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Merged
ktro2828 merged 1 commit into from
Oct 2, 2025
Merged

refactor: apply snake_case to the function names #60

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6 changes: 3 additions & 3 deletions mmros/include/mmros/archetype/exception.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,7 @@ class MmRosException : public std::exception
*
* @param error `MmRosError` object.
*/
explicit MmRosException(const MmRosError & error) : error_(error) { appendMessagePrefix(); }
explicit MmRosException(const MmRosError & error) : error_(error) { append_message_prefix(); }

/**
* @brief Construct a new MmRosException object with error kind and message.
Expand All @@ -75,7 +75,7 @@ class MmRosException : public std::exception
*/
MmRosException(const MmRosError_t & kind, const std::string & msg) : error_(kind, msg)
{
appendMessagePrefix();
append_message_prefix();
}

/**
Expand All @@ -87,7 +87,7 @@ class MmRosException : public std::exception
/**
* @brief Append prefix to the error message depending on its kind.
*/
void appendMessagePrefix() noexcept
void append_message_prefix() noexcept
{
switch (error_.kind) {
case MmRosError_t::TENSORRT:
Expand Down
12 changes: 6 additions & 6 deletions mmros/include/mmros/archetype/result.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -48,14 +48,14 @@ class Result
/**
* @brief Check whether holding value is expected type.
*/
bool isOk() const noexcept { return std::holds_alternative<T>(value_); }
bool is_ok() const noexcept { return std::holds_alternative<T>(value_); }

/**
* @brief Return the expected value if it holds, otherwise throw `MmRosException`.
*/
T unwrap() const
{
if (isOk()) {
if (is_ok()) {
return std::get<T>(value_);
} else {
throw MmRosException(std::get<MmRosError>(value_));
Expand All @@ -74,7 +74,7 @@ class Result
*/

template <typename T>
Result<T> Ok(const T & value) noexcept
Result<T> make_ok(const T & value) noexcept
{
return Result<T>(value);
}
Expand All @@ -86,7 +86,7 @@ Result<T> Ok(const T & value) noexcept
* @param error `MmRosError` object.
*/
template <typename T>
Result<T> Err(const MmRosError & error) noexcept
Result<T> make_err(const MmRosError & error) noexcept
{
return Result<T>(error);
}
Expand All @@ -98,7 +98,7 @@ Result<T> Err(const MmRosError & error) noexcept
* @param kind Error kind.
*/
template <typename T>
Result<T> Err(const MmRosError_t & kind) noexcept
Result<T> make_err(const MmRosError_t & kind) noexcept
{
MmRosError error(kind);
return Result<T>(error);
Expand All @@ -112,7 +112,7 @@ Result<T> Err(const MmRosError_t & kind) noexcept
* @param msg Error message.
*/
template <typename T>
Result<T> Err(const MmRosError_t & kind, const std::string & msg) noexcept
Result<T> make_err(const MmRosError_t & kind, const std::string & msg) noexcept
{
MmRosError error(kind, msg);
return Result<T>(error);
Expand Down
14 changes: 7 additions & 7 deletions mmros/src/detector/detector2d.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -77,36 +77,36 @@ archetype::Result<outputs_type> Detector2D::doInference(
const std::vector<cv::Mat> & images) noexcept
{
if (images.empty()) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
return archetype::make_err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
}

// 1. Init CUDA pointers
try {
initCudaPtr(images.size());
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 2. Execute preprocess
try {
preprocess(images);
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 3. Set tensors
std::vector<void *> buffers{input_d_.get(), out_boxes_d_.get(), out_labels_d_.get()};
if (!trt_common_->setTensorsAddresses(buffers)) {
std::ostringstream os;
os << "@" << __FILE__ << ", #F:" << __FUNCTION__ << ", #L:" << __LINE__;
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
}

// 4. Execute inference
if (!trt_common_->enqueueV3(stream_)) {
std::ostringstream os;
os << "@" << __FILE__ << ", #F:" << __FUNCTION__ << ", #L:" << __LINE__;
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
}

// 5. Execute postprocess
Expand Down Expand Up @@ -184,7 +184,7 @@ archetype::Result<outputs_type> Detector2D::postprocess(
sizeof(int) * batch_size * num_detection * class_dim, ::cudaMemcpyDeviceToHost, stream_));
CHECK_CUDA_ERROR(cudaStreamSynchronize(stream_));
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

outputs_type output;
Expand All @@ -210,6 +210,6 @@ archetype::Result<outputs_type> Detector2D::postprocess(
output.push_back(std::move(boxes));
}

return archetype::Ok(output);
return archetype::make_ok(output);
}
} // namespace mmros::detector
14 changes: 7 additions & 7 deletions mmros/src/detector/instance_segmeter2d.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -79,33 +79,33 @@ archetype::Result<outputs_type> InstanceSegmenter2D::doInference(
const std::vector<cv::Mat> & images) noexcept
{
if (images.empty()) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
return archetype::make_err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
}

