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“2ddb11222adef0545a2691d73281516026b9de10”上不存在“paddle/fluid/operators/lrn_op.cu”
提交 95b32c24 编写于 作者: L liuqi
浏览文件
操作

Update the memory type choose logic and polish some code. 

1. Change DataFormat from enum to enum class.
上级 74dcd617
显示空白变更内容
内联 并排
Showing with 791 addition and 648 deletion
mace/benchmark/benchmark_model.cc
浏览文件 @ 95b32c24
......@@ -83,7 +83,7 @@ DataFormat ParseDataFormat(const std::string &data_format_str) {
} else if (data_format_str == "OIHW") {
return DataFormat::OIHW;
} else {
return DataFormat::DF_NONE;
return DataFormat::NONE;
}
}
......
mace/core/arg_helper.cc
浏览文件 @ 95b32c24
......@@ -123,14 +123,13 @@ MACE_GET_REPEATED_ARGUMENT_FUNC(int64_t, ints, true)
MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, float, f) \
MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, bool, i) \
MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, int, i) \
MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, int64_t, i) \
MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, std::string, s)
MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, int64_t, i)
MACE_SET_OPTIONAL_ARGUMENT_FUNC_MACRO(OperatorDef)
MACE_SET_OPTIONAL_ARGUMENT_FUNC_MACRO(NetDef)
#undef MACE_SET_OPTIONAL_ARGUMENT_FUNC
std::string OutputMemoryTypeTagName() {
const std::string OutputMemoryTypeTagName() {
static const char *kOutputMemTypeArgName = "output_mem_type";
return kOutputMemTypeArgName;
}
......
mace/core/arg_helper.h
浏览文件 @ 95b32c24
......@@ -65,7 +65,7 @@ void SetProtoArg(NetDef *op_def,
const std::string &arg_name,
const T&value);
std::string OutputMemoryTypeTagName();
const std::string OutputMemoryTypeTagName();
bool IsQuantizedModel(const NetDef &def);
......
mace/core/memory_optimizer.cc
浏览文件 @ 95b32c24
......@@ -126,7 +126,8 @@ void MemoryOptimizer::Optimize(
DataFormat data_format = static_cast<DataFormat>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op_def, "data_format", DataFormat::DF_NONE));
*op_def, "data_format",
static_cast<int>(DataFormat::NONE)));
int output_size = op_def->output_size();
for (int i = 0; i < output_size; ++i) {
if (i < op_def->output_type_size()) {
......
mace/core/net.cc
浏览文件 @ 95b32c24
......@@ -76,7 +76,7 @@ SerialNet::SerialNet(const OpRegistryBase *op_registry,
#ifdef MACE_ENABLE_OPENCL
if (target_device_->device_type() == DeviceType::GPU) {
// update the map : output_tensor -> Operation
// update the map : output_tensor -> MemoryType
MemoryType out_mem_type =
static_cast<MemoryType>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
......
mace/core/net_def_adapter.cc
浏览文件 @ 95b32c24
......@@ -37,7 +37,7 @@ DataFormat GetDefaultDataFormat(DeviceType device_type,
return DataFormat::NHWC;
} else {
LOG(FATAL) << "MACE do not support the device " << device_type;
return DataFormat::DF_NONE;
return DataFormat::NONE;
}
}
......@@ -50,19 +50,21 @@ std::string TransformedName(const std::string &input_name,
return ss.str();
}
#ifdef MACE_ENABLE_OPENCL
bool TransformRequiredOp(const std::string &op_type) {
static const std::unordered_set<std::string> kNoTransformOp = {
"Shape", "InferConv2dShape"
};
return kNoTransformOp.count(op_type) == 0;
}
#endif // MACE_ENABLE_OPENCL
void BuildTransposeOpDef(
const std::string &input_name,
const std::string &output_name,
const std::vector<mace::index_t> &output_shape,
const std::vector<index_t> &output_shape,
const std::vector<int> dst_dims,
const mace::DataType dt,
const DataType dt,
DeviceType device_type,
OperatorDef *op_def) {
std::string op_name = "mace_node_" + output_name;
......@@ -89,21 +91,13 @@ void BuildTransposeOpDef(
} // namespace
NetDefAdapter::NetDefAdapter(const mace::OpRegistryBase *op_registry,
const mace::Workspace *ws)
NetDefAdapter::NetDefAdapter(const OpRegistryBase *op_registry,
const Workspace *ws)
: op_registry_(op_registry), ws_(ws) {}
// Adapt original net_def to a better net.
// 1. Adapt device: choose best device for every op in the net.
// 2. Adapt data type: Add data type related transform ops
// for mixing precision.
// 3. Adapt data format: confirm data format of every op
// and add transpose if necessary.
// 4. Adapt memory type: Add BufferTransform if necessary
// for transforming memory type between ops.
MaceStatus NetDefAdapter::AdaptNetDef(
const mace::NetDef *net_def,
mace::Device *target_device,
const NetDef *net_def,
Device *target_device,
NetDef *target_net_def) {
MACE_LATENCY_LOGGER(1, "Adapting original NetDef");
// Copy from original op_def, leave ops alone.
......@@ -115,7 +109,7 @@ MaceStatus NetDefAdapter::AdaptNetDef(
std::unique_ptr<CPUDevice> cpu_device = make_unique<CPUDevice>(
target_device->cpu_runtime()->num_threads(),
target_device->cpu_runtime()->policy(),
target_device->cpu_runtime()->use_gemmlowp());
&(target_device->cpu_runtime()->thread_pool()));
// quantize model flag
bool is_quantized_model = IsQuantizedModel(*net_def);
......@@ -131,9 +125,6 @@ MaceStatus NetDefAdapter::AdaptNetDef(
std::vector<index_t>(tensor.dims().begin(), tensor.dims().end());
}
int input_size = target_net_def->input_info_size();
for (int i = 0; i < input_size; ++i) {
auto input_info = target_net_def->mutable_input_info(i);
MemoryType mem_type = MemoryType::CPU_BUFFER;
if (target_device->device_type() == DeviceType::CPU) {
mem_type = MemoryType::CPU_BUFFER;
......@@ -143,28 +134,31 @@ MaceStatus NetDefAdapter::AdaptNetDef(
LOG(FATAL) << "MACE do not support the device type: "
<< target_device->device_type();
}
DataFormat input_data_format = static_cast<DataFormat>(
int input_size = target_net_def->input_info_size();
for (int i = 0; i < input_size; ++i) {
auto input_info = target_net_def->mutable_input_info(i);
auto input_data_format = static_cast<DataFormat>(
input_info->data_format());
DataFormat expected_data_format = GetDefaultDataFormat(
target_device->device_type(), is_quantized_model);
std::vector<index_t> input_shape =
std::vector<index_t>(input_info->dims().begin(),
std::vector<index_t> input_shape(input_info->dims().begin(),
input_info->dims().end());
if (input_data_format != DataFormat::DF_NONE
if (input_data_format != DataFormat::NONE
&& input_data_format != expected_data_format
&& input_shape.size() == 4) {
if (input_data_format == DataFormat::NHWC
&& expected_data_format == DataFormat::NCHW) {
std::vector<int> dst_dims = {0, 3, 1, 2};
std::vector<int> dst_dims{0, 3, 1, 2};
input_data_format = DataFormat::NCHW;
input_shape = TransposeShape<index_t, index_t>(input_shape, dst_dims);
} else if (input_data_format == DataFormat::NCHW
&& expected_data_format == DataFormat::NHWC) {
std::vector<int> dst_dims = {0, 2, 3, 1};
std::vector<int> dst_dims{0, 2, 3, 1};
input_data_format = DataFormat::NHWC;
input_shape = TransposeShape<index_t, index_t>(input_shape, dst_dims);
}
input_info->set_data_format(input_data_format);
input_info->set_data_format(static_cast<int>(input_data_format));
int input_shape_size = input_shape.size();
for (int j = 0; j < input_shape_size; ++j) {
input_info->set_dims(j, input_shape[j]);
......@@ -287,9 +281,10 @@ MaceStatus NetDefAdapter::AdaptNetDef(
internal_output_info.data_format,
transformed_op_def);
// set data format arg
SetProtoArg<int>(transformed_op_def,
SetProtoArg<int>(
transformed_op_def,
"data_format",
internal_output_info.data_format);
static_cast<int>(internal_output_info.data_format));
// set output memory type argument
SetProtoArg<int>(transformed_op_def,
OutputMemoryTypeTagName(),
......@@ -309,7 +304,7 @@ MaceStatus NetDefAdapter::AdaptDevice(OpConditionContext *context,
const TensorInfoMap &output_map,
const NetDef *net_def,
OperatorDef *op_def) {
VLOG(1) << "Adapt device for op " << op_def->name();
VLOG(3) << "Adapt device for op " << op_def->name();
DeviceType target_device_type = target_device->device_type();
DeviceType device_type = DeviceType::CPU;
context->set_device(cpu_device);
......@@ -335,15 +330,18 @@ MaceStatus NetDefAdapter::AdaptDevice(OpConditionContext *context,
producer_devices);
if (device_type == target_device_type) {
context->set_device(target_device);
} else {
LOG(INFO) << "Op " << op_def->name() << " fall back to CPU";
}
}
op_def->set_device_type(device_type);
return MaceStatus::MACE_SUCCESS;
}
MaceStatus NetDefAdapter::AdaptDataType(mace::OpConditionContext *context,
mace::OperatorDef *op_def) {
MaceStatus NetDefAdapter::AdaptDataType(OpConditionContext *context,
OperatorDef *op_def) {
MACE_UNUSED(context);
// Where to add logic to support mixing precision
// Adjust data type of op ran on CPU
DataType dtype = static_cast<DataType>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
......@@ -355,20 +353,20 @@ MaceStatus NetDefAdapter::AdaptDataType(mace::OpConditionContext *context,
}
MaceStatus NetDefAdapter::AdaptDataFormat(
mace::OpConditionContext *context,
mace::OperatorDef *op_def,
OpConditionContext *context,
OperatorDef *op_def,
bool is_quantized_model,
TensorInfoMap *output_map,
std::unordered_set<std::string> *transformed_set,
DataFormat *op_output_df,
mace::NetDef *target_net_def) {
VLOG(1) << "Adapt data format for op " << op_def->name();
MACE_UNUSED(context);
NetDef *target_net_def) {
VLOG(3) << "Adapt data format for op " << op_def->name();
DataFormat op_data_format =
static_cast<DataFormat>(ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op_def, "data_format", 0));
*op_def, "data_format",
static_cast<int>(DataFormat::NONE)));
// adjust the data format of operation
if (op_data_format == DataFormat::DF_AUTO) {
if (op_data_format == DataFormat::AUTO) {
op_data_format = GetDefaultDataFormat(
static_cast<DeviceType>(op_def->device_type()), is_quantized_model);
SetProtoArg<int>(op_def, "data_format", static_cast<int>(op_data_format));
......@@ -376,7 +374,9 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
int output_shape_size = op_def->output_shape_size();
for (int i = 0; i < output_shape_size; ++i) {
auto output_shape = op_def->mutable_output_shape(i);
if (output_shape->dims_size() == 4) {
MACE_CHECK(output_shape->dims_size() == 4,
"Output shape should be 4D if the of has data format. ",
op_def->name());
// transpose output shape format from NHWC to NCHW
int64_t height = output_shape->dims(1);
int64_t width = output_shape->dims(2);
......@@ -386,7 +386,6 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
}
}
}
}
*op_output_df = op_data_format;
// the output memory type of transpose op is based on the consumer op's device
......@@ -394,8 +393,8 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
if (op_def->device_type() == DeviceType::GPU) {
target_mem_type = MemoryType::GPU_BUFFER;
}
// Use op's data format as inputs' data format for now.
// Could move the logic to OpRegistry if necessary.
auto inputs_data_format = op_registry_->InputsDataFormat(op_def->type(),
context);
DataFormat src_df, dst_df;
int input_size = op_def->input_size();
for (int i = 0; i < input_size; ++i) {
......@@ -408,20 +407,21 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
continue;
}
src_df = output_map->at(op_def->input(i)).data_format;
dst_df = op_data_format;
if (src_df == DataFormat::DF_NONE
|| dst_df == DataFormat::DF_NONE
dst_df = inputs_data_format[i];
if (src_df == DataFormat::NONE
|| dst_df == DataFormat::NONE
|| output_map->at(op_def->input(i)).shape.size() != 4) {
continue;
}
if (src_df != dst_df) {
std::string transformed_name = TransformedName(op_def->input(i),
"data_format", dst_df);
"data_format", static_cast<int>(dst_df));
if (transformed_set->count(transformed_name) == 0) {
VLOG(1) << "Add Transpose operation " << op_def->name()
<< " to transpose tensor "
<< op_def->input(i) << "', from data format "
<< src_df << " to " << dst_df;
<< static_cast<int>(src_df) << " to "
<< static_cast<int>(dst_df);
// Only support transpose between NHWC and NCHW for now.
std::vector<int> dst_dims;
if (src_df == DataFormat::NCHW && dst_df == DataFormat::NHWC) {
......@@ -430,7 +430,8 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
dst_dims = {0, 3, 1, 2};
} else {
LOG(FATAL) << "Encounter unsupported data format transpose from "
<< src_df << " to " << dst_df;
<< static_cast<int>(src_df) << " to "
<< static_cast<int>(dst_df);
}
auto &input_info = output_map->at(op_def->input(i));
auto output_shape = input_info.shape.empty() ?
......@@ -449,7 +450,7 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
// set data format arg
SetProtoArg<int>(transpose_op_def,
"data_format",
dst_df);
static_cast<int>(dst_df));
// set output memory type argument
SetProtoArg<int>(transpose_op_def,
OutputMemoryTypeTagName(),
......@@ -475,20 +476,20 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
}
MaceStatus NetDefAdapter::AdaptMemoryType(
mace::OpConditionContext *context,
mace::OperatorDef *op_def,
mace::NetDefAdapter::TensorInfoMap *output_map,
OpConditionContext *context,
OperatorDef *op_def,
NetDefAdapter::TensorInfoMap *output_map,
std::unordered_set<std::string> *transformed_set,
MemoryType *op_output_mem_types,
mace::NetDef *target_net_def) {
VLOG(1) << "Adapt memory type for op " << op_def->name();
NetDef *target_net_def) {
VLOG(3) << "Adapt memory type for op " << op_def->name();
// Get expected output memory type
// (only support one kind of memory type for multiple outputs)
op_registry_->GetInOutMemoryTypes(op_def->type(), context);
#ifdef MACE_ENABLE_OPENCL
int input_size = op_def->input_size();
// if op is memory-unused op, no transformation
if (TransformRequiredOp(op_def->type())) {
int input_size = op_def->input_size();
for (int i = 0; i < input_size; ++i) {
if (output_map->count(op_def->input(i)) == 0) {
MACE_CHECK(ws_->GetTensor(op_def->input(i)) != nullptr
......@@ -498,14 +499,14 @@ MaceStatus NetDefAdapter::AdaptMemoryType(
continue;
}
auto &input_info = output_map->at(op_def->input(i));
if (input_info.data_format == DataFormat::DF_NONE
|| input_info.shape.size() != 4) {
continue;
}
// check whether to do transform
MemoryType src_mem_type = input_info.mem_type;
MemoryType dst_mem_type = context->GetInputMemType(i);
if (src_mem_type != dst_mem_type) {
auto wanted_input_dtype = context->GetInputDataType(i);
if (src_mem_type != dst_mem_type ||
(input_info.dtype != wanted_input_dtype &&
(src_mem_type != MemoryType::CPU_BUFFER
|| dst_mem_type != MemoryType::CPU_BUFFER))) {
auto transformed_name = TransformedName(op_def->input(i),
"mem_type",
dst_mem_type);
......@@ -521,7 +522,7 @@ MaceStatus NetDefAdapter::AdaptMemoryType(
op_def->input(i),
input_info.shape,
transformed_name,
context->GetInputDataType(i),
wanted_input_dtype,
context->GetInputOpenCLBufferType(i),
dst_mem_type,
input_info.data_format,
......@@ -529,7 +530,7 @@ MaceStatus NetDefAdapter::AdaptMemoryType(
// set data format arg
SetProtoArg<int>(transformed_op_def,
"data_format",
input_info.data_format);
static_cast<int>(input_info.data_format));
// set output memory type argument
SetProtoArg<int>(transformed_op_def,
OutputMemoryTypeTagName(),
......@@ -564,7 +565,7 @@ MaceStatus NetDefAdapter::AdaptMemoryType(
return MaceStatus::MACE_SUCCESS;
}
std::string NetDefAdapter::DebugString(const mace::NetDef *net_def) {
std::string NetDefAdapter::DebugString(const NetDef *net_def) {
std::stringstream sstream;
auto DeviceTypeToStrFunc = [](DeviceType device_type) -> std::string {
if (device_type == DeviceType::CPU) {
......@@ -591,10 +592,10 @@ std::string NetDefAdapter::DebugString(const mace::NetDef *net_def) {
return "NHWC";
} else if (type == DataFormat::NCHW) {
return "NCHW";
} else if (type == DataFormat::DF_NONE) {
return "DF_NONE";
} else if (type == DataFormat::DF_AUTO) {
return "DT_AUTO";
} else if (type == DataFormat::NONE) {
return "NONE";
} else if (type == DataFormat::AUTO) {
return "AUTO";
} else if (type == DataFormat::OIHW) {
return "OIHW";
} else {
......@@ -615,7 +616,7 @@ std::string NetDefAdapter::DebugString(const mace::NetDef *net_def) {
std::string data_format = DataFormatToStrFunc(
static_cast<DataFormat>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
op, "data_format", 0)));
op, "data_format", static_cast<int>(DataFormat::NONE))));
sstream << std::endl;
sstream << "{" << std::endl;
......
mace/core/net_def_adapter.h
浏览文件 @ 95b32c24
......@@ -32,16 +32,22 @@ class OpRegistryBase;
class Workspace;
class Device;
/**
* Conventions:
* 1. DataFormat::DT_AUTO stands for formatted (NHWC or NCHW)
* 2. if Op with DataFormat::DT_AUTO, the arguments of this op
* is formatted to NHWC
*/
/// Conventions:
/// 1. DataFormat::AUTO stands for formatted (NHWC or NCHW)
/// 2. if Op with DataFormat::AUTO, the arguments of this op
/// is formatted to NHWC
class NetDefAdapter {
public:
NetDefAdapter(const OpRegistryBase *op_registry,
const Workspace *ws);
// Adapt original net_def to a better net.
