From 964f19cde65e761a158d5195aa7c7f7f780e9f80 Mon Sep 17 00:00:00 2001 From: wangna11BD Date: Wed, 1 Jun 2022 07:45:48 +0000 Subject: [PATCH 1/3] add c++ infer --- deploy/CMakeLists.txt | 218 +++++++++++++ deploy/README.md | 41 +++ deploy/caddn_main.cc | 109 +++++++ deploy/compile.sh | 46 +++ deploy/grid_sample_3d.cc | 75 +++++ deploy/grid_sample_3d.cu | 642 +++++++++++++++++++++++++++++++++++++++ deploy/grid_sample_3d.h | 32 ++ deploy/run.sh | 17 ++ 8 files changed, 1180 insertions(+) create mode 100755 deploy/CMakeLists.txt create mode 100644 deploy/README.md create mode 100644 deploy/caddn_main.cc create mode 100755 deploy/compile.sh create mode 100644 deploy/grid_sample_3d.cc create mode 100644 deploy/grid_sample_3d.cu create mode 100644 deploy/grid_sample_3d.h create mode 100644 deploy/run.sh diff --git a/deploy/CMakeLists.txt b/deploy/CMakeLists.txt new file mode 100755 index 0000000..fc44f49 --- /dev/null +++ b/deploy/CMakeLists.txt @@ -0,0 +1,218 @@ +cmake_minimum_required(VERSION 3.0) +project(cpp_inference_demo CXX C) +option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON) +option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." ON) +option(USE_TENSORRT "Compile demo with TensorRT." ON) +option(CUSTOM_OPERATOR_FILES "List of file names for custom operators" "") +option(WITH_ONNXRUNTIME "Compile demo with ONNXRuntime" OFF) + +set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake") + +if(WITH_GPU) + find_package(CUDA REQUIRED) + add_definitions("-DPADDLE_WITH_CUDA") +endif() + +if(NOT WITH_STATIC_LIB) + add_definitions("-DPADDLE_WITH_SHARED_LIB") +else() + # PD_INFER_DECL is mainly used to set the dllimport/dllexport attribute in dynamic library mode. + # Set it to empty in static library mode to avoid compilation issues. + add_definitions("/DPD_INFER_DECL=") +endif() + +macro(safe_set_static_flag) + foreach(flag_var + CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE + CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO) + if(${flag_var} MATCHES "/MD") + string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}") + endif(${flag_var} MATCHES "/MD") + endforeach(flag_var) +endmacro() + +if(NOT DEFINED PADDLE_LIB) + message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib") +endif() +if(NOT DEFINED DEMO_NAME) + message(FATAL_ERROR "please set DEMO_NAME with -DDEMO_NAME=demo_name") +endif() + +include_directories("${PADDLE_LIB}/") +set(PADDLE_LIB_THIRD_PARTY_PATH "${PADDLE_LIB}/third_party/install/") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}onnxruntime/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}paddle2onnx/include") + +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") +link_directories("${PADDLE_LIB}/paddle/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}onnxruntime/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}paddle2onnx/lib") + +if (WIN32) + add_definitions("/DGOOGLE_GLOG_DLL_DECL=") + option(MSVC_STATIC_CRT "use static C Runtime library by default" ON) + if (MSVC_STATIC_CRT) + if (WITH_MKL) + set(FLAG_OPENMP "/openmp") + endif() + set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}") + set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}") + set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}") + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}") + safe_set_static_flag() + if (WITH_STATIC_LIB) + add_definitions(-DSTATIC_LIB) + endif() + endif() +else() + if(WITH_MKL) + set(FLAG_OPENMP "-fopenmp") + endif() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 ${FLAG_OPENMP}") +endif() + +if(WITH_GPU) + if(NOT WIN32) + set(CUDA_LIB "/usr/local/cuda/lib64/" CACHE STRING "CUDA Library") + else() + if(CUDA_LIB STREQUAL "") + set(CUDA_LIB "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64") + endif() + endif(NOT WIN32) +endif() + +if (USE_TENSORRT AND WITH_GPU) + set(TENSORRT_ROOT "" CACHE STRING "The root directory of TensorRT library") + if("${TENSORRT_ROOT}" STREQUAL "") + message(FATAL_ERROR "The TENSORRT_ROOT is empty, you must assign it a value with CMake command. Such as: -DTENSORRT_ROOT=TENSORRT_ROOT_PATH ") + endif() + set(TENSORRT_INCLUDE_DIR ${TENSORRT_ROOT}/include) + set(TENSORRT_LIB_DIR ${TENSORRT_ROOT}/lib) +endif() + +if (NOT WIN32) + if (USE_TENSORRT AND WITH_GPU) + include_directories("${TENSORRT_INCLUDE_DIR}") + link_directories("${TENSORRT_LIB_DIR}") + endif() +endif(NOT WIN32) + +if(WITH_MKL) + set(MATH_LIB_PATH "${PADDLE_LIB_THIRD_PARTY_PATH}mklml") + include_directories("${MATH_LIB_PATH}/include") + if(WIN32) + set(MATH_LIB ${MATH_LIB_PATH}/lib/mklml${CMAKE_STATIC_LIBRARY_SUFFIX} + ${MATH_LIB_PATH}/lib/libiomp5md${CMAKE_STATIC_LIBRARY_SUFFIX}) + else() + set(MATH_LIB ${MATH_LIB_PATH}/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} + ${MATH_LIB_PATH}/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX}) + endif() + set(MKLDNN_PATH "${PADDLE_LIB_THIRD_PARTY_PATH}mkldnn") + if(EXISTS ${MKLDNN_PATH}) + include_directories("${MKLDNN_PATH}/include") + if(WIN32) + set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib) + else(WIN32) + set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0) + endif(WIN32) + endif() +else() + set(OPENBLAS_LIB_PATH "${PADDLE_LIB_THIRD_PARTY_PATH}openblas") + include_directories("${OPENBLAS_LIB_PATH}/include/openblas") + if(WIN32) + set(MATH_LIB ${OPENBLAS_LIB_PATH}/lib/openblas${CMAKE_STATIC_LIBRARY_SUFFIX}) + else() + set(MATH_LIB ${OPENBLAS_LIB_PATH}/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() +endif() + +if(WITH_STATIC_LIB) + set(DEPS ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX}) +else() + if(WIN32) + set(DEPS ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX}) + else() + set(DEPS ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX}) + endif() +endif() + +if (WITH_ONNXRUNTIME) + if(WIN32) + set(DEPS ${DEPS} ${PADDLE_LIB_THIRD_PARTY_PATH}onnxruntime/lib/onnxruntime.lib paddle2onnx) + elseif(APPLE) + set(DEPS ${DEPS} ${PADDLE_LIB_THIRD_PARTY_PATH}onnxruntime/lib/libonnxruntime.1.10.0.dylib paddle2onnx) + else() + set(DEPS ${DEPS} ${PADDLE_LIB_THIRD_PARTY_PATH}onnxruntime/lib/libonnxruntime.so.1.10.0 