[ { "path": ".gitignore", "content": ".idea\ndata/wider_face_add_lm_10_10\n\n__pycache__/\n*.py[cod]\n*$py.class\n\ndetect_imgs_results\ndetect_imgs_results_onnx\nwiderface_evaluation\n\nwiderface_evaluate/build\nwiderface_evaluate/*.so\nwiderface_evaluate/*.c" }, { "path": ".gitmodules", "content": "[submodule \"ncnn/3rdparty/ncnn\"]\n\tpath = ncnn/3rdparty/ncnn\n\turl = https://github.com/Tencent/ncnn\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2019 linzai\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "MNN/CMakeLists.txt", "content": "cmake_minimum_required(VERSION 3.10)\nproject(Ultra-face-mnn)\n\nset(CMAKE_CXX_STANDARD 11)\n\nfind_package(OpenCV REQUIRED)\ninclude_directories(\n mnn/include\n src\n)\n\nlink_directories(mnn/lib)\n\nadd_executable(Ultra-face-mnn src/main.cpp src/UltraFace.cpp)\ntarget_link_libraries(Ultra-face-mnn MNN ${OpenCV_LIBS})" }, { "path": "MNN/README.md", "content": "# C++ implemententation of [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) with [MNN](https://github.com/alibaba/MNN)\n\n## Build\n\n```bash\ngit clone --recursive --depth=1 https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB\n\ncd Ultra-Light-Fast-Generic-Face-Detector-1MB/MNN\n```\n\n* Replace **libMNN.so** under ./mnn/lib with your compiled libMNN.so and then :\n\n```bash\nmkdir build && cd build && cmake ..\nmake -j$(nproc)\n```\n\n## Run\n* Use FP32 model and run in FP16 mode:\n```bash\n./Ultra-face-mnn ../model/version-RFB/RFB-320.mnn ../imgs/1.jpg ../imgs/2.jpg ../imgs/3.jpg ../imgs/4.jpg\n```\n* Use quantized INT8 model:\n```bash\n./Ultra-face-mnn ../model/version-RFB/RFB-320-quant-KL-5792.mnn ../imgs/1.jpg ../imgs/2.jpg ../imgs/3.jpg ../imgs/4.jpg\n```\n\n* We provide both converted MNN FP32 and **quantized INT8** models of version-slim-320 and version-RFB-320 in ./MNN/model . The xxx-quant-KL-xxx.mnn is quantified by the **KL** method and xxx-quant-ADMM-xxx.mnn is quantified by the **ADMM** method.\n\n## How to convert pretrained model to MNN\n\n* Code bellow (```vision/ssd/ssd.py```) should be commented out when convert pytorch pretrained model to onnx. Comment it out and use the **convert_to_onnx.py** in official repo to finish this step.\n\n```python\nif self.is_test:\n confidences = F.softmax(confidences, dim=2)\n boxes = locations # this line should be added.\n #boxes = box_utils.convert_locations_to_boxes(\n # locations, self.priors, self.config.center_variance, self.config.size_variance\n #)\n # boxes = box_utils.center_form_to_corner_form(boxes) # these lines should be commented out. detail information and analyze comming soon.\n return confidences, boxes\nelse:\n return confidences, locations\n```\nThen you can generate the onnx model like **version-RFB-320_without_postprocessing.onnx** in onnx directory. (You need to rename your model when convert.)\n* Then we can use this tool to simplify onnx :\nhttps://github.com/daquexian/onnx-simplifier\n\n```\npython3 -m onnxsim version-RFB-320_without_postprocessing.onnx version-RFB-320_simplified.onnx\n\n```\n\nNext, you can convert this onnx model like **version-RFB-320_simplified.onnx** into a MNN model. Here is a website for online conversion : https://convertmodel.com. You can also use the MNN compiled conversion tool **MNNConvert**.\n\n\n\n## PS\n* Since MNN mainly accelerates model inference on mobile, so the INT8 quantified model will run slower on **PC** than FP32 model in CPU mode.\n* If you want to run faster, try using the version-slim model ,using lower-resolution inputs like 160x120 /128x96 or using quantified models(On the mobile).\n\n## Result\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/MNN/result.jpg)\n" }, { "path": "MNN/mnn/include/AutoTime.hpp", "content": "//\n// AutoTime.hpp\n// MNN\n//\n// Created by MNN on 2018/07/27.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef AutoTime_hpp\n#define AutoTime_hpp\n\n#include \n#include \n#include \"MNNDefine.h\"\n\nnamespace MNN {\n\n/** time tracing util. prints duration between init and deinit. */\nclass MNN_PUBLIC AutoTime {\npublic:\n AutoTime(int line, const char* func);\n ~AutoTime();\n AutoTime(const AutoTime&) = delete;\n AutoTime(const AutoTime&&) = delete;\n AutoTime& operator=(const AutoTime&) = delete;\n AutoTime& operator=(const AutoTime&&) = delete;\n\nprivate:\n int mLine;\n char* mName;\n uint64_t mCurrentTime;\n};\n} // namespace MNN\n\n#ifdef MNN_OPEN_TIME_TRACE\n#define AUTOTIME MNN::AutoTime ___t(__LINE__, __func__)\n#else\n#define AUTOTIME\n#endif\n\n#endif /* AutoTime_hpp */\n" }, { "path": "MNN/mnn/include/Backend.hpp", "content": "//\n// Backend.hpp\n// MNN\n//\n// Created by MNN on 2018/07/06.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef Backend_hpp\n#define Backend_hpp\n\n#include \n#include \n#include \n#include \n#include \"ErrorCode.hpp\"\n#include \"MNNForwardType.h\"\n#include \"NonCopyable.hpp\"\n#include \"Tensor.hpp\"\n\nnamespace MNN {\n\nstruct Op;\nstruct GpuLibrary;\nclass Execution;\n\n/** abstract backend */\nclass Backend : public NonCopyable {\npublic:\n /** info used to create backend */\n struct Info {\n /** forward type. */\n MNNForwardType type = MNN_FORWARD_CPU;\n /** for CPU only. number of threads. */\n int numThread = 4;\n /** user data. */\n BackendConfig* user = NULL;\n enum Mode {\n // The Op will be run in execution->onExecute\n DIRECT = 0,\n\n // The Op will be recorded. Run in onExecuteBegin and Wait in onExecuteEnd\n INDIRECT = 1\n };\n Mode mode = DIRECT;\n };\n\n /** backend buffer storage type */\n enum StorageType {\n /**\n use NOT reusable memory.\n - allocates memory when `onAcquireBuffer` is called.\n - releases memory when `onReleaseBuffer` is called or when the backend is deleted.\n - do NOTHING when `onClearBuffer` is called.\n */\n STATIC,\n /**\n use reusable memory.\n - allocates or reuses memory when `onAcquireBuffer` is called. prefers reusing.\n - collects memory for reuse when `onReleaseBuffer` is called.\n - releases memory when `onClearBuffer` is called or when the backend is deleted.\n */\n DYNAMIC,\n /**\n use NOT reusable memory.\n - allocates memory when `onAcquireBuffer` is called.\n - do NOTHING when `onReleaseBuffer` is called.\n - releases memory when `onClearBuffer` is called or when the backend is deleted.\n */\n DYNAMIC_SEPERATE\n };\n\npublic:\n /**\n * @brief initializer.\n * @param type forward type.\n */\n Backend(MNNForwardType type) : mType(type) {\n // nothing to do\n }\n\n /**\n * @brief deinitializer.\n */\n virtual ~Backend() = default;\n\npublic:\n /**\n * @brief measure the cost for op with input and output tensors.\n * @param inputs input tensors.\n * @param outputs output tensors.\n * @param op given op.\n * @return std::make_pair(timeDelayInMs, support);\n */\n virtual std::pair onMeasure(const std::vector& inputs, const std::vector& outputs,\n const MNN::Op* op) {\n return std::make_pair(0.0f, false);\n }\n\n /**\n * @brief create execution for op with input and output tensors.\n * @param inputs input tensors.\n * @param outputs output tensors.\n * @param op given op.\n * @return created execution if op is supported, nullptr otherwise.\n */\n virtual Execution* onCreate(const std::vector& inputs, const std::vector& outputs,\n const MNN::Op* op) = 0;\n\n /**\n * @brief callback before resize ops.\n */\n virtual void onResizeBegin() {\n // nothing to do\n }\n /**\n * @brief callback after resize ops.\n */\n virtual void onResizeEnd() {\n // nothing to do\n }\n\n /**\n * @brief callback before executing ops.\n */\n virtual void onExecuteBegin() const = 0;\n /**\n * @brief callback after executing ops.\n */\n virtual void onExecuteEnd() const = 0;\n /**\n * @brief wait for all async execution to be finished.\n * @return success or not.\n */\n virtual bool onWaitFinish() {\n return true;\n }\n /**\n * @brief load GPU library resource.\n * @param library loading load GPU library.\n * @return success or not.\n */\n virtual bool onLoadLibrary(const GpuLibrary* library) {\n return false;\n }\n\npublic:\n /**\n * @brief allocate buffer of tensor for given storage type.\n * @param tensor buffer provider.\n * @param storageType buffer storage type.\n * @return success or not.\n */\n virtual bool onAcquireBuffer(const Tensor* tensor, StorageType storageType) = 0;\n\n /**\n * @brief release buffer of tensor for given storage type.\n * @param tensor buffer provider.\n * @param storageType buffer storage type.\n * @return success or not.\n */\n virtual bool onReleaseBuffer(const Tensor* tensor, StorageType storageType) = 0;\n\n /**\n * @brief callback after all buffers needed by backend ops were allocated.\n * @return success or not. (result not used currently)\n */\n virtual bool onAllocateBuffer() {\n return true;\n }\n\n /**\n * @brief clear all dynamic buffers.\n * @return success or not.\n */\n virtual bool onClearBuffer() = 0;\n\n /**\n * @brief copy buffer from tensor to tensor.\n * @param srcTensor source buffer provider.\n * @param dstTensor dest buffer provider.\n */\n virtual void onCopyBuffer(const Tensor* srcTensor, const Tensor* dstTensor) const = 0;\n\npublic:\n /**\n * @brief get forward type.\n * @return forward type.\n */\n inline MNNForwardType type() const {\n return mType;\n }\n\nprivate:\n const MNNForwardType mType;\n};\n\n/** abstract backend register */\nclass BackendCreator {\npublic:\n /**\n @brief initializer.\n */\n virtual ~BackendCreator() = default;\n\n /**\n @brief create backend with given info.\n @param info info to create backend.\n @return created backend\n */\n virtual Backend* onCreate(const Backend::Info& info) const = 0;\n\n\n /**\n @brief Turn info to supported.\n @param info info to valid.\n @return success or not\n */\n virtual bool onValid(Backend::Info& info) const {\n info.mode = Backend::Info::DIRECT;\n return true;\n }\nprotected:\n /**\n @brief deinitializer.\n */\n BackendCreator() = default;\n};\n\n/**\n * @brief get registered backend creator for given forward type.\n * @param type given forward type.\n * @return backend creator pointer if registered, nullptr otherwise.\n */\nMNN_PUBLIC const BackendCreator* MNNGetExtraBackendCreator(MNNForwardType type);\n\n/**\n * @brief register backend creator for given forward type.\n * @param type given forward type.\n * @param creator registering backend creator.\n * @return true if backend creator for given forward type was not registered before, false otherwise.\n */\nMNN_PUBLIC bool MNNInsertExtraBackendCreator(MNNForwardType type, const BackendCreator* creator,\n bool needCheck = false);\n\n} // namespace MNN\n\n#endif /* Backend_hpp */\n" }, { "path": "MNN/mnn/include/ErrorCode.hpp", "content": "//\n// ErrorCode.hpp\n// MNN\n//\n// Created by MNN on 2018/09/18.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef ErrorCode_h\n#define ErrorCode_h\n\nnamespace MNN {\nenum ErrorCode {\n#ifdef NO_ERROR\n#undef NO_ERROR\n#endif // NO_ERROR\n NO_ERROR = 0,\n OUT_OF_MEMORY = 1,\n NOT_SUPPORT = 2,\n COMPUTE_SIZE_ERROR = 3,\n NO_EXECUTION = 4,\n INVALID_VALUE = 5,\n\n // User error\n INPUT_DATA_ERROR = 10,\n CALL_BACK_STOP = 11,\n\n // Op Resize Error\n TENSOR_NOT_SUPPORT = 20,\n TENSOR_NEED_DIVIDE = 21,\n};\n} // namespace MNN\n\n#endif /* ErrorCode_h */\n" }, { "path": "MNN/mnn/include/HalideRuntime.h", "content": "#ifndef HALIDE_HALIDERUNTIME_H\n#define HALIDE_HALIDERUNTIME_H\n\n#include \n#include \n#include \n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n// Note that you should not use \"inline\" along with HALIDE_ALWAYS_INLINE;\n// it is not necessary, and may produce warnings for some build configurations.\n#ifdef _MSC_VER\n#define HALIDE_ALWAYS_INLINE __forceinline\n#define HALIDE_NEVER_INLINE __declspec(noinline)\n#else\n#define HALIDE_ALWAYS_INLINE __attribute__((always_inline)) inline\n#define HALIDE_NEVER_INLINE __attribute__((noinline))\n#endif\n\n/** \\file\n *\n * This file declares the routines used by Halide internally in its\n * runtime. On platforms that support weak linking, these can be\n * replaced with user-defined versions by defining an extern \"C\"\n * function with the same name and signature.\n *\n * When doing Just In Time (JIT) compilation methods on the Func being\n * compiled must be called instead. The corresponding methods are\n * documented below.\n *\n * All of these functions take a \"void *user_context\" parameter as their\n * first argument; if the Halide kernel that calls back to any of these\n * functions has been compiled with the UserContext feature set on its Target,\n * then the value of that pointer passed from the code that calls the\n * Halide kernel is piped through to the function.\n *\n * Some of these are also useful to call when using the default\n * implementation. E.g. halide_shutdown_thread_pool.\n *\n * Note that even on platforms with weak linking, some linker setups\n * may not respect the override you provide. E.g. if the override is\n * in a shared library and the halide object files are linked directly\n * into the output, the builtin versions of the runtime functions will\n * be called. See your linker documentation for more details. On\n * Linux, LD_DYNAMIC_WEAK=1 may help.\n *\n */\n\n// Forward-declare to suppress warnings if compiling as C.\nstruct halide_buffer_t;\n\n/** Types in the halide type system. They can be ints, unsigned ints,\n * or floats (of various bit-widths), or a handle (which is always 64-bits).\n * Note that the int/uint/float values do not imply a specific bit width\n * (the bit width is expected to be encoded in a separate value).\n */\ntypedef enum halide_type_code_t\n{\n halide_type_int = 0, //!< signed integers\n halide_type_uint = 1, //!< unsigned integers\n halide_type_float = 2, //!< floating point numbers\n halide_type_handle = 3 //!< opaque pointer type (void *)\n} halide_type_code_t;\n\n// Note that while __attribute__ can go before or after the declaration,\n// __declspec apparently is only allowed before.\n#ifndef HALIDE_ATTRIBUTE_ALIGN\n #ifdef _MSC_VER\n #define HALIDE_ATTRIBUTE_ALIGN(x) __declspec(align(x))\n #else\n #define HALIDE_ATTRIBUTE_ALIGN(x) __attribute__((aligned(x)))\n #endif\n#endif\n\n/** A runtime tag for a type in the halide type system. Can be ints,\n * unsigned ints, or floats of various bit-widths (the 'bits'\n * field). Can also be vectors of the same (by setting the 'lanes'\n * field to something larger than one). This struct should be\n * exactly 32-bits in size. */\nstruct halide_type_t {\n /** The basic type code: signed integer, unsigned integer, or floating point. */\n#if __cplusplus >= 201103L\n HALIDE_ATTRIBUTE_ALIGN(1) halide_type_code_t code; // halide_type_code_t\n#else\n HALIDE_ATTRIBUTE_ALIGN(1) uint8_t code; // halide_type_code_t\n#endif\n\n /** The number of bits of precision of a single scalar value of this type. */\n HALIDE_ATTRIBUTE_ALIGN(1) uint8_t bits;\n\n /** How many elements in a vector. This is 1 for scalar types. */\n HALIDE_ATTRIBUTE_ALIGN(2) uint16_t lanes;\n\n#ifdef __cplusplus\n /** Construct a runtime representation of a Halide type from:\n * code: The fundamental type from an enum.\n * bits: The bit size of one element.\n * lanes: The number of vector elements in the type. */\n HALIDE_ALWAYS_INLINE halide_type_t(halide_type_code_t code, uint8_t bits, uint16_t lanes = 1)\n : code(code), bits(bits), lanes(lanes) {\n }\n\n /** Default constructor is required e.g. to declare halide_trace_event\n * instances. */\n HALIDE_ALWAYS_INLINE halide_type_t() : code((halide_type_code_t)0), bits(0), lanes(0) {}\n\n /** Compare two types for equality. */\n HALIDE_ALWAYS_INLINE bool operator==(const halide_type_t &other) const {\n return (code == other.code &&\n bits == other.bits &&\n lanes == other.lanes);\n }\n\n HALIDE_ALWAYS_INLINE bool operator!=(const halide_type_t &other) const {\n return !(*this == other);\n }\n\n /** Size in bytes for a single element, even if width is not 1, of this type. */\n HALIDE_ALWAYS_INLINE int bytes() const { return (bits + 7) / 8; }\n#endif\n};\n\n/** An opaque struct containing per-GPU API implementations of the\n * device functions. */\nstruct halide_device_interface_impl_t;\n\n/** Each GPU API provides a halide_device_interface_t struct pointing\n * to the code that manages device allocations. You can access these\n * functions directly from the struct member function pointers, or by\n * calling the functions declared below. Note that the global\n * functions are not available when using Halide as a JIT compiler.\n * If you are using raw halide_buffer_t in that context you must use\n * the function pointers in the device_interface struct.\n *\n * The function pointers below are currently the same for every GPU\n * API; only the impl field varies. These top-level functions do the\n * bookkeeping that is common across all GPU APIs, and then dispatch\n * to more API-specific functions via another set of function pointers\n * hidden inside the impl field.\n */\nstruct halide_device_interface_t {\n int (*device_malloc)(void *user_context, struct halide_buffer_t *buf,\n const struct halide_device_interface_t *device_interface);\n int (*device_free)(void *user_context, struct halide_buffer_t *buf);\n int (*device_sync)(void *user_context, struct halide_buffer_t *buf);\n void (*device_release)(void *user_context,\n const struct halide_device_interface_t *device_interface);\n int (*copy_to_host)(void *user_context, struct halide_buffer_t *buf);\n int (*copy_to_device)(void *user_context, struct halide_buffer_t *buf,\n const struct halide_device_interface_t *device_interface);\n int (*device_and_host_malloc)(void *user_context, struct halide_buffer_t *buf,\n const struct halide_device_interface_t *device_interface);\n int (*device_and_host_free)(void *user_context, struct halide_buffer_t *buf);\n int (*buffer_copy)(void *user_context, struct halide_buffer_t *src,\n const struct halide_device_interface_t *dst_device_interface, struct halide_buffer_t *dst);\n int (*device_crop)(void *user_context, const struct halide_buffer_t *src,\n struct halide_buffer_t *dst);\n int (*device_release_crop)(void *user_context, struct halide_buffer_t *buf);\n int (*wrap_native)(void *user_context, struct halide_buffer_t *buf, uint64_t handle,\n const struct halide_device_interface_t *device_interface);\n int (*detach_native)(void *user_context, struct halide_buffer_t *buf);\n const struct halide_device_interface_impl_t *impl;\n};\n\ntypedef struct halide_dimension_t {\n int32_t min, extent, stride;\n\n // Per-dimension flags. None are defined yet (This is reserved for future use).\n uint32_t flags;\n\n#ifdef __cplusplus\n HALIDE_ALWAYS_INLINE halide_dimension_t() : min(0), extent(0), stride(0), flags(0) {}\n HALIDE_ALWAYS_INLINE halide_dimension_t(int32_t m, int32_t e, int32_t s, uint32_t f = 0) :\n min(m), extent(e), stride(s), flags(f) {}\n\n HALIDE_ALWAYS_INLINE bool operator==(const halide_dimension_t &other) const {\n return (min == other.min) &&\n (extent == other.extent) &&\n (stride == other.stride) &&\n (flags == other.flags);\n }\n\n HALIDE_ALWAYS_INLINE bool operator!=(const halide_dimension_t &other) const {\n return !(*this == other);\n }\n#endif\n} halide_dimension_t;\n\n#ifdef __cplusplus\n} // extern \"C\"\n#endif\n\ntypedef enum {halide_buffer_flag_host_dirty = 1,\n halide_buffer_flag_device_dirty = 2} halide_buffer_flags;\n\n/**\n * The raw representation of an image passed around by generated\n * Halide code. It includes some stuff to track whether the image is\n * not actually in main memory, but instead on a device (like a\n * GPU). For a more convenient C++ wrapper, use Halide::Buffer. */\ntypedef struct halide_buffer_t {\n /** A device-handle for e.g. GPU memory used to back this buffer. */\n uint64_t device;\n\n /** The interface used to interpret the above handle. */\n const struct halide_device_interface_t *device_interface;\n\n /** A pointer to the start of the data in main memory. In terms of\n * the Halide coordinate system, this is the address of the min\n * coordinates (defined below). */\n uint8_t* host;\n\n /** flags with various meanings. */\n uint64_t flags;\n\n /** The type of each buffer element. */\n struct halide_type_t type;\n\n /** The dimensionality of the buffer. */\n int32_t dimensions;\n\n /** The shape of the buffer. Halide does not own this array - you\n * must manage the memory for it yourself. */\n halide_dimension_t *dim;\n\n /** Pads the buffer up to a multiple of 8 bytes */\n void *padding;\n} halide_buffer_t;\n\n\n#ifdef __cplusplus\n\nnamespace {\ntemplate struct check_is_pointer;\ntemplate struct check_is_pointer {};\n}\n\n/** Construct the halide equivalent of a C type */\ntemplate\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n // Create a compile-time error if T is not a pointer (without\n // using any includes - this code goes into the runtime).\n check_is_pointer check;\n (void)check;\n return halide_type_t(halide_type_handle, 64);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_float, 32);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_float, 64);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_uint, 1);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_uint, 8);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_uint, 16);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_uint, 32);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_uint, 64);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_int, 8);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_int, 16);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_int, 32);\n}\n\ntemplate<>\nHALIDE_ALWAYS_INLINE halide_type_t halide_type_of() {\n return halide_type_t(halide_type_int, 64);\n}\n\n#endif\n\n#endif // HALIDE_HALIDERUNTIME_H\n" }, { "path": "MNN/mnn/include/ImageProcess.hpp", "content": "//\n// ImageProcess.hpp\n// MNN\n//\n// Created by MNN on 2018/09/19.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef ImageProcess_hpp\n#define ImageProcess_hpp\n\n#include \"ErrorCode.hpp\"\n#include \"Matrix.h\"\n#include \"Tensor.hpp\"\n\nnamespace MNN {\nnamespace CV {\nenum ImageFormat {\n RGBA = 0,\n RGB,\n BGR,\n GRAY,\n BGRA,\n YUV_NV21 = 11,\n};\n\nenum Filter { NEAREST = 0, BILINEAR = 1, BICUBIC = 2 };\n\nenum Wrap { CLAMP_TO_EDGE = 0, ZERO = 1, REPEAT = 2 };\n\n/**\n * handle image process for tensor.\n * step:\n * 1: Do transform compute and get points\n * 2: Sample line and do format convert\n * 3: Turn RGBA to float tensor, and do sub and normalize\n */\nclass MNN_PUBLIC ImageProcess {\npublic:\n struct Inside;\n struct Config {\n /** data filter */\n Filter filterType = NEAREST;\n /** format of source data */\n ImageFormat sourceFormat = RGBA;\n /** format of destination data */\n ImageFormat destFormat = RGBA;\n\n // Only valid if the dest type is float\n float mean[4] = {0.0f, 0.0f, 0.0f, 0.0f};\n float normal[4] = {1.0f, 1.0f, 1.0f, 1.0f};\n\n /** edge wrapper */\n Wrap wrap = CLAMP_TO_EDGE;\n };\n\npublic:\n /**\n * @brief create image process with given config for given tensor.\n * @param config given config.\n * @param dstTensor given tensor.\n * @return image processor.\n */\n static ImageProcess* create(const Config& config, const Tensor* dstTensor = nullptr);\n\n /**\n * @brief create image process with given config for given tensor.\n * @param means given means\n * @param meanCount given means count\n * @param normals given normals\n * @param normalCount given normal count\n * @param sourceFormat format of source data\n * @param destFormat format of destination data\n * @param dstTensor given tensor.\n * @return image processor.\n */\n static ImageProcess* create(const ImageFormat sourceFormat = RGBA, const ImageFormat destFormat = RGBA,\n const float* means = nullptr, const int meanCount = 0, const float* normals = nullptr,\n const int normalCount = 0, const Tensor* dstTensor = nullptr);\n\n ~ImageProcess();\n\n /**\n * @brief get affine transform matrix.\n * @return affine transform matrix.\n */\n inline const Matrix& matrix() const {\n return mTransform;\n }\n void setMatrix(const Matrix& matrix);\n\n /**\n * @brief convert source data to given tensor.\n * @param source source data.\n * @param iw source width.\n * @param ih source height.\n * @param stride number of elements per row. eg: 100 width RGB contains at least 300 elements.\n * @param dest given tensor.\n * @return result code.\n */\n ErrorCode convert(const uint8_t* source, int iw, int ih, int stride, Tensor* dest);\n\n /**\n * @brief convert source data to given tensor.\n * @param source source data.\n * @param iw source width.\n * @param ih source height.\n * @param stride number of elements per row. eg: 100 width RGB contains at least 300 elements.\n * @param dest dest data.\n * @param ow output width.\n * @param oh output height.\n * @param outputBpp output bpp, if 0, set as the save and config.destFormat.\n * @param outputStride output stride, if 0, set as ow * outputBpp.\n * @param type Only support halide_type_of and halide_type_of.\n * @return result code.\n */\n ErrorCode convert(const uint8_t* source, int iw, int ih, int stride, void* dest, int ow, int oh, int outputBpp = 0,\n int outputStride = 0, halide_type_t type = halide_type_of());\n\n /**\n * @brief create tensor with given data.\n * @param w image width.\n * @param h image height.\n * @param bpp bytes per pixel.\n * @param p pixel data pointer.\n * @return created tensor.\n */\n template \n static Tensor* createImageTensor(int w, int h, int bpp, void* p = nullptr) {\n return createImageTensor(halide_type_of(), w, h, bpp, p);\n }\n static Tensor* createImageTensor(halide_type_t type, int w, int h, int bpp, void* p = nullptr);\n\nprivate:\n ImageProcess(const Config& config);\n Matrix mTransform;\n Matrix mTransformInvert;\n Inside* mInside;\n};\n} // namespace CV\n} // namespace MNN\n\n#endif /* ImageProcess_hpp */\n" }, { "path": "MNN/mnn/include/Interpreter.hpp", "content": "//\n// Interpreter.hpp\n// MNN\n//\n// Created by MNN on 2018/07/23.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef Interpreter_hpp\n#define Interpreter_hpp\n\n#include \n#include \n#include \n#include \"ErrorCode.hpp\"\n#include \"MNNForwardType.h\"\n#include \"Tensor.hpp\"\n\nnamespace MNN {\n\n/** session schedule config */\nstruct ScheduleConfig {\n /** which tensor should be kept */\n std::vector saveTensors;\n /** forward type */\n MNNForwardType type = MNN_FORWARD_CPU;\n /** number of threads in parallel */\n int numThread = 4;\n\n /** subpath to run */\n struct Path {\n std::vector inputs;\n std::vector outputs;\n\n enum Mode {\n /**\n * Op Mode\n * - inputs means the source op, can NOT be empty.\n * - outputs means the sink op, can be empty.\n * The path will start from source op, then flow when encounter the sink op.\n * The sink op will not be compute in this path.\n */\n Op = 0,\n\n /**\n * Tensor Mode (NOT supported yet)\n * - inputs means the inputs tensors, can NOT be empty.\n * - outputs means the outputs tensors, can NOT be empty.\n * It will find the pipeline that compute outputs from inputs.\n */\n Tensor = 1\n };\n\n /** running mode */\n Mode mode = Op;\n };\n Path path;\n\n /** backup backend used to create execution when desinated backend do NOT support any op */\n MNNForwardType backupType = MNN_FORWARD_CPU;\n\n /** extra backend config */\n BackendConfig* backendConfig = nullptr;\n};\n\nclass Session;\nstruct Content;\nclass Tensor;\nclass Backend;\n\nclass MNN_PUBLIC OperatorInfo {\n struct Info;\n\npublic:\n /** Operator's name*/\n const std::string& name() const;\n\n /** Operator's type*/\n const std::string& type() const;\n\n /** Operator's flops, in M*/\n float flops() const;\n\nprotected:\n OperatorInfo();\n ~OperatorInfo();\n Info* mContent;\n};\n\ntypedef std::function&, const std::string& /*opName*/)> TensorCallBack;\ntypedef std::function&, const OperatorInfo*)> TensorCallBackWithInfo;\n\n/** net data holder. multiple sessions could share same net. */\nclass MNN_PUBLIC Interpreter {\npublic:\n /**\n * @brief create net from file.\n * @param file given file.\n * @return created net if success, NULL otherwise.\n */\n static Interpreter* createFromFile(const char* file);\n /**\n * @brief create net from buffer.\n * @param buffer given data buffer.\n * @param size size of data buffer.\n * @return created net if success, NULL otherwise.\n */\n static Interpreter* createFromBuffer(const void* buffer, size_t size);\n ~Interpreter();\n\npublic:\n /**\n * @brief create session with schedule config. created session will be managed in net.\n * @param config session schedule config.\n * @return created session if success, NULL otherwise.\n */\n Session* createSession(const ScheduleConfig& config);\n\n /**\n * @brief create multi-path session with schedule configs. created session will be managed in net.\n * @param configs session schedule configs.\n * @return created session if success, NULL otherwise.\n */\n Session* createMultiPathSession(const std::vector& configs);\n\n /**\n * @brief release session.\n * @param session given session.\n * @return true if given session is held by net and is freed.\n */\n bool releaseSession(Session* session);\n\n /**\n * @brief call this function to get tensors ready. output tensor buffer (host or deviceId) should be retrieved\n * after resize of any input tensor.\n * @param session given session.\n */\n void resizeSession(Session* session);\n\n /**\n * @brief call this function if don't need resize or create session any more, it will save a few memory that equal\n * to the size of model buffer\n */\n void releaseModel();\n\n /**\n * @brief Get the model buffer for user to save\n * @return std::make_pair(modleBuffer, modelSize).\n * @example:\n * std::ofstream output(\"trainResult.alinn\")\n * auto buffer = net->getModelBuffer();\n * output.write((const char*)buffer.first, buffer.second);\n */\n std::pair getModelBuffer() const;\n\n /**\n * @brief update Session's Tensor to model's Const Op\n * @param session given session.\n * @return result of running.\n */\n ErrorCode updateSessionToModel(Session* session);\n\n /**\n * @brief run session.\n * @param session given session.\n * @return result of running.\n */\n ErrorCode runSession(Session* session) const;\n\n /*\n * @brief run session.\n * @param session given session.\n * @param before callback before each op. return true to run the op; return false to skip the op.\n * @param after callback after each op. return true to continue running; return false to interrupt the session.\n * @param sync synchronously wait for finish of execution or not.\n * @return result of running.\n */\n ErrorCode runSessionWithCallBack(const Session* session, const TensorCallBack& before, const TensorCallBack& end,\n bool sync = false) const;\n\n /*\n * @brief run session.\n * @param session given session.\n * @param before callback before each op. return true to run the op; return false to skip the op.\n * @param after callback after each op. return true to continue running; return false to interrupt the session.\n * @param sync synchronously wait for finish of execution or not.\n * @return result of running.\n */\n ErrorCode runSessionWithCallBackInfo(const Session* session, const TensorCallBackWithInfo& before,\n const TensorCallBackWithInfo& end, bool sync = false) const;\n\n /**\n * @brief get input tensor for given name.\n * @param session given session.\n * @param name given name. if NULL, return first input.\n * @return tensor if found, NULL otherwise.\n */\n Tensor* getSessionInput(const Session* session, const char* name);\n /**\n * @brief get output tensor for given name.\n * @param session given session.\n * @param name given name. if NULL, return first output.\n * @return tensor if found, NULL otherwise.\n */\n Tensor* getSessionOutput(const Session* session, const char* name);\n\n /**\n * @brief get all input tensors.\n * @param session given session.\n * @return all input tensors mapped with name.\n */\n const std::map& getSessionOutputAll(const Session* session) const;\n /**\n * @brief get all output tensors.\n * @param session given session.\n * @return all output tensors mapped with name.\n */\n const std::map& getSessionInputAll(const Session* session) const;\n\npublic:\n /**\n * @brief resize given tensor.\n * @param tensor given tensor.\n * @param dims new dims. at most 6 dims.\n */\n void resizeTensor(Tensor* tensor, const std::vector& dims);\n\n /**\n * @brief resize given tensor by nchw.\n * @param batch / N.\n * @param channel / C.\n * @param height / H.\n * @param width / W\n */\n void resizeTensor(Tensor* tensor, int batch, int channel, int height, int width);\n\n /**\n * @brief get backend used to create given tensor.\n * @param session given session.\n * @param tensor given tensor.\n * @return backend used to create given tensor, may be NULL.\n */\n const Backend* getBackend(const Session* session, const Tensor* tensor) const;\n\n /**\n * @brief get business code (model identifier).\n * @return business code.\n */\n const char* bizCode() const;\n\nprivate:\n static Interpreter* createFromBufferInternal(Content* net);\n\n Content* mNet = nullptr;\n Interpreter(Content* net);\n\n Interpreter(const Interpreter&) = delete;\n Interpreter(const Interpreter&&) = delete;\n Interpreter& operator=(const Interpreter&) = delete;\n Interpreter& operator=(const Interpreter&&) = delete;\n};\n} // namespace MNN\n\n#endif /* Interpreter_hpp */\n" }, { "path": "MNN/mnn/include/MNNDefine.h", "content": "//\n// MNNDefine.h\n// MNN\n//\n// Created by MNN on 2018/08/09.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef MNNDefine_h\n#define MNNDefine_h\n\n#include \n#include \n\n#if defined(__APPLE__)\n#include \"TargetConditionals.h\"\n#if TARGET_OS_IPHONE\n#define MNN_BUILD_FOR_IOS\n#endif\n#endif\n\n#ifdef MNN_USE_LOGCAT\n#include \n#define MNN_ERROR(format, ...) __android_log_print(ANDROID_LOG_ERROR, \"MNNJNI\", format, ##__VA_ARGS__)\n#define MNN_PRINT(format, ...) __android_log_print(ANDROID_LOG_INFO, \"MNNJNI\", format, ##__VA_ARGS__)\n#else\n#define MNN_PRINT(format, ...) printf(format, ##__VA_ARGS__)\n#define MNN_ERROR(format, ...) printf(format, ##__VA_ARGS__)\n#endif\n\n#ifdef DEBUG\n#define MNN_ASSERT(x) \\\n { \\\n int res = (x); \\\n if (!res) { \\\n MNN_ERROR(\"Error for %s, %d\\n\", __FILE__, __LINE__); \\\n assert(res); \\\n } \\\n }\n#else\n#define MNN_ASSERT(x) \\\n { \\\n int res = (x); \\\n if (!res) { \\\n MNN_ERROR(\"Error for %s, %d\\n\", __FILE__, __LINE__); \\\n } \\\n }\n#endif\n\n#define FUNC_PRINT(x) MNN_PRINT(#x \"=%d in %s, %d \\n\", x, __func__, __LINE__);\n#define FUNC_PRINT_ALL(x, type) MNN_PRINT(#x \"=\" #type \" %\" #type \" in %s, %d \\n\", x, __func__, __LINE__);\n\n#define MNN_CHECK(success, log) \\\nif(!(success)){ \\\nMNN_ERROR(\"Check failed: %s ==> %s\\n\", #success, #log); \\\n}\n\n#if defined(_MSC_VER)\n#if defined(BUILDING_MNN_DLL)\n#define MNN_PUBLIC __declspec(dllexport)\n#elif defined(USING_MNN_DLL)\n#define MNN_PUBLIC __declspec(dllimport)\n#else\n#define MNN_PUBLIC\n#endif\n#else\n#define MNN_PUBLIC __attribute__((visibility(\"default\")))\n#endif\n\n#endif /* MNNDefine_h */\n" }, { "path": "MNN/mnn/include/MNNForwardType.h", "content": "//\n// MNNForwardType.h\n// MNN\n//\n// Created by MNN on 2019/01/19.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef MNNForwardType_h\n#define MNNForwardType_h\n#include \n#include \n\ntypedef enum {\n MNN_FORWARD_CPU = 0,\n\n /*\n Firtly find the first available backends not equal to CPU\n If no other backends, use cpu\n */\n MNN_FORWARD_AUTO = 4,\n\n /*Hand write metal*/\n MNN_FORWARD_METAL = 1,\n\n /*Use IOS's MPS instead of hand-write metal, Not Support yet*/\n MNN_FORWARD_MPS = 2,\n\n /*Android / Common Device GPU API*/\n MNN_FORWARD_OPENCL = 3,\n MNN_FORWARD_OPENGL = 6,\n MNN_FORWARD_VULKAN = 7,\n\n /*Android 8.1's NNAPI, Not Support yet*/\n MNN_FORWARD_NN = 5,\n\n /*User can use API from Backend.hpp to add or search Backend*/\n MNN_FORWARD_USER_0 = 8,\n MNN_FORWARD_USER_1 = 9,\n MNN_FORWARD_USER_2 = 10,\n MNN_FORWARD_USER_3 = 11,\n\n MNN_FORWARD_ALL\n} MNNForwardType;\n#ifdef __cplusplus\nnamespace MNN {\nstruct BackendConfig {\n enum MemoryMode { Memory_Normal = 0, Memory_High, Memory_Low };\n\n MemoryMode memory = Memory_Normal;\n\n enum PowerMode { Power_Normal = 0, Power_High, Power_Low };\n\n PowerMode power = Power_Normal;\n\n enum PrecisionMode { Precision_Normal = 0, Precision_High, Precision_Low };\n\n PrecisionMode precision = Precision_Normal;\n\n /** user defined context */\n union {\n void* sharedContext = nullptr;\n size_t flags; // Valid for CPU Backend\n };\n};\n}; // namespace MNN\n#endif\n#endif /* MNNForwardType_h */\n" }, { "path": "MNN/mnn/include/MNNSharedContext.h", "content": "//\n// MNNSharedContext.h\n// MNN\n//\n// Created by MNN on 2018/10/11.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef MNNSharedContext_h\n#define MNNSharedContext_h\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n#include /*uint32_t*/\n\n#ifndef VK_DEFINE_HANDLE\n#define VK_DEFINE_HANDLE(object) typedef struct object##_T* object;\nVK_DEFINE_HANDLE(VkInstance)\nVK_DEFINE_HANDLE(VkPhysicalDevice)\nVK_DEFINE_HANDLE(VkDevice)\nVK_DEFINE_HANDLE(VkQueue)\n#endif\nstruct MNNVulkanContext {\n VkInstance pInstance;\n VkPhysicalDevice pPhysicalDevice;\n VkDevice pDevice;\n VkQueue pQueue;\n uint32_t iQueueFamilyIndex;\n};\n#ifdef __cplusplus\n}\n#endif\n\n#endif /* MNNSharedContext_h */\n" }, { "path": "MNN/mnn/include/Matrix.h", "content": "/*\n * Copyright 2006 The Android Open Source Project\n *\n * Use of this source code is governed by a BSD-style license that can be\n * found in the LICENSE file.\n */\n\n/* Generated by tools/bookmaker from include/core/Matrix.h and docs/SkMatrix_Reference.bmh\n on 2018-07-13 08:15:11. Additional documentation and examples can be found at:\n https://skia.org/user/api/SkMatrix_Reference\n\n You may edit either file directly. Structural changes to public interfaces require\n editing both files. After editing docs/SkMatrix_Reference.bmh, run:\n bookmaker -b docs -i include/core/Matrix.h -p\n to create an updated version of this file.\n */\n\n\n//\n// Modified by jiangxiaotang on 2018/09/19.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef SkMatrix_DEFINED\n#define SkMatrix_DEFINED\n\n#include \n#include \n#include \"Rect.h\"\n\nnamespace MNN {\nnamespace CV {\n\n/** \\class Matrix\n Matrix holds a 3x3 matrix for transforming coordinates. This allows mapping\n Point and vectors with translation, scaling, skewing, rotation, and\n perspective.\n\n Matrix elements are in row major order. Matrix does not have a constructor,\n so it must be explicitly initialized. setIdentity() initializes Matrix\n so it has no effect. setTranslate(), setScale(), setSkew(), setRotate(), set9 and setAll()\n initializes all Matrix elements with the corresponding mapping.\n\n Matrix includes a hidden variable that classifies the type of matrix to\n improve performance. Matrix is not thread safe unless getType() is called first.\n*/\n\nclass MNN_PUBLIC Matrix {\npublic:\n Matrix() {\n setIdentity();\n }\n\n /** Sets Matrix to scale by (sx, sy). Returned matrix is:\n\n | sx 0 0 |\n | 0 sy 0 |\n | 0 0 1 |\n\n @param sx horizontal scale factor\n @param sy vertical scale factor\n @return Matrix with scale\n */\n static Matrix MakeScale(float sx, float sy) {\n Matrix m;\n m.setScale(sx, sy);\n return m;\n }\n\n /** Sets Matrix to scale by (scale, scale). Returned matrix is:\n\n | scale 0 0 |\n | 0 scale 0 |\n | 0 0 1 |\n\n @param scale horizontal and vertical scale factor\n @return Matrix with scale\n */\n static Matrix MakeScale(float scale) {\n Matrix m;\n m.setScale(scale, scale);\n return m;\n }\n\n /** Sets Matrix to translate by (dx, dy). Returned matrix is:\n\n | 1 0 dx |\n | 0 1 dy |\n | 0 0 1 |\n\n @param dx horizontal translation\n @param dy vertical translation\n @return Matrix with translation\n */\n static Matrix MakeTrans(float dx, float dy) {\n Matrix m;\n m.setTranslate(dx, dy);\n return m;\n }\n\n /** Sets Matrix to:\n\n | scaleX skewX transX |\n | skewY scaleY transY |\n | pers0 pers1 pers2 |\n\n @param scaleX horizontal scale factor\n @param skewX horizontal skew factor\n @param transX horizontal translation\n @param skewY vertical skew factor\n @param scaleY vertical scale factor\n @param transY vertical translation\n @param pers0 input x-axis perspective factor\n @param pers1 input y-axis perspective factor\n @param pers2 perspective scale factor\n @return Matrix constructed from parameters\n */\n static Matrix MakeAll(float scaleX, float skewX, float transX, float skewY, float scaleY, float transY, float pers0,\n float pers1, float pers2) {\n Matrix m;\n m.setAll(scaleX, skewX, transX, skewY, scaleY, transY, pers0, pers1, pers2);\n return m;\n }\n\n /** \\enum Matrix::TypeMask\n Enum of bit fields for mask returned by getType().\n Used to identify the complexity of Matrix, to optimize performance.\n */\n enum TypeMask {\n kIdentity_Mask = 0, //!< identity Matrix; all bits clear\n kTranslate_Mask = 0x01, //!< translation Matrix\n kScale_Mask = 0x02, //!< scale Matrix\n kAffine_Mask = 0x04, //!< skew or rotate Matrix\n kPerspective_Mask = 0x08, //!< perspective Matrix\n };\n\n /** Returns a bit field describing the transformations the matrix may\n perform. The bit field is computed conservatively, so it may include\n false positives. For example, when kPerspective_Mask is set, all\n other bits are set.\n\n @return kIdentity_Mask, or combinations of: kTranslate_Mask, kScale_Mask,\n kAffine_Mask, kPerspective_Mask\n */\n TypeMask getType() const {\n if (fTypeMask & kUnknown_Mask) {\n fTypeMask = this->computeTypeMask();\n }\n // only return the public masks\n return (TypeMask)(fTypeMask & 0xF);\n }\n\n /** Returns true if Matrix is identity. Identity matrix is:\n\n | 1 0 0 |\n | 0 1 0 |\n | 0 0 1 |\n\n @return true if Matrix has no effect\n */\n bool isIdentity() const {\n return this->getType() == 0;\n }\n\n /** Returns true if Matrix at most scales and translates. Matrix may be identity,\n contain only scale elements, only translate elements, or both. Matrix form is:\n\n | scale-x 0 translate-x |\n | 0 scale-y translate-y |\n | 0 0 1 |\n\n @return true if Matrix is identity; or scales, translates, or both\n */\n bool isScaleTranslate() const {\n return !(this->getType() & ~(kScale_Mask | kTranslate_Mask));\n }\n\n /** Returns true if Matrix is identity, or translates. Matrix form is:\n\n | 1 0 translate-x |\n | 0 1 translate-y |\n | 0 0 1 |\n\n @return true if Matrix is identity, or translates\n */\n bool isTranslate() const {\n return !(this->getType() & ~(kTranslate_Mask));\n }\n\n /** Returns true Matrix maps Rect to another Rect. If true, Matrix is identity,\n or scales, or rotates a multiple of 90 degrees, or mirrors on axes. In all\n cases, Matrix may also have translation. Matrix form is either:\n\n | scale-x 0 translate-x |\n | 0 scale-y translate-y |\n | 0 0 1 |\n\n or\n\n | 0 rotate-x translate-x |\n | rotate-y 0 translate-y |\n | 0 0 1 |\n\n for non-zero values of scale-x, scale-y, rotate-x, and rotate-y.\n\n Also called preservesAxisAlignment(); use the one that provides better inline\n documentation.\n\n @return true if Matrix maps one Rect into another\n */\n bool rectStaysRect() const {\n if (fTypeMask & kUnknown_Mask) {\n fTypeMask = this->computeTypeMask();\n }\n return (fTypeMask & kRectStaysRect_Mask) != 0;\n }\n\n /** Returns true Matrix maps Rect to another Rect. If true, Matrix is identity,\n or scales, or rotates a multiple of 90 degrees, or mirrors on axes. In all\n cases, Matrix may also have translation. Matrix form is either:\n\n | scale-x 0 translate-x |\n | 0 scale-y translate-y |\n | 0 0 1 |\n\n or\n\n | 0 rotate-x translate-x |\n | rotate-y 0 translate-y |\n | 0 0 1 |\n\n for non-zero values of scale-x, scale-y, rotate-x, and rotate-y.\n\n Also called rectStaysRect(); use the one that provides better inline\n documentation.\n\n @return true if Matrix maps one Rect into another\n */\n bool preservesAxisAlignment() const {\n return this->rectStaysRect();\n }\n\n /** Matrix organizes its values in row order. These members correspond to\n each value in Matrix.\n */\n static constexpr int kMScaleX = 0; //!< horizontal scale factor\n static constexpr int kMSkewX = 1; //!< horizontal skew factor\n static constexpr int kMTransX = 2; //!< horizontal translation\n static constexpr int kMSkewY = 3; //!< vertical skew factor\n static constexpr int kMScaleY = 4; //!< vertical scale factor\n static constexpr int kMTransY = 5; //!< vertical translation\n static constexpr int kMPersp0 = 6; //!< input x perspective factor\n static constexpr int kMPersp1 = 7; //!< input y perspective factor\n static constexpr int kMPersp2 = 8; //!< perspective bias\n\n /** Affine arrays are in column major order to match the matrix used by\n PDF and XPS.\n */\n static constexpr int kAScaleX = 0; //!< horizontal scale factor\n static constexpr int kASkewY = 1; //!< vertical skew factor\n static constexpr int kASkewX = 2; //!< horizontal skew factor\n static constexpr int kAScaleY = 3; //!< vertical scale factor\n static constexpr int kATransX = 4; //!< horizontal translation\n static constexpr int kATransY = 5; //!< vertical translation\n\n /** Returns one matrix value. Asserts if index is out of range and SK_DEBUG is\n defined.\n\n @param index one of: kMScaleX, kMSkewX, kMTransX, kMSkewY, kMScaleY, kMTransY,\n kMPersp0, kMPersp1, kMPersp2\n @return value corresponding to index\n */\n float operator[](int index) const {\n MNN_ASSERT((unsigned)index < 9);\n return fMat[index];\n }\n\n /** Returns one matrix value. Asserts if index is out of range and SK_DEBUG is\n defined.\n\n @param index one of: kMScaleX, kMSkewX, kMTransX, kMSkewY, kMScaleY, kMTransY,\n kMPersp0, kMPersp1, kMPersp2\n @return value corresponding to index\n */\n float get(int index) const {\n MNN_ASSERT((unsigned)index < 9);\n return fMat[index];\n }\n\n /** Returns scale factor multiplied by x-axis input, contributing to x-axis output.\n With mapPoints(), scales Point along the x-axis.\n\n @return horizontal scale factor\n */\n float getScaleX() const {\n return fMat[kMScaleX];\n }\n\n /** Returns scale factor multiplied by y-axis input, contributing to y-axis output.\n With mapPoints(), scales Point along the y-axis.\n\n @return vertical scale factor\n */\n float getScaleY() const {\n return fMat[kMScaleY];\n }\n\n /** Returns scale factor multiplied by x-axis input, contributing to y-axis output.\n With mapPoints(), skews Point along the y-axis.\n Skewing both axes can rotate Point.\n\n @return vertical skew factor\n */\n float getSkewY() const {\n return fMat[kMSkewY];\n }\n\n /** Returns scale factor multiplied by y-axis input, contributing to x-axis output.\n With mapPoints(), skews Point along the x-axis.\n Skewing both axes can rotate Point.\n\n @return horizontal scale factor\n */\n float getSkewX() const {\n return fMat[kMSkewX];\n }\n\n /** Returns translation contributing to x-axis output.\n With mapPoints(), moves Point along the x-axis.\n\n @return horizontal translation factor\n */\n float getTranslateX() const {\n return fMat[kMTransX];\n }\n\n /** Returns translation contributing to y-axis output.\n With mapPoints(), moves Point along the y-axis.\n\n @return vertical translation factor\n */\n float getTranslateY() const {\n return fMat[kMTransY];\n }\n\n /** Returns factor scaling input x-axis relative to input y-axis.\n\n @return input x-axis perspective factor\n */\n float getPerspX() const {\n return fMat[kMPersp0];\n }\n\n /** Returns factor scaling input y-axis relative to input x-axis.\n\n @return input y-axis perspective factor\n */\n float getPerspY() const {\n return fMat[kMPersp1];\n }\n\n /** Returns writable Matrix value. Asserts if index is out of range and SK_DEBUG is\n defined. Clears internal cache anticipating that caller will change Matrix value.\n\n Next call to read Matrix state may recompute cache; subsequent writes to Matrix\n value must be followed by dirtyMatrixTypeCache().\n\n @param index one of: kMScaleX, kMSkewX, kMTransX, kMSkewY, kMScaleY, kMTransY,\n kMPersp0, kMPersp1, kMPersp2\n @return writable value corresponding to index\n */\n float& operator[](int index) {\n MNN_ASSERT((unsigned)index < 9);\n this->setTypeMask(kUnknown_Mask);\n return fMat[index];\n }\n\n /** Sets Matrix value. Asserts if index is out of range and SK_DEBUG is\n defined. Safer than operator[]; internal cache is always maintained.\n\n @param index one of: kMScaleX, kMSkewX, kMTransX, kMSkewY, kMScaleY, kMTransY,\n kMPersp0, kMPersp1, kMPersp2\n @param value scalar to store in Matrix\n */\n void set(int index, float value) {\n MNN_ASSERT((unsigned)index < 9);\n fMat[index] = value;\n this->setTypeMask(kUnknown_Mask);\n }\n\n /** Sets horizontal scale factor.\n\n @param v horizontal scale factor to store\n */\n void setScaleX(float v) {\n this->set(kMScaleX, v);\n }\n\n /** Sets vertical scale factor.\n\n @param v vertical scale factor to store\n */\n void setScaleY(float v) {\n this->set(kMScaleY, v);\n }\n\n /** Sets vertical skew factor.\n\n @param v vertical skew factor to store\n */\n void setSkewY(float v) {\n this->set(kMSkewY, v);\n }\n\n /** Sets horizontal skew factor.\n\n @param v horizontal skew factor to store\n */\n void setSkewX(float v) {\n this->set(kMSkewX, v);\n }\n\n /** Sets horizontal translation.\n\n @param v horizontal translation to store\n */\n void setTranslateX(float v) {\n this->set(kMTransX, v);\n }\n\n /** Sets vertical translation.\n\n @param v vertical translation to store\n */\n void setTranslateY(float v) {\n this->set(kMTransY, v);\n }\n\n /** Sets input x-axis perspective factor, which causes mapXY() to vary input x-axis values\n inversely proportional to input y-axis values.\n\n @param v perspective factor\n */\n void setPerspX(float v) {\n this->set(kMPersp0, v);\n }\n\n /** Sets input y-axis perspective factor, which causes mapXY() to vary input y-axis values\n inversely proportional to input x-axis values.\n\n @param v perspective factor\n */\n void setPerspY(float v) {\n this->set(kMPersp1, v);\n }\n\n /** Sets all values from parameters. Sets matrix to:\n\n | scaleX skewX transX |\n | skewY scaleY transY |\n | persp0 persp1 persp2 |\n\n @param scaleX horizontal scale factor to store\n @param skewX horizontal skew factor to store\n @param transX horizontal translation to store\n @param skewY vertical skew factor to store\n @param scaleY vertical scale factor to store\n @param transY vertical translation to store\n @param persp0 input x-axis values perspective factor to store\n @param persp1 input y-axis values perspective factor to store\n @param persp2 perspective scale factor to store\n */\n void setAll(float scaleX, float skewX, float transX, float skewY, float scaleY, float transY, float persp0,\n float persp1, float persp2) {\n fMat[kMScaleX] = scaleX;\n fMat[kMSkewX] = skewX;\n fMat[kMTransX] = transX;\n fMat[kMSkewY] = skewY;\n fMat[kMScaleY] = scaleY;\n fMat[kMTransY] = transY;\n fMat[kMPersp0] = persp0;\n fMat[kMPersp1] = persp1;\n fMat[kMPersp2] = persp2;\n this->setTypeMask(kUnknown_Mask);\n }\n\n /** Copies nine scalar values contained by Matrix into buffer, in member value\n ascending order: kMScaleX, kMSkewX, kMTransX, kMSkewY, kMScaleY, kMTransY,\n kMPersp0, kMPersp1, kMPersp2.\n\n @param buffer storage for nine scalar values\n */\n void get9(float buffer[9]) const {\n memcpy(buffer, fMat, 9 * sizeof(float));\n }\n\n /** Sets Matrix to nine scalar values in buffer, in member value ascending order:\n kMScaleX, kMSkewX, kMTransX, kMSkewY, kMScaleY, kMTransY, kMPersp0, kMPersp1,\n kMPersp2.\n\n Sets matrix to:\n\n | buffer[0] buffer[1] buffer[2] |\n | buffer[3] buffer[4] buffer[5] |\n | buffer[6] buffer[7] buffer[8] |\n\n In the future, set9 followed by get9 may not return the same values. Since Matrix\n maps non-homogeneous coordinates, scaling all nine values produces an equivalent\n transformation, possibly improving precision.\n\n @param buffer nine scalar values\n */\n void set9(const float buffer[9]);\n\n /** Sets Matrix to identity; which has no effect on mapped Point. Sets Matrix to:\n\n | 1 0 0 |\n | 0 1 0 |\n | 0 0 1 |\n\n Also called setIdentity(); use the one that provides better inline\n documentation.\n */\n void reset();\n\n /** Sets Matrix to identity; which has no effect on mapped Point. Sets Matrix to:\n\n | 1 0 0 |\n | 0 1 0 |\n | 0 0 1 |\n\n Also called reset(); use the one that provides better inline\n documentation.\n */\n void setIdentity() {\n this->reset();\n }\n\n /** Sets Matrix to translate by (dx, dy).\n\n @param dx horizontal translation\n @param dy vertical translation\n */\n void setTranslate(float dx, float dy);\n\n /** Sets Matrix to scale by sx and sy, about a pivot point at (px, py).\n The pivot point is unchanged when mapped with Matrix.\n\n @param sx horizontal scale factor\n @param sy vertical scale factor\n @param px pivot x\n @param py pivot y\n */\n void setScale(float sx, float sy, float px, float py);\n\n /** Sets Matrix to scale by sx and sy about at pivot point at (0, 0).\n\n @param sx horizontal scale factor\n @param sy vertical scale factor\n */\n void setScale(float sx, float sy);\n\n /** Sets Matrix to rotate by degrees about a pivot point at (px, py).\n The pivot point is unchanged when mapped with Matrix.\n\n Positive degrees rotates clockwise.\n\n @param degrees angle of axes relative to upright axes\n @param px pivot x\n @param py pivot y\n */\n void setRotate(float degrees, float px, float py);\n\n /** Sets Matrix to rotate by degrees about a pivot point at (0, 0).\n Positive degrees rotates clockwise.\n\n @param degrees angle of axes relative to upright axes\n */\n void setRotate(float degrees);\n\n /** Sets Matrix to rotate by sinValue and cosValue, about a pivot point at (px, py).\n The pivot point is unchanged when mapped with Matrix.\n\n Vector (sinValue, cosValue) describes the angle of rotation relative to (0, 1).\n Vector length specifies scale.\n\n @param sinValue rotation vector x-axis component\n @param cosValue rotation vector y-axis component\n @param px pivot x-axis\n @param py pivot y-axis\n */\n void setSinCos(float sinValue, float cosValue, float px, float py);\n\n /** Sets Matrix to rotate by sinValue and cosValue, about a pivot point at (0, 0).\n\n Vector (sinValue, cosValue) describes the angle of rotation relative to (0, 1).\n Vector length specifies scale.\n\n @param sinValue rotation vector x-axis component\n @param cosValue rotation vector y-axis component\n */\n void setSinCos(float sinValue, float cosValue);\n\n /** Sets Matrix to skew by kx and ky, about a pivot point at (px, py).\n The pivot point is unchanged when mapped with Matrix.\n\n @param kx horizontal skew factor\n @param ky vertical skew factor\n @param px pivot x\n @param py pivot y\n */\n void setSkew(float kx, float ky, float px, float py);\n\n /** Sets Matrix to skew by kx and ky, about a pivot point at (0, 0).\n\n @param kx horizontal skew factor\n @param ky vertical skew factor\n */\n void setSkew(float kx, float ky);\n\n /** Sets Matrix to Matrix a multiplied by Matrix b. Either a or b may be this.\n\n Given:\n\n | A B C | | J K L |\n a = | D E F |, b = | M N O |\n | G H I | | P Q R |\n\n sets Matrix to:\n\n | A B C | | J K L | | AJ+BM+CP AK+BN+CQ AL+BO+CR |\n a * b = | D E F | * | M N O | = | DJ+EM+FP DK+EN+FQ DL+EO+FR |\n | G H I | | P Q R | | GJ+HM+IP GK+HN+IQ GL+HO+IR |\n\n @param a Matrix on left side of multiply expression\n @param b Matrix on right side of multiply expression\n */\n void setConcat(const Matrix& a, const Matrix& b);\n\n /** Sets Matrix to Matrix multiplied by Matrix constructed from translation (dx, dy).\n This can be thought of as moving the point to be mapped before applying Matrix.\n\n Given:\n\n | A B C | | 1 0 dx |\n Matrix = | D E F |, T(dx, dy) = | 0 1 dy |\n | G H I | | 0 0 1 |\n\n sets Matrix to:\n\n | A B C | | 1 0 dx | | A B A*dx+B*dy+C |\n Matrix * T(dx, dy) = | D E F | | 0 1 dy | = | D E D*dx+E*dy+F |\n | G H I | | 0 0 1 | | G H G*dx+H*dy+I |\n\n @param dx x-axis translation before applying Matrix\n @param dy y-axis translation before applying Matrix\n */\n void preTranslate(float dx, float dy);\n\n /** Sets Matrix to Matrix multiplied by Matrix constructed from scaling by (sx, sy)\n about pivot point (px, py).\n This can be thought of as scaling about a pivot point before applying Matrix.\n\n Given:\n\n | A B C | | sx 0 dx |\n Matrix = | D E F |, S(sx, sy, px, py) = | 0 sy dy |\n | G H I | | 0 0 1 |\n\n where\n\n dx = px - sx * px\n dy = py - sy * py\n\n sets Matrix to:\n\n | A B C | | sx 0 dx | | A*sx B*sy A*dx+B*dy+C |\n Matrix * S(sx, sy, px, py) = | D E F | | 0 sy dy | = | D*sx E*sy D*dx+E*dy+F |\n | G H I | | 0 0 1 | | G*sx H*sy G*dx+H*dy+I |\n\n @param sx horizontal scale factor\n @param sy vertical scale factor\n @param px pivot x\n @param py pivot y\n */\n void preScale(float sx, float sy, float px, float py);\n\n /** Sets Matrix to Matrix multiplied by Matrix constructed from scaling by (sx, sy)\n about pivot point (0, 0).\n This can be thought of as scaling about the origin before applying Matrix.\n\n Given:\n\n | A B C | | sx 0 0 |\n Matrix = | D E F |, S(sx, sy) = | 0 sy 0 |\n | G H I | | 0 0 1 |\n\n sets Matrix to:\n\n | A B C | | sx 0 0 | | A*sx B*sy C |\n Matrix * S(sx, sy) = | D E F | | 0 sy 0 | = | D*sx E*sy F |\n | G H I | | 0 0 1 | | G*sx H*sy I |\n\n @param sx horizontal scale factor\n @param sy vertical scale factor\n */\n void preScale(float sx, float sy);\n\n /** Sets Matrix to Matrix multiplied by Matrix constructed from rotating by degrees\n about pivot point (px, py).\n This can be thought of as rotating about a pivot point before applying Matrix.\n\n Positive degrees rotates clockwise.\n\n Given:\n\n | A B C | | c -s dx |\n Matrix = | D E F |, R(degrees, px, py) = | s c dy |\n | G H I | | 0 0 1 |\n\n where\n\n c = cos(degrees)\n s = sin(degrees)\n dx = s * py + (1 - c) * px\n dy = -s * px + (1 - c) * py\n\n sets Matrix to:\n\n | A B C | | c -s dx | | Ac+Bs -As+Bc A*dx+B*dy+C |\n Matrix * R(degrees, px, py) = | D E F | | s c dy | = | Dc+Es -Ds+Ec D*dx+E*dy+F |\n | G H I | | 0 0 1 | | Gc+Hs -Gs+Hc G*dx+H*dy+I |\n\n @param degrees angle of axes relative to upright axes\n @param px pivot x\n @param py pivot y\n */\n void preRotate(float degrees, float px, float py);\n\n /** Sets Matrix to Matrix multiplied by Matrix constructed from rotating by degrees\n about pivot point (0, 0).\n This can be thought of as rotating about the origin before applying Matrix.\n\n Positive degrees rotates clockwise.\n\n Given:\n\n | A B C | | c -s 0 |\n Matrix = | D E F |, R(degrees, px, py) = | s c 0 |\n | G H I | | 0 0 1 |\n\n where\n\n c = cos(degrees)\n s = sin(degrees)\n\n sets Matrix to:\n\n | A B C | | c -s 0 | | Ac+Bs -As+Bc C |\n Matrix * R(degrees, px, py) = | D E F | | s c 0 | = | Dc+Es -Ds+Ec F |\n | G H I | | 0 0 1 | | Gc+Hs -Gs+Hc I |\n\n @param degrees angle of axes relative to upright axes\n */\n void preRotate(float degrees);\n\n /** Sets Matrix to Matrix multiplied by Matrix constructed from skewing by (kx, ky)\n about pivot point (px, py).\n This can be thought of as skewing about a pivot point before applying Matrix.\n\n Given:\n\n | A B C | | 1 kx dx |\n Matrix = | D E F |, K(kx, ky, px, py) = | ky 1 dy |\n | G H I | | 0 0 1 |\n\n where\n\n dx = -kx * py\n dy = -ky * px\n\n sets Matrix to:\n\n | A B C | | 1 kx dx | | A+B*ky A*kx+B A*dx+B*dy+C |\n Matrix * K(kx, ky, px, py) = | D E F | | ky 1 dy | = | D+E*ky D*kx+E D*dx+E*dy+F |\n | G H I | | 0 0 1 | | G+H*ky G*kx+H G*dx+H*dy+I |\n\n @param kx horizontal skew factor\n @param ky vertical skew factor\n @param px pivot x\n @param py pivot y\n */\n void preSkew(float kx, float ky, float px, float py);\n\n /** Sets Matrix to Matrix multiplied by Matrix constructed from skewing by (kx, ky)\n about pivot point (0, 0).\n This can be thought of as skewing about the origin before applying Matrix.\n\n Given:\n\n | A B C | | 1 kx 0 |\n Matrix = | D E F |, K(kx, ky) = | ky 1 0 |\n | G H I | | 0 0 1 |\n\n sets Matrix to:\n\n | A B C | | 1 kx 0 | | A+B*ky A*kx+B C |\n Matrix * K(kx, ky) = | D E F | | ky 1 0 | = | D+E*ky D*kx+E F |\n | G H I | | 0 0 1 | | G+H*ky G*kx+H I |\n\n @param kx horizontal skew factor\n @param ky vertical skew factor\n */\n void preSkew(float kx, float ky);\n\n /** Sets Matrix to Matrix multiplied by Matrix other.\n This can be thought of mapping by other before applying Matrix.\n\n Given:\n\n | A B C | | J K L |\n Matrix = | D E F |, other = | M N O |\n | G H I | | P Q R |\n\n sets Matrix to:\n\n | A B C | | J K L | | AJ+BM+CP AK+BN+CQ AL+BO+CR |\n Matrix * other = | D E F | * | M N O | = | DJ+EM+FP DK+EN+FQ DL+EO+FR |\n | G H I | | P Q R | | GJ+HM+IP GK+HN+IQ GL+HO+IR |\n\n @param other Matrix on right side of multiply expression\n */\n void preConcat(const Matrix& other);\n\n /** Sets Matrix to Matrix constructed from translation (dx, dy) multiplied by Matrix.\n This can be thought of as moving the point to be mapped after applying Matrix.\n\n Given:\n\n | J K L | | 1 0 dx |\n Matrix = | M N O |, T(dx, dy) = | 0 1 dy |\n | P Q R | | 0 0 1 |\n\n sets Matrix to:\n\n | 1 0 dx | | J K L | | J+dx*P K+dx*Q L+dx*R |\n T(dx, dy) * Matrix = | 0 1 dy | | M N O | = | M+dy*P N+dy*Q O+dy*R |\n | 0 0 1 | | P Q R | | P Q R |\n\n @param dx x-axis translation after applying Matrix\n @param dy y-axis translation after applying Matrix\n */\n void postTranslate(float dx, float dy);\n\n /** Sets Matrix to Matrix constructed from scaling by (sx, sy) about pivot point\n (px, py), multiplied by Matrix.\n This can be thought of as scaling about a pivot point after applying Matrix.\n\n Given:\n\n | J K L | | sx 0 dx |\n Matrix = | M N O |, S(sx, sy, px, py) = | 0 sy dy |\n | P Q R | | 0 0 1 |\n\n where\n\n dx = px - sx * px\n dy = py - sy * py\n\n sets Matrix to:\n\n | sx 0 dx | | J K L | | sx*J+dx*P sx*K+dx*Q sx*L+dx+R |\n S(sx, sy, px, py) * Matrix = | 0 sy dy | | M N O | = | sy*M+dy*P sy*N+dy*Q sy*O+dy*R |\n | 0 0 1 | | P Q R | | P Q R |\n\n @param sx horizontal scale factor\n @param sy vertical scale factor\n @param px pivot x\n @param py pivot y\n */\n void postScale(float sx, float sy, float px, float py);\n\n /** Sets Matrix to Matrix constructed from scaling by (sx, sy) about pivot point\n (0, 0), multiplied by Matrix.\n This can be thought of as scaling about the origin after applying Matrix.\n\n Given:\n\n | J K L | | sx 0 0 |\n Matrix = | M N O |, S(sx, sy) = | 0 sy 0 |\n | P Q R | | 0 0 1 |\n\n sets Matrix to:\n\n | sx 0 0 | | J K L | | sx*J sx*K sx*L |\n S(sx, sy) * Matrix = | 0 sy 0 | | M N O | = | sy*M sy*N sy*O |\n | 0 0 1 | | P Q R | | P Q R |\n\n @param sx horizontal scale factor\n @param sy vertical scale factor\n */\n void postScale(float sx, float sy);\n\n /** Sets Matrix to Matrix constructed from scaling by (1/divx, 1/divy) about pivot point (px, py), multiplied by\n Matrix.\n\n Returns false if either divx or divy is zero.\n\n Given:\n\n | J K L | | sx 0 0 |\n Matrix = | M N O |, I(divx, divy) = | 0 sy 0 |\n | P Q R | | 0 0 1 |\n\n where\n\n sx = 1 / divx\n sy = 1 / divy\n\n sets Matrix to:\n\n | sx 0 0 | | J K L | | sx*J sx*K sx*L |\n I(divx, divy) * Matrix = | 0 sy 0 | | M N O | = | sy*M sy*N sy*O |\n | 0 0 1 | | P Q R | | P Q R |\n\n @param divx integer divisor for inverse scale in x\n @param divy integer divisor for inverse scale in y\n @return true on successful scale\n */\n bool postIDiv(int divx, int divy);\n\n /** Sets Matrix to Matrix constructed from rotating by degrees about pivot point\n (px, py), multiplied by Matrix.\n This can be thought of as rotating about a pivot point after applying Matrix.\n\n Positive degrees rotates clockwise.\n\n Given:\n\n | J K L | | c -s dx |\n Matrix = | M N O |, R(degrees, px, py) = | s c dy |\n | P Q R | | 0 0 1 |\n\n where\n\n c = cos(degrees)\n s = sin(degrees)\n dx = s * py + (1 - c) * px\n dy = -s * px + (1 - c) * py\n\n sets Matrix to:\n\n |c -s dx| |J K L| |cJ-sM+dx*P cK-sN+dx*Q cL-sO+dx+R|\n R(degrees, px, py) * Matrix = |s c dy| |M N O| = |sJ+cM+dy*P sK+cN+dy*Q sL+cO+dy*R|\n |0 0 1| |P Q R| | P Q R|\n\n @param degrees angle of axes relative to upright axes\n @param px pivot x\n @param py pivot y\n */\n void postRotate(float degrees, float px, float py);\n\n /** Sets Matrix to Matrix constructed from rotating by degrees about pivot point\n (0, 0), multiplied by Matrix.\n This can be thought of as rotating about the origin after applying Matrix.\n\n Positive degrees rotates clockwise.\n\n Given:\n\n | J K L | | c -s 0 |\n Matrix = | M N O |, R(degrees, px, py) = | s c 0 |\n | P Q R | | 0 0 1 |\n\n where\n\n c = cos(degrees)\n s = sin(degrees)\n\n sets Matrix to:\n\n | c -s dx | | J K L | | cJ-sM cK-sN cL-sO |\n R(degrees, px, py) * Matrix = | s c dy | | M N O | = | sJ+cM sK+cN sL+cO |\n | 0 0 1 | | P Q R | | P Q R |\n\n @param degrees angle of axes relative to upright axes\n */\n void postRotate(float degrees);\n\n /** Sets Matrix to Matrix constructed from skewing by (kx, ky) about pivot point\n (px, py), multiplied by Matrix.\n This can be thought of as skewing about a pivot point after applying Matrix.\n\n Given:\n\n | J K L | | 1 kx dx |\n Matrix = | M N O |, K(kx, ky, px, py) = | ky 1 dy |\n | P Q R | | 0 0 1 |\n\n where\n\n dx = -kx * py\n dy = -ky * px\n\n sets Matrix to:\n\n | 1 kx dx| |J K L| |J+kx*M+dx*P K+kx*N+dx*Q L+kx*O+dx+R|\n K(kx, ky, px, py) * Matrix = |ky 1 dy| |M N O| = |ky*J+M+dy*P ky*K+N+dy*Q ky*L+O+dy*R|\n | 0 0 1| |P Q R| | P Q R|\n\n @param kx horizontal skew factor\n @param ky vertical skew factor\n @param px pivot x\n @param py pivot y\n */\n void postSkew(float kx, float ky, float px, float py);\n\n /** Sets Matrix to Matrix constructed from skewing by (kx, ky) about pivot point\n (0, 0), multiplied by Matrix.\n This can be thought of as skewing about the origin after applying Matrix.\n\n Given:\n\n | J K L | | 1 kx 0 |\n Matrix = | M N O |, K(kx, ky) = | ky 1 0 |\n | P Q R | | 0 0 1 |\n\n sets Matrix to:\n\n | 1 kx 0 | | J K L | | J+kx*M K+kx*N L+kx*O |\n K(kx, ky) * Matrix = | ky 1 0 | | M N O | = | ky*J+M ky*K+N ky*L+O |\n | 0 0 1 | | P Q R | | P Q R |\n\n @param kx horizontal skew factor\n @param ky vertical skew factor\n */\n void postSkew(float kx, float ky);\n\n /** Sets Matrix to Matrix other multiplied by Matrix.\n This can be thought of mapping by other after applying Matrix.\n\n Given:\n\n | J K L | | A B C |\n Matrix = | M N O |, other = | D E F |\n | P Q R | | G H I |\n\n sets Matrix to:\n\n | A B C | | J K L | | AJ+BM+CP AK+BN+CQ AL+BO+CR |\n other * Matrix = | D E F | * | M N O | = | DJ+EM+FP DK+EN+FQ DL+EO+FR |\n | G H I | | P Q R | | GJ+HM+IP GK+HN+IQ GL+HO+IR |\n\n @param other Matrix on left side of multiply expression\n */\n void postConcat(const Matrix& other);\n\n /** \\enum Matrix::ScaleToFit\n ScaleToFit describes how Matrix is constructed to map one Rect to another.\n ScaleToFit may allow Matrix to have unequal horizontal and vertical scaling,\n or may restrict Matrix to square scaling. If restricted, ScaleToFit specifies\n how Matrix maps to the side or center of the destination Rect.\n */\n enum ScaleToFit {\n kFill_ScaleToFit, //!< scales in x and y to fill destination Rect\n kStart_ScaleToFit, //!< scales and aligns to left and top\n kCenter_ScaleToFit, //!< scales and aligns to center\n kEnd_ScaleToFit, //!< scales and aligns to right and bottom\n };\n\n /** Sets Matrix to scale and translate src Rect to dst Rect. stf selects whether\n mapping completely fills dst or preserves the aspect ratio, and how to align\n src within dst. Returns false if src is empty, and sets Matrix to identity.\n Returns true if dst is empty, and sets Matrix to:\n\n | 0 0 0 |\n | 0 0 0 |\n | 0 0 1 |\n\n @param src Rect to map from\n @param dst Rect to map to\n @param stf one of: kFill_ScaleToFit, kStart_ScaleToFit,\n kCenter_ScaleToFit, kEnd_ScaleToFit\n @return true if Matrix can represent Rect mapping\n */\n bool setRectToRect(const Rect& src, const Rect& dst, ScaleToFit stf);\n\n /** Returns Matrix set to scale and translate src Rect to dst Rect. stf selects\n whether mapping completely fills dst or preserves the aspect ratio, and how to\n align src within dst. Returns the identity Matrix if src is empty. If dst is\n empty, returns Matrix set to:\n\n | 0 0 0 |\n | 0 0 0 |\n | 0 0 1 |\n\n @param src Rect to map from\n @param dst Rect to map to\n @param stf one of: kFill_ScaleToFit, kStart_ScaleToFit,\n kCenter_ScaleToFit, kEnd_ScaleToFit\n @return Matrix mapping src to dst\n */\n static Matrix MakeRectToRect(const Rect& src, const Rect& dst, ScaleToFit stf) {\n Matrix m;\n m.setRectToRect(src, dst, stf);\n return m;\n }\n\n /** Sets Matrix to map src to dst. count must be zero or greater, and four or less.\n\n If count is zero, sets Matrix to identity and returns true.\n If count is one, sets Matrix to translate and returns true.\n If count is two or more, sets Matrix to map Point if possible; returns false\n if Matrix cannot be constructed. If count is four, Matrix may include\n perspective.\n\n @param src Point to map from\n @param dst Point to map to\n @param count number of Point in src and dst\n @return true if Matrix was constructed successfully\n */\n bool setPolyToPoly(const Point src[], const Point dst[], int count);\n\n /** Sets inverse to reciprocal matrix, returning true if Matrix can be inverted.\n Geometrically, if Matrix maps from source to destination, inverse Matrix\n maps from destination to source. If Matrix can not be inverted, inverse is\n unchanged.\n\n @param inverse storage for inverted Matrix; may be nullptr\n @return true if Matrix can be inverted\n */\n bool invert(Matrix* inverse) const {\n // Allow the trivial case to be inlined.\n if (this->isIdentity()) {\n if (inverse) {\n inverse->reset();\n }\n return true;\n }\n return this->invertNonIdentity(inverse);\n }\n\n /** Fills affine with identity values in column major order.\n Sets affine to:\n\n | 1 0 0 |\n | 0 1 0 |\n\n Affine 3x2 matrices in column major order are used by OpenGL and XPS.\n\n @param affine storage for 3x2 affine matrix\n */\n static void SetAffineIdentity(float affine[6]);\n\n /** Fills affine in column major order. Sets affine to:\n\n | scale-x skew-x translate-x |\n | skew-y scale-y translate-y |\n\n If Matrix contains perspective, returns false and leaves affine unchanged.\n\n @param affine storage for 3x2 affine matrix; may be nullptr\n @return true if Matrix does not contain perspective\n */\n bool asAffine(float affine[6]) const;\n\n /** Sets Matrix to affine values, passed in column major order. Given affine,\n column, then row, as:\n\n | scale-x skew-x translate-x |\n | skew-y scale-y translate-y |\n\n Matrix is set, row, then column, to:\n\n | scale-x skew-x translate-x |\n | skew-y scale-y translate-y |\n | 0 0 1 |\n\n @param affine 3x2 affine matrix\n */\n void setAffine(const float affine[6]);\n\n /** Maps src Point array of length count to dst Point array of equal or greater\n length. Point are mapped by multiplying each Point by Matrix. Given:\n\n | A B C | | x |\n Matrix = | D E F |, pt = | y |\n | G H I | | 1 |\n\n where\n\n for (i = 0; i < count; ++i) {\n x = src[i].fX\n y = src[i].fY\n }\n\n each dst Point is computed as:\n\n |A B C| |x| Ax+By+C Dx+Ey+F\n Matrix * pt = |D E F| |y| = |Ax+By+C Dx+Ey+F Gx+Hy+I| = ------- , -------\n |G H I| |1| Gx+Hy+I Gx+Hy+I\n\n src and dst may point to the same storage.\n\n @param dst storage for mapped Point\n @param src Point to transform\n @param count number of Point to transform\n */\n void mapPoints(Point dst[], const Point src[], int count) const {\n MNN_ASSERT((dst && src && count > 0) || 0 == count);\n // no partial overlap\n MNN_ASSERT(src == dst || &dst[count] <= &src[0] || &src[count] <= &dst[0]);\n this->getMapPtsProc()(*this, dst, src, count);\n }\n\n /** Maps pts Point array of length count in place. Point are mapped by multiplying\n each Point by Matrix. Given:\n\n | A B C | | x |\n Matrix = | D E F |, pt = | y |\n | G H I | | 1 |\n\n where\n\n for (i = 0; i < count; ++i) {\n x = pts[i].fX\n y = pts[i].fY\n }\n\n each resulting pts Point is computed as:\n\n |A B C| |x| Ax+By+C Dx+Ey+F\n Matrix * pt = |D E F| |y| = |Ax+By+C Dx+Ey+F Gx+Hy+I| = ------- , -------\n |G H I| |1| Gx+Hy+I Gx+Hy+I\n\n @param pts storage for mapped Point\n @param count number of Point to transform\n */\n void mapPoints(Point pts[], int count) const {\n this->mapPoints(pts, pts, count);\n }\n\n /** Maps Point (x, y) to result. Point is mapped by multiplying by Matrix. Given:\n\n | A B C | | x |\n Matrix = | D E F |, pt = | y |\n | G H I | | 1 |\n\n result is computed as:\n\n |A B C| |x| Ax+By+C Dx+Ey+F\n Matrix * pt = |D E F| |y| = |Ax+By+C Dx+Ey+F Gx+Hy+I| = ------- , -------\n |G H I| |1| Gx+Hy+I Gx+Hy+I\n\n @param x x-axis value of Point to map\n @param y y-axis value of Point to map\n @param result storage for mapped Point\n */\n void mapXY(float x, float y, Point* result) const {\n this->getMapXYProc()(*this, x, y, result);\n }\n\n /** Returns Point (x, y) multiplied by Matrix. Given:\n\n | A B C | | x |\n Matrix = | D E F |, pt = | y |\n | G H I | | 1 |\n\n result is computed as:\n\n |A B C| |x| Ax+By+C Dx+Ey+F\n Matrix * pt = |D E F| |y| = |Ax+By+C Dx+Ey+F Gx+Hy+I| = ------- , -------\n |G H I| |1| Gx+Hy+I Gx+Hy+I\n\n @param x x-axis value of Point to map\n @param y y-axis value of Point to map\n @return mapped Point\n */\n Point mapXY(float x, float y) const {\n Point result;\n this->getMapXYProc()(*this, x, y, &result);\n return result;\n }\n\n /** Sets dst to bounds of src corners mapped by Matrix.\n Returns true if mapped corners are dst corners.\n\n Returned value is the same as calling rectStaysRect().\n\n @param dst storage for bounds of mapped Point\n @param src Rect to map\n @return true if dst is equivalent to mapped src\n */\n bool mapRect(Rect* dst, const Rect& src) const;\n\n /** Sets rect to bounds of rect corners mapped by Matrix.\n Returns true if mapped corners are computed rect corners.\n\n Returned value is the same as calling rectStaysRect().\n\n @param rect rectangle to map, and storage for bounds of mapped corners\n @return true if result is equivalent to mapped src\n */\n bool mapRect(Rect* rect) const {\n return this->mapRect(rect, *rect);\n }\n\n /** Returns bounds of src corners mapped by Matrix.\n\n @param src rectangle to map\n @return mapped bounds\n */\n Rect mapRect(const Rect& src) const {\n Rect dst;\n (void)this->mapRect(&dst, src);\n return dst;\n }\n\n /** Sets dst to bounds of src corners mapped by Matrix. If matrix contains\n elements other than scale or translate: asserts if SK_DEBUG is defined;\n otherwise, results are undefined.\n\n @param dst storage for bounds of mapped Point\n @param src Rect to map\n */\n void mapRectScaleTranslate(Rect* dst, const Rect& src) const;\n\n /** Returns true if Matrix equals m, using an efficient comparison.\n\n Returns false when the sign of zero values is the different; when one\n matrix has positive zero value and the other has negative zero value.\n\n Returns true even when both Matrix contain NaN.\n\n NaN never equals any value, including itself. To improve performance, NaN values\n are treated as bit patterns that are equal if their bit patterns are equal.\n\n @param m Matrix to compare\n @return true if m and Matrix are represented by identical bit patterns\n */\n bool cheapEqualTo(const Matrix& m) const {\n return 0 == memcmp(fMat, m.fMat, sizeof(fMat));\n }\n\n /** Compares a and b; returns true if a and b are numerically equal. Returns true\n even if sign of zero values are different. Returns false if either Matrix\n contains NaN, even if the other Matrix also contains NaN.\n\n @param a Matrix to compare\n @param b Matrix to compare\n @return true if Matrix a and Matrix b are numerically equal\n */\n friend MNN_PUBLIC bool operator==(const Matrix& a, const Matrix& b);\n\n /** Compares a and b; returns true if a and b are not numerically equal. Returns false\n even if sign of zero values are different. Returns true if either Matrix\n contains NaN, even if the other Matrix also contains NaN.\n\n @param a Matrix to compare\n @param b Matrix to compare\n @return true if Matrix a and Matrix b are numerically not equal\n */\n friend MNN_PUBLIC bool operator!=(const Matrix& a, const Matrix& b) {\n return !(a == b);\n }\n\n /** Writes text representation of Matrix to standard output. Floating point values\n are written with limited precision; it may not be possible to reconstruct\n original Matrix from output.\n */\n void dump() const;\n\n /** Returns the minimum scaling factor of Matrix by decomposing the scaling and\n skewing elements.\n Returns -1 if scale factor overflows or Matrix contains perspective.\n\n @return minimum scale factor\n */\n float getMinScale() const;\n\n /** Returns the maximum scaling factor of Matrix by decomposing the scaling and\n skewing elements.\n Returns -1 if scale factor overflows or Matrix contains perspective.\n\n @return maximum scale factor\n */\n float getMaxScale() const;\n\n /** Sets scaleFactors[0] to the minimum scaling factor, and scaleFactors[1] to the\n maximum scaling factor. Scaling factors are computed by decomposing\n the Matrix scaling and skewing elements.\n\n Returns true if scaleFactors are found; otherwise, returns false and sets\n scaleFactors to undefined values.\n\n @param scaleFactors storage for minimum and maximum scale factors\n @return true if scale factors were computed correctly\n */\n bool getMinMaxScales(float scaleFactors[2]) const;\n\n /** Returns reference to const identity Matrix. Returned Matrix is set to:\n\n | 1 0 0 |\n | 0 1 0 |\n | 0 0 1 |\n\n @return const identity Matrix\n */\n static const Matrix& I();\n\n /** Returns reference to a const Matrix with invalid values. Returned Matrix is set\n to:\n\n | SK_ScalarMax SK_ScalarMax SK_ScalarMax |\n | SK_ScalarMax SK_ScalarMax SK_ScalarMax |\n | SK_ScalarMax SK_ScalarMax SK_ScalarMax |\n\n @return const invalid Matrix\n */\n static const Matrix& InvalidMatrix();\n\n /** Returns Matrix a multiplied by Matrix b.\n\n Given:\n\n | A B C | | J K L |\n a = | D E F |, b = | M N O |\n | G H I | | P Q R |\n\n sets Matrix to:\n\n | A B C | | J K L | | AJ+BM+CP AK+BN+CQ AL+BO+CR |\n a * b = | D E F | * | M N O | = | DJ+EM+FP DK+EN+FQ DL+EO+FR |\n | G H I | | P Q R | | GJ+HM+IP GK+HN+IQ GL+HO+IR |\n\n @param a Matrix on left side of multiply expression\n @param b Matrix on right side of multiply expression\n @return Matrix computed from a times b\n */\n static Matrix Concat(const Matrix& a, const Matrix& b) {\n Matrix result;\n result.setConcat(a, b);\n return result;\n }\n\n /** Sets internal cache to unknown state. Use to force update after repeated\n modifications to Matrix element reference returned by operator[](int index).\n */\n void dirtyMatrixTypeCache() {\n this->setTypeMask(kUnknown_Mask);\n }\n\n /** Initializes Matrix with scale and translate elements.\n\n | sx 0 tx |\n | 0 sy ty |\n | 0 0 1 |\n\n @param sx horizontal scale factor to store\n @param sy vertical scale factor to store\n @param tx horizontal translation to store\n @param ty vertical translation to store\n */\n void setScaleTranslate(float sx, float sy, float tx, float ty) {\n fMat[kMScaleX] = sx;\n fMat[kMSkewX] = 0;\n fMat[kMTransX] = tx;\n\n fMat[kMSkewY] = 0;\n fMat[kMScaleY] = sy;\n fMat[kMTransY] = ty;\n\n fMat[kMPersp0] = 0;\n fMat[kMPersp1] = 0;\n fMat[kMPersp2] = 1;\n\n unsigned mask = 0;\n if (sx != 1 || sy != 1) {\n mask |= kScale_Mask;\n }\n if (tx || ty) {\n mask |= kTranslate_Mask;\n }\n this->setTypeMask(mask | kRectStaysRect_Mask);\n }\n\n /** Returns true if all elements of the matrix are finite. Returns false if any\n element is infinity, or NaN.\n\n @return true if matrix has only finite elements\n */\n\nprivate:\n /** Set if the matrix will map a rectangle to another rectangle. This\n can be true if the matrix is scale-only, or rotates a multiple of\n 90 degrees.\n\n This bit will be set on identity matrices\n */\n static constexpr int kRectStaysRect_Mask = 0x10;\n\n /** Set if the perspective bit is valid even though the rest of\n the matrix is Unknown.\n */\n static constexpr int kOnlyPerspectiveValid_Mask = 0x40;\n\n static constexpr int kUnknown_Mask = 0x80;\n\n static constexpr int kORableMasks = kTranslate_Mask | kScale_Mask | kAffine_Mask | kPerspective_Mask;\n\n static constexpr int kAllMasks =\n kTranslate_Mask | kScale_Mask | kAffine_Mask | kPerspective_Mask | kRectStaysRect_Mask;\n\n float fMat[9];\n mutable uint32_t fTypeMask;\n\n static void ComputeInv(float dst[9], const float src[9], double invDet, bool isPersp);\n\n uint8_t computeTypeMask() const;\n uint8_t computePerspectiveTypeMask() const;\n\n void setTypeMask(int mask) {\n // allow kUnknown or a valid mask\n MNN_ASSERT(kUnknown_Mask == mask || (mask & kAllMasks) == mask ||\n ((kUnknown_Mask | kOnlyPerspectiveValid_Mask) & mask) ==\n (kUnknown_Mask | kOnlyPerspectiveValid_Mask));\n fTypeMask = (uint8_t)(mask);\n }\n\n void orTypeMask(int mask) {\n MNN_ASSERT((mask & kORableMasks) == mask);\n fTypeMask = (uint8_t)(fTypeMask | mask);\n }\n\n void clearTypeMask(int mask) {\n // only allow a valid mask\n MNN_ASSERT((mask & kAllMasks) == mask);\n fTypeMask = fTypeMask & ~mask;\n }\n\n TypeMask getPerspectiveTypeMaskOnly() const {\n if ((fTypeMask & kUnknown_Mask) && !