深海游弋的鱼 – 默默的点滴

如果需要运行 MaskTextSpotter，最少需要 4GB 显存，低于这个容量，运行不起来。

安装最新版本的 cuda-10.1,低版本的编译会出问题：

# 卸载之前已经安装的cuda
$ sudo apt-get remove nvidia-cuda-toolkit

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

$ wget http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb

$ sudo dpkg -i cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb

$ sudo apt-key add /var/cuda-repo-10-1-local-10.1.243-418.87.00/7fa2af80.pub

$ sudo apt-get update

$ sudo apt-get -y install cuda

# 部分驱动可能会更新，需要执行更新，否则可能依旧不正常
$ sudo apt-get dist-upgrade

$ sudo apt-get autoremove

# 可能需要删除一下XWindow的配置文件，否则驱动可能不能正常加载
$ sudo rm -rf ~/.Xauthority 

# 如果出现如下错误
# ubuntu 18.04 "nvidia-340 导致 /usr/lib/x86_64-linux-gnu/libGL.so.1 
# 转移到 /usr/lib/x86_64-linux-gnu/libGL.so.1.distrib"
# 参考 http://www.mobibrw.com/?p=21739 

# 删除安装源，可以节约几个GB的磁盘，安装完成后这部分已经用不上了
$ sudo apt-get remove --purge cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00 

$ sudo apt-get update
 
# 部分驱动可能会更新，需要执行更新，否则可能依旧不正常
$ sudo apt-get dist-upgrade
 
$ sudo apt-get autoremove

# 卸载之前已经安装的cuda

$ sudo apt-get remove nvidia-cuda-toolkit

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

$ wget http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb

$ sudo dpkg -i cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb

$ sudo apt-key add /var/cuda-repo-10-1-local-10.1.243-418.87.00/7fa2af80.pub

$ sudo apt-get update

$ sudo apt-get -y install cuda

# 部分驱动可能会更新，需要执行更新，否则可能依旧不正常

$ sudo apt-get dist-upgrade

$ sudo apt-get autoremove

# 可能需要删除一下XWindow的配置文件，否则驱动可能不能正常加载

$ sudo rm -rf ~/.Xauthority

# 如果出现如下错误

# ubuntu 18.04 "nvidia-340 导致 /usr/lib/x86_64-linux-gnu/libGL.so.1

# 转移到 /usr/lib/x86_64-linux-gnu/libGL.so.1.distrib"

# 参考 http://www.mobibrw.com/?p=21739

# 删除安装源，可以节约几个GB的磁盘，安装完成后这部分已经用不上了

$ sudo apt-get remove --purge cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00

$ sudo apt-get update

# 部分驱动可能会更新，需要执行更新，否则可能依旧不正常

$ sudo apt-get dist-upgrade

$ sudo apt-get autoremove

配置独立环境

# first, make sure that your conda is setup properly with the right environment
# for that, check that `which conda`, `which pip` and `which python` points to the
# right path. From a clean conda env, this is what you need to do

# conda remove -n MaskTextSpotter --all

$ conda create -n MaskTextSpotter -y python=3.6.8 pip

# first, make sure that your conda is setup properly with the right environment

# for that, check that `which conda`, `which pip` and `which python` points to the

# right path. From a clean conda env, this is what you need to do

# conda remove -n MaskTextSpotter --all

$ conda create -n MaskTextSpotter -y python=3.6.8 pip

编译安装 Pytoch

$ sudo apt-get install git

# 进入运行环境
$ source activate MaskTextSpotter

$ conda install numpy pyyaml mkl=2019.1 mkl-include=2019.1 setuptools cmake cffi typing pybind11

$ conda install ninja

# magma-cuda90 magma-cuda91 magma-cuda92 会编译失败 
$ conda install -c pytorch magma-cuda101

$ git clone https://github.com/pytorch/pytorch

# 也可直接本站下载一份同步好的代码 wget https://www.mobibrw.com/wp-content/uploads/2019/11/pytorch.zip

$ cd pytorch

# pytorch 1.0.1 版本支持“Compute Capability” 低于3.0版本的硬件，pytorch 1.2.0需要至少3.5版本的硬件才可以正常运行 
# https://github.com/pytorch/pytorch/blob/v1.3.0/torch/utils/cpp_extension.py
$ git checkout v1.0.1 -b v1.0.1

