mmdeploy/csrc/preprocess/cuda/load_impl.cpp
lzhangzz 640aa03538
Support Windows (#106)
* minor changes

* support windows

* fix GCC build

* fix lint

* reformat

* fix Windows build

* fix GCC build

* search backend ops for onnxruntime

* fix lint

* fix lint

* code clean-up

* code clean-up

* fix clang build

* fix trt support

* fix cmake for ncnn

* fix cmake for openvino

* fix SDK Python API

* handle ops for other backends (ncnn, trt)

* handle SDK Python API library location

* robustify linkage

* fix cuda

* minor fix for openvino & ncnn

* use CMAKE_CUDA_ARCHITECTURES if set

* fix cuda preprocessor

* fix misc

* fix pplnn & pplcv, drop support for pplcv<0.6.0

* robustify cmake

* update build.md (#2)

* build dynamic modules as module library & fix demo (partially)

* fix candidate path for mmdeploy_python

* move "enable CUDA" to cmake config for demo

* refine demo cmake

* add comment

* fix ubuntu build

* revert docs/en/build.md

* fix C API

* fix lint

* Windows build doc (#3)

* check in docs related to mmdeploy build on windows

* update build guide on windows platform

* update build guide on windows platform

* make path of thirdparty libraries consistent

* make path consistency

* correct build command for custom ops

* correct build command for sdk

* update sdk build instructions

* update doc

* correct build command

* fix lint

* correct build command and fix lint

Co-authored-by: lvhan <lvhan@pjlab.org>

* trailing whitespace (#4)

* minor fix

* fix sr sdk model

* fix type deduction

* fix cudaFree after driver shutting down

* update ppl.cv installation warning (#5)

* fix device allocator threshold & fix lint

* update doc (#6)

* update ppl.cv installation warning

* missing 'git clone'

Co-authored-by: chenxin <chenxin2@sensetime.com>
Co-authored-by: zhangli <zhangli@sensetime.com>
Co-authored-by: lvhan028 <lvhan_028@163.com>
Co-authored-by: lvhan <lvhan@pjlab.org>
2022-02-24 20:08:44 +08:00