(fTypeMask & kOnlyPerspectiveValid_Mask)) {\n fTypeMask = this->computePerspectiveTypeMask();\n }\n return (TypeMask)(fTypeMask & 0xF);\n }\n\n /** Returns true if we already know that the matrix is identity;\n false otherwise.\n */\n bool isTriviallyIdentity() const {\n if (fTypeMask & kUnknown_Mask) {\n return false;\n }\n return ((fTypeMask & 0xF) == 0);\n }\n\n inline void updateTranslateMask() {\n if ((fMat[kMTransX] != 0) | (fMat[kMTransY] != 0)) {\n fTypeMask |= kTranslate_Mask;\n } else {\n fTypeMask &= ~kTranslate_Mask;\n }\n }\n\n typedef void (*MapXYProc)(const Matrix& mat, float x, float y, Point* result);\n\n static MapXYProc GetMapXYProc(TypeMask mask) {\n MNN_ASSERT((mask & ~kAllMasks) == 0);\n return gMapXYProcs[mask & kAllMasks];\n }\n\n MapXYProc getMapXYProc() const {\n return GetMapXYProc(this->getType());\n }\n\n typedef void (*MapPtsProc)(const Matrix& mat, Point dst[], const Point src[], int count);\n\n static MapPtsProc GetMapPtsProc(TypeMask mask) {\n MNN_ASSERT((mask & ~kAllMasks) == 0);\n return gMapPtsProcs[mask & kAllMasks];\n }\n\n MapPtsProc getMapPtsProc() const {\n return GetMapPtsProc(this->getType());\n }\n\n bool invertNonIdentity(Matrix* inverse) const;\n\n static void Identity_xy(const Matrix&, float, float, Point*);\n static void Trans_xy(const Matrix&, float, float, Point*);\n static void Scale_xy(const Matrix&, float, float, Point*);\n static void ScaleTrans_xy(const Matrix&, float, float, Point*);\n static void Rot_xy(const Matrix&, float, float, Point*);\n static void RotTrans_xy(const Matrix&, float, float, Point*);\n static void Persp_xy(const Matrix&, float, float, Point*);\n\n static const MapXYProc gMapXYProcs[];\n\n static void Identity_pts(const Matrix&, Point[], const Point[], int);\n static void Trans_pts(const Matrix&, Point dst[], const Point[], int);\n static void Scale_pts(const Matrix&, Point dst[], const Point[], int);\n static void ScaleTrans_pts(const Matrix&, Point dst[], const Point[], int count);\n static void Persp_pts(const Matrix&, Point dst[], const Point[], int);\n\n static void Affine_vpts(const Matrix&, Point dst[], const Point[], int);\n\n static const MapPtsProc gMapPtsProcs[];\n static bool Poly2Proc(const Point srcPt[], Matrix* dst);\n static bool Poly3Proc(const Point srcPt[], Matrix* dst);\n static bool Poly4Proc(const Point srcPt[], Matrix* dst);\n};\n} // namespace CV\n} // namespace MNN\n#endif\n" }, { "path": "MNN/mnn/include/NonCopyable.hpp", "content": "//\n// NonCopyable.hpp\n// MNN\n//\n// Created by MNN on 2018/09/19.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef NonCopyable_hpp\n#define NonCopyable_hpp\n\nnamespace MNN {\n/** protocol class. used to delete assignment operator. */\nclass NonCopyable {\npublic:\n NonCopyable() = default;\n NonCopyable(const NonCopyable&) = delete;\n NonCopyable(const NonCopyable&&) = delete;\n NonCopyable& operator=(const NonCopyable&) = delete;\n NonCopyable& operator=(const NonCopyable&&) = delete;\n};\n} // namespace MNN\n\n#endif /* NonCopyable_hpp */\n" }, { "path": "MNN/mnn/include/Rect.h", "content": "//\n// Rect.h\n// MNN\n//\n// Modified by jiangxiaotang on 2018/09/19.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n/*\n * Copyright 2006 The Android Open Source Project\n *\n * Use of this source code is governed by a BSD-style license that can be\n * found in the LICENSE file.\n */\n\n/* Generated by tools/bookmaker from include/core/Rect.h and docs/SkRect_Reference.bmh\n on 2018-07-13 08:15:11. Additional documentation and examples can be found at:\n https://skia.org/user/api/SkRect_Reference\n\n You may edit either file directly. Structural changes to public interfaces require\n editing both files. After editing docs/SkRect_Reference.bmh, run:\n bookmaker -b docs -i include/core/Rect.h -p\n to create an updated version of this file.\n */\n\n#ifndef SkRect_DEFINED\n#define SkRect_DEFINED\n\n#include \n#include \n#include \n#include \"MNNDefine.h\"\n\nnamespace MNN {\nnamespace CV {\n\nstruct Point {\n float fX;\n float fY;\n\n void set(float x, float y) {\n fX = x;\n fY = y;\n }\n};\n\n/** \\struct Rect\n Rect holds four float coordinates describing the upper and\n lower bounds of a rectangle. Rect may be created from outer bounds or\n from position, width, and height. Rect describes an area; if its right\n is less than or equal to its left, or if its bottom is less than or equal to\n its top, it is considered empty.\n*/\nstruct MNN_PUBLIC Rect {\n float fLeft; //!< smaller x-axis bounds\n float fTop; //!< smaller y-axis bounds\n float fRight; //!< larger x-axis bounds\n float fBottom; //!< larger y-axis bounds\n\n /** Returns constructed Rect set to (0, 0, 0, 0).\n Many other rectangles are empty; if left is equal to or greater than right,\n or if top is equal to or greater than bottom. Setting all members to zero\n is a convenience, but does not designate a special empty rectangle.\n\n @return bounds (0, 0, 0, 0)\n */\n static constexpr Rect MakeEmpty() {\n return Rect{0, 0, 0, 0};\n }\n\n#ifdef SK_SUPPORT_LEGACY_RECTMAKELARGEST\n /** Deprecated.\n */\n static Rect MakeLargest() {\n return {SK_ScalarMin, SK_ScalarMin, SK_ScalarMax, SK_ScalarMax};\n }\n#endif\n\n /** Returns constructed Rect set to float values (0, 0, w, h). Does not\n validate input; w or h may be negative.\n\n Passing integer values may generate a compiler warning since Rect cannot\n represent 32-bit integers exactly. Use SkIRect for an exact integer rectangle.\n\n @param w float width of constructed Rect\n @param h float height of constructed Rect\n @return bounds (0, 0, w, h)\n */\n static constexpr Rect MakeWH(float w, float h) {\n return Rect{0, 0, w, h};\n }\n\n /** Returns constructed Rect set to integer values (0, 0, w, h). Does not validate\n input; w or h may be negative.\n\n Use to avoid a compiler warning that input may lose precision when stored.\n Use SkIRect for an exact integer rectangle.\n\n @param w integer width of constructed Rect\n @param h integer height of constructed Rect\n @return bounds (0, 0, w, h)\n */\n static Rect MakeIWH(int w, int h) {\n Rect r;\n r.set(0, 0, (float)(w), (float)(h));\n return r;\n }\n\n /** Returns constructed Rect set to (l, t, r, b). Does not sort input; Rect may\n result in fLeft greater than fRight, or fTop greater than fBottom.\n\n @param l float stored in fLeft\n @param t float stored in fTop\n @param r float stored in fRight\n @param b float stored in fBottom\n @return bounds (l, t, r, b)\n */\n static constexpr Rect MakeLTRB(float l, float t, float r, float b) {\n return Rect{l, t, r, b};\n }\n\n /** Returns constructed Rect set to (x, y, x + w, y + h). Does not validate input;\n w or h may be negative.\n\n @param x stored in fLeft\n @param y stored in fTop\n @param w added to x and stored in fRight\n @param h added to y and stored in fBottom\n @return bounds at (x, y) with width w and height h\n */\n static constexpr Rect MakeXYWH(float x, float y, float w, float h) {\n return Rect{x, y, x + w, y + h};\n }\n\n /** Returns true if fLeft is equal to or greater than fRight, or if fTop is equal\n to or greater than fBottom. Call sort() to reverse rectangles with negative\n width() or height().\n\n @return true if width() or height() are zero or negative\n */\n bool isEmpty() const {\n // We write it as the NOT of a non-empty rect, so we will return true if any values\n // are NaN.\n return !(fLeft < fRight && fTop < fBottom);\n }\n\n /** Returns true if fLeft is equal to or less than fRight, or if fTop is equal\n to or less than fBottom. Call sort() to reverse rectangles with negative\n width() or height().\n\n @return true if width() or height() are zero or positive\n */\n bool isSorted() const {\n return fLeft <= fRight && fTop <= fBottom;\n }\n\n /** Returns left edge of Rect, if sorted. Call isSorted() to see if Rect is valid.\n Call sort() to reverse fLeft and fRight if needed.\n\n @return fLeft\n */\n float x() const {\n return fLeft;\n }\n\n /** Returns top edge of Rect, if sorted. Call isEmpty() to see if Rect may be invalid,\n and sort() to reverse fTop and fBottom if needed.\n\n @return fTop\n */\n float y() const {\n return fTop;\n }\n\n /** Returns left edge of Rect, if sorted. Call isSorted() to see if Rect is valid.\n Call sort() to reverse fLeft and fRight if needed.\n\n @return fLeft\n */\n float left() const {\n return fLeft;\n }\n\n /** Returns top edge of Rect, if sorted. Call isEmpty() to see if Rect may be invalid,\n and sort() to reverse fTop and fBottom if needed.\n\n @return fTop\n */\n float top() const {\n return fTop;\n }\n\n /** Returns right edge of Rect, if sorted. Call isSorted() to see if Rect is valid.\n Call sort() to reverse fLeft and fRight if needed.\n\n @return fRight\n */\n float right() const {\n return fRight;\n }\n\n /** Returns bottom edge of Rect, if sorted. Call isEmpty() to see if Rect may be invalid,\n and sort() to reverse fTop and fBottom if needed.\n\n @return fBottom\n */\n float bottom() const {\n return fBottom;\n }\n\n /** Returns span on the x-axis. This does not check if Rect is sorted, or if\n result fits in 32-bit float; result may be negative or infinity.\n\n @return fRight minus fLeft\n */\n float width() const {\n return fRight - fLeft;\n }\n\n /** Returns span on the y-axis. This does not check if Rect is sorted, or if\n result fits in 32-bit float; result may be negative or infinity.\n\n @return fBottom minus fTop\n */\n float height() const {\n return fBottom - fTop;\n }\n\n /** Returns average of left edge and right edge. Result does not change if Rect\n is sorted. Result may overflow to infinity if Rect is far from the origin.\n\n @return midpoint in x\n */\n float centerX() const {\n // don't use floatHalf(fLeft + fBottom) as that might overflow before the 0.5\n return 0.5f * (fLeft) + 0.5f * (fRight);\n }\n\n /** Returns average of top edge and bottom edge. Result does not change if Rect\n is sorted.\n\n @return midpoint in y\n */\n float centerY() const {\n // don't use floatHalf(fTop + fBottom) as that might overflow before the 0.5\n return 0.5f * (fTop) + 0.5f * (fBottom);\n }\n\n /** Sets Rect to (0, 0, 0, 0).\n\n Many other rectangles are empty; if left is equal to or greater than right,\n or if top is equal to or greater than bottom. Setting all members to zero\n is a convenience, but does not designate a special empty rectangle.\n */\n void setEmpty() {\n *this = MakeEmpty();\n }\n\n /** Sets Rect to (left, top, right, bottom).\n left and right are not sorted; left is not necessarily less than right.\n top and bottom are not sorted; top is not necessarily less than bottom.\n\n @param left stored in fLeft\n @param top stored in fTop\n @param right stored in fRight\n @param bottom stored in fBottom\n */\n void set(float left, float top, float right, float bottom) {\n fLeft = left;\n fTop = top;\n fRight = right;\n fBottom = bottom;\n }\n\n /** Sets Rect to (left, top, right, bottom).\n left and right are not sorted; left is not necessarily less than right.\n top and bottom are not sorted; top is not necessarily less than bottom.\n\n @param left stored in fLeft\n @param top stored in fTop\n @param right stored in fRight\n @param bottom stored in fBottom\n */\n void setLTRB(float left, float top, float right, float bottom) {\n this->set(left, top, right, bottom);\n }\n\n /** Sets Rect to (left, top, right, bottom).\n All parameters are promoted from integer to scalar.\n left and right are not sorted; left is not necessarily less than right.\n top and bottom are not sorted; top is not necessarily less than bottom.\n\n @param left promoted to float and stored in fLeft\n @param top promoted to float and stored in fTop\n @param right promoted to float and stored in fRight\n @param bottom promoted to float and stored in fBottom\n */\n void iset(int left, int top, int right, int bottom) {\n fLeft = (float)(left);\n fTop = (float)(top);\n fRight = (float)(right);\n fBottom = (float)(bottom);\n }\n\n /** Sets Rect to (0, 0, width, height).\n width and height may be zero or negative. width and height are promoted from\n integer to float, large values may lose precision.\n\n @param width promoted to float and stored in fRight\n @param height promoted to float and stored in fBottom\n */\n void isetWH(int width, int height) {\n fLeft = fTop = 0;\n fRight = (float)(width);\n fBottom = (float)(height);\n }\n\n /** Sets Rect to (x, y, x + width, y + height). Does not validate input;\n width or height may be negative.\n\n @param x stored in fLeft\n @param y stored in fTop\n @param width added to x and stored in fRight\n @param height added to y and stored in fBottom\n */\n void setXYWH(float x, float y, float width, float height) {\n fLeft = x;\n fTop = y;\n fRight = x + width;\n fBottom = y + height;\n }\n\n /** Sets Rect to (0, 0, width, height). Does not validate input;\n width or height may be negative.\n\n @param width stored in fRight\n @param height stored in fBottom\n */\n void setWH(float width, float height) {\n fLeft = 0;\n fTop = 0;\n fRight = width;\n fBottom = height;\n }\n\n /** Returns Rect offset by (dx, dy).\n\n If dx is negative, Rect returned is moved to the left.\n If dx is positive, Rect returned is moved to the right.\n If dy is negative, Rect returned is moved upward.\n If dy is positive, Rect returned is moved downward.\n\n @param dx added to fLeft and fRight\n @param dy added to fTop and fBottom\n @return Rect offset on axes, with original width and height\n */\n Rect makeOffset(float dx, float dy) const {\n return MakeLTRB(fLeft + dx, fTop + dy, fRight + dx, fBottom + dy);\n }\n\n /** Returns Rect, inset by (dx, dy).\n\n If dx is negative, Rect returned is wider.\n If dx is positive, Rect returned is narrower.\n If dy is negative, Rect returned is taller.\n If dy is positive, Rect returned is shorter.\n\n @param dx added to fLeft and subtracted from fRight\n @param dy added to fTop and subtracted from fBottom\n @return Rect inset symmetrically left and right, top and bottom\n */\n Rect makeInset(float dx, float dy) const {\n return MakeLTRB(fLeft + dx, fTop + dy, fRight - dx, fBottom - dy);\n }\n\n /** Returns Rect, outset by (dx, dy).\n\n If dx is negative, Rect returned is narrower.\n If dx is positive, Rect returned is wider.\n If dy is negative, Rect returned is shorter.\n If dy is positive, Rect returned is taller.\n\n @param dx subtracted to fLeft and added from fRight\n @param dy subtracted to fTop and added from fBottom\n @return Rect outset symmetrically left and right, top and bottom\n */\n Rect makeOutset(float dx, float dy) const {\n return MakeLTRB(fLeft - dx, fTop - dy, fRight + dx, fBottom + dy);\n }\n\n /** Offsets Rect by adding dx to fLeft, fRight; and by adding dy to fTop, fBottom.\n\n If dx is negative, moves Rect to the left.\n If dx is positive, moves Rect to the right.\n If dy is negative, moves Rect upward.\n If dy is positive, moves Rect downward.\n\n @param dx offset added to fLeft and fRight\n @param dy offset added to fTop and fBottom\n */\n void offset(float dx, float dy) {\n fLeft += dx;\n fTop += dy;\n fRight += dx;\n fBottom += dy;\n }\n\n /** Offsets Rect so that fLeft equals newX, and fTop equals newY. width and height\n are unchanged.\n\n @param newX stored in fLeft, preserving width()\n @param newY stored in fTop, preserving height()\n */\n void offsetTo(float newX, float newY) {\n fRight += newX - fLeft;\n fBottom += newY - fTop;\n fLeft = newX;\n fTop = newY;\n }\n\n /** Insets Rect by (dx, dy).\n\n If dx is positive, makes Rect narrower.\n If dx is negative, makes Rect wider.\n If dy is positive, makes Rect shorter.\n If dy is negative, makes Rect taller.\n\n @param dx added to fLeft and subtracted from fRight\n @param dy added to fTop and subtracted from fBottom\n */\n void inset(float dx, float dy) {\n fLeft += dx;\n fTop += dy;\n fRight -= dx;\n fBottom -= dy;\n }\n\n /** Outsets Rect by (dx, dy).\n\n If dx is positive, makes Rect wider.\n If dx is negative, makes Rect narrower.\n If dy is positive, makes Rect taller.\n If dy is negative, makes Rect shorter.\n\n @param dx subtracted to fLeft and added from fRight\n @param dy subtracted to fTop and added from fBottom\n */\n void outset(float dx, float dy) {\n this->inset(-dx, -dy);\n }\n\nprivate:\n static bool Intersects(float al, float at, float ar, float ab, float bl, float bt, float br, float bb) {\n float L = std::max(al, bl);\n float R = std::min(ar, br);\n float T = std::max(at, bt);\n float B = std::min(ab, bb);\n return L < R && T < B;\n }\n\npublic:\n /** Constructs Rect to intersect from (left, top, right, bottom). Does not sort\n construction.\n\n Returns true if Rect intersects construction.\n Returns false if either construction or Rect is empty, or do not intersect.\n\n @param left x-axis minimum of constructed Rect\n @param top y-axis minimum of constructed Rect\n @param right x-axis maximum of constructed Rect\n @param bottom y-axis maximum of constructed Rect\n @return true if construction and Rect have area in common\n */\n bool intersects(float left, float top, float right, float bottom) const {\n return Intersects(fLeft, fTop, fRight, fBottom, left, top, right, bottom);\n }\n\n /** Returns true if Rect intersects r.\n Returns false if either r or Rect is empty, or do not intersect.\n\n @param r Rect to intersect\n @return true if r and Rect have area in common\n */\n bool intersects(const Rect& r) const {\n return Intersects(fLeft, fTop, fRight, fBottom, r.fLeft, r.fTop, r.fRight, r.fBottom);\n }\n\n /** Returns true if a intersects b.\n Returns false if either a or b is empty, or do not intersect.\n\n @param a Rect to intersect\n @param b Rect to intersect\n @return true if a and b have area in common\n */\n static bool Intersects(const Rect& a, const Rect& b) {\n return Intersects(a.fLeft, a.fTop, a.fRight, a.fBottom, b.fLeft, b.fTop, b.fRight, b.fBottom);\n }\n\n /** Sets Rect to the union of itself and r.\n\n Asserts if r is empty and SK_DEBUG is defined.\n If Rect is empty, sets Rect to r.\n\n May produce incorrect results if r is empty.\n\n @param r expansion Rect\n */\n void joinNonEmptyArg(const Rect& r) {\n MNN_ASSERT(!r.isEmpty());\n // if we are empty, just assign\n if (fLeft >= fRight || fTop >= fBottom) {\n *this = r;\n } else {\n this->joinPossiblyEmptyRect(r);\n }\n }\n\n /** Sets Rect to the union of itself and the construction.\n\n May produce incorrect results if Rect or r is empty.\n\n @param r expansion Rect\n */\n void joinPossiblyEmptyRect(const Rect& r) {\n fLeft = std::min(fLeft, r.left());\n fTop = std::min(fTop, r.top());\n fRight = std::max(fRight, r.right());\n fBottom = std::max(fBottom, r.bottom());\n }\n\n /** Returns true if: fLeft <= x < fRight && fTop <= y < fBottom.\n Returns false if Rect is empty.\n\n @param x test Point x-coordinate\n @param y test Point y-coordinate\n @return true if (x, y) is inside Rect\n */\n bool contains(float x, float y) const {\n return x >= fLeft && x < fRight && y >= fTop && y < fBottom;\n }\n\n /** Swaps fLeft and fRight if fLeft is greater than fRight; and swaps\n fTop and fBottom if fTop is greater than fBottom. Result may be empty;\n and width() and height() will be zero or positive.\n */\n void sort() {\n using std::swap;\n if (fLeft > fRight) {\n swap(fLeft, fRight);\n }\n\n if (fTop > fBottom) {\n swap(fTop, fBottom);\n }\n }\n\n /** Returns Rect with fLeft and fRight swapped if fLeft is greater than fRight; and\n with fTop and fBottom swapped if fTop is greater than fBottom. Result may be empty;\n and width() and height() will be zero or positive.\n\n @return sorted Rect\n */\n Rect makeSorted() const {\n return MakeLTRB(std::min(fLeft, fRight), std::min(fTop, fBottom), std::max(fLeft, fRight),\n std::max(fTop, fBottom));\n }\n\n /** Returns pointer to first scalar in Rect, to treat it as an array with four\n entries.\n\n @return pointer to fLeft\n */\n const float* asScalars() const {\n return &fLeft;\n }\n};\n\n} // namespace CV\n} // namespace MNN\n#endif\n" }, { "path": "MNN/mnn/include/Tensor.hpp", "content": "//\n// Tensor.hpp\n// MNN\n//\n// Created by MNN on 2018/08/14.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef Tensor_hpp\n#define Tensor_hpp\n\n#include \n#include \"HalideRuntime.h\"\n#include \"MNNDefine.h\"\n\nnamespace MNN {\n\n/**\n * data container.\n * data for host tensor is saved in `host` field. its memory is allocated malloc directly.\n * data for device tensor is saved in `deviceId` field. its memory is allocated by session's backend.\n * usually, device tensors are created by engine (like net, session).\n * meanwhile, host tensors could be created by engine or user.\n */\nclass MNN_PUBLIC Tensor {\npublic:\n struct InsideDescribe;\n\n /** dimension type used to create tensor */\n enum DimensionType {\n /** for tensorflow net type. uses NHWC as data format. */\n TENSORFLOW,\n /** for caffe net type. uses NCHW as data format. */\n CAFFE,\n /** for caffe net type. uses NC4HW4 as data format. */\n CAFFE_C4\n };\n\n /** handle type */\n enum HandleDataType {\n /** default handle type */\n HANDLE_NONE = 0,\n /** string handle type */\n HANDLE_STRING = 1\n };\n\npublic:\n /**\n * @brief create a tensor with dimension size and type without acquire memory for data.\n * @param dimSize dimension size.\n * @param type dimension type.\n */\n Tensor(int dimSize = 4, DimensionType type = CAFFE);\n\n /**\n * @brief create a tensor with same shape as given tensor.\n * @param tensor shape provider.\n * @param type dimension type.\n * @param allocMemory acquire memory for data or not.\n * @warning tensor data won't be copied.\n */\n Tensor(const Tensor* tensor, DimensionType type = CAFFE, bool allocMemory = true);\n\n /** deinitializer */\n ~Tensor();\n\nprivate:\n // remove all assignment operator\n Tensor(const Tensor& tensor) = delete;\n Tensor(const Tensor&& tensor) = delete;\n Tensor& operator=(const Tensor&) = delete;\n Tensor& operator=(const Tensor&&) = delete;\n\npublic:\n /**\n * @brief create tensor with shape, data type and dimension type.\n * @param shape tensor shape.\n * @param type data type.\n * @param dimType dimension type.\n * @return created tensor.\n * @warning memory for data won't be acquired. call backend's onAcquireBuffer to get memory ready.\n */\n static Tensor* createDevice(const std::vector& shape, halide_type_t type, DimensionType dimType = TENSORFLOW);\n\n /**\n * @brief create tensor with shape and dimension type. data type is represented by `T`.\n * @param shape tensor shape.\n * @param dimType dimension type.\n * @return created tensor.\n * @warning memory for data won't be acquired. call backend's onAcquireBuffer to get memory ready.\n */\n template \n static Tensor* createDevice(const std::vector& shape, DimensionType dimType = TENSORFLOW) {\n return createDevice(shape, halide_type_of(), dimType);\n }\n\n /**\n * @brief create tensor with shape, data type, data and dimension type.\n * @param shape tensor shape.\n * @param type data type.\n * @param data data to save.\n * @param dimType dimension type.\n * @return created tensor.\n */\n static Tensor* create(const std::vector& shape, halide_type_t type, void* data = NULL,\n DimensionType dimType = TENSORFLOW);\n\n /**\n * @brief create tensor with shape, data and dimension type. data type is represented by `T`.\n * @param shape tensor shape.\n * @param data data to save.\n * @param dimType dimension type.\n * @return created tensor.\n */\n template \n static Tensor* create(const std::vector& shape, void* data = NULL, DimensionType dimType = TENSORFLOW) {\n return create(shape, halide_type_of(), data, dimType);\n }\n\npublic:\n /**\n * @brief for DEVICE tensor, copy data from given host tensor.\n * @param hostTensor host tensor, the data provider.\n * @return true for DEVICE tensor, and false for HOST tensor.\n */\n bool copyFromHostTensor(const Tensor* hostTensor);\n\n /**\n * @brief for DEVICE tensor, copy data to given host tensor.\n * @param hostTensor host tensor, the data consumer.\n * @return true for DEVICE tensor, and false for HOST tensor.\n */\n bool copyToHostTensor(Tensor* hostTensor) const;\n\n /**\n * @brief create HOST tensor from DEVICE tensor, with or without data copying.\n * @param deviceTensor given device tensor.\n * @param copyData copy data or not.\n * @return created host tensor.\n */\n static Tensor* createHostTensorFromDevice(const Tensor* deviceTensor, bool copyData = true);\n\npublic:\n const halide_buffer_t& buffer() const {\n return mBuffer;\n }\n halide_buffer_t& buffer() {\n return mBuffer;\n }\n\n /**\n * @brief get dimension type.\n * @return dimension type.\n */\n DimensionType getDimensionType() const;\n\n /**\n * @brief handle data type. used when data type code is halide_type_handle.\n * @return handle data type.\n */\n HandleDataType getHandleDataType() const;\n\n /**\n * @brief set data type.\n * @param type data type defined in 'Type_generated.h'.\n */\n void setType(int type);\n\n /**\n * @brief get data type.\n * @return data type.\n */\n inline halide_type_t getType() const {\n return mBuffer.type;\n }\n\n /**\n * @brief visit host memory, data type is represented by `T`.\n * @return data point in `T` type.\n */\n template \n T* host() const {\n return (T*)mBuffer.host;\n }\n\n /**\n * @brief visit device memory.\n * @return device data ID. what the ID means varies between backends.\n */\n uint64_t deviceId() const {\n return mBuffer.device;\n }\n\npublic:\n int dimensions() const {\n return mBuffer.dimensions;\n }\n\n /**\n * @brief get all dimensions' extent.\n * @return dimensions' extent.\n */\n std::vector shape() const;\n\n /**\n * @brief calculate number of bytes needed to store data taking reordering flag into account.\n * @return bytes needed to store data\n */\n int size() const;\n\n /**\n * @brief calculate number of elements needed to store data taking reordering flag into account.\n * @return elements needed to store data\n */\n inline int elementSize() const {\n return size() / mBuffer.type.bytes();\n }\n\npublic:\n inline int width() const {\n if (getDimensionType() == TENSORFLOW) {\n return mBuffer.dim[2].extent;\n }\n \n return mBuffer.dim[3].extent;\n }\n inline int height() const {\n if (getDimensionType() == TENSORFLOW) {\n return mBuffer.dim[1].extent;\n }\n return mBuffer.dim[2].extent;\n }\n inline int channel() const {\n if (getDimensionType() == TENSORFLOW) {\n return mBuffer.dim[3].extent;\n }\n return mBuffer.dim[1].extent;\n }\n inline int batch() const {\n return mBuffer.dim[0].extent;\n }\n\n // visit dimension's extent & stride\n inline int stride(int index) const {\n return mBuffer.dim[index].stride;\n }\n inline int length(int index) const {\n return mBuffer.dim[index].extent;\n }\n inline void setStride(int index, int stride) {\n mBuffer.dim[index].stride = stride;\n }\n inline void setLength(int index, int length) {\n mBuffer.dim[index].extent = length;\n }\n\npublic:\n /**\n * @brief print tensor data. for DEBUG use only.\n */\n void print() const;\n\nprivate:\n halide_buffer_t mBuffer;\n struct InsideDescribe* mDescribe;\n\nprivate:\n friend class TensorUtils;\n};\n} // namespace MNN\n\n#endif /* Tensor_hpp */\n" }, { "path": "MNN/mnn/include/revertMNNModel.hpp", "content": "//\n// revertMNNModel.hpp\n// MNN\n//\n// Created by MNN on 2019/01/31.\n// Copyright © 2018, Alibaba Group Holding Limited\n//\n\n#ifndef REVERTMNNMODEL_HPP\n#define REVERTMNNMODEL_HPP\n\n#include \"mnn/MNN_generated.h\"\n\nclass Revert {\npublic:\n Revert(const char* originalModelFileName);\n ~Revert();\n void* getBuffer() const;\n const size_t getBufferSize() const;\n void initialize();\n static float getRandValue();\n\nprivate:\n Revert();\n std::unique_ptr mMNNNet;\n size_t mBufferSize;\n std::shared_ptr mBuffer;\n void randStart();\n void packMNNNet();\n};\n\n#endif // REVERTMNNMODEL_HPP\n" }, { "path": "MNN/python/README.md", "content": "# Python implemententation of [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) with [MNN](https://github.com/alibaba/MNN)\n\n## How to use MNN in Python\n\n### Install\n#### Install Depencies\n##### graphviz\nfor macOS:\n```bash\nbrew install graphviz\n```\nfor Linux:\n```bash\napt-get install graphviz\n```\n\n#### Python Version Limitation\nPython2.7, 3.5, 3.6, 3.7 are supported, but for Windows, python2.7 is not supported.\nfor macOS:\n```bash\npip install -U MNN\n```\n\nfor Linux:\nAs PyPi requires all wheels to be tagged with \"ManyLinux\", and old version pip can't get the \"ManyLinux\" Tagged wheel, thus you have to upgrade your pip to newer version in order to use \"pip install\"\n```bash\npip install -U pip\npip install -U MNN\n```\n\n## Run\n* Use FP32 model(version-RFB) and run in FP16 mode:\n```bash\npython ultraface_py_mnn.py --model_path ../model/version-RFB/RFB-320.mnn\n```\n* Use quantized INT8 model:\n```bash\npython ultraface_py_mnn.py --model_path ../model/version-RFB/RFB-320-quant-KL-5792.mnn \n```\n\n* We provide both converted MNN FP32 and **quantized INT8** models of version-slim-320 and version-RFB-320 in ./MNN/model . The xxx-quant-KL-xxx.mnn is quantified by the **KL** method and xxx-quant-ADMM-xxx.mnn is quantified by the **ADMM** method.\n\n\n## PS\n* Since MNN mainly accelerates model inference on mobile, so the INT8 quantified model will run slower on **PC** than FP32 model in CPU mode.\n* If you want to run faster, try using the version-slim model ,using lower-resolution inputs like 160x120 /128x96 or using quantified models(On the mobile).\n\n## Result\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/MNN/result.jpg)" }, { "path": "MNN/python/ultraface_py_mnn.py", "content": "#!/usr/bin/env/ python\n# -*- coding: utf-8 -*-\n\"\"\" \n@author:linzai \n@file: ultraface_py_mnn.py \n@time: 2019-11-25 \n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport argparse\nimport sys\nimport time\nfrom math import ceil\n\nimport MNN\nimport cv2\nimport numpy as np\nimport torch\n\nsys.path.append('../../')\nimport vision.utils.box_utils_numpy as box_utils\n\nparser = argparse.ArgumentParser(description='run ultraface with MNN in py')\nparser.add_argument('--model_path', default=\"../model/version-RFB/RFB-320.mnn\", type=str, help='model path')\nparser.add_argument('--input_size', default=\"320,240\", type=str,\n help='define network input size,format: width,height')\nparser.add_argument('--threshold', default=0.7, type=float, help='score threshold')\nparser.add_argument('--imgs_path', default=\"../imgs\", type=str, help='imgs dir')\nparser.add_argument('--results_path', default=\"results\", type=str, help='results dir')\nargs = parser.parse_args()\n\nimage_mean = np.array([127, 127, 127])\nimage_std = 128.0\niou_threshold = 0.3\ncenter_variance = 0.1\nsize_variance = 0.2\nmin_boxes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]\nstrides = [8, 16, 32, 64]\n\n\ndef define_img_size(image_size):\n shrinkage_list = []\n feature_map_w_h_list = []\n for size in image_size:\n feature_map = [ceil(size / stride) for stride in strides]\n feature_map_w_h_list.append(feature_map)\n\n for i in range(0, len(image_size)):\n shrinkage_list.append(strides)\n priors = generate_priors(feature_map_w_h_list, shrinkage_list, image_size, min_boxes)\n return priors\n\n\ndef generate_priors(feature_map_list, shrinkage_list, image_size, min_boxes, clamp=True):\n priors = []\n for index in range(0, len(feature_map_list[0])):\n scale_w = image_size[0] / shrinkage_list[0][index]\n scale_h = image_size[1] / shrinkage_list[1][index]\n for j in range(0, feature_map_list[1][index]):\n for i in range(0, feature_map_list[0][index]):\n x_center = (i + 0.5) / scale_w\n y_center = (j + 0.5) / scale_h\n\n for min_box in min_boxes[index]:\n w = min_box / image_size[0]\n h = min_box / image_size[1]\n priors.append([\n x_center,\n y_center,\n w,\n h\n ])\n print(\"priors nums:{}\".format(len(priors)))\n priors = torch.tensor(priors)\n if clamp:\n torch.clamp(priors, 0.0, 1.0, out=priors)\n return priors\n\n\ndef predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):\n boxes = boxes[0]\n confidences = confidences[0]\n picked_box_probs = []\n picked_labels = []\n for class_index in range(1, confidences.shape[1]):\n probs = confidences[:, class_index]\n mask = probs > prob_threshold\n probs = probs[mask]\n if probs.shape[0] == 0:\n continue\n subset_boxes = boxes[mask, :]\n box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)\n box_probs = box_utils.hard_nms(box_probs,\n iou_threshold=iou_threshold,\n top_k=top_k,\n )\n picked_box_probs.append(box_probs)\n picked_labels.extend([class_index] * box_probs.shape[0])\n if not picked_box_probs:\n return np.array([]), np.array([]), np.array([])\n picked_box_probs = np.concatenate(picked_box_probs)\n picked_box_probs[:, 0] *= width\n picked_box_probs[:, 1] *= height\n picked_box_probs[:, 2] *= width\n picked_box_probs[:, 3] *= height\n return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]\n\n\ndef inference():\n input_size = [int(v.strip()) for v in args.input_size.split(\",\")]\n priors = define_img_size(input_size)\n result_path = args.results_path\n imgs_path = args.imgs_path\n if not os.path.exists(result_path):\n os.makedirs(result_path)\n listdir = os.listdir(imgs_path)\n for file_path in listdir:\n img_path = os.path.join(imgs_path, file_path)\n image_ori = cv2.imread(img_path)\n interpreter = MNN.Interpreter(args.model_path)\n session = interpreter.createSession()\n input_tensor = interpreter.getSessionInput(session)\n image = cv2.cvtColor(image_ori, cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, tuple(input_size))\n image = image.astype(float)\n image = (image - image_mean) / image_std\n image = image.transpose((2, 0, 1))\n tmp_input = MNN.Tensor((1, 3, input_size[1], input_size[0]), MNN.Halide_Type_Float, image, MNN.Tensor_DimensionType_Caffe)\n input_tensor.copyFrom(tmp_input)\n time_time = time.time()\n interpreter.runSession(session)\n scores = interpreter.getSessionOutput(session, \"scores\").getData()\n boxes = interpreter.getSessionOutput(session, \"boxes\").getData()\n boxes = np.expand_dims(np.reshape(boxes, (-1, 4)), axis=0)\n scores = np.expand_dims(np.reshape(scores, (-1, 2)), axis=0)\n print(\"inference time: {} s\".format(round(time.time() - time_time, 4)))\n boxes = box_utils.convert_locations_to_boxes(boxes, priors, center_variance, size_variance)\n boxes = box_utils.center_form_to_corner_form(boxes)\n boxes, labels, probs = predict(image_ori.shape[1], image_ori.shape[0], scores, boxes, args.threshold)\n for i in range(boxes.shape[0]):\n box = boxes[i, :]\n cv2.rectangle(image_ori, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)\n cv2.imwrite(os.path.join(result_path, file_path), image_ori)\n print(\"result_pic is written to {}\".format(os.path.join(result_path, file_path)))\n cv2.imshow(\"UltraFace_mnn_py\", image_ori)\n cv2.waitKey(-1)\n cv2.destroyAllWindows()\n\n\nif __name__ == \"__main__\":\n inference()\n" }, { "path": "MNN/src/UltraFace.cpp", "content": "// Created by Linzaer on 2019/11/15.\n// Copyright © 2019 Linzaer. All rights reserved.\n\n#define clip(x, y) (x < 0 ? 0 : (x > y ? y : x))\n\n#include \"UltraFace.hpp\"\n\nusing namespace std;\n\nUltraFace::UltraFace(const std::string &mnn_path,\n int input_width, int input_length, int num_thread_,\n float score_threshold_, float iou_threshold_, int topk_) {\n num_thread = num_thread_;\n score_threshold = score_threshold_;\n iou_threshold = iou_threshold_;\n in_w = input_width;\n in_h = input_length;\n w_h_list = {in_w, in_h};\n\n for (auto size : w_h_list) {\n std::vector fm_item;\n for (float stride : strides) {\n fm_item.push_back(ceil(size / stride));\n }\n featuremap_size.push_back(fm_item);\n }\n\n for (auto size : w_h_list) {\n shrinkage_size.push_back(strides);\n }\n /* generate prior anchors */\n for (int index = 0; index < num_featuremap; index++) {\n float scale_w = in_w / shrinkage_size[0][index];\n float scale_h = in_h / shrinkage_size[1][index];\n for (int j = 0; j < featuremap_size[1][index]; j++) {\n for (int i = 0; i < featuremap_size[0][index]; i++) {\n float x_center = (i + 0.5) / scale_w;\n float y_center = (j + 0.5) / scale_h;\n\n for (float k : min_boxes[index]) {\n float w = k / in_w;\n float h = k / in_h;\n priors.push_back({clip(x_center, 1), clip(y_center, 1), clip(w, 1), clip(h, 1)});\n }\n }\n }\n }\n /* generate prior anchors finished */\n\n num_anchors = priors.size();\n\n ultraface_interpreter = std::shared_ptr(MNN::Interpreter::createFromFile(mnn_path.c_str()));\n MNN::ScheduleConfig config;\n config.numThread = num_thread;\n MNN::BackendConfig backendConfig;\n backendConfig.precision = (MNN::BackendConfig::PrecisionMode) 2;\n config.backendConfig = &backendConfig;\n\n ultraface_session = ultraface_interpreter->createSession(config);\n\n input_tensor = ultraface_interpreter->getSessionInput(ultraface_session, nullptr);\n\n}\n\nUltraFace::~UltraFace() {\n ultraface_interpreter->releaseModel();\n ultraface_interpreter->releaseSession(ultraface_session);\n}\n\nint UltraFace::detect(cv::Mat &raw_image, std::vector &face_list) {\n if (raw_image.empty()) {\n std::cout << \"image is empty ,please check!\" << std::endl;\n return -1;\n }\n\n image_h = raw_image.rows;\n image_w = raw_image.cols;\n cv::Mat image;\n cv::resize(raw_image, image, cv::Size(in_w, in_h));\n\n ultraface_interpreter->resizeTensor(input_tensor, {1, 3, in_h, in_w});\n ultraface_interpreter->resizeSession(ultraface_session);\n std::shared_ptr pretreat(\n MNN::CV::ImageProcess::create(MNN::CV::BGR, MNN::CV::RGB, mean_vals, 3,\n norm_vals, 3));\n pretreat->convert(image.data, in_w, in_h, image.step[0], input_tensor);\n\n auto start = chrono::steady_clock::now();\n\n\n // run network\n ultraface_interpreter->runSession(ultraface_session);\n\n // get output data\n\n string scores = \"scores\";\n string boxes = \"boxes\";\n MNN::Tensor *tensor_scores = ultraface_interpreter->getSessionOutput(ultraface_session, scores.c_str());\n MNN::Tensor *tensor_boxes = ultraface_interpreter->getSessionOutput(ultraface_session, boxes.c_str());\n\n MNN::Tensor tensor_scores_host(tensor_scores, tensor_scores->getDimensionType());\n\n tensor_scores->copyToHostTensor(&tensor_scores_host);\n\n MNN::Tensor tensor_boxes_host(tensor_boxes, tensor_boxes->getDimensionType());\n\n tensor_boxes->copyToHostTensor(&tensor_boxes_host);\n\n std::vector bbox_collection;\n\n\n auto end = chrono::steady_clock::now();\n chrono::duration elapsed = end - start;\n cout << \"inference time:\" << elapsed.count() << \" s\" << endl;\n\n generateBBox(bbox_collection, tensor_scores, tensor_boxes);\n nms(bbox_collection, face_list);\n return 0;\n}\n\nvoid UltraFace::generateBBox(std::vector &bbox_collection, MNN::Tensor *scores, MNN::Tensor *boxes) {\n for (int i = 0; i < num_anchors; i++) {\n if (scores->host()[i * 2 + 1] > score_threshold) {\n FaceInfo rects;\n float x_center = boxes->host()[i * 4] * center_variance * priors[i][2] + priors[i][0];\n float y_center = boxes->host()[i * 4 + 1] * center_variance * priors[i][3] + priors[i][1];\n float w = exp(boxes->host()[i * 4 + 2] * size_variance) * priors[i][2];\n float h = exp(boxes->host()[i * 4 + 3] * size_variance) * priors[i][3];\n\n rects.x1 = clip(x_center - w / 2.0, 1) * image_w;\n rects.y1 = clip(y_center - h / 2.0, 1) * image_h;\n rects.x2 = clip(x_center + w / 2.0, 1) * image_w;\n rects.y2 = clip(y_center + h / 2.0, 1) * image_h;\n rects.score = clip(scores->host()[i * 2 + 1], 1);\n bbox_collection.push_back(rects);\n }\n }\n}\n\nvoid UltraFace::nms(std::vector &input, std::vector &output, int type) {\n std::sort(input.begin(), input.end(), [](const FaceInfo &a, const FaceInfo &b) { return a.score > b.score; });\n\n int box_num = input.size();\n\n std::vector merged(box_num, 0);\n\n for (int i = 0; i < box_num; i++) {\n if (merged[i])\n continue;\n std::vector buf;\n\n buf.push_back(input[i]);\n merged[i] = 1;\n\n float h0 = input[i].y2 - input[i].y1 + 1;\n float w0 = input[i].x2 - input[i].x1 + 1;\n\n float area0 = h0 * w0;\n\n for (int j = i + 1; j < box_num; j++) {\n if (merged[j])\n continue;\n\n float inner_x0 = input[i].x1 > input[j].x1 ? input[i].x1 : input[j].x1;\n float inner_y0 = input[i].y1 > input[j].y1 ? input[i].y1 : input[j].y1;\n\n float inner_x1 = input[i].x2 < input[j].x2 ? input[i].x2 : input[j].x2;\n float inner_y1 = input[i].y2 < input[j].y2 ? input[i].y2 : input[j].y2;\n\n float inner_h = inner_y1 - inner_y0 + 1;\n float inner_w = inner_x1 - inner_x0 + 1;\n\n if (inner_h <= 0 || inner_w <= 0)\n continue;\n\n float inner_area = inner_h * inner_w;\n\n float h1 = input[j].y2 - input[j].y1 + 1;\n float w1 = input[j].x2 - input[j].x1 + 1;\n\n float area1 = h1 * w1;\n\n float score;\n\n score = inner_area / (area0 + area1 - inner_area);\n\n if (score > iou_threshold) {\n merged[j] = 1;\n buf.push_back(input[j]);\n }\n }\n switch (type) {\n case hard_nms: {\n output.push_back(buf[0]);\n break;\n }\n case blending_nms: {\n float total = 0;\n for (int i = 0; i < buf.size(); i++) {\n total += exp(buf[i].score);\n }\n FaceInfo rects;\n memset(&rects, 0, sizeof(rects));\n for (int i = 0; i < buf.size(); i++) {\n float rate = exp(buf[i].score) / total;\n rects.x1 += buf[i].x1 * rate;\n rects.y1 += buf[i].y1 * rate;\n rects.x2 += buf[i].x2 * rate;\n rects.y2 += buf[i].y2 * rate;\n rects.score += buf[i].score * rate;\n }\n output.push_back(rects);\n break;\n }\n default: {\n printf(\"wrong type of nms.\");\n exit(-1);\n }\n }\n }\n}\n" }, { "path": "MNN/src/UltraFace.hpp", "content": "// Created by Linzaer on 2019/11/15.\n// Copyright © 2019 Linzaer. All rights reserved.\n\n#ifndef UltraFace_hpp\n#define UltraFace_hpp\n\n#pragma once\n\n#include \"Interpreter.hpp\"\n\n#include \"MNNDefine.h\"\n#include \"Tensor.hpp\"\n#include \"ImageProcess.hpp\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#define num_featuremap 4\n#define hard_nms 1\n#define blending_nms 2 /* mix nms was been proposaled in paper blaze face, aims to minimize the temporal jitter*/\ntypedef struct FaceInfo {\n float x1;\n float y1;\n float x2;\n float y2;\n float score;\n\n} FaceInfo;\n\nclass UltraFace {\npublic:\n UltraFace(const std::string &mnn_path,\n int input_width, int input_length, int num_thread_ = 4, float score_threshold_ = 0.7, float iou_threshold_ = 0.3,\n int topk_ = -1);\n\n ~UltraFace();\n\n int detect(cv::Mat &img, std::vector &face_list);\n\nprivate:\n void generateBBox(std::vector &bbox_collection, MNN::Tensor *scores, MNN::Tensor *boxes);\n\n void nms(std::vector &input, std::vector &output, int type = blending_nms);\n\nprivate:\n\n std::shared_ptr ultraface_interpreter;\n MNN::Session *ultraface_session = nullptr;\n MNN::Tensor *input_tensor = nullptr;\n\n int num_thread;\n int image_w;\n int image_h;\n\n int in_w;\n int in_h;\n int num_anchors;\n\n float score_threshold;\n float iou_threshold;\n\n\n const float mean_vals[3] = {127, 127, 127};\n const float norm_vals[3] = {1.0 / 128, 1.0 / 128, 1.0 / 128};\n\n const float center_variance = 0.1;\n const float size_variance = 0.2;\n const std::vector> min_boxes = {\n {10.0f, 16.0f, 24.0f},\n {32.0f, 48.0f},\n {64.0f, 96.0f},\n {128.0f, 192.0f, 256.0f}};\n const std::vector strides = {8.0, 16.0, 32.0, 64.0};\n std::vector> featuremap_size;\n std::vector> shrinkage_size;\n std::vector w_h_list;\n\n std::vector> priors = {};\n};\n\n#endif /* UltraFace_hpp */\n" }, { "path": "MNN/src/main.cpp", "content": "// Created by Linzaer on 2019/11/15.\n// Copyright © 2019 Linzaer. All rights reserved.\n\n#include \"UltraFace.hpp\"\n#include \n#include \n\nusing namespace std;\n\nint main(int argc, char **argv) {\n if (argc <= 2) {\n fprintf(stderr, \"Usage: %s [image files...]\\n\", argv[0]);\n return 1;\n }\n\n string mnn_path = argv[1];\n UltraFace ultraface(mnn_path, 320, 240, 4, 0.65); // config model input\n\n for (int i = 2; i < argc; i++) {\n string image_file = argv[i];\n cout << \"Processing \" << image_file << endl;\n\n cv::Mat frame = cv::imread(image_file);\n auto start = chrono::steady_clock::now();\n vector face_info;\n ultraface.detect(frame, face_info);\n\n for (auto face : face_info) {\n cv::Point pt1(face.x1, face.y1);\n cv::Point pt2(face.x2, face.y2);\n cv::rectangle(frame, pt1, pt2, cv::Scalar(0, 255, 0), 2);\n }\n\n auto end = chrono::steady_clock::now();\n chrono::duration elapsed = end - start;\n cout << \"all time: \" << elapsed.count() << \" s\" << endl;\n cv::imshow(\"UltraFace\", frame);\n cv::waitKey();\n string result_name = \"result\" + to_string(i) + \".jpg\";\n cv::imwrite(result_name, frame);\n }\n return 0;\n}\n" }, { "path": "README.md", "content": "[English](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB ) | [中文简体](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/README_CN.md)\n# Ultra-Light-Fast-Generic-Face-Detector-1MB \n# Ultra-lightweight face detection model\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/27.jpg)\nThis model is a lightweight facedetection model designed for edge computing devices.\n\n- In terms of model size, the default FP32 precision (.pth) file size is **1.04~1.1MB**, and the inference framework int8 quantization size is about **300KB**.\n- In terms of the calculation amount of the model, the input resolution of 320x240 is about **90~109 MFlops**.\n- There are two versions of the model, version-slim (network backbone simplification,slightly faster) and version-RFB (with the modified RFB module, higher precision).\n- Widerface training pre-training model with different input resolutions of 320x240 and 640x480 is provided to better work in different application scenarios.\n- Support for onnx export for ease of migration and inference.\n- [Provide NCNN C++ inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/ncnn).\n- [Provide MNN C++ inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN), [MNN Python inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN/python), [FP32/INT8 quantized models](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN/model).\n- [Provide Caffe model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe/model) and [onnx2caffe conversion code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe).\n- [Caffe python inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_caffe_inference.py) and [OpencvDNN inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_opencvdnn_inference.py).\n \n## Tested the environment that works\n- Ubuntu16.04、Ubuntu18.04、Windows 10（for inference）\n- Python3.6\n- Pytorch1.2\n- CUDA10.0 + CUDNN7.6\n\n## Accuracy, speed, model size comparison\nThe training set is the VOC format data set generated by using the cleaned widerface labels provided by [Retinaface](https://github.com/deepinsight/insightface/blob/master/RetinaFace/README.md) in conjunction with the widerface data set (PS: the following test results were obtained by myself, and the results may be partially inconsistent).\n### Widerface test\n- Test accuracy in the WIDER FACE val set (single-scale input resolution: **320*240 or scaling by the maximum side length of 320**)\n\nModel|Easy Set|Medium Set|Hard Set\n------|--------|----------|--------\nlibfacedetection v1（caffe）|0.65 |0.5 |0.233\nlibfacedetection v2（caffe）|0.714 |0.585 |0.306\nRetinaface-Mobilenet-0.25 (Mxnet) |0.745|0.553|0.232\nversion-slim|0.77 |0.671 |0.395\nversion-RFB|**0.787** |**0.698** |**0.438**\n\n\n- Test accuracy in the WIDER FACE val set (single-scale input resolution: **VGA 640*480 or scaling by the maximum side length of 640** )\n\nModel|Easy Set|Medium Set|Hard Set\n------|--------|----------|--------\nlibfacedetection v1（caffe）|0.741 |0.683 |0.421\nlibfacedetection v2（caffe）|0.773 |0.718 |0.485\nRetinaface-Mobilenet-0.25 (Mxnet) |**0.879**|0.807|0.481\nversion-slim|0.853 |0.819 |0.539\nversion-RFB|0.855 |**0.822** |**0.579**\n\n> - This part mainly tests the effect of the test set under the medium and small resolutions.\n> - RetinaFace-mnet (Retinaface-Mobilenet-0.25), from a great job [insightface](https://github.com/deepinsight/insightface), when testing this network, the original image is scaled by 320 or 640 as the maximum side length, so the face will not be deformed, and the rest of the networks will have a fixed size resize. At the same time, the result of the RetinaFace-mnet optimal 1600 single-scale val set was 0.887 (Easy) / 0.87 (Medium) / 0.791 (Hard).\n\n### Terminal device inference speed\n\n- Raspberry Pi 4B MNN Inference Latency **(unit: ms)** (ARM/A72x4/1.5GHz/input resolution: **320x240** /int8 quantization)\n\nModel|1 core|2 core|3 core|4 core\n------|--------|----------|--------|--------\nlibfacedetection v1|**28** |**16**|**12**|9.7\nOfficial Retinaface-Mobilenet-0.25 (Mxnet) |46|25|18.5|15\nversion-slim|29 |**16** |**12**|**9.5**\nversion-RFB|35 |19.6 |14.8| 11\n\n- iPhone 6s Plus MNN (version tag：0.2.1.5) Inference Latency ( input resolution : **320x240** )[Data comes from MNN official](https://www.yuque.com/mnn/en/demo_zoo#bXsRY)\n\nModel|Inference Latency(ms)\n------|--------\n[slim-320](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/MNN/model/version-slim/slim-320.mnn) |6.33\n[RFB-320](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/MNN/model/version-RFB/RFB-320.mnn)|7.8\n\n- [Kendryte K210](https://kendryte.com/) NNCase Inference Latency (RISC-V/400MHz/input resolution: **320x240** /int8 quantization)[Data comes from NNCase](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect)\n\nModel|Inference Latency(ms)\n------|--------\n[slim-320](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect/k210/kpu_fast_facedetect_example/slim-320.kmodel)|65.6\n[RFB-320](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect/k210/kpu_fast_facedetect_example/RFB-320.kmodel)|164.8\n\n### Model size comparison\n- Comparison of several open source lightweight face detection models:\n\nModel|model file size（MB）\n------|--------\nlibfacedetection v1（caffe）| 2.58\nlibfacedetection v2（caffe）| 3.34\nOfficial Retinaface-Mobilenet-0.25 (Mxnet) | 1.68\nversion-slim| **1.04**\nversion-RFB| **1.11** \n\n## Generate VOC format training data set and training process\n\n1. Download the wideface official website dataset or download the training set I provided and extract it into the ./data folder:\n\n (1) The clean widerface data pack after filtering out the 10px*10px small face: [Baidu cloud disk (extraction code: cbiu)](https://pan.baidu.com/s/1MR0ZOKHUP_ArILjbAn03sw) 、[Google Drive](https://drive.google.com/open?id=1OBY-Pk5hkcVBX1dRBOeLI4e4OCvqJRnH )\n \n (2) Complete widerface data compression package without filtering small faces: [Baidu cloud disk (extraction code: ievk)](https://pan.baidu.com/s/1faHNz9ZrtEmr_yw48GW7ZA)、[Google Drive](https://drive.google.com/open?id=1sbBrDRgctEkymIpCh1OZBrU5qBS-SnCP )\n\n2. **(PS: If you download the filtered packets in (1) above, you don't need to perform this step)** Because the wideface has many small and unclear faces, which is not conducive to the convergence of efficient models, it needs to be filtered for training.By default,faces smaller than 10 pixels by 10 pixels will be filtered.\nrun ./data/wider_face_2_voc_add_landmark.py\n```Python\n python3 ./data/wider_face_2_voc_add_landmark.py\n```\nAfter the program is run and finished, the **wider_face_add_lm_10_10** folder will be generated in the ./data directory. The folder data and data package (1) are the same after decompression. The complete directory structure is as follows:\n```Shell\n data/\n retinaface_labels/\n test/\n train/\n val/\n wider_face/\n WIDER_test/\n WIDER_train/\n WIDER_val/\n wider_face_add_lm_10_10/\n Annotations/\n ImageSets/\n JPEGImages/\n wider_face_2_voc_add_landmark.py\n```\n\n3. At this point, the VOC training set is ready. There are two scripts: **train-version-slim.sh** and **train-version-RFB.sh** in the root directory of the project. The former is used to train the **slim version** model, and the latter is used. Training **RFB version** model, the default parameters have been set, if the parameters need to be changed, please refer to the description of each training parameter in **./train.py**.\n\n4. Run **train-version-slim.sh** **train-version-RFB.sh**\n```Shell\nsh train-version-slim.sh or sh train-version-RFB.sh\n```\n\n## Detecting image effects (input resolution: 640x480)\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/26.jpg)\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/2.jpg)\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/4.jpg)\n## PS\n\n- If the actual production scene is medium-distance, large face, and small number of faces, it is recommended to use input size input_size: 320 (320x240) resolution for training, and use 320x240 ,160x120 or 128x96 image size input for inference, such as using the provided pre-training model **version-slim-320.pth** or **version-RFB-320.pth** .\n- If the actual production scene is medium or long distance, medium or small face and large face number, it is recommended to adopt:\n\n (1) Optimal: input size input_size: 640 (640x480) resolution training, and use the same or larger input size for inference, such as using the provided pre-training model **version-slim-640.pth** or **version-RFB-640.pth** for inference, lower False positives.\n \n (2) Sub-optimal: input size input_size: 320 (320x240) resolution training, and use 480x360 or 640x480 size input for predictive reasoning, more sensitive to small faces, false positives will increase.\n \n- The best results for each scene require adjustment of the input resolution to strike a balance between speed and accuracy.\n- Excessive input resolution will enhance the recall rate of small faces, but it will also increase the false positive rate of large and close-range faces, and the speed of inference will increase exponentially.\n- Too small input resolution will significantly speed up the inference, but it will greatly reduce the recall rate of small faces.\n- The input resolution of the production scene should be as consistent as possible with the input resolution of the model training, and the up and down floating should not be too large.\n\n## TODO LIST\n\n- Add some test data\n\n## Completed list\n - [Widerface test code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/widerface_evaluate)\n - [NCNN C++ inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/ncnn) ([vealocia](https://github.com/vealocia))\n - [MNN C++ inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN), [MNN Python inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN/python)\n - [Caffe model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe/model) and [onnx2caffe conversion code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe)\n - [Caffe python inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_caffe_inference.py) and [OpencvDNN inference code](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_opencvdnn_inference.py)\n \n## Third-party related projects\n - [NNCase C++ inference code](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect)\n - [UltraFaceDotNet (C#)](https://github.com/takuya-takeuchi/UltraFaceDotNet)\n - [faceDetect-ios](https://github.com/Ian778/faceDetect-ios)\n - [Android-FaceDetection-UltraNet-MNN](https://github.com/jackweiwang/Android-FaceDetection-UltraNet-MNN)\n - [Ultra-Tensorflow-Model-Converter](https://github.com/jason9075/Ultra-Light-Fast-Generic-Face-Detector_Tensorflow-Model-Converter)\n - [UltraFace TNN C++ Demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_ultraface.cpp)\n \n## Reference\n- [pytorch-ssd](https://github.com/qfgaohao/pytorch-ssd)\n- [libfacedetection](https://github.com/ShiqiYu/libfacedetection/)\n- [RFBNet](https://github.com/ruinmessi/RFBNet)\n- [RFSong-779](https://github.com/songwsx/RFSong-779)\n- [Retinaface](https://github.com/deepinsight/insightface/blob/master/RetinaFace/README.md)\n" }, { "path": "README_CN.md", "content": "[English](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) | [中文简体](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/README_CN.md )\n# Ultra-Light-Fast-Generic-Face-Detector-1MB \n# 轻量级人脸检测模型\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/27.jpg)\n该模型是针对边缘计算设备设计的轻量人脸检测模型。\n\n - 在模型大小上，默认FP32精度下（.pth）文件大小为 **1.04~1.1MB**，推理框架int8量化后大小为 **300KB** 左右。\n - 在模型计算量上，320x240的输入分辨率下 **90~109 MFlops**左右。\n - 模型有两个版本，version-slim(主干精简速度略快)，version-RFB(加入了修改后的RFB模块，精度更高)。\n - 提供320x240、640x480不同输入分辨率下使用widerface训练的预训练模型，更好的工作于不同的应用场景。\n - 支持onnx导出。\n - [提供NCNN C++推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/ncnn)。\n - [提供MNN C++推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN)、[MNN Python推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN/python)、[FP32/INT8量化后模型](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN/model)。\n - [提供转换后的Caffe model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe/model) 和 [onnx2caffe 转换代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe)。\n - [Caffe python 推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_caffe_inference.py) 和 [OpencvDNN推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_opencvdnn_inference.py)。\n\n## 测试过正常的运行环境\n- Ubuntu16.04、Ubuntu18.04、Windows 10（inference）\n- Python3.6\n- Pytorch1.2\n- CUDA10.0 + CUDNN7.6\n\n## 精度、速度、模型大小比较\n训练集是使用[Retinaface](https://github.com/deepinsight/insightface/blob/master/RetinaFace/README.md )提供的清理过的widerface标签配合widerface数据集生成VOC训练集（PS:以下测试结果为本人测试，结果可能有部分出入）。\n### Widerface测试\n - 在WIDER FACE val集测试精度（单尺度输入分辨率：**320*240 或按最大边长320等比缩放**） \n\n模型|Easy Set|Medium Set|Hard Set\n------|--------|----------|--------\nlibfacedetection v1（caffe）|0.65 |0.5 |0.233\nlibfacedetection v2（caffe）|0.714 |0.585 |0.306\nRetinaface-Mobilenet-0.25 (Mxnet) |0.745|0.553|0.232\nversion-slim|0.77 |0.671 |0.395\nversion-RFB|**0.787** |**0.698** |**0.438**\n\n\n- 在WIDER FACE val集测试精度（单尺度输入分辨率：**VGA 640*480 或按最大边长640等比缩放** ） \n\n模型|Easy Set|Medium Set|Hard Set\n------|--------|----------|--------\nlibfacedetection v1（caffe）|0.741 |0.683 |0.421\nlibfacedetection v2（caffe）|0.773 |0.718 |0.485\nRetinaface-Mobilenet-0.25 (Mxnet) |**0.879**|0.807|0.481\nversion-slim|0.853 |0.819 |0.539\nversion-RFB|0.855 |**0.822** |**0.579**\n\n> - 该部分主要是测试模型在中小分辨率下的测试集效果。\n> - RetinaFace-mnet（Retinaface-Mobilenet-0.25），来自于很棒的工作[insightface](https://github.com/deepinsight/insightface)，测试该网络时是将原图按最大边长320或者640等比缩放，所以人脸不会形变,其余网络采用固定尺寸resize。同时RetinaFace-mnet最优1600单尺度val测试集结果为0.887(Easy)/0.87(Medium)/0.791(Hard)。\n\n### 终端设备推理速度\n\n- 树莓派4B MNN推理测试耗时 **(单位：ms)**（ARM/A72x4/1.5GHz/输入分辨率 : **320x240** /int8 量化 ） \n\n模型|1核|2核|3核|4核\n------|--------|----------|--------|--------\nlibfacedetection v1|**28** |**16**|**12**|9.7\n官方 Retinaface-Mobilenet-0.25 (Mxnet) |46|25|18.5|15\nversion-slim|29 |**16** |**12**|**9.5**\nversion-RFB|35 |19.6 |14.8| 11\n\n- iPhone 6s Plus MNN (版本Tag：0.2.1.5) 推理测试耗时（输入分辨率 : **320x240** ）[数据来自MNN官方](https://www.yuque.com/mnn/en/demo_zoo#bXsRY)\n\n模型|耗时（ms）\n------|--------\n[slim-320](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/MNN/model/version-slim/slim-320.mnn) |6.33\n[RFB-320](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/MNN/model/version-RFB/RFB-320.mnn)|7.8\n\n- [Kendryte K210](https://kendryte.com/) NNCase 推理测试耗时 (RISC-V/400MHz/输入分辨率 : **320x240** /int8 量化) [数据来自NNCase](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect)\n\nModel|Inference Latency(ms)\n------|--------\n[slim-320](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect/k210/kpu_fast_facedetect_example/slim-320.kmodel)|65.6\n[RFB-320](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect/k210/kpu_fast_facedetect_example/RFB-320.kmodel)|164.8\n\n### 模型大小比较\n- 若干开源轻量级人脸检测模型大小比较 ：\n\n模型|模型文件大小（MB）\n------|--------\nlibfacedetection v1（caffe）| 2.58\nlibfacedetection v2（caffe）| 3.34\n官方 Retinaface-Mobilenet-0.25 (Mxnet) | 1.68\nversion-slim| **1.04**\nversion-RFB| **1.11** \n\n## 生成VOC格式训练数据集以及训练流程\n\n1. 下载widerface官网数据集或者下载我提供的训练集解压放入./data文件夹内：\n\n （1）过滤掉10px*10px 小人脸后的干净widerface数据压缩包 ：[百度云盘 (提取码：cbiu)](https://pan.baidu.com/s/1MR0ZOKHUP_ArILjbAn03sw ) 、[Google Drive](https://drive.google.com/open?id=1OBY-Pk5hkcVBX1dRBOeLI4e4OCvqJRnH )\n \n （2）未过滤小人脸的完整widerface数据压缩包 ：[百度云盘 (提取码：ievk)](https://pan.baidu.com/s/1faHNz9ZrtEmr_yw48GW7ZA ) 、[Google Drive](https://drive.google.com/open?id=1sbBrDRgctEkymIpCh1OZBrU5qBS-SnCP )\n \n2. **（PS:如果下载的是过滤后的上述(1)中的数据包，则不需要执行这步）** 由于widerface存在很多极小的不清楚的人脸，不太利于高效模型的收敛，所以需要过滤训练，默认过滤人脸大小10像素x10像素以下的人脸。\n运行./data/wider_face_2_voc_add_landmark.py\n```Python\n python3 ./data/wider_face_2_voc_add_landmark.py\n```\n程序运行和完毕后会在./data目录下生成 **wider_face_add_lm_10_10**文件夹，该文件夹数据和数据包（1）解压后相同，完整目录结构如下：\n```Shell\n data/\n retinaface_labels/\n test/\n train/\n val/\n wider_face/\n WIDER_test/\n WIDER_train/\n WIDER_val/\n wider_face_add_lm_10_10/\n Annotations/\n ImageSets/\n JPEGImages/\n wider_face_2_voc_add_landmark.py\n```\n\n3. 至此VOC训练集准备完毕，项目根目录下分别有 **train-version-slim.sh** 和 **train-version-RFB.sh** 两个脚本，前者用于训练**slim版本**模型，后者用于训练**RFB版本**模型，默认参数已设置好，参数如需微调请参考 **./train.py** 中关于各训练超参数的说明。\n\n4. 运行**train-version-slim.sh** 或 **train-version-RFB.sh**即可\n```Shell\nsh train-version-slim.sh 或者 sh train-version-RFB.sh\n```\n\n## 检测图片效果（输入分辨率：640x480）\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/26.jpg)\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/2.jpg)\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/readme_imgs/4.jpg)\n## PS\n\n - 若生产实际场景为中近距离、人脸大、人脸数少，则建议采用输入尺寸input_size：320（320x240）分辨率训练，并采用 320x240/160x120/128x96 图片大小输入进行预测推理，如使用提供的预训练模型 **version-slim-320.pth** 或者 **version-RFB-320.pth** 进行推理。\n - 若生产实际场景为中远距离、人脸中小、人脸数多，则建议采用：\n \n （1）最优：输入尺寸input_size：640（640x480）分辨率训练，并采用同等或更大输入尺寸进行预测推理,如使用提供的预训练模型 **version-slim-640.pth** 或者 **version-RFB-640.pth** 进行推理，更低的误报。\n \n （2）次优：输入尺寸input_size：320（320x240）分辨率训练，并采用480x360或640x480大小输入进行预测推理，对于小人脸更敏感，误报会增加。\n \n - 各个场景的最佳效果需要调整输入分辨率从而在速度和精度中间取得平衡。\n - 过大的输入分辨率虽然会增强小人脸的召回率，但是也会提高大、近距离人脸的误报率，而且推理速度延迟成倍增加。\n - 过小的输入分辨率虽然会明显加快推理速度，但是会大幅降低小人脸的召回率。\n - 生产场景的输入分辨率尽量与模型训练时的输入分辨率保持一致，上下浮动不宜过大。\n \n\n## TODO LIST\n\n - 完善测试数据\n\n \n## 已完成功能清单\n - [Widerface测试代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/widerface_evaluate)\n - [NCNN C++推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/ncnn) ([vealocia](https://github.com/vealocia))\n - [MNN C++推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN)、[MNN Python推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN/python)\n - [Caffe model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe/model) 和 [onnx2caffe 转换代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe)\n - [Caffe python 推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_caffe_inference.py) 和 [OpencvDNN推理代码](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_opencvdnn_inference.py)\n \n## 第三方相关工程\n - [NNCase C++推理代码](https://github.com/kendryte/nncase/tree/master/examples/fast_facedetect)\n - [UltraFaceDotNet (C#)](https://github.com/takuya-takeuchi/UltraFaceDotNet)\n - [faceDetect-ios](https://github.com/Ian778/faceDetect-ios)\n - [Android-FaceDetection-UltraNet-MNN](https://github.com/jackweiwang/Android-FaceDetection-UltraNet-MNN)\n - [Ultra-Tensorflow-Model-Converter](https://github.com/jason9075/Ultra-Light-Fast-Generic-Face-Detector_Tensorflow-Model-Converter)\n - [UltraFace TNN C++ Demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_ultraface.cpp)\n\n## Reference\n - [pytorch-ssd](https://github.com/qfgaohao/pytorch-ssd)\n - [libfacedetection](https://github.com/ShiqiYu/libfacedetection/)\n - [RFBNet](https://github.com/ruinmessi/RFBNet)\n - [RFSong-779](https://github.com/songwsx/RFSong-779)\n - [Retinaface](https://github.com/deepinsight/insightface/blob/master/RetinaFace/README.md)\n" }, { "path": "caffe/MyCaffe.py", "content": "from collections import OrderedDict, Counter\n\nfrom caffe.proto import caffe_pb2\nfrom google import protobuf\nimport six\n\ndef param_name_dict():\n \"\"\"Find out the correspondence between layer names and parameter names.\"\"\"\n\n layer = caffe_pb2.LayerParameter()\n # get all parameter names (typically underscore case) and corresponding\n # type names (typically camel case), which contain the layer names\n # (note that not all parameters correspond to layers, but we'll ignore that)\n param_names = [f.name for f in layer.DESCRIPTOR.fields if f.name.endswith('_param')]\n param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]\n # strip the final '_param' or 'Parameter'\n param_names = [s[:-len('_param')] for s in param_names]\n param_type_names = [s[:-len('Parameter')] for s in param_type_names]\n return dict(zip(param_type_names, param_names))\n\ndef assign_proto(proto, name, val):\n \"\"\"Assign a Python object to a protobuf message, based on the Python\n type (in recursive fashion). Lists become repeated fields/messages, dicts\n become messages, and other types are assigned directly. For convenience,\n repeated fields whose values are not lists are converted to single-element\n lists; e.g., `my_repeated_int_field=3` is converted to\n `my_repeated_int_field=[3]`.\"\"\"\n\n is_repeated_field = hasattr(getattr(proto, name), 'extend')\n if is_repeated_field and not isinstance(val, list):\n val = [val]\n if isinstance(val, list):\n if isinstance(val[0], dict):\n for item in val:\n proto_item = getattr(proto, name).add()\n for k, v in six.iteritems(item):\n assign_proto(proto_item, k, v)\n else:\n getattr(proto, name).extend(val)\n elif isinstance(val, dict):\n for k, v in six.iteritems(val):\n assign_proto(getattr(proto, name), k, v)\n else:\n setattr(proto, name, val)\n\nclass Function(object):\n \"\"\"A Function specifies a layer, its parameters, and its inputs (which\n are Tops from other layers).\"\"\"\n\n def __init__(self, type_name, layer_name, inputs,outputs, **params):\n self.type_name = type_name\n self.inputs = inputs\n self.outputs = outputs\n self.params = params\n self.layer_name = layer_name\n self.ntop = self.params.get('ntop', 1)\n # use del to make sure kwargs are not double-processed as layer params\n if 'ntop' in self.params:\n del self.params['ntop']\n self.in_place = self.params.get('in_place', False)\n if 'in_place' in self.params:\n del self.params['in_place']\n # self.tops = tuple(Top(self, n) for n in range(self.ntop))l\n\n def _get_name(self, names, autonames):\n if self not in names and self.ntop > 0:\n names[self] = self._get_top_name(self.tops[0], names, autonames)\n elif self not in names:\n autonames[self.type_name] += 1\n names[self] = self.type_name + str(autonames[self.type_name])\n return names[self]\n\n def _get_top_name(self, top, names, autonames):\n if top not in names:\n autonames[top.fn.type_name] += 1\n names[top] = top.fn.type_name + str(autonames[top.fn.type_name])\n return names[top]\n\n def _to_proto(self):\n bottom_names = []\n for inp in self.inputs:\n # inp._to_proto(layers, names, autonames)\n bottom_names.append(inp)\n layer = caffe_pb2.LayerParameter()\n layer.type = self.type_name\n layer.bottom.extend(bottom_names)\n\n if self.in_place:\n layer.top.extend(layer.bottom)\n else:\n for top in self.outputs:\n layer.top.append(top)\n layer.name = self.layer_name\n # print(self.type_name + \"...\")\n for k, v in six.iteritems(self.params):\n # special case to handle generic *params\n # print(\"generating \"+k+\"...\")\n\n if k.endswith('param'):\n assign_proto(layer, k, v)\n else:\n try:\n assign_proto(getattr(layer,\n _param_names[self.type_name] + '_param'), k, v)\n except (AttributeError, KeyError):\n assign_proto(layer, k, v)\n\n return layer\n\nclass Layers(object):\n \"\"\"A Layers object is a pseudo-module which generates functions that specify\n layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top\n specifying a 3x3 convolution applied to bottom.\"\"\"\n\n def __getattr__(self, name):\n def layer_fn(*args, **kwargs):\n fn = Function(name, args, kwargs)\n return fn\n return layer_fn\n\n\n\n\n_param_names = param_name_dict()\n\n" }, { "path": "caffe/README.md", "content": "## I added several operator(Transpose/Permute/Softmax) conversion support based on [onnx2caffe](https://github.com/MTlab/onnx2caffe).\n# Convert pytorch to Caffe by ONNX\nThis tool converts [pytorch](https://github.com/pytorch/pytorch) model to [Caffe](https://github.com/BVLC/caffe) model by [ONNX](https://github.com/onnx/onnx) \nonly use for inference\n\n### Dependencies\n* caffe (with python support)\n* pytorch 0.4+ (optional if you only want to convert onnx)\n* onnx \n\n### Current support operation\n* Conv\n* ConvTranspose\n* BatchNormalization\n* MaxPool\n* AveragePool\n* Relu\n* Sigmoid\n* Dropout\n* Gemm (InnerProduct only)\n* Add\n* Mul\n* Reshape\n* Upsample\n* Concat\n* Flatten\n* Transpose/Permute (new)\n* Softmax (new)\n\n## PS\n* You need to use [onnx-simplifier](https://github.com/daquexian/onnx-simplifier) to simplify onnx model and then run convertCaffe.py to convert it into caffe model.\n* You need to install [ssd-caffe](https://github.com/weiliu89/caffe/tree/ssd) and pycaffe of ssd-caffe.\n" }, { "path": "caffe/convertCaffe.py", "content": "from __future__ import print_function\n\nimport sys\n\nimport caffe\nfrom caffe.proto import caffe_pb2\nimport onnx\n\ncaffe.set_mode_cpu()\nsys.path.append('../')\nfrom onnx2caffe._transformers import ConvAddFuser, ConstantsToInitializers\nfrom onnx2caffe._graph import Graph\n\nimport onnx2caffe._operators as cvt\nimport onnx2caffe._weightloader as wlr\nfrom onnx2caffe._error_utils import ErrorHandling\nfrom onnx import shape_inference\n\ntransformers = [\n ConstantsToInitializers(),\n ConvAddFuser(),\n]\n\n\ndef convertToCaffe(graph, prototxt_save_path, caffe_model_save_path):\n exist_edges = []\n layers = []\n exist_nodes = []\n err = ErrorHandling()\n for i in graph.inputs:\n edge_name = i[0]\n input_layer = cvt.make_input(i)\n layers.append(input_layer)\n exist_edges.append(i[0])\n graph.channel_dims[edge_name] = graph.shape_dict[edge_name][1]\n\n for id, node in enumerate(graph.nodes):\n node_name = node.name\n op_type = node.op_type\n inputs = node.inputs\n inputs_tensor = node.input_tensors\n input_non_exist_flag = False\n\n for inp in inputs:\n if inp not in exist_edges and inp not in inputs_tensor:\n input_non_exist_flag = True\n break\n if input_non_exist_flag:\n continue\n\n if op_type not in cvt._ONNX_NODE_REGISTRY:\n err.unsupported_op(node)\n continue\n converter_fn = cvt._ONNX_NODE_REGISTRY[op_type]\n layer = converter_fn(node, graph, err)\n if type(layer) == tuple:\n for l in layer:\n layers.append(l)\n else:\n layers.append(layer)\n outs = node.outputs\n for out in outs:\n exist_edges.append(out)\n\n net = caffe_pb2.NetParameter()\n for id, layer in enumerate(layers):\n layers[id] = layer._to_proto()\n net.layer.extend(layers)\n\n with open(prototxt_save_path, 'w') as f:\n print(net, file=f)\n\n caffe.set_mode_cpu()\n deploy = prototxt_save_path\n net = caffe.Net(deploy,\n caffe.TEST)\n\n for id, node in enumerate(graph.nodes):\n node_name = node.name\n op_type = node.op_type\n inputs = node.inputs\n inputs_tensor = node.input_tensors\n input_non_exist_flag = False\n if op_type not in wlr._ONNX_NODE_REGISTRY:\n err.unsupported_op(node)\n continue\n converter_fn = wlr._ONNX_NODE_REGISTRY[op_type]\n converter_fn(net, node, graph, err)\n\n net.save(caffe_model_save_path)\n return net\n\n\ndef getGraph(onnx_path):\n model = onnx.load(onnx_path)\n model = shape_inference.infer_shapes(model)\n model_graph = model.graph\n graph = Graph.from_onnx(model_graph)\n graph = graph.transformed(transformers)\n graph.channel_dims = {}\n\n return graph\n\n\nif __name__ == \"__main__\":\n onnx_path = \"../models/onnx/version-RFB-320_simplified.onnx\"\n prototxt_path = \"./RFB-320.prototxt\"\n caffemodel_path = \"./RFB-320.caffemodel\"\n graph = getGraph(onnx_path)\n convertToCaffe(graph, prototxt_path, caffemodel_path)\n" }, { "path": "caffe/model/RFB-320/RFB-320.prototxt", "content": "layer {\n name: \"input\"\n type: \"Input\"\n top: \"input\"\n input_param {\n shape {\n dim: 1\n dim: 3\n dim: 240\n dim: 320\n }\n }\n}\nlayer {\n name: \"245\"\n type: \"Convolution\"\n bottom: \"input\"\n top: \"245\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"247\"\n type: \"ReLU\"\n bottom: \"245\"\n top: \"247\"\n}\nlayer {\n name: \"248\"\n type: \"Convolution\"\n bottom: \"247\"\n top: \"248\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 16\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"250\"\n type: \"ReLU\"\n bottom: \"248\"\n top: \"250\"\n}\nlayer {\n name: \"251\"\n type: \"Convolution\"\n bottom: \"250\"\n top: \"251\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"253\"\n type: \"ReLU\"\n bottom: \"251\"\n top: \"253\"\n}\nlayer {\n name: \"254\"\n type: \"Convolution\"\n bottom: \"253\"\n top: \"254\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 32\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"256\"\n type: \"ReLU\"\n bottom: \"254\"\n top: \"256\"\n}\nlayer {\n name: \"257\"\n type: \"Convolution\"\n bottom: \"256\"\n top: \"257\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"259\"\n type: \"ReLU\"\n bottom: \"257\"\n top: \"259\"\n}\nlayer {\n name: \"260\"\n type: \"Convolution\"\n bottom: \"259\"\n top: \"260\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 32\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"262\"\n type: \"ReLU\"\n bottom: \"260\"\n top: \"262\"\n}\nlayer {\n name: \"263\"\n type: \"Convolution\"\n bottom: \"262\"\n top: \"263\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"265\"\n type: \"ReLU\"\n bottom: \"263\"\n top: \"265\"\n}\nlayer {\n name: \"266\"\n type: \"Convolution\"\n bottom: \"265\"\n top: \"266\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 32\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"268\"\n type: \"ReLU\"\n bottom: \"266\"\n top: \"268\"\n}\nlayer {\n name: \"269\"\n type: \"Convolution\"\n bottom: \"268\"\n top: \"269\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"271\"\n type: \"ReLU\"\n bottom: \"269\"\n top: \"271\"\n}\nlayer {\n name: \"272\"\n type: \"Convolution\"\n bottom: \"271\"\n top: \"272\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"274\"\n type: \"ReLU\"\n bottom: \"272\"\n top: \"274\"\n}\nlayer {\n name: \"275\"\n type: \"Convolution\"\n bottom: \"274\"\n top: \"275\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"277\"\n type: \"ReLU\"\n bottom: \"275\"\n top: \"277\"\n}\nlayer {\n name: \"278\"\n type: \"Convolution\"\n bottom: \"277\"\n top: \"278\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"280\"\n type: \"ReLU\"\n bottom: \"278\"\n top: \"280\"\n}\nlayer {\n name: \"281\"\n type: \"Convolution\"\n bottom: \"280\"\n top: \"281\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"283\"\n type: \"ReLU\"\n bottom: \"281\"\n top: \"283\"\n}\nlayer {\n name: \"284\"\n type: \"Convolution\"\n bottom: \"283\"\n top: \"284\"\n convolution_param {\n num_output: 8\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"286\"\n type: \"Convolution\"\n bottom: \"284\"\n top: \"286\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"288\"\n type: \"ReLU\"\n bottom: \"286\"\n top: \"288\"\n}\nlayer {\n name: \"289\"\n type: \"Convolution\"\n bottom: \"288\"\n top: \"289\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 2\n pad_w: 2\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 2\n }\n}\nlayer {\n name: \"291\"\n type: \"Convolution\"\n bottom: \"283\"\n top: \"291\"\n convolution_param {\n num_output: 8\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"293\"\n type: \"Convolution\"\n bottom: \"291\"\n top: \"293\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"295\"\n type: \"ReLU\"\n bottom: \"293\"\n top: \"295\"\n}\nlayer {\n name: \"296\"\n type: \"Convolution\"\n bottom: \"295\"\n top: \"296\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 3\n pad_w: 3\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 3\n }\n}\nlayer {\n name: \"298\"\n type: \"Convolution\"\n bottom: \"283\"\n top: \"298\"\n convolution_param {\n num_output: 8\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"300\"\n type: \"Convolution\"\n bottom: \"298\"\n top: \"300\"\n convolution_param {\n num_output: 12\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"302\"\n type: \"ReLU\"\n bottom: \"300\"\n top: \"302\"\n}\nlayer {\n name: \"303\"\n type: \"Convolution\"\n bottom: \"302\"\n top: \"303\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"305\"\n type: \"ReLU\"\n bottom: \"303\"\n top: \"305\"\n}\nlayer {\n name: \"306\"\n type: \"Convolution\"\n bottom: \"305\"\n top: \"306\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 5\n pad_w: 5\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 5\n }\n}\nlayer {\n name: \"308\"\n type: \"Concat\"\n bottom: \"289\"\n bottom: \"296\"\n bottom: \"306\"\n top: \"308\"\n concat_param {\n axis: 1\n }\n}\nlayer {\n name: \"309\"\n type: \"Convolution\"\n bottom: \"308\"\n top: \"309\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"311\"\n type: \"Convolution\"\n bottom: \"283\"\n top: \"311\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"313\"\n type: \"Eltwise\"\n bottom: \"309\"\n bottom: \"311\"\n top: \"313\"\n eltwise_param {\n operation: SUM\n }\n}\nlayer {\n name: \"314\"\n type: \"ReLU\"\n bottom: \"313\"\n top: \"314\"\n}\nlayer {\n name: \"315\"\n type: \"Convolution\"\n bottom: \"314\"\n top: \"315\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"316\"\n type: \"ReLU\"\n bottom: \"315\"\n top: \"316\"\n}\nlayer {\n name: \"317\"\n type: \"Convolution\"\n bottom: \"316\"\n top: \"317\"\n convolution_param {\n num_output: 6\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"318\"\n type: \"Permute\"\n bottom: \"317\"\n top: \"318\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"328\"\n type: \"Reshape\"\n bottom: \"318\"\n top: \"328\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"329\"\n type: \"Convolution\"\n bottom: \"314\"\n top: \"329\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"330\"\n type: \"ReLU\"\n bottom: \"329\"\n top: \"330\"\n}\nlayer {\n name: \"331\"\n type: \"Convolution\"\n bottom: \"330\"\n top: \"331\"\n convolution_param {\n num_output: 12\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"332\"\n type: \"Permute\"\n bottom: \"331\"\n top: \"332\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"342\"\n type: \"Reshape\"\n bottom: \"332\"\n top: \"342\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"343\"\n type: \"Convolution\"\n bottom: \"314\"\n top: \"343\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"345\"\n type: \"ReLU\"\n bottom: \"343\"\n top: \"345\"\n}\nlayer {\n name: \"346\"\n type: \"Convolution\"\n bottom: \"345\"\n top: \"346\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"348\"\n type: \"ReLU\"\n bottom: \"346\"\n top: \"348\"\n}\nlayer {\n name: \"349\"\n type: \"Convolution\"\n bottom: \"348\"\n top: \"349\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"351\"\n type: \"ReLU\"\n bottom: \"349\"\n top: \"351\"\n}\nlayer {\n name: \"352\"\n type: \"Convolution\"\n bottom: \"351\"\n top: \"352\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"354\"\n type: \"ReLU\"\n bottom: \"352\"\n top: \"354\"\n}\nlayer {\n name: \"355\"\n type: \"Convolution\"\n bottom: \"354\"\n top: \"355\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"357\"\n type: \"ReLU\"\n bottom: \"355\"\n top: \"357\"\n}\nlayer {\n name: \"358\"\n type: \"Convolution\"\n bottom: \"357\"\n top: \"358\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"360\"\n type: \"ReLU\"\n bottom: \"358\"\n top: \"360\"\n}\nlayer {\n name: \"361\"\n type: \"Convolution\"\n bottom: \"360\"\n top: \"361\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"362\"\n type: \"ReLU\"\n bottom: \"361\"\n top: \"362\"\n}\nlayer {\n name: \"363\"\n type: \"Convolution\"\n bottom: \"362\"\n top: \"363\"\n convolution_param {\n num_output: 4\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"364\"\n type: \"Permute\"\n bottom: \"363\"\n top: \"364\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"374\"\n type: \"Reshape\"\n bottom: \"364\"\n top: \"374\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"375\"\n type: \"Convolution\"\n bottom: \"360\"\n top: \"375\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"376\"\n type: \"ReLU\"\n bottom: \"375\"\n top: \"376\"\n}\nlayer {\n name: \"377\"\n type: \"Convolution\"\n bottom: \"376\"\n top: \"377\"\n convolution_param {\n num_output: 8\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"378\"\n type: \"Permute\"\n bottom: \"377\"\n top: \"378\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"388\"\n type: \"Reshape\"\n bottom: \"378\"\n top: \"388\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"389\"\n type: \"Convolution\"\n bottom: \"360\"\n top: \"389\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"391\"\n type: \"ReLU\"\n bottom: \"389\"\n top: \"391\"\n}\nlayer {\n name: \"392\"\n type: \"Convolution\"\n bottom: \"391\"\n top: \"392\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"394\"\n type: \"ReLU\"\n bottom: \"392\"\n top: \"394\"\n}\nlayer {\n name: \"395\"\n type: \"Convolution\"\n bottom: \"394\"\n top: \"395\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 256\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"397\"\n type: \"ReLU\"\n bottom: \"395\"\n top: \"397\"\n}\nlayer {\n name: \"398\"\n type: \"Convolution\"\n bottom: \"397\"\n top: \"398\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"400\"\n type: \"ReLU\"\n bottom: \"398\"\n top: \"400\"\n}\nlayer {\n name: \"401\"\n type: \"Convolution\"\n bottom: \"400\"\n top: \"401\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 256\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"402\"\n type: \"ReLU\"\n bottom: \"401\"\n top: \"402\"\n}\nlayer {\n name: \"403\"\n type: \"Convolution\"\n bottom: \"402\"\n top: \"403\"\n convolution_param {\n num_output: 4\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"404\"\n type: \"Permute\"\n bottom: \"403\"\n top: \"404\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"414\"\n type: \"Reshape\"\n bottom: \"404\"\n top: \"414\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"415\"\n type: \"Convolution\"\n bottom: \"400\"\n top: \"415\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 256\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"416\"\n type: \"ReLU\"\n bottom: \"415\"\n top: \"416\"\n}\nlayer {\n name: \"417\"\n type: \"Convolution\"\n bottom: \"416\"\n top: \"417\"\n convolution_param {\n num_output: 8\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"418\"\n type: \"Permute\"\n bottom: \"417\"\n top: \"418\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"428\"\n type: \"Reshape\"\n bottom: \"418\"\n top: \"428\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"429\"\n type: \"Convolution\"\n bottom: \"400\"\n top: \"429\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"430\"\n type: \"ReLU\"\n bottom: \"429\"\n top: \"430\"\n}\nlayer {\n name: \"431\"\n type: \"Convolution\"\n bottom: \"430\"\n top: \"431\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"432\"\n type: \"ReLU\"\n bottom: \"431\"\n top: \"432\"\n}\nlayer {\n name: \"433\"\n type: \"Convolution\"\n bottom: \"432\"\n top: \"433\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"434\"\n type: \"ReLU\"\n bottom: \"433\"\n top: \"434\"\n}\nlayer {\n name: \"435\"\n type: \"Convolution\"\n bottom: \"434\"\n top: \"435\"\n convolution_param {\n num_output: 6\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"436\"\n type: \"Permute\"\n bottom: \"435\"\n top: \"436\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"446\"\n type: \"Reshape\"\n bottom: \"436\"\n top: \"446\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"447\"\n type: \"Convolution\"\n bottom: \"434\"\n top: \"447\"\n convolution_param {\n num_output: 12\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"448\"\n type: \"Permute\"\n bottom: \"447\"\n top: \"448\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"458\"\n type: \"Reshape\"\n bottom: \"448\"\n top: \"458\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"459\"\n type: \"Concat\"\n bottom: \"328\"\n bottom: \"374\"\n bottom: \"414\"\n bottom: \"446\"\n top: \"459\"\n concat_param {\n axis: 1\n }\n}\nlayer {\n name: \"boxes\"\n type: \"Concat\"\n bottom: \"342\"\n bottom: \"388\"\n bottom: \"428\"\n bottom: \"458\"\n top: \"boxes\"\n concat_param {\n axis: 1\n }\n}\nlayer {\n name: \"scores\"\n type: \"Softmax\"\n bottom: \"459\"\n top: \"scores\"\n softmax_param {\n axis: 2\n }\n}\n\n" }, { "path": "caffe/model/Slim-320/slim-320.prototxt", "content": "layer {\n name: \"input\"\n type: \"Input\"\n top: \"input\"\n input_param {\n shape {\n dim: 1\n dim: 3\n dim: 240\n dim: 320\n }\n }\n}\nlayer {\n name: \"185\"\n type: \"Convolution\"\n bottom: \"input\"\n top: \"185\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"187\"\n type: \"ReLU\"\n bottom: \"185\"\n top: \"187\"\n}\nlayer {\n name: \"188\"\n type: \"Convolution\"\n bottom: \"187\"\n top: \"188\"\n convolution_param {\n num_output: 16\n bias_term: true\n group: 16\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"190\"\n type: \"ReLU\"\n bottom: \"188\"\n top: \"190\"\n}\nlayer {\n name: \"191\"\n type: \"Convolution\"\n bottom: \"190\"\n top: \"191\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"193\"\n type: \"ReLU\"\n bottom: \"191\"\n top: \"193\"\n}\nlayer {\n name: \"194\"\n type: \"Convolution\"\n bottom: \"193\"\n top: \"194\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 32\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"196\"\n type: \"ReLU\"\n bottom: \"194\"\n top: \"196\"\n}\nlayer {\n name: \"197\"\n type: \"Convolution\"\n bottom: \"196\"\n top: \"197\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"199\"\n type: \"ReLU\"\n bottom: \"197\"\n top: \"199\"\n}\nlayer {\n name: \"200\"\n type: \"Convolution\"\n bottom: \"199\"\n top: \"200\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 32\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"202\"\n type: \"ReLU\"\n bottom: \"200\"\n top: \"202\"\n}\nlayer {\n name: \"203\"\n type: \"Convolution\"\n bottom: \"202\"\n top: \"203\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"205\"\n type: \"ReLU\"\n bottom: \"203\"\n top: \"205\"\n}\nlayer {\n name: \"206\"\n type: \"Convolution\"\n bottom: \"205\"\n top: \"206\"\n convolution_param {\n num_output: 32\n bias_term: true\n group: 32\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"208\"\n type: \"ReLU\"\n bottom: \"206\"\n top: \"208\"\n}\nlayer {\n name: \"209\"\n type: \"Convolution\"\n bottom: \"208\"\n top: \"209\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"211\"\n type: \"ReLU\"\n bottom: \"209\"\n top: \"211\"\n}\nlayer {\n name: \"212\"\n type: \"Convolution\"\n bottom: \"211\"\n top: \"212\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"214\"\n type: \"ReLU\"\n bottom: \"212\"\n top: \"214\"\n}\nlayer {\n name: \"215\"\n type: \"Convolution\"\n bottom: \"214\"\n top: \"215\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"217\"\n type: \"ReLU\"\n bottom: \"215\"\n top: \"217\"\n}\nlayer {\n name: \"218\"\n type: \"Convolution\"\n bottom: \"217\"\n top: \"218\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"220\"\n type: \"ReLU\"\n bottom: \"218\"\n top: \"220\"\n}\nlayer {\n name: \"221\"\n type: \"Convolution\"\n bottom: \"220\"\n top: \"221\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"223\"\n type: \"ReLU\"\n bottom: \"221\"\n top: \"223\"\n}\nlayer {\n name: \"224\"\n type: \"Convolution\"\n bottom: \"223\"\n top: \"224\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"226\"\n type: \"ReLU\"\n bottom: \"224\"\n top: \"226\"\n}\nlayer {\n name: \"227\"\n type: \"Convolution\"\n bottom: \"226\"\n top: \"227\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"229\"\n type: \"ReLU\"\n bottom: \"227\"\n top: \"229\"\n}\nlayer {\n name: \"230\"\n type: \"Convolution\"\n bottom: \"229\"\n top: \"230\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"231\"\n type: \"ReLU\"\n bottom: \"230\"\n top: \"231\"\n}\nlayer {\n name: \"232\"\n type: \"Convolution\"\n bottom: \"231\"\n top: \"232\"\n convolution_param {\n num_output: 6\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"233\"\n type: \"Permute\"\n bottom: \"232\"\n top: \"233\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"243\"\n type: \"Reshape\"\n bottom: \"233\"\n top: \"243\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"244\"\n type: \"Convolution\"\n bottom: \"229\"\n top: \"244\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"245\"\n type: \"ReLU\"\n bottom: \"244\"\n top: \"245\"\n}\nlayer {\n name: \"246\"\n type: \"Convolution\"\n bottom: \"245\"\n top: \"246\"\n convolution_param {\n num_output: 12\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"247\"\n type: \"Permute\"\n bottom: \"246\"\n top: \"247\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"257\"\n type: \"Reshape\"\n bottom: \"247\"\n top: \"257\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"258\"\n type: \"Convolution\"\n bottom: \"229\"\n top: \"258\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"260\"\n type: \"ReLU\"\n bottom: \"258\"\n top: \"260\"\n}\nlayer {\n name: \"261\"\n type: \"Convolution\"\n bottom: \"260\"\n top: \"261\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"263\"\n type: \"ReLU\"\n bottom: \"261\"\n top: \"263\"\n}\nlayer {\n name: \"264\"\n type: \"Convolution\"\n bottom: \"263\"\n top: \"264\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"266\"\n type: \"ReLU\"\n bottom: \"264\"\n top: \"266\"\n}\nlayer {\n name: \"267\"\n type: \"Convolution\"\n bottom: \"266\"\n top: \"267\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"269\"\n type: \"ReLU\"\n bottom: \"267\"\n top: \"269\"\n}\nlayer {\n name: \"270\"\n type: \"Convolution\"\n bottom: \"269\"\n top: \"270\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"272\"\n type: \"ReLU\"\n bottom: \"270\"\n top: \"272\"\n}\nlayer {\n name: \"273\"\n type: \"Convolution\"\n bottom: \"272\"\n top: \"273\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"275\"\n type: \"ReLU\"\n bottom: \"273\"\n top: \"275\"\n}\nlayer {\n name: \"276\"\n type: \"Convolution\"\n bottom: \"275\"\n top: \"276\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"277\"\n type: \"ReLU\"\n bottom: \"276\"\n top: \"277\"\n}\nlayer {\n name: \"278\"\n type: \"Convolution\"\n bottom: \"277\"\n top: \"278\"\n convolution_param {\n num_output: 4\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"279\"\n type: \"Permute\"\n bottom: \"278\"\n top: \"279\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"289\"\n type: \"Reshape\"\n bottom: \"279\"\n top: \"289\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"290\"\n type: \"Convolution\"\n bottom: \"275\"\n top: \"290\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"291\"\n type: \"ReLU\"\n bottom: \"290\"\n top: \"291\"\n}\nlayer {\n name: \"292\"\n type: \"Convolution\"\n bottom: \"291\"\n top: \"292\"\n convolution_param {\n num_output: 8\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"293\"\n type: \"Permute\"\n bottom: \"292\"\n top: \"293\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"303\"\n type: \"Reshape\"\n bottom: \"293\"\n top: \"303\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"304\"\n type: \"Convolution\"\n bottom: \"275\"\n top: \"304\"\n convolution_param {\n num_output: 128\n bias_term: true\n group: 128\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"306\"\n type: \"ReLU\"\n bottom: \"304\"\n top: \"306\"\n}\nlayer {\n name: \"307\"\n type: \"Convolution\"\n bottom: \"306\"\n top: \"307\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"309\"\n type: \"ReLU\"\n bottom: \"307\"\n top: \"309\"\n}\nlayer {\n name: \"310\"\n type: \"Convolution\"\n bottom: \"309\"\n top: \"310\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 256\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"312\"\n type: \"ReLU\"\n bottom: \"310\"\n top: \"312\"\n}\nlayer {\n name: \"313\"\n type: \"Convolution\"\n bottom: \"312\"\n top: \"313\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"315\"\n type: \"ReLU\"\n bottom: \"313\"\n top: \"315\"\n}\nlayer {\n name: \"316\"\n type: \"Convolution\"\n bottom: \"315\"\n top: \"316\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 256\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"317\"\n type: \"ReLU\"\n bottom: \"316\"\n top: \"317\"\n}\nlayer {\n name: \"318\"\n type: \"Convolution\"\n bottom: \"317\"\n top: \"318\"\n convolution_param {\n num_output: 4\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"319\"\n type: \"Permute\"\n bottom: \"318\"\n top: \"319\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"329\"\n type: \"Reshape\"\n bottom: \"319\"\n top: \"329\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"330\"\n type: \"Convolution\"\n bottom: \"315\"\n top: \"330\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 256\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"331\"\n type: \"ReLU\"\n bottom: \"330\"\n top: \"331\"\n}\nlayer {\n name: \"332\"\n type: \"Convolution\"\n bottom: \"331\"\n top: \"332\"\n convolution_param {\n num_output: 8\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"333\"\n type: \"Permute\"\n bottom: \"332\"\n top: \"333\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"343\"\n type: \"Reshape\"\n bottom: \"333\"\n top: \"343\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"344\"\n type: \"Convolution\"\n bottom: \"315\"\n top: \"344\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"345\"\n type: \"ReLU\"\n bottom: \"344\"\n top: \"345\"\n}\nlayer {\n name: \"346\"\n type: \"Convolution\"\n bottom: \"345\"\n top: \"346\"\n convolution_param {\n num_output: 64\n bias_term: true\n group: 64\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 2\n stride_w: 2\n dilation: 1\n }\n}\nlayer {\n name: \"347\"\n type: \"ReLU\"\n bottom: \"346\"\n top: \"347\"\n}\nlayer {\n name: \"348\"\n type: \"Convolution\"\n bottom: \"347\"\n top: \"348\"\n convolution_param {\n num_output: 256\n bias_term: true\n group: 1\n pad_h: 0\n pad_w: 0\n kernel_h: 1\n kernel_w: 1\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"349\"\n type: \"ReLU\"\n bottom: \"348\"\n top: \"349\"\n}\nlayer {\n name: \"350\"\n type: \"Convolution\"\n bottom: \"349\"\n top: \"350\"\n convolution_param {\n num_output: 6\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"351\"\n type: \"Permute\"\n bottom: \"350\"\n top: \"351\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"361\"\n type: \"Reshape\"\n bottom: \"351\"\n top: \"361\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 2\n }\n }\n}\nlayer {\n name: \"362\"\n type: \"Convolution\"\n bottom: \"349\"\n top: \"362\"\n convolution_param {\n num_output: 12\n bias_term: true\n group: 1\n pad_h: 1\n pad_w: 1\n kernel_h: 3\n kernel_w: 3\n stride_h: 1\n stride_w: 1\n dilation: 1\n }\n}\nlayer {\n name: \"363\"\n type: \"Permute\"\n bottom: \"362\"\n top: \"363\"\n permute_param {\n order: 0\n order: 2\n order: 3\n order: 1\n }\n}\nlayer {\n name: \"373\"\n type: \"Reshape\"\n bottom: \"363\"\n top: \"373\"\n reshape_param {\n shape {\n dim: 1\n dim: -1\n dim: 4\n }\n }\n}\nlayer {\n name: \"374\"\n type: \"Concat\"\n bottom: \"243\"\n bottom: \"289\"\n bottom: \"329\"\n bottom: \"361\"\n top: \"374\"\n concat_param {\n axis: 1\n }\n}\nlayer {\n name: \"boxes\"\n type: \"Concat\"\n bottom: \"257\"\n bottom: \"303\"\n bottom: \"343\"\n bottom: \"373\"\n top: \"boxes\"\n concat_param {\n axis: 1\n }\n}\nlayer {\n name: \"scores\"\n type: \"Softmax\"\n bottom: \"374\"\n top: \"scores\"\n softmax_param {\n axis: 2\n }\n}\n\n" }, { "path": "caffe/onnx2caffe/__init__.py", "content": "" }, { "path": "caffe/onnx2caffe/_error_utils.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom typing import Dict, Text, Any, Callable\nfrom ._graph import Node, Graph\n\nclass ErrorHandling(object):\n '''\n To handle errors and addition of custom layers\n '''\n\n def __init__(self,\n add_custom_layers = False, # type: bool\n custom_conversion_functions = dict(), # type: Dict[Text, Any]\n custom_layer_nodes = [], # type : List[Node]\n ):\n # type: (...) -> None\n self.add_custom_layers = add_custom_layers\n self.custom_conversion_functions = custom_conversion_functions\n self.custom_layer_nodes = custom_layer_nodes\n\n\n def unsupported_op(self,\n node, # type: Node\n ):\n # type: (...) -> Callable[[Any, Node, Graph, ErrorHandling], None]\n '''\n Either raise an error for an unsupported op type or return custom layer add function\n '''\n if self.add_custom_layers:\n from ._operators import _convert_custom\n return _convert_custom\n else:\n raise TypeError(\n \"ONNX node of type {} is not supported.\\n\".format(node.op_type,)\n )\n\n\n def unsupported_op_configuration(self,\n node, # type: Node\n err_message, # type: Text\n ):\n raise TypeError(\n \"Error while converting op of type: {}. Error message: {}\\n\".format(node.op_type, err_message, )\n )\n\n\n def missing_initializer(self,\n node, # type: Node\n err_message, # type: Text\n ):\n # type: (...) -> None\n '''\n Missing initializer error\n '''\n raise ValueError(\n \"Missing initializer error in op of type {}, with input name = {}, \"\n \"output name = {}. Error message: {}\\n\".\n format(node.op_type, node.inputs[0], node.outputs[0], err_message)\n )\n\n\n\n" }, { "path": "caffe/onnx2caffe/_graph.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom onnx import numpy_helper, ValueInfoProto, AttributeProto, GraphProto, NodeProto, TensorProto, TensorShapeProto\nfrom typing import Any, Text, Iterable, List, Dict, Sequence, Optional, Tuple, Union\nfrom typing_extensions import Protocol\nimport numpy as np\n\n\nclass Transformer(Protocol):\n def __call__(self, graph): # type: (Graph) -> Graph\n pass\n\n\nEdgeInfo = Tuple[Text, Any, TensorShapeProto]\nAttributeValue = Any # TODO Union[Sequence[float], Sequence[int], Sequence[Text], Sequence[TensorProto], Sequence[GraphProto]]\n\ndef _input_from_onnx_input(input): # type: (ValueInfoProto) -> EdgeInfo\n name = input.name\n type = input.type.tensor_type.elem_type\n shape = tuple([d.dim_value for d in input.type.tensor_type.shape.dim])\n return (name, type, shape)\n\n\ndef _convertAttributeProto(onnx_arg): # type: (AttributeProto) -> AttributeValue\n \"\"\"\n Convert an ONNX AttributeProto into an appropriate Python object\n for the type.\n NB: Tensor attribute gets returned as numpy array\n \"\"\"\n if onnx_arg.HasField('f'):\n return onnx_arg.f\n elif onnx_arg.HasField('i'):\n return onnx_arg.i\n elif onnx_arg.HasField('s'):\n return onnx_arg.s\n elif onnx_arg.HasField('t'):\n return numpy_helper.to_array(onnx_arg.t)\n elif len(onnx_arg.floats):\n return list(onnx_arg.floats)\n elif len(onnx_arg.ints):\n return list(onnx_arg.ints)\n elif len(onnx_arg.strings):\n return list(onnx_arg.strings)\n else:\n raise ValueError(\"Unsupported ONNX attribute: {}\".format(onnx_arg))\n\n\nclass Attributes(Dict[Text, Any]):\n @staticmethod\n def from_onnx(args): # type: (Iterable[AttributeProto]) -> Attributes\n d = Attributes()\n for arg in args:\n d[arg.name] = _convertAttributeProto(arg)\n return d\n\n\nclass Node(object):\n def __init__(self,\n name, # type: Optional[Text]\n op_type, # type: Text\n attrs, # type: Dict[Text, AttributeValue]\n inputs, # type: List[Text]\n outputs, # type: List[Text]\n ):\n # type: (...) -> None\n self.name = name\n self.op_type = op_type\n self.attrs = attrs\n self.inputs = inputs\n self.outputs = outputs\n self.input_tensors = {} # type: Dict[Text, np._ArrayLike[Any]]\n self.parents = [] # type: List[Node]\n self.children = [] # type: List[Node]\n self.metadata = {} # type: Dict[Any, Any]\n\n def add_parent(self, parent_node): # type: (Node) -> None\n assert parent_node not in self.parents\n self.parents.append(parent_node)\n if self not in parent_node.children:\n parent_node.children.append(self)\n\n def add_child(self, child_node): # type: (Node) -> None\n assert child_node not in self.children\n self.children.append(child_node)\n if self not in child_node.parents:\n child_node.parents.append(self)\n\n def get_only_parent(self): # type: () -> Node\n if len(self.parents) != 1:\n raise ValueError('Node ({}) expected to have 1 parent. Found {}.'\n .format(self, len(self.parents)))\n return self.parents[0]\n\n @staticmethod\n def from_onnx(node): # type: (NodeProto) -> Node\n attrs = Attributes.from_onnx(node.attribute)\n name = Text(node.name)\n if len(name) == 0:\n name = \"_\".join(node.output)\n return Node(\n name, node.op_type, attrs, list(node.input), list(node.output)\n )\n\n\nclass Graph(object):\n def __init__(self,\n nodes, # type: List[Node]\n inputs, # type: List[EdgeInfo]\n outputs, # type: List[EdgeInfo]\n shape_dict, # type: Dict[Text,Tuple[int,...]]\n ):\n # type: (...) -> None\n self.nodes = nodes\n self.inputs = inputs\n self.outputs = outputs\n self.shape_dict = shape_dict # data blob name to its shape\n\n # data blob name to the list of op types it feeds into\n self.blob_to_op_type = {} # type: Dict[Text, List[Text]]\n # data blob name to the op_type that generates it\n self.blob_from_op_type = {} # type: Dict[Text, Text]\n\n for node_ in nodes:\n for input_ in node_.inputs:\n if input_ in self.blob_to_op_type:\n self.blob_to_op_type[input_].append(node_.op_type)\n else:\n self.blob_to_op_type[input_] = [node_.op_type]\n for output_ in node_.outputs:\n if output_ in self.blob_from_op_type:\n raise ValueError(\"Data blob: %s, is generated by more than 1 op\" %(output_))\n self.blob_from_op_type[output_] = node_.op_type\n\n\n def transformed(self, transformers): # type: (Iterable[Transformer]) -> Graph\n graph = self\n for transformer in transformers:\n graph = transformer(graph)\n return graph\n\n def has_edge_name(self, name): # type: (Text) -> bool\n '''\n Check if name is already used for graph inputs/outputs or for nodes\n inputs/outputs\n '''\n names = set()\n for input in self.inputs:\n names.add(input[0])\n for output in self.outputs:\n names.add(output[0])\n for node in self.nodes:\n names.update(node.inputs)\n names.update(node.outputs)\n return name in names\n\n def get_unique_edge_name(self, name): # type: (Text) -> Text\n n_ = name\n i = 0\n while self.has_edge_name(n_):\n n_ = \"{}_{}\".format(name, i)\n i += 1\n return n_\n\n @staticmethod\n def from_onnx(graph): # type: (GraphProto) -> Graph\n input_tensors = {\n t.name: numpy_helper.to_array(t) for t in graph.initializer\n }\n nodes_ = []\n nodes_by_input = {} # type: Dict[Text, List[Node]]\n nodes_by_output = {}\n for node in graph.node:\n node_ = Node.from_onnx(node)\n for input_ in node_.inputs:\n if input_ in input_tensors:\n node_.input_tensors[input_] = input_tensors[input_]\n else:\n if input_ in nodes_by_input:\n input_nodes = nodes_by_input[input_]\n else:\n input_nodes = []\n nodes_by_input[input_] = input_nodes\n input_nodes.append(node_)\n for output_ in node_.outputs:\n nodes_by_output[output_] = node_\n nodes_.append(node_)\n\n inputs = []\n for i in graph.input:\n if i.name not in input_tensors:\n inputs.append(_input_from_onnx_input(i))\n\n outputs = []\n for o in graph.output:\n outputs.append(_input_from_onnx_input(o))\n\n for node_ in nodes_:\n for input_ in node_.inputs:\n if input_ in nodes_by_output:\n node_.parents.append(nodes_by_output[input_])\n for output_ in node_.outputs:\n if output_ in nodes_by_input:\n node_.children.extend(nodes_by_input[output_])\n\n # Dictionary to hold the \"value_info\" field from ONNX graph\n shape_dict = {} # type: Dict[Text,Tuple[int,...]]\n\n def extract_value_info(shape_dict, # type: Dict[Text,Tuple[int,...]]\n value_info, # type: ValueInfoProto[...]\n ):\n # type: (...) -> None\n shape_dict[value_info.name] = tuple([int(dim.dim_value) for dim in value_info.type.tensor_type.shape.dim])\n\n for value_info in graph.value_info:\n extract_value_info(shape_dict, value_info)\n for value_info in graph.input:\n extract_value_info(shape_dict, value_info)\n for value_info in graph.output:\n extract_value_info(shape_dict, value_info)\n\n\n return Graph(nodes_, inputs, outputs, shape_dict)\n" }, { "path": "caffe/onnx2caffe/_operators.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom caffe import params as P\nimport math\nimport numpy as np\nfrom ._graph import Node, Graph\nfrom MyCaffe import Function as myf\n\n\ndef _compare(a, b, encoding=\"utf8\"): # type: (Text, Text, Text) -> bool\n if isinstance(a, bytes):\n a = a.decode(encoding)\n if isinstance(b, bytes):\n b = b.decode(encoding)\n return a == b\n\n\ndef make_input(input):\n name = input[0]\n output = input[0]\n output = [output]\n shape = input[2]\n shape = list(shape)\n input_layer = myf(\"Input\", name, [], output, input_param=dict(shape=dict(dim=shape)))\n return input_layer\n\n\ndef _convert_conv(node, graph, err):\n weight_name = node.inputs[1]\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n node_name = node.name\n W = None\n if weight_name in node.input_tensors:\n W = node.input_tensors[weight_name]\n else:\n err.missing_initializer(node,\n \"Weight tensor: {} not found in the graph initializer\".format(weight_name, ))\n is_deconv = False\n if node.op_type.endswith(\"Transpose\"):\n is_deconv = True\n bias_flag = False\n bias = None\n if len(node.inputs) > 2:\n bias = node.input_tensors[node.inputs[2]]\n bias_flag = True\n dilations = node.attrs.get(\"dilations\", [1, 1])\n # groups = 1\n groups = node.attrs.get(\"group\", 1)\n kernel_shape = node.attrs[\"kernel_shape\"]\n pads = node.attrs.get(\"pads\", [0, 0, 0, 0])\n strides = node.attrs[\"strides\"]\n\n layer = myf(\"Convolution\", node_name, [input_name], [output_name],\n kernel_h=kernel_shape[0], kernel_w=kernel_shape[1],\n stride_h=strides[0], stride_w=strides[1], group=groups,\n pad_h=pads[0], pad_w=pads[1],\n num_output=W.shape[0], dilation=dilations[0], bias_term=bias_flag)\n\n graph.channel_dims[output_name] = W.shape[0]\n return layer\n\n\ndef _convert_relu(node, graph, err):\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n name = str(node.name)\n\n if input_name == output_name:\n inplace = True\n else:\n inplace = False\n\n layer = myf(\"ReLU\", name, [input_name], [output_name], in_place=inplace)\n # l_top_relu1 = L.ReLU(l_bottom, name=name, in_place=True)\n\n graph.channel_dims[output_name] = graph.channel_dims[input_name]\n\n return layer\n\n\ndef _convert_sigmoid(node, graph, err):\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n name = str(node.name)\n\n if input_name == output_name:\n inplace = True\n else:\n inplace = False\n\n layer = myf(\"Sigmoid\", name, [input_name], [output_name], in_place=inplace)\n # l_top_relu1 = L.ReLU(l_bottom, name=name, in_place=True)\n\n graph.channel_dims[output_name] = graph.channel_dims[input_name]\n\n return layer\n\n\ndef _convert_BatchNorm(node, graph, err):\n epsilon = node.attrs.get(\"epsilon\", 1e-5)\n scale = node.input_tensors[node.inputs[1]]\n bias = node.input_tensors[node.inputs[2]]\n mean = node.input_tensors[node.inputs[3]]\n var = node.input_tensors[node.inputs[4]]\n node_name = node.name\n\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n\n if input_name == output_name:\n inplace = True\n else:\n inplace = False\n\n bn_layer = myf(\"BatchNorm\", node_name + \"_bn\", [input_name], [output_name], eps=epsilon, use_global_stats=True, in_place=inplace)\n scale_layer = myf(\"Scale\", node_name, [output_name], [output_name], in_place=True, bias_term=True)\n\n graph.channel_dims[output_name] = graph.channel_dims[input_name]\n\n return bn_layer, scale_layer\n\n\ndef _convert_Add(node, graph, err):\n input_name_list = [str(i) for i in node.inputs]\n output_name = str(node.outputs[0])\n node_name = node.name\n\n max_dim = 0\n for name in input_name_list:\n if graph.channel_dims[name] > max_dim:\n max_dim = graph.channel_dims[name]\n\n if 'broadcast' in node.attrs:\n if node.attrs['broadcast'] == 1:\n input_node_number = len(input_name_list)\n if input_node_number != 2:\n return err.unsupported_op_configuration(node, \"Broadcast Add must has 2 input, not {}\".format(input_node_number))\n axis = node.attrs['axis']\n flat_layer = myf(\"Flatten\", node_name + '_flat', [input_name_list[1]], [output_name + '_flat'])\n layer = myf(\"Bias\", node_name, [input_name_list[0], output_name + '_flat'], [output_name], axis=axis)\n # layer = myf(\"Bias\", node_name, input_name_list, [output_name], bias_term = False, axis = axis)\n graph.channel_dims[output_name] = graph.channel_dims[input_name_list[0]]\n return flat_layer, layer\n\n layer = myf(\"Eltwise\", node_name, input_name_list, [output_name], operation=P.Eltwise.SUM)\n graph.channel_dims[output_name] = graph.channel_dims[input_name_list[0]]\n return layer\n\n\ndef _convert_Mul(node, graph, err):\n input_name_list = [str(i) for i in node.inputs]\n output_name = str(node.outputs[0])\n node_name = node.name\n\n # max_dim = 0\n # for name in input_name_list:\n # if graph.channel_dims[name]>max_dim:\n # max_dim = graph.channel_dims[name]\n\n if 'broadcast' in node.attrs:\n if node.attrs['broadcast'] == 1:\n input_node_number = len(input_name_list)\n if input_node_number != 2:\n return err.unsupported_op_configuration(node, \"Broadcast Mul must has 2 input, not {}\".format(input_node_number))\n axis = node.attrs['axis']\n flat_layer = myf(\"Flatten\", node_name + '_flat', [input_name_list[1]], [output_name + '_flat'])\n layer = myf(\"Scale\", node_name, [input_name_list[0], output_name + '_flat'], [output_name], bias_term=False, axis=axis)\n graph.channel_dims[output_name] = graph.channel_dims[input_name_list[0]]\n return flat_layer, layer\n\n layer = myf(\"Eltwise\", node_name, input_name_list, [output_name], operation=P.Eltwise.PROD)\n graph.channel_dims[output_name] = graph.channel_dims[input_name_list[0]]\n return layer\n\n\ndef _convert_Reshape(node, graph, err):\n node_name = node.name\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n if len(node.inputs) == 1:\n shape = tuple(node.attrs.get('shape', ()))\n else:\n shape = tuple(node.input_tensors[node.inputs[1]])\n # if shape == ():\n\n if input_name == output_name:\n inplace = True\n else:\n inplace = False\n if len(shape) == 2:\n layer = myf(\"Flatten\", node_name, [input_name], [output_name], in_place=inplace)\n graph.channel_dims[output_name] = shape[1]\n return layer\n elif len(shape) == 4 or len(shape) == 3:\n graph.channel_dims[output_name] = shape[1]\n layer = myf(\"Reshape\", node_name, [input_name], [output_name], reshape_param=dict(shape=dict(dim=list(shape))))\n return layer\n else:\n return err.unsupported_op_configuration(node, \"Reshape dimention number shall be 2 or 4\")\n\n\ndef _convert_Flatten(node, graph, err):\n node_name = node.name\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n # shape = tuple(node.attrs.get('shape', ()))\n if input_name == output_name:\n inplace = True\n else:\n inplace = False\n layer = myf(\"Flatten\", node_name, [input_name], [output_name], in_place=inplace)\n # graph.channel_dims[output_name] = shape[1]\n return layer\n\n\ndef _convert_Permute(node, graph, err):\n node_name = node.name\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n if len(node.inputs) == 1:\n shape = tuple(node.attrs.get('perm', ()))\n else:\n shape = tuple(node.input_tensors[node.inputs[1]])\n\n if len(shape) == 4:\n layer = myf(\"Permute\", node_name, [input_name], [output_name], permute_param=dict(order=list(shape)))\n return layer\n else:\n return err.unsupported_op_configuration(node, \"Reshape dimention number shall be 2 or 4\")\n\ndef _convert_Softmax(node, graph, err):\n node_name = node.name\n input_name_list = [str(i) for i in node.inputs]\n output_name = str(node.outputs[0])\n axis = node.attrs.get(\"axis\", 1)\n\n layer = myf('Softmax', node_name, input_name_list, [output_name], axis=axis)\n\n return layer\n\ndef _convert_pool(node, graph, err):\n node_name = node.name\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n if node.op_type.endswith(\"MaxPool\"):\n pool_type = P.Pooling.MAX\n elif node.op_type.endswith(\"AveragePool\"):\n pool_type = P.Pooling.AVE\n else:\n return err.unsupported_op_configuration(node, \"Unsupported pool type\")\n\n kernel_shape = node.attrs[\"kernel_shape\"]\n strides = node.attrs.get('strides', [1, 1])\n pads = node.attrs.get('pads', [0, 0, 0, 0])\n\n layer = myf(\"Pooling\", node_name, [input_name], [output_name], pooling_param=dict(pool=pool_type,\n kernel_h=kernel_shape[0],\n kernel_w=kernel_shape[1],\n stride_h=strides[0],\n stride_w=strides[1],\n pad_h=pads[0],\n pad_w=pads[1]))\n graph.channel_dims[output_name] = graph.channel_dims[input_name]\n return layer\n\n\ndef _convert_dropout(node, graph, err):\n node_name = node.name\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n ratio = node.attrs.get('ratio', 0.5)\n layer = myf(\"Dropout\", node_name, [input_name], [output_name], dropout_ratio=ratio)\n graph.channel_dims[output_name] = graph.channel_dims[input_name]\n return layer\n\n\ndef _convert_gemm(node, graph, err):\n node_name = node.name\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n weight_name = node.inputs[1]\n if weight_name in node.input_tensors:\n W = node.input_tensors[weight_name]\n else:\n err.missing_initializer(node,\n \"Weight tensor: {} not found in the graph initializer\".format(weight_name, ))\n return\n\n if node.attrs[\"broadcast\"] != 1 or node.attrs[\"transB\"] != 1:\n return err.unsupported_op_configuration(node, \"Gemm is supported only for inner_product layer\")\n\n b = None\n bias_flag = False\n if len(node.inputs) > 2:\n b = node.input_tensors[node.inputs[2]]\n\n if len(W.shape) != 2 or (b is not None and len(b.shape) != 1):\n return err.unsupported_op_configuration(node, \"Gemm is supported only for inner_product layer\")\n if b is not None:\n bias_flag = True\n if W.shape[0] != b.shape[0]:\n return err.unsupported_op_configuration(node,\n \"Gemm is supported only for inner_product layer\")\n\n layer = myf(\"InnerProduct\", node_name, [input_name], [output_name], num_output=W.shape[0], bias_term=bias_flag)\n graph.channel_dims[output_name] = W.shape[0]\n\n return layer\n\n\ndef _convert_upsample(node, graph, err):\n factor = int(node.attrs[\"height_scale\"])\n node_name = node.name\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n # input_shape = graph.shape_dict[input_name]\n # channels = input_shape[1]\n channels = graph.channel_dims[input_name]\n pad = int(math.ceil((factor - 1) / 2.))\n # layer = myf(\"Deconvolution\", node_name, [input_name], [output_name],\n # kernel_size=2 * factor - factor % 2,\n # stride=factor, group=channels,\n # pad = pad, num_output=channels, bias_term = False)\n mode = node.attrs[\"mode\"]\n # https://github.com/pytorch/pytorch/issues/6900\n if mode == \"bilinear\":\n layer = myf(\"Deconvolution\", node_name, [input_name], [output_name],\n convolution_param=dict(\n num_output=channels,\n kernel_size=2 * factor - factor % 2,\n stride=factor,\n pad=pad,\n group=channels,\n bias_term=False,\n weight_filler=dict(type=\"bilinear_upsampling\")\n ))\n else:\n layer = myf(\"Deconvolution\", node_name, [input_name], [output_name],\n convolution_param=dict(\n num_output=channels,\n kernel_size=factor,\n stride=factor,\n group=channels,\n bias_term=False,\n ))\n\n graph.channel_dims[output_name] = graph.channel_dims[input_name]\n return layer\n\n\ndef _convert_concat(node, graph, err):\n node_name = node.name\n input_name_list = [str(i) for i in node.inputs]\n output_name = str(node.outputs[0])\n axis = node.attrs.get(\"axis\", 1)\n\n layer = myf('Concat', node_name, input_name_list, [output_name], axis=axis)\n if axis == 1:\n dim = 0\n for name in input_name_list:\n dim += graph.channel_dims[name]\n graph.channel_dims[output_name] = dim\n else:\n graph.channel_dims[output_name] = graph.channel_dims[input_name_list[0]]\n\n return layer\n\n\ndef _convert_conv_transpose(node, graph, err):\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n node_name = node.name\n weight_name = node.inputs[1]\n W = None\n if weight_name in node.input_tensors:\n W = node.input_tensors[weight_name]\n else:\n err.missing_initializer(node,\n \"Weight tensor: {} not found in the graph initializer\".format(weight_name, ))\n bias_flag = False\n bias = None\n if len(node.inputs) > 2:\n bias = node.input_tensors[node.inputs[2]]\n bias_flag = True\n dilations = node.attrs.get(\"dilations\", [1, 1])\n # groups = 1\n groups = node.attrs.get(\"group\", 1)\n kernel_shape = node.attrs[\"kernel_shape\"]\n pads = node.attrs.get(\"pads\", [0, 0, 0, 0])\n strides = node.attrs[\"strides\"]\n\n layer = myf('Deconvolution', node_name, [input_name], [output_name],\n convolution_param=dict(\n num_output=W.shape[1],\n kernel_h=kernel_shape[0], kernel_w=kernel_shape[1],\n stride_h=strides[0], stride_w=strides[1],\n group=groups,\n pad_h=pads[0], pad_w=pads[1],\n bias_term=bias_flag,\n ))\n\n graph.channel_dims[output_name] = W.shape[1]\n return layer\n\n # l_top = L.Deconvolution(\n # l_bottom,\n # name=name,\n # convolution_param=dict(\n # num_output=W.shape[1],\n # kernel_h=kernel_h,\n # kernel_w=kernel_w,\n # stride_h=stride_h,\n # stride_w=stride_w,\n # pad_h=pad_h,\n # pad_w=pad_w,\n # group=groups,\n # bias_term=bias_term))\n\n\n_ONNX_NODE_REGISTRY = {\n \"Conv\": _convert_conv,\n \"Relu\": _convert_relu,\n \"BatchNormalization\": _convert_BatchNorm,\n \"Add\": _convert_Add,\n \"Mul\": _convert_Mul,\n \"Reshape\": _convert_Reshape,\n \"MaxPool\": _convert_pool,\n \"AveragePool\": _convert_pool,\n \"Dropout\": _convert_dropout,\n \"Gemm\": _convert_gemm,\n \"Upsample\": _convert_upsample,\n \"Concat\": _convert_concat,\n \"ConvTranspose\": _convert_conv_transpose,\n \"Sigmoid\": _convert_sigmoid,\n \"Flatten\": _convert_Flatten,\n \"Transpose\": _convert_Permute,\n \"Softmax\": _convert_Softmax,\n}\n" }, { "path": "caffe/onnx2caffe/_transformers.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom typing import Sequence, Text, Dict, List\nimport numpy as np\n\nfrom onnx import TensorProto\n\nfrom ._graph import Graph, Node\n\n\nclass NodesFuser(object):\n '''\n An abstract helper for merging nodes\n '''\n def __init__(self,\n num_nodes, # type: int\n ):\n # type: (...) -> None\n assert num_nodes >= 2, \"Algorithm only works if fusing multiple nodes\"\n self.num_nodes = num_nodes\n\n def __call__(self, graph): # type: (Graph) -> Graph\n nodes = graph.nodes\n merged_nodes = {}\n for node in nodes:\n nodes_window = [] # type: List[Node]\n n = node\n for _ in range(self.num_nodes - 1):\n if len(n.parents) != 1:\n # We're only fusing nodes with single parents\n break\n p = n.get_only_parent()\n if len(p.children) != 1:\n # We can only fuse a node if its parent's\n # value isn't used by any other node.\n break\n nodes_window.insert(0, n)\n n = p\n if len(nodes_window) > 0:\n # add parent of chained nodes\n first = nodes_window[0]\n p = first.get_only_parent()\n if len(p.children) == 1:\n nodes_window.insert(0, p)\n if len(nodes_window) != self.num_nodes:\n continue\n if not self.is_eligible(graph, nodes_window):\n continue\n merged = self.merge(graph, nodes_window)\n first, last = nodes_window[0], nodes_window[-1]\n for parent in first.parents:\n parent.children.remove(first)\n if merged[0] not in parent.children:\n parent.add_child(merged[0])\n for child in last.children:\n child.parents.remove(last)\n if merged[-1] not in child.parents:\n child.add_parent(merged[-1])\n for n in nodes_window:\n merged_nodes[n.name] = merged\n\n transformed_nodes = []\n added_merged = [] # type: List[Node]\n for node in nodes:\n if node.name in merged_nodes:\n merged = merged_nodes[node.name]\n if merged[0] not in added_merged:\n for n in merged:\n transformed_nodes.append(n)\n added_merged.append(merged[0])\n else:\n transformed_nodes.append(node)\n return Graph(transformed_nodes, graph.inputs, graph.outputs, graph.shape_dict)\n\n def is_eligible(self, graph, nodes): # type: (Graph, Sequence[Node]) -> bool\n '''Returns true if this subset of nodes is eligible for fusion.'''\n raise NotImplementedError('Must be implemented by subclass.')\n\n def merge(self, graph, nodes): # type: (Graph, Sequence[Node]) -> Sequence[Node]\n '''Merge nodes'''\n nodes[0].outputs = nodes[-1].outputs\n return [nodes[0]]\n\n\nclass ConvAddFuser(NodesFuser):\n '''\n Fuses Add layer into parent convolution layer.\n '''\n def __init__(self): # type: () -> None\n super(ConvAddFuser, self).__init__(2)\n\n def is_eligible(self, graph, nodes): # type: (Graph, Sequence[Node]) -> bool\n parent, child = nodes[0], nodes[1]\n if parent.op_type != 'Conv':\n return False\n if child.op_type != 'Add':\n return False\n if 'broadcast' not in child.attrs:\n return False\n if 'axis' not in child.attrs:\n return False\n if parent.inputs[1] not in parent.input_tensors:\n return False\n if len(parent.inputs) > 2 and parent.inputs[2] not in parent.input_tensors:\n return False\n if child.inputs[1] not in child.input_tensors:\n return False\n\n broadcast = child.attrs['broadcast']\n if broadcast != 1:\n return False\n\n axis = child.attrs['axis']\n if axis != 1:\n return False\n\n return True\n\n def merge(self, graph, nodes): # type: (Graph, Sequence[Node]) -> Sequence[Node]\n parent, child = nodes[0], nodes[1]\n output_channels = parent.input_tensors[parent.inputs[1]].shape[0]\n if len(parent.inputs) > 2:\n bias_input_name = parent.inputs[2]\n bias = parent.input_tensors[bias_input_name]\n else:\n bias_input_name = \"{}_bias\".format(parent.name,)\n parent.inputs.append(bias_input_name)\n bias = np.zeros(\n (output_channels,), dtype=np.float32\n )\n parent.input_tensors[bias_input_name] = bias\n bias = bias + child.input_tensors[child.inputs[1]]\n parent.input_tensors[bias_input_name] = bias\n parent.outputs = child.outputs\n parent.children.remove(child)\n child.parents.remove(parent)\n return [parent]\n\n\nclass BNBroadcastedMulFuser(NodesFuser):\n '''\n Fuses Mul into BatchNorm\n '''\n def __init__(self): # type: () -> None\n super(BNBroadcastedMulFuser, self).__init__(2)\n\n def is_eligible(self, graph, nodes): # type: (Graph, Sequence[Node]) -> bool\n parent, child = nodes[0], nodes[1]\n if parent.op_type != 'BatchNormalization':\n return False\n if child.op_type != 'Mul':\n return False\n if \"broadcast\" not in child.attrs:\n return False\n if child.attrs[\"broadcast\"] != 1:\n return False\n if \"axis\" not in child.attrs:\n return False\n if child.attrs[\"axis\"] != 1:\n return False\n if child.inputs[1] not in child.input_tensors:\n return False\n if parent.inputs[1] not in parent.input_tensors:\n return False\n if parent.inputs[2] not in parent.input_tensors:\n return False\n return True\n\n def merge(self, graph, nodes): # type: (Graph, Sequence[Node]) -> Sequence[Node]\n parent, child = nodes[0], nodes[1]\n weight = parent.input_tensors[parent.inputs[1]]\n bias = parent.input_tensors[parent.inputs[2]]\n W = child.input_tensors[child.inputs[1]]\n parent.input_tensors[parent.inputs[1]] = np.multiply(weight, W)\n parent.input_tensors[parent.inputs[2]] = np.multiply(bias, W)\n parent.outputs = child.outputs\n parent.children.remove(child)\n child.parents.remove(parent)\n return [parent]\n\n\nclass BNBroadcastedAddFuser(NodesFuser):\n '''\n Fuses Add into BatchNorm\n '''\n def __init__(self): # type: () -> None\n super(BNBroadcastedAddFuser, self).__init__(2)\n\n def is_eligible(self, graph, nodes): # type: (Graph, Sequence[Node]) -> bool\n parent, child = nodes[0], nodes[1]\n if parent.op_type != 'BatchNormalization':\n return False\n if child.op_type != 'Add':\n return False\n if \"broadcast\" not in child.attrs:\n return False\n if child.attrs[\"broadcast\"] != 1:\n return False\n if \"axis\" not in child.attrs:\n return False\n if child.attrs[\"axis\"] != 1:\n return False\n if len(child.inputs) != 2:\n return False\n if child.inputs[1] not in child.input_tensors:\n return False\n if parent.inputs[2] not in parent.input_tensors:\n return False\n return True\n\n def merge(self, graph, nodes): # type: (Graph, Sequence[Node]) -> Sequence[Node]\n parent, child = nodes[0], nodes[1]\n bias = parent.input_tensors[parent.inputs[2]]\n b = child.input_tensors[child.inputs[1]]\n parent.input_tensors[parent.inputs[2]] = bias + b\n parent.outputs = child.outputs\n parent.children.remove(child)\n child.parents.remove(parent)\n return [parent]\n\n\nclass DropoutRemover(NodesFuser):\n '''\n Removes Dropout layer\n '''\n def __init__(self): # type: () -> None\n super(DropoutRemover, self).__init__(2)\n\n def is_eligible(self, graph, nodes): # type: (Graph, Sequence[Node]) -> bool\n child = nodes[1]\n return child.op_type == \"Dropout\"\n\n def merge(self, graph, nodes): # type: (Graph, Sequence[Node]) -> Sequence[Node]\n parent, child = nodes[0], nodes[1]\n parent.children.remove(child)\n child.parents.remove(parent)\n parent.outputs = child.outputs\n return [parent]\n\n\nclass ReshapeInitTensorFuser(object):\n '''\n Fuses Reshape operator if it is used only to reshape blob in\n graph initializer. We can reshape here instead of runtime.\n '''\n\n def __call__(self, graph): # type: (Graph) -> Graph\n nodes = graph.nodes\n removed = []\n for node in nodes:\n if node.op_type != 'Reshape':\n continue\n if not (len(node.input_tensors) == 2 or len(node.input_tensors) == 1):\n continue\n tensor_name = node.inputs[0]\n if tensor_name not in node.input_tensors:\n continue\n if len(node.inputs) > 1:\n shape_name = node.inputs[1]\n if shape_name not in node.input_tensors:\n continue\n is_non_constant_parent = False\n if len(node.parents) > 0:\n for parent in node.parents:\n if parent.op_type != 'Constant':\n is_non_constant_parent = True\n break\n if is_non_constant_parent:\n continue\n\n removed.append(node)\n output_name = node.outputs[0]\n\n tensor = node.input_tensors[tensor_name]\n if 'shape' in node.attrs:\n shape = tuple(node.attrs[\"shape\"])\n else:\n shape = node.input_tensors[shape_name] # type: ignore\n\n # ONNX spec supports setting dimension to '0', in which case\n # it should be taken from old dimension.\n # This isn't supported in numpy, so don't transform.\n # TODO Should we support this case?\n if any([s == 0 for s in shape]):\n continue\n\n reshaped_tensor = tensor.reshape(shape)\n\n for child in node.children:\n child.parents.remove(node)\n child.input_tensors[output_name] = reshaped_tensor\n\n transformed_nodes = [node for node in nodes if node not in removed]\n return Graph(transformed_nodes, graph.inputs, graph.outputs, graph.shape_dict)\n\n\nclass OutputRenamer(object):\n '''\n Rename outputs according to mapping\n '''\n def __init__(self,\n mapping, # type: Dict[Text, Text]\n ):\n # type: (...) -> None\n self.mapping = mapping\n\n def __call__(self, graph): # type: (Graph) -> Graph\n mapping = self.mapping.copy()\n nodes = graph.nodes\n for node in nodes:\n for i in range(len(node.outputs)):\n output = node.outputs[i]\n if output not in mapping:\n continue\n node.outputs[i] = mapping[output]\n for child in node.children:\n for j in range(len(child.inputs)):\n input_ = child.inputs[j]\n if input_ != output:\n continue\n child.inputs[j] = mapping[output]\n del mapping[output]\n if len(mapping) == 0:\n break\n return graph\n\n\nclass PixelShuffleFuser(NodesFuser):\n '''\n Fuses 3 operators reshape->transpose->reshape which is equivalent to\n pytorch's pixel_shuffle layer\n '''\n def __init__(self): # type: () -> None\n super(PixelShuffleFuser, self).__init__(3)\n self.num_added = 0\n\n def is_eligible(self, graph, nodes): # type: (Graph, Sequence[Node]) -> bool\n if nodes[0].op_type != 'Reshape':\n return False\n if nodes[1].op_type != 'Transpose':\n return False\n if nodes[2].op_type != 'Reshape':\n return False\n if nodes[0].inputs[1] not in nodes[0].input_tensors:\n return False\n if nodes[2].inputs[1] not in nodes[2].input_tensors:\n return False\n\n shape = nodes[0].input_tensors[nodes[0].inputs[1]]\n if len(shape) != 6:\n return False\n if shape[0] != 1 or shape[2] != shape[3]:\n return False\n\n input_channels = shape[1]\n scale_factor = shape[2]\n input_height = shape[4]\n input_width = shape[5]\n\n if nodes[1].attrs.get('perm', []) != [0, 1, 4, 2, 5, 3]:\n return False\n\n shape = nodes[2].input_tensors[nodes[2].inputs[1]]\n if len(shape) != 4:\n return False\n\n output_channels = shape[1]\n output_height = shape[2]\n output_width = shape[3]\n if input_channels != output_channels:\n return False\n if (input_height * scale_factor) != output_height:\n return False\n if (input_width * scale_factor) != output_width:\n return False\n\n return True\n\n def get_unique_edge_name(self, graph, name): # type: (Graph, Text) -> Text\n self.num_added += 1\n return graph.get_unique_edge_name(name + '_' + str(self.num_added))\n\n def merge(self, graph, nodes): # type: (Graph, Sequence[Node]) -> Sequence[Node]\n '''\n Pixel shuffle is implemented using 3 operators:\n - Reshape(1, channels, scale, scale, height, width)\n - Transpose(0, 1, 4, 2, 5, 3)\n - Reshape(1, channels, height * scale, width * scale)\n CoreML Reshape and Transpose layers don't support tensors with more\n than 4 dimensions. Thus we change above sequence of operators to the\n following equivalent sequence:\n - Reshape(channels, scale * scale, height, width)\n - Transpose(0, 2, 1, 3)\n - Reshape(channels * height, scale, scale, width)\n - Transpose(0, 1, 3, 2)\n - Reshape(1, channels, height * scale, width * scale)\n '''\n reshape_1 = nodes[0]\n transpose_1 = nodes[1]\n transpose_1.children = []\n\n shape = reshape_1.input_tensors[reshape_1.inputs[1]]\n\n channels = shape[1]\n scale = shape[2]\n height = shape[4]\n width = shape[5]\n\n reshape_1.input_tensors[reshape_1.inputs[1]] = np.asarray([channels, scale * scale, height, width])\n transpose_1.attrs['perm'] = [0, 2, 1, 3]\n\n reshape_output_name = 'pixel_shuffle_reshape'\n transpose_output_name = 'pixel_shuffle_transpose'\n\n transpose_1.outputs = [\n self.get_unique_edge_name(graph, transpose_output_name)\n ]\n\n shape_name_second_reshape = self.get_unique_edge_name(graph, reshape_output_name)\n output_name_second_reshape = self.get_unique_edge_name(graph, reshape_output_name)\n reshape_2 = Node(\n reshape_output_name,\n 'Reshape',\n {},\n [transpose_1.outputs[0], shape_name_second_reshape],\n [output_name_second_reshape]\n )\n reshape_2.input_tensors[shape_name_second_reshape] = np.asarray([channels * height, scale, scale, width])\n transpose_1.add_child(reshape_2)\n\n transpose_2 = Node(\n transpose_output_name,\n 'Transpose',\n {'perm': [0, 1, 3, 2]},\n reshape_2.outputs,\n [self.get_unique_edge_name(graph, transpose_output_name)]\n )\n reshape_2.add_child(transpose_2)\n\n final_reshape = nodes[2]\n final_reshape.inputs = [transpose_2.outputs[0], nodes[2].inputs[1]]\n final_reshape.parents = []\n transpose_2.add_child(final_reshape)\n return [reshape_1, transpose_1, reshape_2, transpose_2, final_reshape]\n\n\nclass AddModelInputsOutputs(object):\n '''\n Expose hidden states of recurrent layers as model inputs and outputs\n '''\n def __call__(self, graph): # type: (Graph) -> Graph\n input_names = [str(input_[0]) for input_ in graph.inputs]\n output_names = [str(output_[0]) for output_ in graph.outputs]\n for node in graph.nodes:\n if str(node.op_type) == 'LSTM':\n input_h = node.inputs[5] if len(node.inputs) > 5 else node.inputs[0] + '_h_input'\n input_c = node.inputs[6] if len(node.inputs) > 6 else node.inputs[0] + '_c_input'\n output_h = node.outputs[1] if len(node.outputs) > 1 else node.outputs[0] + '_h_output'\n output_c = node.outputs[2] if len(node.outputs) > 2 else node.outputs[0] + '_c_output'\n h = node.attrs[\"hidden_size\"]\n for input_ in [str(input_h), str(input_c)]:\n if input_ not in input_names:\n graph.inputs.append(tuple((input_, TensorProto.FLOAT, (h,)))) #type: ignore\n if input_ not in graph.blob_to_op_type:\n graph.blob_to_op_type[input_] = ['LSTM']\n for output_ in [str(output_h), str(output_c)]:\n if output_ not in output_names:\n graph.outputs.append(tuple((output_, TensorProto.FLOAT, (h,)))) #type: ignore\n graph.blob_from_op_type[output_] = 'LSTM'\n return graph\n\n\nclass ConstantsToInitializers(object):\n '''\n Takes onnx Constant nodes and puts the tensor into graph initializers instead.\n '''\n def __call__(self, graph): # type: (Graph) -> Graph\n output_names = [str(output_[0]) for output_ in graph.outputs]\n remaining_nodes = []\n for node in graph.nodes:\n if node.op_type != 'Constant' or node.name in output_names:\n remaining_nodes.append(node)\n continue\n for child in node.children:\n child.input_tensors[node.outputs[0]] = node.attrs[\"value\"]\n\n graph.nodes = remaining_nodes\n return graph\n\n\nclass ImageScalerRemover(object):\n '''\n Removes ImageScaler layer if connected to a model input and single parent child nodes\n '''\n\n def __call__(self, graph): # type: (Graph) -> Graph\n input_names = [str(input_[0]) for input_ in graph.inputs]\n nodes_to_be_removed = []\n for node in graph.nodes:\n if (node.op_type != 'ImageScaler') or (len(node.parents) != 0) or (node.inputs[0] not in input_names):\n continue\n is_eligible = True\n for child in node.children:\n if not (len(child.parents) == 1 and child.inputs[0] == node.outputs[0]):\n is_eligible = False\n break\n child.inputs[0] = node.inputs[0]\n child.parents = []\n if not is_eligible:\n continue\n nodes_to_be_removed.append(node.name)\n\n transformed_nodes = []\n for node in graph.nodes:\n if node.name not in nodes_to_be_removed:\n transformed_nodes.append(node)\n return Graph(transformed_nodes, graph.inputs, graph.outputs, graph.shape_dict)" }, { "path": "caffe/onnx2caffe/_weightloader.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n# from caffe import params as P\nimport numpy as np\nfrom ._graph import Node, Graph\n\n\ndef _convert_conv(net, node, graph, err):\n weight_name = node.inputs[1]\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n node_name = node.name\n W = None\n if weight_name in node.input_tensors:\n W = node.input_tensors[weight_name]\n else:\n err.missing_initializer(node,\n \"Weight tensor: {} not found in the graph initializer\".format(weight_name, ))\n bias_flag = False\n bias = None\n if len(node.inputs) > 2:\n bias = node.input_tensors[node.inputs[2]]\n bias_flag = True\n # net.params[node_name][0].data = W\n # if bias_flag:\n # net.params[node_name][1].data = bias\n np.copyto(net.params[node_name][0].data, W, casting='same_kind')\n if bias_flag:\n np.copyto(net.params[node_name][1].data, bias, casting='same_kind')\n\n\ndef _convert_relu(net, node, graph, err):\n pass\n\n\ndef _convert_sigmoid(net, node, graph, err):\n pass\n\n\ndef _convert_BatchNorm(net, node, graph, err):\n scale = node.input_tensors[node.inputs[1]]\n bias = node.input_tensors[node.inputs[2]]\n mean = node.input_tensors[node.inputs[3]]\n var = node.input_tensors[node.inputs[4]]\n node_name = node.name\n np.copyto(net.params[node_name + '_bn'][0].data, mean, casting='same_kind')\n np.copyto(net.params[node_name + '_bn'][1].data, var, casting='same_kind')\n net.params[node_name + '_bn'][2].data[...] = 1.0\n np.copyto(net.params[node_name][0].data, scale, casting='same_kind')\n np.copyto(net.params[node_name][1].data, bias, casting='same_kind')\n # net.params[node_name+'_bn'][1].data = var\n # net.params[node_name][0].data = scale\n # net.params[node_name][1].data = bias\n\n\ndef _convert_Add(net, node, graph, err):\n pass\n\n\ndef _convert_Mul(net, node, graph, err):\n pass\n\n\ndef _convert_Reshape(net, node, graph, err):\n pass\n\n\ndef _convert_Flatten(net, node, graph, err):\n pass\n\n\ndef _convert_pool(net, node, graph, err):\n pass\n\n\ndef _convert_dropout(net, node, graph, err):\n pass\n\n\ndef _convert_Permute(net, node, graph, err):\n pass\n\n\ndef _convert_Softmax(net, node, graph, err):\n pass\n\n\ndef _convert_gemm(net, node, graph, err):\n node_name = node.name\n weight_name = node.inputs[1]\n if weight_name in node.input_tensors:\n W = node.input_tensors[weight_name]\n else:\n err.missing_initializer(node,\n \"Weight tensor: {} not found in the graph initializer\".format(weight_name, ))\n if node.attrs[\"broadcast\"] != 1 or node.attrs[\"transB\"] != 1:\n return err.unsupported_op_configuration(node, \"Gemm is supported only for inner_product layer\")\n b = None\n if len(node.inputs) > 2:\n b = node.input_tensors[node.inputs[2]]\n if len(W.shape) != 2 or (b is not None and len(b.shape) != 1):\n return err.unsupported_op_configuration(node, \"Gemm is supported only for inner_product layer\")\n if b is not None:\n if W.shape[0] != b.shape[0]:\n return err.unsupported_op_configuration(node, \"Gemm is supported only for inner_product layer\")\n net.params[node_name][0].data[...] = W\n net.params[node_name][1].data[...] = b\n\n\ndef _convert_upsample(net, node, graph, err):\n mode = node.attrs[\"mode\"]\n node_name = node.name\n if mode == \"nearest\":\n caffe_params = net.params[node_name][0].data\n weights = np.ones(caffe_params.shape).astype(\"float32\")\n np.copyto(net.params[node_name][0].data, weights, casting='same_kind')\n # net.params[node_name][0].data[]\n\n\ndef _convert_concat(net, node, graph, err):\n pass\n\n\ndef _convert_conv_transpose(net, node, graph, err):\n weight_name = node.inputs[1]\n input_name = str(node.inputs[0])\n output_name = str(node.outputs[0])\n node_name = node.name\n W = None\n if weight_name in node.input_tensors:\n W = node.input_tensors[weight_name]\n else:\n err.missing_initializer(node,\n \"Weight tensor: {} not found in the graph initializer\".format(weight_name, ))\n bias_flag = False\n bias = None\n if len(node.inputs) > 2:\n bias = node.input_tensors[node.inputs[2]]\n bias_flag = True\n # net.params[node_name][0].data = W\n # if bias_flag:\n # net.params[node_name][1].data = bias\n np.copyto(net.params[node_name][0].data, W, casting='same_kind')\n if bias_flag:\n np.copyto(net.params[node_name][1].data, bias, casting='same_kind')\n\n\n_ONNX_NODE_REGISTRY = {\n \"Conv\": _convert_conv,\n \"Relu\": _convert_relu,\n \"BatchNormalization\": _convert_BatchNorm,\n \"Add\": _convert_Add,\n \"Mul\": _convert_Mul,\n \"Reshape\": _convert_Reshape,\n \"MaxPool\": _convert_pool,\n \"AveragePool\": _convert_pool,\n \"Dropout\": _convert_dropout,\n \"Gemm\": _convert_gemm,\n \"Upsample\": _convert_upsample,\n \"Concat\": _convert_concat,\n \"ConvTranspose\": _convert_conv_transpose,\n \"Sigmoid\": _convert_sigmoid,\n \"Flatten\": _convert_Flatten,\n \"Transpose\": _convert_Permute,\n \"Softmax\": _convert_Softmax,\n}\n" }, { "path": "caffe/ultra_face_caffe_inference.py", "content": "# coding=utf-8\nimport argparse\nimport os\nimport time\nfrom math import ceil\n\nimport caffe\nimport cv2\nimport numpy as np\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--caffe_prototxt_path', default=\"model/RFB-320/RFB-320.prototxt\", type=str, help='caffe_prototxt_path')\nparser.add_argument('--caffe_model_path', default=\"model/RFB-320/RFB-320.caffemodel\", type=str, help='caffe_model_path')\nparser.add_argument('--input_size', default=\"320,240\", type=str, help='define network input size,format: width,height')\nparser.add_argument('--threshold', default=0.7, type=float, help='score threshold')\nparser.add_argument('--imgs_path', default=\"../MNN/imgs\", type=str, help='imgs dir')\nparser.add_argument('--results_path', default=\"results\", type=str, help='results dir')\nparser.add_argument('--mode', default=\"cpu\", type=str, help='cpu or gpu')\nargs = parser.parse_args()\n\nif args.mode == \"cpu\":\n caffe.set_mode_cpu()\nelif args.mode == \"gpu\":\n caffe.set_mode_gpu()\nimage_mean = np.array([127, 127, 127])\nimage_std = 128.0\niou_threshold = 0.3\ncenter_variance = 0.1\nsize_variance = 0.2\nmin_boxes = [[10.0, 16.0, 24.0], [32.0, 48.0], [64.0, 96.0], [128.0, 192.0, 256.0]]\nstrides = [8.0, 16.0, 32.0, 64.0]\n\n\ndef define_img_size(image_size):\n shrinkage_list = []\n feature_map_w_h_list = []\n for size in image_size:\n feature_map = [int(ceil(size / stride)) for stride in strides]\n feature_map_w_h_list.append(feature_map)\n\n for i in range(0, len(image_size)):\n shrinkage_list.append(strides)\n priors = generate_priors(feature_map_w_h_list, shrinkage_list, image_size, min_boxes)\n return priors\n\n\ndef generate_priors(feature_map_list, shrinkage_list, image_size, min_boxes):\n priors = []\n for index in range(0, len(feature_map_list[0])):\n scale_w = image_size[0] / shrinkage_list[0][index]\n scale_h = image_size[1] / shrinkage_list[1][index]\n for j in range(0, feature_map_list[1][index]):\n for i in range(0, feature_map_list[0][index]):\n x_center = (i + 0.5) / scale_w\n y_center = (j + 0.5) / scale_h\n\n for min_box in min_boxes[index]:\n w = min_box / image_size[0]\n h = min_box / image_size[1]\n priors.append([\n x_center,\n y_center,\n w,\n h\n ])\n print(\"priors nums:{}\".format(len(priors)))\n return np.clip(priors, 0.0, 1.0)\n\n\ndef hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):\n scores = box_scores[:, -1]\n boxes = box_scores[:, :-1]\n picked = []\n indexes = np.argsort(scores)\n indexes = indexes[-candidate_size:]\n while len(indexes) > 0:\n current = indexes[-1]\n picked.append(current)\n if 0 < top_k == len(picked) or len(indexes) == 1:\n break\n current_box = boxes[current, :]\n indexes = indexes[:-1]\n rest_boxes = boxes[indexes, :]\n iou = iou_of(\n rest_boxes,\n np.expand_dims(current_box, axis=0),\n )\n indexes = indexes[iou <= iou_threshold]\n return box_scores[picked, :]\n\n\ndef area_of(left_top, right_bottom):\n hw = np.clip(right_bottom - left_top, 0.0, None)\n return hw[..., 0] * hw[..., 1]\n\n\ndef iou_of(boxes0, boxes1, eps=1e-5):\n overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])\n overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])\n\n overlap_area = area_of(overlap_left_top, overlap_right_bottom)\n area0 = area_of(boxes0[..., :2], boxes0[..., 2:])\n area1 = area_of(boxes1[..., :2], boxes1[..., 2:])\n return overlap_area / (area0 + area1 - overlap_area + eps)\n\n\ndef predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):\n boxes = boxes[0]\n confidences = confidences[0]\n picked_box_probs = []\n picked_labels = []\n for class_index in range(1, confidences.shape[1]):\n probs = confidences[:, class_index]\n mask = probs > prob_threshold\n probs = probs[mask]\n if probs.shape[0] == 0:\n continue\n subset_boxes = boxes[mask, :]\n box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)\n box_probs = hard_nms(box_probs,\n iou_threshold=iou_threshold,\n top_k=top_k,\n )\n picked_box_probs.append(box_probs)\n picked_labels.extend([class_index] * box_probs.shape[0])\n if not picked_box_probs:\n return np.array([]), np.array([]), np.array([])\n picked_box_probs = np.concatenate(picked_box_probs)\n picked_box_probs[:, 0] *= width\n picked_box_probs[:, 1] *= height\n picked_box_probs[:, 2] *= width\n picked_box_probs[:, 3] *= height\n return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]\n\n\ndef convert_locations_to_boxes(locations, priors, center_variance,\n size_variance):\n if len(priors.shape) + 1 == len(locations.shape):\n priors = np.expand_dims(priors, 0)\n return np.concatenate([\n locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],\n np.exp(locations[..., 2:] * size_variance) * priors[..., 2:]\n ], axis=len(locations.shape) - 1)\n\n\ndef center_form_to_corner_form(locations):\n return np.concatenate([locations[..., :2] - locations[..., 2:] / 2,\n locations[..., :2] + locations[..., 2:] / 2], len(locations.shape) - 1)\n\n\ndef inference():\n net = caffe.Net(args.caffe_prototxt_path, args.caffe_model_path, caffe.TEST)\n input_size = [int(v.strip()) for v in args.input_size.split(\",\")]\n witdh = input_size[0]\n height = input_size[1]\n priors = define_img_size(input_size)\n net.blobs['input'].reshape(1, 3, height, witdh)\n result_path = args.results_path\n imgs_path = args.imgs_path\n if not os.path.exists(result_path):\n os.makedirs(result_path)\n listdir = os.listdir(imgs_path)\n for file_path in listdir:\n img_path = os.path.join(imgs_path, file_path)\n img_ori = cv2.imread(img_path)\n tmp_batch = np.zeros([1, 3, height, witdh], dtype=np.float32)\n rect = cv2.resize(img_ori, (witdh, height))\n rect = cv2.cvtColor(rect, cv2.COLOR_BGR2RGB)\n image = (rect - image_mean) / image_std\n tmp_batch[0, :, :, :] = image.transpose(2, 0, 1)\n net.blobs['input'].data[...] = tmp_batch\n time_time = time.time()\n scores = net.forward()['scores'][0]\n boxes = net.forward()['boxes'][0]\n print(\"inference time: {} s\".format(round(time.time() - time_time, 4)))\n boxes = np.expand_dims(np.reshape(boxes, (-1, 4)), axis=0)\n scores = np.expand_dims(np.reshape(scores, (-1, 2)), axis=0)\n boxes = convert_locations_to_boxes(boxes, priors, center_variance, size_variance)\n boxes = center_form_to_corner_form(boxes)\n boxes, labels, probs = predict(img_ori.shape[1], img_ori.shape[0], scores, boxes, args.threshold)\n for i in range(boxes.shape[0]):\n box = boxes[i, :]\n cv2.rectangle(img_ori, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)\n cv2.imwrite(os.path.join(result_path, file_path), img_ori)\n print(\"result_pic is written to {}\".format(os.path.join(result_path, file_path)))\n cv2.imshow(\"ultraFace_caffe_py\", img_ori)\n cv2.waitKey(-1)\n cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n inference()\n" }, { "path": "caffe/ultra_face_opencvdnn_inference.py", "content": "# coding=utf-8\nimport argparse\nimport os\nimport time\nfrom math import ceil\n\nimport cv2\nimport numpy as np\nfrom cv2 import dnn\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--caffe_prototxt_path', default=\"model/RFB-320/RFB-320.prototxt\", type=str, help='caffe_prototxt_path')\nparser.add_argument('--caffe_model_path', default=\"model/RFB-320/RFB-320.caffemodel\", type=str, help='caffe_model_path')\nparser.add_argument('--onnx_path', default=\"../models/onnx/version-RFB-320_simplified.onnx\", type=str, help='onnx version')\nparser.add_argument('--input_size', default=\"320,240\", type=str, help='define network input size,format: width,height')\nparser.add_argument('--threshold', default=0.7, type=float, help='score threshold')\nparser.add_argument('--imgs_path', default=\"../MNN/imgs\", type=str, help='imgs dir')\nparser.add_argument('--results_path', default=\"results\", type=str, help='results dir')\nargs = parser.parse_args()\n\nimage_mean = np.array([127, 127, 127])\nimage_std = 128.0\niou_threshold = 0.3\ncenter_variance = 0.1\nsize_variance = 0.2\nmin_boxes = [[10.0, 16.0, 24.0], [32.0, 48.0], [64.0, 96.0], [128.0, 192.0, 256.0]]\nstrides = [8.0, 16.0, 32.0, 64.0]\n\n\ndef define_img_size(image_size):\n shrinkage_list = []\n feature_map_w_h_list = []\n for size in image_size:\n feature_map = [int(ceil(size / stride)) for stride in strides]\n feature_map_w_h_list.append(feature_map)\n\n for i in range(0, len(image_size)):\n shrinkage_list.append(strides)\n priors = generate_priors(feature_map_w_h_list, shrinkage_list, image_size, min_boxes)\n return priors\n\n\ndef generate_priors(feature_map_list, shrinkage_list, image_size, min_boxes):\n priors = []\n for index in range(0, len(feature_map_list[0])):\n scale_w = image_size[0] / shrinkage_list[0][index]\n scale_h = image_size[1] / shrinkage_list[1][index]\n for j in range(0, feature_map_list[1][index]):\n for i in range(0, feature_map_list[0][index]):\n x_center = (i + 0.5) / scale_w\n y_center = (j + 0.5) / scale_h\n\n for min_box in min_boxes[index]:\n w = min_box / image_size[0]\n h = min_box / image_size[1]\n priors.append([\n x_center,\n y_center,\n w,\n h\n ])\n print(\"priors nums:{}\".format(len(priors)))\n return np.clip(priors, 0.0, 1.0)\n\n\ndef hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):\n scores = box_scores[:, -1]\n boxes = box_scores[:, :-1]\n picked = []\n indexes = np.argsort(scores)\n indexes = indexes[-candidate_size:]\n while len(indexes) > 0:\n current = indexes[-1]\n picked.append(current)\n if 0 < top_k == len(picked) or len(indexes) == 1:\n break\n current_box = boxes[current, :]\n indexes = indexes[:-1]\n rest_boxes = boxes[indexes, :]\n iou = iou_of(\n rest_boxes,\n np.expand_dims(current_box, axis=0),\n )\n indexes = indexes[iou <= iou_threshold]\n return box_scores[picked, :]\n\n\ndef area_of(left_top, right_bottom):\n hw = np.clip(right_bottom - left_top, 0.0, None)\n return hw[..., 0] * hw[..., 1]\n\n\ndef iou_of(boxes0, boxes1, eps=1e-5):\n overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])\n overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])\n\n overlap_area = area_of(overlap_left_top, overlap_right_bottom)\n area0 = area_of(boxes0[..., :2], boxes0[..., 2:])\n area1 = area_of(boxes1[..., :2], boxes1[..., 2:])\n return overlap_area / (area0 + area1 - overlap_area + eps)\n\n\ndef predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):\n boxes = boxes[0]\n confidences = confidences[0]\n picked_box_probs = []\n picked_labels = []\n for class_index in range(1, confidences.shape[1]):\n probs = confidences[:, class_index]\n mask = probs > prob_threshold\n probs = probs[mask]\n if probs.shape[0] == 0:\n continue\n subset_boxes = boxes[mask, :]\n box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)\n box_probs = hard_nms(box_probs,\n iou_threshold=iou_threshold,\n top_k=top_k,\n )\n picked_box_probs.append(box_probs)\n picked_labels.extend([class_index] * box_probs.shape[0])\n if not picked_box_probs:\n return np.array([]), np.array([]), np.array([])\n picked_box_probs = np.concatenate(picked_box_probs)\n picked_box_probs[:, 0] *= width\n picked_box_probs[:, 1] *= height\n picked_box_probs[:, 2] *= width\n picked_box_probs[:, 3] *= height\n return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]\n\n\ndef convert_locations_to_boxes(locations, priors, center_variance,\n size_variance):\n if len(priors.shape) + 1 == len(locations.shape):\n priors = np.expand_dims(priors, 0)\n return np.concatenate([\n locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],\n np.exp(locations[..., 2:] * size_variance) * priors[..., 2:]\n ], axis=len(locations.shape) - 1)\n\n\ndef center_form_to_corner_form(locations):\n return np.concatenate([locations[..., :2] - locations[..., 2:] / 2,\n locations[..., :2] + locations[..., 2:] / 2], len(locations.shape) - 1)\n\n\ndef inference():\n net = dnn.readNetFromONNX(args.onnx_path) # onnx version\n # net = dnn.readNetFromCaffe(args.caffe_prototxt_path, args.caffe_model_path) # caffe model converted from onnx\n input_size = [int(v.strip()) for v in args.input_size.split(\",\")]\n witdh = input_size[0]\n height = input_size[1]\n priors = define_img_size(input_size)\n result_path = args.results_path\n imgs_path = args.imgs_path\n if not os.path.exists(result_path):\n os.makedirs(result_path)\n listdir = os.listdir(imgs_path)\n for file_path in listdir:\n img_path = os.path.join(imgs_path, file_path)\n img_ori = cv2.imread(img_path)\n rect = cv2.resize(img_ori, (witdh, height))\n rect = cv2.cvtColor(rect, cv2.COLOR_BGR2RGB)\n net.setInput(dnn.blobFromImage(rect, 1 / image_std, (witdh, height), 127))\n time_time = time.time()\n boxes, scores = net.forward([\"boxes\", \"scores\"])\n print(\"inference time: {} s\".format(round(time.time() - time_time, 4)))\n boxes = np.expand_dims(np.reshape(boxes, (-1, 4)), axis=0)\n scores = np.expand_dims(np.reshape(scores, (-1, 2)), axis=0)\n boxes = convert_locations_to_boxes(boxes, priors, center_variance, size_variance)\n boxes = center_form_to_corner_form(boxes)\n boxes, labels, probs = predict(img_ori.shape[1], img_ori.shape[0], scores, boxes, args.threshold)\n for i in range(boxes.shape[0]):\n box = boxes[i, :]\n cv2.rectangle(img_ori, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)\n cv2.imwrite(os.path.join(result_path, file_path), img_ori)\n print(\"result_pic is written to {}\".format(os.path.join(result_path, file_path)))\n cv2.imshow(\"ultra_face_ace_opencvdnn_py\", img_ori)\n cv2.waitKey(-1)\n cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n inference()\n" }, { "path": "cal_flops.py", "content": "\"\"\"\nOutput model complexity\n\"\"\"\nimport time\n\nimport torch\nfrom torchstat import stat\nfrom torchsummary import summary\n\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd\n\ndevice = \"cpu\" # default cpu\nwidth = 320\nheight = 240\n\n# fd = create_mb_tiny_fd(2)\nfd = create_Mb_Tiny_RFB_fd(2)\n\nprint(fd)\nfd.eval()\nfd.to(device)\nx = torch.randn(1, 3, width, height).to(device)\n\nsummary(fd.to(\"cuda\"), (3, width, height))\n\nfrom ptflops import get_model_complexity_info\n\nflops, params = get_model_complexity_info(fd.to(device), (3, width, height), print_per_layer_stat=True, as_strings=True)\nprint(\"FLOPS:\", flops)\nprint(\"PARAMS:\", params)\n\nfor i in range(5):\n time_time = time.time()\n features = fd(x)\n print(\"inference time :{} s\".format(time.time() - time_time))\n\nstat(fd, (3, width, height))\n" }, { "path": "check_gt_box.py", "content": "\"\"\"\nThis code is used to check the data size distribution in the dataset.\n\"\"\"\nimport xml.etree.ElementTree as ET\nfrom math import sqrt as sqrt\n\nimport cv2\nimport matplotlib.pyplot as plt\n\n# sets = [(\"./data/wider_face_add_lm_10_10\", \"trainval\")]\nsets = [(\"./data/wider_face_add_lm_10_10\", \"test\")]\n\nclasses = ['face']\n\nif __name__ == '__main__':\n width = []\n height = []\n\n for image_set, set in sets:\n image_ids = open('{}/ImageSets/Main/{}.txt'.format(image_set, set)).read().strip().split()\n for image_id in image_ids:\n img_path = '{}/JPEGImages/{}.jpg'.format(image_set, image_id)\n label_file = open('{}/Annotations/{}.xml'.format(image_set, image_id))\n tree = ET.parse(label_file)\n root = tree.getroot()\n size = root.find('size')\n img_w = int(size.find('width').text)\n img_h = int(size.find('height').text)\n img = cv2.imread(img_path)\n for obj in root.iter('object'):\n difficult = obj.find('difficult').text\n cls = obj.find('name').text\n if cls not in classes or int(difficult) == 2:\n continue\n cls_id = classes.index(cls)\n\n xmlbox = obj.find('bndbox')\n xmin = int(xmlbox.find('xmin').text)\n ymin = int(xmlbox.find('ymin').text)\n xmax = int(xmlbox.find('xmax').text)\n ymax = int(xmlbox.find('ymax').text)\n w = xmax - xmin\n h = ymax - ymin\n\n # img = cv2.rectangle(img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 8)\n w_change = (w / img_w) * 320\n h_change = (h / img_h) * 240\n s = w_change * h_change\n if w_change / h_change > 6:\n print(\"{}/{}/{}/{}\".format(xmin, xmax, ymin, ymax))\n width.append(sqrt(s))\n height.append(w_change / h_change)\n print(img_path)\n # img = cv2.resize(img, (608, 608))\n # cv2.imwrite('{}_{}'.format(image_set.split('/')[-1], set), img)\n # cv2.waitKey()\n\n plt.plot(width, height, 'ro')\n plt.show()\n" }, { "path": "convert_to_onnx.py", "content": "\"\"\"\nThis code is used to convert the pytorch model into an onnx format model.\n\"\"\"\nimport sys\n\nimport torch.onnx\n\nfrom vision.ssd.config.fd_config import define_img_size\n\ninput_img_size = 320 # define input size ,default optional(128/160/320/480/640/1280)\ndefine_img_size(input_img_size)\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd\n\n# net_type = \"slim\" # inference faster,lower precision\nnet_type = \"RFB\" # inference lower,higher precision\n\nlabel_path = \"models/voc-model-labels.txt\"\nclass_names = [name.strip() for name in open(label_path).readlines()]\nnum_classes = len(class_names)\n\nif net_type == 'slim':\n model_path = \"models/pretrained/version-slim-320.pth\"\n # model_path = \"models/pretrained/version-slim-640.pth\"\n net = create_mb_tiny_fd(len(class_names), is_test=True)\nelif net_type == 'RFB':\n model_path = \"models/pretrained/version-RFB-320.pth\"\n # model_path = \"models/pretrained/version-RFB-640.pth\"\n net = create_Mb_Tiny_RFB_fd(len(class_names), is_test=True)\n\nelse:\n print(\"unsupport network type.\")\n sys.exit(1)\nnet.load(model_path)\nnet.eval()\nnet.to(\"cuda\")\n\nmodel_name = model_path.split(\"/\")[-1].split(\".\")[0]\nmodel_path = f\"models/onnx/{model_name}.onnx\"\n\ndummy_input = torch.randn(1, 3, 240, 320).to(\"cuda\")\n# dummy_input = torch.randn(1, 3, 480, 640).to(\"cuda\") #if input size is 640*480\ntorch.onnx.export(net, dummy_input, model_path, verbose=False, input_names=['input'], output_names=['scores', 'boxes'])\n" }, { "path": "data/retinaface_labels/test/label.txt", "content": "# 0--Parade/0_Parade_marchingband_1_737.jpg\n# 0--Parade/0_Parade_marchingband_1_494.jpg\n# 0--Parade/0_Parade_Parade_0_338.jpg\n# 0--Parade/0_Parade_marchingband_1_533.jpg\n# 0--Parade/0_Parade_marchingband_1_62.jpg\n# 0--Parade/0_Parade_marchingband_1_184.jpg\n# 0--Parade/0_Parade_marchingband_1_120.jpg\n# 0--Parade/0_Parade_Parade_0_832.jpg\n# 0--Parade/0_Parade_marchingband_1_396.jpg\n# 0--Parade/0_Parade_marchingband_1_432.jpg\n# 0--Parade/0_Parade_Parade_0_613.jpg\n# 0--Parade/0_Parade_Parade_0_729.jpg\n# 0--Parade/0_Parade_marchingband_1_389.jpg\n# 0--Parade/0_Parade_marchingband_1_85.jpg\n# 0--Parade/0_Parade_Parade_0_918.jpg\n# 0--Parade/0_Parade_Parade_0_372.jpg\n# 0--Parade/0_Parade_marchingband_1_577.jpg\n# 0--Parade/0_Parade_Parade_0_511.jpg\n# 0--Parade/0_Parade_Parade_0_301.jpg\n# 0--Parade/0_Parade_Parade_0_874.jpg\n# 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0--Parade/0_Parade_marchingband_1_635.jpg\n# 0--Parade/0_Parade_marchingband_1_406.jpg\n# 0--Parade/0_Parade_marchingband_1_121.jpg\n# 0--Parade/0_Parade_marchingband_1_634.jpg\n# 0--Parade/0_Parade_Parade_0_223.jpg\n# 0--Parade/0_Parade_marchingband_1_924.jpg\n# 0--Parade/0_Parade_marchingband_1_70.jpg\n# 0--Parade/0_Parade_marchingband_1_621.jpg\n# 0--Parade/0_Parade_Parade_0_714.jpg\n# 0--Parade/0_Parade_Parade_0_869.jpg\n# 0--Parade/0_Parade_Parade_0_312.jpg\n# 0--Parade/0_Parade_marchingband_1_623.jpg\n# 0--Parade/0_Parade_marchingband_1_181.jpg\n# 0--Parade/0_Parade_marchingband_1_519.jpg\n# 0--Parade/0_Parade_marchingband_1_257.jpg\n# 0--Parade/0_Parade_Parade_0_57.jpg\n# 0--Parade/0_Parade_marchingband_1_60.jpg\n# 0--Parade/0_Parade_marchingband_1_135.jpg\n# 0--Parade/0_Parade_Parade_0_708.jpg\n# 0--Parade/0_Parade_Parade_0_571.jpg\n# 0--Parade/0_Parade_Parade_0_826.jpg\n# 0--Parade/0_Parade_marchingband_1_637.jpg\n# 0--Parade/0_Parade_Parade_0_84.jpg\n# 0--Parade/0_Parade_marchingband_1_144.jpg\n# 0--Parade/0_Parade_Parade_0_210.jpg\n# 0--Parade/0_Parade_marchingband_1_867.jpg\n# 0--Parade/0_Parade_Parade_0_308.jpg\n# 0--Parade/0_Parade_marchingband_1_898.jpg\n# 0--Parade/0_Parade_marchingband_1_466.jpg\n# 0--Parade/0_Parade_Parade_0_23.jpg\n# 0--Parade/0_Parade_marchingband_1_376.jpg\n# 0--Parade/0_Parade_Parade_0_1008.jpg\n# 0--Parade/0_Parade_Parade_0_445.jpg\n# 0--Parade/0_Parade_Parade_0_640.jpg\n# 0--Parade/0_Parade_marchingband_1_229.jpg\n# 0--Parade/0_Parade_Parade_0_298.jpg\n# 0--Parade/0_Parade_marchingband_1_734.jpg\n# 0--Parade/0_Parade_Parade_0_594.jpg\n# 0--Parade/0_Parade_marchingband_1_11.jpg\n# 0--Parade/0_Parade_Parade_0_217.jpg\n# 0--Parade/0_Parade_Parade_0_103.jpg\n# 0--Parade/0_Parade_Parade_0_646.jpg\n# 0--Parade/0_Parade_Parade_0_419.jpg\n# 0--Parade/0_Parade_marchingband_1_179.jpg\n# 0--Parade/0_Parade_Parade_0_802.jpg\n# 0--Parade/0_Parade_Parade_0_885.jpg\n# 0--Parade/0_Parade_Parade_0_173.jpg\n# 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0--Parade/0_Parade_marchingband_1_647.jpg\n# 0--Parade/0_Parade_marchingband_1_240.jpg\n# 0--Parade/0_Parade_marchingband_1_222.jpg\n# 0--Parade/0_Parade_marchingband_1_587.jpg\n# 0--Parade/0_Parade_marchingband_1_763.jpg\n# 0--Parade/0_Parade_marchingband_1_618.jpg\n# 0--Parade/0_Parade_marchingband_1_508.jpg\n# 0--Parade/0_Parade_Parade_0_344.jpg\n# 0--Parade/0_Parade_Parade_0_267.jpg\n# 0--Parade/0_Parade_marchingband_1_105.jpg\n# 0--Parade/0_Parade_Parade_0_134.jpg\n# 0--Parade/0_Parade_marchingband_1_141.jpg\n# 0--Parade/0_Parade_Parade_0_6.jpg\n# 0--Parade/0_Parade_Parade_0_1047.jpg\n# 0--Parade/0_Parade_marchingband_1_605.jpg\n# 0--Parade/0_Parade_Parade_0_333.jpg\n# 0--Parade/0_Parade_Parade_0_1020.jpg\n# 0--Parade/0_Parade_marchingband_1_161.jpg\n# 0--Parade/0_Parade_Parade_0_67.jpg\n# 0--Parade/0_Parade_Parade_0_448.jpg\n# 0--Parade/0_Parade_Parade_0_576.jpg\n# 0--Parade/0_Parade_marchingband_1_1026.jpg\n# 0--Parade/0_Parade_Parade_0_671.jpg\n# 0--Parade/0_Parade_Parade_0_116.jpg\n# 0--Parade/0_Parade_Parade_0_346.jpg\n# 0--Parade/0_Parade_marchingband_1_157.jpg\n# 0--Parade/0_Parade_Parade_0_313.jpg\n# 0--Parade/0_Parade_Parade_0_935.jpg\n# 0--Parade/0_Parade_marchingband_1_32.jpg\n# 0--Parade/0_Parade_Parade_0_297.jpg\n# 0--Parade/0_Parade_Parade_0_197.jpg\n# 0--Parade/0_Parade_Parade_0_18.jpg\n# 0--Parade/0_Parade_Parade_0_575.jpg\n# 0--Parade/0_Parade_marchingband_1_301.jpg\n# 0--Parade/0_Parade_marchingband_1_258.jpg\n# 0--Parade/0_Parade_marchingband_1_909.jpg\n# 0--Parade/0_Parade_Parade_0_131.jpg\n# 0--Parade/0_Parade_Parade_0_182.jpg\n# 0--Parade/0_Parade_Parade_0_833.jpg\n# 0--Parade/0_Parade_Parade_0_151.jpg\n# 0--Parade/0_Parade_Parade_0_51.jpg\n# 0--Parade/0_Parade_Parade_0_366.jpg\n# 0--Parade/0_Parade_Parade_0_263.jpg\n# 0--Parade/0_Parade_marchingband_1_715.jpg\n# 0--Parade/0_Parade_marchingband_1_565.jpg\n# 0--Parade/0_Parade_Parade_0_112.jpg\n# 0--Parade/0_Parade_Parade_0_620.jpg\n# 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30--Surgeons/30_Surgeons_Surgeons_30_593.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_908.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_691.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_99.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_1042.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_438.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_1027.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_184.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_89.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_372.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_711.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_307.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_92.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_214.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_903.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_833.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_98.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_839.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_298.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_677.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_1043.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_193.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_876.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_32.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_390.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_512.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_792.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_357.