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Merge branch 'develop' of https://github.com/PaddlePaddle/FastDeploy into develop

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jiangjiajun
2022-11-07 14:11:02 +00:00
parent b314d66bb7 16266969a1
commit 21836f8af5
7 changed files with 131 additions and 26 deletions
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28
examples/runtime/README.md
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@@ -1,16 +1,16 @@
# FastDeploy Runtime推理示例
# FastDeploy Runtime examples
| 示例代码 | 编程语言 | 说明 |
| Example Code | Program Language | Description |
| :------- | :------- | :---- |
| python/infer_paddle_paddle_inference.py | Python | paddle模型通过paddle inference在cpu/gpu上的推理 |
| python/infer_paddle_tensorrt.py | Python | paddle模型通过tensorrt在gpu上的推理 |
| python/infer_paddle_openvino.py | Python | paddle模型通过openvino在cpu上的推理 |
| python/infer_paddle_onnxruntime.py | Python | paddle模型通过onnx runtime在cpu/gpu上的推理 |
| python/infer_onnx_openvino.py | Python | onnx模型通过openvino在cpu上的推理 |
| python/infer_onnx_tensorrt.py | Python | onnx模型通过tensorrt在gpu上的推理 |
| cpp/infer_paddle_paddle_inference.cc | C++ | paddle模型通过paddle inference在cpu/gpu上的推理 |
| cpp/infer_paddle_tensorrt.cc | C++ | paddle模型通过tensorrt在gpu上的推理 |
| cpp/infer_paddle_openvino.cc | C++ | paddle模型通过openvino在cpu上的推理 |
| cpp/infer_paddle_onnxruntime.cc | C++ | paddle模型通过onnx runtime在cpu/gpu上的推理 |
| cpp/infer_onnx_openvino.cc | C++ | onnx模型通过openvino在cpu上的推理 |
| cpp/infer_onnx_tensorrt.cc | C++ | onnx模型通过tensorrt在gpu上的推理 |
| python/infer_paddle_paddle_inference.py | Python | Deploy Paddle model with Paddle Inference(CPU/GPU) |
| python/infer_paddle_tensorrt.py | Python | Deploy Paddle model with TensorRT(GPU) |
| python/infer_paddle_openvino.py | Python | Deploy Paddle model with OpenVINO(CPU) |
| python/infer_paddle_onnxruntime.py | Python | Deploy Paddle model with ONNX Runtime(CPU/GPU) |
| python/infer_onnx_openvino.py | Python | Deploy ONNX model with OpenVINO(CPU) |
| python/infer_onnx_tensorrt.py | Python | Deploy ONNX model with TensorRT(GPU) |
| cpp/infer_paddle_paddle_inference.cc | C++ | Deploy Paddle model with Paddle Inference(CPU/GPU) |
| cpp/infer_paddle_tensorrt.cc | C++ | Deploy Paddle model with TensorRT(GPU) |
| cpp/infer_paddle_openvino.cc | C++ | Deploy Paddle model with OpenVINO(CPU |
| cpp/infer_paddle_onnxruntime.cc | C++ | Deploy Paddle model with ONNX Runtime(CPU/GPU) |
| cpp/infer_onnx_openvino.cc | C++ | Deploy ONNX model with OpenVINO(CPU) |
| cpp/infer_onnx_tensorrt.cc | C++ | Deploy ONNX model with TensorRT(GPU) |

14
fastdeploy/vision/common/processors/normalize.cc
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@@ -19,7 +19,7 @@ namespace vision {
Normalize::Normalize(const std::vector<float>& mean,
const std::vector<float>& std, bool is_scale,
const std::vector<float>& min,
const std::vector<float>& max) {
const std::vector<float>& max, bool swap_rb) {
FDASSERT(mean.size() == std.size(),
"Normalize: requires the size of mean equal to the size of std.");
std::vector<double> mean_(mean.begin(), mean.end());
@@ -50,6 +50,7 @@ Normalize::Normalize(const std::vector<float>& mean,
alpha_.push_back(alpha);
beta_.push_back(beta);
}
swap_rb_ = swap_rb;
}
bool Normalize::ImplByOpenCV(Mat* mat) {
@@ -57,6 +58,7 @@ bool Normalize::ImplByOpenCV(Mat* mat) {
std::vector<cv::Mat> split_im;
cv::split(*im, split_im);
if (swap_rb_) std::swap(split_im[0], split_im[2]);
for (int c = 0; c < im->channels(); c++) {
split_im[c].convertTo(split_im[c], CV_32FC1, alpha_[c], beta_[c]);
}
@@ -79,9 +81,13 @@ bool Normalize::ImplByFlyCV(Mat* mat) {
std[i] = 1.0 / alpha_[i];
mean[i] = -1 * beta_[i] * std[i];
}
std::vector<uint32_t> channel_reorder_index = {0, 1, 2};
if (swap_rb_) std::swap(channel_reorder_index[0], channel_reorder_index[2]);
fcv::Mat new_im(im->width(), im->height(),
fcv::FCVImageType::PKG_BGR_F32);
fcv::normalize_to_submean_to_reorder(*im, mean, std, std::vector<uint32_t>(),
fcv::normalize_to_submean_to_reorder(*im, mean, std, channel_reorder_index,
new_im, true);
mat->SetMat(new_im);
return true;
@@ -91,8 +97,8 @@ bool Normalize::ImplByFlyCV(Mat* mat) {
bool Normalize::Run(Mat* mat, const std::vector<float>& mean,
const std::vector<float>& std, bool is_scale,
const std::vector<float>& min,
const std::vector<float>& max, ProcLib lib) {
auto n = Normalize(mean, std, is_scale, min, max);
const std::vector<float>& max, ProcLib lib, bool swap_rb) {
auto n = Normalize(mean, std, is_scale, min, max, swap_rb);
return n(mat, lib);
}