$ git submodule sync

$ git submodule update --init --recursive

$ export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}

$ python setup.py clean

# 卸载以前安装的pytorch
$ conda uninstall pytorch

$ pip uninstall pytorch

# 从Nvidia开发网站查询到自己硬件对应的“Compute Capability” 
# 比如 “GeForce GTX 760” 对应 “3.0” 计算能力，能力不正确会导致运行异常
# RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device

$ TORCH_CUDA_ARCH_LIST="3.0" python setup.py install

# 一定要退出 pytorch 的编译目录，在pytorch代码目录下执行命令会出现异常
$ cd ..

# 退出环境
$ conda deactivate

$ sudo apt-get install git

# 进入运行环境

$ source activate MaskTextSpotter

$ conda install numpy pyyaml mkl=2019.1 mkl-include=2019.1 setuptools cmake cffi typing pybind11

$ conda install ninja

# magma-cuda90 magma-cuda91 magma-cuda92 会编译失败

$ conda install -c pytorch magma-cuda101

$ git clone https://github.com/pytorch/pytorch

# 也可直接本站下载一份同步好的代码 wget https://www.mobibrw.com/wp-content/uploads/2019/11/pytorch.zip

$ cd pytorch

# pytorch 1.0.1 版本支持“Compute Capability” 低于3.0版本的硬件，pytorch 1.2.0需要至少3.5版本的硬件才可以正常运行

# https://github.com/pytorch/pytorch/blob/v1.3.0/torch/utils/cpp_extension.py

$ git checkout v1.0.1 -b v1.0.1

$ git submodule sync

$ git submodule update --init --recursive

$ export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}

$ python setup.py clean

# 卸载以前安装的pytorch

$ conda uninstall pytorch

$ pip uninstall pytorch

# 从Nvidia开发网站查询到自己硬件对应的“Compute Capability”

# 比如 “GeForce GTX 760” 对应 “3.0” 计算能力，能力不正确会导致运行异常

# RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device

$ TORCH_CUDA_ARCH_LIST="3.0" python setup.py install

# 一定要退出 pytorch 的编译目录，在pytorch代码目录下执行命令会出现异常

$ cd ..

# 退出环境

$ conda deactivate

如果出现如下错误：

[ 68%] Building NVCC (Device) object caffe2/CMakeFiles/caffe2_gpu.dir/__/aten/src/ATen/native/sparse/cuda/caffe2_gpu_generated_SparseCUDABlas.cu.o
~/pytorch/aten/src/ATen/native/sparse/cuda/SparseCUDABlas.cu(58): error: more than one instance of function "at::native::sparse::cuda::cusparseGetErrorString" matches the argument list:
            function "cusparseGetErrorString(cusparseStatus_t)"
            function "at::native::sparse::cuda::cusparseGetErrorString(cusparseStatus_t)"
            argument types are: (cusparseStatus_t)

[ 68%] Building NVCC (Device) object caffe2/CMakeFiles/caffe2_gpu.dir/__/aten/src/ATen/native/sparse/cuda/caffe2_gpu_generated_SparseCUDABlas.cu.o

~/pytorch/aten/src/ATen/native/sparse/cuda/SparseCUDABlas.cu(58): error: more than one instance of function "at::native::sparse::cuda::cusparseGetErrorString" matches the argument list:

function "cusparseGetErrorString(cusparseStatus_t)"

function "at::native::sparse::cuda::cusparseGetErrorString(cusparseStatus_t)"

argument types are: (cusparseStatus_t)

则需要调整代码 aten/src/ATen/native/sparse/cuda/SparseCUDABlas.cu, 在其中的 cusparseGetErrorString 函数上增加 #if (!((CUSPARSE_VER_MAJOR >= 10) && (CUSPARSE_VER_MINOR >= 2)))

