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Update ppseg with eigen functions (#238)

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* Update ppseg backend support type

* Update ppseg preprocess if condition

* Update README.md

* Update README.md

* Update README.md

* Update ppseg with eigen functions

* Delete old argmax function

* Update README.md

* Delete apply_softmax in ppseg example demo

* Update ppseg code with createFromTensor function

* Delete FDTensor2CVMat function

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update ppseg model.cc with transpose&&softmax in place convert

* Update segmentation_result.md

* Update model.cc

* Update README.md

* Update README.md

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
2022-09-22 21:21:47 +08:00
committed by GitHub
parent 5ad7f64a3a
commit 625845c7d6
13 changed files with 237 additions and 204 deletions
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69
fastdeploy/vision/utils/utils.h
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@@ -20,6 +20,11 @@
#include "fastdeploy/utils/utils.h"
#include "fastdeploy/vision/common/result.h"
// #include "unsupported/Eigen/CXX11/Tensor"
#include "fastdeploy/function/reduce.h"
#include "fastdeploy/function/softmax.h"
#include "fastdeploy/function/transpose.h"
namespace fastdeploy {
namespace vision {
namespace utils {
@@ -51,70 +56,6 @@ std::vector<int32_t> TopKIndices(const T* array, int array_size, int topk) {
return res;
}
template <typename T>
void ArgmaxScoreMap(T infer_result_buffer, SegmentationResult* result,
bool with_softmax) {
int64_t height = result->shape[0];
int64_t width = result->shape[1];
int64_t num_classes = result->shape[2];
int index = 0;
for (size_t i = 0; i < height; ++i) {
for (size_t j = 0; j < width; ++j) {
int64_t s = (i * width + j) * num_classes;
T max_class_score = std::max_element(
infer_result_buffer + s, infer_result_buffer + s + num_classes);
int label_id = std::distance(infer_result_buffer + s, max_class_score);
if (label_id >= 255) {
FDWARNING << "label_id is stored by uint8_t, now the value is bigger "
"than 255, it's "
<< static_cast<int>(label_id) << "." << std::endl;
}
result->label_map[index] = static_cast<uint8_t>(label_id);
if (with_softmax) {
double_t total = 0;
for (int k = 0; k < num_classes; k++) {
total += exp(*(infer_result_buffer + s + k) - *max_class_score);
}
double_t softmax_class_score = 1 / total;
result->score_map[index] = static_cast<float>(softmax_class_score);
} else {
result->score_map[index] = static_cast<float>(*max_class_score);
}
index++;
}
}
}
template <typename T>
void NCHW2NHWC(FDTensor& infer_result) {
T* infer_result_buffer = reinterpret_cast<T*>(infer_result.MutableData());
int num = infer_result.shape[0];
int channel = infer_result.shape[1];
int height = infer_result.shape[2];
int width = infer_result.shape[3];
int chw = channel * height * width;
int wc = width * channel;
int wh = width * height;
std::vector<T> hwc_data(chw);
int index = 0;
for (int n = 0; n < num; n++) {
for (int c = 0; c < channel; c++) {
for (int h = 0; h < height; h++) {
for (int w = 0; w < width; w++) {
hwc_data[n * chw + h * wc + w * channel + c] =
*(infer_result_buffer + index);
index++;
}
}
}
}
std::memcpy(infer_result.MutableData(), hwc_data.data(),
num * chw * sizeof(T));
infer_result.shape = {num, height, width, channel};
}
void NMS(DetectionResult* output, float iou_threshold = 0.5);
void NMS(FaceDetectionResult* result, float iou_threshold = 0.5);