学习率要这两个地方

DB/readme.txt

@@ -1,5 +1,18 @@
 
 
+vi DB/training/learning_rate.py
+
+lr = State(default=0.007) 
+
+in the yaml file
+        learning_rate:
+            class: DecayLearningRate
+            lr: 0.001
+            epochs: 1200
+	# 学习率要这两个地方一起来成一至后，logs 显示才正常
+
+
+
 RuntimeError: transform: failed to synchronize: cudaErrorAssert: device-side assert triggered
 
 I fixed this problem after I change my pytorch version to 1.4.1

readme.txt

@@ -1,223 +1,235 @@
-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
-
-
-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  
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-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
-
-
+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
+
+
+vi DB/training/learning_rate.py
+
+lr = State(default=0.007) 
+
+in the yaml file
+        learning_rate:
+            class: DecayLearningRate
+            lr: 0.001
+            epochs: 1200
+	# 学习率要这两个地方一起来成一至后，logs 显示才正常
+  
+
+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  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+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
+
+