandomInput<DeviceType::GPU, float>("Input0", shape0, true, true);
net.AddRandomInput<DeviceType::OPENCL, float>("Input1", shape1, true, true); net.AddRandomInput<DeviceType::GPU, float>("Input1", shape1, true, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input0", NHWC, net.TransformDataFormat<DeviceType::CPU, float>("Input0", NHWC,
"TInput0", NCHW); "TInput0", NCHW);
...@@ -496,9 +496,9 @@ void RandomTensorEltwise(const kernels::EltwiseType type, ...@@ -496,9 +496,9 @@ void RandomTensorEltwise(const kernels::EltwiseType type,
Tensor expected; Tensor expected;
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
BufferToImage<DeviceType::OPENCL, T>(&net, "Input0", "InputImg0", BufferToImage<DeviceType::GPU, T>(&net, "Input0", "InputImg0",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, T>(&net, "Input1", "InputImg1", BufferToImage<DeviceType::GPU, T>(&net, "Input1", "InputImg1",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("Eltwise", "EltwiseTest") OpDefBuilder("Eltwise", "EltwiseTest")
.Input("InputImg0") .Input("InputImg0")
...@@ -510,15 +510,15 @@ void RandomTensorEltwise(const kernels::EltwiseType type, ...@@ -510,15 +510,15 @@ void RandomTensorEltwise(const kernels::EltwiseType type,
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run // Run
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImg", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImg", "GPUOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
if (DataTypeToEnum<T>::value == DT_FLOAT) { if (DataTypeToEnum<T>::value == DT_FLOAT) {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5); ExpectTensorNear<float>(expected, *net.GetOutput("GPUOutput"), 1e-5);
} else { } else {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2, ExpectTensorNear<float>(expected, *net.GetOutput("GPUOutput"), 1e-2,
1e-2); 1e-2);
} }
} }
...@@ -609,19 +609,19 @@ TEST_F(EltwiseOpTest, RandomTensorTensorFloat) { ...@@ -609,19 +609,19 @@ TEST_F(EltwiseOpTest, RandomTensorTensorFloat) {
TEST_F(EltwiseOpTest, RandomTensorTensorHalf) { TEST_F(EltwiseOpTest, RandomTensorTensorHalf) {
RandomTensorEltwise<half>(kernels::EltwiseType::SUM, RandomTensorEltwise<half>(kernels::EltwiseType::SUM,
{1, 32, 32, 16}, {1, 32, 32, 16}); {1, 32, 32, 16}, {1, 32, 32, 16});
RandomTensorEltwise<half>(kernels::EltwiseType::SUB, RandomTensorEltwise<half>(kernels::EltwiseType::SUB,
{3, 32, 32, 16}, {3, 32, 32, 16}); {3, 32, 32, 16}, {3, 32, 32, 16});
RandomTensorEltwise<half>(kernels::EltwiseType::PROD, RandomTensorEltwise<half>(kernels::EltwiseType::PROD,
{1, 31, 37, 17}, {1, 31, 37, 17}); {1, 31, 37, 17}, {1, 31, 37, 17});
RandomTensorEltwise<half>(kernels::EltwiseType::DIV, RandomTensorEltwise<half>(kernels::EltwiseType::DIV,
{5, 31, 37, 17}, {5, 31, 37, 17}); {5, 31, 37, 17}, {5, 31, 37, 17});
RandomTensorEltwise<half>(kernels::EltwiseType::MIN, RandomTensorEltwise<half>(kernels::EltwiseType::MIN,
{1, 32, 32, 16}, {1, 32, 32, 16}); {1, 32, 32, 16}, {1, 32, 32, 16});
RandomTensorEltwise<half>(kernels::EltwiseType::MAX, RandomTensorEltwise<half>(kernels::EltwiseType::MAX,
{3, 31, 37, 17}, {3, 31, 37, 17}); {3, 31, 37, 17}, {3, 31, 37, 17});
RandomTensorEltwise<half>(kernels::EltwiseType::SQR_DIFF, RandomTensorEltwise<half>(kernels::EltwiseType::SQR_DIFF,
{3, 31, 37, 17}, {3, 31, 37, 17}); {3, 31, 37, 17}, {3, 31, 37, 17});
} }
......