// 1. Init CUDA pointers
try {
initCudaPtr(images.size());
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 2. Execute preprocess
try {
preprocess(images);
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 3. Set tensors
std::vector<void *> buffers{
input_d_.get(), out_boxes_d_.get(), out_labels_d_.get(), out_segments_d_.get()};
if (!trt_common_->setTensorsAddresses(buffers)) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT);
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT);
}

// 4. Execute inference
if (!trt_common_->enqueueV3(stream_)) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT);
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT);
}

// 5. Execute postprocess
Expand Down Expand Up @@ -196,7 +196,7 @@ archetype::Result<outputs_type> InstanceSegmenter2D::postprocess(
::cudaMemcpyDeviceToHost, stream_));
CHECK_CUDA_ERROR(::cudaStreamSynchronize(stream_));
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

outputs_type outputs;
Expand Down Expand Up @@ -234,6 +234,6 @@ archetype::Result<outputs_type> InstanceSegmenter2D::postprocess(
outputs.emplace_back(std::move(boxes), std::move(masks));
}

return archetype::Ok(outputs);
return archetype::make_ok(outputs);
}
} // namespace mmros::detector
14 changes: 7 additions & 7 deletions mmros/src/detector/panoptic_segmenter2d.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -76,21 +76,21 @@ archetype::Result<outputs_type> PanopticSegmenter2D::doInference(
const std::vector<cv::Mat> & images) noexcept
{
if (images.empty()) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
return archetype::make_err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
}

// 1. Init CUDA pointers
try {
initCudaPtr(images.size());
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 2. Execute preprocess
try {
preprocess(images);
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 3. Set tensors
Expand All @@ -100,14 +100,14 @@ archetype::Result<outputs_type> PanopticSegmenter2D::doInference(
if (!trt_common_->setTensorsAddresses(buffers)) {
std::ostringstream os;
os << "@" << __FILE__ << ", #F:" << __FUNCTION__ << ", #L:" << __LINE__;
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
}

// 4. Execute inference
if (!trt_common_->enqueueV3(stream_)) {
std::ostringstream os;
os << "@" << __FILE__ << ", #F:" << __FUNCTION__ << ", #L:" << __LINE__;
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
}

// 5. Execute postprocess
Expand Down Expand Up @@ -216,7 +216,7 @@ archetype::Result<outputs_type> PanopticSegmenter2D::postprocess(
::cudaMemcpyDeviceToHost, stream_));
CHECK_CUDA_ERROR(cudaStreamSynchronize(stream_));
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

outputs_type output;
Expand Down Expand Up @@ -248,6 +248,6 @@ archetype::Result<outputs_type> PanopticSegmenter2D::postprocess(
output.emplace_back(std::move(boxes), std::move(mask));
}

return archetype::Ok(output);
return archetype::make_ok(output);
}
} // namespace mmros::detector
14 changes: 7 additions & 7 deletions mmros/src/detector/semantic_segmenter2d.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -78,36 +78,36 @@ archetype::Result<outputs_type> SemanticSegmenter2D::doInference(
const std::vector<cv::Mat> & images) noexcept
{
if (images.empty()) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
return archetype::make_err<outputs_type>(archetype::MmRosError_t::UNKNOWN, "No image.");
}

// 1. Init CUDA pointers
try {
initCudaPtr(images.size());
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 2. Execute preprocess
try {
preprocess(images);
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

// 3. Set tensors
std::vector<void *> buffers{input_d_.get(), output_d_.get()};
if (!trt_common_->setTensorsAddresses(buffers)) {
std::ostringstream os;
os << "@" << __FILE__ << ", #F:" << __FUNCTION__ << ", #L:" << __LINE__;
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
}

// 4. Execute inference
if (!trt_common_->enqueueV3(stream_)) {
std::ostringstream os;
os << "@" << __FILE__ << ", #F:" << __FUNCTION__ << ", #L:" << __LINE__;
return archetype::Err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::TENSORRT, os.str());
}

// 5. Execute postprocess
Expand Down Expand Up @@ -160,7 +160,7 @@ archetype::Result<outputs_type> SemanticSegmenter2D::postprocess(
output_h.data(), output_d_.get(),
sizeof(int64_t) * batch_size * 1 * output_width * output_height, ::cudaMemcpyDeviceToHost));
} catch (const archetype::MmRosException & e) {
return archetype::Err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
return archetype::make_err<outputs_type>(archetype::MmRosError_t::CUDA, e.what());
}

outputs_type output;
Expand Down Expand Up @@ -197,6 +197,6 @@ archetype::Result<outputs_type> SemanticSegmenter2D::postprocess(
output.push_back(std::move(final_mask));
}

return archetype::Ok(output);
return archetype::make_ok(output);
}
} // namespace mmros::detector
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