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100644 tf-faster-rcnn/tools/.ipynb_checkpoints/demo_1-checkpoint.py create mode 100644 tf-faster-rcnn/tools/.ipynb_checkpoints/demo_pb-checkpoint.py create mode 100644 tf-faster-rcnn/tools/.ipynb_checkpoints/trainval_net-checkpoint.py create mode 100644 tf-faster-rcnn/tools/_init_paths.py create mode 100644 tf-faster-rcnn/tools/convert_from_depre.py create mode 100644 tf-faster-rcnn/tools/demo.py create mode 100644 tf-faster-rcnn/tools/demo_1.py create mode 100644 tf-faster-rcnn/tools/demo_pb.py create mode 100644 tf-faster-rcnn/tools/reval.py create mode 100644 tf-faster-rcnn/tools/test_net.py create mode 100644 tf-faster-rcnn/tools/trainval_net.py create mode 100644 tf-faster-rcnn/val.txt diff --git a/README.md b/README.md new file mode 100644 index 0000000..5c0de61 --- /dev/null +++ b/README.md @@ -0,0 +1,18 @@ +### 我爱996 + +#### 我们的工作共分为三部分： + +##### 一、训练FasterRCNN模型 + +>详情请阅读tf-faster-rcnn中的README.md + +##### 二、tf模型转换Onnx模型 + +>详情请阅读tf-faster-rcnn中的faster_rcnn模型转化.md + +##### 三、TensorRT加速 + +>详情请阅读TensorRT-Base中的README.md + + + diff --git a/TensorRT-Base/.gitignore b/TensorRT-Base/.gitignore new file mode 100644 index 0000000..5830d79 --- /dev/null +++ b/TensorRT-Base/.gitignore @@ -0,0 +1,8 @@ +##ignore this file## +bin/Release/* +build/* +lib/Release/* +lib/*.so +mnist_onnx/model/model.onnx +faster_rcnn/model/VGG16_faster_rcnn_final.caffemodel +faster_rcnn/model/faster_rcnn.bin diff --git a/TensorRT-Base/.vscode/c_cpp_properties.json b/TensorRT-Base/.vscode/c_cpp_properties.json new file mode 100644 index 0000000..d9d9864 --- /dev/null +++ b/TensorRT-Base/.vscode/c_cpp_properties.json @@ -0,0 +1,26 @@ +{ + "configurations": [ + { + "name": "Win32", + "includePath": [ + "${workspaceFolder}/**" + // "/usr/local/opencv/include", + // "/usr/local/opencv/include/opencv", + // "/usr/local/opencv/include/opencv2", + // "/usr/local/cuda/include", + // "/opt/TensorRT-7.2.2.3/include" + ], + "defines": [ + "_DEBUG", + "UNICODE", + "_UNICODE" + ], + "compilerPath": "/usr/bin/gcc", + "cStandard": "c11", + "cppStandard": "c++17", + "intelliSenseMode": "clang-x64", + "configurationProvider": "ms-vscode.cmake-tools" + } + ], + "version": 4 +} \ No newline at end of file diff --git a/TensorRT-Base/.vscode/settings.json b/TensorRT-Base/.vscode/settings.json new file mode 100644 index 0000000..1b6c3a9 --- /dev/null +++ b/TensorRT-Base/.vscode/settings.json @@ -0,0 +1,87 @@ +{ + "files.associations": { + "iosfwd": "cpp", + "iostream": "cpp", + "*.tcc": "cpp", + "chrono": "cpp", + "functional": "cpp", + "iterator": "cpp", + "map": "cpp", + "numeric": "cpp", + "ratio": "cpp", + "cstring": "cpp", + "xstring": "cpp", + "cmath": "cpp", + "array": "cpp", + "atomic": "cpp", + "cctype": "cpp", + "clocale": "cpp", + "cstdarg": "cpp", + "cstddef": "cpp", + "cstdint": "cpp", + "cstdio": "cpp", + "cstdlib": "cpp", + "ctime": "cpp", + "cwchar": "cpp", + "cwctype": "cpp", + "deque": "cpp", + "list": "cpp", + "unordered_map": "cpp", + "vector": "cpp", + "exception": "cpp", + "algorithm": "cpp", + "memory": "cpp", + "memory_resource": "cpp", + "optional": "cpp", + "string": "cpp", + "string_view": "cpp", + "system_error": "cpp", + "tuple": "cpp", + "type_traits": "cpp", + "utility": "cpp", + "fstream": "cpp", + "initializer_list": "cpp", + "iomanip": "cpp", + "istream": "cpp", + "limits": "cpp", + "new": "cpp", + "ostream": "cpp", + "sstream": "cpp", + "stdexcept": "cpp", + "streambuf": "cpp", + "typeinfo": "cpp", + "ios": "cpp", + "locale": "cpp", + "queue": "cpp", + "xfacet": "cpp", + "xhash": "cpp", + "xiosbase": "cpp", + "xlocale": "cpp", + "xlocbuf": "cpp", + "xlocinfo": "cpp", + "xlocmes": "cpp", + "xlocmon": "cpp", + "xlocnum": "cpp", + "xloctime": "cpp", + "xmemory": "cpp", + "xmemory0": "cpp", + "xstddef": "cpp", + "xtr1common": "cpp", + "xtree": "cpp", + "xutility": "cpp", + "complex": "cpp", + "set": "cpp", + "bitset": "cpp", + "codecvt": "cpp", + "condition_variable": "cpp", + "filesystem": "cpp", + "mutex": "cpp", + "thread": "cpp", + "__nullptr": "cpp", + "bit": "cpp", + "compare": "cpp", + "concepts": "cpp", + "stack": "cpp", + "stop_token": "cpp" + } +} \ No newline at end of file diff --git a/TensorRT-Base/CMakeLists.txt b/TensorRT-Base/CMakeLists.txt new file mode 100644 index 0000000..9a61d64 --- /dev/null +++ b/TensorRT-Base/CMakeLists.txt @@ -0,0 +1,76 @@ +# @file - CMakeLists.txt +# @author - wdn (dongnian.wang@outlook.com) +# @brief - 工程编译文件 +# +# @copyright Copyright (c) 2021, wdn. All rights reserved. + +cmake_minimum_required(VERSION 3.0) + +project("tensorrt_base") + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_EXTENSIONS OFF) + +set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/bin) +set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/lib) + +if(WIN32) + set(CUDA_ROOT_DIR "D:/Software/CUDA11.0/development") + set(TRT_ROOT_DIR "D:/Software/CUDA11.0/development") + + # cuda + set(CUDA_INCLUDE_DIR ${CUDA_ROOT_DIR}/include) + set(CUDA_LIB_DIR ${CUDA_ROOT_DIR}/lib/x64) + file(GLOB CUDA_LIBRARIRS "${CUDA_ROOT_DIR}/lib/x64/*.lib") + + # tensorrt + set(TRT_INCLUDE_DIR ${TRT_ROOT_DIR}/include) + set(TRT_LIB_DIR ${TRT_ROOT_DIR}/lib/x64) + file(GLOB TRT_LIBRARIRS "${TRT_ROOT_DIR}/lib/x64/*.lib") + +else() + + + set(CUDA_ROOT_DIR "/usr/local/cuda") + set(TRT_ROOT_DIR "/opt/TensorRT-7.2.2.3") + + # cuda + set(CUDA_INCLUDE_DIR ${CUDA_ROOT_DIR}/include) + set(CUDA_LIB_DIR ${CUDA_ROOT_DIR}/lib64) + file(GLOB CUDA_LIBRARIRS "${CUDA_ROOT_DIR}/lib64/*.so") + + # tensorrt + set(TRT_INCLUDE_DIR ${TRT_ROOT_DIR}/include) + set(TRT_LIB_DIR ${TRT_ROOT_DIR}/lib) + file(GLOB TRT_LIBRARIRS "${TRT_ROOT_DIR}/lib/*.so") + +endif() + +if(WIN32) + # Socket limit is 16K(can be raised arbitrarily) + add_definitions(-DFD_SETSIZE=16384) + add_definitions(-D_CRT_SECURE_NO_WARNINGS) + add_definitions(-D_WINSOCK_DEPRECATED_NO_WARNINGS) +endif() + +# opencv +find_package(OpenCV REQUIRED) +message(STATUS "OpenCV library status:") +message(STATUS " version: ${OpenCV_VERSION}") +message(STATUS " libraries: ${OpenCV_LIBS}") +message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}") + +include_directories(${CUDA_INCLUDE_DIR} ${TRT_INCLUDE_DIR} ${OpenCV_INCLUDE_DIRS}) +include_directories(common plugin) +link_directories(${CUDA_LIB_DIR} ${TRT_LIB_DIR}) + +# 包含子项目。 +add_subdirectory ("common") +add_subdirectory ("plugin") +add_subdirectory ("mnist_caffe") +add_subdirectory ("mnist_onnx") +add_subdirectory ("faster_rcnn") + + + diff --git a/TensorRT-Base/README.md b/TensorRT-Base/README.md new file mode 100644 index 0000000..59b8c79 --- /dev/null +++ b/TensorRT-Base/README.md @@ -0,0 +1,113 @@ +# TensorRT Base + +## 1.Description +该项目封装TensorRT加速深度学习模型，支持caffe和onnx格式模型。 + +主要来自TensorRT C++ sample及部分Github参考代码。 + +# 2.Environment + +推荐使用Ubuntu运行 + +## 2.1 Ubuntu + +* TensorRT 7+ +* GUN工具（g++ 7.5.0） +* CMake工具（3.10+） + +## 2.2 Windows + +* TensorRT 7+ +* Visual Studio 2017 +* CMake工具（3.10+） + +# 3.Directory + +``` + ├── bin: 生成可执行文件目录 + ├── build: CMake工具构建目录 + ├── common: TensorRT基础类和一些共用函数，编译为库供调用 + ├── lib: 生成库目录 + ├── plugin: TensorRT plugin目录，用于存放自定义plugin + ├── CMakeLists.txt: 根目录CMake配置文件 + ├── mnist_caffe: TensorRT解析caffe模型demo + ├── mnist_onnx: TensorRT解析ONNX模型demo + └── faster_rcnn: TensoRT加速Faster RCNN算法 +``` + +# 4.Running + +修改根目录下CMakeLists.txt文件中`CUDA`和`TensorRT`目录，cudnn默认和CUDA安装在同一目录下。 + +```cmake +set(CUDA_ROOT_DIR "D:/Software/CUDA11.0/development") +set(TRT_ROOT_DIR "D:/Software/CUDA11.0/development") +``` + +运行以下命令 + +```shell +cd TensorRT-Base +mkdir build +cd build +cmake .. +make +``` + +编译完成后，在bin文件夹中会生成可执行文件，lib目录下会生成库文件，由于Windows环境下配置动态库较麻烦，默认生成静态库，可自己修改。 + +运行bin文件夹中可执行文件，即可加速深度学习模型，以faster rcnn demo为例 + +```shell +PS E:\TensorRT-Base> .\bin\Release\faster_rcnn.exe +[2021-04-25 20:48:32][ WARN ] : Dynamic size input setting invalid! +[2021-04-25 20:48:32][ DEBUG ] : deserialize engine from faster_rcnn/model/faster_rcnn.bin +[2021-04-25 20:48:38][ DEBUG ] : max batch size of deserialized engine: 1 +[2021-04-25 20:48:38][ DEBUG ] : create execute context and malloc device memory... +[2021-04-25 20:48:38][ DEBUG ] : init engine... +[2021-04-25 20:48:38][ DEBUG ] : malloc device memory +[2021-04-25 20:48:38][ DEBUG ] : nbBingdings: 5 +[2021-04-25 20:48:38][ DEBUG ] : input: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 0, name: data, size in byte: 2250000 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 3 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : input: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 1, name: im_info, size in byte: 12 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 3 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : output: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 2, name: rois, size in byte: 4800 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 3 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : output: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 3, name: bbox_pred, size in byte: 100800 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 4 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : output: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 4, name: cls_prob, size in byte: 25200 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 4 dimemsion +[2021-04-25 20:48:38][ WARN ] : output_bbox_pred: 25200 +[2021-04-25 20:48:38][ WARN ] : output_cls_prob: 6300 +[2021-04-25 20:48:38][ WARN ] : output_rois: 1200 +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 aeroplane +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bicycle +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bird +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 boat +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bottle +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bus +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 car +[2021-04-25 20:48:38][ INFO ] : indices size is: 1 cat +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 chair +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 cow +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 diningtable +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 dog +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 horse +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 motorbike +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 person +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 pottedplant +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 sheep +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 sofa +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 train +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 tvmonitor +``` + + + + + diff --git a/TensorRT-Base/bin/README.md b/TensorRT-Base/bin/README.md new file mode 100644 index 0000000..f0e67aa --- /dev/null +++ b/TensorRT-Base/bin/README.md @@ -0,0 +1 @@ +## 生成可执行文件目录 \ No newline at end of file diff --git a/TensorRT-Base/common/CMakeLists.txt b/TensorRT-Base/common/CMakeLists.txt new file mode 100644 index 0000000..54c3850 --- /dev/null +++ b/TensorRT-Base/common/CMakeLists.txt @@ -0,0 +1,24 @@ +# @file - common/CMakeLists.txt +# @author - wdn (dongnian.wang@outlook.com) +# @brief - common编译文件 +# +# Copyright (c) 2021, wdn. All rights reserved. + +cmake_minimum_required (VERSION 3.0) + +project ("common") + +# aux_source_directory(. COMMON_SRC) +file(GLOB COMMON_SOURCE *.cpp ) + +# file(GLOB_RECURSE trt_source "helper.h" "helper.cpp" "logging.h" "timer.h") + +add_library(common STATIC ${COMMON_SOURCE}) + +# 链接库 +target_link_libraries( + common PRIVATE + trt_plugin + ${CUDA_LIBRARIRS} + ${TRT_LIBRARIRS} + ) diff --git a/TensorRT-Base/common/README.md b/TensorRT-Base/common/README.md new file mode 100644 index 0000000..b86f2f2 --- /dev/null +++ b/TensorRT-Base/common/README.md @@ -0,0 +1,122 @@ +# Common + +## 1.Description + +TensorRT Base common库，包括TensorRT解析ONNX、Caffe模型封装，日志模块，CPU/GPU计时，INT8量化和返回值处理。 + +## 2.file + +``` + ├── base_trt.cpp: TensorRT 推理类实现 + ├── base_trt.h: TensorRT 推理类定义 + ├── CMakeLists.txt: 生成trt动态库CMake配置文件 + ├── helper.cpp: 公共函数实现 + ├── helper.h: 一些公共函数定义 + ├── int8_calibrator.cpp: TensorRT INT8量化类 + ├── int8_calibrator.h: TensorRT INT8量化类 + ├── logger.h: 日志类 + ├── timer.h: CPU/GPU计时类 + └── trt_return_type.h: TensorRT Base返回值 +``` + +## 3.Logger Usage + +在helper公共函数文件中，已定义logger的输出，分别有5个等级，从低到高依次为：`debug`, `info`, `warn`, `err`, `fatal`。 + +```C++ +#include "helper.h" + +// debug信息输出，一般用于调试 +helper::debug << "hello world！" << msg << std::endl; + +// info信息输出，一般用于输出某些提示信息，供用户参考 +helper::info << "hello world！" << msg << std::endl; + +// warn信息输出，警告信息，提示错误，但此时程序可以运行 +helper::warn << "hello world！" << msg << std::endl; + +// err信息输出，错误信息，提示这样操作违规，不可操作，程序不能运行 +helper::err << "hello world！" << msg << std::endl; + +// fatal信息输出，内部错误信息 +helper::debug << "hello world！" << msg << std::endl; +``` + +## 4.Timer Usage + +CPU/GPU计时类，封装在helper.h文件中，可直接调用。 + +CPU计时demo + +```C++ +#include "helper.h" + +helper::PreciseCpuTime cpu_time; +cpu_time.start(); +Sleep(1000); +cpu_time.stop(); +helper::info << "time: " << cpu_time.milliseconds() << "ms" << std::endl; // 1000 +Sleep(1000); +cpu_time.start(); +Sleep(1000); +cpu_time.stop(); +helper::info << "time: " << cpu_time.milliseconds() << "ms" << std::endl; // 1000 +cpu_time.continuation(); +Sleep(1000); +cpu_time.stop(); +helper::info << "time: " << cpu_time.milliseconds() << "ms" << std::endl; // 2000 + +delete gpu_time +``` + +GPU计时demo + +```C++ +#include "helper.h" + +cudaStream_t stream; +helper::PreciseGpuTime gpu_time = helper::PreciseGpuTime(stream); +gpu_time.start(); +Sleep(1000); +gpu_time.stop(); +helper::info << "gpu time: " << gpu_time.milliseconds() << "ms" << std::endl; +gpu_time.start(); +Sleep(1000); +gpu_time.stop(); +helper::info << "gpu time: " << gpu_time.milliseconds() << "ms" << std::endl; +gpu_time.continuation(); +Sleep(1000); +gpu_time.stop(); +helper::info << "gpu time: " << gpu_time.milliseconds() << "ms" << std::endl; + +delete gpu_time +``` + +## 5.TrtBase Usage + +支持解析ONNX模型和Caffe模型，可继承修改继续封装或直接实例化使用； + +支持INT8量化。 + +直接实例化`TrtBase`： + +```C++ +const std::string onnx_model = "mnist_onnx/model/mnist.onnx"; +const std::string engine_file = ""; +const std::vector output_name{"Plus214_Output_0"}; +int batch_size = 1; +infer_precision_t precision = INFER_FP32; +TrtBase trt_base = new TrtBase(); +trt_base->create_engine(onnx_model, engine_file, output_name, batch_size, precision); +int input_data_size = 300; +std::vector input_data(input_data_size, 0.5); +std::vector input_dims{1,10,30}; +trt_base->set_binding_dimentsions(input_dims, 0); +trt_base->mem_host_to_device(input_data, 0); +trt_base->inference(); +std::vector output_data; +trt_base->mem_device_to_host(output_data, 1); +// process output data +``` + +继承`TrtBase`，进一步封装，见 `mnist_onnx` sample。 \ No newline at end of file diff --git a/TensorRT-Base/common/base_trt.cpp b/TensorRT-Base/common/base_trt.cpp new file mode 100644 index 0000000..afdf39b --- /dev/null +++ b/TensorRT-Base/common/base_trt.cpp @@ -0,0 +1,710 @@ +/** + * @file - base_trt.cpp + * @author - wdn + * @brief - tensorrt 推理基类 + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "base_trt.h" +#include "helper.h" + +/** + * 默认构造函数 +*/ +TrtBase::TrtBase() +{ + m_builder = nvinfer1::createInferBuilder(m_logger); + m_config = m_builder->createBuilderConfig(); +} + +/** + * 析构函数 +*/ +TrtBase::~TrtBase() +{ + if(m_context != nullptr) + { + m_context->destroy(); + m_context = nullptr; + } + + if(m_engine != nullptr) + { + m_engine->destroy(); + m_engine = nullptr; + } + + if(m_config != nullptr) + { + m_config->destroy(); + m_config = nullptr; + } + + for(size_t i = 0; i < m_binding.size(); i++) + { + //cudaFree(m_binding[i]); + helper::safe_cuda_free(m_binding[i]); + } +} + +/** + * 从caffe模型创建engine +*/ +trt_ret_t TrtBase::create_engine(const std::string& prototxt, const std::string& caffe_model, + const std::string& engine_file,const std::vector& output_name, int batch_size, infer_precision_t precision) +{ + trt_ret_t ret = TRT_SUCCESS; + m_batch_size = batch_size; + m_precision = precision; + + ret = deserialize_engine(engine_file); + if(ret != TRT_SUCCESS) + { + ret = build_caffe_engine(prototxt, caffe_model, engine_file, output_name); + if(ret != TRT_SUCCESS) + { + helper::err << "Could not build engine!" << std::endl; + return ret; + } + } + + helper::debug << "create execute context and malloc device memory..." << std::endl; + ret = init_engine(); + + return ret; +} + +/** + * 从onnx模型创建engine +*/ +trt_ret_t TrtBase::create_engine(const std::string& onnx_model, const std::string& engine_file, + const std::vector& output_name, int batch_size, infer_precision_t precision) +{ + trt_ret_t ret = TRT_SUCCESS; + m_batch_size = batch_size; + m_precision = precision; + + ret = deserialize_engine(engine_file); + if(ret != TRT_SUCCESS) + { + ret = build_onnx_engine(onnx_model, engine_file, output_name); + if(ret != TRT_SUCCESS) + { + helper::err << "Could not build engine!" << std::endl; + return ret; + } + } + + helper::debug << "create execute context and malloc device memory..." << std::endl; + ret = init_engine(); + + return ret; +} + +/** + * 反序列化文件创建engine +*/ +trt_ret_t TrtBase::create_engine_from_deserialize(const std::string& engine_file) +{ + trt_ret_t ret = TRT_SUCCESS; + ret = deserialize_engine(engine_file); + if(ret != TRT_SUCCESS) + { + helper::err << "Cound NOT deserialize engine!!!" << std::endl; + return ret; + } + + helper::info << "deserialize engine deon!" << std::endl; + helper::debug << "create execute context and malloc device memory..." << std::endl; + ret = init_engine(); + return ret; +} + +/** + * 同步推理 +*/ +trt_ret_t TrtBase::inference() +{ + if(m_context == nullptr) + { + helper::err << "context is empty, infer error! " << std::endl; + return TRT_ERR_CONTEXT; + } + + if(m_flags == 1U << static_cast(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)) + { + m_context->executeV2(&m_binding[0]); + } + else + { + m_context->execute(m_batch_size, &m_binding[0]); + } + + return TRT_SUCCESS; +} + +/** + * 异步推理 +*/ +trt_ret_t TrtBase::inference_async(const cudaStream_t& stream) +{ + if(m_context == nullptr) + { + helper::err << "context is empty, infer error! " << std::endl; + return TRT_ERR_CONTEXT; + } + + if(m_flags == 1U << static_cast(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)) + { + m_context->enqueueV2(&m_binding[0], stream, nullptr); + } + else + { + m_context->enqueue(m_batch_size, &m_binding[0], stream, nullptr); + } + + return TRT_SUCCESS; +} + +/** + * host->device +*/ +trt_ret_t TrtBase::mem_host_to_device(const std::vector& input_data, + int bind_index) +{ + if(input_data.size()*sizeof(float) > m_binding_size[bind_index]) + { + helper::err << "input data out the allocated memory" << std::endl; + return TRT_ERR_MEM_CPY; + } + + cudaError_t cuda_ret = cudaMemcpy(m_binding[bind_index], input_data.data(), + m_binding_size[bind_index], cudaMemcpyHostToDevice); + if(cuda_ret != cudaSuccess) + { + helper::err << "host->device copy mem failed!" << std::endl; + return TRT_ERR_MEM_CPY; + } + + return TRT_SUCCESS; +} + +/** + * host->device +*/ +trt_ret_t TrtBase::mem_host_to_device(const std::vector& input_data, + int bind_index, const cudaStream_t& stream) +{ + if(input_data.size()*sizeof(float) > m_binding_size[bind_index]) + { + helper::err << "input data out the allocated memory" << std::endl; + return TRT_ERR_MEM_CPY; + } + + cudaError_t cuda_ret = cudaMemcpyAsync(m_binding[bind_index], input_data.data(), + m_binding_size[bind_index], cudaMemcpyHostToDevice, stream); + if(cuda_ret != cudaSuccess) + { + helper::err << "host->device copy mem failed!" << std::endl; + return TRT_ERR_MEM_CPY; + } + + return TRT_SUCCESS; +} + +/** + * device->host +*/ +trt_ret_t TrtBase::mem_device_to_host(std::vector& output_data, + int bind_index) +{ + output_data.resize(m_binding_size[bind_index]/sizeof(float)); + + cudaError_t cuda_ret = cudaMemcpy(output_data.data(), m_binding[bind_index], + m_binding_size[bind_index], cudaMemcpyDeviceToHost); + if(cuda_ret != cudaSuccess) + { + helper::err << "device->host copy mem failed!" << std::endl; + return TRT_ERR_MEM_CPY; + } + + return TRT_SUCCESS; +} + +/** + * device->host +*/ +trt_ret_t TrtBase::mem_device_to_host(std::vector& output_data, + int bind_index, const cudaStream_t& stream) +{ + output_data.resize(m_binding_size[bind_index]/sizeof(float)); + + cudaError_t cuda_ret = cudaMemcpyAsync(output_data.data(), m_binding[bind_index], + m_binding_size[bind_index], cudaMemcpyDeviceToHost, stream); + if(cuda_ret != cudaSuccess) + { + helper::err << "device->host copy mem failed!" << std::endl; + return TRT_ERR_MEM_CPY; + } + + return TRT_SUCCESS; +} + +/** + * 设置device +*/ +void TrtBase::set_device(int device) +{ + helper::warn << "warning: make sure save engine file match choosed device" << std::endl; + CUDA_CHECK(cudaSetDevice(device)); +} + +/** + * 获取device +*/ +int TrtBase::get_device() const +{ + int device = -1; + CUDA_CHECK(cudaGetDevice(&device)); + if(device != -1) + { + return device; + } + else + { + helper::err << "Get Device Error" << std::endl; + return -1; + } +} + +trt_ret_t TrtBase::set_int8_calibrator(const std::string& calibrator_type, + const std::vector> calibrator_data) +{ + m_precision = INFER_INT8; + nvinfer1::IInt8Calibrator* calibrator = helper::get_int8_calibrator( + calibrator_type, m_batch_size, calibrator_data, "calibrator", false); + helper::info << "Set INT8 inference mode" << std::endl; + if(!m_builder->platformHasFastInt8()) + { + helper::warn << "Current platform doesn't support INT8 inference!" << std::endl; + } + // m_builder->setInt8Mode(true); + m_config->setFlag(nvinfer1::BuilderFlag::kINT8); + m_config->setInt8Calibrator(calibrator); + + return TRT_SUCCESS; +} + +/** + * 动态调整输入大小 +*/ +trt_ret_t TrtBase::add_dynamic_shape_profile(int batch_size, const std::string& input_name, + const std::vector& min_dims_vec, const std::vector& opt_dims_vec, const std::vector& max_dims_vec) +{ + const nvinfer1::Dims4& min_dims{batch_size, min_dims_vec[0], min_dims_vec[1], min_dims_vec[2]}; + const nvinfer1::Dims4& opt_dims{batch_size, opt_dims_vec[0], opt_dims_vec[1], opt_dims_vec[2]}; + const nvinfer1::Dims4& max_dims{batch_size, max_dims_vec[0], max_dims_vec[1], max_dims_vec[2]}; + nvinfer1::IOptimizationProfile* profile = m_builder->createOptimizationProfile(); + profile->setDimensions(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN, min_dims); + profile->setDimensions(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT, opt_dims); + profile->setDimensions(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX, max_dims); + if(!profile->isValid()) + { + helper::warn << "Dynamic size input setting invalid!" << std::endl; + return TRT_ERR_DYNAMIC_INPUT; + } + m_config->addOptimizationProfile(profile); + + return TRT_SUCCESS; +} + + +/** + * 设置binding dimension +*/ +void TrtBase::set_binding_dimentsions(std::vector& input_dims, int bind_index) +{ + const nvinfer1::Dims3& dims{input_dims[0],input_dims[1],input_dims[2]}; + m_context->setBindingDimensions(bind_index, dims); +} + +/** + * 获取最大batch size +*/ +int TrtBase::get_max_batch_size() const +{ + return m_batch_size; +} + +/** + * 获取设备中的绑定数据指针。 +*/ +void* TrtBase::get_binding_ptr(int bind_index) const +{ + return m_binding[bind_index]; +} + +/** + * 获取绑定数据的大小 +*/ +size_t TrtBase::get_binding_size(int bind_index) const +{ + return m_binding_size[bind_index]; +} + +/** + * 获取绑定数据维度 +*/ +nvinfer1::Dims TrtBase::get_binding_dims(int bind_index) const +{ + return m_binding_dims[bind_index]; +} + +/** + * 获取绑定数据类型 +*/ +nvinfer1::DataType TrtBase::get_binding_data_type(int bind_index) const +{ + return m_binding_data_type[bind_index]; +} + +/** + * 构建engine +*/ +trt_ret_t TrtBase::build_engine() +{ + if(m_precision == 1) + { + helper::debug << "setFp16Mode" << std::endl; + if(!m_builder->platformHasFastFp16()) + { + helper::warn << "the platform do not has fast for fp16" << std::endl; + } + m_config->setFlag(nvinfer1::BuilderFlag::kFP16); + } + m_builder->setMaxBatchSize(m_batch_size); + helper::debug << m_batch_size << std::endl; + m_config->setMaxWorkspaceSize(16 * (1 << 20)); + + helper::debug << "FP16 support: " << m_builder->platformHasFastFp16() << std::endl; + helper::debug << "INT8 support: " << m_builder->platformHasFastInt8() << std::endl; + helper::debug << "Max batchsize: " << m_builder->getMaxBatchSize() << std::endl; + helper::debug << "Max workspace size: " << m_config->getMaxWorkspaceSize() << std::endl; + helper::debug << "Number of DLA core: " << m_builder->getNbDLACores() << std::endl; + helper::debug << "Max DLA batchsize: " << m_builder->getMaxDLABatchSize() << std::endl; + helper::debug << "Current use DLA core: " << m_config->getDLACore() << std::endl; + helper::debug << "build engine ... " << std::endl; + m_engine = m_builder->buildEngineWithConfig(*m_network, *m_config); + if(m_engine == nullptr) + { + helper::err << "build engine fail!!" << std::endl; + return TRT_ERR_ENGINE; + } + + return TRT_SUCCESS; +} + +/** + * 构建caffe engine +*/ +trt_ret_t TrtBase::build_caffe_engine(const std::string& prototxt, const std::string& caffe_model, + const std::string& engine_file, const std::vector& output_name) +{ + helper::debug << "build caffe engine with " << prototxt << " and " << caffe_model << std::endl; + if(m_builder == nullptr) + { + helper::err << "builder is empty!" << std::endl; + return TRT_ERR_BUILDER; + } + + m_network = m_builder->createNetworkV2(m_flags); + if(m_network == nullptr) + { + helper::err << "network is empty!" << std::endl; + return TRT_ERR_NETWOEK; + } + + nvcaffeparser1::ICaffeParser* parser = nvcaffeparser1::createCaffeParser(); + if(parser == nullptr) + { + helper::err << "parser is empty!" << std::endl; + return TRT_ERR_PAESER; + } + nvinfer1::DataType type = m_precision == 1 ? nvinfer1::DataType::kHALF : nvinfer1::DataType::kFLOAT; + const nvcaffeparser1::IBlobNameToTensor* blob_name_to_tensor = parser->parse( + prototxt.c_str(), caffe_model.c_str(), *m_network, nvinfer1::DataType::kFLOAT); + for(auto& s : output_name) + { + m_network->markOutput(*blob_name_to_tensor->find(s.c_str())); + } + + helper::debug << "Number of Network layers: " << m_network->getNbLayers() << std::endl; + helper::debug << "Number of input: " << m_network->getNbInputs() << std::endl; + helper::debug << "Input layer: " << std::endl; + for(int i = 0; i < m_network->getNbInputs(); i++) + { + helper::debug << m_network->getInput(i)->getName() << " : "; + nvinfer1::Dims dims = m_network->getInput(i)->getDimensions(); + for(int j = 0; j < dims.nbDims; j++) + { + helper::debug << dims.d[j] << "x"; + } + helper::debug << "\b" << std::endl; + } + helper::debug << "Number of output: " << m_network->getNbOutputs() << std::endl; + helper::debug << "Output layer: " << std::endl; + for(int i = 0; i < m_network->getNbOutputs(); i++) + { + helper::debug << m_network->getOutput(i)->getName() << " : "; + nvinfer1::Dims dims = m_network->getOutput(i)->getDimensions(); + for(int j = 0; j < dims.nbDims; j++) + { + helper::debug << dims.d[j] << "x"; + } + helper::debug << "\b" << std::endl; + } + + build_engine(); + + helper::debug << "build Network done" << std::endl; + + helper::debug << "serialize engine to " << engine_file << std::endl; + serialize_engine(engine_file); + + m_builder->destroy(); + m_network->destroy(); + parser->destroy(); + + return TRT_SUCCESS; +} + +/** + * 构建onnx engine +*/ +trt_ret_t TrtBase::build_onnx_engine(const std::string& onnx_model, const std::string& engine_file, + const std::vector& output_name) +{ + helper::debug << "build onnx engine from " << onnx_model << " ..."<< std::endl; + if(m_builder == nullptr) + { + helper::err << "builder is empty!" << std::endl; + return TRT_ERR_BUILDER; + } + m_flags = 1U << static_cast(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH); + m_network = m_builder->createNetworkV2(m_flags); + // NMSPlugin nms_plugin("NonMaxSuppressionV3"); + //nvinfer1::ITensor *tensor; + // tensor = m_network->addInput("input0", nvinfer1::DataType::kFLOAT, nvinfer1::Dims4{1, 3, 111, 2222}); + //nvinfer1::ITensor *aInputTensor[] = {tensor}; + //m_network->addPluginV2(aInputTensor, 1, nms_plugin); + if(m_network == nullptr) + { + helper::err << "network is empty!" << std::endl; + return TRT_ERR_NETWOEK; + } + nvonnxparser::IParser* parser = nvonnxparser::createParser(*m_network, m_logger); + + if(parser == nullptr) + { + helper::err << "parser is empty!" << std::endl; + return TRT_ERR_PAESER; + } + if(!parser->parseFromFile(onnx_model.c_str(), static_cast(m_logger.get_reportable_severity()))) + { + helper::err << "could not parse onnx engine" << std::endl; + return TRT_ERR_PAESER; + } + + if(output_name.size() > 0) + { + helper::debug << "unmark original output..." << std::endl; + for(int i = 0; i < m_network->getNbOutputs(); i++) + { + m_network->unmarkOutput(*(m_network->getOutput(i))); + } + helper::debug << "mark custom output..." << std::endl; + for(int i = 0; i < m_network->getNbLayers(); i++) + { + nvinfer1::ITensor* output_tensor = m_network->getLayer(i)->getOutput(0); + for(size_t j = 0; j < output_name.size(); j++) + { + std::string layer_name(output_tensor->getName()); + if(layer_name == output_name[j]) + { + // helper::debug << "111111111" << std::endl; + m_network->markOutput(*output_tensor); + break; + } + } + } + } + + build_engine(); + + helper::debug << "build Network done" << std::endl; + + helper::debug << "serialize engine to " << engine_file << std::endl; + serialize_engine(engine_file); + + m_builder->destroy(); + m_network->destroy(); + parser->destroy(); + + return TRT_SUCCESS; +} + +/** + * 初始化engine + * + * @return @c trt_ret_t +*/ +trt_ret_t TrtBase::init_engine() +{ + helper::debug << "init engine..." << std::endl; + m_context = m_engine->createExecutionContext(); + if(m_context == nullptr) + return TRT_ERR_CONTEXT; + + helper::debug << "malloc device memory" << std::endl; + int num_bindings = m_engine->getNbBindings(); + helper::debug << "nbBingdings: " << num_bindings << std::endl; + m_binding.resize(num_bindings); + m_binding_size.resize(num_bindings); + m_binding_name.resize(num_bindings); + m_binding_dims.resize(num_bindings); + m_binding_data_type.resize(num_bindings); + + for(int i = 0; i < num_bindings; i++) + { + nvinfer1::Dims dims = m_engine->getBindingDimensions(i); + nvinfer1::DataType data_type = m_engine->getBindingDataType(i); + const char* name= m_engine->getBindingName(i); + + // size_t vol = m_batch_size; + // int vec_dims = m_engine->getBindingVectorizedDim(i); + // if(-1 != vec_dims) + // { + // int scalar_per_vec = m_engine->getBindingComponentsPerElement(i); + // dims.d[vec_dims] = helper::divUp(dims.d[vec_dims], scalar_per_vec); + // vol *= scalar_per_vec; + // } + // vol *= helper::volume(dims); + // helper::debug << "vol: " << vol * helper::get_element_size(data_type) << std::endl; + + int64_t total_size = helper::volume(dims) * m_batch_size * helper::get_element_size(data_type); + m_binding_size[i] = total_size; + m_binding_name[i] = name; + m_binding_dims[i] = dims; + m_binding_data_type[i] = data_type; + + if(m_engine->bindingIsInput(i)) + { + helper::debug << "input: " << std::endl; + } + else + { + helper::debug << "output: "<< std::endl; + } + helper::debug << "binding bindIndex: " << i << ", name: " + << name << ", size in byte: " << total_size<< std::endl; + helper::debug << "binding dims with " << dims.nbDims << " dimemsion" << std::endl; + + // for(int j = 0; j < dims.nbDims; j++) + // { + + // } + + m_binding[i] = helper::safe_cuda_malloc(total_size); + if(m_engine->bindingIsInput(i)) + m_input_size++; + } + + return TRT_SUCCESS; +} + +/** + * 序列化engine +*/ +trt_ret_t TrtBase::serialize_engine(const std::string& file_name) +{ + if(file_name == "") + { + helper::warn << "empty engine file name, skip save!" << std::endl; + return TRT_ERR_FILE_NAME; + } + + if(m_engine == nullptr) + { + helper::err << "engine is empty, save engine failed!" << std::endl; + return TRT_ERR_SERIALIZE; + } + + helper::debug << "save engine to " << file_name << " ..." << std::endl; + nvinfer1::IHostMemory* data = m_engine->serialize(); + std::ofstream file; + file.open(file_name, std::ios::binary | std::ios::out); + if(!file.is_open()) + { + helper::err << "read create engine file " << file_name << " failed! " << std::endl; + return TRT_ERR_OPEN_FILE; + } + file.write((const char*)data->data(), data->size()); + file.close(); + data->destroy(); + return TRT_SUCCESS; +} + +/** + * 反序列化engine +*/ +trt_ret_t TrtBase::deserialize_engine(const std::string& engine_file) +{ + if(engine_file == "") + { + helper::warn << "empty engine file name, skip save!" << std::endl; + return TRT_ERR_FILE_NAME; + } + + std::ifstream file(engine_file.c_str(), std::ifstream::binary); + if(!file.is_open()) + { + helper::err << "read engine file " << engine_file << " failed! " << std::endl; + return TRT_ERR_OPEN_FILE; + } + + helper::debug << "deserialize engine from " << engine_file << std::endl; + auto const start_pos = file.tellg(); + file.ignore(std::numeric_limits::max()); + size_t buffer_count = file.gcount(); + file.seekg(start_pos); + std::unique_ptr engine_buffer(new char[buffer_count]); + file.read(engine_buffer.get(), buffer_count); + // initLibNvInferPlugins(&m_logger, ""); + m_runtime = nvinfer1::createInferRuntime(m_logger); + m_engine = m_runtime->deserializeCudaEngine((void*)engine_buffer.get(), buffer_count, nullptr); + if(m_engine == nullptr) + { + m_runtime->destroy(); + return TRT_ERR_DESERIALIZE; + } + m_batch_size = m_engine->getMaxBatchSize(); + helper::debug << "max batch size of deserialized engine: " << m_engine->getMaxBatchSize() << std::endl; + + m_runtime->destroy(); + return TRT_SUCCESS; +} \ No newline at end of file diff --git a/TensorRT-Base/common/base_trt.h b/TensorRT-Base/common/base_trt.h new file mode 100644 index 0000000..5f77133 --- /dev/null +++ b/TensorRT-Base/common/base_trt.h @@ -0,0 +1,351 @@ +/** + * @file - base_trt.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - tensorrt 推理基类 + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#ifndef TRT_BASE_TRT_H +#define TRT_BASE_TRT_H + +#include +#include +#include +#include +#include + +#include + +#include "helper.h" +#include "trt_return_type.h" +#include "int8_calibrator.h" + +/** + * 运行精度 +*/ +typedef enum +{ + INFER_FP32 = 0, + INFER_FP16 = 1, + INFER_INT8 = 2, +}infer_precision_t; + +// /** +// * +// */ +// typedef struct +// { +// std::string name; +// int64_t size; +// void* value; +// }data_t; + + +class TrtBase +{ +public: + /** + * 默认构造函数 + */ + TrtBase(); + + /** + * 析构函数 + */ + virtual ~TrtBase(); + + /** + * 从caffe模型创建engine + * + * @param prototxt - caffe prototxt + * @param caffe_model - caffe模型 + * @param engine_file - file + * @param output_name - 模型输出 + * @param batch_size - max batch size + * @param precision - infer精度 + * + * @return @c trt_ret_t + */ + trt_ret_t create_engine( + const std::string& prototxt, + const std::string& caffe_model, + const std::string& engine_file, + const std::vector& output_name, + int batch_size, + infer_precision_t precision); + + /** + * 从onnx模型创建engine + * + * @param onnx_model - onnx模型 + * @param engine_file - file + * @param output_name - 模型输出 + * @param batch_size - max batch size + * @param precision - infer精度 + * + * @return @c trt_ret_t + */ + trt_ret_t create_engine( + const std::string& onnx_model, + const std::string& engine_file, + const std::vector& output_name, + int batch_size, + infer_precision_t precision); + + /** + * 反序列化文件创建engine + * + * @param engine_file - file + * + * @return @c trt_ret_t + */ + trt_ret_t create_engine_from_deserialize(const std::string& engine_file); + + /** + * 同步推理 + * + * @return @c trt_ret_t + */ + trt_ret_t inference(); + + /** + * 异步推理 + * + * @param stream - cuda流 + * + * @return @c trt_ret_t + */ + trt_ret_t inference_async(const cudaStream_t& stream); + + /** + * host->device + * + * @param input_data - 输入数据（host） + * @param bind_index - + * + * @return @c trt_ret_t + */ + trt_ret_t mem_host_to_device(const std::vector& input_data, int bind_index); + + /** + * host->device + * + * @param input_data - 输入数据（host） + * @param bind_index - + * @param stream - CUDA stream + * + * @return @c trt_ret_t + */ + trt_ret_t mem_host_to_device(const std::vector& input_data, int bind_index, const cudaStream_t& stream); + + /** + * device->host + * + * @param output_data - 输出数据（host） + * @param bind_index - + * + * @return @c trt_ret_t + */ + trt_ret_t mem_device_to_host(std::vector& output_data, int bind_index); + + /** + * device->host + * + * @param output_data - 输出数据（host） + * @param bind_index - + * @param stream - CUDA stream + * + * @return @c trt_ret_t + */ + trt_ret_t mem_device_to_host(std::vector& output_data, int bind_index, const cudaStream_t& stream); + + /** + * 设置device + */ + void set_device(int device); + + /** + * 获取device + */ + int get_device() const; + + + trt_ret_t set_int8_calibrator(const std::string& calibrator_type, + const std::vector> calibrator_data); + + /** + * 动态调整输入大小 + * + * @param batch_size - batch_size + * @param input_name - input_name + * @param min_dims_vec - 输入shape最小 + * @param opt_dims_vec - 最优 shape + * @param max_dims_vec - 输入 shape 最大 + * + * @return @c trt_ret_t + * + * @note example + * + * std::vector min_dims_vec{3, 100, 100}; + * std::vector opt_dims_vec{3, 224, 224}; + * std::vector max_dims_vec{3, 300, 300}; + */ + trt_ret_t add_dynamic_shape_profile(int batch_size, const std::string& input_name, + const std::vector& min_dims_vec, const std::vector& opt_dims_vec, const std::vector& max_dims_vec); + + + /** + * 设置binding dimension + * + * @param input_dims - + * @param bind_index - + * + * @return @c void + */ + void set_binding_dimentsions(std::vector& input_dims, int bind_index); + + /** + * 获取最大batch size + */ + int get_max_batch_size() const; + + /** + * 获取设备中的绑定数据指针。 + * + * @param bind_index - + * + * @return void* - device指针 + */ + void* get_binding_ptr(int bind_index) const; + + /** + * 获取绑定数据的大小 + * + * @param bind_index - + * + * @return @c size_t + */ + size_t get_binding_size(int bind_index) const; + + /** + * 获取绑定数据维度 + * + * @param bind_index - + * + * @return @c nvinfer1::Dims + */ + nvinfer1::Dims get_binding_dims(int bind_index) const; + + /** + * 获取绑定数据类型 + * + * @param bind_index - + * + * @param @c nvinfer1::DataType + */ + nvinfer1::DataType get_binding_data_type(int bind_index) const; + + std::vector m_binding_name; + +protected: + /** + * 构建engine + * + * @return @c trt_ret_t + */ + trt_ret_t build_engine(); + + /** + * 构建caffe engine + * + * @param prototxt - caffe prototxt + * @param caffe_model - caffe 模型 + * @param engine_file - engine 文件名 + * @param output_name - 模型输出名称 + * + * @return @c trt_ret_t + */ + trt_ret_t build_caffe_engine(const std::string& prototxt, const std::string& caffe_model, + const std::string& engine_file, const std::vector& output_name); + + /** + * 构建onnx engine + * + * @param onnx_model - onnx 模型 + * @param engine_file - engine 文件名 + * @param output_name - 模型输出名称 + * + * @return @c trt_ret_t + */ + trt_ret_t build_onnx_engine(const std::string& onnx_model, const std::string& engine_file, + const std::vector& output_name); + + /** + * 初始化engine + * + * @return @c trt_ret_t + */ + trt_ret_t init_engine(); + + /** + * 序列化engine + * + * @param file_name - engine 保存文件名 + * + * @return @c trt_ret_t + */ + trt_ret_t serialize_engine(const std::string& file_name); + + /** + * 反序列化engine + * + * @param engine_file - engine 文件名 + * + * @return @c trt_ret_t + */ + trt_ret_t deserialize_engine(const std::string& engine_file); + +protected: + /** + * 日志 + */ + Logger m_logger; + + /** + * 运行模式 + */ + infer_precision_t m_precision; + + /** + * batch size + */ + int m_batch_size; + + nvinfer1::NetworkDefinitionCreationFlags m_flags = 0; + + nvinfer1::IBuilder* m_builder = nullptr; + + nvinfer1::INetworkDefinition* m_network = nullptr; + + nvinfer1::IBuilderConfig* m_config = nullptr; + + nvinfer1::ICudaEngine* m_engine = nullptr; + + nvinfer1::IExecutionContext* m_context = nullptr; + + nvinfer1::IRuntime* m_runtime = nullptr; + + std::vector m_binding; + + std::vector m_binding_size; + + std::vector m_binding_dims; + + std::vector m_binding_data_type; + + int m_input_size = 0; + +}; // TrtBase + +#endif //TRT_BASE_TRT_H \ No newline at end of file diff --git a/TensorRT-Base/common/helper.cpp b/TensorRT-Base/common/helper.cpp new file mode 100644 index 0000000..3eb0a72 --- /dev/null +++ b/TensorRT-Base/common/helper.cpp @@ -0,0 +1,128 @@ +/** + * @file - helper.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - 一些公共函数实现 + * + * Copyright (c) 2021, wdn. All rights reserved. + * + */ + +#include "helper.h" + +#include +#include +#include +#include +#include + +Logger helper::log_base{Logger::Severity::kVERBOSE}; +LogStreamConsumer helper::debug{LOG_DEBUG(helper::log_base)}; +LogStreamConsumer helper::info{LOG_INFO(helper::log_base)}; +LogStreamConsumer helper::warn{LOG_WARN(helper::log_base)}; +LogStreamConsumer helper::err{LOG_ERROR(helper::log_base)}; +LogStreamConsumer helper::fatal{LOG_FATAL(helper::log_base)}; + +void helper::set_reportable_severity(Logger::Severity severity) +{ + log_base.set_reportable_severity(severity); + info.set_reportable_severity(severity); + debug.set_reportable_severity(severity); + warn.set_reportable_severity(severity); + err.set_reportable_severity(severity); + fatal.set_reportable_severity(severity); +} + +const std::string helper::get_error_string(trt_ret_t status) +{ + switch (status) + { + case TRT_SUCCESS: + return "Error flag: " + std::to_string(status) + ", Error string: Running Succeed!"; + // case VINO_SEG_INPUT_NUM_ERROR: + // return "Error flag: " + std::to_string(status) + ", Error string: Model Supports topologies only with 1 input!!!"; + // case VINO_SEG_INPUT_DIM_ERROR: + // return "Error flag: " + std::to_string(status) + ", Error string: 3-channel 4-dimensional model's input is expected!!!"; + // case VINO_SEG_OUTPUT_NUM_ERROR: + // return "Error flag: " + std::to_string(status) + ", Error string: Model supports topologies only with 1 output!!!"; + // case VINO_SEG_OUTPUT_DIM_ERROR: + // return "Error flag: " + std::to_string(status) + ", Error string: Unexpected output blob shape. Only 4D and 3D output blobs are supported.!!!"; + // case VINO_SPLIT_STRING_ERROR: + // return "Error flag: " + std::to_string(status) + ", Error string: Split String Error!!!"; + // case VINO_PARSE_YOLO_ERROR: + // return "Error flag: " + std::to_string(status) + ", Error string: Parse YOLO outputs Error!!!"; + }; + + return "Unkonw status code " + std::to_string(status); + +} + +int64_t helper::volume(const nvinfer1::Dims& d) +{ + return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies()); +} + +unsigned int helper::get_element_size(nvinfer1::DataType t) +{ + switch (t) + { + case nvinfer1::DataType::kINT32: return 4; + case nvinfer1::DataType::kFLOAT: return 4; + case nvinfer1::DataType::kHALF: return 2; + case nvinfer1::DataType::kBOOL: + case nvinfer1::DataType::kINT8: return 1; + default: throw std::runtime_error("Invalid DataType."); + } +} + +/** + * 安全申请显存 +*/ +void* helper::safe_cuda_malloc(size_t mem_size) +{ + void* device_mem; + // std::cout << "aafffffff: " << mem_size << std::endl; + CUDA_CHECK(cudaMalloc(&device_mem, mem_size)); + if(device_mem == nullptr) + { + helper::err << "Out of memory" << std::endl; + exit(1); + } + + return device_mem; +} + +/** + * 安全释放显存 +*/ +void helper::safe_cuda_free(void* device_mem) +{ + CUDA_CHECK(cudaFree(device_mem)); +} + +void helper::enable_DLA(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config, int useDLACore, bool allow_GPU_fallback) +{ + if (useDLACore >= 0) + { + if (builder->getNbDLACores() == 0) + { + std::cerr << "Trying to use DLA core " << useDLACore << " on a platform that doesn't have any DLA cores" + << std::endl; + assert("Error: use DLA core on a platfrom that doesn't have any DLA cores" && false); + } + if (allow_GPU_fallback) + { + config->setFlag(nvinfer1::BuilderFlag::kGPU_FALLBACK); + } + if (!builder->getInt8Mode() && !config->getFlag(nvinfer1::BuilderFlag::kINT8)) + { + // User has not requested INT8 Mode. + // By default run in FP16 mode. FP32 mode is not permitted. + builder->setFp16Mode(true); + config->setFlag(nvinfer1::BuilderFlag::kFP16); + } + config->setDefaultDeviceType(nvinfer1::DeviceType::kDLA); + config->setDLACore(useDLACore); + config->setFlag(nvinfer1::BuilderFlag::kSTRICT_TYPES); + } +} + diff --git a/TensorRT-Base/common/helper.h b/TensorRT-Base/common/helper.h new file mode 100644 index 0000000..fa8844a --- /dev/null +++ b/TensorRT-Base/common/helper.h @@ -0,0 +1,160 @@ +/** + * @file - helper.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - 一些公共函数定义 + * + * Copyright (c) 2021, wdn. All rights reserved. + * + */ +#ifndef TRT_HELPER_H +#define TRT_HELPER_H + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "logger.h" +#include "timer.h" +#include "trt_return_type.h" + +#ifndef CUDA_CHECK +#define CUDA_CHECK(call_str) \ + { \ + cudaError_t error_code = call_str; \ + if (error_code != cudaSuccess) { \ + helper::err << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__ << std::endl; \ + exit(0); \ + } \ + } +#endif + +namespace helper +{ +/** + * demo: + * + * helper::PreciseCpuTime cpu_time; + * cpu_time.start(); + * Sleep(1000); + * cpu_time.stop(); + * helper::info << "time: " << cpu_time.milliseconds() << "ms" << std::endl; // 1000 + * Sleep(1000); + * cpu_time.start(); + * Sleep(1000); + * cpu_time.stop(); + * helper::info << "time: " << cpu_time.milliseconds() << "ms" << std::endl; // 1000 + * cpu_time.continuation(); + * Sleep(1000); + * cpu_time.stop(); + * helper::info << "time: " << cpu_time.milliseconds() << "ms" << std::endl; // 2000 +*/ +using PreciseCpuTime = CpuTimer; + +/** + * demo: + * + * cudaStream_t stream; + * helper::PreciseGpuTime gpu_time = helper::PreciseGpuTime(stream); + * gpu_time.start(); + * Sleep(1000); + * gpu_time.stop(); + * helper::info << "gpu time: " << gpu_time.milliseconds() << "ms" << std::endl; + * gpu_time.start(); + * Sleep(1000); + * gpu_time.stop(); + * helper::info << "gpu time: " << gpu_time.milliseconds() << "ms" << std::endl; + * gpu_time.continuation(); + * Sleep(1000); + * gpu_time.stop(); + * helper::info << "gpu time: " << gpu_time.milliseconds() << "ms" << std::endl; +*/ +using PreciseGpuTime = GpuTimer; + +/** + * 重载 "" _GiB +*/ +constexpr long long int operator"" _GiB(long long unsigned int val) +{ + return val * (1 << 30); +} +/** + * 重载 "" _MiB +*/ +constexpr long long int operator"" _MiB(long long unsigned int val) +{ + return val * (1 << 20); +} +/** + * 重载 "" _KiB +*/ +constexpr long long int operator"" _KiB(long long unsigned int val) +{ + return val * (1 << 10); +} + +/** + * 日志输出 +*/ +extern Logger log_base; +extern LogStreamConsumer debug; +extern LogStreamConsumer info; +extern LogStreamConsumer warn; +extern LogStreamConsumer err; +extern LogStreamConsumer fatal; + +/** + * 设置日志等级 + * + * @param severity - 日志等级 +*/ +void set_reportable_severity(Logger::Severity severity); + +/** + * 根据错误标志位返回错误字符串 + * + * @param status - 错误标志位 + * + * @return std::string - 错误字符串 + * + */ +const std::string get_error_string(trt_ret_t status); + +/** + * +*/ +int64_t volume(const nvinfer1::Dims& d); + +template +inline A divUp(A x, B n) +{ + return (x + n - 1) / n; +} + +/** + * 获取数据类型大小 +*/ +unsigned int get_element_size(nvinfer1::DataType t); + +/** + * 安全申请显存 +*/ +void* safe_cuda_malloc(size_t mem_size); + +/** + * 安全释放显存 +*/ +void safe_cuda_free(void* device_mem); + +void enable_DLA(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config, + int useDLACore, bool allow_GPU_fallback = true); + +} // namespace helper + +#endif // TRT_HELPER_H \ No newline at end of file diff --git a/TensorRT-Base/common/int8_calibrator.cpp b/TensorRT-Base/common/int8_calibrator.cpp new file mode 100644 index 0000000..c1d5a62 --- /dev/null +++ b/TensorRT-Base/common/int8_calibrator.cpp @@ -0,0 +1,118 @@ +/** + * @file - int8_calibrator.cpp + * @author - wdn (dongnian.wang@outlook.com) + * @brief - TensorRT INT8量化类实现 + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#include +#include +#include +#include +#include + +#include "int8_calibrator.h" + +nvinfer1::IInt8Calibrator* helper::get_int8_calibrator( + const std::string& calibrator_type, + int batch_size, + const std::vector>& data, + const std::string& calib_data_name, + bool read_cache) +{ + if(calibrator_type == "Int8EntropyCalibrator2") + { + return new Int8EntropyCalibrator2(batch_size, data, calib_data_name, read_cache); + } + else + { + helper::debug << "[INT8] : Unsupport calibrator type "<< std::endl; + return nullptr; + } + +} + + +Int8EntropyCalibrator2::Int8EntropyCalibrator2(int batch_size, + const std::vector>& data, + const std::string& calib_data_name , + bool read_cache) + : m_calib_data_name(calib_data_name), + m_batch_size(batch_size), + m_read_cache(read_cache) +{ + m_data.reserve(data.size()); + m_data = data; + + m_input_count = batch_size * data[0].size(); + m_current_batch_data = new float(m_input_count); + m_current_batch_idx = 0; + m_device_input = helper::safe_cuda_malloc(m_input_count * sizeof(float)); +} + +Int8EntropyCalibrator2::~Int8EntropyCalibrator2() +{ + helper::safe_cuda_free(m_device_input); + if(m_current_batch_data) + delete[] m_current_batch_data; +} + +int Int8EntropyCalibrator2::getBatchSize() const +{ + helper::debug << "[INT8] : get batch size : " << m_batch_size << std::endl; + return m_batch_size; +} + +bool Int8EntropyCalibrator2::getBatch(void* bindings[], const char* names[], int nb_bindings) +{ + helper::debug << "[INT8] : name: " << names[0] << ", nbBindings: " << nb_bindings << std::endl; + + if(m_current_batch_idx + m_batch_size > int(m_data.size())) + { + return false; + } + + float* ptr = m_current_batch_data; + size_t img_size = m_input_count / m_batch_size; + auto iter = m_data.begin() + m_current_batch_idx; + + std::for_each(iter, iter + m_batch_size, [=, &ptr](std::vector& val) + { + assert(img_size == val.size()); + memcpy(ptr, val.data(), img_size*sizeof(float)); + + ptr += img_size; + }); + + CUDA_CHECK(cudaMemcpy(m_device_input, m_current_batch_data, + m_input_count*sizeof(float), cudaMemcpyHostToDevice)); + + bindings[0] = m_device_input; + helper::debug << "[INT8] : load batch " << m_current_batch_idx << " to " + << m_current_batch_idx + m_batch_size - 1 << std::endl; + m_current_batch_idx += m_batch_size; + + return true; +} + +const void* Int8EntropyCalibrator2::readCalibrationCache(std::size_t& length) +{ + m_calibration_cache.clear(); + std::ifstream input(m_calib_data_name + ".calib", std::ios::binary); + input >> std::noskipws; + if(m_read_cache && input.good()) + { + std::copy(std::istream_iterator(input), + std::istream_iterator(), std::back_inserter(m_calibration_cache)); + + length = m_calibration_cache.size(); + return length ? &m_calibration_cache[0] : nullptr; + } +} + +void Int8EntropyCalibrator2::writeCalibrationCache(const void* cache, std::size_t length) +{ + std::ofstream output(m_calib_data_name + ".calib", std::ios::binary); + output.write(reinterpret_cast(cache), length); +} \ No newline at end of file diff --git a/TensorRT-Base/common/int8_calibrator.h b/TensorRT-Base/common/int8_calibrator.h new file mode 100644 index 0000000..d2becd4 --- /dev/null +++ b/TensorRT-Base/common/int8_calibrator.h @@ -0,0 +1,62 @@ +/** + * @file - int8_calibrator.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - tensorrt int8量化类声明 + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#ifndef TRT_INT8_CALIBRATOR_H +#define TRT_INT8_CALIBRATOR_H + +#include +#include +#include + +#include +#include + +#include "helper.h" + +namespace helper +{ +nvinfer1::IInt8Calibrator* get_int8_calibrator(const std::string& calibrator_type, + int batch_size, const std::vector>& data, + const std::string& calib_data_name, bool read_cache); +}// helper + +/** + * TensorRT INT8量化类 + */ +class Int8EntropyCalibrator2 : public nvinfer1::IInt8EntropyCalibrator2 +{ +public: + Int8EntropyCalibrator2(int batch_size, const std::vector>& data, + const std::string& calib_data_name = "", bool read_cache = true); + + ~Int8EntropyCalibrator2(); + + int getBatchSize() const override; + + bool getBatch(void* bindings[], const char* names[], int nbBindings) override; + + const void* readCalibrationCache(std::size_t& length) override; + + void writeCalibrationCache(const void* ptr, std::size_t length) override; + +private: + std::string m_calib_data_name; + std::vector> m_data; + int m_batch_size; + + int m_current_batch_idx; + float* m_current_batch_data{nullptr}; + + size_t m_input_count; + bool m_read_cache; + void* m_device_input{nullptr}; + + std::vector m_calibration_cache; +}; + +#endif // TRT_INT8_CALIBRATOR_H diff --git a/TensorRT-Base/common/logger.h b/TensorRT-Base/common/logger.h new file mode 100644 index 0000000..445706e --- /dev/null +++ b/TensorRT-Base/common/logger.h @@ -0,0 +1,317 @@ +/** + * @file - logging.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - 日志类 + * + * @copyright Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. + * + */ +#ifndef TRT_LOGGING_H +#define TRT_LOGGING_H + +#include +#include +#include +#include +#include +#include +#include + +#include + +using Severity = nvinfer1::ILogger::Severity; + +/** + * 日志缓存类 +*/ +class LogStreamConsumerBuffer : public std::stringbuf +{ +public: + LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool should_log) + : m_output(stream), + m_prefix(prefix), + m_should_log(should_log) + { + } + + LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other) + : m_output(other.m_output) + { + } + + ~LogStreamConsumerBuffer() + { + // std::streambuf::pbase() 给出了输出序列的缓冲部分的开头的指针 + // std::streambuf::pptr() 给出了输出序列当前位置的指针 + // 如果指向开头的指针不等于指向当前位置的指针，请调用 put_output() 将输出记录到流 + if(pbase() != pptr()) + { + put_output(); + } + } + + /** + * 同步流缓冲区包括将缓冲区内容插入流中，重置缓冲区并刷新流 + * + * @return @c int - 同步流缓冲区并取得成功返回0 + */ + virtual int sync() + { + put_output(); + return 0; + } + + /** + * 输出 + */ + void put_output() + { + if(m_should_log) + { + std::time_t time_stamp = std::time(nullptr); + tm* tm_local = std::localtime(&time_stamp); + std::cout << "["; + std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-"; + std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "-"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << " "; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "]"; + + // 输出信息 + m_output << m_prefix << " : "<< str(); + // buffer置为空 + str(""); + // 清除stream + m_output.flush(); + } + } + + void set_should_log(bool should_log) + { + m_should_log = should_log; + } + + +private: + std::ostream& m_output; + std::string m_prefix; + bool m_should_log; +}; + +/** + * 日志基类 +*/ +class LogStreamConsumerBase +{ +public: + LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool should_log) + : m_buffer(stream, prefix, should_log) + { + } +protected: + LogStreamConsumerBuffer m_buffer; +}; + +/** + * 日志类 +*/ +class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream +{ +public: + LogStreamConsumer(Severity reportable_severity, Severity severity) + : LogStreamConsumerBase(severity_ostream(severity), severity_prefix(severity), severity <= reportable_severity), + std::ostream(&m_buffer), // 链接stream + m_should_log(severity <= reportable_severity), + m_severity(severity) + { + } + + LogStreamConsumer(LogStreamConsumer&& other) + : LogStreamConsumerBase(severity_ostream(other.m_severity), severity_prefix(other.m_severity), other.m_should_log), + std::ostream(&m_buffer), // 链接stream + m_should_log(other.m_should_log), + m_severity(other.m_severity) + { + } + + void set_reportable_severity(Severity reportable_severity) + { + m_should_log = m_severity <= reportable_severity; + m_buffer.set_should_log(m_should_log); + } + +private: + static std::ostream& severity_ostream(Severity severity) + { + return severity > Severity::kERROR ? std::cout : std::cerr; + } + + static std::string severity_prefix(Severity severity) + { + switch (severity) + { + case Severity::kINTERNAL_ERROR: + return "[ IERROR]"; + case Severity::kERROR: + return "[ ERROR ]"; + case Severity::kWARNING: + return "[ WARN ]"; + case Severity::kINFO: + return "[ INFO ]"; + case Severity::kVERBOSE: + return "[ DEBUG ]"; + default: + assert(0); + return "[ ]"; + } + } + + bool m_should_log; + Severity m_severity; +}; + +/** + * sample日志类 +*/ +class Logger : public nvinfer1::ILogger +{ +public: + Logger(Severity severity = Severity::kWARNING) + : m_reportable_severity(severity) + { + } + + /** + * 用于检索与此记录器相关联的nvinfer1::iLogger的前向兼容方法 + */ + nvinfer1::ILogger& get_trt_logger() + { + return *this; + } + + /** + * 实现输出 + */ + void log(Severity severity, const char* msg) override + { + LogStreamConsumer(m_reportable_severity, severity) << std::string(msg) << std::endl; + //std::cout << std::string(msg) << std::endl; + } + + /** + * 设置控制日志输出的方法 + */ + void set_reportable_severity(Severity severity) + { + m_reportable_severity = severity; + } + + /** + * 获取控制日志输出的方法 + */ + Severity get_reportable_severity() const + { + return m_reportable_severity; + } + +private: + /** + * 返回消息前缀 + */ + static const char* severity_prefix(Severity severity) + { + switch (severity) + { + case Severity::kINTERNAL_ERROR: + return "[ IERROR]"; + case Severity::kERROR: + return "[ ERROR ]"; + case Severity::kWARNING: + return "[ WARN ]"; + case Severity::kINFO: + return "[ INFO ]"; + case Severity::kVERBOSE: + return "[ DEBUG ]"; + default: + assert(0); + return "[ ]"; + } + } + + /** + * 消息流 + */ + static std::ostream& severity_ostream(Severity severity) + { + return severity > Severity::kINFO ? std::cout : std::cerr; + } + + Severity m_reportable_severity; +}; + +namespace +{ +/** + * DEBUG 日志类 + * + * Example usage: + * + * LOG_DEBUG(logger) << "hello world" << std::endl; +*/ +inline LogStreamConsumer LOG_DEBUG(const Logger& logger) +{ + return LogStreamConsumer(logger.get_reportable_severity(), Severity::kVERBOSE); +} + +/** + * INFO 日志类 + * + * Example usage: + * + * LOG_INFO(logger) << "hello world" << std::endl; +*/ +inline LogStreamConsumer LOG_INFO(const Logger& logger) +{ + return LogStreamConsumer(logger.get_reportable_severity(), Severity::kINFO); +} + +/** + * WARN 日志类 + * + * Example usage: + * + * LOG_WARN(logger) << "hello world" << std::endl; +*/ +inline LogStreamConsumer LOG_WARN(const Logger& logger) +{ + return LogStreamConsumer(logger.get_reportable_severity(), Severity::kWARNING); +} + +/** + * ERROR 日志类 + * + * Example usage: + * + * LOG_ERROR(logger) << "hello world" << std::endl; +*/ +inline LogStreamConsumer LOG_ERROR(const Logger& logger) +{ + return LogStreamConsumer(logger.get_reportable_severity(), Severity::kERROR); +} + +/** + * FATAL 日志类 + * + * Example usage: + * + * LOG_FATAL(logger) << "hello world" << std::endl; +*/ +inline LogStreamConsumer LOG_FATAL(const Logger& logger) +{ + return LogStreamConsumer(logger.get_reportable_severity(), Severity::kINTERNAL_ERROR); +} + +} // namespace + +#endif // TRT_LOGGING_H \ No newline at end of file diff --git a/TensorRT-Base/common/timer.h b/TensorRT-Base/common/timer.h new file mode 100644 index 0000000..d01cb42 --- /dev/null +++ b/TensorRT-Base/common/timer.h @@ -0,0 +1,165 @@ +/** + * @file - timer.