// 1. Adapt device: choose best device for every op in the net.
// 2. Adapt data type: Add data type related transform ops
// for mixing precision.
// 3. Adapt data format: confirm data format of every op
// and add transpose if necessary.
// 4. Adapt memory type: Add BufferTransform if necessary
// for transforming memory type between ops.
MaceStatus AdaptNetDef(
const NetDef *net_def,
Device *target_device,
......@@ -91,12 +97,12 @@ class NetDefAdapter {
NetDef *target_net_def);
MaceStatus AdaptMemoryType(
mace::OpConditionContext *context,
mace::OperatorDef *op_def,
OpConditionContext *context,
OperatorDef *op_def,
TensorInfoMap *output_map,
std::unordered_set<std::string> *transformed_set,
MemoryType *op_output_mem_types,
mace::NetDef *target_net_def);
NetDef *target_net_def);
std::string DebugString(const NetDef *net_def);
......
mace/core/net_optimizer.cc
浏览文件 @ 95b32c24
......@@ -19,10 +19,10 @@
namespace mace {
DeviceType NetOptimizer::SelectBestDevice(
const mace::OperatorDef *op_def,
const OperatorDef *op_def,
DeviceType target_device_type,
const std::set<mace::DeviceType> &available_devices,
const std::vector<mace::DeviceType> &inputs_op_devices) {
const std::set<DeviceType> &available_devices,
const std::vector<DeviceType> &inputs_op_devices) {
static const std::set<std::string> kComputeIntensiveOps = {
"Conv2D", "DepthwiseConv2d", "Deconv2D", "DepthwiseDeconv2d",
"FullyConnected"
......
mace/core/net_optimizer.h
浏览文件 @ 95b32c24
......@@ -23,8 +23,21 @@
namespace mace {
/// Any optimization for Net could be put in here in the future.
class NetOptimizer {
public:
/// Select best device for the op to support mixing usage of CPU and GPU.
/// Greedy strategy: one way to the end. If the op fallback to CPU, then
/// the follow-up ops will run on CPU too util meet
/// some compute-intensive ops(Convolution) to
/// reduce the memory copy between CPU and GPU.
/// Simple but effective.
///
/// \param op_def the op
/// \param target_device target device to run on
/// \param available_devices available devices of the op
/// \param inputs_op_devices devices of father ops run on
/// \return Best device for the op_def
DeviceType SelectBestDevice(const OperatorDef *op_def,
DeviceType target_device,
const std::set<DeviceType> &available_devices,
......
mace/core/operator.cc
浏览文件 @ 95b32c24
......@@ -21,22 +21,22 @@
namespace mace {
OpConditionContext::OpConditionContext(
const mace::Workspace *ws,
mace::OpConditionContext::TensorShapeMap *info)
const Workspace *ws,
OpConditionContext::TensorShapeMap *info)
: operator_def_(nullptr),
ws_(ws),
device_(nullptr),
tensor_shape_info_(info) {}
void OpConditionContext::set_operator_def(
const mace::OperatorDef *operator_def) {
const OperatorDef *operator_def) {
operator_def_ = operator_def;
input_data_types_.clear();
}
void OpConditionContext::SetInputInfo(size_t idx,
mace::MemoryType mem_type,
mace::DataType dt) {
MemoryType mem_type,
DataType dt) {
if (input_mem_types_.empty()) {
// the default inputs' memory types are same as output memory type.
input_mem_types_.resize(operator_def_->input_size(), output_mem_type_);
......@@ -53,7 +53,7 @@ void OpConditionContext::SetInputInfo(size_t idx,
input_data_types_[idx] = dt;
}
void OpConditionContext::set_output_mem_type(mace::MemoryType type) {
void OpConditionContext::set_output_mem_type(MemoryType type) {
MACE_CHECK(operator_def_ != nullptr);
output_mem_type_ = type;
input_mem_types_.clear();
......@@ -106,7 +106,7 @@ OpConstructContext::OpConstructContext(Workspace *ws)
device_(nullptr) {}
void OpConstructContext::set_operator_def(
std::shared_ptr<mace::OperatorDef> operator_def) {
std::shared_ptr<OperatorDef> operator_def) {
operator_def_ = operator_def;
}
......@@ -225,9 +225,20 @@ OpRegistrationInfo::OpRegistrationInfo() {
context->set_output_mem_type(MemoryType::CPU_BUFFER);
}
};
data_format_selector = [](OpConditionContext *context)
-> std::vector<DataFormat> {
DataFormat op_data_format =
static_cast<DataFormat>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*context->operator_def(), "data_format",
static_cast<int>(DataFormat::NONE)));
return std::vector<DataFormat>(context->operator_def()->input_size(),
op_data_format);
};
}
void OpRegistrationInfo::AddDevice(mace::DeviceType device) {
void OpRegistrationInfo::AddDevice(DeviceType device) {
devices.insert(device);
}
......@@ -239,9 +250,9 @@ void OpRegistrationInfo::Register(const std::string &key, OpCreator creator) {
MaceStatus OpRegistryBase::Register(
const std::string &op_type,
const mace::DeviceType device_type,
const mace::DataType dt,
mace::OpRegistrationInfo::OpCreator creator) {
const DeviceType device_type,
const DataType dt,
OpRegistrationInfo::OpCreator creator) {
if (registry_.count(op_type) == 0) {
registry_[op_type] = std::unique_ptr<OpRegistrationInfo>(
new OpRegistrationInfo);
......@@ -277,12 +288,20 @@ const std::set<DeviceType> OpRegistryBase::AvailableDevices(
void OpRegistryBase::GetInOutMemoryTypes(
const std::string &op_type,
mace::OpConditionContext *context) const {
OpConditionContext *context) const {
MACE_CHECK(registry_.count(op_type) != 0,
op_type, " operation is not registered.");
return registry_.at(op_type)->memory_type_setter(context);
}
const std::vector<DataFormat> OpRegistryBase::InputsDataFormat(
const std::string &op_type,
OpConditionContext *context) const {
MACE_CHECK(registry_.count(op_type) != 0,
op_type, " operation is not registered.");
return registry_.at(op_type)->data_format_selector(context);
}
std::unique_ptr<Operation> OpRegistryBase::CreateOperation(
OpConstructContext *context,
DeviceType device_type) const {
......@@ -321,11 +340,17 @@ OpConditionBuilder &OpConditionBuilder::SetDevicePlacerFunc(
}
OpConditionBuilder& OpConditionBuilder::SetInputMemoryTypeSetter(
mace::OpRegistrationInfo::MemoryTypeSetter setter) {
OpRegistrationInfo::MemoryTypeSetter setter) {
memory_type_setter_ = setter;
return *this;
}
OpConditionBuilder& OpConditionBuilder::SetInputsDataFormatSelector(
OpRegistrationInfo::DataFormatSelector selector) {
data_format_selector_ = selector;
return *this;
}
void OpConditionBuilder::Finalize(OpRegistrationInfo *info) const {
if (info != nullptr) {
if (placer_) {
......@@ -334,6 +359,10 @@ void OpConditionBuilder::Finalize(OpRegistrationInfo *info) const {
if (memory_type_setter_) {
info->memory_type_setter = memory_type_setter_;
}
if (data_format_selector_) {
info->data_format_selector = data_format_selector_;
}
}
}
......
mace/core/operator.h
浏览文件 @ 95b32c24
......@@ -117,6 +117,14 @@ class OpConstructContext {
inline Device *device() const {
return device_;
}
#ifdef MACE_ENABLE_OPENCL
inline MemoryType GetOpMemoryType() const {
return static_cast<MemoryType>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*operator_def_, OutputMemoryTypeTagName(),
static_cast<int>(MemoryType::CPU_BUFFER)));
}
#endif // MACE_ENABLE_OPENCL
private:
std::shared_ptr<OperatorDef> operator_def_;
......@@ -270,6 +278,9 @@ class OpConditionBuilder {
OpConditionBuilder &SetInputMemoryTypeSetter(
OpRegistrationInfo::MemoryTypeSetter setter);
OpConditionBuilder &SetInputsDataFormatSelector(
OpRegistrationInfo::DataFormatSelector selector);
void Finalize(OpRegistrationInfo *info) const;
private:
......@@ -297,6 +308,9 @@ class OpRegistryBase {
void GetInOutMemoryTypes(
const std::string &op_type, OpConditionContext *context) const;
const std::vector<DataFormat> InputsDataFormat(
const std::string &op_type, OpConditionContext *context) const;
std::unique_ptr<Operation> CreateOperation(
OpConstructContext *context,
DeviceType device_type) const;
......
mace/core/runtime/opencl/opencl_util.cc
浏览文件 @ 95b32c24
......@@ -173,7 +173,7 @@ void OpenCLUtil::BuildTransformOpDef(
arg->set_i(static_cast<int32_t>(dt));
arg = op_def->add_arg();
arg->set_name("data_format");
arg->set_i(data_format);
arg->set_i(static_cast<int>(data_format));
if (!input_shape.empty()) {
OutputShape *shape = op_def->add_output_shape();
for (auto value : input_shape) {
......
mace/core/workspace.cc
浏览文件 @ 95b32c24
......@@ -269,7 +269,7 @@ MaceStatus Workspace::PreallocateOutputTensor(
tensor_mem.second.data_type,
false, tensor_mem.first));
tensor->set_data_format(tensor_mem.second.data_format);
if (tensor_mem.second.data_format != DataFormat::DF_NONE) {
if (tensor_mem.second.data_format != DataFormat::NONE) {
if (mem_blocks[tensor_mem.second.mem_id].mem_type()
== MemoryType::GPU_IMAGE) {
VLOG(1) << "Tensor: " << tensor_mem.first
......
mace/examples/cli/example.cc
浏览文件 @ 95b32c24
......@@ -94,7 +94,7 @@ DataFormat ParseDataFormat(const std::string &data_format_str) {
} else if (data_format_str == "OIHW") {
return DataFormat::OIHW;
} else {
return DataFormat::DF_NONE;
return DataFormat::NONE;
}
}
......
mace/libmace/capability.cc
浏览文件 @ 95b32c24
......@@ -143,7 +143,7 @@ void BMNet::SetUp() {
// Add input and output information
for (size_t i = 0; i < input_names_.size(); ++i) {
InputOutputInfo *info = net_.add_input_info();
info->set_data_format(DataFormat::NHWC);
info->set_data_format(static_cast<int>(DataFormat::NHWC));
info->set_name(input_names_[i]);
for (auto d : input_shapes_[i]) {
info->add_dims(static_cast<int>(d));
......@@ -244,7 +244,7 @@ void BMNet::AddConv(const std::string &conv_type,
op_def->add_output(output_name);
AddIntsArg(op_def, "strides", strides);
AddIntArg(op_def, "padding", padding_type);
AddIntArg(op_def, "has_data_format", 1);
AddIntArg(op_def, "data_format", static_cast<int>(DataFormat::AUTO));
AddIntArg(op_def, "T", DT_HALF);
if (has_relu6) {
AddStringArg(op_def, "activation", "RELUX");
......@@ -271,7 +271,7 @@ void BMNet::AddEltwise(const std::string &op_name,
op_def->add_output(output);
AddIntArg(op_def, "type", type);
AddIntArg(op_def, "T", DT_HALF);
AddIntArg(op_def, "has_data_format", 1);
AddIntArg(op_def, "data_format", static_cast<int>(DataFormat::AUTO));
OutputShape *shape = op_def->add_output_shape();
for (auto dim : output_shape) {
shape->add_dims(dim);
......
mace/libmace/mace.cc
浏览文件 @ 95b32c24
......@@ -283,9 +283,9 @@ MaceTensor::MaceTensor(const std::vector<int64_t> &shape,
std::shared_ptr<void> data,
const DataFormat format) {
MACE_CHECK_NOTNULL(data.get());
MACE_CHECK(format == DataFormat::DF_NONE || format == DataFormat::NHWC
|| format == DataFormat::NCHW || format == OIHW,
"MACE only support DF_NONE, NHWC, NCHW and OIHW "
MACE_CHECK(format == DataFormat::NONE || format == DataFormat::NHWC
|| format == DataFormat::NCHW || format == DataFormat::OIHW,
"MACE only support NONE, NHWC, NCHW and OIHW "
"formats of input now.");
impl_ = make_unique<MaceTensor::Impl>();
impl_->shape = shape;
......@@ -496,7 +496,7 @@ MaceStatus MaceEngine::Impl::Init(
DataType output_dt = output_info_map_[output_name].data_type();
Tensor *output_tensor =
ws_->CreateTensor(output_name, device_->allocator(), output_dt);
output_tensor->set_data_format(NHWC);
output_tensor->set_data_format(DataFormat::NHWC);
#endif
}
#if defined(MACE_ENABLE_HEXAGON) || defined(MACE_ENABLE_HTA)
......@@ -585,14 +585,14 @@ MaceEngine::Impl::~Impl() {
MaceStatus MaceEngine::Impl::TransposeInput(
const std::pair<const std::string, MaceTensor> &input,
Tensor *input_tensor) {
bool has_data_format = input_tensor->data_format() != DataFormat::DF_NONE;
DataFormat data_format = DataFormat::DF_NONE;
bool has_data_format = input_tensor->data_format() != DataFormat::NONE;
DataFormat data_format = DataFormat::NONE;
DataType input_dt = input_tensor->dtype();
if (has_data_format) {
std::vector<int> dst_dims;
if (device_->device_type() == DeviceType::CPU &&
input.second.shape().size() == 4 &&
input.second.data_format() == NHWC &&
input.second.data_format() == DataFormat::NHWC &&
!is_quantized_model_) {
VLOG(1) << "Transform input " << input.first << " from NHWC to NCHW";
input_tensor->set_data_format(DataFormat::NCHW);
......@@ -654,28 +654,28 @@ MaceStatus MaceEngine::Impl::TransposeOutput(
DataType output_dt = output_tensor->dtype();
// save output
if (output_tensor != nullptr && output->second.data() != nullptr) {
if (output_tensor->data_format() != DataFormat::DF_NONE &&
output->second.data_format() != DataFormat::DF_NONE &&
if (output_tensor->data_format() != DataFormat::NONE &&
output->second.data_format() != DataFormat::NONE &&
output->second.shape().size() == 4 &&
output->second.data_format() != output_tensor->data_format()) {
VLOG(1) << "Transform output " << output->first << " from "
<< output_tensor->data_format() << " to "
<< output->second.data_format();
<< static_cast<int>(output_tensor->data_format()) << " to "
<< static_cast<int>(output->second.data_format());
std::vector<int> dst_dims;
if (output_tensor->data_format() == NCHW &&
output->second.data_format() == NHWC) {
if (output_tensor->data_format() == DataFormat::NCHW &&
output->second.data_format() == DataFormat::NHWC) {
dst_dims = {0, 2, 3, 1};
} else if (output_tensor->data_format() == NHWC &&
output->second.data_format() == NCHW) {
} else if (output_tensor->data_format() == DataFormat::NHWC &&
output->second.data_format() == DataFormat::NCHW) {
dst_dims = {0, 3, 1, 2};
} else {
LOG(FATAL) << "Not supported output data format: "
<< output->second.data_format() << " vs "
<< output_tensor->data_format();
<< static_cast<int>(output->second.data_format()) << " vs "
<< static_cast<int>(output_tensor->data_format());
}
VLOG(1) << "Transform output " << output->first << " from "
<< output_tensor->data_format() << " to "
<< output->second.data_format();
<< static_cast<int>(output_tensor->data_format()) << " to "
<< static_cast<int>(output->second.data_format());
std::vector<index_t> shape =
TransposeShape<index_t, index_t>(output_tensor->shape(),
dst_dims);
......