14
fastdeploy/vision/common/processors/normalize.h
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@@ -23,7 +23,8 @@ class FASTDEPLOY_DECL Normalize : public Processor {
Normalize(const std::vector<float>& mean, const std::vector<float>& std,
bool is_scale = true,
const std::vector<float>& min = std::vector<float>(),
const std::vector<float>& max = std::vector<float>());
const std::vector<float>& max = std::vector<float>(),
bool swap_rb = false);
bool ImplByOpenCV(Mat* mat);
#ifdef ENABLE_FLYCV
bool ImplByFlyCV(Mat* mat);
@@ -44,14 +45,23 @@ class FASTDEPLOY_DECL Normalize : public Processor {
const std::vector<float>& std, bool is_scale = true,
const std::vector<float>& min = std::vector<float>(),
const std::vector<float>& max = std::vector<float>(),
ProcLib lib = ProcLib::DEFAULT);
ProcLib lib = ProcLib::DEFAULT, bool swap_rb = false);
std::vector<float> GetAlpha() const { return alpha_; }
std::vector<float> GetBeta() const { return beta_; }
bool GetSwapRB() {
return swap_rb_;
}
void SetSwapRB(bool swap_rb) {
swap_rb_ = swap_rb;
}
private:
std::vector<float> alpha_;
std::vector<float> beta_;
bool swap_rb_;
};
} // namespace vision
} // namespace fastdeploy

16
fastdeploy/vision/common/processors/normalize_and_permute.cc
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@@ -21,7 +21,8 @@ NormalizeAndPermute::NormalizeAndPermute(const std::vector<float>& mean,
const std::vector<float>& std,
bool is_scale,
const std::vector<float>& min,
const std::vector<float>& max) {
const std::vector<float>& max,
bool swap_rb) {
FDASSERT(mean.size() == std.size(),
"Normalize: requires the size of mean equal to the size of std.");
std::vector<double> mean_(mean.begin(), mean.end());
@@ -52,6 +53,7 @@ NormalizeAndPermute::NormalizeAndPermute(const std::vector<float>& mean,
alpha_.push_back(alpha);
beta_.push_back(beta);
}
swap_rb_ = swap_rb;
}
bool NormalizeAndPermute::ImplByOpenCV(Mat* mat) {
@@ -60,6 +62,7 @@ bool NormalizeAndPermute::ImplByOpenCV(Mat* mat) {
int origin_h = im->rows;
std::vector<cv::Mat> split_im;
cv::split(*im, split_im);
if (swap_rb_) std::swap(split_im[0], split_im[2]);
for (int c = 0; c < im->channels(); c++) {
split_im[c].convertTo(split_im[c], CV_32FC1, alpha_[c], beta_[c]);
}
@@ -94,8 +97,12 @@ bool NormalizeAndPermute::ImplByFlyCV(Mat* mat) {
std[i] = 1.0 / alpha_[i];
mean[i] = -1 * beta_[i] * std[i];
}
std::vector<uint32_t> channel_reorder_index = {0, 1, 2};
if (swap_rb_) std::swap(channel_reorder_index[0], channel_reorder_index[2]);
fcv::Mat new_im;
fcv::normalize_to_submean_to_reorder(*im, mean, std, std::vector<uint32_t>(),
fcv::normalize_to_submean_to_reorder(*im, mean, std, channel_reorder_index,
new_im, false);
mat->SetMat(new_im);
mat->layout = Layout::CHW;
@@ -106,8 +113,9 @@ bool NormalizeAndPermute::ImplByFlyCV(Mat* mat) {
bool NormalizeAndPermute::Run(Mat* mat, const std::vector<float>& mean,
const std::vector<float>& std, bool is_scale,
const std::vector<float>& min,
const std::vector<float>& max, ProcLib lib) {
auto n = NormalizeAndPermute(mean, std, is_scale, min, max);
const std::vector<float>& max, ProcLib lib,
bool swap_rb) {
auto n = NormalizeAndPermute(mean, std, is_scale, min, max, swap_rb);
return n(mat, lib);
}