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_153.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_885.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_891.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_527.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_663.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_776.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_42.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_277.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_81.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_626.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_47.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_937.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_212.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_407.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_26.jpg\n# 30--Surgeons/30_Surgeons_Surgeons_30_247.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_108.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_196.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_495.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_437.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_163.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_292.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_284.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_570.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_653.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_132.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_633.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_703.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_638.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_315.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_581.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_51.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_744.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_457.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_474.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_520.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_807.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_511.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_290.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_405.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_639.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_935.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_357.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_874.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_107.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_672.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_737.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_468.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_112.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_178.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_748.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_897.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_14.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_404.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_895.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_603.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_577.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_840.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_63.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_828.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_380.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_456.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_768.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_248.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_880.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_16.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_656.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_395.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_370.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_580.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_394.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_143.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_204.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_698.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_383.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_750.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_944.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_435.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_279.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_556.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_893.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_433.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_217.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_610.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_663.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_901.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_821.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_470.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_498.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_924.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_7.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_775.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_784.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_669.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_759.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_494.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_566.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_813.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_42.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_866.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_90.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_113.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_641.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_476.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_739.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_19.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_75.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_699.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_443.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_56.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_849.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_761.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_421.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_793.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_146.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_427.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_39.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_509.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_193.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_191.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_754.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_288.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_487.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_552.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_344.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_694.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_642.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_441.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_567.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_171.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_130.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_469.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_83.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_903.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_964.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_483.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_224.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_933.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_378.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_960.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_472.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_32.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_160.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_763.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_634.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_125.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_150.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_575.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_762.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_534.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_105.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_527.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_707.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_756.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_387.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_333.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_329.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_619.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_384.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_235.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_29.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_305.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_827.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_452.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_679.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_902.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_418.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_506.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_338.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_282.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_379.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_185.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_11.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_926.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_948.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_412.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_194.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_600.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_621.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_35.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_294.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_558.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_23.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_172.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_801.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_666.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_795.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_116.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_940.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_692.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_247.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_274.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_381.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_811.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_467.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_41.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_524.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_537.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_796.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_671.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_799.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_229.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_269.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_714.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_732.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_912.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_868.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_209.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_409.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_312.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_585.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_689.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_326.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_117.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_814.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_12.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_819.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_541.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_340.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_426.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_835.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_208.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_497.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_826.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_637.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_24.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_830.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_141.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_930.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_738.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_355.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_428.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_374.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_785.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_286.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_9.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_682.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_10.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_240.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_492.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_746.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_538.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_894.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_716.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_425.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_515.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_594.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_693.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_752.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_477.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_563.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_674.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_5.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_845.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_55.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_74.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_202.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_287.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_887.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_571.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_502.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_99.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_700.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_388.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_79.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_265.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_800.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_891.jpg\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_206.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_835.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_582.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_250.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_350.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_343.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_333.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_964.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_348.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_161.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_551.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_885.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_3.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_32.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_887.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_338.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_645.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_156.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_383.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_393.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_682.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_27.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_193.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_649.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_556.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_610.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_72.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_291.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_791.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_796.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_793.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_438.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_313.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_640.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1018.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_619.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_562.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_387.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_265.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_51.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_920.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_186.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1047.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_114.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_162.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_602.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_300.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_629.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_184.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_34.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_871.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1035.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_906.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_604.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_449.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_552.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_66.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_744.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_858.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_495.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_316.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_203.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_337.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_282.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_375.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_155.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_128.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_670.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_158.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_289.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_831.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_805.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_499.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_647.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_572.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_923.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_607.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_125.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_52.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_879.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_355.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_9.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_423.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_327.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_105.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_765.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1042.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_152.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_553.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_312.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_700.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_83.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_46.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_685.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_298.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_947.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_79.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_508.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_814.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_118.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_168.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_941.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_166.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_351.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_601.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_903.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_779.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_627.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_107.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_450.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_646.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_539.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_65.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_88.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_505.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_541.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1011.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_476.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1028.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_875.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_174.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_862.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_16.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_303.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_626.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_126.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_103.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_269.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_336.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_697.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_142.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1007.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_47.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_882.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_567.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_307.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_192.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_389.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_339.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_608.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_352.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_319.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_856.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_783.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_217.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_270.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_469.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_677.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_334.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_907.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_304.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_874.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_661.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_548.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_372.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_761.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_8.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_714.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_534.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_271.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_2.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_447.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_770.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_832.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_260.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_363.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_778.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_36.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_489.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_680.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_756.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_388.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_390.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_362.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_78.jpg\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_702.jpg\n# 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3--Riot/3_Riot_Riot_3_354.jpg\n393 240 23 54\n406 161 23 32\n498 176 22 34\n239 151 19 27\n536 103 12 16\n620 99 14 18\n701 104 13 21\n565 138 11 24\n916 109 18 23\n# 3--Riot/3_Riot_Riot_3_101.jpg\n561 232 47 61\n726 333 55 65\n891 226 57 65\n792 67 46 57\n426 292 50 53\n167 72 54 34\n# 3--Riot/3_Riot_Riot_3_666.jpg\n522 124 156 202\n314 92 148 210\n# 3--Riot/3_Riot_Riot_3_263.jpg\n547 174 18 20\n437 163 17 23\n421 164 14 23\n389 163 14 23\n344 140 18 24\n276 141 13 20\n350 172 11 17\n866 223 20 27\n859 213 12 17\n927 213 12 16\n189 124 31 25\n477 167 21 25\n208 142 15 20\n7 131 22 26\n# 3--Riot/3_Riot_Riot_3_506.jpg\n186 163 86 108\n446 254 35 47\n# 3--Riot/3_Riot_Riot_3_772.jpg\n496 138 168 244\n# 3--Riot/3_Riot_Riot_3_186.jpg\n208 37 67 59\n791 139 20 24\n388 44 43 52\n549 35 40 55\n613 0 37 26\n# 3--Riot/3_Riot_Riot_3_542.jpg\n393 245 9 11\n368 255 11 16\n356 235 6 9\n382 241 5 8\n298 254 11 14\n256 264 15 19\n577 265 7 14\n595 259 9 13\n542 256 7 10\n576 374 11 11\n144 184 7 11\n128 192 7 7\n103 190 7 10\n76 185 9 12\n20 189 7 10\n54 285 12 23\n72 267 14 23\n104 259 15 17\n143 258 12 18\n124 257 14 19\n48 259 11 14\n5 266 8 21\n14 253 13 15\n864 158 6 7\n964 140 7 11\n900 272 8 10\n945 276 9 12\n838 273 12 16\n830 264 8 12\n805 275 11 17\n794 261 8 10\n786 264 8 11\n769 265 9 11\n760 262 8 9\n745 273 9 10\n698 278 8 12\n723 258 9 14\n708 257 10 11\n689 260 9 11\n658 274 9 8\n671 259 9 11\n638 232 9 11\n626 229 4 7\n632 267 6 11\n614 259 8 12\n928 323 11 26\n1012 270 8 10\n953 265 11 18\n891 264 7 10\n331 212 5 5\n317 212 4 5\n304 209 3 4\n182 190 7 8\n203 189 8 9\n176 259 15 24\n229 257 18 20\n209 244 15 24\n357 266 15 18\n197 341 24 29\n424 394 18 22\n456 398 18 35\n318 274 10 19\n543 279 9 11\n493 270 7 13\n478 273 10 16\n459 260 11 17\n477 250 9 10\n509 252 8 9\n550 230 5 7\n595 226 7 6\n495 238 5 5\n419 268 11 15\n401 276 8 10\n420 249 9 16\n441 246 9 15\n392 232 9 9\n# 3--Riot/3_Riot_Riot_3_436.jpg\n260 52 142 208\n# 3--Riot/3_Riot_Riot_3_306.jpg\n263 142 63 65\n479 191 57 60\n659 169 71 71\n746 81 63 85\n924 67 59 79\n145 151 22 23\n472 134 35 54\n324 130 16 25\n238 189 29 43\n289 235 35 72\n841 130 12 17\n# 3--Riot/3_Riot_Riot_3_716.jpg\n104 66 88 144\n352 138 86 120\n736 80 82 130\n# 3--Riot/3_Riot_Riot_3_790.jpg\n207 277 26 30\n245 256 15 19\n262 240 16 19\n328 260 13 16\n431 269 14 16\n511 260 18 20\n485 263 10 14\n812 267 19 24\n786 251 17 20\n929 275 14 15\n987 274 15 17\n345 410 39 54\n# 3--Riot/3_Riot_Riot_3_689.jpg\n620 120 124 168\n320 240 112 128\n184 82 120 118\n# 3--Riot/3_Riot_Riot_3_710.jpg\n491 177 56 64\n616 71 59 73\n29 184 28 32\n0 193 26 35\n292 82 37 54\n743 12 33 66\n153 199 16 19\n# 3--Riot/3_Riot_Riot_3_393.jpg\n727 177 11 16\n672 159 10 18\n442 131 37 45\n542 116 21 31\n787 153 14 18\n814 164 11 15\n853 173 12 16\n168 156 10 14\n223 130 14 19\n344 152 10 16\n330 125 11 22\n125 143 14 16\n195 147 6 9\n# 3--Riot/3_Riot_Riot_3_725.jpg\n387 278 25 34\n389 232 21 27\n635 217 42 41\n262 237 22 26\n279 216 19 22\n306 222 21 25\n297 250 19 23\n324 242 17 24\n410 234 17 23\n418 267 20 29\n461 240 23 30\n519 281 21 28\n816 247 23 33\n728 262 22 30\n736 230 18 24\n865 146 12 18\n902 156 10 14\n940 148 13 16\n542 122 16 21\n687 241 20 21\n# 3--Riot/3_Riot_Riot_3_123.jpg\n592 478 68 58\n# 3--Riot/3_Riot_Riot_3_963.jpg\n237 80 525 680\n# 3--Riot/3_Riot_Riot_3_993.jpg\n280 50 106 142\n544 28 92 138\n556 346 102 150\n250 358 112 170\n# 3--Riot/3_Riot_Riot_3_958.jpg\n864 17 34 47\n925 77 20 31\n782 41 23 28\n824 49 19 24\n773 44 16 26\n706 28 27 37\n666 54 19 26\n522 44 24 36\n500 32 22 30\n607 47 15 22\n14 9 37 43\n109 33 24 30\n404 23 34 53\n337 33 19 30\n372 32 25 32\n237 11 24 35\n315 37 18 27\n306 28 16 27\n206 27 24 25\n137 4 25 33\n71 2 22 31\n53 24 25 33\n565 122 61 58\n183 27 21 25\n399 19 25 34\n950 62 29 35\n972 20 21 29\n# 3--Riot/3_Riot_Riot_3_1037.jpg\n70 152 78 76\n104 204 68 61\n300 154 31 47\n602 181 29 36\n680 135 30 46\n717 155 26 26\n770 173 29 38\n56 493 52 67\n672 410 46 59\n9 710 84 93\n973 211 34 41\n320 449 8 12\n332 445 8 10\n279 453 9 13\n420 453 11 14\n448 454 8 9\n434 445 9 11\n359 899 205 125\n# 3--Riot/3_Riot_Riot_3_273.jpg\n174 97 45 58\n400 99 39 52\n306 235 26 35\n238 323 28 34\n349 336 20 30\n379 323 16 22\n103 419 37 53\n462 384 45 55\n65 173 19 22\n# 3--Riot/3_Riot_Riot_3_750.jpg\n526 124 336 402\n# 3--Riot/3_Riot_Riot_3_166.jpg\n451 345 45 53\n# 3--Riot/3_Riot_Riot_3_405.jpg\n300 232 228 276\n62 490 192 224\n694 2 210 204\n# 3--Riot/3_Riot_Riot_3_137.jpg\n489 320 38 58\n661 360 42 58\n921 351 60 65\n976 320 20 35\n924 312 29 35\n789 318 17 28\n777 315 14 25\n547 302 11 13\n528 310 9 13\n461 306 18 27\n994 277 9 9\n979 284 7 10\n962 274 7 7\n901 328 15 21\n749 305 9 15\n761 288 9 15\n672 300 15 17\n588 289 7 13\n784 298 22 26\n809 296 10 11\n277 365 36 44\n337 385 49 66\n402 335 28 41\n349 324 18 25\n110 409 28 46\n141 404 20 44\n60 386 39 40\n233 319 11 14\n338 330 9 13\n373 316 10 12\n18 311 9 9\n25 328 10 12\n26 354 14 25\n152 332 8 11\n154 346 13 17\n144 349 12 13\n134 340 8 10\n966 309 10 14\n947 279 7 9\n950 288 7 13\n1009 288 8 10\n265 382 22 34\n190 316 8 7\n0 329 6 13\n573 298 6 8\n566 295 5 6\n717 313 10 16\n259 341 19 22\n252 329 16 23\n814 341 22 37\n748 292 11 14\n654 348 34 50\n# 3--Riot/3_Riot_Riot_3_26.jpg\n424 374 31 40\n717 386 32 48\n674 354 36 32\n790 341 23 29\n756 430 14 31\n853 385 19 43\n359 224 25 33\n470 213 22 27\n561 235 25 35\n677 229 24 31\n649 212 21 30\n660 62 21 32\n540 97 19 25\n508 73 22 29\n467 75 19 29\n358 69 25 35\n297 50 22 31\n246 91 27 31\n193 43 19 30\n204 210 30 36\n253 224 26 42\n285 212 17 27\n429 222 19 23\n234 350 32 53\n257 331 27 34\n277 354 27 41\n322 355 28 42\n416 347 31 42\n338 419 24 42\n216 442 11 26\n93 356 26 36\n55 197 28 30\n51 246 31 33\n22 104 15 21\n849 32 19 32\n894 49 20 26\n1003 44 14 21\n939 160 20 38\n989 188 25 35\n790 219 22 28\n903 160 21 29\n763 41 17 23\n818 8 19 28\n941 3 20 20\n976 22 15 20\n645 0 23 20\n791 67 22 27\n861 345 27 33\n866 412 23 39\n940 406 22 44\n955 371 27 35\n958 414 24 46\n130 434 21 24\n726 357 23 35\n644 344 19 34\n778 393 27 32\n981 410 11 37\n782 367 28 36\n# 3--Riot/3_Riot_Riot_3_184.jpg\n608 177 20 25\n691 169 20 30\n729 173 19 23\n753 160 23 33\n916 152 35 44\n968 171 31 39\n68 66 45 63\n212 84 32 57\n127 164 29 40\n# 3--Riot/3_Riot_Riot_3_438.jpg\n836 209 42 39\n653 145 17 22\n451 151 18 23\n216 163 15 29\n273 189 8 12\n295 194 6 10\n# 3--Riot/3_Riot_Riot_3_765.jpg\n695 340 106 146\n100 134 195 286\n# 3--Riot/3_Riot_Riot_3_480.jpg\n164 56 106 168\n502 140 96 152\n750 242 76 92\n# 3--Riot/3_Riot_Riot_3_322.jpg\n243 14 120 156\n# 3--Riot/3_Riot_Riot_3_106.jpg\n670 176 194 212\n# 30--Surgeons/30_Surgeons_Surgeons_30_107.jpg\n263 372 238 205\n673 314 212 222\n# 30--Surgeons/30_Surgeons_Surgeons_30_491.jpg\n244 74 250 274\n530 120 338 236\n# 30--Surgeons/30_Surgeons_Surgeons_30_862.jpg\n441 186 195 267\n# 30--Surgeons/30_Surgeons_Surgeons_30_819.jpg\n357 21 54 77\n465 23 45 63\n579 39 52 73\n# 30--Surgeons/30_Surgeons_Surgeons_30_256.jpg\n670 466 116 136\n690 200 166 100\n436 34 122 120\n268 256 150 88\n322 434 146 142\n# 30--Surgeons/30_Surgeons_Surgeons_30_708.jpg\n340 226 88 138\n668 194 108 158\n# 30--Surgeons/30_Surgeons_Surgeons_30_533.jpg\n495 257 51 67\n# 30--Surgeons/30_Surgeons_Surgeons_30_988.jpg\n324 168 535 766\n# 30--Surgeons/30_Surgeons_Surgeons_30_77.jpg\n394 252 164 240\n112 108 260 184\n694 12 262 192\n766 252 168 178\n# 30--Surgeons/30_Surgeons_Surgeons_30_63.jpg\n226 196 130 148\n448 48 172 180\n# 30--Surgeons/30_Surgeons_Surgeons_30_95.jpg\n365 200 53 67\n561 155 49 61\n593 191 58 88\n758 227 54 103\n# 30--Surgeons/30_Surgeons_Surgeons_30_554.jpg\n766 214 44 56\n847 226 53 64\n477 219 47 63\n336 252 64 66\n202 224 70 98\n643 335 67 95\n# 30--Surgeons/30_Surgeons_Surgeons_30_264.jpg\n201 63 144 234\n483 246 123 183\n730 117 132 198\n# 30--Surgeons/30_Surgeons_Surgeons_30_8.jpg\n306 182 58 74\n412 166 68 100\n588 188 62 76\n706 136 54 82\n# 30--Surgeons/30_Surgeons_Surgeons_30_749.jpg\n358 32 92 102\n# 30--Surgeons/30_Surgeons_Surgeons_30_932.jpg\n379 137 277 373\n# 30--Surgeons/30_Surgeons_Surgeons_30_115.jpg\n79 256 43 57\n199 278 60 61\n311 277 70 90\n477 307 71 90\n560 207 86 81\n847 257 77 150\n# 30--Surgeons/30_Surgeons_Surgeons_30_40.jpg\n396 136 174 226\n# 30--Surgeons/30_Surgeons_Surgeons_30_43.jpg\n660 268 230 238\n304 96 242 272\n8 2 214 250\n# 30--Surgeons/30_Surgeons_Surgeons_30_696.jpg\n490 187 213 310\n# 30--Surgeons/30_Surgeons_Surgeons_30_746.jpg\n152 40 110 188\n632 68 134 150\n854 164 90 178\n# 30--Surgeons/30_Surgeons_Surgeons_30_397.jpg\n200 170 102 122\n476 128 100 140\n680 186 104 114\n# 30--Surgeons/30_Surgeons_Surgeons_30_914.jpg\n215 147 512 693\n# 30--Surgeons/30_Surgeons_Surgeons_30_525.jpg\n438 80 84 106\n620 98 58 80\n218 156 92 182\n# 30--Surgeons/30_Surgeons_Surgeons_30_482.jpg\n356 350 408 509\n# 30--Surgeons/30_Surgeons_Surgeons_30_122.jpg\n257 268 25 46\n279 305 33 40\n522 288 40 45\n738 274 38 39\n852 432 32 30\n# 30--Surgeons/30_Surgeons_Surgeons_30_555.jpg\n138 37 55 68\n228 119 48 69\n321 46 49 64\n421 36 51 66\n537 48 58 75\n# 30--Surgeons/30_Surgeons_Surgeons_30_486.jpg\n328 141 397 538\n# 30--Surgeons/30_Surgeons_Surgeons_30_552.jpg\n214 257 27 44\n384 224 32 37\n228 325 34 22\n# 30--Surgeons/30_Surgeons_Surgeons_30_705.jpg\n121 382 27 41\n545 323 23 35\n# 30--Surgeons/30_Surgeons_Surgeons_30_490.jpg\n290 333 496 734\n# 30--Surgeons/30_Surgeons_Surgeons_30_343.jpg\n806 82 78 100\n694 32 72 88\n540 44 54 64\n334 0 62 84\n186 142 78 108\n# 30--Surgeons/30_Surgeons_Surgeons_30_722.jpg\n124 97 46 59\n168 27 54 76\n455 139 49 53\n507 39 55 74\n810 91 56 74\n856 29 65 87\n# 30--Surgeons/30_Surgeons_Surgeons_30_861.jpg\n309 261 456 580\n# 30--Surgeons/30_Surgeons_Surgeons_30_840.jpg\n316 328 355 493\n# 30--Surgeons/30_Surgeons_Surgeons_30_778.jpg\n193 62 39 53\n320 92 46 60\n677 149 50 71\n785 166 47 65\n# 30--Surgeons/30_Surgeons_Surgeons_30_979.jpg\n326 206 433 613\n# 30--Surgeons/30_Surgeons_Surgeons_30_160.jpg\n516 170 62 94\n790 250 58 60\n298 114 52 110\n# 30--Surgeons/30_Surgeons_Surgeons_30_911.jpg\n574 408 148 236\n# 30--Surgeons/30_Surgeons_Surgeons_30_823.jpg\n448 132 88 138\n662 140 86 100\n946 246 74 86\n# 30--Surgeons/30_Surgeons_Surgeons_30_865.jpg\n682 4 118 120\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_932.jpg\n472 288 100 146\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_304.jpg\n396 158 236 332\n680 110 256 320\n86 120 216 282\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_667.jpg\n280 105 72 130\n704 127 82 97\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_769.jpg\n430 76 142 180\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_740.jpg\n408 100 88 112\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_162.jpg\n453 333 243 320\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_21.jpg\n316 96 164 230\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_93.jpg\n146 218 112 148\n588 74 118 162\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_613.jpg\n826 216 54 64\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_484.jpg\n334 160 116 166\n570 56 122 188\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_339.jpg\n400 42 128 196\n576 80 124 176\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_818.jpg\n436 173 77 105\n183 129 111 145\n144 136 30 38\n721 148 68 70\n576 72 73 86\n644 95 57 68\n774 66 59 67\n665 52 45 51\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_788.jpg\n457 0 62 62\n717 218 30 84\n249 186 33 64\n169 195 41 67\n107 229 32 62\n902 132 27 37\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_227.jpg\n260 90 148 192\n619 213 145 204\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_43.jpg\n300 50 108 158\n658 26 114 172\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_200.jpg\n158 60 124 164\n678 50 120 162\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_327.jpg\n453 344 112 171\n304 349 99 141\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_215.jpg\n304 154 120 176\n582 60 136 192\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_212.jpg\n582 162 124 102\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_34.jpg\n420 57 168 267\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_230.jpg\n386 206 122 116\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_927.jpg\n420 222 237 300\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_118.jpg\n366 84 170 210\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_225.jpg\n206 86 132 152\n714 60 120 176\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_220.jpg\n242 74 114 166\n678 74 116 158\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_465.jpg\n272 236 122 176\n658 102 130 172\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_276.jpg\n199 259 564 805\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_742.jpg\n491 229 41 49\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_420.jpg\n41 221 20 27\n122 297 16 24\n147 267 14 19\n186 292 14 26\n194 290 21 23\n239 316 20 26\n280 320 8 21\n334 301 20 30\n374 302 21 26\n409 236 18 26\n419 220 17 26\n465 183 29 42\n492 221 21 24\n537 207 18 27\n535 232 21 26\n549 219 25 38\n583 225 28 28\n651 223 29 32\n691 243 27 32\n695 211 25 28\n869 314 22 25\n890 313 20 30\n916 214 28 38\n1020 240 4 30\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_572.jpg\n663 117 145 194\n311 65 149 197\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_726.jpg\n288 94 142 180\n700 66 136 202\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_214.jpg\n168 44 116 154\n334 84 120 134\n828 58 126 172\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_858.jpg\n392 57 126 168\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_410.jpg\n464 80 122 176\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_683.jpg\n552 92 132 150\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_847.jpg\n385 95 240 296\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_685.jpg\n524 62 80 108\n828 58 84 110\n150 50 78 104\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_722.jpg\n469 72 155 213\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_267.jpg\n458 122 150 198\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_188.jpg\n571 344 96 144\n304 229 99 125\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_195.jpg\n183 57 147 198\n617 91 143 206\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_517.jpg\n354 112 124 180\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_842.jpg\n343 134 175 247\n743 574 175 244\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_176.jpg\n116 52 254 316\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_111.jpg\n192 128 156 190\n690 76 124 182\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_358.jpg\n802 185 84 65\n500 290 67 93\n310 284 60 70\n175 318 36 57\n136 260 19 25\n683 368 24 27\n609 384 21 33\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_888.jpg\n293 217 179 239\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_373.jpg\n80 6 302 328\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_720.jpg\n574 24 200 260\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_351.jpg\n871 79 47 62\n299 176 141 175\n144 149 18 37\n844 159 10 20\n160 155 22 29\n# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_915.jpg\n466 186 114 176\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_812.jpg\n388 72 88 130\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_529.jpg\n481 268 65 102\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_932.jpg\n277 89 76 108\n957 783 53 67\n99 781 66 76\n70 850 72 62\n80 910 75 84\n246 790 50 63\n276 859 71 95\n357 832 61 67\n369 772 48 60\n520 808 53 76\n645 930 55 66\n685 775 53 65\n809 796 65 86\n860 783 46 67\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_987.jpg\n766 52 54 80\n612 58 56 80\n270 212 60 78\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_101.jpg\n202 26 98 126\n576 110 92 142\n776 512 104 144\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_723.jpg\n299 83 51 71\n432 143 49 93\n169 0 57 40\n6 202 42 86\n782 49 39 77\n936 134 47 90\n498 185 40 84\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_135.jpg\n316 87 327 442\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_357.jpg\n206 188 64 88\n718 404 82 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32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_209.jpg\n736 136 80 98\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_1038.jpg\n431 180 119 132\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_462.jpg\n440 178 84 106\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_860.jpg\n352 330 364 542\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_400.jpg\n697 182 47 52\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_443.jpg\n516 24 404 662\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_170.jpg\n474 576 134 104\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_110.jpg\n698 55 94 100\n490 333 42 39\n169 242 17 20\n204 139 18 14\n107 193 19 15\n455 12 20 26\n281 152 20 10\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_262.jpg\n401 401 142 192\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_26.jpg\n202 257 36 38\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_204.jpg\n396 232 130 226\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_408.jpg\n281 309 91 130\n443 128 77 111\n712 329 82 105\n528 723 96 128\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_116.jpg\n210 72 86 114\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_738.jpg\n285 324 123 168\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_68.jpg\n405 258 323 426\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_434.jpg\n448 152 234 329\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_90.jpg\n380 226 121 186\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_595.jpg\n397 182 39 45\n# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_566.jpg\n658 354 60 84\n# 33--Running/33_Running_Running_33_547.jpg\n117 44 57 54\n255 87 49 47\n363 107 48 49\n452 54 51 52\n523 112 51 58\n622 141 47 48\n743 69 55 56\n938 177 13 14\n7 138 5 7\n# 33--Running/33_Running_Running_33_332.jpg\n592 38 142 216\n# 33--Running/33_Running_Running_33_17.jpg\n477 208 47 73\n# 33--Running/33_Running_Running_33_107.jpg\n458 160 35 50\n598 189 32 46\n744 167 35 50\n# 33--Running/33_Running_Running_33_760.jpg\n422 341 107 138\n# 33--Running/33_Running_Running_33_490.jpg\n614 178 74 86\n# 33--Running/33_Running_Running_33_786.jpg\n283 228 13 20\n128 376 27 35\n75 381 17 28\n505 134 31 28\n703 132 29 23\n847 139 6 6\n900 123 14 16\n922 135 7 8\n952 115 14 16\n57 240 4 8\n113 248 4 8\n138 248 5 8\n520 238 30 35\n707 249 19 15\n889 341 12 18\n943 354 14 15\n975 343 10 16\n450 339 9 12\n514 343 7 9\n550 338 9 13\n314 374 17 21\n50 467 11 14\n90 459 13 14\n141 463 14 15\n248 458 8 8\n343 466 9 12\n502 465 35 36\n732 462 30 37\n916 448 22 35\n706 338 29 48\n335 255 15 19\n# 33--Running/33_Running_Running_33_119.jpg\n287 164 51 65\n555 145 47 74\n894 183 58 70\n# 33--Running/33_Running_Running_33_771.jpg\n419 332 116 148\n332 132 106 142\n# 33--Running/33_Running_Running_33_316.jpg\n855 111 15 25\n829 111 9 18\n894 120 13 14\n915 132 13 18\n856 197 47 68\n758 146 51 70\n423 148 37 48\n680 65 10 11\n369 139 18 14\n220 163 45 62\n267 163 33 56\n326 173 22 33\n354 168 38 58\n402 180 44 62\n527 222 38 46\n628 181 42 62\n413 106 13 19\n430 121 14 22\n472 132 9 13\n573 129 15 21\n542 123 22 23\n52 111 8 17\n100 159 20 17\n184 131 14 22\n215 101 15 21\n289 101 15 19\n325 103 17 19\n153 187 41 49\n603 124 7 10\n789 123 14 16\n666 131 12 19\n693 150 19 29\n718 138 14 15\n479 231 33 36\n# 33--Running/33_Running_Running_33_569.jpg\n492 110 396 538\n# 33--Running/33_Running_Running_33_341.jpg\n510 249 70 107\n# 33--Running/33_Running_Running_33_286.jpg\n481 292 48 70\n# 33--Running/33_Running_Running_33_891.jpg\n418 122 158 198\n# 33--Running/33_Running_Running_33_475.jpg\n617 149 44 69\n# 33--Running/33_Running_Running_33_577.jpg\n475 192 333 267\n213 267 187 163\n101 237 131 117\n# 33--Running/33_Running_Running_33_209.jpg\n596 144 114 142\n# 33--Running/33_Running_Running_33_35.jpg\n638 278 98 161\n# 33--Running/33_Running_Running_33_538.jpg\n696 42 44 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35--Basketball/35_Basketball_Basketball_35_635.jpg\n814 304 35 39\n753 316 32 36\n697 343 31 37\n621 306 34 39\n599 265 34 40\n544 293 29 35\n459 278 29 34\n485 326 27 36\n422 320 28 35\n358 334 30 33\n282 310 32 35\n276 274 32 35\n211 318 29 28\n370 415 27 29\n447 420 33 39\n570 451 31 38\n510 474 30 37\n310 453 28 35\n# 35--Basketball/35_Basketball_basketballgame_ball_35_391.jpg\n208 48 86 120\n370 90 86 112\n534 64 88 126\n702 76 92 132\n# 35--Basketball/35_Basketball_playingbasketball_35_730.jpg\n419 164 336 422\n# 35--Basketball/35_Basketball_basketballgame_ball_35_662.jpg\n62 41 27 34\n36 117 24 28\n64 101 38 60\n111 66 31 38\n113 111 31 40\n208 146 39 59\n228 122 33 43\n287 136 34 39\n243 66 36 41\n305 96 34 41\n378 69 32 39\n361 140 35 37\n393 189 26 38\n411 146 31 38\n429 13 30 36\n480 76 54 80\n492 0 27 18\n558 36 24 31\n595 43 53 72\n680 125 56 58\n659 3 27 31\n683 48 28 39\n708 80 34 42\n737 123 37 68\n760 3 26 32\n805 44 31 32\n798 120 52 74\n841 141 27 45\n0 175 32 37\n170 1 31 34\n282 1 28 30\n0 0 18 28\n# 35--Basketball/35_Basketball_playingbasketball_35_350.jpg\n488 148 156 184\n210 142 132 202\n# 35--Basketball/35_Basketball_basketballgame_ball_35_133.jpg\n332 74 176 240\n736 62 198 320\n118 42 162 206\n700 48 120 188\n# 35--Basketball/35_Basketball_playingbasketball_35_585.jpg\n627 188 194 329\n240 218 218 283\n# 35--Basketball/35_Basketball_Basketball_35_185.jpg\n295 437 60 63\n712 475 52 92\n454 637 53 57\n607 720 20 30\n# 35--Basketball/35_Basketball_basketballgame_ball_35_858.jpg\n566 222 92 102\n778 506 98 38\n# 35--Basketball/35_Basketball_playingbasketball_35_431.jpg\n446 147 46 68\n# 35--Basketball/35_Basketball_playingbasketball_35_794.jpg\n750 88 44 59\n831 138 33 48\n555 123 33 42\n512 136 30 39\n424 177 21 27\n440 198 20 30\n389 145 20 24\n380 172 19 23\n284 179 29 31\n321 164 23 29\n265 150 18 25\n242 181 20 32\n97 159 31 35\n0 169 24 44\n189 166 14 22\n149 17 17 27\n379 194 18 26\n68 202 18 23\n# 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35--Basketball/35_Basketball_playingbasketball_35_476.jpg\n314 12 96 152\n590 82 100 154\n# 35--Basketball/35_Basketball_Basketball_35_180.jpg\n684 165 28 38\n413 397 34 48\n610 355 33 45\n219 175 32 40\n283 187 33 43\n336 189 26 39\n385 193 28 38\n441 178 27 38\n476 216 28 45\n529 155 27 39\n602 166 30 38\n# 35--Basketball/35_Basketball_playingbasketball_35_127.jpg\n170 8 17 19\n320 57 26 37\n424 87 13 17\n497 117 34 40\n534 172 35 42\n599 195 46 54\n640 37 29 41\n762 15 16 25\n852 5 23 25\n889 66 21 32\n15 66 15 19\n91 20 15 19\n403 6 22 17\n# 35--Basketball/35_Basketball_Basketball_35_209.jpg\n51 244 7 10\n66 271 9 10\n171 190 20 28\n10 263 11 12\n50 220 7 10\n86 240 7 9\n419 110 32 42\n500 135 32 47\n898 181 20 26\n613 236 8 12\n834 263 9 13\n321 250 6 8\n18 234 6 9\n183 249 6 9\n176 276 6 9\n75 192 5 6\n23 205 4 6\n73 183 4 5\n22 184 5 6\n98 210 5 6\n359 272 5 7\n290 225 4 5\n336 215 3 5\n300 203 4 5\n278 214 4 5\n277 199 4 5\n743 270 7 9\n761 240 6 9\n117 269 7 8\n714 265 7 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9\n117 528 6 9\n142 524 10 10\n172 524 7 9\n149 506 6 9\n97 486 5 8\n26 497 6 5\n40 504 9 10\n243 526 9 10\n186 505 6 7\n268 523 6 7\n281 518 6 8\n299 519 6 8\n317 521 8 9\n292 526 8 9\n373 529 6 9\n432 529 5 7\n51 466 6 7\n71 474 5 8\n92 475 7 8\n113 475 6 7\n137 487 6 6\n1016 478 7 9\n206 488 12 14\n791 496 7 7\n# 35--Basketball/35_Basketball_playingbasketball_35_582.jpg\n679 108 40 60\n513 138 49 44\n427 123 36 48\n# 35--Basketball/35_Basketball_Basketball_35_529.jpg\n266 212 29 38\n376 180 28 37\n440 165 28 42\n504 161 32 45\n564 176 29 43\n639 174 30 43\n697 214 29 40\n766 238 28 37\n# 35--Basketball/35_Basketball_playingbasketball_35_612.jpg\n357 141 147 183\n544 444 162 231\n# 35--Basketball/35_Basketball_basketballgame_ball_35_937.jpg\n714 134 106 148\n544 88 108 154\n426 162 90 132\n212 186 96 128\n# 35--Basketball/35_Basketball_basketballgame_ball_35_290.jpg\n472 174 36 51\n409 252 43 58\n57 202 21 27\n4 318 13 18\n53 286 6 8\n35 268 7 10\n9 268 10 12\n9 258 8 10\n8 243 6 9\n36 235 6 8\n40 224 6 7\n6 225 5 8\n15 211 6 10\n13 202 6 6\n23 204 7 9\n38 196 5 7\n27 176 5 8\n2 192 7 8\n22 192 6 7\n60 235 7 10\n81 191 6 7\n89 198 5 8\n104 213 5 8\n111 225 5 6\n112 234 7 8\n125 286 7 10\n138 302 8 12\n153 309 6 11\n144 327 10 14\n108 206 6 7\n132 190 5 7\n114 200 8 9\n127 213 6 7\n123 225 5 7\n131 233 5 8\n150 243 7 9\n159 228 6 6\n181 238 6 7\n164 242 5 7\n164 253 8 8\n183 256 6 8\n121 275 7 7\n124 253 6 9\n147 267 6 10\n154 281 6 8\n161 280 6 7\n161 296 7 11\n171 291 5 7\n197 223 5 7\n186 225 5 6\n197 183 5 9\n152 181 5 7\n132 208 5 9\n144 210 6 6\n4 151 3 8\n24 154 4 6\n56 157 4 8\n69 170 4 6\n83 172 3 5\n109 175 3 6\n122 166 4 8\n130 168 4 7\n113 157 5 7\n88 140 5 7\n71 144 5 6\n61 138 5 6\n141 172 4 4\n121 136 2 6\n208 232 5 6\n201 250 6 4\n179 249 7 6\n215 239 5 9\n228 237 6 7\n229 249 5 7\n197 259 15 20\n228 281 6 7\n271 294 7 8\n260 261 5 8\n241 260 5 8\n273 252 5 8\n296 240 5 9\n264 222 4 6\n285 239 4 7\n463 324 6 9\n470 318 7 9\n444 329 7 9\n392 256 4 7\n365 238 5 6\n353 239 4 6\n395 241 4 6\n579 330 8 10\n638 117 34 48\n907 135 31 43\n766 230 14 22\n608 339 6 11\n627 329 5 8\n625 347 5 9\n618 353 6 9\n610 357 5 9\n# 35--Basketball/35_Basketball_playingbasketball_35_199.jpg\n586 202 260 228\n# 35--Basketball/35_Basketball_playingbasketball_35_495.jpg\n545 402 88 78\n217 460 15 21\n290 451 13 21\n347 475 15 21\n361 424 14 17\n203 432 14 21\n124 442 14 21\n48 454 16 18\n61 408 15 16\n145 438 12 18\n184 459 10 16\n2 448 12 16\n1 420 9 16\n705 474 16 24\n680 438 13 18\n654 481 14 22\n781 489 23 16\n857 484 12 13\n848 507 14 18\n894 510 16 22\n167 633 18 22\n108 629 17 20\n135 672 17 23\n232 637 17 25\n260 604 17 24\n287 580 15 20\n305 630 17 23\n207 608 15 22\n220 586 14 21\n167 568 17 24\n104 573 15 20\n44 568 19 22\n285 672 17 23\n288 704 17 21\n243 707 19 20\n175 706 17 24\n225 743 20 27\n286 741 20 23\n342 758 60 68\n263 773 20 28\n288 815 18 26\n342 737 16 23\n350 715 16 21\n180 923 23 36\n203 968 26 29\n172 1116 26 35\n235 1157 22 32\n250 1102 25 32\n0 779 97 75\n522 153 15 21\n630 160 13 20\n699 183 13 19\n606 125 18 20\n608 63 15 22\n798 83 15 14\n767 452 17 16\n1004 447 14 17\n710 622 16 22\n740 599 15 20\n788 645 15 19\n818 657 16 21\n830 630 15 19\n844 604 13 19\n874 629 14 21\n870 659 14 21\n919 673 14 17\n942 630 19 22\n934 603 16 20\n992 609 15 21\n981 639 14 16\n988 694 16 21\n934 695 16 22\n802 726 17 21\n726 657 16 21\n690 692 14 21\n696 717 18 23\n592 712 19 23\n588 685 17 23\n692 752 17 25\n739 754 17 21\n793 757 18 19\n728 812 19 26\n733 789 17 24\n676 822 19 25\n660 856 19 24\n720 850 22 25\n770 859 19 22\n782 817 20 26\n835 796 16 21\n861 764 16 20\n906 816 19 23\n944 792 18 21\n935 730 14 20\n961 739 17 22\n867 724 20 24\n997 757 18 24\n951 830 16 21\n974 844 15 15\n946 864 17 19\n971 897 18 24\n1008 898 16 24\n1015 832 9 23\n737 1058 24 35\n740 991 23 32\n669 986 21 30\n685 959 22 29\n1002 1095 22 31\n766 896 18 25\n721 905 19 19\n652 798 23 28\n883 690 15 21\n701 31 16 23\n867 91 12 18\n678 86 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64\n284 257 49 56\n990 1 34 78\n801 408 21 28\n770 409 23 26\n704 310 22 30\n650 407 19 24\n605 414 16 21\n566 415 18 22\n634 379 13 16\n787 382 16 20\n785 329 14 21\n740 329 13 17\n647 321 16 18\n755 276 13 18\n803 275 10 13\n741 272 11 14\n734 304 15 17\n583 408 11 14\n620 401 13 16\n387 421 15 21\n381 405 16 16\n111 371 15 19\n32 375 13 18\n51 368 13 18\n77 403 10 14\n107 298 13 18\n88 317 15 17\n10 289 14 18\n32 288 10 14\n43 264 11 11\n154 305 11 16\n207 295 12 18\n210 404 15 19\n11 389 17 17\n47 221 14 17\n29 207 13 16\n78 207 10 16\n104 226 9 13\n114 212 13 16\n142 230 13 16\n172 229 12 13\n185 250 11 14\n171 271 12 16\n139 267 12 17\n206 264 9 11\n240 241 12 16\n252 228 9 14\n249 188 10 14\n197 184 10 14\n154 203 13 16\n294 194 12 16\n342 222 13 18\n87 179 13 14\n48 177 13 15\n36 321 14 18\n104 151 10 14\n115 166 10 13\n158 170 11 11\n193 158 11 15\n220 148 11 15\n252 169 11 14\n260 142 11 16\n239 121 13 15\n235 104 13 14\n237 84 13 13\n201 96 13 12\n192 121 13 14\n153 109 10 13\n166 99 13 14\n123 107 12 13\n108 127 13 14\n130 73 12 13\n39 160 12 14\n63 135 10 14\n34 138 11 13\n29 118 12 13\n44 94 11 15\n52 71 10 14\n89 74 13 16\n100 34 11 13\n57 41 11 14\n5 82 11 14\n14 62 11 14\n28 22 12 14\n141 47 9 14\n198 53 10 13\n161 44 11 14\n266 6 10 14\n225 23 11 14\n188 3 8 11\n244 55 10 14\n227 66 9 11\n87 94 11 15\n64 110 11 14\n0 142 9 18\n654 250 11 16\n706 257 13 14\n726 234 10 14\n763 221 13 15\n688 218 13 16\n726 214 12 17\n723 196 11 15\n642 195 12 17\n682 194 12 16\n611 216 12 18\n646 216 13 15\n638 234 10 12\n593 230 10 13\n605 194 11 14\n615 174 11 13\n555 185 12 14\n513 189 13 14\n516 175 13 15\n437 155 13 14\n411 192 11 15\n396 212 11 15\n420 222 12 15\n400 155 13 14\n392 136 12 13\n437 137 11 14\n496 120 13 14\n466 115 11 14\n499 96 10 13\n538 124 9 13\n570 122 10 13\n629 149 14 16\n568 174 13 13\n673 152 14 17\n662 139 14 17\n699 134 13 16\n634 122 11 13\n601 145 12 16\n733 142 13 14\n684 100 12 14\n755 120 10 13\n778 113 12 15\n800 95 13 16\n726 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35--Basketball/35_Basketball_Basketball_35_664.jpg\n752 247 16 18\n646 285 21 17\n790 303 16 14\n675 326 21 15\n885 381 10 10\n889 393 9 12\n38 327 6 11\n46 324 8 10\n22 319 9 10\n137 357 5 9\n121 337 5 7\n153 331 5 8\n101 308 6 8\n# 35--Basketball/35_Basketball_basketballgame_ball_35_64.jpg\n870 59 22 34\n953 186 71 113\n904 173 61 90\n692 223 73 87\n806 186 39 45\n590 216 69 87\n476 227 64 80\n294 240 65 81\n126 295 57 69\n32 246 59 65\n99 200 43 54\n189 216 32 33\n241 248 31 40\n722 96 39 32\n655 108 34 39\n593 78 27 35\n491 157 34 39\n523 92 26 32\n440 110 29 37\n397 149 25 31\n414 113 22 30\n466 65 21 28\n536 19 24 30\n389 69 24 30\n340 154 29 31\n234 193 30 34\n271 128 27 31\n245 167 26 31\n148 141 28 30\n217 102 24 35\n138 110 24 30\n96 130 25 34\n180 72 25 34\n30 150 30 34\n415 17 20 19\n449 0 25 32\n624 10 25 25\n208 10 18 22\n157 3 18 25\n267 6 17 24\n296 0 23 19\n92 42 22 28\n23 76 21 28\n46 42 20 24\n565 198 32 41\n296 95 28 31\n55 194 22 27\n0 292 42 71\n336 0 20 20\n# 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8\n954 382 6 9\n979 384 6 8\n975 377 5 7\n1007 381 7 11\n953 398 7 9\n931 400 10 14\n994 406 9 11\n950 423 9 13\n1001 426 7 10\n972 451 9 14\n990 375 5 9\n890 447 11 15\n901 440 8 10\n875 355 7 8\n897 380 8 10\n793 361 8 10\n822 334 5 8\n760 391 10 16\n206 462 10 12\n575 335 5 6\n282 326 4 6\n# 35--Basketball/35_Basketball_playingbasketball_35_491.jpg\n337 122 227 307\n# 35--Basketball/35_Basketball_basketballgame_ball_35_389.jpg\n505 227 35 64\n62 582 6 11\n28 595 7 13\n16 586 4 9\n18 611 7 14\n85 618 8 15\n90 578 6 13\n137 588 6 12\n158 590 6 11\n153 622 11 14\n223 610 8 14\n185 596 5 9\n139 569 5 10\n113 572 6 12\n102 572 5 9\n112 556 5 8\n264 601 6 11\n256 625 10 14\n322 613 9 17\n288 606 7 12\n315 596 5 10\n22 561 7 14\n54 568 6 10\n66 560 6 11\n351 606 6 11\n344 564 7 12\n311 572 9 12\n303 559 6 12\n235 555 5 11\n237 619 6 12\n203 569 6 10\n208 556 5 10\n415 614 6 12\n503 584 7 14\n548 613 7 11\n545 630 8 9\n567 618 10 21\n625 587 14 27\n464 565 5 8\n471 580 7 16\n680 598 6 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35--Basketball/35_Basketball_basketballgame_ball_35_393.jpg\n418 299 12 18\n664 291 10 19\n677 286 11 16\n683 296 9 14\n713 289 12 19\n702 300 13 18\n780 290 13 16\n856 282 15 20\n118 227 9 9\n110 237 7 8\n96 230 7 9\n70 226 8 9\n71 240 8 9\n78 251 9 11\n65 268 8 9\n80 281 7 11\n126 313 9 11\n95 314 8 9\n49 252 8 9\n20 240 8 9\n36 256 7 8\n47 282 8 11\n15 271 8 10\n11 287 8 9\n15 299 8 12\n3 327 9 13\n24 328 9 11\n52 333 10 11\n83 333 9 11\n110 333 8 11\n132 330 8 11\n166 292 12 17\n# 35--Basketball/35_Basketball_Basketball_35_684.jpg\n890 32 120 202\n# 35--Basketball/35_Basketball_Basketball_35_549.jpg\n724 378 48 53\n685 378 40 55\n# 35--Basketball/35_Basketball_playingbasketball_35_632.jpg\n326 520 49 31\n130 446 16 20\n459 336 35 22\n911 483 15 16\n# 35--Basketball/35_Basketball_basketballgame_ball_35_446.jpg\n466 307 120 179\n# 35--Basketball/35_Basketball_playingbasketball_35_283.jpg\n646 354 171 96\n# 35--Basketball/35_Basketball_Basketball_35_449.jpg\n79 409 11 15\n288 388 12 17\n493 243 22 33\n563 425 8 11\n855 397 11 15\n905 416 9 11\n982 409 8 12\n# 35--Basketball/35_Basketball_Basketball_35_327.jpg\n187 182 18 27\n251 170 19 26\n324 184 18 26\n384 177 22 29\n435 163 20 28\n465 177 22 34\n412 234 54 74\n491 177 17 24\n538 153 28 40\n599 160 18 31\n637 171 18 27\n664 153 29 34\n737 153 23 35\n763 143 29 40\n790 169 30 42\n828 149 27 35\n915 139 37 59\n970 181 28 42\n101 164 16 28\n508 194 18 21\n981 159 25 32\n563 160 16 28\n83 162 25 40\n24 176 25 33\n# 35--Basketball/35_Basketball_basketballgame_ball_35_827.jpg\n372 387 222 327\n715 892 258 399\n# 35--Basketball/35_Basketball_playingbasketball_35_113.jpg\n477 169 51 55\n577 125 44 47\n# 35--Basketball/35_Basketball_basketballgame_ball_35_513.jpg\n616 149 169 235\n227 86 191 244\n# 35--Basketball/35_Basketball_playingbasketball_35_11.jpg\n481 139 87 139\n# 35--Basketball/35_Basketball_basketballgame_ball_35_681.jpg\n452 20 92 126\n# 35--Basketball/35_Basketball_playingbasketball_35_248.jpg\n172 894 24 25\n286 857 12 13\n145 859 16 19\n154 959 30 35\n396 833 31 40\n493 930 29 32\n651 864 20 20\n816 863 19 17\n624 905 41 50\n27 895 31 38\n# 35--Basketball/35_Basketball_basketballgame_ball_35_736.jpg\n388 128 70 136\n480 186 62 96\n716 142 72 128\n# 35--Basketball/35_Basketball_basketballgame_ball_35_153.jpg\n419 225 167 219\n709 290 151 204\n89 207 133 225\n86 15 126 123\n# 35--Basketball/35_Basketball_playingbasketball_35_19.jpg\n115 299 30 57\n520 250 33 26\n# 35--Basketball/35_Basketball_basketballgame_ball_35_542.jpg\n320 257 38 55\n551 252 43 57\n86 256 44 55\n# 35--Basketball/35_Basketball_playingbasketball_35_795.jpg\n83 36 23 29\n137 110 20 30\n220 73 24 30\n198 128 11 15\n39 16 10 15\n28 67 5 9\n37 67 6 8\n316 93 4 6\n325 90 3 5\n338 90 3 5\n317 81 4 4\n411 89 4 4\n408 80 3 4\n366 82 7 10\n393 48 8 7\n557 53 42 61\n845 86 41 59\n53 268 64 88\n402 395 23 35\n9 458 26 26\n609 519 12 16\n483 499 15 19\n575 558 6 8\n442 539 5 7\n665 553 6 9\n682 562 7 8\n692 551 6 8\n770 552 7 10\n785 551 7 9\n793 521 6 9\n784 518 6 9\n752 526 6 8\n726 522 5 7\n715 525 7 9\n701 522 6 9\n684 524 6 8\n912 437 20 33\n851 595 12 16\n937 572 10 14\n984 533 9 12\n575 533 6 9\n595 499 5 6\n622 498 5 7\n341 35 6 11\n# 35--Basketball/35_Basketball_playingbasketball_35_252.jpg\n560 3 34 39\n599 64 48 60\n469 77 59 67\n104 19 44 55\n452 12 43 57\n# 35--Basketball/35_Basketball_basketballgame_ball_35_423.jpg\n392 98 298 440\n# 35--Basketball/35_Basketball_playingbasketball_35_195.jpg\n1012 399 11 13\n210 275 15 27\n55 404 9 10\n114 410 10 9\n152 404 9 12\n303 406 9 9\n461 395 15 16\n505 387 11 11\n359 401 7 10\n432 403 7 10\n538 404 8 10\n583 385 15 17\n737 401 8 8\n775 405 8 9\n780 391 9 9\n711 400 9 10\n971 387 12 18\n557 399 14 17\n617 426 9 11\n253 405 4 7\n135 416 10 12\n344 397 8 8\n290 435 9 11\n389 431 6 8\n# 35--Basketball/35_Basketball_playingbasketball_35_555.jpg\n382 10 86 112\n526 50 100 102\n# 35--Basketball/35_Basketball_Basketball_35_653.jpg\n872 33 55 73\n734 48 57 70\n599 28 59 68\n460 76 56 69\n369 54 58 74\n268 49 56 68\n99 38 53 71\n90 276 59 69\n209 314 46 69\n380 301 53 63\n536 299 58 69\n653 276 54 64\n813 283 51 66\n921 278 61 76\n# 35--Basketball/35_Basketball_Basketball_35_107.jpg\n41 199 75 117\n419 58 128 186\n619 450 24 35\n609 518 36 56\n691 547 48 40\n931 443 33 39\n851 386 26 35\n892 381 27 31\n949 373 26 33\n992 483 32 82\n743 481 27 42\n947 489 41 69\n667 446 30 37\n858 354 20 25\n818 410 22 24\n748 379 18 22\n788 348 15 25\n734 350 19 26\n712 360 15 23\n853 301 21 30\n816 336 26 30\n1007 343 16 29\n1010 383 14 26\n1011 431 13 40\n727 232 20 25\n838 217 20 27\n845 135 17 22\n917 122 17 23\n881 92 15 20\n818 93 15 21\n786 162 14 23\n690 200 19 27\n615 110 16 20\n311 166 15 17\n263 170 13 18\n231 119 16 20\n291 123 14 18\n336 125 15 18\n202 329 16 22\n165 288 16 24\n94 161 21 17\n229 358 19 25\n682 509 37 49\n320 66 14 20\n221 70 14 17\n273 71 12 16\n167 72 14 15\n780 226 18 21\n741 196 17 23\n798 187 16 22\n795 387 22 29\n775 99 17 18\n683 81 13 17\n867 218 17 22\n957 357 18 20\n365 88 14 18\n210 103 13 17\n320 99 13 15\n198 45 12 17\n258 34 12 16\n293 42 10 14\n339 31 12 16\n389 32 11 16\n146 45 10 13\n630 82 12 16\n746 80 11 14\n746 47 13 16\n749 12 11 13\n788 12 13 15\n675 18 14 16\n923 362 22 27\n772 406 17 21\n982 155 16 23\n658 254 17 19\n270 5 15 17\n297 14 14 17\n# 35--Basketball/35_Basketball_playingbasketball_35_251.jpg\n432 99 84 180\n# 35--Basketball/35_Basketball_basketballgame_ball_35_276.jpg\n17 0 58 53\n236 0 56 48\n18 125 40 65\n67 184 58 62\n277 160 56 81\n492 119 55 77\n432 50 48 45\n671 120 66 89\n961 34 52 75\n940 151 61 76\n# 35--Basketball/35_Basketball_playingbasketball_35_619.jpg\n199 282 16 20\n82 278 12 14\n781 281 14 20\n609 285 14 14\n576 260 17 26\n449 274 22 24\n526 178 21 28\n# 35--Basketball/35_Basketball_Basketball_35_304.jpg\n706 250 25 39\n765 269 33 36\n861 342 60 69\n# 35--Basketball/35_Basketball_Basketball_35_457.jpg\n938 320 15 17\n606 151 36 39\n410 336 7 12\n380 327 8 15\n304 364 6 9\n16 393 9 11\n69 385 9 10\n50 383 9 8\n40 360 8 7\n42 351 8 8\n94 381 7 8\n126 388 8 10\n171 387 8 6\n136 387 7 7\n127 373 7 5\n125 381 6 7\n159 374 5 6\n213 386 5 7\n194 371 6 7\n213 360 5 6\n248 393 6 7\n282 377 5 8\n269 364 6 7\n234 351 5 7\n81 347 7 8\n# 35--Basketball/35_Basketball_basketballgame_ball_35_341.jpg\n240 240 105 175\n501 190 125 163\n653 25 125 183\n# 35--Basketball/35_Basketball_playingbasketball_35_405.jpg\n576 308 16 14\n826 295 16 25\n358 312 18 16\n343 316 13 17\n979 453 45 190\n# 35--Basketball/35_Basketball_basketballgame_ball_35_375.jpg\n83 567 7 16\n871 518 9 18\n994 535 11 19\n1017 636 7 18\n960 493 6 12\n102 562 9 17\n987 481 11 18\n994 471 10 11\n19 77 5 8\n42 77 5 7\n66 78 5 6\n84 79 6 7\n104 84 3 5\n123 83 5 6\n122 96 4 5\n125 112 4 5\n106 112 5 7\n106 91 5 7\n86 98 5 5\n82 108 6 7\n69 98 4 5\n56 95 4 5\n31 94 4 6\n9 94 5 5\n30 107 5 6\n7 159 5 6\n22 162 6 7\n53 140 6 6\n69 142 5 6\n34 161 6 7\n37 174 6 5\n75 169 7 6\n93 157 7 8\n111 158 6 8\n119 172 7 8\n101 168 5 6\n86 167 6 7\n58 165 6 7\n97 130 5 7\n81 131 5 6\n63 124 5 6\n1 116 4 9\n0 133 6 9\n155 129 5 5\n156 141 7 7\n163 152 6 7\n163 163 5 8\n147 161 7 9\n134 159 5 9\n142 176 5 7\n155 178 7 7\n132 144 6 8\n137 99 4 5\n143 81 5 7\n160 83 5 7\n27 64 5 6\n55 63 4 5\n54 49 6 6\n36 49 4 5\n89 51 5 7\n79 63 6 8\n96 65 5 6\n110 64 6 8\n112 51 5 7\n131 54 5 6\n134 67 5 6\n145 53 4 6\n148 66 5 6\n161 56 5 6\n182 69 6 7\n179 58 5 5\n165 68 4 6\n174 132 6 7\n180 149 6 8\n179 164 6 7\n123 3 4 7\n102 4 4 6\n127 16 4 6\n108 16 5 7\n119 33 4 6\n104 33 5 6\n86 31 5 6\n68 33 4 6\n52 32 4 6\n55 18 4 5\n65 6 4 5\n93 18 4 6\n77 8 5 10\n150 5 5 5\n161 8 5 5\n156 18 4 7\n158 31 5 7\n174 35 5 7\n137 35 5 7\n141 18 4 7\n259 8 4 5\n246 6 4 6\n230 7 3 6\n215 5 5 7\n196 5 4 6\n182 5 4 6\n191 18 4 7\n210 20 4 5\n229 24 6 7\n245 18 5 6\n177 21 4 6\n188 35 5 7\n207 38 4 5\n221 39 6 6\n206 56 5 6\n254 60 5 6\n256 73 5 7\n256 89 5 7\n224 82 7 8\n204 96 5 8\n235 70 6 7\n235 81 5 9\n350 10 4 5\n358 13 4 5\n373 15 4 6\n392 13 5 7\n378 27 4 5\n360 29 5 5\n350 30 4 5\n335 26 4 7\n322 24 4 5\n301 23 4 5\n280 37 5 7\n300 36 5 7\n314 40 5 6\n352 43 5 6\n341 43 3 5\n329 44 4 5\n382 45 4 5\n391 45 6 7\n406 47 5 7\n395 65 5 7\n380 60 5 7\n363 61 5 8\n351 64 4 7\n331 59 4 7\n293 58 6 7\n313 81 4 7\n331 78 6 8\n354 77 5 7\n372 82 6 8\n404 96 6 9\n385 80 4 6\n382 115 4 8\n369 90 5 12\n362 115 6 7\n348 109 6 8\n335 108 6 10\n350 128 7 8\n339 131 5 8\n320 126 6 10\n308 126 7 8\n291 124 4 7\n315 108 5 7\n297 106 6 7\n280 73 8 10\n289 91 7 9\n269 75 5 7\n272 92 6 6\n270 105 5 7\n239 119 6 9\n244 138 6 7\n263 143 6 7\n283 140 6 7\n299 142 5 7\n221 118 5 7\n199 119 6 6\n197 134 6 7\n214 133 5 8\n224 138 5 6\n194 148 5 7\n214 154 7 7\n230 153 7 9\n250 158 8 8\n273 159 5 7\n289 162 6 8\n291 177 5 7\n272 177 8 9\n256 175 6 8\n238 173 7 8\n219 173 6 7\n202 168 7 8\n187 168 6 7\n173 175 6 7\n196 185 7 9\n221 185 5 7\n238 187 6 8\n251 186 4 7\n256 198 5 7\n279 192 7 9\n303 193 5 9\n328 6 8 8\n409 0 6 5\n409 15 4 5\n440 6 4 6\n446 4 4 6\n454 4 5 6\n470 4 3 6\n463 15 5 6\n445 17 4 5\n422 0 5 4\n463 29 5 6\n442 31 4 4\n422 32 4 5\n412 42 5 7\n427 48 3 3\n451 44 4 6\n473 48 7 6\n448 67 7 7\n416 65 6 8\n337 145 6 7\n363 145 6 10\n396 151 7 11\n412 155 6 7\n428 159 5 7\n451 157 5 8\n468 160 8 11\n473 140 6 6\n478 105 6 9\n448 142 6 8\n427 136 6 8\n414 135 6 9\n430 128 6 10\n429 119 8 11\n458 120 7 8\n456 100 7 9\n435 69 5 7\n477 89 6 6\n534 6 5 6\n523 6 6 7\n522 18 5 6\n505 4 4 4\n493 4 4 5\n500 18 5 7\n301 75 6 8\n272 60 5 6\n321 95 5 6\n275 123 5 6\n259 122 6 7\n324 145 5 6\n428 16 4 5\n487 3 4 5\n485 14 5 6\n496 29 5 6\n475 35 4 6\n495 50 4 5\n415 82 4 6\n428 81 5 6\n498 88 3 5\n510 73 5 7\n496 105 6 8\n27 124 6 8\n26 143 4 9\n32 142 4 7\n633 9 4 6\n615 8 4 5\n592 7 6 7\n577 18 5 6\n593 19 6 7\n615 26 5 8\n622 26 3 5\n626 43 4 5\n612 39 3 6\n561 39 5 6\n578 41 4 5\n593 56 6 7\n604 61 6 7\n627 60 5 6\n622 78 5 7\n597 78 6 7\n581 77 6 7\n561 75 5 6\n567 55 5 7\n554 53 5 7\n534 50 6 7\n526 72 6 8\n542 72 6 8\n518 89 4 6\n541 88 5 9\n556 91 5 8\n576 91 6 9\n574 113 5 7\n559 107 5 9\n543 107 5 7\n518 104 6 9\n570 129 6 7\n557 127 6 7\n531 126 6 8\n506 128 6 8\n490 128 5 6\n318 156 4 5\n312 165 5 6\n318 196 7 8\n360 166 8 9\n379 178 7 8\n397 175 7 7\n414 176 7 8\n410 195 7 10\n378 195 8 7\n345 185 7 9\n320 182 7 9\n312 179 6 8\n330 234 7 5\n639 76 6 8\n661 79 5 8\n598 96 5 7\n613 96 5 7\n635 101 6 6\n651 100 5 6\n638 118 5 9\n612 118 5 7\n594 112 6 9\n592 134 7 8\n610 136 7 8\n635 133 9 8\n659 136 8 9\n651 161 7 8\n625 155 6 9\n612 152 7 9\n594 152 6 8\n574 150 6 9\n568 168 6 10\n584 180 5 7\n609 176 6 8\n625 178 7 9\n650 180 5 9\n606 193 7 8\n559 187 6 8\n390 96 4 5\n441 179 5 8\n459 180 5 8\n493 141 5 7\n507 150 5 8\n527 151 6 7\n541 149 6 8\n549 167 7 8\n518 164 7 9\n501 168 6 8\n483 160 6 9\n575 193 6 9\n535 184 6 9\n521 185 5 8\n494 186 6 8\n480 181 7 9\n526 205 5 9\n543 207 6 8\n568 213 7 9\n587 215 6 9\n558 223 7 8\n558 235 8 9\n523 227 6 9\n501 219 8 10\n434 200 8 9\n451 212 8 12\n420 196 6 7\n478 124 5 7\n469 201 9 8\n623 203 5 7\n614 216 7 9\n629 216 7 10\n611 232 8 8\n636 233 7 10\n646 242 6 7\n657 221 6 8\n682 203 5 10\n696 205 6 7\n674 221 7 12\n685 236 7 11\n705 243 7 11\n540 261 5 10\n587 266 6 12\n550 257 8 9\n525 259 7 11\n592 229 8 9\n645 6 4 5\n671 9 5 7\n679 9 4 6\n699 16 4 4\n682 27 5 5\n679 33 5 7\n679 42 5 7\n672 41 5 7\n660 30 5 7\n643 25 5 8\n647 57 6 8\n661 62 5 5\n682 66 5 7\n702 60 5 8\n703 43 5 7\n724 45 5 6\n746 41 6 9\n741 25 5 6\n729 30 4 6\n703 24 5 7\n720 9 5 7\n740 11 5 7\n731 63 5 8\n749 65 7 7\n738 85 5 7\n717 84 5 7\n701 83 5 7\n679 84 6 7\n738 100 6 10\n720 99 8 9\n696 105 5 6\n715 127 5 8\n695 120 6 8\n677 117 7 8\n680 101 7 8\n757 18 5 5\n755 30 7 7\n783 12 4 7\n801 13 5 8\n818 13 5 8\n840 13 5 6\n803 33 6 11\n781 26 6 8\n778 41 8 9\n766 44 7 10\n763 70 6 9\n763 85 6 8\n831 98 6 7\n853 95 6 9\n851 116 5 8\n831 119 5 7\n928 4 7 8\n948 5 6 8\n979 3 5 8\n1007 17 5 7\n998 39 6 6\n976 24 5 7\n960 21 4 6\n939 18 5 6\n912 18 4 6\n1000 58 5 7\n975 58 6 8\n966 41 4 7\n955 35 6 8\n942 37 5 10\n915 38 5 7\n890 41 5 7\n878 53 6 7\n899 59 5 6\n921 58 6 8\n926 73 6 8\n947 58 5 8\n948 79 6 8\n968 82 6 7\n999 83 5 6\n1017 80 6 11\n1001 103 6 10\n978 104 6 8\n959 100 6 8\n934 101 6 10\n903 100 5 7\n906 79 5 6\n882 74 5 8\n869 69 6 12\n885 100 5 6\n878 118 6 9\n937 122 6 6\n973 122 6 8\n1020 129 2 10\n964 146 6 9\n947 139 6 8\n915 137 7 11\n892 138 5 8\n988 128 5 9\n918 118 6 9\n1018 169 5 9\n995 170 7 8\n972 185 7 9\n964 167 6 10\n940 165 6 9\n952 187 6 9\n935 208 5 10\n917 189 6 7\n908 206 6 8\n917 228 6 9\n948 239 6 10\n896 181 8 11\n892 158 6 8\n861 136 7 8\n863 157 6 8\n845 151 8 9\n822 153 8 7\n817 175 7 8\n840 176 6 8\n864 183 6 7\n861 199 7 9\n882 211 6 8\n865 224 7 9\n886 236 6 7\n832 216 7 9\n823 203 5 9\n800 226 5 7\n775 215 7 9\n775 144 7 12\n806 189 8 10\n1011 47 7 12\n987 84 9 6\n916 161 6 7\n1018 196 5 10\n1007 195 5 9\n1015 260 7 12\n1006 277 8 12\n845 267 8 7\n875 271 7 7\n725 185 7 10\n752 164 8 8\n754 211 6 8\n749 226 8 10\n809 266 8 9\n750 260 6 7\n177 113 6 6\n243 104 6 6\n260 107 5 7\n208 129 4 7\n184 180 7 7\n# 35--Basketball/35_Basketball_basketballgame_ball_35_479.jpg\n139 291 5 6\n133 286 4 6\n130 284 3 4\n138 296 4 5\n138 320 4 5\n123 334 4 6\n114 287 4 6\n103 283 3 5\n92 280 4 4\n101 295 4 5\n103 332 4 6\n96 335 5 5\n85 342 6 5\n80 306 4 6\n87 294 3 5\n80 289 4 6\n62 310 3 4\n46 340 5 9\n23 338 4 6\n22 313 4 6\n40 305 4 5\n62 300 5 6\n56 310 4 6\n61 336 6 9\n92 344 4 7\n39 322 4 5\n28 367 4 6\n22 373 5 8\n22 404 4 6\n43 384 5 7\n54 389 5 7\n64 391 4 6\n51 409 5 7\n70 402 4 6\n73 412 6 6\n85 415 6 8\n28 295 3 4\n20 282 3 6\n21 296 2 5\n57 293 4 5\n16 340 5 7\n27 343 4 6\n126 426 7 9\n139 438 5 9\n134 439 5 8\n111 437 6 8\n76 441 6 9\n102 451 5 10\n84 447 4 8\n91 479 8 11\n94 468 6 7\n61 434 7 9\n49 430 6 8\n35 426 6 6\n22 446 7 6\n36 443 5 7\n26 461 5 9\n144 487 5 8\n92 518 10 14\n139 531 8 13\n42 379 5 5\n32 377 5 4\n10 393 5 5\n9 398 5 7\n8 410 5 7\n4 422 3 6\n2 407 4 6\n7 420 4 6\n103 423 5 9\n7 450 6 9\n102 472 8 9\n103 622 19 31\n87 575 7 15\n221 486 6 10\n251 471 4 7\n269 475 5 8\n279 487 4 9\n175 453 6 6\n164 449 5 8\n195 466 5 6\n196 478 5 6\n323 554 8 11\n304 583 11 14\n481 584 11 12\n628 600 5 10\n816 665 9 16\n951 645 8 12\n982 638 9 11\n179 229 3 3\n189 239 2 4\n216 281 3 4\n174 274 4 4\n160 253 3 4\n155 231 3 4\n144 232 3 4\n177 247 4 4\n171 255 4 5\n168 294 5 6\n249 227 4 5\n240 228 3 4\n225 229 2 3\n260 279 4 6\n268 270 3 4\n251 288 4 4\n371 339 4 5\n421 366 5 8\n503 360 4 7\n477 357 4 5\n539 351 4 6\n558 350 3 6\n570 369 5 7\n626 377 4 7\n268 289 2 5\n238 292 4 5\n225 302 4 5\n221 310 5 5\n246 328 3 5\n237 305 5 7\n255 303 4 4\n273 300 4 4\n255 309 4 5\n285 300 3 5\n290 306 3 5\n299 304 4 5\n314 304 4 5\n319 295 4 5\n308 289 4 6\n268 303 3 6\n203 280 5 7\n256 261 6 4\n272 249 5 6\n253 270 3 5\n239 310 5 5\n324 256 3 5\n335 255 4 8\n311 255 3 6\n337 280 4 5\n312 279 3 4\n348 253 3 5\n370 264 3 4\n374 290 5 5\n357 290 2 4\n323 299 3 6\n320 279 4 6\n386 274 4 5\n398 272 4 6\n397 291 4 7\n390 290 5 5\n354 235 3 5\n336 244 2 3\n324 247 2 3\n349 277 3 4\n401 265 4 5\n399 255 3 4\n394 249 3 4\n425 238 3 4\n416 248 3 4\n408 248 3 5\n413 239 2 4\n399 240 3 4\n440 285 3 5\n430 294 3 4\n430 305 3 5\n444 309 3 5\n455 311 4 5\n462 311 3 5\n477 315 3 4\n489 316 5 6\n482 299 4 5\n473 299 4 4\n468 286 4 5\n479 288 4 4\n456 295 4 5\n501 319 4 6\n466 317 4 4\n533 313 3 6\n511 322 3 5\n531 323 3 5\n500 292 4 6\n491 290 4 6\n534 273 3 4\n519 272 3 4\n497 270 3 4\n484 271 2 3\n474 270 3 4\n447 267 3 4\n454 277 3 4\n505 261 3 5\n521 262 3 4\n530 264 3 4\n515 283 4 5\n493 279 3 5\n548 323 4 5\n558 327 4 5\n570 328 4 6\n584 330 3 6\n586 297 4 6\n575 297 4 5\n563 298 3 4\n549 295 4 6\n558 283 4 5\n546 308 4 4\n581 267 3 4\n594 264 4 5\n544 262 3 4\n587 285 2 3\n596 290 2 4\n606 289 2 3\n606 305 3 3\n600 312 4 5\n594 310 4 5\n705 268 2 4\n695 268 4 4\n703 277 3 4\n695 277 3 4\n680 277 4 5\n673 280 4 5\n666 283 4 5\n657 285 3 3\n643 282 3 5\n635 280 4 6\n602 271 3 4\n611 282 2 3\n648 294 3 6\n661 297 3 4\n666 298 4 4\n677 307 3 5\n709 314 3 5\n716 328 3 6\n706 327 4 6\n698 312 4 5\n690 327 5 6\n683 325 4 5\n657 306 5 4\n665 333 4 5\n646 341 4 5\n646 332 4 5\n628 341 4 6\n620 336 4 6\n615 327 4 5\n602 340 4 5\n601 334 3 4\n603 327 3 5\n593 324 6 5\n575 357 6 4\n657 360 5 6\n626 292 6 8\n639 297 7 8\n623 317 4 5\n639 318 3 5\n580 308 4 6\n468 258 3 4\n460 248 3 5\n464 240 2 4\n381 281 3 7\n416 217 3 4\n459 267 3 5\n569 306 3 4\n580 279 2 5\n526 252 3 4\n537 253 3 4\n548 254 3 3\n559 257 2 3\n560 246 3 5\n545 244 2 3\n497 229 2 3\n517 230 2 4\n515 248 4 6\n510 269 2 5\n500 250 3 5\n565 222 3 6\n561 232 3 4\n522 220 4 4\n507 211 3 4\n576 224 2 4\n588 225 2 5\n494 262 3 4\n474 248 3 4\n422 226 3 4\n765 237 2 4\n755 238 3 3\n729 243 4 5\n748 249 3 4\n732 259 3 4\n791 249 3 4\n829 253 4 4\n843 255 4 5\n835 268 4 6\n818 277 4 5\n822 266 3 5\n798 263 3 6\n785 263 4 5\n774 265 4 5\n782 273 3 5\n773 275 4 5\n756 272 3 5\n738 281 5 5\n750 285 3 5\n789 274 3 5\n807 274 5 5\n837 280 4 4\n845 295 4 5\n831 291 4 5\n795 286 6 7\n741 294 4 5\n730 301 4 6\n749 306 3 6\n774 298 4 5\n775 289 3 5\n792 299 3 4\n805 300 4 5\n816 296 5 7\n831 306 4 5\n814 315 6 9\n797 313 4 6\n801 327 3 6\n811 328 5 7\n844 318 4 5\n775 309 3 4\n763 305 5 8\n735 318 4 5\n743 319 4 6\n756 321 4 5\n751 335 4 5\n781 337 3 5\n785 321 3 8\n774 324 3 6\n795 340 3 4\n768 337 4 5\n731 307 4 5\n722 304 4 5\n710 301 5 5\n719 296 4 6\n720 319 3 4\n705 342 4 5\n713 341 5 7\n725 350 4 4\n751 351 4 5\n754 352 4 7\n766 361 5 7\n721 333 4 5\n638 260 3 4\n644 258 5 5\n608 257 2 4\n601 275 3 5\n628 261 4 4\n603 248 4 6\n655 216 4 7\n634 236 3 5\n671 246 3 5\n688 242 3 5\n696 240 3 5\n707 242 3 5\n357 278 5 7\n333 290 3 4\n310 261 4 5\n358 262 4 6\n364 235 4 6\n350 243 5 6\n502 310 4 5\n523 312 4 6\n531 302 3 4\n561 321 4 5\n570 319 4 5\n567 285 3 5\n558 307 3 5\n853 256 4 5\n792 219 3 3\n782 221 3 4\n819 228 5 4\n832 229 4 4\n846 212 3 3\n621 241 3 6\n620 260 3 5\n645 250 3 4\n650 228 3 4\n585 207 4 7\n665 210 4 5\n577 207 4 4\n596 207 3 4\n609 216 2 4\n588 198 2 4\n629 199 3 4\n609 190 3 4\n626 190 4 6\n607 197 4 6\n599 219 4 5\n765 285 4 6\n797 365 5 6\n799 354 4 6\n789 354 4 7\n812 337 5 7\n827 342 5 7\n786 364 4 6\n810 372 5 5\n831 367 7 8\n814 359 4 6\n957 238 5 7\n947 232 2 4\n934 224 4 5\n918 226 4 5\n908 245 3 6\n906 233 4 9\n930 244 6 6\n916 247 6 8\n945 249 4 6\n933 276 7 7\n919 273 5 8\n903 273 4 5\n924 286 6 7\n886 246 4 4\n881 257 3 5\n922 262 4 5\n934 263 4 5\n949 265 3 6\n959 258 7 6\n957 317 5 8\n941 327 5 6\n926 342 4 6\n918 355 5 5\n905 357 5 8\n891 353 6 8\n903 259 6 6\n896 259 3 5\n894 224 4 4\n864 236 3 4\n868 257 4 5\n847 269 4 5\n862 269 4 6\n877 271 4 5\n889 274 3 5\n904 298 4 6\n898 287 5 7\n871 283 4 5\n889 286 4 6\n860 283 4 6\n886 297 5 6\n890 309 5 7\n869 306 5 6\n859 321 4 6\n877 323 4 7\n889 325 3 4\n914 303 5 5\n854 339 4 4\n845 347 5 5\n860 304 5 6\n844 303 6 10\n908 380 6 8\n896 377 5 7\n863 338 5 6\n886 366 4 8\n876 364 4 6\n843 371 7 8\n841 364 6 11\n861 373 6 7\n856 362 6 6\n976 241 4 5\n965 252 4 5\n1000 230 7 7\n997 242 7 9\n1009 286 5 6\n967 307 3 5\n1013 296 6 8\n859 291 4 9\n830 318 4 6\n973 253 7 8\n1017 312 5 7\n1009 304 7 7\n1008 336 6 9\n1006 324 3 5\n988 338 5 6\n991 359 5 5\n1006 358 4 6\n972 338 5 6\n958 346 4 7\n987 368 4 6\n945 346 5 7\n937 351 2 4\n936 368 4 4\n996 339 6 7\n875 376 5 7\n936 378 7 8\n957 389 8 11\n1007 393 7 11\n988 243 3 5\n1014 218 5 7\n1011 208 5 6\n995 207 4 7\n985 220 3 5\n970 219 4 6\n956 204 4 5\n945 205 4 4\n969 197 3 5\n932 214 4 5\n971 370 5 5\n992 405 6 7\n847 235 3 4\n892 235 4 5\n875 241 4 4\n906 215 3 4\n897 215 4 5\n883 211 4 5\n868 212 4 4\n908 203 3 5\n895 201 4 7\n894 193 4 5\n882 221 4 5\n922 215 4 5\n904 191 4 5\n905 226 4 6\n966 205 5 5\n980 185 3 4\n967 185 3 3\n995 187 4 4\n955 185 4 5\n944 181 4 5\n966 175 4 5\n976 176 4 4\n998 178 2 3\n943 195 3 4\n906 166 4 5\n916 192 4 6\n894 170 4 6\n894 181 3 6\n918 174 3 4\n932 185 4 4\n928 173 2 5\n955 165 4 4\n940 162 4 5\n930 165 3 4\n915 154 4 6\n440 220 3 5\n429 216 4 6\n421 215 5 5\n437 198 4 6\n456 196 3 3\n883 201 3 7\n875 195 4 6\n868 198 4 6\n842 187 4 6\n854 212 4 7\n859 196 5 8\n829 190 5 7\n851 168 4 5\n846 167 4 6\n712 256 4 5\n697 255 3 4\n759 224 4 6\n735 222 5 5\n746 223 4 4\n736 235 3 3\n729 236 4 3\n717 227 5 8\n683 211 4 4\n676 208 6 6\n664 220 3 3\n687 230 3 5\n696 223 3 3\n702 213 3 4\n696 202 3 5\n688 201 2 4\n754 196 4 4\n717 214 3 7\n724 215 3 6\n736 215 3 5\n765 225 4 6\n760 207 3 4\n745 195 3 6\n705 194 3 6\n711 192 4 4\n830 221 4 5\n819 199 3 5\n810 198 3 5\n792 208 3 5\n794 188 3 4\n794 199 3 3\n488 205 2 4\n519 211 3 4\n508 231 2 4\n982 206 5 6\n988 167 4 6\n1000 142 4 5\n973 135 3 4\n988 155 4 7\n952 134 3 5\n938 136 4 4\n992 184 2 4\n1003 186 5 10\n1009 158 5 5\n1007 143 4 8\n# 35--Basketball/35_Basketball_Basketball_35_158.jpg\n198 462 120 174\n955 565 60 84\n# 35--Basketball/35_Basketball_basketballgame_ball_35_254.jpg\n627 312 92 78\n312 237 61 106\n12 143 80 92\n# 35--Basketball/35_Basketball_playingbasketball_35_219.jpg\n468 968 61 69\n580 871 30 37\n684 927 23 34\n702 1053 66 83\n815 959 68 94\n924 871 68 88\n246 683 96 113\n829 877 22 26\n759 870 28 35\n# 35--Basketball/35_Basketball_basketballgame_ball_35_460.jpg\n444 427 213 288\n# 35--Basketball/35_Basketball_basketballgame_ball_35_709.jpg\n767 996 35 45\n749 971 22 26\n782 974 35 42\n865 978 21 26\n882 1012 30 38\n894 978 31 40\n937 983 27 31\n960 972 23 23\n994 978 29 35\n1003 1002 21 36\n993 930 22 28\n972 909 18 24\n930 908 23 25\n774 923 24 29\n851 918 20 32\n908 955 23 28\n904 925 25 30\n851 896 23 29\n868 863 18 24\n913 867 20 26\n972 861 24 28\n973 834 19 25\n998 816 19 25\n981 781 26 29\n976 759 20 24\n991 749 23 23\n983 720 23 23\n979 700 18 20\n1000 680 18 25\n942 652 19 25\n949 684 18 25\n897 675 20 24\n836 653 18 18\n804 668 16 20\n841 677 17 20\n790 649 20 18\n776 672 18 20\n771 692 21 20\n739 701 18 23\n758 725 17 20\n758 742 18 20\n779 715 19 25\n824 697 19 24\n824 721 25 28\n888 707 18 23\n880 737 20 23\n923 734 20 21\n925 766 19 21\n921 807 24 28\n923 786 20 19\n904 837 22 23\n860 847 18 19\n859 808 20 29\n877 810 21 30\n789 836 24 24\n795 808 23 25\n801 778 24 24\n832 773 18 25\n865 771 20 19\n874 787 18 22\n796 867 22 25\n893 641 21 25\n971 607 18 23\n972 579 15 21\n928 587 17 22\n880 583 14 17\n851 621 17 19\n791 620 19 19\n810 607 14 16\n807 558 15 14\n813 541 14 15\n673 567 24 22\n715 569 17 21\n748 493 19 21\n717 479 17 17\n695 462 16 18\n667 427 11 12\n684 434 13 15\n703 454 14 16\n729 468 11 15\n738 452 11 15\n728 430 10 11\n695 406 13 15\n766 476 12 14\n788 482 14 19\n803 482 11 12\n827 491 13 17\n793 518 12 13\n875 504 12 15\n857 470 12 14\n838 482 11 15\n868 492 12 15\n887 470 11 15\n909 489 10 18\n926 477 12 17\n956 508 15 16\n976 459 10 15\n988 492 12 17\n1003 476 11 17\n848 450 12 11\n829 444 9 12\n810 457 12 12\n790 440 12 14\n801 432 12 15\n773 461 12 13\n812 415 12 16\n866 443 11 16\n890 451 11 12\n933 443 13 19\n954 482 13 16\n310 388 15 15\n331 371 17 19\n364 363 14 13\n369 383 14 16\n390 402 11 13\n410 409 15 19\n354 420 14 14\n337 394 12 19\n350 386 13 16\n314 422 11 15\n322 439 10 12\n341 441 12 14\n376 434 11 16\n400 423 11 14\n401 449 14 17\n423 477 14 19\n346 490 19 21\n320 484 13 16\n304 555 17 20\n408 550 21 24\n520 401 76 69\n11 560 15 15\n22 586 17 16\n14 622 18 20\n49 635 18 24\n78 567 18 22\n98 587 18 24\n108 646 15 18\n129 623 18 19\n143 607 16 20\n161 592 16 19\n178 563 21 23\n240 552 18 21\n197 556 19 22\n217 603 19 22\n179 616 19 20\n243 627 20 29\n254 615 15 16\n291 622 16 23\n306 598 18 21\n259 579 19 25\n223 584 20 22\n16 461 17 21\n39 473 15 16\n63 466 15 18\n109 468 15 15\n109 441 16 18\n150 444 13 16\n190 436 13 16\n169 432 11 12\n233 455 15 17\n267 445 18 13\n262 467 15 14\n298 433 15 16\n276 482 15 17\n58 594 19 22\n51 619 20 22\n361 602 17 19\n390 580 17 22\n419 599 17 23\n393 629 16 22\n368 645 20 25\n328 644 21 23\n26 774 22 29\n50 804 26 27\n27 829 27 29\n17 858 22 26\n3 888 19 27\n3 933 24 29\n3 966 23 29\n5 