mace/ops/activation.cc
浏览文件 @ 95b32c24
......@@ -96,7 +96,7 @@ class ActivationOp<DeviceType::GPU, T> : public Operation {
auto leakyrelu_coefficient = static_cast<T>(
Operation::GetOptionalArg<float>("leakyrelu_coefficient", 0.0f));
MemoryType mem_type;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
mem_type = MemoryType::GPU_IMAGE;
kernel_ = make_unique<opencl::image::ActivationKernel<T>>(
type, relux_max_limit, leakyrelu_coefficient);
......@@ -140,11 +140,13 @@ void RegisterActivation(OpRegistryBase *op_registry) {
.SetDevicePlacerFunc(
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU, DeviceType::GPU };
}
int has_data_format =
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op, "has_data_format", 0);
if (!has_data_format ||
(op->output_shape_size() != op->output_size()) ||
op->output_shape(0).dims_size() != 4) {
return { DeviceType::CPU };
}
......
mace/ops/activation_test.cc
浏览文件 @ 95b32c24
......@@ -207,7 +207,8 @@ void TestSimplePrelu() {
// Run
net.RunOp(D);
} else {
net.TransformDataFormat<D, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformDataFormat<D, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Activation", "PreluTest")
.Input("InputNCHW")
.Input("Alpha")
......@@ -217,7 +218,8 @@ void TestSimplePrelu() {
// Run
net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<D, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
auto expected = net.CreateTensor<float>(
......
mace/ops/addn.cc
浏览文件 @ 95b32c24
......@@ -69,7 +69,7 @@ class AddNOp<DeviceType::GPU, T> : public Operation {
public:
explicit AddNOp(OpConstructContext *context)
: Operation(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::AddNKernel<T>>();
} else {
MACE_NOT_IMPLEMENTED;
......@@ -109,11 +109,13 @@ void RegisterAddN(OpRegistryBase *op_registry) {
.SetDevicePlacerFunc(
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU, DeviceType::GPU };
}
int has_data_format =
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op, "has_data_format", 0);
if (!has_data_format ||
(op->output_shape_size() != op->output_size()) ||
op->output_shape(0).dims_size() != 4) {
return { DeviceType::CPU };
}
......
mace/ops/arm/fp32/deconv_2d.cc
浏览文件 @ 95b32c24
......@@ -54,7 +54,7 @@ MaceStatus Deconv2dBase::ResizeOutAndPadOut(
out_pad_size,
&padded_out_shape,
framework_type_,
NCHW);
DataFormat::NCHW);
MACE_RETURN_IF_ERROR(output->Resize(out_shape));
......
mace/ops/batch_norm.cc
浏览文件 @ 95b32c24
......@@ -174,7 +174,7 @@ class BatchNormOp<DeviceType::GPU, T> : public Operation {
float leakyrelu_coefficient = Operation::GetOptionalArg<float>(
"leakyrelu_coefficient", 0.0f);
MemoryType mem_type;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
mem_type = MemoryType::GPU_IMAGE;
kernel_ = make_unique<opencl::image::BatchNormKernel<T>>(
epsilon, activation, relux_max_limit, leakyrelu_coefficient);
......
mace/ops/batch_norm_test.cc
浏览文件 @ 95b32c24
......@@ -34,7 +34,8 @@ void Simple() {
net.AddInputFromArray<D, float>("Var", {1}, {11.67f}, true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<D, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformDataFormat<D, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("InputNCHW")
.Input("Scale")
......@@ -47,7 +48,8 @@ void Simple() {
// Run
net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<D, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("Input")
......@@ -93,8 +95,8 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Var", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("BatchNorm", "BatchNormTest")
......@@ -112,8 +114,8 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -163,8 +165,8 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) {
net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Var", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("InputNCHW")
......@@ -179,8 +181,8 @@ TEST_F(BatchNormOpTest, SimpleRandomHalfOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -230,8 +232,8 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Var", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("InputNCHW")
......@@ -246,8 +248,8 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -296,8 +298,8 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) {
net.AddRandomInput<DeviceType::GPU, float>("Mean", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Var", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("InputNCHW")
......@@ -312,8 +314,8 @@ TEST_F(BatchNormOpTest, ComplexRandomHalfOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......
mace/ops/batch_to_space.cc
浏览文件 @ 95b32c24
......@@ -264,7 +264,7 @@ class BatchToSpaceNDOp<DeviceType::GPU, T> : public BatchToSpaceOpBase {
public:
explicit BatchToSpaceNDOp(OpConstructContext *context)
: BatchToSpaceOpBase(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::BatchToSpaceKernel<T>>();
} else {
MACE_NOT_IMPLEMENTED;
......
mace/ops/bias_add.cc
浏览文件 @ 95b32c24
......@@ -103,7 +103,7 @@ class BiasAddOp<DeviceType::GPU, T> : public Operation {
: Operation(context),
has_data_format_(Operation::GetOptionalArg<int>("has_data_format", 1)) {
MemoryType mem_type = MemoryType::CPU_BUFFER;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
mem_type = MemoryType::GPU_IMAGE;
kernel_ = make_unique<opencl::image::BiasAddKernel<T>>();
} else {
......@@ -151,11 +151,13 @@ void RegisterBiasAdd(OpRegistryBase *op_registry) {
.SetDevicePlacerFunc(
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU, DeviceType::GPU };
}
int has_data_format =
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op, "has_data_format", 0);
if (!has_data_format ||
(op->output_shape_size() != op->output_size()) ||
op->output_shape(0).dims_size() != 4) {
return { DeviceType::CPU };
}
......
mace/ops/bias_add_benchmark.cc
浏览文件 @ 95b32c24
......@@ -27,9 +27,7 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) {
OpsTestNet net;
// Add input data
DataFormat data_format = NHWC;
if (D == DeviceType::CPU) {
data_format = NCHW;
net.AddRandomInput<D, T>("Input", {batch, channels, height, width});
} else if (D == DeviceType::GPU) {
net.AddRandomInput<D, T>("Input", {batch, height, width, channels});
......
mace/ops/bias_add_test.cc
浏览文件 @ 95b32c24
......@@ -31,8 +31,8 @@ void BiasAddSimple() {
net.AddInputFromArray<D, float>("Bias", {1}, {0.5f}, true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BiasAdd", "BiasAddTest")
.Input("InputNCHW")
.Input("Bias")
......@@ -41,8 +41,8 @@ void BiasAddSimple() {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("BiasAdd", "BiasAddTest")
.Input("Input")
......@@ -83,8 +83,8 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) {
{batch, height, width, channels});
net.AddRandomInput<DeviceType::GPU, float>("Bias", {channels}, true, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("BiasAdd", "BiasAddTest")
......@@ -97,8 +97,8 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -132,8 +132,8 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) {
{batch, height, width, channels});
net.AddRandomInput<DeviceType::GPU, float>("Bias", {channels}, true, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("BiasAdd", "BiasAddTest")
......@@ -146,8 +146,8 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......
mace/ops/buffer_to_image_benchmark.cc
浏览文件 @ 95b32c24
......@@ -48,7 +48,6 @@ void FilterBufferToImage(int iters,
OpenCLBufferType::IN_OUT_CHANNEL,
MemoryType::GPU_IMAGE,
0,
DataFormat::NHWC,
b2i_output);
};
......
mace/ops/buffer_to_image_test.cc
浏览文件 @ 95b32c24
......@@ -37,14 +37,14 @@ void TestBidirectionTransform(const OpenCLBufferType type,
OpenCLBufferTransformer<T>(MemoryType::GPU_BUFFER, MemoryType::GPU_IMAGE)
.Transform(&context, net.ws()->GetTensor("Input"),
type, MemoryType::GPU_IMAGE, 0, DataFormat::NHWC, b2i_output);
type, MemoryType::GPU_IMAGE, 0, b2i_output);
// Inverse Transform
Tensor *i2b_output = net.ws()->CreateTensor(
"I2BOutput", context.device()->allocator(), DataTypeToEnum<T>::value);
OpenCLBufferTransformer<T>(MemoryType::GPU_IMAGE, MemoryType::GPU_BUFFER)
.Transform(&context, b2i_output,
type, MemoryType::GPU_BUFFER, 0, DataFormat::NHWC, i2b_output);
type, MemoryType::GPU_BUFFER, 0, i2b_output);
// Check
ExpectTensorNear<T>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"),
......@@ -178,14 +178,14 @@ void TestDiffTypeBidirectionTransform(const OpenCLBufferType type,
OpenCLBufferTransformer<T>(MemoryType::GPU_BUFFER, MemoryType::GPU_IMAGE)
.Transform(&context, net.ws()->GetTensor("Input"),
type, MemoryType::GPU_IMAGE, 0, DataFormat::NHWC, b2i_output);
type, MemoryType::GPU_IMAGE, 0, b2i_output);
// Inverse Transform
Tensor *i2b_output = net.ws()->CreateTensor(
"I2BOutput", context.device()->allocator(), DT_FLOAT);
OpenCLBufferTransformer<float>(MemoryType::GPU_IMAGE, MemoryType::GPU_BUFFER)
.Transform(&context, b2i_output,
type, MemoryType::GPU_BUFFER, 0, DataFormat::NHWC, i2b_output);
type, MemoryType::GPU_BUFFER, 0, i2b_output);
// Check
ExpectTensorNear<float>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"),
......@@ -218,14 +218,14 @@ void TestStringHalfBidirectionTransform(const OpenCLBufferType type,
// Transform
OpenCLBufferTransformer<T>(MemoryType::GPU_BUFFER, MemoryType::GPU_IMAGE)
.Transform(&context, net.ws()->GetTensor("Input"),
type, MemoryType::GPU_IMAGE, 0, DataFormat::NHWC, b2i_output);
type, MemoryType::GPU_IMAGE, 0, b2i_output);
// Inverse Transform
Tensor *i2b_output = net.ws()->CreateTensor(
"I2BOutput", context.device()->allocator(), DataTypeToEnum<T>::value);
OpenCLBufferTransformer<T>(MemoryType::GPU_IMAGE, MemoryType::GPU_BUFFER)
.Transform(&context, b2i_output,
type, MemoryType::GPU_BUFFER, 0, DataFormat::NHWC, i2b_output);
type, MemoryType::GPU_BUFFER, 0, i2b_output);
// Check
ExpectTensorNear<half>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"),
......
mace/ops/buffer_transform.cc
浏览文件 @ 95b32c24
......@@ -39,14 +39,11 @@ class BufferTransformOp<DeviceType::GPU, T> : public Operation {
auto type =
static_cast<OpenCLBufferType>(Operation::GetOptionalArg<int>(
"buffer_type", static_cast<int>(CONV2D_FILTER)));
DataFormat data_format = static_cast<DataFormat>(
Operation::GetOptionalArg<int>("data_format", DataFormat::DF_NONE));
MemoryType in_mem_type = context->workspace()->GetTensor(
operator_def_->input(0))->memory_type();
return OpenCLBufferTransformer<T>(in_mem_type, out_mem_type_).Transform(
context, input, type, out_mem_type_, wino_blk_size_,
data_format, output);
context, input, type, out_mem_type_, wino_blk_size_, output);
}
private:
......
mace/ops/buffer_transform_test.cc
浏览文件 @ 95b32c24
......@@ -48,7 +48,7 @@ void TestBidirectionTransform(const OpenCLBufferType type,
OpenCLBufferTransformer<DstType>(MemoryType::GPU_BUFFER,
MemoryType::GPU_BUFFER)
.Transform(&context, net.ws()->GetTensor("Input"),
type, MemoryType::GPU_BUFFER, 0, DataFormat::NHWC, bt_output);
type, MemoryType::GPU_BUFFER, 0, bt_output);
// Inverse Transform
Tensor *output = net.ws()->CreateTensor(
......@@ -57,7 +57,7 @@ void TestBidirectionTransform(const OpenCLBufferType type,
OpenCLBufferTransformer<OrgType>(MemoryType::GPU_BUFFER,
MemoryType::GPU_BUFFER)
.Transform(&context, bt_output,
type, MemoryType::GPU_BUFFER, 0, DataFormat::NHWC, output);
type, MemoryType::GPU_BUFFER, 0, output);
if (DataTypeToEnum<OrgType>::value == DataTypeToEnum<DstType>::value) {
EXPECT_EQ(net.GetOutput("Input")->UnderlyingBuffer(),
......@@ -94,7 +94,7 @@ void TestArgumentTransform(const index_t input_size) {
MemoryType::GPU_BUFFER)
.Transform(&context, net.ws()->GetTensor("Input"),
OpenCLBufferType::ARGUMENT, MemoryType::GPU_BUFFER,
0, DataFormat::NHWC, output);
0, output);
index_t expected_size = RoundUp<index_t>(input_size, 4);
EXPECT_EQ(expected_size, output->buffer_shape()[0]);
......
mace/ops/channel_shuffle.cc
浏览文件 @ 95b32c24
......@@ -82,7 +82,7 @@ class ChannelShuffleOp<DeviceType::GPU, T> : public Operation {
explicit ChannelShuffleOp(OpConstructContext *context)
: Operation(context) {
const int groups = Operation::GetOptionalArg<int>("group", 1);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::ChannelShuffleKernel<T>>(groups);
} else {
MACE_NOT_IMPLEMENTED;
......@@ -119,7 +119,7 @@ void RegisterChannelShuffle(OpRegistryBase *op_registry) {
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU };
return { DeviceType::CPU, DeviceType::GPU };
}
int groups = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op, "group", 1);
......
mace/ops/channel_shuffle_test.cc
浏览文件 @ 95b32c24
......@@ -28,8 +28,8 @@ TEST_F(ChannelShuffleOpTest, C8G4_CPU) {
"Input", {1, 1, 2, 8},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("ChannelShuffle", "ChannelShuffleTest")
......@@ -40,8 +40,8 @@ TEST_F(ChannelShuffleOpTest, C8G4_CPU) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>(
......