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - CPU/GPU计时类 + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#ifndef TRT_TIME_H +#define TRT_TIME_H + +#include +#include + +#include +#include + +/** + * Timer基类 +*/ +class TimerBase +{ +public: + /** + * 计时开始 + */ + virtual void start() = 0; + + /** + * 计时结束 + */ + virtual void stop() = 0; + + virtual void continuation() = 0; + + /** + * 返回微妙 + */ + float microseconds() const noexcept + { + return m_ms * 1000.f; + } + + /** + * 返回毫秒 + */ + float milliseconds() const noexcept + { + return m_ms; + } + + /** + * 返回秒 + */ + float seconds() const noexcept + { + return m_ms / 1000.f; + } + + /** + * 重置 + */ + void reset() noexcept + { + m_ms = 0.f; + } + +protected: + float m_ms{0.0f}; +}; + +/** + * 获取GPU时间 +*/ +template +class CpuTimer : public TimerBase +{ +public: + using clock_type = Clock; + + /** + * 开始 + */ + void start() override + { + reset(); + m_start = Clock::now(); + } + + /** + * 继续 + */ + void continuation() override + { + m_start = Clock::now(); + } + + /** + * 停止 + */ + void stop() override + { + m_stop = Clock::now(); + m_ms += std::chrono::duration {m_stop - m_start}.count(); + } + +private: + std::chrono::time_point m_start; + std::chrono::time_point m_stop; +}; + +/** + * GPU计时 +*/ +class GpuTimer : public TimerBase +{ +public: + GpuTimer(cudaStream_t stream) : m_stream(stream) + { + cudaEventCreate(&m_start); + cudaEventCreate(&m_stop); + } + + ~GpuTimer() + { + cudaEventDestroy(m_start); + cudaEventDestroy(m_stop); + } + + /** + * 开始 + */ + void start() override + { + reset(); + cudaEventRecord(m_start, m_stream); + } + + /** + * 继续 + */ + void continuation() override + { + cudaEventRecord(m_start, m_stream); + } + + /** + * 停止 + */ + void stop() override + { + cudaEventRecord(m_stop, m_stream); + float ms{0.0f}; + cudaEventSynchronize(m_stop); + cudaEventElapsedTime(&ms, m_start, m_stop); + m_ms += ms; + } + +private: + cudaEvent_t m_start; + cudaEvent_t m_stop; + cudaStream_t m_stream; +}; + +#endif // TRT_TIME_H \ No newline at end of file diff --git a/TensorRT-Base/common/trt_return_type.h b/TensorRT-Base/common/trt_return_type.h new file mode 100644 index 0000000..36ef7a6 --- /dev/null +++ b/TensorRT-Base/common/trt_return_type.h @@ -0,0 +1,33 @@ +/** + * @file - trt_return_type.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#ifndef TRT_RETURN_TYPE_H +#define TRT_RETURN_TYPE_H + +#include + +/** + * TensorRT 推理返回值 + */ +typedef enum { + TRT_SUCCESS = 0, + TRT_ERR_FILE_NAME = -1, + TRT_ERR_ENGINE = -2, + TRT_ERR_OPEN_FILE = -3, + TRT_ERR_CONTEXT = -4, + TRT_ERR_DESERIALIZE = -5, + TRT_ERR_SERIALIZE = -6, + TRT_ERR_BUILDER = -7, + TRT_ERR_NETWOEK = -8, + TRT_ERR_PAESER = -9, + TRT_ERR_MEM_CPY = -10, + TRT_ERR_DYNAMIC_INPUT = -11, + +} trt_ret_t; + +#endif // TRT_RETURN_TYPE_H diff --git a/TensorRT-Base/faster_rcnn/CMakeLists.txt b/TensorRT-Base/faster_rcnn/CMakeLists.txt new file mode 100644 index 0000000..4336814 --- /dev/null +++ b/TensorRT-Base/faster_rcnn/CMakeLists.txt @@ -0,0 +1,20 @@ +# @file - faster_rcnn/CMakeLists.txt +# @author - wdn (dongnian.wang@outlook.com) +# @brief - mnist编译文件 +# +# Copyright (c) 2021, wdn. All rights reserved. + +cmake_minimum_required (VERSION 3.0) + +project ("faster_rcnn") + +add_executable (faster_rcnn "main_faster_rcnn.cpp" "faster_rcnn.cpp" "plugin/plugin_faster_rcnn.h") + +# 链接 +target_link_libraries( + faster_rcnn PRIVATE + common + ${CUDA_LIBRARIRS} + ${TRT_LIBRARIRS} + ${OpenCV_LIBS} + ) diff --git a/TensorRT-Base/faster_rcnn/README.md b/TensorRT-Base/faster_rcnn/README.md new file mode 100644 index 0000000..b0c7ccd --- /dev/null +++ b/TensorRT-Base/faster_rcnn/README.md @@ -0,0 +1,79 @@ +# Faster RCNN + +## 1.Description + +继承`TrtBase`类实现`Faster RCNN`算法。 + +## 2.Running + +### 2.1 下载[faster rcnn model](https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz) dataset。 + +```shell +wget --no-check-certificate https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0 -O faster_rcnn/model/faster-rcnn/faster-rcnn.tgz +``` + +### 2.2 解压模型文件 + +此demo使用VGG16的网络，将VGG16权重文件放在`faster_rcnn/model`目录下，修改主函数中model路径即可。 + +### 2.3 编译生成可执行文件 + +```shell +cd TensorRT-Base +mkdir build +cd build +cmake .. +make +``` + +### 2.4 运行 faster rcnn + +```shell +PS E:\TensorRT-Base> .\bin\Release\faster_rcnn.exe +[2021-04-25 20:48:32][ WARN ] : Dynamic size input setting invalid! +[2021-04-25 20:48:32][ DEBUG ] : deserialize engine from faster_rcnn/model/faster_rcnn.bin +[2021-04-25 20:48:38][ DEBUG ] : max batch size of deserialized engine: 1 +[2021-04-25 20:48:38][ DEBUG ] : create execute context and malloc device memory... +[2021-04-25 20:48:38][ DEBUG ] : init engine... +[2021-04-25 20:48:38][ DEBUG ] : malloc device memory +[2021-04-25 20:48:38][ DEBUG ] : nbBingdings: 5 +[2021-04-25 20:48:38][ DEBUG ] : input: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 0, name: data, size in byte: 2250000 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 3 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : input: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 1, name: im_info, size in byte: 12 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 3 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : output: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 2, name: rois, size in byte: 4800 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 3 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : output: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 3, name: bbox_pred, size in byte: 100800 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 4 dimemsion +[2021-04-25 20:48:38][ DEBUG ] : output: +[2021-04-25 20:48:38][ DEBUG ] : binding bindIndex: 4, name: cls_prob, size in byte: 25200 +[2021-04-25 20:48:38][ DEBUG ] : binding dims with 4 dimemsion +[2021-04-25 20:48:38][ WARN ] : output_bbox_pred: 25200 +[2021-04-25 20:48:38][ WARN ] : output_cls_prob: 6300 +[2021-04-25 20:48:38][ WARN ] : output_rois: 1200 +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 aeroplane +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bicycle +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bird +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 boat +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bottle +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 bus +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 car +[2021-04-25 20:48:38][ INFO ] : indices size is: 1 cat +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 chair +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 cow +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 diningtable +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 dog +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 horse +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 motorbike +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 person +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 pottedplant +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 sheep +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 sofa +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 train +[2021-04-25 20:48:38][ INFO ] : indices size is: 0 tvmonitor +``` + diff --git a/TensorRT-Base/faster_rcnn/faster_rcnn.cpp b/TensorRT-Base/faster_rcnn/faster_rcnn.cpp new file mode 100644 index 0000000..d40e99f --- /dev/null +++ b/TensorRT-Base/faster_rcnn/faster_rcnn.cpp @@ -0,0 +1,278 @@ +/** + * @file - faster_rcnn.cpp + * @author - wdn (dongnian.wang@outlook.com) + * @brief - faster_rcnn + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#include "faster_rcnn.h" +#include + +trt_ret_t FasterRcnnCaffe::build() +{ + std::vector min_dim_vec{1, 50, 50}; + std::vector opt_dim_vec{1, 28, 28}; + std::vector max_dim_vec{1, 20, 20}; + add_dynamic_shape_profile(m_batch_size,m_input_name[0], min_dim_vec, opt_dim_vec, max_dim_vec); + initLibNvInferPlugins(&m_logger, ""); + create_engine(m_caffe_prototxt, m_caffe_model, m_engine_file, m_output_name, m_batch_size, m_precision); + + nvinfer1::Dims data_dim = get_binding_dims(m_engine->getBindingIndex(m_input_name[0].c_str())); + m_input_shape = {data_dim.d[0], data_dim.d[1], data_dim.d[2]}; // CHW + // helper::info << "111111111:" << data_dim.MAX_DIMS << std::endl; + // helper::info << "111111111:" << data_dim.nbDims << std::endl; + // helper::info << "111111111:" << data_dim.d[0] << std::endl; + // helper::info << "111111111:" << data_dim.d[1] << std::endl; + // helper::info << "111111111:" << data_dim.d[2] << std::endl; + + return TRT_SUCCESS; +} + +// trt_ret_t FasterRcnnCaffe::process_input(std::string& pgm_name) +// { +// int pgm_height = 28; +// int pgm_width = 28; +// int pgm_channel = 1; +// std::vector file_buffer(pgm_height * pgm_width); +// m_input_data.resize(pgm_height*pgm_width*pgm_channel); +// read_pgm(pgm_name, file_buffer.data(), 28, 28); +// for (int i = 0; i < pgm_height * pgm_width; i++) +// { +// std::cout << (" .:-=+*#%@"[file_buffer[i] / 26]) << (((i + 1) % pgm_width) ? "" : "\n"); +// m_input_data[i] = 1.0 - float(file_buffer[i] / 255.0); +// } +// return TRT_SUCCESS; +// } + +trt_ret_t FasterRcnnCaffe::process_input(std::string& img_name) +{ + helper::warn << "11111111111" << std::endl; + cv::Mat resize_img; + cv::Mat image = cv::imread(img_name); + float pixe_mean[3] {102.9801f, 115.9465f, 122.7717f}; + cv::resize(image, resize_img, cv::Size(m_input_shape[2], m_input_shape[1])); + m_infer_img = resize_img; + int img_height = resize_img.rows; + int img_width = resize_img.cols; + int img_channel = resize_img.channels(); + + resize_img.convertTo(resize_img, CV_32FC3); + for(int row = 0; row < img_height; row++) + { + for(int col = 0; col < img_width; col++) + { + resize_img.at(row, col)[0] -= pixe_mean[0]; + resize_img.at(row, col)[1] -= pixe_mean[1]; + resize_img.at(row, col)[2] -= pixe_mean[2]; + } + } + + + std::vector input_channels(img_channel); + cv::split(resize_img, input_channels); + float* result = new float[img_height * img_width * img_channel]; + std::vector test_data; + float *data = result; + for(int i = 0; i < img_channel; i++) + { + memcpy(data, input_channels[i].data, img_height*img_width * sizeof(float)); + data += img_height*img_width; + } + + int input_index0 = m_engine->getBindingIndex(m_input_name[0].c_str()); + std::vector img_data; + img_data.insert(img_data.begin(), result, result + get_binding_size(input_index0)/ helper::get_element_size(get_binding_data_type(input_index0))); + m_input_data.insert(m_input_data.begin(), img_data); + + return TRT_SUCCESS; +} + +void FasterRcnnCaffe::bbox_transform_clip(std::vector &rois, std::vector& bbox_pred, std::vector& pred_bbox, + std::vector &img_info) +{ + for(int i = 0; i < m_batch_size * m_nms_max_out; i++) + { + float width = rois[i * 4 + 2] - rois[i * 4] + 1; + float height = rois[i * 4 + 3] - rois[i * 4 + 1] + 1; + float ctr_x = rois[i * 4] + 0.5 * width; + float ctr_y = rois[i * 4 + 1] + 0.5 * height; + float* img_offset = img_info.data() + i/m_nms_max_out * 3; + for(int j = 0; j < m_output_class_size; j++) + { + float dx = bbox_pred[i * m_output_class_size * 4 + j * 4 + 0]; + float dy = bbox_pred[i * m_output_class_size * 4 + j * 4 + 1]; + float dw = bbox_pred[i * m_output_class_size * 4 + j * 4 + 2]; + float dh = bbox_pred[i * m_output_class_size * 4 + j * 4 + 3]; + + float pred_ctr_x = dx * width + ctr_x; + float pred_ctr_y = dy * height + ctr_y; + float pred_w = exp(dw) * width; + float pred_h = exp(dh) * height; + + pred_bbox[i * m_output_class_size * 4 + j * 4 + 0] + = std::max(std::min(pred_ctr_x - 0.5f * pred_w, img_offset[1] - 1.f), 0.f); + pred_bbox[i * m_output_class_size * 4 + j * 4 + 1] + = std::max(std::min(pred_ctr_y - 0.5f * pred_h, img_offset[0] - 1.f), 0.f); + pred_bbox[i * m_output_class_size * 4 + j * 4 + 2] + = std::max(std::min(pred_ctr_x + 0.5f * pred_w, img_offset[1] - 1.f), 0.f); + pred_bbox[i * m_output_class_size * 4 + j * 4 + 3] + = std::max(std::min(pred_ctr_y + 0.5f * pred_h, img_offset[0] - 1.f), 0.f); + } + + } +} + +std::vector FasterRcnnCaffe::nms(std::vector> score_index, float* bbox, + const int class_num, float nms_threshold) +{ + auto overlap1D = [](float x1min, float x1max, float x2min, float x2max)->float + { + if(x1min > x2min) + { + std::swap(x1min, x2min); + std::swap(x1max, x2max); + } + return x1max < x2min ? 0 : std::min(x1max, x2max) - x2min; + }; + auto compute_iou = [&overlap1D](float* bbox1, float* bbox2) -> float { + float overlapX = overlap1D(bbox1[0], bbox1[2], bbox2[0], bbox2[2]); + float overlapY = overlap1D(bbox1[1], bbox1[3], bbox2[1], bbox2[3]); + float area1 = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1]); + float area2 = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1]); + float overlap2D = overlapX * overlapY; + float u = area1 + area2 - overlap2D; + return u == 0 ? 0 : overlap2D / u; + }; + + std::vector indices; + for(auto score_index : score_index) + { + const int index = score_index.second; + bool keep = true; + for(int k = 0; k < indices.size(); k++) + { + if(keep) + { + int idx = indices[k]; + float iou = compute_iou(&bbox[(index * m_output_class_size + class_num) * 4], + &bbox[(idx * m_output_class_size + class_num) * 4]); + keep = (iou < nms_threshold); + } + else + { + break; + } + } + if (keep) + indices.push_back(index); + } + return indices; +} + +trt_ret_t FasterRcnnCaffe::infer() +{ + // std::vector input_dims{pgm_channel, pgm_width, pgm_height}; + // set_binding_dimentsions(input_dims, 0); + // mem_host_to_device(m_input_data, 0); + // inference(); + // mem_device_to_host(m_output_data, 1); + helper::warn << "bbbbbbbbbbbb: " << m_engine->getNbBindings() << std::endl; + int input_index0 = m_engine->getBindingIndex(m_input_name[0].c_str()); + int input_index1 = m_engine->getBindingIndex(m_input_name[1].c_str()); + int output_index0 = m_engine->getBindingIndex(m_output_name[0].c_str()); + int output_index1 = m_engine->getBindingIndex(m_output_name[1].c_str()); + int output_index2 = m_engine->getBindingIndex(m_output_name[2].c_str()); + + //const int data_size = get_binding_size(input_index0)/ helper::get_element_size(get_binding_data_type(input_index1)); + //m_input_data[1].resize(get_binding_size(input_index1)/ helper::get_element_size(get_binding_data_type(input_index1))); + std::vector img_info_vec = {(float)m_input_shape[1], (float)m_input_shape[2], 1}; + m_input_data.insert(m_input_data.begin() + 1, img_info_vec); + helper::warn << "2222222222222: " << m_input_data[0].size() << std::endl; + helper::warn << "2222222222222: " << m_input_data[1].size() << std::endl; + helper::warn << "2222222222222: " << get_binding_size(input_index0) << std::endl; + + cudaStream_t stream; + cudaStreamCreate(&stream); + mem_host_to_device(m_input_data[0], input_index0, stream); + mem_host_to_device(m_input_data[1], input_index1, stream); + inference_async(stream); + std::vector output_bbox_pred; + std::vector output_cls_prob; + std::vector output_rois; + mem_device_to_host(output_bbox_pred, output_index0, stream); + mem_device_to_host(output_cls_prob, output_index1, stream); + mem_device_to_host(output_rois, output_index2, stream); + helper::warn << "output_bbox_pred: " << output_bbox_pred.size() << std::endl; + helper::warn << "output_cls_prob: " << output_cls_prob.size() << std::endl; + helper::warn << "output_rois: " << output_rois.size() << std::endl; + cudaStreamSynchronize(stream); + cudaStreamDestroy(stream); + + for(int i = 0; i < m_batch_size; i++) + { + for(int j = 0; j < m_nms_max_out * 4; j++) + { + output_rois[i * m_nms_max_out * 4 + j] /= img_info_vec[i * 3 + 2]; + } + } + + std::vector pred_bbox(output_bbox_pred.size()); + + bbox_transform_clip(output_rois, output_bbox_pred, pred_bbox, img_info_vec); + + const float nms_threshold = 0.3f; + const float score_threshold = 0.5f; + for(int i = 0; i < m_batch_size; i++) + { + float* bbox = pred_bbox.data() + i * m_nms_max_out * m_output_class_size * 4; + float* scores = output_cls_prob.data() + i * m_nms_max_out * m_output_class_size; + for(int cls = 1; cls < m_output_class_size; cls++) + { + std::vector> score_index; + for(int idx = 0; idx < m_nms_max_out; idx++) + { + if(scores[idx * m_output_class_size + cls] > score_threshold) + { + score_index.push_back(std::make_pair(scores[idx * m_output_class_size + cls], idx)); + std::stable_sort(score_index.begin(), score_index.end(), + [](const std::pair& pair1, const std::pair& pair2){ + return pair1.first > pair2.first; + }); + } + } + std::vector indices = nms(score_index, bbox, cls, nms_threshold); + + helper::info << "indices size is: " << indices.size() << " " << m_output_class[cls] << std::endl; + // + std::string class_name = m_output_class[cls]; + for(int k = 0; k < indices.size(); k++) + { + int index = indices[k]; + cv::rectangle(m_infer_img, cv::Point(bbox[index * m_output_class_size * 4 + cls * 4], + bbox[index * m_output_class_size * 4 + cls * 4 + 1]), + cv::Point(bbox[index * m_output_class_size * 4 + cls * 4 + 2], + bbox[index * m_output_class_size * 4 + cls * 4 + 3]), cv::Scalar(255,0,0), 1); + + cv::putText(m_infer_img, class_name, cv::Point(50,80), cv::FONT_HERSHEY_COMPLEX, 2, 1, 8, 0); + } + } + } + cv::imwrite("faster_rcnn/result/test.jpg", m_infer_img); + + // cv::imshow("aaa", m_infer_img); + // cv::waitKey(0); + + return TRT_SUCCESS; +} + +trt_ret_t FasterRcnnCaffe::read_pgm(const std::string& file_name, uint8_t* buffer, int in_h, int in_w) +{ + std::ifstream infile(file_name, std::ifstream::binary); + assert(infile.is_open() && "Attempting to read from a file that is not open."); + std::string magic, h, w, max; + infile >> magic >> h >> w >> max; + infile.seekg(1, infile.cur); + infile.read(reinterpret_cast(buffer), in_h * in_w); + return TRT_SUCCESS; +} \ No newline at end of file diff --git a/TensorRT-Base/faster_rcnn/faster_rcnn.h b/TensorRT-Base/faster_rcnn/faster_rcnn.h new file mode 100644 index 0000000..ac1c7ff --- /dev/null +++ b/TensorRT-Base/faster_rcnn/faster_rcnn.h @@ -0,0 +1,97 @@ +/** + * @file - faster_rcnn.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - faster_rcnn + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ + +#ifndef FASTER_RCNN_H +#define FASTER_RCNN_H + +#include +#include + +#include "helper.h" +#include "timer.h" +#include "base_trt.h" + +// struct detection_object_t +// { +// int xmin, ymin, xmax, ymax; + +// //float + +// detection_object_t(float x, float y, float h, float w) +// { +// this->xmin = static_cast (x - w / 2); +// this->ymin = static_cast (y - h / 2); +// this->xmax = static_cast (this->xmin + w); +// this->ymax = static_cast (this->ymin + h); +// } + +// detection_object_t(float xmin, float ymin, float xmax, float ymax) +// { +// this->xmin = static_cast (xmin); +// this->ymin = static_cast (ymin); +// this->xmax = static_cast (xmax); +// this->ymax = static_cast (ymax); +// } + +// }; + + +class FasterRcnnCaffe : public TrtBase +{ +public: + FasterRcnnCaffe(const std::string& caffe_prototxt, const std::string& caffe_model, const std::string& engine_file, + int batch_size, const std::vector& input_name, const std::vector& output_name, infer_precision_t precision) + : TrtBase(), m_caffe_prototxt(caffe_prototxt), m_caffe_model(caffe_model), m_batch_size(batch_size), + m_input_name(input_name), m_output_name(output_name), m_precision(precision), m_engine_file(engine_file) + { + m_output_class = {"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", + "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", + "train", "tvmonitor"}; + } + + trt_ret_t build(); + + trt_ret_t process_input(std::string& img_name); + + trt_ret_t infer(); + + trt_ret_t process_result(); + + void bbox_transform_clip(std::vector &rois, std::vector& deltas, std::vector& pred_bbox, + std::vector &img_info); + + std::vector nms(std::vector> score_index, float* bbox, + const int class_num, float nms_threshold); + +private: + std::string m_caffe_model; + std::string m_caffe_prototxt; + std::string m_engine_file; + int m_batch_size; + std::vector m_output_name; + std::vector m_input_name; + infer_precision_t m_precision; + + int m_output_class_size{21}; + int m_nms_max_out{300}; + + std::vector m_output_class; + + // std::map> m_input_data; + std::vector> m_input_data; + std::vector> m_output_data; + + std::vector m_input_shape; + + cv::Mat m_infer_img; + + trt_ret_t read_pgm(const std::string& file_name, uint8_t* buffer, int in_h, int in_w); +}; + +#endif // FASTER_RCNN_H \ No newline at end of file diff --git a/TensorRT-Base/faster_rcnn/image/000456.ppm b/TensorRT-Base/faster_rcnn/image/000456.ppm new file mode 100644 index 0000000000000000000000000000000000000000..1bc8fe34691903035f2895d3225c3d7d84802468 GIT binary patch literal 562515 zcmXuL^>^Fpy5(8t6r{rBn3-7&7Be$5Gc#CZNtR_>X6D3+o!G%jRi(J<(CvHs&br;x z{mcA4^H$HAtnboN9NV#FX+Qh@?EP4RzJ-E9dl4AaEjS8w%dF*k-5Q%&6b~9zYPOh% zF-b{!Db{YHdTcxq2j{k#ECzjfF6Z$Wn6#P7^2)FO_5aY6c1tjsDwcaj#@x~P`O9mi zO3ET*Y+8j;C7JB+otYj@MI0I>3q95C(Qw^5uEW44Bm0X%qff%(C2CX4v%w%?dV&BMt5@e@(ZCpDZ;3 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zo?r;tC$3*!)mk^*qjxQ~^IWr?htTU@h2X8^OKl6BPX7RE4IhW;=`G34d+!AQ0J_ut zrVp%gHMo6UTeyRU84T2rFqU66zQ%=Ap(m>dI#dci@xlqf~^35#azY|_lVkLI} z05vn^FRTWGsrW_?U{*<&JJJnaChcBQ;wB7r9;ce>nq`(HrukXRO3>lUR^*pq?-f7k zXq?7<9UVth=e?AaF_K!?*hvCGQ;_^Z=!C@05{}7HdbIK?J4SKdC@!+q$aRvLz8>{a z)9p%HG8L_hE?C)zy?HHkb*@Ot*RJ9-qk490#_Hoz6vR`q1f@8it!Ub0KTx z=+Uz@q`K>T4~K8!9eai>64$z${VTozLDIAEJU{$?8qG6nO!-~kt!d9rwaMRCs@hKy zd%IP$RnYHIcHV0EjJ;@Q8LJt~Mi|Gw z^j5x(X;Yo?-h*1_R^x`#QtST!xj*Y~SJ!HqaNZi<`J73p+y4NkNlRq@>$aHebn{(% z0@5b4@YY$QrDc|w2oAwdnSn~>`8u}aUT20(utiuzNNqwPwMja;tZXri2en@@EHvO=o#}a{sKXyC{{YZ_w8@s2b*81Tu!xD#HvV?1)74e7_%AmN zleygdnyU!eAx@PdVeDdpbCsf#bKIqYY4tytstgfOdpl-oOta%|^hgnxZ%VWyJ^uAl zITs&)YQG?3??<5sL`?MYQ!)(Ctz1YcQ5lKpiZp`tkItzvlh%$q)g}yj;+>$lj7I({ z7Q}`h6v*ySq%kcmqZJB5y|><=K?zqI{{X6`$}oAVlwz)W0gP=zhwVoPS*k=?{&}Y; zpcG+!=B$^ejjF*}%}ua$rbTg>;dLIRt$6KB-1uM4y?-8u2db{t;=F?`-At`^f6w^K zg-qo|V_Khe|FBP17AsPyYaA`MKZHrAnUv z0Qpxxt%kX8U$qzfgkw9@sa&|!%Q%U5r(3DaOVFuOv!QOO5t;8xzlyU+m%MFKr8xi| zJR_D#-xXS%RkwxqV^ZJkW78EXRm2TVg4erNZa_&*Q$n(nG%&6(@oKDFMr1Rj+tS3k-6zE?F<-la;-NHsV5)Tu-@ z{*?zcDpH~(;jA2HyL0$v#Lt?QD!Kg(wEYVu=4w=`>+qVi?B-Ygqm#WlrAo}v>{RJe Ur2%VIF;b-f7cTWGRHh&Q* + +#include "helper.h" +#include "faster_rcnn.h" + +int main() +{ + const std::string caffe_prototxt = "faster_rcnn/model/faster_rcnn_test_iplugin.prototxt"; + const std::string caffe_model = "faster_rcnn/model/VGG16_faster_rcnn_final.caffemodel"; + const std::string engine_file = "faster_rcnn/model/faster_rcnn.bin"; + int batch_size = 1; + const std::vector input_name{"data", "im_info"}; + const std::vector output_name{"bbox_pred", "cls_prob", "rois"}; + infer_precision_t precision = INFER_FP32; + + + FasterRcnnCaffe* faster_rcnn = new FasterRcnnCaffe(caffe_prototxt, caffe_model, engine_file, batch_size, input_name, output_name, precision); + + faster_rcnn->build(); + std::string pgm_name = "faster_rcnn/image/3.jpg"; + faster_rcnn->process_input(pgm_name); + + faster_rcnn->infer(); + + + // faster_rcnn->process_result(); + + delete faster_rcnn; + return 0; +} \ No newline at end of file diff --git a/TensorRT-Base/faster_rcnn/model/faster_rcnn_test_iplugin.prototxt b/TensorRT-Base/faster_rcnn/model/faster_rcnn_test_iplugin.prototxt new file mode 100644 index 0000000..44d316c --- /dev/null +++ b/TensorRT-Base/faster_rcnn/model/faster_rcnn_test_iplugin.prototxt @@ -0,0 +1,428 @@ +name: "VGG_ILSVRC_16_layers" + +input: "data" +input_shape { + dim: 2 + dim: 3 + dim: 375 + dim: 500 +} + +input: "im_info" +input_shape { + dim: 2 + dim: 1 + dim: 1 + dim: 3 +} + +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + convolution_param { + num_output: 64 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + convolution_param { + num_output: 64 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + convolution_param { + num_output: 128 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + convolution_param { + num_output: 128 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + convolution_param { + num_output: 256 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + convolution_param { + num_output: 256 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + convolution_param { + num_output: 256 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + convolution_param { + num_output: 512 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + convolution_param { + num_output: 512 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + convolution_param { + num_output: 512 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + convolution_param { + num_output: 512 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + convolution_param { + num_output: 512 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + convolution_param { + num_output: 512 + pad: 1 kernel_size: 3 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} + +#========= RPN ============ + +layer { + name: "rpn_conv/3x3" + type: "Convolution" + bottom: "conv5_3" + top: "rpn/output" + convolution_param { + num_output: 512 + kernel_size: 3 pad: 1 stride: 1 + } +} +layer { + name: "rpn_relu/3x3" + type: "ReLU" + bottom: "rpn/output" + top: "rpn/output" +} + +layer { + name: "rpn_cls_score" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_cls_score" + convolution_param { + num_output: 18 # 2(bg/fg) * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + } +} +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + convolution_param { + num_output: 36 # 4 * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + } +} +layer { + bottom: "rpn_cls_score" + top: "rpn_cls_score_reshape" + name: "ReshapeCTo2" + type: "Reshape" + reshape_param { + shape { + dim: 2 + dim: -1 + dim: 0 + } + axis: 1 + } +} + +#========= RoI Proposal ============ + +layer { + name: "rpn_cls_prob" + type: "Softmax" + bottom: "rpn_cls_score_reshape" + top: "rpn_cls_prob" +} +layer { + name: 'ReshapeCTo18' + type: 'Reshape' + bottom: 'rpn_cls_prob' + top: 'rpn_cls_prob_reshape' + reshape_param { + shape { + dim: 18 + dim: -1 + dim: 0 + } + axis: 1 + } +} +layer { + name: "RPROIFused" + type: "RPROI" + bottom: 'rpn_cls_prob_reshape' + bottom: 'rpn_bbox_pred' + bottom: 'conv5_3' + bottom: 'im_info' + top: 'rois' + top: 'pool5' + region_proposal_param { + feature_stride: 16 + prenms_top: 6000 + nms_max_out: 300 + anchor_ratio_count: 3 + anchor_scale_count: 3 + iou_threshold: 0.7 + min_box_size: 16 + anchor_ratio: 0.5 + anchor_ratio: 1.0 + anchor_ratio: 2.0 + anchor_scale: 8.0 + anchor_scale: 16.0 + anchor_scale: 32.0 + } + roi_pooling_param { + pooled_h: 7 + pooled_w: 7 + spatial_scale: 0.0625 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + inner_product_param { + num_output: 21 + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + inner_product_param { + num_output: 84 + } +} +layer { + name: "cls_prob" + type: "Softmax" + bottom: "cls_score" + top: "cls_prob" +} + diff --git a/TensorRT-Base/faster_rcnn/plugin/plugin_faster_rcnn.h b/TensorRT-Base/faster_rcnn/plugin/plugin_faster_rcnn.h new file mode 100644 index 0000000..4b33c65 --- /dev/null +++ b/TensorRT-Base/faster_rcnn/plugin/plugin_faster_rcnn.h @@ -0,0 +1,84 @@ +/** + * @file - plugin_faster_rcnn.h + * @author - wdn(dongnian.wang@outlook.com) + * @brief - plugin_faster_rcnn + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#ifndef PLUGIN_FATSER_RCNN_H +#define PLUGIN_FATSER_RCNN_H + +#include +#include +#include + + +#include +#include + +const int pooling_wide = 7; +const int pooling_height = 7; +const int feature_stride = 16; +const int pre_nums_top = 6000; +const int nms_max_out = 300; +const int anchors_ration_count = 3; +const int anchors_scale_count = 3; +const float iou_threshold = 0.7f; +const float min_box_size = 16; +const float spatial_scale = 0.0625f; +const float anchors_ratios[anchors_ration_count] = {0.5f, 1.0f, 2.0f}; +const float anchors_scales[anchors_scale_count] = {8.0f, 16.0f, 32.0f}; + +class FRCNNPluginFactory : public nvcaffeparser1::IPluginFactoryV2 +{ +public: + + virtual nvinfer1::IPluginV2* createPlugin(const char* layerName, const nvinfer1::Weights* weights, + int nbWeights, const char* libNamespace) override + { + assert(isPluginV2(layerName)); + if(!strcmp(layerName, "RPROIFused")) + { + assert(m_plugin_RPROI == nullptr); + assert(nbWeights == 0 && weights == nullptr); + m_plugin_RPROI == std::unique_ptr( + createRPNROIPlugin(feature_stride, pre_nums_top, nms_max_out,iou_threshold, + min_box_size, spatial_scale, nvinfer1::DimsHW(pooling_height, pooling_wide), + nvinfer1::Weights{nvinfer1::DataType::kFLOAT, anchors_ratios, anchors_ration_count}, + nvinfer1::Weights{nvinfer1::DataType::kFLOAT, anchors_scales, anchors_scale_count}), + plugin_deleter); + + m_plugin_RPROI.get()->setPluginNamespace(libNamespace); + return m_plugin_RPROI.get(); + } + else + { + assert(0); + return nullptr; + } + } + + bool isPluginV2(const char* name) override + { + return !strcmp(name, "RPROIFused"); + } + + void destroyPlugin() + { + m_plugin_RPROI.reset(); + } + + void (*plugin_deleter)(nvinfer1::IPluginV2*) + { + [](nvinfer1::IPluginV2* ptr) + { + ptr->destroy(); + } + }; + + std::unique_ptr m_plugin_RPROI{nullptr, plugin_deleter}; +}; + + +#endif // PLUGIN_FATSER_RCNN_H \ No newline at end of file diff --git a/TensorRT-Base/lib/README.md b/TensorRT-Base/lib/README.md new file mode 100644 index 0000000..b737b62 --- /dev/null +++ b/TensorRT-Base/lib/README.md @@ -0,0 +1 @@ +## 生成库目录 \ No newline at end of file diff --git a/TensorRT-Base/mnist_caffe/CMakeLists.txt b/TensorRT-Base/mnist_caffe/CMakeLists.txt new file mode 100644 index 0000000..6e47eb9 --- /dev/null +++ b/TensorRT-Base/mnist_caffe/CMakeLists.txt @@ -0,0 +1,18 @@ +# @file - mnist_caffe/CMakeLists.txt +# @author - wdn (dongnian.wang@outlook.com) +# @brief - mnist编译文件 +# +# @copyright Copyright (c) 2021, wdn. All rights reserved. + +cmake_minimum_required (VERSION 3.0) + +project ("mnist_caffe") + +add_executable (mnist_caffe "main_mnist_caffe.cpp" "mnist_caffe.cpp") + +# 链接库 +target_link_libraries( + mnist_caffe PRIVATE + common + ${CUDA_LIBRARIRS} + ) diff --git a/TensorRT-Base/mnist_caffe/README.md b/TensorRT-Base/mnist_caffe/README.md new file mode 100644 index 0000000..ad5aca4 --- /dev/null +++ b/TensorRT-Base/mnist_caffe/README.md @@ -0,0 +1,96 @@ +# MNIST Caffe + +## 1.Description + +继承`TrtBase`类实现`mnist`手写数字算法。 + +## 2.Running + +### 2.1 编译生成可执行文件 + +```shell +cd TensorRT-Base +mkdir build +cd build +cmake .. +make +``` + +### 2.2 运行 faster rcnn + +```shell +PS E:\TensorRT-Base> .\bin\Release\mnist_caffe.exe +[2021-04-26 21:29:51][ WARN ] : Dynamic size input setting invalid! +[2021-04-26 21:29:51][ DEBUG ] : deserialize engine from mnist_caffe/model/mnist.bin +[2021-04-26 21:29:51][ ERROR ] : INVALID_CONFIG: Deserialize the cuda engine failed. +[2021-04-26 21:29:51][ DEBUG ] : build caffe engine with mnist_caffe/model/mnist.prototxt and mnist_caffe/model/mnist.caffemodel +[2021-04-26 21:29:51][ DEBUG ] : Number of Network layers: 9 +[2021-04-26 21:29:51][ DEBUG ] : Number of input: 1 +[2021-04-26 21:29:51][ DEBUG ] : Input layer: +[2021-04-26 21:29:51][ DEBUG ] : data : 1x28x28x +[2021-04-26 21:29:51][ DEBUG ] : Number of output: 1 +[2021-04-26 21:29:51][ DEBUG ] : Output layer: +[2021-04-26 21:29:51][ DEBUG ] : prob : 10x1x1x +[2021-04-26 21:29:51][ DEBUG ] : FP16 support: 1 +[2021-04-26 21:29:51][ DEBUG ] : INT8 support: 1 +[2021-04-26 21:29:51][ DEBUG ] : Max batchsize: 1 +[2021-04-26 21:29:51][ DEBUG ] : Max workspace size: 16777216 +[2021-04-26 21:29:51][ DEBUG ] : Number of DLA core: 0 +[2021-04-26 21:29:51][ DEBUG ] : Max DLA batchsize: 268435456 +[2021-04-26 21:29:51][ DEBUG ] : Current use DLA core: 0 +[2021-04-26 21:29:51][ DEBUG ] : build engine ... +[2021-04-26 21:29:57][ DEBUG ] : build Network done +[2021-04-26 21:29:57][ DEBUG ] : serialize engine to mnist_caffe/model/mnist.bin +[2021-04-26 21:29:57][ DEBUG ] : save engine to mnist_caffe/model/mnist.bin ... +[2021-04-26 21:29:57][ DEBUG ] : create execute context and malloc device memory... +[2021-04-26 21:29:57][ DEBUG ] : init engine... +[2021-04-26 21:29:57][ DEBUG ] : malloc device memory +[2021-04-26 21:29:57][ DEBUG ] : nbBingdings: 2 +[2021-04-26 21:29:57][ DEBUG ] : input: +[2021-04-26 21:29:57][ DEBUG ] : binding bindIndex: 0, name: data, size in byte: 3136 +[2021-04-26 21:29:57][ DEBUG ] : binding dims with 3 dimemsion +[2021-04-26 21:29:57][ DEBUG ] : output: +[2021-04-26 21:29:57][ DEBUG ] : binding bindIndex: 1, name: prob, size in byte: 40 +[2021-04-26 21:29:57][ DEBUG ] : binding dims with 3 dimemsion +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@#=. +*=#@@@@@@@ +@@@@@@@@@@@* :. -@@@@@@@ +@@@@@@@@@@# :#@@: +@@@@@@@ +@@@@@@@@@* :@@@* .@@@@@@@@ +@@@@@@@@= =@@@@. *@@@@@@@@ +@@@@@@@= -@@@@* =@@@@@@@@@ +@@@@@@@ -@@@%: -@@@@@@@@@@ +@@@@@@% %%+: *@@@@@@@@@@ +@@@@@@@ .. @@@@@@@@@@@ +@@@@@@@# .=%%: =@@@@@@@@@@@ +@@@@@@@@@@@@@# +@@@@@@@@@@@ +@@@@@@@@@@@@@# @@@@@@@@@@@@ +@@@@@@@@@@@@@@ @@@@@@@@@@@@ +@@@@@@@@@@@@@# @@@@@@@@@@@@ +@@@@@@@@@@@@@+ @@@@@@@@@@@@ +@@@@@@@@@@@@@% @@@@@@@@@@@@ +@@@@@@@@@@@@@@. #@@@@@@@@@@@ +@@@@@@@@@@@@@@* :%@@@@@@@@@@ +@@@@@@@@@@@@@@@: -@@@@@@@@@@ +@@@@@@@@@@@@@@@@= %@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +[2021-04-26 21:29:57][ INFO ] : Output: +[2021-04-26 21:29:57][ INFO ] : 0: 1.04952e-19 +[2021-04-26 21:29:57][ INFO ] : 1: 5.1673e-09 +[2021-04-26 21:29:57][ INFO ] : 2: 3.33257e-17 +[2021-04-26 21:29:57][ INFO ] : 3: 4.6637e-10 +[2021-04-26 21:29:57][ INFO ] : 4: 1.66628e-07 +[2021-04-26 21:29:57][ INFO ] : 5: 5.3007e-13 +[2021-04-26 21:29:57][ INFO ] : 6: 1.51152e-21 +[2021-04-26 21:29:57][ INFO ] : 7: 2.91715e-06 +[2021-04-26 21:29:57][ INFO ] : 8: 3.99939e-17 +[2021-04-26 21:29:57][ INFO ] : 9: 0.999997 +[2021-04-26 21:29:57][ INFO ] : +``` + diff --git a/TensorRT-Base/mnist_caffe/image/0.pgm b/TensorRT-Base/mnist_caffe/image/0.pgm new file mode 100644 index 0000000000000000000000000000000000000000..0be23e4338e4853bcc961aa47b6c0e39af86b7f4 GIT binary patch literal 797 zcmWGAGBxG;Kd|6T0#m|S>`pi@$jrO?0h-6e%sk(TDFR*Yj35(}V=t5Rg$)ehXmTKD_|s823j7lj~;~2S~4CR)Fz-)G*8KfN?GBxG;Kd3=76T<-<4xG=;!gLUaTmuVEx!uAnIuE{M)puB$g=HaD zng9P&n3$Y!NWEra5!s7f?!FNdOCxrv|I=BR+&^HKThGs8_X4}reI_QR1ng4(Ggw$y zZeW)?uf)ONqHf4p+f M?QrYEWga==03$=pQvd(} literal 0 HcmV?d00001 diff --git a/TensorRT-Base/mnist_caffe/image/2.pgm b/TensorRT-Base/mnist_caffe/image/2.pgm new file mode 100644 index 0000000000000000000000000000000000000000..22d816e07ed666d05ea7e2f543108b107d3e40c1 GIT binary patch literal 797 zcmWGAGBxG;KX}2XXR}*>V0GTT41H#1=3Fdt*VDO~ndEht#jm4Fen=Mp z>QR4ii<$W_y4-xA471v!|JdZbn3u;6hNwuah#&Jkp`O%*+y9KT#!rZe(U|I`aQ5C|sCVpoH45 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2021, wdn. All rights reserved. + * + */ + +#include + +#include "helper.h" +#include "mnist_caffe.h" + +int main() +{ + const std::string caffe_model = "mnist_caffe/model/mnist.caffemodel"; + const std::string caffe_prototxt = "mnist_caffe/model/mnist.prototxt"; + const std::string engine_file = "mnist_caffe/model/mnist.bin"; + int batch_size = 1; + const std::vector output_name{"prob"}; + infer_precision_t precision = INFER_FP32; + + // MnistCaffe(std::string& caffe_prototxt, std::string& caffe_model, std::string& engine_file, int batch_size, + // std::vector output_name, infer_precision_t precision) ; + MnistCaffe* mnist_caffe = new MnistCaffe(caffe_prototxt, caffe_model, engine_file, batch_size, output_name, precision); + + mnist_caffe->build(); + std::string pgm_name = "mnist_caffe/image/9.pgm"; + mnist_caffe->process_input(pgm_name); + mnist_caffe->infer(); + mnist_caffe->process_result(); + + delete mnist_caffe; + return 0; +} \ No newline at end of file diff --git a/TensorRT-Base/mnist_caffe/mnist_caffe.cpp b/TensorRT-Base/mnist_caffe/mnist_caffe.cpp new file mode 100644 index 0000000..0c21eb2 --- /dev/null +++ b/TensorRT-Base/mnist_caffe/mnist_caffe.cpp @@ -0,0 +1,77 @@ +/** + * @file - mnist_caffe.cpp + * @author - wdn (dongnian.wang@outlook.com) + * @brief - mnist_caffe + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#include "mnist_caffe.h" + +trt_ret_t MnistCaffe::build() +{ + std::string input_name = "loss"; + std::vector min_dim_vec{1, 50, 50}; + std::vector opt_dim_vec{1, 28, 28}; + std::vector max_dim_vec{1, 20, 20}; + add_dynamic_shape_profile(m_batch_size,input_name, min_dim_vec, opt_dim_vec, max_dim_vec); + create_engine(m_caffe_prototxt, m_caffe_model, m_engine_file, m_output_name, m_batch_size, m_precision); + + return TRT_SUCCESS; +} + +trt_ret_t MnistCaffe::process_input(std::string& pgm_name) +{ + m_pgm_height = 28; + m_pgm_width = 28; + m_pgm_channel = 1; + std::vector file_buffer(m_pgm_height * m_pgm_width); + m_input_data.resize(m_pgm_height*m_pgm_width*m_pgm_channel); + read_pgm(pgm_name, file_buffer.data(), 28, 28); + for (int i = 0; i < m_pgm_height * m_pgm_width; i++) + { + std::cout << (" .:-=+*#%@"[file_buffer[i] / 26]) << (((i + 1) % m_pgm_width) ? "" : "\n"); + m_input_data[i] = float(file_buffer[i]); + } + return TRT_SUCCESS; +} + + +trt_ret_t MnistCaffe::infer() +{ + std::vector input_dims{m_pgm_channel, m_pgm_width, m_pgm_height}; + set_binding_dimentsions(input_dims, 0); + mem_host_to_device(m_input_data, 0); + inference(); + mem_device_to_host(m_output_data, 1); + + return TRT_SUCCESS; +} + +trt_ret_t MnistCaffe::process_result() +{ + helper::info << "Output:" << std::endl; + float val{0.0f}; + int idx{0}; + const int kDIGITS = 10; + + for (int i = 0; i < kDIGITS; i++) + { + helper::info << i << ": " << m_output_data[i] << std::endl; + } + helper::info << std::endl; + + return TRT_SUCCESS; +} + + +trt_ret_t MnistCaffe::read_pgm(const std::string& file_name, uint8_t* buffer, int in_h, int in_w) +{ + std::ifstream infile(file_name, std::ifstream::binary); + assert(infile.is_open() && "Attempting to read from a file that is not open."); + std::string magic, h, w, max; + infile >> magic >> h >> w >> max; + infile.seekg(1, infile.cur); + infile.read(reinterpret_cast(buffer), in_h * in_w); + return TRT_SUCCESS; +} \ No newline at end of file diff --git a/TensorRT-Base/mnist_caffe/mnist_caffe.h b/TensorRT-Base/mnist_caffe/mnist_caffe.h new file mode 100644 index 0000000..907fccf --- /dev/null +++ b/TensorRT-Base/mnist_caffe/mnist_caffe.h @@ -0,0 +1,51 @@ +/** + * @file - mnist_caffe.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - mnist_caffe + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ + +#ifndef MNIST_CAFFE +#define MNIST_CAFFE + +#include "helper.h" +#include "timer.h" +#include "base_trt.h" + +class MnistCaffe : public TrtBase +{ +public: + MnistCaffe(const std::string& caffe_prototxt, const std::string& caffe_model, const std::string& engine_file, int batch_size, + const std::vector output_name, infer_precision_t precision) + : TrtBase(), m_caffe_prototxt(caffe_prototxt), m_caffe_model(caffe_model), m_engine_file(engine_file), + m_batch_size(batch_size), m_output_name(output_name), m_precision(precision) {} + + trt_ret_t build(); + + trt_ret_t process_input(std::string& pgm_name); + + trt_ret_t infer(); + + trt_ret_t process_result(); + +private: + std::string m_caffe_model; + std::string m_caffe_prototxt; + std::string m_engine_file; + int m_batch_size; + std::vector m_output_name; + infer_precision_t m_precision; + + std::vector m_input_data; + std::vector m_output_data; + + int m_pgm_height; + int m_pgm_width; + int m_pgm_channel; + + trt_ret_t read_pgm(const std::string& file_name, uint8_t* buffer, int in_h, int in_w); +}; + +#endif // MNIST_CAFFE \ No newline at end of file diff --git a/TensorRT-Base/mnist_caffe/model/mnist.caffemodel b/TensorRT-Base/mnist_caffe/model/mnist.caffemodel new file mode 100644 index 0000000000000000000000000000000000000000..fcf5406055a002640a4a736d1d99ae7056d5d45b GIT binary patch literal 1725135 zcmbTdX;@ER^#9#lDp8sx4Jwi*nm%XmPo#kgp-Gbl35_(EY1X7sny8ROld14IYaPB6 z5}8txdCrs}nTosr?|0u%{txdbXIJ#Up}(LnG`&q=ekY?s3qWZxSE7!6bG~(1uWxRcpi6#>M`BVO?}^tckg) znWd?@ne{BQIi^R#Z;x+`Wjas=X zSZnTF;e!%-(&DipK@p(};%?C!LpP}Zzw`Z{b8bR+B*m3(Tz4f0B{Jlt#Y3W_63i52 z=8uKwi1@g*(NXIE`>@XcUNRS-A|#Y(`#(WwF0X%r;jIbA)AsTwrSte4vNq@)=8voiUTtJV3zoe6w9gK~bHZYF{W>d*gGKa9`P zUy+vw#5v6giJbJMLH6VFel}@G3H#r+C`|d}$2UJ(Ov&R-R7ld<4iRlP^u$J^-l

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UQkcPC_#*uKdOJxg_EM4mFMp*=&;S4c literal 0 HcmV?d00001 diff --git a/TensorRT-Base/plugin/CMakeLists.txt b/TensorRT-Base/plugin/CMakeLists.txt new file mode 100644 index 0000000..11f0d29 --- /dev/null +++ b/TensorRT-Base/plugin/CMakeLists.txt @@ -0,0 +1,22 @@ +# @file - plugin/CMakeLists.txt +# @author - wdn (dongnian.wang@outlook.com) +# @brief - plugin编译文件 +# +# @copyright Copyright (c) 2021, wdn. All rights reserved. + +cmake_minimum_required (VERSION 3.0) + +project ("trt_plugin") + +file(GLOB PLUGIN_SOURCE + *.cpp + demo_plugin/*.cpp) + +add_library(trt_plugin STATIC ${PLUGIN_SOURCE}) + +# 链接库 +target_link_libraries( + trt_plugin PRIVATE + ${CUDA_LIBRARIRS} + ${TRT_LIBRARIRS} + ) diff --git a/TensorRT-Base/plugin/README.md b/TensorRT-Base/plugin/README.md new file mode 100644 index 0000000..89f5c16 --- /dev/null +++ b/TensorRT-Base/plugin/README.md @@ -0,0 +1,162 @@ +# Plugin + +## 1.Description + +TensorRT已经只支持了许多常见的神经网络层,比如卷积, 池化, BN等等. 但是依然还有很多操作和算子是不支持的,所以TensorRT提供了接口让我们可以编写插件来实现自己的自定义层。 + +随着tensorRT的不断发展(v5->v6->v7)，TensorRT的插件的使用方式也在不断更新。插件接口也在不断地变化，由v5版本的`IPluginV2Ext`，到v6版本的`IPluginV2IOExt`和`IPluginV2DynamicExt`。 + +| TensorRT版本 | 混合精度 | 动态大小输入 | Requires extended runtime | | +| ------------------- | -------- | ------------ | ------------------------- | ---- | +| IPluginV2Ext | 5.1 | Limited | No | No | +| IPluginV2IOExt | 6.0.1 | General | No | No | +| IPluginV2DynamicExt | 6.0.1 | General | Yes | Yes | + +## 2.Plugin Class + +需要写两个类： + +- `DemoPlugin`，继承`IPluginV2Ext`，是插件类，用于写插件具体的实现 +- `DemoPluginCreator`，继承`IPluginCreator`，是插件工厂类，用于根据需求创建该插件 + +对了，插件类继承`IPluginV2DynamicExt`才可以支持动态尺寸，其他插件类接口例如`IPluginV2IOExt`和前者大部分是相似的。 + +```cpp + +class DemoPlugin : public nvinfer1::IPluginV2Ext + +class DemoPluginCreator : public nvinfer1::IPluginCreator +``` + +## 3.DemoPlugin Class + +```C++ +class DemoPlugin : public nvinfer1::IPluginV2Ext +{ +public: + DemoPlugin(const std::string layer_name); + DemoPlugin(const std::string layer_name, const void* data, size_t length); + DemoPlugin() = delete; + const char* getPluginType() const override; + const char* getPluginVersion() const override; + int getNbOutputs() const override; + nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, int nbInputDims) override; + bool supportsFormat(nvinfer1::DataType type, nvinfer1::PluginFormat format) const override; + int initialize() override; + void terminate() override; + size_t getWorkspaceSize(int maxBatchSize) const override; + int enqueue(int batchSize, const void* const* inputs, void** outputs, + void* workspace, cudaStream_t stream) override; + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override; + bool canBroadcastInputAcrossBatch(int inputIndex) const override; + void configurePlugin(const nvinfer1::Dims* inputDims, int nbInputs, const nvinfer1::Dims* outputDims, + int nbOutputs, const nvinfer1::DataType* inputTypes, + const nvinfer1::DataType* outputTypes, + const bool* inputIsBroadcast, const bool* outputIsBroadcast, + nvinfer1::PluginFormat floatFormat, int maxBatchSize) override; + void destroy() override; + nvinfer1::IPluginV2Ext* clone() const override; + void setPluginNamespace(const char* pluginNamespace) override; + const char* getPluginNamespace() const override; +private: + const std::string m_layer_name; + size_t m_cpoy_size; + std::string m_namespace; + std::string m_plugin_version; + std::string m_plugin_name; + +}; //DemoPlugin +``` + +## 4.IPluginV2插件的工作流 + +### 4.1 parse phase/ parse阶段 + +在模型的parse阶段会通过`CustomPlugin(const Weights *weights, int nbWeights)`创建模型中每一个自定义层的实例，在这个阶段还会调用到`getNbOutputs()`和`getOutputDimensions()`来获取自定义层的输出个数和维度，这个步骤的目的是为了构建整一个模型的工作流。 + +如果自定义层的输出个数和维度跟其他层匹配不上，parse就会失败.所以如果你的自定义层在parse阶段就parse失败了，可以先检查一下这两个函数的实现。 这个阶段创建的CustomPlugin实例会在engine构建阶段被析构掉。 + +### 4.2 build engine phase / engine构建阶段 + +engine构建阶段会再次通过`CustomPlugin(const Weights *weights, int nbWeights)`创建自定义层的实例.然后调用`supportFormat()`函数来检查自定义层的支持的`Datatype`和`PluginFormat`。 + + 在build的过程中，会调用`configureWithFormat`，根据设定的类型(见参数)对插件进行配置。调用完这个函数之后，自定义层内部的状态和变量应该被配置好了。在这里也会调用getWorksapceSize()，但是这个函数不怎么重要。 + +最后会调用initialize()，进行初始化。此时已经准备好所有准备的数据和参数可以进行执行了。构建结束后当调用builder， network或者 engine的destroy()函数时，会调用CustomPlugin的destroy()方法析构掉CustomPlugin对象。 + +### 4.3 save engine phase / 引擎保存阶段 + +保存引擎到序列化文件会调用`getSerializationSize()`函数来获取序列化所需要的空间，在保存的过程中会调用serialize()函数将自定义层的相关信息序列化到引擎文件。 + +### 4.4 engine running phase / 引擎推理阶段 + +在这个阶段会调用用`enqueue()`进行模型推理 + +### 4.5 inference with engine file / 使用引擎文件进行推理 + +在使用引擎文件进行推理的过程中,从序列化文件恢复权重和参数,所以会先调用`DemoPlugins(const void *data, size_t length)`读取自定义层的相关信息，然后调用initialize() 进行初始化。 + +在推理的过程中调用enqueue()进行推理，推理结束后如果在调用engine的destroy方法的时候会调用terminate()函数，释放 掉initialize()申请的资源。 + +## 5.IPluginCreator Class + +IPluginCreator主要用于将编写好的IPlugin插件注册到Plugin Registry。 + +```c++ +class DemoPluginCreator : public nvinfer1::IPluginCreator +{ +public: + /** + * 默认构造函数 + */ + DemoPluginCreator(); + /** + * 获取 plugin 名称 + */ + const char* getPluginName() const override; + /** + * 获取 plugin 版本 + */ + const char* getPluginVersion() const override; + /** + * 获取 file 名称 + */ + const nvinfer1::PluginFieldCollection* getFieldNames() override; + /** + * 创建 plugin + */ + nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override; + /** + * 序列化 plugin + */ + nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + /** + * 设置 namespace + */ + void setPluginNamespace(const char* pluginNamespace) override; + /** + * 获取 namespace + */ + const char* getPluginNamespace() const override; +private: + nvinfer1::PluginFieldCollection m_fc; + std::string m_namespace; + std::string m_plugin_version; + std::string m_plugin_name; +}; // DemoPluginCreator +``` + +## 6.REGISTER_TENSORRT_PLUGIN + +注册plugin宏定义。 + +```C++ +/** + * 注册plugin ！！！！！！！！！！！！！！ +*/ +REGISTER_TENSORRT_PLUGIN(DemoPluginCreator); +``` + diff --git a/TensorRT-Base/plugin/demo_plugin/demo_plugin.cpp b/TensorRT-Base/plugin/demo_plugin/demo_plugin.cpp new file mode 100644 index 0000000..e9a840c --- /dev/null +++ b/TensorRT-Base/plugin/demo_plugin/demo_plugin.cpp @@ -0,0 +1,215 @@ +/** + * @file - demo_plugin.cpp + * @author - wdn (dongnian.wang@outlook.com) + * @brief + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#include "demo_plugin.h" +#include "helper.h" + +using namespace nvinfer1; +using nvinfer1::plugin::DemoPlugin; +using nvinfer1::plugin::DemoPluginCreator; + +// log +#define log(...) { \ + char str[100]; \ + sprintf(str, __VA_ARGS__); \ + helper::debug << "(@ _ @) Here ---> call " << "[" << __FILE__ \ + << __FUNCTION__ << "][Line " << __LINE__ << "]" \ + << str << std::endl; \ +} + +DemoPlugin::DemoPlugin(const std::string layer_name) : m_layer_name(layer_name) +{ + log(" run here now! "); + m_plugin_name = "demo_plugin"; + m_plugin_version = "01"; +} + +DemoPlugin::DemoPlugin(const std::string layer_name, const void* data, size_t length) + : m_layer_name(layer_name) +{ + log(" run here now! "); +} + +const char* DemoPlugin::getPluginType() const +{ + log(" run here now! "); + return m_plugin_name.c_str(); +} + +const char* DemoPlugin::getPluginVersion() const +{ + log(" run here now! "); + return m_plugin_version.c_str(); +} + +int DemoPlugin::getNbOutputs() const +{ + log(" run here now! "); + return 1; +} + +nvinfer1::Dims DemoPlugin::getOutputDimensions(int index, + const nvinfer1::Dims* inputs, int nbInputDims) +{ + log(" run here now! "); + return nvinfer1::Dims3(inputs[1].d[1],inputs[1].d[2],inputs[1].d[3]); +} + +bool DemoPlugin::supportsFormat(nvinfer1::DataType type, nvinfer1::PluginFormat format) const +{ + log(" run here now! "); + return false; +} + +int DemoPlugin::initialize() +{ + log(" run here now! "); + return 0; +} + +void DemoPlugin::terminate() +{ + log(" run here now! "); +} + +size_t DemoPlugin::getWorkspaceSize(int maxBatchSize) const +{ + log(" run here now! "); + return 0; +} + +int DemoPlugin::enqueue(int batchSize, const void* const* inputs, + void** outputs, void* workspace, cudaStream_t stream) +{ + log(" run here now! "); + return 0; +} + +size_t DemoPlugin::getSerializationSize() const +{ + log(" run here now! "); + return 0; +} + +void DemoPlugin::serialize(void* buffer) const +{ + log(" run here now! "); +} + +nvinfer1::DataType DemoPlugin::getOutputDataType(int index, + const nvinfer1::DataType* inputTypes, int nbInputs) const +{ + log(" run here now! "); + return nvinfer1::DataType::kFLOAT; +} + +bool DemoPlugin::isOutputBroadcastAcrossBatch(int outputIndex, + const bool* inputIsBroadcasted, int nbInputs) const +{ + log(" run here now! "); + return false; +} + +bool DemoPlugin::canBroadcastInputAcrossBatch(int inputIndex) const +{ + log(" run here now! "); + return false; +} + +void DemoPlugin::configurePlugin(const nvinfer1::Dims* inputDims, + int nbInputs, const nvinfer1::Dims* outputDims, int nbOutputs, + const nvinfer1::DataType* inputTypes, const nvinfer1::DataType* outputTypes, + const bool* inputIsBroadcast, const bool* outputIsBroadcast, + nvinfer1::PluginFormat floatFormat, int maxBatchSize) +{ + log(" run here now! "); +} + +void DemoPlugin::destroy() +{ + log(" run here now! "); +} + +nvinfer1::IPluginV2Ext* DemoPlugin::clone() const +{ + log(" run here now! "); + auto* plugin = new DemoPlugin(m_layer_name); + plugin->setPluginNamespace(m_namespace.c_str()); + return plugin; +} + +void DemoPlugin::setPluginNamespace(const char* pluginNamespace) +{ + log(" run here now! "); +} + +const char* DemoPlugin::getPluginNamespace() const +{ + log(" run here now! "); + return m_namespace.c_str(); +} + +/******DemoPluginCreator*******/ +DemoPluginCreator::DemoPluginCreator() +{ + log(" run here now! "); + m_fc.nbFields = 0; + m_fc.fields = nullptr; + m_plugin_name = "demo_plugin"; + m_plugin_version = "01"; +} + +const char* DemoPluginCreator::getPluginName() const +{ + log(" run here now! "); + return m_plugin_name.c_str(); +} + +const char* DemoPluginCreator::getPluginVersion() const +{ + log(" run here now! "); + return m_plugin_version.c_str(); +} + +const nvinfer1::PluginFieldCollection* DemoPluginCreator::getFieldNames() +{ + log(" run here now! "); + return &m_fc; +} + +nvinfer1::IPluginV2* DemoPluginCreator::createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) +{ + log(" run here now! "); + auto* plugin = new DemoPlugin(name); + plugin->setPluginNamespace(m_namespace.c_str()); + return plugin; +} + +nvinfer1::IPluginV2* DemoPluginCreator::deserializePlugin(const char* name, + const void* serialData, size_t serialLength) +{ + log(" run here now! "); + return new DemoPlugin(name, serialData, serialLength); +} + +void DemoPluginCreator::setPluginNamespace(const char* pluginNamespace) +{ + log(" run here now! "); + m_namespace = pluginNamespace; +} + +const char* DemoPluginCreator::getPluginNamespace() const +{ + log(" run here now! "); + return m_namespace.c_str(); +} + +/** + * 注册plugin ！！！！！！！！！！！！！！ +*/ +REGISTER_TENSORRT_PLUGIN(DemoPluginCreator); \ No newline at end of file diff --git a/TensorRT-Base/plugin/demo_plugin/demo_plugin.h b/TensorRT-Base/plugin/demo_plugin/demo_plugin.h new file mode 100644 index 0000000..0ccaccb --- /dev/null +++ b/TensorRT-Base/plugin/demo_plugin/demo_plugin.h @@ -0,0 +1,200 @@ +/** + * @file - demo_plugin.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#ifndef TRT_DEMO_PLUGIN_H +#define TRT_DEMO_PLUGIN_H + +#include +#include + +#include +#include + +#include "helper.h" + +namespace nvinfer1 +{ +namespace plugin +{ +class DemoPlugin : public nvinfer1::IPluginV2Ext +{ +public: + /** + * parse 阶段构造函数 + */ + DemoPlugin(const std::string layer_name); + + /** + * deserialize 阶段构造函数 + */ + DemoPlugin(const std::string layer_name, const void* data, size_t length); + + /** + * 注意：删掉默认构造函数 + */ + DemoPlugin() = delete; + + /** + * 返回 plugin 名称 + */ + const char* getPluginType() const override; + + /** + * 返回 plugin 版本 + */ + const char* getPluginVersion() const override; + + /** + * 返回op返回多少个Tensor + */ + int getNbOutputs() const override; + + /** + * 输出 tensor 维度 + */ + nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, int nbInputDims) override; + + bool supportsFormat(nvinfer1::DataType type, nvinfer1::PluginFormat format) const override; + // void configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs, + // const nvinfer1::Dims* outputDims, int nbOutputs, nvinfer1::DataType type, + // nvinfer1::PluginFormat format, int maxBatchSize) override; + + /** + * 初始化函数，一般分配内存等 + */ + int initialize() override; + + /** + * 终止函数，一般释放内存等 + */ + void terminate() override; + + /** + * 返回此 plugin 所需要显存大小 + */ + size_t getWorkspaceSize(int maxBatchSize) const override; + + /** + * plugin 实际执行函数 + */ + int enqueue(int batchSize, const void* const* inputs, void** outputs, + void* workspace, cudaStream_t stream) override; + + /** + * 获取序列化 plugin 大小 + */ + size_t getSerializationSize() const override; + + /** + * 获取序列化 plugin + */ + void serialize(void* buffer) const override; + + /** + * 返回结果的类型 + */ + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + + bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override; + + bool canBroadcastInputAcrossBatch(int inputIndex) const override; + + /** + * 判断输入和输出类型数量是否正确。 + * + * 官方还提到通过这个配置信息可以告知TensorRT去选择合适的算法(algorithm)去调优这个模型。 + */ + void configurePlugin(const nvinfer1::Dims* inputDims, int nbInputs, const nvinfer1::Dims* outputDims, + int nbOutputs, const nvinfer1::DataType* inputTypes, + const nvinfer1::DataType* outputTypes, + const bool* inputIsBroadcast, const bool* outputIsBroadcast, + nvinfer1::PluginFormat floatFormat, int maxBatchSize) override; + + /** + * 销毁 + */ + void destroy() override; + + /** + * 深拷贝 + */ + nvinfer1::IPluginV2Ext* clone() const override; + + /** + * 设置 namespace + * + * 注意：同一个namespace下的plugin如果名字相同会冲突 + */ + void setPluginNamespace(const char* pluginNamespace) override; + + /** + * 获取 namespace + */ + const char* getPluginNamespace() const override; + +private: + const std::string m_layer_name; + size_t m_cpoy_size; + std::string m_namespace; + std::string m_plugin_version; + std::string m_plugin_name; + +}; //DemoPlugin + +class DemoPluginCreator : public nvinfer1::IPluginCreator +{ +public: + /** + * 默认构造函数 + */ + DemoPluginCreator(); + + /** + * 获取 plugin 名称 + */ + const char* getPluginName() const override; + + /** + * 获取 plugin 版本 + */ + const char* getPluginVersion() const override; + + /** + * 获取 file 名称 + */ + const nvinfer1::PluginFieldCollection* getFieldNames() override; + + /** + * 创建 plugin + */ + nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override; + + /** + * 序列化 plugin + */ + nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + + /** + * 设置 namespace + */ + void setPluginNamespace(const char* pluginNamespace) override; + + /** + * 获取 namespace + */ + const char* getPluginNamespace() const override; +private: + nvinfer1::PluginFieldCollection m_fc; + std::string m_namespace; + std::string m_plugin_version; + std::string m_plugin_name; +}; // DemoPluginCreator + +} // namespace plugin +} // namespace nvinfer1 +#endif // TRT_DEMO_PLUGIN_H diff --git a/tf-faster-rcnn/.gitignore b/tf-faster-rcnn/.gitignore new file mode 100644 index 0000000..7f81bbe --- /dev/null +++ b/tf-faster-rcnn/.gitignore @@ -0,0 +1,9 @@ +*.so +*.pyc +output +tensorboard +lib/build +lib/pycocotools +lib/pycocotools/_mask.c +lib/pycocotools/_mask.so +.idea diff --git a/tf-faster-rcnn/123.png b/tf-faster-rcnn/123.png new file mode 100644 index 0000000000000000000000000000000000000000..99fb5c6fb18de0978861f52874f46072ad7fdd1c GIT binary patch literal 167658 zcmbSz2UL^U+BGxISjLJC3q?UJQ~?2LVWdbGr9}vkQ3yy02nZM;FpiC)L~5juNI+^N zgg_|4u@E2>sgZ=D(g|fqs0o4pyxx1~-hch`-EXZwYq5+e?|aJg?7g3T&PmMGD<->t zI`)%@h{$d;QzIJ@ksn?|f7`c$S2!mtI>0|a1R0pwZ3mBt?e5Xw|2uA*ItGb|?7ac~ zefLVgzze*1@s9EJJGTBFcS3IjqD0U)?%ekEzvJt5^F#Ab>;TX*i< z)>c;j_xnozfu71+F-1KhA}2)5j4s*T&72v;Bp>=PxVbR@=1r@J+Pk6?q60t2>@+uW z&i&Qm2hFSdt?Ygr|KA0FBM*kKn?u-WydZF*HBClcT|IDZ3_n4e(LLBpG(N88@ua7x 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    cW6K^XJ!YYo+J%4L+{3pF<0aep<`GjTCF$i^tKzlN>vO{uMFakmSM9x(rvmaF zjPGbidj0Xx6otH^+N|+1?V<7;M+LIK`t78~*MasLMzaiMU%dOavlJJ|x+QC>u!w?d zR>&iL**K`C|Iotoy;|%LZlo@2qs5V`rpH3Fw%@>Po0$;hSIx;rU|sc!Sb{wNxvc*< zD6PhP2OBrVo!bh1;fgIn6*&^os<8V+zf92nuC=v%_K>(7J=0`nyl5KxfnNLcXjx{) zR|uFIU#cnvezADV=g%$_Fs9mOilzr~&U{XbNO#H7(ypvUH%q2PF0L{JtO1on!JhhO zK?2|Ram(?jHUUa{mzn)OyD9}o!v*T*vjC=}@=Y2f1-27(AM0Ara*qwV+4kwNdA_+5 z<2f#$^R!Cb&Szy-$+z%}^x?SHi$B@3C=x&h-j>oi|l4vywYADWm^bFeP@de>qs zu%dY;19;xo?QSzN8&2_TO6|()SK5WgoZs<6*8195H^9e~zw>DftZ&ut6pH<_a>Kz% z;$8mz@Kd#R=vubxZ%-7ZRU_qqhjTus&!I)&#o$;j-SfQ{*K?deP`Y3Jtakew2)Iuu z4suw7;6O@LYg#2fKdG7A7X9+oHpd%F_!AZT>WGT{AqWKG_Es|Twz2ZIm9h4;1wKF` zLc)^IgoU07i|YxC%ZQ4~h)FyX5|a@ULOLgS{68 I2eFL!KO*WYp#T5? literal 0 HcmV?d00001 diff --git a/tf-faster-rcnn/data/scripts/fetch_faster_rcnn_models.sh b/tf-faster-rcnn/data/scripts/fetch_faster_rcnn_models.sh new file mode 100644 index 0000000..b5fcab4 --- /dev/null +++ b/tf-faster-rcnn/data/scripts/fetch_faster_rcnn_models.