prepared for docker

config/test_all_class.txt

@@ -1,6 +1,7 @@
 [data]
-data_root         = [data/Brats17ValidationData_renamed]
-data_names        =
+data_root         = [/home/guotwang/data/brats_docker_data/pre_process]
+data_names        = config/test_names.txt
+modality_postfix  = [Flair, T1, T1c, T2]
 
 [network1ax]
 net_type            = MSNet
@@ -9,7 +10,7 @@ downsample_twice    = True
 data_shape          = [19, 180, 160, 4]
 label_shape         = [11, 180, 160, 1]
 class_num           = 2
-model_file          = model_pretrain/msnet_wt32_20000.ckpt
+model_file          = model/msnet_wt32_10000.ckpt
 
 [network1sg]
 net_type            = MSNet
@@ -18,7 +19,7 @@ downsample_twice    = True
 data_shape          = [19, 160, 180, 4]
 label_shape         = [11, 160, 180, 1]
 class_num           = 2
-model_file          = model_pretrain/msnet_wt32sg_20000.ckpt
+model_file          = model/msnet_wt32sg_5000.ckpt
 
 [network1cr]
 net_type            = MSNet
@@ -27,7 +28,7 @@ downsample_twice    = True
 data_shape          = [19, 160, 160, 4]
 label_shape         = [11, 160, 160, 1]
 class_num           = 2
-model_file          = model_pretrain/msnet_wt32cr_20000.ckpt
+model_file          = model/msnet_wt32cr_5000.ckpt
 
 
 [network2ax]
@@ -37,7 +38,7 @@ downsample_twice    = True
 data_shape          = [19, 96, 96, 4]
 label_shape         = [11, 96, 96, 1]
 class_num           = 2
-model_file          = model_pretrain/msnet_tc32_20000.ckpt
+model_file          = model/msnet_tc32_20000.ckpt
 
 [network2sg]
 net_type            = MSNet
@@ -46,7 +47,7 @@ downsample_twice    = True
 data_shape          = [19, 96, 96, 4]
 label_shape         = [11, 96, 96, 1]
 class_num           = 2
-model_file          = model_pretrain/msnet_tc32sg_20000.ckpt
+model_file          = model/msnet_tc32sg_5000.ckpt
 
 [network2cr]
 net_type            = MSNet
@@ -55,37 +56,35 @@ downsample_twice    = True
 data_shape          = [19, 96, 96, 4]
 label_shape         = [11, 96, 96, 1]
 class_num           = 2
-model_file          = model_pretrain/msnet_tc32cr_20000.ckpt
+model_file          = model/msnet_tc32cr_5000.ckpt
 
 [network3ax]
 net_type            = MSNet
 net_name            = MSNet_EN32
 downsample_twice    = False
 data_shape          = [19, 96, 96, 4]
 label_shape         = [11, 96, 96, 1]
-class_num           = 2
-model_file          = model_pretrain/msnet_en32_20000.ckpt
+class_num           = 3
+model_file          = model/msnet_en32_20000.ckpt
 
 [network3sg]
 net_type            = MSNet
 net_name            = MSNet_EN32sg
 downsample_twice    = False
 data_shape          = [19, 96, 96, 4]
 label_shape         = [11, 96, 96, 1]
-class_num           = 2
-model_file          = model_pretrain/msnet_en32sg_20000.ckpt
+class_num           = 3
+model_file          = model/msnet_en32sg_5000.ckpt
 
 [network3cr]
 net_type            = MSNet
 net_name            = MSNet_EN32cr
 downsample_twice    = False
 data_shape          = [19, 96, 96, 4]
 label_shape         = [11, 96, 96, 1]
-class_num           = 2
-model_file          = model_pretrain/msnet_en32cr_20000.ckpt
+class_num           = 3
+model_file          = model/msnet_en32cr_5000.ckpt
 
 [testing]
 test_slice_direction = all
-label_convert_source = [0, 1, 2, 3]
-label_convert_target = [0, 1, 2, 4]
-save_folder = results
+save_folder = result_all

config/test_names.txt

@@ -0,0 +1,50 @@
+HGG_brats_tcia_pat417_0001
+HGG_brats_tcia_pat205_0001
+HGG_brats_tcia_pat455_0001
+HGG_brats_tcia_pat374_0356
+HGG_brats_tcia_pat473_0001
+HGG_brats_tcia_pat277_0001
+HGG_brats_tcia_pat396_0117
+LGG_brats_tcia_pat299_0001
+HGG_brats_tcia_pat332_0001
+HGG_brats_tcia_pat178_0002
+HGG_brats_tcia_pat199_0001
+HGG_brats_tcia_pat170_0002
+HGG_brats_tcia_pat156_0001
+HGG_brats_tcia_pat447_0313
+HGG_brats_tcia_pat198_0283
+HGG_brats_tcia_pat105_0001
+HGG_brats_2013_pat0002_1
+HGG_brats_tcia_pat406_0001
+LGG_brats_tcia_pat393_0001
+HGG_brats_tcia_pat274_0001
+LGG_brats_tcia_pat141_0001
+HGG_brats_tcia_pat138_0001
+LGG_brats_2013_pat0006_1
+HGG_brats_tcia_pat314_0001
+LGG_brats_2013_pat0014_1
+HGG_brats_tcia_pat447_0122
+HGG_brats_tcia_pat372_0001
+HGG_brats_tcia_pat230_0637
+HGG_brats_tcia_pat247_0001
+HGG_brats_tcia_pat171_0780
+LGG_brats_tcia_pat351_0001
+HGG_brats_tcia_pat399_0527
+HGG_brats_tcia_pat193_0002
+HGG_brats_tcia_pat374_0557
+HGG_brats_2013_pat0024_1
+HGG_brats_tcia_pat374_0909
+HGG_brats_tcia_pat278_0001
+HGG_brats_tcia_pat180_0001
+HGG_brats_tcia_pat399_0595
+HGG_brats_tcia_pat396_0217
+HGG_brats_tcia_pat257_0001
+HGG_brats_tcia_pat157_0001
+HGG_brats_tcia_pat165_0001
+HGG_brats_2013_pat0022_1
+HGG_brats_tcia_pat444_0001
+HGG_brats_tcia_pat399_0002
+HGG_brats_tcia_pat399_0156
+LGG_brats_tcia_pat428_0001
+HGG_brats_tcia_pat171_0950
+HGG_brats_tcia_pat399_0479

