modify readme.txt

readme.txt

@@ -1,24 +1,38 @@
-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
-  
+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 && 
@@ -72,18 +86,40 @@ sed -i 's/epochs\:\ 1200/epochs\:\ 30/1' ~/DB/experiments/seg_detector/td500_res
 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
-          
+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：
 
-def main():
 
+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' )
@@ -92,29 +128,28 @@ def main():
     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( '--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 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  
-
-
+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