如下：

#if (!((CUSPARSE_VER_MAJOR >= 10) && (CUSPARSE_VER_MINOR >= 2)))
const char* cusparseGetErrorString(cusparseStatus_t status) {
  switch(status)
  {
    case CUSPARSE_STATUS_SUCCESS:
      return "success";

    case CUSPARSE_STATUS_NOT_INITIALIZED:
      return "library not initialized";

    case CUSPARSE_STATUS_ALLOC_FAILED:
      return "resource allocation failed";

    case CUSPARSE_STATUS_INVALID_VALUE:
      return "an invalid numeric value was used as an argument";

    case CUSPARSE_STATUS_ARCH_MISMATCH:
      return "an absent device architectural feature is required";

    case CUSPARSE_STATUS_MAPPING_ERROR:
      return "an access to GPU memory space failed";

    case CUSPARSE_STATUS_EXECUTION_FAILED:
      return "the GPU program failed to execute";

    case CUSPARSE_STATUS_INTERNAL_ERROR:
      return "an internal operation failed";

    case CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
      return "the matrix type is not supported by this function";

    case CUSPARSE_STATUS_ZERO_PIVOT:
      return "an entry of the matrix is either structural zero or numerical zero (singular block)";

    default:
      return "unknown error";
  }
}
#endif

#if (!((CUSPARSE_VER_MAJOR >= 10) && (CUSPARSE_VER_MINOR >= 2)))

const char* cusparseGetErrorString(cusparseStatus_t status) {

switch(status)

{

case CUSPARSE_STATUS_SUCCESS:

return "success";

case CUSPARSE_STATUS_NOT_INITIALIZED:

return "library not initialized";

case CUSPARSE_STATUS_ALLOC_FAILED:

return "resource allocation failed";

case CUSPARSE_STATUS_INVALID_VALUE:

return "an invalid numeric value was used as an argument";

case CUSPARSE_STATUS_ARCH_MISMATCH:

return "an absent device architectural feature is required";

case CUSPARSE_STATUS_MAPPING_ERROR:

return "an access to GPU memory space failed";

case CUSPARSE_STATUS_EXECUTION_FAILED:

return "the GPU program failed to execute";

case CUSPARSE_STATUS_INTERNAL_ERROR:

return "an internal operation failed";

case CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED:

return "the matrix type is not supported by this function";

case CUSPARSE_STATUS_ZERO_PIVOT:

return "an entry of the matrix is either structural zero or numerical zero (singular block)";

default:

return "unknown error";

}

#endif

这样解决跟 CUDA-10.1自带函数的冲突问题。

具体参考： https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/sparse/cuda/SparseCUDABlas.cu

编译安装 TorchVision

$ sudo apt-get install git

# 进入运行环境
$ source activate MaskTextSpotter

$ git clone https://github.com/pytorch/vision.git

# 也可本站下载一份拷贝 wget https://www.mobibrw.com/wp-content/uploads/2019/11/vision.zip

$ cd vision

$ git checkout v0.2.1 -b v0.2.1

$ python setup.py install

# 退出环境 
$ conda deactivate

$ sudo apt-get install git

# 进入运行环境

$ source activate MaskTextSpotter

$ git clone https://github.com/pytorch/vision.git

# 也可本站下载一份拷贝 wget https://www.mobibrw.com/wp-content/uploads/2019/11/vision.zip

$ cd vision

$ git checkout v0.2.1 -b v0.2.1

$ python setup.py install

# 退出环境

$ conda deactivate

源代码编译

$ source activate MaskTextSpotter

# this installs the right pip and dependencies for the fresh python
$ conda install ipython pip

# python dependencies
$ pip install ninja yacs cython matplotlib tqdm opencv-python shapely scipy tensorboardX

$ export INSTALL_DIR=$PWD

# install pycocotools
$ cd $INSTALL_DIR
$ git clone https://github.com/cocodataset/cocoapi.git
$ cd cocoapi/PythonAPI
$ python setup.py build_ext install

# 本站下载 https://www.mobibrw.com/wp-content/uploads/2019/11/cocoapi.zip

# install apex (optional)
$ cd $INSTALL_DIR
$ git clone https://github.com/NVIDIA/apex.git
$ cd apex
$ python setup.py install --cuda_ext --cpp_ext