mace/ops/folded_batch_norm.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_FoldedBatchNorm(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_FoldedBatchNorm(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("FoldedBatchNorm") REGISTER_OPERATOR(op_registry, OpKeyBuilder("FoldedBatchNorm")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
FoldedBatchNormOp<DeviceType::OPENCL, float>); FoldedBatchNormOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("FoldedBatchNorm") REGISTER_OPERATOR(op_registry, OpKeyBuilder("FoldedBatchNorm")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
FoldedBatchNormOp<DeviceType::OPENCL, half>); FoldedBatchNormOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/folded_batch_norm_test.cc
浏览文件 @ 3ba07e0b
...@@ -60,7 +60,7 @@ void Simple() { ...@@ -60,7 +60,7 @@ void Simple() {
// Run // Run
net.RunOp(D); net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC); net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Scale", "ScaleImage", BufferToImage<D, float>(&net, "Scale", "ScaleImage",
...@@ -94,7 +94,7 @@ void Simple() { ...@@ -94,7 +94,7 @@ void Simple() {
TEST_F(FoldedBatchNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); } TEST_F(FoldedBatchNormOpTest, SimpleCPU) { Simple<DeviceType::CPU>(); }
TEST_F(FoldedBatchNormOpTest, SimpleOPENCL) { Simple<DeviceType::OPENCL>(); } TEST_F(FoldedBatchNormOpTest, SimpleOPENCL) { Simple<DeviceType::GPU>(); }
TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) { TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) {
// generate random input // generate random input
...@@ -108,10 +108,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) { ...@@ -108,10 +108,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -138,11 +138,11 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) { ...@@ -138,11 +138,11 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, float>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, float>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, float>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest") OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest")
...@@ -153,10 +153,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) { ...@@ -153,10 +153,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4);
} }
...@@ -173,10 +173,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) { ...@@ -173,10 +173,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -203,11 +203,11 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) { ...@@ -203,11 +203,11 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, half>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, half>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, half>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, half>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, half>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest") OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest")
...@@ -219,10 +219,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) { ...@@ -219,10 +219,10 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
net.Sync(); net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2, 1e-2); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2, 1e-2);
} }
...@@ -239,10 +239,10 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) { ...@@ -239,10 +239,10 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -269,11 +269,11 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) { ...@@ -269,11 +269,11 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, float>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, float>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, float>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, float>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest") OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest")
...@@ -284,9 +284,9 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) { ...@@ -284,9 +284,9 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5, 1e-4);
} }
...@@ -303,10 +303,10 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) { ...@@ -303,10 +303,10 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) {
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", net.TransformDataFormat<DeviceType::CPU, float>("Input",
NHWC, NHWC,
...@@ -333,11 +333,11 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) { ...@@ -333,11 +333,11 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) {
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, half>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, half>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, half>(&net, "Scale", "ScaleImage", BufferToImage<DeviceType::GPU, half>(&net, "Scale", "ScaleImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, half>(&net, "Offset", "OffsetImage", BufferToImage<DeviceType::GPU, half>(&net, "Offset", "OffsetImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest") OpDefBuilder("FoldedBatchNorm", "FoldedBatchNormTest")
...@@ -349,9 +349,9 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) { ...@@ -349,9 +349,9 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2, 1e-2); ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2, 1e-2);
} }
......
mace/ops/fully_connected.cc
浏览文件 @ 3ba07e0b
...@@ -26,16 +26,16 @@ void Register_FullyConnected(OperatorRegistry *op_registry) { ...@@ -26,16 +26,16 @@ void Register_FullyConnected(OperatorRegistry *op_registry) {
#ifdef MACE_ENABLE_OPENCL #ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("FC") REGISTER_OPERATOR(op_registry, OpKeyBuilder("FC")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<float>("T") .TypeConstraint<float>("T")
.Build(), .Build(),
FullyConnectedOp<DeviceType::OPENCL, float>); FullyConnectedOp<DeviceType::GPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("FC") REGISTER_OPERATOR(op_registry, OpKeyBuilder("FC")
.Device(DeviceType::OPENCL) .Device(DeviceType::GPU)
.TypeConstraint<half>("T") .TypeConstraint<half>("T")
.Build(), .Build(),
FullyConnectedOp<DeviceType::OPENCL, half>); FullyConnectedOp<DeviceType::GPU, half>);
#endif // MACE_ENABLE_OPENCL #endif // MACE_ENABLE_OPENCL
} }
......
mace/ops/fully_connected_benchmark.cc
浏览文件 @ 3ba07e0b
...@@ -43,7 +43,7 @@ void FCBenchmark( ...@@ -43,7 +43,7 @@ void FCBenchmark(
.Input("Bias") .Input("Bias")
.Output("Output") .Output("Output")
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
kernels::BufferType weight_type = kernels::BufferType::WEIGHT_WIDTH; kernels::BufferType weight_type = kernels::BufferType::WEIGHT_WIDTH;
BufferToImage<D, T>(&net, "Weight", "WeightImage", BufferToImage<D, T>(&net, "Weight", "WeightImage",
weight_type); weight_type);
...@@ -93,8 +93,8 @@ void FCBenchmark( ...@@ -93,8 +93,8 @@ void FCBenchmark(
#define BM_FC(N, H, W, C, OC) \ #define BM_FC(N, H, W, C, OC) \
BM_FC_MACRO(N, H, W, C, OC, float, CPU); \ BM_FC_MACRO(N, H, W, C, OC, float, CPU); \
BM_FC_MACRO(N, H, W, C, OC, float, OPENCL); \ BM_FC_MACRO(N, H, W, C, OC, float, GPU); \
BM_FC_MACRO(N, H, W, C, OC, half, OPENCL); BM_FC_MACRO(N, H, W, C, OC, half, GPU);
BM_FC(1, 16, 16, 32, 32); BM_FC(1, 16, 16, 32, 32);
BM_FC(1, 8, 8, 32, 1000); BM_FC(1, 8, 8, 32, 1000);
......