paddle2onnx) + endif() +endif() + +if (NOT WIN32) + set(EXTERNAL_LIB "-lrt -ldl -lpthread") + set(DEPS ${DEPS} + ${MATH_LIB} ${MKLDNN_LIB} + glog gflags protobuf xxhash + ${EXTERNAL_LIB}) +else() + set(DEPS ${DEPS} + ${MATH_LIB} ${MKLDNN_LIB} + glog gflags_static libprotobuf xxhash ${EXTERNAL_LIB}) + set(DEPS ${DEPS} shlwapi.lib) +endif(NOT WIN32) + +if(WITH_GPU) + if(NOT WIN32) + if (USE_TENSORRT) + set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX}) + endif() + set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX}) + else() + if(USE_TENSORRT) + set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_STATIC_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() + set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} ) + set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} ) + set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX} ) + endif() +endif() + +cuda_add_library(pd_infer_custom_op ${CUSTOM_OPERATOR_FILES} SHARED) +add_executable(${DEMO_NAME} ${DEMO_NAME}.cc) +set(DEPS ${DEPS} pd_infer_custom_op) + +if(WIN32) + if(USE_TENSORRT) + add_custom_command(TARGET ${DEMO_NAME} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_SHARED_LIBRARY_SUFFIX} + ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} + COMMAND ${CMAKE_COMMAND} -E copy ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX} + ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} + ) + endif() + if(WITH_MKL) + add_custom_command(TARGET ${DEMO_NAME} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy ${MATH_LIB_PATH}/lib/mklml.dll ${CMAKE_BINARY_DIR}/Release + COMMAND ${CMAKE_COMMAND} -E copy ${MATH_LIB_PATH}/lib/libiomp5md.dll ${CMAKE_BINARY_DIR}/Release + COMMAND ${CMAKE_COMMAND} -E copy ${MKLDNN_PATH}/lib/mkldnn.dll ${CMAKE_BINARY_DIR}/Release + ) + else() + add_custom_command(TARGET ${DEMO_NAME} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy ${OPENBLAS_LIB_PATH}/lib/openblas.dll ${CMAKE_BINARY_DIR}/Release + ) + endif() + if(NOT WITH_STATIC_LIB) + add_custom_command(TARGET ${DEMO_NAME} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy "${PADDLE_LIB}/paddle/lib/paddle_fluid.dll" ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} + ) + endif() +endif() + +target_link_libraries(${DEMO_NAME} ${DEPS}) \ No newline at end of file diff --git a/deploy/README.md b/deploy/README.md new file mode 100644 index 0000000..ebca540 --- /dev/null +++ b/deploy/README.md @@ -0,0 +1,41 @@ +## 一. 将模型参数放到caddn_infer_model目录下 +模型放好后的目录结构 +deploy + ├── caddn_infer_model + | ├── model.pdiparams + | └── model.pdmodel + ├── caddn_main.cc + ... + +## 二. 修改`compile.sh` + +打开`compile.sh`，我们对以下的几处信息进行修改： + +```shell +# 根据预编译库中的version.txt信息判断是否将以下三个标记打开 +WITH_MKL=ON +WITH_GPU=ON +USE_TENSORRT=OFF + +# 配置预测库的根目录 +LIB_DIR=${work_path}/../lib/paddle_inference + +# 如果上述的WITH_GPU 或 USE_TENSORRT设为ON，请设置对应的CUDA， CUDNN， TENSORRT的路径。 +CUDNN_LIB=/usr/lib/x86_64-linux-gnu/ +CUDA_LIB=/usr/local/cuda/lib64 +TENSORRT_ROOT=/usr/local/TensorRT-7.0.0.11 +``` + +运行 `bash compile.sh`， 会在目录下产生build目录。 + + +## 三. 运行样例 + +```shell +# 运行样例 +./build/caddn_main +``` + +运行结束后，程序会将模型结果打印到屏幕，说明运行成功。 + +> 注：确保路径配置正确后，也可执行执行 `bash run.sh` ，一次性完成以上两个步骤的执行 diff --git a/deploy/caddn_main.cc b/deploy/caddn_main.cc new file mode 100644 index 0000000..20b7151 --- /dev/null +++ b/deploy/caddn_main.cc @@ -0,0 +1,109 @@ +/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include +#include +#include + +#include "paddle/include/paddle_inference_api.h" + +using paddle_infer::Config; +using paddle_infer::Predictor; +using paddle_infer::CreatePredictor; + +void run(Predictor *predictor, + const std::vector &images_shape, + const std::vector &images_data, + const std::vector &cam_shape, + const std::vector &cam_data, + const std::vector &lidar_shape, + const std::vector &lidar_data, + std::vector *boxes, + std::vector *labels, + std::vector *scores) { + + auto input_names = predictor->GetInputNames(); + for (const auto& tensor_name : input_names) { + auto in_tensor = predictor->GetInputHandle(tensor_name); + if (tensor_name == "images") { + in_tensor->Reshape(images_shape); + in_tensor->CopyFromCpu(images_data.data()); + } else if (tensor_name == "trans_cam_to_img") { + in_tensor->Reshape(cam_shape); + in_tensor->CopyFromCpu(cam_data.data()); + } else if (tensor_name == "trans_lidar_to_cam") { + in_tensor->Reshape(lidar_shape); + in_tensor->CopyFromCpu(lidar_data.data()); + } + } + + clock_t start,stop; + start = clock(); + for (int i = 0; i < 100; i++) { + CHECK(predictor->Run()); + + auto output_names = predictor->GetOutputNames(); + for (size_t i = 0; i != output_names.size(); i++) { + auto output = predictor->GetOutputHandle(output_names[i]); + std::vector output_shape = output->shape(); + int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, + std::multiplies()); + if (i == 0) { + boxes->resize(out_num); + output->CopyToCpu(boxes->data()); + } else if (i == 1) { + labels->resize(out_num); + output->CopyToCpu(labels->data()); + } else if (i == 2) { + scores->resize(out_num); + output->CopyToCpu(scores->data()); + } + } + } + stop = clock(); + double endtime = (double)(stop - start) / CLOCKS_PER_SEC; + LOG(INFO) << "time:" << endtime << "s"; +} + +int main() { + paddle::AnalysisConfig config; + config.EnableUseGpu(100, 0); + config.SetModel("caddn_infer_model/model.pdmodel", + "caddn_infer_model/model.pdiparams"); + auto predictor{paddle_infer::CreatePredictor(config)}; + std::vector images_shape = {1, 3, 375, 1242}; + std::vector images_data(1 * 3 * 375 * 1242, 1); + std::vector cam_shape = {1, 3, 4}; + std::vector cam_data(1 * 3 * 4, 1); + std::vector lidar_shape = {1, 4, 4}; + std::vector lidar_data(1 * 4 * 4, 1); + std::vector boxes; + std::vector labels; + std::vector scores; + run(predictor.get(), images_shape, images_data, + cam_shape, cam_data, + lidar_shape, lidar_data, + &boxes, &labels, &scores); + for (auto e : boxes) { + LOG(INFO) << e; + } + for (auto e : labels) { + LOG(INFO) << e; + } + for (auto e : scores) { + LOG(INFO) << e; + } + return 0; +} diff --git a/deploy/compile.sh b/deploy/compile.sh new file mode 100755 index 0000000..b90d853 --- /dev/null +++ b/deploy/compile.sh @@ -0,0 +1,46 @@ +#!