14
fastdeploy/vision/common/processors/normalize_and_permute.h
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@@ -23,7 +23,8 @@ class FASTDEPLOY_DECL NormalizeAndPermute : public Processor {
NormalizeAndPermute(const std::vector<float>& mean,
const std::vector<float>& std, bool is_scale = true,
const std::vector<float>& min = std::vector<float>(),
const std::vector<float>& max = std::vector<float>());
const std::vector<float>& max = std::vector<float>(),
bool swap_rb = false);
bool ImplByOpenCV(Mat* mat);
#ifdef ENABLE_FLYCV
bool ImplByFlyCV(Mat* mat);
@@ -44,7 +45,7 @@ class FASTDEPLOY_DECL NormalizeAndPermute : public Processor {
const std::vector<float>& std, bool is_scale = true,
const std::vector<float>& min = std::vector<float>(),
const std::vector<float>& max = std::vector<float>(),
ProcLib lib = ProcLib::DEFAULT);
ProcLib lib = ProcLib::DEFAULT, bool swap_rb = false);
void SetAlpha(const std::vector<float>& alpha) {
alpha_.clear();
@@ -58,9 +59,18 @@ class FASTDEPLOY_DECL NormalizeAndPermute : public Processor {
beta_.assign(beta.begin(), beta.end());
}
bool GetSwapRB() {
return swap_rb_;
}
void SetSwapRB(bool swap_rb) {
swap_rb_ = swap_rb;
}
private:
std::vector<float> alpha_;
std::vector<float> beta_;
bool swap_rb_;
};
} // namespace vision
} // namespace fastdeploy

67
fastdeploy/vision/common/processors/transform.cc
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@@ -95,10 +95,77 @@ void FuseNormalizeHWC2CHW(
<< std::endl;
}
void FuseNormalizeColorConvert(
std::vector<std::shared_ptr<Processor>>* processors) {
// Fuse Normalize and BGR2RGB/RGB2BGR
int normalize_index = -1;
int color_convert_index = -1;
// If these middle processors are after BGR2RGB/RGB2BGR and before Normalize,
// we can still fuse Normalize and BGR2RGB/RGB2BGR
static std::unordered_set<std::string> middle_processors(
{"Resize", "ResizeByShort", "ResizeByLong", "Crop", "CenterCrop",
"LimitByStride", "LimitShort", "Pad", "PadToSize", "StridePad",
"WarpAffine"});
for (size_t i = 0; i < processors->size(); ++i) {
if ((*processors)[i]->Name() == "BGR2RGB" ||
(*processors)[i]->Name() == "RGB2BGR") {
color_convert_index = i;
for (size_t j = color_convert_index + 1; j < processors->size(); ++j) {
if ((*processors)[j]->Name() == "Normalize" ||
(*processors)[j]->Name() == "NormalizeAndPermute") {
normalize_index = j;
break;
}
}
if (normalize_index < 0) {
return;
}
for (size_t j = color_convert_index + 1; j < normalize_index; ++j) {
if (middle_processors.count((*processors)[j]->Name())) {
continue;
}
return;
}
}
}
if (color_convert_index < 0) {
return;
}
// Delete Color Space Convert
std::string color_processor_name = (*processors)[color_convert_index]->Name();
processors->erase(processors->begin() + color_convert_index);
// Toggle the swap_rb option of the Normalize processor
std::string normalize_processor_name =
(*processors)[normalize_index - 1]->Name();
bool swap_rb;
if (normalize_processor_name == "Normalize") {
auto processor = dynamic_cast<Normalize*>(
(*processors)[normalize_index - 1].get());
swap_rb = processor->GetSwapRB();
processor->SetSwapRB(!swap_rb);
} else if (normalize_processor_name == "NormalizeAndPermute") {
auto processor = dynamic_cast<NormalizeAndPermute*>(
(*processors)[normalize_index - 1].get());
swap_rb = processor->GetSwapRB();
processor->SetSwapRB(!swap_rb);
} else {
FDASSERT(false, "Something wrong in FuseNormalizeColorConvert().");
}
FDINFO << color_processor_name << " and " << normalize_processor_name
<< " are fused to " << normalize_processor_name
<< " with swap_rb=" << !swap_rb << std::endl;
}
void FuseTransforms(
std::vector<std::shared_ptr<Processor>>* processors) {
FuseNormalizeCast(processors);
FuseNormalizeHWC2CHW(processors);
FuseNormalizeColorConvert(processors);
}

4
fastdeploy/vision/common/processors/transform.h
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@@ -31,6 +31,7 @@
#include "fastdeploy/vision/common/processors/resize_by_short.h"
#include "fastdeploy/vision/common/processors/stride_pad.h"
#include "fastdeploy/vision/common/processors/warp_affine.h"
#include <unordered_set>
namespace fastdeploy {
namespace vision {
@@ -41,6 +42,9 @@ void FuseTransforms(std::vector<std::shared_ptr<Processor>>* processors);
void FuseNormalizeCast(std::vector<std::shared_ptr<Processor>>* processors);
// Fuse Normalize + HWC2CHW to NormalizeAndPermute
void FuseNormalizeHWC2CHW(std::vector<std::shared_ptr<Processor>>* processors);
// Fuse Normalize + Color Convert
void FuseNormalizeColorConvert(
std::vector<std::shared_ptr<Processor>>* processors);
} // namespace vision
} // namespace fastdeploy