####baseline command:
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 3 --batch_size 8
####分辨率对比:
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 1 --batch_size 1
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 2 --batch_size 2
rl=1 or 2 or 3
1:input size [512,512,256]
2:input size [256,256,128]
3:input size [128,128,64]
####增加attention:
python direct_seg.py --fold $i --channel 4 --model "FCN_AG" --rl 3 --batch_size 8
####增加通道:
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 3 --batch_size 8
python direct_seg.py --fold $i --channel 12 --model "FCN" --rl 3 --batch_size 2
####预处理:
python patch_process.py --fold $i --patch_size 32 --pools 32 --Direct_parameter "Low_resolution_4_Dice"
python patch_process.py --fold $i --patch_size 64 --pools 32 --Direct_parameter "Low_resolution_4_Dice"
保证在命令 python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 3 --batch_size 8 已经运行过,并对训练,测试进行推理,生成Low_resolution_4_Dice参数结果
####baseline:
python patch_seg.py --fold $i --patch_size 32 --pools 32 --num_workers 8 --is_train 1 --frangi 0 --load_num 0 --batch_size 64 --Direct_parameter "Low_resolution_4_Dice" ##experment 1
####数据增强影响:
python patch_seg.py --fold $i --patch_size 32 --pools 32 --num_workers 8 --is_train 1 --frangi 0 --load_num 0 --batch_size 64 --flip_prob 0.5 --rotate_prob 0.5 --Direct_parameter "Low_resolution_4_Dice"
python patch_seg.py --fold $i --patch_size 32 --pools 32 --num_workers 8 --is_train 1 --frangi 0 --load_num 0 --batch_size 64 --flip_prob 0 --rotate_prob 0 --Direct_parameter "Low_resolution_4_Dice"
####增加frangi通道:
python patch_seg.py --fold $i --patch_size 32 --pools 32 --num_workers 8 --is_train 1 --frangi 1 --load_num 0 --batch_size 64 --Direct_parameter "Low_resolution_4_Dice" ##experment 4 add frangi
python patch_seg.py --fold $i --patch_size 64 --pools 32 --num_workers 8 --is_train 1 --frangi 0 --load_num 0 --batch_size 10 --Direct_parameter "Low_resolution_4_Dice" ##experment5
保证在命令 python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 3 --batch_size 8 已经运行过,并对训练,测试进行推理,生成Low_resolution_4_Dice参数结果
python morphology_process.py --fold $i --Direct_parameter "Low_resolution_4_Dice"
####预处理
python tree_process.py --fold $i --patch_size 16 --z_size 4 --Direct_parameter "Low_resolution_4_Dice"
python morphology_process.py --fold $i --Direct_parameter "Low_resolution_4_Dice_dilation" --pools 32
####baseline
python tree_seg.py --gpu_index 0 --fold $i --patch_size 16 --z_size 4 --model "TreeConvGRU" --Direct_parameter "Low_resolution_4_Dice"
####model
python tree_seg.py --gpu_index 0 --fold $i --patch_size 16 --z_size 4 --model "TreeConvLSTM" --Direct_parameter "Low_resolution_4_Dice"
p
####patch
python tree_seg.py --gpu_index 0 --fold $i --patch_size 16 --z_size 8 --model "TreeConvGRU" --Direct_parameter "Low_resolution_4_Dice"
python tree_seg.py --gpu_index 0 --fold $i --patch_size 16 --z_size 4 --model "TreeConvGRU" --Direct_parameter "High_resolution_4_Dice"
ps:先运行 python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 1 --batch_size 1 并对训练,测试集进行推理,生成High_resolution_4_Dice参数结果
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 3 --batch_size 8 并对训练,测试集进行推理,测试进行推理,生成Low_resolution_4_Dice参数结果
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 1 --batch_size 1 并对训练,测试集进行推理,生成High_resolution_4_Dice参数结果
python morphology_process.py --fold $i --Direct_parameter "Low_resolution_4_Dice_dilation" --pools 32
python morphology_process.py --fold $i --Direct_parameter "High_resolution_4_Dice_dilation" --pools 32
python graph_process.py --fold $i --Direct_parameter "Low_resolution_4_Dice"
python graph_seg.py --fold $i --Direct_parameter "Low_resolution_4_Dice"
python graph_seg.py --fold $i --Direct_parameter "High_resolution_4_Dice"
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 3 --batch_size 8 --loss "Dice" # 如果direct 已经训练可以不重新训练
python morphology_process.py --fold $i --Direct_parameter "Low_resolution_4_Dice" --pools 32
python prior_patch_process.py --fold $i --pools 32 --Direct_parameter "Low_resolution_4_Dice"
python prior_patch_seg.py --fold $i --patch_size 16 --pools 32 --num_workers 8 --is_train 1 --load_num 0 --batch_size 512 --Direct_parameter "Low_resolution_4_Dice"
python prior_patch_seg.py --fold $i --patch_size 32 --pools 32 --num_workers 8 --is_train 1 --load_num 0 --batch_size 64 --Direct_parameter "Low_resolution_4_Dice"
python prior_patch_seg.py --fold $i --patch_size 64 --pools 32 --num_workers 8 --is_train 1 --load_num 0 --batch_size 8 --Direct_parameter "Low_resolution_4_Dice"
python direct_seg.py --fold $i --channel 4 --model "FCN" --rl 3 --batch_size 8 --loss "Dice_dilation"
python morphology_process.py --fold $i --Direct_parameter "Low_resolution_4_Dice_dilation" --pools 32
python prior_patch_process.py --fold $i --pools 32 --Direct_parameter "Low_resolution_4_Dice_dilation"
python prior_patch_seg.py --fold $i --patch_size 16 --pools 32 --num_workers 8 --is_train 1 --load_num 0 --batch_size 512 --Direct_parameter "Low_resolution_4_Dice_dilation"
python prior_patch_seg.py --fold $i --patch_size 32 --pools 32 --num_workers 8 --is_train 1 --load_num 0 --batch_size 64 --Direct_parameter "Low_resolution_4_Dice_dilation"
python prior_patch_seg.py --fold $i --patch_size 64 --pools 32 --num_workers 8 --is_train 1 --load_num 0 --batch_size 8 --Direct_parameter "Low_resolution_4_Dice_dilation"
project/
-config/
-config.yaml
-data/
-data_loader.py
-.....
-utils/
-....
-model/
-net
-.....
-Intermediate_data/
-Patch/
-Tree/
-Graph/
-Prior_Patch/
-result/
-Direct/
-Patch/
-/Pre_seg_name
-different_parameters_name/
-pre_label
-model_save
-result.csv
-Tree/
-Graph/
-Prior_Patch/
-main.py
-run.sh