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[Other] Optimize code style (#1032)

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* Optimize code

* optimize code

* optimize code

* fix compile error
This commit is contained in:
Jason
2023-01-03 19:54:12 +08:00
committed by GitHub
parent ab49b41080
commit f51697d745
31 changed files with 594 additions and 580 deletions
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58
fastdeploy/backends/rknpu/rknpu2/rknpu2_backend.cc
View File

@@ -27,14 +27,14 @@ RKNPU2Backend::~RKNPU2Backend() {
for (uint32_t i = 0; i < io_num.n_input; i++) {
rknn_destroy_mem(ctx, input_mems_[i]);
}
if(input_mems_ != nullptr){
if (input_mems_ != nullptr) {
free(input_mems_);
}
for (uint32_t i = 0; i < io_num.n_output; i++) {
rknn_destroy_mem(ctx, output_mems_[i]);
}
if(output_mems_ != nullptr){
if (output_mems_ != nullptr) {
free(output_mems_);
}
}
@@ -173,16 +173,15 @@ bool RKNPU2Backend::GetModelInputOutputInfos() {
// create input tensor memory
// rknn_tensor_mem* input_mems[io_num.n_input];
input_mems_ = (rknn_tensor_mem**)malloc(sizeof(rknn_tensor_mem*) * io_num.n_input);
input_mems_ =
(rknn_tensor_mem**)malloc(sizeof(rknn_tensor_mem*) * io_num.n_input);
// get input info and copy to input tensor info
for (uint32_t i = 0; i < io_num.n_input; i++) {
input_attrs_[i].index = i;
// query info
ret = rknn_query(ctx,
RKNN_QUERY_INPUT_ATTR,
&(input_attrs_[i]),
ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs_[i]),
sizeof(rknn_tensor_attr));
DumpTensorAttr(input_attrs_[i]);
@@ -190,12 +189,12 @@ bool RKNPU2Backend::GetModelInputOutputInfos() {
printf("rknn_init error! ret=%d\n", ret);
return false;
}
if((input_attrs_[i].fmt != RKNN_TENSOR_NHWC) &&
(input_attrs_[i].fmt != RKNN_TENSOR_UNDEFINED)){
FDERROR << "rknpu2_backend only support input format is NHWC or UNDEFINED" << std::endl;
if ((input_attrs_[i].fmt != RKNN_TENSOR_NHWC) &&
(input_attrs_[i].fmt != RKNN_TENSOR_UNDEFINED)) {
FDERROR << "rknpu2_backend only support input format is NHWC or UNDEFINED"
<< std::endl;
}
// copy input_attrs_ to input tensor info
std::string temp_name = input_attrs_[i].name;
std::vector<int> temp_shape{};
@@ -203,25 +202,28 @@ bool RKNPU2Backend::GetModelInputOutputInfos() {
for (int j = 0; j < input_attrs_[i].n_dims; j++) {
temp_shape[j] = (int)input_attrs_[i].dims[j];
}
FDDataType temp_dtype = fastdeploy::RKNPU2Backend::RknnTensorTypeToFDDataType(input_attrs_[i].type);
FDDataType temp_dtype =
fastdeploy::RKNPU2Backend::RknnTensorTypeToFDDataType(
input_attrs_[i].type);
TensorInfo temp_input_info = {temp_name, temp_shape, temp_dtype};
inputs_desc_[i] = temp_input_info;
}
// Get detailed output parameters
output_attrs_ = (rknn_tensor_attr*)malloc(sizeof(rknn_tensor_attr) * io_num.n_output);
output_attrs_ =
(rknn_tensor_attr*)malloc(sizeof(rknn_tensor_attr) * io_num.n_output);
memset(output_attrs_, 0, io_num.n_output * sizeof(rknn_tensor_attr));
outputs_desc_.resize(io_num.n_output);
// Create output tensor memory
output_mems_ = (rknn_tensor_mem**)malloc(sizeof(rknn_tensor_mem*) * io_num.n_output);;
output_mems_ =
(rknn_tensor_mem**)malloc(sizeof(rknn_tensor_mem*) * io_num.n_output);
;
for (uint32_t i = 0; i < io_num.n_output; i++) {
output_attrs_[i].index = i;
// query info
ret = rknn_query(ctx,
RKNN_QUERY_OUTPUT_ATTR,
&(output_attrs_[i]),
ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs_[i]),
sizeof(rknn_tensor_attr));
DumpTensorAttr(output_attrs_[i]);
@@ -233,7 +235,7 @@ bool RKNPU2Backend::GetModelInputOutputInfos() {
// If the output dimension is 3, the runtime will automatically change it to 4.
// Obviously, this is wrong, and manual correction is required here.
int n_dims = output_attrs_[i].n_dims;
if((n_dims == 4) && (output_attrs_[i].dims[3] == 1)){
if ((n_dims == 4) && (output_attrs_[i].dims[3] == 1)) {
n_dims--;
}
@@ -292,8 +294,7 @@ std::vector<TensorInfo> RKNPU2Backend::GetOutputInfos() {
}
bool RKNPU2Backend::Infer(std::vector<FDTensor>& inputs,
std::vector<FDTensor>* outputs,
bool copy_to_fd) {
std::vector<FDTensor>* outputs, bool copy_to_fd) {
int ret = RKNN_SUCC;
// Judge whether the input and output size are the same
if (inputs.size() != inputs_desc_.size()) {
@@ -303,15 +304,17 @@ bool RKNPU2Backend::Infer(std::vector<FDTensor>& inputs,
return false;
}
if(!this->infer_init){
if (!this->infer_init) {
for (uint32_t i = 0; i < io_num.n_input; i++) {
// Judge whether the input and output types are the same
rknn_tensor_type input_type =
fastdeploy::RKNPU2Backend::FDDataTypeToRknnTensorType(inputs[i].dtype);
fastdeploy::RKNPU2Backend::FDDataTypeToRknnTensorType(
inputs[i].dtype);
if (input_type != input_attrs_[i].type) {
FDWARNING << "The input tensor type != model's inputs type."
<< "The input_type need " << get_type_string(input_attrs_[i].type)
<< ",but inputs["<< i << "].type is " << get_type_string(input_type)
<< "The input_type need "
<< get_type_string(input_attrs_[i].type) << ",but inputs["
<< i << "].type is " << get_type_string(input_type)
<< std::endl;
}
@@ -319,10 +322,11 @@ bool RKNPU2Backend::Infer(std::vector<FDTensor>& inputs,
input_attrs_[i].type = input_type;
input_attrs_[i].size = inputs[0].Nbytes();
input_attrs_[i].size_with_stride = inputs[0].Nbytes();
if(input_attrs_[i].type == RKNN_TENSOR_FLOAT16 ||
input_attrs_[i].type == RKNN_TENSOR_FLOAT32){
if (input_attrs_[i].type == RKNN_TENSOR_FLOAT16 ||
input_attrs_[i].type == RKNN_TENSOR_FLOAT32) {
FDINFO << "The input model is not a quantitative model. "
"Close the normalize operation." << std::endl;
"Close the normalize operation."
<< std::endl;
}
input_mems_[i] = rknn_create_mem(ctx, inputs[i].Nbytes());
@@ -474,4 +478,4 @@ RKNPU2Backend::FDDataTypeToRknnTensorType(fastdeploy::FDDataType type) {
FDERROR << "rknn_tensor_type don't support this type" << std::endl;
return RKNN_TENSOR_TYPE_MAX;
}
} // namespace fastdeploy
} // namespace fastdeploy

