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[Fix] Fix ncnn float bugs for segmentor sdk (#572)

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* fix ncnn float bugs for segmentor sdk

* fix segment for all cases

* fix ut

Co-authored-by: RunningLeon <mnsheng@yeah.net>
This commit is contained in:
hanrui1sensetime 2022-06-08 22:30:23 +08:00 committed by GitHub
parent e69b7a5838
commit c99379cc76
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 30 additions and 33 deletions
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61
csrc/codebase/mmseg/segment.cpp
View File

@ -1,6 +1,7 @@
// Copyright (c) OpenMMLab. All rights reserved.
#include "codebase/mmseg/mmseg.h"
#include "core/logger.h"
#include "core/tensor.h"
#include "core/utils/device_utils.h"
#include "core/utils/formatter.h"
@ -9,6 +10,9 @@
namespace mmdeploy::mmseg {
// TODO: resize masks on device
// TODO: when network output is on device, cast it to a smaller type (e.g. int16_t or int8_t
// according to num classes) to reduce DtoH footprint
class ResizeMask : public MMSegmentation {
public:
explicit ResizeMask(const Value &cfg) : MMSegmentation(cfg) {
@ -39,45 +43,38 @@ class ResizeMask : public MMSegmentation {
Device host{"cpu"};
OUTCOME_TRY(auto host_tensor, MakeAvailableOnDevice(mask, host, stream_));
OUTCOME_TRY(stream_.Wait());
if (mask.data_type() == DataType::kINT64) {
// change kINT64 to 2 INT32
TensorDesc desc{
host_tensor.device(), DataType::kINT32, {1, 2, height, width}, host_tensor.name()};
Tensor _host_tensor(desc, host_tensor.buffer());
return MaskResize(_host_tensor, input_height, input_width);
} else if (mask.data_type() == DataType::kINT32) {
return MaskResize(host_tensor, input_height, input_width);
} else {
MMDEPLOY_ERROR("unsupported `output` tensor, dtype: {}", (int)mask.data_type());
return Status(eNotSupported);
OUTCOME_TRY(auto cv_type, GetCvType(mask.data_type()));
cv::Mat mask_mat(height, width, cv_type, host_tensor.data());
if (mask_mat.channels() > 1) {
cv::extractChannel(mask_mat, mask_mat, little_endian_ ? 0 : mask_mat.channels() - 1);
}
if (mask_mat.type() != CV_32S) {
mask_mat.convertTo(mask_mat, CV_32S);
}
cv::Mat resized_mask = cpu::Resize(mask_mat, input_height, input_width, "nearest");
SegmentorOutput output{cpu::CVMat2Tensor(resized_mask), input_height, input_width, classes_};
return to_value(output);
}
private:
Result<Value> MaskResize(Tensor &tensor, int dst_height, int dst_width) {
auto channel = tensor.shape(1);
auto height = tensor.shape(2);
auto width = tensor.shape(3);
// reshape tensor to convert it to cv::Mat
tensor.Reshape({1, height, width, channel});
auto mat = cpu::Tensor2CVMat(tensor);
auto dst = cpu::Resize(mat, dst_height, dst_width, "nearest");
if (channel == 1) {
auto output_tensor = cpu::CVMat2Tensor(dst);
SegmentorOutput output{output_tensor, dst_height, dst_width, classes_};
return to_value(output);
} else {
cv::Mat _dst;
int channel = little_endian_ ? 0 : dst.dims - 1;
cv::extractChannel(dst, _dst, channel);
auto output_tensor = cpu::CVMat2Tensor(_dst);
SegmentorOutput output{output_tensor, dst_height, dst_width, classes_};
return to_value(output);
static Result<int> GetCvType(DataType type) {
switch (type) {
case DataType::kFLOAT:
return CV_32F;
case DataType::kINT64:
return CV_32SC2;
case DataType::kINT32:
return CV_32S;
default:
return Status(eNotSupported);
}
}
bool IsLittleEndian() {
static bool IsLittleEndian() {
union Un {
char a;
int b;

2
tests/test_codebase/test_mmdet/test_mmdet_core.py
View File

@ -213,7 +213,7 @@ def test_multiclass_nms_with_keep_top_k(pre_top_k):
model_inputs = {'boxes': test_boxes, 'scores': test_scores}
import mmdeploy.backend.onnxruntime as ort_apis
backend_model = ort_apis.ORTWrapper(onnx_model_path, 'cuda:0', None)
backend_model = ort_apis.ORTWrapper(onnx_model_path, 'cpu', None)
output = backend_model.forward(model_inputs)
output = backend_model.output_to_list(output)
dets = output[0]