see 深入理解神经网络：从逻辑回归到CNN.md -> autodl


see doc\lang\programming\pytorch\文本检测\DBNET 论文代码都有

pip install torch==2.0.0+cu118 -f https://download.pytorch.org/whl/torch_stable.html
	# apt install -y libsm6 libxrender1 libxext6 libgl1-mesa-glx
    	# 实测 vgpu-32G 要装这个
        # 能正常训练
        # 这套用官方训练好的权重 eval 比论文的精度低了很多，但确实框出来了
        	# 试试用它再继续微调会不会好点
        
        
conda create --name DB python==3.7 ipython pip -y \
  && conda activate DB \
  && pip install https://download.pytorch.org/whl/cu100/torch-1.2.0-cp37-cp37m-manylinux1_x86_64.whl \
  


https://download.pytorch.org/whl/cu100/torch-1.2.0-cp37-cp37m-manylinux1_x86_64.whl
	# 最接近 cu10.1 + torch 1.2.0 的是这个
    # numpy-1.21.6
    # 这套用官方训练好的权重 eval 能达到论文的精度
    https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_410.48_linux
        # cuda10.0 for ubuntu 18.04 
        
    
  # python dependencies
  pip install -r requirement.txt

  # install PyTorch with cuda-10.1
  conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
        
        

update-alternatives --remove cuda /usr/local/cuda-11.6
update-alternatives --install /usr/local/cuda cuda /usr/local/cuda-11.8 118 && 
ln -sfT /usr/local/cuda-11.8 /etc/alternatives/cuda && 
ln -sfT /etc/alternatives/cuda /usr/local/cuda  
	# 切换版本

vi ~/.bashrc 

if [ -z $LD_LIBRARY_PATH ]; then
  LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64
else
  LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.8/lib64
fi
export LD_LIBRARY_PATH

export PATH=/usr/local/cuda/bin:$PATH


source ~/.bashrc 

nvcc --version
        
        
apt install build-essential  && \
export CUDA_HOME=/usr/local/cuda && \
echo $CUDA_HOME && \
nvcc --version && \
ldconfig -p | grep cuda && \
cd ~/DB/assets/ops/dcn/ && \
sed -i 's/AT_CHECK/TORCH_CHECK/1' /root/DB/assets/ops/dcn/src/deform_conv_cuda.cpp && \
sed -i 's/AT_CHECK/TORCH_CHECK/1' /root/DB/assets/ops/dcn/src/deform_pool_cuda.cpp && \
python setup.py build_ext --inplace
	# python setup.py clean --all \
	    && rm -rf *.so build/
	

cd ~/DB && \
pip install -r requirement.txt && \
pip install --upgrade protobuf==3.20.0

~/DB/backbones# vi resnet.py
	# 这里可以注释掉下载预训练模型的代码

sed -i 's/batch_size\:\ 16/batch_size\:\ 10/1' ~/DB/experiments/seg_detector/td500_resnet18_deform_thre.yaml && \
sed -i 's/num_workers\:\ 16/num_workers\:\ 10/1' ~/DB/experiments/seg_detector/td500_resnet18_deform_thre.yaml && \
sed -i 's/save_interval\:\ 18000/save_interval\:\ 450/1' ~/DB/experiments/seg_detector/td500_resnet18_deform_thre.yaml && \
sed -i 's/epochs\:\ 1200/epochs\:\ 30/1' ~/DB/experiments/seg_detector/td500_resnet18_deform_thre.yaml

# 禁用 cudnn
torch.backends.cudnn.enabled = False


https://matpool.com/supports/doc-vscode-connect-matpool/
    Remote Development 安装插件
    VS Code 远程连接矩池云机器教程

# vscode 打开远程文件夹 DB, ctrl + x 安装 python 扩展, ctrl+shift+p 输入 Python，选择选conda的python ，vscode 中修改train.py 在main 函数下加入命令行参数：


vi DB/training/learning_rate.py

lr = State(default=0.007) 

vi experiments/seg_detector/ic15_resnet18_deform_thre.yaml
        learning_rate:
            class: DecayLearningRate
            lr: 0.001
            epochs: 1200
	# 学习率要这两个地方一起改成一至后，logs 显示才正常
        
      
vi data/image_dataset.py
    if 'TD' in self.data_dir[0] and label == '1':
        label = '###' 
    # 注释掉这两行 
    # https://github.com/MhLiao/DB/issues/186
    
