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Merge branch 'develop' into rknn_pybind

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Jason
2023-03-07 18:53:53 +08:00
committed by GitHub
parent 458523fc29 8ec1353cd8
commit 7e4ef4a72c
3 changed files with 97 additions and 49 deletions
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4
fastdeploy/benchmark/option.h
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@@ -20,8 +20,8 @@ namespace fastdeploy {
*/
namespace benchmark {
/*! @brief Option object used to control the behavior of the benchmark profiling.
*/
// @brief Option object used to control the behavior of the benchmark profiling.
//
struct BenchmarkOption {
int warmup = 50; ///< Warmup for backend inference.
int repeats = 100; ///< Repeats for backend inference.

114
fastdeploy/core/fd_tensor.h
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@@ -25,11 +25,89 @@
namespace fastdeploy {
/*! @brief FDTensor object used to represend data matrix
*
*/
struct FASTDEPLOY_DECL FDTensor {
// std::vector<int8_t> data;
/** \brief Set data buffer for a FDTensor, e.g
* ```
* std::vector<float> buffer(1 * 3 * 224 * 224, 0);
* FDTensor tensor;
* tensor.SetData({1, 3, 224, 224}, FDDataType::FLOAT, buffer.data());
* ```
* \param[in] tensor_shape The shape of tensor
* \param[in] data_type The data type of tensor
* \param[in] data_buffer The pointer of data buffer memory
* \param[in] copy Whether to copy memory from data_buffer to tensor, if false, this tensor will share memory with data_buffer, and the data is managed by userself
* \param[in] data_device The device of data_buffer, e.g if data_buffer is a pointer to GPU data, the device should be Device::GPU
* \param[in] data_device_id The device id of data_buffer
*/
void SetData(const std::vector<int64_t>& tensor_shape, const FDDataType& data_type, void* data_buffer, bool copy = false, const Device& data_device = Device::CPU, int data_device_id = -1) {
SetExternalData(tensor_shape, data_type, data_buffer, data_device, data_device_id);
if (copy) {
StopSharing();
}
}
/// Get data pointer of tensor
void* GetData() {
return MutableData();
}
/// Get data pointer of tensor
const void* GetData() const {
return Data();
}
/// Expand the shape of tensor, it will not change the data memory, just modify its attribute `shape`
void ExpandDim(int64_t axis = 0);
/// Squeeze the shape of tensor, it will not change the data memory, just modify its attribute `shape`
void Squeeze(int64_t axis = 0);
/// Reshape the tensor, it will not change the data memory, just modify its attribute `shape`
bool Reshape(const std::vector<int64_t>& new_shape);
/// Total size of tensor memory buffer in bytes
int Nbytes() const;
/// Total number of elements in tensor
int Numel() const;
/// Get shape of tensor
std::vector<int64_t> Shape() const { return shape; }
/// Get dtype of tensor
FDDataType Dtype() const { return dtype; }
/** \brief Allocate cpu data buffer for a FDTensor, e.g
* ```
* FDTensor tensor;
* tensor.Allocate(FDDataType::FLOAT, {1, 3, 224, 224};
* ```
* \param[in] data_type The data type of tensor
* \param[in] tensor_shape The shape of tensor
*/
void Allocate(const FDDataType& data_type, const std::vector<int64_t>& data_shape) {
Allocate(data_shape, data_type, name);
}
/// Debug function, print shape, dtype, mean, max, min of tensor
void PrintInfo(const std::string& prefix = "Debug TensorInfo: ") const;
/// Name of tensor, while feed to runtime, this need be defined
std::string name = "";
/// Whether the tensor is owned the data buffer or share the data buffer from outside
bool IsShared() { return external_data_ptr != nullptr; }
/// If the tensor is share the data buffer from outside, `StopSharing` will copy to its own structure; Otherwise, do nothing
void StopSharing();
// ******************************************************
// The following member and function only used by inside FastDeploy, maybe removed in next version
void* buffer_ = nullptr;
std::vector<int64_t> shape = {0};
std::string name = "";
FDDataType dtype = FDDataType::INT8;
// This use to skip memory copy step
@@ -64,10 +142,6 @@ struct FASTDEPLOY_DECL FDTensor {
void* Data();
bool IsShared() { return external_data_ptr != nullptr; }
void StopSharing();
const void* Data() const;
// Use this data to get the tensor data to process
@@ -78,6 +152,7 @@ struct FASTDEPLOY_DECL FDTensor {
// will copy to cpu store in `temporary_cpu_buffer`
const void* CpuData() const;
// void SetDataBuffer(const std::vector<int64_t>& new_shape, const FDDataType& data_type, void* data_buffer, bool copy = false, const Device& new_device = Device::CPU, int new_device_id = -1);
// Set user memory buffer for Tensor, the memory is managed by
// the user it self, but the Tensor will share the memory with user
// So take care with the user buffer
@@ -85,15 +160,6 @@ struct FASTDEPLOY_DECL FDTensor {
const FDDataType& data_type, void* data_buffer,
const Device& new_device = Device::CPU,
int new_device_id = -1);
// Expand the shape of a Tensor. Insert a new axis that will appear
// at the `axis` position in the expanded Tensor shape.
void ExpandDim(int64_t axis = 0);
// Squeeze the shape of a Tensor. Erase the axis that will appear
// at the `axis` position in the squeezed Tensor shape.
void Squeeze(int64_t axis = 0);
// Initialize Tensor
// Include setting attribute for tensor
// and allocate cpu memory buffer
@@ -102,18 +168,6 @@ struct FASTDEPLOY_DECL FDTensor {
const std::string& tensor_name = "",
const Device& new_device = Device::CPU);
// Total size of tensor memory buffer in bytes
int Nbytes() const;
// Total number of elements in this tensor
int Numel() const;
// Get shape of FDTensor
std::vector<int64_t> Shape() const { return shape; }
// Get dtype of FDTensor
FDDataType Dtype() const { return dtype; }
void Resize(size_t nbytes);
void Resize(const std::vector<int64_t>& new_shape);
@@ -122,12 +176,6 @@ struct FASTDEPLOY_DECL FDTensor {
const FDDataType& data_type, const std::string& tensor_name = "",
const Device& new_device = Device::CPU);
bool Reshape(const std::vector<int64_t>& new_shape);
// Debug function
// Use this function to print shape, dtype, mean, max, min
// prefix will also be printed as tag
void PrintInfo(const std::string& prefix = "TensorInfo: ") const;
bool ReallocFn(size_t nbytes);
void FreeFn();