# 本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/11/apex.zip

# clone repo
$ cd $INSTALL_DIR
$ git clone https://github.com/MhLiao/MaskTextSpotter.git
$ cd MaskTextSpotter

# 本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/11/MaskTextSpotter.zip

# build
$ python setup.py build develop

$ unset INSTALL_DIR

$ source activate MaskTextSpotter

# this installs the right pip and dependencies for the fresh python

$ conda install ipython pip

# python dependencies

$ pip install ninja yacs cython matplotlib tqdm opencv-python shapely scipy tensorboardX

$ export INSTALL_DIR=$PWD

# install pycocotools

$ cd $INSTALL_DIR

$ git clone https://github.com/cocodataset/cocoapi.git

$ cd cocoapi/PythonAPI

$ python setup.py build_ext install

# 本站下载 https://www.mobibrw.com/wp-content/uploads/2019/11/cocoapi.zip

# install apex (optional)

$ cd $INSTALL_DIR

$ git clone https://github.com/NVIDIA/apex.git

$ cd apex

$ python setup.py install --cuda_ext --cpp_ext

# 本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/11/apex.zip

# clone repo

$ cd $INSTALL_DIR

$ git clone https://github.com/MhLiao/MaskTextSpotter.git

$ cd MaskTextSpotter

# 本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/11/MaskTextSpotter.zip

# build

$ python setup.py build develop

$ unset INSTALL_DIR

准备测试数据

# 创建目录(源代码根目录)
$ mkdir outputs

$ cd outputs

$ mkdir finetune

$ cd finetune

# 下载已经训练好的模型 https://drive.google.com/open?id=1pPRS7qS_K1keXjSye0kksqhvoyD0SARz

# 本站下载
$ wget https://www.mobibrw.com/wp-content/uploads/2019/11/model_finetune.zip

$ unzip model_finetune.zip

$ cd ../../

$ mkdir datasets

$ cd datasets

# 下载 icdar2013 数据集
$ wget https://www.mobibrw.com/wp-content/uploads/2019/11/icdar2013.zip

$ unzip icdar2013.zip

$ cd icdar2013

# 下载测试集文件
$ git clone https://github.com/zazaliu/ICDAR2PASCAL_VOC.git

# 本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/11/ICDAR2PASCAL_VOC.zip

$ cp -r ICDAR2PASCAL_VOC/ICDAR2015/ch4_training_localization_transcription_gt/ test_gts

# 执行测试

$ cd ../../

# 预先删除生成的文件，否则可能会启动之后就崩溃退出
$ rm -rf outputs/finetune/inference/

$ bash test.sh

$ mkdir outputs

$ cd outputs

$ mkdir finetune

$ cd finetune

# 下载已经训练好的模型 https://drive.google.com/open?id=1pPRS7qS_K1keXjSye0kksqhvoyD0SARz

# 本站下载

$ wget https://www.mobibrw.com/wp-content/uploads/2019/11/model_finetune.zip

$ unzip model_finetune.zip

$ cd ../../

$ mkdir datasets

$ cd datasets

# 下载 icdar2013 数据集

$ wget https://www.mobibrw.com/wp-content/uploads/2019/11/icdar2013.zip

$ unzip icdar2013.zip

$ cd icdar2013

# 下载测试集文件

$ git clone https://github.com/zazaliu/ICDAR2PASCAL_VOC.git

# 本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/11/ICDAR2PASCAL_VOC.zip

$ cp -r ICDAR2PASCAL_VOC/ICDAR2015/ch4_training_localization_transcription_gt/ test_gts