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )" +cd $DIR + +NET=res101 +FILE=voc_0712_80k-110k.tgz +# replace it with gs11655.sp.cs.cmu.edu if ladoga.graphics.cs.cmu.edu does not work +URL=http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/$NET/$FILE +CHECKSUM=cb32e9df553153d311cc5095b2f8c340 + +if [ -f $FILE ]; then + echo "File already exists. Checking md5..." + os=`uname -s` + if [ "$os" = "Linux" ]; then + checksum=`md5sum $FILE | awk '{ print $1 }'` + elif [ "$os" = "Darwin" ]; then + checksum=`cat $FILE | md5` + fi + if [ "$checksum" = "$CHECKSUM" ]; then + echo "Checksum is correct. No need to download." + exit 0 + else + echo "Checksum is incorrect. Need to download again." + fi +fi + +echo "Downloading Resnet 101 Faster R-CNN models Pret-trained on VOC 07+12 (340M)..." + +wget $URL -O $FILE + +echo "Unzipping..." + +tar zxvf $FILE + +echo "Done. Please run this command again to verify that checksum = $CHECKSUM." diff --git a/tf-faster-rcnn/docker/Dockerfile.cuda-7.5 b/tf-faster-rcnn/docker/Dockerfile.cuda-7.5 new file mode 100644 index 0000000..9837691 --- /dev/null +++ b/tf-faster-rcnn/docker/Dockerfile.cuda-7.5 @@ -0,0 +1,49 @@ +# mbuckler/tf-faster-rcnn-deps +# +# Dockerfile to hold dependencies for the Tensorflow implmentation +# of Faster RCNN + +FROM mbuckler/tensorflow:cuda-7.5 +WORKDIR /root + +# Get required packages +RUN apt-get update && \ + apt-get install vim \ + python-pip \ + python-dev \ + python-opencv \ + python-tk \ + libjpeg-dev \ + libfreetype6 \ + libfreetype6-dev \ + zlib1g-dev \ + cmake \ + wget \ + cython \ + git \ + -y + +# Get required python modules +RUN pip install --upgrade pip +RUN pip install image \ + scipy \ + matplotlib \ + PyYAML \ + numpy \ + easydict + +# Update numpy +RUN pip install -U numpy + +# Install python interface for COCO +RUN git clone https://github.com/pdollar/coco.git +WORKDIR /root/coco/PythonAPI +RUN make +WORKDIR /root + +# Add CUDA to the path +ENV LD_LIBRARY_PATH $LD_LIBRARY_PATH:/usr/local/cuda/lib64 +ENV CUDA_HOME /usr/local/cuda +ENV PYTHONPATH /root/coco/PythonAPI +RUN ldconfig + diff --git a/tf-faster-rcnn/docker/Dockerfile.cuda-8.0 b/tf-faster-rcnn/docker/Dockerfile.cuda-8.0 new file mode 100644 index 0000000..bcf1522 --- /dev/null +++ b/tf-faster-rcnn/docker/Dockerfile.cuda-8.0 @@ -0,0 +1,49 @@ +# mbuckler/tf-faster-rcnn-deps +# +# Dockerfile to hold dependencies for the Tensorflow implmentation +# of Faster RCNN + +FROM nvidia/cuda:8.0-cudnn5-devel +WORKDIR /root + +# Get required packages +RUN apt-get update && \ + apt-get install vim \ + python-pip \ + python-dev \ + python-opencv \ + python-tk \ + libjpeg-dev \ + libfreetype6 \ + libfreetype6-dev \ + zlib1g-dev \ + cmake \ + wget \ + cython \ + git \ + -y + +# Get required python modules +RUN pip install --upgrade pip +RUN pip install image \ + scipy \ + matplotlib \ + PyYAML \ + numpy \ + easydict \ + tensorflow-gpu +# Update numpy +RUN pip install -U numpy + +# Install python interface for COCO +RUN git clone https://github.com/pdollar/coco.git +WORKDIR /root/coco/PythonAPI +RUN make +WORKDIR /root + +# Add CUDA to the path +ENV LD_LIBRARY_PATH $LD_LIBRARY_PATH:/usr/local/cuda/lib64 +ENV CUDA_HOME /usr/local/cuda +ENV PYTHONPATH /root/coco/PythonAPI +RUN ldconfig + diff --git a/tf-faster-rcnn/experiments/cfgs/.ipynb_checkpoints/mobile-checkpoint.yml b/tf-faster-rcnn/experiments/cfgs/.ipynb_checkpoints/mobile-checkpoint.yml new file mode 100644 index 0000000..8adbf94 --- /dev/null +++ b/tf-faster-rcnn/experiments/cfgs/.ipynb_checkpoints/mobile-checkpoint.yml @@ -0,0 +1,16 @@ +EXP_DIR: mobile +TRAIN: + HAS_RPN: True + IMS_PER_BATCH: 1 + BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True + RPN_POSITIVE_OVERLAP: 0.7 + RPN_BATCHSIZE: 256 + PROPOSAL_METHOD: gt + BG_THRESH_LO: 0.0 + DISPLAY: 20 + BATCH_SIZE: 256 + DOUBLE_BIAS: False + SNAPSHOT_PREFIX: mobile_faster_rcnn +TEST: + HAS_RPN: True +POOLING_MODE: crop diff --git a/tf-faster-rcnn/experiments/cfgs/.ipynb_checkpoints/vgg16-checkpoint.yml b/tf-faster-rcnn/experiments/cfgs/.ipynb_checkpoints/vgg16-checkpoint.yml new file mode 100644 index 0000000..de1ac68 --- /dev/null +++ b/tf-faster-rcnn/experiments/cfgs/.ipynb_checkpoints/vgg16-checkpoint.yml @@ -0,0 +1,15 @@ +EXP_DIR: vgg16 +TRAIN: + HAS_RPN: True + IMS_PER_BATCH: 1 + BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True + RPN_POSITIVE_OVERLAP: 0.7 + RPN_BATCHSIZE: 256 + PROPOSAL_METHOD: gt + BG_THRESH_LO: 0.0 + DISPLAY: 20 + BATCH_SIZE: 256 + SNAPSHOT_PREFIX: vgg16_faster_rcnn +TEST: + HAS_RPN: True +POOLING_MODE: crop diff --git a/tf-faster-rcnn/experiments/cfgs/mobile.yml b/tf-faster-rcnn/experiments/cfgs/mobile.yml new file mode 100644 index 0000000..8adbf94 --- /dev/null +++ b/tf-faster-rcnn/experiments/cfgs/mobile.yml @@ -0,0 +1,16 @@ +EXP_DIR: mobile +TRAIN: + HAS_RPN: True + IMS_PER_BATCH: 1 + BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True + RPN_POSITIVE_OVERLAP: 0.7 + RPN_BATCHSIZE: 256 + PROPOSAL_METHOD: gt + BG_THRESH_LO: 0.0 + DISPLAY: 20 + BATCH_SIZE: 256 + DOUBLE_BIAS: False + SNAPSHOT_PREFIX: mobile_faster_rcnn +TEST: + HAS_RPN: True +POOLING_MODE: crop diff --git a/tf-faster-rcnn/experiments/cfgs/res101-lg.yml b/tf-faster-rcnn/experiments/cfgs/res101-lg.yml new file mode 100644 index 0000000..ce4aefb --- /dev/null +++ b/tf-faster-rcnn/experiments/cfgs/res101-lg.yml @@ -0,0 +1,22 @@ +EXP_DIR: res101-lg +TRAIN: + HAS_RPN: True + IMS_PER_BATCH: 1 + BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True + RPN_POSITIVE_OVERLAP: 0.7 + RPN_BATCHSIZE: 256 + PROPOSAL_METHOD: gt + BG_THRESH_LO: 0.0 + DISPLAY: 20 + BATCH_SIZE: 256 + DOUBLE_BIAS: False + SNAPSHOT_PREFIX: res101_faster_rcnn + SCALES: [800] + MAX_SIZE: 1333 +TEST: + HAS_RPN: True + SCALES: [800] + MAX_SIZE: 1333 + RPN_POST_NMS_TOP_N: 1000 +POOLING_MODE: crop +ANCHOR_SCALES: [2,4,8,16,32] diff --git a/tf-faster-rcnn/experiments/cfgs/res101.yml b/tf-faster-rcnn/experiments/cfgs/res101.yml new file mode 100644 index 0000000..367cad3 --- /dev/null +++ b/tf-faster-rcnn/experiments/cfgs/res101.yml @@ -0,0 +1,16 @@ +EXP_DIR: res101 +TRAIN: + HAS_RPN: True + IMS_PER_BATCH: 1 + BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True + RPN_POSITIVE_OVERLAP: 0.7 + RPN_BATCHSIZE: 256 + PROPOSAL_METHOD: gt + BG_THRESH_LO: 0.0 + DISPLAY: 20 + BATCH_SIZE: 256 + DOUBLE_BIAS: False + SNAPSHOT_PREFIX: res101_faster_rcnn +TEST: + HAS_RPN: True +POOLING_MODE: crop diff --git a/tf-faster-rcnn/experiments/cfgs/res50.yml b/tf-faster-rcnn/experiments/cfgs/res50.yml new file mode 100644 index 0000000..7b38b2a --- /dev/null +++ b/tf-faster-rcnn/experiments/cfgs/res50.yml @@ -0,0 +1,16 @@ +EXP_DIR: res50 +TRAIN: + HAS_RPN: True + IMS_PER_BATCH: 1 + BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True + RPN_POSITIVE_OVERLAP: 0.7 + RPN_BATCHSIZE: 256 + PROPOSAL_METHOD: gt + BG_THRESH_LO: 0.0 + DISPLAY: 20 + BATCH_SIZE: 256 + DOUBLE_BIAS: False + SNAPSHOT_PREFIX: res50_faster_rcnn +TEST: + HAS_RPN: True +POOLING_MODE: crop diff --git a/tf-faster-rcnn/experiments/cfgs/vgg16.yml b/tf-faster-rcnn/experiments/cfgs/vgg16.yml new file mode 100644 index 0000000..de1ac68 --- /dev/null +++ b/tf-faster-rcnn/experiments/cfgs/vgg16.yml @@ -0,0 +1,15 @@ +EXP_DIR: vgg16 +TRAIN: + HAS_RPN: True + IMS_PER_BATCH: 1 + BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True + RPN_POSITIVE_OVERLAP: 0.7 + RPN_BATCHSIZE: 256 + PROPOSAL_METHOD: gt + BG_THRESH_LO: 0.0 + DISPLAY: 20 + BATCH_SIZE: 256 + SNAPSHOT_PREFIX: vgg16_faster_rcnn +TEST: + HAS_RPN: True +POOLING_MODE: crop diff --git a/tf-faster-rcnn/experiments/logs/.gitignore b/tf-faster-rcnn/experiments/logs/.gitignore new file mode 100644 index 0000000..d0f34f6 --- /dev/null +++ b/tf-faster-rcnn/experiments/logs/.gitignore @@ -0,0 +1 @@ +*.txt.* diff --git a/tf-faster-rcnn/experiments/scripts/.ipynb_checkpoints/test_faster_rcnn-checkpoint.sh b/tf-faster-rcnn/experiments/scripts/.ipynb_checkpoints/test_faster_rcnn-checkpoint.sh new file mode 100644 index 0000000..a8914dd --- /dev/null +++ b/tf-faster-rcnn/experiments/scripts/.ipynb_checkpoints/test_faster_rcnn-checkpoint.sh @@ -0,0 +1,75 @@ +#!/bin/bash + +set -x +set -e + +export PYTHONUNBUFFERED="True" + +GPU_ID=$1 +DATASET=$2 +NET=$3 + +array=( $@ ) +len=${#array[@]} +EXTRA_ARGS=${array[@]:3:$len} +EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} + +case ${DATASET} in + pascal_voc) + TRAIN_IMDB="voc_2007_trainval" + TEST_IMDB="voc_2007_test" + ITERS=10000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + pascal_voc_0712) + TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" + TEST_IMDB="voc_2007_test" + ITERS=110000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + coco) + TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" + TEST_IMDB="coco_2014_minival" + ITERS=490000 + ANCHORS="[4,8,16,32]" + RATIOS="[0.5,1,2]" + ;; + *) + echo "No dataset given" + exit + ;; +esac + +LOG="experiments/logs/test_${NET}_${TRAIN_IMDB}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" +exec &> >(tee -a "$LOG") +echo Logging output to "$LOG" + +set +x +if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt +else + NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt +fi +set -x + +if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/test_net.py \ + --imdb ${TEST_IMDB} \ + --model ${NET_FINAL} \ + --cfg experiments/cfgs/${NET}.yml \ + --tag ${EXTRA_ARGS_SLUG} \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + ${EXTRA_ARGS} +else + CUDA_VISIBLE_DEVICES=${GPU_ID} &&time python ./tools/test_net.py \ + --imdb ${TEST_IMDB} \ + --model ${NET_FINAL} \ + --cfg experiments/cfgs/${NET}.yml \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + ${EXTRA_ARGS} +fi + diff --git a/tf-faster-rcnn/experiments/scripts/.ipynb_checkpoints/train_faster_rcnn-checkpoint.sh b/tf-faster-rcnn/experiments/scripts/.ipynb_checkpoints/train_faster_rcnn-checkpoint.sh new file mode 100644 index 0000000..0466fac --- /dev/null +++ b/tf-faster-rcnn/experiments/scripts/.ipynb_checkpoints/train_faster_rcnn-checkpoint.sh @@ -0,0 +1,85 @@ +#!/bin/bash + +set -x +set -e + +export PYTHONUNBUFFERED="True" + +GPU_ID=$1 +DATASET=$2 +NET=$3 + +array=( $@ ) +len=${#array[@]} +EXTRA_ARGS=${array[@]:3:$len} +EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} + +case ${DATASET} in + pascal_voc) + TRAIN_IMDB="voc_2007_trainval" + TEST_IMDB="voc_2007_test" + STEPSIZE="[50000]" + ITERS=10000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + pascal_voc_0712) + TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" + TEST_IMDB="voc_2007_test" + STEPSIZE="[80000]" + ITERS=110000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + coco) + TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" + TEST_IMDB="coco_2014_minival" + STEPSIZE="[350000]" + ITERS=490000 + ANCHORS="[4,8,16,32]" + RATIOS="[0.5,1,2]" + ;; + *) + echo "No dataset given" + exit + ;; +esac + +LOG="experiments/logs/${NET}_${TRAIN_IMDB}_${EXTRA_ARGS_SLUG}_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" +exec &> >(tee -a "$LOG") +echo Logging output to "$LOG" + +set +x +if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt +else + NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt +fi +set -x + +if [ ! -f ${NET_FINAL}.index ]; then + if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \ + --weight data/imagenet_weights/${NET}.ckpt \ + --imdb ${TRAIN_IMDB} \ + --imdbval ${TEST_IMDB} \ + --iters ${ITERS} \ + --cfg experiments/cfgs/${NET}.yml \ + --tag ${EXTRA_ARGS_SLUG} \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} + else + CUDA_VISIBLE_DEVICES=${GPU_ID} &&time python ./tools/trainval_net.py \ + --weight data/imagenet_weights/${NET}.ckpt \ + --imdb ${TRAIN_IMDB} \ + --imdbval ${TEST_IMDB} \ + --iters ${ITERS} \ + --cfg experiments/cfgs/${NET}.yml \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} + fi +fi + +./experiments/scripts/test_faster_rcnn.sh $@ diff --git a/tf-faster-rcnn/experiments/scripts/convert_vgg16.sh b/tf-faster-rcnn/experiments/scripts/convert_vgg16.sh new file mode 100644 index 0000000..7fae1b4 --- /dev/null +++ b/tf-faster-rcnn/experiments/scripts/convert_vgg16.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +set -x +set -e + +export PYTHONUNBUFFERED="True" + +GPU_ID=$1 +DATASET=$2 +NET=vgg16 + +array=( $@ ) +len=${#array[@]} +EXTRA_ARGS=${array[@]:2:$len} +EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} + +case ${DATASET} in + pascal_voc) + TRAIN_IMDB="voc_2007_trainval" + TEST_IMDB="voc_2007_test" + ITERS=70000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + pascal_voc_0712) + TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" + TEST_IMDB="voc_2007_test" + ITERS=110000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + coco) + TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" + TEST_IMDB="coco_2014_minival" + ITERS=490000 + ANCHORS="[4,8,16,32]" + RATIOS="[0.5,1,2]" + ;; + *) + echo "No dataset given" + exit + ;; +esac + +set +x +NET_FINAL=${NET}_faster_rcnn_iter_${ITERS} +set -x + +if [ ! -f ${NET_FINAL}.index ]; then + if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/convert_from_depre.py \ + --snapshot ${NET_FINAL} \ + --imdb ${TRAIN_IMDB} \ + --iters ${ITERS} \ + --cfg experiments/cfgs/${NET}.yml \ + --tag ${EXTRA_ARGS_SLUG} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} ${EXTRA_ARGS} + else + CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/convert_from_depre.py \ + --snapshot ${NET_FINAL} \ + --imdb ${TRAIN_IMDB} \ + --iters ${ITERS} \ + --cfg experiments/cfgs/${NET}.yml \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} ${EXTRA_ARGS} + fi +fi + diff --git a/tf-faster-rcnn/experiments/scripts/test_faster_rcnn.sh b/tf-faster-rcnn/experiments/scripts/test_faster_rcnn.sh new file mode 100644 index 0000000..a8914dd --- /dev/null +++ b/tf-faster-rcnn/experiments/scripts/test_faster_rcnn.sh @@ -0,0 +1,75 @@ +#!/bin/bash + +set -x +set -e + +export PYTHONUNBUFFERED="True" + +GPU_ID=$1 +DATASET=$2 +NET=$3 + +array=( $@ ) +len=${#array[@]} +EXTRA_ARGS=${array[@]:3:$len} +EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} + +case ${DATASET} in + pascal_voc) + TRAIN_IMDB="voc_2007_trainval" + TEST_IMDB="voc_2007_test" + ITERS=10000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + pascal_voc_0712) + TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" + TEST_IMDB="voc_2007_test" + ITERS=110000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + coco) + TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" + TEST_IMDB="coco_2014_minival" + ITERS=490000 + ANCHORS="[4,8,16,32]" + RATIOS="[0.5,1,2]" + ;; + *) + echo "No dataset given" + exit + ;; +esac + +LOG="experiments/logs/test_${NET}_${TRAIN_IMDB}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" +exec &> >(tee -a "$LOG") +echo Logging output to "$LOG" + +set +x +if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt +else + NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt +fi +set -x + +if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/test_net.py \ + --imdb ${TEST_IMDB} \ + --model ${NET_FINAL} \ + --cfg experiments/cfgs/${NET}.yml \ + --tag ${EXTRA_ARGS_SLUG} \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + ${EXTRA_ARGS} +else + CUDA_VISIBLE_DEVICES=${GPU_ID} &&time python ./tools/test_net.py \ + --imdb ${TEST_IMDB} \ + --model ${NET_FINAL} \ + --cfg experiments/cfgs/${NET}.yml \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + ${EXTRA_ARGS} +fi + diff --git a/tf-faster-rcnn/experiments/scripts/train_faster_rcnn.sh b/tf-faster-rcnn/experiments/scripts/train_faster_rcnn.sh new file mode 100644 index 0000000..0466fac --- /dev/null +++ b/tf-faster-rcnn/experiments/scripts/train_faster_rcnn.sh @@ -0,0 +1,85 @@ +#!/bin/bash + +set -x +set -e + +export PYTHONUNBUFFERED="True" + +GPU_ID=$1 +DATASET=$2 +NET=$3 + +array=( $@ ) +len=${#array[@]} +EXTRA_ARGS=${array[@]:3:$len} +EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} + +case ${DATASET} in + pascal_voc) + TRAIN_IMDB="voc_2007_trainval" + TEST_IMDB="voc_2007_test" + STEPSIZE="[50000]" + ITERS=10000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + pascal_voc_0712) + TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" + TEST_IMDB="voc_2007_test" + STEPSIZE="[80000]" + ITERS=110000 + ANCHORS="[8,16,32]" + RATIOS="[0.5,1,2]" + ;; + coco) + TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" + TEST_IMDB="coco_2014_minival" + STEPSIZE="[350000]" + ITERS=490000 + ANCHORS="[4,8,16,32]" + RATIOS="[0.5,1,2]" + ;; + *) + echo "No dataset given" + exit + ;; +esac + +LOG="experiments/logs/${NET}_${TRAIN_IMDB}_${EXTRA_ARGS_SLUG}_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" +exec &> >(tee -a "$LOG") +echo Logging output to "$LOG" + +set +x +if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt +else + NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt +fi +set -x + +if [ ! -f ${NET_FINAL}.index ]; then + if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then + CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \ + --weight data/imagenet_weights/${NET}.ckpt \ + --imdb ${TRAIN_IMDB} \ + --imdbval ${TEST_IMDB} \ + --iters ${ITERS} \ + --cfg experiments/cfgs/${NET}.yml \ + --tag ${EXTRA_ARGS_SLUG} \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} + else + CUDA_VISIBLE_DEVICES=${GPU_ID} &&time python ./tools/trainval_net.py \ + --weight data/imagenet_weights/${NET}.ckpt \ + --imdb ${TRAIN_IMDB} \ + --imdbval ${TEST_IMDB} \ + --iters ${ITERS} \ + --cfg experiments/cfgs/${NET}.yml \ + --net ${NET} \ + --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \ + TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} + fi +fi + +./experiments/scripts/test_faster_rcnn.sh $@ diff --git "a/tf-faster-rcnn/faster_rcnn\346\250\241\345\236\213\350\275\254\345\214\226.md" "b/tf-faster-rcnn/faster_rcnn\346\250\241\345\236\213\350\275\254\345\214\226.md" new file mode 100644 index 0000000..2976523 --- /dev/null +++ "b/tf-faster-rcnn/faster_rcnn\346\250\241\345\236\213\350\275\254\345\214\226.md" @@ -0,0 +1,128 @@ +## faster_rcnn模型转化 + +1. 查看输入输出节点 + + 使用网络训练后会生成3个ckpt加一个checkpoint文件： + + ``` + checkpoint + + voc_8001model.ckpt.data-00000-of-00001 + + voc_8001model.ckpt.index + + voc_8001model.ckpt.meta + ``` + + 输入和输出tensor的确定是ckpt模型转pb模型的关键，采用Netron软件可以直接打开`.meta`文件，查看网络结构图得到模型的输入输出节点名称。也可以通过如下代码通过ckpt文件直接生成输入输出节点名称。 + + ``` + import tensorflow as tf + ckpt = 'F:/gree_test/tensorflow/voc_8001model.ckpt' + b=open('a.txt',mode='a+') + with tf.Session() as sess: + saver = tf.train.import_meta_graph(ckpt + '.meta', clear_devices=True) + graph_def = tf.get_default_graph().as_graph_def(add_shapes=True) + node_list = [n.name for n in graph_def.node] + for node in node_list: + b.write(str(node)+"\n") + print("node_name", node) + ``` + + 因为faster_rcnn模型比较大，打印出来的节点名称非常多，需要熟悉网络结构的输入输出。最方便的就是直接在网络测试代码中寻找网络节点，在原始文件lib/nets/network.py找到如下代码： + + def test_image(self, sess, image, im_info): + feed_dict = {self._image: image, + self._im_info: im_info} + cls_score, cls_prob, bbox_pred, rois = sess.run([self._predictions["cls_score"], + self._predictions['cls_prob'], + self._predictions['bbox_pred'], + self._predictions['rois']], + feed_dict=feed_dict) + return cls_score, cls_prob, bbox_pred, rois + + 这里就是tensorflow开始run模型，输入的参数就是我们想要的，根据run()的调用方式，前面的列表是输出的tensor，后面的feed_dict字典是喂给图的数据和输入的tensor，这里可以提取打印一下这几个参数，最终得到我们的输入和输出节点。输入节点：Placeholder：0，Placeholder_1：0，输出节点：vgg_16_3/cls_score/BiasAdd:0, vgg_16_3/cls_prob:0, add:0, vgg_16_1/rois/concat:0. + +2. 固化pb模型 + + 将CKPT 转换成 PB格式的文件的过程可简述如下： + + - 通过传入 CKPT 模型的路径得到模型的图和变量数据 + + - 通过 import_meta_graph 导入模型中的图 + + - 通过 saver.restore 从模型中恢复图中各个变量的数据 + + - 通过 graph_util.convert_variables_to_constants 将模型持久化 + + 注意改变输出节点，转化代码如下： + + ``` + import tensorflow as tf + from tensorflow.python.framework import graph_util + from tensorflow.python.platform import gfile + def freeze_graph(ckpt, output_graph): + output_node_names = 'vgg_16_3/cls_score/BiasAdd:0,vgg_16_3/cls_prob:0,add:0,vgg_16_1/rois/concat:0' + saver = tf.compat.v1.train.import_meta_graph(ckpt + '.meta', clear_devices=True) + graph = tf.get_default_graph() + input_graph_def = graph.as_graph_def() + with tf.Session() as sess: + saver.restore(sess, ckpt) + output_graph_def = graph_util.convert_variables_to_constants( + sess=sess, + input_graph_def=input_graph_def, + output_node_names=output_node_names.split(',') + ) + with tf.gfile.GFile(output_graph, 'wb') as fw: + fw.write(output_graph_def.SerializeToString()) + print('{} ops in the final graph.'.format(len(output_graph_def.node))) + ckpt = 'F:/gree_test/tensorflow/voc_8001model.ckpt' + pb = 'F:/gree_test/tensorflow/bert_model.pb' + if __name__ == '__main__': + freeze_graph(ckpt, pb) + ``` + +3. ONNX模型转化 + + 安装tensorflow转换onnx模型的转化工具 + + `pip install tf2onnx` + + 运行下列命令将pb模型转化为onnx模型 + + ```bash + python -m tf2onnx.convert\ + --input bert_model.pb\ + --inputs Placeholder：0,Placeholder_1：0\ + --outputs vgg_16_3/cls_score/BiasAdd:0,vgg_16_3/cls_prob:0,add:0,vgg_16_1/rois/concat:0.\ + --output model.onnx\ + --opset10 + ``` + + + +4. 存在问题 + + （1）在利用tf2onnx转化onnx模型时，在过程中会出现以下错误 + + `ERROR - Tensorflow op [vgg_16_1/rois/non_max_suppression/NonMaxSuppressionV3: NonMaxSuppressionV3] is not supported` + + 由于有不支持的op，需要在转换代码中加上--continue_on_error，成功转成onnx。 + + （2）在利用转成的onnx模型进行tensorrt测试时，出现了如下错误： + + `ERROR - Failed to convert node vgg_16_1/pool5/crops` + `OP=CropAndResize` + + 这里使用ONNX-graphsurgeon来修改onnx model + + 将vgg_16_1/pool5/crops这个节点的名称修改为"CropAndResize" + + （3）运行修改节点后的onnx模型会出现如下错误： + + `[E] [TRT] (Unnamed Layer* 154) [Constant]: invalid weights type of Bool` + + + + + diff --git a/tf-faster-rcnn/lib/.ipynb_checkpoints/setup-checkpoint.py b/tf-faster-rcnn/lib/.ipynb_checkpoints/setup-checkpoint.py new file mode 100644 index 0000000..27d0bad --- /dev/null +++ b/tf-faster-rcnn/lib/.ipynb_checkpoints/setup-checkpoint.py @@ -0,0 +1,144 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import os +from os.path import join as pjoin +import numpy as np +from distutils.core import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext + +def find_in_path(name, path): + "Find a file in a search path" + #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ + for dir in path.split(os.pathsep): + binpath = pjoin(dir, name) + if os.path.exists(binpath): + return os.path.abspath(binpath) + return None + +def locate_cuda(): + """Locate the CUDA environment on the system + + Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' + and values giving the absolute path to each directory. + + Starts by looking for the CUDAHOME env variable. If not found, everything + is based on finding 'nvcc' in the PATH. + """ + + # first check if the CUDAHOME env variable is in use + if 'CUDAHOME' in os.environ: + home = os.environ['CUDAHOME'] + nvcc = pjoin(home, 'bin', 'nvcc') + else: + # otherwise, search the PATH for NVCC + default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') + nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) + if nvcc is None: + raise EnvironmentError('The nvcc binary could not be ' + 'located in your $PATH. Either add it to your path, or set $CUDAHOME') + home = os.path.dirname(os.path.dirname(nvcc)) + + cudaconfig = {'home':home, 'nvcc':nvcc, + 'include': pjoin(home, 'include'), + 'lib64': pjoin(home, 'lib64')} + for k, v in cudaconfig.items(): + if not os.path.exists(v): + raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) + + return cudaconfig +CUDA = locate_cuda() + +# Obtain the numpy include directory. This logic works across numpy versions. +try: + numpy_include = np.get_include() +except AttributeError: + numpy_include = np.get_numpy_include() + +def customize_compiler_for_nvcc(self): + """inject deep into distutils to customize how the dispatch + to gcc/nvcc works. + + If you subclass UnixCCompiler, it's not trivial to get your subclass + injected in, and still have the right customizations (i.e. + distutils.sysconfig.customize_compiler) run on it. So instead of going + the OO route, I have this. Note, it's kindof like a wierd functional + subclassing going on.""" + + # tell the compiler it can processes .cu + self.src_extensions.append('.cu') + + # save references to the default compiler_so and _comple methods + default_compiler_so = self.compiler_so + super = self._compile + + # now redefine the _compile method. This gets executed for each + # object but distutils doesn't have the ability to change compilers + # based on source extension: we add it. + def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): + print(extra_postargs) + if os.path.splitext(src)[1] == '.cu': + # use the cuda for .cu files + self.set_executable('compiler_so', CUDA['nvcc']) + # use only a subset of the extra_postargs, which are 1-1 translated + # from the extra_compile_args in the Extension class + postargs = extra_postargs['nvcc'] + else: + postargs = extra_postargs['gcc'] + + super(obj, src, ext, cc_args, postargs, pp_opts) + # reset the default compiler_so, which we might have changed for cuda + self.compiler_so = default_compiler_so + + # inject our redefined _compile method into the class + self._compile = _compile + +# run the customize_compiler +class custom_build_ext(build_ext): + def build_extensions(self): + customize_compiler_for_nvcc(self.compiler) + build_ext.build_extensions(self) + +ext_modules = [ + Extension( + "utils.cython_bbox", + ["utils/bbox.pyx"], + extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + include_dirs = [numpy_include] + ), + Extension( + "nms.cpu_nms", + ["nms/cpu_nms.pyx"], + extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + include_dirs = [numpy_include] + ), + Extension('nms.gpu_nms', + ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'], + library_dirs=[CUDA['lib64']], + libraries=['cudart'], + language='c++', + runtime_library_dirs=[CUDA['lib64']], + # this syntax is specific to this build system + # we're only going to use certain compiler args with nvcc and not with gcc + # the implementation of this trick is in customize_compiler() below + extra_compile_args={'gcc': ["-Wno-unused-function"], + 'nvcc': ['-arch=sm_60', + '--ptxas-options=-v', + '-c', + '--compiler-options', + "'-fPIC'"]}, + include_dirs = [numpy_include, CUDA['include']] + ) +] + +setup( + name='tf_faster_rcnn', + ext_modules=ext_modules, + # inject our custom trigger + cmdclass={'build_ext': custom_build_ext}, +) diff --git a/tf-faster-rcnn/lib/Makefile b/tf-faster-rcnn/lib/Makefile new file mode 100644 index 0000000..1e9e686 --- /dev/null +++ b/tf-faster-rcnn/lib/Makefile @@ -0,0 +1,6 @@ +all: + python setup.py build_ext --inplace + rm -rf build +clean: + rm -rf */*.pyc + rm -rf */*.so diff --git a/tf-faster-rcnn/lib/datasets/.ipynb_checkpoints/pascal_voc-checkpoint.py b/tf-faster-rcnn/lib/datasets/.ipynb_checkpoints/pascal_voc-checkpoint.py new file mode 100644 index 0000000..ce5ad5d --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/.ipynb_checkpoints/pascal_voc-checkpoint.py @@ -0,0 +1,310 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +from datasets.imdb import imdb +import datasets.ds_utils as ds_utils +import xml.etree.ElementTree as ET +import numpy as np +import scipy.sparse +import scipy.io as sio +import utils.cython_bbox +import pickle +import subprocess +import uuid +from .voc_eval import voc_eval +from model.config import cfg + + +class pascal_voc(imdb): + def __init__(self, image_set, year, use_diff=False): + name = 'voc_' + year + '_' + image_set + if use_diff: + name += '_diff' + imdb.__init__(self, name) + self._year = year + self._image_set = image_set + self._devkit_path = self._get_default_path() + self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year) +# self._classes = ('__background__', # always index 0 +# 'aeroplane', 'bicycle', 'bird', 'boat', +# 'bottle', 'bus', 'car', 'cat', 'chair', +# 'cow', 'diningtable', 'dog', 'horse', +# 'motorbike', 'person', 'pottedplant', +# 'sheep', 'sofa', 'train', 'tvmonitor') + self._classes = ('__background__', 'node') + self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) + self._image_ext = '.png' + self._image_index = self._load_image_set_index() + # Default to roidb handler + self._roidb_handler = self.gt_roidb + self._salt = str(uuid.uuid4()) + self._comp_id = 'comp4' + + # PASCAL specific config options + self.config = {'cleanup': True, + 'use_salt': True, + 'use_diff': use_diff, + 'matlab_eval': False, + 'rpn_file': None} + + assert os.path.exists(self._devkit_path), \ + 'VOCdevkit path does not exist: {}'.format(self._devkit_path) + assert os.path.exists(self._data_path), \ + 'Path does not exist: {}'.format(self._data_path) + + def image_path_at(self, i): + """ + Return the absolute path to image i in the image sequence. + """ + return self.image_path_from_index(self._image_index[i]) + + def image_path_from_index(self, index): + """ + Construct an image path from the image's "index" identifier. + """ + image_path = os.path.join(self._data_path, 'JPEGImages', + index + self._image_ext) + assert os.path.exists(image_path), \ + 'Path does not exist: {}'.format(image_path) + return image_path + + def _load_image_set_index(self): + """ + Load the indexes listed in this dataset's image set file. + """ + # Example path to image set file: + # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt + image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main', + self._image_set + '.txt') + assert os.path.exists(image_set_file), \ + 'Path does not exist: {}'.format(image_set_file) + with open(image_set_file) as f: + image_index = [x.strip() for x in f.readlines()] + return image_index + + def _get_default_path(self): + """ + Return the default path where PASCAL VOC is expected to be installed. + """ + return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year) + + def gt_roidb(self): + """ + Return the database of ground-truth regions of interest. + + This function loads/saves from/to a cache file to speed up future calls. + """ + cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') + if os.path.exists(cache_file): + with open(cache_file, 'rb') as fid: + try: + roidb = pickle.load(fid) + except: + roidb = pickle.load(fid, encoding='bytes') + print('{} gt roidb loaded from {}'.format(self.name, cache_file)) + return roidb + + gt_roidb = [self._load_pascal_annotation(index) + for index in self.image_index] + with open(cache_file, 'wb') as fid: + pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL) + print('wrote gt roidb to {}'.format(cache_file)) + + return gt_roidb + + def rpn_roidb(self): + if int(self._year) == 2007 or self._image_set != 'test': + gt_roidb = self.gt_roidb() + rpn_roidb = self._load_rpn_roidb(gt_roidb) + roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) + else: + roidb = self._load_rpn_roidb(None) + + return roidb + + def _load_rpn_roidb(self, gt_roidb): + filename = self.config['rpn_file'] + print('loading {}'.format(filename)) + assert os.path.exists(filename), \ + 'rpn data not found at: {}'.format(filename) + with open(filename, 'rb') as f: + box_list = pickle.load(f) + return self.create_roidb_from_box_list(box_list, gt_roidb) + + def _load_pascal_annotation(self, index): + """ + Load image and bounding boxes info from XML file in the PASCAL VOC + format. + """ + filename = os.path.join(self._data_path, 'Annotations', index + '.xml') + tree = ET.parse(filename) + objs = tree.findall('object') + if not self.config['use_diff']: + # Exclude the samples labeled as difficult + non_diff_objs = [ + obj for obj in objs if int(obj.find('difficult').text) == 0] + # if len(non_diff_objs) != len(objs): + # print 'Removed {} difficult objects'.format( + # len(objs) - len(non_diff_objs)) + objs = non_diff_objs + num_objs = len(objs) + + boxes = np.zeros((num_objs, 4), dtype=np.uint16) + gt_classes = np.zeros((num_objs), dtype=np.int32) + overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) + # "Seg" area for pascal is just the box area + seg_areas = np.zeros((num_objs), dtype=np.float32) + + # Load object bounding boxes into a data frame. + for ix, obj in enumerate(objs): + bbox = obj.find('bndbox') + # Make pixel indexes 0-based + x1 = float(bbox.find('xmin').text) - 1 + y1 = float(bbox.find('ymin').text) - 1 + x2 = float(bbox.find('xmax').text) - 1 + y2 = float(bbox.find('ymax').text) - 1 + cls = self._class_to_ind[obj.find('name').text.lower().strip()] + boxes[ix, :] = [x1, y1, x2, y2] + gt_classes[ix] = cls + overlaps[ix, cls] = 1.0 + seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) + + overlaps = scipy.sparse.csr_matrix(overlaps) + + return {'boxes': boxes, + 'gt_classes': gt_classes, + 'gt_overlaps': overlaps, + 'flipped': False, + 'seg_areas': seg_areas} + + def _get_comp_id(self): + comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt'] + else self._comp_id) + return comp_id + + def _get_voc_results_file_template(self): + # VOCdevkit/results/VOC2007/Main/_det_test_aeroplane.txt + filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt' + path = os.path.join( + self._devkit_path, + 'results', + 'VOC' + self._year, + 'Main', + filename) + return path + + def _write_voc_results_file(self, all_boxes): + for cls_ind, cls in enumerate(self.classes): + if cls == '__background__': + continue + print('Writing {} VOC results file'.format(cls)) + filename = self._get_voc_results_file_template().format(cls) + with open(filename, 'wt') as f: + for im_ind, index in enumerate(self.image_index): + dets = all_boxes[cls_ind][im_ind] + if dets == []: + continue + # the VOCdevkit expects 1-based indices + for k in range(dets.shape[0]): + f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. + format(index, dets[k, -1], + dets[k, 0] + 1, dets[k, 1] + 1, + dets[k, 2] + 1, dets[k, 3] + 1)) + + def _do_python_eval(self, output_dir='output'): + annopath = os.path.join( + self._devkit_path, + 'VOC' + self._year, + 'Annotations', + '{:s}.xml') + imagesetfile = os.path.join( + self._devkit_path, + 'VOC' + self._year, + 'ImageSets', + 'Main', + self._image_set + '.txt') + cachedir = os.path.join(self._devkit_path, 'annotations_cache') + aps = [] + # The PASCAL VOC metric changed in 2010 + use_07_metric = True if int(self._year) < 2010 else False + print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No')) + if not os.path.isdir(output_dir): + os.mkdir(output_dir) + for i, cls in enumerate(self._classes): + if cls == '__background__': + continue + filename = self._get_voc_results_file_template().format(cls) + rec, prec, ap = voc_eval( + filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, + use_07_metric=use_07_metric, use_diff=self.config['use_diff']) + aps += [ap] + print(('AP for {} = {:.4f}'.format(cls, ap))) + with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f: + pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f) + print(('Mean AP = {:.4f}'.format(np.mean(aps)))) + print('~~~~~~~~') + print('Results:') + for ap in aps: + print(('{:.3f}'.format(ap))) + print(('{:.3f}'.format(np.mean(aps)))) + print('~~~~~~~~') + print('') + print('--------------------------------------------------------------') + print('Results computed with the **unofficial** Python eval code.') + print('Results should be very close to the official MATLAB eval code.') + print('Recompute with `./tools/reval.py --matlab ...` for your paper.') + print('-- Thanks, The Management') + print('--------------------------------------------------------------') + + def _do_matlab_eval(self, output_dir='output'): + print('-----------------------------------------------------') + print('Computing results with the official MATLAB eval code.') + print('-----------------------------------------------------') + path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets', + 'VOCdevkit-matlab-wrapper') + cmd = 'cd {} && '.format(path) + cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB) + cmd += '-r "dbstop if error; ' + cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \ + .format(self._devkit_path, self._get_comp_id(), + self._image_set, output_dir) + print(('Running:\n{}'.format(cmd))) + status = subprocess.call(cmd, shell=True) + + def evaluate_detections(self, all_boxes, output_dir): + self._write_voc_results_file(all_boxes) + self._do_python_eval(output_dir) + if self.config['matlab_eval']: + self._do_matlab_eval(output_dir) + if self.config['cleanup']: + for cls in self._classes: + if cls == '__background__': + continue + filename = self._get_voc_results_file_template().format(cls) + os.remove(filename) + + def competition_mode(self, on): + if on: + self.config['use_salt'] = False + self.config['cleanup'] = False + else: + self.config['use_salt'] = True + self.config['cleanup'] = True + + +if __name__ == '__main__': + from datasets.pascal_voc import pascal_voc + + d = pascal_voc('trainval', '2007') + res = d.roidb + from IPython import embed; + + embed() diff --git a/tf-faster-rcnn/lib/datasets/.ipynb_checkpoints/voc_eval-checkpoint.py b/tf-faster-rcnn/lib/datasets/.ipynb_checkpoints/voc_eval-checkpoint.py new file mode 100644 index 0000000..0201887 --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/.ipynb_checkpoints/voc_eval-checkpoint.py @@ -0,0 +1,215 @@ +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Bharath Hariharan +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import xml.etree.ElementTree as ET +import os +import pickle +import numpy as np + +def parse_rec(filename): + """ Parse a PASCAL VOC xml file """ + tree = ET.parse(filename) + objects = [] + for obj in tree.findall('object'): + obj_struct = {} + obj_struct['name'] = obj.find('name').text + obj_struct['pose'] = obj.find('pose').text + obj_struct['truncated'] = int(obj.find('truncated').text) + obj_struct['difficult'] = int(obj.find('difficult').text) + bbox = obj.find('bndbox') + obj_struct['bbox'] = [int(bbox.find('xmin').text), + int(bbox.find('ymin').text), + int(bbox.find('xmax').text), + int(bbox.find('ymax').text)] + objects.append(obj_struct) + + return objects + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def voc_eval(detpath, + annopath, + imagesetfile, + classname, + cachedir, + ovthresh=0.5, + use_07_metric=False, + use_diff=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + # first load gt + if not os.path.isdir(cachedir): + os.mkdir(cachedir) + #cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile) + cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile.split("/")[-1].split(".")[0]) + # read list of images + with open(imagesetfile, 'r') as f: + lines = f.readlines() + imagenames = [x.strip() for x in lines] + + if not os.path.isfile(cachefile): + # load annotations + recs = {} + for i, imagename in enumerate(imagenames): + recs[imagename] = parse_rec(annopath.format(imagename)) + if i % 100 == 0: + print('Reading annotation for {:d}/{:d}'.format( + i + 1, len(imagenames))) + # save + print('Saving cached annotations to {:s}'.format(cachefile)) + with open(cachefile, 'wb') as f: + pickle.dump(recs, f) + else: + # load + with open(cachefile, 'rb') as f: + try: + recs = pickle.load(f) + except: + recs = pickle.load(f, encoding='bytes') + + # extract gt objects for this class + class_recs = {} + npos = 0 + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj['name'] == classname] + bbox = np.array([x['bbox'] for x in R]) + if use_diff: + difficult = np.array([False for x in R]).astype(np.bool) + else: + difficult = np.array([x['difficult'] for x in R]).astype(np.bool) + det = [False] * len(R) + npos = npos + sum(~difficult) + class_recs[imagename] = {'bbox': bbox, + 'difficult': difficult, + 'det': det} + + # read dets + detfile = detpath.format(classname) + with open(detfile, 'r') as f: + lines = f.readlines() + + splitlines = [x.strip().split(' ') for x in lines] + image_ids = [x[0] for x in splitlines] + confidence = np.array([float(x[1]) for x in splitlines]) + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) + + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + + if BB.shape[0] > 0: + # sort by confidence + sorted_ind = np.argsort(-confidence) + sorted_scores = np.sort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R['bbox'].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1., 0.) + ih = np.maximum(iymax - iymin + 1., 0.) + inters = iw * ih + + # union + uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + + (BBGT[:, 2] - BBGT[:, 0] + 1.) * + (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > ovthresh: + if not R['difficult'][jmax]: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap diff --git a/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m b/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m new file mode 100644 index 0000000..629597a --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m @@ -0,0 +1,14 @@ +function VOCopts = get_voc_opts(path) + +tmp = pwd; +cd(path); +try + addpath('VOCcode'); + VOCinit; +catch + rmpath('VOCcode'); + cd(tmp); + error(sprintf('VOCcode directory not found under %s', path)); +end +rmpath('VOCcode'); +cd(tmp); diff --git a/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m b/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m new file mode 100644 index 0000000..1911a0e --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m @@ -0,0 +1,56 @@ +function res = voc_eval(path, comp_id, test_set, output_dir) + +VOCopts = get_voc_opts(path); +VOCopts.testset = test_set; + +for i = 1:length(VOCopts.classes) + cls = VOCopts.classes{i}; + res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir); +end + +fprintf('\n~~~~~~~~~~~~~~~~~~~~\n'); +fprintf('Results:\n'); +aps = [res(:).ap]'; +fprintf('%.1f\n', aps * 100); +fprintf('%.1f\n', mean(aps) * 100); +fprintf('~~~~~~~~~~~~~~~~~~~~\n'); + +function res = voc_eval_cls(cls, VOCopts, comp_id, output_dir) + +test_set = VOCopts.testset; +year = VOCopts.dataset(4:end); + +addpath(fullfile(VOCopts.datadir, 'VOCcode')); + +res_fn = sprintf(VOCopts.detrespath, comp_id, cls); + +recall = []; +prec = []; +ap = 0; +ap_auc = 0; + +do_eval = (str2num(year) <= 2007) | ~strcmp(test_set, 'test'); +if do_eval + % Bug in VOCevaldet requires that tic has been called first + tic; + [recall, prec, ap] = VOCevaldet(VOCopts, comp_id, cls, true); + ap_auc = xVOCap(recall, prec); + + % force plot limits + ylim([0 1]); + xlim([0 1]); + + print(gcf, '-djpeg', '-r0', ... + [output_dir '/' cls '_pr.jpg']); +end +fprintf('!!! %s : %.4f %.4f\n', cls, ap, ap_auc); + +res.recall = recall; +res.prec = prec; +res.ap = ap; +res.ap_auc = ap_auc; + +save([output_dir '/' cls '_pr.mat'], ... + 'res', 'recall', 'prec', 'ap', 'ap_auc'); + +rmpath(fullfile(VOCopts.datadir, 'VOCcode')); diff --git a/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m b/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m new file mode 100644 index 0000000..7e8024f --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m @@ -0,0 +1,10 @@ +function ap = xVOCap(rec,prec) +% From the PASCAL VOC 2011 devkit + +mrec=[0 ; rec ; 1]; +mpre=[0 ; prec ; 0]; +for i=numel(mpre)-1:-1:1 + mpre(i)=max(mpre(i),mpre(i+1)); +end +i=find(mrec(2:end)~=mrec(1:end-1))+1; +ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); diff --git a/tf-faster-rcnn/lib/datasets/__init__.py b/tf-faster-rcnn/lib/datasets/__init__.py new file mode 100644 index 0000000..7ba6a65 --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/__init__.py @@ -0,0 +1,6 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- diff --git a/tf-faster-rcnn/lib/datasets/coco.py b/tf-faster-rcnn/lib/datasets/coco.py new file mode 100644 index 0000000..fba89f0 --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/coco.py @@ -0,0 +1,316 @@ +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from datasets.imdb import imdb +import datasets.ds_utils as ds_utils +from model.config import cfg +import os.path as osp +import sys +import os +import numpy as np +import scipy.sparse +import scipy.io as sio +import pickle +import json +import uuid +# COCO API +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from pycocotools import mask as COCOmask + +class coco(imdb): + def __init__(self, image_set, year): + imdb.__init__(self, 'coco_' + year + '_' + image_set) + # COCO specific config options + self.config = {'use_salt': True, + 'cleanup': True} + # name, paths + self._year = year + self._image_set = image_set + self._data_path = osp.join(cfg.DATA_DIR, 'coco') + # load COCO API, classes, class <-> id mappings + self._COCO = COCO(self._get_ann_file()) + cats = self._COCO.loadCats(self._COCO.getCatIds()) + self._classes = tuple(['__background__'] + [c['name'] for c in cats]) + self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) + self._class_to_coco_cat_id = dict(list(zip([c['name'] for c in cats], + self._COCO.getCatIds()))) + self._image_index = self._load_image_set_index() + # Default to roidb handler + self.set_proposal_method('gt') + self.competition_mode(False) + + # Some image sets are "views" (i.e. subsets) into others. + # For example, minival2014 is a random 5000 image subset of val2014. + # This mapping tells us where the view's images and proposals come from. + self._view_map = { + 'minival2014': 'val2014', # 5k val2014 subset + 'valminusminival2014': 'val2014', # val2014 \setminus minival2014 + 'test-dev2015': 'test2015', + } + coco_name = image_set + year # e.g., "val2014" + self._data_name = (self._view_map[coco_name] + if coco_name in self._view_map + else coco_name) + # Dataset splits that have ground-truth annotations (test splits + # do not have gt annotations) + self._gt_splits = ('train', 'val', 'minival') + + def _get_ann_file(self): + prefix = 'instances' if self._image_set.find('test') == -1 \ + else 'image_info' + return osp.join(self._data_path, 'annotations', + prefix + '_' + self._image_set + self._year + '.json') + + def _load_image_set_index(self): + """ + Load image ids. + """ + image_ids = self._COCO.getImgIds() + return image_ids + + def _get_widths(self): + anns = self._COCO.loadImgs(self._image_index) + widths = [ann['width'] for ann in anns] + return widths + + def image_path_at(self, i): + """ + Return the absolute path to image i in the image sequence. + """ + return self.image_path_from_index(self._image_index[i]) + + def image_path_from_index(self, index): + """ + Construct an image path from the image's "index" identifier. + """ + # Example image path for index=119993: + # images/train2014/COCO_train2014_000000119993.jpg + file_name = ('COCO_' + self._data_name + '_' + + str(index).zfill(12) + '.jpg') + image_path = osp.join(self._data_path, 'images', + self._data_name, file_name) + assert osp.exists(image_path), \ + 'Path does not exist: {}'.format(image_path) + return image_path + + def gt_roidb(self): + """ + Return the database of ground-truth regions of interest. + This function loads/saves from/to a cache file to speed up future calls. + """ + cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl') + if osp.exists(cache_file): + with open(cache_file, 'rb') as fid: + roidb = pickle.load(fid) + print('{} gt roidb loaded from {}'.format(self.name, cache_file)) + return roidb + + gt_roidb = [self._load_coco_annotation(index) + for index in self._image_index] + + with open(cache_file, 'wb') as fid: + pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL) + print('wrote gt roidb to {}'.format(cache_file)) + return gt_roidb + + def _load_coco_annotation(self, index): + """ + Loads COCO bounding-box instance annotations. Crowd instances are + handled by marking their overlaps (with all categories) to -1. This + overlap value means that crowd "instances" are excluded from training. + """ + im_ann = self._COCO.loadImgs(index)[0] + width = im_ann['width'] + height = im_ann['height'] + + annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None) + objs = self._COCO.loadAnns(annIds) + # Sanitize bboxes -- some are invalid + valid_objs = [] + for obj in objs: + x1 = np.max((0, obj['bbox'][0])) + y1 = np.max((0, obj['bbox'][1])) + x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1)))) + y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1)))) + if obj['area'] > 0 and x2 >= x1 and y2 >= y1: + obj['clean_bbox'] = [x1, y1, x2, y2] + valid_objs.append(obj) + objs = valid_objs + num_objs = len(objs) + + boxes = np.zeros((num_objs, 4), dtype=np.uint16) + gt_classes = np.zeros((num_objs), dtype=np.int32) + overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) + seg_areas = np.zeros((num_objs), dtype=np.float32) + + # Lookup table to map from COCO category ids to our internal class + # indices + coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls], + self._class_to_ind[cls]) + for cls in self._classes[1:]]) + + for ix, obj in enumerate(objs): + cls = coco_cat_id_to_class_ind[obj['category_id']] + boxes[ix, :] = obj['clean_bbox'] + gt_classes[ix] = cls + seg_areas[ix] = obj['area'] + if obj['iscrowd']: + # Set overlap to -1 for all classes for crowd objects + # so they will be excluded during training + overlaps[ix, :] = -1.0 + else: + overlaps[ix, cls] = 1.0 + + ds_utils.validate_boxes(boxes, width=width, height=height) + overlaps = scipy.sparse.csr_matrix(overlaps) + return {'width': width, + 'height': height, + 'boxes': boxes, + 'gt_classes': gt_classes, + 'gt_overlaps': overlaps, + 'flipped': False, + 'seg_areas': seg_areas} + + def _get_widths(self): + return [r['width'] for r in self.roidb] + + def append_flipped_images(self): + num_images = self.num_images + widths = self._get_widths() + for i in range(num_images): + boxes = self.roidb[i]['boxes'].copy() + oldx1 = boxes[:, 0].copy() + oldx2 = boxes[:, 2].copy() + boxes[:, 0] = widths[i] - oldx2 - 1 + boxes[:, 2] = widths[i] - oldx1 - 1 + assert (boxes[:, 2] >= boxes[:, 0]).all() + entry = {'width': widths[i], + 'height': self.roidb[i]['height'], + 'boxes': boxes, + 'gt_classes': self.roidb[i]['gt_classes'], + 'gt_overlaps': self.roidb[i]['gt_overlaps'], + 'flipped': True, + 'seg_areas': self.roidb[i]['seg_areas']} + + self.roidb.append(entry) + self._image_index = self._image_index * 2 + + def _get_box_file(self, index): + # first 14 chars / first 22 chars / all chars + .mat + # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat + file_name = ('COCO_' + self._data_name + + '_' + str(index).zfill(12) + '.mat') + return osp.join(file_name[:14], file_name[:22], file_name) + + def _print_detection_eval_metrics(self, coco_eval): + IoU_lo_thresh = 0.5 + IoU_hi_thresh = 0.95 + + def _get_thr_ind(coco_eval, thr): + ind = np.where((coco_eval.params.iouThrs > thr - 1e-5) & + (coco_eval.params.iouThrs < thr + 1e-5))[0][0] + iou_thr = coco_eval.params.iouThrs[ind] + assert np.isclose(iou_thr, thr) + return ind + + ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh) + ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh) + # precision has dims (iou, recall, cls, area range, max dets) + # area range index 0: all area ranges + # max dets index 2: 100 per image + precision = \ + coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2] + ap_default = np.mean(precision[precision > -1]) + print(('~~~~ Mean and per-category AP @ IoU=[{:.2f},{:.2f}] ' + '~~~~').format(IoU_lo_thresh, IoU_hi_thresh)) + print('{:.1f}'.format(100 * ap_default)) + for cls_ind, cls in enumerate(self.classes): + if cls == '__background__': + continue + # minus 1 because of __background__ + precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, cls_ind - 1, 0, 2] + ap = np.mean(precision[precision > -1]) + print('{:.1f}'.format(100 * ap)) + + print('~~~~ Summary metrics ~~~~') + coco_eval.summarize() + + def _do_detection_eval(self, res_file, output_dir): + ann_type = 'bbox' + coco_dt = self._COCO.loadRes(res_file) + coco_eval = COCOeval(self._COCO, coco_dt) + coco_eval.params.useSegm = (ann_type == 'segm') + coco_eval.evaluate() + coco_eval.accumulate() + self._print_detection_eval_metrics(coco_eval) + eval_file = osp.join(output_dir, 'detection_results.pkl') + with open(eval_file, 'wb') as fid: + pickle.dump(coco_eval, fid, pickle.HIGHEST_PROTOCOL) + print('Wrote COCO eval results to: {}'.format(eval_file)) + + def _coco_results_one_category(self, boxes, cat_id): + results = [] + for im_ind, index in enumerate(self.image_index): + dets = boxes[im_ind].astype(np.float) + if dets == []: + continue + scores = dets[:, -1] + xs = dets[:, 0] + ys = dets[:, 1] + ws = dets[:, 2] - xs + 1 + hs = dets[:, 3] - ys + 1 + results.extend( + [{'image_id': index, + 'category_id': cat_id, + 'bbox': [xs[k], ys[k], ws[k], hs[k]], + 'score': scores[k]} for k in range(dets.shape[0])]) + return results + + def _write_coco_results_file(self, all_boxes, res_file): + # [{"image_id": 42, + # "category_id": 18, + # "bbox": [258.15,41.29,348.26,243.78], + # "score": 0.236}, ...] + results = [] + for cls_ind, cls in enumerate(self.classes): + if cls == '__background__': + continue + print('Collecting {} results ({:d}/{:d})'.format(cls, cls_ind, + self.num_classes - 1)) + coco_cat_id = self._class_to_coco_cat_id[cls] + results.extend(self._coco_results_one_category(all_boxes[cls_ind], + coco_cat_id)) + print('Writing results json to {}'.format(res_file)) + with open(res_file, 'w') as fid: + json.dump(results, fid) + + def evaluate_detections(self, all_boxes, output_dir): + res_file = osp.join(output_dir, ('detections_' + + self._image_set + + self._year + + '_results')) + if self.config['use_salt']: + res_file += '_{}'.format(str(uuid.uuid4())) + res_file += '.json' + self._write_coco_results_file(all_boxes, res_file) + # Only do evaluation on non-test sets + if self._image_set.find('test') == -1: + self._do_detection_eval(res_file, output_dir) + # Optionally cleanup results json file + if self.config['cleanup']: + os.remove(res_file) + + def competition_mode(self, on): + if on: + self.config['use_salt'] = False + self.config['cleanup'] = False + else: + self.config['use_salt'] = True + self.config['cleanup'] = True diff --git a/tf-faster-rcnn/lib/datasets/ds_utils.py b/tf-faster-rcnn/lib/datasets/ds_utils.py new file mode 100644 index 0000000..fd5ca4b --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/ds_utils.py @@ -0,0 +1,49 @@ +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +def unique_boxes(boxes, scale=1.0): + """Return indices of unique boxes.""" + v = np.array([1, 1e3, 1e6, 1e9]) + hashes = np.round(boxes * scale).dot(v) + _, index = np.unique(hashes, return_index=True) + return np.sort(index) + + +def xywh_to_xyxy(boxes): + """Convert [x y w h] box format to [x1 y1 x2 y2] format.""" + return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1)) + + +def xyxy_to_xywh(boxes): + """Convert [x1 y1 x2 y2] box format to [x y w h] format.""" + return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1)) + + +def validate_boxes(boxes, width=0, height=0): + """Check that a set of boxes are valid.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + assert (x1 >= 0).all() + assert (y1 >= 0).all() + assert (x2 >= x1).all() + assert (y2 >= y1).all() + assert (x2 < width).all() + assert (y2 < height).all() + + +def filter_small_boxes(boxes, min_size): + w = boxes[:, 2] - boxes[:, 0] + h = boxes[:, 3] - boxes[:, 1] + keep = np.where((w >= min_size) & (h > min_size))[0] + return keep diff --git a/tf-faster-rcnn/lib/datasets/factory.py b/tf-faster-rcnn/lib/datasets/factory.py new file mode 100644 index 0000000..3283d0b --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/factory.py @@ -0,0 +1,52 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Factory method for easily getting imdbs by name.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +__sets = {} +from datasets.pascal_voc import pascal_voc +from datasets.coco import coco + +import numpy as np + +# Set up voc__ +for year in ['2007', '2012']: + for split in ['train', 'val', 'trainval', 'test']: + name = 'voc_{}_{}'.format(year, split) + __sets[name] = (lambda split=split, year=year: pascal_voc(split, year)) + +for year in ['2007', '2012']: + for split in ['train', 'val', 'trainval', 'test']: + name = 'voc_{}_{}_diff'.format(year, split) + __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, use_diff=True)) + +# Set up coco_2014_ +for year in ['2014']: + for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']: + name = 'coco_{}_{}'.format(year, split) + __sets[name] = (lambda split=split, year=year: coco(split, year)) + +# Set up coco_2015_ +for year in ['2015']: + for split in ['test', 'test-dev']: + name = 'coco_{}_{}'.format(year, split) + __sets[name] = (lambda split=split, year=year: coco(split, year)) + + +def get_imdb(name): + """Get an imdb (image database) by name.""" + if name not in __sets: + raise KeyError('Unknown dataset: {}'.format(name)) + return __sets[name]() + + +def list_imdbs(): + """List all registered imdbs.""" + return list(__sets.keys()) diff --git a/tf-faster-rcnn/lib/datasets/imdb.py b/tf-faster-rcnn/lib/datasets/imdb.py new file mode 100644 index 0000000..2544279 --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/imdb.py @@ -0,0 +1,260 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import os.path as osp +import PIL +from utils.cython_bbox import bbox_overlaps +import numpy as np +import scipy.sparse +from model.config import cfg + + +class imdb(object): + """Image database.""" + + def __init__(self, name, classes=None): + self._name = name + self._num_classes = 0 + if not classes: + self._classes = [] + else: + self._classes = classes + self._image_index = [] + self._obj_proposer = 'gt' + self._roidb = None + self._roidb_handler = self.default_roidb + # Use this dict for storing dataset specific config options + self.config = {} + + @property + def name(self): + return self._name + + @property + def num_classes(self): + return len(self._classes) + + @property + def classes(self): + return self._classes + + @property + def image_index(self): + return self._image_index + + @property + def roidb_handler(self): + return self._roidb_handler + + @roidb_handler.setter + def roidb_handler(self, val): + self._roidb_handler = val + + def set_proposal_method(self, method): + method = eval('self.' + method + '_roidb') + self.roidb_handler = method + + @property + def roidb(self): + # A roidb is a list of dictionaries, each with the following keys: + # boxes + # gt_overlaps + # gt_classes + # flipped + if self._roidb is not None: + return self._roidb + self._roidb = self.roidb_handler() + return self._roidb + + @property + def cache_path(self): + cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache')) + if not os.path.exists(cache_path): + os.makedirs(cache_path) + return cache_path + + @property + def num_images(self): + return len(self.image_index) + + def image_path_at(self, i): + raise NotImplementedError + + def default_roidb(self): + raise NotImplementedError + + def evaluate_detections(self, all_boxes, output_dir=None): + """ + all_boxes is a list of length number-of-classes. + Each list element is a list of length number-of-images. + Each of those list elements is either an empty list [] + or a numpy array of detection. + + all_boxes[class][image] = [] or np.array of shape #dets x 5 + """ + raise NotImplementedError + + def _get_widths(self): + return [PIL.Image.open(self.image_path_at(i)).size[0] + for i in range(self.num_images)] + + def append_flipped_images(self): + num_images = self.num_images + widths = self._get_widths() + for i in range(num_images): + boxes = self.roidb[i]['boxes'].copy() + oldx1 = boxes[:, 0].copy() + oldx2 = boxes[:, 2].copy() + boxes[:, 0] = widths[i] - oldx2 - 1 + boxes[:, 2] = widths[i] - oldx1 - 1 + assert (boxes[:, 2] >= boxes[:, 0]).all() + entry = {'boxes': boxes, + 'gt_overlaps': self.roidb[i]['gt_overlaps'], + 'gt_classes': self.roidb[i]['gt_classes'], + 'flipped': True} + self.roidb.append(entry) + self._image_index = self._image_index * 2 + + def evaluate_recall(self, candidate_boxes=None, thresholds=None, + area='all', limit=None): + """Evaluate detection proposal recall metrics. + + Returns: + results: dictionary of results with keys + 'ar': average recall + 'recalls': vector recalls at each IoU overlap threshold + 'thresholds': vector of IoU overlap thresholds + 'gt_overlaps': vector of all ground-truth overlaps + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3, + '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7} + area_ranges = [[0 ** 2, 1e5 ** 2], # all + [0 ** 2, 32 ** 2], # small + [32 ** 2, 96 ** 2], # medium + [96 ** 2, 1e5 ** 2], # large + [96 ** 2, 128 ** 2], # 96-128 + [128 ** 2, 256 ** 2], # 128-256 + [256 ** 2, 512 ** 2], # 256-512 + [512 ** 2, 1e5 ** 2], # 512-inf + ] + assert area in areas, 'unknown area range: {}'.format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = np.zeros(0) + num_pos = 0 + for i in range(self.num_images): + # Checking for max_overlaps == 1 avoids including crowd annotations + # (...pretty hacking :/) + max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1) + gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) & + (max_gt_overlaps == 1))[0] + gt_boxes = self.roidb[i]['boxes'][gt_inds, :] + gt_areas = self.roidb[i]['seg_areas'][gt_inds] + valid_gt_inds = np.where((gt_areas >= area_range[0]) & + (gt_areas <= area_range[1]))[0] + gt_boxes = gt_boxes[valid_gt_inds, :] + num_pos += len(valid_gt_inds) + + if candidate_boxes is None: + # If candidate_boxes is not supplied, the default is to use the + # non-ground-truth boxes from this roidb + non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0] + boxes = self.roidb[i]['boxes'][non_gt_inds, :] + else: + boxes = candidate_boxes[i] + if boxes.shape[0] == 0: + continue + if limit is not None and boxes.shape[0] > limit: + boxes = boxes[:limit, :] + + overlaps = bbox_overlaps(boxes.astype(np.float), + gt_boxes.astype(np.float)) + + _gt_overlaps = np.zeros((gt_boxes.shape[0])) + for j in range(gt_boxes.shape[0]): + # find which proposal box maximally covers each gt box + argmax_overlaps = overlaps.argmax(axis=0) + # and get the iou amount of coverage for each gt box + max_overlaps = overlaps.max(axis=0) + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ind = max_overlaps.argmax() + gt_ovr = max_overlaps.max() + assert (gt_ovr >= 0) + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert (_gt_overlaps[j] == gt_ovr) + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + # append recorded iou coverage level + gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps)) + + gt_overlaps = np.sort(gt_overlaps) + if thresholds is None: + step = 0.05 + thresholds = np.arange(0.5, 0.95 + 1e-5, step) + recalls = np.