1003 36 46\n34 988 29 38\n65 985 29 42\n53 962 27 25\n91 978 25 31\n120 1000 27 33\n137 959 28 36\n98 931 25 29\n83 856 24 31\n105 828 24 29\n135 822 30 33\n173 939 21 33\n180 983 37 43\n212 973 23 30\n165 989 29 40\n219 1009 40 42\n249 928 23 33\n268 958 27 25\n268 987 26 36\n288 997 37 38\n348 966 29 36\n79 667 16 20\n148 674 17 22\n193 663 17 22\n182 689 22 24\n152 706 23 29\n186 711 25 25\n182 735 25 25\n227 685 19 26\n261 664 19 18\n294 688 21 23\n266 705 20 23\n238 736 21 25\n216 761 25 23\n263 775 20 31\n273 758 23 23\n300 732 21 25\n320 705 21 26\n356 691 14 18\n360 722 18 23\n352 752 22 29\n320 778 25 30\n304 805 23 27\n352 831 23 29\n372 820 20 23\n386 787 26 27\n413 756 23 27\n399 690 16 19\n437 703 20 25\n392 865 27 33\n496 994 25 36\n506 937 23 30\n133 505 22 23\n632 595 89 74\n184 481 14 17\n64 433 14 18\n44 394 15 17\n43 426 13 16\n42 456 11 14\n75 409 11 13\n88 397 11 13\n108 409 15 17\n127 424 12 16\n134 400 12 15\n67 376 13 16\n71 360 12 16\n55 353 11 14\n48 321 13 15\n76 336 10 15\n86 353 11 15\n108 361 12 16\n106 338 13 15\n148 355 12 17\n164 376 13 17\n188 384 13 16\n206 376 15 15\n220 433 14 18\n230 414 15 19\n251 402 15 16\n290 378 13 15\n281 355 13 17\n233 384 10 13\n250 325 11 15\n171 358 14 16\n206 332 12 14\n222 344 12 18\n174 327 11 15\n246 359 12 17\n763 416 14 19\n757 390 12 14\n852 402 12 16\n947 432 12 14\n420 647 20 22\n374 667 18 24\n356 556 16 17\n0 810 15 25\n# 35--Basketball/35_Basketball_playingbasketball_35_732.jpg\n645 256 36 48\n# 35--Basketball/35_Basketball_playingbasketball_35_13.jpg\n443 160 98 122\n# 35--Basketball/35_Basketball_playingbasketball_35_433.jpg\n529 120 117 201\n# 35--Basketball/35_Basketball_Basketball_35_393.jpg\n752 170 72 68\n188 6 198 228\n# 35--Basketball/35_Basketball_basketballgame_ball_35_178.jpg\n86 395 25 32\n195 470 21 25\n266 506 15 18\n303 484 17 26\n567 264 32 22\n455 383 29 27\n13 658 7 9\n24 676 7 8\n31 656 8 9\n47 667 7 8\n42 660 4 7\n30 640 5 6\n20 646 5 5\n39 646 4 6\n50 647 4 6\n48 655 5 5\n55 644 3 6\n60 646 4 5\n66 647 3 5\n72 645 4 5\n58 659 6 9\n68 662 6 9\n75 666 4 7\n86 668 4 8\n120 675 5 8\n126 676 4 7\n151 674 7 7\n146 681 6 6\n228 688 3 6\n241 690 4 7\n235 691 5 5\n294 686 3 4\n313 701 4 4\n290 699 3 6\n328 694 3 6\n341 695 3 5\n355 694 4 5\n370 694 3 4\n372 704 4 5\n382 695 4 5\n390 703 5 5\n397 697 3 4\n409 697 3 5\n420 693 3 5\n435 683 3 5\n434 695 3 4\n426 683 3 4\n478 691 3 6\n463 694 3 4\n406 690 4 6\n396 690 3 5\n483 685 3 4\n490 683 4 5\n501 681 4 6\n505 680 3 4\n491 678 3 4\n535 697 4 6\n542 698 4 4\n569 694 3 6\n583 694 4 5\n592 696 3 7\n605 696 3 5\n603 683 3 4\n551 687 4 5\n601 704 4 5\n618 693 4 6\n653 697 3 5\n662 696 3 4\n633 695 4 5\n621 670 3 5\n719 689 4 5\n743 691 5 6\n750 681 4 6\n759 703 5 6\n761 692 4 6\n787 689 4 5\n794 686 4 5\n798 685 5 5\n809 685 3 4\n811 681 3 6\n818 686 3 5\n825 682 3 5\n833 680 4 5\n832 687 5 6\n843 670 5 7\n759 666 2 4\n748 667 3 4\n768 664 3 4\n784 665 2 3\n762 677 5 8\n791 673 4 6\n861 656 3 5\n855 678 5 6\n867 680 5 5\n881 675 6 8\n863 676 4 5\n887 677 6 6\n886 671 4 5\n916 673 6 7\n905 675 5 7\n900 674 4 7\n897 675 4 6\n894 676 4 5\n932 668 6 8\n924 664 3 5\n917 658 4 5\n899 666 5 5\n928 657 3 5\n934 659 3 4\n945 668 6 6\n952 663 5 7\n961 667 6 7\n962 653 6 7\n970 663 7 8\n1015 666 6 5\n1013 677 7 7\n986 661 5 7\n976 666 6 7\n981 663 5 7\n977 655 4 6\n1009 646 5 6\n995 645 5 6\n988 652 3 6\n985 644 4 4\n975 646 3 5\n970 650 4 5\n983 635 3 4\n690 387 25 28\n839 190 27 36\n# 35--Basketball/35_Basketball_playingbasketball_35_366.jpg\n410 324 40 45\n# 35--Basketball/35_Basketball_playingbasketball_35_65.jpg\n681 234 11 11\n744 380 14 18\n850 416 16 16\n907 354 16 22\n918 376 24 32\n968 405 22 27\n1005 416 14 20\n800 425 14 16\n621 391 16 22\n452 349 22 23\n529 311 18 25\n227 428 23 36\n717 416 11 14\n# 35--Basketball/35_Basketball_basketballgame_ball_35_309.jpg\n396 70 82 110\n214 6 84 58\n136 30 68 116\n766 42 180 258\n# 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312 23 63\n839 218 27 40\n447 145 27 37\n570 152 29 35\n502 337 39 41\n826 100 33 37\n114 119 23 24\n0 316 24 35\n513 50 27 33\n588 53 27 40\n270 88 23 32\n177 121 28 27\n909 210 38 37\n866 303 31 40\n476 229 25 32\n392 183 33 36\n2 242 13 40\n# 35--Basketball/35_Basketball_basketballgame_ball_35_689.jpg\n617 71 11 15\n657 71 8 12\n370 78 7 12\n323 99 11 15\n352 104 13 14\n388 113 9 10\n393 82 11 9\n423 89 9 11\n445 83 9 12\n491 86 11 15\n520 90 11 17\n551 91 12 15\n575 94 12 15\n624 103 11 14\n653 104 12 16\n581 133 9 13\n548 131 10 13\n517 131 8 13\n479 117 11 14\n452 115 9 16\n411 109 12 14\n343 140 11 12\n341 170 12 23\n382 141 8 14\n411 143 9 12\n437 146 9 12\n475 145 11 12\n557 163 10 12\n392 189 12 15\n421 175 10 14\n447 181 11 14\n421 202 11 14\n449 202 10 15\n469 223 11 12\n491 188 8 14\n514 190 11 15\n524 210 11 12\n550 194 12 15\n582 193 9 15\n590 216 14 17\n552 216 15 14\n632 201 9 15\n660 169 12 15\n653 201 12 17\n665 226 12 16\n595 15 8 14\n677 27 8 10\n698 19 13 18\n742 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8\n962 105 5 8\n922 97 6 8\n937 97 6 8\n891 96 5 8\n879 96 5 8\n997 106 8 10\n# 35--Basketball/35_Basketball_playingbasketball_35_135.jpg\n498 269 31 36\n196 245 35 52\n# 35--Basketball/35_Basketball_playingbasketball_35_674.jpg\n634 45 61 80\n370 154 66 72\n183 139 51 55\n152 68 36 42\n214 77 25 32\n269 83 28 40\n289 115 31 39\n357 94 26 30\n90 8 31 36\n175 15 27 37\n405 20 27 34\n351 8 24 30\n450 47 25 30\n478 103 25 23\n561 104 29 37\n505 29 23 34\n450 19 26 31\n553 32 28 32\n603 22 23 33\n859 259 28 36\n598 274 34 44\n646 259 36 47\n950 245 23 33\n999 246 23 32\n817 158 26 37\n803 127 31 40\n876 160 26 37\n901 73 24 34\n803 50 24 31\n789 89 23 28\n742 40 25 37\n981 31 43 78\n16 235 33 39\n0 89 27 47\n977 120 25 30\n926 81 26 32\n481 153 28 31\n844 135 20 31\n578 77 25 26\n269 3 20 32\n# 35--Basketball/35_Basketball_playingbasketball_35_876.jpg\n461 110 82 118\n980 522 21 27\n956 506 20 25\n929 521 19 24\n923 491 16 23\n929 452 19 22\n964 449 15 22\n1010 506 14 27\n1008 477 16 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36--Football/36_Football_americanfootball_ball_36_234.jpg\n335 91 101 148\n# 36--Football/36_Football_americanfootball_ball_36_162.jpg\n402 272 196 284\n# 36--Football/36_Football_americanfootball_ball_36_257.jpg\n71 222 75 95\n429 193 63 81\n292 179 49 65\n100 166 55 65\n629 132 54 72\n864 152 83 123\n503 205 35 53\n387 191 29 48\n780 209 44 61\n62 185 19 27\n# 36--Football/36_Football_americanfootball_ball_36_301.jpg\n454 116 187 235\n# 36--Football/36_Football_americanfootball_ball_36_279.jpg\n337 881 84 148\n# 36--Football/36_Football_americanfootball_ball_36_510.jpg\n566 144 76 78\n191 56 48 73\n# 36--Football/36_Football_americanfootball_ball_36_16.jpg\n540 88 132 152\n292 266 128 160\n# 36--Football/36_Football_americanfootball_ball_36_396.jpg\n252 200 126 166\n# 36--Football/36_Football_Football_36_157.jpg\n230 174 18 22\n95 240 23 28\n409 55 22 22\n240 152 16 18\n427 266 20 25\n50 131 17 25\n121 113 15 17\n144 98 17 22\n180 96 18 28\n248 117 17 23\n152 152 20 22\n157 177 20 25\n186 185 21 25\n78 148 16 22\n1 55 8 31\n566 133 16 17\n610 143 18 25\n584 162 17 20\n544 194 17 22\n362 116 15 22\n152 28 15 15\n102 174 18 17\n44 188 20 22\n820 18 13 19\n900 26 18 24\n409 125 19 19\n765 147 20 26\n812 107 18 19\n869 84 23 28\n905 106 17 23\n897 86 24 24\n983 246 22 27\n642 0 18 18\n669 35 18 20\n701 17 14 17\n583 192 19 22\n504 253 19 23\n1 188 15 20\n75 200 19 21\n90 138 18 21\n433 219 15 26\n484 207 17 25\n115 203 19 21\n357 177 16 20\n338 105 18 21\n91 108 18 21\n199 5 20 17\n273 44 20 22\n579 86 21 24\n615 74 20 24\n963 140 18 22\n906 215 24 31\n839 190 16 23\n384 367 32 45\n410 141 19 23\n441 165 17 20\n479 122 22 27\n425 96 20 25\n454 104 17 19\n382 129 18 22\n392 81 19 24\n96 10 18 20\n608 244 28 32\n504 347 34 39\n483 357 25 31\n11 170 16 23\n345 134 20 24\n845 45 21 27\n337 41 20 24\n144 269 19 27\n237 250 26 33\n905 170 20 25\n614 333 39 47\n570 332 33 47\n335 179 18 24\n337 164 16 21\n703 144 19 21\n15 119 18 23\n856 4 17 19\n970 23 20 24\n935 165 17 19\n975 159 17 25\n981 200 16 24\n667 173 19 24\n635 173 24 19\n274 185 19 21\n246 188 20 25\n229 174 18 22\n567 104 17 25\n459 33 26 30\n516 54 14 24\n493 8 16 21\n112 138 20 25\n615 42 20 22\n947 97 17 23\n535 76 18 27\n490 88 17 23\n348 5 20 22\n417 1 22 19\n96 243 23 28\n42 258 22 25\n851 168 17 20\n510 176 17 24\n522 113 20 23\n832 108 16 22\n285 136 18 24\n218 163 16 22\n787 174 22 23\n232 77 21 29\n877 174 20 25\n752 189 21 21\n739 217 26 32\n661 257 22 22\n631 196 24 24\n434 348 21 32\n163 204 21 25\n301 159 20 26\n783 112 18 25\n789 58 14 20\n319 82 23 24\n542 30 18 23\n581 8 17 24\n546 109 18 24\n392 197 37 42\n788 232 29 40\n516 141 18 27\n303 115 18 24\n732 55 18 20\n755 118 21 24\n37 17 25 24\n294 89 19 23\n890 124 17 22\n298 211 23 26\n302 254 24 29\n227 128 19 23\n271 166 18 21\n250 172 19 18\n253 154 16 19\n32 154 19 24\n0 146 16 21\n957 101 18 21\n589 139 19 24\n# 36--Football/36_Football_americanfootball_ball_36_526.jpg\n624 160 108 144\n# 36--Football/36_Football_americanfootball_ball_36_132.jpg\n112 208 50 47\n530 19 39 41\n793 79 37 37\n376 244 50 39\n780 225 49 45\n690 130 20 21\n# 36--Football/36_Football_americanfootball_ball_36_358.jpg\n318 174 56 80\n400 330 56 78\n# 36--Football/36_Football_Football_36_62.jpg\n313 114 40 56\n440 190 41 61\n773 140 48 60\n807 114 38 48\n# 36--Football/36_Football_americanfootball_ball_36_853.jpg\n212 96 100 148\n# 36--Football/36_Football_americanfootball_ball_36_25.jpg\n356 124 58 94\n574 86 66 98\n# 36--Football/36_Football_americanfootball_ball_36_615.jpg\n200 104 25 47\n385 140 17 37\n454 155 15 25\n84 136 13 18\n532 186 18 23\n645 165 11 18\n741 138 14 22\n669 174 13 18\n862 153 13 18\n923 102 19 32\n522 152 11 24\n# 36--Football/36_Football_americanfootball_ball_36_487.jpg\n920 212 8 12\n915 234 8 12\n939 226 10 11\n969 226 7 10\n534 197 10 14\n547 197 9 13\n961 177 10 11\n85 181 10 12\n843 212 8 12\n860 149 8 11\n850 134 10 12\n863 108 8 11\n743 174 9 11\n953 29 8 9\n341 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8 14\n322 255 10 16\n337 281 11 11\n297 291 14 17\n356 297 15 17\n969 14 8 10\n731 12 8 8\n64 89 10 16\n95 92 10 13\n113 86 10 18\n88 104 11 15\n628 74 8 11\n645 89 8 9\n702 78 8 11\n661 32 10 11\n708 14 7 11\n698 35 8 11\n736 73 9 11\n642 47 9 11\n870 44 8 12\n29 13 7 9\n54 13 8 11\n67 27 8 11\n80 18 9 14\n84 33 9 12\n62 40 10 12\n83 68 9 12\n115 52 9 13\n131 47 9 13\n60 54 10 13\n8 217 10 14\n6 259 9 13\n140 34 9 11\n185 35 7 11\n192 34 9 11\n176 48 10 12\n159 64 7 9\n142 66 10 11\n139 105 11 13\n351 49 8 8\n204 78 9 9\n184 65 8 10\n843 175 10 9\n874 177 11 13\n874 234 9 11\n899 228 9 12\n884 204 10 11\n274 106 10 11\n248 84 8 10\n258 76 7 7\n976 146 10 13\n978 127 9 11\n977 102 9 14\n775 244 10 13\n728 199 8 14\n689 184 9 10\n699 187 10 13\n656 180 12 15\n651 150 11 11\n615 149 10 11\n635 153 9 10\n598 142 9 8\n621 135 10 10\n634 134 9 11\n628 100 8 9\n651 117 9 11\n663 114 9 10\n698 119 9 13\n681 111 10 11\n137 284 18 17\n924 273 15 13\n461 290 17 17\n517 281 14 18\n79 138 9 12\n62 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9\n363 4 10 8\n394 15 7 9\n424 32 9 9\n266 160 9 13\n113 104 11 15\n40 165 12 18\n557 27 13 14\n737 123 10 13\n838 25 9 13\n786 34 11 12\n832 91 5 8\n882 62 5 7\n939 96 5 9\n833 185 7 9\n810 161 6 7\n913 116 8 8\n763 147 9 11\n754 264 8 10\n16 244 15 17\n119 255 9 14\n162 35 11 10\n0 216 5 12\n20 306 12 19\n1010 241 11 13\n585 273 14 19\n# 36--Football/36_Football_americanfootball_ball_36_693.jpg\n493 300 50 72\n604 481 71 46\n465 575 68 63\n270 127 78 65\n552 164 71 73\n# 36--Football/36_Football_Football_36_138.jpg\n133 57 24 35\n284 80 30 38\n747 131 23 31\n878 86 29 39\n839 261 22 22\n326 200 8 25\n406 214 21 34\n538 230 17 29\n520 203 13 16\n471 210 10 16\n560 202 11 18\n495 314 21 26\n644 218 22 27\n684 197 9 16\n980 75 11 16\n387 190 7 9\n863 78 11 13\n424 213 11 14\n634 303 22 22\n452 191 14 14\n922 93 10 14\n# 36--Football/36_Football_americanfootball_ball_36_114.jpg\n725 105 69 82\n217 32 33 47\n602 20 33 44\n437 13 42 41\n43 27 38 42\n914 31 26 48\n# 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30 48\n995 235 20 26\n# 54--Rescue/54_Rescue_rescuepeople_54_711.jpg\n408 375 14 18\n483 305 10 20\n421 318 11 16\n# 54--Rescue/54_Rescue_rescuepeople_54_581.jpg\n623 188 12 15\n653 197 9 14\n671 200 9 8\n691 191 9 14\n748 199 9 11\n803 196 9 11\n814 183 11 12\n364 230 10 9\n849 183 10 10\n902 207 9 12\n883 197 8 11\n935 198 9 11\n915 197 9 12\n925 208 7 12\n957 193 11 13\n966 192 12 12\n975 200 11 12\n907 373 10 12\n997 368 9 12\n1011 371 12 14\n1015 307 9 14\n399 356 7 13\n481 316 11 15\n479 352 10 15\n535 314 14 17\n506 181 10 13\n842 370 9 12\n815 368 8 11\n792 364 10 12\n789 390 8 12\n919 315 9 12\n964 308 10 12\n887 313 9 12\n874 378 9 12\n973 423 10 12\n480 179 8 12\n471 197 9 12\n452 193 8 12\n434 183 10 12\n403 182 9 9\n393 177 9 11\n384 189 7 10\n234 195 9 11\n248 187 8 10\n287 191 10 11\n304 191 8 11\n323 198 9 10\n210 202 7 10\n193 184 8 11\n32 185 8 11\n7 190 8 10\n105 194 9 11\n110 220 8 9\n97 360 10 11\n92 337 10 12\n56 348 8 12\n0 336 8 12\n118 387 7 9\n167 353 11 13\n203 329 8 11\n212 331 10 13\n234 353 9 11\n262 331 10 11\n315 339 11 13\n360 342 10 12\n363 361 7 10\n373 354 10 14\n335 357 7 10\n420 331 11 14\n594 355 10 14\n536 369 10 12\n539 337 10 12\n531 429 10 12\n620 364 7 12\n642 367 10 11\n690 374 9 12\n698 357 8 12\n742 372 11 13\n# 54--Rescue/54_Rescue_rescuepeople_54_222.jpg\n509 213 41 57\n677 43 47 66\n845 40 51 63\n325 80 52 63\n217 67 40 49\n173 46 39 53\n124 32 31 39\n51 32 35 46\n# 54--Rescue/54_Rescue_firemanrescue_54_724.jpg\n330 158 80 120\n# 54--Rescue/54_Rescue_rescuepeople_54_589.jpg\n108 386 66 82\n292 358 58 76\n460 380 68 78\n602 374 64 74\n830 412 62 82\n954 496 54 56\n# 54--Rescue/54_Rescue_rescuepeople_54_845.jpg\n258 210 74 106\n592 212 68 116\n# 54--Rescue/54_Rescue_rescuepeople_54_8.jpg\n164 178 142 140\n442 190 134 146\n524 74 118 150\n# 54--Rescue/54_Rescue_firemanrescue_54_789.jpg\n358 36 208 310\n# 54--Rescue/54_Rescue_rescuepeople_54_143.jpg\n239 135 47 54\n421 205 27 46\n371 70 36 51\n# 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22\n268 322 26 26\n395 501 27 45\n210 290 14 19\n97 199 20 21\n89 214 40 65\n295 197 11 14\n250 187 10 11\n238 195 9 13\n653 270 15 25\n727 174 10 15\n615 177 10 13\n707 184 9 11\n418 60 6 9\n748 297 23 20\n3 70 12 11\n567 428 24 27\n684 294 16 14\n619 297 11 19\n512 174 11 18\n379 257 15 19\n302 219 11 17\n# 54--Rescue/54_Rescue_rescuepeople_54_325.jpg\n367 172 63 88\n558 292 63 84\n620 245 47 93\n25 256 35 60\n186 284 31 58\n846 234 46 93\n# 54--Rescue/54_Rescue_rescuepeople_54_557.jpg\n699 136 34 48\n# 54--Rescue/54_Rescue_rescuepeople_54_406.jpg\n428 349 40 45\n737 321 39 45\n# 54--Rescue/54_Rescue_rescuepeople_54_335.jpg\n380 374 114 114\n526 238 102 120\n828 152 114 132\n# 54--Rescue/54_Rescue_rescuepeople_54_54.jpg\n638 298 26 20\n730 393 35 34\n530 101 18 15\n520 77 12 13\n# 54--Rescue/54_Rescue_rescuepeople_54_860.jpg\n320 437 160 243\n603 117 179 211\n# 54--Rescue/54_Rescue_firemanrescue_54_969.jpg\n506 99 35 41\n# 54--Rescue/54_Rescue_rescuepeople_54_529.jpg\n310 214 52 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55\n747 163 30 37\n535 196 36 35\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_620.jpg\n474 146 43 52\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_686.jpg\n451 95 27 26\n394 99 13 16\n311 95 11 17\n107 117 51 43\n751 67 29 40\n579 68 37 47\n854 78 51 66\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_786.jpg\n362 262 96 140\n472 114 98 142\n554 244 100 152\n714 250 106 140\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_708.jpg\n212 226 58 80\n384 270 54 70\n542 246 60 74\n718 262 72 82\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_867.jpg\n808 254 17 26\n716 284 22 32\n561 287 24 31\n508 264 31 44\n390 259 32 35\n232 280 24 29\n60 298 28 34\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_188.jpg\n222 44 100 140\n538 110 82 138\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_466.jpg\n228 568 69 120\n553 526 60 93\n# 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52\n816 269 24 41\n840 208 22 39\n873 229 24 37\n868 302 24 41\n872 395 36 47\n965 217 35 42\n775 272 26 36\n593 291 24 27\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_838.jpg\n606 6 366 500\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1015.jpg\n305 152 582 821\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_154.jpg\n657 19 32 36\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_894.jpg\n422 22 186 238\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_305.jpg\n219 58 52 64\n435 129 55 69\n606 94 41 49\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_531.jpg\n460 349 23 31\n530 373 22 29\n681 354 18 21\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_343.jpg\n380 102 336 462\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_637.jpg\n284 0 442 428\n# 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67\n234 184 35 64\n199 147 35 56\n148 215 45 71\n64 242 44 62\n89 174 39 54\n54 130 36 55\n823 315 38 62\n765 171 37 61\n731 335 38 54\n630 277 41 61\n672 198 38 64\n617 116 34 64\n592 218 34 58\n544 257 38 56\n454 242 42 64\n392 221 36 61\n467 161 36 59\n424 128 35 47\n922 162 40 60\n931 328 39 60\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_588.jpg\n593 248 20 28\n664 247 26 35\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_12.jpg\n770 174 84 128\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_183.jpg\n382 128 238 344\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_721.jpg\n278 284 506 659\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_774.jpg\n383 6 13 20\n408 15 13 18\n354 30 12 18\n345 5 11 12\n332 0 7 9\n424 41 12 16\n375 25 9 16\n357 18 11 15\n845 61 56 82\n753 119 58 92\n795 277 56 83\n519 293 41 48\n126 265 41 51\n660 102 23 33\n603 143 27 39\n473 197 29 36\n483 173 26 26\n444 171 23 33\n363 174 28 39\n418 104 20 24\n346 96 19 31\n326 80 23 24\n381 72 12 23\n395 74 18 24\n673 10 11 14\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_764.jpg\n118 76 72 96\n340 86 70 100\n554 64 66 96\n770 4 78 98\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_155.jpg\n284 36 70 54\n478 224 58 52\n584 252 72 74\n644 32 66 72\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_726.jpg\n480 106 144 210\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_424.jpg\n309 335 20 25\n418 267 45 37\n590 310 15 19\n606 362 10 16\n644 372 18 22\n675 351 10 20\n715 328 10 20\n832 384 10 20\n886 387 9 17\n931 383 11 18\n973 359 14 18\n1014 385 10 24\n360 147 15 17\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_770.jpg\n621 21 50 58\n# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_208.jpg\n414 28 248 350\n# 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24\n938 89 10 23\n916 73 15 18\n893 62 15 17\n783 38 11 20\n938 49 11 16\n482 24 17 28\n418 46 16 26\n320 59 18 27\n616 56 10 29\n700 88 11 24\n179 61 18 32\n262 60 15 29\n# 56--Voter/56_Voter_peoplevoting_56_558.jpg\n409 253 34 86\n621 369 6 10\n645 372 10 10\n635 365 8 11\n572 386 5 8\n542 389 8 8\n518 385 6 9\n522 398 6 8\n554 348 5 6\n558 359 5 8\n579 346 4 8\n519 355 6 7\n517 366 9 9\n531 349 6 8\n538 355 6 6\n513 341 5 7\n493 362 9 8\n485 360 5 7\n466 383 5 8\n489 397 6 9\n474 351 5 8\n569 327 6 6\n611 329 5 5\n620 337 4 4\n628 354 6 8\n583 361 6 7\n527 329 6 7\n79 409 9 10\n109 407 6 12\n117 398 8 13\n136 395 6 9\n148 408 7 12\n46 411 8 10\n16 411 8 11\n51 400 7 10\n23 379 6 8\n10 370 6 9\n6 387 6 8\n38 369 7 11\n42 360 8 9\n164 396 7 9\n152 398 9 11\n130 377 7 8\n148 373 5 7\n110 389 8 12\n106 376 6 9\n95 372 6 8\n86 385 8 9\n78 357 7 7\n99 362 6 8\n86 339 6 9\n57 349 4 6\n58 383 8 11\n44 379 8 9\n31 386 8 10\n185 385 7 11\n211 399 7 9\n173 368 6 11\n170 341 6 7\n217 357 7 7\n201 366 8 9\n193 360 6 8\n214 341 7 10\n146 350 7 7\n190 378 7 10\n203 378 8 8\n157 361 8 10\n23 360 6 7\n74 396 7 7\n645 280 51 77\n835 275 19 30\n894 426 10 12\n935 418 9 13\n926 413 11 13\n921 404 7 12\n927 386 7 13\n956 399 7 12\n964 408 7 12\n951 388 7 11\n962 381 8 12\n945 371 7 9\n916 372 8 9\n887 389 7 8\n904 332 6 9\n883 336 6 7\n596 422 10 14\n625 413 9 13\n651 406 8 13\n613 412 10 12\n604 403 9 9\n584 414 11 12\n489 421 9 11\n474 409 8 13\n482 404 7 12\n520 413 7 10\n601 385 7 11\n635 389 6 8\n659 381 5 10\n595 375 6 9\n# 56--Voter/56_Voter_peoplevoting_56_122.jpg\n964 15 22 43\n691 48 33 59\n559 80 29 50\n619 55 26 45\n486 18 27 38\n531 14 33 40\n607 17 29 32\n800 7 31 35\n82 66 39 48\n160 27 28 29\n151 59 33 40\n211 30 29 34\n263 19 26 29\n350 27 24 32\n404 6 29 39\n440 33 25 39\n423 133 27 40\n299 12 19 33\n118 18 23 39\n90 17 34 48\n28 19 22 35\n912 194 39 47\n936 77 33 57\n839 57 31 50\n902 34 26 47\n326 98 30 68\n282 60 34 50\n663 0 33 41\n# 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8 14\n514 299 11 16\n410 346 13 16\n331 374 8 15\n395 369 7 14\n426 378 7 14\n475 382 8 13\n570 392 10 14\n553 315 11 16\n626 299 13 14\n678 295 9 14\n716 282 11 12\n693 276 9 12\n662 261 10 14\n653 286 10 11\n558 304 13 13\n678 388 8 13\n703 303 8 10\n759 357 5 13\n816 297 11 15\n808 315 11 13\n782 255 10 12\n851 349 10 14\n822 334 8 15\n878 316 7 9\n965 326 10 15\n890 235 8 11\n676 322 6 14\n546 388 10 10\n861 276 10 16\n966 241 6 6\n770 291 12 15\n620 322 9 14\n# 57--Angler/57_Angler_peoplefishing_57_933.jpg\n584 254 86 114\n418 398 58 76\n# 57--Angler/57_Angler_peoplefishing_57_411.jpg\n307 264 35 57\n779 209 48 67\n# 57--Angler/57_Angler_peoplefishing_57_600.jpg\n452 142 124 154\n# 57--Angler/57_Angler_peoplefishing_57_764.jpg\n321 112 175 230\n# 57--Angler/57_Angler_peoplefishing_57_866.jpg\n614 94 136 170\n250 150 126 166\n# 57--Angler/57_Angler_peoplefishing_57_442.jpg\n452 104 58 94\n692 166 64 88\n# 57--Angler/57_Angler_peoplefishing_57_1012.jpg\n222 320 70 68\n398 158 102 112\n632 214 112 128\n# 58--Hockey/58_Hockey_icehockey_puck_58_507.jpg\n852 221 59 72\n724 120 61 70\n411 109 38 40\n462 64 36 40\n293 46 20 31\n343 54 24 32\n# 58--Hockey/58_Hockey_icehockey_puck_58_653.jpg\n62 154 64 82\n252 182 58 74\n466 100 62 76\n686 150 56 74\n896 26 66 82\n# 58--Hockey/58_Hockey_icehockey_puck_58_895.jpg\n634 264 390 456\n81 750 75 74\n373 823 34 30\n386 876 38 30\n192 797 52 67\n# 58--Hockey/58_Hockey_icehockey_puck_58_431.jpg\n212 191 154 195\n233 305 34 41\n890 201 50 63\n# 58--Hockey/58_Hockey_icehockey_puck_58_17.jpg\n68 93 63 70\n446 488 25 35\n165 499 45 46\n277 454 21 25\n542 449 20 25\n375 238 18 22\n309 360 25 17\n193 464 15 14\n856 63 53 70\n17 309 92 119\n# 58--Hockey/58_Hockey_icehockey_puck_58_493.jpg\n447 394 93 60\n472 94 70 83\n317 179 74 86\n773 149 38 36\n905 129 38 42\n611 145 35 42\n655 57 32 43\n236 101 43 54\n250 43 34 45\n439 55 28 39\n752 64 25 35\n809 5 36 31\n719 4 30 27\n598 1 31 35\n112 134 38 40\n161 131 28 32\n345 0 36 25\n226 1 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33 38\n641 235 33 35\n552 261 32 38\n578 246 28 33\n603 221 32 36\n513 251 37 40\n513 227 30 32\n388 237 29 34\n444 231 34 37\n405 236 29 34\n416 282 32 40\n343 300 36 38\n297 224 30 34\n256 286 34 44\n266 275 32 38\n207 236 31 35\n327 226 29 35\n207 307 39 40\n260 241 26 29\n150 304 39 44\n69 313 39 45\n21 294 41 45\n614 275 32 42\n558 222 20 34\n356 233 31 34\n# 8--Election_Campain/8_Election_Campain_Election_Campaign_8_25.jpg\n590 154 180 240\n# 8--Election_Campain/8_Election_Campain_Election_Campaign_8_113.jpg\n238 178 52 82\n358 114 70 96\n696 38 52 70\n812 336 54 72\n# 8--Election_Campain/8_Election_Campain_Election_Campaign_8_347.jpg\n211 128 33 50\n750 163 29 47\n21 571 16 21\n178 563 9 18\n476 583 9 17\n852 575 13 15\n921 590 14 15\n959 587 10 16\n1009 575 15 18\n# 8--Election_Campain/8_Election_Campain_Election_Campaign_8_452.jpg\n0 414 31 57\n65 445 39 46\n77 449 46 53\n152 450 48 48\n240 418 60 59\n369 400 50 66\n421 389 67 74\n645 293 69 83\n788 333 91 91\n969 389 55 72\n# 8--Election_Campain/8_Election_Campain_Election_Campaign_8_133.jpg\n944 186 28 28\n902 190 31 30\n854 192 29 33\n729 186 32 26\n624 170 33 36\n184 88 83 148\n435 178 25 22\n21 119 33 36\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_907.jpg\n410 156 344 458\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_784.jpg\n204 90 388 466\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_518.jpg\n401 316 169 253\n765 163 160 259\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_352.jpg\n181 395 560 708\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_214.jpg\n56 200 122 146\n392 140 102 158\n838 164 116 140\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_492.jpg\n262 6 454 478\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_141.jpg\n248 295 546 716\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_35.jpg\n378 245 12 17\n291 210 15 18\n312 215 12 16\n334 213 10 14\n401 187 12 15\n216 397 42 35\n200 287 16 21\n268 308 23 35\n303 284 17 26\n368 310 17 35\n498 305 15 25\n687 215 14 21\n623 216 14 19\n853 143 12 15\n602 196 13 18\n957 187 28 38\n957 223 27 31\n190 191 17 22\n374 214 12 15\n355 420 38 61\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_655.jpg\n63 207 24 30\n137 223 20 32\n115 218 21 21\n161 202 28 34\n199 189 24 32\n231 218 25 26\n307 201 24 34\n392 198 23 23\n422 191 20 32\n346 211 21 30\n365 228 26 40\n465 190 19 25\n488 199 24 36\n497 225 36 41\n552 185 20 26\n543 183 17 22\n579 210 26 38\n651 204 22 27\n708 187 23 31\n726 192 26 26\n757 207 27 39\n661 216 40 61\n793 240 49 64\n848 189 25 30\n884 213 29 38\n942 184 28 40\n957 214 28 43\n1010 205 14 41\n138 202 20 28\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_357.jpg\n338 195 375 561\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_571.jpg\n369 342 252 348\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_165.jpg\n90 54 72 88\n474 66 68 78\n844 58 74 94\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_344.jpg\n608 294 268 368\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_849.jpg\n268 361 449 629\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_31.jpg\n586 211 16 20\n442 206 14 20\n48 286 14 19\n20 310 11 15\n927 361 13 21\n958 431 17 29\n195 375 14 24\n121 370 14 23\n82 433 12 27\n166 428 13 26\n82 481 19 33\n799 444 13 26\n812 374 14 28\n920 231 12 17\n957 221 13 19\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_520.jpg\n282 495 195 264\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_297.jpg\n410 176 262 362\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_66.jpg\n216 175 73 88\n453 285 53 76\n884 267 75 88\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_332.jpg\n163 241 796 1101\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_325.jpg\n332 192 60 76\n684 114 56 110\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_41.jpg\n152 194 78 94\n700 188 84 110\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_432.jpg\n437 166 244 364\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_40.jpg\n12 170 19 21\n108 186 17 18\n49 226 21 24\n187 178 17 23\n247 168 15 19\n206 173 14 16\n299 176 14 20\n334 180 11 15\n353 169 14 17\n372 177 13 17\n406 162 16 22\n423 164 15 23\n463 178 19 23\n460 156 17 22\n369 236 19 22\n263 219 18 26\n220 228 39 57\n316 213 26 71\n713 162 32 36\n751 116 25 38\n665 174 25 36\n693 159 20 31\n591 127 26 36\n528 168 21 27\n513 168 14 23\n970 168 54 49\n898 171 35 43\n116 179 12 16\n331 221 11 16\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_257.jpg\n270 192 153 213\n685 351 132 171\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_924.jpg\n398 156 256 364\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_209.jpg\n262 70 98 136\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_636.jpg\n202 296 495 661\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_346.jpg\n256 238 140 199\n422 223 147 224\n81 634 50 72\n264 659 52 68\n417 671 51 62\n614 708 36 57\n362 981 86 52\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_397.jpg\n87 90 47 63\n208 134 46 52\n355 103 45 61\n483 118 41 55\n627 111 45 61\n712 153 40 52\n783 161 45 53\n869 142 53 61\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_252.jpg\n296 124 400 542\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_615.jpg\n350 158 298 358\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_933.jpg\n304 265 425 593\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_945.jpg\n227 227 372 607\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_648.jpg\n461 187 293 373\n19 475 112 253\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_748.jpg\n308 215 378 547\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_12.jpg\n930 203 37 56\n390 312 19 27\n317 317 22 25\n261 306 19 22\n326 232 16 22\n107 221 32 37\n182 259 16 19\n117 382 26 44\n205 339 18 30\n254 331 14 29\n420 342 20 32\n802 427 27 58\n673 260 15 21\n615 287 15 23\n567 285 13 20\n512 291 13 17\n830 302 20 24\n469 342 20 35\n52 245 8 12\n18 246 6 12\n201 312 19 24\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_105.jpg\n58 91 55 68\n113 121 48 62\n276 145 41 60\n333 192 28 34\n486 56 68 92\n601 79 48 68\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_607.jpg\n333 153 249 348\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_34.jpg\n257 286 408 465\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_258.jpg\n283 338 479 654\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_431.jpg\n306 258 342 453\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_872.jpg\n411 321 207 288\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_883.jpg\n274 196 286 375\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_161.jpg\n214 62 196 236\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_343.jpg\n496 120 123 179\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_710.jpg\n269 266 411 607\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_74.jpg\n140 278 84 82\n458 264 82 112\n856 282 76 90\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_658.jpg\n247 178 364 491\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_632.jpg\n177 303 501 751\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_375.jpg\n128 132 72 98\n298 122 72 96\n546 126 54 84\n660 104 56 92\n842 128 66 92\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_693.jpg\n8 108 52 118\n264 186 72 122\n658 116 88 130\n880 214 82 128\n522 236 80 90\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_196.jpg\n287 160 367 451\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_767.jpg\n281 167 399 573\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_552.jpg\n389 120 243 355\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_100.jpg\n493 271 32 38\n639 268 28 35\n747 268 28 37\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_595.jpg\n252 156 324 454\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_43.jpg\n400 70 144 184\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_828.jpg\n266 269 545 784\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_424.jpg\n59 336 92 151\n489 67 157 238\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_563.jpg\n402 114 188 252\n780 220 132 152\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_278.jpg\n596 116 78 112\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_391.jpg\n322 12 414 590\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_345.jpg\n229 440 133 169\n392 259 232 322\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_757.jpg\n143 223 370 397\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_930.jpg\n268 96 343 521\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_328.jpg\n236 114 120 204\n554 166 128 188\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_114.jpg\n464 92 205 316\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_60.jpg\n123 387 27 37\n263 394 31 42\n393 387 29 38\n555 314 29 39\n680 392 31 40\n768 390 27 35\n862 400 24 39\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_182.jpg\n440 160 94 118\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_147.jpg\n458 154 100 136\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_45.jpg\n68 270 48 65\n478 261 50 74\n266 246 51 77\n838 258 61 78\n680 273 59 75\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_89.jpg\n132 116 73 130\n403 154 80 136\n661 199 75 107\n784 222 53 70\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_594.jpg\n334 182 300 400\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_613.jpg\n316 224 270 347\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_521.jpg\n332 172 294 372\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_129.jpg\n336 242 152 202\n712 278 126 152\n# 9--Press_Conference/9_Press_Conference_Press_Conference_9_183.jpg\n218 190 112 160\n302 224 74 140\n560 136 170 204\n" }, { "path": "data/wider_face_2_voc_add_landmark.py", "content": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nimport os\nimport shutil\nfrom xml.dom.minidom import Document\n\nimport cv2\n\nrootdir = \"./wider_face_add_lm_10_10\"\nos.makedirs(rootdir)\nretinaface_gt_file_path = \"./retinaface_labels/\"\nconvet2yoloformat = False\nconvert2vocformat = True\nresized_dim = (48, 48)\n\nminsize2select = 10 # min face size\nusepadding = True\n\ndatasetprefix = \"./wider_face\" #\n\n\ndef convertimgset(img_set=\"train\"):\n imgdir = datasetprefix + \"/WIDER_\" + img_set + \"/images\"\n gtfilepath = retinaface_gt_file_path + img_set + \"/label.txt\"\n imagesdir = rootdir + \"/JPEGImages\"\n vocannotationdir = rootdir + \"/Annotations\"\n labelsdir = rootdir + \"/labels\"\n if not os.path.exists(imagesdir):\n os.mkdir(imagesdir)\n if not os.path.exists(rootdir + \"/ImageSets\"):\n os.mkdir(rootdir + \"/ImageSets\")\n if not os.path.exists(rootdir + \"/ImageSets/Main\"):\n os.mkdir(rootdir + \"/ImageSets/Main\")\n\n if convet2yoloformat:\n if not os.path.exists(labelsdir):\n os.mkdir(labelsdir)\n if convert2vocformat:\n if not os.path.exists(vocannotationdir):\n os.mkdir(vocannotationdir)\n index = 0\n\n f_set = open(rootdir + \"/ImageSets/Main/\" + img_set + \".txt\", 'w')\n current_filename = \"\"\n bboxes = []\n lms = []\n with open(gtfilepath, 'r') as gtfile:\n while True: # and len(faces)<10\n line = gtfile.readline().strip()\n if line == \"\":\n if len(bboxes) != 0:\n method_name(bboxes, filename, saveimg, vocannotationdir, lms, img_set)\n cv2.imwrite(imagesdir + \"/\" + filename, saveimg)\n imgfilepath = filename[:-4]\n f_set.write(imgfilepath + '\\n')\n print(\"end!\")\n break\n if line.startswith(\"#\"):\n if index != 0 and convert2vocformat:\n if len(bboxes) != 0:\n method_name(bboxes, filename, saveimg, vocannotationdir, lms, img_set)\n cv2.imwrite(imagesdir + \"/\" + filename, saveimg)\n imgfilepath = filename[:-4]\n f_set.write(imgfilepath + '\\n')\n else:\n print(\"no face\")\n\n current_filename = filename = line[1:].strip()\n print((\"\\r\" + str(index) + \":\" + filename + \"\\t\\t\\t\"))\n index = index + 1\n bboxes = []\n lms = []\n continue\n else:\n imgpath = imgdir + \"/\" + current_filename\n img = cv2.imread(imgpath)\n if not img.data:\n break\n saveimg = img.copy()\n showimg = saveimg.copy()\n line = [float(x) for x in line.strip().split()]\n if int(line[3]) <= 0 or int(line[2]) <= 0:\n continue\n x = int(line[0])\n y = int(line[1])\n width = int(line[2])\n height = int(line[3])\n bbox = (x, y, width, height)\n x2 = x + width\n y2 = y + height\n if width >= minsize2select and height >= minsize2select:\n bboxes.append(bbox)\n if img_set == \"train\":\n if line[4] == -1:\n lms.append(-1)\n else:\n lm = []\n for i in range(5):\n x = line[4 + 3 * i]\n y = line[4 + 3 * i + 1]\n lm.append((x, y))\n lm.append(int(line[4 + 3 * i + 2]))\n lm.append(line[19])\n lms.append(lm)\n cv2.rectangle(showimg, (int(x), int(y)), (int(x2), int(y2)), (0, 255, 0))\n else:\n saveimg[y:y2, x:x2, :] = (104, 117, 123)\n cv2.rectangle(showimg, (x, y), (x2, y2), (0, 0, 255))\n filename = filename.replace(\"/\", \"_\")\n\n if convet2yoloformat:\n height = saveimg.shape[0]\n width = saveimg.shape[1]\n txtpath = labelsdir + \"/\" + filename\n txtpath = txtpath[:-3] + \"txt\"\n ftxt = open(txtpath, 'w')\n for i in range(len(bboxes)):\n bbox = bboxes[i]\n xcenter = (bbox[0] + bbox[2] * 0.5) / width\n ycenter = (bbox[1] + bbox[3] * 0.5) / height\n wr = bbox[2] * 1.0 / width\n hr = bbox[3] * 1.0 / height\n txtline = \"0 \" + str(xcenter) + \" \" + str(ycenter) + \" \" + str(wr) + \" \" + str(hr) + \"\\n\"\n ftxt.write(txtline)\n ftxt.close()\n f_set.close()\n\n\ndef method_name(bboxes, filename, saveimg, vocannotationdir, lms, img_set):\n xmlpath = vocannotationdir + \"/\" + filename\n xmlpath = xmlpath[:-3] + \"xml\"\n doc = Document()\n annotation = doc.createElement('annotation')\n doc.appendChild(annotation)\n folder = doc.createElement('folder')\n folder_name = doc.createTextNode('widerface')\n folder.appendChild(folder_name)\n annotation.appendChild(folder)\n filenamenode = doc.createElement('filename')\n filename_name = doc.createTextNode(filename)\n filenamenode.appendChild(filename_name)\n annotation.appendChild(filenamenode)\n source = doc.createElement('source')\n annotation.appendChild(source)\n database = doc.createElement('database')\n database.appendChild(doc.createTextNode('wider face Database'))\n source.appendChild(database)\n annotation_s = doc.createElement('annotation')\n annotation_s.appendChild(doc.createTextNode('PASCAL VOC2007'))\n source.appendChild(annotation_s)\n image = doc.createElement('image')\n image.appendChild(doc.createTextNode('flickr'))\n source.appendChild(image)\n flickrid = doc.createElement('flickrid')\n flickrid.appendChild(doc.createTextNode('-1'))\n source.appendChild(flickrid)\n owner = doc.createElement('owner')\n annotation.appendChild(owner)\n flickrid_o = doc.createElement('flickrid')\n flickrid_o.appendChild(doc.createTextNode('yanyu'))\n owner.appendChild(flickrid_o)\n name_o = doc.createElement('name')\n name_o.appendChild(doc.createTextNode('yanyu'))\n owner.appendChild(name_o)\n size = doc.createElement('size')\n annotation.appendChild(size)\n width = doc.createElement('width')\n width.appendChild(doc.createTextNode(str(saveimg.shape[1])))\n height = doc.createElement('height')\n height.appendChild(doc.createTextNode(str(saveimg.shape[0])))\n depth = doc.createElement('depth')\n depth.appendChild(doc.createTextNode(str(saveimg.shape[2])))\n size.appendChild(width)\n size.appendChild(height)\n size.appendChild(depth)\n segmented = doc.createElement('segmented')\n segmented.appendChild(doc.createTextNode('0'))\n annotation.appendChild(segmented)\n for i in range(len(bboxes)):\n bbox = bboxes[i]\n objects = doc.createElement('object')\n annotation.appendChild(objects)\n object_name = doc.createElement('name')\n object_name.appendChild(doc.createTextNode('face'))\n objects.appendChild(object_name)\n pose = doc.createElement('pose')\n pose.appendChild(doc.createTextNode('Unspecified'))\n objects.appendChild(pose)\n truncated = doc.createElement('truncated')\n truncated.appendChild(doc.createTextNode('1'))\n objects.appendChild(truncated)\n difficult = doc.createElement('difficult')\n difficult.appendChild(doc.createTextNode('0'))\n objects.appendChild(difficult)\n bndbox = doc.createElement('bndbox')\n objects.appendChild(bndbox)\n xmin = doc.createElement('xmin')\n xmin.appendChild(doc.createTextNode(str(bbox[0])))\n bndbox.appendChild(xmin)\n ymin = doc.createElement('ymin')\n ymin.appendChild(doc.createTextNode(str(bbox[1])))\n bndbox.appendChild(ymin)\n xmax = doc.createElement('xmax')\n xmax.appendChild(doc.createTextNode(str(bbox[0] + bbox[2])))\n bndbox.appendChild(xmax)\n ymax = doc.createElement('ymax')\n ymax.appendChild(doc.createTextNode(str(bbox[1] + bbox[3])))\n bndbox.appendChild(ymax)\n\n if img_set == \"train\":\n has_lm = doc.createElement('has_lm')\n\n if lms[i] == -1:\n has_lm.appendChild(doc.createTextNode('0'))\n else:\n has_lm.appendChild(doc.createTextNode('1'))\n lm = doc.createElement('lm')\n objects.appendChild(lm)\n\n x1 = doc.createElement('x1')\n x1.appendChild(doc.createTextNode(str(lms[i][0][0])))\n lm.appendChild(x1)\n\n y1 = doc.createElement('y1')\n y1.appendChild(doc.createTextNode(str(lms[i][0][1])))\n lm.appendChild(y1)\n\n x2 = doc.createElement('x2')\n x2.appendChild(doc.createTextNode(str(lms[i][1][0])))\n lm.appendChild(x2)\n\n y2 = doc.createElement('y2')\n y2.appendChild(doc.createTextNode(str(lms[i][1][1])))\n lm.appendChild(y2)\n\n x3 = doc.createElement('x3')\n x3.appendChild(doc.createTextNode(str(lms[i][2][0])))\n lm.appendChild(x3)\n\n y3 = doc.createElement('y3')\n y3.appendChild(doc.createTextNode(str(lms[i][2][1])))\n lm.appendChild(y3)\n\n x4 = doc.createElement('x4')\n x4.appendChild(doc.createTextNode(str(lms[i][3][0])))\n lm.appendChild(x4)\n\n y4 = doc.createElement('y4')\n y4.appendChild(doc.createTextNode(str(lms[i][3][1])))\n lm.appendChild(y4)\n\n x5 = doc.createElement('x5')\n x5.appendChild(doc.createTextNode(str(lms[i][4][0])))\n lm.appendChild(x5)\n\n y5 = doc.createElement('y5')\n y5.appendChild(doc.createTextNode(str(lms[i][4][1])))\n lm.appendChild(y5)\n\n visible = doc.createElement('visible')\n visible.appendChild(doc.createTextNode(str(lms[i][5])))\n lm.appendChild(visible)\n\n blur = doc.createElement('blur')\n blur.appendChild(doc.createTextNode(str(lms[i][6])))\n lm.appendChild(blur)\n objects.appendChild(has_lm)\n f = open(xmlpath, \"w\")\n f.write(doc.toprettyxml(indent=''))\n f.close()\n\n\ndef generatetxt(img_set=\"train\"):\n gtfilepath = rootdir + \"/wider_face_split/wider_face_\" + img_set + \"_bbx_gt.txt\"\n f = open(rootdir + \"/\" + img_set + \".txt\", \"w\")\n with open(gtfilepath, 'r') as gtfile:\n while (True): # and len(faces)<10\n filename = gtfile.readline()[:-1]\n if (filename == \"\"):\n break\n filename = filename.replace(\"/\", \"_\")\n imgfilepath = datasetprefix + \"/images/\" + filename\n f.write(imgfilepath + '\\n')\n numbbox = int(gtfile.readline())\n for i in range(numbbox):\n line = gtfile.readline()\n f.close()\n\n\ndef generatevocsets(img_set=\"train\"):\n if not os.path.exists(rootdir + \"/ImageSets\"):\n os.mkdir(rootdir + \"/ImageSets\")\n if not os.path.exists(rootdir + \"/ImageSets/Main\"):\n os.mkdir(rootdir + \"/ImageSets/Main\")\n gtfilepath = rootdir + \"/wider_face_split/wider_face_\" + img_set + \"_bbx_gt.txt\"\n f = open(rootdir + \"/ImageSets/Main/\" + img_set + \".txt\", 'w')\n with open(gtfilepath, 'r') as gtfile:\n while (True): # and len(faces)<10\n filename = gtfile.readline()[:-1]\n if (filename == \"\"):\n break\n filename = filename.replace(\"/\", \"_\")\n imgfilepath = filename[:-4]\n f.write(imgfilepath + '\\n')\n numbbox = int(gtfile.readline())\n for i in range(numbbox):\n line = gtfile.readline()\n f.close()\n\n\ndef convertdataset():\n img_sets = [\"train\", \"val\"]\n for img_set in img_sets:\n convertimgset(img_set)\n\n\nif __name__ == \"__main__\":\n convertdataset()\n shutil.move(rootdir + \"/ImageSets/Main/\" + \"train.txt\", rootdir + \"/ImageSets/Main/\" + \"trainval.txt\")\n shutil.move(rootdir + \"/ImageSets/Main/\" + \"val.txt\", rootdir + \"/ImageSets/Main/\" + \"test.txt\")\n" }, { "path": "detect_imgs.py", "content": "\"\"\"\nThis code is used to batch detect images in a folder.\n\"\"\"\nimport argparse\nimport os\nimport sys\n\nimport cv2\n\nfrom vision.ssd.config.fd_config import define_img_size\n\nparser = argparse.ArgumentParser(\n description='detect_imgs')\n\nparser.add_argument('--net_type', default=\"RFB\", type=str,\n help='The network architecture ,optional: RFB (higher precision) or slim (faster)')\nparser.add_argument('--input_size', default=640, type=int,\n help='define network input size,default optional value 128/160/320/480/640/1280')\nparser.add_argument('--threshold', default=0.6, type=float,\n help='score threshold')\nparser.add_argument('--candidate_size', default=1500, type=int,\n help='nms candidate size')\nparser.add_argument('--path', default=\"imgs\", type=str,\n help='imgs dir')\nparser.add_argument('--test_device', default=\"cuda:0\", type=str,\n help='cuda:0 or cpu')\nargs = parser.parse_args()\ndefine_img_size(args.input_size) # must put define_img_size() before 'import create_mb_tiny_fd, create_mb_tiny_fd_predictor'\n\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd, create_mb_tiny_fd_predictor\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd, create_Mb_Tiny_RFB_fd_predictor\n\nresult_path = \"./detect_imgs_results\"\nlabel_path = \"./models/voc-model-labels.txt\"\ntest_device = args.test_device\n\nclass_names = [name.strip() for name in open(label_path).readlines()]\nif args.net_type == 'slim':\n model_path = \"models/pretrained/version-slim-320.pth\"\n # model_path = \"models/pretrained/version-slim-640.pth\"\n net = create_mb_tiny_fd(len(class_names), is_test=True, device=test_device)\n predictor = create_mb_tiny_fd_predictor(net, candidate_size=args.candidate_size, device=test_device)\nelif args.net_type == 'RFB':\n model_path = \"models/pretrained/version-RFB-320.pth\"\n # model_path = \"models/pretrained/version-RFB-640.pth\"\n net = create_Mb_Tiny_RFB_fd(len(class_names), is_test=True, device=test_device)\n predictor = create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=args.candidate_size, device=test_device)\nelse:\n print(\"The net type is wrong!\")\n sys.exit(1)\nnet.load(model_path)\n\nif not os.path.exists(result_path):\n os.makedirs(result_path)\nlistdir = os.listdir(args.path)\nsum = 0\nfor file_path in listdir:\n img_path = os.path.join(args.path, file_path)\n orig_image = cv2.imread(img_path)\n image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)\n boxes, labels, probs = predictor.predict(image, args.candidate_size / 2, args.threshold)\n sum += boxes.size(0)\n for i in range(boxes.size(0)):\n box = boxes[i, :]\n cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), (0, 0, 255), 2)\n # label = f\"\"\"{voc_dataset.class_names[labels[i]]}: {probs[i]:.2f}\"\"\"\n label = f\"{probs[i]:.2f}\"\n # cv2.putText(orig_image, label, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n cv2.putText(orig_image, str(boxes.size(0)), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n cv2.imwrite(os.path.join(result_path, file_path), orig_image)\n print(f\"Found {len(probs)} faces. The output image is {result_path}\")\nprint(sum)\n" }, { "path": "detect_imgs_onnx.py", "content": "\"\"\"\nThis code uses the onnx model to detect faces from live video or cameras.\n\"\"\"\nimport os\nimport time\n\nimport cv2\nimport numpy as np\nimport onnx\nimport vision.utils.box_utils_numpy as box_utils\nfrom caffe2.python.onnx import backend\n\n# onnx runtime\nimport onnxruntime as ort\n\n\ndef predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):\n boxes = boxes[0]\n confidences = confidences[0]\n picked_box_probs = []\n picked_labels = []\n for class_index in range(1, confidences.shape[1]):\n probs = confidences[:, class_index]\n mask = probs > prob_threshold\n probs = probs[mask]\n if probs.shape[0] == 0:\n continue\n subset_boxes = boxes[mask, :]\n box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)\n box_probs = box_utils.hard_nms(box_probs,\n iou_threshold=iou_threshold,\n top_k=top_k,\n )\n picked_box_probs.append(box_probs)\n picked_labels.extend([class_index] * box_probs.shape[0])\n if not picked_box_probs:\n return np.array([]), np.array([]), np.array([])\n picked_box_probs = np.concatenate(picked_box_probs)\n picked_box_probs[:, 0] *= width\n picked_box_probs[:, 1] *= height\n picked_box_probs[:, 2] *= width\n picked_box_probs[:, 3] *= height\n return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]\n\n\nlabel_path = \"models/voc-model-labels.txt\"\n\nonnx_path = \"models/onnx/version-RFB-320.onnx\"\nclass_names = [name.strip() for name in open(label_path).readlines()]\n\npredictor = onnx.load(onnx_path)\nonnx.checker.check_model(predictor)\nonnx.helper.printable_graph(predictor.graph)\npredictor = backend.prepare(predictor, device=\"CPU\") # default CPU\n\nort_session = ort.InferenceSession(onnx_path)\ninput_name = ort_session.get_inputs()[0].name\nresult_path = \"./detect_imgs_results_onnx\"\n\nthreshold = 0.7\npath = \"imgs\"\nsum = 0\nif not os.path.exists(result_path):\n os.makedirs(result_path)\nlistdir = os.listdir(path)\nsum = 0\nfor file_path in listdir:\n img_path = os.path.join(path, file_path)\n orig_image = cv2.imread(img_path)\n image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, (320, 240))\n # image = cv2.resize(image, (640, 480))\n image_mean = np.array([127, 127, 127])\n image = (image - image_mean) / 128\n image = np.transpose(image, [2, 0, 1])\n image = np.expand_dims(image, axis=0)\n image = image.astype(np.float32)\n # confidences, boxes = predictor.run(image)\n time_time = time.time()\n confidences, boxes = ort_session.run(None, {input_name: image})\n print(\"cost time:{}\".format(time.time() - time_time))\n boxes, labels, probs = predict(orig_image.shape[1], orig_image.shape[0], confidences, boxes, threshold)\n for i in range(boxes.shape[0]):\n box = boxes[i, :]\n label = f\"{class_names[labels[i]]}: {probs[i]:.2f}\"\n\n cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), (255, 255, 0), 4)\n\n # cv2.putText(orig_image, label,\n # (box[0] + 20, box[1] + 40),\n # cv2.FONT_HERSHEY_SIMPLEX,\n # 1, # font scale\n # (255, 0, 255),\n # 2) # line type\n cv2.imwrite(os.path.join(result_path, file_path), orig_image)\n sum += boxes.shape[0]\nprint(\"sum:{}\".format(sum))\n" }, { "path": "masked_face/README.md", "content": "# Masked Face Detection \n\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img4.jpeg)\n\n# Extending Ultra-L face model for masked facial detection\n\nUltra-L face detection model achieves great popularity in edge and client based applications. It has a surprising balance of model size and accuracy performance, e.g.,\n- The default FP32 *.pth model size is **1.04~1.1MB**, and the inference framework int8 quantization size is about **300KB**.\n- Only **90~109 MFlops** for 320x240 input resolution.\n- Supported inference code for [NCNN](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/ncnn), [MNN](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN), [INT8](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/MNN/model),\n[Onnx](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/caffe), [OpencvDNN](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_opencvdnn_inference.py), etc.\n\nCOVID-19 has ravaged the world in the past two years, and wearing masks has become the norm in our lives on many occasions. However, most traditional face datasets such as Wider Face currently lack face samples with masks. Therefore, the face detection model based on conventional datasets will fail in the scenario where all attendants wear masks.\n[Face-Mask-Detection](https://github.com/chandrikadeb7/Face-Mask-Detection) is the most popular face detection model we can find on Github that supports Mask detection. \nHowever, this model is trained only using 4095 images (2165 masked / 1930 without mask), which is a pretty small dataset. \nWe will experience many false positives in the actual application scenarios.\n\nThis original intention inspired me to build a larger dataset to provide better open-source masked facial detection models and help the world survive the pandemic.\nThe main contribution of this project is to provide balanced facial training data combining the [wider_face_add_lm_10_10](https://drive.google.com/open?id=1OBY-Pk5hkcVBX1dRBOeLI4e4OCvqJRnH) and [MAFA face](https://imsg.ac.cn/research/maskedface.html) dataset. The [MAFA](https://imsg.ac.cn/research/maskedface.html) data was converted to pascal-VOC format and merged into the [wider_face_add_lm_10_10](https://drive.google.com/open?id=1OBY-Pk5hkcVBX1dRBOeLI4e4OCvqJRnH).\n\n## About the WIDER_MAFA_Balanced dataset\nThe *Wider_MAFA_Balanced* dataset (**4.8GB**) can be downloaded at [HPI owncloud](https://owncloud.hpi.de/s/L4MUGqrpeENLbSv).\nIt contains 38225 images in total where 31084 for training and 7141 for testing, respectively.\nThe specific composition information is shown in the following table:\n\nSource| Class | Train | Test |Total|\n----|------|-------|------|-----\nMAFA face| masked_face | 15542 | 3922 | 19464 |\nWider face| face | 12859 | 3219 | 16078 |\n*MAFA human body* | face | 2683 | 0 |2683\n\n*MAFA human body* indicates the extracted training samples with human body occlusions. \n\nI use this script for converting MAFA data format to pascal VOC:\n```Shell\nmasked_face/mafa2voc.py\n```\n\n## About the pre-trained models\n```Shell\nmasked_face/\n pretrained/\n RFB-320-masked_face-v2.pth # trained with 320x240\n RFB-640-masked_face-v2.pth # trained with 640x480\n RFB-640-masked_face-v2.onnx # suitable for 640x480\n RFB-1280-masked_face-v2.onnx # suitable for 1280x960\n```\n\n## Detection Result (input resolution: 1280x960)\n\nThe following visual results are created by using this script:\n```Shell\nmasked_face/detect_imgs.py\n```\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img1.jpeg)\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img2.jpeg)\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img3.jpg)\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img5.jpeg)\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img6.webp)\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img7.webp)\n![img1](https://github.com/yanghaojin/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/masked_face/readme_imgs/img8.jpeg)\n\nAuthor: Haojin Yang\n" }, { "path": "masked_face/detect_imgs.py", "content": "\"\"\"\nThis code is used to batch detect images in a folder.\n\"\"\"\nimport os, sys\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(currentdir)\nsys.path.append(parentdir)\n\nimport argparse\nimport os\nimport sys\n\nimport cv2\n\nfrom vision.ssd.config.fd_config import define_img_size\n\nparser = argparse.ArgumentParser(\n description='detect_imgs')\n\nparser.add_argument('--net_type', default=\"RFB\", type=str,\n help='The network architecture ,optional: RFB (higher precision) or slim (faster)')\nparser.add_argument('--input_size', default=1280, type=int,\n help='define network input size,default optional value 128/160/320/480/640/1280')\nparser.add_argument('--threshold', default=0.3, type=float,\n help='score threshold')\nparser.add_argument('--candidate_size', default=1200, type=int,\n help='nms candidate size')\nparser.add_argument('--path', default=\"imgs\", type=str,\n help='imgs dir')\nparser.add_argument('--test_device', default=\"cpu\", type=str,\n help='cuda:0 or cpu')\nargs = parser.parse_args()\ndefine_img_size(args.input_size) # must put define_img_size() before 'import create_mb_tiny_fd, create_mb_tiny_fd_predictor'\n\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd, create_mb_tiny_fd_predictor\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd, create_Mb_Tiny_RFB_fd_predictor\n\nresult_path = \"detect_imgs_results\"\nlabel_path = \"./voc-model-labels.txt\"\ntest_device = args.test_device\n\nclass_names = [name.strip() for name in open(label_path).readlines()]\n\nif args.net_type == 'RFB':\n model_path = \"pretrained/RFB-640-masked_face-v2.pth\"\n net = create_Mb_Tiny_RFB_fd(len(class_names), is_test=True, device=test_device)\n predictor = create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=args.candidate_size, device=test_device)\nelse:\n print(\"The net type is wrong!\")\n sys.exit(1)\nnet.load(model_path)\n\nif not os.path.exists(result_path):\n os.makedirs(result_path)\nlistdir = os.listdir(args.path)\nsum = 0\nfor file_path in listdir:\n img_path = os.path.join(args.path, file_path)\n orig_image = cv2.imread(img_path)\n if orig_image is None: continue\n image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)\n boxes, labels, probs = predictor.predict(image, args.candidate_size / 2, args.threshold)\n sum += boxes.size(0)\n for i in range(boxes.size(0)):\n box = boxes[i, :]\n label_index = labels[i].item()\n cv2.rectangle(orig_image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 165, 255), 2)\n # label = f\"\"\"{voc_dataset.class_names[labels[i]]}: {probs[i]:.2f}\"\"\"\n label = f\"{probs[i]:.2f}\"\n # cv2.putText(orig_image, label, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n cv2.putText(orig_image, class_names[label_index],\n (int(box[0]), int(box[1]) - 10),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.5, # font scale\n (100, 0, 255),\n 1) # line type\n cv2.putText(orig_image, str(boxes.size(0)), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n cv2.imwrite(os.path.join(result_path, file_path), orig_image)\n print(f\"Found {len(probs)} faces. The output image is {result_path}\")\nprint(sum)\n" }, { "path": "masked_face/mafa2voc.py", "content": "# Original Author: Rahul Mangalampalli@https://www.kaggle.com/rahulmangalampalli/mafa-data\n# Haojin Yang: extended to convert the MAFA dataset to the pascal voc compatible format.\n\nfrom argparse import ArgumentParser\nfrom os import path\nimport os\n\nimport cv2\nimport cv2 as cv\nimport hdf5storage\nfrom tqdm import tqdm\nimport shutil\nimport xml.etree.cElementTree as ET\n\n## Argparser\nargparser = ArgumentParser()\nargparser.add_argument(\"--mafa_root\", type=str, default=None,\n help=\"MAFA dataset root folder.\")\nargparser.add_argument(\"--train_mat\", type=str, default=None,\n help=\"The mat file contains the train labels.\")\nargparser.add_argument(\"--test_mat\", type=str, default=None,\n help=\"The mat file contains the test labels.\")\nargparser.add_argument(\"--export_dir\", default=None, type=str,\n help=\"Where the extracted images should be saved.\")\n\n##=============== All about the visualization of bboxes ==================#\ndef expand_box(square_box, scale_ratio=1.2):\n \"\"\"Scale up the box\"\"\"\n assert (scale_ratio >= 1), \"Scale ratio should be greater than 1.\"\n delta = int((square_box[2] - square_box[0]) * (scale_ratio - 1) / 2)\n left_x = square_box[0] - delta\n left_y = square_box[1] - delta\n right_x = square_box[2] + delta\n right_y = square_box[3] + delta\n return [left_x, left_y, right_x, right_y]\n\n\ndef fit_by_shifting(box, rows, cols):\n \"\"\"Method 1: Try to move the box.\"\"\"\n # Face box points.\n left_x = box[0]\n top_y = box[1]\n right_x = box[2]\n bottom_y = box[3]\n\n # Check if moving is possible.\n if right_x - left_x <= cols and bottom_y - top_y <= rows:\n if left_x < 0: # left edge crossed, move right.\n right_x += abs(left_x)\n left_x = 0\n if right_x > cols: # right edge crossed, move left.\n left_x -= (right_x - cols)\n right_x = cols\n if top_y < 0: # top edge crossed, move down.\n bottom_y += abs(top_y)\n top_y = 0\n if bottom_y > rows: # bottom edge crossed, move up.\n top_y -= (bottom_y - rows)\n bottom_y = rows\n\n return [left_x, top_y, right_x, bottom_y]\n\n\ndef get_minimal_box(points):\n \"\"\"\n Get the minimal bounding box of a group of points.\n The coordinates are also converted to int numbers.\n \"\"\"\n min_x = int(min([point[0] for point in points]))\n max_x = int(max([point[0] for point in points]))\n min_y = int(min([point[1] for point in points]))\n max_y = int(max([point[1] for point in points]))\n return [min_x, min_y, max_x, max_y]\n\n\ndef points_in_box(points, box):\n \"\"\"Check if box contains all the points\"\"\"\n minimal_box = get_minimal_box(points)\n return box[0] <= minimal_box[0] and \\\n box[1] <= minimal_box[1] and \\\n box[2] >= minimal_box[2] and \\\n box[3] >= minimal_box[3]\n\n\ndef box_in_image(box, image):\n \"\"\"Check if the box is in image\"\"\"\n rows = image.shape[0]\n cols = image.shape[1]\n return box[0] >= 0 and box[1] >= 0 and box[2] <= cols and box[3] <= rows\n\n\ndef box_is_valid(image, points, box):\n \"\"\"Check if box is valid.\"\"\"\n # Box contains all the points.\n points_is_in_box = points_in_box(points, box)\n\n # Box is in image.\n box_is_in_image = box_in_image(box, image)\n\n # Box is square.\n w_equal_h = (box[2] - box[0]) == (box[3] - box[1])\n\n # Return the result.\n return box_is_in_image and points_is_in_box and w_equal_h\n\n\n\ndef fit_by_shrinking(box, rows, cols):\n \"\"\"Method 2: Try to shrink the box.\"\"\"\n # Face box points.\n left_x = box[0]\n top_y = box[1]\n right_x = box[2]\n bottom_y = box[3]\n\n # The first step would be get the interlaced area.\n if left_x < 0: # left edge crossed, set zero.\n left_x = 0\n if right_x > cols: # right edge crossed, set max.\n right_x = cols\n if top_y < 0: # top edge crossed, set zero.\n top_y = 0\n if bottom_y > rows: # bottom edge crossed, set max.\n bottom_y = rows\n\n # Then found out which is larger: the width or height. This will\n # be used to decide in which dimension the size would be shrunken.\n width = right_x - left_x\n height = bottom_y - top_y\n delta = abs(width - height)\n # Find out which dimension should be altered.\n if width > height: # x should be altered.\n if left_x != 0 and right_x != cols: # shrink from center.\n left_x += int(delta / 2)\n right_x -= int(delta / 2) + delta % 2\n elif left_x == 0: # shrink from right.\n right_x -= delta\n else: # shrink from left.\n left_x += delta\n else: # y should be altered.\n if top_y != 0 and bottom_y != rows: # shrink from center.\n top_y += int(delta / 2) + delta % 2\n bottom_y -= int(delta / 2)\n elif top_y == 0: # shrink from bottom.\n bottom_y -= delta\n else: # shrink from top.\n top_y += delta\n\n return [left_x, top_y, right_x, bottom_y]\n\n\ndef fit_box(box, image: object, points: object):\n \"\"\"\n Try to fit the box, make sure it satisfy following conditions:\n - A square.\n - Inside the image.\n - Contains all the points.\n If all above failed, return None.\n \"\"\"\n rows = image.shape[0]\n cols = image.shape[1]\n\n # First try to move the box.\n box_moved = fit_by_shifting(box, rows, cols)\n\n # If moving fails ,try to shrink.\n if box_is_valid(image, points, box_moved):\n return box_moved\n else:\n box_shrunken = fit_by_shrinking(box, rows, cols)\n\n # If shrink failed, return None\n if box_is_valid(image, points, box_shrunken):\n return box_shrunken\n\n # Finally, Worst situation.\n print(\"Fitting failed!\")\n return None\n\n##=============== END of All about the visualization of bboxes ==================#\n\n\n##=============== All about the visualization of bboxes ==================#\n\ndef load_labels(label_file, is_train):\n out = hdf5storage.loadmat(label_file)\n samples = []\n if is_train:\n record = out['label_train'][0]\n for item in record:\n samples.append(\n {\n 'image_file': item[1][0],\n 'lables': [v for v in item[2].astype(int)]\n }\n )\n else:\n record = out['LabelTest'][0]\n for item in record:\n samples.append(\n {\n 'image_file': item[0][0],\n 'lables': [v for v in item[1].astype(int)]\n }\n )\n return samples\n\n\ndef parse_labels(raw_labels, is_train=True):\n \"\"\"\n FOR TRAIN LABELS\n raw labels form: [x,y,w,h, x1,y1,x2,y2, x3,y3,w3,h3, occ_type, occ_degree,\n gender, race, orientation, x4,y4,w4,h4]\n (a) (x,y,w,h) is the bounding box of a face,\n (b) (x1,y1,x2,y2) is the position of two eyes.\n (c) (x3,y3,w3,h3) is the bounding box of the occluder. Note that (x3,y3)\n is related to the face bounding box position (x,y)\n (d) occ_type stands for the occluder type and has: 1 for simple, 2 for\n complex and 3 for human body.\n (e) occ_degree stands for the number of occluded face parts\n (f) gender and race stand for the gender and race of one face\n (g) orientation stands for the face orientation/pose, and has: 1-left,\n 2-left frontal, 3-frontal, 4-right frontal, 5-right\n (h) (x4,y4,w4,h4) is the bounding box of the glasses and is set to\n (-1,-1,-1,-1) when no glasses. Note that (x4,y4) is related to the\n face bounding box position (x,y)\n\n FOR TEST LABELS\n The format is stored in a 18d array (x,y,w,h,face_type,x1,y1,w1,h1, occ_type,\n occ_degree, gender, race, orientation, x2,y2,w2,h2), where\n (a) (x,y,w,h) is the bounding box of a face, \n (b) face_type stands for the face type and has: 1 for masked face, 2 for\n unmasked face and 3 for invalid face.\n (c) (x1,y1,w1,h1) is the bounding box of the occluder. Note that (x1,y1)\n is related to the face bounding box position (x,y)\n (d) occ_type stands for the occluder type and has: 1 for simple, 2 for \n complex and 3 for human body.\n (e) occ_degree stands for the number of occluded face parts\n (f) gender and race stand for the gender and race of one face\n (g) orientation stands for the face orientation/pose, and has: 1-left, \n 2-left frontal, 3-frontal, 4-right frontal, 5-right\n (h) (x2,y2,w2,h2) is the bounding box of the glasses and is set to \n (-1,-1,-1,-1) when no glasses. Note that (x2,y2) is related to the \n face bounding box position (x,y)\n\n \"\"\"\n labels = []\n\n # For the purpose of creating a dataset benchmark for masked face detection, we will exclude samples with a\n # occlude degree lower than 3. We also exclude the samples with human body (e.g., hands) occlusion.\n\n if is_train:\n for raw_label in raw_labels:\n # filtering out the less occluded faces\n if raw_label[13] < 3 or raw_label[12] == 3:\n continue\n\n labels.append(\n {\n 'face': [raw_label[0], raw_label[1], raw_label[2], raw_label[3]],\n 'eyes': [raw_label[4], raw_label[5], raw_label[6], raw_label[7]],\n 'occlude': {\n 'location': [raw_label[8], raw_label[9], raw_label[10], raw_label[11]],\n 'type': raw_label[12],\n 'degree': raw_label[13]},\n 'gender': raw_label[14],\n 'race': raw_label[15],\n 'orientation': raw_label[16],\n 'glass': [raw_label[17], raw_label[18], raw_label[19], raw_label[20]]\n }\n )\n else:\n for raw_label in raw_labels:\n # filtering out the less occluded faces and non-occluded faces\n if raw_label[4] != 1 or raw_label[10] < 3 or raw_label[9] == 3:\n continue\n\n labels.append(\n {\n 'face': [raw_label[0], raw_label[1], raw_label[2], raw_label[3]],\n 'face_type': raw_label[4],\n 'occlude': {\n 'location': [raw_label[5], raw_label[6], raw_label[7], raw_label[8]],\n 'type': raw_label[9], # exclude human body\n 'degree': raw_label[10]}, # degree 1 and 2 not acceptable\n 'gender': raw_label[11],\n 'race': raw_label[12],\n 'orientation': raw_label[13],\n 'glass': [raw_label[14], raw_label[15], raw_label[16], raw_label[17]]\n }\n )\n\n return labels\n\n\ndef draw_face(image, labels, color=(0, 255, 0)):\n for label in labels:\n x, y, w, h = label['face']\n cv.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 3)\n\n\ndef draw_mask(image, labels, color=(0, 0, 255)):\n for label in labels:\n x, y, w, h = label['face']\n cv.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 3)\n _x, _y, _w, _h = label['occlude']['location']\n _x =_x+x\n _y=_y+y\n cv.rectangle(image, (_x, _y), (_x + _w, _y + _h), (0, 0, 255), 3)\n\n\ndef export_face(image, labels, export_file, occ_types=[1, 2, 3], min_size=120, export_size=112):\n \"\"\"\n Export face areas in an image.\n Args:\n image: the image as a numpy array.\n labels: MAFA labels.\n export_file: the output file name. If more than one face exported, a subfix \n number will be appended to the file name.\n occ_types: a list of occlusion type which should be exported.\n min_size: the minimal size of faces should be exported.\n exprot_size: the output size of the square image.\n Returns:\n the exported image, or None.\n \"\"\"\n # Crop the face\n idx_for_face = 0\n image_faces = []\n for label in labels:\n # Not all faces in label is occluded. Filter the image by occlusion,\n # size, etc.\n x, y, w, h = label['face']\n if w < min_size or h < min_size:\n continue\n\n if label['occlude']['type'] not in occ_types:\n continue\n\n # Enlarge the face area and make it a square.\n box = expand_box([x, y, x+w, y+h], 1.3)\n box = fit_box(box, image, [(box[0], box[1]), (box[2], box[3])])\n if box is not None:\n image_face = image[box[1]:box[3], box[0]:box[2]]\n else:\n return None\n\n # Resize and save image.\n image_face = cv2.resize(image_face, (export_size, export_size))\n new_file = export_file.rstrip(\n '.jpg') + '-{}.jpg'.format(idx_for_face)\n cv2.imwrite(new_file, image_face)\n image_faces.append(image_face)\n\n idx_for_face += 1\n\n return image_faces\n\n\ndef write_voc_style_ann(labels, img_file_name, num_human_occ):\n # example of pascal voc annotation\n # \n # face\n # Unspecified\n # 1\n # 0\n # \n # 146\n # 215\n # 179\n # 248\n # \n # 0\n # \n\n root = ET.Element(\"annotation\")\n ET.SubElement(root, \"folder\").text = \"MAFA\"\n ET.SubElement(root, \"filename\").text = img_file_name\n ET.SubElement(root, \"segmented\").text = '0'\n\n for label in labels:\n obj = ET.SubElement(root, \"object\")\n # we label the human body occlusion as the 'face', since our purpose is to build a dataset of face/masked_face.\n # Human body occlusion should not be considered as masked_face.\n occ_type = label['occlude']['type']\n if occ_type == 3: # human body\n ET.SubElement(obj, \"name\").text = 'face'\n num_human_occ +=1\n else:\n ET.SubElement(obj, \"name\").text = 'masked_face'\n ET.SubElement(obj, \"pose\").text = 'Unspecified'\n ET.SubElement(obj, \"truncated\").text = '1'\n ET.SubElement(obj, \"difficult\").text = '0'\n ET.SubElement(obj, \"has_lm\").text = '0'\n\n x, y, w, h = label['face']\n bnbbox = ET.SubElement(obj, \"bndbox\")\n ET.SubElement(bnbbox, \"xmin\").text = str(x)\n ET.SubElement(bnbbox, \"ymin\").text = str(y)\n ET.SubElement(bnbbox, \"xmax\").text = str(x+w)\n ET.SubElement(bnbbox, \"ymax\").text = str(y+h)\n\n _x, _y, _w, _h = label['occlude']['location']\n _x = _x + x\n _y = _y + y\n occluder = ET.SubElement(obj, \"occluder\")\n ET.SubElement(occluder, \"xmin\").text = str(_x)\n ET.SubElement(occluder, \"ymin\").text = str(_y)\n ET.SubElement(occluder, \"xmax\").text = str(_x+_w)\n ET.SubElement(occluder, \"ymax\").text = str(_y+_h)\n\n # We could further extend the annotations using more information such as glasses and eyes etc.\n tree = ET.ElementTree(root)\n return tree, num_human_occ\n\n\nif __name__ == \"__main__\":\n # Get all args.\n args = argparser.parse_args()\n\n # is_train = False\n is_train = True\n\n img_dir = 'train-images' if is_train else 'test-images'\n mat = args.train_mat if is_train else args.test_mat\n\n # Load annotations from the mat file.\n samples = load_labels(mat, is_train=is_train)\n\n TARGET_SAMPLES = 15542\n num = 0\n num_human_occ = 0\n\n # Extract the face images.\n if args.export_dir is not None:\n export_face_image_dir = path.join(args.export_dir, 'JPEGImages')\n export_imageSets_dir = path.join(args.export_dir, 'ImageSets')\n main_imageSets_dir = path.join(export_imageSets_dir, 'Main')\n export_annotations_dir = path.join(args.export_dir, 'Annotations')\n if not os.path.exists(export_face_image_dir):\n os.makedirs(export_face_image_dir)\n if not os.path.exists(export_imageSets_dir):\n os.makedirs(export_imageSets_dir)\n if not os.path.exists(main_imageSets_dir):\n os.makedirs(main_imageSets_dir)\n if not os.path.exists(export_annotations_dir):\n os.makedirs(export_annotations_dir)\n\n # create image set list files for train and test\n listfile = None\n if is_train:\n listfile = path.join(main_imageSets_dir, 'trainval.txt')\n else:\n listfile = path.join(main_imageSets_dir, 'test.txt')\n with open(listfile, 'w') as f:\n # loop through all the annotations and do processing. Here we are going to\n # extract all the occluded faces and save them in a new image file.\n for sample in tqdm(samples):\n labels = parse_labels(sample['lables'], is_train=is_train)\n if len(labels) == 0:\n continue\n\n img_file_name = sample['image_file']\n img_url = path.join(args.mafa_root, img_dir, img_file_name)\n image = cv2.imread(img_url)\n assert not isinstance(image, type(None)), 'image not found'\n\n # We could use the following method to visually check the annotation quality.\n # draw_face(image, labels)\n draw_mask(image, labels)\n\n # write the image name without extension\n img_name_wo_ext = os.path.splitext(img_file_name)[0]\n # only for creating image name of human body split\n # img_name_wo_ext = img_name_wo_ext + '_human_occ'\n\n f.write(img_name_wo_ext)\n f.write('\\n')\n\n # create xml annotation file\n xml_annotation_file = path.join(export_annotations_dir, img_name_wo_ext + '.xml')\n f_xml, num_human_occ = write_voc_style_ann(labels, img_file_name, num_human_occ)\n f_xml.write(xml_annotation_file)\n\n # Extract the face images.\n if args.export_dir is not None:\n export_face_image_file = path.join(export_face_image_dir, img_name_wo_ext + '.jpg')\n # copy images\n shutil.copyfile(img_url, export_face_image_file)\n\n # visualize faces for validation purpose.\n # exported_face = export_face(image, labels, export_face_image_file, occ_types=[1, 2, 3], min_size=60, export_size=112)\n num += 1\n if num == TARGET_SAMPLES:\n break\n f.close()\n print('{} human body occluded faces'.format(num_human_occ))\n\n" }, { "path": "masked_face/voc-model-labels.txt", "content": "BACKGROUND\nface\nmasked_face" }, { "path": "models/readme", "content": "A place to store your models.\n\n./pretrained:\n version-RFB-320.pth : train with img input size 320x240\n version-RFB-640.pth : train with img input size 640x480\n version-slim-320.pth : train with img input size 320x240\n version-slim-640.pth : train with img input size 640x480\n./onnx\n version-RFB-320.onnx :version-RFB / train with 320x240/ with postprocessing\n version-RFB-320_simplified.onnx :version-RFB / train with 320x240/ without postprocessing /for ncnn & mnn\n version-RFB-320_without_postprocessing.onnx :version-RFB / train with 320x240/ without postprocessing\n version-slim-320.onnx :version-slim/ train with 320x240/ with postprocessing\n version-slim-320_simplified.onnx :version-slim/ train with 320x240/ without postprocessing /for ncnn & mnn\n version-slim-320_without_postprocessing.onnx :version-slim/ train with 320x240/ without postprocessing" }, { "path": "models/voc-model-labels.txt", "content": "BACKGROUND\nface" }, { "path": "ncnn/.clang-format", "content": "---\nBasedOnStyle: LLVM\nBreakBeforeBraces: Linux\nIndentWidth: '4'\n\n...\n" }, { "path": "ncnn/CMakeLists.txt", "content": "cmake_minimum_required(VERSION 3.10)\n\nset(CMAKE_CXX_FLAGS \"-Wall\")\nset(CMAKE_CXX_FLAGS_RELEASE \"-O2 -DNDEBUG\")\nset(CMAKE_CXX_FLAGS_DEBUG \"-g\")\nset(CMAKE_CXX_STANDARD 11)\n\nif (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)\n message(STATUS \"No build type selected, default to Release\")\n set(CMAKE_BUILD_TYPE \"Release\" CACHE STRING \"Build type (default Debug)\" FORCE)\nendif()\n\nproject(Ultra-Light-Fast-Generic-Face-Detector-1MB)\n\nadd_subdirectory(3rdparty/ncnn ncnn_build)\ninclude_directories(3rdparty/ncnn/src)\n\nfind_package(OpenCV REQUIRED)\n\nadd_executable(main src/main.cpp src/UltraFace.cpp)\ntarget_link_libraries(main ncnn ${OpenCV_LIBS})\n" }, { "path": "ncnn/README.md", "content": "# C++ implemententation of [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) with [NCNN](https://github.com/Tencent/ncnn)\n\n## Build\n\n```bash\ngit clone --recursive --depth=1 https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB\n\ncd Ultra-Light-Fast-Generic-Face-Detector-1MB/ncnn\n\nmkdir build && cd build && cmake ..\nmake -j$(nproc)\n```\n\n## Run\n\n```bash\n./main ../data/version-RFB/RFB-320.bin ../data/version-RFB/RFB-320.param ../data/test.jpg\n```\n* We provide converted NCNN models of version-slim-320 and version-RFB-320 in ./ncnn/data .\n\n## How to convert pretrained model to ncnn\n\n* Code bellow (```vision/ssd/ssd.py```) should be commented out when convert pytorch pretrained model to onnx. Comment it out and use the **convert_to_onnx.py** in official repo to finish this step.\n\n```python\nif self.is_test:\n confidences = F.softmax(confidences, dim=2)\n boxes = locations # this line should be added.\n #boxes = box_utils.convert_locations_to_boxes(\n # locations, self.priors, self.config.center_variance, self.config.size_variance\n #)\n # boxes = box_utils.center_form_to_corner_form(boxes) # these lines should be commented out. detail information and analyze comming soon.\n return confidences, boxes\nelse:\n return confidences, locations\n```\nThen you can generate the onnx model like **version-RFB-320_without_postprocessing.onnx** in onnx directory. (You need to rename your model when convert.)\n* But the exported onnx model may contains many redundant operators such as Shape, Gather and Unsqueeze that is not supported in ncnn.\n\n```\nShape not supported yet!\nGather not supported yet!\n # axis=0\nUnsqueeze not supported yet!\n # axes 7\nUnsqueeze not supported yet!\n # axes 7\n```\n\nFortunately, we can use this tool to eliminate them :\nhttps://github.com/daquexian/onnx-simplifier\n\n```\npython3 -m onnxsim version-RFB-320_without_postprocessing.onnx version-RFB-320_simplified.onnx\n\n```\n\nNext, you can convert this onnx model like **version-RFB-320_simplified.onnx** into a ncnn model. Here is a website for online conversion : https://convertmodel.com/?tdsourcetag=s_pctim_aiomsg. You can also use the NCNN compiled conversion tool **onnx2ncnn**.\n\n## PS\n* If you want to run faster, try using the version-slim model or using lower-resolution inputs like 160x120 or 128x96.