mace/ops/common/conv_pool_2d_util.cc
浏览文件 @ 95b32c24
......@@ -40,19 +40,19 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,
index_t input_height = 0, input_width = 0;
index_t kernel_height = 0, kernel_width = 0;
if (input_format == NCHW) {
if (input_format == DataFormat::NCHW) {
input_height = input_shape[2];
input_width = input_shape[3];
} else if (input_format == NHWC) {
} else if (input_format == DataFormat::NHWC) {
input_height = input_shape[1];
input_width = input_shape[2];
} else {
MACE_NOT_IMPLEMENTED;
}
if (filter_format == OIHW) {
if (filter_format == DataFormat::OIHW) {
kernel_height = filter_shape[2];
kernel_width = filter_shape[3];
} else if (filter_format == OHWI) {
} else if (filter_format == DataFormat::OHWI) {
kernel_height = filter_shape[1];
kernel_width = filter_shape[2];
} else {
......@@ -97,11 +97,11 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,
0, (output_width - 1) * strides[1] + k_extent_width - input_width);
output_shape[0] = input_shape[0];
if (input_format == NCHW) {
if (input_format == DataFormat::NCHW) {
output_shape[1] = output_channels;
output_shape[2] = output_height;
output_shape[3] = output_width;
} else if (input_format == NHWC) {
} else if (input_format == DataFormat::NHWC) {
output_shape[1] = output_height;
output_shape[2] = output_width;
output_shape[3] = output_channels;
......@@ -117,7 +117,8 @@ void CalcNCHWPaddingAndOutputSize(const index_t *input_shape, // NCHW
Padding padding,
index_t *output_shape,
int *padding_size) {
CalcPaddingAndOutputSize(input_shape, NCHW, filter_shape, OIHW, dilations,
CalcPaddingAndOutputSize(input_shape, DataFormat::NCHW, filter_shape,
DataFormat::OIHW, dilations,
strides, padding, output_shape, padding_size);
}
......@@ -128,7 +129,8 @@ void CalcNHWCPaddingAndOutputSize(const index_t *input_shape, // NHWC
Padding padding,
index_t *output_shape,
int *padding_size) {
CalcPaddingAndOutputSize(input_shape, NHWC, filter_shape, OIHW, dilations,
CalcPaddingAndOutputSize(input_shape, DataFormat::NHWC, filter_shape,
DataFormat::OIHW, dilations,
strides, padding, output_shape, padding_size);
}
......@@ -151,19 +153,19 @@ void CalcOutputSize(const index_t *input_shape,
index_t input_height = 0, input_width = 0;
index_t kernel_height = 0, kernel_width = 0;
if (input_format == NCHW) {
if (input_format == DataFormat::NCHW) {
input_height = input_shape[2];
input_width = input_shape[3];
} else if (input_format == NHWC) {
} else if (input_format == DataFormat::NHWC) {
input_height = input_shape[1];
input_width = input_shape[2];
} else {
MACE_NOT_IMPLEMENTED;
}
if (filter_format == OIHW) {
if (filter_format == DataFormat::OIHW) {
kernel_height = filter_shape[2];
kernel_width = filter_shape[3];
} else if (filter_format == OHWI) {
} else if (filter_format == DataFormat::OHWI) {
kernel_height = filter_shape[1];
kernel_width = filter_shape[2];
} else {
......@@ -195,11 +197,11 @@ void CalcOutputSize(const index_t *input_shape,
}
output_shape[0] = input_shape[0];
if (input_format == NCHW) {
if (input_format == DataFormat::NCHW) {
output_shape[1] = output_channels;
output_shape[2] = output_height;
output_shape[3] = output_width;
} else if (input_format == NHWC) {
} else if (input_format == DataFormat::NHWC) {
output_shape[1] = output_height;
output_shape[2] = output_width;
output_shape[3] = output_channels;
......@@ -215,7 +217,8 @@ void CalcOutputSize(const index_t *input_shape, // NHWC
const int *strides,
const RoundType round_type,
index_t *output_shape) {
CalcOutputSize(input_shape, NHWC, filter_shape, OIHW, padding_size, dilations,
CalcOutputSize(input_shape, DataFormat::NHWC, filter_shape,
DataFormat::OIHW, padding_size, dilations,
strides, round_type, output_shape);
}
......@@ -226,7 +229,8 @@ void CalcNCHWOutputSize(const index_t *input_shape, // NCHW
const int *strides,
const RoundType round_type,
index_t *output_shape) {
CalcOutputSize(input_shape, NCHW, filter_shape, OIHW, padding_size, dilations,
CalcOutputSize(input_shape, DataFormat::NCHW, filter_shape,
DataFormat::OIHW, padding_size, dilations,
strides, round_type, output_shape);
}
......@@ -241,14 +245,18 @@ void CalcDeconvShape_TF(const std::vector<index_t> &input_shape,
std::vector<index_t> *padded_out_shape,
DataFormat data_format) {
const index_t
in_height = data_format == NCHW ? input_shape[2] : input_shape[1];
in_height =
data_format == DataFormat::NCHW ? input_shape[2] : input_shape[1];
const index_t
in_width = data_format == NCHW ? input_shape[3] : input_shape[2];
in_width =
data_format == DataFormat::NCHW ? input_shape[3] : input_shape[2];
const index_t
out_height = data_format == NCHW ? output_shape[2] : output_shape[1];
out_height =
data_format == DataFormat::NCHW ? output_shape[2] : output_shape[1];
const index_t
out_width = data_format == NCHW ? output_shape[3] : output_shape[2];
out_width =
data_format == DataFormat::NCHW ? output_shape[3] : output_shape[2];
const index_t extended_in_height = (in_height - 1) * strides[0] + 1;
const index_t extended_in_width = (in_width - 1) * strides[1] + 1;
......@@ -307,11 +315,11 @@ void CalcDeconvShape_TF(const std::vector<index_t> &input_shape,
padded_out_shape->resize(4);
(*padded_out_shape)[0] = output_shape[0];
(*padded_out_shape)[1] =
data_format == NCHW ? output_channel : padded_out_height;
data_format == DataFormat::NCHW ? output_channel : padded_out_height;
(*padded_out_shape)[2] =
data_format == NCHW ? padded_out_height : padded_out_width;
data_format == DataFormat::NCHW ? padded_out_height : padded_out_width;
(*padded_out_shape)[3] =
data_format == NCHW ? padded_out_width : output_channel;
data_format == DataFormat::NCHW ? padded_out_width : output_channel;
}
}
......@@ -325,9 +333,11 @@ void CalcDeconvShape_Caffe(const std::vector<index_t> &input_shape,
std::vector<index_t> *padded_out_shape,
DataFormat data_format) {
const index_t
in_height = data_format == NCHW ? input_shape[2] : input_shape[1];
in_height =
data_format == DataFormat::NCHW ? input_shape[2] : input_shape[1];
const index_t
in_width = data_format == NCHW ? input_shape[3] : input_shape[2];
in_width =
data_format == DataFormat::NCHW ? input_shape[3] : input_shape[2];
const index_t output_channel = filter_shape[0] * group;
......@@ -351,11 +361,11 @@ void CalcDeconvShape_Caffe(const std::vector<index_t> &input_shape,
padded_out_shape->resize(4);
(*padded_out_shape)[0] = input_shape[0];
(*padded_out_shape)[1] =
data_format == NCHW ? output_channel : padded_out_height;
data_format == DataFormat::NCHW ? output_channel : padded_out_height;
(*padded_out_shape)[2] =
data_format == NCHW ? padded_out_height : padded_out_width;
data_format == DataFormat::NCHW ? padded_out_height : padded_out_width;
(*padded_out_shape)[3] =
data_format == NCHW ? padded_out_width : output_channel;
data_format == DataFormat::NCHW ? padded_out_width : output_channel;
}
if (out_shape != nullptr) {
......@@ -363,9 +373,11 @@ void CalcDeconvShape_Caffe(const std::vector<index_t> &input_shape,
index_t out_width = padded_out_width - out_pad_size[1];
out_shape->resize(4);
(*out_shape)[0] = input_shape[0];
(*out_shape)[1] = data_format == NCHW ? output_channel : out_height;
(*out_shape)[2] = data_format == NCHW ? out_height : out_width;
(*out_shape)[3] = data_format == NCHW ? out_width : output_channel;
(*out_shape)[1] =
data_format == DataFormat::NCHW ? output_channel : out_height;
(*out_shape)[2] = data_format == DataFormat::NCHW ? out_height : out_width;
(*out_shape)[3] =
data_format == DataFormat::NCHW ? out_width : output_channel;
}
}
......@@ -385,7 +397,7 @@ void CalDeconvOutputShapeAndPadSize(const std::vector<index_t> &input_shape,
MACE_CHECK(output_shape->size() == 4,
"deconv output shape shoud be 4-dims");
std::vector<index_t> &out_shape = *output_shape;
if (data_format == NCHW) {
if (data_format == DataFormat::NCHW) {
const index_t t = out_shape[1];
out_shape[1] = out_shape[3];
out_shape[3] = out_shape[2];
......
mace/ops/concat.cc
浏览文件 @ 95b32c24
......@@ -199,7 +199,7 @@ class ConcatOp<DeviceType::GPU, T> : public ConcatOpBase {
public:
explicit ConcatOp(OpConstructContext *context)
: ConcatOpBase(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::ConcatKernel<T>>();
} else {
MACE_NOT_IMPLEMENTED;
......@@ -243,9 +243,11 @@ void RegisterConcat(OpRegistryBase *op_registry) {
.SetDevicePlacerFunc(
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU, DeviceType::GPU };
}
auto tensor_shape_info = context->tensor_shape_info();
if (op->output_shape_size() != op->output_size() ||
op->output_shape(0).dims_size() != 4) {
if (op->output_shape(0).dims_size() != 4) {
return { DeviceType::CPU };
} else {
int has_data_format =
......
mace/ops/conv_2d.cc
浏览文件 @ 95b32c24
......@@ -231,9 +231,9 @@ class Conv2dOp<DeviceType::CPU, uint8_t> : public ConvPool2dOpBase {
std::vector<int> paddings(2);
if (paddings_.empty()) {
CalcPaddingAndOutputSize(input->shape().data(),
NHWC,
DataFormat::NHWC,
filter->shape().data(),
OHWI,
DataFormat::OHWI,
dilations_.data(),
strides_.data(),
padding_type_,
......@@ -242,9 +242,9 @@ class Conv2dOp<DeviceType::CPU, uint8_t> : public ConvPool2dOpBase {
} else {
paddings = paddings_;
CalcOutputSize(input->shape().data(),
NHWC,
DataFormat::NHWC,
filter->shape().data(),
OHWI,
DataFormat::OHWI,
paddings_.data(),
dilations_.data(),
strides_.data(),
......@@ -459,7 +459,7 @@ class Conv2dOp<DeviceType::GPU, T> : public ConvPool2dOpBase {
"leakyrelu_coefficient", 0.0f)),
wino_block_size_(Operation::GetOptionalArg<int>("wino_block_size", 0)) {
MemoryType mem_type;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
mem_type = MemoryType::GPU_IMAGE;
kernel_ = make_unique<opencl::image::Conv2dKernel<T>>();
} else {
......
mace/ops/conv_2d_test.cc
浏览文件 @ 95b32c24
......@@ -47,8 +47,8 @@ void TestNHWCSimple3x3VALID(int wino_blk_size = 0) {
const std::vector<index_t> output_shape = {1, 1, 1, 1};
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -60,8 +60,8 @@ void TestNHWCSimple3x3VALID(int wino_blk_size = 0) {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("Input")
......@@ -105,8 +105,8 @@ void TestNHWCSimple3x3SAME(int wino_blk_size = 0) {
const std::vector<index_t> output_shape = {1, 3, 3, 1};
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -118,8 +118,8 @@ void TestNHWCSimple3x3SAME(int wino_blk_size = 0) {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("Input")
......@@ -189,8 +189,8 @@ void TestNHWCSimple3x3WithoutBias() {
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -203,8 +203,8 @@ void TestNHWCSimple3x3WithoutBias() {
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("Input")
......@@ -256,8 +256,8 @@ void TestNHWCCombined3x3() {
net.AddInputFromArray<D, T>("Bias", {2}, {0.1f, 0.2f}, true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -270,8 +270,8 @@ void TestNHWCCombined3x3() {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("Input")
......@@ -321,8 +321,8 @@ void TestFusedNHWCSimple3x3VALID(int wino_blk_size = 0) {
const std::vector<index_t> output_shape = {1, 1, 1, 1};
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -336,8 +336,8 @@ void TestFusedNHWCSimple3x3VALID(int wino_blk_size = 0) {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("Input")
......@@ -376,8 +376,8 @@ void TestFusedNHWCSimple3x3WithoutBias(int wino_blk_size = 0) {
const std::vector<index_t> output_shape = {1, 1, 1, 1};
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -391,8 +391,8 @@ void TestFusedNHWCSimple3x3WithoutBias(int wino_blk_size = 0) {
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("Input")
......@@ -459,8 +459,8 @@ void TestConv1x1() {
net.AddInputFromArray<D, float>("Bias", {2}, {0.1f, 0.2f}, true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -472,8 +472,8 @@ void TestConv1x1() {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Conv2D", "Conv2DTest")
.Input("Input")
......@@ -532,8 +532,8 @@ void TestComplexConvNxNS12(const std::vector<index_t> &shape,
"Filter", {output_channels, input_channels, kernel_h, kernel_w}, true,
false);
net.AddRandomInput<D, T>("Bias", {output_channels}, true, false);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("Conv2D", "Conv2dTest")
......@@ -552,8 +552,8 @@ void TestComplexConvNxNS12(const std::vector<index_t> &shape,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -651,8 +651,8 @@ void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
float_bias_data,
true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
......@@ -667,8 +667,8 @@ void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -811,8 +811,8 @@ void TestDilationConvNxN(const std::vector<index_t> &shape,
"Filter", {output_channels, input_channels, kernel_h, kernel_w}, true);
net.AddRandomInput<D, T>("Bias", {output_channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("Conv2D", "Conv2dTest")
......@@ -828,8 +828,8 @@ void TestDilationConvNxN(const std::vector<index_t> &shape,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -900,8 +900,8 @@ void TestGeneralHalfAtrousConv(const std::vector<index_t> &image_shape,
"Filter", {output_channels, input_channels, kernel_h, kernel_w}, true);
net.AddRandomInput<D, float>("Bias", {output_channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
......@@ -916,8 +916,8 @@ void TestGeneralHalfAtrousConv(const std::vector<index_t> &image_shape,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......@@ -979,8 +979,8 @@ void TestArbitraryPadConvNxN(const std::vector<index_t> &shape,
"Filter", {output_channels, input_channels, kernel_h, kernel_w}, true);
net.AddRandomInput<D, float>("Bias", {output_channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Construct graph
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
......@@ -994,8 +994,8 @@ void TestArbitraryPadConvNxN(const std::vector<index_t> &shape,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -1118,12 +1118,12 @@ void TestQuant(const index_t batch,
net.AddRandomInput<CPU, float>("Filter", {out_channels, k_height, k_width,
in_channels}, true);
net.AddRandomInput<CPU, float>("Bias", {out_channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.TransformFilterDataFormat<DeviceType::CPU, float>("Filter",
OHWI,
DataFormat::OHWI,
"FilterOIHW",
OIHW);
DataFormat::OIHW);
OpDefBuilder("Conv2D", "Conv2dTest")
.Input("InputNCHW")
......@@ -1136,8 +1136,8 @@ void TestQuant(const index_t batch,
.AddIntArg("T", static_cast<int>(DT_FLOAT))
.Finalize(net.NewOperatorDef());
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("Quantize", "QuantizeFilter")
.Input("Filter")
......
mace/ops/crop.cc
浏览文件 @ 95b32c24
......@@ -117,7 +117,7 @@ class CropOp<DeviceType::GPU, T> : public Operation {
public:
explicit CropOp(OpConstructContext *context)
: Operation(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::CropKernel<T>>(
Operation::GetRepeatedArgs<int>("offset"));
} else {
......@@ -151,11 +151,13 @@ void RegisterCrop(OpRegistryBase *op_registry) {
.SetDevicePlacerFunc(
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU, DeviceType::GPU };
}
int has_data_format =
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op, "has_data_format", 0);
if (!has_data_format ||
(op->output_shape_size() != op->output_size()) ||
op->output_shape(0).dims_size() != 4) {
return { DeviceType::CPU };
}
......
mace/ops/crop_test.cc
浏览文件 @ 95b32c24
......@@ -42,13 +42,13 @@ void RunCrop(const std::vector<index_t> &input_shape,
.Finalize(net.NewOperatorDef());
} else if (D == CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input0",
NHWC,
DataFormat::NHWC,
"InputNCHW0",
NCHW);
DataFormat::NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Input1",
NHWC,
DataFormat::NHWC,
"InputNCHW1",
NCHW);
DataFormat::NCHW);
OpDefBuilder("Crop", "CropTest")
.Input("InputNCHW0")
.Input("InputNCHW1")
......@@ -62,8 +62,8 @@ void RunCrop(const std::vector<index_t> &input_shape,
net.RunOp(D);
if (D == CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
// Check
auto expected = net.CreateTensor<float>(expected_shape, expected_data);
......
mace/ops/cumsum_test.cc
浏览文件 @ 95b32c24
......@@ -32,8 +32,8 @@ void SimpleTestWithDataFormat(const std::vector<index_t> &shape,
OpsTestNet net;
net.AddInputFromArray<CPU, T>("Input", shape, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Cumsum", "CumsumTest")
.Input("InputNCHW")
......@@ -48,8 +48,8 @@ void SimpleTestWithDataFormat(const std::vector<index_t> &shape,
// Run
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
net.AddInputFromArray<CPU, T>("ExpectedOutput", shape, output);
ExpectTensorNear<T>(*net.GetOutput("ExpectedOutput"),
......
mace/ops/deconv_2d.cc
浏览文件 @ 95b32c24
......@@ -173,7 +173,7 @@ class Deconv2dOp<DeviceType::GPU, T> : public Deconv2dOpBase {
explicit Deconv2dOp(OpConstructContext *context)
: Deconv2dOpBase(context) {
MemoryType mem_type = MemoryType::GPU_IMAGE;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::Deconv2dKernel<T>>();
} else {
MACE_NOT_IMPLEMENTED;
......@@ -240,7 +240,7 @@ class Deconv2dOp<DeviceType::GPU, T> : public Deconv2dOpBase {
&out_paddings,
nullptr,
model_type_,
NHWC);
DataFormat::NHWC);
return kernel_->Compute(context, input, filter, bias,
strides_.data(), in_paddings.data(), activation_,
......@@ -276,7 +276,7 @@ void RegisterDeconv2D(OpRegistryBase *op_registry) {
MACE_NOT_IMPLEMENTED;
}
FrameworkType framework_type =
static_cast<ops::FrameworkType>(
static_cast<FrameworkType>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*(context->operator_def()), "framework_type",
FrameworkType::TENSORFLOW));
......
mace/ops/deconv_2d_test.cc
浏览文件 @ 95b32c24
......@@ -47,7 +47,8 @@ void RunTestSimple(const std::vector<index_t> &input_shape,
net.AddInputFromArray<D, float>("Filter", filter_shape, filter_data, true);
net.AddInputFromArray<D, float>("Bias", {out_channels}, bias_data, true);
// TODO(liutuo): remove the unused transform
net.TransformFilterDataFormat<D, float>("Filter", HWOI, "FilterOIHW", OIHW);
net.TransformFilterDataFormat<D, float>(
"Filter", DataFormat::HWOI, "FilterOIHW", DataFormat::OIHW);
if (D == DeviceType::GPU) {
if (model_type == FrameworkType::CAFFE) {
OpDefBuilder("Deconv2D", "Deconv2dTest")
......@@ -77,8 +78,8 @@ void RunTestSimple(const std::vector<index_t> &input_shape,
}
net.RunOp(D);
} else {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
if (model_type == FrameworkType::CAFFE) {
OpDefBuilder("Deconv2D", "Deconv2dTest")
......@@ -109,8 +110,8 @@ void RunTestSimple(const std::vector<index_t> &input_shape,
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
auto expected = net.CreateTensor<float>(expected_shape, expected_data);
......@@ -380,8 +381,8 @@ void TestComplexDeconvNxN(const int batch,
"Filter", {output_channels, input_channels, kernel_h, kernel_w}, true,
false);
net.AddRandomInput<D, T>("Bias", {output_channels}, true, false);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
int out_h = 0;
int out_w = 0;
......@@ -440,8 +441,8 @@ void TestComplexDeconvNxN(const int batch,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......