mace/ops/fully_connected_test.cc
浏览文件 @ 3ba07e0b
...@@ -51,7 +51,7 @@ void Simple(const std::vector<index_t> &input_shape, ...@@ -51,7 +51,7 @@ void Simple(const std::vector<index_t> &input_shape,
// Run // Run
net.RunOp(D); net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC); net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
} else if (D == DeviceType::OPENCL) { } else if (D == DeviceType::GPU) {
BufferToImage<D, float>(&net, "Input", "InputImage", BufferToImage<D, float>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, float>(&net, "Weight", "WeightImage", BufferToImage<D, float>(&net, "Weight", "WeightImage",
...@@ -104,14 +104,14 @@ TEST_F(FullyConnectedOpTest, SimpleCPUWithBatch) { ...@@ -104,14 +104,14 @@ TEST_F(FullyConnectedOpTest, SimpleCPUWithBatch) {
} }
TEST_F(FullyConnectedOpTest, SimpleOPENCL) { TEST_F(FullyConnectedOpTest, SimpleOPENCL) {
Simple<DeviceType::OPENCL>({1, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 8}, Simple<DeviceType::GPU>({1, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 8},
{1, 2, 3, 4, 5, 6, 7, 8}, {1}, {2}, {1, 1, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8}, {1}, {2}, {1, 1, 1, 1},
{206}); {206});
Simple<DeviceType::OPENCL>( Simple<DeviceType::GPU>(
{1, 1, 2, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {2, 10}, {1, 1, 2, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {2, 10},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100},
{2}, {2, 3}, {1, 1, 1, 2}, {387, 3853}); {2}, {2, 3}, {1, 1, 1, 2}, {387, 3853});
Simple<DeviceType::OPENCL>( Simple<DeviceType::GPU>(
{1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {5, 6}, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {5, 6},
{1, 2, 3, 4, 5, 6, 10, 20, 30, 40, 50, 60, 1, 2, 3, {1, 2, 3, 4, 5, 6, 10, 20, 30, 40, 50, 60, 1, 2, 3,
4, 5, 6, 10, 20, 30, 40, 50, 60, 1, 2, 3, 4, 5, 6}, 4, 5, 6, 10, 20, 30, 40, 50, 60, 1, 2, 3, 4, 5, 6},
...@@ -119,7 +119,7 @@ TEST_F(FullyConnectedOpTest, SimpleOPENCL) { ...@@ -119,7 +119,7 @@ TEST_F(FullyConnectedOpTest, SimpleOPENCL) {
} }
TEST_F(FullyConnectedOpTest, SimpleGPUWithBatch) { TEST_F(FullyConnectedOpTest, SimpleGPUWithBatch) {
Simple<DeviceType::OPENCL>({2, 1, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 4}, Simple<DeviceType::GPU>({2, 1, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 4},
{1, 2, 3, 4}, {1}, {2}, {2, 1, 1, 1}, {32, 72}); {1, 2, 3, 4}, {1}, {2}, {2, 1, 1, 1}, {32, 72});
} }
...@@ -136,11 +136,11 @@ void Complex(const index_t batch, ...@@ -136,11 +136,11 @@ void Complex(const index_t batch,
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Weight", {out_channel, height * width * channels}); "Weight", {out_channel, height * width * channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Bias", {out_channel}); net.AddRandomInput<DeviceType::GPU, float>("Bias", {out_channel});
OpDefBuilder("FC", "FullyConnectedTest") OpDefBuilder("FC", "FullyConnectedTest")
.Input("Input") .Input("Input")
...@@ -159,11 +159,11 @@ void Complex(const index_t batch, ...@@ -159,11 +159,11 @@ void Complex(const index_t batch,
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, T>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, T>(&net, "Weight", "WeightImage", BufferToImage<DeviceType::GPU, T>(&net, "Weight", "WeightImage",
kernels::BufferType::WEIGHT_HEIGHT); kernels::BufferType::WEIGHT_HEIGHT);
BufferToImage<DeviceType::OPENCL, float>(&net, "Bias", "BiasImage", BufferToImage<DeviceType::GPU, float>(&net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("FC", "FullyConnectedTest") OpDefBuilder("FC", "FullyConnectedTest")
...@@ -176,9 +176,9 @@ void Complex(const index_t batch, ...@@ -176,9 +176,9 @@ void Complex(const index_t batch,
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run on opencl // Run on opencl
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
if (DataTypeToEnum<T>::value == DataType::DT_HALF) { if (DataTypeToEnum<T>::value == DataType::DT_HALF) {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"),
...@@ -225,11 +225,11 @@ void TestWXFormat(const index_t batch, ...@@ -225,11 +225,11 @@ void TestWXFormat(const index_t batch,
OpsTestNet net; OpsTestNet net;
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Input", {batch, height, width, channels}); "Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>( net.AddRandomInput<DeviceType::GPU, float>(