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds, + 'gt_overlaps': gt_overlaps} + + def create_roidb_from_box_list(self, box_list, gt_roidb): + assert len(box_list) == self.num_images, \ + 'Number of boxes must match number of ground-truth images' + roidb = [] + for i in range(self.num_images): + boxes = box_list[i] + num_boxes = boxes.shape[0] + overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32) + + if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0: + gt_boxes = gt_roidb[i]['boxes'] + gt_classes = gt_roidb[i]['gt_classes'] + gt_overlaps = bbox_overlaps(boxes.astype(np.float), + gt_boxes.astype(np.float)) + argmaxes = gt_overlaps.argmax(axis=1) + maxes = gt_overlaps.max(axis=1) + I = np.where(maxes > 0)[0] + overlaps[I, gt_classes[argmaxes[I]]] = maxes[I] + + overlaps = scipy.sparse.csr_matrix(overlaps) + roidb.append({ + 'boxes': boxes, + 'gt_classes': np.zeros((num_boxes,), dtype=np.int32), + 'gt_overlaps': overlaps, + 'flipped': False, + 'seg_areas': np.zeros((num_boxes,), dtype=np.float32), + }) + return roidb + + @staticmethod + def merge_roidbs(a, b): + assert len(a) == len(b) + for i in range(len(a)): + a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes'])) + a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'], + b[i]['gt_classes'])) + a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'], + b[i]['gt_overlaps']]) + a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'], + b[i]['seg_areas'])) + return a + + def competition_mode(self, on): + """Turn competition mode on or off.""" + pass diff --git a/tf-faster-rcnn/lib/datasets/pascal_voc.py b/tf-faster-rcnn/lib/datasets/pascal_voc.py new file mode 100644 index 0000000..ce5ad5d --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/pascal_voc.py @@ -0,0 +1,310 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +from datasets.imdb import imdb +import datasets.ds_utils as ds_utils +import xml.etree.ElementTree as ET +import numpy as np +import scipy.sparse +import scipy.io as sio +import utils.cython_bbox +import pickle +import subprocess +import uuid +from .voc_eval import voc_eval +from model.config import cfg + + +class pascal_voc(imdb): + def __init__(self, image_set, year, use_diff=False): + name = 'voc_' + year + '_' + image_set + if use_diff: + name += '_diff' + imdb.__init__(self, name) + self._year = year + self._image_set = image_set + self._devkit_path = self._get_default_path() + self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year) +# self._classes = ('__background__', # always index 0 +# 'aeroplane', 'bicycle', 'bird', 'boat', +# 'bottle', 'bus', 'car', 'cat', 'chair', +# 'cow', 'diningtable', 'dog', 'horse', +# 'motorbike', 'person', 'pottedplant', +# 'sheep', 'sofa', 'train', 'tvmonitor') + self._classes = ('__background__', 'node') + self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) + self._image_ext = '.png' + self._image_index = self._load_image_set_index() + # Default to roidb handler + self._roidb_handler = self.gt_roidb + self._salt = str(uuid.uuid4()) + self._comp_id = 'comp4' + + # PASCAL specific config options + self.config = {'cleanup': True, + 'use_salt': True, + 'use_diff': use_diff, + 'matlab_eval': False, + 'rpn_file': None} + + assert os.path.exists(self._devkit_path), \ + 'VOCdevkit path does not exist: {}'.format(self._devkit_path) + assert os.path.exists(self._data_path), \ + 'Path does not exist: {}'.format(self._data_path) + + def image_path_at(self, i): + """ + Return the absolute path to image i in the image sequence. + """ + return self.image_path_from_index(self._image_index[i]) + + def image_path_from_index(self, index): + """ + Construct an image path from the image's "index" identifier. + """ + image_path = os.path.join(self._data_path, 'JPEGImages', + index + self._image_ext) + assert os.path.exists(image_path), \ + 'Path does not exist: {}'.format(image_path) + return image_path + + def _load_image_set_index(self): + """ + Load the indexes listed in this dataset's image set file. + """ + # Example path to image set file: + # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt + image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main', + self._image_set + '.txt') + assert os.path.exists(image_set_file), \ + 'Path does not exist: {}'.format(image_set_file) + with open(image_set_file) as f: + image_index = [x.strip() for x in f.readlines()] + return image_index + + def _get_default_path(self): + """ + Return the default path where PASCAL VOC is expected to be installed. + """ + return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year) + + def gt_roidb(self): + """ + Return the database of ground-truth regions of interest. + + This function loads/saves from/to a cache file to speed up future calls. + """ + cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') + if os.path.exists(cache_file): + with open(cache_file, 'rb') as fid: + try: + roidb = pickle.load(fid) + except: + roidb = pickle.load(fid, encoding='bytes') + print('{} gt roidb loaded from {}'.format(self.name, cache_file)) + return roidb + + gt_roidb = [self._load_pascal_annotation(index) + for index in self.image_index] + with open(cache_file, 'wb') as fid: + pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL) + print('wrote gt roidb to {}'.format(cache_file)) + + return gt_roidb + + def rpn_roidb(self): + if int(self._year) == 2007 or self._image_set != 'test': + gt_roidb = self.gt_roidb() + rpn_roidb = self._load_rpn_roidb(gt_roidb) + roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) + else: + roidb = self._load_rpn_roidb(None) + + return roidb + + def _load_rpn_roidb(self, gt_roidb): + filename = self.config['rpn_file'] + print('loading {}'.format(filename)) + assert os.path.exists(filename), \ + 'rpn data not found at: {}'.format(filename) + with open(filename, 'rb') as f: + box_list = pickle.load(f) + return self.create_roidb_from_box_list(box_list, gt_roidb) + + def _load_pascal_annotation(self, index): + """ + Load image and bounding boxes info from XML file in the PASCAL VOC + format. + """ + filename = os.path.join(self._data_path, 'Annotations', index + '.xml') + tree = ET.parse(filename) + objs = tree.findall('object') + if not self.config['use_diff']: + # Exclude the samples labeled as difficult + non_diff_objs = [ + obj for obj in objs if int(obj.find('difficult').text) == 0] + # if len(non_diff_objs) != len(objs): + # print 'Removed {} difficult objects'.format( + # len(objs) - len(non_diff_objs)) + objs = non_diff_objs + num_objs = len(objs) + + boxes = np.zeros((num_objs, 4), dtype=np.uint16) + gt_classes = np.zeros((num_objs), dtype=np.int32) + overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) + # "Seg" area for pascal is just the box area + seg_areas = np.zeros((num_objs), dtype=np.float32) + + # Load object bounding boxes into a data frame. + for ix, obj in enumerate(objs): + bbox = obj.find('bndbox') + # Make pixel indexes 0-based + x1 = float(bbox.find('xmin').text) - 1 + y1 = float(bbox.find('ymin').text) - 1 + x2 = float(bbox.find('xmax').text) - 1 + y2 = float(bbox.find('ymax').text) - 1 + cls = self._class_to_ind[obj.find('name').text.lower().strip()] + boxes[ix, :] = [x1, y1, x2, y2] + gt_classes[ix] = cls + overlaps[ix, cls] = 1.0 + seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) + + overlaps = scipy.sparse.csr_matrix(overlaps) + + return {'boxes': boxes, + 'gt_classes': gt_classes, + 'gt_overlaps': overlaps, + 'flipped': False, + 'seg_areas': seg_areas} + + def _get_comp_id(self): + comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt'] + else self._comp_id) + return comp_id + + def _get_voc_results_file_template(self): + # VOCdevkit/results/VOC2007/Main/_det_test_aeroplane.txt + filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt' + path = os.path.join( + self._devkit_path, + 'results', + 'VOC' + self._year, + 'Main', + filename) + return path + + def _write_voc_results_file(self, all_boxes): + for cls_ind, cls in enumerate(self.classes): + if cls == '__background__': + continue + print('Writing {} VOC results file'.format(cls)) + filename = self._get_voc_results_file_template().format(cls) + with open(filename, 'wt') as f: + for im_ind, index in enumerate(self.image_index): + dets = all_boxes[cls_ind][im_ind] + if dets == []: + continue + # the VOCdevkit expects 1-based indices + for k in range(dets.shape[0]): + f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. + format(index, dets[k, -1], + dets[k, 0] + 1, dets[k, 1] + 1, + dets[k, 2] + 1, dets[k, 3] + 1)) + + def _do_python_eval(self, output_dir='output'): + annopath = os.path.join( + self._devkit_path, + 'VOC' + self._year, + 'Annotations', + '{:s}.xml') + imagesetfile = os.path.join( + self._devkit_path, + 'VOC' + self._year, + 'ImageSets', + 'Main', + self._image_set + '.txt') + cachedir = os.path.join(self._devkit_path, 'annotations_cache') + aps = [] + # The PASCAL VOC metric changed in 2010 + use_07_metric = True if int(self._year) < 2010 else False + print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No')) + if not os.path.isdir(output_dir): + os.mkdir(output_dir) + for i, cls in enumerate(self._classes): + if cls == '__background__': + continue + filename = self._get_voc_results_file_template().format(cls) + rec, prec, ap = voc_eval( + filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, + use_07_metric=use_07_metric, use_diff=self.config['use_diff']) + aps += [ap] + print(('AP for {} = {:.4f}'.format(cls, ap))) + with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f: + pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f) + print(('Mean AP = {:.4f}'.format(np.mean(aps)))) + print('~~~~~~~~') + print('Results:') + for ap in aps: + print(('{:.3f}'.format(ap))) + print(('{:.3f}'.format(np.mean(aps)))) + print('~~~~~~~~') + print('') + print('--------------------------------------------------------------') + print('Results computed with the **unofficial** Python eval code.') + print('Results should be very close to the official MATLAB eval code.') + print('Recompute with `./tools/reval.py --matlab ...` for your paper.') + print('-- Thanks, The Management') + print('--------------------------------------------------------------') + + def _do_matlab_eval(self, output_dir='output'): + print('-----------------------------------------------------') + print('Computing results with the official MATLAB eval code.') + print('-----------------------------------------------------') + path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets', + 'VOCdevkit-matlab-wrapper') + cmd = 'cd {} && '.format(path) + cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB) + cmd += '-r "dbstop if error; ' + cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \ + .format(self._devkit_path, self._get_comp_id(), + self._image_set, output_dir) + print(('Running:\n{}'.format(cmd))) + status = subprocess.call(cmd, shell=True) + + def evaluate_detections(self, all_boxes, output_dir): + self._write_voc_results_file(all_boxes) + self._do_python_eval(output_dir) + if self.config['matlab_eval']: + self._do_matlab_eval(output_dir) + if self.config['cleanup']: + for cls in self._classes: + if cls == '__background__': + continue + filename = self._get_voc_results_file_template().format(cls) + os.remove(filename) + + def competition_mode(self, on): + if on: + self.config['use_salt'] = False + self.config['cleanup'] = False + else: + self.config['use_salt'] = True + self.config['cleanup'] = True + + +if __name__ == '__main__': + from datasets.pascal_voc import pascal_voc + + d = pascal_voc('trainval', '2007') + res = d.roidb + from IPython import embed; + + embed() diff --git a/tf-faster-rcnn/lib/datasets/tools/mcg_munge.py b/tf-faster-rcnn/lib/datasets/tools/mcg_munge.py new file mode 100644 index 0000000..1392aa3 --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/tools/mcg_munge.py @@ -0,0 +1,38 @@ +import os +import sys + +"""Hacky tool to convert file system layout of MCG boxes downloaded from +http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/ +so that it's consistent with those computed by Jan Hosang (see: +http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal- + computing/research/object-recognition-and-scene-understanding/how- + good-are-detection-proposals-really/) + +NB: Boxes from the MCG website are in (y1, x1, y2, x2) order. +Boxes from Hosang et al. are in (x1, y1, x2, y2) order. +""" + +def munge(src_dir): + # stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat + # want: ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_000000141334.mat + + files = os.listdir(src_dir) + for fn in files: + base, ext = os.path.splitext(fn) + # first 14 chars / first 22 chars / all chars + .mat + # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat + first = base[:14] + second = base[:22] + dst_dir = os.path.join('MCG', 'mat', first, second) + if not os.path.exists(dst_dir): + os.makedirs(dst_dir) + src = os.path.join(src_dir, fn) + dst = os.path.join(dst_dir, fn) + print 'MV: {} -> {}'.format(src, dst) + os.rename(src, dst) + +if __name__ == '__main__': + # src_dir should look something like: + # src_dir = 'MCG-COCO-val2014-boxes' + src_dir = sys.argv[1] + munge(src_dir) diff --git a/tf-faster-rcnn/lib/datasets/voc_eval.py b/tf-faster-rcnn/lib/datasets/voc_eval.py new file mode 100644 index 0000000..0201887 --- /dev/null +++ b/tf-faster-rcnn/lib/datasets/voc_eval.py @@ -0,0 +1,215 @@ +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Bharath Hariharan +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import xml.etree.ElementTree as ET +import os +import pickle +import numpy as np + +def parse_rec(filename): + """ Parse a PASCAL VOC xml file """ + tree = ET.parse(filename) + objects = [] + for obj in tree.findall('object'): + obj_struct = {} + obj_struct['name'] = obj.find('name').text + obj_struct['pose'] = obj.find('pose').text + obj_struct['truncated'] = int(obj.find('truncated').text) + obj_struct['difficult'] = int(obj.find('difficult').text) + bbox = obj.find('bndbox') + obj_struct['bbox'] = [int(bbox.find('xmin').text), + int(bbox.find('ymin').text), + int(bbox.find('xmax').text), + int(bbox.find('ymax').text)] + objects.append(obj_struct) + + return objects + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def voc_eval(detpath, + annopath, + imagesetfile, + classname, + cachedir, + ovthresh=0.5, + use_07_metric=False, + use_diff=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + # first load gt + if not os.path.isdir(cachedir): + os.mkdir(cachedir) + #cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile) + cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile.split("/")[-1].split(".")[0]) + # read list of images + with open(imagesetfile, 'r') as f: + lines = f.readlines() + imagenames = [x.strip() for x in lines] + + if not os.path.isfile(cachefile): + # load annotations + recs = {} + for i, imagename in enumerate(imagenames): + recs[imagename] = parse_rec(annopath.format(imagename)) + if i % 100 == 0: + print('Reading annotation for {:d}/{:d}'.format( + i + 1, len(imagenames))) + # save + print('Saving cached annotations to {:s}'.format(cachefile)) + with open(cachefile, 'wb') as f: + pickle.dump(recs, f) + else: + # load + with open(cachefile, 'rb') as f: + try: + recs = pickle.load(f) + except: + recs = pickle.load(f, encoding='bytes') + + # extract gt objects for this class + class_recs = {} + npos = 0 + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj['name'] == classname] + bbox = np.array([x['bbox'] for x in R]) + if use_diff: + difficult = np.array([False for x in R]).astype(np.bool) + else: + difficult = np.array([x['difficult'] for x in R]).astype(np.bool) + det = [False] * len(R) + npos = npos + sum(~difficult) + class_recs[imagename] = {'bbox': bbox, + 'difficult': difficult, + 'det': det} + + # read dets + detfile = detpath.format(classname) + with open(detfile, 'r') as f: + lines = f.readlines() + + splitlines = [x.strip().split(' ') for x in lines] + image_ids = [x[0] for x in splitlines] + confidence = np.array([float(x[1]) for x in splitlines]) + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) + + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + + if BB.shape[0] > 0: + # sort by confidence + sorted_ind = np.argsort(-confidence) + sorted_scores = np.sort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R['bbox'].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1., 0.) + ih = np.maximum(iymax - iymin + 1., 0.) + inters = iw * ih + + # union + uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + + (BBGT[:, 2] - BBGT[:, 0] + 1.) * + (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > ovthresh: + if not R['difficult'][jmax]: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap diff --git a/tf-faster-rcnn/lib/layer_utils/__init__.py b/tf-faster-rcnn/lib/layer_utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tf-faster-rcnn/lib/layer_utils/anchor_target_layer.py b/tf-faster-rcnn/lib/layer_utils/anchor_target_layer.py new file mode 100644 index 0000000..22ab573 --- /dev/null +++ b/tf-faster-rcnn/lib/layer_utils/anchor_target_layer.py @@ -0,0 +1,162 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +from model.config import cfg +import numpy as np +import numpy.random as npr +from utils.cython_bbox import bbox_overlaps +from model.bbox_transform import bbox_transform + +def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride, all_anchors, num_anchors): + """Same as the anchor target layer in original Fast/er RCNN """ + A = num_anchors + total_anchors = all_anchors.shape[0] + K = total_anchors / num_anchors + + # allow boxes to sit over the edge by a small amount + _allowed_border = 0 + + # map of shape (..., H, W) + height, width = rpn_cls_score.shape[1:3] + + # only keep anchors inside the image + inds_inside = np.where( + (all_anchors[:, 0] >= -_allowed_border) & + (all_anchors[:, 1] >= -_allowed_border) & + (all_anchors[:, 2] < im_info[1] + _allowed_border) & # width + (all_anchors[:, 3] < im_info[0] + _allowed_border) # height + )[0] + + # keep only inside anchors + anchors = all_anchors[inds_inside, :] + + # label: 1 is positive, 0 is negative, -1 is dont care + labels = np.empty((len(inds_inside),), dtype=np.float32) + labels.fill(-1) + + # overlaps between the anchors and the gt boxes + # overlaps (ex, gt) + overlaps = bbox_overlaps( + np.ascontiguousarray(anchors, dtype=np.float), + np.ascontiguousarray(gt_boxes, dtype=np.float)) + argmax_overlaps = overlaps.argmax(axis=1) + max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps] + gt_argmax_overlaps = overlaps.argmax(axis=0) + gt_max_overlaps = overlaps[gt_argmax_overlaps, + np.arange(overlaps.shape[1])] + gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0] + + if not cfg.TRAIN.RPN_CLOBBER_POSITIVES: + # assign bg labels first so that positive labels can clobber them + # first set the negatives + labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 + + # fg label: for each gt, anchor with highest overlap + labels[gt_argmax_overlaps] = 1 + + # fg label: above threshold IOU + labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 + + if cfg.TRAIN.RPN_CLOBBER_POSITIVES: + # assign bg labels last so that negative labels can clobber positives + labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 + + # subsample positive labels if we have too many + num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE) + fg_inds = np.where(labels == 1)[0] + if len(fg_inds) > num_fg: + disable_inds = npr.choice( + fg_inds, size=(len(fg_inds) - num_fg), replace=False) + labels[disable_inds] = -1 + + # subsample negative labels if we have too many + num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1) + bg_inds = np.where(labels == 0)[0] + if len(bg_inds) > num_bg: + disable_inds = npr.choice( + bg_inds, size=(len(bg_inds) - num_bg), replace=False) + labels[disable_inds] = -1 + + bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32) + bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :]) + + bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32) + # only the positive ones have regression targets + bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS) + + bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32) + if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0: + # uniform weighting of examples (given non-uniform sampling) + num_examples = np.sum(labels >= 0) + positive_weights = np.ones((1, 4)) * 1.0 / num_examples + negative_weights = np.ones((1, 4)) * 1.0 / num_examples + else: + assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) & + (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1)) + positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT / + np.sum(labels == 1)) + negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) / + np.sum(labels == 0)) + bbox_outside_weights[labels == 1, :] = positive_weights + bbox_outside_weights[labels == 0, :] = negative_weights + + # map up to original set of anchors + labels = _unmap(labels, total_anchors, inds_inside, fill=-1) + bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0) + bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0) + bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0) + + # labels + labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2) + labels = labels.reshape((1, 1, A * height, width)) + rpn_labels = labels + + # bbox_targets + bbox_targets = bbox_targets \ + .reshape((1, height, width, A * 4)) + + rpn_bbox_targets = bbox_targets + # bbox_inside_weights + bbox_inside_weights = bbox_inside_weights \ + .reshape((1, height, width, A * 4)) + + rpn_bbox_inside_weights = bbox_inside_weights + + # bbox_outside_weights + bbox_outside_weights = bbox_outside_weights \ + .reshape((1, height, width, A * 4)) + + rpn_bbox_outside_weights = bbox_outside_weights + return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights + + +def _unmap(data, count, inds, fill=0): + """ Unmap a subset of item (data) back to the original set of items (of + size count) """ + if len(data.shape) == 1: + ret = np.empty((count,), dtype=np.float32) + ret.fill(fill) + ret[inds] = data + else: + ret = np.empty((count,) + data.shape[1:], dtype=np.float32) + ret.fill(fill) + ret[inds, :] = data + return ret + + +def _compute_targets(ex_rois, gt_rois): + """Compute bounding-box regression targets for an image.""" + + assert ex_rois.shape[0] == gt_rois.shape[0] + assert ex_rois.shape[1] == 4 + assert gt_rois.shape[1] == 5 + + return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False) diff --git a/tf-faster-rcnn/lib/layer_utils/generate_anchors.py b/tf-faster-rcnn/lib/layer_utils/generate_anchors.py new file mode 100644 index 0000000..0270203 --- /dev/null +++ b/tf-faster-rcnn/lib/layer_utils/generate_anchors.py @@ -0,0 +1,117 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Sean Bell +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +# Verify that we compute the same anchors as Shaoqing's matlab implementation: +# +# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat +# >> anchors +# +# anchors = +# +# -83 -39 100 56 +# -175 -87 192 104 +# -359 -183 376 200 +# -55 -55 72 72 +# -119 -119 136 136 +# -247 -247 264 264 +# -35 -79 52 96 +# -79 -167 96 184 +# -167 -343 184 360 + +# array([[ -83., -39., 100., 56.], +# [-175., -87., 192., 104.], +# [-359., -183., 376., 200.], +# [ -55., -55., 72., 72.], +# [-119., -119., 136., 136.], +# [-247., -247., 264., 264.], +# [ -35., -79., 52., 96.], +# [ -79., -167., 96., 184.], +# [-167., -343., 184., 360.]]) + +def generate_anchors(base_size=16, ratios=[0.5, 1, 2], + scales=2 ** np.arange(3, 6)): + """ + Generate anchor (reference) windows by enumerating aspect ratios X + scales wrt a reference (0, 0, 15, 15) window. + """ + + base_anchor = np.array([1, 1, base_size, base_size]) - 1 + ratio_anchors = _ratio_enum(base_anchor, ratios) + anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales) + for i in range(ratio_anchors.shape[0])]) + return anchors + + +def _whctrs(anchor): + """ + Return width, height, x center, and y center for an anchor (window). + """ + + w = anchor[2] - anchor[0] + 1 + h = anchor[3] - anchor[1] + 1 + x_ctr = anchor[0] + 0.5 * (w - 1) + y_ctr = anchor[1] + 0.5 * (h - 1) + return w, h, x_ctr, y_ctr + + +def _mkanchors(ws, hs, x_ctr, y_ctr): + """ + Given a vector of widths (ws) and heights (hs) around a center + (x_ctr, y_ctr), output a set of anchors (windows). + """ + + ws = ws[:, np.newaxis] + hs = hs[:, np.newaxis] + anchors = np.hstack((x_ctr - 0.5 * (ws - 1), + y_ctr - 0.5 * (hs - 1), + x_ctr + 0.5 * (ws - 1), + y_ctr + 0.5 * (hs - 1))) + return anchors + + +def _ratio_enum(anchor, ratios): + """ + Enumerate a set of anchors for each aspect ratio wrt an anchor. + """ + + w, h, x_ctr, y_ctr = _whctrs(anchor) + size = w * h + size_ratios = size / ratios + ws = np.round(np.sqrt(size_ratios)) + hs = np.round(ws * ratios) + anchors = _mkanchors(ws, hs, x_ctr, y_ctr) + return anchors + + +def _scale_enum(anchor, scales): + """ + Enumerate a set of anchors for each scale wrt an anchor. + """ + + w, h, x_ctr, y_ctr = _whctrs(anchor) + ws = w * scales + hs = h * scales + anchors = _mkanchors(ws, hs, x_ctr, y_ctr) + return anchors + + +if __name__ == '__main__': + import time + + t = time.time() + a = generate_anchors() + print(time.time() - t) + print(a) + from IPython import embed; + + embed() diff --git a/tf-faster-rcnn/lib/layer_utils/proposal_layer.py b/tf-faster-rcnn/lib/layer_utils/proposal_layer.py new file mode 100644 index 0000000..b0b1415 --- /dev/null +++ b/tf-faster-rcnn/lib/layer_utils/proposal_layer.py @@ -0,0 +1,85 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import numpy as np +from model.config import cfg +from model.bbox_transform import bbox_transform_inv, clip_boxes, bbox_transform_inv_tf, clip_boxes_tf +from model.nms_wrapper import nms + +def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors): + """A simplified version compared to fast/er RCNN + For details please see the technical report + """ + if type(cfg_key) == bytes: + cfg_key = cfg_key.decode('utf-8') + pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N + post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N + nms_thresh = cfg[cfg_key].RPN_NMS_THRESH + + # Get the scores and bounding boxes + scores = rpn_cls_prob[:, :, :, num_anchors:] + rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4)) + scores = scores.reshape((-1, 1)) + proposals = bbox_transform_inv(anchors, rpn_bbox_pred) + proposals = clip_boxes(proposals, im_info[:2]) + + # Pick the top region proposals + order = scores.ravel().argsort()[::-1] + if pre_nms_topN > 0: + order = order[:pre_nms_topN] + proposals = proposals[order, :] + scores = scores[order] + + # Non-maximal suppression + keep = nms(np.hstack((proposals, scores)), nms_thresh) + + # Pick th top region proposals after NMS + if post_nms_topN > 0: + keep = keep[:post_nms_topN] + proposals = proposals[keep, :] + scores = scores[keep] + + # Only support single image as input + batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32) + blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False))) + + return blob, scores + + +def proposal_layer_tf(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors): + if type(cfg_key) == bytes: + cfg_key = cfg_key.decode('utf-8') + pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N + post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N + nms_thresh = cfg[cfg_key].RPN_NMS_THRESH + + # Get the scores and bounding boxes + scores = rpn_cls_prob[:, :, :, num_anchors:] + scores = tf.reshape(scores, shape=(-1,)) + rpn_bbox_pred = tf.reshape(rpn_bbox_pred, shape=(-1, 4)) + + proposals = bbox_transform_inv_tf(anchors, rpn_bbox_pred) + proposals = clip_boxes_tf(proposals, im_info[:2]) + + # Non-maximal suppression + indices = tf.image.non_max_suppression(proposals, scores, max_output_size=post_nms_topN, iou_threshold=nms_thresh) + + boxes = tf.gather(proposals, indices) + boxes = tf.to_float(boxes) + scores = tf.gather(scores, indices) + scores = tf.reshape(scores, shape=(-1, 1)) + + # Only support single image as input + batch_inds = tf.zeros((tf.shape(indices)[0], 1), dtype=tf.float32) + blob = tf.concat([batch_inds, boxes], 1) + + return blob, scores + + diff --git a/tf-faster-rcnn/lib/layer_utils/proposal_target_layer.py b/tf-faster-rcnn/lib/layer_utils/proposal_target_layer.py new file mode 100644 index 0000000..f77a28e --- /dev/null +++ b/tf-faster-rcnn/lib/layer_utils/proposal_target_layer.py @@ -0,0 +1,152 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick, Sean Bell and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import numpy.random as npr +from model.config import cfg +from model.bbox_transform import bbox_transform +from utils.cython_bbox import bbox_overlaps + + +def proposal_target_layer(rpn_rois, rpn_scores, gt_boxes, _num_classes): + """ + Assign object detection proposals to ground-truth targets. Produces proposal + classification labels and bounding-box regression targets. + """ + + # Proposal ROIs (0, x1, y1, x2, y2) coming from RPN + # (i.e., rpn.proposal_layer.ProposalLayer), or any other source + all_rois = rpn_rois + all_scores = rpn_scores + + # Include ground-truth boxes in the set of candidate rois + if cfg.TRAIN.USE_GT: + zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype) + all_rois = np.vstack( + (all_rois, np.hstack((zeros, gt_boxes[:, :-1]))) + ) + # not sure if it a wise appending, but anyway i am not using it + all_scores = np.vstack((all_scores, zeros)) + + num_images = 1 + rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images + fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image) + + # Sample rois with classification labels and bounding box regression + # targets + labels, rois, roi_scores, bbox_targets, bbox_inside_weights = _sample_rois( + all_rois, all_scores, gt_boxes, fg_rois_per_image, + rois_per_image, _num_classes) + + rois = rois.reshape(-1, 5) + roi_scores = roi_scores.reshape(-1) + labels = labels.reshape(-1, 1) + bbox_targets = bbox_targets.reshape(-1, _num_classes * 4) + bbox_inside_weights = bbox_inside_weights.reshape(-1, _num_classes * 4) + bbox_outside_weights = np.array(bbox_inside_weights > 0).astype(np.float32) + + return rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights + + +def _get_bbox_regression_labels(bbox_target_data, num_classes): + """Bounding-box regression targets (bbox_target_data) are stored in a + compact form N x (class, tx, ty, tw, th) + + This function expands those targets into the 4-of-4*K representation used + by the network (i.e. only one class has non-zero targets). + + Returns: + bbox_target (ndarray): N x 4K blob of regression targets + bbox_inside_weights (ndarray): N x 4K blob of loss weights + """ + + clss = bbox_target_data[:, 0] + bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32) + bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32) + inds = np.where(clss > 0)[0] + for ind in inds: + cls = clss[ind] + start = int(4 * cls) + end = start + 4 + bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] + bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS + return bbox_targets, bbox_inside_weights + + +def _compute_targets(ex_rois, gt_rois, labels): + """Compute bounding-box regression targets for an image.""" + + assert ex_rois.shape[0] == gt_rois.shape[0] + assert ex_rois.shape[1] == 4 + assert gt_rois.shape[1] == 4 + + targets = bbox_transform(ex_rois, gt_rois) + if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: + # Optionally normalize targets by a precomputed mean and stdev + targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS)) + / np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS)) + return np.hstack( + (labels[:, np.newaxis], targets)).astype(np.float32, copy=False) + + +def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, num_classes): + """Generate a random sample of RoIs comprising foreground and background + examples. + """ + # overlaps: (rois x gt_boxes) + overlaps = bbox_overlaps( + np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float), + np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float)) + gt_assignment = overlaps.argmax(axis=1) + max_overlaps = overlaps.max(axis=1) + labels = gt_boxes[gt_assignment, 4] + + # Select foreground RoIs as those with >= FG_THRESH overlap + fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0] + # Guard against the case when an image has fewer than fg_rois_per_image + # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) + bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) & + (max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] + + # Small modification to the original version where we ensure a fixed number of regions are sampled + if fg_inds.size > 0 and bg_inds.size > 0: + fg_rois_per_image = min(fg_rois_per_image, fg_inds.size) + fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_image), replace=False) + bg_rois_per_image = rois_per_image - fg_rois_per_image + to_replace = bg_inds.size < bg_rois_per_image + bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_image), replace=to_replace) + elif fg_inds.size > 0: + to_replace = fg_inds.size < rois_per_image + fg_inds = npr.choice(fg_inds, size=int(rois_per_image), replace=to_replace) + fg_rois_per_image = rois_per_image + elif bg_inds.size > 0: + to_replace = bg_inds.size < rois_per_image + bg_inds = npr.choice(bg_inds, size=int(rois_per_image), replace=to_replace) + fg_rois_per_image = 0 + else: + import pdb + pdb.set_trace() + + # The indices that we're selecting (both fg and bg) + keep_inds = np.append(fg_inds, bg_inds) + # Select sampled values from various arrays: + labels = labels[keep_inds] + # Clamp labels for the background RoIs to 0 + labels[int(fg_rois_per_image):] = 0 + rois = all_rois[keep_inds] + roi_scores = all_scores[keep_inds] + + bbox_target_data = _compute_targets( + rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels) + + bbox_targets, bbox_inside_weights = \ + _get_bbox_regression_labels(bbox_target_data, num_classes) + + return labels, rois, roi_scores, bbox_targets, bbox_inside_weights diff --git a/tf-faster-rcnn/lib/layer_utils/proposal_top_layer.py b/tf-faster-rcnn/lib/layer_utils/proposal_top_layer.py new file mode 100644 index 0000000..873bf19 --- /dev/null +++ b/tf-faster-rcnn/lib/layer_utils/proposal_top_layer.py @@ -0,0 +1,85 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from model.config import cfg +from model.bbox_transform import bbox_transform_inv, clip_boxes, bbox_transform_inv_tf, clip_boxes_tf + +import tensorflow as tf +import numpy as np +import numpy.random as npr + +def proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, im_info, _feat_stride, anchors, num_anchors): + """A layer that just selects the top region proposals + without using non-maximal suppression, + For details please see the technical report + """ + rpn_top_n = cfg.TEST.RPN_TOP_N + + scores = rpn_cls_prob[:, :, :, num_anchors:] + + rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4)) + scores = scores.reshape((-1, 1)) + + length = scores.shape[0] + if length < rpn_top_n: + # Random selection, maybe unnecessary and loses good proposals + # But such case rarely happens + top_inds = npr.choice(length, size=rpn_top_n, replace=True) + else: + top_inds = scores.argsort(0)[::-1] + top_inds = top_inds[:rpn_top_n] + top_inds = top_inds.reshape(rpn_top_n, ) + + # Do the selection here + anchors = anchors[top_inds, :] + rpn_bbox_pred = rpn_bbox_pred[top_inds, :] + scores = scores[top_inds] + + # Convert anchors into proposals via bbox transformations + proposals = bbox_transform_inv(anchors, rpn_bbox_pred) + + # Clip predicted boxes to image + proposals = clip_boxes(proposals, im_info[:2]) + + # Output rois blob + # Our RPN implementation only supports a single input image, so all + # batch inds are 0 + batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32) + blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False))) + return blob, scores + + +def proposal_top_layer_tf(rpn_cls_prob, rpn_bbox_pred, im_info, _feat_stride, anchors, num_anchors): + """A layer that just selects the top region proposals + without using non-maximal suppression, + For details please see the technical report + """ + rpn_top_n = cfg.TEST.RPN_TOP_N + + scores = rpn_cls_prob[:, :, :, num_anchors:] + rpn_bbox_pred = tf.reshape(rpn_bbox_pred, shape=(-1, 4)) + scores = tf.reshape(scores, shape=(-1,)) + + # Do the selection here + top_scores, top_inds = tf.nn.top_k(scores, k=rpn_top_n) + top_scores = tf.reshape(top_scores, shape=(-1, 1)) + top_anchors = tf.gather(anchors, top_inds) + top_rpn_bbox = tf.gather(rpn_bbox_pred, top_inds) + proposals = bbox_transform_inv_tf(top_anchors, top_rpn_bbox) + + # Clip predicted boxes to image + proposals = clip_boxes_tf(proposals, im_info[:2]) + + # Output rois blob + # Our RPN implementation only supports a single input image, so all + # batch inds are 0 + proposals = tf.to_float(proposals) + batch_inds = tf.zeros((rpn_top_n, 1)) + blob = tf.concat([batch_inds, proposals], 1) + return blob, top_scores diff --git a/tf-faster-rcnn/lib/layer_utils/snippets.py b/tf-faster-rcnn/lib/layer_utils/snippets.py new file mode 100644 index 0000000..cfa69a8 --- /dev/null +++ b/tf-faster-rcnn/lib/layer_utils/snippets.py @@ -0,0 +1,49 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import numpy as np +from layer_utils.generate_anchors import generate_anchors + +def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)): + """ A wrapper function to generate anchors given different scales + Also return the number of anchors in variable 'length' + """ + anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales)) + A = anchors.shape[0] + shift_x = np.arange(0, width) * feat_stride + shift_y = np.arange(0, height) * feat_stride + shift_x, shift_y = np.meshgrid(shift_x, shift_y) + shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() + K = shifts.shape[0] + # width changes faster, so here it is H, W, C + anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)) + anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False) + length = np.int32(anchors.shape[0]) + + return anchors, length + +def generate_anchors_pre_tf(height, width, feat_stride=16, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)): + shift_x = tf.range(width) * feat_stride # width + shift_y = tf.range(height) * feat_stride # height + shift_x, shift_y = tf.meshgrid(shift_x, shift_y) + sx = tf.reshape(shift_x, shape=(-1,)) + sy = tf.reshape(shift_y, shape=(-1,)) + shifts = tf.transpose(tf.stack([sx, sy, sx, sy])) + K = tf.multiply(width, height) + shifts = tf.transpose(tf.reshape(shifts, shape=[1, K, 4]), perm=(1, 0, 2)) + + anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales)) + A = anchors.shape[0] + anchor_constant = tf.constant(anchors.reshape((1, A, 4)), dtype=tf.int32) + + length = K * A + anchors_tf = tf.reshape(tf.add(anchor_constant, shifts), shape=(length, 4)) + + return tf.cast(anchors_tf, dtype=tf.float32), length diff --git a/tf-faster-rcnn/lib/model/.ipynb_checkpoints/test-checkpoint.py b/tf-faster-rcnn/lib/model/.ipynb_checkpoints/test-checkpoint.py new file mode 100644 index 0000000..45f05bc --- /dev/null +++ b/tf-faster-rcnn/lib/model/.ipynb_checkpoints/test-checkpoint.py @@ -0,0 +1,193 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import cv2 +import numpy as np +try: + import cPickle as pickle +except ImportError: + import pickle +import os +import math + +from utils.timer import Timer +from utils.blob import im_list_to_blob + +from model.config import cfg, get_output_dir +from model.bbox_transform import clip_boxes, bbox_transform_inv +from model.nms_wrapper import nms + +def _get_image_blob(im): + """Converts an image into a network input. + Arguments: + im (ndarray): a color image in BGR order + Returns: + blob (ndarray): a data blob holding an image pyramid + im_scale_factors (list): list of image scales (relative to im) used + in the image pyramid + """ + im_orig = im.astype(np.float32, copy=True) + im_orig -= cfg.PIXEL_MEANS + + im_shape = im_orig.shape + im_size_min = np.min(im_shape[0:2]) + im_size_max = np.max(im_shape[0:2]) + + processed_ims = [] + im_scale_factors = [] + + for target_size in cfg.TEST.SCALES: + im_scale = float(target_size) / float(im_size_min) + # Prevent the biggest axis from being more than MAX_SIZE + if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: + im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) + im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, + interpolation=cv2.INTER_LINEAR) + im_scale_factors.append(im_scale) + processed_ims.append(im) + + # Create a blob to hold the input images + blob = im_list_to_blob(processed_ims) + + return blob, np.array(im_scale_factors) + +def _get_blobs(im): + """Convert an image and RoIs within that image into network inputs.""" + blobs = {} + blobs['data'], im_scale_factors = _get_image_blob(im) + + return blobs, im_scale_factors + +def _clip_boxes(boxes, im_shape): + """Clip boxes to image boundaries.""" + # x1 >= 0 + boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0) + # y1 >= 0 + boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0) + # x2 < im_shape[1] + boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1) + # y2 < im_shape[0] + boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1) + return boxes + +def _rescale_boxes(boxes, inds, scales): + """Rescale boxes according to image rescaling.""" + for i in range(boxes.shape[0]): + boxes[i,:] = boxes[i,:] / scales[int(inds[i])] + + return boxes + +def im_detect(sess, net, im): + blobs, im_scales = _get_blobs(im) + assert len(im_scales) == 1, "Only single-image batch implemented" + + im_blob = blobs['data'] + blobs['im_info'] = np.array([im_blob.shape[1], im_blob.shape[2], im_scales[0]], dtype=np.float32) + + _, scores, bbox_pred, rois = net.test_image(sess, blobs['data'], blobs['im_info']) + + boxes = rois[:, 1:5] / im_scales[0] + scores = np.reshape(scores, [scores.shape[0], -1]) + bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1]) + if cfg.TEST.BBOX_REG: + # Apply bounding-box regression deltas + box_deltas = bbox_pred + pred_boxes = bbox_transform_inv(boxes, box_deltas) + pred_boxes = _clip_boxes(pred_boxes, im.shape) + else: + # Simply repeat the boxes, once for each class + pred_boxes = np.tile(boxes, (1, scores.shape[1])) + + return scores, pred_boxes + +def apply_nms(all_boxes, thresh): + """Apply non-maximum suppression to all predicted boxes output by the + test_net method. + """ + num_classes = len(all_boxes) + num_images = len(all_boxes[0]) + nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)] + for cls_ind in range(num_classes): + for im_ind in range(num_images): + dets = all_boxes[cls_ind][im_ind] + if dets == []: + continue + + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + inds = np.where((x2 > x1) & (y2 > y1))[0] + dets = dets[inds,:] + if dets == []: + continue + + keep = nms(dets, thresh) + if len(keep) == 0: + continue + nms_boxes[cls_ind][im_ind] = dets[keep, :].copy() + return nms_boxes + +def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.): + np.random.seed(cfg.RNG_SEED) + """Test a Fast R-CNN network on an image database.""" + num_images = len(imdb.image_index) + # all detections are collected into: + # all_boxes[cls][image] = N x 5 array of detections in + # (x1, y1, x2, y2, score) + all_boxes = [[[] for _ in range(num_images)] + for _ in range(imdb.num_classes)] + + output_dir = get_output_dir(imdb, weights_filename) + # timers + _t = {'im_detect' : Timer(), 'misc' : Timer()} + + for i in range(num_images): + im = cv2.imread(imdb.image_path_at(i)) + + _t['im_detect'].tic() + scores, boxes = im_detect(sess, net, im) + _t['im_detect'].toc() + + _t['misc'].tic() + + # skip j = 0, because it's the background class + for j in range(1, imdb.num_classes): + inds = np.where(scores[:, j] > thresh)[0] + cls_scores = scores[inds, j] + cls_boxes = boxes[inds, j*4:(j+1)*4] + cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \ + .astype(np.float32, copy=False) + keep = nms(cls_dets, cfg.TEST.NMS) + cls_dets = cls_dets[keep, :] + all_boxes[j][i] = cls_dets + + # Limit to max_per_image detections *over all classes* + if max_per_image > 0: + image_scores = np.hstack([all_boxes[j][i][:, -1] + for j in range(1, imdb.num_classes)]) + if len(image_scores) > max_per_image: + image_thresh = np.sort(image_scores)[-max_per_image] + for j in range(1, imdb.num_classes): + keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] + all_boxes[j][i] = all_boxes[j][i][keep, :] + _t['misc'].toc() + + print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \ + .format(i + 1, num_images, _t['im_detect'].average_time, + _t['misc'].average_time)) + + det_file = os.path.join(output_dir, 'detections.pkl') + with open(det_file, 'wb') as f: + pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) + + print('Evaluating detections') + imdb.evaluate_detections(all_boxes, output_dir) + diff --git a/tf-faster-rcnn/lib/model/.ipynb_checkpoints/train_val-checkpoint.py b/tf-faster-rcnn/lib/model/.ipynb_checkpoints/train_val-checkpoint.py new file mode 100644 index 0000000..e303baf --- /dev/null +++ b/tf-faster-rcnn/lib/model/.ipynb_checkpoints/train_val-checkpoint.py @@ -0,0 +1,378 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen and Zheqi He +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from model.config import cfg +import roi_data_layer.roidb as rdl_roidb +from roi_data_layer.layer import RoIDataLayer +from utils.timer import Timer +try: + import cPickle as pickle +except ImportError: + import pickle +import numpy as np +import os +import sys +import glob +import time + +import tensorflow as tf +from tensorflow.python import pywrap_tensorflow + +class SolverWrapper(object): + """ + A wrapper class for the training process + """ + + def __init__(self, sess, network, imdb, roidb, valroidb, output_dir, tbdir, pretrained_model=None): + self.net = network + self.imdb = imdb + self.roidb = roidb + self.valroidb = valroidb + self.output_dir = output_dir + self.tbdir = tbdir + # Simply put '_val' at the end to save the summaries from the validation set + self.tbvaldir = tbdir + '_val' + if not os.path.exists(self.tbvaldir): + os.makedirs(self.tbvaldir) + self.pretrained_model = pretrained_model + + def snapshot(self, sess, iter): + net = self.net + + if not os.path.exists(self.output_dir): + os.makedirs(self.output_dir) + + # Store the model snapshot + filename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.ckpt' + filename = os.path.join(self.output_dir, filename) + self.saver.save(sess, filename) + print('Wrote snapshot to: {:s}'.format(filename)) + + # Also store some meta information, random state, etc. + nfilename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.pkl' + nfilename = os.path.join(self.output_dir, nfilename) + # current state of numpy random + st0 = np.random.get_state() + # current position in the database + cur = self.data_layer._cur + # current shuffled indexes of the database + perm = self.data_layer._perm + # current position in the validation database + cur_val = self.data_layer_val._cur + # current shuffled indexes of the validation database + perm_val = self.data_layer_val._perm + + # Dump the meta info + with open(nfilename, 'wb') as fid: + pickle.dump(st0, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(cur, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(perm, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(cur_val, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(perm_val, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(iter, fid, pickle.HIGHEST_PROTOCOL) + + return filename, nfilename + + def from_snapshot(self, sess, sfile, nfile): + print('Restoring model snapshots from {:s}'.format(sfile)) + self.saver.restore(sess, sfile) + print('Restored.') + # Needs to restore the other hyper-parameters/states for training, (TODO xinlei) I have + # tried my best to find the random states so that it can be recovered exactly + # However the Tensorflow state is currently not available + with open(nfile, 'rb') as fid: + st0 = pickle.load(fid) + cur = pickle.load(fid) + perm = pickle.load(fid) + cur_val = pickle.load(fid) + perm_val = pickle.load(fid) + last_snapshot_iter = pickle.load(fid) + + np.random.set_state(st0) + self.data_layer._cur = cur + self.data_layer._perm = perm + self.data_layer_val._cur = cur_val + self.data_layer_val._perm = perm_val + + return last_snapshot_iter + + def get_variables_in_checkpoint_file(self, file_name): + try: + reader = pywrap_tensorflow.NewCheckpointReader(file_name) + var_to_shape_map = reader.get_variable_to_shape_map() + return var_to_shape_map + except Exception as e: # pylint: disable=broad-except + print(str(e)) + if "corrupted compressed block contents" in str(e): + print("It's likely that your checkpoint file has been compressed " + "with SNAPPY.") + + def construct_graph(self, sess): + with sess.graph.as_default(): + # Set the random seed for tensorflow + tf.set_random_seed(cfg.RNG_SEED) + # Build the main computation graph + layers = self.net.create_architecture('TRAIN', self.imdb.num_classes, tag='default', + anchor_scales=cfg.ANCHOR_SCALES, + anchor_ratios=cfg.ANCHOR_RATIOS) + # Define the loss + loss = layers['total_loss'] + # Set learning rate and momentum + lr = tf.Variable(cfg.TRAIN.LEARNING_RATE, trainable=False) + self.optimizer = tf.train.MomentumOptimizer(lr, cfg.TRAIN.MOMENTUM) + + # Compute the gradients with regard to the loss + gvs = self.optimizer.compute_gradients(loss) + # Double the gradient of the bias if set + if cfg.TRAIN.DOUBLE_BIAS: + final_gvs = [] + with tf.variable_scope('Gradient_Mult') as scope: + for grad, var in gvs: + scale = 1. + if cfg.TRAIN.DOUBLE_BIAS and '/biases:' in var.name: + scale *= 2. + if not np.allclose(scale, 1.0): + grad = tf.multiply(grad, scale) + final_gvs.append((grad, var)) + train_op = self.optimizer.apply_gradients(final_gvs) + else: + train_op = self.optimizer.apply_gradients(gvs) + + # We will handle the snapshots ourselves + self.saver = tf.train.Saver(max_to_keep=100000) + # Write the train and validation information to tensorboard + self.writer = tf.summary.FileWriter(self.tbdir, sess.graph) + self.valwriter = tf.summary.FileWriter(self.tbvaldir) + + return lr, train_op + + def find_previous(self): + sfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.ckpt.meta') + sfiles = glob.glob(sfiles) + sfiles.sort(key=os.path.getmtime) + # Get the snapshot name in TensorFlow + redfiles = [] + for stepsize in cfg.TRAIN.STEPSIZE: + redfiles.append(os.path.join(self.output_dir, + cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}.ckpt.meta'.format(stepsize+1))) + sfiles = [ss.replace('.meta', '') for ss in sfiles if ss not in redfiles] + + nfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.pkl') + nfiles = glob.glob(nfiles) + nfiles.sort(key=os.path.getmtime) + redfiles = [redfile.replace('.ckpt.meta', '.pkl') for redfile in redfiles] + nfiles = [nn for nn in nfiles if nn not in redfiles] + + lsf = len(sfiles) + assert len(nfiles) == lsf + + return lsf, nfiles, sfiles + + def initialize(self, sess): + # Initial file lists are empty + np_paths = [] + ss_paths = [] + # Fresh train directly from ImageNet weights + print('Loading initial model weights from {:s}'.format(self.pretrained_model)) + variables = tf.global_variables() + # Initialize all variables first + sess.run(tf.variables_initializer(variables, name='init')) + var_keep_dic = self.get_variables_in_checkpoint_file(self.pretrained_model) + # Get the variables to restore, ignoring the variables to fix + variables_to_restore = self.net.get_variables_to_restore(variables, var_keep_dic) + + restorer = tf.train.Saver(variables_to_restore) + restorer.restore(sess, self.pretrained_model) + print('Loaded.') + # Need to fix the variables before loading, so that the RGB weights are changed to BGR + # For VGG16 it also changes the convolutional weights fc6 and fc7 to + # fully connected weights + self.net.fix_variables(sess, self.pretrained_model) + print('Fixed.') + last_snapshot_iter = 0 + rate = cfg.TRAIN.LEARNING_RATE + stepsizes = list(cfg.TRAIN.STEPSIZE) + + return rate, last_snapshot_iter, stepsizes, np_paths, ss_paths + + def restore(self, sess, sfile, nfile): + # Get the most recent snapshot and restore + np_paths = [nfile] + ss_paths = [sfile] + # Restore model from snapshots + last_snapshot_iter = self.from_snapshot(sess, sfile, nfile) + # Set the learning rate + rate = cfg.TRAIN.LEARNING_RATE + stepsizes = [] + for stepsize in cfg.TRAIN.STEPSIZE: + if last_snapshot_iter > stepsize: + rate *= cfg.TRAIN.GAMMA + else: + stepsizes.append(stepsize) + + return rate, last_snapshot_iter, stepsizes, np_paths, ss_paths + + def remove_snapshot(self, np_paths, ss_paths): + to_remove = len(np_paths) - cfg.TRAIN.SNAPSHOT_KEPT + for c in range(to_remove): + nfile = np_paths[0] + os.remove(str(nfile)) + np_paths.remove(nfile) + + to_remove = len(ss_paths) - cfg.TRAIN.SNAPSHOT_KEPT + for c in range(to_remove): + sfile = ss_paths[0] + # To make the code compatible to earlier versions of Tensorflow, + # where the naming tradition for checkpoints are different + if os.path.exists(str(sfile)): + os.remove(str(sfile)) + else: + os.remove(str(sfile + '.data-00000-of-00001')) + os.remove(str(sfile + '.index')) + sfile_meta = sfile + '.meta' + os.remove(str(sfile_meta)) + ss_paths.remove(sfile) + + def train_model(self, sess, max_iters): + # Build data layers for both training and validation set + self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes) + self.data_layer_val = RoIDataLayer(self.valroidb, self.imdb.num_classes, random=True) + + # Construct the computation graph + lr, train_op = self.construct_graph(sess) + + # Find previous snapshots if there is any to restore from + lsf, nfiles, sfiles = self.find_previous() + + # Initialize the variables or restore them from the last snapshot + if lsf == 0: + rate, last_snapshot_iter, stepsizes, np_paths, ss_paths = self.initialize(sess) + else: + rate, last_snapshot_iter, stepsizes, np_paths, ss_paths = self.restore(sess, + str(sfiles[-1]), + str(nfiles[-1])) + timer = Timer() + iter = last_snapshot_iter + 1 + last_summary_time = time.time() + # Make sure the lists are not empty + stepsizes.append(max_iters) + stepsizes.reverse() + next_stepsize = stepsizes.pop() + while iter < max_iters + 1: + # Learning rate + if iter == next_stepsize + 1: + # Add snapshot here before reducing the learning rate + self.snapshot(sess, iter) + rate *= cfg.TRAIN.GAMMA + sess.run(tf.assign(lr, rate)) + next_stepsize = stepsizes.pop() + + timer.tic() + # Get training data, one batch at a time + blobs = self.data_layer.forward() + + now = time.time() + if iter == 1 or now - last_summary_time > cfg.TRAIN.SUMMARY_INTERVAL: + # Compute the graph with summary + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss, summary = \ + self.net.train_step_with_summary(sess, blobs, train_op) + self.writer.add_summary(summary, float(iter)) + # Also check the summary on the validation set + blobs_val = self.data_layer_val.forward() + summary_val = self.net.get_summary(sess, blobs_val) + self.valwriter.add_summary(summary_val, float(iter)) + last_summary_time = now + else: + # Compute the graph without summary + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss = \ + self.net.train_step(sess, blobs, train_op) + timer.toc() + + # Display training information + if iter % (cfg.TRAIN.DISPLAY) == 0: + print('iter: %d / %d, total loss: %.6f\n >>> rpn_loss_cls: %.6f\n ' + '>>> rpn_loss_box: %.6f\n >>> loss_cls: %.6f\n >>> loss_box: %.6f\n >>> lr: %f' % \ + (iter, max_iters, total_loss, rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, lr.eval())) + print('speed: {:.3f}s / iter'.format(timer.average_time)) + + # Snapshotting + if iter % cfg.TRAIN.SNAPSHOT_ITERS == 0: + last_snapshot_iter = iter + ss_path, np_path = self.snapshot(sess, iter) + np_paths.append(np_path) + ss_paths.append(ss_path) + + # Remove the old snapshots if there are too many + if len(np_paths) > cfg.TRAIN.SNAPSHOT_KEPT: + self.remove_snapshot(np_paths, ss_paths) + + iter += 1 + + if last_snapshot_iter != iter - 1: + self.snapshot(sess, iter - 1) + + self.writer.close() + self.valwriter.close() + + +def get_training_roidb(imdb): + """Returns a roidb (Region of Interest database) for use in training.""" + if cfg.TRAIN.USE_FLIPPED: + print('Appending horizontally-flipped training examples...') + imdb.append_flipped_images() + print('done') + + print('Preparing training data...') + rdl_roidb.prepare_roidb(imdb) + print('done') + + return imdb.roidb + + +def filter_roidb(roidb): + """Remove roidb entries that have no usable RoIs.""" + + def is_valid(entry): + # Valid images have: + # (1) At least one foreground RoI OR + # (2) At least one background RoI + overlaps = entry['max_overlaps'] + # find boxes with sufficient overlap + fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0] + # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) + bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) & + (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] + # image is only valid if such boxes exist + valid = len(fg_inds) > 0 or len(bg_inds) > 0 + return valid + + num = len(roidb) + filtered_roidb = [entry for entry in roidb if is_valid(entry)] + num_after = len(filtered_roidb) + print('Filtered {} roidb entries: {} -> {}'.format(num - num_after, + num, num_after)) + return filtered_roidb + + +def train_net(network, imdb, roidb, valroidb, output_dir, tb_dir, + pretrained_model=None, + max_iters=40000): + """Train a Faster R-CNN network.""" + roidb = filter_roidb(roidb) + valroidb = filter_roidb(valroidb) + + tfconfig = tf.ConfigProto(allow_soft_placement=True) + tfconfig.gpu_options.allow_growth = True + + with tf.Session(config=tfconfig) as sess: + sw = SolverWrapper(sess, network, imdb, roidb, valroidb, output_dir, tb_dir, + pretrained_model=pretrained_model) + print('Solving...') + sw.train_model(sess, max_iters) + print('done solving') diff --git a/tf-faster-rcnn/lib/model/__init__.py b/tf-faster-rcnn/lib/model/__init__.py new file mode 100644 index 0000000..d63bc18 --- /dev/null +++ b/tf-faster-rcnn/lib/model/__init__.py @@ -0,0 +1 @@ +from . import config diff --git a/tf-faster-rcnn/lib/model/bbox_transform.py b/tf-faster-rcnn/lib/model/bbox_transform.py new file mode 100644 index 0000000..9cc7be1 --- /dev/null +++ b/tf-faster-rcnn/lib/model/bbox_transform.py @@ -0,0 +1,117 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +def bbox_transform(ex_rois, gt_rois): + ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 + ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 + ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths + ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights + + gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 + gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 + gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths + gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights + + targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths + targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights + targets_dw = np.log(gt_widths / ex_widths) + targets_dh = np.log(gt_heights / ex_heights) + + targets = np.vstack( + (targets_dx, targets_dy, targets_dw, targets_dh)).transpose() + return targets + + +def bbox_transform_inv(boxes, deltas): + if boxes.shape[0] == 0: + return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype) + + boxes = boxes.astype(deltas.dtype, copy=False) + widths = boxes[:, 2] - boxes[:, 0] + 1.0 + heights = boxes[:, 3] - boxes[:, 1] + 1.0 + ctr_x = boxes[:, 0] + 0.5 * widths + ctr_y = boxes[:, 1] + 0.5 * heights + + dx = deltas[:, 0::4] + dy = deltas[:, 1::4] + dw = deltas[:, 2::4] + dh = deltas[:, 3::4] + + pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis] + pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis] + pred_w = np.exp(dw) * widths[:, np.newaxis] + pred_h = np.exp(dh) * heights[:, np.newaxis] + + pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype) + # x1 + pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w + # y1 + pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h + # x2 + pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w + # y2 + pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h + + return pred_boxes + + +def clip_boxes(boxes, im_shape): + """ + Clip boxes to image boundaries. + """ + + # x1 >= 0 + boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) + # y1 >= 0 + boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0) + # x2 < im_shape[1] + boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0) + # y2 < im_shape[0] + boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0) + return boxes + + + +def bbox_transform_inv_tf(boxes, deltas): + boxes = tf.cast(boxes, deltas.dtype) + widths = tf.subtract(boxes[:, 2], boxes[:, 0]) + 1.0 + heights = tf.subtract(boxes[:, 3], boxes[:, 1]) + 1.0 + ctr_x = tf.add(boxes[:, 0], widths * 0.5) + ctr_y = tf.add(boxes[:, 1], heights * 0.5) + + dx = deltas[:, 0] + dy = deltas[:, 1] + dw = deltas[:, 2] + dh = deltas[:, 3] + + pred_ctr_x = tf.add(tf.multiply(dx, widths), ctr_x) + pred_ctr_y = tf.add(tf.multiply(dy, heights), ctr_y) + pred_w = tf.multiply(tf.exp(dw), widths) + pred_h = tf.multiply(tf.exp(dh), heights) + + pred_boxes0 = tf.subtract(pred_ctr_x, pred_w * 0.5) + pred_boxes1 = tf.subtract(pred_ctr_y, pred_h * 0.5) + pred_boxes2 = tf.add(pred_ctr_x, pred_w * 0.5) + pred_boxes3 = tf.add(pred_ctr_y, pred_h * 0.5) + + return tf.stack([pred_boxes0, pred_boxes1, pred_boxes2, pred_boxes3], axis=1) + + +def clip_boxes_tf(boxes, im_info): + b0 = tf.maximum(tf.minimum(boxes[:, 0], im_info[1] - 1), 0) + b1 = tf.maximum(tf.minimum(boxes[:, 1], im_info[0] - 1), 0) + b2 = tf.maximum(tf.minimum(boxes[:, 2], im_info[1] - 1), 0) + b3 = tf.maximum(tf.minimum(boxes[:, 3], im_info[0] - 1), 0) + return tf.stack([b0, b1, b2, b3], axis=1) + + diff --git a/tf-faster-rcnn/lib/model/config.py b/tf-faster-rcnn/lib/model/config.py new file mode 100644 index 0000000..539116f --- /dev/null +++ b/tf-faster-rcnn/lib/model/config.py @@ -0,0 +1,387 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import os.path as osp +import numpy as np +# `pip install easydict` if you don't have it +from easydict import EasyDict as edict + +__C = edict() +# Consumers can get config by: +# from fast_rcnn_config import cfg +cfg = __C + +# +# Training options +# +__C.TRAIN = edict() + +# Initial learning rate +__C.TRAIN.LEARNING_RATE = 0.001 + +# Momentum +__C.TRAIN.MOMENTUM = 0.9 + +# Weight decay, for regularization +__C.TRAIN.WEIGHT_DECAY = 0.0001 + +# Factor for reducing the learning rate +__C.TRAIN.GAMMA = 0.1 + +# Step size for reducing the learning rate, currently only support one step +__C.TRAIN.STEPSIZE = [30000] + +# Iteration intervals for showing the loss during training, on command line interface +__C.TRAIN.DISPLAY = 10 + +# Whether to double the learning rate for bias +__C.TRAIN.DOUBLE_BIAS = True + +# Whether to initialize the weights with truncated normal distribution +__C.TRAIN.TRUNCATED = False + +# Whether to have weight decay on bias as well +__C.TRAIN.BIAS_DECAY = False + +# Whether to add ground truth boxes to the pool when sampling regions +__C.TRAIN.USE_GT = False + +# Whether to use aspect-ratio grouping of training images, introduced merely for saving +# GPU memory +__C.TRAIN.ASPECT_GROUPING = False + +# The number of snapshots kept, older ones are deleted to save space +__C.TRAIN.SNAPSHOT_KEPT = 3 + +# The time interval for saving tensorflow summaries +__C.TRAIN.SUMMARY_INTERVAL = 180 + +# Scale to use during training (can list multiple scales) +# The scale is the pixel size of an image's shortest side +__C.TRAIN.SCALES = (600,) + +# Max pixel size of the longest side of a scaled input image +__C.TRAIN.MAX_SIZE = 1000 + +# Images to use per minibatch +__C.TRAIN.IMS_PER_BATCH = 1 + +# Minibatch size (number of regions of interest [ROIs]) +__C.TRAIN.BATCH_SIZE = 128 + +# Fraction of minibatch that is labeled foreground (i.e. class > 0) +__C.TRAIN.FG_FRACTION = 0.25 + +# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH) +__C.TRAIN.FG_THRESH = 0.5 + +# Overlap threshold for a ROI to be considered background (class = 0 if +# overlap in [LO, HI)) +__C.TRAIN.BG_THRESH_HI = 0.5 +__C.TRAIN.BG_THRESH_LO = 0.1 + +# Use horizontally-flipped images during training? +__C.TRAIN.USE_FLIPPED = True + +# Train bounding-box regressors +__C.TRAIN.BBOX_REG = True + +# Overlap required between a ROI and ground-truth box in order for that ROI to +# be used as a bounding-box regression training example +__C.TRAIN.BBOX_THRESH = 0.5 + +# Iterations between snapshots +__C.TRAIN.SNAPSHOT_ITERS = 5000 + +# solver.prototxt specifies the snapshot path prefix, this adds an optional +# infix to yield the path: [_]_iters_XYZ.caffemodel +__C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn' + +# Normalize the targets (subtract empirical mean, divide by empirical stddev) +__C.TRAIN.BBOX_NORMALIZE_TARGETS = True + +# Deprecated (inside weights) +__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) + +# Normalize the targets using "precomputed" (or made up) means and stdevs +# (BBOX_NORMALIZE_TARGETS must also be True) +__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True + +__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0) + +__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2) + +# Train using these proposals +__C.TRAIN.PROPOSAL_METHOD = 'gt' + +# Make minibatches from images that have similar aspect ratios (i.e. both +# tall and thin or both short and wide) in order to avoid wasting computation +# on zero-padding. + +# Use RPN to detect objects +__C.TRAIN.HAS_RPN = True + +# IOU >= thresh: positive example +__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7 + +# IOU < thresh: negative example +__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3 + +# If an anchor satisfied by positive and negative conditions set to negative +__C.TRAIN.RPN_CLOBBER_POSITIVES = False + +# Max number of foreground examples +__C.TRAIN.RPN_FG_FRACTION = 0.5 + +# Total number of examples +__C.TRAIN.RPN_BATCHSIZE = 256 + +# NMS threshold used on RPN proposals +__C.TRAIN.RPN_NMS_THRESH = 0.7 + +# Number of top scoring boxes to keep before apply NMS to RPN proposals +__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 + +# Number of top scoring boxes to keep after applying NMS to RPN proposals +__C.TRAIN.RPN_POST_NMS_TOP_N = 2000 + +# Deprecated (outside weights) +__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) + +# Give the positive RPN examples weight of p * 1 / {num positives} +# and give negatives a weight of (1 - p) +# Set to -1.0 to use uniform example weighting +__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0 + +# Whether to use all ground truth bounding boxes for training, +# For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd'' +__C.TRAIN.USE_ALL_GT = True + +# +# Testing options +# +__C.TEST = edict() + +# Scale to use during testing (can NOT list multiple scales) +# The scale is the pixel size of an image's shortest side +__C.TEST.SCALES = (600,) + +# Max pixel size of the longest side of a scaled input image +__C.TEST.MAX_SIZE = 1000 + +# Overlap threshold used for non-maximum suppression (suppress boxes with +# IoU >= this threshold) +__C.TEST.NMS = 0.3 + +# Experimental: treat the (K+1) units in the cls_score layer as linear +# predictors (trained, eg, with one-vs-rest SVMs). +__C.TEST.SVM = False + +# Test using bounding-box regressors +__C.TEST.BBOX_REG = True + +# Propose boxes +__C.TEST.HAS_RPN = False + +# Test using these proposals +__C.TEST.PROPOSAL_METHOD = 'gt' + +## NMS threshold used on RPN proposals +__C.TEST.RPN_NMS_THRESH = 0.7 + +# Number of top scoring boxes to keep before apply NMS to RPN proposals +__C.TEST.RPN_PRE_NMS_TOP_N = 6000 + +# Number of top scoring boxes to keep after applying NMS to RPN proposals +__C.TEST.RPN_POST_NMS_TOP_N = 300 + +# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) +# __C.TEST.RPN_MIN_SIZE = 16 + +# Testing mode, default to be 'nms', 'top' is slower but better +# See report for details +__C.TEST.MODE = 'nms' + +# Only useful when TEST.MODE is 'top', specifies the number of top proposals to select +__C.TEST.RPN_TOP_N = 5000 + +# +# ResNet options +# + +__C.RESNET = edict() + +# Option to set if max-pooling is appended after crop_and_resize. +# if true, the region will be resized to a square of 2xPOOLING_SIZE, +# then 2x2 max-pooling is applied; otherwise the region will be directly +# resized to a square of POOLING_SIZE +__C.RESNET.MAX_POOL = False + +# Number of fixed blocks during training, by default the first of all 4 blocks is fixed +# Range: 0 (none) to 3 (all) +__C.RESNET.FIXED_BLOCKS = 1 + +# +# MobileNet options +# + +__C.MOBILENET = edict() + +# Whether to regularize the depth-wise filters during training +__C.MOBILENET.REGU_DEPTH = False + +# Number of fixed layers during training, by default the bottom 5 of 14 layers is fixed +# Range: 0 (none) to 12 (all) +__C.MOBILENET.FIXED_LAYERS = 5 + +# Weight decay for the mobilenet weights +__C.MOBILENET.WEIGHT_DECAY = 0.00004 + +# Depth multiplier +__C.MOBILENET.DEPTH_MULTIPLIER = 1. + +# +# MISC +# + +# Pixel mean values (BGR order) as a (1, 1, 3) array +# We use the same pixel mean for all networks even though it's not exactly what +# they were trained with +__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]]) + +# For reproducibility +__C.RNG_SEED = 3 + +# Root directory of project +__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) + +# Data directory +__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) + +# Name (or path to) the matlab executable +__C.MATLAB = 'matlab' + +# Place outputs under an experiments directory +__C.EXP_DIR = 'default' + +# Use GPU implementation of non-maximum suppression +__C.USE_GPU_NMS = True + +# Use an end-to-end tensorflow model. +# Note: models in E2E tensorflow mode have only been tested in feed-forward mode, +# but these models are exportable to other tensorflow instances as GraphDef files. +__C.USE_E2E_TF = True + +# Default pooling mode, only 'crop' is available +__C.POOLING_MODE = 'crop' + +# Size of the pooled region after RoI pooling +__C.POOLING_SIZE = 7 + +# Anchor scales for RPN +__C.ANCHOR_SCALES = [8,16,32] + +# Anchor ratios for RPN +__C.ANCHOR_RATIOS = [0.5,1,2] + +# Number of filters for the RPN layer +__C.RPN_CHANNELS = 512 + + +def get_output_dir(imdb, weights_filename): + """Return the directory where experimental artifacts are placed. + If the directory does not exist, it is created. + + A canonical path is built using the name from an imdb and a network + (if not None). + """ + outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name)) + if weights_filename is None: + weights_filename = 'default' + outdir = osp.join(outdir, weights_filename) + if not os.path.exists(outdir): + os.makedirs(outdir) + return outdir + + +def get_output_tb_dir(imdb, weights_filename): + """Return the directory where tensorflow summaries are placed. + If the directory does not exist, it is created. + + A canonical path is built using the name from an imdb and a network + (if not None). + """ + outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name)) + if weights_filename is None: + weights_filename = 'default' + outdir = osp.join(outdir, weights_filename) + if not os.path.exists(outdir): + os.makedirs(outdir) + return outdir + + +def _merge_a_into_b(a, b): + """Merge config dictionary a into config dictionary b, clobbering the + options in b whenever they are also specified in a. + """ + if type(a) is not edict: + return + + for k, v in a.items(): + # a must specify keys that are in b + if k not in b: + raise KeyError('{} is not a valid config key'.format(k)) + + # the types must match, too + old_type = type(b[k]) + if old_type is not type(v): + if isinstance(b[k], np.ndarray): + v = np.array(v, dtype=b[k].dtype) + else: + raise ValueError(('Type mismatch ({} vs. {}) ' + 'for config key: {}').format(type(b[k]), + type(v), k)) + + # recursively merge dicts + if type(v) is edict: + try: + _merge_a_into_b(a[k], b[k]) + except: + print(('Error under config key: {}'.format(k))) + raise + else: + b[k] = v + + +def cfg_from_file(filename): + """Load a config file and merge it into the default options.""" + import yaml + with open(filename, 'r') as f: + yaml_cfg = edict(yaml.load(f)) + + _merge_a_into_b(yaml_cfg, __C) + + +def cfg_from_list(cfg_list): + """Set config keys via list (e.g., from command line).""" + from ast import literal_eval + assert len(cfg_list) % 2 == 0 + for k, v in zip(cfg_list[0::2], cfg_list[1::2]): + key_list = k.split('.') + d = __C + for subkey in key_list[:-1]: + assert subkey in d + d = d[subkey] + subkey = key_list[-1] + assert subkey in d + try: + value = literal_eval(v) + except: + # handle the case when v is a string literal + value = v + assert type(value) == type(d[subkey]), \ + 'type {} does not match original type {}'.format( + type(value), type(d[subkey])) + d[subkey] = value diff --git a/tf-faster-rcnn/lib/model/nms_wrapper.py b/tf-faster-rcnn/lib/model/nms_wrapper.py new file mode 100644 index 0000000..f594922 --- /dev/null +++ b/tf-faster-rcnn/lib/model/nms_wrapper.py @@ -0,0 +1,23 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from model.config import cfg +from nms.gpu_nms import gpu_nms +from nms.cpu_nms import cpu_nms + +def nms(dets, thresh, force_cpu=False): + """Dispatch to either CPU or GPU NMS implementations.""" + + if dets.shape[0] == 0: + return [] + if cfg.USE_GPU_NMS and not force_cpu: + return gpu_nms(dets, thresh, device_id=0) + else: + return cpu_nms(dets, thresh) diff --git a/tf-faster-rcnn/lib/model/test.py b/tf-faster-rcnn/lib/model/test.py new file mode 100644 index 0000000..45f05bc --- /dev/null +++ b/tf-faster-rcnn/lib/model/test.py @@ -0,0 +1,193 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import cv2 +import numpy as np +try: + import cPickle as pickle +except ImportError: + import pickle +import os +import math + +from utils.timer import Timer +from utils.blob import im_list_to_blob + +from model.config import cfg, get_output_dir +from model.bbox_transform import clip_boxes, bbox_transform_inv +from model.nms_wrapper import nms + +def _get_image_blob(im): + """Converts an image into a network input. + Arguments: + im (ndarray): a color image in BGR order + Returns: + blob (ndarray): a data blob holding an image pyramid + im_scale_factors (list): list of image scales (relative to im) used + in the image pyramid + """ + im_orig = im.astype(np.float32, copy=True) + im_orig -= cfg.PIXEL_MEANS + + im_shape = im_orig.shape + im_size_min = np.min(im_shape[0:2]) + im_size_max = np.max(im_shape[0:2]) + + processed_ims = [] + im_scale_factors = [] + + for target_size in cfg.TEST.SCALES: + im_scale = float(target_size) / float(im_size_min) + # Prevent the biggest axis from being more than MAX_SIZE + if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: + im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) + im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, + interpolation=cv2.INTER_LINEAR) + im_scale_factors.append(im_scale) + processed_ims.append(im) + + # Create a blob to hold the input images + blob = im_list_to_blob(processed_ims) + + return blob, np.array(im_scale_factors) + +def _get_blobs(im): + """Convert an image and RoIs within that image into network inputs.""" + blobs = {} + blobs['data'], im_scale_factors = _get_image_blob(im) + + return blobs, im_scale_factors + +def _clip_boxes(boxes, im_shape): + """Clip boxes to image boundaries.""" + # x1 >= 0 + boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0) + # y1 >= 0 + boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0) + # x2 < im_shape[1] + boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1) + # y2 < im_shape[0] + boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1) + return boxes + +def _rescale_boxes(boxes, inds, scales): + """Rescale boxes according to image rescaling.""" + for i in range(boxes.shape[0]): + boxes[i,:] = boxes[i,:] / scales[int(inds[i])] + + return boxes + +def im_detect(sess, net, im): + blobs, im_scales = _get_blobs(im) + assert len(im_scales) == 1, "Only single-image batch implemented" + + im_blob = blobs['data'] + blobs['im_info'] = np.array([im_blob.shape[1], im_blob.shape[2], im_scales[0]], dtype=np.float32) + + _, scores, bbox_pred, rois = net.test_image(sess, blobs['data'], blobs['im_info']) + + boxes = rois[:, 1:5] / im_scales[0] + scores = np.reshape(scores, [scores.shape[0], -1]) + bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1]) + if cfg.TEST.BBOX_REG: + # Apply bounding-box regression deltas + box_deltas = bbox_pred + pred_boxes = bbox_transform_inv(boxes, box_deltas) + pred_boxes = _clip_boxes(pred_boxes, im.shape) + else: + # Simply repeat the boxes, once for each class + pred_boxes = np.tile(boxes, (1, scores.shape[1])) + + return scores, pred_boxes + +def apply_nms(all_boxes, thresh): + """Apply non-maximum suppression to all predicted boxes output by the + test_net method. + """ + num_classes = len(all_boxes) + num_images = len(all_boxes[0]) + nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)] + for cls_ind in range(num_classes): + for im_ind in range(num_images): + dets = all_boxes[cls_ind][im_ind] + if dets == []: + continue + + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + inds = np.where((x2 > x1) & (y2 > y1))[0] + dets = dets[inds,:] + if dets == []: + continue + + keep = nms(dets, thresh) + if len(keep) == 0: + continue + nms_boxes[cls_ind][im_ind] = dets[keep, :].copy() + return nms_boxes + +def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.): + np.random.seed(cfg.RNG_SEED) + """Test a Fast R-CNN network on an image database.""" + num_images = len(imdb.image_index) + # all detections are collected into: + # all_boxes[cls][image] = N x 5 array of detections in + # (x1, y1, x2, y2, score) + all_boxes = [[[] for _ in range(num_images)] + for _ in range(imdb.num_classes)] + + output_dir = get_output_dir(imdb, weights_filename) + # timers + _t = {'im_detect' : Timer(), 'misc' : Timer()} + + for i in range(num_images): + im = cv2.imread(imdb.image_path_at(i)) + + _t['im_detect'].tic() + scores, boxes = im_detect(sess, net, im) + _t['im_detect'].toc() + + _t['misc'].tic() + + # skip j = 0, because it's the background class + for j in range(1, imdb.num_classes): + inds = np.where(scores[:, j] > thresh)[0] + cls_scores = scores[inds, j] + cls_boxes = boxes[inds, j*4:(j+1)*4] + cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \ + .astype(np.float32, copy=False) + keep = nms(cls_dets, cfg.TEST.NMS) + cls_dets = cls_dets[keep, :] + all_boxes[j][i] = cls_dets + + # Limit to max_per_image detections *over all classes* + if max_per_image > 0: + image_scores = np.hstack([all_boxes[j][i][:, -1] + for j in range(1, imdb.num_classes)]) + if len(image_scores) > max_per_image: + image_thresh = np.sort(image_scores)[-max_per_image] + for j in range(1, imdb.num_classes): + keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] + all_boxes[j][i] = all_boxes[j][i][keep, :] + _t['misc'].toc() + + print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \ + .format(i + 1, num_images, _t['im_detect'].average_time, + _t['misc'].average_time)) + + det_file = os.path.join(output_dir, 'detections.pkl') + with open(det_file, 'wb') as f: + pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) + + print('Evaluating detections') + imdb.evaluate_detections(all_boxes, output_dir) + diff --git a/tf-faster-rcnn/lib/model/train_val.py b/tf-faster-rcnn/lib/model/train_val.py new file mode 100644 index 0000000..e303baf --- /dev/null +++ b/tf-faster-rcnn/lib/model/train_val.py @@ -0,0 +1,378 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen and Zheqi He +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from model.config import cfg +import roi_data_layer.roidb as rdl_roidb +from roi_data_layer.layer import RoIDataLayer +from utils.timer import Timer +try: + import cPickle as pickle +except ImportError: + import pickle +import numpy as np +import os +import sys +import glob +import time + +import tensorflow as tf +from tensorflow.python import pywrap_tensorflow + +class SolverWrapper(object): + """ + A wrapper class for the training process + """ + + def __init__(self, sess, network, imdb, roidb, valroidb, output_dir, tbdir, pretrained_model=None): + self.net = network + self.imdb = imdb + self.roidb = roidb + self.valroidb = valroidb + self.output_dir = output_dir + self.tbdir = tbdir + # Simply put '_val' at the end to save the summaries from the validation set + self.tbvaldir = tbdir + '_val' + if not os.path.exists(self.tbvaldir): + os.makedirs(self.tbvaldir) + self.pretrained_model = pretrained_model + + def snapshot(self, sess, iter): + net = self.net + + if not os.path.exists(self.output_dir): + os.makedirs(self.output_dir) + + # Store the model snapshot + filename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.ckpt' + filename = os.path.join(self.output_dir, filename) + self.saver.save(sess, filename) + print('Wrote snapshot to: {:s}'.format(filename)) + + # Also store some meta information, random state, etc. + nfilename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.pkl' + nfilename = os.path.join(self.output_dir, nfilename) + # current state of numpy random + st0 = np.random.get_state() + # current position in the database + cur = self.data_layer._cur + # current shuffled indexes of the database + perm = self.data_layer._perm + # current position in the validation database + cur_val = self.data_layer_val._cur + # current shuffled indexes of the validation database + perm_val = self.data_layer_val._perm + + # Dump the meta info + with open(nfilename, 'wb') as fid: + pickle.dump(st0, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(cur, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(perm, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(cur_val, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(perm_val, fid, pickle.HIGHEST_PROTOCOL) + pickle.dump(iter, fid, pickle.HIGHEST_PROTOCOL) + + return filename, nfilename + + def from_snapshot(self, sess, sfile, nfile): + print('Restoring model snapshots from {:s}'.format(sfile)) + self.saver.restore(sess, sfile) + print('Restored.') + # Needs to restore the other hyper-parameters/states for training, (TODO xinlei) I have + # tried my best to find the random states so that it can be recovered exactly + # However the Tensorflow state is currently not available + with open(nfile, 'rb') as fid: + st0 = pickle.load(fid) + cur = pickle.load(fid) + perm = pickle.load(fid) + cur_val = pickle.load(fid) + perm_val = pickle.load(fid) + last_snapshot_iter = pickle.load(fid) + + np.random.set_state(st0) + self.data_layer._cur = cur + self.data_layer._perm = perm + self.data_layer_val._cur = cur_val + self.data_layer_val._perm = perm_val + + return last_snapshot_iter + + def get_variables_in_checkpoint_file(self, file_name): + try: + reader = pywrap_tensorflow.NewCheckpointReader(file_name) + var_to_shape_map = reader.get_variable_to_shape_map() + return var_to_shape_map + except Exception as e: # pylint: disable=broad-except + print(str(e)) + if "corrupted compressed block contents" in str(e): + print("It's likely that your checkpoint file has been compressed " + "with SNAPPY.") + + def construct_graph(self, sess): + with sess.graph.as_default(): + # Set the random seed for tensorflow + tf.set_random_seed(cfg.RNG_SEED) + # Build the main computation graph + layers = self.net.create_architecture('TRAIN', self.imdb.num_classes, tag='default', + anchor_scales=cfg.ANCHOR_SCALES, + anchor_ratios=cfg.ANCHOR_RATIOS) + # Define the loss + loss = layers['total_loss'] + # Set learning rate and momentum + lr = tf.Variable(cfg.TRAIN.LEARNING_RATE, trainable=False) + self.optimizer = tf.train.MomentumOptimizer(lr, cfg.TRAIN.MOMENTUM) + + # Compute the gradients with regard to the loss + gvs = self.optimizer.compute_gradients(loss) + # Double the gradient of the bias if set + if cfg.TRAIN.DOUBLE_BIAS: + final_gvs = [] + with tf.variable_scope('Gradient_Mult') as scope: + for grad, var in gvs: + scale = 1. + if cfg.TRAIN.DOUBLE_BIAS and '/biases:' in var.name: + scale *= 2. + if not np.allclose(scale, 1.0): + grad = tf.multiply(grad, scale) + final_gvs.append((grad, var)) + train_op = self.optimizer.apply_gradients(final_gvs) + else: + train_op = self.optimizer.apply_gradients(gvs) + + # We will handle the snapshots ourselves + self.saver = tf.train.Saver(max_to_keep=100000) + # Write the train and validation information to tensorboard + self.writer = tf.summary.FileWriter(self.tbdir, sess.graph) + self.valwriter = tf.summary.FileWriter(self.tbvaldir) + + return lr, train_op + + def find_previous(self): + sfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.ckpt.meta') + sfiles = glob.glob(sfiles) + sfiles.sort(key=os.path.getmtime) + # Get the snapshot name in TensorFlow + redfiles = [] + for stepsize in cfg.TRAIN.STEPSIZE: + redfiles.append(os.path.join(self.output_dir, + cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}.ckpt.meta'.format(stepsize+1))) + sfiles = [ss.replace('.meta', '') for ss in sfiles if ss not in redfiles] + + nfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.pkl') + nfiles = glob.glob(nfiles) + nfiles.sort(key=os.path.getmtime) + redfiles = [redfile.replace('.ckpt.meta', '.pkl') for redfile in redfiles] + nfiles = [nn for nn in nfiles if nn not in redfiles] + + lsf = len(sfiles) + assert len(nfiles) == lsf + + return lsf, nfiles, sfiles + + def initialize(self, sess): + # Initial file lists are empty + np_paths = [] + ss_paths = [] + # Fresh train directly from ImageNet weights + print('Loading initial model weights from {:s}'.format(self.pretrained_model)) + variables = tf.global_variables() + # Initialize all variables first + sess.run(tf.variables_initializer(variables, name='init')) + var_keep_dic = self.get_variables_in_checkpoint_file(self.pretrained_model) + # Get the variables to restore, ignoring the variables to fix + variables_to_restore = self.net.get_variables_to_restore(variables, var_keep_dic) + + restorer = tf.train.Saver(variables_to_restore) + restorer.restore(sess, self.pretrained_model) + print('Loaded.') + # Need to fix the variables before loading, so that the RGB weights are changed to BGR + # For VGG16 it also changes the convolutional weights fc6 and fc7 to + # fully connected weights + self.net.fix_variables(sess, self.pretrained_model) + print('Fixed.') + last_snapshot_iter = 0 + rate = cfg.TRAIN.LEARNING_RATE + stepsizes = list(cfg.TRAIN.STEPSIZE) + + return rate, last_snapshot_iter, stepsizes, np_paths, ss_paths + + def restore(self, sess, sfile, nfile): + # Get the most recent snapshot and restore + np_paths = [nfile] + ss_paths = [sfile] + # Restore model from snapshots + last_snapshot_iter = self.from_snapshot(sess, sfile, nfile) + # Set the learning rate + rate = cfg.TRAIN.LEARNING_RATE + stepsizes = [] + for stepsize in cfg.TRAIN.STEPSIZE: + if last_snapshot_iter > stepsize: + rate *= cfg.TRAIN.GAMMA + else: + stepsizes.append(stepsize) + + return rate, last_snapshot_iter, stepsizes, np_paths, ss_paths + + def remove_snapshot(self, np_paths, ss_paths): + to_remove = len(np_paths) - cfg.TRAIN.SNAPSHOT_KEPT + for c in range(to_remove): + nfile = np_paths[0] + os.remove(str(nfile)) + np_paths.remove(nfile) + + to_remove = len(ss_paths) - cfg.TRAIN.SNAPSHOT_KEPT + for c in range(to_remove): + sfile = ss_paths[0] + # To make the code compatible to earlier versions of Tensorflow, + # where the naming tradition for checkpoints are different + if os.path.exists(str(sfile)): + os.remove(str(sfile)) + else: + os.remove(str(sfile + '.data-00000-of-00001')) + os.remove(str(sfile + '.index')) + sfile_meta = sfile + '.meta' + os.remove(str(sfile_meta)) + ss_paths.remove(sfile) + + def train_model(self, sess, max_iters): + # Build data layers for both training and validation set + self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes) + self.data_layer_val = RoIDataLayer(self.valroidb, self.imdb.num_classes, random=True) + + # Construct the computation graph + lr, train_op = self.construct_graph(sess) + + # Find previous snapshots if there is any to restore from + lsf, nfiles, sfiles = self.find_previous() + + # Initialize the variables or restore them from the last snapshot + if lsf == 0: + rate, last_snapshot_iter, stepsizes, np_paths, ss_paths = self.initialize(sess) + else: + rate, last_snapshot_iter, stepsizes, np_paths, ss_paths = self.restore(sess, + str(sfiles[-1]), + str(nfiles[-1])) + timer = Timer() + iter = last_snapshot_iter + 1 + last_summary_time = time.time() + # Make sure the lists are not empty + stepsizes.append(max_iters) + stepsizes.reverse() + next_stepsize = stepsizes.pop() + while iter < max_iters + 1: + # Learning rate + if iter == next_stepsize + 1: + # Add snapshot here before reducing the learning rate + self.snapshot(sess, iter) + rate *= cfg.TRAIN.GAMMA + sess.run(tf.assign(lr, rate)) + next_stepsize = stepsizes.pop() + + timer.tic() + # Get training data, one batch at a time + blobs = self.data_layer.forward() + + now = time.time() + if iter == 1 or now - last_summary_time > cfg.TRAIN.SUMMARY_INTERVAL: + # Compute the graph with summary + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss, summary = \ + self.net.train_step_with_summary(sess, blobs, train_op) + self.writer.add_summary(summary, float(iter)) + # Also check the summary on the validation set + blobs_val = self.data_layer_val.forward() + summary_val = self.net.get_summary(sess, blobs_val) + self.valwriter.add_summary(summary_val, float(iter)) + last_summary_time = now + else: + # Compute the graph without summary + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss = \ + self.net.train_step(sess, blobs, train_op) + timer.toc() + + # Display training information + if iter % (cfg.TRAIN.DISPLAY) == 0: + print('iter: %d / %d, total loss: %.6f\n >>> rpn_loss_cls: %.6f\n ' + '>>> rpn_loss_box: %.6f\n >>> loss_cls: %.6f\n >>> loss_box: %.6f\n >>> lr: %f' % \ + (iter, max_iters, total_loss, rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, lr.eval())) + print('speed: {:.3f}s / iter'.format(timer.average_time)) + + # Snapshotting + if iter % cfg.TRAIN.SNAPSHOT_ITERS == 0: + last_snapshot_iter = iter + ss_path, np_path = self.snapshot(sess, iter) + np_paths.append(np_path) + ss_paths.append(ss_path) + + # Remove the old snapshots if there are too many + if len(np_paths) > cfg.TRAIN.SNAPSHOT_KEPT: + self.remove_snapshot(np_paths, ss_paths) + + iter += 1 + + if last_snapshot_iter != iter - 1: + self.snapshot(sess, iter - 1) + + self.writer.close() + self.valwriter.close() + + +def get_training_roidb(imdb): + """Returns a roidb (Region of Interest database) for use in training.""" + if cfg.TRAIN.USE_FLIPPED: + print('Appending horizontally-flipped training examples...') + imdb.append_flipped_images() + print('done') + + print('Preparing training data...') + rdl_roidb.prepare_roidb(imdb) + print('done') + + return imdb.roidb + + +def filter_roidb(roidb): + """Remove roidb entries that have no usable RoIs.""" + + def is_valid(entry): + # Valid images have: + # (1) At least one foreground RoI OR + # (2) At least one background RoI + overlaps = entry['max_overlaps'] + # find boxes with sufficient overlap + fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0] + # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) + bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) & + (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] + # image is only valid if such boxes exist + valid = len(fg_inds) > 0 or len(bg_inds) > 0 + return valid + + num = len(roidb) + filtered_roidb = [entry for entry in roidb if is_valid(entry)] + num_after = len(filtered_roidb) + print('Filtered {} roidb entries: {} -> {}'.format(num - num_after, + num, num_after)) + return filtered_roidb + + +def train_net(network, imdb, roidb, valroidb, output_dir, tb_dir, + pretrained_model=None, + max_iters=40000): + """Train a Faster R-CNN network.""" + roidb = filter_roidb(roidb) + valroidb = filter_roidb(valroidb) + + tfconfig = tf.ConfigProto(allow_soft_placement=True) + tfconfig.gpu_options.allow_growth = True + + with tf.Session(config=tfconfig) as sess: + sw = SolverWrapper(sess, network, imdb, roidb, valroidb, output_dir, tb_dir, + pretrained_model=pretrained_model) + print('Solving...') + sw.train_model(sess, max_iters) + print('done solving') diff --git a/tf-faster-rcnn/lib/nets/.ipynb_checkpoints/network-checkpoint.py b/tf-faster-rcnn/lib/nets/.ipynb_checkpoints/network-checkpoint.py new file mode 100644 index 0000000..a0329c0 --- /dev/null +++ b/tf-faster-rcnn/lib/nets/.ipynb_checkpoints/network-checkpoint.py @@ -0,0 +1,524 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim +from tensorflow.contrib.slim import losses +from tensorflow.contrib.slim import arg_scope + +import numpy as np + +from layer_utils.snippets import generate_anchors_pre, generate_anchors_pre_tf +from layer_utils.proposal_layer import proposal_layer, proposal_layer_tf +from layer_utils.proposal_top_layer import proposal_top_layer, proposal_top_layer_tf +from layer_utils.anchor_target_layer import anchor_target_layer +from layer_utils.proposal_target_layer import proposal_target_layer +from utils.visualization import draw_bounding_boxes + +from model.config import cfg + +class Network(object): + def __init__(self): + self._predictions = {} + self._losses = {} + self._anchor_targets = {} + self._proposal_targets = {} + self._layers = {} + self._gt_image = None + self._act_summaries = [] + self._score_summaries = {} + self._train_summaries = [] + self._event_summaries = {} + self._variables_to_fix = {} + + def _add_gt_image(self): + # add back mean + image = self._image + cfg.PIXEL_MEANS + # BGR to RGB (opencv uses BGR) + resized = tf.image.resize_bilinear(image, tf.to_int32(self._im_info[:2] / self._im_info[2])) + self._gt_image = tf.reverse(resized, axis=[-1]) + + def _add_gt_image_summary(self): + # use a customized visualization function to visualize the boxes + if self._gt_image is None: + self._add_gt_image() + image = tf.py_func(draw_bounding_boxes, + [self._gt_image, self._gt_boxes, self._im_info], + tf.float32, name="gt_boxes") + + return tf.summary.image('GROUND_TRUTH', image) + + def _add_act_summary(self, tensor): + tf.summary.histogram('ACT/' + tensor.op.name + '/activations', tensor) + tf.summary.scalar('ACT/' + tensor.op.name + '/zero_fraction', + tf.nn.zero_fraction(tensor)) + + def _add_score_summary(self, key, tensor): + tf.summary.histogram('SCORE/' + tensor.op.name + '/' + key + '/scores', tensor) + + def _add_train_summary(self, var): + tf.summary.histogram('TRAIN/' + var.op.name, var) + + def _reshape_layer(self, bottom, num_dim, name): + input_shape = tf.shape(bottom) + with tf.variable_scope(name) as scope: + # change the channel to the caffe format + to_caffe = tf.transpose(bottom, [0, 3, 1, 2]) + # then force it to have channel 2 + reshaped = tf.reshape(to_caffe, + tf.concat(axis=0, values=[[1, num_dim, -1], [input_shape[2]]])) + # then swap the channel back + to_tf = tf.transpose(reshaped, [0, 2, 3, 1]) + return to_tf + + def _softmax_layer(self, bottom, name): + if name.startswith('rpn_cls_prob_reshape'): + input_shape = tf.shape(bottom) + bottom_reshaped = tf.reshape(bottom, [-1, input_shape[-1]]) + reshaped_score = tf.nn.softmax(bottom_reshaped, name=name) + return tf.reshape(reshaped_score, input_shape) + return tf.nn.softmax(bottom, name=name) + + def _proposal_top_layer(self, rpn_cls_prob, rpn_bbox_pred, name): + with tf.variable_scope(name) as scope: + if cfg.USE_E2E_TF: + rois, rpn_scores = proposal_top_layer_tf( + rpn_cls_prob, + rpn_bbox_pred, + self._im_info, + self._feat_stride, + self._anchors, + self._num_anchors + ) + else: + rois, rpn_scores = tf.py_func(proposal_top_layer, + [rpn_cls_prob, rpn_bbox_pred, self._im_info, + self._feat_stride, self._anchors, self._num_anchors], + [tf.float32, tf.float32], name="proposal_top") + + rois.set_shape([cfg.TEST.RPN_TOP_N, 5]) + rpn_scores.set_shape([cfg.TEST.RPN_TOP_N, 1]) + + return rois, rpn_scores + + def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name): + with tf.variable_scope(name) as scope: + if cfg.USE_E2E_TF: + rois, rpn_scores = proposal_layer_tf( + rpn_cls_prob, + rpn_bbox_pred, + self._im_info, + self._mode, + self._feat_stride, + self._anchors, + self._num_anchors + ) + else: + rois, rpn_scores = tf.py_func(proposal_layer, + [rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode, + self._feat_stride, self._anchors, self._num_anchors], + [tf.float32, tf.float32], name="proposal") + + rois.set_shape([None, 5]) + rpn_scores.set_shape([None, 1]) + + return rois, rpn_scores + + # Only use it if you have roi_pooling op written in tf.image + def _roi_pool_layer(self, bootom, rois, name): + with tf.variable_scope(name) as scope: + return tf.image.roi_pooling(bootom, rois, + pooled_height=cfg.POOLING_SIZE, + pooled_width=cfg.POOLING_SIZE, + spatial_scale=1. / 16.)[0] + + def _crop_pool_layer(self, bottom, rois, name): + with tf.variable_scope(name) as scope: + batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1]) + # Get the normalized coordinates of bounding boxes + bottom_shape = tf.shape(bottom) + height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0]) + width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0]) + x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width + y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height + x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width + y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height + # Won't be back-propagated to rois anyway, but to save time + bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1)) + pre_pool_size = cfg.POOLING_SIZE * 2 + crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops") + + return slim.max_pool2d(crops, [2, 2], padding='SAME') + + def _dropout_layer(self, bottom, name, ratio=0.5): + return tf.nn.dropout(bottom, ratio, name=name) + + def _anchor_target_layer(self, rpn_cls_score, name): + with tf.variable_scope(name) as scope: + rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = tf.py_func( + anchor_target_layer, + [rpn_cls_score, self._gt_boxes, self._im_info, self._feat_stride, self._anchors, self._num_anchors], + [tf.float32, tf.float32, tf.float32, tf.float32], + name="anchor_target") + + rpn_labels.set_shape([1, 1, None, None]) + rpn_bbox_targets.set_shape([1, None, None, self._num_anchors * 4]) + rpn_bbox_inside_weights.set_shape([1, None, None, self._num_anchors * 4]) + rpn_bbox_outside_weights.set_shape([1, None, None, self._num_anchors * 4]) + + rpn_labels = tf.to_int32(rpn_labels, name="to_int32") + self._anchor_targets['rpn_labels'] = rpn_labels + self._anchor_targets['rpn_bbox_targets'] = rpn_bbox_targets + self._anchor_targets['rpn_bbox_inside_weights'] = rpn_bbox_inside_weights + self._anchor_targets['rpn_bbox_outside_weights'] = rpn_bbox_outside_weights + + self._score_summaries.update(self._anchor_targets) + + return rpn_labels + + def _proposal_target_layer(self, rois, roi_scores, name): + with tf.variable_scope(name) as scope: + rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = tf.py_func( + proposal_target_layer, + [rois, roi_scores, self._gt_boxes, self._num_classes], + [tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32], + name="proposal_target") + + rois.set_shape([cfg.TRAIN.BATCH_SIZE, 5]) + roi_scores.set_shape([cfg.TRAIN.BATCH_SIZE]) + labels.set_shape([cfg.TRAIN.BATCH_SIZE, 1]) + bbox_targets.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) + bbox_inside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) + bbox_outside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) + + self._proposal_targets['rois'] = rois + self._proposal_targets['labels'] = tf.to_int32(labels, name="to_int32") + self._proposal_targets['bbox_targets'] = bbox_targets + self._proposal_targets['bbox_inside_weights'] = bbox_inside_weights + self._proposal_targets['bbox_outside_weights'] = bbox_outside_weights + + self._score_summaries.update(self._proposal_targets) + + return rois, roi_scores + + def _anchor_component(self): + with tf.variable_scope('ANCHOR_' + self._tag) as scope: + # just to get the shape right + height = tf.to_int32(tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0]))) + width = tf.to_int32(tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0]))) + if cfg.USE_E2E_TF: + anchors, anchor_length = generate_anchors_pre_tf( + height, + width, + self._feat_stride, + self._anchor_scales, + self._anchor_ratios + ) + else: + anchors, anchor_length = tf.py_func(generate_anchors_pre, + [height, width, + self._feat_stride, self._anchor_scales, self._anchor_ratios], + [tf.float32, tf.int32], name="generate_anchors") + anchors.set_shape([None, 4]) + anchor_length.set_shape([]) + self._anchors = anchors + self._anchor_length = anchor_length + + def _build_network(self, is_training=True): + # select initializers + if cfg.TRAIN.TRUNCATED: + initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01) + initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001) + else: + initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01) + initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001) + + net_conv = self._image_to_head(is_training) + with tf.variable_scope(self._scope, self._scope): + # build the anchors for the image + self._anchor_component() + # region proposal network + rois = self._region_proposal(net_conv, is_training, initializer) + # region of interest pooling + if cfg.POOLING_MODE == 'crop': + pool5 = self._crop_pool_layer(net_conv, rois, "pool5") + else: + raise NotImplementedError + + fc7 = self._head_to_tail(pool5, is_training) + with tf.variable_scope(self._scope, self._scope): + # region classification + cls_prob, bbox_pred = self._region_classification(fc7, is_training, + initializer, initializer_bbox) + + self._score_summaries.update(self._predictions) + + return rois, cls_prob, bbox_pred + + def _smooth_l1_loss(self, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights, sigma=1.0, dim=[1]): + sigma_2 = sigma ** 2 + box_diff = bbox_pred - bbox_targets + in_box_diff = bbox_inside_weights * box_diff + abs_in_box_diff = tf.abs(in_box_diff) + smoothL1_sign = tf.stop_gradient(tf.to_float(tf.less(abs_in_box_diff, 1. / sigma_2))) + in_loss_box = tf.pow(in_box_diff, 2) * (sigma_2 / 2.) * smoothL1_sign \ + + (abs_in_box_diff - (0.5 / sigma_2)) * (1. - smoothL1_sign) + out_loss_box = bbox_outside_weights * in_loss_box + loss_box = tf.reduce_mean(tf.reduce_sum( + out_loss_box, + axis=dim + )) + return loss_box + + def _add_losses(self, sigma_rpn=3.0): + with tf.variable_scope('LOSS_' + self._tag) as scope: + # RPN, class loss + rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2]) + rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1]) + rpn_select = tf.where(tf.not_equal(rpn_label, -1)) + rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) + rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1]) + rpn_cross_entropy = tf.reduce_mean( + tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label)) + + # RPN, bbox loss + rpn_bbox_pred = self._predictions['rpn_bbox_pred'] + rpn_bbox_targets = self._anchor_targets['rpn_bbox_targets'] + rpn_bbox_inside_weights = self._anchor_targets['rpn_bbox_inside_weights'] + rpn_bbox_outside_weights = self._anchor_targets['rpn_bbox_outside_weights'] + rpn_loss_box = self._smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, + rpn_bbox_outside_weights, sigma=sigma_rpn, dim=[1, 2, 3]) + + # RCNN, class loss + cls_score = self._predictions["cls_score"] + label = tf.reshape(self._proposal_targets["labels"], [-1]) + cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label)) + + # RCNN, bbox loss + bbox_pred = self._predictions['bbox_pred'] + bbox_targets = self._proposal_targets['bbox_targets'] + bbox_inside_weights = self._proposal_targets['bbox_inside_weights'] + bbox_outside_weights = self._proposal_targets['bbox_outside_weights'] + loss_box = self._smooth_l1_loss(bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights) + + self._losses['cross_entropy'] = cross_entropy + self._losses['loss_box'] = loss_box + self._losses['rpn_cross_entropy'] = rpn_cross_entropy + self._losses['rpn_loss_box'] = rpn_loss_box + + loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box + regularization_loss = tf.add_n(tf.losses.get_regularization_losses(), 'regu') + self._losses['total_loss'] = loss + regularization_loss + + self._event_summaries.update(self._losses) + + return loss + + def _region_proposal(self, net_conv, is_training, initializer): + rpn = slim.conv2d(net_conv, cfg.RPN_CHANNELS, [3, 3], trainable=is_training, weights_initializer=initializer, + scope="rpn_conv/3x3") + self._act_summaries.append(rpn) + rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, + weights_initializer=initializer, + padding='VALID', activation_fn=None, scope='rpn_cls_score') + # change it so that the score has 2 as its channel size + rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape') + rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape") + rpn_cls_pred = tf.argmax(tf.reshape(rpn_cls_score_reshape, [-1, 2]), axis=1, name="rpn_cls_pred") + rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob") + rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, + weights_initializer=initializer, + padding='VALID', activation_fn=None, scope='rpn_bbox_pred') + if is_training: + rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") + rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor") + # Try to have a deterministic order for the computing graph, for reproducibility + with tf.control_dependencies([rpn_labels]): + rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois") + else: + if cfg.TEST.MODE == 'nms': + rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") + elif cfg.TEST.MODE == 'top': + rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois") + else: + raise NotImplementedError + + self._predictions["rpn_cls_score"] = rpn_cls_score + self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape + self._predictions["rpn_cls_prob"] = rpn_cls_prob + self._predictions["rpn_cls_pred"] = rpn_cls_pred + self._predictions["rpn_bbox_pred"] = rpn_bbox_pred + self._predictions["rois"] = rois + + return rois + + def _region_classification(self, fc7, is_training, initializer, initializer_bbox): + cls_score = slim.fully_connected(fc7, self._num_classes, + weights_initializer=initializer, + trainable=is_training, + activation_fn=None, scope='cls_score') + cls_prob = self._softmax_layer(cls_score, "cls_prob") + cls_pred = tf.argmax(cls_score, axis=1, name="cls_pred") + bbox_pred = slim.fully_connected(fc7, self._num_classes * 4, + weights_initializer=initializer_bbox, + trainable=is_training, + activation_fn=None, scope='bbox_pred') + + self._predictions["cls_score"] = cls_score + self._predictions["cls_pred"] = cls_pred + self._predictions["cls_prob"] = cls_prob + self._predictions["bbox_pred"] = bbox_pred + + return cls_prob, bbox_pred + + def _image_to_head(self, is_training, reuse=None): + raise NotImplementedError + + def _head_to_tail(self, pool5, is_training, reuse=None): + raise NotImplementedError + + def create_architecture(self, mode, num_classes, tag=None, + anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)): + self._image = tf.placeholder(tf.float32, shape=[1, None, None, 3]) + self._im_info = tf.placeholder(tf.float32, shape=[3]) + self._gt_boxes = tf.placeholder(tf.float32, shape=[None, 5]) + self._tag = tag + + self._num_classes = num_classes + self._mode = mode + self._anchor_scales = anchor_scales + self._num_scales = len(anchor_scales) + + self._anchor_ratios = anchor_ratios + self._num_ratios = len(anchor_ratios) + + self._num_anchors = self._num_scales * self._num_ratios + + training = mode == 'TRAIN' + testing = mode == 'TEST' + + assert tag != None + + # handle most of the regularizers here + weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY) + if cfg.TRAIN.BIAS_DECAY: + biases_regularizer = weights_regularizer + else: + biases_regularizer = tf.no_regularizer + + # list as many types of layers as possible, even if they are not used now + with arg_scope([slim.conv2d, slim.conv2d_in_plane, \ + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + rois, cls_prob, bbox_pred = self._build_network(training) + + layers_to_output = {'rois': rois} + + for var in tf.trainable_variables(): + self._train_summaries.append(var) + + if testing: + stds = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (self._num_classes)) + means = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (self._num_classes)) + self._predictions["bbox_pred"] *= stds + self._predictions["bbox_pred"] += means + else: + self._add_losses() + layers_to_output.update(self._losses) + + val_summaries = [] + with tf.device("/cpu:0"): + val_summaries.append(self._add_gt_image_summary()) + for key, var in self._event_summaries.items(): + val_summaries.append(tf.summary.scalar(key, var)) + for key, var in self._score_summaries.items(): + self._add_score_summary(key, var) + for var in self._act_summaries: + self._add_act_summary(var) + for var in self._train_summaries: + self._add_train_summary(var) + + self._summary_op = tf.summary.merge_all() + self._summary_op_val = tf.summary.merge(val_summaries) + + layers_to_output.update(self._predictions) + + return layers_to_output + + def get_variables_to_restore(self, variables, var_keep_dic): + raise NotImplementedError + + def fix_variables(self, sess, pretrained_model): + raise NotImplementedError + + # Extract the head feature maps, for example for vgg16 it is conv5_3 + # only useful during testing mode + def extract_head(self, sess, image): + feed_dict = {self._image: image} + feat = sess.run(self._layers["head"], feed_dict=feed_dict) + return feat + + # only useful during testing mode + def test_image(self, sess, image, im_info): + feed_dict = {self._image: image, + self._im_info: im_info} + print("input_tensor") + print(self._image) + print(self._im_info) + print("out_put_tensor") + print(self._predictions["cls_score"]) + print(self._predictions["cls_prob"]) + print(self._predictions["bbox_pred"]) + print(self._predictions["rois"]) + cls_score, cls_prob, bbox_pred, rois = sess.run([self._predictions["cls_score"], + self._predictions['cls_prob'], + self._predictions['bbox_pred'], + self._predictions['rois']], + feed_dict=feed_dict) + return cls_score, cls_prob, bbox_pred, rois + + def get_summary(self, sess, blobs): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + summary = sess.run(self._summary_op_val, feed_dict=feed_dict) + + return summary + + def train_step(self, sess, blobs, train_op): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, _ = sess.run([self._losses["rpn_cross_entropy"], + self._losses['rpn_loss_box'], + self._losses['cross_entropy'], + self._losses['loss_box'], + self._losses['total_loss'], + train_op], + feed_dict=feed_dict) + return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss + + def train_step_with_summary(self, sess, blobs, train_op): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary, _ = sess.run([self._losses["rpn_cross_entropy"], + self._losses['rpn_loss_box'], + self._losses['cross_entropy'], + self._losses['loss_box'], + self._losses['total_loss'], + self._summary_op, + train_op], + feed_dict=feed_dict) + return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary + + def train_step_no_return(self, sess, blobs, train_op): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + sess.run([train_op], feed_dict=feed_dict) + diff --git a/tf-faster-rcnn/lib/nets/.ipynb_checkpoints/vgg16-checkpoint.py b/tf-faster-rcnn/lib/nets/.ipynb_checkpoints/vgg16-checkpoint.py new file mode 100644 index 0000000..38f7c21 --- /dev/null +++ b/tf-faster-rcnn/lib/nets/.ipynb_checkpoints/vgg16-checkpoint.py @@ -0,0 +1,100 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim +from tensorflow.contrib.slim import losses +from tensorflow.contrib.slim import arg_scope +import numpy as np + +from nets.network import Network +from model.config import cfg + +class vgg16(Network): + def __init__(self): + Network.__init__(self) + self._feat_stride = [16, ] + self._feat_compress = [1. / float(self._feat_stride[0]), ] + self._scope = 'vgg_16' + + def _image_to_head(self, is_training, reuse=None): + with tf.variable_scope(self._scope, self._scope, reuse=reuse): + net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], + trainable=False, scope='conv1') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1') + net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], + trainable=False, scope='conv2') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2') + net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], + trainable=is_training, scope='conv3') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], + trainable=is_training, scope='conv4') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], + trainable=is_training, scope='conv5') + + self._act_summaries.append(net) + self._layers['head'] = net + + return net + + def _head_to_tail(self, pool5, is_training, reuse=None): + with tf.variable_scope(self._scope, self._scope, reuse=reuse): + pool5_flat = slim.flatten(pool5, scope='flatten') + fc6 = slim.fully_connected(pool5_flat, 4096, scope='fc6') + if is_training: + fc6 = slim.dropout(fc6, keep_prob=0.5, is_training=True, + scope='dropout6') + fc7 = slim.fully_connected(fc6, 4096, scope='fc7') + if is_training: + fc7 = slim.dropout(fc7, keep_prob=0.5, is_training=True, + scope='dropout7') + + return fc7 + + def get_variables_to_restore(self, variables, var_keep_dic): + variables_to_restore = [] + + for v in variables: + # exclude the conv weights that are fc weights in vgg16 + if v.name == (self._scope + '/fc6/weights:0') or \ + v.name == (self._scope + '/fc7/weights:0'): + self._variables_to_fix[v.name] = v + continue + # exclude the first conv layer to swap RGB to BGR + if v.name == (self._scope + '/conv1/conv1_1/weights:0'): + self._variables_to_fix[v.name] = v + continue + if v.name.split(':')[0] in var_keep_dic: + print('Variables restored: %s' % v.name) + variables_to_restore.append(v) + + return variables_to_restore + + def fix_variables(self, sess, pretrained_model): + print('Fix VGG16 layers..') + with tf.variable_scope('Fix_VGG16') as scope: + with tf.device("/cpu:0"): + # fix the vgg16 issue from conv weights to fc weights + # fix RGB to BGR + fc6_conv = tf.get_variable("fc6_conv", [7, 7, 512, 4096], trainable=False) + fc7_conv = tf.get_variable("fc7_conv", [1, 1, 4096, 4096], trainable=False) + conv1_rgb = tf.get_variable("conv1_rgb", [3, 3, 3, 64], trainable=False) + restorer_fc = tf.train.Saver({self._scope + "/fc6/weights": fc6_conv, + self._scope + "/fc7/weights": fc7_conv, + self._scope + "/conv1/conv1_1/weights": conv1_rgb}) + restorer_fc.restore(sess, pretrained_model) + + sess.run(tf.assign(self._variables_to_fix[self._scope + '/fc6/weights:0'], tf.reshape(fc6_conv, + self._variables_to_fix[self._scope + '/fc6/weights:0'].get_shape()))) + sess.run(tf.assign(self._variables_to_fix[self._scope + '/fc7/weights:0'], tf.reshape(fc7_conv, + self._variables_to_fix[self._scope + '/fc7/weights:0'].get_shape()))) + sess.run(tf.assign(self._variables_to_fix[self._scope + '/conv1/conv1_1/weights:0'], + tf.reverse(conv1_rgb, [2]))) diff --git a/tf-faster-rcnn/lib/nets/__init__.py b/tf-faster-rcnn/lib/nets/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tf-faster-rcnn/lib/nets/mobilenet_v1.py b/tf-faster-rcnn/lib/nets/mobilenet_v1.py new file mode 100644 index 0000000..44b52e2 --- /dev/null +++ b/tf-faster-rcnn/lib/nets/mobilenet_v1.py @@ -0,0 +1,278 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim +from tensorflow.contrib.slim import losses +from tensorflow.contrib.slim import arg_scope +from tensorflow.contrib.slim.python.slim.nets import resnet_utils +import numpy as np +from collections import namedtuple + +from nets.network import Network +from model.config import cfg + +def separable_conv2d_same(inputs, kernel_size, stride, rate=1, scope=None): + """Strided 2-D separable convolution with 'SAME' padding. + Args: + inputs: A 4-D tensor of size [batch, height_in, width_in, channels]. + kernel_size: An int with the kernel_size of the filters. + stride: An integer, the output stride. + rate: An integer, rate for atrous convolution. + scope: Scope. + Returns: + output: A 4-D tensor of size [batch, height_out, width_out, channels] with + the convolution output. + """ + + # By passing filters=None + # separable_conv2d produces only a depth-wise convolution layer + if stride == 1: + return slim.separable_conv2d(inputs, None, kernel_size, + depth_multiplier=1, stride=1, rate=rate, + padding='SAME', scope=scope) + else: + kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) + pad_total = kernel_size_effective - 1 + pad_beg = pad_total // 2 + pad_end = pad_total - pad_beg + inputs = tf.pad(inputs, + [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) + return slim.separable_conv2d(inputs, None, kernel_size, + depth_multiplier=1, stride=stride, rate=rate, + padding='VALID', scope=scope) + +# The following is adapted from: +# https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py + +# Conv and DepthSepConv named tuple define layers of the MobileNet architecture +# Conv defines 3x3 convolution layers +# DepthSepConv defines 3x3 depthwise convolution followed by 1x1 convolution. +# stride is the stride of the convolution +# depth is the number of channels or filters in a layer +Conv = namedtuple('Conv', ['kernel', 'stride', 'depth']) +DepthSepConv = namedtuple('DepthSepConv', ['kernel', 'stride', 'depth']) + +# _CONV_DEFS specifies the MobileNet body +_CONV_DEFS = [ + Conv(kernel=3, stride=2, depth=32), + DepthSepConv(kernel=3, stride=1, depth=64), + DepthSepConv(kernel=3, stride=2, depth=128), + DepthSepConv(kernel=3, stride=1, depth=128), + DepthSepConv(kernel=3, stride=2, depth=256), + DepthSepConv(kernel=3, stride=1, depth=256), + DepthSepConv(kernel=3, stride=2, depth=512), + DepthSepConv(kernel=3, stride=1, depth=512), + DepthSepConv(kernel=3, stride=1, depth=512), + DepthSepConv(kernel=3, stride=1, depth=512), + DepthSepConv(kernel=3, stride=1, depth=512), + DepthSepConv(kernel=3, stride=1, depth=512), + # use stride 1 for the 13th layer + DepthSepConv(kernel=3, stride=1, depth=1024), + DepthSepConv(kernel=3, stride=1, depth=1024) +] + +# Modified mobilenet_v1 +def mobilenet_v1_base(inputs, + conv_defs, + starting_layer=0, + min_depth=8, + depth_multiplier=1.0, + output_stride=None, + reuse=None, + scope=None): + """Mobilenet v1. + Constructs a Mobilenet v1 network from inputs to the given final endpoint. + Args: + inputs: a tensor of shape [batch_size, height, width, channels]. + starting_layer: specifies the current starting layer. For region proposal + network it is 0, for region classification it is 12 by default. + min_depth: Minimum depth value (number of channels) for all convolution ops. + Enforced when depth_multiplier < 1, and not an active constraint when + depth_multiplier >= 1. + depth_multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + conv_defs: A list of ConvDef named tuples specifying the net architecture. + output_stride: An integer that specifies the requested ratio of input to + output spatial resolution. If not None, then we invoke atrous convolution + if necessary to prevent the network from reducing the spatial resolution + of the activation maps. + scope: Optional variable_scope. + Returns: + tensor_out: output tensor corresponding to the final_endpoint. + Raises: + ValueError: if depth_multiplier <= 0, or convolution type is not defined. + """ + depth = lambda d: max(int(d * depth_multiplier), min_depth) + end_points = {} + + # Used to find thinned depths for each layer. + if depth_multiplier <= 0: + raise ValueError('depth_multiplier is not greater than zero.') + + with tf.variable_scope(scope, 'MobilenetV1', [inputs], reuse=reuse): + # The current_stride variable keeps track of the output stride of the + # activations, i.e., the running product of convolution strides up to the + # current network layer. This allows us to invoke atrous convolution + # whenever applying the next convolution would result in the activations + # having output stride larger than the target output_stride. + current_stride = 1 + + # The atrous convolution rate parameter. + rate = 1 + + net = inputs + for i, conv_def in enumerate(conv_defs): + end_point_base = 'Conv2d_%d' % (i + starting_layer) + + if output_stride is not None and current_stride == output_stride: + # If we have reached the target output_stride, then we need to employ + # atrous convolution with stride=1 and multiply the atrous rate by the + # current unit's stride for use in subsequent layers. + layer_stride = 1 + layer_rate = rate + rate *= conv_def.stride + else: + layer_stride = conv_def.stride + layer_rate = 1 + current_stride *= conv_def.stride + + if isinstance(conv_def, Conv): + end_point = end_point_base + net = resnet_utils.conv2d_same(net, depth(conv_def.depth), conv_def.kernel, + stride=conv_def.stride, + scope=end_point) + + elif isinstance(conv_def, DepthSepConv): + end_point = end_point_base + '_depthwise' + + net = separable_conv2d_same(net, conv_def.kernel, + stride=layer_stride, + rate=layer_rate, + scope=end_point) + + end_point = end_point_base + '_pointwise' + + net = slim.conv2d(net, depth(conv_def.depth), [1, 1], + stride=1, + scope=end_point) + + else: + raise ValueError('Unknown convolution type %s for layer %d' + % (conv_def.ltype, i)) + + return net + +# Modified arg_scope to incorporate configs +def mobilenet_v1_arg_scope(is_training=True, + stddev=0.09): + batch_norm_params = { + 'is_training': False, + 'center': True, + 'scale': True, + 'decay': 0.9997, + 'epsilon': 0.001, + 'trainable': False, + } + + # Set weight_decay for weights in Conv and DepthSepConv layers. + weights_init = tf.truncated_normal_initializer(stddev=stddev) + regularizer = tf.contrib.layers.l2_regularizer(cfg.MOBILENET.WEIGHT_DECAY) + if cfg.MOBILENET.REGU_DEPTH: + depthwise_regularizer = regularizer + else: + depthwise_regularizer = None + + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], + trainable=is_training, + weights_initializer=weights_init, + activation_fn=tf.nn.relu6, + normalizer_fn=slim.batch_norm, + padding='SAME'): + with slim.arg_scope([slim.batch_norm], **batch_norm_params): + with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer): + with slim.arg_scope([slim.separable_conv2d], + weights_regularizer=depthwise_regularizer) as sc: + return sc + +class mobilenetv1(Network): + def __init__(self): + Network.