Ivan Mukhin's questions

有一个项目收集了2个库TinyEXIF和tinyxml2，其中TinyEXIF需要tinyxml2。 CMakeLists.txt：

option(BUILD_TINYEXIF "Build 3rdparty library TinyEXIF with tinyxml2" OFF)

if(BUILD_TINYEXIF)
    file(MAKE_DIRECTORY ${CMAKE_BINARY_DIR}/tinyxml2)
    execute_process(
        COMMAND ${CMAKE_COMMAND} ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tinyxml2
                                 -DCMAKE_INSTALL_PREFIX=tinyxml2_install
                                 -DBUILD_SHARED_LIBS=ON
        WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/tinyxml2
    )

    execute_process(
        COMMAND ${CMAKE_COMMAND} --build .
        WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/tinyxml2
    )

    execute_process(
        COMMAND ${CMAKE_COMMAND} --install .
        WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/tinyxml2
    )

    file(MAKE_DIRECTORY ${CMAKE_BINARY_DIR}/TinyEXIF)
    execute_process(
        COMMAND ${CMAKE_COMMAND} ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/TinyEXIF
                                -DCMAKE_INSTALL_PREFIX=TinyEXIF_install
                                -DBUILD_SHARED_LIBS=ON -DBUILD_STATIC_LIBS=OFF
                                -Dtinyxml2_DIR=${CMAKE_BINARY_DIR}/tinyxml2/tinyxml2_install/lib/cmake/tinyxml2
        WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/TinyEXIF
    )
    
    execute_process(
        COMMAND ${CMAKE_COMMAND} --build .
        WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/TinyEXIF
    )
    
    execute_process(
        COMMAND ${CMAKE_COMMAND} --install .
        WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/TinyEXIF
    )
    set(tinyxml2_DIR "${CMAKE_BINARY_DIR}/tinyxml2/tinyxml2_install/lib/cmake/tinyxml2")
    set(TinyEXIF_DIR "${CMAKE_BINARY_DIR}/TinyEXIF/TinyEXIF_install/lib/cmake/TinyEXIF")

    #list(APPEND CMAKE_BUILD_RPATH "${CMAKE_BINARY_DIR}/TinyEXIF/TinyEXIF_install/lib")
    #list(APPEND CMAKE_BUILD_RPATH "${CMAKE_BINARY_DIR}/tinyxml2/tinyxml2_install/lib")

endif()

find_package(tinyxml2 CONFIG REQUIRED)

find_package(TinyEXIF CONFIG REQUIRED)

file(GLOB_RECURSE SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/src/*")

file(GLOB_RECURSE HEADERS "${CMAKE_CURRENT_SOURCE_DIR}/include/*")

add_executable(Geotags ${SOURCES} ${HEADERS})

include_directories("${CMAKE_BINARY_DIR}/TinyEXIF/TinyEXIF_install/include")

include_directories("${CMAKE_CURRENT_SOURCE_DIR}/include")
target_link_libraries(Geotags
    TinyEXIF
)

配置完成后，在build文件夹中会出现必要的库，以及这个库的安装文件夹，结构如下：

build ->
    TinyEXIF ->
       libTinyEXIF.so
       libTinyEXIF.so.1
       libTinyEXIF.so.1.0.3
       TinyEXIF_install ->
           lib ->
               libTinyEXIF.so
               libTinyEXIF.so.1
               libTinyEXIF.so.1.0.3
           ....
     tinyxml2 ->
       libtinyxml2.so
       libtinyxml2.so.10
       libtinyxml2.so.10.0.0
       tinyxml2_install ->
           lib ->
               libtinyxml2.so
               libtinyxml2.so.10
               libtinyxml2.so.10.0.0

当我启动它时，弹出以下内容：

./Geotags: error while loading shared libraries: libtinyxml2.so.10: cannot open shared object file: No such file or directory

针对 build/TinyEXIF/TinyEXIF_install/libTinyEXIF.so 文件的 ldd 命令输出：

        linux-vdso.so.1 (0x00007ffd8979b000)
        libtinyxml2.so.10 => not found
        libstdc++.so.6 => /lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007fa640735000)
        libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007fa64064c000)
        libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007fa64061e000)
        libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fa64040c000)
        /lib64/ld-linux-x86-64.so.2 (0x00007fa6409d5000)

针对 build/TinyEXIF/libTinyEXIF.so 文件的 ldd 命令输出：

        linux-vdso.so.1 (0x00007ffd6d9db000)
        libtinyxml2.so.10 => build/tinyxml2/tinyxml2_install/lib/libtinyxml2.so.10 (0x00007f4ad3c46000)
        libstdc++.so.6 => /lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007f4ad39bd000)
        libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f4ad38d4000)
        libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007f4ad38a6000)
        libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f4ad3694000)
        /lib64/ld-linux-x86-64.so.2 (0x00007f4ad3c7e000)