config/train_en_ax.txt

@@ -1,22 +1,24 @@
 [data]
-data_root             = [data/Brats17TrainingData_crop_renamed]
+data_root             = [/home/guotwang/data/brats_docker_data/pre_process]
 data_names            = config/train_names_all_lggx3.txt 
+modality_postfix      = [Flair, T1, T1c, T2]
+label_postfix         = OT  
 with_ground_truth     = True
 batch_size            = 5
 data_shape            = [19, 64, 64, 4]
 label_shape           = [11, 64, 64, 1]
-label_convert_source  = [0, 1, 2, 4]
-label_convert_target  = [0, 0, 0, 1] 
+label_convert_source  = [0, 1, 2, 3, 4]
+label_convert_target  = [0, 1, 0, 0, 2] 
 batch_slice_direction = axial
 train_with_roi_patch  = True
-label_roi_mask        = [1,4]
+label_roi_mask        = [1,3,4]
 roi_patch_margin      = 5
 
 [network]
 net_type            = MSNet
 net_name            = MSNet_EN32
 downsample_twice    = False
-class_num           = 2
+class_num           = 3
 
 [training]
 learning_rate      = 1e-3
@@ -27,4 +29,4 @@ start_iteration    = 0
 test_iteration     = 100
 test_step          = 10
 model_pre_trained  = 
-model_save_prefix  = model/msnet_en32
+model_save_prefix  = model/msnet_en32

config/train_en_cr.txt

@@ -1,30 +1,32 @@
 [data]
-data_root             = [data/Brats17TrainingData_crop_renamed]
+data_root             = [/home/guotwang/data/brats_docker_data/pre_process]
 data_names            = config/train_names_all_lggx3.txt 
+modality_postfix      = [Flair, T1, T1c, T2]
+label_postfix         = OT  
 with_ground_truth     = True
 batch_size            = 5
 data_shape            = [19, 64, 64, 4]
 label_shape           = [11, 64, 64, 1]
-label_convert_source  = [0, 1, 2, 4]
-label_convert_target  = [0, 0, 0, 1]
+label_convert_source  = [0, 1, 2, 3, 4]
+label_convert_target  = [0, 1, 0, 0, 2] 
 batch_slice_direction = coronal
 train_with_roi_patch  = True
-label_roi_mask        = [1,4]
+label_roi_mask        = [1,3,4]
 roi_patch_margin      = 5
 
 [network]
 net_type            = MSNet
 net_name            = MSNet_EN32cr
 downsample_twice    = False
-class_num           = 2
+class_num           = 3
 
 [training]
 learning_rate      = 1e-3
 decay              = 1e-7
-maximal_iteration  = 20000
+maximal_iteration  = 5000
 snapshot_iteration = 5000
-start_iteration    = 0
+start_iteration    = 1
 test_iteration     = 100
 test_step          = 10
-model_pre_trained  = 
+model_pre_trained  = model/msnet_en32cr_init
 model_save_prefix  = model/msnet_en32cr

config/train_en_sg.txt

@@ -1,30 +1,32 @@
 [data]
-data_root             = [data/Brats17TrainingData_crop_renamed]
+data_root             = [/home/guotwang/data/brats_docker_data/pre_process]
 data_names            = config/train_names_all_lggx3.txt 
+modality_postfix      = [Flair, T1, T1c, T2]
+label_postfix         = OT  
 with_ground_truth     = True
 batch_size            = 5
 data_shape            = [19, 64, 64, 4]
 label_shape           = [11, 64, 64, 1]
-label_convert_source  = [0, 1, 2, 4]
-label_convert_target  = [0, 0, 0, 1]
+label_convert_source  = [0, 1, 2, 3, 4]
+label_convert_target  = [0, 1, 0, 0, 2] 
 batch_slice_direction = sagittal
 train_with_roi_patch  = True
-label_roi_mask        = [1,4]
+label_roi_mask        = [1,3,4]
 roi_patch_margin      = 5
 
 [network]
 net_type            = MSNet
 net_name            = MSNet_EN32sg
 downsample_twice    = False
-class_num           = 2
+class_num           = 3
 
 [training]
 learning_rate      = 1e-3
 decay              = 1e-7
-maximal_iteration  = 20000
+maximal_iteration  = 5000
 snapshot_iteration = 5000
-start_iteration    = 0
+start_iteration    = 1
 test_iteration     = 100
 test_step          = 10
-model_pre_trained  = 
+model_pre_trained  = model/msnet_en32sg_init
 model_save_prefix  = model/msnet_en32sg