mace/ops/depth_to_space.cc
浏览文件 @ 95b32c24
......@@ -96,7 +96,7 @@ class DepthToSpaceOp<DeviceType::GPU, T> : public Operation {
explicit DepthToSpaceOp(OpConstructContext *context)
: Operation(context) {
int block_size = Operation::GetOptionalArg<int>("block_size", 1);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::DepthToSpaceKernel<T>>(block_size);
} else {
MACE_NOT_IMPLEMENTED;
......
mace/ops/depth_to_space_test.cc
浏览文件 @ 95b32c24
......@@ -32,8 +32,8 @@ void RunDepthToSpace(const std::vector<index_t> &input_shape,
net.AddInputFromArray<D, float>("Input", input_shape, input_data);
// Construct graph
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("DepthToSpace", "DepthToSpaceTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -41,8 +41,8 @@ void RunDepthToSpace(const std::vector<index_t> &input_shape,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else {
OpDefBuilder("DepthToSpace", "DepthToSpaceTest")
......@@ -114,8 +114,8 @@ void RandomTest(const int block_size,
// Add input data
net.AddRandomInput<D, float>("Input", shape);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("DepthToSpace", "DepthToSpaceTest")
.Input("InputNCHW")
.AddIntArg("block_size", block_size)
......@@ -125,8 +125,8 @@ void RandomTest(const int block_size,
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("DepthToSpace", "DepthToSpaceTest")
.Input("Input")
......
mace/ops/depthwise_conv2d.cc
浏览文件 @ 95b32c24
......@@ -188,9 +188,9 @@ class DepthwiseConv2dOp<DeviceType::CPU, uint8_t>
filter->dim(2) * filter->dim(3), filter->dim(0), filter->dim(1), 1};
if (paddings_.empty()) {
CalcPaddingAndOutputSize(input->shape().data(),
NHWC,
DataFormat::NHWC,
ohwi_shape.data(),
OHWI,
DataFormat::OHWI,
dilations_.data(),
strides_.data(),
padding_type_,
......@@ -199,9 +199,9 @@ class DepthwiseConv2dOp<DeviceType::CPU, uint8_t>
} else {
paddings = paddings_;
CalcOutputSize(input->shape().data(),
NHWC,
DataFormat::NHWC,
ohwi_shape.data(),
OHWI,
DataFormat::OHWI,
paddings_.data(),
dilations_.data(),
strides_.data(),
......@@ -375,7 +375,7 @@ class DepthwiseConv2dOp<DeviceType::GPU, T> : public DepthwiseConv2dOpBase {
explicit DepthwiseConv2dOp(OpConstructContext *context)
: DepthwiseConv2dOpBase(context) {
MemoryType mem_type;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
mem_type = MemoryType::GPU_IMAGE;
kernel_ = make_unique<opencl::image::DepthwiseConv2dKernel<T>>();
} else {
......@@ -459,6 +459,18 @@ void RegisterDepthwiseConv2d(OpRegistryBase *op_registry) {
context->set_output_mem_type(mem_type);
}));
#endif // MACE_ENABLE_OPENCL
MACE_REGISTER_OP_CONDITION(
op_registry,
OpConditionBuilder("DepthwiseConv2d")
.SetInputsDataFormatSelector(
[](OpConditionContext *context) -> std::vector<DataFormat> {
DataFormat op_data_format =
static_cast<DataFormat>(
ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*context->operator_def(), "data_format",
static_cast<int>(DataFormat::NONE)));
return {op_data_format, DataFormat::OIHW, DataFormat::NONE};
}));
}
} // namespace ops
......
mace/ops/depthwise_conv2d_test.cc
浏览文件 @ 95b32c24
......@@ -39,8 +39,8 @@ void SimpleValidTest() {
true);
net.AddInputFromArray<D, float>("Bias", {2}, {.1f, .2f}, true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -52,8 +52,8 @@ void SimpleValidTest() {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("Input")
......@@ -127,8 +127,8 @@ void ComplexValidTest(index_t batch,
true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -141,8 +141,8 @@ void ComplexValidTest(index_t batch,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("Input")
......@@ -249,8 +249,8 @@ void TestNxNS12(const index_t height, const index_t width) {
{multiplier * channel},
true, false);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -267,8 +267,8 @@ void TestNxNS12(const index_t height, const index_t width) {
// Run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -389,9 +389,9 @@ void TestQuant(const index_t batch,
"Filter", {k_height, k_width, in_channels, multiplier}, true, false);
net.AddRandomInput<CPU, float>("Bias", {out_channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", NHWC, "InputNCHW", NCHW);
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.TransformFilterDataFormat<DeviceType::CPU, float>(
"Filter", HWIO, "FilterOIHW", OIHW);
"Filter", DataFormat::HWIO, "FilterOIHW", DataFormat::OIHW);
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("InputNCHW")
......@@ -405,7 +405,7 @@ void TestQuant(const index_t batch,
.Finalize(net.NewOperatorDef());
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", NCHW, "Output", NHWC);
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("Quantize", "QuantizeFilter")
.Input("Filter")
......
mace/ops/depthwise_deconv2d.cc
浏览文件 @ 95b32c24
......@@ -190,7 +190,7 @@ class DepthwiseDeconv2dOp<DeviceType::GPU, T> : public Deconv2dOpBase {
explicit DepthwiseDeconv2dOp(OpConstructContext *context)
: Deconv2dOpBase(context) {
MemoryType mem_type = MemoryType::GPU_IMAGE;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::DepthwiseDeconv2dKernel<T>>();
} else {
MACE_NOT_IMPLEMENTED;
......@@ -230,7 +230,7 @@ class DepthwiseDeconv2dOp<DeviceType::GPU, T> : public Deconv2dOpBase {
&out_paddings,
nullptr,
CAFFE,
NHWC);
DataFormat::NHWC);
return kernel_->Compute(context,
input,
......
mace/ops/depthwise_deconv2d_test.cc
浏览文件 @ 95b32c24
......@@ -39,7 +39,8 @@ void RunTestSimple(const int group,
// Add input data
net.AddInputFromArray<D, float>("Input", input_shape, input_data);
net.AddInputFromArray<D, float>("Filter", filter_shape, filter_data, true);
net.TransformFilterDataFormat<D, float>("Filter", HWOI, "FilterOIHW", OIHW);
net.TransformFilterDataFormat<D, float>(
"Filter", DataFormat::HWOI, "FilterOIHW", DataFormat::OIHW);
const index_t out_channels = expected_shape[3];
net.AddInputFromArray<D, float>("Bias", {out_channels}, bias_data, true);
......@@ -56,8 +57,8 @@ void RunTestSimple(const int group,
net.RunOp(D);
} else {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC,
"InputNCHW", NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("DepthwiseDeconv2d", "DepthwiseDeconv2dTest")
.Input("InputNCHW")
.Input("FilterOIHW")
......@@ -69,8 +70,8 @@ void RunTestSimple(const int group,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
auto expected = net.CreateTensor<float>(expected_shape, expected_data);
......@@ -193,8 +194,8 @@ void RandomTest(index_t batch,
{channel * multiplier},
bias_data, true, false);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("DepthwiseDeconv2d", "DepthwiseDeconv2dTest")
.Input("InputNCHW")
.Input("Filter")
......@@ -210,8 +211,8 @@ void RandomTest(index_t batch,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
......
mace/ops/eltwise.cc
浏览文件 @ 95b32c24
......@@ -1145,7 +1145,7 @@ class EltwiseOp<DeviceType::GPU, T> : public Operation {
int32_t scalar_input_index = Operation::GetOptionalArg<int32_t>(
"scalar_input_index", 1);
MemoryType mem_type;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
mem_type = MemoryType::GPU_IMAGE;
kernel_ = make_unique<opencl::image::EltwiseKernel<T>>(
type, coeff, scalar_input, scalar_input_index);
......
mace/ops/eltwise_test.cc
浏览文件 @ 95b32c24
......@@ -69,7 +69,8 @@ void SimpleTensorScalar(const ops::EltwiseType type,
net.AddInputFromArray<D, T>("Input", shape, input);
if (D == DeviceType::CPU) {
net.TransformDataFormat<D, T>("Input", NHWC, "TInput", NCHW);
net.TransformDataFormat<D, T>(
"Input", DataFormat::NHWC, "TInput", DataFormat::NCHW);
OpDefBuilder("Eltwise", "EltwiseTest")
.Input("TInput")
.AddIntArg("T", DataTypeToEnum<T>::v())
......@@ -81,7 +82,8 @@ void SimpleTensorScalar(const ops::EltwiseType type,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<D, DstType>("TOutput", NCHW, "Output", NHWC);
net.TransformDataFormat<D, DstType>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else {
OpDefBuilder("Eltwise", "EltwiseTest")
.Input("Input")
......@@ -124,13 +126,15 @@ void SimpleTensorEltwise(const ops::EltwiseType type,
.OutputType({ops::IsLogicalType(type) ? DT_INT32 : DT_FLOAT})
.Output("TOutput");
if (shape0.size() > 1) {
net.TransformDataFormat<D, T>("Input0", NHWC, "TInput0", NCHW);
net.TransformDataFormat<D, T>(
"Input0", DataFormat::NHWC, "TInput0", DataFormat::NCHW);
op_builder.Input("TInput0");
} else {
op_builder.Input("Input0");
}
if (shape1.size() > 1) {
net.TransformDataFormat<D, T>("Input1", NHWC, "TInput1", NCHW);
net.TransformDataFormat<D, T>(
"Input1", DataFormat::NHWC, "TInput1", DataFormat::NCHW);
op_builder.Input("TInput1");
} else {
op_builder.Input("Input1");
......@@ -139,7 +143,8 @@ void SimpleTensorEltwise(const ops::EltwiseType type,
// Run
net.RunOp(D);
net.TransformDataFormat<D, DstType>("TOutput", NCHW, "Output", NHWC);
net.TransformDataFormat<D, DstType>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else {
OpDefBuilder("Eltwise", "EltwiseTest")
.Input("Input0")
......@@ -560,7 +565,8 @@ void GPUOverflowTest(const ops::EltwiseType type,
net.AddInputFromArray<DeviceType::GPU, T>(
"Filter",
{output_shape.back(), shape0.back(), 3, 3},
std::vector<float>(output_shape.back() * shape0.back() * 9, 1));
std::vector<float>(output_shape.back() * shape0.back() * 9, 1),
true);
OpDefBuilder("Conv2D", "Conv2D")
.AddIntArg("T", DataTypeToEnum<T>::v())
.Input("EltOutput")
......@@ -636,8 +642,8 @@ void RandomTensorScalar(const ops::EltwiseType type,
// Add input data
net.AddRandomInput<DeviceType::GPU, float>("Input", shape, false, true, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "TInput",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "TInput", DataFormat::NCHW);
OpDefBuilder("Eltwise", "EltwiseTest")
.Input("TInput")
.AddIntArg("type", static_cast<int>(type))
......@@ -647,8 +653,8 @@ void RandomTensorScalar(const ops::EltwiseType type,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("TOutput", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......@@ -690,10 +696,10 @@ void RandomTensorEltwise(const ops::EltwiseType type,
true,
true);
net.TransformDataFormat<DeviceType::CPU, float>("Input0", NHWC, "TInput0",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Input1", NHWC, "TInput1",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input0", DataFormat::NHWC, "TInput0", DataFormat::NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input1", DataFormat::NHWC, "TInput1", DataFormat::NCHW);
OpDefBuilder("Eltwise", "EltwiseTest")
.Input("TInput0")
.Input("TInput1")
......@@ -705,8 +711,8 @@ void RandomTensorEltwise(const ops::EltwiseType type,
// Run
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("TOutput", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......@@ -746,10 +752,10 @@ void Quantized(const std::vector<index_t> &shape,
true,
true);
net.TransformDataFormat<DeviceType::CPU, float>("Input0", NHWC, "TInput0",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Input1", NHWC, "TInput1",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input0", DataFormat::NHWC, "TInput0", DataFormat::NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input1", DataFormat::NHWC, "TInput1", DataFormat::NCHW);
OpDefBuilder("Eltwise", "EltwiseTest")
.Input("TInput0")
......@@ -761,8 +767,8 @@ void Quantized(const std::vector<index_t> &shape,
// Run
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("TOutput", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("Quantize", "QuantizeInput0")
.Input("Input0")
......
mace/ops/folded_batch_norm_test.cc
浏览文件 @ 95b32c24
......@@ -49,7 +49,8 @@ void Simple() {
net.AddInputFromArray<D, float>("Offset", {1}, offset, true);
if (D == DeviceType::CPU) {
net.TransformDataFormat<D, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformDataFormat<D, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "FoldedBatchNormTest")
.Input("InputNCHW")
.Input("Scale")
......@@ -58,7 +59,8 @@ void Simple() {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<D, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("BatchNorm", "FoldedBatchNormTest")
.Input("Input")
......@@ -100,8 +102,8 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) {
net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "FoldedBatchNormTest")
.Input("InputNCHW")
......@@ -113,8 +115,8 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -151,8 +153,8 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) {
net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "FoldedBatchNormTest")
.Input("InputNCHW")
......@@ -164,8 +166,8 @@ TEST_F(FoldedBatchNormOpTest, SimpleRandomHalfOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -205,8 +207,8 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) {
net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "FoldedBatchNormTest")
.Input("InputNCHW")
......@@ -218,8 +220,8 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -254,11 +256,11 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) {
// Add input data
net.AddRandomInput<DeviceType::GPU, float>("Input",
{batch, height, width, channels});
net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels});
net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels});
net.AddRandomInput<DeviceType::GPU, float>("Scale", {channels}, true);
net.AddRandomInput<DeviceType::GPU, float>("Offset", {channels}, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchNorm", "FoldedBatchNormTest")
.Input("InputNCHW")
......@@ -270,8 +272,8 @@ TEST_F(FoldedBatchNormOpTest, ComplexRandomHalfOPENCL) {
// run cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......
mace/ops/fully_connected.cc
浏览文件 @ 95b32c24
......@@ -190,7 +190,7 @@ class FullyConnectedOp<DeviceType::GPU, T> : public FullyConnectedOpBase {
explicit FullyConnectedOp(OpConstructContext *context)
: FullyConnectedOpBase(context) {
MemoryType mem_type = MemoryType::CPU_BUFFER;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
mem_type = MemoryType::GPU_IMAGE;
kernel_ = make_unique<opencl::image::FullyConnectedKernel<T>>();
} else {
......
mace/ops/fully_connected_test.cc
浏览文件 @ 95b32c24
......@@ -48,7 +48,8 @@ void Simple(const std::vector<index_t> &input_shape,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<D, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("FullyConnected", "FullyConnectedTest")
.Input("Input")
......@@ -129,8 +130,8 @@ void Random(const index_t batch,
net.AddRandomInput<DeviceType::GPU, float>("Bias", {out_channel}, true,
false);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("FullyConnected", "FullyConnectedTest")
.Input("InputNCHW")
.Input("Weight")
......@@ -143,7 +144,8 @@ void Random(const index_t batch,
// run cpu
net.RunOp();
net.TransformDataFormat<CPU, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>();
......@@ -215,8 +217,10 @@ void QuantRandom(const index_t batch,
net.AddRandomInput<CPU, float>(
"Weight", {out_channel, height, width, channels}, true);
net.AddRandomInput<CPU, float>("Bias", {out_channel}, true);
net.TransformDataFormat<CPU, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformFilterDataFormat<CPU, float>("Weight", OHWI, "WeightOIHW", OIHW);
net.TransformDataFormat<CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.TransformFilterDataFormat<CPU, float>(
"Weight", DataFormat::OHWI, "WeightOIHW", DataFormat::OIHW);
OpDefBuilder("FullyConnected", "FullyConnectedTest")
.Input("InputNCHW")
......@@ -226,7 +230,8 @@ void QuantRandom(const index_t batch,
.AddIntArg("T", DT_FLOAT)
.Finalize(net.NewOperatorDef());
net.RunOp();
net.TransformDataFormat<CPU, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("Quantize", "QuantizeWeight")
.Input("Weight")
......
mace/ops/local_response_norm_test.cc
浏览文件 @ 95b32c24
......@@ -29,7 +29,8 @@ void Simple() {
{5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15, 15});
if (D == DeviceType::CPU) {
net.TransformDataFormat<D, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformDataFormat<D, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("LocalResponseNorm", "LocalResponseNormTest")
.Input("InputNCHW")
......@@ -41,7 +42,8 @@ void Simple() {
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<D, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
// Check
......
mace/ops/lstm_cell.cc
浏览文件 @ 95b32c24
......@@ -36,7 +36,7 @@ class LSTMCellOp<DeviceType::GPU, T> : public Operation {
Operation::GetOptionalArg<float>("scalar_input",
0.0));
MemoryType mem_type = MemoryType::GPU_IMAGE;
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::LSTMCellKernel<T>>(forget_bias);
} else {
MACE_NOT_IMPLEMENTED;
......
mace/ops/opencl/buffer_transformer.h
浏览文件 @ 95b32c24
......@@ -47,7 +47,6 @@ class OpenCLBufferTransformer {
const OpenCLBufferType type,
const MemoryType out_mem_type,
const int wino_blk_size,
DataFormat data_format,
Tensor *output) {
Workspace *ws = context->workspace();
DataType dt = DataTypeToEnum<T>::value;
......@@ -66,7 +65,6 @@ class OpenCLBufferTransformer {
VLOG(2) << "Transform CPU Buffer " << input->name()
<< " to GPU Buffer " << internal_tensor->name()
<< " with data type " << dt;
MACE_CHECK(data_format == DataFormat::NHWC);
internal_tensor->Resize(input->shape());
const uint8_t *input_ptr = input->data<uint8_t>();
Tensor::MappingGuard guard(internal_tensor);
......@@ -88,7 +86,6 @@ class OpenCLBufferTransformer {
VLOG(2) << "Transform GPU Buffer " << internal_tensor.name()
<< " to CPU Buffer " << output->name()
<< " with data type " << dt;
MACE_CHECK(data_format == DataFormat::NHWC);
Tensor::MappingGuard guard(&internal_tensor);
const T *internal_ptr = internal_tensor.data<T>();
output->Resize(internal_tensor.shape());
......@@ -135,7 +132,7 @@ MaceStatus TransformFilter(
input->MarkUnused();
return OpenCLBufferTransformer<T>(input->memory_type(), mem_type).