File "/root/miniforge3/envs/DB/lib/python3.7/site-packages/anyconfig/processors/utils.py", line 14, in <module>
    import importlib.metadata
ModuleNotFoundError: No module named 'importlib.metadata' 
    # 只有 py3.7 会错
    # pip install importlib-metadata
    # import importlib_metadata as metadata  # 改成这样
    
    
def main():
    import sys
    sys.argv.append( 'experiments/seg_detector/ic15_resnet18_deform_thre.yaml' )
    sys.argv.append( '--num_gpus' )
    sys.argv.append( '1' )
    sys.argv.append( '--batch_size' )
    sys.argv.append( '16' )
    sys.argv.append( '--epochs' )
    sys.argv.append( '1200' )
    sys.argv.append( '--lr' )
    sys.argv.append( '0.0001' )
    sys.argv.append( '--resume' )       # 继续上一次训练
    sys.argv.append( '/root/final7' )
    #sys.argv.append( '--start_iter' )
    #sys.argv.append( '18000' )
    #sys.argv.append( '--start_epoch' )
    #sys.argv.append( '107' )
    torch.backends.cudnn.enabled = False

vscode 中然后F5 调试运行train.py 

CUDA_VISIBLE_DEVICES=0 python eval.py experiments/seg_detector/ic15_resnet18_deform_thre.yaml --resume /root/final8 --box_thresh 0.55

CUDA_VISIBLE_DEVICES=0 python demo.py experiments/seg_detector/ic15_resnet18_deform_thre.yaml --image_path datasets/icdar2015/test_images/img_2.jpg --resume /root/final8 --polygon --box_thresh 0.55 --visualize 
	# img_1 用官方训练好的模型也是框不出的！！！

CUDA_VISIBLE_DEVICES=0 python eval.py experiments/seg_detector/ic15_resnet18_deform_thre.yaml --resume /root/ic15_resnet18 --box_thresh 0.55
	# 官方
    
CUDA_VISIBLE_DEVICES=0 python demo.py experiments/seg_detector/ic15_resnet18_deform_thre.yaml --image_path datasets/icdar2015/test_images/img_2.jpg --resume /root/ic15_resnet18 --polygon --box_thresh 0.55 --visualize
	# 官方














https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.39_linux.run
	# 下载安装

22.04 安装 cuda 10.1 需要降级 gcc

echo "deb http://archive.ubuntu.com/ubuntu focal main universe" | sudo tee /etc/apt/sources.list.d/focal.list \
  && sudo apt update \
  && sudo apt install gcc-7 g++-7 -y

# 设置默认GCC版本
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 2 \
  && sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 1 \
  && sudo update-alternatives --config gcc
  	# 选择gcc-7

./cuda_10.1.105_418.39_linux.run
	# 安装，不要选驱动	

Please make sure that
 -   PATH includes /usr/local/cuda-10.1/bin
 -   LD_LIBRARY_PATH includes /usr/local/cuda-10.1/lib64, or, add /usr/local/cuda-10.1/lib64 to /etc/ld.so.conf and run ldconfig as root



update-alternatives --remove cuda /usr/local/cuda-11.8
update-alternatives --install /usr/local/cuda cuda /usr/local/cuda-10.1 101 && 
ln -sfT /usr/local/cuda-10.1 /etc/alternatives/cuda && 
ln -sfT /etc/alternatives/cuda /usr/local/cuda  
	# 切换版本

vi ~/.bashrc 

if [ -z $LD_LIBRARY_PATH ]; then
  LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64
else
  LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.1/lib64
fi
export LD_LIBRARY_PATH

export PATH=/usr/local/cuda/bin:$PATH


source ~/.bashrc 

nvcc --version




conda install pytorch==1.2.0  -c pytorch
	# 官方文档是这个版本
	# 但是现在下载不了了
	

https://download.pytorch.org/whl/torch_stable.html
	# 这里看有什么可以装
	
pip install https://download.pytorch.org/whl/cu101/torch-1.4.0-cp38-cp38-linux_x86_64.whl
	# 这样装 1.4.0+cu101
	# 实测可以成功训练
	# 但是，为什么会自动下载 Downloading: "https://download.pytorch.org/models/resnet18-5c106cde.pth" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth
    # DB\backbones\resnet.py 
      # def deformable_resnet18(pretrained=True, **kwargs)
        # 注释这里就不会下载了



字符级标注

测试
CUDA_VISIBLE_DEVICES=0 python demo.py experiments/seg_detector/ic15_resnet18_deform_thre.yaml --image_path datasets/icdar2015/test_images/img_00000028.jpg --resume /root/final --polygon --box_thresh 0.7 --visualize

实测一张图拆出来的每一行一个图，字符级标注：结果完全不得行

todo:
    优化训练原码，自已写


# pip install numpy==1.26.4 opencv-python==4.6.0.66


see /root/DB/experiments/seg_detector/base_ic15.yaml

    processes:
        - class: AugmentDetectionData
          augmenter_args:
              - ['Fliplr', 0.5]
              - {'cls': 'Affine', 'rotate': [-10, 10]}
              - ['Resize', [0.5, 3.0]]
          only_resize: False
          keep_ratio: False
        - class: RandomCropData
          size: [640, 640]
          max_tries: 10
        - class: MakeICDARData
        - class: MakeSegDetectionData
        - class: MakeBorderMap
        - class: NormalizeImage
        - class: FilterKeys
          superfluous: ['polygons', 'filename', 'shape', 'ignore_tags', 'is_training']

训练阶段图片要经过这些处理