174 lines
6.4 KiB
C++

// Copyright (c) OpenMMLab. All rights reserved.
#include <cuda_runtime.h>
#include "core/utils/device_utils.h"
#include "core/utils/formatter.h"
#include "ppl/cv/cuda/cvtcolor.h"
#include "preprocess/transform/load.h"
using namespace std;
using namespace ppl::cv::cuda;
namespace mmdeploy {
namespace cuda {
template <int channels>
void CastToFloat(const uint8_t* src, int height, int width, float* dst, cudaStream_t stream);
class PrepareImageImpl : public ::mmdeploy::PrepareImageImpl {
public:
explicit PrepareImageImpl(const Value& args) : ::mmdeploy::PrepareImageImpl(args){};
~PrepareImageImpl() override = default;
protected:
Tensor Mat2Tensor(const mmdeploy::Mat& mat) {
TensorDesc desc{
mat.buffer().GetDevice(), mat.type(), {1, mat.height(), mat.width(), mat.channel()}, ""};
shared_ptr<void> data(mat.data<void>(), [mat = mat](void* p) {});
return Tensor(desc, data);
}
protected:
Result<Tensor> ConvertToBGR(const Mat& img) override {
auto _img = MakeAvailableOnDevice(img, device_, stream_);
auto src_mat = _img.value();
if (img.pixel_format() == PixelFormat::kBGR) {
return Mat2Tensor(src_mat);
}
cudaStream_t stream = ::mmdeploy::GetNative<cudaStream_t>(stream_);
Mat dst_mat(src_mat.height(), src_mat.width(), PixelFormat::kBGR, src_mat.type(), device_);
ppl::common::RetCode ret = 0;
int src_h = src_mat.height();
int src_w = src_mat.width();
int src_c = src_mat.channel();
int src_stride = src_w * src_mat.channel();
uint8_t* src_ptr = src_mat.data<uint8_t>();
int dst_w = dst_mat.width();
int dst_stride = dst_w * dst_mat.channel();
uint8_t* dst_ptr = dst_mat.data<uint8_t>();
switch (img.pixel_format()) {
case PixelFormat::kRGB:
ret = RGB2BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
case PixelFormat::kGRAYSCALE:
ret = GRAY2BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
case PixelFormat::kNV12:
assert(src_c == 1);
NV122BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
case PixelFormat::kNV21:
assert(src_c == 1);
NV212BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
case PixelFormat::kBGRA:
BGRA2BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
default:
MMDEPLOY_ERROR("src type: unknown type {}", img.pixel_format());
return Status(eNotSupported);
}
if (ret != 0) {
MMDEPLOY_ERROR("color transfer from {} to BGR failed, ret {}", img.pixel_format(), ret);
return Status(eFail);
}
if (arg_.to_float32) {
TensorDesc desc{device_, DataType::kFLOAT, {1, src_h, src_w, dst_mat.channel()}, ""};
Tensor dst_tensor{desc};
CastToFloat<3>(dst_ptr, src_h, src_w, dst_tensor.data<float>(), stream);
return dst_tensor;
} else {
return Mat2Tensor(dst_mat);
}
}
Result<Tensor> ConvertToGray(const Mat& img) override {
OUTCOME_TRY(auto src_mat, MakeAvailableOnDevice(img, device_, stream_));
if (img.pixel_format() == PixelFormat::kGRAYSCALE) {
return Mat2Tensor(src_mat);
}
cudaStream_t stream = ::mmdeploy::GetNative<cudaStream_t>(stream_);
Mat dst_mat(src_mat.height(), src_mat.width(), PixelFormat::kGRAYSCALE, src_mat.type(),
device_);
ppl::common::RetCode ret = 0;
int src_h = src_mat.height();
int src_w = src_mat.width();
int src_c = src_mat.channel();
int src_stride = src_w * src_mat.channel();
uint8_t* src_ptr = src_mat.data<uint8_t>();
int dst_w = dst_mat.width();
int dst_stride = dst_w * dst_mat.channel();
uint8_t* dst_ptr = dst_mat.data<uint8_t>();
switch (img.pixel_format()) {
case PixelFormat::kRGB:
ret = RGB2GRAY<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
case PixelFormat::kBGR:
ret = BGR2GRAY<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
case PixelFormat::kNV12: {
assert(src_c == 1);
Mat rgb_mat(src_mat.height(), src_mat.width(), PixelFormat::kRGB, src_mat.type(), device_);
NV122RGB<uint8_t>(stream, src_h, src_w, src_stride, src_ptr,
rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>());
RGB2GRAY<uint8_t>(stream, rgb_mat.height(), rgb_mat.width(),
rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>(), dst_stride,
dst_mat.data<uint8_t>());
break;
}
case PixelFormat::kNV21: {
assert(src_c == 1);
Mat rgb_mat(src_mat.height(), src_mat.width(), PixelFormat::kRGB, src_mat.type(), device_);
NV212RGB<uint8_t>(stream, src_h, src_w, src_stride, src_ptr,
rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>());
RGB2GRAY<uint8_t>(stream, rgb_mat.height(), rgb_mat.width(),
rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>(), dst_stride,
dst_mat.data<uint8_t>());
break;
}
case PixelFormat::kBGRA:
BGRA2GRAY<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
break;
default:
MMDEPLOY_ERROR("src type: unknown type {}", img.pixel_format());
throw Status(eNotSupported);
}
if (ret != 0) {
MMDEPLOY_ERROR("color transfer from {} to Gray failed", img.pixel_format());
throw Status(eFail);
}
if (arg_.to_float32) {
TensorDesc desc{device_, DataType::kFLOAT, {1, src_h, src_w, dst_mat.channel()}, ""};
Tensor dst_tensor{desc};
CastToFloat<1>(dst_ptr, src_h, src_w, dst_tensor.data<float>(), stream);
return dst_tensor;
} else {
return Mat2Tensor(dst_mat);
}
}
};
class PrepareImageImplCreator : public Creator<::mmdeploy::PrepareImageImpl> {
public:
const char* GetName() const override { return "cuda"; }
int GetVersion() const override { return 1; }
ReturnType Create(const Value& args) override { return make_unique<PrepareImageImpl>(args); }
};
} // namespace cuda
} // namespace mmdeploy
using mmdeploy::PrepareImageImpl;
using mmdeploy::cuda::PrepareImageImplCreator;
REGISTER_MODULE(PrepareImageImpl, PrepareImageImplCreator);