Transform(&op_context, input, buffer_type, mem_type, wino_blk_size,
DataFormat::DF_NONE, output);
output);
}
} // namespace ops
......
mace/ops/ops_test_util.cc
浏览文件 @ 95b32c24
......@@ -15,6 +15,7 @@
#include "mace/ops/ops_test_util.h"
#include "mace/core/memory_optimizer.h"
#include "mace/utils/memory.h"
#include "mace/core/net_def_adapter.h"
namespace mace {
namespace ops {
......@@ -164,26 +165,27 @@ void OpTestContext::SetOCLImageAndBufferTestFlag() {
bool OpsTestNet::Setup(mace::DeviceType device) {
NetDef net_def;
for (auto &op_def : op_defs_) {
net_def.add_op()->CopyFrom(op_def);
auto target_op = net_def.add_op();
target_op->CopyFrom(op_def);
auto has_data_format = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
op_def, "has_data_format", 0);
auto is_quantized_op = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
op_def, "T", static_cast<int>(DT_FLOAT))
== static_cast<int>(DT_UINT8);
for (auto input : op_def.input()) {
if (ws_.GetTensor(input) != nullptr &&
!ws_.GetTensor(input)->is_weight()) {
auto input_info = net_def.add_input_info();
input_info->set_name(input);
auto has_data_format = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
op_def, "has_data_format", 1);
auto is_quantized_op = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
op_def, "T", static_cast<int>(DT_FLOAT))
== static_cast<int>(DT_UINT8);
if (has_data_format) {
if (is_quantized_op || device == DeviceType::GPU) {
input_info->set_data_format(NHWC);
input_info->set_data_format(static_cast<int>(DataFormat::NHWC));
} else {
input_info->set_data_format(NCHW);
input_info->set_data_format(static_cast<int>(DataFormat::NCHW));
}
} else {
input_info->set_data_format(DataFormat::DF_NONE);
input_info->set_data_format(static_cast<int>(DataFormat::NONE));
}
auto &shape = ws_.GetTensor(input)->shape();
for (auto d : shape) {
......@@ -191,6 +193,10 @@ bool OpsTestNet::Setup(mace::DeviceType device) {
}
}
}
if (has_data_format) {
SetProtoArg<int>(target_op, "data_format",
static_cast<int>(DataFormat::AUTO));
}
}
if (!op_defs_.empty()) {
auto op_def = op_defs_.back();
......@@ -205,15 +211,21 @@ bool OpsTestNet::Setup(mace::DeviceType device) {
}
}
}
NetDef adapted_net_def;
NetDefAdapter net_def_adapter(op_registry_.get(), &ws_);
net_def_adapter.AdaptNetDef(&net_def,
OpTestContext::Get()->GetDevice(device),
&adapted_net_def);
MemoryOptimizer mem_optimizer;
net_ = make_unique<SerialNet>(
op_registry_.get(),
&net_def,
&adapted_net_def,
&ws_,
OpTestContext::Get()->GetDevice(device),
&mem_optimizer);
MaceStatus status = (ws_.PreallocateOutputTensor(
net_def,
adapted_net_def,
&mem_optimizer,
OpTestContext::Get()->GetDevice(device)));
if (status != MaceStatus::MACE_SUCCESS) return false;
......@@ -252,15 +264,20 @@ MaceStatus OpsTestNet::RunOp() {
MaceStatus OpsTestNet::RunNet(const mace::NetDef &net_def,
const mace::DeviceType device) {
device_type_ = device;
NetDef adapted_net_def;
NetDefAdapter net_def_adapter(op_registry_.get(), &ws_);
net_def_adapter.AdaptNetDef(&net_def,
OpTestContext::Get()->GetDevice(device),
&adapted_net_def);
MemoryOptimizer mem_optimizer;
net_ = make_unique<SerialNet>(
op_registry_.get(),
&net_def,
&adapted_net_def,
&ws_,
OpTestContext::Get()->GetDevice(device),
&mem_optimizer);
MACE_RETURN_IF_ERROR(ws_.PreallocateOutputTensor(
net_def,
adapted_net_def,
&mem_optimizer,
OpTestContext::Get()->GetDevice(device)));
MACE_RETURN_IF_ERROR(net_->Init());
......
mace/ops/ops_test_util.h
浏览文件 @ 95b32c24
......@@ -216,7 +216,7 @@ class OpsTestNet {
const std::vector<index_t> input_shape = input->shape();
MACE_CHECK(input_shape.size() == 4, "input shape != 4");
if (src_format == NHWC && dst_format == NCHW) {
if (src_format == DataFormat::NHWC && dst_format == DataFormat::NCHW) {
index_t batch = input_shape[0];
index_t height = input_shape[1];
index_t width = input_shape[2];
......@@ -236,7 +236,8 @@ class OpsTestNet {
}
}
}
} else if (src_format == NCHW && dst_format == NHWC) {
} else if (src_format == DataFormat::NCHW &&
dst_format == DataFormat::NHWC) {
index_t batch = input_shape[0];
index_t channels = input_shape[1];
index_t height = input_shape[2];
......@@ -274,7 +275,7 @@ class OpsTestNet {
input->is_weight());
const std::vector<index_t> input_shape = input->shape();
MACE_CHECK(input_shape.size() == 4, "input shape != 4");
if (src_format == HWOI && dst_format == OIHW) {
if (src_format == DataFormat::HWOI && dst_format == DataFormat::OIHW) {
index_t height = input_shape[0];
index_t width = input_shape[1];
index_t out_channels = input_shape[2];
......@@ -292,7 +293,8 @@ class OpsTestNet {
input_data[j * out_channels * in_channels + i];
}
}
} else if (src_format == OIHW && dst_format == HWOI) {
} else if (src_format == DataFormat::OIHW &&
dst_format == DataFormat::HWOI) {
index_t out_channels = input_shape[0];
index_t in_channels = input_shape[1];
index_t height = input_shape[2];
......@@ -310,7 +312,8 @@ class OpsTestNet {
input_data[j * height * width + i];
}
}
} else if (src_format == HWIO && dst_format == OIHW) {
} else if (src_format == DataFormat::HWIO &&
dst_format == DataFormat::OIHW) {
index_t height = input_shape[0];
index_t width = input_shape[1];
index_t in_channels = input_shape[2];
......@@ -330,7 +333,8 @@ class OpsTestNet {
}
}
}
} else if (src_format == OHWI && dst_format == OIHW) {
} else if (src_format == DataFormat::OHWI &&
dst_format == DataFormat::OIHW) {
index_t out_channels = input_shape[0];
index_t height = input_shape[1];
index_t width = input_shape[2];
......
mace/ops/pad.cc
浏览文件 @ 95b32c24
......@@ -179,7 +179,7 @@ class PadOp<DeviceType::GPU, T> : public Operation {
std::vector<int> paddings = Operation::GetRepeatedArgs<int>("paddings");
float constant_value = Operation::GetOptionalArg<float>(
"constant_value", 0.0);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::PadKernel<T>>(
type, paddings, constant_value);
} else {
......
mace/ops/pad_test.cc
浏览文件 @ 95b32c24
......@@ -45,8 +45,8 @@ void SimpleConstant() {
// Run
net.RunOp(D);
} else {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "TInput",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "TInput", DataFormat::NCHW);
OpDefBuilder("Pad", "PadTest")
.Input("TInput")
.Output("TOutput")
......@@ -58,8 +58,8 @@ void SimpleConstant() {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("TOutput", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
auto output = net.GetTensor("Output");
......@@ -93,7 +93,8 @@ void Result(const std::vector<index_t> &input_shape,
if (D == DeviceType::CPU) {
t_input = "TInput";
t_output = "TOutput";
net.TransformDataFormat<DeviceType::CPU, T>(input, NHWC, t_input, NCHW);
net.TransformDataFormat<DeviceType::CPU, T>(
input, DataFormat::NHWC, t_input, DataFormat::NCHW);
}
OpDefBuilder("Pad", "PadTest")
......@@ -108,7 +109,8 @@ void Result(const std::vector<index_t> &input_shape,
net.RunOp(D);
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, T>(t_output, NCHW, output, NHWC);
net.TransformDataFormat<DeviceType::CPU, T>(
t_output, DataFormat::NCHW, output, DataFormat::NHWC);
}
auto actual = net.GetTensor(output.c_str());
......@@ -172,8 +174,8 @@ TEST_F(PadTest, ComplexCPU) {
// Add input data
net.AddRepeatedInput<DeviceType::CPU, float>("Input", {1, 1, 1, 2}, 2);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "TInput",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "TInput", DataFormat::NCHW);
OpDefBuilder("Pad", "PadTest")
.Input("TInput")
.Output("TOutput")
......@@ -184,8 +186,8 @@ TEST_F(PadTest, ComplexCPU) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("TOutput", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto output = net.GetTensor("Output");
......@@ -209,8 +211,8 @@ void Complex(const std::vector<index_t> &input_shape,
// Add input data
net.AddRandomInput<DeviceType::GPU, float>("Input", input_shape);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "TInput",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "TInput", DataFormat::NCHW);
OpDefBuilder("Pad", "PadTest")
.Input("TInput")
.Output("TOutput")
......@@ -222,8 +224,8 @@ void Complex(const std::vector<index_t> &input_shape,
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("TOutput", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"TOutput", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......
mace/ops/pooling.cc
浏览文件 @ 95b32c24
......@@ -270,9 +270,9 @@ class PoolingOp<DeviceType::CPU, uint8_t> : public PoolingOpBase {
std::vector<int> paddings(2);
if (paddings_.empty()) {
CalcPaddingAndOutputSize(input_tensor->shape().data(),
NHWC,
DataFormat::NHWC,
filter_shape.data(),
OHWI,
DataFormat::OHWI,
dilations_.data(),
strides_.data(),
padding_type_,
......@@ -281,9 +281,9 @@ class PoolingOp<DeviceType::CPU, uint8_t> : public PoolingOpBase {
} else {
paddings = paddings_;
CalcOutputSize(input_tensor->shape().data(),
NHWC,
DataFormat::NHWC,
filter_shape.data(),
OHWI,
DataFormat::OHWI,
paddings_.data(),
dilations_.data(),
strides_.data(),
......@@ -477,7 +477,7 @@ class PoolingOp<DeviceType::GPU, T> : public PoolingOpBase {
public:
explicit PoolingOp(OpConstructContext *context)
: PoolingOpBase(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::PoolingKernel<T>>();
} else {
kernel_ = make_unique<opencl::buffer::PoolingKernel<T>>();
......
mace/ops/pooling_test.cc
浏览文件 @ 95b32c24
......@@ -34,8 +34,8 @@ TEST_F(PoolingOpTest, MAX_VALID) {
{0, 16, 1, 17, 2, 18, 3, 19, 4, 20, 5, 21, 6, 22, 7, 23,
8, 24, 9, 25, 10, 26, 11, 27, 12, 28, 13, 29, 14, 30, 15, 31});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -50,8 +50,8 @@ TEST_F(PoolingOpTest, MAX_VALID) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected =
......@@ -68,8 +68,8 @@ TEST_F(PoolingOpTest, MAX_SAME) {
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 3, 3, 1},
{0, 1, 2, 3, 4, 5, 6, 7, 8});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -84,8 +84,8 @@ TEST_F(PoolingOpTest, MAX_SAME) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 2, 2, 1}, {4, 5, 7, 8});
......@@ -102,8 +102,8 @@ TEST_F(PoolingOpTest, MAX_VALID_DILATION) {
"Input", {1, 4, 4, 1},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -118,8 +118,8 @@ TEST_F(PoolingOpTest, MAX_VALID_DILATION) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 2, 2, 1}, {10, 11, 14, 15});
......@@ -136,8 +136,8 @@ TEST_F(PoolingOpTest, MAX_k2x2s2x2) {
"Input", {1, 2, 9, 1},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -152,8 +152,8 @@ TEST_F(PoolingOpTest, MAX_k2x2s2x2) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 1, 5, 1}, {10, 12, 14, 16, 17});
......@@ -174,8 +174,8 @@ void SimpleMaxPooling3S2() {
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26});
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
// Run
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -187,8 +187,8 @@ void SimpleMaxPooling3S2() {
.AddIntsArg("dilations", {1, 1})
.Finalize(net.NewOperatorDef());
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else if (D == DeviceType::GPU) {
OpDefBuilder("Pooling", "PoolingTest")
.Input("Input")
......@@ -224,8 +224,8 @@ void MaxPooling3S2(const std::vector<index_t> &input_shape,
// Add input data
net.AddRandomInput<D, float>("Input", input_shape);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -240,8 +240,8 @@ void MaxPooling3S2(const std::vector<index_t> &input_shape,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......@@ -304,8 +304,8 @@ TEST_F(PoolingOpTest, AVG_VALID) {
{0, 16, 1, 17, 2, 18, 3, 19, 4, 20, 5, 21, 6, 22, 7, 23,
8, 24, 9, 25, 10, 26, 11, 27, 12, 28, 13, 29, 14, 30, 15, 31});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -320,8 +320,8 @@ TEST_F(PoolingOpTest, AVG_VALID) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>(
......@@ -373,8 +373,8 @@ void AvgPoolingTest(const std::vector<index_t> &shape,
// Add input data
net.AddRandomInput<D, float>("Input", shape);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Pooling", "PoolingTest")
.Input("InputNCHW")
......@@ -389,8 +389,8 @@ void AvgPoolingTest(const std::vector<index_t> &shape,
// run on cpu
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......@@ -563,7 +563,7 @@ void TestQuant(const index_t batch,
net.AddRandomInput<CPU, float>(
"Input", input_shape, false, false);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", NHWC, "InputNCHW", NCHW);
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.AddRandomInput<DeviceType::CPU, float>(
"OutputNCHW", input_shape, false, true, true);
......@@ -580,7 +580,7 @@ void TestQuant(const index_t batch,
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", NCHW, "Output", NHWC);
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("Quantize", "QuantizeInput")
.Input("Input")
......
mace/ops/reduce.cc
浏览文件 @ 95b32c24
......@@ -873,7 +873,7 @@ class ReduceOp<DeviceType::GPU, T> : public ReduceOpBase {
public:
explicit ReduceOp(OpConstructContext *context)
: ReduceOpBase(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::ReduceKernel<T>>(reduce_type_,
axis_,
keep_dims_);
......@@ -914,6 +914,9 @@ void RegisterReduce(OpRegistryBase *op_registry) {
.SetDevicePlacerFunc(
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU, DeviceType::GPU };
}
bool keep_dims =
ProtoArgHelper::GetOptionalArg<OperatorDef, bool>(
*op, "keepdims", false);
......@@ -923,7 +926,7 @@ void RegisterReduce(OpRegistryBase *op_registry) {
auto axis =
ProtoArgHelper::GetRepeatedArgs<OperatorDef, int>(
*op, "axis");
if (axis.size() != 2 || axis[0] != 1 || axis[1] == 2) {
if (axis.size() != 2 || axis[0] != 1 || axis[1] != 2) {
return { DeviceType::CPU };
}
auto tensor_shape_info = context->tensor_shape_info();
......