也就是说，安装后该库就与其依赖项失去连接。但是，如果您强制沿着路径 build/TinyEXIF/libTinyEXIF.so 推送库，那么一切都会正常。另外，如果您通过 clang 进行编译，ldd 输出是相同的，但程序启动并运行。但是在gcc的情况下，就会出现这个错误。这与什么有关？我阅读了有关 RPATH 的文档，但是在研究了相应的变量后，它并没有帮助我。

有必要对目录中的图像实现迭代器。

class DatasetReader(Reader):
    def __init__(self, log):
        super().__init__(log)

    @memory_profiler.profile
    def _get_arguments(self):
        self.dataset_name = self.args['DatasetName']
        self.dataset_path = self.args['DatasetPath']

        self.channel_swap = (np.asarray(ast.literal_eval(self.args['ChannelSwap']), dtype=np.float32)
                             if self.args['ChannelSwap'] is not None else [2, 1, 0])

        self.norm = (ast.literal_eval(self.args['Normalization'])
                     if self.args['Normalization'] is not None else False)

        self.layout = self.args['Layout']

        self.mean = (np.asarray(ast.literal_eval(self.args['Mean']), dtype=np.float32)
                     if self.args['Mean'] is not None else [0., 0., 0.])

        self.std = (np.asarray(ast.literal_eval(self.args['Std']), dtype=np.float32)
                    if self.args['Std'] is not None else [1., 1., 1.])

        self.image_size = ast.literal_eval(self.args['ImageSize'])
        self.dataset = list(Path(self.dataset_path).glob('*'))
        self.batch = int(self.args['BatchSize'])
        self.gg = Path(self.dataset_path).glob('*')
        self.max = len(self.dataset)
        self.transformer = Transformer(self.dict_for_transformer())

    def dict_for_transformer(self):
        dictionary = {
            'channel_swap': self.channel_swap,
            'mean': self.mean,
            'std': self.std,
            'norm': self.norm,
            'layout': self.layout,
        }
        return dictionary

    @memory_profiler.profile
    def _preprocess_image(self, image_path):
        image = cv2.imread(image_path)
        image_resize = cv2.resize(image, self.image_size)
        del image
        return image_resize

    def __iter__(self):
        self.n = 0
        return self

    #@memory_profiler.profile
    def __next__(self):
        if self.n <= self.max:
            res = []
            for _ in range(self.batch):
                self.n += 1
                image = self._preprocess_image(str(next(self.gg).absolute()))
                res.append(image)
            res = np.array(res)
            result = self.transformer.transform_images(res, None, np.float32, 'data')
            del image
            del res
            return result
        else:
            raise StopIteration

该对象被传递给外部框架函数。然而，每次迭代之后，内存消耗都会增加很多（del我插入它是为了尝试解决这个问题）。10 次迭代的示例。

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67    615.1 MiB    615.1 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69    618.5 MiB      3.4 MiB           1           image = cv2.imread(image_path)
    70    618.8 MiB      0.4 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71    618.8 MiB      0.0 MiB           1           del image
    72    618.8 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67    882.0 MiB    882.0 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69    883.1 MiB      1.1 MiB           1           image = cv2.imread(image_path)
    70    883.1 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71    883.1 MiB      0.0 MiB           1           del image
    72    883.1 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1001.2 MiB   1001.2 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1002.1 MiB      0.9 MiB           1           image = cv2.imread(image_path)
    70   1002.1 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1002.1 MiB      0.0 MiB           1           del image
    72   1002.1 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1121.3 MiB   1121.3 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1122.5 MiB      1.1 MiB           1           image = cv2.imread(image_path)
    70   1122.5 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1122.5 MiB      0.0 MiB           1           del image
    72   1122.5 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1241.6 MiB   1241.6 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1243.7 MiB      2.1 MiB           1           image = cv2.imread(image_path)
    70   1243.7 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1243.7 MiB      0.0 MiB           1           del image
    72   1243.7 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1361.7 MiB   1361.7 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1362.7 MiB      1.0 MiB           1           image = cv2.imread(image_path)
    70   1362.7 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1362.7 MiB      0.0 MiB           1           del image
    72   1362.7 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1482.0 MiB   1482.0 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1482.3 MiB      0.4 MiB           1           image = cv2.imread(image_path)
    70   1482.3 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1482.3 MiB      0.0 MiB           1           del image
    72   1482.3 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1602.2 MiB   1602.2 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1603.2 MiB      1.0 MiB           1           image = cv2.imread(image_path)
    70   1603.2 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1603.2 MiB      0.0 MiB           1           del image
    72   1603.2 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1722.3 MiB   1722.3 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1723.5 MiB      1.1 MiB           1           image = cv2.imread(image_path)
    70   1723.5 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1723.5 MiB      0.0 MiB           1           del image
    72   1723.5 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1842.6 MiB   1842.6 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1843.7 MiB      1.1 MiB           1           image = cv2.imread(image_path)
    70   1843.7 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1843.7 MiB      0.0 MiB           1           del image
    72   1843.7 MiB      0.0 MiB           1           return image_resize


