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[RKNPU2] Update quantitative model (#879)

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* 对RKNPU2后端进行修改，当模型为非量化模型时，不在NPU执行normalize操作，当模型为量化模型时，在NUP上执行normalize操作

* 更新RKNPU2框架，输出数据的数据类型统一返回fp32类型

* 更新scrfd，拆分disable_normalize和disable_permute

* 更新scrfd代码，支持量化

* 更新scrfd python example代码

* 更新模型转换代码，支持量化模型

* 更新文档

* 按照要求修改

* 按照要求修改

* 修正模型转换文档

* 更新一下转换脚本
This commit is contained in:
Zheng_Bicheng
2022-12-19 13:58:43 +08:00
committed by GitHub
parent 383887dede
commit dc13eb7049
18 changed files with 92 additions and 148 deletions
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17
fastdeploy/backends/rknpu/rknpu2/rknpu2_backend.cc
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@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/backends/rknpu/rknpu2/rknpu2_backend.h"
#include "fastdeploy/utils/perf.h"
namespace fastdeploy {
RKNPU2Backend::~RKNPU2Backend() {
// Release memory uniformly here
@@ -254,12 +254,11 @@ bool RKNPU2Backend::GetModelInputOutputInfos() {
void RKNPU2Backend::DumpTensorAttr(rknn_tensor_attr& attr) {
printf("index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], "
"n_elems=%d, size=%d, fmt=%s, type=%s, "
"qnt_type=%s, zp=%d, scale=%f, pass_through=%d",
"qnt_type=%s, zp=%d, scale=%f\n",
attr.index, attr.name, attr.n_dims, attr.dims[0], attr.dims[1],
attr.dims[2], attr.dims[3], attr.n_elems, attr.size,
get_format_string(attr.fmt), get_type_string(attr.type),
get_qnt_type_string(attr.qnt_type), attr.zp, attr.scale,
attr.pass_through);
get_qnt_type_string(attr.qnt_type), attr.zp, attr.scale);
}
TensorInfo RKNPU2Backend::GetInputInfo(int index) {
@@ -310,12 +309,7 @@ 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){
FDINFO << "The input model is not a quantitative model. "
"Close the normalize operation." << std::endl;
}
input_attrs_[i].pass_through = 0;
input_mems_[i] = rknn_create_mem(ctx, inputs[i].Nbytes());
if (input_mems_[i] == nullptr) {
FDERROR << "rknn_create_mem input_mems_ error." << std::endl;
@@ -346,7 +340,6 @@ bool RKNPU2Backend::Infer(std::vector<FDTensor>& inputs,
// default output type is depend on model, this requires float32 to compute top5
ret = rknn_set_io_mem(ctx, output_mems_[i], &output_attrs_[i]);
// set output memory and attribute
if (ret != RKNN_SUCC) {
FDERROR << "output tensor memory rknn_set_io_mem fail! ret=" << ret
@@ -357,7 +350,7 @@ bool RKNPU2Backend::Infer(std::vector<FDTensor>& inputs,
this->infer_init = true;
}
// Copy input data to input tensor memory
for (uint32_t i = 0; i < io_num.n_input; i++) {
uint32_t width = input_attrs_[i].dims[2];