# 执行测试

$ cd ../../

# 预先删除生成的文件，否则可能会启动之后就崩溃退出

$ rm -rf outputs/finetune/inference/

$ bash test.sh

执行测试的时候，如果出现如下错误信息：

  File "tools/test_net.py", line 95, in <module>
    main()
  File "tools/test_net.py", line 89, in main
    cfg=cfg,
  File "~/MaskTextSpotter/maskrcnn_benchmark/engine/text_inference.py", line 380, in inference
    predictions = compute_on_dataset(model, data_loader, device)
  File "~/MaskTextSpotter/maskrcnn_benchmark/engine/text_inference.py", line 55, in compute_on_dataset
    for i, batch in tqdm(enumerate(data_loader)):
  File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/tqdm/std.py", line 1091, in __iter__
    for obj in iterable:
  File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 637, in __next__
    return self._process_next_batch(batch)
  File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 658, in _process_next_batch
    raise batch.exc_type(batch.exc_msg)
ValueError: Traceback (most recent call last):
  File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in <listcomp>
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "~/MaskTextSpotter/maskrcnn_benchmark/data/datasets/icdar.py", line 32, in __getitem__
    words,boxes,charsbbs,segmentations=self.load_gt_from_txt(gt_path,height,width)
  File "~/MaskTextSpotter/maskrcnn_benchmark/data/datasets/icdar.py", line 94, in load_gt_from_txt
    strs, loc = self.line2boxes(line)
  File "~/MaskTextSpotter/maskrcnn_benchmark/data/datasets/icdar.py", line 153, in line2boxes
    loc = np.vstack(v).transpose()
  File "<__array_function__ internals>", line 6, in vstack
  File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/numpy/core/shape_base.py", line 282, in vstack
    return _nx.concatenate(arrs, 0)
  File "<__array_function__ internals>", line 6, in concatenate
ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 2 and the array at index 1 has size 1

File "tools/test_net.py", line 95, in <module>

main()

File "tools/test_net.py", line 89, in main

cfg=cfg,

File "~/MaskTextSpotter/maskrcnn_benchmark/engine/text_inference.py", line 380, in inference

predictions = compute_on_dataset(model, data_loader, device)

File "~/MaskTextSpotter/maskrcnn_benchmark/engine/text_inference.py", line 55, in compute_on_dataset

for i, batch in tqdm(enumerate(data_loader)):

File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/tqdm/std.py", line 1091, in __iter__

for obj in iterable:

File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 637, in __next__

return self._process_next_batch(batch)

File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 658, in _process_next_batch

raise batch.exc_type(batch.exc_msg)

ValueError: Traceback (most recent call last):

File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop

samples = collate_fn([dataset[i] for i in batch_indices])

File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in <listcomp>

samples = collate_fn([dataset[i] for i in batch_indices])

File "~/MaskTextSpotter/maskrcnn_benchmark/data/datasets/icdar.py", line 32, in __getitem__

words,boxes,charsbbs,segmentations=self.load_gt_from_txt(gt_path,height,width)

File "~/MaskTextSpotter/maskrcnn_benchmark/data/datasets/icdar.py", line 94, in load_gt_from_txt

strs, loc = self.line2boxes(line)

File "~/MaskTextSpotter/maskrcnn_benchmark/data/datasets/icdar.py", line 153, in line2boxes

loc = np.vstack(v).transpose()

File "<__array_function__ internals>", line 6, in vstack

File "~.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/numpy/core/shape_base.py", line 282, in vstack

return _nx.concatenate(arrs, 0)

File "<__array_function__ internals>", line 6, in concatenate

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 2 and the array at index 1 has size 1

那么问题出现的原因是maskrcnn_benchmark/data/datasets/icdar.py解析文件的时候，遇到了478,239,511,241,511,255,478,253,$5,000这样的数据，测试代码如下：

import numpy as np

line = '478,239,511,241,511,255,478,253,$5,000'
def line2boxes(line):
    parts = line.strip().split(',')
    if '\xef\xbb\xbf' in parts[0]:
        parts[0] = parts[0][3:]
    if '\ufeff' in parts[0]:
        parts[0] = parts[0].replace('\ufeff', '')
    x1 = np.array([int(float(x)) for x in parts[::9]])
    y1 = np.array([int(float(x)) for x in parts[1::9]])
    x2 = np.array([int(float(x)) for x in parts[2::9]])
    y2 = np.array([int(float(x)) for x in parts[3::9]])
    x3 = np.array([int(float(x)) for x in parts[4::9]])
    y3 = np.array([int(float(x)) for x in parts[5::9]])
    x4 = np.array([int(float(x)) for x in parts[6::9]])
    y4 = np.array([int(float(x)) for x in parts[7::9]])
    strs = parts[8::9]
    print(x1)
    loc = np.vstack((x1, y1, x2, y2, x3, y3, x4, y4)).transpose()
    print(loc)
    return strs, loc