__init__(self) + self._feat_stride = [16, ] + self._feat_compress = [1. / float(self._feat_stride[0]), ] + self._depth_multiplier = cfg.MOBILENET.DEPTH_MULTIPLIER + self._scope = 'MobilenetV1' + + def _image_to_head(self, is_training, reuse=None): + # Base bottleneck + assert (0 <= cfg.MOBILENET.FIXED_LAYERS <= 12) + net_conv = self._image + if cfg.MOBILENET.FIXED_LAYERS > 0: + with slim.arg_scope(mobilenet_v1_arg_scope(is_training=False)): + net_conv = mobilenet_v1_base(net_conv, + _CONV_DEFS[:cfg.MOBILENET.FIXED_LAYERS], + starting_layer=0, + depth_multiplier=self._depth_multiplier, + reuse=reuse, + scope=self._scope) + if cfg.MOBILENET.FIXED_LAYERS < 12: + with slim.arg_scope(mobilenet_v1_arg_scope(is_training=is_training)): + net_conv = mobilenet_v1_base(net_conv, + _CONV_DEFS[cfg.MOBILENET.FIXED_LAYERS:12], + starting_layer=cfg.MOBILENET.FIXED_LAYERS, + depth_multiplier=self._depth_multiplier, + reuse=reuse, + scope=self._scope) + + self._act_summaries.append(net_conv) + self._layers['head'] = net_conv + + return net_conv + + def _head_to_tail(self, pool5, is_training, reuse=None): + with slim.arg_scope(mobilenet_v1_arg_scope(is_training=is_training)): + fc7 = mobilenet_v1_base(pool5, + _CONV_DEFS[12:], + starting_layer=12, + depth_multiplier=self._depth_multiplier, + reuse=reuse, + scope=self._scope) + # average pooling done by reduce_mean + fc7 = tf.reduce_mean(fc7, axis=[1, 2]) + return fc7 + + def get_variables_to_restore(self, variables, var_keep_dic): + variables_to_restore = [] + + for v in variables: + # exclude the first conv layer to swap RGB to BGR + if v.name == (self._scope + '/Conv2d_0/weights:0'): + self._variables_to_fix[v.name] = v + continue + if v.name.split(':')[0] in var_keep_dic: + print('Variables restored: %s' % v.name) + variables_to_restore.append(v) + + return variables_to_restore + + def fix_variables(self, sess, pretrained_model): + print('Fix MobileNet V1 layers..') + with tf.variable_scope('Fix_MobileNet_V1') as scope: + with tf.device("/cpu:0"): + # fix RGB to BGR, and match the scale by (255.0 / 2.0) + Conv2d_0_rgb = tf.get_variable("Conv2d_0_rgb", + [3, 3, 3, max(int(32 * self._depth_multiplier), 8)], + trainable=False) + restorer_fc = tf.train.Saver({self._scope + "/Conv2d_0/weights": Conv2d_0_rgb}) + restorer_fc.restore(sess, pretrained_model) + + sess.run(tf.assign(self._variables_to_fix[self._scope + "/Conv2d_0/weights:0"], + tf.reverse(Conv2d_0_rgb / (255.0 / 2.0), [2]))) diff --git a/tf-faster-rcnn/lib/nets/network.py b/tf-faster-rcnn/lib/nets/network.py new file mode 100644 index 0000000..a0329c0 --- /dev/null +++ b/tf-faster-rcnn/lib/nets/network.py @@ -0,0 +1,524 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim +from tensorflow.contrib.slim import losses +from tensorflow.contrib.slim import arg_scope + +import numpy as np + +from layer_utils.snippets import generate_anchors_pre, generate_anchors_pre_tf +from layer_utils.proposal_layer import proposal_layer, proposal_layer_tf +from layer_utils.proposal_top_layer import proposal_top_layer, proposal_top_layer_tf +from layer_utils.anchor_target_layer import anchor_target_layer +from layer_utils.proposal_target_layer import proposal_target_layer +from utils.visualization import draw_bounding_boxes + +from model.config import cfg + +class Network(object): + def __init__(self): + self._predictions = {} + self._losses = {} + self._anchor_targets = {} + self._proposal_targets = {} + self._layers = {} + self._gt_image = None + self._act_summaries = [] + self._score_summaries = {} + self._train_summaries = [] + self._event_summaries = {} + self._variables_to_fix = {} + + def _add_gt_image(self): + # add back mean + image = self._image + cfg.PIXEL_MEANS + # BGR to RGB (opencv uses BGR) + resized = tf.image.resize_bilinear(image, tf.to_int32(self._im_info[:2] / self._im_info[2])) + self._gt_image = tf.reverse(resized, axis=[-1]) + + def _add_gt_image_summary(self): + # use a customized visualization function to visualize the boxes + if self._gt_image is None: + self._add_gt_image() + image = tf.py_func(draw_bounding_boxes, + [self._gt_image, self._gt_boxes, self._im_info], + tf.float32, name="gt_boxes") + + return tf.summary.image('GROUND_TRUTH', image) + + def _add_act_summary(self, tensor): + tf.summary.histogram('ACT/' + tensor.op.name + '/activations', tensor) + tf.summary.scalar('ACT/' + tensor.op.name + '/zero_fraction', + tf.nn.zero_fraction(tensor)) + + def _add_score_summary(self, key, tensor): + tf.summary.histogram('SCORE/' + tensor.op.name + '/' + key + '/scores', tensor) + + def _add_train_summary(self, var): + tf.summary.histogram('TRAIN/' + var.op.name, var) + + def _reshape_layer(self, bottom, num_dim, name): + input_shape = tf.shape(bottom) + with tf.variable_scope(name) as scope: + # change the channel to the caffe format + to_caffe = tf.transpose(bottom, [0, 3, 1, 2]) + # then force it to have channel 2 + reshaped = tf.reshape(to_caffe, + tf.concat(axis=0, values=[[1, num_dim, -1], [input_shape[2]]])) + # then swap the channel back + to_tf = tf.transpose(reshaped, [0, 2, 3, 1]) + return to_tf + + def _softmax_layer(self, bottom, name): + if name.startswith('rpn_cls_prob_reshape'): + input_shape = tf.shape(bottom) + bottom_reshaped = tf.reshape(bottom, [-1, input_shape[-1]]) + reshaped_score = tf.nn.softmax(bottom_reshaped, name=name) + return tf.reshape(reshaped_score, input_shape) + return tf.nn.softmax(bottom, name=name) + + def _proposal_top_layer(self, rpn_cls_prob, rpn_bbox_pred, name): + with tf.variable_scope(name) as scope: + if cfg.USE_E2E_TF: + rois, rpn_scores = proposal_top_layer_tf( + rpn_cls_prob, + rpn_bbox_pred, + self._im_info, + self._feat_stride, + self._anchors, + self._num_anchors + ) + else: + rois, rpn_scores = tf.py_func(proposal_top_layer, + [rpn_cls_prob, rpn_bbox_pred, self._im_info, + self._feat_stride, self._anchors, self._num_anchors], + [tf.float32, tf.float32], name="proposal_top") + + rois.set_shape([cfg.TEST.RPN_TOP_N, 5]) + rpn_scores.set_shape([cfg.TEST.RPN_TOP_N, 1]) + + return rois, rpn_scores + + def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name): + with tf.variable_scope(name) as scope: + if cfg.USE_E2E_TF: + rois, rpn_scores = proposal_layer_tf( + rpn_cls_prob, + rpn_bbox_pred, + self._im_info, + self._mode, + self._feat_stride, + self._anchors, + self._num_anchors + ) + else: + rois, rpn_scores = tf.py_func(proposal_layer, + [rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode, + self._feat_stride, self._anchors, self._num_anchors], + [tf.float32, tf.float32], name="proposal") + + rois.set_shape([None, 5]) + rpn_scores.set_shape([None, 1]) + + return rois, rpn_scores + + # Only use it if you have roi_pooling op written in tf.image + def _roi_pool_layer(self, bootom, rois, name): + with tf.variable_scope(name) as scope: + return tf.image.roi_pooling(bootom, rois, + pooled_height=cfg.POOLING_SIZE, + pooled_width=cfg.POOLING_SIZE, + spatial_scale=1. / 16.)[0] + + def _crop_pool_layer(self, bottom, rois, name): + with tf.variable_scope(name) as scope: + batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1]) + # Get the normalized coordinates of bounding boxes + bottom_shape = tf.shape(bottom) + height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0]) + width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0]) + x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width + y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height + x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width + y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height + # Won't be back-propagated to rois anyway, but to save time + bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1)) + pre_pool_size = cfg.POOLING_SIZE * 2 + crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops") + + return slim.max_pool2d(crops, [2, 2], padding='SAME') + + def _dropout_layer(self, bottom, name, ratio=0.5): + return tf.nn.dropout(bottom, ratio, name=name) + + def _anchor_target_layer(self, rpn_cls_score, name): + with tf.variable_scope(name) as scope: + rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = tf.py_func( + anchor_target_layer, + [rpn_cls_score, self._gt_boxes, self._im_info, self._feat_stride, self._anchors, self._num_anchors], + [tf.float32, tf.float32, tf.float32, tf.float32], + name="anchor_target") + + rpn_labels.set_shape([1, 1, None, None]) + rpn_bbox_targets.set_shape([1, None, None, self._num_anchors * 4]) + rpn_bbox_inside_weights.set_shape([1, None, None, self._num_anchors * 4]) + rpn_bbox_outside_weights.set_shape([1, None, None, self._num_anchors * 4]) + + rpn_labels = tf.to_int32(rpn_labels, name="to_int32") + self._anchor_targets['rpn_labels'] = rpn_labels + self._anchor_targets['rpn_bbox_targets'] = rpn_bbox_targets + self._anchor_targets['rpn_bbox_inside_weights'] = rpn_bbox_inside_weights + self._anchor_targets['rpn_bbox_outside_weights'] = rpn_bbox_outside_weights + + self._score_summaries.update(self._anchor_targets) + + return rpn_labels + + def _proposal_target_layer(self, rois, roi_scores, name): + with tf.variable_scope(name) as scope: + rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = tf.py_func( + proposal_target_layer, + [rois, roi_scores, self._gt_boxes, self._num_classes], + [tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32], + name="proposal_target") + + rois.set_shape([cfg.TRAIN.BATCH_SIZE, 5]) + roi_scores.set_shape([cfg.TRAIN.BATCH_SIZE]) + labels.set_shape([cfg.TRAIN.BATCH_SIZE, 1]) + bbox_targets.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) + bbox_inside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) + bbox_outside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) + + self._proposal_targets['rois'] = rois + self._proposal_targets['labels'] = tf.to_int32(labels, name="to_int32") + self._proposal_targets['bbox_targets'] = bbox_targets + self._proposal_targets['bbox_inside_weights'] = bbox_inside_weights + self._proposal_targets['bbox_outside_weights'] = bbox_outside_weights + + self._score_summaries.update(self._proposal_targets) + + return rois, roi_scores + + def _anchor_component(self): + with tf.variable_scope('ANCHOR_' + self._tag) as scope: + # just to get the shape right + height = tf.to_int32(tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0]))) + width = tf.to_int32(tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0]))) + if cfg.USE_E2E_TF: + anchors, anchor_length = generate_anchors_pre_tf( + height, + width, + self._feat_stride, + self._anchor_scales, + self._anchor_ratios + ) + else: + anchors, anchor_length = tf.py_func(generate_anchors_pre, + [height, width, + self._feat_stride, self._anchor_scales, self._anchor_ratios], + [tf.float32, tf.int32], name="generate_anchors") + anchors.set_shape([None, 4]) + anchor_length.set_shape([]) + self._anchors = anchors + self._anchor_length = anchor_length + + def _build_network(self, is_training=True): + # select initializers + if cfg.TRAIN.TRUNCATED: + initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01) + initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001) + else: + initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01) + initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001) + + net_conv = self._image_to_head(is_training) + with tf.variable_scope(self._scope, self._scope): + # build the anchors for the image + self._anchor_component() + # region proposal network + rois = self._region_proposal(net_conv, is_training, initializer) + # region of interest pooling + if cfg.POOLING_MODE == 'crop': + pool5 = self._crop_pool_layer(net_conv, rois, "pool5") + else: + raise NotImplementedError + + fc7 = self._head_to_tail(pool5, is_training) + with tf.variable_scope(self._scope, self._scope): + # region classification + cls_prob, bbox_pred = self._region_classification(fc7, is_training, + initializer, initializer_bbox) + + self._score_summaries.update(self._predictions) + + return rois, cls_prob, bbox_pred + + def _smooth_l1_loss(self, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights, sigma=1.0, dim=[1]): + sigma_2 = sigma ** 2 + box_diff = bbox_pred - bbox_targets + in_box_diff = bbox_inside_weights * box_diff + abs_in_box_diff = tf.abs(in_box_diff) + smoothL1_sign = tf.stop_gradient(tf.to_float(tf.less(abs_in_box_diff, 1. / sigma_2))) + in_loss_box = tf.pow(in_box_diff, 2) * (sigma_2 / 2.) * smoothL1_sign \ + + (abs_in_box_diff - (0.5 / sigma_2)) * (1. - smoothL1_sign) + out_loss_box = bbox_outside_weights * in_loss_box + loss_box = tf.reduce_mean(tf.reduce_sum( + out_loss_box, + axis=dim + )) + return loss_box + + def _add_losses(self, sigma_rpn=3.0): + with tf.variable_scope('LOSS_' + self._tag) as scope: + # RPN, class loss + rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2]) + rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1]) + rpn_select = tf.where(tf.not_equal(rpn_label, -1)) + rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) + rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1]) + rpn_cross_entropy = tf.reduce_mean( + tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label)) + + # RPN, bbox loss + rpn_bbox_pred = self._predictions['rpn_bbox_pred'] + rpn_bbox_targets = self._anchor_targets['rpn_bbox_targets'] + rpn_bbox_inside_weights = self._anchor_targets['rpn_bbox_inside_weights'] + rpn_bbox_outside_weights = self._anchor_targets['rpn_bbox_outside_weights'] + rpn_loss_box = self._smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, + rpn_bbox_outside_weights, sigma=sigma_rpn, dim=[1, 2, 3]) + + # RCNN, class loss + cls_score = self._predictions["cls_score"] + label = tf.reshape(self._proposal_targets["labels"], [-1]) + cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label)) + + # RCNN, bbox loss + bbox_pred = self._predictions['bbox_pred'] + bbox_targets = self._proposal_targets['bbox_targets'] + bbox_inside_weights = self._proposal_targets['bbox_inside_weights'] + bbox_outside_weights = self._proposal_targets['bbox_outside_weights'] + loss_box = self._smooth_l1_loss(bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights) + + self._losses['cross_entropy'] = cross_entropy + self._losses['loss_box'] = loss_box + self._losses['rpn_cross_entropy'] = rpn_cross_entropy + self._losses['rpn_loss_box'] = rpn_loss_box + + loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box + regularization_loss = tf.add_n(tf.losses.get_regularization_losses(), 'regu') + self._losses['total_loss'] = loss + regularization_loss + + self._event_summaries.update(self._losses) + + return loss + + def _region_proposal(self, net_conv, is_training, initializer): + rpn = slim.conv2d(net_conv, cfg.RPN_CHANNELS, [3, 3], trainable=is_training, weights_initializer=initializer, + scope="rpn_conv/3x3") + self._act_summaries.append(rpn) + rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, + weights_initializer=initializer, + padding='VALID', activation_fn=None, scope='rpn_cls_score') + # change it so that the score has 2 as its channel size + rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape') + rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape") + rpn_cls_pred = tf.argmax(tf.reshape(rpn_cls_score_reshape, [-1, 2]), axis=1, name="rpn_cls_pred") + rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob") + rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, + weights_initializer=initializer, + padding='VALID', activation_fn=None, scope='rpn_bbox_pred') + if is_training: + rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") + rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor") + # Try to have a deterministic order for the computing graph, for reproducibility + with tf.control_dependencies([rpn_labels]): + rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois") + else: + if cfg.TEST.MODE == 'nms': + rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") + elif cfg.TEST.MODE == 'top': + rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois") + else: + raise NotImplementedError + + self._predictions["rpn_cls_score"] = rpn_cls_score + self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape + self._predictions["rpn_cls_prob"] = rpn_cls_prob + self._predictions["rpn_cls_pred"] = rpn_cls_pred + self._predictions["rpn_bbox_pred"] = rpn_bbox_pred + self._predictions["rois"] = rois + + return rois + + def _region_classification(self, fc7, is_training, initializer, initializer_bbox): + cls_score = slim.fully_connected(fc7, self._num_classes, + weights_initializer=initializer, + trainable=is_training, + activation_fn=None, scope='cls_score') + cls_prob = self._softmax_layer(cls_score, "cls_prob") + cls_pred = tf.argmax(cls_score, axis=1, name="cls_pred") + bbox_pred = slim.fully_connected(fc7, self._num_classes * 4, + weights_initializer=initializer_bbox, + trainable=is_training, + activation_fn=None, scope='bbox_pred') + + self._predictions["cls_score"] = cls_score + self._predictions["cls_pred"] = cls_pred + self._predictions["cls_prob"] = cls_prob + self._predictions["bbox_pred"] = bbox_pred + + return cls_prob, bbox_pred + + def _image_to_head(self, is_training, reuse=None): + raise NotImplementedError + + def _head_to_tail(self, pool5, is_training, reuse=None): + raise NotImplementedError + + def create_architecture(self, mode, num_classes, tag=None, + anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)): + self._image = tf.placeholder(tf.float32, shape=[1, None, None, 3]) + self._im_info = tf.placeholder(tf.float32, shape=[3]) + self._gt_boxes = tf.placeholder(tf.float32, shape=[None, 5]) + self._tag = tag + + self._num_classes = num_classes + self._mode = mode + self._anchor_scales = anchor_scales + self._num_scales = len(anchor_scales) + + self._anchor_ratios = anchor_ratios + self._num_ratios = len(anchor_ratios) + + self._num_anchors = self._num_scales * self._num_ratios + + training = mode == 'TRAIN' + testing = mode == 'TEST' + + assert tag != None + + # handle most of the regularizers here + weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY) + if cfg.TRAIN.BIAS_DECAY: + biases_regularizer = weights_regularizer + else: + biases_regularizer = tf.no_regularizer + + # list as many types of layers as possible, even if they are not used now + with arg_scope([slim.conv2d, slim.conv2d_in_plane, \ + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + rois, cls_prob, bbox_pred = self._build_network(training) + + layers_to_output = {'rois': rois} + + for var in tf.trainable_variables(): + self._train_summaries.append(var) + + if testing: + stds = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (self._num_classes)) + means = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (self._num_classes)) + self._predictions["bbox_pred"] *= stds + self._predictions["bbox_pred"] += means + else: + self._add_losses() + layers_to_output.update(self._losses) + + val_summaries = [] + with tf.device("/cpu:0"): + val_summaries.append(self._add_gt_image_summary()) + for key, var in self._event_summaries.items(): + val_summaries.append(tf.summary.scalar(key, var)) + for key, var in self._score_summaries.items(): + self._add_score_summary(key, var) + for var in self._act_summaries: + self._add_act_summary(var) + for var in self._train_summaries: + self._add_train_summary(var) + + self._summary_op = tf.summary.merge_all() + self._summary_op_val = tf.summary.merge(val_summaries) + + layers_to_output.update(self._predictions) + + return layers_to_output + + def get_variables_to_restore(self, variables, var_keep_dic): + raise NotImplementedError + + def fix_variables(self, sess, pretrained_model): + raise NotImplementedError + + # Extract the head feature maps, for example for vgg16 it is conv5_3 + # only useful during testing mode + def extract_head(self, sess, image): + feed_dict = {self._image: image} + feat = sess.run(self._layers["head"], feed_dict=feed_dict) + return feat + + # only useful during testing mode + def test_image(self, sess, image, im_info): + feed_dict = {self._image: image, + self._im_info: im_info} + print("input_tensor") + print(self._image) + print(self._im_info) + print("out_put_tensor") + print(self._predictions["cls_score"]) + print(self._predictions["cls_prob"]) + print(self._predictions["bbox_pred"]) + print(self._predictions["rois"]) + cls_score, cls_prob, bbox_pred, rois = sess.run([self._predictions["cls_score"], + self._predictions['cls_prob'], + self._predictions['bbox_pred'], + self._predictions['rois']], + feed_dict=feed_dict) + return cls_score, cls_prob, bbox_pred, rois + + def get_summary(self, sess, blobs): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + summary = sess.run(self._summary_op_val, feed_dict=feed_dict) + + return summary + + def train_step(self, sess, blobs, train_op): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, _ = sess.run([self._losses["rpn_cross_entropy"], + self._losses['rpn_loss_box'], + self._losses['cross_entropy'], + self._losses['loss_box'], + self._losses['total_loss'], + train_op], + feed_dict=feed_dict) + return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss + + def train_step_with_summary(self, sess, blobs, train_op): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary, _ = sess.run([self._losses["rpn_cross_entropy"], + self._losses['rpn_loss_box'], + self._losses['cross_entropy'], + self._losses['loss_box'], + self._losses['total_loss'], + self._summary_op, + train_op], + feed_dict=feed_dict) + return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary + + def train_step_no_return(self, sess, blobs, train_op): + feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], + self._gt_boxes: blobs['gt_boxes']} + sess.run([train_op], feed_dict=feed_dict) + diff --git a/tf-faster-rcnn/lib/nets/resnet_v1.py b/tf-faster-rcnn/lib/nets/resnet_v1.py new file mode 100644 index 0000000..ffdf115 --- /dev/null +++ b/tf-faster-rcnn/lib/nets/resnet_v1.py @@ -0,0 +1,178 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Zheqi He and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim +from tensorflow.contrib.slim import losses +from tensorflow.contrib.slim import arg_scope +from tensorflow.contrib.slim.python.slim.nets import resnet_utils +from tensorflow.contrib.slim.python.slim.nets import resnet_v1 +from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block +import numpy as np + +from nets.network import Network +from model.config import cfg + +def resnet_arg_scope(is_training=True, + batch_norm_decay=0.997, + batch_norm_epsilon=1e-5, + batch_norm_scale=True): + batch_norm_params = { + 'is_training': False, + 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, + 'scale': batch_norm_scale, + 'trainable': False, + 'updates_collections': tf.GraphKeys.UPDATE_OPS + } + + with arg_scope( + [slim.conv2d], + weights_regularizer=slim.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), + weights_initializer=slim.variance_scaling_initializer(), + trainable=is_training, + activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params): + with arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc: + return arg_sc + +class resnetv1(Network): + def __init__(self, num_layers=50): + Network.__init__(self) + self._feat_stride = [16, ] + self._feat_compress = [1. / float(self._feat_stride[0]), ] + self._num_layers = num_layers + self._scope = 'resnet_v1_%d' % num_layers + self._decide_blocks() + + def _crop_pool_layer(self, bottom, rois, name): + with tf.variable_scope(name) as scope: + batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1]) + # Get the normalized coordinates of bboxes + bottom_shape = tf.shape(bottom) + height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0]) + width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0]) + x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width + y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height + x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width + y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height + # Won't be back-propagated to rois anyway, but to save time + bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], 1)) + if cfg.RESNET.MAX_POOL: + pre_pool_size = cfg.POOLING_SIZE * 2 + crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], + name="crops") + crops = slim.max_pool2d(crops, [2, 2], padding='SAME') + else: + crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [cfg.POOLING_SIZE, cfg.POOLING_SIZE], + name="crops") + return crops + + # Do the first few layers manually, because 'SAME' padding can behave inconsistently + # for images of different sizes: sometimes 0, sometimes 1 + def _build_base(self): + with tf.variable_scope(self._scope, self._scope): + net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1') + net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1') + + return net + + def _image_to_head(self, is_training, reuse=None): + assert (0 <= cfg.RESNET.FIXED_BLOCKS <= 3) + # Now the base is always fixed during training + with slim.arg_scope(resnet_arg_scope(is_training=False)): + net_conv = self._build_base() + if cfg.RESNET.FIXED_BLOCKS > 0: + with slim.arg_scope(resnet_arg_scope(is_training=False)): + net_conv, _ = resnet_v1.resnet_v1(net_conv, + self._blocks[0:cfg.RESNET.FIXED_BLOCKS], + global_pool=False, + include_root_block=False, + reuse=reuse, + scope=self._scope) + if cfg.RESNET.FIXED_BLOCKS < 3: + with slim.arg_scope(resnet_arg_scope(is_training=is_training)): + net_conv, _ = resnet_v1.resnet_v1(net_conv, + self._blocks[cfg.RESNET.FIXED_BLOCKS:-1], + global_pool=False, + include_root_block=False, + reuse=reuse, + scope=self._scope) + + self._act_summaries.append(net_conv) + self._layers['head'] = net_conv + + return net_conv + + def _head_to_tail(self, pool5, is_training, reuse=None): + with slim.arg_scope(resnet_arg_scope(is_training=is_training)): + fc7, _ = resnet_v1.resnet_v1(pool5, + self._blocks[-1:], + global_pool=False, + include_root_block=False, + reuse=reuse, + scope=self._scope) + # average pooling done by reduce_mean + fc7 = tf.reduce_mean(fc7, axis=[1, 2]) + return fc7 + + def _decide_blocks(self): + # choose different blocks for different number of layers + if self._num_layers == 50: + self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), + # use stride 1 for the last conv4 layer + resnet_v1_block('block3', base_depth=256, num_units=6, stride=1), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)] + + elif self._num_layers == 101: + self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), + # use stride 1 for the last conv4 layer + resnet_v1_block('block3', base_depth=256, num_units=23, stride=1), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)] + + elif self._num_layers == 152: + self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=8, stride=2), + # use stride 1 for the last conv4 layer + resnet_v1_block('block3', base_depth=256, num_units=36, stride=1), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)] + + else: + # other numbers are not supported + raise NotImplementedError + + def get_variables_to_restore(self, variables, var_keep_dic): + variables_to_restore = [] + + for v in variables: + # exclude the first conv layer to swap RGB to BGR + if v.name == (self._scope + '/conv1/weights:0'): + self._variables_to_fix[v.name] = v + continue + if v.name.split(':')[0] in var_keep_dic: + print('Variables restored: %s' % v.name) + variables_to_restore.append(v) + + return variables_to_restore + + def fix_variables(self, sess, pretrained_model): + print('Fix Resnet V1 layers..') + with tf.variable_scope('Fix_Resnet_V1') as scope: + with tf.device("/cpu:0"): + # fix RGB to BGR + conv1_rgb = tf.get_variable("conv1_rgb", [7, 7, 3, 64], trainable=False) + restorer_fc = tf.train.Saver({self._scope + "/conv1/weights": conv1_rgb}) + restorer_fc.restore(sess, pretrained_model) + + sess.run(tf.assign(self._variables_to_fix[self._scope + '/conv1/weights:0'], + tf.reverse(conv1_rgb, [2]))) diff --git a/tf-faster-rcnn/lib/nets/vgg16.py b/tf-faster-rcnn/lib/nets/vgg16.py new file mode 100644 index 0000000..38f7c21 --- /dev/null +++ b/tf-faster-rcnn/lib/nets/vgg16.py @@ -0,0 +1,100 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim +from tensorflow.contrib.slim import losses +from tensorflow.contrib.slim import arg_scope +import numpy as np + +from nets.network import Network +from model.config import cfg + +class vgg16(Network): + def __init__(self): + Network.__init__(self) + self._feat_stride = [16, ] + self._feat_compress = [1. / float(self._feat_stride[0]), ] + self._scope = 'vgg_16' + + def _image_to_head(self, is_training, reuse=None): + with tf.variable_scope(self._scope, self._scope, reuse=reuse): + net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], + trainable=False, scope='conv1') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1') + net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], + trainable=False, scope='conv2') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2') + net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], + trainable=is_training, scope='conv3') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], + trainable=is_training, scope='conv4') + net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], + trainable=is_training, scope='conv5') + + self._act_summaries.append(net) + self._layers['head'] = net + + return net + + def _head_to_tail(self, pool5, is_training, reuse=None): + with tf.variable_scope(self._scope, self._scope, reuse=reuse): + pool5_flat = slim.flatten(pool5, scope='flatten') + fc6 = slim.fully_connected(pool5_flat, 4096, scope='fc6') + if is_training: + fc6 = slim.dropout(fc6, keep_prob=0.5, is_training=True, + scope='dropout6') + fc7 = slim.fully_connected(fc6, 4096, scope='fc7') + if is_training: + fc7 = slim.dropout(fc7, keep_prob=0.5, is_training=True, + scope='dropout7') + + return fc7 + + def get_variables_to_restore(self, variables, var_keep_dic): + variables_to_restore = [] + + for v in variables: + # exclude the conv weights that are fc weights in vgg16 + if v.name == (self._scope + '/fc6/weights:0') or \ + v.name == (self._scope + '/fc7/weights:0'): + self._variables_to_fix[v.name] = v + continue + # exclude the first conv layer to swap RGB to BGR + if v.name == (self._scope + '/conv1/conv1_1/weights:0'): + self._variables_to_fix[v.name] = v + continue + if v.name.split(':')[0] in var_keep_dic: + print('Variables restored: %s' % v.name) + variables_to_restore.append(v) + + return variables_to_restore + + def fix_variables(self, sess, pretrained_model): + print('Fix VGG16 layers..') + with tf.variable_scope('Fix_VGG16') as scope: + with tf.device("/cpu:0"): + # fix the vgg16 issue from conv weights to fc weights + # fix RGB to BGR + fc6_conv = tf.get_variable("fc6_conv", [7, 7, 512, 4096], trainable=False) + fc7_conv = tf.get_variable("fc7_conv", [1, 1, 4096, 4096], trainable=False) + conv1_rgb = tf.get_variable("conv1_rgb", [3, 3, 3, 64], trainable=False) + restorer_fc = tf.train.Saver({self._scope + "/fc6/weights": fc6_conv, + self._scope + "/fc7/weights": fc7_conv, + self._scope + "/conv1/conv1_1/weights": conv1_rgb}) + restorer_fc.restore(sess, pretrained_model) + + sess.run(tf.assign(self._variables_to_fix[self._scope + '/fc6/weights:0'], tf.reshape(fc6_conv, + self._variables_to_fix[self._scope + '/fc6/weights:0'].get_shape()))) + sess.run(tf.assign(self._variables_to_fix[self._scope + '/fc7/weights:0'], tf.reshape(fc7_conv, + self._variables_to_fix[self._scope + '/fc7/weights:0'].get_shape()))) + sess.run(tf.assign(self._variables_to_fix[self._scope + '/conv1/conv1_1/weights:0'], + tf.reverse(conv1_rgb, [2]))) diff --git a/tf-faster-rcnn/lib/nms/.gitignore b/tf-faster-rcnn/lib/nms/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/tf-faster-rcnn/lib/nms/__init__.py b/tf-faster-rcnn/lib/nms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tf-faster-rcnn/lib/nms/cpu_nms.c b/tf-faster-rcnn/lib/nms/cpu_nms.c new file mode 100644 index 0000000..96f7ec4 --- /dev/null +++ b/tf-faster-rcnn/lib/nms/cpu_nms.c @@ -0,0 +1,6869 @@ +/* Generated by Cython 0.20.1 on Wed Oct 5 13:15:30 2016 */ + +#define PY_SSIZE_T_CLEAN +#ifndef CYTHON_USE_PYLONG_INTERNALS +#ifdef PYLONG_BITS_IN_DIGIT +#define CYTHON_USE_PYLONG_INTERNALS 0 +#else +#include "pyconfig.h" +#ifdef PYLONG_BITS_IN_DIGIT +#define CYTHON_USE_PYLONG_INTERNALS 1 +#else +#define CYTHON_USE_PYLONG_INTERNALS 0 +#endif +#endif +#endif +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02040000 + #error Cython requires Python 2.4+. +#else +#define CYTHON_ABI "0_20_1" +#include /* For offsetof */ +#ifndef offsetof +#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION +#define CYTHON_COMPILING_IN_PYPY 1 +#define CYTHON_COMPILING_IN_CPYTHON 0 +#else +#define CYTHON_COMPILING_IN_PYPY 0 +#define CYTHON_COMPILING_IN_CPYTHON 1 +#endif +#if CYTHON_COMPILING_IN_PYPY +#define Py_OptimizeFlag 0 +#endif +#if PY_VERSION_HEX < 0x02050000 + typedef int Py_ssize_t; + #define PY_SSIZE_T_MAX INT_MAX + #define PY_SSIZE_T_MIN INT_MIN + #define PY_FORMAT_SIZE_T "" + #define CYTHON_FORMAT_SSIZE_T "" + #define PyInt_FromSsize_t(z) PyInt_FromLong(z) + #define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o) + #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \ + (PyErr_Format(PyExc_TypeError, \ + "expected index value, got %.200s", Py_TYPE(o)->tp_name), \ + (PyObject*)0)) + #define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \ + !PyComplex_Check(o)) + #define PyIndex_Check __Pyx_PyIndex_Check + #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) + #define __PYX_BUILD_PY_SSIZE_T "i" +#else + #define __PYX_BUILD_PY_SSIZE_T "n" + #define CYTHON_FORMAT_SSIZE_T "z" + #define __Pyx_PyIndex_Check PyIndex_Check +#endif +#if PY_VERSION_HEX < 0x02060000 + #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) + #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) + #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) + #define PyVarObject_HEAD_INIT(type, size) \ + PyObject_HEAD_INIT(type) size, + #define PyType_Modified(t) + typedef struct { + void *buf; + PyObject *obj; + Py_ssize_t len; + Py_ssize_t itemsize; + int readonly; + int ndim; + char *format; + Py_ssize_t *shape; + Py_ssize_t *strides; + Py_ssize_t *suboffsets; + void *internal; + } Py_buffer; + #define PyBUF_SIMPLE 0 + #define PyBUF_WRITABLE 0x0001 + #define PyBUF_FORMAT 0x0004 + #define PyBUF_ND 0x0008 + #define PyBUF_STRIDES (0x0010 | PyBUF_ND) + #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) + #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) + #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) + #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) + #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE) + #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE) + typedef int (*getbufferproc)(PyObject *, Py_buffer *, int); + typedef void (*releasebufferproc)(PyObject *, Py_buffer *); +#endif +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#if PY_VERSION_HEX < 0x02060000 + #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict") +#endif +#if PY_MAJOR_VERSION >= 3 + #define Py_TPFLAGS_CHECKTYPES 0 + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#if PY_VERSION_HEX < 0x02060000 + #define Py_TPFLAGS_HAVE_VERSION_TAG 0 +#endif +#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT) + #define Py_TPFLAGS_IS_ABSTRACT 0 +#endif +#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE) + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) +#else + #define CYTHON_PEP393_ENABLED 0 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_VERSION_HEX < 0x02060000 + #define PyBytesObject PyStringObject + #define PyBytes_Type PyString_Type + #define PyBytes_Check PyString_Check + #define PyBytes_CheckExact PyString_CheckExact + #define PyBytes_FromString PyString_FromString + #define PyBytes_FromStringAndSize PyString_FromStringAndSize + #define PyBytes_FromFormat PyString_FromFormat + #define PyBytes_DecodeEscape PyString_DecodeEscape + #define PyBytes_AsString PyString_AsString + #define PyBytes_AsStringAndSize PyString_AsStringAndSize + #define PyBytes_Size PyString_Size + #define PyBytes_AS_STRING PyString_AS_STRING + #define PyBytes_GET_SIZE PyString_GET_SIZE + #define PyBytes_Repr PyString_Repr + #define PyBytes_Concat PyString_Concat + #define PyBytes_ConcatAndDel PyString_ConcatAndDel +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \ + PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#if PY_VERSION_HEX < 0x02060000 + #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type) + #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300) + #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b) + #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value) + #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b) +#else + #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \ + (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \ + (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \ + (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0))) + #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \ + (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ + (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \ + (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1))) + #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \ + (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ + (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \ + (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1))) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#endif +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) +#else + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) +#endif +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_NAMESTR(n) ((char *)(n)) + #define __Pyx_DOCSTR(n) ((char *)(n)) +#else + #define __Pyx_NAMESTR(n) (n) + #define __Pyx_DOCSTR(n) (n) +#endif +#ifndef CYTHON_INLINE + #if defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and + a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is + a quiet NaN. */ + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif + + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) +#define _USE_MATH_DEFINES +#endif +#include +#define __PYX_HAVE__nms__cpu_nms +#define __PYX_HAVE_API__nms__cpu_nms +#include "string.h" +#include "stdio.h" +#include "stdlib.h" +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \ + (sizeof(type) < sizeof(Py_ssize_t)) || \ + (sizeof(type) > sizeof(Py_ssize_t) && \ + likely(v < (type)PY_SSIZE_T_MAX || \ + v == (type)PY_SSIZE_T_MAX) && \ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \ + v == (type)PY_SSIZE_T_MIN))) || \ + (sizeof(type) == sizeof(Py_ssize_t) && \ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX || \ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((char*)s) +#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s) +#define __Pyx_PyByteArray_FromUString(s) __Pyx_PyByteArray_FromString((char*)s) +#define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((char*)s) +#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return u_end - u - 1; +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_COMPILING_IN_CPYTHON +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys = NULL; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + sys = PyImport_ImportModule("sys"); + if (sys == NULL) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + if (default_encoding == NULL) goto bad; + if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + const char* default_encoding_c = PyBytes_AS_STRING(default_encoding); + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (ascii_chars_u == NULL) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + } + Py_XDECREF(sys); + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return 0; +bad: + Py_XDECREF(sys); + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys = NULL; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (sys == NULL) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + if (default_encoding == NULL) goto bad; + default_encoding_c = PyBytes_AS_STRING(default_encoding); + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(sys); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(sys); + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +#ifdef __GNUC__ + /* Test for GCC > 2.95 */ + #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) + #else /* __GNUC__ > 2 ... */ + #define likely(x) (x) + #define unlikely(x) (x) + #endif /* __GNUC__ > 2 ... */ +#else /* __GNUC__ */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "cpu_nms.pyx", + "__init__.pxd", + "type.pxd", +}; +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; /* for error messages only */ + struct __Pyx_StructField_* fields; + size_t size; /* sizeof(type) */ + size_t arraysize[8]; /* length of array in each dimension */ + int ndim; + char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject, c_H_ar */ + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":723 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":724 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":737 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":738 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":747 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":748 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":752 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":756 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":759 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif + + +/*--- Type declarations ---*/ + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":763 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764 + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cdouble complex_t + */ +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766 + * ctypedef npy_clongdouble clongdouble_t + * + * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< + * + * cdef inline object PyArray_MultiIterNew1(a): + */ +typedef npy_cdouble __pyx_t_5numpy_complex_t; +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/ + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil) \ + if (acquire_gil) { \ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \ + PyGILState_Release(__pyx_gilstate_save); \ + } else { \ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil) \ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) +#endif + #define __Pyx_RefNannyFinishContext() \ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif /* CYTHON_REFNANNY */ +#define __Pyx_XDECREF_SET(r, v) do { \ + PyObject *tmp = (PyObject *) r; \ + r = v; __Pyx_XDECREF(tmp); \ + } while (0) +#define __Pyx_DECREF_SET(r, v) do { \ + PyObject *tmp = (PyObject *) r; \ + r = v; __Pyx_DECREF(tmp); \ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/ + +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/ + +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/ + +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \ + const char* function_name); /*proto*/ + +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); /*proto*/ + +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/ +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/ + +static void __Pyx_RaiseBufferIndexError(int axis); /*proto*/ + +#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/ + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/ + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eqf(a, b) ((a)==(b)) + #define __Pyx_c_sumf(a, b) ((a)+(b)) + #define __Pyx_c_difff(a, b) ((a)-(b)) + #define __Pyx_c_prodf(a, b) ((a)*(b)) + #define __Pyx_c_quotf(a, b) ((a)/(b)) + #define __Pyx_c_negf(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zerof(z) ((z)==(float)0) + #define __Pyx_c_conjf(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_absf(z) (::std::abs(z)) + #define __Pyx_c_powf(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zerof(z) ((z)==0) + #define __Pyx_c_conjf(z) (conjf(z)) + #if 1 + #define __Pyx_c_absf(z) (cabsf(z)) + #define __Pyx_c_powf(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs(z) (::std::abs(z)) + #define __Pyx_c_pow(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + #if 1 + #define __Pyx_c_abs(z) (cabs(z)) + #define __Pyx_c_pow(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +static int __Pyx_check_binary_version(void); + +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ + +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ + +typedef struct { + int code_line; + PyCodeObject* code_object; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); /*proto*/ + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ + + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'nms.cpu_nms' */ +static CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_3nms_7cpu_nms_max(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/ +static CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_3nms_7cpu_nms_min(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t = { "float32_t", NULL, sizeof(__pyx_t_5numpy_float32_t), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { "int_t", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int_t), 0 }; +#define __Pyx_MODULE_NAME "nms.cpu_nms" +int __pyx_module_is_main_nms__cpu_nms = 0; + +/* Implementation of 'nms.cpu_nms' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_pf_3nms_7cpu_nms_cpu_nms(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_dets, PyObject *__pyx_v_thresh); /* proto */ +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ +static char __pyx_k_B[] = "B"; +static char __pyx_k_H[] = "H"; +static char __pyx_k_I[] = "I"; +static char __pyx_k_L[] = "L"; +static char __pyx_k_O[] = "O"; +static char __pyx_k_Q[] = "Q"; +static char __pyx_k_b[] = "b"; +static char __pyx_k_d[] = "d"; +static char __pyx_k_f[] = "f"; +static char __pyx_k_g[] = "g"; +static char __pyx_k_h[] = "h"; +static char __pyx_k_i[] = "i"; +static char __pyx_k_j[] = "_j"; +static char __pyx_k_l[] = "l"; +static char __pyx_k_q[] = "q"; +static char __pyx_k_w[] = "w"; +static char __pyx_k_Zd[] = "Zd"; +static char __pyx_k_Zf[] = "Zf"; +static char __pyx_k_Zg[] = "Zg"; +static char __pyx_k_np[] = "np"; +static char __pyx_k_x1[] = "x1"; +static char __pyx_k_x2[] = "x2"; +static char __pyx_k_y1[] = "y1"; +static char __pyx_k_y2[] = "y2"; +static char __pyx_k_i_2[] = "_i"; +static char __pyx_k_int[] = "int"; +static char __pyx_k_ix1[] = "ix1"; +static char __pyx_k_ix2[] = "ix2"; +static char __pyx_k_iy1[] = "iy1"; +static char __pyx_k_iy2[] = "iy2"; +static char __pyx_k_j_2[] = "j"; +static char __pyx_k_ovr[] = "ovr"; +static char __pyx_k_xx1[] = "xx1"; +static char __pyx_k_xx2[] = "xx2"; +static char __pyx_k_yy1[] = "yy1"; +static char __pyx_k_yy2[] = "yy2"; +static char __pyx_k_dets[] = "dets"; +static char __pyx_k_keep[] = "keep"; 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__pyx_L1_error;} + } + } + #endif + /*--- Builtin init code ---*/ + if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Constants init code ---*/ + if (unlikely(__Pyx_InitCachedConstants() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Global init code ---*/ + /*--- Variable export code ---*/ + /*--- Function export code ---*/ + /*--- Type init code ---*/ + /*--- Type import code ---*/ + __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "type", + #if CYTHON_COMPILING_IN_PYPY + sizeof(PyTypeObject), + #else + sizeof(PyHeapTypeObject), + #endif + 0); if (unlikely(!__pyx_ptype_7cpython_4type_type)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_ptype_5numpy_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr), 0); if (unlikely(!__pyx_ptype_5numpy_dtype)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 155; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_ptype_5numpy_flatiter = __Pyx_ImportType("numpy", "flatiter", sizeof(PyArrayIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_flatiter)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 165; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_ptype_5numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_broadcast)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 169; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject), 0); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 178; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_ptype_5numpy_ufunc = __Pyx_ImportType("numpy", "ufunc", sizeof(PyUFuncObject), 0); if (unlikely(!