\n\n## Result\n![img1](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/ncnn/data/result.jpg)" }, { "path": "ncnn/data/version-RFB/RFB-320.param", "content": "7767517\n116 126\nInput input 0 1 input\nConvolution 245 1 1 input 245 0=16 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 15=1 16=1 5=1 6=432\nReLU 247 1 1 245 247\nConvolutionDepthWise 248 1 1 247 248 0=16 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=144 7=16\nReLU 250 1 1 248 250\nConvolution 251 1 1 250 251 0=32 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=512\nReLU 253 1 1 251 253\nConvolutionDepthWise 254 1 1 253 254 0=32 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 15=1 16=1 5=1 6=288 7=32\nReLU 256 1 1 254 256\nConvolution 257 1 1 256 257 0=32 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=1024\nReLU 259 1 1 257 259\nConvolutionDepthWise 260 1 1 259 260 0=32 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=288 7=32\nReLU 262 1 1 260 262\nConvolution 263 1 1 262 263 0=32 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=1024\nReLU 265 1 1 263 265\nConvolutionDepthWise 266 1 1 265 266 0=32 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 15=1 16=1 5=1 6=288 7=32\nReLU 268 1 1 266 268\nConvolution 269 1 1 268 269 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=2048\nReLU 271 1 1 269 271\nConvolutionDepthWise 272 1 1 271 272 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=576 7=64\nReLU 274 1 1 272 274\nConvolution 275 1 1 274 275 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=4096\nReLU 277 1 1 275 277\nConvolutionDepthWise 278 1 1 277 278 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=576 7=64\nReLU 280 1 1 278 280\nConvolution 281 1 1 280 281 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=4096\nReLU 283 1 1 281 283\nSplit splitncnn_0 1 4 283 283_splitncnn_0 283_splitncnn_1 283_splitncnn_2 283_splitncnn_3\nConvolution 284 1 1 283_splitncnn_3 284 0=8 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=512\nConvolution 286 1 1 284 286 0=16 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=1152\nReLU 288 1 1 286 288\nConvolution 289 1 1 288 289 0=16 1=3 11=3 2=2 12=2 3=1 13=1 4=2 14=2 15=2 16=2 5=1 6=2304\nConvolution 291 1 1 283_splitncnn_2 291 0=8 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=512\nConvolution 293 1 1 291 293 0=16 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=1152\nReLU 295 1 1 293 295\nConvolution 296 1 1 295 296 0=16 1=3 11=3 2=3 12=3 3=1 13=1 4=3 14=3 15=3 16=3 5=1 6=2304\nConvolution 298 1 1 283_splitncnn_1 298 0=8 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=512\nConvolution 300 1 1 298 300 0=12 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=864\nReLU 302 1 1 300 302\nConvolution 303 1 1 302 303 0=16 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=1728\nReLU 305 1 1 303 305\nConvolution 306 1 1 305 306 0=16 1=3 11=3 2=5 12=5 3=1 13=1 4=5 14=5 15=5 16=5 5=1 6=2304\nConcat 308 3 1 289 296 306 308 0=0\nConvolution 309 1 1 308 309 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=3072\nConvolution 311 1 1 283_splitncnn_0 311 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=4096\nBinaryOp 313 2 1 309 311 313 0=0\nReLU 314 1 1 313 314\nSplit splitncnn_1 1 3 314 314_splitncnn_0 314_splitncnn_1 314_splitncnn_2\nConvolutionDepthWise 315 1 1 314_splitncnn_2 315 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=576 7=64\nReLU 316 1 1 315 316\nConvolution 317 1 1 316 317 0=6 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=384\nPermute 318 1 1 317 318 0=3\nReshape 328 1 1 318 328 0=2 1=-1\nConvolutionDepthWise 329 1 1 314_splitncnn_1 329 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=576 7=64\nReLU 330 1 1 329 330\nConvolution 331 1 1 330 331 0=12 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=768\nPermute 332 1 1 331 332 0=3\nReshape 342 1 1 332 342 0=4 1=-1\nConvolutionDepthWise 343 1 1 314_splitncnn_0 343 0=64 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 15=1 16=1 5=1 6=576 7=64\nReLU 345 1 1 343 345\nConvolution 346 1 1 345 346 0=128 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=8192\nReLU 348 1 1 346 348\nConvolutionDepthWise 349 1 1 348 349 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=1152 7=128\nReLU 351 1 1 349 351\nConvolution 352 1 1 351 352 0=128 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=16384\nReLU 354 1 1 352 354\nConvolutionDepthWise 355 1 1 354 355 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=1152 7=128\nReLU 357 1 1 355 357\nConvolution 358 1 1 357 358 0=128 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=16384\nReLU 360 1 1 358 360\nSplit splitncnn_2 1 3 360 360_splitncnn_0 360_splitncnn_1 360_splitncnn_2\nConvolutionDepthWise 361 1 1 360_splitncnn_2 361 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=1152 7=128\nReLU 362 1 1 361 362\nConvolution 363 1 1 362 363 0=4 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=512\nPermute 364 1 1 363 364 0=3\nReshape 374 1 1 364 374 0=2 1=-1\nConvolutionDepthWise 375 1 1 360_splitncnn_1 375 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=1152 7=128\nReLU 376 1 1 375 376\nConvolution 377 1 1 376 377 0=8 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=1024\nPermute 378 1 1 377 378 0=3\nReshape 388 1 1 378 388 0=4 1=-1\nConvolutionDepthWise 389 1 1 360_splitncnn_0 389 0=128 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 15=1 16=1 5=1 6=1152 7=128\nReLU 391 1 1 389 391\nConvolution 392 1 1 391 392 0=256 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=32768\nReLU 394 1 1 392 394\nConvolutionDepthWise 395 1 1 394 395 0=256 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=2304 7=256\nReLU 397 1 1 395 397\nConvolution 398 1 1 397 398 0=256 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=65536\nReLU 400 1 1 398 400\nSplit splitncnn_3 1 3 400 400_splitncnn_0 400_splitncnn_1 400_splitncnn_2\nConvolutionDepthWise 401 1 1 400_splitncnn_2 401 0=256 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=2304 7=256\nReLU 402 1 1 401 402\nConvolution 403 1 1 402 403 0=4 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=1024\nPermute 404 1 1 403 404 0=3\nReshape 414 1 1 404 414 0=2 1=-1\nConvolutionDepthWise 415 1 1 400_splitncnn_1 415 0=256 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=2304 7=256\nReLU 416 1 1 415 416\nConvolution 417 1 1 416 417 0=8 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=2048\nPermute 418 1 1 417 418 0=3\nReshape 428 1 1 418 428 0=4 1=-1\nConvolution 429 1 1 400_splitncnn_0 429 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=16384\nReLU 430 1 1 429 430\nConvolutionDepthWise 431 1 1 430 431 0=64 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 15=1 16=1 5=1 6=576 7=64\nReLU 432 1 1 431 432\nConvolution 433 1 1 432 433 0=256 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=1 6=16384\nReLU 434 1 1 433 434\nSplit splitncnn_4 1 2 434 434_splitncnn_0 434_splitncnn_1\nConvolution 435 1 1 434_splitncnn_1 435 0=6 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=13824\nPermute 436 1 1 435 436 0=3\nReshape 446 1 1 436 446 0=2 1=-1\nConvolution 447 1 1 434_splitncnn_0 447 0=12 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=27648\nPermute 448 1 1 447 448 0=3\nReshape 458 1 1 448 458 0=4 1=-1\nConcat 459 4 1 328 374 414 446 459 0=0\nConcat boxes 4 1 342 388 428 458 boxes 0=0\nSoftmax scores 1 1 459 scores 0=1 1=1\n" }, { "path": "ncnn/data/version-slim/slim_320.param", "content": "7767517\n100 107\nInput input 0 1 input\nConvolution 185 1 1 input 185 0=16 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 5=1 6=432\nReLU 187 1 1 185 187\nConvolutionDepthWise 188 1 1 187 188 0=16 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=144 7=16\nReLU 190 1 1 188 190\nConvolution 191 1 1 190 191 0=32 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=512\nReLU 193 1 1 191 193\nConvolutionDepthWise 194 1 1 193 194 0=32 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 5=1 6=288 7=32\nReLU 196 1 1 194 196\nConvolution 197 1 1 196 197 0=32 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=1024\nReLU 199 1 1 197 199\nConvolutionDepthWise 200 1 1 199 200 0=32 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=288 7=32\nReLU 202 1 1 200 202\nConvolution 203 1 1 202 203 0=32 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=1024\nReLU 205 1 1 203 205\nConvolutionDepthWise 206 1 1 205 206 0=32 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 5=1 6=288 7=32\nReLU 208 1 1 206 208\nConvolution 209 1 1 208 209 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=2048\nReLU 211 1 1 209 211\nConvolutionDepthWise 212 1 1 211 212 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=576 7=64\nReLU 214 1 1 212 214\nConvolution 215 1 1 214 215 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=4096\nReLU 217 1 1 215 217\nConvolutionDepthWise 218 1 1 217 218 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=576 7=64\nReLU 220 1 1 218 220\nConvolution 221 1 1 220 221 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=4096\nReLU 223 1 1 221 223\nConvolutionDepthWise 224 1 1 223 224 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=576 7=64\nReLU 226 1 1 224 226\nConvolution 227 1 1 226 227 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=4096\nReLU 229 1 1 227 229\nSplit splitncnn_0 1 3 229 229_splitncnn_0 229_splitncnn_1 229_splitncnn_2\nConvolutionDepthWise 230 1 1 229_splitncnn_2 230 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=576 7=64\nReLU 231 1 1 230 231\nConvolution 232 1 1 231 232 0=6 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=384\nPermute 233 1 1 232 233 0=3\nReshape 243 1 1 233 243 0=2 1=-1\nConvolutionDepthWise 244 1 1 229_splitncnn_1 244 0=64 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=576 7=64\nReLU 245 1 1 244 245\nConvolution 246 1 1 245 246 0=12 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=768\nPermute 247 1 1 246 247 0=3\nReshape 257 1 1 247 257 0=4 1=-1\nConvolutionDepthWise 258 1 1 229_splitncnn_0 258 0=64 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 5=1 6=576 7=64\nReLU 260 1 1 258 260\nConvolution 261 1 1 260 261 0=128 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=8192\nReLU 263 1 1 261 263\nConvolutionDepthWise 264 1 1 263 264 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=1152 7=128\nReLU 266 1 1 264 266\nConvolution 267 1 1 266 267 0=128 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=16384\nReLU 269 1 1 267 269\nConvolutionDepthWise 270 1 1 269 270 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=1152 7=128\nReLU 272 1 1 270 272\nConvolution 273 1 1 272 273 0=128 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=16384\nReLU 275 1 1 273 275\nSplit splitncnn_1 1 3 275 275_splitncnn_0 275_splitncnn_1 275_splitncnn_2\nConvolutionDepthWise 276 1 1 275_splitncnn_2 276 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=1152 7=128\nReLU 277 1 1 276 277\nConvolution 278 1 1 277 278 0=4 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=512\nPermute 279 1 1 278 279 0=3\nReshape 289 1 1 279 289 0=2 1=-1\nConvolutionDepthWise 290 1 1 275_splitncnn_1 290 0=128 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=1152 7=128\nReLU 291 1 1 290 291\nConvolution 292 1 1 291 292 0=8 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=1024\nPermute 293 1 1 292 293 0=3\nReshape 303 1 1 293 303 0=4 1=-1\nConvolutionDepthWise 304 1 1 275_splitncnn_0 304 0=128 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 5=1 6=1152 7=128\nReLU 306 1 1 304 306\nConvolution 307 1 1 306 307 0=256 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=32768\nReLU 309 1 1 307 309\nConvolutionDepthWise 310 1 1 309 310 0=256 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=2304 7=256\nReLU 312 1 1 310 312\nConvolution 313 1 1 312 313 0=256 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=65536\nReLU 315 1 1 313 315\nSplit splitncnn_2 1 3 315 315_splitncnn_0 315_splitncnn_1 315_splitncnn_2\nConvolutionDepthWise 316 1 1 315_splitncnn_2 316 0=256 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=2304 7=256\nReLU 317 1 1 316 317\nConvolution 318 1 1 317 318 0=4 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=1024\nPermute 319 1 1 318 319 0=3\nReshape 329 1 1 319 329 0=2 1=-1\nConvolutionDepthWise 330 1 1 315_splitncnn_1 330 0=256 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=2304 7=256\nReLU 331 1 1 330 331\nConvolution 332 1 1 331 332 0=8 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=2048\nPermute 333 1 1 332 333 0=3\nReshape 343 1 1 333 343 0=4 1=-1\nConvolution 344 1 1 315_splitncnn_0 344 0=64 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=16384\nReLU 345 1 1 344 345\nConvolutionDepthWise 346 1 1 345 346 0=64 1=3 11=3 2=1 12=1 3=2 13=2 4=1 14=1 5=1 6=576 7=64\nReLU 347 1 1 346 347\nConvolution 348 1 1 347 348 0=256 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 5=1 6=16384\nReLU 349 1 1 348 349\nSplit splitncnn_3 1 2 349 349_splitncnn_0 349_splitncnn_1\nConvolution 350 1 1 349_splitncnn_1 350 0=6 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=13824\nPermute 351 1 1 350 351 0=3\nReshape 361 1 1 351 361 0=2 1=-1\nConvolution 362 1 1 349_splitncnn_0 362 0=12 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 5=1 6=27648\nPermute 363 1 1 362 363 0=3\nReshape 373 1 1 363 373 0=4 1=-1\nConcat 374 4 1 243 289 329 361 374 0=0\nConcat boxes 4 1 257 303 343 373 boxes 0=0\nSoftmax scores 1 1 374 scores 0=1 1=1\n" }, { "path": "ncnn/src/UltraFace.cpp", "content": "//\n// UltraFace.cpp\n// UltraFaceTest\n//\n// Created by vealocia on 2019/10/17.\n// Copyright © 2019 vealocia. All rights reserved.\n//\n\n#define clip(x, y) (x < 0 ? 0 : (x > y ? y : x))\n\n#include \"UltraFace.hpp\"\n#include \"mat.h\"\n\nUltraFace::UltraFace(const std::string &bin_path, const std::string ¶m_path,\n int input_width, int input_length, int num_thread_,\n float score_threshold_, float iou_threshold_, int topk_) {\n num_thread = num_thread_;\n topk = topk_;\n score_threshold = score_threshold_;\n iou_threshold = iou_threshold_;\n in_w = input_width;\n in_h = input_length;\n w_h_list = {in_w, in_h};\n\n for (auto size : w_h_list) {\n std::vector fm_item;\n for (float stride : strides) {\n fm_item.push_back(ceil(size / stride));\n }\n featuremap_size.push_back(fm_item);\n }\n\n for (auto size : w_h_list) {\n shrinkage_size.push_back(strides);\n }\n\n /* generate prior anchors */\n for (int index = 0; index < num_featuremap; index++) {\n float scale_w = in_w / shrinkage_size[0][index];\n float scale_h = in_h / shrinkage_size[1][index];\n for (int j = 0; j < featuremap_size[1][index]; j++) {\n for (int i = 0; i < featuremap_size[0][index]; i++) {\n float x_center = (i + 0.5) / scale_w;\n float y_center = (j + 0.5) / scale_h;\n\n for (float k : min_boxes[index]) {\n float w = k / in_w;\n float h = k / in_h;\n priors.push_back({clip(x_center, 1), clip(y_center, 1), clip(w, 1), clip(h, 1)});\n }\n }\n }\n }\n num_anchors = priors.size();\n /* generate prior anchors finished */\n\n ultraface.load_param(param_path.data());\n ultraface.load_model(bin_path.data());\n}\n\nUltraFace::~UltraFace() { ultraface.clear(); }\n\nint UltraFace::detect(ncnn::Mat &img, std::vector &face_list) {\n if (img.empty()) {\n std::cout << \"image is empty ,please check!\" << std::endl;\n return -1;\n }\n\n image_h = img.h;\n image_w = img.w;\n\n ncnn::Mat in;\n ncnn::resize_bilinear(img, in, in_w, in_h);\n ncnn::Mat ncnn_img = in;\n ncnn_img.substract_mean_normalize(mean_vals, norm_vals);\n\n std::vector bbox_collection;\n std::vector valid_input;\n\n ncnn::Extractor ex = ultraface.create_extractor();\n ex.set_num_threads(num_thread);\n ex.input(\"input\", ncnn_img);\n\n ncnn::Mat scores;\n ncnn::Mat boxes;\n ex.extract(\"scores\", scores);\n ex.extract(\"boxes\", boxes);\n generateBBox(bbox_collection, scores, boxes, score_threshold, num_anchors);\n nms(bbox_collection, face_list);\n return 0;\n}\n\nvoid UltraFace::generateBBox(std::vector &bbox_collection, ncnn::Mat scores, ncnn::Mat boxes, float score_threshold, int num_anchors) {\n for (int i = 0; i < num_anchors; i++) {\n if (scores.channel(0)[i * 2 + 1] > score_threshold) {\n FaceInfo rects;\n float x_center = boxes.channel(0)[i * 4] * center_variance * priors[i][2] + priors[i][0];\n float y_center = boxes.channel(0)[i * 4 + 1] * center_variance * priors[i][3] + priors[i][1];\n float w = exp(boxes.channel(0)[i * 4 + 2] * size_variance) * priors[i][2];\n float h = exp(boxes.channel(0)[i * 4 + 3] * size_variance) * priors[i][3];\n\n rects.x1 = clip(x_center - w / 2.0, 1) * image_w;\n rects.y1 = clip(y_center - h / 2.0, 1) * image_h;\n rects.x2 = clip(x_center + w / 2.0, 1) * image_w;\n rects.y2 = clip(y_center + h / 2.0, 1) * image_h;\n rects.score = clip(scores.channel(0)[i * 2 + 1], 1);\n bbox_collection.push_back(rects);\n }\n }\n}\n\nvoid UltraFace::nms(std::vector &input, std::vector &output, int type) {\n std::sort(input.begin(), input.end(), [](const FaceInfo &a, const FaceInfo &b) { return a.score > b.score; });\n\n int box_num = input.size();\n\n std::vector merged(box_num, 0);\n\n for (int i = 0; i < box_num; i++) {\n if (merged[i])\n continue;\n std::vector buf;\n\n buf.push_back(input[i]);\n merged[i] = 1;\n\n float h0 = input[i].y2 - input[i].y1 + 1;\n float w0 = input[i].x2 - input[i].x1 + 1;\n\n float area0 = h0 * w0;\n\n for (int j = i + 1; j < box_num; j++) {\n if (merged[j])\n continue;\n\n float inner_x0 = input[i].x1 > input[j].x1 ? input[i].x1 : input[j].x1;\n float inner_y0 = input[i].y1 > input[j].y1 ? input[i].y1 : input[j].y1;\n\n float inner_x1 = input[i].x2 < input[j].x2 ? input[i].x2 : input[j].x2;\n float inner_y1 = input[i].y2 < input[j].y2 ? input[i].y2 : input[j].y2;\n\n float inner_h = inner_y1 - inner_y0 + 1;\n float inner_w = inner_x1 - inner_x0 + 1;\n\n if (inner_h <= 0 || inner_w <= 0)\n continue;\n\n float inner_area = inner_h * inner_w;\n\n float h1 = input[j].y2 - input[j].y1 + 1;\n float w1 = input[j].x2 - input[j].x1 + 1;\n\n float area1 = h1 * w1;\n\n float score;\n\n score = inner_area / (area0 + area1 - inner_area);\n\n if (score > iou_threshold) {\n merged[j] = 1;\n buf.push_back(input[j]);\n }\n }\n switch (type) {\n case hard_nms: {\n output.push_back(buf[0]);\n break;\n }\n case blending_nms: {\n float total = 0;\n for (int i = 0; i < buf.size(); i++) {\n total += exp(buf[i].score);\n }\n FaceInfo rects;\n memset(&rects, 0, sizeof(rects));\n for (int i = 0; i < buf.size(); i++) {\n float rate = exp(buf[i].score) / total;\n rects.x1 += buf[i].x1 * rate;\n rects.y1 += buf[i].y1 * rate;\n rects.x2 += buf[i].x2 * rate;\n rects.y2 += buf[i].y2 * rate;\n rects.score += buf[i].score * rate;\n }\n output.push_back(rects);\n break;\n }\n default: {\n printf(\"wrong type of nms.\");\n exit(-1);\n }\n }\n }\n}\n" }, { "path": "ncnn/src/UltraFace.hpp", "content": "//\n// UltraFace.hpp\n// UltraFaceTest\n//\n// Created by vealocia on 2019/10/17.\n// Copyright © 2019 vealocia. All rights reserved.\n//\n\n#ifndef UltraFace_hpp\n#define UltraFace_hpp\n\n#pragma once\n\n#include \"gpu.h\"\n#include \"net.h\"\n#include \n#include \n#include \n#include \n\n#define num_featuremap 4\n#define hard_nms 1\n#define blending_nms 2 /* mix nms was been proposaled in paper blaze face, aims to minimize the temporal jitter*/\n\ntypedef struct FaceInfo {\n float x1;\n float y1;\n float x2;\n float y2;\n float score;\n\n float *landmarks;\n} FaceInfo;\n\nclass UltraFace {\npublic:\n UltraFace(const std::string &bin_path, const std::string ¶m_path,\n int input_width, int input_length, int num_thread_ = 4, float score_threshold_ = 0.7, float iou_threshold_ = 0.3, int topk_ = -1);\n\n ~UltraFace();\n\n int detect(ncnn::Mat &img, std::vector &face_list);\n\nprivate:\n void generateBBox(std::vector &bbox_collection, ncnn::Mat scores, ncnn::Mat boxes, float score_threshold, int num_anchors);\n\n void nms(std::vector &input, std::vector &output, int type = blending_nms);\n\nprivate:\n ncnn::Net ultraface;\n\n int num_thread;\n int image_w;\n int image_h;\n\n int in_w;\n int in_h;\n int num_anchors;\n\n int topk;\n float score_threshold;\n float iou_threshold;\n\n\n const float mean_vals[3] = {127, 127, 127};\n const float norm_vals[3] = {1.0 / 128, 1.0 / 128, 1.0 / 128};\n\n const float center_variance = 0.1;\n const float size_variance = 0.2;\n const std::vector> min_boxes = {\n {10.0f, 16.0f, 24.0f},\n {32.0f, 48.0f},\n {64.0f, 96.0f},\n {128.0f, 192.0f, 256.0f}};\n const std::vector strides = {8.0, 16.0, 32.0, 64.0};\n std::vector> featuremap_size;\n std::vector> shrinkage_size;\n std::vector w_h_list;\n\n std::vector> priors = {};\n};\n\n#endif /* UltraFace_hpp */\n" }, { "path": "ncnn/src/main.cpp", "content": "//\n// main.cpp\n// UltraFaceTest\n//\n// Created by vealocia on 2019/10/17.\n// Copyright © 2019 vealocia. All rights reserved.\n//\n\n#include \"UltraFace.hpp\"\n#include \n#include \n\nint main(int argc, char **argv) {\n if (argc <= 3) {\n fprintf(stderr, \"Usage: %s [image files...]\\n\", argv[0]);\n return 1;\n }\n\n std::string bin_path = argv[1];\n std::string param_path = argv[2];\n UltraFace ultraface(bin_path, param_path, 320, 240, 1, 0.7); // config model input\n\n for (int i = 3; i < argc; i++) {\n std::string image_file = argv[i];\n std::cout << \"Processing \" << image_file << std::endl;\n\n cv::Mat frame = cv::imread(image_file);\n ncnn::Mat inmat = ncnn::Mat::from_pixels(frame.data, ncnn::Mat::PIXEL_BGR2RGB, frame.cols, frame.rows);\n\n std::vector face_info;\n ultraface.detect(inmat, face_info);\n\n for (int i = 0; i < face_info.size(); i++) {\n auto face = face_info[i];\n cv::Point pt1(face.x1, face.y1);\n cv::Point pt2(face.x2, face.y2);\n cv::rectangle(frame, pt1, pt2, cv::Scalar(0, 255, 0), 2);\n }\n\n cv::imshow(\"UltraFace\", frame);\n cv::waitKey();\n cv::imwrite(\"result.jpg\", frame);\n }\n return 0;\n}\n" }, { "path": "opencv_dnn/cv_dnn_ultraface.cpp", "content": "#include \"stdafx.h\"\n#include \"cv_dnn_ultraface.h\"\n\n#define clip(x, y) (x < 0 ? 0 : (x > y ? y : x))\n\nUltraFace::UltraFace(const std::string model_path,\n\tint input_width, int input_length, int num_thread_,\n\tfloat score_threshold_, float iou_threshold_, int topk_) {\n\tnum_thread = num_thread_;\n\ttopk = topk_;\n\tscore_threshold = score_threshold_;\n\tiou_threshold = iou_threshold_;\n\tin_w = input_width;\n\tin_h = input_length;\n\tw_h_list = { in_w, in_h };\n\n\tfor (auto size : w_h_list) {\n\t\tstd::vector fm_item;\n\t\tfor (float stride : strides) {\n\t\t\tfm_item.push_back(ceil(size / stride));\n\t\t}\n\t\tfeaturemap_size.push_back(fm_item);\n\t}\n\n\tfor (auto size : w_h_list) {\n\t\tshrinkage_size.push_back(strides);\n\t}\n\n\t/* generate prior anchors */\n\tfor (int index = 0; index < num_featuremap; index++) {\n\t\tfloat scale_w = in_w / shrinkage_size[0][index];\n\t\tfloat scale_h = in_h / shrinkage_size[1][index];\n\t\tfor (int j = 0; j < featuremap_size[1][index]; j++) {\n\t\t\tfor (int i = 0; i < featuremap_size[0][index]; i++) {\n\t\t\t\tfloat x_center = (i + 0.5) / scale_w;\n\t\t\t\tfloat y_center = (j + 0.5) / scale_h;\n\n\t\t\t\tfor (float k : min_boxes[index]) {\n\t\t\t\t\tfloat w = k / in_w;\n\t\t\t\t\tfloat h = k / in_h;\n\t\t\t\t\tpriors.push_back({ clip(x_center, 1), clip(y_center, 1), clip(w, 1), clip(h, 1) });\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\tnum_anchors = priors.size();\n\t/* generate prior anchors finished */\n\n\tultraface = cv::dnn::readNetFromONNX(model_path + \"/version-slim-320_without_postprocessing.onnx\");\n\n}\n\nUltraFace::~UltraFace() { }\n\nint UltraFace::detect(cv::Mat &img, std::vector &face_list) {\n\tif (img.empty()) {\n\t\tstd::cout << \"image is empty ,please check!\" << std::endl;\n\t\treturn -1;\n\t}\n\n\timage_h = img.rows;\n\timage_w = img.cols;\n\n\t//cv::Mat in;\n\t//cv::resize(img, in, cv::Size(in_w, in_h));\n\n\tstd::vector bbox_collection;\n\tstd::vector valid_input;\n\n\tcv::Mat inputBlob = cv::dnn::blobFromImage(img, 1.0 / 128, cv::Size(in_w, in_h), cv::Scalar(127, 127, 127), true);\n\tultraface.setInput(inputBlob);\n\n\tstd::vector output_names = { \"scores\",\"boxes\"};\n\tstd::vector out_blobs;\n\tultraface.forward(out_blobs, output_names);\n\tgenerateBBox(bbox_collection,out_blobs[0], out_blobs[1], score_threshold, num_anchors);\n\tnms(bbox_collection, face_list);\n\treturn 0;\n}\n\nvoid UltraFace::generateBBox(std::vector &bbox_collection, cv::Mat scores, cv::Mat boxes, float score_threshold, int num_anchors) {\n\t\n\tfloat* score_value = (float*)(scores.data);\n\tfloat* bbox_value = (float*)(boxes.data);\n\tfor (int i = 0; i < num_anchors; i++) {\n\t\tfloat score = score_value[2 * i + 1];\n\t\tif (score_value[2 * i + 1] > score_threshold) {\n\t\t\tFaceInfo rects = {0};\n\t\t\tfloat x_center = bbox_value[i * 4] * center_variance * priors[i][2] + priors[i][0];\n\t\t\tfloat y_center = bbox_value[i * 4 + 1] * center_variance * priors[i][3] + priors[i][1];\n\t\t\tfloat w = exp(bbox_value[i * 4 + 2] * size_variance) * priors[i][2];\n\t\t\tfloat h = exp(bbox_value[i * 4 + 3] * size_variance) * priors[i][3];\n\n\t\t\trects.x1 = clip(x_center - w / 2.0, 1) * image_w;\n\t\t\trects.y1 = clip(y_center - h / 2.0, 1) * image_h;\n\t\t\trects.x2 = clip(x_center + w / 2.0, 1) * image_w;\n\t\t\trects.y2 = clip(y_center + h / 2.0, 1) * image_h;\n\t\t\trects.score = clip(score_value[2 * i + 1], 1);\n\t\t\tbbox_collection.push_back(rects);\n\t\t}\n\t}\n}\n\nvoid UltraFace::nms(std::vector &input, std::vector &output, int type) {\n\tstd::sort(input.begin(), input.end(), [](const FaceInfo &a, const FaceInfo &b) { return a.score > b.score; });\n\n\tint box_num = input.size();\n\n\tstd::vector merged(box_num, 0);\n\n\tfor (int i = 0; i < box_num; i++) {\n\t\tif (merged[i])\n\t\t\tcontinue;\n\t\tstd::vector buf;\n\n\t\tbuf.push_back(input[i]);\n\t\tmerged[i] = 1;\n\n\t\tfloat h0 = input[i].y2 - input[i].y1 + 1;\n\t\tfloat w0 = input[i].x2 - input[i].x1 + 1;\n\n\t\tfloat area0 = h0 * w0;\n\n\t\tfor (int j = i + 1; j < box_num; j++) {\n\t\t\tif (merged[j])\n\t\t\t\tcontinue;\n\n\t\t\tfloat inner_x0 = input[i].x1 > input[j].x1 ? input[i].x1 : input[j].x1;\n\t\t\tfloat inner_y0 = input[i].y1 > input[j].y1 ? input[i].y1 : input[j].y1;\n\n\t\t\tfloat inner_x1 = input[i].x2 < input[j].x2 ? input[i].x2 : input[j].x2;\n\t\t\tfloat inner_y1 = input[i].y2 < input[j].y2 ? input[i].y2 : input[j].y2;\n\n\t\t\tfloat inner_h = inner_y1 - inner_y0 + 1;\n\t\t\tfloat inner_w = inner_x1 - inner_x0 + 1;\n\n\t\t\tif (inner_h <= 0 || inner_w <= 0)\n\t\t\t\tcontinue;\n\n\t\t\tfloat inner_area = inner_h * inner_w;\n\n\t\t\tfloat h1 = input[j].y2 - input[j].y1 + 1;\n\t\t\tfloat w1 = input[j].x2 - input[j].x1 + 1;\n\n\t\t\tfloat area1 = h1 * w1;\n\n\t\t\tfloat score;\n\n\t\t\tscore = inner_area / (area0 + area1 - inner_area);\n\n\t\t\tif (score > iou_threshold) {\n\t\t\t\tmerged[j] = 1;\n\t\t\t\tbuf.push_back(input[j]);\n\t\t\t}\n\t\t}\n\t\tswitch (type) {\n\t\tcase hard_nms: {\n\t\t\toutput.push_back(buf[0]);\n\t\t\tbreak;\n\t\t}\n\t\tcase blending_nms: {\n\t\t\tfloat total = 0;\n\t\t\tfor (int i = 0; i < buf.size(); i++) {\n\t\t\t\ttotal += exp(buf[i].score);\n\t\t\t}\n\t\t\tFaceInfo rects;\n\t\t\tmemset(&rects, 0, sizeof(rects));\n\t\t\tfor (int i = 0; i < buf.size(); i++) {\n\t\t\t\tfloat rate = exp(buf[i].score) / total;\n\t\t\t\trects.x1 += buf[i].x1 * rate;\n\t\t\t\trects.y1 += buf[i].y1 * rate;\n\t\t\t\trects.x2 += buf[i].x2 * rate;\n\t\t\t\trects.y2 += buf[i].y2 * rate;\n\t\t\t\trects.score += buf[i].score * rate;\n\t\t\t}\n\t\t\toutput.push_back(rects);\n\t\t\tbreak;\n\t\t}\n\t\tdefault: {\n\t\t\tprintf(\"wrong type of nms.\");\n\t\t\texit(-1);\n\t\t}\n\t\t}\n\t}\n}" }, { "path": "opencv_dnn/cv_dnn_ultraface.h", "content": "#ifndef UltraFace_hpp\n#define UltraFace_hpp\n\n#pragma once\n\n#include \n#include \n#include \n#include \n#include \"opencv2/opencv.hpp\"\n\n#define num_featuremap 4\n#define hard_nms 1\n#define blending_nms 2 /* mix nms was been proposaled in paper blaze face, aims to minimize the temporal jitter*/\n\ntypedef struct FaceInfo {\n\tfloat x1;\n\tfloat y1;\n\tfloat x2;\n\tfloat y2;\n\tfloat score;\n\n\tfloat landmarks[10];\n} FaceInfo;\n\nclass UltraFace {\npublic:\n\tUltraFace(const std::string model_path,\n\t\tint input_width, int input_length, int num_thread_ = 4, float score_threshold_ = 0.7, float iou_threshold_ = 0.3, int topk_ = -1);\n\n\t~UltraFace();\n\n\tint detect(cv::Mat &img, std::vector &face_list);\n\nprivate:\n\tvoid generateBBox(std::vector &bbox_collection, cv::Mat scores, cv::Mat boxes, float score_threshold, int num_anchors);\n\n\tvoid nms(std::vector &input, std::vector &output, int type = blending_nms);\n\nprivate:\n\tcv::dnn::Net ultraface;\n\n\tint num_thread;\n\tint image_w;\n\tint image_h;\n\n\tint in_w;\n\tint in_h;\n\tint num_anchors;\n\n\tint topk;\n\tfloat score_threshold;\n\tfloat iou_threshold;\n\n\n\tconst float mean_vals[3] = { 127, 127, 127 };\n\tconst float norm_vals[3] = { 1.0 / 128, 1.0 / 128, 1.0 / 128 };\n\n\tconst float center_variance = 0.1;\n\tconst float size_variance = 0.2;\n\tconst std::vector> min_boxes = {\n\t\t\t{10.0f, 16.0f, 24.0f},\n\t\t\t{32.0f, 48.0f},\n\t\t\t{64.0f, 96.0f},\n\t\t\t{128.0f, 192.0f, 256.0f} };\n\tconst std::vector strides = { 8.0, 16.0, 32.0, 64.0 };\n\tstd::vector> featuremap_size;\n\tstd::vector> shrinkage_size;\n\tstd::vector w_h_list;\n\n\tstd::vector> priors = {};\n};\n\n#endif /* UltraFace_hpp */" }, { "path": "paddle/train-version-RFB.sh", "content": "#!/usr/bin/env bash\nmodel_root_path=\"./models/train-version-RFB\"\nlog_dir=\"$model_root_path/logs\"\nlog=\"$log_dir/log\"\nmkdir -p \"$log_dir\"\n\npython3 -u train.py \\\n --datasets \\\n ./data/wider_face_add_lm_10_10 \\\n --validation_dataset \\\n ./data/wider_face_add_lm_10_10 \\\n --net \\\n RFB \\\n --num_epochs \\\n 200 \\\n --milestones \\\n \"95,150\" \\\n --lr \\\n 1e-2 \\\n --batch_size \\\n 24 \\\n --input_size \\\n 320 \\\n --checkpoint_folder \\\n ${model_root_path} \\\n --num_workers \\\n 4 \\\n --log_dir \\\n ${log_dir} \\\n --cuda_index \\\n 0 \\\n 2>&1 | tee \"$log\"\n" }, { "path": "paddle/train-version-slim.sh", "content": "#!/usr/bin/env bash\nmodel_root_path=\"./models/train-version-slim\"\nlog_dir=\"$model_root_path/logs\"\nlog=\"$log_dir/log\"\nmkdir -p \"$log_dir\"\n\npython3 -u train.py \\\n --datasets \\\n ./data/wider_face_add_lm_10_10 \\\n --validation_dataset \\\n ./data/wider_face_add_lm_10_10 \\\n --net \\\n slim \\\n --num_epochs \\\n 200 \\\n --milestones \\\n \"95,150\" \\\n --lr \\\n 1e-2 \\\n --batch_size \\\n 24 \\\n --input_size \\\n 320 \\\n --checkpoint_folder \\\n ${model_root_path} \\\n --num_workers \\\n 4 \\\n --log_dir \\\n ${log_dir} \\\n --cuda_index \\\n 0 \\\n 2>&1 | tee \"$log\"\n" }, { "path": "paddle/train.py", "content": "\"\"\"\nThis code is the main training code.\n\"\"\"\nimport argparse\nimport itertools\nimport logging\nimport os\nimport sys\n\nimport paddle\nfrom paddle.optimizer.lr import CosineAnnealingDecay, MultiStepDecay\nfrom paddle.io import DataLoader, ComposeDataset\n\nfrom vision.datasets.voc_dataset import VOCDataset\nfrom vision.nn.multibox_loss import MultiboxLoss\nfrom vision.ssd.config.fd_config import define_img_size\nfrom vision.utils.misc import str2bool, Timer, freeze_net_layers, store_labels\n\nparser = argparse.ArgumentParser(\n description='train With Pytorch')\n\nparser.add_argument(\"--dataset_type\", default=\"voc\", type=str,\n help='Specify dataset type. Currently support voc.')\n\nparser.add_argument('--datasets', nargs='+', help='Dataset directory path')\nparser.add_argument('--validation_dataset', help='Dataset directory path')\nparser.add_argument('--balance_data', action='store_true',\n help=\"Balance training data by down-sampling more frequent labels.\")\n\nparser.add_argument('--net', default=\"RFB\",\n help=\"The network architecture ,optional(RFB , slim)\")\nparser.add_argument('--freeze_base_net', action='store_true',\n help=\"Freeze base net layers.\")\nparser.add_argument('--freeze_net', action='store_true',\n help=\"Freeze all the layers except the prediction head.\")\n\n# Params for SGD\nparser.add_argument('--lr', '--learning-rate', default=1e-2, type=float,\n help='initial learning rate')\nparser.add_argument('--momentum', default=0.9, type=float,\n help='Momentum value for optim')\nparser.add_argument('--weight_decay', default=5e-4, type=float,\n help='Weight decay for SGD')\nparser.add_argument('--gamma', default=0.1, type=float,\n help='Gamma update for SGD')\nparser.add_argument('--base_net_lr', default=None, type=float,\n help='initial learning rate for base net.')\nparser.add_argument('--extra_layers_lr', default=None, type=float,\n help='initial learning rate for the layers not in base net and prediction heads.')\n\n# Params for loading pretrained basenet or checkpoints.\nparser.add_argument('--base_net',\n help='Pretrained base model')\nparser.add_argument('--pretrained_ssd', help='Pre-trained base model')\nparser.add_argument('--resume', default=None, type=str,\n help='Checkpoint state_dict file to resume training from')\n\n# Scheduler\nparser.add_argument('--scheduler', default=\"multi-step\", type=str,\n help=\"Scheduler for SGD. It can one of multi-step and cosine\")\n\n# Params for Multi-step Scheduler\nparser.add_argument('--milestones', default=\"80,100\", type=str,\n help=\"milestones for MultiStepLR\")\n\n# Params for Cosine Annealing\nparser.add_argument('--t_max', default=120, type=float,\n help='T_max value for Cosine Annealing Scheduler.')\n\n# Train params\nparser.add_argument('--batch_size', default=24, type=int,\n help='Batch size for training')\nparser.add_argument('--num_epochs', default=200, type=int,\n help='the number epochs')\nparser.add_argument('--num_workers', default=4, type=int,\n help='Number of workers used in dataloading')\nparser.add_argument('--validation_epochs', default=5, type=int,\n help='the number epochs')\nparser.add_argument('--debug_steps', default=100, type=int,\n help='Set the debug log output frequency.')\nparser.add_argument('--use_cuda', default=True, type=str2bool,\n help='Use CUDA to train model')\n\nparser.add_argument('--checkpoint_folder', default='models/',\n help='Directory for saving checkpoint models')\nparser.add_argument('--log_dir', default='./models/Ultra-Light(1MB)_&_Fast_Face_Detector/logs',\n help='lod dir')\nparser.add_argument('--cuda_index', default=\"0\", type=str,\n help='Choose cuda index.If you have 4 GPUs, you can set it like 0,1,2,3')\nparser.add_argument('--power', default=2, type=int,\n help='poly lr pow')\nparser.add_argument('--overlap_threshold', default=0.35, type=float,\n help='overlap_threshold')\nparser.add_argument('--optimizer_type', default=\"SGD\", type=str,\n help='optimizer_type')\nparser.add_argument('--input_size', default=320, type=int,\n help='define network input size,default optional value 128/160/320/480/640/1280')\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nargs = parser.parse_args()\n\ninput_img_size = args.input_size # define input size ,default optional(128/160/320/480/640/1280)\nlogging.info(\"inpu size :{}\".format(input_img_size))\ndefine_img_size(input_img_size) # must put define_img_size() before 'import fd_config'\n\nfrom vision.ssd.config import fd_config\nfrom vision.ssd.data_preprocessing import TrainAugmentation, TestTransform\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd\nfrom vision.ssd.ssd import MatchPrior\n\n# GPU settings\nif paddle.is_compiled_with_cuda() and args.use_cuda:\n paddle.set_device(\"gpu\")\n logging.info(\"Use gpu.\")\nelse:\n paddle.set_device(\"cpu\")\n logging.info(\"Use cpu.\")\n\n\ndef lr_poly(base_lr, iter):\n \"\"\"\n learning rate poly decay\n Args:\n base_lr: the base learning rate\n iter: the current iter\n\n Returns:\n the current learning rate\n \"\"\"\n return base_lr * ((1 - float(iter) / args.num_epochs) ** (args.power))\n\n\ndef adjust_learning_rate(optimizer, i_iter):\n \"\"\"Sets the learning rate to the initial LR divided by 5 at 60th, 120th and 160th epochs\"\"\"\n lr = lr_poly(args.lr, i_iter)\n optimizer.param_groups[0]['lr'] = lr\n\n\ndef train(loader, net, criterion, optimizer, debug_steps=100, epoch=-1):\n net.train()\n running_loss = 0.0\n running_regression_loss = 0.0\n running_classification_loss = 0.0\n for i, data in enumerate(loader):\n print(\".\", end=\"\", flush=True)\n images, boxes, labels = data\n\n optimizer.clear_grad()\n confidence, locations = net(images)\n regression_loss, classification_loss = criterion(confidence, locations, labels, boxes) # TODO CHANGE BOXES\n loss = regression_loss + classification_loss\n loss.backward()\n optimizer.step()\n\n running_loss += loss.numpy().tolist()[0]\n running_regression_loss += regression_loss.numpy().tolist()[0]\n running_classification_loss += classification_loss.numpy().tolist()[0]\n if i and i % debug_steps == 0:\n print(\".\", flush=True)\n avg_loss = running_loss / debug_steps\n avg_reg_loss = running_regression_loss / debug_steps\n avg_clf_loss = running_classification_loss / debug_steps\n logging.info(\n f\"Epoch: {epoch}, Step: {i}, \" +\n f\"Average Loss: {avg_loss:.4f}, \" +\n f\"Average Regression Loss {avg_reg_loss:.4f}, \" +\n f\"Average Classification Loss: {avg_clf_loss:.4f}\"\n )\n\n running_loss = 0.0\n running_regression_loss = 0.0\n running_classification_loss = 0.0\n\n\ndef test(loader, net, criterion):\n net.eval()\n running_loss = 0.0\n running_regression_loss = 0.0\n running_classification_loss = 0.0\n num = 0\n for _, data in enumerate(loader):\n images, boxes, labels = data\n num += 1\n\n with paddle.no_grad():\n confidence, locations = net(images)\n regression_loss, classification_loss = criterion(confidence, locations, labels, boxes)\n loss = regression_loss + classification_loss\n\n running_loss += loss.numpy().tolist()[0]\n running_regression_loss += regression_loss.numpy().tolist()[0]\n running_classification_loss += classification_loss.numpy().tolist()[0]\n return running_loss / num, running_regression_loss / num, running_classification_loss / num\n\n\nif __name__ == '__main__':\n timer = Timer()\n\n logging.info(args)\n if args.net == 'slim':\n create_net = create_mb_tiny_fd\n config = fd_config\n elif args.net == 'RFB':\n create_net = create_Mb_Tiny_RFB_fd\n config = fd_config\n else:\n logging.fatal(\"The net type is wrong.\")\n parser.print_help(sys.stderr)\n sys.exit(1)\n\n train_transform = TrainAugmentation(config.image_size, config.image_mean, config.image_std)\n target_transform = MatchPrior(config.priors, config.center_variance,\n config.size_variance, args.overlap_threshold)\n\n test_transform = TestTransform(config.image_size, config.image_mean_test, config.image_std)\n\n if not os.path.exists(args.checkpoint_folder):\n os.makedirs(args.checkpoint_folder)\n logging.info(\"Prepare training datasets.\")\n datasets = []\n for dataset_path in args.datasets:\n if args.dataset_type == 'voc':\n dataset = VOCDataset(dataset_path, transform=train_transform,\n target_transform=target_transform)\n label_file = os.path.join(args.checkpoint_folder, \"voc-model-labels.txt\")\n store_labels(label_file, dataset.class_names)\n num_classes = len(dataset.class_names)\n\n else:\n raise ValueError(f\"Dataset tpye {args.dataset_type} is not supported.\")\n datasets.append(dataset)\n logging.info(f\"Stored labels into file {label_file}.\")\n train_dataset = ComposeDataset(datasets)\n logging.info(\"Train dataset size: {}\".format(len(train_dataset)))\n train_loader = DataLoader(train_dataset,\n batch_size=args.batch_size,\n num_workers=args.num_workers,\n shuffle=True)\n logging.info(\"Prepare Validation datasets.\")\n if args.dataset_type == \"voc\":\n val_dataset = VOCDataset(args.validation_dataset,\n transform=test_transform,\n target_transform=target_transform,\n is_test=True)\n logging.info(\"validation dataset size: {}\".format(len(val_dataset)))\n\n val_loader = DataLoader(val_dataset,\n batch_size=args.batch_size,\n num_workers=args.num_workers,\n shuffle=False)\n logging.info(\"Build network.\")\n net = create_net(num_classes)\n\n # add multigpu_train\n if paddle.distributed.get_world_size() != 1:\n # cuda_index_list = [int(v.strip()) for v in args.cuda_index.split(\",\")]\n paddle.distributed.init_parallel_env()\n net = paddle.DataParallel(net)\n logging.info(\"use multi gpu.\")\n\n min_loss = -10000.0\n last_epoch = -1\n\n base_net_lr = args.base_net_lr if args.base_net_lr is not None else args.lr\n extra_layers_lr = args.extra_layers_lr if args.extra_layers_lr is not None else args.lr\n if args.freeze_base_net:\n logging.info(\"Freeze base net.\")\n freeze_net_layers(net.base_net)\n params = itertools.chain(net.source_layer_add_ons.parameters(), net.extras.parameters(),\n net.regression_headers.parameters(), net.classification_headers.parameters())\n params = [\n {'params': itertools.chain(\n net.source_layer_add_ons.parameters(),\n net.extras.parameters()\n ), 'lr': extra_layers_lr},\n {'params': itertools.chain(\n net.regression_headers.parameters(),\n net.classification_headers.parameters()\n )}\n ]\n elif args.freeze_net:\n freeze_net_layers(net.base_net)\n freeze_net_layers(net.source_layer_add_ons)\n freeze_net_layers(net.extras)\n params = itertools.chain(net.regression_headers.parameters(), net.classification_headers.parameters())\n logging.info(\"Freeze all the layers except prediction heads.\")\n else:\n params = [\n {'params': net.base_net.parameters(), 'lr': base_net_lr},\n {'params': itertools.chain(\n net.source_layer_add_ons.parameters(),\n net.extras.parameters()\n ), 'lr': extra_layers_lr},\n {'params': itertools.chain(\n net.regression_headers.parameters(),\n net.classification_headers.parameters()\n )}\n ]\n\n timer.start(\"Load Model\")\n if args.resume:\n logging.info(f\"Resume from the model {args.resume}\")\n net.load(args.resume)\n elif args.base_net:\n logging.info(f\"Init from base net {args.base_net}\")\n net.init_from_base_net(args.base_net)\n elif args.pretrained_ssd:\n logging.info(f\"Init from pretrained ssd {args.pretrained_ssd}\")\n net.init_from_pretrained_ssd(args.pretrained_ssd)\n logging.info(f'Took {timer.end(\"Load Model\"):.2f} seconds to load the model.')\n\n criterion = MultiboxLoss(config.priors, neg_pos_ratio=3, center_variance=0.1, size_variance=0.2)\n\n if args.optimizer_type != \"Adam\":\n if args.scheduler == 'multi-step':\n logging.info(\"Uses MultiStepLR scheduler.\")\n milestones = [int(v.strip()) for v in args.milestones.split(\",\")]\n scheduler = MultiStepDecay(args.lr, milestones=milestones,\n gamma=0.1, last_epoch=last_epoch)\n elif args.scheduler == 'cosine':\n logging.info(\"Uses CosineAnnealingLR scheduler.\")\n scheduler = CosineAnnealingDecay(args.lr, args.t_max, last_epoch=last_epoch)\n elif args.scheduler == 'poly':\n logging.info(\"Uses PolyLR scheduler.\")\n else:\n logging.fatal(f\"Unsupported Scheduler: {args.scheduler}.\")\n parser.print_help(sys.stderr)\n sys.exit(1)\n\n if args.optimizer_type == \"SGD\":\n optimizer = paddle.optimizer.Momentum(learning_rate=scheduler,\n momentum=args.momentum,\n parameters=net.parameters(), #######################,\n weight_decay=args.weight_decay)\n elif args.optimizer_type == \"Adam\":\n optimizer = paddle.optimizer.Adam(learning_rate=scheduler, parameters=params)\n logging.info(\"use Adam optimizer\")\n else:\n logging.fatal(f\"Unsupported optimizer: {args.scheduler}.\")\n parser.print_help(sys.stderr)\n sys.exit(1)\n logging.info(f\"Learning rate: {args.lr}, Base net learning rate: {base_net_lr}, \"\n + f\"Extra Layers learning rate: {extra_layers_lr}.\")\n\n logging.info(f\"Start training from epoch {last_epoch + 1}.\")\n for epoch in range(last_epoch + 1, args.num_epochs):\n if args.optimizer_type != \"Adam\":\n if args.scheduler != \"poly\":\n if epoch != 0:\n scheduler.step()\n train(train_loader, net, criterion, optimizer, debug_steps=args.debug_steps, epoch=epoch)\n if args.scheduler == \"poly\":\n adjust_learning_rate(optimizer, epoch)\n logging.info(\"lr rate :{}\".format(scheduler.get_lr()))\n\n if epoch % args.validation_epochs == 0 or epoch == args.num_epochs - 1:\n logging.info(\"lr rate :{}\".format(scheduler.get_lr()))\n val_loss, val_regression_loss, val_classification_loss = test(val_loader, net, criterion)\n logging.info(\n f\"Epoch: {epoch}, \" +\n f\"Validation Loss: {val_loss:.4f}, \" +\n f\"Validation Regression Loss {val_regression_loss:.4f}, \" +\n f\"Validation Classification Loss: {val_classification_loss:.4f}\"\n )\n model_path = os.path.join(args.checkpoint_folder, f\"{args.net}-Epoch-{epoch}-Loss-{val_loss}.ppmodel\")\n net.save(model_path)\n logging.info(f\"Saved model {model_path}\")\n" }, { "path": "paddle/vision/__init__.py", "content": "" }, { "path": "paddle/vision/datasets/__init__.py", "content": "" }, { "path": "paddle/vision/datasets/voc_dataset.py", "content": "import logging\nimport os\nimport pathlib\nimport xml.etree.ElementTree as ET\n\nimport cv2\nimport numpy as np\nfrom paddle.io import Dataset\n\nclass VOCDataset(Dataset):\n def __init__(self, root, transform=None, target_transform=None, is_test=False, keep_difficult=False):\n \"\"\"Dataset for VOC data.\n Args:\n root: the root of the VOC2007 or VOC2012 dataset, the directory contains the following sub-directories:\n Annotations, ImageSets, JPEGImages, SegmentationClass, SegmentationObject.\n \"\"\"\n super(VOCDataset, self).__init__()\n self.root = pathlib.Path(root)\n self.transform = transform\n self.target_transform = target_transform\n if is_test:\n image_sets_file = self.root / \"ImageSets/Main/test.txt\"\n else:\n image_sets_file = self.root / \"ImageSets/Main/trainval.txt\"\n self.ids = VOCDataset._read_image_ids(image_sets_file)\n self.keep_difficult = keep_difficult\n\n # if the labels file exists, read in the class names\n label_file_name = self.root / \"labels.txt\"\n\n if os.path.isfile(label_file_name):\n class_string = \"\"\n with open(label_file_name, 'r') as infile:\n for line in infile:\n class_string += line.rstrip()\n\n # classes should be a comma separated list\n classes = class_string.split(',')\n # prepend BACKGROUND as first class\n classes.insert(0, 'BACKGROUND')\n classes = [elem.replace(\" \", \"\") for elem in classes]\n self.class_names = tuple(classes)\n logging.info(\"VOC Labels read from file: \" + str(self.class_names))\n\n else:\n logging.info(\"No labels file, using default VOC classes.\")\n self.class_names = ('BACKGROUND',\n 'face')\n\n self.class_dict = {class_name: i for i, class_name in enumerate(self.class_names)}\n\n def __getitem__(self, index):\n image_id = self.ids[index]\n boxes, labels, is_difficult = self._get_annotation(image_id)\n if not self.keep_difficult:\n boxes = boxes[is_difficult == 0]\n labels = labels[is_difficult == 0]\n image = self._read_image(image_id)\n if self.transform:\n image, boxes, labels = self.transform(image, boxes, labels)\n if self.target_transform:\n boxes, labels = self.target_transform(boxes, labels)\n return image, boxes, labels\n\n def get_image(self, index):\n image_id = self.ids[index]\n image = self._read_image(image_id)\n if self.transform:\n image, _ = self.transform(image)\n return image\n\n def get_annotation(self, index):\n image_id = self.ids[index]\n return image_id, self._get_annotation(image_id)\n\n def __len__(self):\n return len(self.ids)\n\n @staticmethod\n def _read_image_ids(image_sets_file):\n ids = []\n with open(image_sets_file) as f:\n for line in f:\n ids.append(line.rstrip())\n return ids\n\n def _get_annotation(self, image_id):\n annotation_file = self.root / f\"Annotations/{image_id}.xml\"\n objects = ET.parse(annotation_file).findall(\"object\")\n boxes = []\n labels = []\n is_difficult = []\n for object in objects:\n class_name = object.find('name').text.lower().strip()\n # we're only concerned with clases in our list\n if class_name in self.class_dict:\n bbox = object.find('bndbox')\n\n # VOC dataset format follows Matlab, in which indexes start from 0\n x1 = float(bbox.find('xmin').text) - 1\n y1 = float(bbox.find('ymin').text) - 1\n x2 = float(bbox.find('xmax').text) - 1\n y2 = float(bbox.find('ymax').text) - 1\n boxes.append([x1, y1, x2, y2])\n\n labels.append(self.class_dict[class_name])\n is_difficult_str = object.find('difficult').text\n is_difficult.append(int(is_difficult_str) if is_difficult_str else 0)\n\n return (np.array(boxes, dtype=np.float32),\n np.array(labels, dtype=np.int64),\n np.array(is_difficult, dtype=np.uint8))\n\n def _read_image(self, image_id):\n image_file = self.root / f\"JPEGImages/{image_id}.jpg\"\n image = cv2.imread(str(image_file))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return image\n" }, { "path": "paddle/vision/nn/__init__.py", "content": "" }, { "path": "paddle/vision/nn/mb_tiny.py", "content": "import paddle.nn as nn\nimport paddle.nn.functional as F\n\n\nclass Mb_Tiny(nn.Layer):\n\n def __init__(self, num_classes=2):\n super(Mb_Tiny, self).__init__()\n self.base_channel = 8 * 2\n\n def conv_bn(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2D(inp, oup, 3, stride, 1, bias_attr=None),\n nn.BatchNorm2D(oup),\n nn.ReLU()\n )\n\n def conv_dw(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2D(inp, inp, 3, stride, 1, groups=inp, bias_attr=None),\n nn.BatchNorm2D(inp),\n nn.ReLU(),\n\n nn.Conv2D(inp, oup, 1, 1, 0, bias_attr=None),\n nn.BatchNorm2D(oup),\n nn.ReLU(),\n )\n\n self.model = nn.Sequential(\n conv_bn(3, self.base_channel, 2), # 160*120\n conv_dw(self.base_channel, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 2, 2), # 80*60\n conv_dw(self.base_channel * 2, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 4, 2), # 40*30\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 8, 2), # 20*15\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 16, 2), # 10*8\n conv_dw(self.base_channel * 16, self.base_channel * 16, 1)\n )\n self.fc = nn.Linear(1024, num_classes)\n\n def forward(self, x):\n x = self.model(x)\n x = F.avg_pool2d(x, 7)\n x = x.view(-1, 1024)\n x = self.fc(x)\n return x\n" }, { "path": "paddle/vision/nn/mb_tiny_RFB.py", "content": "import paddle\nimport paddle.nn as nn\nimport paddle.nn.functional as F\n\n\nclass BasicConv(nn.Layer):\n\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n if bn:\n self.conv = nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias_attr=None)\n self.bn = nn.BatchNorm2D(out_planes, epsilon=1e-5, momentum=0.01)\n self.relu = nn.ReLU() if relu else None\n else:\n self.conv = nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias_attr=None)\n self.bn = None\n self.relu = nn.ReLU() if relu else None\n\n def forward(self, x):\n x = self.conv(x)\n if self.bn is not None:\n x = self.bn(x)\n if self.relu is not None:\n x = self.relu(x)\n return x\n\n\nclass BasicRFB(nn.Layer):\n\n def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_reduce=8, vision=1, groups=1):\n super(BasicRFB, self).__init__()\n self.scale = scale\n self.out_channels = out_planes\n inter_planes = in_planes // map_reduce\n\n self.branch0 = nn.Sequential(\n BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),\n BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=1, groups=groups),\n BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 1, dilation=vision + 1, relu=False, groups=groups)\n )\n self.branch1 = nn.Sequential(\n BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),\n BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=1, groups=groups),\n BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 2, dilation=vision + 2, relu=False, groups=groups)\n )\n self.branch2 = nn.Sequential(\n BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),\n BasicConv(inter_planes, (inter_planes // 2) * 3, kernel_size=3, stride=1, padding=1, groups=groups),\n BasicConv((inter_planes // 2) * 3, 2 * inter_planes, kernel_size=3, stride=stride, padding=1, groups=groups),\n BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 4, dilation=vision + 4, relu=False, groups=groups)\n )\n\n self.ConvLinear = BasicConv(6 * inter_planes, out_planes, kernel_size=1, stride=1, relu=False)\n self.shortcut = BasicConv(in_planes, out_planes, kernel_size=1, stride=stride, relu=False)\n self.relu = nn.ReLU()\n\n def forward(self, x):\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n x2 = self.branch2(x)\n\n out = paddle.concat((x0, x1, x2), 1)\n out = self.ConvLinear(out)\n short = self.shortcut(x)\n out = out * self.scale + short\n out = self.relu(out)\n\n return out\n\n\nclass Mb_Tiny_RFB(nn.Layer):\n\n def __init__(self, num_classes=2):\n super(Mb_Tiny_RFB, self).__init__()\n self.base_channel = 8 * 2\n\n def conv_bn(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2D(inp, oup, 3, stride, 1, bias_attr=None),\n nn.BatchNorm2D(oup, momentum=0.1, epsilon=1e-05),\n nn.ReLU()\n )\n\n def conv_dw(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2D(inp, inp, 3, stride, 1, groups=inp, bias_attr=None),\n nn.BatchNorm2D(inp, momentum=0.1, epsilon=1e-05),\n nn.ReLU(),\n\n nn.Conv2D(inp, oup, 1, 1, 0, bias_attr=None),\n nn.BatchNorm2D(oup, momentum=0.1, epsilon=1e-05),\n nn.ReLU(),\n )\n\n self.model = nn.Sequential(\n conv_bn(3, self.base_channel, 2), # 160*120\n conv_dw(self.base_channel, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 2, 2), # 80*60\n conv_dw(self.base_channel * 2, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 4, 2), # 40*30\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n BasicRFB(self.base_channel * 4, self.base_channel * 4, stride=1, scale=1.0),\n conv_dw(self.base_channel * 4, self.base_channel * 8, 2), # 20*15\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 16, 2), # 10*8\n conv_dw(self.base_channel * 16, self.base_channel * 16, 1)\n )\n self.fc = nn.Linear(1024, num_classes)\n\n def forward(self, x):\n x = self.model(x)\n x = F.avg_pool2d(x, 7)\n x = x.view(-1, 1024)\n x = self.fc(x)\n return x\n" }, { "path": "paddle/vision/nn/multibox_loss.py", "content": "import paddle\nimport paddle.nn as nn\nimport paddle.nn.functional as F\n\nfrom ..utils import box_utils\n\n\n# class MultiboxLoss(object):\nclass MultiboxLoss(nn.Layer):\n def __init__(self, priors, neg_pos_ratio, center_variance, size_variance):\n \"\"\"Implement SSD Multibox Loss.\n\n Basically, Multibox loss combines classification loss\n and Smooth L1 regression loss.\n \"\"\"\n super(MultiboxLoss, self).__init__()\n self.neg_pos_ratio = neg_pos_ratio\n self.center_variance = center_variance\n self.size_variance = size_variance\n self.priors = priors\n\n def forward(self, confidence, predicted_locations, labels, gt_locations):\n \"\"\"Compute classification loss and smooth l1 loss.\n\n Args:\n confidence (batch_size, num_priors, num_classes): class predictions.\n locations (batch_size, num_priors, 4): predicted locations.\n labels (batch_size, num_priors): real labels of all the priors.\n boxes (batch_size, num_priors, 4): real boxes corresponding all the priors.\n \"\"\"\n num_classes = confidence.shape[2]\n with paddle.no_grad():\n # derived from cross_entropy=sum(log(p))\n loss = -F.log_softmax(confidence, 2)[:, :, 0]\n mask = box_utils.hard_negative_mining(loss, labels, self.neg_pos_ratio)\n\n confidence = paddle.concat([confidence[:, :, 0].masked_select(mask).reshape([-1, 1]),\n confidence[:, :,1].masked_select(mask).reshape([-1, 1])], axis=1)\n classification_loss = F.cross_entropy(confidence.reshape([-1, num_classes]), labels.masked_select(mask), reduction='sum')\n pos_mask = labels > 0\n predicted_locations = predicted_locations.masked_select(paddle.concat([pos_mask.reshape(pos_mask.shape+[1]), pos_mask.reshape(pos_mask.shape+[1]), pos_mask.reshape(pos_mask.shape+[1]), pos_mask.reshape(pos_mask.shape+[1])], axis=2)).reshape([-1, 4])\n gt_locations = gt_locations.masked_select(paddle.concat([pos_mask.reshape(pos_mask.shape+[1]), pos_mask.reshape(pos_mask.shape+[1]), pos_mask.reshape(pos_mask.shape+[1]), pos_mask.reshape(pos_mask.shape+[1])], axis=2)).reshape([-1, 4])\n smooth_l1_loss = F.smooth_l1_loss(predicted_locations, gt_locations.cast('float32'), reduction='sum') # smooth_l1_loss\n # smooth_l1_loss = F.mse_loss(predicted_locations, gt_locations, reduction='sum') #l2 loss\n num_pos = gt_locations.shape[0]\n return smooth_l1_loss / num_pos, classification_loss / num_pos\n" }, { "path": "paddle/vision/ssd/__init__.py", "content": "" }, { "path": "paddle/vision/ssd/config/__init__.py", "content": "" }, { "path": "paddle/vision/ssd/config/fd_config.py", "content": "import numpy as np\n\nfrom vision.utils.box_utils import generate_priors\n\nimage_mean_test = image_mean = np.array([127, 127, 127])\nimage_std = 128.0\niou_threshold = 0.3\ncenter_variance = 0.1\nsize_variance = 0.2\n\nmin_boxes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]\nshrinkage_list = []\nimage_size = [320, 240] # default input size 320*240\nfeature_map_w_h_list = [[40, 20, 10, 5], [30, 15, 8, 4]] # default feature map size\npriors = []\n\n\ndef define_img_size(size):\n global image_size, feature_map_w_h_list, priors\n img_size_dict = {128: [128, 96],\n 160: [160, 120],\n 320: [320, 240],\n 480: [480, 360],\n 640: [640, 480],\n 1280: [1280, 960]}\n image_size = img_size_dict[size]\n\n feature_map_w_h_list_dict = {128: [[16, 8, 4, 2], [12, 6, 3, 2]],\n 160: [[20, 10, 5, 3], [15, 8, 4, 2]],\n 320: [[40, 20, 10, 5], [30, 15, 8, 4]],\n 480: [[60, 30, 15, 8], [45, 23, 12, 6]],\n 640: [[80, 40, 20, 10], [60, 30, 15, 8]],\n 1280: [[160, 80, 40, 20], [120, 60, 30, 15]]}\n feature_map_w_h_list = feature_map_w_h_list_dict[size]\n\n for i in range(0, len(image_size)):\n item_list = []\n for k in range(0, len(feature_map_w_h_list[i])):\n item_list.append(image_size[i] / feature_map_w_h_list[i][k])\n shrinkage_list.append(item_list)\n priors = generate_priors(feature_map_w_h_list, shrinkage_list, image_size, min_boxes)\n" }, { "path": "paddle/vision/ssd/data_preprocessing.py", "content": "from ..transforms.transforms import *\n\n\nclass TrainAugmentation:\n def __init__(self, size, mean=0, std=1.0):\n \"\"\"\n Args:\n size: the size the of final image.\n mean: mean pixel value per channel.\n \"\"\"\n self.mean = mean\n self.size = size\n self.augment = Compose([\n ConvertFromInts(),\n PhotometricDistort(),\n RandomSampleCrop_v2(),\n RandomMirror(),\n ToPercentCoords(),\n Resize(self.size),\n SubtractMeans(self.mean),\n lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n ToTensor(),\n ])\n\n def __call__(self, img, boxes, labels):\n \"\"\"\n\n Args:\n img: the output of cv.imread in RGB layout.\n boxes: boundding boxes in the form of (x1, y1, x2, y2).\n labels: labels of boxes.\n \"\"\"\n return self.augment(img, boxes, labels)\n\n\nclass TestTransform:\n def __init__(self, size, mean=0.0, std=1.0):\n self.transform = Compose([\n ToPercentCoords(),\n Resize(size),\n SubtractMeans(mean),\n lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n ToTensor(),\n ])\n\n def __call__(self, image, boxes, labels):\n return self.transform(image, boxes, labels)\n\n\nclass PredictionTransform:\n def __init__(self, size, mean=0.0, std=1.0):\n self.transform = Compose([\n Resize(size),\n SubtractMeans(mean),\n lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n ToTensor()\n ])\n\n def __call__(self, image):\n image, _, _ = self.transform(image)\n return image\n" }, { "path": "paddle/vision/ssd/mb_tiny_RFB_fd.py", "content": "from paddle.nn import Conv2D, Sequential, LayerList, ReLU\n\nfrom vision.nn.mb_tiny_RFB import Mb_Tiny_RFB\nfrom vision.ssd.config import fd_config as config\nfrom vision.ssd.predictor import Predictor\nfrom vision.ssd.ssd import SSD\n\n\ndef SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0):\n \"\"\"Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.\n \"\"\"\n return Sequential(\n Conv2D(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,\n groups=in_channels, stride=stride, padding=padding),\n ReLU(),\n Conv2D(in_channels=in_channels, out_channels=out_channels, kernel_size=1),\n )\n\n\ndef create_Mb_Tiny_RFB_fd(num_classes, is_test=False, device=\"cuda\"):\n base_net = Mb_Tiny_RFB(2)\n base_net_model = base_net.model # disable dropout layer\n\n source_layer_indexes = [\n 8,\n 11,\n 13\n ]\n extras = LayerList([\n Sequential(\n Conv2D(in_channels=base_net.base_channel * 16, out_channels=base_net.base_channel * 4, kernel_size=1),\n ReLU(),\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=base_net.base_channel * 16, kernel_size=3, stride=2, padding=1),\n ReLU()\n )\n ])\n\n regression_headers = LayerList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * 4, kernel_size=3, padding=1),\n Conv2D(in_channels=base_net.base_channel * 16, out_channels=3 * 4, kernel_size=3, padding=1)\n ])\n\n classification_headers = LayerList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * num_classes, kernel_size=3, padding=1),\n Conv2D(in_channels=base_net.base_channel * 16, out_channels=3 * num_classes, kernel_size=3, padding=1)\n ])\n\n return SSD(num_classes, base_net_model, source_layer_indexes,\n extras, classification_headers, regression_headers, is_test=is_test, config=config, device=device)\n\n\ndef create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):\n predictor = Predictor(net, config.image_size, config.image_mean_test,\n config.image_std,\n nms_method=nms_method,\n iou_threshold=config.iou_threshold,\n candidate_size=candidate_size,\n sigma=sigma,\n device=device)\n return predictor\n" }, { "path": "paddle/vision/ssd/mb_tiny_fd.py", "content": "from paddle.nn import Conv2D, Sequential, LayerList, ReLU\n\nfrom vision.nn.mb_tiny import Mb_Tiny\nfrom vision.ssd.config import fd_config as config\nfrom vision.ssd.predictor import Predictor\nfrom vision.ssd.ssd import SSD\n\n\ndef SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0):\n \"\"\"Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.\n \"\"\"\n return Sequential(\n Conv2D(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,\n groups=in_channels, stride=stride, padding=padding),\n ReLU(),\n Conv2D(in_channels=in_channels, out_channels=out_channels, kernel_size=1),\n )\n\n\ndef create_mb_tiny_fd(num_classes, is_test=False, device=\"cuda\"):\n base_net = Mb_Tiny(2)\n base_net_model = base_net.model # disable dropout layer\n\n source_layer_indexes = [\n 8,\n 11,\n 13\n ]\n extras = LayerList([\n Sequential(\n Conv2D(in_channels=base_net.base_channel * 16, out_channels=base_net.base_channel * 4, kernel_size=1),\n ReLU(),\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=base_net.base_channel * 16, kernel_size=3, stride=2, padding=1),\n ReLU()\n )\n ])\n\n regression_headers = LayerList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * 4, kernel_size=3, padding=1),\n Conv2D(in_channels=base_net.base_channel * 16, out_channels=3 * 4, kernel_size=3, padding=1)\n ])\n\n classification_headers = LayerList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * num_classes, kernel_size=3, padding=1),\n Conv2D(in_channels=base_net.base_channel * 16, out_channels=3 * num_classes, kernel_size=3, padding=1)\n ])\n\n return SSD(num_classes, base_net_model, source_layer_indexes,\n extras, classification_headers, regression_headers, is_test=is_test, config=config, device=device)\n\n\ndef create_mb_tiny_fd_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):\n predictor = Predictor(net, config.image_size, config.image_mean_test,\n config.image_std,\n nms_method=nms_method,\n iou_threshold=config.iou_threshold,\n candidate_size=candidate_size,\n sigma=sigma,\n device=device)\n return predictor\n" }, { "path": "paddle/vision/ssd/predictor.py", "content": "import paddle\n\nfrom ..utils import box_utils\nfrom .data_preprocessing import PredictionTransform\nfrom ..utils.misc import Timer\n\n\nclass Predictor:\n def __init__(self, net, size, mean=0.0, std=1.0, nms_method=None,\n iou_threshold=0.3, filter_threshold=0.01, candidate_size=200, sigma=0.5, device=None):\n self.net = net\n self.transform = PredictionTransform(size, mean, std)\n self.iou_threshold = iou_threshold\n self.filter_threshold = filter_threshold\n self.candidate_size = candidate_size\n self.nms_method = nms_method\n\n self.sigma = sigma\n if device:\n self.device = device\n else:\n self.device = paddle.set_device(\"cuda\" if paddle.is_compiled_with_cuda() else \"cpu\")\n\n self.net.to(self.device)\n self.net.eval()\n\n self.timer = Timer()\n\n def predict(self, image, top_k=-1, prob_threshold=None):\n cpu_device = paddle.set_device(\"cpu\")\n height, width, _ = image.shape\n image = self.transform(image)\n images = image.unsqueeze(0)\n images = images.to(self.device)\n with paddle.no_grad():\n for i in range(1):\n self.timer.start()\n scores, boxes = self.net.forward(images)\n print(\"Inference time: \", self.timer.end())\n boxes = boxes[0]\n scores = scores[0]\n if not prob_threshold:\n prob_threshold = self.filter_threshold\n # this version of nms is slower on GPU, so we move data to CPU.\n boxes = boxes.to(cpu_device)\n scores = scores.to(cpu_device)\n picked_box_probs = []\n picked_labels = []\n for class_index in range(1, scores.size(1)):\n probs = scores[:, class_index]\n mask = probs > prob_threshold\n probs = probs[mask]\n if probs.size(0) == 0:\n continue\n subset_boxes = boxes[mask, :]\n box_probs = paddle.concat([subset_boxes, probs.reshape(-1, 1)], 1)\n box_probs = box_utils.nms(box_probs, self.nms_method,\n score_threshold=prob_threshold,\n iou_threshold=self.iou_threshold,\n sigma=self.sigma,\n top_k=top_k,\n candidate_size=self.candidate_size)\n picked_box_probs.append(box_probs)\n picked_labels.extend([class_index] * box_probs.size(0))\n if not picked_box_probs:\n return paddle.to_tensor([]), paddle.to_tensor([]), paddle.to_tensor([])\n picked_box_probs = paddle.concat(picked_box_probs)\n picked_box_probs[:, 0] *= width\n picked_box_probs[:, 1] *= height\n picked_box_probs[:, 2] *= width\n picked_box_probs[:, 3] *= height\n return picked_box_probs[:, :4], paddle.to_tensor(picked_labels), picked_box_probs[:, 4]\n" }, { "path": "paddle/vision/ssd/ssd.py", "content": "from collections import namedtuple\nfrom typing import List, Tuple\n\nimport numpy as np\nimport paddle\nimport paddle.nn as nn\nimport paddle.nn.functional as F\n\nfrom vision.utils import box_utils\n\nGraphPath = namedtuple(\"GraphPath\", ['s0', 'name', 's1'])\n\n\nclass SSD(nn.Layer):\n def __init__(self, num_classes: int, base_net: nn.LayerList, source_layer_indexes: List[int],\n extras: nn.LayerList, classification_headers: nn.LayerList,\n regression_headers: nn.LayerList, is_test=False, config=None, device=None):\n \"\"\"Compose a SSD model using the given components.\n \"\"\"\n super(SSD, self).__init__()\n\n self.num_classes = num_classes\n self.base_net = base_net\n self.source_layer_indexes = source_layer_indexes\n self.extras = extras\n self.classification_headers = classification_headers\n self.regression_headers = regression_headers\n self.is_test = is_test\n self.config = config\n\n # register layers in source_layer_indexes by adding them to a module list\n self.source_layer_add_ons = nn.LayerList([t[1] for t in source_layer_indexes\n if isinstance(t, tuple) and not isinstance(t, GraphPath)])\n if device:\n self.device = device\n else:\n self.device = paddle.to_device(\"cuda\" if paddle.is_compiled_with_cuda() else \"cpu\")\n if is_test:\n self.config = config\n self.priors = config.priors.to(self.device)\n\n def forward(self, x: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:\n confidences = []\n locations = []\n start_layer_index = 0\n header_index = 0\n end_layer_index = 0\n for end_layer_index in self.source_layer_indexes:\n if isinstance(end_layer_index, GraphPath):\n path = end_layer_index\n end_layer_index = end_layer_index.s0\n added_layer = None\n elif isinstance(end_layer_index, tuple):\n added_layer = end_layer_index[1]\n end_layer_index = end_layer_index[0]\n path = None\n else:\n added_layer = None\n path = None\n for layer in self.base_net[start_layer_index: end_layer_index]:\n x = layer(x)\n if added_layer:\n y = added_layer(x)\n else:\n y = x\n if path:\n sub = getattr(self.base_net[end_layer_index], path.name)\n for layer in sub[:path.s1]:\n x = layer(x)\n y = x\n for layer in sub[path.s1:]:\n x = layer(x)\n end_layer_index += 1\n start_layer_index = end_layer_index\n confidence, location = self.compute_header(header_index, y)\n header_index += 1\n confidences.append(confidence)\n locations.append(location)\n\n for layer in self.base_net[end_layer_index:]:\n x = layer(x)\n\n for layer in self.extras:\n x = layer(x)\n confidence, location = self.compute_header(header_index, x)\n header_index += 1\n confidences.append(confidence)\n locations.append(location)\n\n confidences = paddle.concat(confidences, 1)\n locations = paddle.concat(locations, 1)\n\n if self.is_test:\n confidences = F.softmax(confidences, 2)\n boxes = box_utils.convert_locations_to_boxes(\n locations, self.priors, self.config.center_variance, self.config.size_variance\n )\n boxes = box_utils.center_form_to_corner_form(boxes)\n return confidences, boxes\n else:\n return confidences, locations\n\n def compute_header(self, i, x):\n confidence = self.classification_headers[i](x)\n confidence = confidence.transpose([0, 2, 3, 1])\n confidence = confidence.reshape([confidence.shape[0], -1, self.num_classes])\n\n location = self.regression_headers[i](x)\n location = location.transpose([0, 2, 3, 1])\n location = location.reshape([location.shape[0], -1, 4])\n\n return confidence, location\n\n def init_from_base_net(self, model):\n self.base_net.load_state_dict(paddle.load(model, map_location=lambda storage, loc: storage), strict=True)\n self.source_layer_add_ons.apply(_xavier_init_)\n self.extras.apply(_xavier_init_)\n self.classification_headers.apply(_xavier_init_)\n self.regression_headers.apply(_xavier_init_)\n\n def init_from_pretrained_ssd(self, model):\n state_dict = paddle.load(model, map_location=lambda storage, loc: storage)\n state_dict = {k: v for k, v in state_dict.items() if not (k.startswith(\"classification_headers\") or k.startswith(\"regression_headers\"))}\n model_dict = self.state_dict()\n model_dict.update(state_dict)\n self.load_state_dict(model_dict)\n self.classification_headers.apply(_xavier_init_)\n self.regression_headers.apply(_xavier_init_)\n\n def init(self):\n self.base_net.apply(_xavier_init_)\n self.source_layer_add_ons.apply(_xavier_init_)\n self.extras.apply(_xavier_init_)\n self.classification_headers.apply(_xavier_init_)\n self.regression_headers.apply(_xavier_init_)\n\n def load(self, model):\n self.load_state_dict(paddle.load(model, map_location=lambda storage, loc: storage))\n\n def save(self, model_path):\n paddle.save(self.state_dict(), model_path)\n\n\nclass MatchPrior(object):\n def __init__(self, center_form_priors, center_variance, size_variance, iou_threshold):\n self.center_form_priors = center_form_priors\n self.corner_form_priors = box_utils.center_form_to_corner_form(center_form_priors)\n self.center_variance = center_variance\n self.size_variance = size_variance\n self.iou_threshold = iou_threshold\n\n def __call__(self, gt_boxes, gt_labels):\n boxes, labels = box_utils.assign_priors(gt_boxes, gt_labels,\n self.corner_form_priors, self.iou_threshold)\n boxes = box_utils.corner_form_to_center_form(boxes)\n locations = box_utils.convert_boxes_to_locations(boxes, self.center_form_priors, self.center_variance, self.size_variance)\n return locations, labels\n\n\ndef _xavier_init_(m: nn.Layer):\n if isinstance(m, nn.Conv2D):\n nn.initializer.XavierUniform(m.weight)\n" }, { "path": "paddle/vision/transforms/__init__.py", "content": "" }, { "path": "paddle/vision/transforms/transforms.py", "content": "# from https://github.com/amdegroot/ssd.pytorch\n\n\nimport types\n\nimport cv2\nimport numpy as np\nimport paddle\nfrom numpy import random\nfrom paddle.vision import transforms\n\n\ndef intersect(box_a, box_b):\n max_xy = np.minimum(box_a[:, 2:], box_b[2:])\n min_xy = np.maximum(box_a[:, :2], box_b[:2])\n inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf)\n return inter[:, 0] * inter[:, 1]\n\n\ndef jaccard_numpy(box_a, box_b):\n \"\"\"Compute the jaccard overlap of two sets of boxes. The jaccard overlap\n is simply the intersection over union of two boxes.\n E.g.:\n A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)\n Args:\n box_a: Multiple bounding boxes, Shape: [num_boxes,4]\n box_b: Single bounding box, Shape: [4]\n Return:\n jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]]\n \"\"\"\n inter = intersect(box_a, box_b)\n area_a = ((box_a[:, 2] - box_a[:, 0]) *\n (box_a[:, 3] - box_a[:, 1])) # [A,B]\n area_b = ((box_b[2] - box_b[0]) *\n (box_b[3] - box_b[1])) # [A,B]\n union = area_a + area_b - inter\n return inter / union # [A,B]\n\n\ndef object_converage_numpy(box_a, box_b):\n \"\"\"Compute the jaccard overlap of two sets of boxes. The jaccard overlap\n is simply the intersection over union of two boxes.\n E.g.:\n A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)\n Args:\n box_a: Multiple bounding boxes, Shape: [num_boxes,4]\n box_b: Single bounding box, Shape: [4]\n Return:\n jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]]\n \"\"\"\n inter = intersect(box_a, box_b)\n area_a = ((box_a[:, 2] - box_a[:, 0]) *\n (box_a[:, 3] - box_a[:, 1])) # [A,B]\n area_b = ((box_b[2] - box_b[0]) *\n (box_b[3] - box_b[1])) # [A,B]\n return inter / area_a # [A,B]\n\n\nclass Compose(object):\n \"\"\"Composes several augmentations together.\n Args:\n transforms (List[Transform]): list of transforms to compose.\n Example:\n >>> augmentations.Compose([\n >>> transforms.CenterCrop(10),\n >>> transforms.ToTensor(),\n >>> ])\n \"\"\"\n\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, img, boxes=None, labels=None):\n for t in self.transforms:\n img, boxes, labels = t(img, boxes, labels)\n return img, boxes, labels\n\n\nclass Lambda(object):\n \"\"\"Applies a lambda as a transform.\"\"\"\n\n def __init__(self, lambd):\n assert isinstance(lambd, types.LambdaType)\n self.lambd = lambd\n\n def __call__(self, img, boxes=None, labels=None):\n return self.lambd(img, boxes, labels)\n\n\nclass ConvertFromInts(object):\n def __call__(self, image, boxes=None, labels=None):\n return image.astype(np.float32), boxes, labels\n\n\nclass SubtractMeans(object):\n def __init__(self, mean):\n self.mean = np.array(mean, dtype=np.float32)\n\n def __call__(self, image, boxes=None, labels=None):\n image = image.astype(np.float32)\n image -= self.mean\n return image.astype(np.float32), boxes, labels\n\n\nclass imgprocess(object):\n def __init__(self, std):\n self.std = np.array(std, dtype=np.float32)\n\n def __call__(self, image, boxes=None, labels=None):\n image = image.astype(np.float32)\n image /= self.std\n return image.astype(np.float32), boxes, labels\n\n\nclass ToAbsoluteCoords(object):\n def __call__(self, image, boxes=None, labels=None):\n height, width, channels = image.shape\n boxes[:, 0] *= width\n boxes[:, 2] *= width\n boxes[:, 1] *= height\n boxes[:, 3] *= height\n\n return image, boxes, labels\n\n\nclass ToPercentCoords(object):\n def __call__(self, image, boxes=None, labels=None):\n height, width, channels = image.shape\n boxes[:, 0] /= width\n boxes[:, 2] /= width\n boxes[:, 1] /= height\n boxes[:, 3] /= height\n\n return image, boxes, labels\n\n\nclass Resize(object):\n def __init__(self, size=(300, 300)):\n self.size = size\n\n def __call__(self, image, boxes=None, labels=None):\n image = cv2.resize(image, (self.size[0],\n self.size[1]))\n return image, boxes, labels\n\n\nclass RandomSaturation(object):\n def __init__(self, lower=0.5, upper=1.5):\n self.lower = lower\n self.upper = upper\n assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n image[:, :, 1] *= random.uniform(self.lower, self.upper)\n\n return image, boxes, labels\n\n\nclass RandomHue(object):\n def __init__(self, delta=18.0):\n assert delta >= 0.0 and delta <= 360.0\n self.delta = delta\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n image[:, :, 0] += random.uniform(-self.delta, self.delta)\n image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0\n image[:, :, 0][image[:, :, 0] < 0.0] += 360.0\n return image, boxes, labels\n\n\nclass RandomLightingNoise(object):\n def __init__(self):\n self.perms = ((0, 1, 2), (0, 2, 1),\n (1, 0, 2), (1, 2, 0),\n (2, 0, 1), (2, 1, 0))\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n swap = self.perms[random.randint(len(self.perms))]\n shuffle = SwapChannels(swap) # shuffle channels\n image = shuffle(image)\n return image, boxes, labels\n\n\nclass ConvertColor(object):\n def __init__(self, current, transform):\n self.transform = transform\n self.current = current\n\n def __call__(self, image, boxes=None, labels=None):\n if self.current == 'BGR' and self.transform == 'HSV':\n image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n elif self.current == 'RGB' and self.transform == 'HSV':\n image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n elif self.current == 'BGR' and self.transform == 'RGB':\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n elif self.current == 'HSV' and self.transform == 'BGR':\n image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\n elif self.current == 'HSV' and self.transform == \"RGB\":\n image = cv2.cvtColor(image, cv2.COLOR_HSV2RGB)\n else:\n raise NotImplementedError\n return image, boxes, labels\n\n\nclass RandomContrast(object):\n def __init__(self, lower=0.5, upper=1.5):\n self.lower = lower\n self.upper = upper\n assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n # expects float image\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n alpha = random.uniform(self.lower, self.upper)\n image *= alpha\n return image, boxes, labels\n\n\nclass RandomBrightness(object):\n def __init__(self, delta=32):\n assert delta >= 0.0\n assert delta <= 255.0\n self.delta = delta\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n delta = random.uniform(-self.delta, self.delta)\n image += delta\n return image, boxes, labels\n\n\nclass ToCV2Image(object):\n def __call__(self, tensor, boxes=None, labels=None):\n return tensor.cpu().numpy().astype(np.float32).transpose((1, 2, 0)), boxes, labels\n\n\nclass ToTensor(object):\n def __call__(self, cvimage, boxes=None, labels=None):\n return np.transpose(cvimage, (2, 0, 1)).astype(np.float32), boxes, labels\n\n\nclass RandomSampleCrop(object):\n \"\"\"Crop\n Arguments:\n img (Image): the image being input during training\n boxes (Tensor): the original bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n mode (float tuple): the min and max jaccard overlaps\n Return:\n (img, boxes, classes)\n img (Image): the cropped image\n boxes (Tensor): the adjusted bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n \"\"\"\n\n def __init__(self):\n self.sample_options = (\n # using entire original input image\n None,\n # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9\n (0.1, None),\n (0.3, None),\n (0.7, None),\n (0.9, None),\n # randomly sample a patch\n (None, None),\n )\n\n def __call__(self, image, boxes=None, labels=None):\n height, width, _ = image.shape\n while True:\n # randomly choose a mode\n mode = random.choice(self.sample_options)\n if mode is None:\n return image, boxes, labels\n\n min_iou, max_iou = mode\n if min_iou is None:\n min_iou = float('-inf')\n if max_iou is None:\n max_iou = float('inf')\n\n # max trails (50)\n for _ in range(50):\n current_image = image\n\n w = random.uniform(0.3 * width, width)\n h = random.uniform(0.3 * height, height)\n\n # aspect ratio constraint b/t .5 & 2\n if h / w < 0.5 or h / w > 2:\n continue\n\n left = random.uniform(width - w)\n top = random.uniform(height - h)\n\n # convert to integer rect x1,y1,x2,y2\n rect = np.array([int(left), int(top), int(left + w), int(top + h)])\n\n # calculate IoU (jaccard overlap) b/t the cropped and gt boxes\n overlap = jaccard_numpy(boxes, rect)\n\n # is min and max overlap constraint satisfied? if not try again\n if overlap.max() < min_iou or overlap.min() > max_iou:\n continue\n\n # cut the crop from the image\n current_image = current_image[rect[1]:rect[3], rect[0]:rect[2],\n :]\n\n # keep overlap with gt box IF center in sampled patch\n centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0\n\n # mask in all gt boxes that above and to the left of centers\n m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])\n\n # mask in all gt boxes that under and to the right of centers\n m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])\n\n # mask in that both m1 and m2 are true\n mask = m1 * m2\n\n # have any valid boxes? try again if not\n if not paddle.any(mask):\n continue\n\n # take only matching gt boxes\n current_boxes = boxes[mask, :].copy()\n\n # take only matching gt labels\n current_labels = labels[mask]\n\n # should we use the box left and top corner or the crop's\n current_boxes[:, :2] = np.maximum(current_boxes[:, :2],\n rect[:2])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, :2] -= rect[:2]\n\n current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],\n rect[2:])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, 2:] -= rect[:2]\n\n return current_image, current_boxes, current_labels\n\n\nclass RandomSampleCrop_v2(object):\n \"\"\"Crop\n Arguments:\n img (Image): the image being input during training\n boxes (Tensor): the original bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n mode (float tuple): the min and max jaccard overlaps\n Return:\n (img, boxes, classes)\n img (Image): the cropped image\n boxes (Tensor): the adjusted bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n \"\"\"\n\n def __init__(self):\n self.sample_options = (\n # using entire original input image\n None,\n # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9\n\n # randomly sample a patch\n (1, None),\n (1, None),\n (1, None),\n (1, None),\n )\n\n def __call__(self, image, boxes=None, labels=None):\n height, width, _ = image.shape\n while True:\n # randomly choose a mode\n mode = self.sample_options[np.random.randint(len(self.sample_options))]\n if mode is None:\n return image, boxes, labels\n\n min_iou, max_iou = mode\n if min_iou is None:\n min_iou = float('-inf')\n if max_iou is None:\n max_iou = float('inf')\n\n # max trails (50)\n for _ in range(50):\n current_image = image\n\n w = random.uniform(0.3 * width, width)\n h = random.uniform(0.3 * height, height)\n\n # aspect ratio constraint b/t .5 & 2\n if h / w != 1:\n continue\n left = random.uniform(width - w)\n top = random.uniform(height - h)\n\n # convert to integer rect x1,y1,x2,y2\n rect = np.array([int(left), int(top), int(left + w), int(top + h)])\n\n # calculate IoU (jaccard overlap) b/t the cropped and gt boxes\n overlap = object_converage_numpy(boxes, rect)\n\n # is min and max overlap constraint satisfied? if not try again\n if overlap.max() < min_iou or overlap.min() > max_iou:\n continue\n\n # cut the crop from the image\n current_image = current_image[rect[1]:rect[3], rect[0]:rect[2],\n :]\n\n # keep overlap with gt box IF center in sampled patch\n centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0\n\n # mask in all gt boxes that above and to the left of centers\n m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])\n\n # mask in all gt boxes that under and to the right of centers\n m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])\n\n # mask in that both m1 and m2 are true\n mask = m1 * m2\n\n # have any valid boxes? try again if not\n if not paddle.any(mask):\n continue\n\n # take only matching gt boxes\n current_boxes = boxes[mask, :].copy()\n\n # take only matching gt labels\n current_labels = labels[mask]\n\n # should we use the box left and top corner or the crop's\n current_boxes[:, :2] = np.maximum(current_boxes[:, :2],\n rect[:2])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, :2] -= rect[:2]\n\n current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],\n rect[2:])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, 2:] -= rect[:2]\n\n return current_image, current_boxes, current_labels\n\n\nclass Expand(object):\n def __init__(self, mean):\n self.mean = mean\n\n def __call__(self, image, boxes, labels):\n if random.randint(2):\n return image, boxes, labels\n\n height, width, depth = image.shape\n ratio = random.uniform(1, 4)\n left = random.uniform(0, width * ratio - width)\n top = random.uniform(0, height * ratio - height)\n\n expand_image = np.zeros(\n (int(height * ratio), int(width * ratio), depth),\n dtype=image.dtype)\n expand_image[:, :, :] = self.mean\n expand_image[int(top):int(top + height),\n int(left):int(left + width)] = image\n image = expand_image\n\n boxes = boxes.copy()\n boxes[:, :2] += (int(left), int(top))\n boxes[:, 2:] += (int(left), int(top))\n\n return image, boxes, labels\n\n\nclass RandomMirror(object):\n def __call__(self, image, boxes, classes):\n _, width, _ = image.shape\n if random.randint(2):\n image = image[:, ::-1]\n boxes = boxes.copy()\n boxes[:, 0::2] = width - boxes[:, 2::-2]\n return image, boxes, classes\n\n\nclass SwapChannels(object):\n \"\"\"Transforms a tensorized image by swapping the channels in the order\n specified in the swap tuple.