line2boxes(line)

import numpy as np

line = '478,239,511,241,511,255,478,253,$5,000'

def line2boxes(line):

parts = line.strip().split(',')

if '\xef\xbb\xbf' in parts[0]:

parts[0] = parts[0][3:]

if '\ufeff' in parts[0]:

parts[0] = parts[0].replace('\ufeff', '')

x1 = np.array([int(float(x)) for x in parts[::9]])

y1 = np.array([int(float(x)) for x in parts[1::9]])

x2 = np.array([int(float(x)) for x in parts[2::9]])

y2 = np.array([int(float(x)) for x in parts[3::9]])

x3 = np.array([int(float(x)) for x in parts[4::9]])

y3 = np.array([int(float(x)) for x in parts[5::9]])

x4 = np.array([int(float(x)) for x in parts[6::9]])

y4 = np.array([int(float(x)) for x in parts[7::9]])

strs = parts[8::9]

print(x1)

loc = np.vstack((x1, y1, x2, y2, x3, y3, x4, y4)).transpose()

print(loc)

return strs, loc

line2boxes(line)

修正后的代码如下：

import numpy as np

line = '478,239,511,241,511,255,478,253,$5,000'
def line2boxes(line):
    parts = line.strip().split(',', 8)
    if '\xef\xbb\xbf' in parts[0]:
        parts[0] = parts[0][3:]
    if '\ufeff' in parts[0]:
        parts[0] = parts[0].replace('\ufeff', '')
    x1 = np.array([int(float(x)) for x in parts[::9]])
    y1 = np.array([int(float(x)) for x in parts[1::9]])
    x2 = np.array([int(float(x)) for x in parts[2::9]])
    y2 = np.array([int(float(x)) for x in parts[3::9]])
    x3 = np.array([int(float(x)) for x in parts[4::9]])
    y3 = np.array([int(float(x)) for x in parts[5::9]])
    x4 = np.array([int(float(x)) for x in parts[6::9]])
    y4 = np.array([int(float(x)) for x in parts[7::9]])
    strs = parts[8::9]
    print(x1)
    loc = np.vstack((x1, y1, x2, y2, x3, y3, x4, y4)).transpose()
    print(loc)
    return strs, loc

line2boxes(line)

import numpy as np

line = '478,239,511,241,511,255,478,253,$5,000'

def line2boxes(line):

parts = line.strip().split(',', 8)

if '\xef\xbb\xbf' in parts[0]:

parts[0] = parts[0][3:]

if '\ufeff' in parts[0]:

parts[0] = parts[0].replace('\ufeff', '')

x1 = np.array([int(float(x)) for x in parts[::9]])

y1 = np.array([int(float(x)) for x in parts[1::9]])

x2 = np.array([int(float(x)) for x in parts[2::9]])

y2 = np.array([int(float(x)) for x in parts[3::9]])

x3 = np.array([int(float(x)) for x in parts[4::9]])

y3 = np.array([int(float(x)) for x in parts[5::9]])

x4 = np.array([int(float(x)) for x in parts[6::9]])

y4 = np.array([int(float(x)) for x in parts[7::9]])

strs = parts[8::9]

print(x1)

loc = np.vstack((x1, y1, x2, y2, x3, y3, x4, y4)).transpose()

print(loc)

return strs, loc

line2boxes(line)

其他错误，可能是中途软件安装卸载造成的软件版本冲突，则直接删除环境，重新创建一个干净的环境重新构建。

在ubuntu 18.04(GeForce GTX 760 4GB显存)编译/测试MaskTextSpotter(CUDA-10.1)

参考链接

发布者

默默

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发布者

默默

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