mace/ops/reduce_test.cc
浏览文件 @ 95b32c24
......@@ -38,7 +38,8 @@ void Simple(const std::vector<index_t> &input_shape,
net.AddInputFromArray<D, float>("Input", input_shape, input);
if (D == DeviceType::CPU) {
net.TransformDataFormat<D, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformDataFormat<D, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Reduce", "ReduceTest")
.Input("InputNCHW")
.AddIntsArg("axis", axis)
......@@ -49,7 +50,8 @@ void Simple(const std::vector<index_t> &input_shape,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<D, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<D, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else {
OpDefBuilder("Reduce", "ReduceTest")
.Input("Input")
......@@ -289,8 +291,8 @@ void RandomTest(const std::vector<index_t> &input_shape,
// Add input data
net.AddRandomInput<D, float>("Input", input_shape);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Reduce", "ReduceTest")
.Input("InputNCHW")
.AddIntsArg("axis", axis)
......@@ -301,8 +303,8 @@ void RandomTest(const std::vector<index_t> &input_shape,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("Reduce", "ReduceTest")
.Input("Input")
.AddIntsArg("axis", axis)
......@@ -353,7 +355,7 @@ void TestQuant(const std::vector<index_t> &input_shape,
net.AddRandomInput<CPU, float>(
"Input", input_shape, false, false);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", NHWC, "InputNCHW", NCHW);
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.AddRandomInput<DeviceType::CPU, float>(
"OutputNCHW", input_shape, false, true, true);
......@@ -368,7 +370,7 @@ void TestQuant(const std::vector<index_t> &input_shape,
.Finalize(net.NewOperatorDef());
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", NCHW, "Output", NHWC);
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("Quantize", "QuantizeInput")
.Input("Input")
......
mace/ops/ref/deconv_2d.cc
浏览文件 @ 95b32c24
......@@ -51,7 +51,7 @@ MaceStatus Deconv2d<float>::Compute(const OpContext *context,
&out_pad_size,
&padded_out_shape,
framework_type_,
NCHW);
DataFormat::NCHW);
MACE_RETURN_IF_ERROR(output->Resize(out_shape));
......
mace/ops/ref/depthwise_deconv_2d.cc
浏览文件 @ 95b32c24
......@@ -50,7 +50,7 @@ MaceStatus DepthwiseDeconv2d<float>::Compute(const OpContext *context,
&out_pad_size,
&padded_out_shape,
framework_type_,
NCHW);
DataFormat::NCHW);
MACE_RETURN_IF_ERROR(output->Resize(out_shape));
......@@ -185,7 +185,7 @@ MaceStatus GroupDeconv2d<float>::Compute(const OpContext *context,
&out_pad_size,
&padded_out_shape,
framework_type_,
NCHW);
DataFormat::NCHW);
MACE_RETURN_IF_ERROR(output->Resize(out_shape));
......
mace/ops/resize_bicubic.cc
浏览文件 @ 95b32c24
......@@ -212,7 +212,7 @@ class ResizeBicubicOp<DeviceType::GPU, T> : public Operation {
std::vector<index_t> size = Operation::GetRepeatedArgs<index_t>(
"size", {-1, -1});
MACE_CHECK(size.size() == 2);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::ResizeBicubicKernel<T>>(
align_corners, size[0], size[1]);
} else {
......
mace/ops/resize_bicubic_test.cc
浏览文件 @ 95b32c24
......@@ -31,8 +31,8 @@ TEST_F(ResizeBicubicTest, CPUResizeBicubicWOAlignCorners) {
std::vector<float> input(24);
std::iota(begin(input), end(input), 0);
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 2, 4, 3}, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBicubic", "ResizeBicubicTest")
.Input("InputNCHW")
......@@ -42,8 +42,8 @@ TEST_F(ResizeBicubicTest, CPUResizeBicubicWOAlignCorners) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 1, 2, 3}, {0, 1, 2, 6, 7, 8});
......@@ -60,8 +60,8 @@ TEST_F(ResizeBicubicTest, CPUResizeBicubicWOAlignCornersFloat) {
std::vector<float> input(48);
std::iota(begin(input), end(input), 0);
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 4, 4, 3}, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBicubic", "ResizeBicubicTest")
.Input("InputNCHW")
......@@ -71,8 +71,8 @@ TEST_F(ResizeBicubicTest, CPUResizeBicubicWOAlignCornersFloat) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 2, 3, 3},
......@@ -92,8 +92,8 @@ TEST_F(ResizeBicubicTest, ResizeBicubicWAlignCorners) {
std::vector<float> input(24);
std::iota(begin(input), end(input), 0);
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 2, 4, 3}, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBicubic", "ResizeBicubicTest")
.Input("InputNCHW")
......@@ -104,8 +104,8 @@ TEST_F(ResizeBicubicTest, ResizeBicubicWAlignCorners) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 1, 2, 3}, {0, 1, 2, 9, 10, 11});
......@@ -133,8 +133,8 @@ void TestRandomResizeBicubic() {
net.AddRandomInput<D, float>("Input",
{batch, in_height, in_width, channels},
false, true, true);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBicubic", "ResizeBicubicTest")
.Input("InputNCHW")
......@@ -144,8 +144,8 @@ void TestRandomResizeBicubic() {
.Finalize(net.NewOperatorDef());
// Run on CPU
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
Tensor expected;
expected.Copy(*net.GetOutput("Output"));
......
mace/ops/resize_bilinear.cc
浏览文件 @ 95b32c24
......@@ -346,7 +346,7 @@ class ResizeBilinearOp<DeviceType::GPU, T> : public Operation {
std::vector<index_t> size = Operation::GetRepeatedArgs<index_t>(
"size", {-1, -1});
MACE_CHECK(size.size() == 2);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::ResizeBilinearKernel<T>>(
align_corners, size[0], size[1]);
} else {
......
mace/ops/resize_bilinear_test.cc
浏览文件 @ 95b32c24
......@@ -31,8 +31,8 @@ TEST_F(ResizeBilinearTest, CPUResizeBilinearWOAlignCorners) {
std::vector<float> input(24);
std::iota(begin(input), end(input), 0);
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 2, 4, 3}, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
.Input("InputNCHW")
......@@ -42,8 +42,8 @@ TEST_F(ResizeBilinearTest, CPUResizeBilinearWOAlignCorners) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 1, 2, 3}, {0, 1, 2, 6, 7, 8});
......@@ -60,8 +60,8 @@ TEST_F(ResizeBilinearTest, ResizeBilinearWAlignCorners) {
std::vector<float> input(24);
std::iota(begin(input), end(input), 0);
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 2, 4, 3}, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
.Input("InputNCHW")
......@@ -72,8 +72,8 @@ TEST_F(ResizeBilinearTest, ResizeBilinearWAlignCorners) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 1, 2, 3}, {0, 1, 2, 9, 10, 11});
......@@ -100,8 +100,8 @@ void TestRandomResizeBilinear() {
// Add input data
net.AddRandomInput<D, float>("Input",
{batch, in_height, in_width, channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
.Input("InputNCHW")
......@@ -111,8 +111,8 @@ void TestRandomResizeBilinear() {
.Finalize(net.NewOperatorDef());
// Run on CPU
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......@@ -155,8 +155,8 @@ void TestQuantizedResizeBilinear() {
true,
-1.f,
1.f);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
.Input("InputNCHW")
......@@ -166,8 +166,8 @@ void TestQuantizedResizeBilinear() {
.Finalize(net.NewOperatorDef());
// Run on CPU
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// run quantize
OpDefBuilder("Quantize", "QuantizeInput")
......
mace/ops/resize_nearest_neighbor.cc
浏览文件 @ 95b32c24
......@@ -149,7 +149,7 @@ class ResizeNearestNeighborOp<DeviceType::GPU, T> : public Operation {
: Operation(context) {
bool align_corners = Operation::GetOptionalArg<bool>(
"align_corners", false);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::ResizeNearestNeighborKernel<T>>(
align_corners);
} else {
......
mace/ops/resize_nearest_neighbor_test.cc
浏览文件 @ 95b32c24
......@@ -32,8 +32,8 @@ TEST_F(ResizeNearestNeighborTest, CPUResizeNearestNeighborWOAlignCorners) {
std::iota(begin(input), end(input), 0);
std::vector<int32_t> size = {1, 2};
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 2, 4, 3}, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.AddInputFromArray<DeviceType::CPU, int32_t>("Size", {2}, size);
OpDefBuilder("ResizeNearestNeighbor", "ResizeNearestNeighborTest")
......@@ -45,8 +45,8 @@ TEST_F(ResizeNearestNeighborTest, CPUResizeNearestNeighborWOAlignCorners) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 1, 2, 3}, {0, 1, 2, 6, 7, 8});
......@@ -64,8 +64,8 @@ TEST_F(ResizeNearestNeighborTest, ResizeNearestNeighborWAlignCorners) {
std::iota(begin(input), end(input), 0);
std::vector<int32_t> size = {1, 2};
net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 2, 4, 3}, input);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.AddInputFromArray<DeviceType::CPU, int32_t>("Size", {2}, size);
OpDefBuilder("ResizeNearestNeighbor", "ResizeNearestNeighborTest")
......@@ -78,8 +78,8 @@ TEST_F(ResizeNearestNeighborTest, ResizeNearestNeighborWAlignCorners) {
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
// Check
auto expected = net.CreateTensor<float>({1, 1, 2, 3}, {0, 1, 2, 9, 10, 11});
......@@ -105,8 +105,8 @@ void TestRandomResizeNearestNeighbor() {
std::vector<int32_t> size = {20, 40};
net.AddRandomInput<D, float>("Input",
{batch, in_height, in_width, channels});
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
net.AddInputFromArray<D, int32_t>("Size", {2}, size);
OpDefBuilder("ResizeNearestNeighbor", "ResizeNearestNeighborTest")
.Input("InputNCHW")
......@@ -116,8 +116,8 @@ void TestRandomResizeNearestNeighbor() {
.Finalize(net.NewOperatorDef());
// Run on CPU
net.RunOp(DeviceType::CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
auto expected = net.CreateTensor<float>();
expected->Copy(*net.GetOutput("Output"));
......
mace/ops/softmax.cc
浏览文件 @ 95b32c24
......@@ -414,7 +414,7 @@ class SoftmaxOp<DeviceType::GPU, T> : public Operation {
: Operation(context) {
bool use_log = (
Operation::GetOptionalArg<bool>("use_log", false));
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::SoftmaxKernel<T>>(use_log);
} else {
kernel_ = make_unique<opencl::buffer::SoftmaxKernel<T>>(use_log);
......
mace/ops/softmax_test.cc
浏览文件 @ 95b32c24
......@@ -50,7 +50,8 @@ void Simple(bool use_log = false) {
if (D == DeviceType::CPU) {
// test 4d softmax
net.TransformDataFormat<CPU, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformDataFormat<CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Softmax", "SoftmaxTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -59,7 +60,8 @@ void Simple(bool use_log = false) {
// Run
net.RunOp(D);
net.TransformDataFormat<CPU, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
......@@ -109,7 +111,8 @@ void Complex(const std::vector<index_t> &logits_shape,
net.AddRandomInput<D, float>("Input", logits_shape);
if (logits_shape.size() == 4) {
net.TransformDataFormat<CPU, float>("Input", NHWC, "InputNCHW", NCHW);
net.TransformDataFormat<CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("Softmax", "SoftmaxTest")
.Input("InputNCHW")
......@@ -127,7 +130,8 @@ void Complex(const std::vector<index_t> &logits_shape,
net.RunOp();
if (logits_shape.size() == 4) {
net.TransformDataFormat<CPU, float>("OutputNCHW", NCHW, "Output", NHWC);
net.TransformDataFormat<CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
auto expected = net.CreateTensor<float>();
......
mace/ops/space_to_batch.cc
浏览文件 @ 95b32c24
......@@ -307,7 +307,7 @@ class SpaceToBatchNDOp<DeviceType::GPU, T> : public SpaceToBatchOpBase {
public:
explicit SpaceToBatchNDOp(OpConstructContext *context)
: SpaceToBatchOpBase(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::SpaceToBatchKernel<T>>();
} else {
MACE_NOT_IMPLEMENTED;
......
mace/ops/space_to_batch_test.cc
浏览文件 @ 95b32c24
......@@ -39,8 +39,8 @@ void RunSpaceToBatch(const std::vector<index_t> &input_shape,
.AddIntsArg("block_shape", block_shape_data)
.Finalize(net.NewOperatorDef());
} else if (D == CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("SpaceToBatchND", "SpaceToBatchNDTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -53,8 +53,8 @@ void RunSpaceToBatch(const std::vector<index_t> &input_shape,
net.RunOp(D);
if (D == CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
// Check
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"));
......@@ -78,8 +78,8 @@ void RunBatchToSpace(const std::vector<index_t> &input_shape,
.AddIntsArg("block_shape", block_shape_data)
.Finalize(net.NewOperatorDef());
} else if (D == CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchToSpaceND", "BatchToSpaceNDTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -92,8 +92,8 @@ void RunBatchToSpace(const std::vector<index_t> &input_shape,
net.RunOp(D);
if (D == CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
}
// Check
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"));
......@@ -155,8 +155,8 @@ void TestSpaceToBatchLargeInput(const std::vector<index_t> &input_shape,
net.RunOp(GPU);
// run cpu
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("SpaceToBatchND", "SpaceToBatchNDTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -164,8 +164,8 @@ void TestSpaceToBatchLargeInput(const std::vector<index_t> &input_shape,
.AddIntsArg("block_shape", block_shape_data)
.Finalize(net.NewOperatorDef());
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"OutputCPU", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "OutputCPU", DataFormat::NHWC);
// Check
ExpectTensorNear<float>(*net.GetOutput("OutputCPU"),
......@@ -188,8 +188,8 @@ void TestoBatchToSpaceLargeInput(const std::vector<index_t> &input_shape,
net.RunOp(GPU);
// run cpu
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchToSpaceND", "BatchToSpaceNDTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -197,8 +197,8 @@ void TestoBatchToSpaceLargeInput(const std::vector<index_t> &input_shape,
.AddIntsArg("block_shape", block_shape_data)
.Finalize(net.NewOperatorDef());
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"OutputCPU", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "OutputCPU", DataFormat::NHWC);
// Check
ExpectTensorNear<float>(*net.GetOutput("OutputCPU"),
......@@ -218,8 +218,8 @@ void TestSpaceToBatchQuantize(const std::vector<index_t> &input_shape,
1.f);
// run cpu
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("SpaceToBatchND", "SpaceToBatchNDTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -227,8 +227,8 @@ void TestSpaceToBatchQuantize(const std::vector<index_t> &input_shape,
.AddIntsArg("block_shape", block_shape_data)
.Finalize(net.NewOperatorDef());
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"OutputCPU", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "OutputCPU", DataFormat::NHWC);
// run quantize
OpDefBuilder("Quantize", "QuantizeInput")
......@@ -279,8 +279,8 @@ void TestoBatchToSpaceQuantize(const std::vector<index_t> &input_shape,
1.f);
// run cpu
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("BatchToSpaceND", "BatchToSpaceNDTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -288,8 +288,8 @@ void TestoBatchToSpaceQuantize(const std::vector<index_t> &input_shape,
.AddIntsArg("block_shape", block_shape_data)
.Finalize(net.NewOperatorDef());
net.RunOp(CPU);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"OutputCPU", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "OutputCPU", DataFormat::NHWC);
// run quantize
OpDefBuilder("Quantize", "QuantizeInput")
......
mace/ops/space_to_depth.cc
浏览文件 @ 95b32c24
......@@ -94,7 +94,7 @@ class SpaceToDepthOp<DeviceType::GPU, T> : public Operation {
explicit SpaceToDepthOp(OpConstructContext *context)
: Operation(context) {
int block_size = Operation::GetOptionalArg<int>("block_size", 1);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::SpaceToDepthKernel<T>>(block_size);
} else {
MACE_NOT_IMPLEMENTED;
......
mace/ops/space_to_depth_test.cc
浏览文件 @ 95b32c24
......@@ -32,8 +32,8 @@ void RunSpaceToDepth(const std::vector<index_t> &input_shape,
net.AddInputFromArray<D, float>("Input", input_shape, input_data);
// Construct graph
if (D == DeviceType::CPU) {
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("SpaceToDepth", "SpaceToDepthTest")
.Input("InputNCHW")
.Output("OutputNCHW")
......@@ -41,8 +41,8 @@ void RunSpaceToDepth(const std::vector<index_t> &input_shape,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
} else {
OpDefBuilder("SpaceToDepth", "SpaceToDepthTest")
......@@ -107,8 +107,8 @@ void RandomTest(const int block_size,
// Add input data
net.AddRandomInput<D, float>("Input", shape);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("SpaceToDepth", "SpaceToDepthTest")
.Input("InputNCHW")
.AddIntArg("block_size", block_size)
......@@ -118,8 +118,8 @@ void RandomTest(const int block_size,
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("SpaceToDepth", "SpaceToDepthTest")
.Input("Input")
......