\n Args:\n swaps (int triple): final order of channels\n eg: (2, 1, 0)\n \"\"\"\n\n def __init__(self, swaps):\n self.swaps = swaps\n\n def __call__(self, image):\n \"\"\"\n Args:\n image (Tensor): image tensor to be transformed\n Return:\n a tensor with channels swapped according to swap\n \"\"\"\n # if paddle.is_tensor(image):\n # image = image.data.cpu().numpy()\n # else:\n # image = np.array(image)\n image = image[:, :, self.swaps]\n return image\n\n\nclass PhotometricDistort(object):\n def __init__(self):\n self.pd = [\n RandomContrast(), # RGB\n ConvertColor(current=\"RGB\", transform='HSV'), # HSV\n RandomSaturation(), # HSV\n RandomHue(), # HSV\n ConvertColor(current='HSV', transform='RGB'), # RGB\n RandomContrast() # RGB\n ]\n self.rand_brightness = RandomBrightness()\n self.rand_light_noise = RandomLightingNoise()\n\n def __call__(self, image, boxes, labels):\n im = image.copy()\n im, boxes, labels = self.rand_brightness(im, boxes, labels)\n if random.randint(2):\n distort = Compose(self.pd[:-1])\n else:\n distort = Compose(self.pd[1:])\n im, boxes, labels = distort(im, boxes, labels)\n return self.rand_light_noise(im, boxes, labels)\n" }, { "path": "paddle/vision/utils/__init__.py", "content": "from .misc import *\n" }, { "path": "paddle/vision/utils/box_utils.py", "content": "import math\nimport numpy as np\nimport paddle\n\n\ndef generate_priors(feature_map_list, shrinkage_list, image_size, min_boxes, clamp=True) -> paddle.Tensor:\n priors = []\n for index in range(0, len(feature_map_list[0])):\n scale_w = image_size[0] / shrinkage_list[0][index]\n scale_h = image_size[1] / shrinkage_list[1][index]\n for j in range(0, feature_map_list[1][index]):\n for i in range(0, feature_map_list[0][index]):\n x_center = (i + 0.5) / scale_w\n y_center = (j + 0.5) / scale_h\n\n for min_box in min_boxes[index]:\n w = min_box / image_size[0]\n h = min_box / image_size[1]\n priors.append([x_center, y_center, w, h])\n print(\"priors nums:{}\".format(len(priors)))\n priors = np.array(priors)\n if clamp:\n priors = np.clip(priors, 0.0, 1.0)\n return priors\n\n\ndef convert_locations_to_boxes(locations, priors, center_variance, size_variance):\n \"\"\"Convert regressional location results of SSD into boxes in the form of (center_x, center_y, h, w).\n\n The conversion:\n $$predicted\\_center * center_variance = \\frac {real\\_center - prior\\_center} {prior\\_hw}$$\n $$exp(predicted\\_hw * size_variance) = \\frac {real\\_hw} {prior\\_hw}$$\n We do it in the inverse direction here.\n Args:\n locations (batch_size, num_priors, 4): the regression output of SSD. It will contain the outputs as well.\n priors (num_priors, 4) or (batch_size/1, num_priors, 4): prior boxes.\n center_variance: a float used to change the scale of center.\n size_variance: a float used to change of scale of size.\n Returns:\n boxes: priors: [[center_x, center_y, h, w]]. All the values\n are relative to the image size.\n \"\"\"\n # priors can have one dimension less.\n if priors.dim() + 1 == locations.dim():\n priors = priors.unsqueeze(0)\n return paddle.concat([\n locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],\n paddle.exp(locations[..., 2:] * size_variance) * priors[..., 2:]\n ], locations.dim() - 1)\n\n\ndef convert_boxes_to_locations(center_form_boxes, center_form_priors, center_variance, size_variance):\n # priors can have one dimension less\n if center_form_priors.ndim + 1 == center_form_boxes.ndim:\n center_form_priors = np.expand_dims(center_form_priors, axis=0)\n return np.concatenate([\n (center_form_boxes[..., :2] - center_form_priors[..., :2]) / center_form_priors[..., 2:] / center_variance,\n np.log(center_form_boxes[..., 2:] / center_form_priors[..., 2:]) / size_variance\n ], center_form_boxes.ndim - 1)\n\n\ndef area_of(left_top, right_bottom) -> paddle.Tensor:\n \"\"\"Compute the areas of rectangles given two corners.\n\n Args:\n left_top (N, 2): left top corner.\n right_bottom (N, 2): right bottom corner.\n\n Returns:\n area (N): return the area.\n \"\"\"\n hw = np.clip(right_bottom - left_top, 0.0, None)\n return hw[..., 0] * hw[..., 1]\n\n\ndef iou_of(boxes0, boxes1, eps=1e-5):\n \"\"\"Return intersection-over-union (Jaccard index) of boxes.\n\n Args:\n boxes0 (N, 4): ground truth boxes.\n boxes1 (N or 1, 4): predicted boxes.\n eps: a small number to avoid 0 as denominator.\n Returns:\n iou (N): IoU values.\n \"\"\"\n expand_b0_left_top = np.repeat(boxes0[..., :2], repeats=boxes1.shape[0], axis=0)\n expand_b1_left_top = np.repeat(boxes1[..., :2], repeats=boxes0.shape[1], axis=1)\n expand_b0_right_bottom = np.repeat(boxes0[..., 2:], repeats=boxes1.shape[0], axis=0)\n expand_b1_right_bottom = np.repeat(boxes1[..., 2:], repeats=boxes0.shape[1], axis=1)\n\n overlap_left_top = expand_b0_left_top * (expand_b0_left_top > expand_b1_left_top) \\\n + expand_b1_left_top * (expand_b0_left_top <= expand_b1_left_top)\n overlap_right_bottom = expand_b0_right_bottom * (expand_b0_right_bottom < expand_b1_right_bottom) \\\n + expand_b0_right_bottom * (expand_b0_right_bottom >= expand_b1_right_bottom)\n\n overlap_area = area_of(overlap_left_top, overlap_right_bottom)\n area0 = area_of(boxes0[..., :2], boxes0[..., 2:])\n area1 = area_of(boxes1[..., :2], boxes1[..., 2:])\n return overlap_area / (area0 + area1 - overlap_area + eps)\n\n\ndef assign_priors(gt_boxes, gt_labels, corner_form_priors,\n iou_threshold):\n \"\"\"Assign ground truth boxes and targets to priors.\n\n Args:\n gt_boxes (num_targets, 4): ground truth boxes.\n gt_labels (num_targets): labels of targets.\n priors (num_priors, 4): corner form priors\n Returns:\n boxes (num_priors, 4): real values for priors.\n labels (num_priros): labels for priors.\n \"\"\"\n # size: num_priors x num_targets\n ious = iou_of(np.expand_dims(gt_boxes, axis=0), np.expand_dims(corner_form_priors, axis=1))\n # size: num_priors\n best_target_per_prior = np.max(ious, axis=1)\n best_target_per_prior_index = np.argmax(ious, axis=1)\n # size: num_targets\n best_prior_per_target = np.max(ious, axis=0)\n best_prior_per_target_index = np.argmax(ious, axis=0)\n\n for target_index, prior_index in enumerate(best_prior_per_target_index):\n best_target_per_prior_index[prior_index] = target_index\n # 2.0 is used to make sure every target has a prior assigned\n best_target_per_prior[best_prior_per_target_index, ...] = 2\n # size: num_priors\n labels = gt_labels[best_target_per_prior_index]\n labels[best_target_per_prior < iou_threshold] = 0 # the backgournd id\n boxes = gt_boxes[best_target_per_prior_index]\n return boxes, labels\n\n\ndef hard_negative_mining(loss, labels, neg_pos_ratio):\n \"\"\"\n It used to suppress the presence of a large number of negative prediction.\n It works on image level not batch level.\n For any example/image, it keeps all the positive predictions and\n cut the number of negative predictions to make sure the ratio\n between the negative examples and positive examples is no more\n the given ratio for an image.\n\n Args:\n loss (N, num_priors): the loss for each example.\n labels (N, num_priors): the labels.\n neg_pos_ratio: the ratio between the negative examples and positive examples.\n \"\"\"\n pos_mask = labels > 0\n num_pos = pos_mask.cast('int').sum(axis=1, keepdim=True)\n num_neg = num_pos * neg_pos_ratio\n loss = pos_mask.cast('int') * (-1e8) + loss * (1 - pos_mask.cast('int'))\n indexes = paddle.argsort(loss, axis=1, descending=True)\n orders = paddle.argsort(indexes, axis=1)\n neg_mask = orders < num_neg\n return paddle.logical_or(pos_mask, neg_mask)\n\n\ndef center_form_to_corner_form(locations):\n return np.concatenate([locations[:, :2] - locations[:, 2:] / 2,\n locations[:, :2] + locations[:, 2:] / 2], locations.ndim - 1)\n\n\ndef corner_form_to_center_form(boxes):\n return np.concatenate([(boxes[..., :2] + boxes[..., 2:]) / 2,\n boxes[..., 2:] - boxes[..., :2]], boxes.ndim - 1)\n\n\ndef hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):\n \"\"\"\n\n Args:\n box_scores (N, 5): boxes in corner-form and probabilities.\n iou_threshold: intersection over union threshold.\n top_k: keep top_k results. If k <= 0, keep all the results.\n candidate_size: only consider the candidates with the highest scores.\n Returns:\n picked: a list of indexes of the kept boxes\n \"\"\"\n scores = box_scores[:, -1]\n boxes = box_scores[:, :-1]\n picked = []\n _, indexes = paddle.sort(scores, descending=True)\n indexes = indexes[:candidate_size]\n while len(indexes) > 0:\n current = indexes[0]\n picked.append(current.item())\n if 0 < top_k == len(picked) or len(indexes) == 1:\n break\n current_box = boxes[current, :]\n indexes = indexes[1:]\n rest_boxes = boxes[indexes, :]\n iou = iou_of(\n rest_boxes,\n current_box.unsqueeze(0),\n )\n indexes = indexes[iou <= iou_threshold]\n\n return box_scores[picked, :]\n\n\ndef nms(box_scores, nms_method=None, score_threshold=None, iou_threshold=None,\n sigma=0.5, top_k=-1, candidate_size=200):\n if nms_method == \"soft\":\n return soft_nms(box_scores, score_threshold, sigma, top_k)\n else:\n return hard_nms(box_scores, iou_threshold, top_k, candidate_size=candidate_size)\n\n\ndef soft_nms(box_scores, score_threshold, sigma=0.5, top_k=-1):\n \"\"\"Soft NMS implementation.\n\n References:\n https://arxiv.org/abs/1704.04503\n https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/cython_nms.pyx\n\n Args:\n box_scores (N, 5): boxes in corner-form and probabilities.\n score_threshold: boxes with scores less than value are not considered.\n sigma: the parameter in score re-computation.\n scores[i] = scores[i] * exp(-(iou_i)^2 / simga)\n top_k: keep top_k results. If k <= 0, keep all the results.\n Returns:\n picked_box_scores (K, 5): results of NMS.\n \"\"\"\n picked_box_scores = []\n while box_scores.size(0) > 0:\n max_score_index = paddle.argmax(box_scores[:, 4])\n cur_box_prob = paddle.to_tensor(box_scores[max_score_index, :])\n picked_box_scores.append(cur_box_prob)\n if len(picked_box_scores) == top_k > 0 or box_scores.size(0) == 1:\n break\n cur_box = cur_box_prob[:-1]\n box_scores[max_score_index, :] = box_scores[-1, :]\n box_scores = box_scores[:-1, :]\n ious = iou_of(cur_box.unsqueeze(0), box_scores[:, :-1])\n box_scores[:, -1] = box_scores[:, -1] * paddle.exp(-(ious * ious) / sigma)\n box_scores = box_scores[box_scores[:, -1] > score_threshold, :]\n if len(picked_box_scores) > 0:\n return paddle.stack(picked_box_scores)\n else:\n return paddle.to_tensor([])\n" }, { "path": "paddle/vision/utils/box_utils_numpy.py", "content": "import numpy as np\n\n\ndef convert_locations_to_boxes(locations, priors, center_variance, size_variance):\n \"\"\"Convert regressional location results of SSD into boxes in the form of (center_x, center_y, h, w).\n\n The conversion:\n $$predicted\\_center * center_variance = \\frac {real\\_center - prior\\_center} {prior\\_hw}$$\n $$exp(predicted\\_hw * size_variance) = \\frac {real\\_hw} {prior\\_hw}$$\n We do it in the inverse direction here.\n Args:\n locations (batch_size, num_priors, 4): the regression output of SSD. It will contain the outputs as well.\n priors (num_priors, 4) or (batch_size/1, num_priors, 4): prior boxes.\n center_variance: a float used to change the scale of center.\n size_variance: a float used to change of scale of size.\n Returns:\n boxes: priors: [[center_x, center_y, h, w]]. All the values\n are relative to the image size.\n \"\"\"\n # priors can have one dimension less.\n if len(priors.shape) + 1 == len(locations.shape):\n priors = np.expand_dims(priors, 0)\n return np.concatenate([\n locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],\n np.exp(locations[..., 2:] * size_variance) * priors[..., 2:]\n ], axis=len(locations.shape) - 1)\n\n\ndef convert_boxes_to_locations(center_form_boxes, center_form_priors, center_variance, size_variance):\n # priors can have one dimension less\n if len(center_form_priors.shape) + 1 == len(center_form_boxes.shape):\n center_form_priors = np.expand_dims(center_form_priors, 0)\n return np.concatenate([\n (center_form_boxes[..., :2] - center_form_priors[..., :2]) / center_form_priors[..., 2:] / center_variance,\n np.log(center_form_boxes[..., 2:] / center_form_priors[..., 2:]) / size_variance\n ], axis=len(center_form_boxes.shape) - 1)\n\n\ndef area_of(left_top, right_bottom):\n \"\"\"Compute the areas of rectangles given two corners.\n\n Args:\n left_top (N, 2): left top corner.\n right_bottom (N, 2): right bottom corner.\n\n Returns:\n area (N): return the area.\n \"\"\"\n hw = np.clip(right_bottom - left_top, 0.0, None)\n return hw[..., 0] * hw[..., 1]\n\n\ndef iou_of(boxes0, boxes1, eps=1e-5):\n \"\"\"Return intersection-over-union (Jaccard index) of boxes.\n\n Args:\n boxes0 (N, 4): ground truth boxes.\n boxes1 (N or 1, 4): predicted boxes.\n eps: a small number to avoid 0 as denominator.\n Returns:\n iou (N): IoU values.\n \"\"\"\n overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])\n overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])\n\n overlap_area = area_of(overlap_left_top, overlap_right_bottom)\n area0 = area_of(boxes0[..., :2], boxes0[..., 2:])\n area1 = area_of(boxes1[..., :2], boxes1[..., 2:])\n return overlap_area / (area0 + area1 - overlap_area + eps)\n\n\ndef center_form_to_corner_form(locations):\n return np.concatenate([locations[..., :2] - locations[..., 2:] / 2,\n locations[..., :2] + locations[..., 2:] / 2], len(locations.shape) - 1)\n\n\ndef corner_form_to_center_form(boxes):\n return np.concatenate([\n (boxes[..., :2] + boxes[..., 2:]) / 2,\n boxes[..., 2:] - boxes[..., :2]\n ], len(boxes.shape) - 1)\n\n\ndef hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):\n \"\"\"\n\n Args:\n box_scores (N, 5): boxes in corner-form and probabilities.\n iou_threshold: intersection over union threshold.\n top_k: keep top_k results. If k <= 0, keep all the results.\n candidate_size: only consider the candidates with the highest scores.\n Returns:\n picked: a list of indexes of the kept boxes\n \"\"\"\n scores = box_scores[:, -1]\n boxes = box_scores[:, :-1]\n picked = []\n # _, indexes = scores.sort(descending=True)\n indexes = np.argsort(scores)\n # indexes = indexes[:candidate_size]\n indexes = indexes[-candidate_size:]\n while len(indexes) > 0:\n # current = indexes[0]\n current = indexes[-1]\n picked.append(current)\n if 0 < top_k == len(picked) or len(indexes) == 1:\n break\n current_box = boxes[current, :]\n # indexes = indexes[1:]\n indexes = indexes[:-1]\n rest_boxes = boxes[indexes, :]\n iou = iou_of(\n rest_boxes,\n np.expand_dims(current_box, axis=0),\n )\n indexes = indexes[iou <= iou_threshold]\n\n return box_scores[picked, :]\n" }, { "path": "paddle/vision/utils/misc.py", "content": "import datetime\n\nimport paddle\n\n\ndef str2bool(s):\n return s.lower() in ('true', '1')\n\n\nclass Timer:\n def __init__(self):\n self.clock = {}\n\n def start(self, key=\"default\"):\n self.clock[key] = datetime.datetime.now()\n\n def end(self, key=\"default\"):\n if key not in self.clock:\n raise Exception(f\"{key} is not in the clock.\")\n interval = datetime.datetime.now() - self.clock[key]\n del self.clock[key]\n return interval.total_seconds()\n \n\ndef save_checkpoint(epoch, net_state_dict, optimizer_state_dict, best_score, checkpoint_path, model_path):\n paddle.save({\n 'epoch': epoch,\n 'model': net_state_dict,\n 'optimizer': optimizer_state_dict,\n 'best_score': best_score\n }, checkpoint_path)\n paddle.save(net_state_dict, model_path)\n \n \ndef load_checkpoint(checkpoint_path):\n return paddle.load(checkpoint_path)\n\n\ndef freeze_net_layers(net):\n for param in net.parameters():\n param.stop_gradient = True\n\n\ndef store_labels(path, labels):\n with open(path, \"w\") as f:\n f.write(\"\\n\".join(labels))\n" }, { "path": "requirements.txt", "content": "numpy\ntorch\nopencv_python\ntorchvision\ntyping\ntorchstat\ntorchsummary\nptflops\nmatplotlib\nonnx\nonnxruntime" }, { "path": "run_video_face_detect.py", "content": "\"\"\"\nThis code uses the pytorch model to detect faces from live video or camera.\n\"\"\"\nimport argparse\nimport sys\nimport cv2\n\nfrom vision.ssd.config.fd_config import define_img_size\n\nparser = argparse.ArgumentParser(\n description='detect_video')\n\nparser.add_argument('--net_type', default=\"RFB\", type=str,\n help='The network architecture ,optional: RFB (higher precision) or slim (faster)')\nparser.add_argument('--input_size', default=480, type=int,\n help='define network input size,default optional value 128/160/320/480/640/1280')\nparser.add_argument('--threshold', default=0.7, type=float,\n help='score threshold')\nparser.add_argument('--candidate_size', default=1000, type=int,\n help='nms candidate size')\nparser.add_argument('--path', default=\"imgs\", type=str,\n help='imgs dir')\nparser.add_argument('--test_device', default=\"cuda:0\", type=str,\n help='cuda:0 or cpu')\nparser.add_argument('--video_path', default=\"/home/linzai/Videos/video/16_1.MP4\", type=str,\n help='path of video')\nargs = parser.parse_args()\n\ninput_img_size = args.input_size\ndefine_img_size(input_img_size) # must put define_img_size() before 'import create_mb_tiny_fd, create_mb_tiny_fd_predictor'\n\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd, create_mb_tiny_fd_predictor\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd, create_Mb_Tiny_RFB_fd_predictor\nfrom vision.utils.misc import Timer\n\nlabel_path = \"./models/voc-model-labels.txt\"\n\nnet_type = args.net_type\n\ncap = cv2.VideoCapture(args.video_path) # capture from video\n# cap = cv2.VideoCapture(0) # capture from camera\n\nclass_names = [name.strip() for name in open(label_path).readlines()]\nnum_classes = len(class_names)\ntest_device = args.test_device\n\ncandidate_size = args.candidate_size\nthreshold = args.threshold\n\nif net_type == 'slim':\n model_path = \"models/pretrained/version-slim-320.pth\"\n # model_path = \"models/pretrained/version-slim-640.pth\"\n net = create_mb_tiny_fd(len(class_names), is_test=True, device=test_device)\n predictor = create_mb_tiny_fd_predictor(net, candidate_size=candidate_size, device=test_device)\nelif net_type == 'RFB':\n model_path = \"models/pretrained/version-RFB-320.pth\"\n # model_path = \"models/pretrained/version-RFB-640.pth\"\n net = create_Mb_Tiny_RFB_fd(len(class_names), is_test=True, device=test_device)\n predictor = create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=candidate_size, device=test_device)\nelse:\n print(\"The net type is wrong!\")\n sys.exit(1)\nnet.load(model_path)\n\ntimer = Timer()\nsum = 0\nwhile True:\n ret, orig_image = cap.read()\n if orig_image is None:\n print(\"end\")\n break\n image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)\n timer.start()\n boxes, labels, probs = predictor.predict(image, candidate_size / 2, threshold)\n interval = timer.end()\n print('Time: {:.6f}s, Detect Objects: {:d}.'.format(interval, labels.size(0)))\n for i in range(boxes.size(0)):\n box = boxes[i, :]\n label = f\" {probs[i]:.2f}\"\n cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 4)\n\n # cv2.putText(orig_image, label,\n # (box[0], box[1] - 10),\n # cv2.FONT_HERSHEY_SIMPLEX,\n # 0.5, # font scale\n # (0, 0, 255),\n # 2) # line type\n orig_image = cv2.resize(orig_image, None, None, fx=0.8, fy=0.8)\n sum += boxes.size(0)\n cv2.imshow('annotated', orig_image)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\ncap.release()\ncv2.destroyAllWindows()\nprint(\"all face num:{}\".format(sum))\n" }, { "path": "run_video_face_detect_onnx.py", "content": "\"\"\"\nThis code uses the onnx model to detect faces from live video or cameras.\n\"\"\"\nimport time\n\nimport cv2\nimport numpy as np\nimport onnx\nimport vision.utils.box_utils_numpy as box_utils\nfrom caffe2.python.onnx import backend\n\n# onnx runtime\nimport onnxruntime as ort\n\n\ndef predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):\n boxes = boxes[0]\n confidences = confidences[0]\n picked_box_probs = []\n picked_labels = []\n for class_index in range(1, confidences.shape[1]):\n probs = confidences[:, class_index]\n mask = probs > prob_threshold\n probs = probs[mask]\n if probs.shape[0] == 0:\n continue\n subset_boxes = boxes[mask, :]\n box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)\n box_probs = box_utils.hard_nms(box_probs,\n iou_threshold=iou_threshold,\n top_k=top_k,\n )\n picked_box_probs.append(box_probs)\n picked_labels.extend([class_index] * box_probs.shape[0])\n if not picked_box_probs:\n return np.array([]), np.array([]), np.array([])\n picked_box_probs = np.concatenate(picked_box_probs)\n picked_box_probs[:, 0] *= width\n picked_box_probs[:, 1] *= height\n picked_box_probs[:, 2] *= width\n picked_box_probs[:, 3] *= height\n return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]\n\n\nlabel_path = \"models/voc-model-labels.txt\"\n\nonnx_path = \"models/onnx/version-RFB-320.onnx\"\nclass_names = [name.strip() for name in open(label_path).readlines()]\n\npredictor = onnx.load(onnx_path)\nonnx.checker.check_model(predictor)\nonnx.helper.printable_graph(predictor.graph)\npredictor = backend.prepare(predictor, device=\"CPU\") # default CPU\n\nort_session = ort.InferenceSession(onnx_path)\ninput_name = ort_session.get_inputs()[0].name\n\ncap = cv2.VideoCapture(\"/home/linzai/Videos/video/16_6.MP4\") # capture from camera\n\nthreshold = 0.7\n\nsum = 0\nwhile True:\n ret, orig_image = cap.read()\n if orig_image is None:\n print(\"no img\")\n break\n image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, (320, 240))\n # image = cv2.resize(image, (640, 480))\n image_mean = np.array([127, 127, 127])\n image = (image - image_mean) / 128\n image = np.transpose(image, [2, 0, 1])\n image = np.expand_dims(image, axis=0)\n image = image.astype(np.float32)\n # confidences, boxes = predictor.run(image)\n time_time = time.time()\n confidences, boxes = ort_session.run(None, {input_name: image})\n print(\"cost time:{}\".format(time.time() - time_time))\n boxes, labels, probs = predict(orig_image.shape[1], orig_image.shape[0], confidences, boxes, threshold)\n for i in range(boxes.shape[0]):\n box = boxes[i, :]\n label = f\"{class_names[labels[i]]}: {probs[i]:.2f}\"\n\n cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), (255, 255, 0), 4)\n\n # cv2.putText(orig_image, label,\n # (box[0] + 20, box[1] + 40),\n # cv2.FONT_HERSHEY_SIMPLEX,\n # 1, # font scale\n # (255, 0, 255),\n # 2) # line type\n sum += boxes.shape[0]\n orig_image = cv2.resize(orig_image, (0, 0), fx=0.7, fy=0.7)\n cv2.imshow('annotated', orig_image)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\ncap.release()\ncv2.destroyAllWindows()\nprint(\"sum:{}\".format(sum))\n" }, { "path": "tf/README.md", "content": "# Tensorflow implementation of Ultra-Light-Fast-Generic-Face-Detector-1MB with converter\n\nYou can use this script to converter origin model to tensorflow version.\n\n## Run\nCovert model\n```Python\n python3 ./convert_tensorflow.py --net_type \n```\n\nInference on image\n```Python\n python3 ./det_image.py --net_type --img_path \n```\n\n## Result\n![img1](https://github.com/jason9075/Ultra-Light-Fast-Generic-Face-Detector_Tensorflow-Model-Converter/blob/master/imgs/test_output_RFB.jpg)\n\n## Reference\n- [Ultra-Light-Fast-Generic-Face-Detector_Tensorflow-Model-Converter](https://github.com/jason9075/Ultra-Light-Fast-Generic-Face-Detector_Tensorflow-Model-Converter)\n" }, { "path": "tf/backend/op.py", "content": "import tensorflow as tf\n\n\ndef basic_conv(x, out_ch, kernel_size, stride=(1, 1), padding=0, dilation=1, relu=True,\n bn=True, prefix='basic_conv'):\n if 0 < padding:\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}_padding')(x)\n else:\n out = x\n out = tf.keras.layers.Conv2D(out_ch,\n kernel_size,\n strides=stride,\n dilation_rate=dilation,\n use_bias=(not bn),\n name=f'{prefix}_conv')(out)\n if bn:\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}_bn')(out)\n if relu:\n out = tf.keras.layers.ReLU(name=f'{prefix}_relu')(out)\n\n return out\n\n\ndef basic_rfb(x, in_ch, out_ch, stride=1, scale=0.1, map_reduce=8, vision=1, prefix='basic_rfb'):\n inter_ch = in_ch // map_reduce\n\n branch0 = basic_conv(x, inter_ch, kernel_size=1, stride=1, relu=False,\n prefix=f'{prefix}.branch0.0')\n branch0 = basic_conv(branch0, 2 * inter_ch, kernel_size=3, stride=stride, padding=1,\n prefix=f'{prefix}.branch0.1')\n branch0 = basic_conv(branch0, 2 * inter_ch, kernel_size=3, stride=1, dilation=vision + 1,\n padding=vision + 1, relu=False, prefix=f'{prefix}.branch0.2')\n\n branch1 = basic_conv(x, inter_ch, kernel_size=1, stride=1, relu=False,\n prefix=f'{prefix}.branch1.0')\n branch1 = basic_conv(branch1, 2 * inter_ch, kernel_size=3, stride=stride, padding=1,\n prefix=f'{prefix}.branch1.1')\n branch1 = basic_conv(branch1, 2 * inter_ch, kernel_size=3, stride=1, dilation=vision + 2,\n padding=vision + 2, relu=False, prefix=f'{prefix}.branch1.2')\n\n branch2 = basic_conv(x, inter_ch, kernel_size=1, stride=1, relu=False,\n prefix=f'{prefix}.branch2.0')\n branch2 = basic_conv(branch2, (inter_ch // 2) * 3, kernel_size=3, stride=1, padding=1,\n prefix=f'{prefix}.branch2.1')\n branch2 = basic_conv(branch2, 2 * inter_ch, kernel_size=3, stride=stride, padding=1,\n prefix=f'{prefix}.branch2.2')\n branch2 = basic_conv(branch2, 2 * inter_ch, kernel_size=3, stride=1, dilation=vision + 4,\n padding=vision + 4, relu=False, prefix=f'{prefix}.branch2.3')\n\n out = tf.keras.layers.Concatenate(axis=-1, name=f'{prefix}_cat')([branch0, branch1, branch2])\n out = basic_conv(out, out_ch, kernel_size=1, stride=1, relu=False, prefix=f'{prefix}.convlinear')\n shortcut = basic_conv(x, out_ch, kernel_size=1, stride=stride, relu=False, prefix=f'{prefix}.shortcut')\n out = tf.multiply(out, scale, name=f'{prefix}_mul')\n out = tf.keras.layers.Add(name=f'{prefix}_add')([out, shortcut])\n out = tf.keras.layers.ReLU(name=f'{prefix}_relu')(out)\n\n return out\n\n\ndef separable_conv(x, out_ch, kernel_size, stride, padding, prefix='separable_conv'):\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}_dconv_padding')(x)\n\n out = tf.keras.layers.DepthwiseConv2D(kernel_size,\n strides=stride,\n name=f'{prefix}_dconvbias')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}_relu')(out)\n out = tf.keras.layers.Conv2D(out_ch, 1,\n name=f'{prefix}_convbias')(out)\n\n return out\n\n\ndef conv_bn(x, out_ch, stride, padding=1, prefix='conv_bn'):\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}.0_padding')(x)\n out = tf.keras.layers.Conv2D(out_ch,\n (3, 3),\n strides=stride,\n use_bias=False,\n name=f'{prefix}.0_conv')(out)\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}.1_bn')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}.2_relu')(out)\n\n return out\n\n\ndef conv_dw(x, out_ch, stride, padding=1, prefix='conv_dw'):\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}.0_padding')(x)\n out = tf.keras.layers.DepthwiseConv2D(3, strides=stride,\n use_bias=False,\n name=f'{prefix}.0_dconv')(out)\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}.1_bn')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}.2_relu')(out)\n\n out = tf.keras.layers.Conv2D(out_ch, 1, use_bias=False, name=f'{prefix}.3_conv')(out)\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}.4_bn')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}.5_relu')(out)\n\n return out\n" }, { "path": "tf/backend/utils.py", "content": "import json\n\nimport numpy as np\nimport tensorflow as tf\nimport torch\n\n\ndef post_processing(reg_list, cls_list, num_classes, image_size, feature_map_wh_list, min_boxes,\n center_variance, size_variance,\n conf_threshold=0.6, nms_max_output_size=100, nms_iou_threshold=0.3, top_k=100):\n reg_list = [tf.keras.layers.Reshape([-1, 4])(reg) for reg in reg_list]\n cls_list = [tf.keras.layers.Reshape([-1, num_classes])(cls) for cls in cls_list]\n\n reg = tf.keras.layers.Concatenate(axis=1)(reg_list)\n cls = tf.keras.layers.Concatenate(axis=1)(cls_list)\n\n # post process\n cls = tf.keras.layers.Softmax(axis=-1)(cls)\n loc = decode_regression(reg, image_size, feature_map_wh_list, min_boxes,\n center_variance, size_variance)\n\n result = tf.keras.layers.Concatenate(axis=-1)([cls, loc])\n\n # confidence thresholding\n mask = conf_threshold < cls[..., 1]\n result = tf.boolean_mask(tensor=result, mask=mask)\n\n # non-maximum suppression\n mask = tf.image.non_max_suppression(boxes=result[..., -4:],\n scores=result[..., 1],\n max_output_size=nms_max_output_size,\n iou_threshold=nms_iou_threshold,\n name='non_maximum_suppresion')\n result = tf.gather(params=result, indices=mask, axis=0)\n\n # top-k filtering\n top_k_value = tf.math.minimum(tf.constant(top_k), tf.shape(result)[0])\n mask = tf.nn.top_k(result[..., 1], k=top_k_value, sorted=True).indices\n result = tf.gather(params=result, indices=mask, axis=0)\n\n return result\n\n\ndef decode_regression(reg, image_size, feature_map_w_h_list, min_boxes,\n center_variance, size_variance):\n priors = []\n for feature_map_w_h, min_box in zip(feature_map_w_h_list, min_boxes):\n xy_grid = np.meshgrid(range(feature_map_w_h[0]), range(feature_map_w_h[1]))\n xy_grid = np.add(xy_grid, 0.5)\n xy_grid[0, :, :] /= feature_map_w_h[0]\n xy_grid[1, :, :] /= feature_map_w_h[1]\n xy_grid = np.stack(xy_grid, axis=-1)\n xy_grid = np.tile(xy_grid, [1, 1, len(min_box)])\n xy_grid = np.reshape(xy_grid, (-1, 2))\n\n wh_grid = np.array(min_box) / np.array(image_size)[:, np.newaxis]\n wh_grid = np.tile(np.transpose(wh_grid), [np.product(feature_map_w_h), 1])\n\n prior = np.concatenate((xy_grid, wh_grid), axis=-1)\n priors.append(prior)\n\n priors = np.concatenate(priors, axis=0)\n print(f'priors nums:{priors.shape[0]}')\n\n priors = tf.constant(priors, dtype=tf.float32, shape=priors.shape, name='priors')\n\n center_xy = reg[..., :2] * center_variance * priors[..., 2:] + priors[..., :2]\n center_wh = tf.exp(reg[..., 2:] * size_variance) * priors[..., 2:]\n\n # center to corner\n start_xy = center_xy - center_wh / 2\n end_xy = center_xy + center_wh / 2\n\n loc = tf.concat([start_xy, end_xy], axis=-1)\n loc = tf.clip_by_value(loc, clip_value_min=0.0, clip_value_max=1.0)\n\n return loc\n\n\ndef load_weight(model, torch_path, mapping_table_path):\n torch_weights = torch.load(torch_path, map_location=torch.device('cpu'))\n\n with open(mapping_table_path, 'r') as f:\n mapping_table = json.load(f)\n mapping_table = {layer['name']: layer['weight'] for layer in mapping_table}\n\n for layer in model.layers:\n if layer.name in mapping_table:\n print(f'Set layer: {layer.name}')\n layer_type = layer.name.split('_')[-1]\n\n torch_layer_names = mapping_table[layer.name]\n if layer_type == 'conv':\n weight = np.array(torch_weights[torch_layer_names[0]])\n weight = np.transpose(weight, [2, 3, 1, 0])\n layer.set_weights([weight])\n elif layer_type == 'dconv':\n weight = np.array(torch_weights[torch_layer_names[0]])\n weight = np.transpose(weight, [2, 3, 0, 1])\n layer.set_weights([weight])\n elif layer_type == 'bn':\n gamma = np.array(torch_weights[torch_layer_names[0]])\n beta = np.array(torch_weights[torch_layer_names[1]])\n running_mean = np.array(torch_weights[torch_layer_names[2]])\n running_var = np.array(torch_weights[torch_layer_names[3]])\n layer.set_weights([gamma, beta, running_mean, running_var])\n elif layer_type == 'convbias':\n weight = np.array(torch_weights[torch_layer_names[0]])\n bias = np.array(torch_weights[torch_layer_names[1]])\n weight = np.transpose(weight, [2, 3, 1, 0])\n layer.set_weights([weight, bias])\n elif layer_type == 'dconvbias':\n weight = np.array(torch_weights[torch_layer_names[0]])\n bias = np.array(torch_weights[torch_layer_names[1]])\n weight = np.transpose(weight, [2, 3, 0, 1])\n layer.set_weights([weight, bias])\n else:\n raise RuntimeError(f'Unknown Layer type \\'{layer_type}\\'.')\n else:\n print(f'Ignore layer: {layer.name}')\n" }, { "path": "tf/convert_tensorflow.py", "content": "import argparse\nimport sys\n\nfrom tf.backend.utils import load_weight\nfrom tf.model.rfb_320 import create_rfb_net\nfrom tf.model.slim_320 import create_slim_net\n\nparser = argparse.ArgumentParser(\n description='convert model')\n\nparser.add_argument('--net_type', default=\"RFB\", type=str,\n help='The network architecture ,optional: RFB (higher precision) or slim (faster)')\nargs = parser.parse_args()\n\n\ndef main():\n input_shape = (240, 320) # H,W\n base_channel = 8 * 2\n num_classes = 2\n\n if args.net_type == 'slim':\n torch_path = \"../models/pretrained/version-slim-320.pth\"\n mapping_table = \"mapping_tables/slim_320.json\"\n model = create_slim_net(input_shape, base_channel, num_classes)\n elif args.net_type == 'RFB':\n torch_path = \"../models/pretrained/version-RFB-320.pth\"\n mapping_table = \"mapping_tables/rfb_320.json\"\n model = create_rfb_net(input_shape, base_channel, num_classes)\n else:\n print(\"The net type is wrong!\")\n sys.exit(1)\n\n load_weight(model, torch_path, mapping_table)\n model.save(f'export_models/{args.net_type}/', include_optimizer=False)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tf/det_image.py", "content": "import argparse\nimport sys\n\nimport cv2\nimport tensorflow as tf\nimport numpy as np\n\nparser = argparse.ArgumentParser(\n description='convert model')\n\nparser.add_argument('--net_type', default=\"RFB\", type=str,\n help='The network architecture ,optional: RFB (higher precision) or slim (faster)')\nparser.add_argument('--img_path', default='imgs/test_input.jpg', type=str,\n help='Image path for inference')\nargs = parser.parse_args()\n\n\ndef main():\n if args.net_type == 'slim':\n model_path = \"export_models/slim/\"\n elif args.net_type == 'RFB':\n model_path = \"export_models/RFB/\"\n else:\n print(\"The net type is wrong!\")\n sys.exit(1)\n\n model = tf.keras.models.load_model(model_path)\n\n img = cv2.imread(args.img_path)\n h, w, _ = img.shape\n img_resize = cv2.resize(img, (320, 240))\n img_resize = cv2.cvtColor(img_resize, cv2.COLOR_BGR2RGB)\n img_resize = img_resize - 127.0\n img_resize = img_resize / 128.0\n\n results = model.predict(np.expand_dims(img_resize, axis=0)) # result=[background,face,x1,y1,x2,y2]\n\n for result in results:\n start_x = int(result[2] * w)\n start_y = int(result[3] * h)\n end_x = int(result[4] * w)\n end_y = int(result[5] * h)\n\n cv2.rectangle(img, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)\n\n cv2.imwrite(f'imgs/test_output_{args.net_type}.jpg', img)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tf/mapping_tables/rfb_320.json", "content": "[\n {\n \"name\": \"basenet.0.0_conv\",\n \"weight\": [\n \"base_net.0.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.0.1_bn\",\n \"weight\": [\n \"base_net.0.1.weight\",\n \"base_net.0.1.bias\",\n \"base_net.0.1.running_mean\",\n \"base_net.0.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.1.0_dconv\",\n \"weight\": [\n \"base_net.1.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.1.1_bn\",\n \"weight\": [\n \"base_net.1.1.weight\",\n \"base_net.1.1.bias\",\n \"base_net.1.1.running_mean\",\n \"base_net.1.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.1.3_conv\",\n \"weight\": [\n \"base_net.1.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.1.4_bn\",\n \"weight\": [\n \"base_net.1.4.weight\",\n \"base_net.1.4.bias\",\n \"base_net.1.4.running_mean\",\n \"base_net.1.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.2.0_dconv\",\n \"weight\": [\n \"base_net.2.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.2.1_bn\",\n \"weight\": [\n \"base_net.2.1.weight\",\n \"base_net.2.1.bias\",\n \"base_net.2.1.running_mean\",\n \"base_net.2.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.2.3_conv\",\n \"weight\": [\n \"base_net.2.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.2.4_bn\",\n \"weight\": [\n \"base_net.2.4.weight\",\n \"base_net.2.4.bias\",\n \"base_net.2.4.running_mean\",\n \"base_net.2.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.3.0_dconv\",\n \"weight\": [\n \"base_net.3.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.3.1_bn\",\n \"weight\": [\n \"base_net.3.1.weight\",\n \"base_net.3.1.bias\",\n \"base_net.3.1.running_mean\",\n \"base_net.3.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.3.3_conv\",\n \"weight\": [\n \"base_net.3.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.3.4_bn\",\n \"weight\": [\n \"base_net.3.4.weight\",\n \"base_net.3.4.bias\",\n \"base_net.3.4.running_mean\",\n \"base_net.3.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.4.0_dconv\",\n \"weight\": [\n \"base_net.4.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.4.1_bn\",\n \"weight\": [\n \"base_net.4.1.weight\",\n \"base_net.4.1.bias\",\n \"base_net.4.1.running_mean\",\n \"base_net.4.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.4.3_conv\",\n \"weight\": [\n \"base_net.4.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.4.4_bn\",\n \"weight\": [\n \"base_net.4.4.weight\",\n \"base_net.4.4.bias\",\n \"base_net.4.4.running_mean\",\n \"base_net.4.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.5.0_dconv\",\n \"weight\": [\n \"base_net.5.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.5.1_bn\",\n \"weight\": [\n \"base_net.5.1.weight\",\n \"base_net.5.1.bias\",\n \"base_net.5.1.running_mean\",\n \"base_net.5.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.5.3_conv\",\n \"weight\": [\n \"base_net.5.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.5.4_bn\",\n \"weight\": [\n \"base_net.5.4.weight\",\n \"base_net.5.4.bias\",\n \"base_net.5.4.running_mean\",\n \"base_net.5.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.6.0_dconv\",\n \"weight\": [\n \"base_net.6.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.6.1_bn\",\n \"weight\": [\n \"base_net.6.1.weight\",\n \"base_net.6.1.bias\",\n \"base_net.6.1.running_mean\",\n \"base_net.6.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.6.3_conv\",\n \"weight\": [\n \"base_net.6.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.6.4_bn\",\n \"weight\": [\n \"base_net.6.4.weight\",\n \"base_net.6.4.bias\",\n \"base_net.6.4.running_mean\",\n \"base_net.6.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch0.0_conv\",\n \"weight\": [\n \"base_net.7.branch0.0.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch0.0_bn\",\n \"weight\": [\n \"base_net.7.branch0.0.bn.weight\",\n \"base_net.7.branch0.0.bn.bias\",\n \"base_net.7.branch0.0.bn.running_mean\",\n \"base_net.7.branch0.0.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch0.1_conv\",\n \"weight\": [\n \"base_net.7.branch0.1.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch0.1_bn\",\n \"weight\": [\n \"base_net.7.branch0.1.bn.weight\",\n \"base_net.7.branch0.1.bn.bias\",\n \"base_net.7.branch0.1.bn.running_mean\",\n \"base_net.7.branch0.1.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch0.2_conv\",\n \"weight\": [\n \"base_net.7.branch0.2.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch0.2_bn\",\n \"weight\": [\n \"base_net.7.branch0.2.bn.weight\",\n \"base_net.7.branch0.2.bn.bias\",\n \"base_net.7.branch0.2.bn.running_mean\",\n \"base_net.7.branch0.2.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch1.0_conv\",\n \"weight\": [\n \"base_net.7.branch1.0.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch1.0_bn\",\n \"weight\": [\n \"base_net.7.branch1.0.bn.weight\",\n \"base_net.7.branch1.0.bn.bias\",\n \"base_net.7.branch1.0.bn.running_mean\",\n \"base_net.7.branch1.0.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch1.1_conv\",\n \"weight\": [\n \"base_net.7.branch1.1.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch1.1_bn\",\n \"weight\": [\n \"base_net.7.branch1.1.bn.weight\",\n \"base_net.7.branch1.1.bn.bias\",\n \"base_net.7.branch1.1.bn.running_mean\",\n \"base_net.7.branch1.1.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch1.2_conv\",\n \"weight\": [\n \"base_net.7.branch1.2.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch1.2_bn\",\n \"weight\": [\n \"base_net.7.branch1.2.bn.weight\",\n \"base_net.7.branch1.2.bn.bias\",\n \"base_net.7.branch1.2.bn.running_mean\",\n \"base_net.7.branch1.2.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.0_conv\",\n \"weight\": [\n \"base_net.7.branch2.0.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.0_bn\",\n \"weight\": [\n \"base_net.7.branch2.0.bn.weight\",\n \"base_net.7.branch2.0.bn.bias\",\n \"base_net.7.branch2.0.bn.running_mean\",\n \"base_net.7.branch2.0.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.1_conv\",\n \"weight\": [\n \"base_net.7.branch2.1.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.1_bn\",\n \"weight\": [\n \"base_net.7.branch2.1.bn.weight\",\n \"base_net.7.branch2.1.bn.bias\",\n \"base_net.7.branch2.1.bn.running_mean\",\n \"base_net.7.branch2.1.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.2_conv\",\n \"weight\": [\n \"base_net.7.branch2.2.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.2_bn\",\n \"weight\": [\n \"base_net.7.branch2.2.bn.weight\",\n \"base_net.7.branch2.2.bn.bias\",\n \"base_net.7.branch2.2.bn.running_mean\",\n \"base_net.7.branch2.2.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.3_conv\",\n \"weight\": [\n \"base_net.7.branch2.3.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.branch2.3_bn\",\n \"weight\": [\n \"base_net.7.branch2.3.bn.weight\",\n \"base_net.7.branch2.3.bn.bias\",\n \"base_net.7.branch2.3.bn.running_mean\",\n \"base_net.7.branch2.3.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.convlinear_conv\",\n \"weight\": [\n \"base_net.7.ConvLinear.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.convlinear_bn\",\n \"weight\": [\n \"base_net.7.ConvLinear.bn.weight\",\n \"base_net.7.ConvLinear.bn.bias\",\n \"base_net.7.ConvLinear.bn.running_mean\",\n \"base_net.7.ConvLinear.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.shortcut_conv\",\n \"weight\": [\n \"base_net.7.shortcut.conv.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.shortcut_bn\",\n \"weight\": [\n \"base_net.7.shortcut.bn.weight\",\n \"base_net.7.shortcut.bn.bias\",\n \"base_net.7.shortcut.bn.running_mean\",\n \"base_net.7.shortcut.bn.running_var\"\n ]\n },\n {\n \"name\": \"basenet.8.0_dconv\",\n \"weight\": [\n \"base_net.8.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.8.1_bn\",\n \"weight\": [\n \"base_net.8.1.weight\",\n \"base_net.8.1.bias\",\n \"base_net.8.1.running_mean\",\n \"base_net.8.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.8.3_conv\",\n \"weight\": [\n \"base_net.8.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.8.4_bn\",\n \"weight\": [\n \"base_net.8.4.weight\",\n \"base_net.8.4.bias\",\n \"base_net.8.4.running_mean\",\n \"base_net.8.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.9.0_dconv\",\n \"weight\": [\n \"base_net.9.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.9.1_bn\",\n \"weight\": [\n \"base_net.9.1.weight\",\n \"base_net.9.1.bias\",\n \"base_net.9.1.running_mean\",\n \"base_net.9.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.9.3_conv\",\n \"weight\": [\n \"base_net.9.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.9.4_bn\",\n \"weight\": [\n \"base_net.9.4.weight\",\n \"base_net.9.4.bias\",\n \"base_net.9.4.running_mean\",\n \"base_net.9.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.10.0_dconv\",\n \"weight\": [\n \"base_net.10.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.10.1_bn\",\n \"weight\": [\n \"base_net.10.1.weight\",\n \"base_net.10.1.bias\",\n \"base_net.10.1.running_mean\",\n \"base_net.10.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.10.3_conv\",\n \"weight\": [\n \"base_net.10.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.10.4_bn\",\n \"weight\": [\n \"base_net.10.4.weight\",\n \"base_net.10.4.bias\",\n \"base_net.10.4.running_mean\",\n \"base_net.10.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.11.0_dconv\",\n \"weight\": [\n \"base_net.11.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.11.1_bn\",\n \"weight\": [\n \"base_net.11.1.weight\",\n \"base_net.11.1.bias\",\n \"base_net.11.1.running_mean\",\n \"base_net.11.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.11.3_conv\",\n \"weight\": [\n \"base_net.11.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.11.4_bn\",\n \"weight\": [\n \"base_net.11.4.weight\",\n \"base_net.11.4.bias\",\n \"base_net.11.4.running_mean\",\n \"base_net.11.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.12.0_dconv\",\n \"weight\": [\n \"base_net.12.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.12.1_bn\",\n \"weight\": [\n \"base_net.12.1.weight\",\n \"base_net.12.1.bias\",\n \"base_net.12.1.running_mean\",\n \"base_net.12.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.12.3_conv\",\n \"weight\": [\n \"base_net.12.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.12.4_bn\",\n \"weight\": [\n \"base_net.12.4.weight\",\n \"base_net.12.4.bias\",\n \"base_net.12.4.running_mean\",\n \"base_net.12.4.running_var\"\n ]\n },\n {\n \"name\": \"extras_convbias\",\n \"weight\": [\n \"extras.0.0.weight\",\n \"extras.0.0.bias\"\n ]\n },\n {\n \"name\": \"extras_sep_dconvbias\",\n \"weight\": [\n \"extras.0.2.0.weight\",\n \"extras.0.2.0.bias\"\n ]\n },\n {\n \"name\": \"extras_sep_convbias\",\n \"weight\": [\n \"extras.0.2.2.weight\",\n \"extras.0.2.2.bias\"\n ]\n },\n {\n \"name\": \"cls_0_sep_dconvbias\",\n \"weight\": [\n \"classification_headers.0.0.weight\",\n \"classification_headers.0.0.bias\"\n ]\n },\n {\n \"name\": \"cls_0_sep_convbias\",\n \"weight\": [\n \"classification_headers.0.2.weight\",\n \"classification_headers.0.2.bias\"\n ]\n },\n {\n \"name\": \"cls_1_sep_dconvbias\",\n \"weight\": [\n \"classification_headers.1.0.weight\",\n \"classification_headers.1.0.bias\"\n ]\n },\n {\n \"name\": \"cls_1_sep_convbias\",\n \"weight\": [\n \"classification_headers.1.2.weight\",\n \"classification_headers.1.2.bias\"\n ]\n },\n {\n \"name\": \"cls_2_sep_dconvbias\",\n \"weight\": [\n \"classification_headers.2.0.weight\",\n \"classification_headers.2.0.bias\"\n ]\n },\n {\n \"name\": \"cls_2_sep_convbias\",\n \"weight\": [\n \"classification_headers.2.2.weight\",\n \"classification_headers.2.2.bias\"\n ]\n },\n {\n \"name\": \"cls_3_convbias\",\n \"weight\": [\n \"classification_headers.3.weight\",\n \"classification_headers.3.bias\"\n ]\n },\n {\n \"name\": \"reg_0_sep_dconvbias\",\n \"weight\": [\n \"regression_headers.0.0.weight\",\n \"regression_headers.0.0.bias\"\n ]\n },\n {\n \"name\": \"reg_0_sep_convbias\",\n \"weight\": [\n \"regression_headers.0.2.weight\",\n \"regression_headers.0.2.bias\"\n ]\n },\n {\n \"name\": \"reg_1_sep_dconvbias\",\n \"weight\": [\n \"regression_headers.1.0.weight\",\n \"regression_headers.1.0.bias\"\n ]\n },\n {\n \"name\": \"reg_1_sep_convbias\",\n \"weight\": [\n \"regression_headers.1.2.weight\",\n \"regression_headers.1.2.bias\"\n ]\n },\n {\n \"name\": \"reg_2_sep_dconvbias\",\n \"weight\": [\n \"regression_headers.2.0.weight\",\n \"regression_headers.2.0.bias\"\n ]\n },\n {\n \"name\": \"reg_2_sep_convbias\",\n \"weight\": [\n \"regression_headers.2.2.weight\",\n \"regression_headers.2.2.bias\"\n ]\n },\n {\n \"name\": \"reg_3_convbias\",\n \"weight\": [\n \"regression_headers.3.weight\",\n \"regression_headers.3.bias\"\n ]\n }\n]" }, { "path": "tf/mapping_tables/slim_320.json", "content": "[\n {\n \"name\": \"basenet.0.0_conv\",\n \"weight\": [\n \"base_net.0.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.0.1_bn\",\n \"weight\": [\n \"base_net.0.1.weight\",\n \"base_net.0.1.bias\",\n \"base_net.0.1.running_mean\",\n \"base_net.0.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.1.0_dconv\",\n \"weight\": [\n \"base_net.1.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.1.1_bn\",\n \"weight\": [\n \"base_net.1.1.weight\",\n \"base_net.1.1.bias\",\n \"base_net.1.1.running_mean\",\n \"base_net.1.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.1.3_conv\",\n \"weight\": [\n \"base_net.1.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.1.4_bn\",\n \"weight\": [\n \"base_net.1.4.weight\",\n \"base_net.1.4.bias\",\n \"base_net.1.4.running_mean\",\n \"base_net.1.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.2.0_dconv\",\n \"weight\": [\n \"base_net.2.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.2.1_bn\",\n \"weight\": [\n \"base_net.2.1.weight\",\n \"base_net.2.1.bias\",\n \"base_net.2.1.running_mean\",\n \"base_net.2.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.2.3_conv\",\n \"weight\": [\n \"base_net.2.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.2.4_bn\",\n \"weight\": [\n \"base_net.2.4.weight\",\n \"base_net.2.4.bias\",\n \"base_net.2.4.running_mean\",\n \"base_net.2.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.3.0_dconv\",\n \"weight\": [\n \"base_net.3.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.3.1_bn\",\n \"weight\": [\n \"base_net.3.1.weight\",\n \"base_net.3.1.bias\",\n \"base_net.3.1.running_mean\",\n \"base_net.3.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.3.3_conv\",\n \"weight\": [\n \"base_net.3.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.3.4_bn\",\n \"weight\": [\n \"base_net.3.4.weight\",\n \"base_net.3.4.bias\",\n \"base_net.3.4.running_mean\",\n \"base_net.3.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.4.0_dconv\",\n \"weight\": [\n \"base_net.4.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.4.1_bn\",\n \"weight\": [\n \"base_net.4.1.weight\",\n \"base_net.4.1.bias\",\n \"base_net.4.1.running_mean\",\n \"base_net.4.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.4.3_conv\",\n \"weight\": [\n \"base_net.4.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.4.4_bn\",\n \"weight\": [\n \"base_net.4.4.weight\",\n \"base_net.4.4.bias\",\n \"base_net.4.4.running_mean\",\n \"base_net.4.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.5.0_dconv\",\n \"weight\": [\n \"base_net.5.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.5.1_bn\",\n \"weight\": [\n \"base_net.5.1.weight\",\n \"base_net.5.1.bias\",\n \"base_net.5.1.running_mean\",\n \"base_net.5.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.5.3_conv\",\n \"weight\": [\n \"base_net.5.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.5.4_bn\",\n \"weight\": [\n \"base_net.5.4.weight\",\n \"base_net.5.4.bias\",\n \"base_net.5.4.running_mean\",\n \"base_net.5.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.6.0_dconv\",\n \"weight\": [\n \"base_net.6.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.6.1_bn\",\n \"weight\": [\n \"base_net.6.1.weight\",\n \"base_net.6.1.bias\",\n \"base_net.6.1.running_mean\",\n \"base_net.6.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.6.3_conv\",\n \"weight\": [\n \"base_net.6.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.6.4_bn\",\n \"weight\": [\n \"base_net.6.4.weight\",\n \"base_net.6.4.bias\",\n \"base_net.6.4.running_mean\",\n \"base_net.6.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.0_dconv\",\n \"weight\": [\n \"base_net.7.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.1_bn\",\n \"weight\": [\n \"base_net.7.1.weight\",\n \"base_net.7.1.bias\",\n \"base_net.7.1.running_mean\",\n \"base_net.7.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.7.3_conv\",\n \"weight\": [\n \"base_net.7.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.7.4_bn\",\n \"weight\": [\n \"base_net.7.4.weight\",\n \"base_net.7.4.bias\",\n \"base_net.7.4.running_mean\",\n \"base_net.7.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.8.0_dconv\",\n \"weight\": [\n \"base_net.8.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.8.1_bn\",\n \"weight\": [\n \"base_net.8.1.weight\",\n \"base_net.8.1.bias\",\n \"base_net.8.1.running_mean\",\n \"base_net.8.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.8.3_conv\",\n \"weight\": [\n \"base_net.8.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.8.4_bn\",\n \"weight\": [\n \"base_net.8.4.weight\",\n \"base_net.8.4.bias\",\n \"base_net.8.4.running_mean\",\n \"base_net.8.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.9.0_dconv\",\n \"weight\": [\n \"base_net.9.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.9.1_bn\",\n \"weight\": [\n \"base_net.9.1.weight\",\n \"base_net.9.1.bias\",\n \"base_net.9.1.running_mean\",\n \"base_net.9.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.9.3_conv\",\n \"weight\": [\n \"base_net.9.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.9.4_bn\",\n \"weight\": [\n \"base_net.9.4.weight\",\n \"base_net.9.4.bias\",\n \"base_net.9.4.running_mean\",\n \"base_net.9.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.10.0_dconv\",\n \"weight\": [\n \"base_net.10.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.10.1_bn\",\n \"weight\": [\n \"base_net.10.1.weight\",\n \"base_net.10.1.bias\",\n \"base_net.10.1.running_mean\",\n \"base_net.10.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.10.3_conv\",\n \"weight\": [\n \"base_net.10.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.10.4_bn\",\n \"weight\": [\n \"base_net.10.4.weight\",\n \"base_net.10.4.bias\",\n \"base_net.10.4.running_mean\",\n \"base_net.10.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.11.0_dconv\",\n \"weight\": [\n \"base_net.11.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.11.1_bn\",\n \"weight\": [\n \"base_net.11.1.weight\",\n \"base_net.11.1.bias\",\n \"base_net.11.1.running_mean\",\n \"base_net.11.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.11.3_conv\",\n \"weight\": [\n \"base_net.11.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.11.4_bn\",\n \"weight\": [\n \"base_net.11.4.weight\",\n \"base_net.11.4.bias\",\n \"base_net.11.4.running_mean\",\n \"base_net.11.4.running_var\"\n ]\n },\n {\n \"name\": \"basenet.12.0_dconv\",\n \"weight\": [\n \"base_net.12.0.weight\"\n ]\n },\n {\n \"name\": \"basenet.12.1_bn\",\n \"weight\": [\n \"base_net.12.1.weight\",\n \"base_net.12.1.bias\",\n \"base_net.12.1.running_mean\",\n \"base_net.12.1.running_var\"\n ]\n },\n {\n \"name\": \"basenet.12.3_conv\",\n \"weight\": [\n \"base_net.12.3.weight\"\n ]\n },\n {\n \"name\": \"basenet.12.4_bn\",\n \"weight\": [\n \"base_net.12.4.weight\",\n \"base_net.12.4.bias\",\n \"base_net.12.4.running_mean\",\n \"base_net.12.4.running_var\"\n ]\n },\n {\n \"name\": \"extras_convbias\",\n \"weight\": [\n \"extras.0.0.weight\",\n \"extras.0.0.bias\"\n ]\n },\n {\n \"name\": \"extras_sep_dconvbias\",\n \"weight\": [\n \"extras.0.2.0.weight\",\n \"extras.0.2.0.bias\"\n ]\n },\n {\n \"name\": \"extras_sep_convbias\",\n \"weight\": [\n \"extras.0.2.2.weight\",\n \"extras.0.2.2.bias\"\n ]\n },\n {\n \"name\": \"cls_0_sep_dconvbias\",\n \"weight\": [\n \"classification_headers.0.0.weight\",\n \"classification_headers.0.0.bias\"\n ]\n },\n {\n \"name\": \"cls_0_sep_convbias\",\n \"weight\": [\n \"classification_headers.0.2.weight\",\n \"classification_headers.0.2.bias\"\n ]\n },\n {\n \"name\": \"cls_1_sep_dconvbias\",\n \"weight\": [\n \"classification_headers.1.0.weight\",\n \"classification_headers.1.0.bias\"\n ]\n },\n {\n \"name\": \"cls_1_sep_convbias\",\n \"weight\": [\n \"classification_headers.1.2.weight\",\n \"classification_headers.1.2.bias\"\n ]\n },\n {\n \"name\": \"cls_2_sep_dconvbias\",\n \"weight\": [\n \"classification_headers.2.0.weight\",\n \"classification_headers.2.0.bias\"\n ]\n },\n {\n \"name\": \"cls_2_sep_convbias\",\n \"weight\": [\n \"classification_headers.2.2.weight\",\n \"classification_headers.2.2.bias\"\n ]\n },\n {\n \"name\": \"cls_3_convbias\",\n \"weight\": [\n \"classification_headers.3.weight\",\n \"classification_headers.3.bias\"\n ]\n },\n {\n \"name\": \"reg_0_sep_dconvbias\",\n \"weight\": [\n \"regression_headers.0.0.weight\",\n \"regression_headers.0.0.bias\"\n ]\n },\n {\n \"name\": \"reg_0_sep_convbias\",\n \"weight\": [\n \"regression_headers.0.2.weight\",\n \"regression_headers.0.2.bias\"\n ]\n },\n {\n \"name\": \"reg_1_sep_dconvbias\",\n \"weight\": [\n \"regression_headers.1.0.weight\",\n \"regression_headers.1.0.bias\"\n ]\n },\n {\n \"name\": \"reg_1_sep_convbias\",\n \"weight\": [\n \"regression_headers.1.2.weight\",\n \"regression_headers.1.2.bias\"\n ]\n },\n {\n \"name\": \"reg_2_sep_dconvbias\",\n \"weight\": [\n \"regression_headers.2.0.weight\",\n \"regression_headers.2.0.bias\"\n ]\n },\n {\n \"name\": \"reg_2_sep_convbias\",\n \"weight\": [\n \"regression_headers.2.2.weight\",\n \"regression_headers.2.2.bias\"\n ]\n },\n {\n \"name\": \"reg_3_convbias\",\n \"weight\": [\n \"regression_headers.3.weight\",\n \"regression_headers.3.bias\"\n ]\n }\n]" }, { "path": "tf/model/rfb_320.py", "content": "import tensorflow as tf\n\nfrom tf.backend.op import conv_bn, conv_dw, basic_rfb, separable_conv\nfrom tf.backend.utils import post_processing\n\nconf_threshold = 0.6\nnms_iou_threshold = 0.3\nnms_max_output_size = 200\ntop_k = 100\ncenter_variance = 0.1\nsize_variance = 0.2\n\nimage_size = [320, 240] # default input size 320*240\nfeature_map_wh_list = [[40, 30], [20, 15], [10, 8], [5, 4]] # default feature map size\nmin_boxes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]\n\n\ndef create_rfb_net(input_shape, base_channel, num_classes):\n input_node = tf.keras.layers.Input(shape=(input_shape[0], input_shape[1], 3))\n\n net = conv_bn(input_node, base_channel, stride=2, prefix='basenet.0') # 120x160\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.1')\n net = conv_dw(net, base_channel * 2, stride=2, prefix='basenet.2') # 60x80\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.3')\n net = conv_dw(net, base_channel * 4, stride=2, prefix='basenet.4') # 30x40\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.5')\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.6')\n header_0 = basic_rfb(net, base_channel * 4, base_channel * 4, stride=1, scale=1.0, prefix='basenet.7')\n net = conv_dw(header_0, base_channel * 8, stride=2, prefix='basenet.8') # 15x20\n net = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.9')\n header_1 = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.10')\n net = conv_dw(header_1, base_channel * 16, stride=2, prefix='basenet.11') # 8x10\n header_2 = conv_dw(net, base_channel * 16, stride=1, prefix='basenet.12')\n\n out = tf.keras.layers.Conv2D(base_channel * 4, 1, padding='SAME', name='extras_convbias')(header_2)\n out = tf.keras.layers.ReLU(name='extras_relu1')(out)\n out = separable_conv(out, base_channel * 16, kernel_size=3, stride=2, padding=1,\n prefix='extras_sep')\n header_3 = tf.keras.layers.ReLU(name='extras_relu2')(out)\n\n reg_0 = separable_conv(header_0, 3 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_0_sep')\n cls_0 = separable_conv(header_0, 3 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_0_sep')\n\n reg_1 = separable_conv(header_1, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_1_sep')\n cls_1 = separable_conv(header_1, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_1_sep')\n\n reg_2 = separable_conv(header_2, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_2_sep')\n cls_2 = separable_conv(header_2, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_2_sep')\n\n reg_3 = tf.keras.layers.Conv2D(3 * 4, kernel_size=3, padding='SAME',\n name='reg_3_convbias')(header_3)\n cls_3 = tf.keras.layers.Conv2D(3 * num_classes, kernel_size=3, padding='SAME',\n name='cls_3_convbias')(header_3)\n\n result = post_processing([reg_0, reg_1, reg_2, reg_3],\n [cls_0, cls_1, cls_2, cls_3],\n num_classes, image_size, feature_map_wh_list, min_boxes,\n center_variance, size_variance)\n\n model = tf.keras.Model(inputs=[input_node], outputs=[result])\n model.summary()\n\n return model\n" }, { "path": "tf/model/slim_320.py", "content": "import tensorflow as tf\n\nfrom tf.backend.op import conv_bn, conv_dw, separable_conv\nfrom tf.backend.utils import post_processing\n\nconf_threshold = 0.6\nnms_iou_threshold = 0.3\nnms_max_output_size = 200\ntop_k = 100\ncenter_variance = 0.1\nsize_variance = 0.2\n\nimage_size = [320, 240] # default input size 320*240\nfeature_map_wh_list = [[40, 30], [20, 15], [10, 8], [5, 4]] # default feature map size\nmin_boxes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]\n\n\ndef create_slim_net(input_shape, base_channel, num_classes):\n input_node = tf.keras.layers.Input(shape=(input_shape[0], input_shape[1], 3))\n\n net = conv_bn(input_node, base_channel, stride=2, prefix='basenet.0') # 120x160\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.1')\n net = conv_dw(net, base_channel * 2, stride=2, prefix='basenet.2') # 60x80\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.3')\n net = conv_dw(net, base_channel * 4, stride=2, prefix='basenet.4') # 30x40\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.5')\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.6')\n header_0 = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.7')\n net = conv_dw(header_0, base_channel * 8, stride=2, prefix='basenet.8') # 15x20\n net = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.9')\n header_1 = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.10')\n net = conv_dw(header_1, base_channel * 16, stride=2, prefix='basenet.11') # 8x10\n header_2 = conv_dw(net, base_channel * 16, stride=1, prefix='basenet.12')\n\n out = tf.keras.layers.Conv2D(base_channel * 4, 1, padding='SAME', name='extras_convbias')(header_2)\n out = tf.keras.layers.ReLU(name='extras_relu1')(out)\n out = separable_conv(out, base_channel * 16, kernel_size=3, stride=2, padding=1,\n prefix='extras_sep')\n header_3 = tf.keras.layers.ReLU(name='extras_relu2')(out)\n\n reg_0 = separable_conv(header_0, 3 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_0_sep')\n cls_0 = separable_conv(header_0, 3 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_0_sep')\n\n reg_1 = separable_conv(header_1, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_1_sep')\n cls_1 = separable_conv(header_1, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_1_sep')\n\n reg_2 = separable_conv(header_2, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_2_sep')\n cls_2 = separable_conv(header_2, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_2_sep')\n\n reg_3 = tf.keras.layers.Conv2D(3 * 4, kernel_size=3, padding='SAME',\n name='reg_3_convbias')(header_3)\n cls_3 = tf.keras.layers.Conv2D(3 * num_classes, kernel_size=3, padding='SAME',\n name='cls_3_convbias')(header_3)\n\n result = post_processing([reg_0, reg_1, reg_2, reg_3],\n [cls_0, cls_1, cls_2, cls_3],\n num_classes, image_size, feature_map_wh_list, min_boxes,\n center_variance, size_variance)\n\n model = tf.keras.Model(inputs=[input_node], outputs=[result])\n model.summary()\n\n return model\n" }, { "path": "tflite/README.md", "content": "# TFLite implementation of Ultra-Light-Fast-Generic-Face-Detector-1MB\n\nTFLite model is suitable for edge computing devices.\nPlease refer to the official [Android Demo](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android) for learning how to deploy the face detection model on your phones.\n\n![Dwq3dS.jpg](https://s3.ax1x.com/2020/11/26/Dwq3dS.jpg)\n\n## Run\n\nInference on image\n\n``` bash\npython3 inference_test.py --net_type --img_path \n```\n\nInference on video\n\n``` bash\npython3 inference_test.py --net_type --video_path \n```\n\n## Import\n\n``` python\nfrom TFLiteFaceDetector import UltraLightFaceDetecion\n\nfd = UltraLightFaceDetecion(model_path,\n input_size=(320, 240), conf_threshold=0.6,\n center_variance=0.1, size_variance=0.2,\n nms_max_output_size=200, nms_iou_threshold=0.3)\n```\n\n## Files Tree\n\nThe pretrained weights are converted form `onnx -> protobuf -> tflite`.\n\n``` bash\n.\n├── model # keras defined model architecture\n│ ├── tflite_RFB_320_without_postprocessing.py\n│ └── tflite_slim_320_without_postprocessing.py\n├── pretrained # pretrained model without post-processing\n│ ├── version-RFB-320_without_postprocessing.tflite\n│ └── version-slim-320_without_postprocessing.tflite\n├── README.md\n├── inference_test.py # detector test script\n└── TFLiteFaceDetector.py # class file of the tflite detector\n```\n\n## Special Thanks\n\nPart of the code for this work is referenced from the following repositories:\n\n- [Ultra-Light-Fast-Generic-Face-Detector_Tensorflow-Model-Converter](https://github.com/jason9075/Ultra-Light-Fast-Generic-Face-Detector_Tensorflow-Model-Converter)\n" }, { "path": "tflite/TFLiteFaceDetector.py", "content": "from functools import partial\nimport cv2\nimport tensorflow as tf\nimport numpy as np\n\n\nclass UltraLightFaceDetecion():\n def __init__(self, filepath, input_size=(320, 240), conf_threshold=0.6,\n center_variance=0.1, size_variance=0.2,\n nms_max_output_size=200, nms_iou_threshold=0.3) -> None:\n\n self._feature_maps = np.array([[40, 30], [20, 15], [10, 8], [5, 4]])\n self._min_boxes = np.array([[10, 16, 24], [32, 48],\n [64, 96], [128, 192, 256]])\n\n self._resize = partial(cv2.resize, dsize=input_size)\n self._input_size = np.array(input_size)[:, None]\n\n self._anchors_xy, self._anchors_wh = self._generate_anchors()\n self._conf_threshold = conf_threshold\n self._center_variance = center_variance\n self._size_variance = size_variance\n self._nms = partial(tf.image.non_max_suppression,\n max_output_size=nms_max_output_size,\n iou_threshold=nms_iou_threshold)\n\n # tflite model init\n self._interpreter = tf.lite.Interpreter(model_path=filepath)\n self._interpreter.allocate_tensors()\n\n # model details\n input_details = self._interpreter.get_input_details()\n output_details = self._interpreter.get_output_details()\n\n # inference helper\n self._set_input_tensor = partial(self._interpreter.set_tensor,\n input_details[0][\"index\"])\n self._get_boxes_tensor = partial(self._interpreter.get_tensor,\n output_details[0][\"index\"])\n self._get_scores_tensor = partial(self._interpreter.get_tensor,\n output_details[1][\"index\"])\n\n def _generate_anchors(self):\n anchors = []\n for feature_map_w_h, min_box in zip(self._feature_maps, self._min_boxes):\n\n wh_grid = min_box / self._input_size\n wh_grid = np.tile(wh_grid.T, (np.prod(feature_map_w_h), 1))\n\n xy_grid = np.meshgrid(range(feature_map_w_h[0]),\n range(feature_map_w_h[1]))\n xy_grid = np.add(xy_grid, 0.5)\n\n xy_grid /= feature_map_w_h[..., None, None]\n\n xy_grid = np.stack(xy_grid, axis=-1)\n xy_grid = np.tile(xy_grid, [1, 1, len(min_box)])\n xy_grid = xy_grid.reshape(-1, 2)\n\n prior = np.concatenate((xy_grid, wh_grid), axis=-1)\n anchors.append(prior)\n\n anchors = np.concatenate(anchors, axis=0)\n anchors = np.clip(anchors, 0.0, 1.0)\n\n return anchors[:, :2], anchors[:, 2:]\n\n def _pre_processing(self, img):\n resized = self._resize(img)\n image_rgb = resized[..., ::-1]\n image_norm = image_rgb.astype(np.float32)\n cv2.normalize(image_norm, image_norm,\n alpha=-1, beta=1, norm_type=cv2.NORM_MINMAX)\n return image_norm[None, ...]\n\n def inference(self, img):\n # BGR image to tensor\n input_tensor = self._pre_processing(img)\n\n # set tensor and invoke\n self._set_input_tensor(input_tensor)\n self._interpreter.invoke()\n\n # get results\n boxes = self._get_boxes_tensor()[0]\n scores = self._get_scores_tensor()[0]\n\n # decode boxes to corner format\n boxes, scores = self._post_processing(boxes, scores)\n boxes *= np.tile(img.shape[1::-1], 2)\n\n return boxes, scores\n\n def _post_processing(self, boxes, scores):\n # bounding box regression\n boxes = self._decode_regression(boxes)\n scores = scores[:, 1]\n\n # confidence threshold filter\n conf_mask = self._conf_threshold < scores\n boxes, scores = boxes[conf_mask], scores[conf_mask]\n\n # non-maximum suppression\n nms_mask = self._nms(boxes=boxes, scores=scores)\n boxes = np.take(boxes, nms_mask, axis=0)\n\n return boxes, scores\n\n def _decode_regression(self, reg):\n # bounding box regression\n center_xy = reg[:, :2] * self._center_variance * \\\n self._anchors_wh + self._anchors_xy\n center_wh = np.exp(\n reg[:, 2:] * self._size_variance) * self._anchors_wh / 2\n\n # center to corner\n start_xy = center_xy - center_wh\n end_xy = center_xy + center_wh\n\n boxes = np.concatenate((start_xy, end_xy), axis=-1)\n boxes = np.clip(boxes, 0.0, 1.0)\n\n return boxes\n" }, { "path": "tflite/inference_test.py", "content": "import argparse\nimport cv2\nimport time\n\nfrom TFLiteFaceDetector import UltraLightFaceDetecion\n\n\nparser = argparse.ArgumentParser(description='TFLite Face Detector')\n\nparser.add_argument('--net_type', default=\"RFB\", type=str,\n help='The network architecture ,optional: RFB (higher precision) or slim (faster)')\nparser.add_argument('--img_path', type=str, help='Image path for inference')\nparser.add_argument('--video_path', type=str, help='Video path for inference')\n\nargs = parser.parse_args()\n\n\ndef image_inference(image_path, model_path, color=(125, 255, 0)):\n\n fd = UltraLightFaceDetecion(model_path,\n conf_threshold=0.6)\n\n img = cv2.imread(image_path)\n\n boxes, scores = fd.inference(img)\n\n for result in boxes.astype(int):\n cv2.rectangle(img, (result[0], result[1]),\n (result[2], result[3]), color, 2)\n\n cv2.imshow('res', img)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n\ndef video_inference(video, model_path, color=(125, 255, 0)):\n\n fd = UltraLightFaceDetecion(model_path,\n conf_threshold=0.88)\n\n cap = cv2.VideoCapture(video)\n\n while True:\n ret, frame = cap.read()\n\n if not ret:\n break\n\n start_time = time.perf_counter()\n boxes, scores = fd.inference(frame)\n print(time.perf_counter() - start_time)\n\n for result in boxes.astype(int):\n cv2.rectangle(frame, (result[0], result[1]),\n (result[2], result[3]), color, 2)\n\n cv2.imshow('res', frame)\n if cv2.waitKey(1) == ord('q'):\n break\n\n cap.release()\n cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n\n filepath = f\"pretrained/version-{args.net_type}-320_without_postprocessing.tflite\"\n\n if args.img_path:\n image_inference(args.img_path, filepath)\n elif args.video_path:\n video_inference(args.video_path, filepath)\n else:\n print('--ima_path or --video_path must be filled')\n" }, { "path": "tflite/model/tflite_RFB_320_without_postprocessing.py", "content": "import tensorflow as tf\n\nimport sys\nsys.path.append(\"../tf\")\nfrom backend.op import conv_bn, conv_dw, basic_rfb, separable_conv\n\ndef basic_conv(x, out_ch, kernel_size, stride=(1, 1), padding=0, dilation=1, relu=True,\n bn=True, prefix='basic_conv'):\n if 0 < padding:\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}_padding')(x)\n else:\n out = x\n out = tf.keras.layers.Conv2D(out_ch,\n kernel_size,\n strides=stride,\n dilation_rate=dilation,\n use_bias=(not bn),\n name=f'{prefix}_conv')(out)\n if bn:\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}_bn')(out)\n if relu:\n out = tf.keras.layers.ReLU(name=f'{prefix}_relu')(out)\n\n return out\n\n\ndef basic_rfb(x, in_ch, out_ch, stride=1, scale=0.1, map_reduce=8, vision=1, prefix='basic_rfb'):\n inter_ch = in_ch // map_reduce\n\n branch0 = basic_conv(x, inter_ch, kernel_size=1, stride=1, relu=False,\n prefix=f'{prefix}.branch0.0')\n branch0 = basic_conv(branch0, 2 * inter_ch, kernel_size=3, stride=stride, padding=1,\n prefix=f'{prefix}.branch0.1')\n branch0 = basic_conv(branch0, 2 * inter_ch, kernel_size=3, stride=1, dilation=vision + 1,\n padding=vision + 1, relu=False, prefix=f'{prefix}.branch0.2')\n\n branch1 = basic_conv(x, inter_ch, kernel_size=1, stride=1, relu=False,\n prefix=f'{prefix}.branch1.0')\n branch1 = basic_conv(branch1, 2 * inter_ch, kernel_size=3, stride=stride, padding=1,\n prefix=f'{prefix}.branch1.1')\n branch1 = basic_conv(branch1, 2 * inter_ch, kernel_size=3, stride=1, dilation=vision + 2,\n padding=vision + 2, relu=False, prefix=f'{prefix}.branch1.2')\n\n branch2 = basic_conv(x, inter_ch, kernel_size=1, stride=1, relu=False,\n prefix=f'{prefix}.branch2.0')\n branch2 = basic_conv(branch2, (inter_ch // 2) * 3, kernel_size=3, stride=1, padding=1,\n prefix=f'{prefix}.branch2.1')\n branch2 = basic_conv(branch2, 2 * inter_ch, kernel_size=3, stride=stride, padding=1,\n prefix=f'{prefix}.branch2.2')\n branch2 = basic_conv(branch2, 2 * inter_ch, kernel_size=3, stride=1, dilation=vision + 4,\n padding=vision + 4, relu=False, prefix=f'{prefix}.branch2.3')\n\n out = tf.keras.layers.Concatenate(axis=-1, name=f'{prefix}_cat')([branch0, branch1, branch2])\n out = basic_conv(out, out_ch, kernel_size=1, stride=1, relu=False, prefix=f'{prefix}.convlinear')\n shortcut = basic_conv(x, out_ch, kernel_size=1, stride=stride, relu=False, prefix=f'{prefix}.shortcut')\n out = tf.multiply(out, scale, name=f'{prefix}_mul')\n out = tf.keras.layers.Add(name=f'{prefix}_add')([out, shortcut])\n out = tf.keras.layers.ReLU(name=f'{prefix}_relu')(out)\n\n return out\n\n\ndef separable_conv(x, out_ch, kernel_size, stride, padding, prefix='separable_conv'):\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}_dconv_padding')(x)\n\n out = tf.keras.layers.DepthwiseConv2D(kernel_size,\n strides=stride,\n name=f'{prefix}_dconvbias')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}_relu')(out)\n out = tf.keras.layers.Conv2D(out_ch, 1,\n name=f'{prefix}_convbias')(out)\n\n return out\n\n\ndef conv_bn(x, out_ch, stride, padding=1, prefix='conv_bn'):\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}.0_padding')(x)\n out = tf.keras.layers.Conv2D(out_ch,\n (3, 3),\n strides=stride,\n use_bias=False,\n name=f'{prefix}.0_conv')(out)\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}.1_bn')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}.2_relu')(out)\n\n return out\n\n\ndef conv_dw(x, out_ch, stride, padding=1, prefix='conv_dw'):\n out = tf.keras.layers.ZeroPadding2D(padding=padding, name=f'{prefix}.0_padding')(x)\n out = tf.keras.layers.DepthwiseConv2D(3, strides=stride,\n use_bias=False,\n name=f'{prefix}.0_dconv')(out)\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}.1_bn')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}.2_relu')(out)\n\n out = tf.keras.layers.Conv2D(out_ch, 1, use_bias=False, name=f'{prefix}.3_conv')(out)\n out = tf.keras.layers.BatchNormalization(epsilon=1e-5, name=f'{prefix}.4_bn')(out)\n out = tf.keras.layers.ReLU(name=f'{prefix}.5_relu')(out)\n\n return out\n\n\ndef create_rfb_net(input_shape, base_channel, num_classes):\n input_node = tf.keras.layers.Input(shape=(input_shape[0], input_shape[1], 3))\n\n net = conv_bn(input_node, base_channel, stride=2, prefix='basenet.0') # 120x160\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.1')\n net = conv_dw(net, base_channel * 2, stride=2, prefix='basenet.2') # 60x80\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.3')\n net = conv_dw(net, base_channel * 4, stride=2, prefix='basenet.4') # 30x40\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.5')\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.6')\n header_0 = basic_rfb(net, base_channel * 4, base_channel * 4, stride=1, scale=1.0, prefix='basenet.7')\n net = conv_dw(header_0, base_channel * 8, stride=2, prefix='basenet.8') # 15x20\n net = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.9')\n header_1 = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.10')\n net = conv_dw(header_1, base_channel * 16, stride=2, prefix='basenet.11') # 8x10\n header_2 = conv_dw(net, base_channel * 16, stride=1, prefix='basenet.12')\n\n out = tf.keras.layers.Conv2D(base_channel * 4, 1, padding='SAME', name='extras_convbias')(header_2)\n out = tf.keras.layers.ReLU(name='extras_relu1')(out)\n out = separable_conv(out, base_channel * 16, kernel_size=3, stride=2, padding=1,\n prefix='extras_sep')\n header_3 = tf.keras.layers.ReLU(name='extras_relu2')(out)\n\n reg_0 = separable_conv(header_0, 3 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_0_sep')\n cls_0 = separable_conv(header_0, 3 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_0_sep')\n\n reg_1 = separable_conv(header_1, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_1_sep')\n cls_1 = separable_conv(header_1, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_1_sep')\n\n reg_2 = separable_conv(header_2, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_2_sep')\n cls_2 = separable_conv(header_2, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_2_sep')\n\n reg_3 = tf.keras.layers.Conv2D(3 * 4, kernel_size=3, padding='SAME',\n name='reg_3_convbias')(header_3)\n cls_3 = tf.keras.layers.Conv2D(3 * num_classes, kernel_size=3, padding='SAME',\n name='cls_3_convbias')(header_3)\n\n reg_list = [tf.keras.layers.Reshape([-1, 4])(reg) for reg in [reg_0, reg_1, reg_2, reg_3]]\n cls_list = [tf.keras.layers.Reshape([-1, num_classes])(cls) for cls in [cls_0, cls_1, cls_2, cls_3]]\n\n reg = tf.keras.layers.Concatenate(axis=1, name='face_boxes')(reg_list)\n cls = tf.keras.layers.Concatenate(axis=1)(cls_list)\n\n cls = tf.keras.layers.Softmax(axis=-1, name='face_scores')(cls)\n\n model = tf.keras.Model(inputs=[input_node], outputs=[reg, cls])\n model.summary()\n\n return model\n" }, { "path": "tflite/model/tflite_slim_320_without_postprocessing.py", "content": "import tensorflow as tf\n\nimport sys\nsys.path.append(\"../tf\")\nfrom backend.op import conv_bn, conv_dw, separable_conv\n\n\ndef create_slim_net(input_shape, base_channel, num_classes):\n input_node = tf.keras.layers.Input(\n shape=(input_shape[0], input_shape[1], 3))\n\n net = conv_bn(input_node, base_channel, stride=2,\n prefix='basenet.0') # 120x160\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.1')\n net = conv_dw(net, base_channel * 2, stride=2, prefix='basenet.2') # 60x80\n net = conv_dw(net, base_channel * 2, stride=1, prefix='basenet.3')\n net = conv_dw(net, base_channel * 4, stride=2, prefix='basenet.4') # 30x40\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.5')\n net = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.6')\n header_0 = conv_dw(net, base_channel * 4, stride=1, prefix='basenet.7')\n net = conv_dw(header_0, base_channel * 8, stride=2,\n prefix='basenet.8') # 15x20\n net = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.9')\n header_1 = conv_dw(net, base_channel * 8, stride=1, prefix='basenet.10')\n net = conv_dw(header_1, base_channel * 16,\n stride=2, prefix='basenet.11') # 8x10\n header_2 = conv_dw(net, base_channel * 16, stride=1, prefix='basenet.12')\n\n out = tf.keras.layers.Conv2D(\n base_channel * 4, 1, padding='SAME', name='extras_convbias')(header_2)\n out = tf.keras.layers.ReLU(name='extras_relu1')(out)\n out = separable_conv(out, base_channel * 16, kernel_size=3, stride=2, padding=1,\n prefix='extras_sep')\n header_3 = tf.keras.layers.ReLU(name='extras_relu2')(out)\n\n reg_0 = separable_conv(header_0, 3 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_0_sep')\n cls_0 = separable_conv(header_0, 3 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_0_sep')\n\n reg_1 = separable_conv(header_1, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_1_sep')\n cls_1 = separable_conv(header_1, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_1_sep')\n\n reg_2 = separable_conv(header_2, 2 * 4, kernel_size=3, stride=1, padding=1,\n prefix='reg_2_sep')\n cls_2 = separable_conv(header_2, 2 * num_classes, kernel_size=3, stride=1, padding=1,\n prefix='cls_2_sep')\n\n reg_3 = tf.keras.layers.Conv2D(3 * 4, kernel_size=3, padding='SAME',\n name='reg_3_convbias')(header_3)\n cls_3 = tf.keras.layers.Conv2D(3 * num_classes, kernel_size=3, padding='SAME',\n name='cls_3_convbias')(header_3)\n \n reg_list = [tf.keras.layers.Reshape([-1, 4])(reg) for reg in [reg_0, reg_1, reg_2, reg_3]]\n cls_list = [tf.keras.layers.Reshape([-1, num_classes])(cls) for cls in [cls_0, cls_1, cls_2, cls_3]]\n\n reg = tf.keras.layers.Concatenate(axis=1, name='face_boxes')(reg_list)\n cls = tf.keras.layers.Concatenate(axis=1)(cls_list)\n\n cls = tf.keras.layers.Softmax(axis=-1, name='face_scores')(cls)\n \n model = tf.keras.Model(inputs=[input_node], outputs=[reg, cls])\n\n model.summary()\n\n return model\n" }, { "path": "train-version-RFB.sh", "content": "#!/usr/bin/env bash\nmodel_root_path=\"./models/train-version-RFB\"\nlog_dir=\"$model_root_path/logs\"\nlog=\"$log_dir/log\"\nmkdir -p \"$log_dir\"\n\npython3 -u train.py \\\n --datasets \\\n ./data/wider_face_add_lm_10_10 \\\n --validation_dataset \\\n ./data/wider_face_add_lm_10_10 \\\n --net \\\n RFB \\\n --num_epochs \\\n 200 \\\n --milestones \\\n \"95,150\" \\\n --lr \\\n 1e-2 \\\n --batch_size \\\n 24 \\\n --input_size \\\n 320 \\\n --checkpoint_folder \\\n ${model_root_path} \\\n --num_workers \\\n 0 \\\n --log_dir \\\n ${log_dir} \\\n --cuda_index \\\n 0 \\\n 2>&1 | tee \"$log\"\n" }, { "path": "train-version-slim.sh", "content": "#!/usr/bin/env bash\nmodel_root_path=\"./models/train-version-slim\"\nlog_dir=\"$model_root_path/logs\"\nlog=\"$log_dir/log\"\nmkdir -p \"$log_dir\"\n\npython3 -u train.py \\\n --datasets \\\n ./data/wider_face_add_lm_10_10 \\\n --validation_dataset \\\n ./data/wider_face_add_lm_10_10 \\\n --net \\\n slim \\\n --num_epochs \\\n 200 \\\n --milestones \\\n \"95,150\" \\\n --lr \\\n 1e-2 \\\n --batch_size \\\n 24 \\\n --input_size \\\n 320 \\\n --checkpoint_folder \\\n ${model_root_path} \\\n --num_workers \\\n 4 \\\n --log_dir \\\n ${log_dir} \\\n --cuda_index \\\n 0 \\\n 2>&1 | tee \"$log\"\n" }, { "path": "train.py", "content": "\"\"\"\nThis code is the main training code.\n\"\"\"\nimport argparse\nimport itertools\nimport logging\nimport os\nimport sys\n\nimport torch\nfrom torch import nn\nfrom torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR\nfrom torch.utils.data import DataLoader, ConcatDataset\n\nfrom vision.datasets.voc_dataset import VOCDataset\nfrom vision.nn.multibox_loss import MultiboxLoss\nfrom vision.ssd.config.fd_config import define_img_size\nfrom vision.utils.misc import str2bool, Timer, freeze_net_layers, store_labels\n\nparser = argparse.ArgumentParser(\n description='train With Pytorch')\n\nparser.add_argument(\"--dataset_type\", default=\"voc\", type=str,\n help='Specify dataset type. Currently support voc.')\n\nparser.add_argument('--datasets', nargs='+', help='Dataset directory path')\nparser.add_argument('--validation_dataset', help='Dataset directory path')\nparser.add_argument('--balance_data', action='store_true',\n help=\"Balance training data by down-sampling more frequent labels.