/bin/bash +set +x +set -e + +work_path=$(dirname $(readlink -f $0)) + +# 1. check paddle_inference exists +if [ ! -d "/workspace/cadnn/paddle_inference" ]; then + echo "Please download paddle_inference lib and move it in Paddle-Inference-Demo/c++/lib" + exit 1 +fi + +# 2. compile +mkdir -p build +cd build + +DEMO_NAME=caddn_main + +WITH_MKL=ON +WITH_GPU=ON +USE_TENSORRT=ON +WITH_ONNXRUNTIME=ON + +LIB_DIR=/workspace/cadnn/paddle_inference + +CUDNN_LIB=/usr/local/x86_64-pc-linux-gnu/ +CUDA_LIB=/usr/local/cuda/lib64 +TENSORRT_ROOT=/usr/local/TensorRT-6.0.1.8 + +# CUSTOM_OPERATOR_FILES="custom_relu_op.cc;custom_relu_op.cu" +CUSTOM_OPERATOR_FILES="grid_sample_3d.cc;grid_sample_3d.cu" + + +cmake .. -DPADDLE_LIB=${LIB_DIR} \ + -DWITH_MKL=${WITH_MKL} \ + -DDEMO_NAME=${DEMO_NAME} \ + -DWITH_GPU=${WITH_GPU} \ + -DWITH_STATIC_LIB=OFF \ + -DUSE_TENSORRT=${USE_TENSORRT} \ + -DCUDNN_LIB=${CUDNN_LIB} \ + -DCUDA_LIB=${CUDA_LIB} \ + -DTENSORRT_ROOT=${TENSORRT_ROOT} \ + -DCUSTOM_OPERATOR_FILES=${CUSTOM_OPERATOR_FILES} \ + -DWITH_ONNXRUNTIME=${WITH_ONNXRUNTIME} + +make -j diff --git a/deploy/grid_sample_3d.cc b/deploy/grid_sample_3d.cc new file mode 100644 index 0000000..6d3823c --- /dev/null +++ b/deploy/grid_sample_3d.cc @@ -0,0 +1,75 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include "grid_sample_3d.h" +#include "paddle/include/experimental/ext_all.h" + +std::vector GridSample3DCUDAForward(const paddle::Tensor& x, + const paddle::Tensor& grid, + const std::string& mode, + const std::string& padding_mode, + bool align_corners); + +std::vector GridSample3DForward(const paddle::Tensor& x, + const paddle::Tensor& grid, + const std::string& mode, + const std::string& padding_mode, + bool align_corners) { + return GridSample3DCUDAForward(x, grid, mode, padding_mode, align_corners); +} + +std::vector GridSample3DCUDABackward(const paddle::Tensor& x, + const paddle::Tensor& grid, + const paddle::Tensor& grad_out, + const std::string& mode, + const std::string& padding_mode, + bool align_corners); + +std::vector GridSample3DBackward(const paddle::Tensor& x, + const paddle::Tensor& grid, + const paddle::Tensor& grad_out, + const std::string& mode, + const std::string& padding_mode, + bool align_corners) { + return GridSample3DCUDABackward(x, grid, grad_out, mode, padding_mode, align_corners); +} + +std::vector> GridSample3DInferShape(std::vector x_shape, std::vector grid_shape) { + return {{x_shape[0], x_shape[1], grid_shape[1], grid_shape[2], grid_shape[3]}}; +} + +std::vector> GridSample3DInferBackShape(std::vector x_shape, std::vector grid_shape) { + return {x_shape}; +} + +std::vector GridSample3DInferDtype(paddle::DataType x_dtype, paddle::DataType grid_dtype) { + return {x_dtype}; +} + +PD_BUILD_OP(grid_sample_3d) + .Inputs({"x", "grid"}) + .Attrs({"mode: std::string", "padding_mode: std::string", "align_corners: bool"}) + .Outputs({"out"}) + .SetKernelFn(PD_KERNEL(GridSample3DForward)) + .SetInferShapeFn(PD_INFER_SHAPE(GridSample3DInferShape)) + .SetInferDtypeFn(PD_INFER_DTYPE(GridSample3DInferDtype)); + + +PD_BUILD_GRAD_OP(grid_sample_3d) + .Inputs({"x", "grid", paddle::Grad("out")}) + .Attrs({"mode: std::string", "padding_mode: std::string", "align_corners: bool"}) + .Outputs({paddle::Grad("x")}) + .SetKernelFn(PD_KERNEL(GridSample3DBackward)) + .SetInferShapeFn(PD_INFER_SHAPE(GridSample3DInferBackShape)); \ No newline at end of file diff --git a/deploy/grid_sample_3d.cu b/deploy/grid_sample_3d.cu new file mode 100644 index 0000000..313e004 --- /dev/null +++ b/deploy/grid_sample_3d.cu @@ -0,0 +1,642 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/include/experimental/ext_all.h" +#include "grid_sample_3d.h" +#include + +#define CHECK_INPUT_GPU(x) PD_CHECK(x.place() == paddle::PlaceType::kGPU, #x " must be a GPU Tensor.") + + +static __forceinline__ __device__ bool InBounds3D(int64_t d, int64_t h, int64_t w, int64_t D, int64_t H, int64_t W) { + return d >= 0 && d < D && h >= 0 && h < H && w >= 0 && w < W; +} + +#define CUDA_KERNEL_LOOP_TYPE(i, n, index_type) \ + index_type _i_n_d_e_x = blockIdx.x * blockDim.x + threadIdx.x; \ + for (index_type i=_i_n_d_e_x; _i_n_d_e_x < (n); _i_n_d_e_x+=blockDim.x * gridDim.x, i=_i_n_d_e_x) + +#define CUDA_KERNEL_LOOP(i, n) CUDA_KERNEL_LOOP_TYPE(i, n, int) + +template +static __forceinline__ __device__ T Unnormalize(T coord, + int size, + bool align_corners) { + if (align_corners) { + return ((coord + 1.f) / 2) * (size - 1); + } else { + return ((coord + 1.f) * size - 1) / 2; + } +} + +template +static __forceinline__ __device__ T ClipIndexes(T in, int max_value) { + return min(static_cast(max_value), max(in, static_cast(0))); +} + +template +static __forceinline__ __device__ T ReflectIndexes(T in, + int twice_low, + int twice_high) { + if (twice_low == twice_high) { + return static_cast(0); + } + T min = static_cast(twice_low) / 2; + T span = static_cast(twice_high - twice_low) / 2; + in = fabs(in - min); + T extra = fmod(in, span); + int flips = static_cast(floor(in / span)); + if (flips % 2 == 0) { + return extra + min; + } else { + return span - extra + min; + } +} + +template +static __forceinline__ __device__ T ComputePositions(T coord, + int size, + PaddingMode padding_mode, + bool align_corners) { + coord = Unnormalize(coord, size, align_corners); + if (padding_mode == PaddingMode::border) { + coord = ClipIndexes(coord, size - 1); + } else if (padding_mode == PaddingMode::reflect) { + if (align_corners) { + coord = ReflectIndexes(coord, 0, 2 * (size - 1)); + } else { + coord = ReflectIndexes(coord, -1, 2 * size - 1); + } + coord = ClipIndexes(coord, size - 1); + } + return coord; +} + + +template + __global__ void GridSample3DCudaKernel( + const index_t nthreads, + index_t out_c, + index_t out_d, + index_t out_h, + index_t out_w, + index_t in_d, + index_t in_h, + index_t in_w, + const T* input, + const T* grid, + T* output, + const Mode interpolation_mode, + const PaddingMode padding_mode, + bool align_corners) { + //printf("size: %d, %d, %d, %d, %d, %d \n", out_c, out_d, out_w, out_h, in_d, in_w); + index_t inp_sW = 1; + index_t inp_sH = in_w; + index_t inp_sD = in_h * in_w; + index_t inp_sC = in_d * inp_sD; + index_t inp_sN = out_c * inp_sC; + + index_t grid_sCoor = 1; + index_t grid_sW = 3; + index_t grid_sH = out_w * grid_sW; + index_t grid_sD = out_h * grid_sH; + index_t grid_sN = out_d * grid_sD; + + index_t out_sW = 1; + index_t out_sH = out_w; + index_t out_sD = out_h * out_w; + index_t out_sC = out_d * out_sD; + index_t out_sN = out_c * out_sC; + + CUDA_KERNEL_LOOP_TYPE(index, nthreads, index_t) { + const index_t w = index % out_w; + const index_t h = (index / out_w) % out_h; + const index_t d = (index / (out_h * out_w)) % out_d; + const index_t n = index / (out_d * out_h * out_w); + const index_t grid_offset = n * grid_sN + d * grid_sD + h * grid_sH + w * grid_sW; + // get the corresponding input x, y, z co-ordinates from grid + T ix = grid[grid_offset]; + T iy = grid[grid_offset + grid_sCoor]; + T iz = grid[grid_offset + 2 * grid_sCoor]; + ix = ComputePositions(ix, in_w, padding_mode, align_corners); + iy = ComputePositions(iy, in_h, padding_mode, align_corners); + iz = ComputePositions(iz, in_d, padding_mode, align_corners); + // printf("ix: %f, iy: %f, iz: %f \n", ix, iy, iz); + if (interpolation_mode == Mode::bilinear) { + // get corner pixel values from (x, y, z) + // for 4d, we used north-east-south-west + // for 5d, we add top-bottom + index_t ix_tnw = static_cast(std::floor(ix)); + index_t iy_tnw = static_cast(std::floor(iy)); + index_t iz_tnw = static_cast(std::floor(iz)); + + index_t ix_tne = ix_tnw + 1; + index_t iy_tne = iy_tnw; + index_t iz_tne = iz_tnw; + + index_t ix_tsw = ix_tnw; + index_t iy_tsw = iy_tnw + 1; + index_t iz_tsw = iz_tnw; + + index_t ix_tse = ix_tnw + 1; + index_t iy_tse = iy_tnw + 1; + index_t iz_tse = iz_tnw; + + index_t ix_bnw = ix_tnw; + index_t iy_bnw = iy_tnw; + index_t iz_bnw = iz_tnw + 1; + + index_t ix_bne = ix_tnw + 1; + index_t iy_bne = iy_tnw; + index_t iz_bne = iz_tnw + 1; + + index_t ix_bsw = ix_tnw; + index_t iy_bsw = iy_tnw + 1; + index_t iz_bsw = iz_tnw + 1; + + index_t ix_bse = ix_tnw + 1; + index_t iy_bse = iy_tnw + 1; + index_t iz_bse = iz_tnw + 1; + + // get surfaces to each neighbor: + T tnw = (ix_bse - ix) * (iy_bse - iy) * (iz_bse - iz); + T tne = (ix - ix_bsw) * (iy_bsw - iy) * (iz_bsw - iz); + T tsw = (ix_bne - ix) * (iy - iy_bne) * (iz_bne - iz); + T tse = (ix - ix_bnw) * (iy - iy_bnw) * (iz_bnw - iz); + T bnw = (ix_tse - ix) * (iy_tse - iy) * (iz - iz_tse); + T bne = (ix - ix_tsw) * (iy_tsw - iy) * (iz - iz_tsw); + T bsw = (ix_tne - ix) * (iy - iy_tne) * (iz - iz_tne); + T bse = (ix - ix_tnw) * (iy - iy_tnw) * (iz - iz_tnw); + + auto inp_ptr_NC = input + n * inp_sN; + auto out_ptr_NCDHW = output + n * out_sN + d * out_sD + h * out_sH + w * out_sW; + for (index_t c = 0; c < out_c; ++c, inp_ptr_NC += inp_sC, out_ptr_NCDHW += out_sC) { + *out_ptr_NCDHW = static_cast(0); + if (InBounds3D(iz_tnw, iy_tnw, ix_tnw, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_tnw * inp_sD + iy_tnw * inp_sH + ix_tnw * inp_sW] * tnw; + } + if (InBounds3D(iz_tne, iy_tne, ix_tne, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_tne * inp_sD + iy_tne * inp_sH + ix_tne * inp_sW] * tne; + } + if (InBounds3D(iz_tsw, iy_tsw, ix_tsw, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_tsw * inp_sD + iy_tsw * inp_sH + ix_tsw * inp_sW] * tsw; + } + if (InBounds3D(iz_tse, iy_tse, ix_tse, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_tse * inp_sD + iy_tse * inp_sH + ix_tse * inp_sW] * tse; + } + if (InBounds3D(iz_bnw, iy_bnw, ix_bnw, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_bnw * inp_sD + iy_bnw * inp_sH + ix_bnw * inp_sW] * bnw; + } + if (InBounds3D(iz_bne, iy_bne, ix_bne, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_bne * inp_sD + iy_bne * inp_sH + ix_bne * inp_sW] * bne; + } + if (InBounds3D(iz_bsw, iy_bsw, ix_bsw, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_bsw * inp_sD + iy_bsw * inp_sH + ix_bsw * inp_sW] * bsw; + } + if (InBounds3D(iz_bse, iy_bse, ix_bse, in_d, in_h, in_w)) { + *out_ptr_NCDHW += inp_ptr_NC[iz_bse * inp_sD + iy_bse * inp_sH + ix_bse * inp_sW] * bse; + } + } + } else if (interpolation_mode == Mode::nearest) { + index_t ix_nearest = static_cast(std::round(ix)); + index_t iy_nearest = static_cast(std::round(iy)); + index_t iz_nearest = static_cast(std::round(iz)); + + // assign nearest neighor pixel value to output pixel + auto inp_ptr_NC = input + n * inp_sN; + auto out_ptr_NCDHW = output + n * out_sN + d * out_sD + h * out_sH + w * out_sW; + for (index_t c = 0; c < out_c; ++c, inp_ptr_NC += inp_sC, out_ptr_NCDHW += out_sC) { + if (InBounds3D(iz_nearest, iy_nearest, ix_nearest, in_d, in_h, in_w)) { + *out_ptr_NCDHW = inp_ptr_NC[iz_nearest * inp_sD + iy_nearest * inp_sH + ix_nearest * inp_sW]; + } else { + *out_ptr_NCDHW = static_cast(0); + } + } + } + } + } + + +std::vector GridSample3DCUDAForward(const paddle::Tensor& x, + const paddle::Tensor& grid, + const std::string& mode, + const std::string& padding_mode, + bool align_corners) { + CHECK_INPUT_GPU(x); + CHECK_INPUT_GPU(grid); + PaddingMode enum_padding_mode; + Mode enum_mode; + if (padding_mode == "border") { + enum_padding_mode = PaddingMode::border; + } else if (padding_mode == "reflection") { + enum_padding_mode = PaddingMode::reflect; + } else { + enum_padding_mode = PaddingMode::zeros; + } + + if (mode == "nearest") { + enum_mode = Mode::nearest; + } else { + enum_mode = Mode::bilinear; + } + const int n = grid.shape()[0]; + const int out_d = grid.shape()[1]; + const int out_h = grid.shape()[2]; + const int out_w = grid.shape()[3]; + const int c = x.shape()[1]; + const int in_d = x.shape()[2]; + const int in_h = x.shape()[3]; + const int in_w = x.shape()[4]; + + auto output = paddle::Tensor(paddle::PlaceType::kGPU, {n, c, out_d, out_h, out_w}); + const int count = static_cast(n * out_d * out_h * out_w); + + int max_threads_per_block = 512; + int block_num = (count - 1) / max_threads_per_block + 1; + // printf("size: %d, %d, %d, %d, %d, %d \n", n, c, out_d, out_h, count, block_num); + GridSample3DCudaKernel + <<>>( + count, + c, + out_d, + out_h, + out_w, + in_d, + in_h, + in_w, + x.data(), + grid.data(), + output.mutable_data(), + enum_mode, + enum_padding_mode, + align_corners); + + cudaError_t error_check ; + error_check = cudaGetLastError(); + if( error_check != cudaSuccess ){ printf("%s\n" , cudaGetErrorString( error_check ) ); } + // printf("size: %d, %d, %d, %d, %d, %d \n", n, c, out_d, out_h, count, block_num); + return {output}; +} + +template +static __forceinline__ __device__ T +UnnormalizeWithMask(T coord, int size, bool align_corners, T* grad_in) { + if (align_corners) { + *grad_in = static_cast(size - 1) / 2; + return ((coord + 1.f) / 2) * (size - 1); + } else { + *grad_in = static_cast(size) / 2; + return ((coord + 1.f) * size - 1) / 2; + } +} + +template +static __forceinline__ __device__ T ClipIndexesWithMask(T in, + int clip_limit, + T* grad_in) { + if (in <= static_cast(0)) { + *grad_in = static_cast(0); + return static_cast(0); + } else { + T max = static_cast(clip_limit - 1); + if (in >= max) { + *grad_in = static_cast(0); + return max; + } else { + *grad_in = static_cast(1); + return in; + } + } +} + +template +static __forceinline__ __device__ T +ReflectIndexesWithMask(T in, int twice_low, int twice_high, T* grad_in) { + if (twice_low == twice_high) { + *grad_in = static_cast(0); + return static_cast(0); + } + int grad_in_mult_; + T min = static_cast(twice_low) / 2; + T span = static_cast(twice_high - twice_low) / 2; + in = in - min; + if (in < static_cast(0)) { + grad_in_mult_ = -1; + in = -in; + } else { + grad_in_mult_ = 1; + } + T extra = fmod(in, span); + int flips = static_cast(floor(in / span)); + if (flips % 2 == 0) { + *grad_in = static_cast(grad_in_mult_); + return extra + min; + } else { + *grad_in = static_cast(-grad_in_mult_); + return span - extra + min; + } +} + +template +static __forceinline__ __device__ T +ComputePositionsWithMask(T coord, + int size, + PaddingMode padding_mode, + bool align_corners, + T* grad_in) { + T grad_clip, grad_refl; + coord = UnnormalizeWithMask(coord, size, align_corners, grad_in); + if (padding_mode == PaddingMode::border) { + coord = ClipIndexesWithMask(coord, size, &grad_clip); + *grad_in = (*grad_in) * grad_clip; + } else if (padding_mode == PaddingMode::reflect) { + if (align_corners) { + coord = ReflectIndexesWithMask(coord, 0, 2 * (size - 1), &grad_refl); + } else { + coord = ReflectIndexesWithMask(coord, -1, 2 * size - 1, &grad_refl); + } + coord = ClipIndexesWithMask(coord, size, &grad_clip); + *grad_in = (*grad_in) * grad_refl * grad_clip; + } + + return coord; +} + + +template +static __forceinline__ __device__ void AtomicAdd3D( + T* data, int64_t d, int64_t h, int64_t w, int64_t sD, int64_t sH, int64_t sW, int64_t D, int64_t H, int64_t W, T delta) { + if (InBounds3D(d, h, w, D, H, W)) { + atomicAdd(data + d * sD + h * sH + w * sW, delta); + } +} + +template + __global__ void GridSample3DCudaBackwardKernel( + const index_t nthreads, + const T* grad_output, + const T* input, + const T* grid, + index_t out_c, + index_t out_d, + index_t out_h, + index_t out_w, + index_t in_d, + index_t in_h, + index_t in_w, + T* grad_input, + T* grad_grid, + const Mode mode, + const PaddingMode padding_mode, + bool align_corners) { + + index_t inp_sW = 1; + index_t inp_sH = in_w; + index_t inp_sD = in_h * in_w; + index_t inp_sC = in_d * inp_sD; + index_t inp_sN = out_c * inp_sC; + + index_t grid_sCoor = 1; + index_t grid_sW = 3; + index_t grid_sH = out_w * grid_sW; + index_t grid_sD = out_h * grid_sH; + index_t grid_sN = out_d * grid_sD; + + index_t gOut_sW = 1; + index_t gOut_sH = out_w; + index_t gOut_sD = out_h * out_w; + index_t gOut_sC = out_d * gOut_sD; + index_t gOut_sN = out_c * gOut_sC; + + + CUDA_KERNEL_LOOP_TYPE(index, nthreads, index_t) { + const index_t w = index % out_w; + const index_t h = (index / out_w) % out_h; + const index_t d = (index / (out_h * out_w)) % out_d; + const index_t n = index / (out_d * out_h * out_w); + const auto grid_offset = n * grid_sN + d * grid_sD + h * grid_sH + w * grid_sW; + + // get the corresponding input x, y, z co-ordinates from grid + T ix = grid[grid_offset]; + T iy = grid[grid_offset + grid_sCoor]; + T iz = grid[grid_offset + 2 * grid_sCoor]; + + // multipliers for gradients on ix, iy, and iz + T gix_mult, giy_mult, giz_mult; + ix = ComputePositionsWithMask(ix, in_w, padding_mode, align_corners, &gix_mult); + iy = ComputePositionsWithMask(iy, in_h, padding_mode, align_corners, &giy_mult); + iz = ComputePositionsWithMask(iz, in_d, padding_mode, align_corners, &giz_mult); + + if (mode == Mode::bilinear) { + // get corner pixel values from (x, y, z) + // for 4d, we used north-east-south-west + // for 5d, we add top-bottom + index_t ix_tnw = static_cast(std::floor(ix)); + index_t iy_tnw = static_cast(std::floor(iy)); + index_t iz_tnw = static_cast(std::floor(iz)); + + index_t ix_tne = ix_tnw + 1; + index_t iy_tne = iy_tnw; + index_t iz_tne = iz_tnw; + + index_t ix_tsw = ix_tnw; + index_t iy_tsw = iy_tnw + 1; + index_t iz_tsw = iz_tnw; + + index_t ix_tse = ix_tnw + 1; + index_t iy_tse = iy_tnw + 1; + index_t iz_tse = iz_tnw; + + index_t ix_bnw = ix_tnw; + index_t iy_bnw = iy_tnw; + index_t iz_bnw = iz_tnw + 1; + + index_t ix_bne = ix_tnw + 1; + index_t iy_bne = iy_tnw; + index_t iz_bne = iz_tnw + 1; + + index_t ix_bsw = ix_tnw; + index_t iy_bsw = iy_tnw + 1; + index_t iz_bsw = iz_tnw + 1; + + index_t ix_bse = ix_tnw + 1; + index_t iy_bse = iy_tnw + 1; + index_t iz_bse = iz_tnw + 1; + + // get surfaces to each neighbor: + T tnw = (ix_bse - ix) * (iy_bse - iy) * (iz_bse - iz); + T tne = (ix - ix_bsw) * (iy_bsw - iy) * (iz_bsw - iz); + T tsw = (ix_bne - ix) * (iy - iy_bne) * (iz_bne - iz); + T tse = (ix - ix_bnw) * (iy - iy_bnw) * (iz_bnw - iz); + T bnw = (ix_tse - ix) * (iy_tse - iy) * (iz - iz_tse); + T bne = (ix - ix_tsw) * (iy_tsw - iy) * (iz - iz_tsw); + T bsw = (ix_tne - ix) * (iy - iy_tne) * (iz - iz_tne); + T bse = (ix - ix_tnw) * (iy - iy_tnw) * (iz - iz_tnw); + + T gix = static_cast(0), giy = static_cast(0), giz = static_cast(0); + index_t gOut_offset = n * gOut_sN + d * gOut_sD + h * gOut_sH + w * gOut_sW; + index_t inp_offset_NC = n * inp_sN; + T* gInp_ptr_NC = grad_input + n * inp_sN; + for (index_t c = 0; c < out_c; ++c, gOut_offset += gOut_sC, gInp_ptr_NC += inp_sC, inp_offset_NC += inp_sC) { + T gOut = grad_output[gOut_offset]; + + AtomicAdd3D(gInp_ptr_NC, iz_tnw, iy_tnw, ix_tnw, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, tnw * gOut); + AtomicAdd3D(gInp_ptr_NC, iz_tne, iy_tne, ix_tne, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, tne * gOut); + AtomicAdd3D(gInp_ptr_NC, iz_tsw, iy_tsw, ix_tsw, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, tsw * gOut); + AtomicAdd3D(gInp_ptr_NC, iz_tse, iy_tse, ix_tse, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, tse * gOut); + AtomicAdd3D(gInp_ptr_NC, iz_bnw, iy_bnw, ix_bnw, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, bnw * gOut); + AtomicAdd3D(gInp_ptr_NC, iz_bne, iy_bne, ix_bne, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, bne * gOut); + AtomicAdd3D(gInp_ptr_NC, iz_bsw, iy_bsw, ix_bsw, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, bsw * gOut); + AtomicAdd3D(gInp_ptr_NC, iz_bse, iy_bse, ix_bse, inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, bse * gOut); + + // calculate grad_grid + if (InBounds3D(iz_tnw, iy_tnw, ix_tnw, in_d, in_h, in_w)) { + T tnw_val = input[inp_offset_NC + iz_tnw * inp_sD + iy_tnw * inp_sH + ix_tnw * inp_sW]; + gix -= tnw_val * (iy_bse - iy) * (iz_bse - iz) * gOut; + giy -= tnw_val * (ix_bse - ix) * (iz_bse - iz) * gOut; + giz -= tnw_val * (ix_bse - ix) * (iy_bse - iy) * gOut; + } + if (InBounds3D(iz_tne, iy_tne, ix_tne, in_d, in_h, in_w)) { + T tne_val = input[inp_offset_NC + iz_tne * inp_sD + iy_tne * inp_sH + ix_tne * inp_sW]; + gix += tne_val * (iy_bsw - iy) * (iz_bsw - iz) * gOut; + giy -= tne_val * (ix - ix_bsw) * (iz_bsw - iz) * gOut; + giz -= tne_val * (ix - ix_bsw) * (iy_bsw - iy) * gOut; + } + if (InBounds3D(iz_tsw, iy_tsw, ix_tsw, in_d, in_h, in_w)) { + T tsw_val = input[inp_offset_NC + iz_tsw * inp_sD + iy_tsw * inp_sH + ix_tsw * inp_sW]; + gix -= tsw_val * (iy - iy_bne) * (iz_bne - iz) * gOut; + giy += tsw_val * (ix_bne - ix) * (iz_bne - iz) * gOut; + giz -= tsw_val * (ix_bne - ix) * (iy - iy_bne) * gOut; + } + if (InBounds3D(iz_tse, iy_tse, ix_tse, in_d, in_h, in_w)) { + T tse_val = input[inp_offset_NC + iz_tse * inp_sD + iy_tse * inp_sH + ix_tse * inp_sW]; + gix += tse_val * (iy - iy_bnw) * (iz_bnw - iz) * gOut; + giy += tse_val * (ix - ix_bnw) * (iz_bnw - iz) * gOut; + giz -= tse_val * (ix - ix_bnw) * (iy - iy_bnw) * gOut; + } + if (InBounds3D(iz_bnw, iy_bnw, ix_bnw, in_d, in_h, in_w)) { + T bnw_val = input[inp_offset_NC + iz_bnw * inp_sD + iy_bnw * inp_sH + ix_bnw * inp_sW]; + gix -= bnw_val * (iy_tse - iy) * (iz - iz_tse) * gOut; + giy -= bnw_val * (ix_tse - ix) * (iz - iz_tse) * gOut; + giz += bnw_val * (ix_tse - ix) * (iy_tse - iy) * gOut; + } + if (InBounds3D(iz_bne, iy_bne, ix_bne, in_d, in_h, in_w)) { + T bne_val = input[inp_offset_NC + iz_bne * inp_sD + iy_bne * inp_sH + ix_bne * inp_sW]; + gix += bne_val * (iy_tsw - iy) * (iz - iz_tsw) * gOut; + giy -= bne_val * (ix - ix_tsw) * (iz - iz_tsw) * gOut; + giz += bne_val * (ix - ix_tsw) * (iy_tsw - iy) * gOut; + } + if (InBounds3D(iz_bsw, iy_bsw, ix_bsw, in_d, in_h, in_w)) { + T bsw_val = input[inp_offset_NC + iz_bsw * inp_sD + iy_bsw * inp_sH + ix_bsw * inp_sW]; + gix -= bsw_val * (iy - iy_tne) * (iz - iz_tne) * gOut; + giy += bsw_val * (ix_tne - ix) * (iz - iz_tne) * gOut; + giz += bsw_val * (ix_tne - ix) * (iy - iy_tne) * gOut; + } + if (InBounds3D(iz_bse, iy_bse, ix_bse, in_d, in_h, in_w)) { + T bse_val = input[inp_offset_NC + iz_bse * inp_sD + iy_bse * inp_sH + ix_bse * inp_sW]; + gix += bse_val * (iy - iy_tnw) * (iz - iz_tnw) * gOut; + giy += bse_val * (ix - ix_tnw) * (iz - iz_tnw) * gOut; + giz += bse_val * (ix - ix_tnw) * (iy - iy_tnw) * gOut; + } + } + if (grad_grid != nullptr) { + T *gGrid_ptr_NDHW = grad_grid + index * grid_sW; + gGrid_ptr_NDHW[0] = gix_mult * gix; + gGrid_ptr_NDHW[1] = giy_mult * giy; + gGrid_ptr_NDHW[2] = giz_mult * giz; + } + } else if (mode == Mode::nearest) { + + auto ix_nearest = static_cast(std::round(ix)); + auto iy_nearest = static_cast(std::round(iy)); + auto iz_nearest = static_cast(std::round(iz)); + + // assign nearest neighor pixel value to output pixel + index_t gOut_offset = n * gOut_sN + d * gOut_sD + h * gOut_sH + w * gOut_sW; + T* gInp_ptr_NC = grad_input + n * inp_sN; + for (index_t c = 0; c < out_c; ++c, gOut_offset += gOut_sC, gInp_ptr_NC += inp_sC) { + AtomicAdd3D(gInp_ptr_NC, iz_nearest, iy_nearest, ix_nearest, + inp_sD, inp_sH, inp_sW, in_d, in_h, in_w, grad_output[gOut_offset]); + } + if (grad_grid != nullptr) { + T *gGrid_ptr_NDHW = grad_grid + index * grid_sW; + gGrid_ptr_NDHW[0] = static_cast(0); + gGrid_ptr_NDHW[1] = static_cast(0); + gGrid_ptr_NDHW[2] = static_cast(0); + } + } + } + } + + +std::vector GridSample3DCUDABackward(const paddle::Tensor& x, + const paddle::Tensor& grid, + const paddle::Tensor& grad_out, + const std::string& mode, + const std::string& padding_mode, + bool align_corners) { + PaddingMode enum_padding_mode; + Mode enum_mode; + if (padding_mode == "border") { + enum_padding_mode = PaddingMode::border; + } else if (padding_mode == "reflection") { + enum_padding_mode = PaddingMode::reflect; + } else { + enum_padding_mode = PaddingMode::zeros; + } + + if (mode == "nearest") { + enum_mode = Mode::nearest; + } else { + enum_mode = Mode::bilinear; + } + + const int out_d = grid.shape()[1]; + const int out_h = grid.shape()[2]; + const int out_w = grid.shape()[3]; + const int n = x.shape()[0]; + const int c = x.shape()[1]; + const int in_d = x.shape()[2]; + const int in_h = x.shape()[3]; + const int in_w = x.shape()[4]; + + auto grid_grad_output = paddle::Tensor(paddle::PlaceType::kGPU, {n, out_d, out_h, out_w, 3}); + auto x_grad_output = paddle::Tensor(paddle::PlaceType::kGPU, {n, c, in_d, in_h, in_w}); + const int count = static_cast(n * out_d * out_h * out_w); + + int max_threads_per_block = 512; + int block_num = (count - 1) / max_threads_per_block + 1; + + GridSample3DCudaBackwardKernel + <<>>( + count, + grad_out.data(), + x.data(), + grid.data(), + c, + out_d, + out_h, + out_w, + in_d, + in_h, + in_w, + x_grad_output.mutable_data(), + grid_grad_output.mutable_data(), + enum_mode, + enum_padding_mode, + align_corners); + + + return {x_grad_output}; // , grid_grad_output}; +} diff --git a/deploy/grid_sample_3d.h b/deploy/grid_sample_3d.h new file mode 100644 index 0000000..34411a4 --- /dev/null +++ b/deploy/grid_sample_3d.h @@ -0,0 +1,32 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// + +#ifndef GRID_SAMPLE_3D_H +#define GRID_SAMPLE_3D_H + +#include +#include +#include + +#define HOST_DEVICE __host__ __device__ +#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__ + +enum class Mode {bilinear, nearest}; + +enum class PaddingMode { zeros, border, reflect }; + +namespace { +} +#endif \ No newline at end of file diff --git a/deploy/run.sh b/deploy/run.sh new file mode 100644 index 0000000..e0a6a50 --- /dev/null +++ b/deploy/run.sh @@ -0,0 +1,17 @@ +#!/bin/bash +set +x +set -e + +work_path=$(dirname $(readlink -f $0)) + +# 1. compile +bash ${work_path}/compile.sh + +# 2. download model +# if [ ! -d caddn_infer_model ]; then +# wget +# tar xzf caddn_infer_model.tgz +# fi + +# 3. run +./build/caddn_main \ No newline at end of file From 09f9216602f2be4679d952c32833f9f1a9a1e608 Mon Sep 17 00:00:00 2001 From: wangna11BD Date: Thu, 2 Jun 2022 04:58:36 +0000 Subject: [PATCH 2/3] add trrt --- deploy/caddn_main.cc | 24 + deploy/shape_range_info.pbtxt | 12714 ++++++++++++++++++++++++++++++++ 2 files changed, 12738 insertions(+) create mode 100644 deploy/shape_range_info.pbtxt diff --git a/deploy/caddn_main.cc b/deploy/caddn_main.cc index 20b7151..8a25120 100644 --- a/deploy/caddn_main.cc +++ b/deploy/caddn_main.cc @@ -19,10 +19,24 @@ limitations under the License. */ #include "paddle/include/paddle_inference_api.h" +DEFINE_bool(use_trt, true, "use trt."); +DEFINE_string(trt_precision, "trt_fp32", "trt_fp32, trt_fp16, etc."); +DEFINE_bool(tune, false, "tune to get shape range."); + using paddle_infer::Config; using paddle_infer::Predictor; using paddle_infer::CreatePredictor; +const std::string shape_range_info = "shape_range_info.pbtxt"; + +paddle_infer::PrecisionType GetPrecisionType(const std::string& ptype) { + if (ptype == "trt_fp32") + return paddle_infer::PrecisionType::kFloat32; + if (ptype == "trt_fp16") + return paddle_infer::PrecisionType::kHalf; + return paddle_infer::PrecisionType::kFloat32; +} + void run(Predictor *predictor, const std::vector &images_shape, const std::vector &images_data, @@ -82,6 +96,16 @@ int main() { config.EnableUseGpu(100, 0); config.SetModel("caddn_infer_model/model.pdmodel", "caddn_infer_model/model.pdiparams"); + // use trt + if (FLAGS_use_trt) { + config.EnableTensorRtEngine(1 << 30, 8, 3, + GetPrecisionType(FLAGS_trt_precision), false, false); + config.EnableTunedTensorRtDynamicShape(shape_range_info, true); + if (FLAGS_tune) { + config.CollectShapeRangeInfo(shape_range_info); + } + } + auto predictor{paddle_infer::CreatePredictor(config)}; std::vector images_shape = {1, 3, 375, 1242}; std::vector images_data(1 * 3 * 375 * 1242, 1); diff --git a/deploy/shape_range_info.pbtxt b/deploy/shape_range_info.pbtxt new file mode 100644 index 0000000..ebc7216 --- /dev/null +++ b/deploy/shape_range_info.pbtxt @@ -0,0 +1,12714 @@ +shape_range_info { + name: "_generated_var_0" + min_shape: 1 + min_shape: 64 + min_shape: 280 + min_shape: 376 + min_shape: 25 + max_shape: 1 + max_shape: 64 + max_shape: 280 + max_shape: 376 + max_shape: 25 + opt_shape: 1 + opt_shape: 64 + opt_shape: 280 + opt_shape: 376 + opt_shape: 25 +} +shape_range_info { + name: "_generated_var_1" + min_shape: 1 + max_shape: 1 + opt_shape: 1 +} +shape_range_info { + name: "_generated_var_2" + min_shape: 1 + max_shape: 1 + opt_shape: 1 +} +shape_range_info { + name: "_generated_var_22" + min_shape: 0 + max_shape: 0 + opt_shape: 0 +} +shape_range_info { + name: "_generated_var_23" + min_shape: 0 + max_shape: 0 + opt_shape: 0 +} +shape_range_info { + name: "_generated_var_24" + min_shape: 0 + max_shape: 0 + opt_shape: 0 +} +shape_range_info { + name: "_generated_var_25" + min_shape: 0 + max_shape: 0 + opt_shape: 0 +} +shape_range_info { + name: "_generated_var_26" + min_shape: 0 + max_shape: 0 + opt_shape: 0 +} +shape_range_info { + name: "_generated_var_27" + min_shape: 0 + max_shape: 0 + opt_shape: 0 +} +shape_range_info { + name: "_generated_var_28" + min_shape: 0 + max_shape: 0 + opt_shape: 0 +} +shape_range_info { + name: "_generated_var_3" + min_shape: 1 + min_shape: 7 + max_shape: 1 + max_shape: 7 + opt_shape: 1 + opt_shape: 7 +} +shape_range_info { + name: "_generated_var_30" + min_shape: 1 + max_shape: 1 + opt_shape: 1 +} +shape_range_info { + name: "_generated_var_31" + min_shape: 1 + max_shape: 1 + opt_shape: 1 +} +shape_range_info { + name: "_generated_var_32" + min_shape: 1 + min_shape: 7 + max_shape: 1 + max_shape: 7 + opt_shape: 1 + opt_shape: 7 +} +shape_range_info { + name: "_generated_var_4" + min_shape: 157920 + min_shape: 7 + max_shape: 157920 + max_shape: 7 + opt_shape: 157920 + opt_shape: 7 +} +shape_range_info { + name: "_generated_var_5" + min_shape: 157920 + max_shape: 157920 + opt_shape: 157920 +} +shape_range_info { + name: "_generated_var_6" + min_shape: 157920 + max_shape: 157920 + opt_shape: 157920 +} +shape_range_info { + name: "_generated_var_7" + min_shape: 0 + min_shape: 1 + max_shape: 0 + max_shape: 1 + opt_shape: 0 + opt_shape: 1 +} +shape_range_info { + name: "abs_0.tmp_0" + min_shape: 105280 + min_shape: 25 + min_shape: 1 + max_shape: 105280 + max_shape: 25 + max_shape: 1 + opt_shape: 105280 + opt_shape: 25 + opt_shape: 1 +} +shape_range_info { + name: "abs_1.tmp_0" + min_shape: 1 + min_shape: 280 + min_shape: 376 + min_shape: 25 + min_shape: 1 + max_shape: 1 + max_shape: 280 + max_shape: 376 + max_shape: 25 + max_shape: 1 + opt_shape: 1 + opt_shape: 280 + opt_shape: 376 + opt_shape: 25 + opt_shape: 1 +} +shape_range_info { + name: "argmax_0.tmp_0" + min_shape: 1 + min_shape: 157920 + max_shape: 1 + max_shape: 157920 + opt_shape: 1 + opt_shape: 