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[Other] Update deprecated segmentation apis && segmentation label map resize interinterpolation (#790)

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* Refactor PaddleSeg with preprocessor && postprocessor

* Fix bugs

* Delete redundancy code

* Modify by comments

* Refactor according to comments

* Add batch evaluation

* Add single test script

* Add ppliteseg single test script && fix eval(raise) error

* fix bug

* Fix evaluation segmentation.py batch predict

* Fix segmentation evaluation bug

* Fix evaluation segmentation bugs

* Update segmentation result docs

* Update old predict api and DisableNormalizeAndPermute

* Update resize segmentation label map with cv::INTER_NEAREST

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
2022-12-05 09:57:54 +08:00
committed by GitHub
parent d74e1209ae
commit 6c31198342
4 changed files with 13 additions and 9 deletions
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6
examples/vision/segmentation/paddleseg/cpp/infer.cc
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@@ -37,7 +37,7 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
auto im = cv::imread(image_file); auto im = cv::imread(image_file);
fastdeploy::vision::SegmentationResult res; fastdeploy::vision::SegmentationResult res;
if (!model.Predict(&im, &res)) { if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl; std::cerr << "Failed to predict." << std::endl;
return; return;
} }
@@ -66,7 +66,7 @@ void GpuInfer(const std::string& model_dir, const std::string& image_file) {
auto im = cv::imread(image_file); auto im = cv::imread(image_file);
fastdeploy::vision::SegmentationResult res; fastdeploy::vision::SegmentationResult res;
if (!model.Predict(&im, &res)) { if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl; std::cerr << "Failed to predict." << std::endl;
return; return;
} }
@@ -96,7 +96,7 @@ void TrtInfer(const std::string& model_dir, const std::string& image_file) {
auto im = cv::imread(image_file); auto im = cv::imread(image_file);
fastdeploy::vision::SegmentationResult res; fastdeploy::vision::SegmentationResult res;
if (!model.Predict(&im, &res)) { if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl; std::cerr << "Failed to predict." << std::endl;
return; return;
} }

2
examples/vision/segmentation/paddleseg/quantize/cpp/infer.cc
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@@ -34,7 +34,7 @@ void InitAndInfer(const std::string& model_dir, const std::string& image_file,
auto im_bak = im.clone(); auto im_bak = im.clone();
fastdeploy::vision::SegmentationResult res; fastdeploy::vision::SegmentationResult res;
if (!model.Predict(&im, &res)) { if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl; std::cerr << "Failed to predict." << std::endl;
return; return;
} }

4
examples/vision/segmentation/paddleseg/rknpu2/cpp/infer.cc
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@@ -74,12 +74,12 @@ void InferHumanPPHumansegv2Lite(const std::string& device) {
auto im = cv::imread(image_file); auto im = cv::imread(image_file);
if (device == "npu") { if (device == "npu") {
model.DisableNormalizeAndPermute(); model.GetPreprocessor().DisableNormalizeAndPermute();
} }
fastdeploy::vision::SegmentationResult res; fastdeploy::vision::SegmentationResult res;
clock_t start = clock(); clock_t start = clock();
if (!model.Predict(&im, &res)) { if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl; std::cerr << "Failed to predict." << std::endl;
return; return;
} }

10
fastdeploy/vision/segmentation/ppseg/postprocessor.cc
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@@ -280,13 +280,17 @@ bool PaddleSegPostprocessor::Run(
FDMat mat; FDMat mat;
std::vector<uint8_t> uint8_result_buffer; std::vector<uint8_t> uint8_result_buffer;
// Resize interpration
int interpolation = cv::INTER_LINEAR;
if (is_resized) { if (is_resized) {
if (infer_results_dtype == FDDataType::INT64 || if (infer_results_dtype == FDDataType::INT64 ||
infer_results_dtype == FDDataType::INT32 ){ infer_results_dtype == FDDataType::INT32 ){
FDTensorCast2Uint8(&infer_result, infer_chw, &uint8_result_buffer); FDTensorCast2Uint8(&infer_result, infer_chw, &uint8_result_buffer);
} // label map resize with nearest interpolation
interpolation = cv::INTER_NEAREST;
}
mat = std::move(Mat::Create(infer_result, ProcLib::OPENCV)); mat = std::move(Mat::Create(infer_result, ProcLib::OPENCV));
Resize::Run(&mat, input_width, input_height, -1.0f, -1.0f, 1, false, ProcLib::OPENCV); Resize::Run(&mat, input_width, input_height, -1.0f, -1.0f, interpolation, false, ProcLib::OPENCV);
mat.ShareWithTensor(&infer_result); mat.ShareWithTensor(&infer_result);
} }
result->shape = infer_result.shape; result->shape = infer_result.shape;