__pyx_ptype_5numpy_ufunc)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 861; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Variable import code ---*/ + /*--- Function import code ---*/ + /*--- Execution code ---*/ + + /* "nms/cpu_nms.pyx":8 + * # -------------------------------------------------------- + * + * import numpy as np # <<<<<<<<<<<<<< + * cimport numpy as np + * + */ + __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "nms/cpu_nms.pyx":17 + * return a if a <= b else b + * + * def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): # <<<<<<<<<<<<<< + * cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + * cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + */ + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_3nms_7cpu_nms_1cpu_nms, NULL, __pyx_n_s_nms_cpu_nms); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 17; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_cpu_nms, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 17; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "nms/cpu_nms.pyx":1 + * # -------------------------------------------------------- # <<<<<<<<<<<<<< + * # Fast R-CNN + * # Copyright (c) 2015 Microsoft + */ + __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":976 + * arr.base = baseptr + * + * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< + * if arr.base is NULL: + * return None + */ + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + if (__pyx_m) { + __Pyx_AddTraceback("init nms.cpu_nms", __pyx_clineno, __pyx_lineno, __pyx_filename); + Py_DECREF(__pyx_m); __pyx_m = 0; + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init nms.cpu_nms"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if PY_MAJOR_VERSION < 3 + return; + #else + return __pyx_m; + #endif +} + +/* Runtime support code */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif /* CYTHON_REFNANNY */ + +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) /* First char was not a digit */ + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; /* Consume from buffer string */ + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; /* breaks both loops as ctx->enc_count == 0 */ + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; /* empty struct */ + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; /* not a 'break' in the loop */ + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case 10: + case 13: + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': /* substruct */ + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; /* Erase processed last struct element */ + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': /* end of substruct; either repeat or move on */ + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; /* Erase processed last struct element */ + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } /* fall through */ + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 's': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + } else { + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + } + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); +#if PY_VERSION_HEX >= 0x02060000 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; +#endif + result = (*call)(func, arg, kw); +#if PY_VERSION_HEX >= 0x02060000 + Py_LeaveRecursiveCall(); +#endif + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON + result = PyDict_GetItem(__pyx_d, name); + if (result) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyThreadState *tstate = PyThreadState_GET(); + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_Restore(type, value, tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(type, value, tb); +#endif +} + +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + #if PY_VERSION_HEX < 0x02050000 + if (PyClass_Check(type)) { + #else + if (PyType_Check(type)) { + #endif +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + #if PY_VERSION_HEX < 0x02050000 + if (PyInstance_Check(type)) { + type = (PyObject*) ((PyInstanceObject*)type)->in_class; + Py_INCREF(type); + } else { + type = 0; + PyErr_SetString(PyExc_TypeError, + "raise: exception must be an old-style class or instance"); + goto raise_error; + } + #else + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + #endif + } + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else /* Python 3+ */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + if (PyObject_IsSubclass(instance_class, type)) { + type = instance_class; + } else { + instance_class = NULL; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + #if PY_VERSION_HEX >= 0x02060000 + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + #endif + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + #if PY_VERSION_HEX < 0x02060000 + if (obj->ob_type->tp_dict) { + PyObject *getbuffer_cobj = PyObject_GetItem( + obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer); + if (getbuffer_cobj) { + getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); + Py_DECREF(getbuffer_cobj); + if (!func) + goto fail; + return func(obj, view, flags); + } else { + PyErr_Clear(); + } + } + #endif + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); +#if PY_VERSION_HEX < 0x02060000 +fail: +#endif + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + #if PY_VERSION_HEX >= 0x02060000 + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + #endif + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + #if PY_VERSION_HEX < 0x02060000 + if (obj->ob_type->tp_dict) { + PyObject *releasebuffer_cobj = PyObject_GetItem( + obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer); + if (releasebuffer_cobj) { + releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); + Py_DECREF(releasebuffer_cobj); + if (!func) + goto fail; + func(obj, view); + return; + } else { + PyErr_Clear(); + } + } + #endif + goto nofail; +#if PY_VERSION_HEX < 0x02060000 +fail: +#endif + PyErr_WriteUnraisable(obj); +nofail: + Py_DECREF(obj); + view->obj = NULL; +} +#endif /* PY_MAJOR_VERSION < 3 */ + + + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + #if PY_VERSION_HEX >= 0x02050000 + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; /* try absolute import on failure */ + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } + #else + if (level>0) { + PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); + goto bad; + } + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, NULL); + #endif +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(int) <= sizeof(unsigned long long)) { + return PyLong_FromUnsignedLongLong((unsigned long long) value); + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(long long)) { + return PyLong_FromLongLong((long long) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \ + { \ + func_type value = func(x); \ + if (sizeof(target_type) < sizeof(func_type)) { \ + if (unlikely(value != (func_type) (target_type) value)) { \ + func_type zero = 0; \ + PyErr_SetString(PyExc_OverflowError, \ + (is_unsigned && unlikely(value < zero)) ? \ + "can't convert negative value to " #target_type : \ + "value too large to convert to " #target_type); \ + return (target_type) -1; \ + } \ + } \ + return (target_type) value; \ + } + +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(int)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return (int) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; + } + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong) + } else if (sizeof(int) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(int)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return +(int) ((PyLongObject*)x)->ob_digit[0]; + case -1: return -(int) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong) + } else if (sizeof(int) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(long) <= sizeof(unsigned long long)) { + return PyLong_FromUnsignedLongLong((unsigned long long) value); + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(long long)) { + return PyLong_FromLongLong((long long) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(a, a); + case 3: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, a); + case 4: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_absf(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(a, a); + case 3: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, a); + case 4: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_abs(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(long)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; + } + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong) + } else if (sizeof(long) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(long)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; + case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong) + } else if (sizeof(long) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +} + +static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + #if PY_VERSION_HEX < 0x02050000 + return PyErr_Warn(NULL, message); + #else + return PyErr_WarnEx(NULL, message, 1); + #endif + } + return 0; +} + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + #if PY_VERSION_HEX < 0x02050000 + if (PyErr_Warn(NULL, warning) < 0) goto bad; + #else + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + #endif + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = (start + end) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, /*int argcount,*/ + 0, /*int kwonlyargcount,*/ + 0, /*int nlocals,*/ + 0, /*int stacksize,*/ + 0, /*int flags,*/ + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, /*int firstlineno,*/ + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_globals = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_globals = PyModule_GetDict(__pyx_m); + if (!py_globals) goto bad; + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + py_globals, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = py_line; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else /* Python 3+ has unicode identifiers */ + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else /* PY_VERSION_HEX < 0x03030000 */ + if (PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_DATA_SIZE(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ + return PyUnicode_AsUTF8AndSize(o, length); +#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ +#endif /* PY_VERSION_HEX < 0x03030000 */ + } else +#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ +#if !CYTHON_COMPILING_IN_PYPY +#if PY_VERSION_HEX >= 0x02060000 + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return Py_INCREF(x), x; + m = Py_TYPE(x)->tp_as_number; +#if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) + return PyInt_AS_LONG(b); +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(b)) { + case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; + case 0: return 0; + case 1: return ((PyLongObject*)b)->ob_digit[0]; + } + #endif + #endif + #if PY_VERSION_HEX < 0x02060000 + return PyInt_AsSsize_t(b); + #else + return PyLong_AsSsize_t(b); + #endif + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { +#if PY_VERSION_HEX < 0x02050000 + if (ival <= LONG_MAX) + return PyInt_FromLong((long)ival); + else { + unsigned char *bytes = (unsigned char *) &ival; + int one = 1; int little = (int)*(unsigned char*)&one; + return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); + } +#else + return PyInt_FromSize_t(ival); +#endif +} + + +#endif /* Py_PYTHON_H */ diff --git a/tf-faster-rcnn/lib/nms/cpu_nms.pyx b/tf-faster-rcnn/lib/nms/cpu_nms.pyx new file mode 100644 index 0000000..1d0bef3 --- /dev/null +++ b/tf-faster-rcnn/lib/nms/cpu_nms.pyx @@ -0,0 +1,68 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import numpy as np +cimport numpy as np + +cdef inline np.float32_t max(np.float32_t a, np.float32_t b): + return a if a >= b else b + +cdef inline np.float32_t min(np.float32_t a, np.float32_t b): + return a if a <= b else b + +def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): + cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] + cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] + cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] + + cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) + cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] + + cdef int ndets = dets.shape[0] + cdef np.ndarray[np.int_t, ndim=1] suppressed = \ + np.zeros((ndets), dtype=np.int) + + # nominal indices + cdef int _i, _j + # sorted indices + cdef int i, j + # temp variables for box i's (the box currently under consideration) + cdef np.float32_t ix1, iy1, ix2, iy2, iarea + # variables for computing overlap with box j (lower scoring box) + cdef np.float32_t xx1, yy1, xx2, yy2 + cdef np.float32_t w, h + cdef np.float32_t inter, ovr + + keep = [] + for _i in range(ndets): + i = order[_i] + if suppressed[i] == 1: + continue + keep.append(i) + ix1 = x1[i] + iy1 = y1[i] + ix2 = x2[i] + iy2 = y2[i] + iarea = areas[i] + for _j in range(_i + 1, ndets): + j = order[_j] + if suppressed[j] == 1: + continue + xx1 = max(ix1, x1[j]) + yy1 = max(iy1, y1[j]) + xx2 = min(ix2, x2[j]) + yy2 = min(iy2, y2[j]) + w = max(0.0, xx2 - xx1 + 1) + h = max(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (iarea + areas[j] - inter) + if ovr >= thresh: + suppressed[j] = 1 + + return keep diff --git a/tf-faster-rcnn/lib/nms/gpu_nms.cpp b/tf-faster-rcnn/lib/nms/gpu_nms.cpp new file mode 100644 index 0000000..b6fdcea --- /dev/null +++ b/tf-faster-rcnn/lib/nms/gpu_nms.cpp @@ -0,0 +1,6391 @@ +/* Generated by Cython 0.20.1 on Wed Oct 5 13:15:30 2016 */ + +#define PY_SSIZE_T_CLEAN +#ifndef CYTHON_USE_PYLONG_INTERNALS +#ifdef PYLONG_BITS_IN_DIGIT +#define CYTHON_USE_PYLONG_INTERNALS 0 +#else +#include "pyconfig.h" +#ifdef PYLONG_BITS_IN_DIGIT +#define CYTHON_USE_PYLONG_INTERNALS 1 +#else +#define CYTHON_USE_PYLONG_INTERNALS 0 +#endif +#endif +#endif +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02040000 + #error Cython requires Python 2.4+. +#else +#define CYTHON_ABI "0_20_1" +#include /* For offsetof */ +#ifndef offsetof +#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION +#define CYTHON_COMPILING_IN_PYPY 1 +#define CYTHON_COMPILING_IN_CPYTHON 0 +#else +#define CYTHON_COMPILING_IN_PYPY 0 +#define CYTHON_COMPILING_IN_CPYTHON 1 +#endif +#if CYTHON_COMPILING_IN_PYPY +#define Py_OptimizeFlag 0 +#endif +#if PY_VERSION_HEX < 0x02050000 + typedef int Py_ssize_t; + #define PY_SSIZE_T_MAX INT_MAX + #define PY_SSIZE_T_MIN INT_MIN + #define PY_FORMAT_SIZE_T "" + #define CYTHON_FORMAT_SSIZE_T "" + #define PyInt_FromSsize_t(z) PyInt_FromLong(z) + #define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o) + #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \ + (PyErr_Format(PyExc_TypeError, \ + "expected index value, got %.200s", Py_TYPE(o)->tp_name), \ + (PyObject*)0)) + #define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \ + !PyComplex_Check(o)) + #define PyIndex_Check __Pyx_PyIndex_Check + #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) + #define __PYX_BUILD_PY_SSIZE_T "i" +#else + #define __PYX_BUILD_PY_SSIZE_T "n" + #define CYTHON_FORMAT_SSIZE_T "z" + #define __Pyx_PyIndex_Check PyIndex_Check +#endif +#if PY_VERSION_HEX < 0x02060000 + #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) + #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) + #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) + #define PyVarObject_HEAD_INIT(type, size) \ + PyObject_HEAD_INIT(type) size, + #define PyType_Modified(t) + typedef struct { + void *buf; + PyObject *obj; + Py_ssize_t len; + Py_ssize_t itemsize; + int readonly; + int ndim; + char *format; + Py_ssize_t *shape; + Py_ssize_t *strides; + Py_ssize_t *suboffsets; + void *internal; + } Py_buffer; + #define PyBUF_SIMPLE 0 + #define PyBUF_WRITABLE 0x0001 + #define PyBUF_FORMAT 0x0004 + #define PyBUF_ND 0x0008 + #define PyBUF_STRIDES (0x0010 | PyBUF_ND) + #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) + #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) + #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) + #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) + #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE) + #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE) + typedef int (*getbufferproc)(PyObject *, Py_buffer *, int); + typedef void (*releasebufferproc)(PyObject *, Py_buffer *); +#endif +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#if PY_VERSION_HEX < 0x02060000 + #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict") +#endif +#if PY_MAJOR_VERSION >= 3 + #define Py_TPFLAGS_CHECKTYPES 0 + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#if PY_VERSION_HEX < 0x02060000 + #define Py_TPFLAGS_HAVE_VERSION_TAG 0 +#endif +#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT) + #define Py_TPFLAGS_IS_ABSTRACT 0 +#endif +#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE) + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) +#else + #define CYTHON_PEP393_ENABLED 0 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_VERSION_HEX < 0x02060000 + #define PyBytesObject PyStringObject + #define PyBytes_Type PyString_Type + #define PyBytes_Check PyString_Check + #define PyBytes_CheckExact PyString_CheckExact + #define PyBytes_FromString PyString_FromString + #define PyBytes_FromStringAndSize PyString_FromStringAndSize + #define PyBytes_FromFormat PyString_FromFormat + #define PyBytes_DecodeEscape PyString_DecodeEscape + #define PyBytes_AsString PyString_AsString + #define PyBytes_AsStringAndSize PyString_AsStringAndSize + #define PyBytes_Size PyString_Size + #define PyBytes_AS_STRING PyString_AS_STRING + #define PyBytes_GET_SIZE PyString_GET_SIZE + #define PyBytes_Repr PyString_Repr + #define PyBytes_Concat PyString_Concat + #define PyBytes_ConcatAndDel PyString_ConcatAndDel +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \ + PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#if PY_VERSION_HEX < 0x02060000 + #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type) + #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300) + #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b) + #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value) + #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b) +#else + #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \ + (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \ + (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \ + (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0))) + #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \ + (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ + (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \ + (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1))) + #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \ + (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ + (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \ + (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1))) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#endif +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) +#else + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) +#endif +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_NAMESTR(n) ((char *)(n)) + #define __Pyx_DOCSTR(n) ((char *)(n)) +#else + #define __Pyx_NAMESTR(n) (n) + #define __Pyx_DOCSTR(n) (n) +#endif +#ifndef CYTHON_INLINE + #if defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and + a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is + a quiet NaN. */ + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif + + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) +#define _USE_MATH_DEFINES +#endif +#include +#define __PYX_HAVE__nms__gpu_nms +#define __PYX_HAVE_API__nms__gpu_nms +#include "string.h" +#include "stdio.h" +#include "stdlib.h" +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "gpu_nms.hpp" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \ + (sizeof(type) < sizeof(Py_ssize_t)) || \ + (sizeof(type) > sizeof(Py_ssize_t) && \ + likely(v < (type)PY_SSIZE_T_MAX || \ + v == (type)PY_SSIZE_T_MAX) && \ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \ + v == (type)PY_SSIZE_T_MIN))) || \ + (sizeof(type) == sizeof(Py_ssize_t) && \ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX || \ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((char*)s) +#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s) +#define __Pyx_PyByteArray_FromUString(s) __Pyx_PyByteArray_FromString((char*)s) +#define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((char*)s) +#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return u_end - u - 1; +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_COMPILING_IN_CPYTHON +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys = NULL; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + sys = PyImport_ImportModule("sys"); + if (sys == NULL) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + if (default_encoding == NULL) goto bad; + if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + const char* default_encoding_c = PyBytes_AS_STRING(default_encoding); + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (ascii_chars_u == NULL) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + } + Py_XDECREF(sys); + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return 0; +bad: + Py_XDECREF(sys); + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys = NULL; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (sys == NULL) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + if (default_encoding == NULL) goto bad; + default_encoding_c = PyBytes_AS_STRING(default_encoding); + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(sys); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(sys); + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +#ifdef __GNUC__ + /* Test for GCC > 2.95 */ + #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) + #else /* __GNUC__ > 2 ... */ + #define likely(x) (x) + #define unlikely(x) (x) + #endif /* __GNUC__ > 2 ... */ +#else /* __GNUC__ */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "gpu_nms.pyx", + "__init__.pxd", + "type.pxd", +}; +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; /* for error messages only */ + struct __Pyx_StructField_* fields; + size_t size; /* sizeof(type) */ + size_t arraysize[8]; /* length of array in each dimension */ + int ndim; + char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject, c_H_ar */ + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":723 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":724 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":737 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":738 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":747 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":748 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":752 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":756 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":759 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif + + +/*--- Type declarations ---*/ + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":763 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764 + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cdouble complex_t + */ +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766 + * ctypedef npy_clongdouble clongdouble_t + * + * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< + * + * cdef inline object PyArray_MultiIterNew1(a): + */ +typedef npy_cdouble __pyx_t_5numpy_complex_t; +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/ + +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/ + +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \ + const char* function_name); /*proto*/ + +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); /*proto*/ + +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/ + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/ + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/ +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ + +static void __Pyx_RaiseBufferIndexError(int axis); /*proto*/ + +#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) +#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( + PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, + PyObject** py_start, PyObject** py_stop, PyObject** py_slice, + int has_cstart, int has_cstop, int wraparound); + +static void __Pyx_RaiseBufferFallbackError(void); /*proto*/ + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/ + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/ + +static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); 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+ static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs(z) (::std::abs(z)) + #define __Pyx_c_pow(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + #if 1 + #define __Pyx_c_abs(z) (cabs(z)) + #define __Pyx_c_pow(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); 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+ +static int __Pyx_check_binary_version(void); + +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ + +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ + +typedef struct { + int code_line; + PyCodeObject* code_object; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); /*proto*/ + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ + + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'nms.gpu_nms' */ +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t = { "float32_t", NULL, sizeof(__pyx_t_5numpy_float32_t), { 0 }, 0, 'R', 0, 0 }; 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'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int32_t), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { "int_t", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 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+ case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + values[0] = PyTuple_GET_ITEM(__pyx_args, 0); + break; + default: goto __pyx_L5_argtuple_error; + } + } + __pyx_v_dets = ((PyArrayObject *)values[0]); + __pyx_v_thresh = ((PyObject*)values[1]); + if (values[2]) { + __pyx_v_device_id = __Pyx_PyInt_As_npy_int32(values[2]); if (unlikely((__pyx_v_device_id == (npy_int32)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 17; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } else { + __pyx_v_device_id = ((__pyx_t_5numpy_int32_t)0); + } + } + goto __pyx_L4_argument_unpacking_done; + __pyx_L5_argtuple_error:; + __Pyx_RaiseArgtupleInvalid("gpu_nms", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 16; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_L3_error:; + __Pyx_AddTraceback("nms.gpu_nms.gpu_nms", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_RefNannyFinishContext(); + return NULL; + __pyx_L4_argument_unpacking_done:; 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+ const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) /* First char was not a digit */ + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; /* Consume from buffer string */ + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; /* breaks both loops as ctx->enc_count == 0 */ + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; /* empty struct */ + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; /* not a 'break' in the loop */ + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case 10: + case 13: + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': /* substruct */ + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; /* Erase processed last struct element */ + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': /* end of substruct; either repeat or move on */ + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; /* Erase processed last struct element */ + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } /* fall through */ + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 's': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + } else { + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + } + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON + result = PyDict_GetItem(__pyx_d, name); + if (result) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); +#if PY_VERSION_HEX >= 0x02060000 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; +#endif + result = (*call)(func, arg, kw); +#if PY_VERSION_HEX >= 0x02060000 + Py_LeaveRecursiveCall(); +#endif + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( + PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, + PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, + int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { +#if CYTHON_COMPILING_IN_CPYTHON + PyMappingMethods* mp; +#if PY_MAJOR_VERSION < 3 + PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; + if (likely(ms && ms->sq_slice)) { + if (!has_cstart) { + if (_py_start && (*_py_start != Py_None)) { + cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); + if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; + } else + cstart = 0; + } + if (!has_cstop) { + if (_py_stop && (*_py_stop != Py_None)) { + cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); + if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; + } else + cstop = PY_SSIZE_T_MAX; + } + if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { + Py_ssize_t l = ms->sq_length(obj); + if (likely(l >= 0)) { + if (cstop < 0) { + cstop += l; + if (cstop < 0) cstop = 0; + } + if (cstart < 0) { + cstart += l; + if (cstart < 0) cstart = 0; + } + } else { + if (PyErr_ExceptionMatches(PyExc_OverflowError)) + PyErr_Clear(); + else + goto bad; + } + } + return ms->sq_slice(obj, cstart, cstop); + } +#endif + mp = Py_TYPE(obj)->tp_as_mapping; + if (likely(mp && mp->mp_subscript)) +#endif + { + PyObject* result; + PyObject *py_slice, *py_start, *py_stop; + if (_py_slice) { + py_slice = *_py_slice; + } else { + PyObject* owned_start = NULL; + PyObject* owned_stop = NULL; + if (_py_start) { + py_start = *_py_start; + } else { + if (has_cstart) { + owned_start = py_start = PyInt_FromSsize_t(cstart); + if (unlikely(!py_start)) goto bad; + } else + py_start = Py_None; + } + if (_py_stop) { + py_stop = *_py_stop; + } else { + if (has_cstop) { + owned_stop = py_stop = PyInt_FromSsize_t(cstop); + if (unlikely(!py_stop)) { + Py_XDECREF(owned_start); + goto bad; + } + } else + py_stop = Py_None; + } + py_slice = PySlice_New(py_start, py_stop, Py_None); + Py_XDECREF(owned_start); + Py_XDECREF(owned_stop); + if (unlikely(!py_slice)) goto bad; + } +#if CYTHON_COMPILING_IN_CPYTHON + result = mp->mp_subscript(obj, py_slice); +#else + result = PyObject_GetItem(obj, py_slice); +#endif + if (!_py_slice) { + Py_DECREF(py_slice); + } + return result; + } + PyErr_Format(PyExc_TypeError, + "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); +bad: + return NULL; +} + +static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_SetString(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyThreadState *tstate = PyThreadState_GET(); + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_Restore(type, value, tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(type, value, tb); +#endif +} + +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + #if PY_VERSION_HEX < 0x02050000 + if (PyClass_Check(type)) { + #else + if (PyType_Check(type)) { + #endif +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + #if PY_VERSION_HEX < 0x02050000 + if (PyInstance_Check(type)) { + type = (PyObject*) ((PyInstanceObject*)type)->in_class; + Py_INCREF(type); + } else { + type = 0; + PyErr_SetString(PyExc_TypeError, + "raise: exception must be an old-style class or instance"); + goto raise_error; + } + #else + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + #endif + } + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else /* Python 3+ */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + if (PyObject_IsSubclass(instance_class, type)) { + type = instance_class; + } else { + instance_class = NULL; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + #if PY_VERSION_HEX >= 0x02060000 + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + #endif + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + #if PY_VERSION_HEX < 0x02060000 + if (obj->ob_type->tp_dict) { + PyObject *getbuffer_cobj = PyObject_GetItem( + obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer); + if (getbuffer_cobj) { + getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); + Py_DECREF(getbuffer_cobj); + if (!func) + goto fail; + return func(obj, view, flags); + } else { + PyErr_Clear(); + } + } + #endif + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); +#if PY_VERSION_HEX < 0x02060000 +fail: +#endif + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + #if PY_VERSION_HEX >= 0x02060000 + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + #endif + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + #if PY_VERSION_HEX < 0x02060000 + if (obj->ob_type->tp_dict) { + PyObject *releasebuffer_cobj = PyObject_GetItem( + obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer); + if (releasebuffer_cobj) { + releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); + Py_DECREF(releasebuffer_cobj); + if (!func) + goto fail; + func(obj, view); + return; + } else { + PyErr_Clear(); + } + } + #endif + goto nofail; +#if PY_VERSION_HEX < 0x02060000 +fail: +#endif + PyErr_WriteUnraisable(obj); +nofail: + Py_DECREF(obj); + view->obj = NULL; +} +#endif /* PY_MAJOR_VERSION < 3 */ + + + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + #if PY_VERSION_HEX >= 0x02050000 + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; /* try absolute import on failure */ + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } + #else + if (level>0) { + PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); + goto bad; + } + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, NULL); + #endif +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \ + { \ + func_type value = func(x); \ + if (sizeof(target_type) < sizeof(func_type)) { \ + if (unlikely(value != (func_type) (target_type) value)) { \ + func_type zero = 0; \ + PyErr_SetString(PyExc_OverflowError, \ + (is_unsigned && unlikely(value < zero)) ? \ + "can't convert negative value to " #target_type : \ + "value too large to convert to " #target_type); \ + return (target_type) -1; \ + } \ + } \ + return (target_type) value; \ + } + +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif +static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) { + const npy_int32 neg_one = (npy_int32) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(npy_int32) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to npy_int32"); + return (npy_int32) -1; + } + return (npy_int32) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(npy_int32)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return (npy_int32) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to npy_int32"); + return (npy_int32) -1; + } + if (sizeof(npy_int32) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, PyLong_AsUnsignedLong) + } else if (sizeof(npy_int32) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long long, PyLong_AsUnsignedLongLong) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(npy_int32)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return +(npy_int32) ((PyLongObject*)x)->ob_digit[0]; + case -1: return -(npy_int32) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (sizeof(npy_int32) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(npy_int32, long, PyLong_AsLong) + } else if (sizeof(npy_int32) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(npy_int32, long long, PyLong_AsLongLong) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + npy_int32 val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (npy_int32) -1; + } + } else { + npy_int32 val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (npy_int32) -1; + val = __Pyx_PyInt_As_npy_int32(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(int) <= sizeof(unsigned long long)) { + return PyLong_FromUnsignedLongLong((unsigned long long) value); + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(long long)) { + return PyLong_FromLongLong((long long) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(a, a); + case 3: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, a); + case 4: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_absf(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(a, a); + case 3: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, a); + case 4: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_abs(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(int)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return (int) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; + } + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong) + } else if (sizeof(int) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(int)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return +(int) ((PyLongObject*)x)->ob_digit[0]; + case -1: return -(int) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong) + } else if (sizeof(int) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(long) <= sizeof(unsigned long long)) { + return PyLong_FromUnsignedLongLong((unsigned long long) value); + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(long long)) { + return PyLong_FromLongLong((long long) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(long)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; + } + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong) + } else if (sizeof(long) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + if (sizeof(digit) <= sizeof(long)) { + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; + case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; + } + } + #endif +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong) + } else if (sizeof(long) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +} + +static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + #if PY_VERSION_HEX < 0x02050000 + return PyErr_Warn(NULL, message); + #else + return PyErr_WarnEx(NULL, message, 1); + #endif + } + return 0; +} + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + #if PY_VERSION_HEX < 0x02050000 + if (PyErr_Warn(NULL, warning) < 0) goto bad; + #else + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + #endif + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = (start + end) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, /*int argcount,*/ + 0, /*int kwonlyargcount,*/ + 0, /*int nlocals,*/ + 0, /*int stacksize,*/ + 0, /*int flags,*/ + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, /*int firstlineno,*/ + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_globals = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_globals = PyModule_GetDict(__pyx_m); + if (!py_globals) goto bad; + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + py_globals, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = py_line; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else /* Python 3+ has unicode identifiers */ + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else /* PY_VERSION_HEX < 0x03030000 */ + if (PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_DATA_SIZE(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ + return PyUnicode_AsUTF8AndSize(o, length); +#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ +#endif /* PY_VERSION_HEX < 0x03030000 */ + } else +#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ +#if !CYTHON_COMPILING_IN_PYPY +#if PY_VERSION_HEX >= 0x02060000 + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return Py_INCREF(x), x; + m = Py_TYPE(x)->tp_as_number; +#if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) + return PyInt_AS_LONG(b); +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(b)) { + case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; + case 0: return 0; + case 1: return ((PyLongObject*)b)->ob_digit[0]; + } + #endif + #endif + #if PY_VERSION_HEX < 0x02060000 + return PyInt_AsSsize_t(b); + #else + return PyLong_AsSsize_t(b); + #endif + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { +#if PY_VERSION_HEX < 0x02050000 + if (ival <= LONG_MAX) + return PyInt_FromLong((long)ival); + else { + unsigned char *bytes = (unsigned char *) &ival; + int one = 1; int little = (int)*(unsigned char*)&one; + return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); + } +#else + return PyInt_FromSize_t(ival); +#endif +} + + +#endif /* Py_PYTHON_H */ diff --git a/tf-faster-rcnn/lib/nms/gpu_nms.hpp b/tf-faster-rcnn/lib/nms/gpu_nms.hpp new file mode 100644 index 0000000..68b6d42 --- /dev/null +++ b/tf-faster-rcnn/lib/nms/gpu_nms.hpp @@ -0,0 +1,2 @@ +void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, + int boxes_dim, float nms_overlap_thresh, int device_id); diff --git a/tf-faster-rcnn/lib/nms/gpu_nms.pyx b/tf-faster-rcnn/lib/nms/gpu_nms.pyx new file mode 100644 index 0000000..59d84af --- /dev/null +++ b/tf-faster-rcnn/lib/nms/gpu_nms.pyx @@ -0,0 +1,31 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import numpy as np +cimport numpy as np + +assert sizeof(int) == sizeof(np.int32_t) + +cdef extern from "gpu_nms.hpp": + void _nms(np.int32_t*, int*, np.float32_t*, int, int, float, int) + +def gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh, + np.int32_t device_id=0): + cdef int boxes_num = dets.shape[0] + cdef int boxes_dim = dets.shape[1] + cdef int num_out + cdef np.ndarray[np.int32_t, ndim=1] \ + keep = np.zeros(boxes_num, dtype=np.int32) + cdef np.ndarray[np.float32_t, ndim=1] \ + scores = dets[:, 4] + cdef np.ndarray[np.int_t, ndim=1] \ + order = scores.argsort()[::-1] + cdef np.ndarray[np.float32_t, ndim=2] \ + sorted_dets = dets[order, :] + _nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id) + keep = keep[:num_out] + return list(order[keep]) diff --git a/tf-faster-rcnn/lib/nms/nms_kernel.cu b/tf-faster-rcnn/lib/nms/nms_kernel.cu new file mode 100644 index 0000000..038a590 --- /dev/null +++ b/tf-faster-rcnn/lib/nms/nms_kernel.cu @@ -0,0 +1,144 @@ +// ------------------------------------------------------------------ +// Faster R-CNN +// Copyright (c) 2015 Microsoft +// Licensed under The MIT License [see fast-rcnn/LICENSE for details] +// Written by Shaoqing Ren +// ------------------------------------------------------------------ + +#include "gpu_nms.hpp" +#include +#include + +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + cudaError_t error = condition; \ + if (error != cudaSuccess) { \ + std::cout << cudaGetErrorString(error) << std::endl; \ + } \ + } while (0) + +#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +__device__ inline float devIoU(float const * const a, float const * const b) { + float left = max(a[0], b[0]), right = min(a[2], b[2]); + float top = max(a[1], b[1]), bottom = min(a[3], b[3]); + float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); + float interS = width * height; + float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); + float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); + return interS / (Sa + Sb - interS); +} + +__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, + const float *dev_boxes, unsigned long long *dev_mask) { + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + __shared__ float block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const float *cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = DIVUP(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +void _set_device(int device_id) { + int current_device; + CUDA_CHECK(cudaGetDevice(¤t_device)); + if (current_device == device_id) { + return; + } + // The call to cudaSetDevice must come before any calls to Get, which + // may perform initialization using the GPU. + CUDA_CHECK(cudaSetDevice(device_id)); +} + +void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, + int boxes_dim, float nms_overlap_thresh, int device_id) { + _set_device(device_id); + + float* boxes_dev = NULL; + unsigned long long* mask_dev = NULL; + + const int col_blocks = DIVUP(boxes_num, threadsPerBlock); + + CUDA_CHECK(cudaMalloc(&boxes_dev, + boxes_num * boxes_dim * sizeof(float))); + CUDA_CHECK(cudaMemcpy(boxes_dev, + boxes_host, + boxes_num * boxes_dim * sizeof(float), + cudaMemcpyHostToDevice)); + + CUDA_CHECK(cudaMalloc(&mask_dev, + boxes_num * col_blocks * sizeof(unsigned long long))); + + dim3 blocks(DIVUP(boxes_num, threadsPerBlock), + DIVUP(boxes_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); + nms_kernel<<>>(boxes_num, + nms_overlap_thresh, + boxes_dev, + mask_dev); + + std::vector mask_host(boxes_num * col_blocks); + CUDA_CHECK(cudaMemcpy(&mask_host[0], + mask_dev, + sizeof(unsigned long long) * boxes_num * col_blocks, + cudaMemcpyDeviceToHost)); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + int num_to_keep = 0; + for (int i = 0; i < boxes_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + *num_out = num_to_keep; + + CUDA_CHECK(cudaFree(boxes_dev)); + CUDA_CHECK(cudaFree(mask_dev)); +} diff --git a/tf-faster-rcnn/lib/nms/py_cpu_nms.py b/tf-faster-rcnn/lib/nms/py_cpu_nms.py new file mode 100644 index 0000000..54e7b25 --- /dev/null +++ b/tf-faster-rcnn/lib/nms/py_cpu_nms.py @@ -0,0 +1,38 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import numpy as np + +def py_cpu_nms(dets, thresh): + """Pure Python NMS baseline.""" + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep diff --git a/tf-faster-rcnn/lib/roi_data_layer/__init__.py b/tf-faster-rcnn/lib/roi_data_layer/__init__.py new file mode 100644 index 0000000..7ba6a65 --- /dev/null +++ b/tf-faster-rcnn/lib/roi_data_layer/__init__.py @@ -0,0 +1,6 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- diff --git a/tf-faster-rcnn/lib/roi_data_layer/layer.py b/tf-faster-rcnn/lib/roi_data_layer/layer.py new file mode 100644 index 0000000..dbe5cbc --- /dev/null +++ b/tf-faster-rcnn/lib/roi_data_layer/layer.py @@ -0,0 +1,88 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- + +"""The data layer used during training to train a Fast R-CNN network. + +RoIDataLayer implements a Caffe Python layer. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from model.config import cfg +from roi_data_layer.minibatch import get_minibatch +import numpy as np +import time + +class RoIDataLayer(object): + """Fast R-CNN data layer used for training.""" + + def __init__(self, roidb, num_classes, random=False): + """Set the roidb to be used by this layer during training.""" + self._roidb = roidb + self._num_classes = num_classes + # Also set a random flag + self._random = random + self._shuffle_roidb_inds() + + def _shuffle_roidb_inds(self): + """Randomly permute the training roidb.""" + # If the random flag is set, + # then the database is shuffled according to system time + # Useful for the validation set + if self._random: + st0 = np.random.get_state() + millis = int(round(time.time() * 1000)) % 4294967295 + np.random.seed(millis) + + if cfg.TRAIN.ASPECT_GROUPING: + widths = np.array([r['width'] for r in self._roidb]) + heights = np.array([r['height'] for r in self._roidb]) + horz = (widths >= heights) + vert = np.logical_not(horz) + horz_inds = np.where(horz)[0] + vert_inds = np.where(vert)[0] + inds = np.hstack(( + np.random.permutation(horz_inds), + np.random.permutation(vert_inds))) + inds = np.reshape(inds, (-1, 2)) + row_perm = np.random.permutation(np.arange(inds.shape[0])) + inds = np.reshape(inds[row_perm, :], (-1,)) + self._perm = inds + else: + self._perm = np.random.permutation(np.arange(len(self._roidb))) + # Restore the random state + if self._random: + np.random.set_state(st0) + + self._cur = 0 + + def _get_next_minibatch_inds(self): + """Return the roidb indices for the next minibatch.""" + + if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb): + self._shuffle_roidb_inds() + + db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH] + self._cur += cfg.TRAIN.IMS_PER_BATCH + + return db_inds + + def _get_next_minibatch(self): + """Return the blobs to be used for the next minibatch. + + If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a + separate process and made available through self._blob_queue. + """ + db_inds = self._get_next_minibatch_inds() + minibatch_db = [self._roidb[i] for i in db_inds] + return get_minibatch(minibatch_db, self._num_classes) + + def forward(self): + """Get blobs and copy them into this layer's top blob vector.""" + blobs = self._get_next_minibatch() + return blobs diff --git a/tf-faster-rcnn/lib/roi_data_layer/minibatch.py b/tf-faster-rcnn/lib/roi_data_layer/minibatch.py new file mode 100644 index 0000000..889f6e5 --- /dev/null +++ b/tf-faster-rcnn/lib/roi_data_layer/minibatch.py @@ -0,0 +1,74 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- + +"""Compute minibatch blobs for training a Fast R-CNN network.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import numpy.random as npr +import cv2 +from model.config import cfg +from utils.blob import prep_im_for_blob, im_list_to_blob + +def get_minibatch(roidb, num_classes): + """Given a roidb, construct a minibatch sampled from it.""" + num_images = len(roidb) + # Sample random scales to use for each image in this batch + random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES), + size=num_images) + assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \ + 'num_images ({}) must divide BATCH_SIZE ({})'. \ + format(num_images, cfg.TRAIN.BATCH_SIZE) + + # Get the input image blob, formatted for caffe + im_blob, im_scales = _get_image_blob(roidb, random_scale_inds) + + blobs = {'data': im_blob} + + assert len(im_scales) == 1, "Single batch only" + assert len(roidb) == 1, "Single batch only" + + # gt boxes: (x1, y1, x2, y2, cls) + if cfg.TRAIN.USE_ALL_GT: + # Include all ground truth boxes + gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0] + else: + # For the COCO ground truth boxes, exclude the ones that are ''iscrowd'' + gt_inds = np.where(roidb[0]['gt_classes'] != 0 & np.all(roidb[0]['gt_overlaps'].toarray() > -1.0, axis=1))[0] + gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32) + gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0] + gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds] + blobs['gt_boxes'] = gt_boxes + blobs['im_info'] = np.array( + [im_blob.shape[1], im_blob.shape[2], im_scales[0]], + dtype=np.float32) + + return blobs + +def _get_image_blob(roidb, scale_inds): + """Builds an input blob from the images in the roidb at the specified + scales. + """ + num_images = len(roidb) + processed_ims = [] + im_scales = [] + for i in range(num_images): + im = cv2.imread(roidb[i]['image']) + if roidb[i]['flipped']: + im = im[:, ::-1, :] + target_size = cfg.TRAIN.SCALES[scale_inds[i]] + im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, + cfg.TRAIN.MAX_SIZE) + im_scales.append(im_scale) + processed_ims.append(im) + + # Create a blob to hold the input images + blob = im_list_to_blob(processed_ims) + + return blob, im_scales diff --git a/tf-faster-rcnn/lib/roi_data_layer/roidb.py b/tf-faster-rcnn/lib/roi_data_layer/roidb.py new file mode 100644 index 0000000..0a7a6e7 --- /dev/null +++ b/tf-faster-rcnn/lib/roi_data_layer/roidb.py @@ -0,0 +1,49 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Transform a roidb into a trainable roidb by adding a bunch of metadata.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from model.config import cfg +from model.bbox_transform import bbox_transform +from utils.cython_bbox import bbox_overlaps +import PIL + +def prepare_roidb(imdb): + """Enrich the imdb's roidb by adding some derived quantities that + are useful for training. This function precomputes the maximum + overlap, taken over ground-truth boxes, between each ROI and + each ground-truth box. The class with maximum overlap is also + recorded. + """ + roidb = imdb.roidb + if not (imdb.name.startswith('coco')): + sizes = [PIL.Image.open(imdb.image_path_at(i)).size + for i in range(imdb.num_images)] + for i in range(len(imdb.image_index)): + roidb[i]['image'] = imdb.image_path_at(i) + if not (imdb.name.startswith('coco')): + roidb[i]['width'] = sizes[i][0] + roidb[i]['height'] = sizes[i][1] + # need gt_overlaps as a dense array for argmax + gt_overlaps = roidb[i]['gt_overlaps'].toarray() + # max overlap with gt over classes (columns) + max_overlaps = gt_overlaps.max(axis=1) + # gt class that had the max overlap + max_classes = gt_overlaps.argmax(axis=1) + roidb[i]['max_classes'] = max_classes + roidb[i]['max_overlaps'] = max_overlaps + # sanity checks + # max overlap of 0 => class should be zero (background) + zero_inds = np.where(max_overlaps == 0)[0] + assert all(max_classes[zero_inds] == 0) + # max overlap > 0 => class should not be zero (must be a fg class) + nonzero_inds = np.where(max_overlaps > 0)[0] + assert all(max_classes[nonzero_inds] != 0) diff --git a/tf-faster-rcnn/lib/setup.py b/tf-faster-rcnn/lib/setup.py new file mode 100644 index 0000000..27d0bad --- /dev/null +++ b/tf-faster-rcnn/lib/setup.py @@ -0,0 +1,144 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import os +from os.path import join as pjoin +import numpy as np +from distutils.core import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext + +def find_in_path(name, path): + "Find a file in a search path" + #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ + for dir in path.split(os.pathsep): + binpath = pjoin(dir, name) + if os.path.exists(binpath): + return os.path.abspath(binpath) + return None + +def locate_cuda(): + """Locate the CUDA environment on the system + + Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' + and values giving the absolute path to each directory. + + Starts by looking for the CUDAHOME env variable. If not found, everything + is based on finding 'nvcc' in the PATH. + """ + + # first check if the CUDAHOME env variable is in use + if 'CUDAHOME' in os.environ: + home = os.environ['CUDAHOME'] + nvcc = pjoin(home, 'bin', 'nvcc') + else: + # otherwise, search the PATH for NVCC + default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') + nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) + if nvcc is None: + raise EnvironmentError('The nvcc binary could not be ' + 'located in your $PATH. Either add it to your path, or set $CUDAHOME') + home = os.path.dirname(os.path.dirname(nvcc)) + + cudaconfig = {'home':home, 'nvcc':nvcc, + 'include': pjoin(home, 'include'), + 'lib64': pjoin(home, 'lib64')} + for k, v in cudaconfig.items(): + if not os.path.exists(v): + raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) + + return cudaconfig +CUDA = locate_cuda() + +# Obtain the numpy include directory. This logic works across numpy versions. +try: + numpy_include = np.get_include() +except AttributeError: + numpy_include = np.get_numpy_include() + +def customize_compiler_for_nvcc(self): + """inject deep into distutils to customize how the dispatch + to gcc/nvcc works. + + If you subclass UnixCCompiler, it's not trivial to get your subclass + injected in, and still have the right customizations (i.e. + distutils.sysconfig.customize_compiler) run on it. So instead of going + the OO route, I have this. Note, it's kindof like a wierd functional + subclassing going on.""" + + # tell the compiler it can processes .cu + self.src_extensions.append('.cu') + + # save references to the default compiler_so and _comple methods + default_compiler_so = self.compiler_so + super = self._compile + + # now redefine the _compile method. This gets executed for each + # object but distutils doesn't have the ability to change compilers + # based on source extension: we add it. + def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): + print(extra_postargs) + if os.path.splitext(src)[1] == '.cu': + # use the cuda for .cu files + self.set_executable('compiler_so', CUDA['nvcc']) + # use only a subset of the extra_postargs, which are 1-1 translated + # from the extra_compile_args in the Extension class + postargs = extra_postargs['nvcc'] + else: + postargs = extra_postargs['gcc'] + + super(obj, src, ext, cc_args, postargs, pp_opts) + # reset the default compiler_so, which we might have changed for cuda + self.compiler_so = default_compiler_so + + # inject our redefined _compile method into the class + self._compile = _compile + +# run the customize_compiler +class custom_build_ext(build_ext): + def build_extensions(self): + customize_compiler_for_nvcc(self.compiler) + build_ext.build_extensions(self) + +ext_modules = [ + Extension( + "utils.cython_bbox", + ["utils/bbox.pyx"], + extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + include_dirs = [numpy_include] + ), + Extension( + "nms.cpu_nms", + ["nms/cpu_nms.pyx"], + extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + include_dirs = [numpy_include] + ), + Extension('nms.gpu_nms', + ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'], + library_dirs=[CUDA['lib64']], + libraries=['cudart'], + language='c++', + runtime_library_dirs=[CUDA['lib64']], + # this syntax is specific to this build system + # we're only going to use certain compiler args with nvcc and not with gcc + # the implementation of this trick is in customize_compiler() below + extra_compile_args={'gcc': ["-Wno-unused-function"], + 'nvcc': ['-arch=sm_60', + '--ptxas-options=-v', + '-c', + '--compiler-options', + "'-fPIC'"]}, + include_dirs = [numpy_include, CUDA['include']] + ) +] + +setup( + name='tf_faster_rcnn', + ext_modules=ext_modules, + # inject our custom trigger + cmdclass={'build_ext': custom_build_ext}, +) diff --git a/tf-faster-rcnn/lib/utils/.gitignore b/tf-faster-rcnn/lib/utils/.gitignore new file mode 100644 index 0000000..3347b69 --- /dev/null +++ b/tf-faster-rcnn/lib/utils/.gitignore @@ -0,0 +1,4 @@ +*.c +*.cpp +*.h +*.hpp diff --git a/tf-faster-rcnn/lib/utils/__init__.py b/tf-faster-rcnn/lib/utils/__init__.py new file mode 100644 index 0000000..7ba6a65 --- /dev/null +++ b/tf-faster-rcnn/lib/utils/__init__.py @@ -0,0 +1,6 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- diff --git a/tf-faster-rcnn/lib/utils/bbox.pyx b/tf-faster-rcnn/lib/utils/bbox.pyx new file mode 100644 index 0000000..0f9c696 --- /dev/null +++ b/tf-faster-rcnn/lib/utils/bbox.pyx @@ -0,0 +1,56 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Sergey Karayev +# -------------------------------------------------------- + +cimport cython +import numpy as np +cimport numpy as np + +DTYPE = np.float +ctypedef np.float_t DTYPE_t + +def bbox_overlaps( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + ua = float( + (boxes[n, 2] - boxes[n, 0] + 1) * + (boxes[n, 3] - boxes[n, 1] + 1) + + box_area - iw * ih + ) + overlaps[n, k] = iw * ih / ua + return overlaps + diff --git a/tf-faster-rcnn/lib/utils/blob.py b/tf-faster-rcnn/lib/utils/blob.py new file mode 100644 index 0000000..0399fdd --- /dev/null +++ b/tf-faster-rcnn/lib/utils/blob.py @@ -0,0 +1,47 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Blob helper functions.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 + + +def im_list_to_blob(ims): + """Convert a list of images into a network input. + + Assumes images are already prepared (means subtracted, BGR order, ...). + """ + max_shape = np.array([im.shape for im in ims]).max(axis=0) + num_images = len(ims) + blob = np.zeros((num_images, max_shape[0], max_shape[1], 3), + dtype=np.float32) + for i in range(num_images): + im = ims[i] + blob[i, 0:im.shape[0], 0:im.shape[1], :] = im + + return blob + + +def prep_im_for_blob(im, pixel_means, target_size, max_size): + """Mean subtract and scale an image for use in a blob.""" + im = im.astype(np.float32, copy=False) + im -= pixel_means + im_shape = im.shape + im_size_min = np.min(im_shape[0:2]) + im_size_max = np.max(im_shape[0:2]) + im_scale = float(target_size) / float(im_size_min) + # Prevent the biggest axis from being more than MAX_SIZE + if np.round(im_scale * im_size_max) > max_size: + im_scale = float(max_size) / float(im_size_max) + im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, + interpolation=cv2.INTER_LINEAR) + + return im, im_scale diff --git a/tf-faster-rcnn/lib/utils/timer.py b/tf-faster-rcnn/lib/utils/timer.py new file mode 100644 index 0000000..dacc942 --- /dev/null +++ b/tf-faster-rcnn/lib/utils/timer.py @@ -0,0 +1,32 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import time + +class Timer(object): + """A simple timer.""" + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + if average: + return self.average_time + else: + return self.diff diff --git a/tf-faster-rcnn/lib/utils/visualization.py b/tf-faster-rcnn/lib/utils/visualization.py new file mode 100644 index 0000000..a34c843 --- /dev/null +++ b/tf-faster-rcnn/lib/utils/visualization.py @@ -0,0 +1,89 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from six.moves import range +import PIL.Image as Image +import PIL.ImageColor as ImageColor +import PIL.ImageDraw as ImageDraw +import PIL.ImageFont as ImageFont + +STANDARD_COLORS = [ + 'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque', + 'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite', + 'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan', + 'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange', + 'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet', + 'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite', + 'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod', + 'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki', + 'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue', + 'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey', + 'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue', + 'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime', + 'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid', + 'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen', + 'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin', + 'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed', + 'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed', + 'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple', + 'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown', + 'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue', + 'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow', + 'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White', + 'WhiteSmoke', 'Yellow', 'YellowGreen' +] + +NUM_COLORS = len(STANDARD_COLORS) + +try: + FONT = ImageFont.truetype('arial.ttf', 24) +except IOError: + FONT = ImageFont.load_default() + +def _draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='black', thickness=4): + draw = ImageDraw.Draw(image) + (left, right, top, bottom) = (xmin, xmax, ymin, ymax) + draw.line([(left, top), (left, bottom), (right, bottom), + (right, top), (left, top)], width=thickness, fill=color) + text_bottom = bottom + # Reverse list and print from bottom to top. + text_width, text_height = font.getsize(display_str) + margin = np.ceil(0.05 * text_height) + draw.rectangle( + [(left, text_bottom - text_height - 2 * margin), (left + text_width, + text_bottom)], + fill=color) + draw.text( + (left + margin, text_bottom - text_height - margin), + display_str, + fill='black', + font=font) + + return image + +def draw_bounding_boxes(image, gt_boxes, im_info): + num_boxes = gt_boxes.shape[0] + gt_boxes_new = gt_boxes.copy() + gt_boxes_new[:,:4] = np.round(gt_boxes_new[:,:4].copy() / im_info[2]) + disp_image = Image.fromarray(np.uint8(image[0])) + + for i in range(num_boxes): + this_class = int(gt_boxes_new[i, 4]) + disp_image = _draw_single_box(disp_image, + gt_boxes_new[i, 0], + gt_boxes_new[i, 1], + gt_boxes_new[i, 2], + gt_boxes_new[i, 3], + 'N%02d-C%02d' % (i, this_class), + FONT, + color=STANDARD_COLORS[this_class % NUM_COLORS]) + + image[0, :] = np.array(disp_image) + return image diff --git a/tf-faster-rcnn/tools/.ipynb_checkpoints/demo-checkpoint.py b/tf-faster-rcnn/tools/.ipynb_checkpoints/demo-checkpoint.py new file mode 100644 index 0000000..4251976 --- /dev/null +++ b/tf-faster-rcnn/tools/.ipynb_checkpoints/demo-checkpoint.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python + +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- + +""" +Demo script showing detections in sample images. + +See README.md for installation instructions before running. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.config import cfg +from model.test import im_detect +from model.nms_wrapper import nms + +from utils.timer import Timer +import tensorflow as tf +import matplotlib.pyplot as plt +import numpy as np +import os, cv2 +import argparse +from tensorflow.python.framework import graph_util + +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 + +# CLASSES = ('__background__', +# 'aeroplane', 'bicycle', 'bird', 'boat', +# 'bottle', 'bus', 'car', 'cat', 'chair', +# 'cow', 'diningtable', 'dog', 'horse', +# 'motorbike', 'person', 'pottedplant', +# 'sheep', 'sofa', 'train', 'tvmonitor') +CLASSES = ('__background__','node') +NETS = {'vgg16': ('vgg16_faster_rcnn_iter_10000.ckpt',),'res101': ('res101_faster_rcnn_iter_10000.ckpt',)} +DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} + +def vis_detections(im, class_name, dets, thresh=0.5): + """Draw detected bounding boxes.""" + inds = np.where(dets[:, -1] >= thresh)[0] + if len(inds) == 0: + return + + im = im[:, :, (2, 1, 0)] + fig, ax = plt.subplots(figsize=(12, 12)) + ax.imshow(im, aspect='equal') + for i in inds: + bbox = dets[i, :4] + score = dets[i, -1] + + ax.add_patch( + plt.Rectangle((bbox[0], bbox[1]), + bbox[2] - bbox[0], + bbox[3] - bbox[1], fill=False, + edgecolor='red', linewidth=3.5) + ) + ax.text(bbox[0], bbox[1] - 2, + '{:s} {:.3f}'.format(class_name, score), + bbox=dict(facecolor='blue', alpha=0.5), + fontsize=14, color='white') + + ax.set_title(('{} detections with ' + 'p({} | box) >= {:.1f}').format(class_name, class_name, + thresh), + fontsize=14) + plt.axis('off') + plt.tight_layout() + plt.draw() + plt.savefig('/home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/123.png') +# cv2.imwrite('123.png',im) + +def demo(sess, net, image_name): + """Detect object classes in an image using pre-computed object proposals.""" + + # Load the demo image + im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) + im = cv2.imread(im_file) + + # Detect all object classes and regress object bounds + timer = Timer() + timer.tic() + scores, boxes = im_detect(sess, net, im) + timer.toc() + print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) + + # Visualize detections for each class + CONF_THRESH = 0.8 + NMS_THRESH = 0.3 + for cls_ind, cls in enumerate(CLASSES[1:]): + cls_ind += 1 # because we skipped background + cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] + cls_scores = scores[:, cls_ind] + dets = np.hstack((cls_boxes, + cls_scores[:, np.newaxis])).astype(np.float32) + keep = nms(dets, NMS_THRESH) + dets = dets[keep, :] + vis_detections(im, cls, dets, thresh=CONF_THRESH) + +def parse_args(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') + parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', + choices=NETS.keys(), default='res101') + parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', + choices=DATASETS.keys(), default='pascal_voc_0712') + args = parser.parse_args() + + return args + +if __name__ == '__main__': +# cfg.TEST.HAS_RPN = True # Use RPN for proposals +# args = parse_args() + +# # model path +# demonet = args.demo_net +# dataset = args.dataset +# tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', +# NETS[demonet][0]) + + +# if not os.path.isfile(tfmodel + '.meta'): +# raise IOError(('{:s} not found.