update readme.txt

readme.txt

@@ -1,6 +1,121 @@
 see doc\lang\programming\pytorch\文本检测\DBNET 论文代码都有
 
-see 深入理解神经网络：从逻辑回归到CNN.md -> autodl -> cuda 多版本共存
+see 深入理解神经网络：从逻辑回归到CNN.md -> autodl
+
+
+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
+
+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
+
+
+vi experiments\seg_detector\ic15_resnet18_deform_thre.yaml
+            learning_rate:  
+                class: DecayLearningRate
+                epochs: 1200
+        epochs: 1200
+          # 实测这里的 epochs 要和 train 中的 epochs 一致，否则学习率要减到 0
+          
+
+# 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：
+
+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( '--resume' )       # 继续上一次训练
+    #sys.argv.append( '/root/model_epoch_120_minibatch_12000' )     
+    #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 train.py experiments/seg_detector/td500_resnet18_deform_thre.yaml --num_gpus 1
+
+权重转换：
+Usage: python convert_to_onnx.py /path/to/exp/yaml /path/to/pretrained/weight /path/to/save/onnx.  
+
+// 验证    
+CUDA_VISIBLE_DEVICES=0 python demo.py experiments/seg_detector/td500_resnet18_deform_thre.yaml --image_path datasets/GD500/test_images/IMG_0000.JPG --resume /root/final --polygon --box_thresh 0.7 --visualize  
+
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+
 
 https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.39_linux.run
 	# 下载安装