whisper训练自己数据 psenet训练自己的数据

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liutao988 2024-05-16 08:18:17

文章标签 whisper训练自己数据 psenet python CUDA OpenCV 文章分类 深度学习人工智能

作者的github链接：https://github.com/whai362/PSENet
Requirements
Python 2.7
PyTorch v0.4.1+
pyclipper
Polygon2
OpenCV 3.4 (for c++ version pse)
opencv-python 3.4
首先是需要安装这些依赖，因为我们自己电脑上面已经安装了各种版本的opencv，python，不建议在自己电脑上面再安装，可以创建虚拟环境anaconda
anaconda安装自行百度
anaconda 常用命令参考链接：

conda create -n PSENet_1 python=2.7 OpenCV=3.4

这里创建虚拟环境可以同时安装必要的包，当然上面命令都是经过n次失败之后摸索出来的，一开始这么写的 conda create -n PSENet_1 python=2.7 OpenCV=3.4 opencv-python=3.4，报错，可能opencv-python没有吧

下一步重新打开一个终端，

source activate PSENet_1

可以看到(PSENet_1) yhl@B-Y:~$ 说明可以了
接着安装pytorch 我电脑配置是ubuntu16.04，显卡 1080，cuda8.0
到pytorch官网可以看到已经没有cuda-8了，但是历史版本有，直接conda install pytorch torchvision cudatoolkit=9.0 -c pytorch改成8就可以

conda install pytorch torchvision cudatoolkit=8.0 -c pytorch

其他版本的pytorch安装
https://pytorch.org/get-started/previous-versions/

# CUDA 10.0
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

# CUDA 10.1
conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.1 -c pytorch

# CUDA 10.2
conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch

# CUDA 10.1
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch

# CUDA 10.2
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.2 -c pytorch

# CUDA 11.0
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=11.0 -c pytorch

装torch命令或者：

pip install torch==1.0.0 torchvision==0.2.1 -i https://mirror.baidu.com/pypi/simple

然后完成，敲 pip -V 看是否是对应python2.7， pip -V
pip 19.1.1 from /media/data_1/software_install/Anaconda0515/envs/PSENet_1/lib/python2.7/site-packages/pip (python 2.7)

pip install pyclipper
 pip install Polygon2

需求表上面的opencv-python 3.4 我没法安装制定的3.4版本，之前好像敲的pip install opencv-python 安装的是4.1版本的，实践证明这个可以不要安装，安装了opencv3.4就已经可以了
转到PSENet根目录，并训练，前提是你需要准备好数据，数据需要放在根目录下面的data文件夹下，data-ICDAR2015-Challenge4-（ ch4_training_localization_transcription_gt ch4_training_images ch4_test_images）
还有ctw-1500的数据集比较难下载，还好我问同事要到了，晚点上传发链接到这里吧
同样的，把自己的数据也按照它存放的文件路径就可以训练自己的数据集

cd /media/data_1/project/2019/project/PSENet #
 CUDA_VISIBLE_DEVICES=0 python train_ic15.py --batch_size 2

报错 ImportError: No module named matplotlib

pip install matplotlib

再敲上面训练的命令就可以训练了，显示如下

CUDA_VISIBLE_DEVICES=0 python train_ic15.py --batch_size 2
 checkpoint path: checkpoints/ic15_resnet50_bs_2_ep_600
 init lr: 0.00100000
 ('schedule: ', [200, 400])
 Training from scratch.Epoch: [1 | 600] LR: 0.001000
 /media/data_1/Yang/software_install/Anaconda0515/envs/PSENet_1/lib/python2.7/site-packages/torch/nn/functional.py:2351: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
 warnings.warn(“nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.”)
 /media/data_1/Yang/software_install/Anaconda0515/envs/PSENet_1/lib/python2.7/site-packages/torch/nn/functional.py:2423: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
 “See the documentation of nn.Upsample for details.”.format(mode))
 (1/6849) Batch: 1.927s | TOTAL: 0min | ETA: 220min | Loss: 0.8001 | Acc_t: 0.6196 | IOU_t: 0.2853 | IOU_k: 0.4976
 (21/6849) Batch: 0.516s | TOTAL: 0min | ETA: 59min | Loss: 0.7719 | Acc_t: 0.6852 | IOU_t: 0.3115 | IOU_k: 0.4986
 (41/6849) Batch: 0.481s | TOTAL: 0min | ETA: 55min | Loss: 0.7681 | Acc_t: 0.7403 | IOU_t: 0.3354 | IOU_k: 0.4985