157920 +} +shape_range_info { + name: "argmax_1.tmp_0" + min_shape: 157920 + max_shape: 157920 + opt_shape: 157920 +} +shape_range_info { + name: "assign_0.tmp_0" + min_shape: 1 + max_shape: 1 + opt_shape: 1 +} +shape_range_info { + name: "assign_1.tmp_0" + min_shape: 1 + max_shape: 1 + opt_shape: 1 +} +shape_range_info { + name: "assign_2.tmp_0" + min_shape: 1 + max_shape: 1 + opt_shape: 1 +} +shape_range_info { + name: "assign_3.tmp_0" + min_shape: 1 + min_shape: 7 + max_shape: 1 + max_shape: 7 + opt_shape: 1 + opt_shape: 7 +} +shape_range_info { + name: "batch_norm_0.tmp_0" + min_shape: 64 + max_shape: 64 + opt_shape: 64 +} +shape_range_info { + name: "batch_norm_0.tmp_1" + min_shape: 64 + max_shape: 64 + opt_shape: 64 +} +shape_range_info { + name: "batch_norm_0.tmp_2" + min_shape: 1 + min_shape: 64 + min_shape: 188 + min_shape: 621 + max_shape: 1 + max_shape: 64 + max_shape: 188 + max_shape: 621 + opt_shape: 1 + opt_shape: 64 + opt_shape: 188 + opt_shape: 621 +} +shape_range_info { + name: "batch_norm_1.tmp_0" + min_shape: 64 + max_shape: 64 + opt_shape: 64 +} +shape_range_info { + name: "batch_norm_1.tmp_1" + min_shape: 64 + max_shape: 64 + opt_shape: 64 +} +shape_range_info { + name: "batch_norm_1.tmp_2" + min_shape: 1 + min_shape: 64 + min_shape: 94 + min_shape: 311 + max_shape: 1 + max_shape: 64 + max_shape: 94 + max_shape: 311 + opt_shape: 1 + opt_shape: 64 + opt_shape: 94 + opt_shape: 311 +} +shape_range_info { + name: "batch_norm_10.tmp_0" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_10.tmp_1" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_10.tmp_2" + min_shape: 1 + min_shape: 256 + min_shape: 94 + min_shape: 311 + max_shape: 1 + max_shape: 256 + max_shape: 94 + max_shape: 311 + opt_shape: 1 + opt_shape: 256 + opt_shape: 94 + opt_shape: 311 +} +shape_range_info { + name: "batch_norm_100.tmp_0" + min_shape: 2048 + max_shape: 2048 + opt_shape: 2048 +} +shape_range_info { + name: "batch_norm_100.tmp_1" + min_shape: 2048 + max_shape: 2048 + opt_shape: 2048 +} +shape_range_info { + name: "batch_norm_100.tmp_2" + min_shape: 1 + min_shape: 2048 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 2048 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 2048 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_101.tmp_0" + min_shape: 512 + max_shape: 512 + opt_shape: 512 +} +shape_range_info { + name: "batch_norm_101.tmp_1" + min_shape: 512 + max_shape: 512 + opt_shape: 512 +} +shape_range_info { + name: "batch_norm_101.tmp_2" + min_shape: 1 + min_shape: 512 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 512 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 512 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_102.tmp_0" + min_shape: 512 + max_shape: 512 + opt_shape: 512 +} +shape_range_info { + name: "batch_norm_102.tmp_1" + min_shape: 512 + max_shape: 512 + opt_shape: 512 +} +shape_range_info { + name: "batch_norm_102.tmp_2" + min_shape: 1 + min_shape: 512 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 512 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 512 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_103.tmp_0" + min_shape: 2048 + max_shape: 2048 + opt_shape: 2048 +} +shape_range_info { + name: "batch_norm_103.tmp_1" + min_shape: 2048 + max_shape: 2048 + opt_shape: 2048 +} +shape_range_info { + name: "batch_norm_103.tmp_2" + min_shape: 1 + min_shape: 2048 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 2048 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 2048 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_104.tmp_0" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_104.tmp_1" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_104.tmp_2" + min_shape: 1 + min_shape: 256 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 256 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 256 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_105.tmp_0" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_105.tmp_1" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_105.tmp_2" + min_shape: 1 + min_shape: 256 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 256 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 256 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_106.tmp_0" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_106.tmp_1" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_106.tmp_2" + min_shape: 1 + min_shape: 256 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 256 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 256 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_107.tmp_0" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_107.tmp_1" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: "batch_norm_107.tmp_2" + min_shape: 1 + min_shape: 256 + min_shape: 47 + min_shape: 156 + max_shape: 1 + max_shape: 256 + max_shape: 47 + max_shape: 156 + opt_shape: 1 + opt_shape: 256 + opt_shape: 47 + opt_shape: 156 +} +shape_range_info { + name: "batch_norm_108.tmp_0" + min_shape: 256 + max_shape: 256 + opt_shape: 256 +} +shape_range_info { + name: 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opt_shape: 376 + opt_shape: 25 + opt_shape: 1 +} +shape_range_info { + name: "where_2.tmp_0" + min_shape: 1 + min_shape: 280 + min_shape: 376 + min_shape: 25 + min_shape: 3 + max_shape: 1 + max_shape: 280 + max_shape: 376 + max_shape: 25 + max_shape: 3 + opt_shape: 1 + opt_shape: 280 + opt_shape: 376 + opt_shape: 25 + opt_shape: 3 +} +shape_range_info { + name: "where_index_0.tmp_0" + min_shape: 0 + min_shape: 1 + max_shape: 0 + max_shape: 1 + opt_shape: 0 + opt_shape: 1 +} From 47f3b505ae257d9468a08421c67ca670ce78bcbf Mon Sep 17 00:00:00 2001 From: wangna11BD Date: Thu, 2 Jun 2022 05:02:18 +0000 Subject: [PATCH 3/3] add trt --- deploy/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deploy/README.md b/deploy/README.md index ebca540..177a68f 100644 --- a/deploy/README.md +++ b/deploy/README.md @@ -1,11 +1,13 @@ ## 一. 将模型参数放到caddn_infer_model目录下 模型放好后的目录结构 +``` deploy ├── caddn_infer_model | ├── model.pdiparams | └── model.pdmodel ├── caddn_main.cc ... +``` ## 二. 修改`compile.sh`