mace/ops/split.cc
浏览文件 @ 95b32c24
......@@ -106,7 +106,7 @@ class SplitOp<DeviceType::GPU, T> : public Operation {
explicit SplitOp(OpConstructContext *context)
: Operation(context) {
int32_t axis = Operation::GetOptionalArg<int>("axis", 3);
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::SplitKernel<T>>(axis);
} else {
MACE_NOT_IMPLEMENTED;
......@@ -147,7 +147,7 @@ void RegisterSplit(OpRegistryBase *op_registry) {
[](OpConditionContext *context) -> std::set<DeviceType> {
auto op = context->operator_def();
if (op->output_shape_size() != op->output_size()) {
return { DeviceType::CPU };
return {DeviceType::CPU, DeviceType::GPU};
}
int axis = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
*op, "axis", 3);
......
mace/ops/sqrdiff_mean.cc
浏览文件 @ 95b32c24
......@@ -83,7 +83,7 @@ class SqrDiffMeanOp<DeviceType::GPU, T> : public Operation {
public:
explicit SqrDiffMeanOp(OpConstructContext *context)
: Operation(context) {
if (context->device()->gpu_runtime()->UseImageMemory()) {
if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) {
kernel_ = make_unique<opencl::image::SqrDiffMeanKernel<T>>();
} else {
MACE_NOT_IMPLEMENTED;
......
mace/ops/sqrdiff_mean_test.cc
浏览文件 @ 95b32c24
......@@ -36,13 +36,13 @@ void Simple(const std::vector<index_t> &input_shape0,
net.AddInputFromArray<D, float>("Input1", input_shape1, input1);
net.TransformDataFormat<DeviceType::CPU, float>("Input0",
NHWC,
DataFormat::NHWC,
"InputNCHW0",
NCHW);
DataFormat::NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Input1",
NHWC,
DataFormat::NHWC,
"InputNCHW1",
NCHW);
DataFormat::NCHW);
if (D == DeviceType::CPU) {
OpDefBuilder("SqrDiffMean", "SqrDiffMeanTest")
......@@ -54,9 +54,9 @@ void Simple(const std::vector<index_t> &input_shape0,
net.RunOp(D);
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW",
NCHW,
DataFormat::NCHW,
"Output",
NHWC);
DataFormat::NHWC);
} else {
OpDefBuilder("SqrDiffMean", "SqrDiffMeanTest")
.Input("Input0")
......@@ -107,10 +107,10 @@ void RandomTest(const std::vector<index_t> &input_shape0,
net.AddRandomInput<D, float>("Input0", input_shape0);
net.AddRandomInput<D, float>("Input1", input_shape1);
net.TransformDataFormat<DeviceType::CPU, float>("Input0", NHWC, "InputNCHW0",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>("Input1", NHWC, "InputNCHW1",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input0", DataFormat::NHWC, "InputNCHW0", DataFormat::NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input1", DataFormat::NHWC, "InputNCHW1", DataFormat::NCHW);
OpDefBuilder("SqrDiffMean", "SqrDiffMeanTest")
.Input("InputNCHW0")
.Input("InputNCHW1")
......@@ -118,8 +118,8 @@ void RandomTest(const std::vector<index_t> &input_shape0,
.Finalize(net.NewOperatorDef());
// Run
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW,
"Output", NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
OpDefBuilder("SqrDiffMean", "SqrDiffMeanTest")
.Input("Input0")
.Input("Input1")
......
mace/ops/strided_slice_test.cc
浏览文件 @ 95b32c24
......@@ -86,8 +86,8 @@ void TestStridedSliceWithDataFormat(const std::vector<index_t> &input_shape,
net.AddInputFromArray<CPU, int32_t>(
"Strides", {static_cast<int32_t>(strides.size())}, strides);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("StridedSlice", "StridedSliceOpTest")
.Input("InputNCHW")
......@@ -105,8 +105,8 @@ void TestStridedSliceWithDataFormat(const std::vector<index_t> &input_shape,
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
net.AddInputFromArray<CPU, float>("ExpectedOutput", output_shape, output);
ExpectTensorNear<float>(*net.GetOutput("ExpectedOutput"),
*net.GetOutput("Output"));
......@@ -154,8 +154,8 @@ void TestSliceWithDataFormat(const std::vector<index_t> &input_shape,
net.AddInputFromArray<CPU, int32_t>(
"IndicesSize", {static_cast<int32_t>(indices_size.size())}, indices_size);
net.TransformDataFormat<DeviceType::CPU, float>("Input", NHWC, "InputNCHW",
NCHW);
net.TransformDataFormat<DeviceType::CPU, float>(
"Input", DataFormat::NHWC, "InputNCHW", DataFormat::NCHW);
OpDefBuilder("StridedSlice", "StridedSliceOpTest")
.Input("InputNCHW")
......@@ -168,8 +168,8 @@ void TestSliceWithDataFormat(const std::vector<index_t> &input_shape,
net.RunOp();
net.TransformDataFormat<DeviceType::CPU, float>("OutputNCHW", NCHW, "Output",
NHWC);
net.TransformDataFormat<DeviceType::CPU, float>(
"OutputNCHW", DataFormat::NCHW, "Output", DataFormat::NHWC);
net.AddInputFromArray<CPU, float>("ExpectedOutput", output_shape, output);
ExpectTensorNear<float>(*net.GetOutput("ExpectedOutput"),
*net.GetOutput("Output"));
......
mace/public/mace.h
浏览文件 @ 95b32c24
......@@ -34,10 +34,10 @@ class NetDef;
enum DeviceType { CPU = 0, GPU = 2, HEXAGON = 3, HTA = 4 };
enum DataFormat {
DF_NONE = 0, NHWC = 1, NCHW = 2,
enum class DataFormat {
NONE = 0, NHWC = 1, NCHW = 2,
HWOI = 100, OIHW = 101, HWIO = 102, OHWI = 103,
DF_AUTO = 1000,
AUTO = 1000,
};
enum GPUPerfHint {
......
mace/python/tools/converter.py
浏览文件 @ 95b32c24
......@@ -41,7 +41,7 @@ device_type_map = {'cpu': cvt.DeviceType.CPU.value,
'cpu+gpu': cvt.DeviceType.CPU.value}
data_format_map = {
'NONE': cvt.DataFormat.DF_NONE,
'NONE': cvt.DataFormat.NONE,
'NHWC': cvt.DataFormat.NHWC,
'NCHW': cvt.DataFormat.NCHW,
'OIHW': cvt.DataFormat.OIHW,
......
mace/python/tools/converter_tool/base_converter.py
浏览文件 @ 95b32c24
......@@ -26,14 +26,14 @@ class DeviceType(Enum):
class DataFormat(Enum):
DF_NONE = 0
NONE = 0
NHWC = 1
NCHW = 2
HWIO = 100
OIHW = 101
HWOI = 102
OHWI = 103
DF_AUTO = 1000
AUTO = 1000
# SAME_LOWER: if the amount of paddings to be added is odd,
......@@ -598,8 +598,8 @@ class ConverterUtil(object):
return DataFormat.NHWC
elif arg.i == DataFormat.NCHW.value:
return DataFormat.NCHW
elif arg.i == DataFormat.DF_AUTO.value:
return DataFormat.DF_AUTO
elif arg.i == DataFormat.AUTO.value:
return DataFormat.AUTO
else:
return None
......
mace/python/tools/converter_tool/onnx_converter.py
浏览文件 @ 95b32c24
......@@ -387,7 +387,8 @@ class OnnxConverter(base_converter.ConverterInterface):
self._mace_net_def = mace_pb2.NetDef()
self._data_format = DataFormat.NCHW
ConverterUtil.set_filter_format(self._mace_net_def, DataFormat.OIHW)
ConverterUtil.add_data_format_arg(self._mace_net_def, self._data_format)
ConverterUtil.add_data_format_arg(self._mace_net_def,
self._data_format)
onnx_model = onnx.load(src_model_file)
ir_version = onnx_model.ir_version
......@@ -403,7 +404,7 @@ class OnnxConverter(base_converter.ConverterInterface):
print("constains ops domain: ", domain, "version:", version)
if 'kaldi2onnx' in domain:
polish_available = False
self._data_format = DataFormat.DF_NONE
self._data_format = DataFormat.NONE
self._isKaldi = True
if polish_available:
onnx_model = onnx.utils.polish_model(onnx_model)
......
mace/python/tools/converter_tool/transformer.py
浏览文件 @ 95b32c24
......@@ -27,7 +27,7 @@ from mace.python.tools.converter_tool.base_converter import EltwiseType
from mace.python.tools.converter_tool.base_converter import FrameworkType
from mace.python.tools.converter_tool.base_converter import MaceKeyword
from mace.python.tools.converter_tool.base_converter import MaceOp
from mace.python.tools.converter_tool.base_converter import MaceHasDataFormatOps
from mace.python.tools.converter_tool.base_converter import MaceHasDataFormatOps # noqa
from mace.python.tools.converter_tool.base_converter import MaceMayHasDataFormatOps # noqa
from mace.python.tools.converter_tool.base_converter import PaddingMode
from mace.python.tools.converter_tool.base_converter import ReduceType
......@@ -200,15 +200,15 @@ class Transformer(base_converter.ConverterInterface):
op.output.extend([input_node.name])
output_shape = op.output_shape.add()
output_shape.dims.extend(input_node.shape)
if input_node.data_format != DataFormat.DF_NONE:
if input_node.data_format != DataFormat.NONE:
if input_node.data_format == DataFormat.NCHW:
self.transpose_shape(output_shape.dims,
[0, 3, 1, 2])
ConverterUtil.add_data_format_arg(op,
DataFormat.DF_AUTO)
DataFormat.AUTO)
else:
ConverterUtil.add_data_format_arg(op,
DataFormat.DF_NONE)
DataFormat.NONE)
self._producer[op.output[0]] = op
@staticmethod
......@@ -261,7 +261,7 @@ class Transformer(base_converter.ConverterInterface):
producer = self._producer[tensor]
return ConverterUtil.data_format(producer)
else:
return DataFormat.DF_NONE
return DataFormat.NONE
def consumer_count(self, tensor_name):
return len(self._consumers.get(tensor_name, []))
......@@ -1021,7 +1021,6 @@ class Transformer(base_converter.ConverterInterface):
filter_format.name)
return False
def add_winograd_arg(self):
if self._wino_arg == 0:
return False
......@@ -1350,20 +1349,21 @@ class Transformer(base_converter.ConverterInterface):
df_arg = op.arg.add()
df_arg.name = MaceKeyword.mace_data_format_str
if op.type in MaceHasDataFormatOps:
df_arg.i = DataFormat.DF_AUTO.value
df_arg.i = DataFormat.AUTO.value
elif op.type in MaceMayHasDataFormatOps:
input_df = DataFormat.DF_AUTO.value
input_df = DataFormat.AUTO.value
for input_tensor in op.input:
if input_tensor in self._consts:
continue
mace_check(input_tensor in self._producer,
mace_check(
input_tensor in self._producer,
"Input tensor %s not in producer" % input_tensor)
father_op = self._producer[input_tensor]
temp_input_df = ConverterUtil.get_arg(
father_op, MaceKeyword.mace_data_format_str)
if temp_input_df.i != DataFormat.DF_AUTO.value:
if temp_input_df.i != DataFormat.AUTO.value:
input_df = temp_input_df.i
if input_df == DataFormat.DF_AUTO.value:
if input_df == DataFormat.AUTO.value:
df_arg.i = input_df
# add flag to mark the ops may has data format
has_data_format_arg = op.arg.add()
......@@ -1379,7 +1379,7 @@ class Transformer(base_converter.ConverterInterface):
src_data_format = ConverterUtil.data_format(net)
for op in net.op:
has_data_format = ConverterUtil.data_format(op) == \
DataFormat.DF_AUTO
DataFormat.AUTO
# transpose args
if op.type == MaceOp.Pad.name:
for arg in op.arg:
......
mace/python/tools/model.jinja2
浏览文件 @ 95b32c24
......@@ -80,7 +80,7 @@ void CreateInputInfo(NetDef *net_def) {
input_info = net_def->add_input_info();
input_info->set_name({{ net.input_info[idx].name|tojson }});
input_info->set_data_type(static_cast<DataType>({{ net.input_info[idx].data_type }}));
input_info->set_data_format(static_cast<DataFormat>({{ net.input_info[idx].data_format }}));
input_info->set_data_format({{ net.input_info[idx].data_format }});
input_info->mutable_dims()->Reserve({{ net.input_info[idx].dims|length }});
{% for dim in net.input_info[idx].dims %}
input_info->add_dims({{ dim }});
......@@ -97,7 +97,7 @@ void CreateOutputInfo(NetDef *net_def) {
output_info = net_def->add_output_info();
output_info->set_name({{ net.output_info[idx].name|tojson }});
output_info->set_data_type(static_cast<DataType>({{ net.output_info[idx].data_type }}));
output_info->set_data_format(static_cast<DataFormat>({{ net.output_info[idx].data_format }}));
output_info->set_data_format({{ net.output_info[idx].data_format }});
output_info->mutable_dims()->Reserve({{ net.output_info[idx].dims|length }});
{% for dim in net.output_info[idx].dims %}
output_info->add_dims({{dim}});
......
mace/test/mace_api_mt_test.cc
浏览文件 @ 95b32c24
......@@ -48,7 +48,7 @@ void MaceRunFunc(const int in_out_size) {
for (size_t i = 0; i < input_names.size(); ++i) {
InputOutputInfo *info = net_def->add_input_info();
info->set_data_format(DataFormat::NHWC);
info->set_data_format(static_cast<int>(DataFormat::NHWC));
info->set_name(input_names[i]);
for (auto d : input_shapes[0]) {
info->add_dims(static_cast<int>(d));
......
mace/test/mace_api_test.cc
浏览文件 @ 95b32c24
......@@ -45,7 +45,7 @@ void MaceRun(const int in_out_size,
for (size_t i = 0; i < input_names.size(); ++i) {
InputOutputInfo *info = net_def->add_input_info();
info->set_data_format(DataFormat::NHWC);
info->set_data_format(static_cast<int>(DataFormat::NHWC));
info->set_name(input_names[i]);
for (auto d : max_shape) {
info->add_dims(static_cast<int>(d));
......
mace/test/mace_api_test.h
浏览文件 @ 95b32c24
......@@ -76,7 +76,7 @@ void Conv3x3(const std::string &input_name,
.AddIntArg("padding", Padding::SAME)
.AddIntsArg("dilations", {1, 1})
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddIntArg("has_data_format", 1)
.AddIntArg("data_format", static_cast<int>(DataFormat::AUTO))
.Finalize(&operator_def);
OutputShape *shape = operator_def.add_output_shape();
......@@ -99,7 +99,7 @@ void Relu(const std::string &input_name,
.AddStringArg("activation", "RELU")
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.AddIntArg("device", static_cast<int>(device_type))
.AddIntArg("has_data_format", 1)
.AddIntArg("data_format", static_cast<int>(DataFormat::AUTO))
.Finalize(&operator_def);
net_def->add_op()->CopyFrom(operator_def);
......@@ -139,7 +139,8 @@ void CheckOutputs(const NetDef &net_def,
if (D == DeviceType::CPU) {
std::string input_name = input.first + "NHWC";
net.AddInputFromArray<D, float>(input_name, input_shape, input_data);
net.TransformDataFormat<D, float>(input_name, NHWC, input.first, NCHW);
net.TransformDataFormat<D, float>(
input_name, DataFormat::NHWC, input.first, DataFormat::NCHW);
} else {
net.AddInputFromArray<D, float>(input.first, input_shape, input_data);
}
......@@ -154,7 +155,7 @@ void CheckOutputs(const NetDef &net_def,
memcpy(data.data(),
reinterpret_cast<const T *>(tensor_data.data()) + tensor.offset(),
tensor.data_size() * sizeof(T));
net.AddInputFromArray<D, T>(tensor.name(), shape, data);
net.AddInputFromArray<D, T>(tensor.name(), shape, data, true);
}
net.RunNet(net_def, D);
......@@ -175,9 +176,9 @@ void CheckOutputs(const NetDef &net_def,
if (D == DeviceType::CPU) {
output_name = output.first + "NHWC";
net.TransformDataFormat<CPU, float>(output.first,
NCHW,
DataFormat::NCHW,
output_name,
NHWC);
DataFormat::NHWC);
}
ops::test::ExpectTensorNear<float>(*tmp_tensor,
*net.GetOutput(output_name.data()),
......
mace/tools/validation/mace_run.cc
浏览文件 @ 95b32c24
......@@ -91,7 +91,7 @@ DataFormat ParseDataFormat(const std::string &data_format_str) {
} else if (data_format_str == "OIHW") {
return DataFormat::OIHW;
} else {
return DataFormat::DF_NONE;
return DataFormat::NONE;
}
}
......
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