\nDid you download the proper networks from ' +# 'our server and place them properly?').format(tfmodel + '.meta')) + +# # set config +# tfconfig = tf.ConfigProto(allow_soft_placement=True) +# tfconfig.gpu_options.allow_growth=True + +# # init session +# sess = tf.Session(config=tfconfig) +# # load network +# if demonet == 'vgg16': +# net = vgg16() +# elif demonet == 'res101': +# net = resnetv1(num_layers=101) +# else: +# raise NotImplementedError +# net.create_architecture("TEST", 2, +# tag='default', anchor_scales=[8, 16, 32]) +# saver = tf.train.Saver() +# saver.restore(sess, tfmodel) + +# print('Loaded network {:s}'.format(tfmodel)) + +# # im_names = ['000456.jpg', '000542.jpg', '001150.jpg', +# # '001763.jpg', '004545.jpg'] +# im_names = ['004.png','000.png','001.png'] +# for im_name in im_names: +# print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +# print('Demo for data/demo/{}'.format(im_name)) +# demo(sess, net, im_name) + +# plt.show() + + cfg.TEST.HAS_RPN = True # Use RPN for proposals + #args = parse_args() + + f_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + + # model path + # demonet = args.demo_net + # dataset = args.dataset + # input_dir = args.input + # output_dir = args.output + demonet = "vgg16" + dataset = "pascal_voc" + # input_dir = sys.argv[1] + # output_dir = sys.argv[2] + input_dir = "./data/demo/demo" + output_dir = "./data/oo" + tfmodel = os.path.join(f_path,'output', demonet, DATASETS[dataset][0], 'default', + NETS[demonet][0]) + + if not os.path.isfile(tfmodel + '.meta'): + raise IOError(('{:s} not found.\nDid you download the proper networks from ' + 'our server and place them properly?').format(tfmodel + '.meta')) + + # set config + tfconfig = tf.ConfigProto(allow_soft_placement=True) + print(tfconfig) + tfconfig.gpu_options.allow_growth=True + + # init session + sess = tf.Session(config=tfconfig) + # load network + if demonet == 'vgg16': + net = vgg16() + elif demonet == 'res101': + net = resnetv1(num_layers=101) + else: + raise NotImplementedError + net.create_architecture("TEST", len(CLASSES), + tag='default', anchor_scales=[8, 16, 32]) + saver = tf.train.Saver() + saver.restore(sess, tfmodel) + + #ckpt to pb + graph = tf.get_default_graph() + input_graph_def = graph.as_graph_def() + output_graph = "./data/oo/ship_model_from_demo3.pb" + output_node_names = "vgg_16_3/cls_prob,add,vgg_16_1/rois/concat,vgg_16_3/cls_score/BiasAdd" + output_graph_def = graph_util.convert_variables_to_constants(sess, input_graph_def, output_node_names.split(",")) + with tf.gfile.GFile(output_graph, "wb") as f: + f.write(output_graph_def.SerializeToString()) + diff --git a/tf-faster-rcnn/tools/.ipynb_checkpoints/demo_1-checkpoint.py b/tf-faster-rcnn/tools/.ipynb_checkpoints/demo_1-checkpoint.py new file mode 100644 index 0000000..4ea81b9 --- /dev/null +++ b/tf-faster-rcnn/tools/.ipynb_checkpoints/demo_1-checkpoint.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- + +""" +Demo script showing detections in sample images. + +See README.md for installation instructions before running. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.config import cfg +from model.test import im_detect +from model.nms_wrapper import nms + +from utils.timer import Timer +import tensorflow as tf +import matplotlib.pyplot as plt +import numpy as np +import os, cv2 +import argparse +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 + +CLASSES = ('__background__', + 'node') # 修改自己的类别 + +NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_10000.ckpt',)} # 修改自己的模型名字 +DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} + +def vis_detections(image_name, im, class_name, dets, thresh=0.5): # 此处的函数添加一个形参 + """Draw detected bounding boxes.""" + inds = np.where(dets[:, -1] >= thresh)[0] + if len(inds) == 0: + return + + im = im[:, :, (2, 1, 0)] + fig, ax = plt.subplots(figsize=(12, 12)) + ax.imshow(im, aspect='equal') + for i in inds: + bbox = dets[i, :4] + score = dets[i, -1] + + ax.add_patch( + plt.Rectangle((bbox[0], bbox[1]), + bbox[2] - bbox[0], + bbox[3] - bbox[1], fill=False, + edgecolor='red', linewidth=3.5) + ) + ax.text(bbox[0], bbox[1] - 2, + '{:s} {:.3f}'.format(class_name, score), + bbox=dict(facecolor='blue', alpha=0.5), + fontsize=14, color='white') + + ax.set_title(('{} detections with ' + 'p({} | box) >= {:.1f}').format(class_name, class_name, + thresh), + fontsize=14) + plt.axis('off') + plt.tight_layout() + plt.draw() + # 添加下面两行，注意修改路径 + plt.savefig('/home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/'+image_name) # 保存结果的路径 + print("save image to /home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/{}".format(image_name)) + + +def demo(image_name, sess, net): #第一个形参 + """Detect object classes in an image using pre-computed object proposals.""" + + # Load the demo image + im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) + im = cv2.imread(im_file) + + # Detect all object classes and regress object bounds + timer = Timer() + timer.tic() + scores, boxes = im_detect(sess, net, im) + timer.toc() + print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) + + # Visualize detections for each class + CONF_THRESH = 0.8 + NMS_THRESH = 0.3 + for cls_ind, cls in enumerate(CLASSES[1:]): + cls_ind += 1 # because we skipped background + cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] + cls_scores = scores[:, cls_ind] + dets = np.hstack((cls_boxes, + cls_scores[:, np.newaxis])).astype(np.float32) + keep = nms(dets, NMS_THRESH) + dets = dets[keep, :] + vis_detections(image_name, im, cls, dets, thresh=CONF_THRESH) # 添加此处调用的参数 + +def parse_args(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') + parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', + choices=NETS.keys(), default='res101') + parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', + choices=DATASETS.keys(), default='pascal_voc_0712') + args = parser.parse_args() + + return args + +if __name__ == '__main__': + cfg.TEST.HAS_RPN = True # Use RPN for proposals + args = parse_args() + + # model path + demonet = args.demo_net + dataset = args.dataset + tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', + NETS[demonet][0]) + + + if not os.path.isfile(tfmodel + '.meta'): + raise IOError(('{:s} not found.\nDid you download the proper networks from ' + 'our server and place them properly?').format(tfmodel + '.meta')) + + # set config + tfconfig = tf.ConfigProto(allow_soft_placement=True) + tfconfig.gpu_options.allow_growth=True + + # init session + sess = tf.Session(config=tfconfig) + # load network + if demonet == 'vgg16': + net = vgg16() + elif demonet == 'res101': + net = resnetv1(num_layers=101) + else: + raise NotImplementedError + net.create_architecture("TEST", 2, # 类别+1 + tag='default', anchor_scales=[8, 16, 32]) + saver = tf.train.Saver() + saver.restore(sess, tfmodel) + + print('Loaded network {:s}'.format(tfmodel)) + # 添加下面几行 + fi=open('/home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/val.txt')#输入要批量检测的图片名字合集，直接用训练时的test.txt就行。 + + txt=fi.readlines() + im_names = [] + for line in txt: + line=line.strip('\n') + line=(line+'.png') + im_names.append(line) + print(im_names) + fi.close() + + # 把之前的这几行注释或删去 + #im_names = ['000033.jpg', '000062.jpg', '000279.jpg', + # '000603.jpg', '000798.jpg', '001080.jpg', + # '001084.jpg', '001210.jpg', '001587.jpg', + # '001851.jpg', '001852.jpg', '000000.jpg'] + + for im_name in im_names: + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + print('Demo for data/demo/{}'.format(im_name)) + demo(im_name, sess, net) + + #plt.show() #最好注释这一行，不然会将大量图片全部显示出来 \ No newline at end of file diff --git a/tf-faster-rcnn/tools/.ipynb_checkpoints/demo_pb-checkpoint.py b/tf-faster-rcnn/tools/.ipynb_checkpoints/demo_pb-checkpoint.py new file mode 100644 index 0000000..a7be334 --- /dev/null +++ b/tf-faster-rcnn/tools/.ipynb_checkpoints/demo_pb-checkpoint.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python + +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- + +""" +Demo script showing detections in sample images. + +See README.md for installation instructions before running. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import _init_paths +from model.config import cfg +from model.test import im_detect +from model.nms_wrapper import nms +from model.bbox_transform import bbox_transform_inv +from model.test import _clip_boxes,_get_blobs +from utils.timer import Timer +import tensorflow as tf +#import matplotlib.pyplot as plt +import numpy as np +import cv2 +import argparse +import glob +#import gdal + +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 +from tensorflow.python.framework import graph_util + +CLASSES = ('__background__','node') +# CLASSES = ('__background__', +# 'aeroplane', 'bicycle', 'bird', 'boat', +# 'bottle', 'bus', 'car', 'cat', 'chair', +# 'cow', 'diningtable', 'dog', 'horse', +# 'motorbike', 'person', 'pottedplant', +# 'sheep', 'sofa', 'train', 'tvmonitor') + +NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_10000.ckpt',)} +DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} + +def vis_detections(im,class_name, dets,outfile, thresh=0.5): + """Draw detected bounding boxes.""" + inds = np.where(dets[:, -1] >= thresh)[0] + if len(inds) == 0: + return + + for i in inds: + bbox = dets[i, :4] + #print(bbox) + score = dets[i, -1] + + im[int(bbox[1]),int(bbox[0]):int(bbox[2]),0]=0 + im[int(bbox[3]),int(bbox[0]):int(bbox[2]),0] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[0]),0] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[2]),0] = 0 + + im[int(bbox[1]),int(bbox[0]):int(bbox[2]),1]=0 + im[int(bbox[3]),int(bbox[0]):int(bbox[2]),1] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[0]),1] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[2]),1] = 0 + + im[int(bbox[1]),int(bbox[0]):int(bbox[2]),2]=255 + im[int(bbox[3]),int(bbox[0]):int(bbox[2]),2] = 255 + im[int(bbox[1]):int(bbox[3]),int(bbox[0]),2] = 255 + im[int(bbox[1]):int(bbox[3]),int(bbox[2]),2] = 255 + +def demo(image_name,out_file,sess): + """Detect object classes in an image using pre-computed object proposals.""" + + # Load the demo image + im=cv2.imread(image_name) + + blobs, im_scales = _get_blobs(im) + assert len(im_scales) == 1, "Only single-image batch implemented" + + im_blob = blobs['data'] + blobs['im_info'] = np.array([im_blob.shape[1], im_blob.shape[2], im_scales[0]], dtype=np.float32) + print(blobs["im_info"]) + + #Detect all object classes and regress object bounds + timer = Timer() + timer.tic() + scores, boxes = freeze_graph_test(sess, blobs) + timer.toc() + print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) + + # Visualize detections for each class + CONF_THRESH = 0.5 + NMS_THRESH = 0.3 + # + + im = im[:, :, (2, 1, 0)] + + for cls_ind, cls in enumerate(CLASSES[1:]): + cls_ind += 1 # because we skipped background + cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] + cls_scores = scores[:, cls_ind] + dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32) + keep = nms(dets, NMS_THRESH) + dets = dets[keep, :] + vis_detections(im,cls, dets,out_file, thresh=CONF_THRESH) + + cv2.imencode('.png',im)[1].tofile(out_file) + +def parse_args(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') + parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', + choices=NETS.keys(), default='vgg16') + parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', + choices=DATASETS.keys(), default='pascal_voc') + + parser.add_argument('--input',dest='input',help='input folder') + parser.add_argument('--output',dest='output',help='output folder') + args = parser.parse_args() + + return args + +def freeze_graph_test(sess, blobs): + ''' + :param pb_path:pb文件的路径 + :param image_path:测试图片的路径 + :return: + ''' + # 定义输入的张量名称,对应网络结构的输入张量 + # input:0作为输入图像,keep_prob:0作为dropout的参数,测试时值为1,is_training:0训练参数 + # 定义输出的张量名称 + input_image_tensor = sess.graph.get_tensor_by_name("Placeholder:0") + tensor_info = sess.graph.get_tensor_by_name("Placeholder_1:0") + + biasadd = sess.graph.get_tensor_by_name("vgg_16_3/cls_score/BiasAdd:0") + score = sess.graph.get_tensor_by_name("vgg_16_3/cls_prob:0") + bbox = sess.graph.get_tensor_by_name("add:0") + rois = sess.graph.get_tensor_by_name("vgg_16_1/rois/concat:0") + + _, scores, bbox_pred, rois = sess.run([biasadd, score, bbox, rois], + feed_dict={input_image_tensor: blobs['data'], tensor_info: blobs['im_info']}) + + im_scales=blobs['im_info'][2] + boxes = rois[:, 1:5] / im_scales + scores = np.reshape(scores, [scores.shape[0], -1]) + bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1]) + # Apply bounding-box regression deltas + box_deltas = bbox_pred + pred_boxes = bbox_transform_inv(boxes, box_deltas) + pred_boxes = _clip_boxes(pred_boxes, (255,255,0)) + + return scores, pred_boxes + +if __name__ == '__main__': + + input_dir = "./data/demo/demo" + output_dir = "./data/oo" + pb_path = "./data/oo/ship_model_from_demo3.pb" + im_names = glob.glob(os.path.join(input_dir,"*.png")) + print(im_names) + + with tf.Graph().as_default(): + output_graph_def = tf.GraphDef() + with open(pb_path, "rb") as f: + output_graph_def.ParseFromString(f.read()) + tf.import_graph_def(output_graph_def, name="") + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + + for im_name in im_names: + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + img_basename = os.path.basename(im_name) + out_file = os.path.join(output_dir,img_basename) + print(im_name) + demo(im_name,out_file,sess) + + #plt.show() diff --git a/tf-faster-rcnn/tools/.ipynb_checkpoints/trainval_net-checkpoint.py b/tf-faster-rcnn/tools/.ipynb_checkpoints/trainval_net-checkpoint.py new file mode 100644 index 0000000..e7b4dab --- /dev/null +++ b/tf-faster-rcnn/tools/.ipynb_checkpoints/trainval_net-checkpoint.py @@ -0,0 +1,139 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Zheqi He, Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.train_val import get_training_roidb, train_net +from model.config import cfg, cfg_from_file, cfg_from_list, get_output_dir, get_output_tb_dir +from datasets.factory import get_imdb +import datasets.imdb +import argparse +import pprint +import numpy as np +import sys + +import tensorflow as tf +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 +from nets.mobilenet_v1 import mobilenetv1 + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') + parser.add_argument('--cfg', dest='cfg_file', + help='optional config file', + default=None, type=str) + parser.add_argument('--weight', dest='weight', + help='initialize with pretrained model weights', + type=str) + parser.add_argument('--imdb', dest='imdb_name', + help='dataset to train on', + default='voc_2007_trainval', type=str) + parser.add_argument('--imdbval', dest='imdbval_name', + help='dataset to validate on', + default='voc_2007_test', type=str) + parser.add_argument('--iters', dest='max_iters', + help='number of iterations to train', + default=70000, type=int) + parser.add_argument('--tag', dest='tag', + help='tag of the model', + default=None, type=str) + parser.add_argument('--net', dest='net', + help='vgg16, res50, res101, res152, mobile', + default='res50', type=str) + parser.add_argument('--set', dest='set_cfgs', + help='set config keys', default=None, + nargs=argparse.REMAINDER) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + + +def combined_roidb(imdb_names): + """ + Combine multiple roidbs + """ + + def get_roidb(imdb_name): + imdb = get_imdb(imdb_name) + print('Loaded dataset `{:s}` for training'.format(imdb.name)) + imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD) + print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)) + roidb = get_training_roidb(imdb) + return roidb + + roidbs = [get_roidb(s) for s in imdb_names.split('+')] + roidb = roidbs[0] + if len(roidbs) > 1: + for r in roidbs[1:]: + roidb.extend(r) + tmp = get_imdb(imdb_names.split('+')[1]) + imdb = datasets.imdb.imdb(imdb_names, tmp.classes) + else: + imdb = get_imdb(imdb_names) + return imdb, roidb + + +if __name__ == '__main__': + args = parse_args() + + print('Called with args:') + print(args) + + if args.cfg_file is not None: + cfg_from_file(args.cfg_file) + if args.set_cfgs is not None: + cfg_from_list(args.set_cfgs) + + print('Using config:') + pprint.pprint(cfg) + + np.random.seed(cfg.RNG_SEED) + + # train set + imdb, roidb = combined_roidb(args.imdb_name) + print('{:d} roidb entries'.format(len(roidb))) + + # output directory where the models are saved + output_dir = get_output_dir(imdb, args.tag) + print('Output will be saved to `{:s}`'.format(output_dir)) + + # tensorboard directory where the summaries are saved during training + tb_dir = get_output_tb_dir(imdb, args.tag) + print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir)) + + # also add the validation set, but with no flipping images + orgflip = cfg.TRAIN.USE_FLIPPED + cfg.TRAIN.USE_FLIPPED = False + _, valroidb = combined_roidb(args.imdbval_name) + print('{:d} validation roidb entries'.format(len(valroidb))) + cfg.TRAIN.USE_FLIPPED = orgflip + + # load network + if args.net == 'vgg16': + net = vgg16() + elif args.net == 'res50': + net = resnetv1(num_layers=50) + elif args.net == 'res101': + net = resnetv1(num_layers=101) + elif args.net == 'res152': + net = resnetv1(num_layers=152) + elif args.net == 'mobile': + net = mobilenetv1() + else: + raise NotImplementedError + + train_net(net, imdb, roidb, valroidb, output_dir, tb_dir, + pretrained_model=args.weight, + max_iters=args.max_iters) diff --git a/tf-faster-rcnn/tools/_init_paths.py b/tf-faster-rcnn/tools/_init_paths.py new file mode 100644 index 0000000..08c54dd --- /dev/null +++ b/tf-faster-rcnn/tools/_init_paths.py @@ -0,0 +1,15 @@ +import os.path as osp +import sys + +def add_path(path): + if path not in sys.path: + sys.path.insert(0, path) + +this_dir = osp.dirname(__file__) + +# Add lib to PYTHONPATH +lib_path = osp.join(this_dir, '..', 'lib') +add_path(lib_path) + +coco_path = osp.join(this_dir, '..', 'data', 'coco', 'PythonAPI') +add_path(coco_path) diff --git a/tf-faster-rcnn/tools/convert_from_depre.py b/tf-faster-rcnn/tools/convert_from_depre.py new file mode 100644 index 0000000..4ed7125 --- /dev/null +++ b/tf-faster-rcnn/tools/convert_from_depre.py @@ -0,0 +1,205 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen +# -------------------------------------------------------- +""" +Convert depreciated VGG16 snapshots to the ones that support tensorflow format + +It will check the specific snapshot at the vgg16_depre folder, and copy it to the same location at vgg16 folder +See experimental/scripts/convert_vgg16.sh for how to use it. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.config import cfg, cfg_from_file, cfg_from_list, get_output_dir +from model.train_val import filter_roidb, get_training_roidb +from datasets.factory import get_imdb +import datasets.imdb +import argparse +import pprint +import numpy as np +import sys +import os +import os.path as osp +import shutil + +try: + import cPickle as pickle +except ImportError: + import pickle + +import tensorflow as tf +from tensorflow.python import pywrap_tensorflow + +from nets.vgg16 import vgg16 + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Convert an old VGG16 snapshot to new format') + parser.add_argument('--cfg', dest='cfg_file', + help='optional config file', + default=None, type=str) + parser.add_argument('--snapshot', dest='snapshot', + help='vgg snapshot prefix', + type=str) + parser.add_argument('--imdb', dest='imdb_name', + help='dataset to train on', + default='voc_2007_trainval', type=str) + parser.add_argument('--iters', dest='max_iters', + help='number of iterations to train', + default=70000, type=int) + parser.add_argument('--tag', dest='tag', + help='tag of the model', + default=None, type=str) + parser.add_argument('--set', dest='set_cfgs', + help='set config keys', default=None, + nargs=argparse.REMAINDER) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + +def combined_roidb(imdb_names): + """ + Combine multiple roidbs + """ + + def get_roidb(imdb_name): + imdb = get_imdb(imdb_name) + print('Loaded dataset `{:s}` for training'.format(imdb.name)) + imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD) + print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)) + roidb = get_training_roidb(imdb) + return roidb + + roidbs = [get_roidb(s) for s in imdb_names.split('+')] + roidb = roidbs[0] + if len(roidbs) > 1: + for r in roidbs[1:]: + roidb.extend(r) + tmp = get_imdb(imdb_names.split('+')[1]) + imdb = datasets.imdb.imdb(imdb_names, tmp.classes) + else: + imdb = get_imdb(imdb_names) + return imdb, roidb + +def get_variables_in_checkpoint_file(file_name): + try: + reader = pywrap_tensorflow.NewCheckpointReader(file_name) + var_to_shape_map = reader.get_variable_to_shape_map() + return var_to_shape_map + except Exception as e: # pylint: disable=broad-except + print(str(e)) + if "corrupted compressed block contents" in str(e): + print("It's likely that your checkpoint file has been compressed " + "with SNAPPY.") + +def convert_names(name): + # removing :0 + name = name.replace(':0', '') + # replace + name = name.replace('vgg_16/', 'vgg16_default/') + name = name.replace('/biases', '/bias') + name = name.replace('/weights', '/weight') + name = name.replace('/conv1/', '/') + name = name.replace('/conv2/', '/') + name = name.replace('/conv3/', '/') + name = name.replace('/conv4/', '/') + name = name.replace('/conv5/', '/') + + return name + +# Just build the graph, load the weights/statistics, and save them +def convert_from_depre(net, imdb, input_dir, output_dir, snapshot, max_iters): + if not osp.exists(output_dir): + os.makedirs(output_dir) + + tfconfig = tf.ConfigProto(allow_soft_placement=True) + tfconfig.gpu_options.allow_growth = True + sess = tf.Session(config=tfconfig) + + num_classes = imdb.num_classes + with sess.graph.as_default(): + tf.set_random_seed(cfg.RNG_SEED) + layers = net.create_architecture(sess, 'TRAIN', num_classes, tag='default', + anchor_scales=cfg.ANCHOR_SCALES, + anchor_ratios=cfg.ANCHOR_RATIOS) + loss = layers['total_loss'] + # Learning rate should be reduced already + lr = tf.Variable(cfg.TRAIN.LEARNING_RATE * cfg.TRAIN.GAMMA, trainable=False) + momentum = cfg.TRAIN.MOMENTUM + optimizer = tf.train.MomentumOptimizer(lr, momentum) + gvs = optimizer.compute_gradients(loss) + if cfg.TRAIN.DOUBLE_BIAS: + final_gvs = [] + with tf.variable_scope('Gradient_Mult') as scope: + for grad, var in gvs: + scale = 1. + if cfg.TRAIN.DOUBLE_BIAS and '/biases:' in var.name: + scale *= 2. + if not np.allclose(scale, 1.0): + grad = tf.multiply(grad, scale) + final_gvs.append((grad, var)) + train_op = optimizer.apply_gradients(final_gvs) + else: + train_op = optimizer.apply_gradients(gvs) + + checkpoint = osp.join(input_dir, snapshot + '.ckpt') + variables = tf.global_variables() + name2var = {convert_names(v.name): v for v in variables} + target_names = get_variables_in_checkpoint_file(checkpoint) + restorer = tf.train.Saver(name2var) + saver = tf.train.Saver() + + print('Importing...') + restorer.restore(sess, checkpoint) + checkpoint = osp.join(output_dir, snapshot + '.ckpt') + print('Exporting...') + saver.save(sess, checkpoint) + + # also copy the pkl file + index = osp.join(input_dir, snapshot + '.pkl') + outdex = osp.join(output_dir, snapshot + '.pkl') + shutil.copy(index, outdex) + + sess.close() + + +if __name__ == '__main__': + args = parse_args() + + print('Called with args:') + print(args) + + if args.cfg_file is not None: + cfg_from_file(args.cfg_file) + if args.set_cfgs is not None: + cfg_from_list(args.set_cfgs) + + print('Using config:') + pprint.pprint(cfg) + + np.random.seed(cfg.RNG_SEED) + + # train set + imdb, _ = combined_roidb(args.imdb_name) + + # output directory where the snapshot will be exported + output_dir = get_output_dir(imdb, args.tag) + print('Output will be exported to `{:s}`'.format(output_dir)) + + # input directory where the snapshot will be imported + input_dir = output_dir.replace('/vgg16/', '/vgg16_depre/') + print('Input will be imported from `{:s}`'.format(input_dir)) + + net = vgg16() + + convert_from_depre(net, imdb, input_dir, output_dir, args.snapshot, args.max_iters) diff --git a/tf-faster-rcnn/tools/demo.py b/tf-faster-rcnn/tools/demo.py new file mode 100644 index 0000000..4251976 --- /dev/null +++ b/tf-faster-rcnn/tools/demo.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python + +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- + +""" +Demo script showing detections in sample images. + +See README.md for installation instructions before running. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.config import cfg +from model.test import im_detect +from model.nms_wrapper import nms + +from utils.timer import Timer +import tensorflow as tf +import matplotlib.pyplot as plt +import numpy as np +import os, cv2 +import argparse +from tensorflow.python.framework import graph_util + +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 + +# CLASSES = ('__background__', +# 'aeroplane', 'bicycle', 'bird', 'boat', +# 'bottle', 'bus', 'car', 'cat', 'chair', +# 'cow', 'diningtable', 'dog', 'horse', +# 'motorbike', 'person', 'pottedplant', +# 'sheep', 'sofa', 'train', 'tvmonitor') +CLASSES = ('__background__','node') +NETS = {'vgg16': ('vgg16_faster_rcnn_iter_10000.ckpt',),'res101': ('res101_faster_rcnn_iter_10000.ckpt',)} +DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} + +def vis_detections(im, class_name, dets, thresh=0.5): + """Draw detected bounding boxes.""" + inds = np.where(dets[:, -1] >= thresh)[0] + if len(inds) == 0: + return + + im = im[:, :, (2, 1, 0)] + fig, ax = plt.subplots(figsize=(12, 12)) + ax.imshow(im, aspect='equal') + for i in inds: + bbox = dets[i, :4] + score = dets[i, -1] + + ax.add_patch( + plt.Rectangle((bbox[0], bbox[1]), + bbox[2] - bbox[0], + bbox[3] - bbox[1], fill=False, + edgecolor='red', linewidth=3.5) + ) + ax.text(bbox[0], bbox[1] - 2, + '{:s} {:.3f}'.format(class_name, score), + bbox=dict(facecolor='blue', alpha=0.5), + fontsize=14, color='white') + + ax.set_title(('{} detections with ' + 'p({} | box) >= {:.1f}').format(class_name, class_name, + thresh), + fontsize=14) + plt.axis('off') + plt.tight_layout() + plt.draw() + plt.savefig('/home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/123.png') +# cv2.imwrite('123.png',im) + +def demo(sess, net, image_name): + """Detect object classes in an image using pre-computed object proposals.""" + + # Load the demo image + im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) + im = cv2.imread(im_file) + + # Detect all object classes and regress object bounds + timer = Timer() + timer.tic() + scores, boxes = im_detect(sess, net, im) + timer.toc() + print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) + + # Visualize detections for each class + CONF_THRESH = 0.8 + NMS_THRESH = 0.3 + for cls_ind, cls in enumerate(CLASSES[1:]): + cls_ind += 1 # because we skipped background + cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] + cls_scores = scores[:, cls_ind] + dets = np.hstack((cls_boxes, + cls_scores[:, np.newaxis])).astype(np.float32) + keep = nms(dets, NMS_THRESH) + dets = dets[keep, :] + vis_detections(im, cls, dets, thresh=CONF_THRESH) + +def parse_args(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') + parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', + choices=NETS.keys(), default='res101') + parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', + choices=DATASETS.keys(), default='pascal_voc_0712') + args = parser.parse_args() + + return args + +if __name__ == '__main__': +# cfg.TEST.HAS_RPN = True # Use RPN for proposals +# args = parse_args() + +# # model path +# demonet = args.demo_net +# dataset = args.dataset +# tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', +# NETS[demonet][0]) + + +# if not os.path.isfile(tfmodel + '.meta'): +# raise IOError(('{:s} not found.\nDid you download the proper networks from ' +# 'our server and place them properly?').format(tfmodel + '.meta')) + +# # set config +# tfconfig = tf.ConfigProto(allow_soft_placement=True) +# tfconfig.gpu_options.allow_growth=True + +# # init session +# sess = tf.Session(config=tfconfig) +# # load network +# if demonet == 'vgg16': +# net = vgg16() +# elif demonet == 'res101': +# net = resnetv1(num_layers=101) +# else: +# raise NotImplementedError +# net.create_architecture("TEST", 2, +# tag='default', anchor_scales=[8, 16, 32]) +# saver = tf.train.Saver() +# saver.restore(sess, tfmodel) + +# print('Loaded network {:s}'.format(tfmodel)) + +# # im_names = ['000456.jpg', '000542.jpg', '001150.jpg', +# # '001763.jpg', '004545.jpg'] +# im_names = ['004.png','000.png','001.png'] +# for im_name in im_names: +# print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +# print('Demo for data/demo/{}'.format(im_name)) +# demo(sess, net, im_name) + +# plt.show() + + cfg.TEST.HAS_RPN = True # Use RPN for proposals + #args = parse_args() + + f_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + + # model path + # demonet = args.demo_net + # dataset = args.dataset + # input_dir = args.input + # output_dir = args.output + demonet = "vgg16" + dataset = "pascal_voc" + # input_dir = sys.argv[1] + # output_dir = sys.argv[2] + input_dir = "./data/demo/demo" + output_dir = "./data/oo" + tfmodel = os.path.join(f_path,'output', demonet, DATASETS[dataset][0], 'default', + NETS[demonet][0]) + + if not os.path.isfile(tfmodel + '.meta'): + raise IOError(('{:s} not found.\nDid you download the proper networks from ' + 'our server and place them properly?').format(tfmodel + '.meta')) + + # set config + tfconfig = tf.ConfigProto(allow_soft_placement=True) + print(tfconfig) + tfconfig.gpu_options.allow_growth=True + + # init session + sess = tf.Session(config=tfconfig) + # load network + if demonet == 'vgg16': + net = vgg16() + elif demonet == 'res101': + net = resnetv1(num_layers=101) + else: + raise NotImplementedError + net.create_architecture("TEST", len(CLASSES), + tag='default', anchor_scales=[8, 16, 32]) + saver = tf.train.Saver() + saver.restore(sess, tfmodel) + + #ckpt to pb + graph = tf.get_default_graph() + input_graph_def = graph.as_graph_def() + output_graph = "./data/oo/ship_model_from_demo3.pb" + output_node_names = "vgg_16_3/cls_prob,add,vgg_16_1/rois/concat,vgg_16_3/cls_score/BiasAdd" + output_graph_def = graph_util.convert_variables_to_constants(sess, input_graph_def, output_node_names.split(",")) + with tf.gfile.GFile(output_graph, "wb") as f: + f.write(output_graph_def.SerializeToString()) + diff --git a/tf-faster-rcnn/tools/demo_1.py b/tf-faster-rcnn/tools/demo_1.py new file mode 100644 index 0000000..4ea81b9 --- /dev/null +++ b/tf-faster-rcnn/tools/demo_1.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- + +""" +Demo script showing detections in sample images. + +See README.md for installation instructions before running. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.config import cfg +from model.test import im_detect +from model.nms_wrapper import nms + +from utils.timer import Timer +import tensorflow as tf +import matplotlib.pyplot as plt +import numpy as np +import os, cv2 +import argparse +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 + +CLASSES = ('__background__', + 'node') # 修改自己的类别 + +NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_10000.ckpt',)} # 修改自己的模型名字 +DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} + +def vis_detections(image_name, im, class_name, dets, thresh=0.5): # 此处的函数添加一个形参 + """Draw detected bounding boxes.""" + inds = np.where(dets[:, -1] >= thresh)[0] + if len(inds) == 0: + return + + im = im[:, :, (2, 1, 0)] + fig, ax = plt.subplots(figsize=(12, 12)) + ax.imshow(im, aspect='equal') + for i in inds: + bbox = dets[i, :4] + score = dets[i, -1] + + ax.add_patch( + plt.Rectangle((bbox[0], bbox[1]), + bbox[2] - bbox[0], + bbox[3] - bbox[1], fill=False, + edgecolor='red', linewidth=3.5) + ) + ax.text(bbox[0], bbox[1] - 2, + '{:s} {:.3f}'.format(class_name, score), + bbox=dict(facecolor='blue', alpha=0.5), + fontsize=14, color='white') + + ax.set_title(('{} detections with ' + 'p({} | box) >= {:.1f}').format(class_name, class_name, + thresh), + fontsize=14) + plt.axis('off') + plt.tight_layout() + plt.draw() + # 添加下面两行，注意修改路径 + plt.savefig('/home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/'+image_name) # 保存结果的路径 + print("save image to /home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/{}".format(image_name)) + + +def demo(image_name, sess, net): #第一个形参 + """Detect object classes in an image using pre-computed object proposals.""" + + # Load the demo image + im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) + im = cv2.imread(im_file) + + # Detect all object classes and regress object bounds + timer = Timer() + timer.tic() + scores, boxes = im_detect(sess, net, im) + timer.toc() + print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) + + # Visualize detections for each class + CONF_THRESH = 0.8 + NMS_THRESH = 0.3 + for cls_ind, cls in enumerate(CLASSES[1:]): + cls_ind += 1 # because we skipped background + cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] + cls_scores = scores[:, cls_ind] + dets = np.hstack((cls_boxes, + cls_scores[:, np.newaxis])).astype(np.float32) + keep = nms(dets, NMS_THRESH) + dets = dets[keep, :] + vis_detections(image_name, im, cls, dets, thresh=CONF_THRESH) # 添加此处调用的参数 + +def parse_args(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') + parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', + choices=NETS.keys(), default='res101') + parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', + choices=DATASETS.keys(), default='pascal_voc_0712') + args = parser.parse_args() + + return args + +if __name__ == '__main__': + cfg.TEST.HAS_RPN = True # Use RPN for proposals + args = parse_args() + + # model path + demonet = args.demo_net + dataset = args.dataset + tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', + NETS[demonet][0]) + + + if not os.path.isfile(tfmodel + '.meta'): + raise IOError(('{:s} not found.\nDid you download the proper networks from ' + 'our server and place them properly?').format(tfmodel + '.meta')) + + # set config + tfconfig = tf.ConfigProto(allow_soft_placement=True) + tfconfig.gpu_options.allow_growth=True + + # init session + sess = tf.Session(config=tfconfig) + # load network + if demonet == 'vgg16': + net = vgg16() + elif demonet == 'res101': + net = resnetv1(num_layers=101) + else: + raise NotImplementedError + net.create_architecture("TEST", 2, # 类别+1 + tag='default', anchor_scales=[8, 16, 32]) + saver = tf.train.Saver() + saver.restore(sess, tfmodel) + + print('Loaded network {:s}'.format(tfmodel)) + # 添加下面几行 + fi=open('/home/jovyan/lcc/faster_rcnn_endernewton/tf-faster-rcnn-master/val.txt')#输入要批量检测的图片名字合集，直接用训练时的test.txt就行。 + + txt=fi.readlines() + im_names = [] + for line in txt: + line=line.strip('\n') + line=(line+'.png') + im_names.append(line) + print(im_names) + fi.close() + + # 把之前的这几行注释或删去 + #im_names = ['000033.jpg', '000062.jpg', '000279.jpg', + # '000603.jpg', '000798.jpg', '001080.jpg', + # '001084.jpg', '001210.jpg', '001587.jpg', + # '001851.jpg', '001852.jpg', '000000.jpg'] + + for im_name in im_names: + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + print('Demo for data/demo/{}'.format(im_name)) + demo(im_name, sess, net) + + #plt.show() #最好注释这一行，不然会将大量图片全部显示出来 \ No newline at end of file diff --git a/tf-faster-rcnn/tools/demo_pb.py b/tf-faster-rcnn/tools/demo_pb.py new file mode 100644 index 0000000..a7be334 --- /dev/null +++ b/tf-faster-rcnn/tools/demo_pb.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python + +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- + +""" +Demo script showing detections in sample images. + +See README.md for installation instructions before running. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import _init_paths +from model.config import cfg +from model.test import im_detect +from model.nms_wrapper import nms +from model.bbox_transform import bbox_transform_inv +from model.test import _clip_boxes,_get_blobs +from utils.timer import Timer +import tensorflow as tf +#import matplotlib.pyplot as plt +import numpy as np +import cv2 +import argparse +import glob +#import gdal + +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 +from tensorflow.python.framework import graph_util + +CLASSES = ('__background__','node') +# CLASSES = ('__background__', +# 'aeroplane', 'bicycle', 'bird', 'boat', +# 'bottle', 'bus', 'car', 'cat', 'chair', +# 'cow', 'diningtable', 'dog', 'horse', +# 'motorbike', 'person', 'pottedplant', +# 'sheep', 'sofa', 'train', 'tvmonitor') + +NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_10000.ckpt',)} +DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} + +def vis_detections(im,class_name, dets,outfile, thresh=0.5): + """Draw detected bounding boxes.""" + inds = np.where(dets[:, -1] >= thresh)[0] + if len(inds) == 0: + return + + for i in inds: + bbox = dets[i, :4] + #print(bbox) + score = dets[i, -1] + + im[int(bbox[1]),int(bbox[0]):int(bbox[2]),0]=0 + im[int(bbox[3]),int(bbox[0]):int(bbox[2]),0] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[0]),0] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[2]),0] = 0 + + im[int(bbox[1]),int(bbox[0]):int(bbox[2]),1]=0 + im[int(bbox[3]),int(bbox[0]):int(bbox[2]),1] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[0]),1] = 0 + im[int(bbox[1]):int(bbox[3]),int(bbox[2]),1] = 0 + + im[int(bbox[1]),int(bbox[0]):int(bbox[2]),2]=255 + im[int(bbox[3]),int(bbox[0]):int(bbox[2]),2] = 255 + im[int(bbox[1]):int(bbox[3]),int(bbox[0]),2] = 255 + im[int(bbox[1]):int(bbox[3]),int(bbox[2]),2] = 255 + +def demo(image_name,out_file,sess): + """Detect object classes in an image using pre-computed object proposals.""" + + # Load the demo image + im=cv2.imread(image_name) + + blobs, im_scales = _get_blobs(im) + assert len(im_scales) == 1, "Only single-image batch implemented" + + im_blob = blobs['data'] + blobs['im_info'] = np.array([im_blob.shape[1], im_blob.shape[2], im_scales[0]], dtype=np.float32) + print(blobs["im_info"]) + + #Detect all object classes and regress object bounds + timer = Timer() + timer.tic() + scores, boxes = freeze_graph_test(sess, blobs) + timer.toc() + print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) + + # Visualize detections for each class + CONF_THRESH = 0.5 + NMS_THRESH = 0.3 + # + + im = im[:, :, (2, 1, 0)] + + for cls_ind, cls in enumerate(CLASSES[1:]): + cls_ind += 1 # because we skipped background + cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] + cls_scores = scores[:, cls_ind] + dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32) + keep = nms(dets, NMS_THRESH) + dets = dets[keep, :] + vis_detections(im,cls, dets,out_file, thresh=CONF_THRESH) + + cv2.imencode('.png',im)[1].tofile(out_file) + +def parse_args(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') + parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', + choices=NETS.keys(), default='vgg16') + parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', + choices=DATASETS.keys(), default='pascal_voc') + + parser.add_argument('--input',dest='input',help='input folder') + parser.add_argument('--output',dest='output',help='output folder') + args = parser.parse_args() + + return args + +def freeze_graph_test(sess, blobs): + ''' + :param pb_path:pb文件的路径 + :param image_path:测试图片的路径 + :return: + ''' + # 定义输入的张量名称,对应网络结构的输入张量 + # input:0作为输入图像,keep_prob:0作为dropout的参数,测试时值为1,is_training:0训练参数 + # 定义输出的张量名称 + input_image_tensor = sess.graph.get_tensor_by_name("Placeholder:0") + tensor_info = sess.graph.get_tensor_by_name("Placeholder_1:0") + + biasadd = sess.graph.get_tensor_by_name("vgg_16_3/cls_score/BiasAdd:0") + score = sess.graph.get_tensor_by_name("vgg_16_3/cls_prob:0") + bbox = sess.graph.get_tensor_by_name("add:0") + rois = sess.graph.get_tensor_by_name("vgg_16_1/rois/concat:0") + + _, scores, bbox_pred, rois = sess.run([biasadd, score, bbox, rois], + feed_dict={input_image_tensor: blobs['data'], tensor_info: blobs['im_info']}) + + im_scales=blobs['im_info'][2] + boxes = rois[:, 1:5] / im_scales + scores = np.reshape(scores, [scores.shape[0], -1]) + bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1]) + # Apply bounding-box regression deltas + box_deltas = bbox_pred + pred_boxes = bbox_transform_inv(boxes, box_deltas) + pred_boxes = _clip_boxes(pred_boxes, (255,255,0)) + + return scores, pred_boxes + +if __name__ == '__main__': + + input_dir = "./data/demo/demo" + output_dir = "./data/oo" + pb_path = "./data/oo/ship_model_from_demo3.pb" + im_names = glob.glob(os.path.join(input_dir,"*.png")) + print(im_names) + + with tf.Graph().as_default(): + output_graph_def = tf.GraphDef() + with open(pb_path, "rb") as f: + output_graph_def.ParseFromString(f.read()) + tf.import_graph_def(output_graph_def, name="") + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + + for im_name in im_names: + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + img_basename = os.path.basename(im_name) + out_file = os.path.join(output_dir,img_basename) + print(im_name) + demo(im_name,out_file,sess) + + #plt.show() diff --git a/tf-faster-rcnn/tools/reval.py b/tf-faster-rcnn/tools/reval.py new file mode 100644 index 0000000..612ae31 --- /dev/null +++ b/tf-faster-rcnn/tools/reval.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python + +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +# Reval = re-eval. Re-evaluate saved detections. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.test import apply_nms +from model.config import cfg +from datasets.factory import get_imdb +import pickle +import os, sys, argparse +import numpy as np + + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Re-evaluate results') + parser.add_argument('output_dir', nargs=1, help='results directory', + type=str) + parser.add_argument('--imdb', dest='imdb_name', + help='dataset to re-evaluate', + default='voc_2007_test', type=str) + parser.add_argument('--matlab', dest='matlab_eval', + help='use matlab for evaluation', + action='store_true') + parser.add_argument('--comp', dest='comp_mode', help='competition mode', + action='store_true') + parser.add_argument('--nms', dest='apply_nms', help='apply nms', + action='store_true') + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + + +def from_dets(imdb_name, output_dir, args): + imdb = get_imdb(imdb_name) + imdb.competition_mode(args.comp_mode) + imdb.config['matlab_eval'] = args.matlab_eval + with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f: + dets = pickle.load(f) + + if args.apply_nms: + print('Applying NMS to all detections') + nms_dets = apply_nms(dets, cfg.TEST.NMS) + else: + nms_dets = dets + + print('Evaluating detections') + imdb.evaluate_detections(nms_dets, output_dir) + + +if __name__ == '__main__': + args = parse_args() + + output_dir = os.path.abspath(args.output_dir[0]) + imdb_name = args.imdb_name + from_dets(imdb_name, output_dir, args) diff --git a/tf-faster-rcnn/tools/test_net.py b/tf-faster-rcnn/tools/test_net.py new file mode 100644 index 0000000..c9cfc53 --- /dev/null +++ b/tf-faster-rcnn/tools/test_net.py @@ -0,0 +1,122 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Zheqi he, Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.test import test_net +from model.config import cfg, cfg_from_file, cfg_from_list +from datasets.factory import get_imdb +import argparse +import pprint +import time, os, sys + +import tensorflow as tf +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 +from nets.mobilenet_v1 import mobilenetv1 + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Test a Fast R-CNN network') + parser.add_argument('--cfg', dest='cfg_file', + help='optional config file', default=None, type=str) + parser.add_argument('--model', dest='model', + help='model to test', + default=None, type=str) + parser.add_argument('--imdb', dest='imdb_name', + help='dataset to test', + default='voc_2007_test', type=str) + parser.add_argument('--comp', dest='comp_mode', help='competition mode', + action='store_true') + parser.add_argument('--num_dets', dest='max_per_image', + help='max number of detections per image', + default=100, type=int) + parser.add_argument('--tag', dest='tag', + help='tag of the model', + default='', type=str) + parser.add_argument('--net', dest='net', + help='vgg16, res50, res101, res152, mobile', + default='res50', type=str) + parser.add_argument('--set', dest='set_cfgs', + help='set config keys', default=None, + nargs=argparse.REMAINDER) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + +if __name__ == '__main__': + args = parse_args() + + print('Called with args:') + print(args) + + if args.cfg_file is not None: + cfg_from_file(args.cfg_file) + if args.set_cfgs is not None: + cfg_from_list(args.set_cfgs) + + print('Using config:') + pprint.pprint(cfg) + + # if has model, get the name from it + # if does not, then just use the initialization weights + if args.model: + filename = os.path.splitext(os.path.basename(args.model))[0] + else: + filename = os.path.splitext(os.path.basename(args.weight))[0] + + tag = args.tag + tag = tag if tag else 'default' + filename = tag + '/' + filename + + imdb = get_imdb(args.imdb_name) + imdb.competition_mode(args.comp_mode) + + tfconfig = tf.ConfigProto(allow_soft_placement=True) + tfconfig.gpu_options.allow_growth=True + + # init session + sess = tf.Session(config=tfconfig) + # load network + if args.net == 'vgg16': + net = vgg16() + elif args.net == 'res50': + net = resnetv1(num_layers=50) + elif args.net == 'res101': + net = resnetv1(num_layers=101) + elif args.net == 'res152': + net = resnetv1(num_layers=152) + elif args.net == 'mobile': + net = mobilenetv1() + else: + raise NotImplementedError + + # load model + net.create_architecture("TEST", imdb.num_classes, tag='default', + anchor_scales=cfg.ANCHOR_SCALES, + anchor_ratios=cfg.ANCHOR_RATIOS) + + if args.model: + print(('Loading model check point from {:s}').format(args.model)) + saver = tf.train.Saver() + saver.restore(sess, args.model) + print('Loaded.') + else: + print(('Loading initial weights from {:s}').format(args.weight)) + sess.run(tf.global_variables_initializer()) + print('Loaded.') + + test_net(sess, net, imdb, filename, max_per_image=args.max_per_image) + + sess.close() diff --git a/tf-faster-rcnn/tools/trainval_net.py b/tf-faster-rcnn/tools/trainval_net.py new file mode 100644 index 0000000..e7b4dab --- /dev/null +++ b/tf-faster-rcnn/tools/trainval_net.py @@ -0,0 +1,139 @@ +# -------------------------------------------------------- +# Tensorflow Faster R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Zheqi He, Xinlei Chen, based on code from Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import _init_paths +from model.train_val import get_training_roidb, train_net +from model.config import cfg, cfg_from_file, cfg_from_list, get_output_dir, get_output_tb_dir +from datasets.factory import get_imdb +import datasets.imdb +import argparse +import pprint +import numpy as np +import sys + +import tensorflow as tf +from nets.vgg16 import vgg16 +from nets.resnet_v1 import resnetv1 +from nets.mobilenet_v1 import mobilenetv1 + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') + parser.add_argument('--cfg', dest='cfg_file', + help='optional config file', + default=None, type=str) + parser.add_argument('--weight', dest='weight', + help='initialize with pretrained model weights', + type=str) + parser.add_argument('--imdb', dest='imdb_name', + help='dataset to train on', + default='voc_2007_trainval', type=str) + parser.add_argument('--imdbval', dest='imdbval_name', + help='dataset to validate on', + default='voc_2007_test', type=str) + parser.add_argument('--iters', dest='max_iters', + help='number of iterations to train', + default=70000, type=int) + parser.add_argument('--tag', dest='tag', + help='tag of the model', + default=None, type=str) + parser.add_argument('--net', dest='net', + help='vgg16, res50, res101, res152, mobile', + default='res50', type=str) + parser.add_argument('--set', dest='set_cfgs', + help='set config keys', default=None, + nargs=argparse.REMAINDER) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + + +def combined_roidb(imdb_names): + """ + Combine multiple roidbs + """ + + def get_roidb(imdb_name): + imdb = get_imdb(imdb_name) + print('Loaded dataset `{:s}` for training'.format(imdb.name)) + imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD) + print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)) + roidb = get_training_roidb(imdb) + return roidb + + roidbs = [get_roidb(s) for s in imdb_names.split('+')] + roidb = roidbs[0] + if len(roidbs) > 1: + for r in roidbs[1:]: + roidb.extend(r) + tmp = get_imdb(imdb_names.split('+')[1]) + imdb = datasets.imdb.imdb(imdb_names, tmp.classes) + else: + imdb = get_imdb(imdb_names) + return imdb, roidb + + +if __name__ == '__main__': + args = parse_args() + + print('Called with args:') + print(args) + + if args.cfg_file is not None: + cfg_from_file(args.cfg_file) + if args.set_cfgs is not None: + cfg_from_list(args.set_cfgs) + + print('Using config:') + pprint.pprint(cfg) + + np.random.seed(cfg.RNG_SEED) + + # train set + imdb, roidb = combined_roidb(args.imdb_name) + print('{:d} roidb entries'.format(len(roidb))) + + # output directory where the models are saved + output_dir = get_output_dir(imdb, args.tag) + print('Output will be saved to `{:s}`'.format(output_dir)) + + # tensorboard directory where the summaries are saved during training + tb_dir = get_output_tb_dir(imdb, args.tag) + print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir)) + + # also add the validation set, but with no flipping images + orgflip = cfg.TRAIN.USE_FLIPPED + cfg.TRAIN.USE_FLIPPED = False + _, valroidb = combined_roidb(args.imdbval_name) + print('{:d} validation roidb entries'.format(len(valroidb))) + cfg.TRAIN.USE_FLIPPED = orgflip + + # load network + if args.net == 'vgg16': + net = vgg16() + elif args.net == 'res50': + net = resnetv1(num_layers=50) + elif args.net == 'res101': + net = resnetv1(num_layers=101) + elif args.net == 'res152': + net = resnetv1(num_layers=152) + elif args.net == 'mobile': + net = mobilenetv1() + else: + raise NotImplementedError + + train_net(net, imdb, roidb, valroidb, output_dir, tb_dir, + pretrained_model=args.weight, + max_iters=args.max_iters) diff --git a/tf-faster-rcnn/val.txt b/tf-faster-rcnn/val.txt new file mode 100644 index 0000000..61328a8 --- /dev/null +++ b/tf-faster-rcnn/val.txt @@ -0,0 +1,21 @@ +385 +386 +387 +388 +389 +390 +391 +392 +393 +394 +395 +396 +397 +398 +399 +400 +401 +402 +403 +404 +405

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zCF`^bkiTgvC@(mJ{g$d!Jv9hZeT?wGucP?c{}p{Qb20RV>cW5j4xx7Y5t*33<0*sCg+qO|3@C zD5B8sU{086B^~Hd1kctDXyQe14>?s+Ys7Dr-epBVgh2{1&nkSFRFS}O;2Xa zS9< .\bin\Release\mnist_onnx.exe +[2021-04-26 21:25:52][ WARN ] : empty engine file name, skip save! +[2021-04-26 21:25:52][ DEBUG ] : build onnx engine from mnist_onnx/model/mnist.onnx ... +---------------------------------------------------------------- +Input filename: mnist_onnx/model/mnist.onnx +ONNX IR version: 0.0.3 +Opset version: 8 +Producer name: CNTK +Producer version: 2.5.1 +Domain: ai.cntk +Model version: 1 +Doc string: +---------------------------------------------------------------- +[2021-04-26 21:25:52][ WARN ] : onnx2trt_utils.cpp:220: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. +[2021-04-26 21:25:52][ DEBUG ] : unmark original output... +[2021-04-26 21:25:52][ DEBUG ] : mark custom output... +[2021-04-26 21:25:52][ DEBUG ] : FP16 support: 1 +[2021-04-26 21:25:52][ DEBUG ] : INT8 support: 1 +[2021-04-26 21:25:52][ DEBUG ] : Max batchsize: 1 +[2021-04-26 21:25:52][ DEBUG ] : Max workspace size: 16777216 +[2021-04-26 21:25:52][ DEBUG ] : Number of DLA core: 0 +[2021-04-26 21:25:52][ DEBUG ] : Max DLA batchsize: 268435456 +[2021-04-26 21:25:52][ DEBUG ] : Current use DLA core: 0 +[2021-04-26 21:25:52][ DEBUG ] : build engine ... +[2021-04-26 21:26:00][ DEBUG ] : build Network done +[2021-04-26 21:26:00][ DEBUG ] : serialize engine to +[2021-04-26 21:26:00][ WARN ] : empty engine file name, skip save! +[2021-04-26 21:26:00][ DEBUG ] : create execute context and malloc device memory... +[2021-04-26 21:26:00][ DEBUG ] : init engine... +[2021-04-26 21:26:00][ DEBUG ] : malloc device memory +[2021-04-26 21:26:00][ DEBUG ] : nbBingdings: 2 +[2021-04-26 21:26:00][ DEBUG ] : input: +[2021-04-26 21:26:00][ DEBUG ] : binding bindIndex: 0, name: Input3, size in byte: 3136 +[2021-04-26 21:26:00][ DEBUG ] : binding dims with 4 dimemsion +[2021-04-26 21:26:00][ DEBUG ] : output: +[2021-04-26 21:26:00][ DEBUG ] : binding bindIndex: 1, name: Plus214_Output_0, size in byte: 40 +[2021-04-26 21:26:00][ DEBUG ] : binding dims with 2 dimemsion +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +@@@@@@@@@@@@#=. +*=#@@@@@@@ +@@@@@@@@@@@* :. -@@@@@@@ +@@@@@@@@@@# :#@@: +@@@@@@@ +@@@@@@@@@* :@@@* .@@@@@@@@ +@@@@@@@@= =@@@@. *@@@@@@@@ +@@@@@@@= -@@@@* =@@@@@@@@@ +@@@@@@@ -@@@%: -@@@@@@@@@@ +@@@@@@% %%+: *@@@@@@@@@@ +@@@@@@@ .. @@@@@@@@@@@ +@@@@@@@# .=%%: =@@@@@@@@@@@ +@@@@@@@@@@@@@# +@@@@@@@@@@@ +@@@@@@@@@@@@@# @@@@@@@@@@@@ +@@@@@@@@@@@@@@ @@@@@@@@@@@@ +@@@@@@@@@@@@@# @@@@@@@@@@@@ +@@@@@@@@@@@@@+ @@@@@@@@@@@@ +@@@@@@@@@@@@@% @@@@@@@@@@@@ +@@@@@@@@@@@@@@. #@@@@@@@@@@@ +@@@@@@@@@@@@@@* :%@@@@@@@@@@ +@@@@@@@@@@@@@@@: -@@@@@@@@@@ +@@@@@@@@@@@@@@@@= %@@@@@@@@@ +@@@@@@@@@@@@@@@@@@@@@@@@@@@@ +[2021-04-26 21:26:00][ INFO ] : Output: +[2021-04-26 21:26:00][ INFO ] : paob0: 1.87643e-14 +[2021-04-26 21:26:00][ INFO ] : paob1: 2.81554e-11 +[2021-04-26 21:26:00][ INFO ] : paob2: 1.40004e-11 +[2021-04-26 21:26:00][ INFO ] : paob3: 3.43565e-11 +[2021-04-26 21:26:00][ INFO ] : paob4: 8.02621e-06 +[2021-04-26 21:26:00][ INFO ] : paob5: 9.98186e-13 +[2021-04-26 21:26:00][ INFO ] : paob6: 8.26894e-15 +[2021-04-26 21:26:00][ INFO ] : paob7: 2.78337e-07 +[2021-04-26 21:26:00][ INFO ] : paob8: 5.86764e-08 +[2021-04-26 21:26:00][ INFO ] : paob9: 0.999992 +``` + diff --git a/TensorRT-Base/mnist_onnx/image/0.pgm b/TensorRT-Base/mnist_onnx/image/0.pgm new file mode 100644 index 0000000000000000000000000000000000000000..0be23e4338e4853bcc961aa47b6c0e39af86b7f4 GIT binary 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All rights reserved. + * + */ + + +#include + +#include "helper.h" +#include "mnist_onnx.h" + +int main() +{ + const std::string onnx_model = "mnist_onnx/model/mnist.onnx"; + const std::string engine_file = ""; + //const std::string engine_file = "mnist_onnx/model/mnist_onnx.bin"; + int batch_size = 1; + const std::vector output_name{"Plus214_Output_0"}; + // const std::vector output_name{"identity_out"}; + infer_precision_t precision = INFER_FP32; + + MnistOnnx* mnist_onnx = new MnistOnnx(onnx_model, engine_file, batch_size, output_name, precision); + + mnist_onnx->build(); + std::string pgm_name = "mnist_onnx/image/9.pgm"; + mnist_onnx->process_input(pgm_name); + mnist_onnx->infer(); + mnist_onnx->process_result(); + + delete mnist_onnx; + return 0; +} \ No newline at end of file diff --git a/TensorRT-Base/mnist_onnx/mnist_onnx.cpp b/TensorRT-Base/mnist_onnx/mnist_onnx.cpp new file mode 100644 index 0000000..93a2c72 --- /dev/null +++ b/TensorRT-Base/mnist_onnx/mnist_onnx.cpp @@ -0,0 +1,89 @@ +/** + * @file - mnist_onnx.cpp + * @author - wdn (dongnian.wang@outlook.com) + * @brief - mnist_onnx + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ +#include "mnist_onnx.h" +#include + +trt_ret_t MnistOnnx::build() +{ + std::string input_name = "Input3"; + std::vector min_dim_vec{1, 50, 50}; + std::vector opt_dim_vec{1, 28, 28}; + std::vector max_dim_vec{1, 20, 20}; + add_dynamic_shape_profile(m_batch_size,input_name, min_dim_vec, opt_dim_vec, max_dim_vec); + create_engine(m_onnx_model, m_engine_file, m_output_name, m_batch_size, m_precision); + + return TRT_SUCCESS; +} + +trt_ret_t MnistOnnx::process_input(std::string& pgm_name) +{ + m_pgm_height = 28; + m_pgm_width = 28; + m_pgm_channel = 1; + std::vector file_buffer(m_pgm_height * m_pgm_width); + m_input_data.resize(m_pgm_height*m_pgm_width*m_pgm_channel); + read_pgm(pgm_name, file_buffer.data(), 28, 28); + for (int i = 0; i < m_pgm_height * m_pgm_width; i++) + { + std::cout << (" .:-=+*#%@"[file_buffer[i] / 26]) << (((i + 1) % m_pgm_width) ? "" : "\n"); + m_input_data[i] = 1.0 - float(file_buffer[i] / 255.0); + } + return TRT_SUCCESS; +} + + +trt_ret_t MnistOnnx::infer() +{ + std::vector input_dims{m_pgm_channel, m_pgm_width, m_pgm_height}; + set_binding_dimentsions(input_dims, 0); + mem_host_to_device(m_input_data, 0); + inference(); + mem_device_to_host(m_output_data, 1); + + return TRT_SUCCESS; +} + +trt_ret_t MnistOnnx::process_result() +{ + float sum{0.0f}; + float val{0.0f}; + int idx{0}; + int output_size = 10; + for(int i = 0; i < output_size; i++) + { + m_output_data[i] = exp(m_output_data[i]); + sum += m_output_data[i]; + } + helper::info << "Output:" << std::endl; + for (int i = 0; i < output_size; i++) + { + m_output_data[i] /= sum; + val = val > m_output_data[i] ? val : m_output_data[i]; + // val = (std::max)(val, host_output[i]); + if (val == m_output_data[i]) + { + idx = i; + } + helper::info << "paob" << i << ": " << m_output_data[i] << std::endl; + } + + return TRT_SUCCESS; +} + + +trt_ret_t MnistOnnx::read_pgm(const std::string& file_name, uint8_t* buffer, int in_h, int in_w) +{ + std::ifstream infile(file_name, std::ifstream::binary); + assert(infile.is_open() && "Attempting to read from a file that is not open."); + std::string magic, h, w, max; + infile >> magic >> h >> w >> max; + infile.seekg(1, infile.cur); + infile.read(reinterpret_cast(buffer), in_h * in_w); + return TRT_SUCCESS; +} \ No newline at end of file diff --git a/TensorRT-Base/mnist_onnx/mnist_onnx.h b/TensorRT-Base/mnist_onnx/mnist_onnx.h new file mode 100644 index 0000000..a686a41 --- /dev/null +++ b/TensorRT-Base/mnist_onnx/mnist_onnx.h @@ -0,0 +1,50 @@ +/** + * @file - mnist_onnx.h + * @author - wdn (dongnian.wang@outlook.com) + * @brief - mnist_onnx + * + * @copyright Copyright (c) 2021, wdn. All rights reserved. + * + */ + +#ifndef MNIST_ONNX +#define MNIST_ONNX + +#include "helper.h" +#include "timer.h" +#include "base_trt.h" + +class MnistOnnx : public TrtBase +{ +public: + MnistOnnx(const std::string& onnx_model, const std::string& engine_file, int batch_size, + const std::vector& output_name, infer_precision_t precision) + : TrtBase(), m_onnx_model(onnx_model),m_batch_size(batch_size), + m_output_name(output_name), m_precision(precision), m_engine_file(engine_file) {} + + trt_ret_t build(); + + trt_ret_t process_input(std::string& pgm_name); + + trt_ret_t infer(); + + trt_ret_t process_result(); + +private: + std::string m_onnx_model; + std::string m_engine_file; + int m_batch_size; + std::vector m_output_name; + infer_precision_t m_precision; + + std::vector m_input_data; + std::vector m_output_data; + + int m_pgm_height; + int m_pgm_width; + int m_pgm_channel; + + trt_ret_t read_pgm(const std::string& file_name, uint8_t* buffer, int in_h, int in_w); +}; + +#endif // 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