再测试

CUDA_VISIBLE_DEVICES=0 python test_ic15.py --scale 1 --resume [path of model]

可以看到根目录生成了outputs文件夹，里面有效果图：下面是ICDAR2015测试集效果图：train_ic15.py

whisper训练自己数据 psenet训练自己的数据_OpenCV

下面是我自己训练的数据效果图：这个是用train_ctw1500.py训练的

whisper训练自己数据 psenet训练自己的数据_CUDA_02

###下面这个错误当正确安装了opencv的时候可以无视，创建虚拟环境的时候 conda create -n PSENet_1 python=2.7 OpenCV=3.4 这么写好像就没有问题
看起来一帆风顺，其实一开始弄的时候各种报错，一开始报错说pse文件夹下connectedComponents找不到，大概是这个错误，我百度了一下发现这个函数是opencv的，在pse文件夹下有MakeFile敲make 确实报错，看它里面OPENCV = pkg-config --cflags --libs opencv 这个语句我看它好像是调用系统下面的opencv了，各种尝试，最后确定makefile这么写可以通过：

CXXFLAGS = -I include -std=c++11 -O3
DEPS = lanms.h $(shell find include -xtype f)
 CXX_SOURCES = adaptor.cpp include/clipper/clipper.cpp#OPENCV = pkg-config --cflags --libs opencv INCLUDES = -I/media/data_1/Yang/software_install/Anaconda0515/envs/PSENet/include
 LIBS = -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ml
 LIBDIRS = -L/media/data_1/Yang/software_install/Anaconda0515/envs/PSENet/libLIB_SO = adaptor.so
$(LIB_SO): $(CXX_SOURCES) $(DEPS)
 $(CXX) -o $@ $(CXXFLAGS) $(LDFLAGS) $(CXX_SOURCES) --shared -fPIC $(OPENCV)clean:
 rm -rf $(LIB_SO)

20201216编译一个版本的psenet，编译pse又报错

adaptor.cpp: In function ‘void pse_adaptor::growing_text_line(std::vector<cv::Mat>&, std::vector<std::vector<int> >&, float)’:
adaptor.cpp:35:86: error: ‘connectedComponents’ was not declared in this scope
         int label_num = connectedComponents(kernals[kernals.size() - 1], label_mat, 4);
                                                                                      ^
In file included from include/pybind11/pytypes.h:12:0,
                 from include/pybind11/cast.h:13,
                 from include/pybind11/attr.h:13,
                 from include/pybind11/pybind11.h:43,
                 from adaptor.cpp:1:
adaptor.cpp: In function ‘PyObject* PyInit_adaptor()’:

想着之前编译成功，怎么这次换了一个环境又编译不过。。。。各种重新装opencv，发现pip install opencv-python在虚拟环境的lib下面没有opencv的so
只有conda install OpenCV=3.4
才会有，然而还是报错！！！！
找来jiamin，给我解释了一波，说

$(LIB_SO): $(CXX_SOURCES) $(DEPS)
	$(CXX) -o $@ $(CXXFLAGS) $(LDFLAGS) $(CXX_SOURCES) --shared -fPIC $(OPENCV)

这里的OPENCV被我注释了。。。但是之前可以就不知道了。。
正确的makefile如下：

CXXFLAGS = -I include  -std=c++11 -O3

DEPS = lanms.h $(shell find include -xtype f)
CXX_SOURCES = adaptor.cpp include/clipper/clipper.cpp
#OPENCV = `pkg-config --cflags --libs opencv`

INCLUDES = -I/data_1/Yang/software_install/Anaconda1105/envs/libtorch1.0/include
LIBDIRS = -L/data_1/Yang/software_install/Anaconda1105/envs/libtorch1.0/lib
LIBS = -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ml

LIB_SO = adaptor.so

$(LIB_SO): $(CXX_SOURCES) $(DEPS)
	$(CXX) -o $@ $(CXXFLAGS) $(LDFLAGS) $(CXX_SOURCES) --shared -fPIC $(INCLUDES) $(LIBDIRS) $(LIBS)

clean:
	rm -rf $(LIB_SO)

psenet的后处理理解–戳我