28
fastdeploy/runtime/runtime_option.h
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@@ -158,12 +158,12 @@ struct FASTDEPLOY_DECL RuntimeOption {
/// Option to configure RKNPU2 backend
RKNPU2BackendOption rknpu2_option;
/** \brief Set the profile mode as 'true'.
*
* \param[in] inclue_h2d_d2h Whether to include time of H2D_D2H for time of runtime.
* \param[in] repeat Repeat times for runtime inference.
* \param[in] warmup Warmup times for runtime inference.
*/
// \brief Set the profile mode as 'true'.
//
// \param[in] inclue_h2d_d2h Whether to include time of H2D_D2H for time of runtime.
// \param[in] repeat Repeat times for runtime inference.
// \param[in] warmup Warmup times for runtime inference.
//
void EnableProfiling(bool inclue_h2d_d2h = false,
int repeat = 100, int warmup = 50) {
benchmark_option.enable_profile = true;
@@ -172,24 +172,24 @@ struct FASTDEPLOY_DECL RuntimeOption {
benchmark_option.include_h2d_d2h = inclue_h2d_d2h;
}
/** \brief Set the profile mode as 'false'.
*/
// \brief Set the profile mode as 'false'.
//
void DisableProfiling() {
benchmark_option.enable_profile = false;
}
/** \brief Enable to check if current backend set by user can be found at valid_xxx_backend.
*/
// \brief Enable to check if current backend set by user can be found at valid_xxx_backend.
//
void EnableValidBackendCheck() {
enable_valid_backend_check = true;
}
/** \brief Disable to check if current backend set by user can be found at valid_xxx_backend.
*/
// \brief Disable to check if current backend set by user can be found at valid_xxx_backend.
//
void DisableValidBackendCheck() {
enable_valid_backend_check = false;
}
/// Benchmark option
// Benchmark option
benchmark::BenchmarkOption benchmark_option;
// enable the check for valid backend, default true.
bool enable_valid_backend_check = true;
@@ -200,7 +200,7 @@ struct FASTDEPLOY_DECL RuntimeOption {
std::string model_file = "";
std::string params_file = "";
bool model_from_memory_ = false;
/// format of input model
// format of input model
ModelFormat model_format = ModelFormat::PADDLE;
std::string encryption_key_ = "";