"Weight", {out_channel, height * width * channels}); "Weight", {out_channel, height * width * channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Bias", {out_channel}); net.AddRandomInput<DeviceType::GPU, float>("Bias", {out_channel});
OpDefBuilder("FC", "FullyConnectedTest") OpDefBuilder("FC", "FullyConnectedTest")
.Input("Input") .Input("Input")
...@@ -248,11 +248,11 @@ void TestWXFormat(const index_t batch, ...@@ -248,11 +248,11 @@ void TestWXFormat(const index_t batch,
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// Run on opencl // Run on opencl
BufferToImage<DeviceType::OPENCL, T>(&net, "Input", "InputImage", BufferToImage<DeviceType::GPU, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<DeviceType::OPENCL, T>(&net, "Weight", "WeightImage", BufferToImage<DeviceType::GPU, T>(&net, "Weight", "WeightImage",
kernels::BufferType::WEIGHT_WIDTH); kernels::BufferType::WEIGHT_WIDTH);
BufferToImage<DeviceType::OPENCL, T>(&net, "Bias", "BiasImage", BufferToImage<DeviceType::GPU, T>(&net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT); kernels::BufferType::ARGUMENT);
OpDefBuilder("FC", "FullyConnectedTest") OpDefBuilder("FC", "FullyConnectedTest")
...@@ -264,9 +264,9 @@ void TestWXFormat(const index_t batch, ...@@ -264,9 +264,9 @@ void TestWXFormat(const index_t batch,
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Run // Run
net.RunOp(DeviceType::OPENCL); net.RunOp(DeviceType::GPU);
ImageToBuffer<DeviceType::OPENCL, float>(&net, "OutputImage", "OPENCLOutput", ImageToBuffer<DeviceType::GPU, float>(&net, "OutputImage", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL); kernels::BufferType::IN_OUT_CHANNEL);
if (DataTypeToEnum<T>::value == DataType::DT_HALF) { if (DataTypeToEnum<T>::value == DataType::DT_HALF) {
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"),
......
mace/ops/fused_conv_2d.cc 已删除 100644 → 0
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// Copyright 2018 Xiaomi, Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "mace/ops/fused_conv_2d.h"
namespace mace {
namespace ops {
void Register_FusedConv2D(OperatorRegistry *op_registry) {
REGISTER_OPERATOR(op_registry, OpKeyBuilder("FusedConv2D")
.Device(DeviceType::CPU)
.TypeConstraint<float>("T")
.Build(),
FusedConv2dOp<DeviceType::CPU, float>);
#ifdef MACE_ENABLE_OPENCL
REGISTER_OPERATOR(op_registry, OpKeyBuilder("FusedConv2D")
.Device(DeviceType::OPENCL)
.TypeConstraint<float>("T")
.Build(),
FusedConv2dOp<DeviceType::OPENCL, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("FusedConv2D")
.Device(DeviceType::OPENCL)
.TypeConstraint<half>("T")
.Build(),
FusedConv2dOp<DeviceType::OPENCL, half>);
#endif // MACE_ENABLE_OPENCL
}
} // namespace ops
} // namespace mace
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mace/ops/image_to_buffer.cc
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mace/ops/matmul.cc
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mace/ops/matmul_benchmark.cc
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mace/ops/matmul_test.cc
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mace/ops/ops_test_util.h
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mace/ops/pad.cc
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mace/ops/pad_benchmark.cc
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mace/ops/pad_test.cc
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mace/ops/pooling.cc
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mace/ops/pooling_benchmark.cc
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mace/ops/pooling_test.cc
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mace/ops/resize_bilinear.cc
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mace/ops/resize_bilinear_test.cc
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mace/ops/slice.cc
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mace/ops/slice_benchmark.cc
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mace/ops/slice_test.cc
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mace/ops/softmax.cc
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mace/ops/softmax_test.cc
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mace/ops/space_to_batch.cc
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mace/ops/space_to_batch_test.cc
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mace/ops/space_to_depth.cc
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mace/ops/space_to_depth.h
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mace/ops/winograd_transform.cc
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