Filename: /home/vanya/projects/dl-benchmark/src/quantization/tvm/parameters.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    67   1962.8 MiB   1962.8 MiB           1       @memory_profiler.profile
    68                                             def _preprocess_image(self, image_path):
    69   1964.3 MiB      1.5 MiB           1           image = cv2.imread(image_path)
    70   1964.3 MiB      0.0 MiB           1           image_resize = cv2.resize(image, self.image_size)
    71   1964.3 MiB      0.0 MiB           1           del image
    72   1964.3 MiB      0.0 MiB           1           return image_resize

这个实现可能存在什么问题？需要迭代 32 个图像，结果导致 RAM 出现故障。变压器代码：

class Transformer:
    def __init__(self, converting):
        self._converting = converting

    def get_shape_in_chw_order(self, shape, input_name):
        layout = self._converting['layout']
        sort = np.argsort(LAYER_LAYOUT_TO_IMAGE[layout])
        shape = np.array(shape)[sort]
        chw = shape[1:]
        if len(shape) in [4, 5]:
            chw = shape[-1], shape[-3], shape[-2]
        return chw

    def __set_channel_swap(self, image, input_name):
        channel_swap = self._converting['channel_swap']
        if channel_swap is not None:
            image = image[:, :, :, channel_swap]

    def __set_norm(self, image, input_name):
        image /= [np.float32(255), np.float32(255), np.float32(255)]

    def __set_mean(self, image, input_name):
        mean = self._converting['mean']
        if mean is not None:
            image -= mean

    def __set_input_scale(self, image, input_name):
        input_scale = self._converting['std']
        if input_scale is not None:
            image /= input_scale

    def __set_layout_order(self, image, input_name):
        layout = self._converting['layout']
        if layout is not None:
            layout = LAYER_LAYOUT_TO_IMAGE[layout]
            image = image.transpose(layout)
        return image

    def _transform(self, image, input_name):
        transformed_image = np.copy(image).astype(np.float64)
        self.__set_channel_swap(transformed_image, input_name)
        if self._converting['norm']:
            self.__set_norm(transformed_image, input_name)
        self.__set_mean(transformed_image, input_name)
        self.__set_input_scale(transformed_image, input_name)
        transformed_image = self.__set_layout_order(transformed_image, input_name)
        return transformed_image

    def transform_images(self, images, shape, element_type, input_name):
        transformed_images = self._transform(images, input_name)
        return transformed_images.astype(element_type)

基于迭代器创建生成器并将其发送到外部框架的代码（函数 quantization_tvm、calibrate_dataset - 生成器）：

class QuantizationProcess:
    def __init__(self, log, model, dataset, quant_params):
        self.log = log
        self.quant_model = None
        self.model = model
        self.dataset = dataset
        self.quant_params = quant_params

    @memory_profiler.profile
    def calibrate_dataset(self):
        for i, data in enumerate(self.dataset):
            if i * self.dataset.batch >= self.quant_params.calib_samples:
                break
            yield {'data': data}

    @memory_profiler.profile
    def quantization_tvm(self):
        self.log.info(f'Starting quantization with calibration mode {self.quant_params.calib_mode}')
        if self.quant_params.calib_mode.lower() == 'kl_divergence':
            with tvm.relay.quantize.qconfig(calibrate_mode=self.quant_params.calib_mode.lower(),
                                            weight_scale=self.quant_params.weights_scale.lower(),
                                            dtype_input='int32', dtype_weight='int32', dtype_activation='int32',
                                            partition_conversions='enabled'):

                self.quant_model = tvm.relay.quantize.quantize(self.model.mod,
                                                               self.model.params,
                                                               dataset=self.calibrate_dataset())

        elif self.quant_params.calib_mode.lower() == 'global_scale':
            with tvm.relay.quantize.qconfig(calibrate_mode=self.quant_params.calib_mode.lower(),
                                            global_scale=self.quant_params.global_scale,
                                            dtype_input='int32', dtype_weight='int32', dtype_activation='int32'):

                self.quant_model = tvm.relay.quantize.quantize(self.model.mod,
                                                               self.model.params)

        else:
            raise ValueError('Wrong calibration mode parameter.'
                             'Supported modes: kl_divergence, global_scale')

https://github.com/apache/tvm/blob/main/python/tvm/relay/quantize/_calibrate.py中的 _kl_scale 函数迭代数据集

matplotlib 中有一个关于时间表的问题。有一个汽车数据集。最后一列的数据类型是“numpy.float64”，决定绘制。第一张图很好，但第二张图不行。这里有什么问题？为什么 x 轴看起来像这样？我对 Price 和 Mileage 进行了排序并制作了 numpy 数组，第一个一切都很好，第二个不一样。