7
fastdeploy/backends/rknpu/rknpu2/rknpu2_backend.h
View File

@@ -14,9 +14,9 @@
#pragma once
#include "fastdeploy/backends/backend.h"
#include "fastdeploy/core/fd_tensor.h"
#include "rknn_api.h" // NOLINT
#include "fastdeploy/backends/rknpu/rknpu2/rknpu2_config.h"
#include "fastdeploy/core/fd_tensor.h"
#include "rknn_api.h" // NOLINT
#include <cstring>
#include <iostream>
#include <memory>
@@ -71,8 +71,7 @@ class RKNPU2Backend : public BaseBackend {
TensorInfo GetOutputInfo(int index) override;
std::vector<TensorInfo> GetInputInfos() override;
std::vector<TensorInfo> GetOutputInfos() override;
bool Infer(std::vector<FDTensor>& inputs,
std::vector<FDTensor>* outputs,
bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs,
bool copy_to_fd = true) override;
private:

6
fastdeploy/backends/rknpu/rknpu2/rknpu2_config.h
View File

@@ -24,9 +24,9 @@ typedef enum _rknpu2_cpu_name {
/*! RKNPU2 core mask for mobile device. */
typedef enum _rknpu2_core_mask {
RKNN_NPU_CORE_AUTO = 0, //< default, run on NPU core randomly.
RKNN_NPU_CORE_0 = 1, //< run on NPU core 0.
RKNN_NPU_CORE_1 = 2, //< run on NPU core 1.
RKNN_NPU_CORE_2 = 4, //< run on NPU core 2.
RKNN_NPU_CORE_0 = 1, //< run on NPU core 0.
RKNN_NPU_CORE_1 = 2, //< run on NPU core 1.
RKNN_NPU_CORE_2 = 4, //< run on NPU core 2.
RKNN_NPU_CORE_0_1 =
RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1, //< run on NPU core 1 and core 2.
RKNN_NPU_CORE_0_1_2 =