\")\n\nparser.add_argument('--net', default=\"RFB\",\n help=\"The network architecture ,optional(RFB , slim)\")\nparser.add_argument('--freeze_base_net', action='store_true',\n help=\"Freeze base net layers.\")\nparser.add_argument('--freeze_net', action='store_true',\n help=\"Freeze all the layers except the prediction head.\")\n\n# Params for SGD\nparser.add_argument('--lr', '--learning-rate', default=1e-2, type=float,\n help='initial learning rate')\nparser.add_argument('--momentum', default=0.9, type=float,\n help='Momentum value for optim')\nparser.add_argument('--weight_decay', default=5e-4, type=float,\n help='Weight decay for SGD')\nparser.add_argument('--gamma', default=0.1, type=float,\n help='Gamma update for SGD')\nparser.add_argument('--base_net_lr', default=None, type=float,\n help='initial learning rate for base net.')\nparser.add_argument('--extra_layers_lr', default=None, type=float,\n help='initial learning rate for the layers not in base net and prediction heads.')\n\n# Params for loading pretrained basenet or checkpoints.\nparser.add_argument('--base_net',\n help='Pretrained base model')\nparser.add_argument('--pretrained_ssd', help='Pre-trained base model')\nparser.add_argument('--resume', default=None, type=str,\n help='Checkpoint state_dict file to resume training from')\n\n# Scheduler\nparser.add_argument('--scheduler', default=\"multi-step\", type=str,\n help=\"Scheduler for SGD. It can one of multi-step and cosine\")\n\n# Params for Multi-step Scheduler\nparser.add_argument('--milestones', default=\"80,100\", type=str,\n help=\"milestones for MultiStepLR\")\n\n# Params for Cosine Annealing\nparser.add_argument('--t_max', default=120, type=float,\n help='T_max value for Cosine Annealing Scheduler.')\n\n# Train params\nparser.add_argument('--batch_size', default=24, type=int,\n help='Batch size for training')\nparser.add_argument('--num_epochs', default=200, type=int,\n help='the number epochs')\nparser.add_argument('--num_workers', default=4, type=int,\n help='Number of workers used in dataloading')\nparser.add_argument('--validation_epochs', default=5, type=int,\n help='the number epochs')\nparser.add_argument('--debug_steps', default=100, type=int,\n help='Set the debug log output frequency.')\nparser.add_argument('--use_cuda', default=True, type=str2bool,\n help='Use CUDA to train model')\n\nparser.add_argument('--checkpoint_folder', default='models/',\n help='Directory for saving checkpoint models')\nparser.add_argument('--log_dir', default='./models/Ultra-Light(1MB)_&_Fast_Face_Detector/logs',\n help='lod dir')\nparser.add_argument('--cuda_index', default=\"0\", type=str,\n help='Choose cuda index.If you have 4 GPUs, you can set it like 0,1,2,3')\nparser.add_argument('--power', default=2, type=int,\n help='poly lr pow')\nparser.add_argument('--overlap_threshold', default=0.35, type=float,\n help='overlap_threshold')\nparser.add_argument('--optimizer_type', default=\"SGD\", type=str,\n help='optimizer_type')\nparser.add_argument('--input_size', default=320, type=int,\n help='define network input size,default optional value 128/160/320/480/640/1280')\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nargs = parser.parse_args()\n\ninput_img_size = args.input_size # define input size ,default optional(128/160/320/480/640/1280)\nlogging.info(\"inpu size :{}\".format(input_img_size))\ndefine_img_size(input_img_size) # must put define_img_size() before 'import fd_config'\n\nfrom vision.ssd.config import fd_config\nfrom vision.ssd.data_preprocessing import TrainAugmentation, TestTransform\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd\nfrom vision.ssd.ssd import MatchPrior\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() and args.use_cuda else \"cpu\")\n\nif args.use_cuda and torch.cuda.is_available():\n torch.backends.cudnn.benchmark = True\n logging.info(\"Use Cuda.\")\n\n\ndef lr_poly(base_lr, iter):\n return base_lr * ((1 - float(iter) / args.num_epochs) ** (args.power))\n\n\ndef adjust_learning_rate(optimizer, i_iter):\n \"\"\"Sets the learning rate to the initial LR divided by 5 at 60th, 120th and 160th epochs\"\"\"\n lr = lr_poly(args.lr, i_iter)\n optimizer.param_groups[0]['lr'] = lr\n\n\ndef train(loader, net, criterion, optimizer, device, debug_steps=100, epoch=-1):\n net.train(True)\n running_loss = 0.0\n running_regression_loss = 0.0\n running_classification_loss = 0.0\n for i, data in enumerate(loader):\n print(\".\", end=\"\", flush=True)\n images, boxes, labels = data\n images = images.to(device)\n boxes = boxes.to(device)\n labels = labels.to(device)\n\n optimizer.zero_grad()\n confidence, locations = net(images)\n regression_loss, classification_loss = criterion(confidence, locations, labels, boxes) # TODO CHANGE BOXES\n loss = regression_loss + classification_loss\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n running_regression_loss += regression_loss.item()\n running_classification_loss += classification_loss.item()\n if i and i % debug_steps == 0:\n print(\".\", flush=True)\n avg_loss = running_loss / debug_steps\n avg_reg_loss = running_regression_loss / debug_steps\n avg_clf_loss = running_classification_loss / debug_steps\n logging.info(\n f\"Epoch: {epoch}, Step: {i}, \" +\n f\"Average Loss: {avg_loss:.4f}, \" +\n f\"Average Regression Loss {avg_reg_loss:.4f}, \" +\n f\"Average Classification Loss: {avg_clf_loss:.4f}\"\n )\n\n running_loss = 0.0\n running_regression_loss = 0.0\n running_classification_loss = 0.0\n\n\ndef test(loader, net, criterion, device):\n net.eval()\n running_loss = 0.0\n running_regression_loss = 0.0\n running_classification_loss = 0.0\n num = 0\n for _, data in enumerate(loader):\n images, boxes, labels = data\n images = images.to(device)\n boxes = boxes.to(device)\n labels = labels.to(device)\n num += 1\n\n with torch.no_grad():\n confidence, locations = net(images)\n regression_loss, classification_loss = criterion(confidence, locations, labels, boxes)\n loss = regression_loss + classification_loss\n\n running_loss += loss.item()\n running_regression_loss += regression_loss.item()\n running_classification_loss += classification_loss.item()\n return running_loss / num, running_regression_loss / num, running_classification_loss / num\n\n\nif __name__ == '__main__':\n timer = Timer()\n\n logging.info(args)\n if args.net == 'slim':\n create_net = create_mb_tiny_fd\n config = fd_config\n elif args.net == 'RFB':\n create_net = create_Mb_Tiny_RFB_fd\n config = fd_config\n else:\n logging.fatal(\"The net type is wrong.\")\n parser.print_help(sys.stderr)\n sys.exit(1)\n\n train_transform = TrainAugmentation(config.image_size, config.image_mean, config.image_std)\n target_transform = MatchPrior(config.priors, config.center_variance,\n config.size_variance, args.overlap_threshold)\n\n test_transform = TestTransform(config.image_size, config.image_mean_test, config.image_std)\n\n if not os.path.exists(args.checkpoint_folder):\n os.makedirs(args.checkpoint_folder)\n logging.info(\"Prepare training datasets.\")\n datasets = []\n for dataset_path in args.datasets:\n if args.dataset_type == 'voc':\n dataset = VOCDataset(dataset_path, transform=train_transform,\n target_transform=target_transform)\n label_file = os.path.join(args.checkpoint_folder, \"voc-model-labels.txt\")\n store_labels(label_file, dataset.class_names)\n num_classes = len(dataset.class_names)\n\n else:\n raise ValueError(f\"Dataset tpye {args.dataset_type} is not supported.\")\n datasets.append(dataset)\n logging.info(f\"Stored labels into file {label_file}.\")\n train_dataset = ConcatDataset(datasets)\n logging.info(\"Train dataset size: {}\".format(len(train_dataset)))\n train_loader = DataLoader(train_dataset, args.batch_size,\n num_workers=args.num_workers,\n shuffle=True)\n logging.info(\"Prepare Validation datasets.\")\n if args.dataset_type == \"voc\":\n val_dataset = VOCDataset(args.validation_dataset, transform=test_transform,\n target_transform=target_transform, is_test=True)\n logging.info(\"validation dataset size: {}\".format(len(val_dataset)))\n\n val_loader = DataLoader(val_dataset, args.batch_size,\n num_workers=args.num_workers,\n shuffle=False)\n logging.info(\"Build network.\")\n net = create_net(num_classes)\n\n # add multigpu_train\n cuda_index_list = None\n if torch.cuda.device_count() >= 1:\n cuda_index_list = [int(v.strip()) for v in args.cuda_index.split(\",\")]\n net = nn.DataParallel(net, device_ids=cuda_index_list)\n logging.info(\"use gpu :{}\".format(cuda_index_list))\n\n min_loss = -10000.0\n last_epoch = -1\n\n base_net_lr = args.base_net_lr if args.base_net_lr is not None else args.lr\n extra_layers_lr = args.extra_layers_lr if args.extra_layers_lr is not None else args.lr\n if args.freeze_base_net:\n logging.info(\"Freeze base net.\")\n freeze_net_layers(net.base_net)\n params = itertools.chain(net.source_layer_add_ons.parameters(), net.extras.parameters(),\n net.regression_headers.parameters(), net.classification_headers.parameters())\n params = [\n {'params': itertools.chain(\n net.source_layer_add_ons.parameters(),\n net.extras.parameters()\n ), 'lr': extra_layers_lr},\n {'params': itertools.chain(\n net.regression_headers.parameters(),\n net.classification_headers.parameters()\n )}\n ]\n elif args.freeze_net:\n freeze_net_layers(net.base_net)\n freeze_net_layers(net.source_layer_add_ons)\n freeze_net_layers(net.extras)\n params = itertools.chain(net.regression_headers.parameters(), net.classification_headers.parameters())\n logging.info(\"Freeze all the layers except prediction heads.\")\n else:\n if cuda_index_list:\n params = [\n {'params': net.module.base_net.parameters(), 'lr': base_net_lr},\n {'params': itertools.chain(\n net.module.source_layer_add_ons.parameters(),\n net.module.extras.parameters()\n ), 'lr': extra_layers_lr},\n {'params': itertools.chain(\n net.module.regression_headers.parameters(),\n net.module.classification_headers.parameters()\n )}\n ]\n else:\n params = [\n {'params': net.base_net.parameters(), 'lr': base_net_lr},\n {'params': itertools.chain(\n net.source_layer_add_ons.parameters(),\n net.extras.parameters()\n ), 'lr': extra_layers_lr},\n {'params': itertools.chain(\n net.regression_headers.parameters(),\n net.classification_headers.parameters()\n )}\n ]\n\n timer.start(\"Load Model\")\n if args.resume:\n logging.info(f\"Resume from the model {args.resume}\")\n net.load(args.resume)\n elif args.base_net:\n logging.info(f\"Init from base net {args.base_net}\")\n net.init_from_base_net(args.base_net)\n elif args.pretrained_ssd:\n logging.info(f\"Init from pretrained ssd {args.pretrained_ssd}\")\n net.init_from_pretrained_ssd(args.pretrained_ssd)\n logging.info(f'Took {timer.end(\"Load Model\"):.2f} seconds to load the model.')\n\n net.to(DEVICE)\n\n criterion = MultiboxLoss(config.priors, neg_pos_ratio=3,\n center_variance=0.1, size_variance=0.2, device=DEVICE)\n if args.optimizer_type == \"SGD\":\n optimizer = torch.optim.SGD(params, lr=args.lr, momentum=args.momentum,\n weight_decay=args.weight_decay)\n elif args.optimizer_type == \"Adam\":\n optimizer = torch.optim.Adam(params, lr=args.lr)\n logging.info(\"use Adam optimizer\")\n else:\n logging.fatal(f\"Unsupported optimizer: {args.scheduler}.\")\n parser.print_help(sys.stderr)\n sys.exit(1)\n logging.info(f\"Learning rate: {args.lr}, Base net learning rate: {base_net_lr}, \"\n + f\"Extra Layers learning rate: {extra_layers_lr}.\")\n if args.optimizer_type != \"Adam\":\n if args.scheduler == 'multi-step':\n logging.info(\"Uses MultiStepLR scheduler.\")\n milestones = [int(v.strip()) for v in args.milestones.split(\",\")]\n scheduler = MultiStepLR(optimizer, milestones=milestones,\n gamma=0.1, last_epoch=last_epoch)\n elif args.scheduler == 'cosine':\n logging.info(\"Uses CosineAnnealingLR scheduler.\")\n scheduler = CosineAnnealingLR(optimizer, args.t_max, last_epoch=last_epoch)\n elif args.scheduler == 'poly':\n logging.info(\"Uses PolyLR scheduler.\")\n else:\n logging.fatal(f\"Unsupported Scheduler: {args.scheduler}.\")\n parser.print_help(sys.stderr)\n sys.exit(1)\n\n logging.info(f\"Start training from epoch {last_epoch + 1}.\")\n for epoch in range(last_epoch + 1, args.num_epochs):\n if args.optimizer_type != \"Adam\":\n if args.scheduler != \"poly\":\n if epoch != 0:\n scheduler.step()\n train(train_loader, net, criterion, optimizer,\n device=DEVICE, debug_steps=args.debug_steps, epoch=epoch)\n if args.scheduler == \"poly\":\n adjust_learning_rate(optimizer, epoch)\n logging.info(\"lr rate :{}\".format(optimizer.param_groups[0]['lr']))\n\n if epoch % args.validation_epochs == 0 or epoch == args.num_epochs - 1:\n logging.info(\"lr rate :{}\".format(optimizer.param_groups[0]['lr']))\n val_loss, val_regression_loss, val_classification_loss = test(val_loader, net, criterion, DEVICE)\n logging.info(\n f\"Epoch: {epoch}, \" +\n f\"Validation Loss: {val_loss:.4f}, \" +\n f\"Validation Regression Loss {val_regression_loss:.4f}, \" +\n f\"Validation Classification Loss: {val_classification_loss:.4f}\"\n )\n model_path = os.path.join(args.checkpoint_folder, f\"{args.net}-Epoch-{epoch}-Loss-{val_loss}.pth\")\n if cuda_index_list:\n net.module.save(model_path)\n else:\n net.save(model_path)\n logging.info(f\"Saved model {model_path}\")\n" }, { "path": "vision/__init__.py", "content": "" }, { "path": "vision/datasets/__init__.py", "content": "" }, { "path": "vision/datasets/voc_dataset.py", "content": "import logging\nimport os\nimport pathlib\nimport xml.etree.ElementTree as ET\n\nimport cv2\nimport numpy as np\n\n\nclass VOCDataset:\n\n def __init__(self, root, transform=None, target_transform=None, is_test=False, keep_difficult=False, label_file=None):\n \"\"\"Dataset for VOC data.\n Args:\n root: the root of the VOC2007 or VOC2012 dataset, the directory contains the following sub-directories:\n Annotations, ImageSets, JPEGImages, SegmentationClass, SegmentationObject.\n \"\"\"\n self.root = pathlib.Path(root)\n self.transform = transform\n self.target_transform = target_transform\n if is_test:\n image_sets_file = self.root / \"ImageSets/Main/test.txt\"\n else:\n image_sets_file = self.root / \"ImageSets/Main/trainval.txt\"\n self.ids = VOCDataset._read_image_ids(image_sets_file)\n self.keep_difficult = keep_difficult\n\n # if the labels file exists, read in the class names\n label_file_name = self.root / \"labels.txt\"\n\n if os.path.isfile(label_file_name):\n class_string = \"\"\n with open(label_file_name, 'r') as infile:\n for line in infile:\n class_string += line.rstrip()\n\n # classes should be a comma separated list\n\n classes = class_string.split(',')\n # prepend BACKGROUND as first class\n classes.insert(0, 'BACKGROUND')\n classes = [elem.replace(\" \", \"\") for elem in classes]\n self.class_names = tuple(classes)\n logging.info(\"VOC Labels read from file: \" + str(self.class_names))\n\n else:\n logging.info(\"No labels file, using default VOC classes.\")\n self.class_names = ('BACKGROUND',\n 'face')\n\n self.class_dict = {class_name: i for i, class_name in enumerate(self.class_names)}\n\n def __getitem__(self, index):\n image_id = self.ids[index]\n boxes, labels, is_difficult = self._get_annotation(image_id)\n if not self.keep_difficult:\n boxes = boxes[is_difficult == 0]\n labels = labels[is_difficult == 0]\n image = self._read_image(image_id)\n if self.transform:\n image, boxes, labels = self.transform(image, boxes, labels)\n if self.target_transform:\n boxes, labels = self.target_transform(boxes, labels)\n return image, boxes, labels\n\n def get_image(self, index):\n image_id = self.ids[index]\n image = self._read_image(image_id)\n if self.transform:\n image, _ = self.transform(image)\n return image\n\n def get_annotation(self, index):\n image_id = self.ids[index]\n return image_id, self._get_annotation(image_id)\n\n def __len__(self):\n return len(self.ids)\n\n @staticmethod\n def _read_image_ids(image_sets_file):\n ids = []\n with open(image_sets_file) as f:\n for line in f:\n ids.append(line.rstrip())\n return ids\n\n def _get_annotation(self, image_id):\n annotation_file = self.root / f\"Annotations/{image_id}.xml\"\n objects = ET.parse(annotation_file).findall(\"object\")\n boxes = []\n labels = []\n is_difficult = []\n for object in objects:\n class_name = object.find('name').text.lower().strip()\n # we're only concerned with clases in our list\n if class_name in self.class_dict:\n bbox = object.find('bndbox')\n\n # VOC dataset format follows Matlab, in which indexes start from 0\n x1 = float(bbox.find('xmin').text) - 1\n y1 = float(bbox.find('ymin').text) - 1\n x2 = float(bbox.find('xmax').text) - 1\n y2 = float(bbox.find('ymax').text) - 1\n boxes.append([x1, y1, x2, y2])\n\n labels.append(self.class_dict[class_name])\n is_difficult_str = object.find('difficult').text\n is_difficult.append(int(is_difficult_str) if is_difficult_str else 0)\n\n return (np.array(boxes, dtype=np.float32),\n np.array(labels, dtype=np.int64),\n np.array(is_difficult, dtype=np.uint8))\n\n def _read_image(self, image_id):\n image_file = self.root / f\"JPEGImages/{image_id}.jpg\"\n image = cv2.imread(str(image_file))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return image\n" }, { "path": "vision/nn/__init__.py", "content": "" }, { "path": "vision/nn/mb_tiny.py", "content": "import torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Mb_Tiny(nn.Module):\n\n def __init__(self, num_classes=2):\n super(Mb_Tiny, self).__init__()\n self.base_channel = 8 * 2\n\n def conv_bn(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n nn.BatchNorm2d(oup),\n nn.ReLU(inplace=True)\n )\n\n def conv_dw(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n nn.BatchNorm2d(inp),\n nn.ReLU(inplace=True),\n\n nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n nn.BatchNorm2d(oup),\n nn.ReLU(inplace=True),\n )\n\n self.model = nn.Sequential(\n conv_bn(3, self.base_channel, 2), # 160*120\n conv_dw(self.base_channel, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 2, 2), # 80*60\n conv_dw(self.base_channel * 2, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 4, 2), # 40*30\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 8, 2), # 20*15\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 16, 2), # 10*8\n conv_dw(self.base_channel * 16, self.base_channel * 16, 1)\n )\n self.fc = nn.Linear(1024, num_classes)\n\n def forward(self, x):\n x = self.model(x)\n x = F.avg_pool2d(x, 7)\n x = x.view(-1, 1024)\n x = self.fc(x)\n return x\n" }, { "path": "vision/nn/mb_tiny_RFB.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass BasicConv(nn.Module):\n\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n if bn:\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False)\n self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True)\n self.relu = nn.ReLU(inplace=True) if relu else None\n else:\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)\n self.bn = None\n self.relu = nn.ReLU(inplace=True) if relu else None\n\n def forward(self, x):\n x = self.conv(x)\n if self.bn is not None:\n x = self.bn(x)\n if self.relu is not None:\n x = self.relu(x)\n return x\n\n\nclass BasicRFB(nn.Module):\n\n def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_reduce=8, vision=1, groups=1):\n super(BasicRFB, self).__init__()\n self.scale = scale\n self.out_channels = out_planes\n inter_planes = in_planes // map_reduce\n\n self.branch0 = nn.Sequential(\n BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),\n BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),\n BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 1, dilation=vision + 1, relu=False, groups=groups)\n )\n self.branch1 = nn.Sequential(\n BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),\n BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),\n BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 2, dilation=vision + 2, relu=False, groups=groups)\n )\n self.branch2 = nn.Sequential(\n BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),\n BasicConv(inter_planes, (inter_planes // 2) * 3, kernel_size=3, stride=1, padding=1, groups=groups),\n BasicConv((inter_planes // 2) * 3, 2 * inter_planes, kernel_size=3, stride=stride, padding=1, groups=groups),\n BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 4, dilation=vision + 4, relu=False, groups=groups)\n )\n\n self.ConvLinear = BasicConv(6 * inter_planes, out_planes, kernel_size=1, stride=1, relu=False)\n self.shortcut = BasicConv(in_planes, out_planes, kernel_size=1, stride=stride, relu=False)\n self.relu = nn.ReLU(inplace=False)\n\n def forward(self, x):\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n x2 = self.branch2(x)\n\n out = torch.cat((x0, x1, x2), 1)\n out = self.ConvLinear(out)\n short = self.shortcut(x)\n out = out * self.scale + short\n out = self.relu(out)\n\n return out\n\n\nclass Mb_Tiny_RFB(nn.Module):\n\n def __init__(self, num_classes=2):\n super(Mb_Tiny_RFB, self).__init__()\n self.base_channel = 8 * 2\n\n def conv_bn(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n nn.BatchNorm2d(oup),\n nn.ReLU(inplace=True)\n )\n\n def conv_dw(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n nn.BatchNorm2d(inp),\n nn.ReLU(inplace=True),\n\n nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n nn.BatchNorm2d(oup),\n nn.ReLU(inplace=True),\n )\n\n self.model = nn.Sequential(\n conv_bn(3, self.base_channel, 2), # 160*120\n conv_dw(self.base_channel, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 2, 2), # 80*60\n conv_dw(self.base_channel * 2, self.base_channel * 2, 1),\n conv_dw(self.base_channel * 2, self.base_channel * 4, 2), # 40*30\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n conv_dw(self.base_channel * 4, self.base_channel * 4, 1),\n BasicRFB(self.base_channel * 4, self.base_channel * 4, stride=1, scale=1.0),\n conv_dw(self.base_channel * 4, self.base_channel * 8, 2), # 20*15\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 8, 1),\n conv_dw(self.base_channel * 8, self.base_channel * 16, 2), # 10*8\n conv_dw(self.base_channel * 16, self.base_channel * 16, 1)\n )\n self.fc = nn.Linear(1024, num_classes)\n\n def forward(self, x):\n x = self.model(x)\n x = F.avg_pool2d(x, 7)\n x = x.view(-1, 1024)\n x = self.fc(x)\n return x\n" }, { "path": "vision/nn/multibox_loss.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..utils import box_utils\n\n\nclass MultiboxLoss(nn.Module):\n def __init__(self, priors, neg_pos_ratio,\n center_variance, size_variance, device):\n \"\"\"Implement SSD Multibox Loss.\n\n Basically, Multibox loss combines classification loss\n and Smooth L1 regression loss.\n \"\"\"\n super(MultiboxLoss, self).__init__()\n self.neg_pos_ratio = neg_pos_ratio\n self.center_variance = center_variance\n self.size_variance = size_variance\n self.priors = priors\n self.priors.to(device)\n\n def forward(self, confidence, predicted_locations, labels, gt_locations):\n \"\"\"Compute classification loss and smooth l1 loss.\n\n Args:\n confidence (batch_size, num_priors, num_classes): class predictions.\n locations (batch_size, num_priors, 4): predicted locations.\n labels (batch_size, num_priors): real labels of all the priors.\n boxes (batch_size, num_priors, 4): real boxes corresponding all the priors.\n \"\"\"\n num_classes = confidence.size(2)\n with torch.no_grad():\n # derived from cross_entropy=sum(log(p))\n loss = -F.log_softmax(confidence, dim=2)[:, :, 0]\n mask = box_utils.hard_negative_mining(loss, labels, self.neg_pos_ratio)\n\n confidence = confidence[mask, :]\n classification_loss = F.cross_entropy(confidence.reshape(-1, num_classes), labels[mask], reduction='sum')\n pos_mask = labels > 0\n predicted_locations = predicted_locations[pos_mask, :].reshape(-1, 4)\n gt_locations = gt_locations[pos_mask, :].reshape(-1, 4)\n smooth_l1_loss = F.smooth_l1_loss(predicted_locations, gt_locations, reduction='sum') # smooth_l1_loss\n # smooth_l1_loss = F.mse_loss(predicted_locations, gt_locations, reduction='sum') #l2 loss\n num_pos = gt_locations.size(0)\n return smooth_l1_loss / num_pos, classification_loss / num_pos\n" }, { "path": "vision/ssd/__init__.py", "content": "" }, { "path": "vision/ssd/config/__init__.py", "content": "" }, { "path": "vision/ssd/config/fd_config.py", "content": "import numpy as np\n\nfrom vision.utils.box_utils import generate_priors\n\nimage_mean_test = image_mean = np.array([127, 127, 127])\nimage_std = 128.0\niou_threshold = 0.3\ncenter_variance = 0.1\nsize_variance = 0.2\n\nmin_boxes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]\nshrinkage_list = []\nimage_size = [320, 240] # default input size 320*240\nfeature_map_w_h_list = [[40, 20, 10, 5], [30, 15, 8, 4]] # default feature map size\npriors = []\n\n\ndef define_img_size(size):\n global image_size, feature_map_w_h_list, priors\n img_size_dict = {128: [128, 96],\n 160: [160, 120],\n 320: [320, 240],\n 480: [480, 360],\n 640: [640, 480],\n 1280: [1280, 960]}\n image_size = img_size_dict[size]\n\n feature_map_w_h_list_dict = {128: [[16, 8, 4, 2], [12, 6, 3, 2]],\n 160: [[20, 10, 5, 3], [15, 8, 4, 2]],\n 320: [[40, 20, 10, 5], [30, 15, 8, 4]],\n 480: [[60, 30, 15, 8], [45, 23, 12, 6]],\n 640: [[80, 40, 20, 10], [60, 30, 15, 8]],\n 1280: [[160, 80, 40, 20], [120, 60, 30, 15]]}\n feature_map_w_h_list = feature_map_w_h_list_dict[size]\n\n for i in range(0, len(image_size)):\n item_list = []\n for k in range(0, len(feature_map_w_h_list[i])):\n item_list.append(image_size[i] / feature_map_w_h_list[i][k])\n shrinkage_list.append(item_list)\n priors = generate_priors(feature_map_w_h_list, shrinkage_list, image_size, min_boxes)\n" }, { "path": "vision/ssd/data_preprocessing.py", "content": "from ..transforms.transforms import *\n\n\nclass TrainAugmentation:\n def __init__(self, size, mean=0, std=1.0):\n \"\"\"\n Args:\n size: the size the of final image.\n mean: mean pixel value per channel.\n \"\"\"\n self.mean = mean\n self.size = size\n self.augment = Compose([\n ConvertFromInts(),\n PhotometricDistort(),\n RandomSampleCrop_v2(),\n RandomMirror(),\n ToPercentCoords(),\n Resize(self.size),\n SubtractMeans(self.mean),\n lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n ToTensor(),\n ])\n\n def __call__(self, img, boxes, labels):\n \"\"\"\n\n Args:\n img: the output of cv.imread in RGB layout.\n boxes: boundding boxes in the form of (x1, y1, x2, y2).\n labels: labels of boxes.\n \"\"\"\n return self.augment(img, boxes, labels)\n\n\nclass TestTransform:\n def __init__(self, size, mean=0.0, std=1.0):\n self.transform = Compose([\n ToPercentCoords(),\n Resize(size),\n SubtractMeans(mean),\n lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n ToTensor(),\n ])\n\n def __call__(self, image, boxes, labels):\n return self.transform(image, boxes, labels)\n\n\nclass PredictionTransform:\n def __init__(self, size, mean=0.0, std=1.0):\n self.transform = Compose([\n Resize(size),\n SubtractMeans(mean),\n lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n ToTensor()\n ])\n\n def __call__(self, image):\n image, _, _ = self.transform(image)\n return image\n" }, { "path": "vision/ssd/mb_tiny_RFB_fd.py", "content": "from torch.nn import Conv2d, Sequential, ModuleList, ReLU\n\nfrom vision.nn.mb_tiny_RFB import Mb_Tiny_RFB\nfrom vision.ssd.config import fd_config as config\nfrom vision.ssd.predictor import Predictor\nfrom vision.ssd.ssd import SSD\n\n\ndef SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0):\n \"\"\"Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.\n \"\"\"\n return Sequential(\n Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,\n groups=in_channels, stride=stride, padding=padding),\n ReLU(),\n Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),\n )\n\n\ndef create_Mb_Tiny_RFB_fd(num_classes, is_test=False, device=\"cuda\"):\n base_net = Mb_Tiny_RFB(2)\n base_net_model = base_net.model # disable dropout layer\n\n source_layer_indexes = [\n 8,\n 11,\n 13\n ]\n extras = ModuleList([\n Sequential(\n Conv2d(in_channels=base_net.base_channel * 16, out_channels=base_net.base_channel * 4, kernel_size=1),\n ReLU(),\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=base_net.base_channel * 16, kernel_size=3, stride=2, padding=1),\n ReLU()\n )\n ])\n\n regression_headers = ModuleList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * 4, kernel_size=3, padding=1),\n Conv2d(in_channels=base_net.base_channel * 16, out_channels=3 * 4, kernel_size=3, padding=1)\n ])\n\n classification_headers = ModuleList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * num_classes, kernel_size=3, padding=1),\n Conv2d(in_channels=base_net.base_channel * 16, out_channels=3 * num_classes, kernel_size=3, padding=1)\n ])\n\n return SSD(num_classes, base_net_model, source_layer_indexes,\n extras, classification_headers, regression_headers, is_test=is_test, config=config, device=device)\n\n\ndef create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):\n predictor = Predictor(net, config.image_size, config.image_mean_test,\n config.image_std,\n nms_method=nms_method,\n iou_threshold=config.iou_threshold,\n candidate_size=candidate_size,\n sigma=sigma,\n device=device)\n return predictor\n" }, { "path": "vision/ssd/mb_tiny_fd.py", "content": "from torch.nn import Conv2d, Sequential, ModuleList, ReLU\n\nfrom vision.nn.mb_tiny import Mb_Tiny\nfrom vision.ssd.config import fd_config as config\nfrom vision.ssd.predictor import Predictor\nfrom vision.ssd.ssd import SSD\n\n\ndef SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0):\n \"\"\"Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.\n \"\"\"\n return Sequential(\n Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,\n groups=in_channels, stride=stride, padding=padding),\n ReLU(),\n Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),\n )\n\n\ndef create_mb_tiny_fd(num_classes, is_test=False, device=\"cuda\"):\n base_net = Mb_Tiny(2)\n base_net_model = base_net.model # disable dropout layer\n\n source_layer_indexes = [\n 8,\n 11,\n 13\n ]\n extras = ModuleList([\n Sequential(\n Conv2d(in_channels=base_net.base_channel * 16, out_channels=base_net.base_channel * 4, kernel_size=1),\n ReLU(),\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=base_net.base_channel * 16, kernel_size=3, stride=2, padding=1),\n ReLU()\n )\n ])\n\n regression_headers = ModuleList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * 4, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * 4, kernel_size=3, padding=1),\n Conv2d(in_channels=base_net.base_channel * 16, out_channels=3 * 4, kernel_size=3, padding=1)\n ])\n\n classification_headers = ModuleList([\n SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * num_classes, kernel_size=3, padding=1),\n SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * num_classes, kernel_size=3, padding=1),\n Conv2d(in_channels=base_net.base_channel * 16, out_channels=3 * num_classes, kernel_size=3, padding=1)\n ])\n\n return SSD(num_classes, base_net_model, source_layer_indexes,\n extras, classification_headers, regression_headers, is_test=is_test, config=config, device=device)\n\n\ndef create_mb_tiny_fd_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):\n predictor = Predictor(net, config.image_size, config.image_mean_test,\n config.image_std,\n nms_method=nms_method,\n iou_threshold=config.iou_threshold,\n candidate_size=candidate_size,\n sigma=sigma,\n device=device)\n return predictor\n" }, { "path": "vision/ssd/predictor.py", "content": "import torch\n\nfrom ..utils import box_utils\nfrom .data_preprocessing import PredictionTransform\nfrom ..utils.misc import Timer\n\n\nclass Predictor:\n def __init__(self, net, size, mean=0.0, std=1.0, nms_method=None,\n iou_threshold=0.3, filter_threshold=0.01, candidate_size=200, sigma=0.5, device=None):\n self.net = net\n self.transform = PredictionTransform(size, mean, std)\n self.iou_threshold = iou_threshold\n self.filter_threshold = filter_threshold\n self.candidate_size = candidate_size\n self.nms_method = nms_method\n\n self.sigma = sigma\n if device:\n self.device = device\n else:\n self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n self.net.to(self.device)\n self.net.eval()\n\n self.timer = Timer()\n\n def predict(self, image, top_k=-1, prob_threshold=None):\n cpu_device = torch.device(\"cpu\")\n height, width, _ = image.shape\n image = self.transform(image)\n images = image.unsqueeze(0)\n images = images.to(self.device)\n with torch.no_grad():\n for i in range(1):\n self.timer.start()\n scores, boxes = self.net.forward(images)\n print(\"Inference time: \", self.timer.end())\n boxes = boxes[0]\n scores = scores[0]\n if not prob_threshold:\n prob_threshold = self.filter_threshold\n # this version of nms is slower on GPU, so we move data to CPU.\n boxes = boxes.to(cpu_device)\n scores = scores.to(cpu_device)\n picked_box_probs = []\n picked_labels = []\n for class_index in range(1, scores.size(1)):\n probs = scores[:, class_index]\n mask = probs > prob_threshold\n probs = probs[mask]\n if probs.size(0) == 0:\n continue\n subset_boxes = boxes[mask, :]\n box_probs = torch.cat([subset_boxes, probs.reshape(-1, 1)], dim=1)\n box_probs = box_utils.nms(box_probs, self.nms_method,\n score_threshold=prob_threshold,\n iou_threshold=self.iou_threshold,\n sigma=self.sigma,\n top_k=top_k,\n candidate_size=self.candidate_size)\n picked_box_probs.append(box_probs)\n picked_labels.extend([class_index] * box_probs.size(0))\n if not picked_box_probs:\n return torch.tensor([]), torch.tensor([]), torch.tensor([])\n picked_box_probs = torch.cat(picked_box_probs)\n picked_box_probs[:, 0] *= width\n picked_box_probs[:, 1] *= height\n picked_box_probs[:, 2] *= width\n picked_box_probs[:, 3] *= height\n return picked_box_probs[:, :4], torch.tensor(picked_labels), picked_box_probs[:, 4]\n" }, { "path": "vision/ssd/ssd.py", "content": "from collections import namedtuple\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom vision.utils import box_utils\n\nGraphPath = namedtuple(\"GraphPath\", ['s0', 'name', 's1'])\n\n\nclass SSD(nn.Module):\n def __init__(self, num_classes: int, base_net: nn.ModuleList, source_layer_indexes: List[int],\n extras: nn.ModuleList, classification_headers: nn.ModuleList,\n regression_headers: nn.ModuleList, is_test=False, config=None, device=None):\n \"\"\"Compose a SSD model using the given components.\n \"\"\"\n super(SSD, self).__init__()\n\n self.num_classes = num_classes\n self.base_net = base_net\n self.source_layer_indexes = source_layer_indexes\n self.extras = extras\n self.classification_headers = classification_headers\n self.regression_headers = regression_headers\n self.is_test = is_test\n self.config = config\n\n # register layers in source_layer_indexes by adding them to a module list\n self.source_layer_add_ons = nn.ModuleList([t[1] for t in source_layer_indexes\n if isinstance(t, tuple) and not isinstance(t, GraphPath)])\n if device:\n self.device = device\n else:\n self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n if is_test:\n self.config = config\n self.priors = config.priors.to(self.device)\n\n def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n confidences = []\n locations = []\n start_layer_index = 0\n header_index = 0\n end_layer_index = 0\n for end_layer_index in self.source_layer_indexes:\n if isinstance(end_layer_index, GraphPath):\n path = end_layer_index\n end_layer_index = end_layer_index.s0\n added_layer = None\n elif isinstance(end_layer_index, tuple):\n added_layer = end_layer_index[1]\n end_layer_index = end_layer_index[0]\n path = None\n else:\n added_layer = None\n path = None\n for layer in self.base_net[start_layer_index: end_layer_index]:\n x = layer(x)\n if added_layer:\n y = added_layer(x)\n else:\n y = x\n if path:\n sub = getattr(self.base_net[end_layer_index], path.name)\n for layer in sub[:path.s1]:\n x = layer(x)\n y = x\n for layer in sub[path.s1:]:\n x = layer(x)\n end_layer_index += 1\n start_layer_index = end_layer_index\n confidence, location = self.compute_header(header_index, y)\n header_index += 1\n confidences.append(confidence)\n locations.append(location)\n\n for layer in self.base_net[end_layer_index:]:\n x = layer(x)\n\n for layer in self.extras:\n x = layer(x)\n confidence, location = self.compute_header(header_index, x)\n header_index += 1\n confidences.append(confidence)\n locations.append(location)\n\n confidences = torch.cat(confidences, 1)\n locations = torch.cat(locations, 1)\n\n if self.is_test:\n confidences = F.softmax(confidences, dim=2)\n boxes = box_utils.convert_locations_to_boxes(\n locations, self.priors, self.config.center_variance, self.config.size_variance\n )\n boxes = box_utils.center_form_to_corner_form(boxes)\n return confidences, boxes\n else:\n return confidences, locations\n\n def compute_header(self, i, x):\n confidence = self.classification_headers[i](x)\n confidence = confidence.permute(0, 2, 3, 1).contiguous()\n confidence = confidence.view(confidence.size(0), -1, self.num_classes)\n\n location = self.regression_headers[i](x)\n location = location.permute(0, 2, 3, 1).contiguous()\n location = location.view(location.size(0), -1, 4)\n\n return confidence, location\n\n def init_from_base_net(self, model):\n self.base_net.load_state_dict(torch.load(model, map_location=lambda storage, loc: storage), strict=True)\n self.source_layer_add_ons.apply(_xavier_init_)\n self.extras.apply(_xavier_init_)\n self.classification_headers.apply(_xavier_init_)\n self.regression_headers.apply(_xavier_init_)\n\n def init_from_pretrained_ssd(self, model):\n state_dict = torch.load(model, map_location=lambda storage, loc: storage)\n state_dict = {k: v for k, v in state_dict.items() if not (k.startswith(\"classification_headers\") or k.startswith(\"regression_headers\"))}\n model_dict = self.state_dict()\n model_dict.update(state_dict)\n self.load_state_dict(model_dict)\n self.classification_headers.apply(_xavier_init_)\n self.regression_headers.apply(_xavier_init_)\n\n def init(self):\n self.base_net.apply(_xavier_init_)\n self.source_layer_add_ons.apply(_xavier_init_)\n self.extras.apply(_xavier_init_)\n self.classification_headers.apply(_xavier_init_)\n self.regression_headers.apply(_xavier_init_)\n\n def load(self, model):\n self.load_state_dict(torch.load(model, map_location=lambda storage, loc: storage))\n\n def save(self, model_path):\n torch.save(self.state_dict(), model_path)\n\n\nclass MatchPrior(object):\n def __init__(self, center_form_priors, center_variance, size_variance, iou_threshold):\n self.center_form_priors = center_form_priors\n self.corner_form_priors = box_utils.center_form_to_corner_form(center_form_priors)\n self.center_variance = center_variance\n self.size_variance = size_variance\n self.iou_threshold = iou_threshold\n\n def __call__(self, gt_boxes, gt_labels):\n if type(gt_boxes) is np.ndarray:\n gt_boxes = torch.from_numpy(gt_boxes)\n if type(gt_labels) is np.ndarray:\n gt_labels = torch.from_numpy(gt_labels)\n boxes, labels = box_utils.assign_priors(gt_boxes, gt_labels,\n self.corner_form_priors, self.iou_threshold)\n boxes = box_utils.corner_form_to_center_form(boxes)\n locations = box_utils.convert_boxes_to_locations(boxes, self.center_form_priors, self.center_variance, self.size_variance)\n return locations, labels\n\n\ndef _xavier_init_(m: nn.Module):\n if isinstance(m, nn.Conv2d):\n nn.init.xavier_uniform_(m.weight)\n" }, { "path": "vision/transforms/__init__.py", "content": "" }, { "path": "vision/transforms/transforms.py", "content": "# from https://github.com/amdegroot/ssd.pytorch\n\n\nimport types\n\nimport cv2\nimport numpy as np\nimport torch\nfrom numpy import random\nfrom torchvision import transforms\n\n\ndef intersect(box_a, box_b):\n max_xy = np.minimum(box_a[:, 2:], box_b[2:])\n min_xy = np.maximum(box_a[:, :2], box_b[:2])\n inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf)\n return inter[:, 0] * inter[:, 1]\n\n\ndef jaccard_numpy(box_a, box_b):\n \"\"\"Compute the jaccard overlap of two sets of boxes. The jaccard overlap\n is simply the intersection over union of two boxes.\n E.g.:\n A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)\n Args:\n box_a: Multiple bounding boxes, Shape: [num_boxes,4]\n box_b: Single bounding box, Shape: [4]\n Return:\n jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]]\n \"\"\"\n inter = intersect(box_a, box_b)\n area_a = ((box_a[:, 2] - box_a[:, 0]) *\n (box_a[:, 3] - box_a[:, 1])) # [A,B]\n area_b = ((box_b[2] - box_b[0]) *\n (box_b[3] - box_b[1])) # [A,B]\n union = area_a + area_b - inter\n return inter / union # [A,B]\n\n\ndef object_converage_numpy(box_a, box_b):\n \"\"\"Compute the jaccard overlap of two sets of boxes. The jaccard overlap\n is simply the intersection over union of two boxes.\n E.g.:\n A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)\n Args:\n box_a: Multiple bounding boxes, Shape: [num_boxes,4]\n box_b: Single bounding box, Shape: [4]\n Return:\n jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]]\n \"\"\"\n inter = intersect(box_a, box_b)\n area_a = ((box_a[:, 2] - box_a[:, 0]) *\n (box_a[:, 3] - box_a[:, 1])) # [A,B]\n area_b = ((box_b[2] - box_b[0]) *\n (box_b[3] - box_b[1])) # [A,B]\n return inter / area_a # [A,B]\n\n\nclass Compose(object):\n \"\"\"Composes several augmentations together.\n Args:\n transforms (List[Transform]): list of transforms to compose.\n Example:\n >>> augmentations.Compose([\n >>> transforms.CenterCrop(10),\n >>> transforms.ToTensor(),\n >>> ])\n \"\"\"\n\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, img, boxes=None, labels=None):\n for t in self.transforms:\n img, boxes, labels = t(img, boxes, labels)\n return img, boxes, labels\n\n\nclass Lambda(object):\n \"\"\"Applies a lambda as a transform.\"\"\"\n\n def __init__(self, lambd):\n assert isinstance(lambd, types.LambdaType)\n self.lambd = lambd\n\n def __call__(self, img, boxes=None, labels=None):\n return self.lambd(img, boxes, labels)\n\n\nclass ConvertFromInts(object):\n def __call__(self, image, boxes=None, labels=None):\n return image.astype(np.float32), boxes, labels\n\n\nclass SubtractMeans(object):\n def __init__(self, mean):\n self.mean = np.array(mean, dtype=np.float32)\n\n def __call__(self, image, boxes=None, labels=None):\n image = image.astype(np.float32)\n image -= self.mean\n return image.astype(np.float32), boxes, labels\n\n\nclass imgprocess(object):\n def __init__(self, std):\n self.std = np.array(std, dtype=np.float32)\n\n def __call__(self, image, boxes=None, labels=None):\n image = image.astype(np.float32)\n image /= self.std\n return image.astype(np.float32), boxes, labels\n\n\nclass ToAbsoluteCoords(object):\n def __call__(self, image, boxes=None, labels=None):\n height, width, channels = image.shape\n boxes[:, 0] *= width\n boxes[:, 2] *= width\n boxes[:, 1] *= height\n boxes[:, 3] *= height\n\n return image, boxes, labels\n\n\nclass ToPercentCoords(object):\n def __call__(self, image, boxes=None, labels=None):\n height, width, channels = image.shape\n boxes[:, 0] /= width\n boxes[:, 2] /= width\n boxes[:, 1] /= height\n boxes[:, 3] /= height\n\n return image, boxes, labels\n\n\nclass Resize(object):\n def __init__(self, size=(300, 300)):\n self.size = size\n\n def __call__(self, image, boxes=None, labels=None):\n image = cv2.resize(image, (self.size[0],\n self.size[1]))\n return image, boxes, labels\n\n\nclass RandomSaturation(object):\n def __init__(self, lower=0.5, upper=1.5):\n self.lower = lower\n self.upper = upper\n assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n image[:, :, 1] *= random.uniform(self.lower, self.upper)\n\n return image, boxes, labels\n\n\nclass RandomHue(object):\n def __init__(self, delta=18.0):\n assert delta >= 0.0 and delta <= 360.0\n self.delta = delta\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n image[:, :, 0] += random.uniform(-self.delta, self.delta)\n image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0\n image[:, :, 0][image[:, :, 0] < 0.0] += 360.0\n return image, boxes, labels\n\n\nclass RandomLightingNoise(object):\n def __init__(self):\n self.perms = ((0, 1, 2), (0, 2, 1),\n (1, 0, 2), (1, 2, 0),\n (2, 0, 1), (2, 1, 0))\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n swap = self.perms[random.randint(len(self.perms))]\n shuffle = SwapChannels(swap) # shuffle channels\n image = shuffle(image)\n return image, boxes, labels\n\n\nclass ConvertColor(object):\n def __init__(self, current, transform):\n self.transform = transform\n self.current = current\n\n def __call__(self, image, boxes=None, labels=None):\n if self.current == 'BGR' and self.transform == 'HSV':\n image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n elif self.current == 'RGB' and self.transform == 'HSV':\n image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n elif self.current == 'BGR' and self.transform == 'RGB':\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n elif self.current == 'HSV' and self.transform == 'BGR':\n image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\n elif self.current == 'HSV' and self.transform == \"RGB\":\n image = cv2.cvtColor(image, cv2.COLOR_HSV2RGB)\n else:\n raise NotImplementedError\n return image, boxes, labels\n\n\nclass RandomContrast(object):\n def __init__(self, lower=0.5, upper=1.5):\n self.lower = lower\n self.upper = upper\n assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n # expects float image\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n alpha = random.uniform(self.lower, self.upper)\n image *= alpha\n return image, boxes, labels\n\n\nclass RandomBrightness(object):\n def __init__(self, delta=32):\n assert delta >= 0.0\n assert delta <= 255.0\n self.delta = delta\n\n def __call__(self, image, boxes=None, labels=None):\n if random.randint(2):\n delta = random.uniform(-self.delta, self.delta)\n image += delta\n return image, boxes, labels\n\n\nclass ToCV2Image(object):\n def __call__(self, tensor, boxes=None, labels=None):\n return tensor.cpu().numpy().astype(np.float32).transpose((1, 2, 0)), boxes, labels\n\n\nclass ToTensor(object):\n def __call__(self, cvimage, boxes=None, labels=None):\n return torch.from_numpy(cvimage.astype(np.float32)).permute(2, 0, 1), boxes, labels\n\n\nclass RandomSampleCrop(object):\n \"\"\"Crop\n Arguments:\n img (Image): the image being input during training\n boxes (Tensor): the original bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n mode (float tuple): the min and max jaccard overlaps\n Return:\n (img, boxes, classes)\n img (Image): the cropped image\n boxes (Tensor): the adjusted bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n \"\"\"\n\n def __init__(self):\n self.sample_options = (\n # using entire original input image\n None,\n # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9\n (0.1, None),\n (0.3, None),\n (0.7, None),\n (0.9, None),\n # randomly sample a patch\n (None, None),\n )\n\n def __call__(self, image, boxes=None, labels=None):\n height, width, _ = image.shape\n while True:\n # randomly choose a mode\n mode = random.choice(self.sample_options)\n if mode is None:\n return image, boxes, labels\n\n min_iou, max_iou = mode\n if min_iou is None:\n min_iou = float('-inf')\n if max_iou is None:\n max_iou = float('inf')\n\n # max trails (50)\n for _ in range(50):\n current_image = image\n\n w = random.uniform(0.3 * width, width)\n h = random.uniform(0.3 * height, height)\n\n # aspect ratio constraint b/t .5 & 2\n if h / w < 0.5 or h / w > 2:\n continue\n\n left = random.uniform(width - w)\n top = random.uniform(height - h)\n\n # convert to integer rect x1,y1,x2,y2\n rect = np.array([int(left), int(top), int(left + w), int(top + h)])\n\n # calculate IoU (jaccard overlap) b/t the cropped and gt boxes\n overlap = jaccard_numpy(boxes, rect)\n\n # is min and max overlap constraint satisfied? if not try again\n if overlap.max() < min_iou or overlap.min() > max_iou:\n continue\n\n # cut the crop from the image\n current_image = current_image[rect[1]:rect[3], rect[0]:rect[2],\n :]\n\n # keep overlap with gt box IF center in sampled patch\n centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0\n\n # mask in all gt boxes that above and to the left of centers\n m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])\n\n # mask in all gt boxes that under and to the right of centers\n m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])\n\n # mask in that both m1 and m2 are true\n mask = m1 * m2\n\n # have any valid boxes? try again if not\n if not mask.any():\n continue\n\n # take only matching gt boxes\n current_boxes = boxes[mask, :].copy()\n\n # take only matching gt labels\n current_labels = labels[mask]\n\n # should we use the box left and top corner or the crop's\n current_boxes[:, :2] = np.maximum(current_boxes[:, :2],\n rect[:2])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, :2] -= rect[:2]\n\n current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],\n rect[2:])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, 2:] -= rect[:2]\n\n return current_image, current_boxes, current_labels\n\n\nclass RandomSampleCrop_v2(object):\n \"\"\"Crop\n Arguments:\n img (Image): the image being input during training\n boxes (Tensor): the original bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n mode (float tuple): the min and max jaccard overlaps\n Return:\n (img, boxes, classes)\n img (Image): the cropped image\n boxes (Tensor): the adjusted bounding boxes in pt form\n labels (Tensor): the class labels for each bbox\n \"\"\"\n\n def __init__(self):\n self.sample_options = (\n # using entire original input image\n None,\n # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9\n\n # randomly sample a patch\n (1, None),\n (1, None),\n (1, None),\n (1, None),\n )\n\n def __call__(self, image, boxes=None, labels=None):\n height, width, _ = image.shape\n while True:\n # randomly choose a mode\n mode = random.choice(self.sample_options)\n if mode is None:\n return image, boxes, labels\n\n min_iou, max_iou = mode\n if min_iou is None:\n min_iou = float('-inf')\n if max_iou is None:\n max_iou = float('inf')\n\n # max trails (50)\n for _ in range(50):\n current_image = image\n\n w = random.uniform(0.3 * width, width)\n h = random.uniform(0.3 * height, height)\n\n # aspect ratio constraint b/t .5 & 2\n if h / w != 1:\n continue\n left = random.uniform(width - w)\n top = random.uniform(height - h)\n\n # convert to integer rect x1,y1,x2,y2\n rect = np.array([int(left), int(top), int(left + w), int(top + h)])\n\n # calculate IoU (jaccard overlap) b/t the cropped and gt boxes\n overlap = object_converage_numpy(boxes, rect)\n\n # is min and max overlap constraint satisfied? if not try again\n if overlap.max() < min_iou or overlap.min() > max_iou:\n continue\n\n # cut the crop from the image\n current_image = current_image[rect[1]:rect[3], rect[0]:rect[2],\n :]\n\n # keep overlap with gt box IF center in sampled patch\n centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0\n\n # mask in all gt boxes that above and to the left of centers\n m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])\n\n # mask in all gt boxes that under and to the right of centers\n m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])\n\n # mask in that both m1 and m2 are true\n mask = m1 * m2\n\n # have any valid boxes? try again if not\n if not mask.any():\n continue\n\n # take only matching gt boxes\n current_boxes = boxes[mask, :].copy()\n\n # take only matching gt labels\n current_labels = labels[mask]\n\n # should we use the box left and top corner or the crop's\n current_boxes[:, :2] = np.maximum(current_boxes[:, :2],\n rect[:2])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, :2] -= rect[:2]\n\n current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],\n rect[2:])\n # adjust to crop (by substracting crop's left,top)\n current_boxes[:, 2:] -= rect[:2]\n\n return current_image, current_boxes, current_labels\n\n\nclass Expand(object):\n def __init__(self, mean):\n self.mean = mean\n\n def __call__(self, image, boxes, labels):\n if random.randint(2):\n return image, boxes, labels\n\n height, width, depth = image.shape\n ratio = random.uniform(1, 4)\n left = random.uniform(0, width * ratio - width)\n top = random.uniform(0, height * ratio - height)\n\n expand_image = np.zeros(\n (int(height * ratio), int(width * ratio), depth),\n dtype=image.dtype)\n expand_image[:, :, :] = self.mean\n expand_image[int(top):int(top + height),\n int(left):int(left + width)] = image\n image = expand_image\n\n boxes = boxes.copy()\n boxes[:, :2] += (int(left), int(top))\n boxes[:, 2:] += (int(left), int(top))\n\n return image, boxes, labels\n\n\nclass RandomMirror(object):\n def __call__(self, image, boxes, classes):\n _, width, _ = image.shape\n if random.randint(2):\n image = image[:, ::-1]\n boxes = boxes.copy()\n boxes[:, 0::2] = width - boxes[:, 2::-2]\n return image, boxes, classes\n\n\nclass SwapChannels(object):\n \"\"\"Transforms a tensorized image by swapping the channels in the order\n specified in the swap tuple.\n Args:\n swaps (int triple): final order of channels\n eg: (2, 1, 0)\n \"\"\"\n\n def __init__(self, swaps):\n self.swaps = swaps\n\n def __call__(self, image):\n \"\"\"\n Args:\n image (Tensor): image tensor to be transformed\n Return:\n a tensor with channels swapped according to swap\n \"\"\"\n # if torch.is_tensor(image):\n # image = image.data.cpu().numpy()\n # else:\n # image = np.array(image)\n image = image[:, :, self.swaps]\n return image\n\n\nclass PhotometricDistort(object):\n def __init__(self):\n self.pd = [\n RandomContrast(), # RGB\n ConvertColor(current=\"RGB\", transform='HSV'), # HSV\n RandomSaturation(), # HSV\n RandomHue(), # HSV\n ConvertColor(current='HSV', transform='RGB'), # RGB\n RandomContrast() # RGB\n ]\n self.rand_brightness = RandomBrightness()\n self.rand_light_noise = RandomLightingNoise()\n\n def __call__(self, image, boxes, labels):\n im = image.copy()\n im, boxes, labels = self.rand_brightness(im, boxes, labels)\n if random.randint(2):\n distort = Compose(self.pd[:-1])\n else:\n distort = Compose(self.pd[1:])\n im, boxes, labels = distort(im, boxes, labels)\n return self.rand_light_noise(im, boxes, labels)\n" }, { "path": "vision/utils/__init__.py", "content": "from .misc import *\n" }, { "path": "vision/utils/box_utils.py", "content": "import math\n\nimport torch\n\n\ndef generate_priors(feature_map_list, shrinkage_list, image_size, min_boxes, clamp=True) -> torch.Tensor:\n priors = []\n for index in range(0, len(feature_map_list[0])):\n scale_w = image_size[0] / shrinkage_list[0][index]\n scale_h = image_size[1] / shrinkage_list[1][index]\n for j in range(0, feature_map_list[1][index]):\n for i in range(0, feature_map_list[0][index]):\n x_center = (i + 0.5) / scale_w\n y_center = (j + 0.5) / scale_h\n\n for min_box in min_boxes[index]:\n w = min_box / image_size[0]\n h = min_box / image_size[1]\n priors.append([\n x_center,\n y_center,\n w,\n h\n ])\n print(\"priors nums:{}\".format(len(priors)))\n priors = torch.tensor(priors)\n if clamp:\n torch.clamp(priors, 0.0, 1.0, out=priors)\n return priors\n\n\ndef convert_locations_to_boxes(locations, priors, center_variance,\n size_variance):\n \"\"\"Convert regressional location results of SSD into boxes in the form of (center_x, center_y, h, w).\n\n The conversion:\n $$predicted\\_center * center_variance = \\frac {real\\_center - prior\\_center} {prior\\_hw}$$\n $$exp(predicted\\_hw * size_variance) = \\frac {real\\_hw} {prior\\_hw}$$\n We do it in the inverse direction here.\n Args:\n locations (batch_size, num_priors, 4): the regression output of SSD. It will contain the outputs as well.\n priors (num_priors, 4) or (batch_size/1, num_priors, 4): prior boxes.\n center_variance: a float used to change the scale of center.\n size_variance: a float used to change of scale of size.\n Returns:\n boxes: priors: [[center_x, center_y, h, w]]. All the values\n are relative to the image size.\n \"\"\"\n # priors can have one dimension less.\n if priors.dim() + 1 == locations.dim():\n priors = priors.unsqueeze(0)\n return torch.cat([\n locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],\n torch.exp(locations[..., 2:] * size_variance) * priors[..., 2:]\n ], dim=locations.dim() - 1)\n\n\ndef convert_boxes_to_locations(center_form_boxes, center_form_priors, center_variance, size_variance):\n # priors can have one dimension less\n if center_form_priors.dim() + 1 == center_form_boxes.dim():\n center_form_priors = center_form_priors.unsqueeze(0)\n return torch.cat([\n (center_form_boxes[..., :2] - center_form_priors[..., :2]) / center_form_priors[..., 2:] / center_variance,\n torch.log(center_form_boxes[..., 2:] / center_form_priors[..., 2:]) / size_variance\n ], dim=center_form_boxes.dim() - 1)\n\n\ndef area_of(left_top, right_bottom) -> torch.Tensor:\n \"\"\"Compute the areas of rectangles given two corners.\n\n Args:\n left_top (N, 2): left top corner.\n right_bottom (N, 2): right bottom corner.\n\n Returns:\n area (N): return the area.\n \"\"\"\n hw = torch.clamp(right_bottom - left_top, min=0.0)\n return hw[..., 0] * hw[..., 1]\n\n\ndef iou_of(boxes0, boxes1, eps=1e-5):\n \"\"\"Return intersection-over-union (Jaccard index) of boxes.\n\n Args:\n boxes0 (N, 4): ground truth boxes.\n boxes1 (N or 1, 4): predicted boxes.\n eps: a small number to avoid 0 as denominator.\n Returns:\n iou (N): IoU values.\n \"\"\"\n overlap_left_top = torch.max(boxes0[..., :2], boxes1[..., :2])\n overlap_right_bottom = torch.min(boxes0[..., 2:], boxes1[..., 2:])\n\n overlap_area = area_of(overlap_left_top, overlap_right_bottom)\n area0 = area_of(boxes0[..., :2], boxes0[..., 2:])\n area1 = area_of(boxes1[..., :2], boxes1[..., 2:])\n return overlap_area / (area0 + area1 - overlap_area + eps)\n\n\ndef assign_priors(gt_boxes, gt_labels, corner_form_priors,\n iou_threshold):\n \"\"\"Assign ground truth boxes and targets to priors.\n\n Args:\n gt_boxes (num_targets, 4): ground truth boxes.\n gt_labels (num_targets): labels of targets.\n priors (num_priors, 4): corner form priors\n Returns:\n boxes (num_priors, 4): real values for priors.\n labels (num_priros): labels for priors.\n \"\"\"\n # size: num_priors x num_targets\n ious = iou_of(gt_boxes.unsqueeze(0), corner_form_priors.unsqueeze(1))\n # size: num_priors\n best_target_per_prior, best_target_per_prior_index = ious.max(1)\n # size: num_targets\n best_prior_per_target, best_prior_per_target_index = ious.max(0)\n\n for target_index, prior_index in enumerate(best_prior_per_target_index):\n best_target_per_prior_index[prior_index] = target_index\n # 2.0 is used to make sure every target has a prior assigned\n best_target_per_prior.index_fill_(0, best_prior_per_target_index, 2)\n # size: num_priors\n labels = gt_labels[best_target_per_prior_index]\n labels[best_target_per_prior < iou_threshold] = 0 # the backgournd id\n boxes = gt_boxes[best_target_per_prior_index]\n return boxes, labels\n\n\ndef hard_negative_mining(loss, labels, neg_pos_ratio):\n \"\"\"\n It used to suppress the presence of a large number of negative prediction.\n It works on image level not batch level.\n For any example/image, it keeps all the positive predictions and\n cut the number of negative predictions to make sure the ratio\n between the negative examples and positive examples is no more\n the given ratio for an image.\n\n Args:\n loss (N, num_priors): the loss for each example.\n labels (N, num_priors): the labels.\n neg_pos_ratio: the ratio between the negative examples and positive examples.\n \"\"\"\n pos_mask = labels > 0\n num_pos = pos_mask.long().sum(dim=1, keepdim=True)\n num_neg = num_pos * neg_pos_ratio\n\n loss[pos_mask] = -math.inf\n _, indexes = loss.sort(dim=1, descending=True)\n _, orders = indexes.sort(dim=1)\n neg_mask = orders < num_neg\n return pos_mask | neg_mask\n\n\ndef center_form_to_corner_form(locations):\n return torch.cat([locations[..., :2] - locations[..., 2:] / 2,\n locations[..., :2] + locations[..., 2:] / 2], locations.dim() - 1)\n\n\ndef corner_form_to_center_form(boxes):\n return torch.cat([\n (boxes[..., :2] + boxes[..., 2:]) / 2,\n boxes[..., 2:] - boxes[..., :2]\n ], boxes.dim() - 1)\n\n\ndef hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):\n \"\"\"\n\n Args:\n box_scores (N, 5): boxes in corner-form and probabilities.\n iou_threshold: intersection over union threshold.\n top_k: keep top_k results. If k <= 0, keep all the results.\n candidate_size: only consider the candidates with the highest scores.\n Returns:\n picked: a list of indexes of the kept boxes\n \"\"\"\n scores = box_scores[:, -1]\n boxes = box_scores[:, :-1]\n picked = []\n _, indexes = scores.sort(descending=True)\n indexes = indexes[:candidate_size]\n while len(indexes) > 0:\n current = indexes[0]\n picked.append(current.item())\n if 0 < top_k == len(picked) or len(indexes) == 1:\n break\n current_box = boxes[current, :]\n indexes = indexes[1:]\n rest_boxes = boxes[indexes, :]\n iou = iou_of(\n rest_boxes,\n current_box.unsqueeze(0),\n )\n indexes = indexes[iou <= iou_threshold]\n\n return box_scores[picked, :]\n\n\ndef nms(box_scores, nms_method=None, score_threshold=None, iou_threshold=None,\n sigma=0.5, top_k=-1, candidate_size=200):\n if nms_method == \"soft\":\n return soft_nms(box_scores, score_threshold, sigma, top_k)\n else:\n return hard_nms(box_scores, iou_threshold, top_k, candidate_size=candidate_size)\n\n\ndef soft_nms(box_scores, score_threshold, sigma=0.5, top_k=-1):\n \"\"\"Soft NMS implementation.\n\n References:\n https://arxiv.org/abs/1704.04503\n https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/cython_nms.pyx\n\n Args:\n box_scores (N, 5): boxes in corner-form and probabilities.\n score_threshold: boxes with scores less than value are not considered.\n sigma: the parameter in score re-computation.\n scores[i] = scores[i] * exp(-(iou_i)^2 / simga)\n top_k: keep top_k results. If k <= 0, keep all the results.\n Returns:\n picked_box_scores (K, 5): results of NMS.\n \"\"\"\n picked_box_scores = []\n while box_scores.size(0) > 0:\n max_score_index = torch.argmax(box_scores[:, 4])\n cur_box_prob = torch.tensor(box_scores[max_score_index, :])\n picked_box_scores.append(cur_box_prob)\n if len(picked_box_scores) == top_k > 0 or box_scores.size(0) == 1:\n break\n cur_box = cur_box_prob[:-1]\n box_scores[max_score_index, :] = box_scores[-1, :]\n box_scores = box_scores[:-1, :]\n ious = iou_of(cur_box.unsqueeze(0), box_scores[:, :-1])\n box_scores[:, -1] = box_scores[:, -1] * torch.exp(-(ious * ious) / sigma)\n box_scores = box_scores[box_scores[:, -1] > score_threshold, :]\n if len(picked_box_scores) > 0:\n return torch.stack(picked_box_scores)\n else:\n return torch.tensor([])\n" }, { "path": "vision/utils/box_utils_numpy.py", "content": "import numpy as np\n\n\ndef convert_locations_to_boxes(locations, priors, center_variance,\n size_variance):\n \"\"\"Convert regressional location results of SSD into boxes in the form of (center_x, center_y, h, w).\n\n The conversion:\n $$predicted\\_center * center_variance = \\frac {real\\_center - prior\\_center} {prior\\_hw}$$\n $$exp(predicted\\_hw * size_variance) = \\frac {real\\_hw} {prior\\_hw}$$\n We do it in the inverse direction here.\n Args:\n locations (batch_size, num_priors, 4): the regression output of SSD. It will contain the outputs as well.\n priors (num_priors, 4) or (batch_size/1, num_priors, 4): prior boxes.\n center_variance: a float used to change the scale of center.\n size_variance: a float used to change of scale of size.\n Returns:\n boxes: priors: [[center_x, center_y, h, w]]. All the values\n are relative to the image size.\n \"\"\"\n # priors can have one dimension less.\n if len(priors.shape) + 1 == len(locations.shape):\n priors = np.expand_dims(priors, 0)\n return np.concatenate([\n locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],\n np.exp(locations[..., 2:] * size_variance) * priors[..., 2:]\n ], axis=len(locations.shape) - 1)\n\n\ndef convert_boxes_to_locations(center_form_boxes, center_form_priors, center_variance, size_variance):\n # priors can have one dimension less\n if len(center_form_priors.shape) + 1 == len(center_form_boxes.shape):\n center_form_priors = np.expand_dims(center_form_priors, 0)\n return np.concatenate([\n (center_form_boxes[..., :2] - center_form_priors[..., :2]) / center_form_priors[..., 2:] / center_variance,\n np.log(center_form_boxes[..., 2:] / center_form_priors[..., 2:]) / size_variance\n ], axis=len(center_form_boxes.shape) - 1)\n\n\ndef area_of(left_top, right_bottom):\n \"\"\"Compute the areas of rectangles given two corners.\n\n Args:\n left_top (N, 2): left top corner.\n right_bottom (N, 2): right bottom corner.\n\n Returns:\n area (N): return the area.\n \"\"\"\n hw = np.clip(right_bottom - left_top, 0.0, None)\n return hw[..., 0] * hw[..., 1]\n\n\ndef iou_of(boxes0, boxes1, eps=1e-5):\n \"\"\"Return intersection-over-union (Jaccard index) of boxes.\n\n Args:\n boxes0 (N, 4): ground truth boxes.\n boxes1 (N or 1, 4): predicted boxes.\n eps: a small number to avoid 0 as denominator.\n Returns:\n iou (N): IoU values.\n \"\"\"\n overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])\n overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])\n\n overlap_area = area_of(overlap_left_top, overlap_right_bottom)\n area0 = area_of(boxes0[..., :2], boxes0[..., 2:])\n area1 = area_of(boxes1[..., :2], boxes1[..., 2:])\n return overlap_area / (area0 + area1 - overlap_area + eps)\n\n\ndef center_form_to_corner_form(locations):\n return np.concatenate([locations[..., :2] - locations[..., 2:] / 2,\n locations[..., :2] + locations[..., 2:] / 2], len(locations.shape) - 1)\n\n\ndef corner_form_to_center_form(boxes):\n return np.concatenate([\n (boxes[..., :2] + boxes[..., 2:]) / 2,\n boxes[..., 2:] - boxes[..., :2]\n ], len(boxes.shape) - 1)\n\n\ndef hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):\n \"\"\"\n\n Args:\n box_scores (N, 5): boxes in corner-form and probabilities.\n iou_threshold: intersection over union threshold.\n top_k: keep top_k results. If k <= 0, keep all the results.\n candidate_size: only consider the candidates with the highest scores.\n Returns:\n picked: a list of indexes of the kept boxes\n \"\"\"\n scores = box_scores[:, -1]\n boxes = box_scores[:, :-1]\n picked = []\n # _, indexes = scores.sort(descending=True)\n indexes = np.argsort(scores)\n # indexes = indexes[:candidate_size]\n indexes = indexes[-candidate_size:]\n while len(indexes) > 0:\n # current = indexes[0]\n current = indexes[-1]\n picked.append(current)\n if 0 < top_k == len(picked) or len(indexes) == 1:\n break\n current_box = boxes[current, :]\n # indexes = indexes[1:]\n indexes = indexes[:-1]\n rest_boxes = boxes[indexes, :]\n iou = iou_of(\n rest_boxes,\n np.expand_dims(current_box, axis=0),\n )\n indexes = indexes[iou <= iou_threshold]\n\n return box_scores[picked, :]\n" }, { "path": "vision/utils/misc.py", "content": "import datetime\n\nimport torch\n\n\ndef str2bool(s):\n return s.lower() in ('true', '1')\n\n\nclass Timer:\n def __init__(self):\n self.clock = {}\n\n def start(self, key=\"default\"):\n self.clock[key] = datetime.datetime.now()\n\n def end(self, key=\"default\"):\n if key not in self.clock:\n raise Exception(f\"{key} is not in the clock.\")\n interval = datetime.datetime.now() - self.clock[key]\n del self.clock[key]\n return interval.total_seconds()\n \n\ndef save_checkpoint(epoch, net_state_dict, optimizer_state_dict, best_score, checkpoint_path, model_path):\n torch.save({\n 'epoch': epoch,\n 'model': net_state_dict,\n 'optimizer': optimizer_state_dict,\n 'best_score': best_score\n }, checkpoint_path)\n torch.save(net_state_dict, model_path)\n \n \ndef load_checkpoint(checkpoint_path):\n return torch.load(checkpoint_path)\n\n\ndef freeze_net_layers(net):\n for param in net.parameters():\n param.requires_grad = False\n\n\ndef store_labels(path, labels):\n with open(path, \"w\") as f:\n f.write(\"\\n\".join(labels))\n" }, { "path": "widerface_evaluate/README.md", "content": "# WiderFace-Evaluation\nPython Evaluation Code for [Wider Face Dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/)\n\n\n## Usage\n\n##### run evaluation_on_widerface.py\n\n````\npython3 evaluation_on_widerface.py\n````\n##### before evaluating \n\n````\npython3 setup.py build_ext --inplace\n````\n\n##### evaluating\n\n**GroungTruth:** `wider_face_val.mat`, `wider_easy_val.mat`, `wider_medium_val.mat`,`wider_hard_val.mat`\n\n````\npython3 evaluation.py -p -g \n````\n\n## Acknowledgements\n\nsome code borrowed from Sergey Karayev\n" }, { "path": "widerface_evaluate/box_overlaps.pyx", "content": "# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Sergey Karayev\n# --------------------------------------------------------\n\ncimport cython\nimport numpy as np\ncimport numpy as np\n\nDTYPE = np.float\nctypedef np.float_t DTYPE_t\n\ndef bbox_overlaps(\n np.ndarray[DTYPE_t, ndim=2] boxes,\n np.ndarray[DTYPE_t, ndim=2] query_boxes):\n \"\"\"\n Parameters\n ----------\n boxes: (N, 4) ndarray of float\n query_boxes: (K, 4) ndarray of float\n Returns\n -------\n overlaps: (N, K) ndarray of overlap between boxes and query_boxes\n \"\"\"\n cdef unsigned int N = boxes.shape[0]\n cdef unsigned int K = query_boxes.shape[0]\n cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)\n cdef DTYPE_t iw, ih, box_area\n cdef DTYPE_t ua\n cdef unsigned int k, n\n for k in range(K):\n box_area = (\n (query_boxes[k, 2] - query_boxes[k, 0] + 1) *\n (query_boxes[k, 3] - query_boxes[k, 1] + 1)\n )\n for n in range(N):\n iw = (\n min(boxes[n, 2], query_boxes[k, 2]) -\n max(boxes[n, 0], query_boxes[k, 0]) + 1\n )\n if iw > 0:\n ih = (\n min(boxes[n, 3], query_boxes[k, 3]) -\n max(boxes[n, 1], query_boxes[k, 1]) + 1\n )\n if ih > 0:\n ua = float(\n (boxes[n, 2] - boxes[n, 0] + 1) *\n (boxes[n, 3] - boxes[n, 1] + 1) +\n box_area - iw * ih\n )\n overlaps[n, k] = iw * ih / ua\n return overlaps" }, { "path": "widerface_evaluate/evaluation.py", "content": "\"\"\"\nWiderFace evaluation code\nauthor: wondervictor\nmail: tianhengcheng@gmail.com\ncopyright@wondervictor\n\"\"\"\n\nimport os\nimport tqdm\nimport pickle\nimport argparse\nimport numpy as np\nfrom scipy.io import loadmat\nfrom bbox import bbox_overlaps\n\n\ndef get_gt_boxes(gt_dir):\n \"\"\" gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)\"\"\"\n\n gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat'))\n hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat'))\n medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat'))\n easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat'))\n\n facebox_list = gt_mat['face_bbx_list']\n event_list = gt_mat['event_list']\n file_list = gt_mat['file_list']\n\n hard_gt_list = hard_mat['gt_list']\n medium_gt_list = medium_mat['gt_list']\n easy_gt_list = easy_mat['gt_list']\n\n return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list\n\n\ndef get_gt_boxes_from_txt(gt_path, cache_dir):\n\n cache_file = os.path.join(cache_dir, 'gt_cache.pkl')\n if os.path.exists(cache_file):\n f = open(cache_file, 'rb')\n boxes = pickle.load(f)\n f.close()\n return boxes\n\n f = open(gt_path, 'r')\n state = 0\n lines = f.readlines()\n lines = list(map(lambda x: x.rstrip('\\r\\n'), lines))\n boxes = {}\n print(len(lines))\n f.close()\n current_boxes = []\n current_name = None\n for line in lines:\n if state == 0 and '--' in line:\n state = 1\n current_name = line\n continue\n if state == 1:\n state = 2\n continue\n\n if state == 2 and '--' in line:\n state = 1\n boxes[current_name] = np.array(current_boxes).astype('float32')\n current_name = line\n current_boxes = []\n continue\n\n if state == 2:\n box = [float(x) for x in line.split(' ')[:4]]\n current_boxes.append(box)\n continue\n\n f = open(cache_file, 'wb')\n pickle.dump(boxes, f)\n f.close()\n return boxes\n\n\ndef read_pred_file(filepath):\n\n with open(filepath, 'r') as f:\n lines = f.readlines()\n img_file = lines[0].rstrip('\\n\\r')\n lines = lines[2:]\n\n # b = lines[0].rstrip('\\r\\n').split(' ')[:-1]\n # c = float(b)\n # a = map(lambda x: [[float(a[0]), float(a[1]), float(a[2]), float(a[3]), float(a[4])] for a in x.rstrip('\\r\\n').split(' ')], lines)\n boxes = []\n for line in lines:\n line = line.rstrip('\\r\\n').split(' ')\n if line[0] == '':\n continue\n # a = float(line[4])\n boxes.append([float(line[0]), float(line[1]), float(line[2]), float(line[3]), float(line[4])])\n boxes = np.array(boxes)\n # boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\\r\\n').split(' ')], lines))).astype('float')\n return img_file.split('/')[-1], boxes\n\n\ndef get_preds(pred_dir):\n events = os.listdir(pred_dir)\n boxes = dict()\n pbar = tqdm.tqdm(events)\n\n for event in pbar:\n pbar.set_description('Reading Predictions ')\n event_dir = os.path.join(pred_dir, event)\n event_images = os.listdir(event_dir)\n current_event = dict()\n for imgtxt in event_images:\n imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt))\n current_event[imgname.rstrip('.jpg')] = _boxes\n boxes[event] = current_event\n return boxes\n\n\ndef norm_score(pred):\n \"\"\" norm score\n pred {key: [[x1,y1,x2,y2,s]]}\n \"\"\"\n\n max_score = 0\n min_score = 1\n\n for _, k in pred.items():\n for _, v in k.items():\n if len(v) == 0:\n continue\n _min = np.min(v[:, -1])\n _max = np.max(v[:, -1])\n max_score = max(_max, max_score)\n min_score = min(_min, min_score)\n\n diff = max_score - min_score\n for _, k in pred.items():\n for _, v in k.items():\n if len(v) == 0:\n continue\n v[:, -1] = (v[:, -1] - min_score)/diff\n\n\ndef image_eval(pred, gt, ignore, iou_thresh):\n \"\"\" single image evaluation\n pred: Nx5\n gt: Nx4\n ignore:\n \"\"\"\n\n _pred = pred.copy()\n _gt = gt.copy()\n pred_recall = np.zeros(_pred.shape[0])\n recall_list = np.zeros(_gt.shape[0])\n proposal_list = np.ones(_pred.shape[0])\n\n _pred[:, 2] = _pred[:, 2] + _pred[:, 0]\n _pred[:, 3] = _pred[:, 3] + _pred[:, 1]\n _gt[:, 2] = _gt[:, 2] + _gt[:, 0]\n _gt[:, 3] = _gt[:, 3] + _gt[:, 1]\n\n overlaps = bbox_overlaps(_pred[:, :4], _gt)\n\n for h in range(_pred.shape[0]):\n\n gt_overlap = overlaps[h]\n max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()\n if max_overlap >= iou_thresh:\n if ignore[max_idx] == 0:\n recall_list[max_idx] = -1\n proposal_list[h] = -1\n elif recall_list[max_idx] == 0:\n recall_list[max_idx] = 1\n\n r_keep_index = np.where(recall_list == 1)[0]\n pred_recall[h] = len(r_keep_index)\n return pred_recall, proposal_list\n\n\ndef img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):\n pr_info = np.zeros((thresh_num, 2)).astype('float')\n for t in range(thresh_num):\n\n thresh = 1 - (t+1)/thresh_num\n r_index = np.where(pred_info[:, 4] >= thresh)[0]\n if len(r_index) == 0:\n pr_info[t, 0] = 0\n pr_info[t, 1] = 0\n else:\n r_index = r_index[-1]\n p_index = np.where(proposal_list[:r_index+1] == 1)[0]\n pr_info[t, 0] = len(p_index)\n pr_info[t, 1] = pred_recall[r_index]\n return pr_info\n\n\ndef dataset_pr_info(thresh_num, pr_curve, count_face):\n _pr_curve = np.zeros((thresh_num, 2))\n for i in range(thresh_num):\n _pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0]\n _pr_curve[i, 1] = pr_curve[i, 1] / count_face\n return _pr_curve\n\n\ndef voc_ap(rec, prec):\n\n # correct AP calculation\n # first append sentinel values at the end\n mrec = np.concatenate(([0.], rec, [1.]))\n mpre = np.concatenate(([0.], prec, [0.]))\n\n # compute the precision envelope\n for i in range(mpre.size - 1, 0, -1):\n mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n\n # to calculate area under PR curve, look for points\n # where X axis (recall) changes value\n i = np.where(mrec[1:] != mrec[:-1])[0]\n\n # and sum (\\Delta recall) * prec\n ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n return ap\n\n\ndef evaluation(pred, gt_path, iou_thresh=0.5):\n pred = get_preds(pred)\n norm_score(pred)\n facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path)\n event_num = len(event_list)\n thresh_num = 1000\n settings = ['easy', 'medium', 'hard']\n setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list]\n aps = []\n for setting_id in range(3):\n # different setting\n gt_list = setting_gts[setting_id]\n count_face = 0\n pr_curve = np.zeros((thresh_num, 2)).astype('float')\n # [hard, medium, easy]\n pbar = tqdm.tqdm(range(event_num))\n for i in pbar:\n pbar.set_description('Processing {}'.format(settings[setting_id]))\n event_name = str(event_list[i][0][0])\n img_list = file_list[i][0]\n pred_list = pred[event_name]\n sub_gt_list = gt_list[i][0]\n # img_pr_info_list = np.zeros((len(img_list), thresh_num, 2))\n gt_bbx_list = facebox_list[i][0]\n\n for j in range(len(img_list)):\n pred_info = pred_list[str(img_list[j][0][0])]\n\n gt_boxes = gt_bbx_list[j][0].astype('float')\n keep_index = sub_gt_list[j][0]\n count_face += len(keep_index)\n\n if len(gt_boxes) == 0 or len(pred_info) == 0:\n continue\n ignore = np.zeros(gt_boxes.shape[0])\n if len(keep_index) != 0:\n ignore[keep_index-1] = 1\n pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_thresh)\n\n _img_pr_info = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall)\n\n pr_curve += _img_pr_info\n pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face)\n\n propose = pr_curve[:, 0]\n recall = pr_curve[:, 1]\n\n ap = voc_ap(recall, propose)\n aps.append(ap)\n\n print(\"==================== Results ====================\")\n print(\"Easy Val AP: {}\".format(aps[0]))\n print(\"Medium Val AP: {}\".format(aps[1]))\n print(\"Hard Val AP: {}\".format(aps[2]))\n print(\"=================================================\")\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--pred', default=\"./widerface_txt/\")\n parser.add_argument('-g', '--gt', default='./ground_truth/')\n\n args = parser.parse_args()\n evaluation(args.pred, args.gt)\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "widerface_evaluate/evaluation_on_widerface.py", "content": "#!/usr/bin/ python3\n# -*- coding: utf-8 -*-\n# @Time : 2019-10-17\n# @Author : vealocia\n# @FileName: evaluation_on_widerface.py\n\nimport math\nimport os\nimport sys\n\nimport cv2\nsys.path.append('../')\nfrom vision.ssd.config.fd_config import define_img_size\n\ninput_img_size = 320 # define input size ,default optional(128/160/320/480/640/1280)\ndefine_img_size(input_img_size) # must put define_img_size() before 'import create_mb_tiny_fd, create_mb_tiny_fd_predictor'\n\nfrom vision.ssd.mb_tiny_fd import create_mb_tiny_fd, create_mb_tiny_fd_predictor\nfrom vision.ssd.mb_tiny_RFB_fd import create_Mb_Tiny_RFB_fd, create_Mb_Tiny_RFB_fd_predictor\n\nlabel_path = \"../models/voc-model-labels.txt\"\n\n# net_type = \"slim\" # inference faster,lower precision\nnet_type = \"RFB\" # inference lower,higher precision\n\nclass_names = [name.strip() for name in open(label_path).readlines()]\nnum_classes = len(class_names)\ntest_device = \"cuda:0\"\n# test_device = \"cpu\"\ncandidate_size = 800\nthreshold = 0.1\n\nval_image_root = \"/pic/linzai/1080Ti/home_linzai/PycharmProjects/insightface/RetinaFace/data/retinaface/val\" # path to widerface valuation image root\nval_result_txt_save_root = \"./widerface_evaluation/\" # result directory\n\nif net_type == 'slim':\n model_path = \"../models/pretrained/version-slim-320.pth\"\n # model_path = \"../models/pretrained/version-slim-640.pth\"\n net = create_mb_tiny_fd(len(class_names), is_test=True, device=test_device)\n predictor = create_mb_tiny_fd_predictor(net, candidate_size=candidate_size, device=test_device)\nelif net_type == 'RFB':\n model_path = \"../models/pretrained/version-RFB-320.pth\"\n # model_path = \"../models/pretrained/version-RFB-640.pth\"\n net = create_Mb_Tiny_RFB_fd(len(class_names), is_test=True, device=test_device)\n predictor = create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=candidate_size, device=test_device)\nelse:\n print(\"The net type is wrong!\")\n sys.exit(1)\nnet.load(model_path)\n\ncounter = 0\nfor parent, dir_names, file_names in os.walk(val_image_root):\n for file_name in file_names:\n if not file_name.lower().endswith('jpg'):\n continue\n im = cv2.imread(os.path.join(parent, file_name), cv2.IMREAD_COLOR)\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n boxes, labels, probs = predictor.predict(im, candidate_size / 2, threshold)\n\n event_name = parent.split('/')[-1]\n if not os.path.exists(os.path.join(val_result_txt_save_root, event_name)):\n os.makedirs(os.path.join(val_result_txt_save_root, event_name))\n fout = open(os.path.join(val_result_txt_save_root, event_name, file_name.split('.')[0] + '.txt'), 'w')\n fout.write(file_name.split('.')[0] + '\\n')\n fout.write(str(boxes.size(0)) + '\\n')\n for i in range(boxes.size(0)):\n bbox = boxes[i, :]\n fout.write('%d %d %d %d %.03f' % (math.floor(bbox[0]), math.floor(bbox[1]), math.ceil(bbox[2] - bbox[0]), math.ceil(bbox[3] - bbox[1]), probs[i] if probs[i] <= 1 else 1) + '\\n')\n fout.close()\n counter += 1\n print('[%d] %s is processed.' % (counter, file_name))\n\n# note: with score_threshold = 0.11 and hard_nms, MAP of 320-input model on widerface val set is: 0.785/0.695/0.431\n" }, { "path": "widerface_evaluate/setup.py", "content": "\"\"\"\nWiderFace evaluation code\nauthor: wondervictor\nmail: tianhengcheng@gmail.com\ncopyright@wondervictor\n\"\"\"\n\nfrom distutils.core import setup, Extension\nfrom Cython.Build import cythonize\nimport numpy\n\npackage = Extension('bbox', ['box_overlaps.pyx'], include_dirs=[numpy.get_include()])\nsetup(ext_modules=cythonize([package]))\n" } ]