update wsl methods

update wsl methods

Author: taigw

seg_wsl/ACDC/README.md

@@ -72,7 +72,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
-deep_supervise= False
+multiscale_pred = False
 ```
 
 For training, we use the CrossEntropyLoss with pixel weighting (i.e., partial CE loss), and train the network by the  `Adam` optimizer. The maximal iteration is 20k, and the training is early stopped if there is not performance improvement on the validation set for 8k iteratins. The learning rate scheduler is `ReduceLROnPlateau`. The corresponding configuration is:

seg_wsl/ACDC/config/unet2d_baseline.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_wsl/ACDC/config/unet2d_em.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_wsl/ACDC/config/unet2d_gcrf.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_wsl/ACDC/config/unet2d_mumford.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_wsl/ACDC/config/unet2d_tv.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_wsl/ACDC/config/unet2d_ustm.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus
@@ -59,16 +59,16 @@ weight_decay  = 1e-5
 # for lr schedular 
 lr_scheduler  = ReduceLROnPlateau
 lr_gamma      = 0.5
-ReduceLROnPlateau_patience = 2000
-early_stop_patience = 8000
+ReduceLROnPlateau_patience = 4000
+early_stop_patience = 10000
 
 ckpt_save_dir    = model/unet2d_ustm
 
 # start iter
 iter_start = 0
-iter_max   = 20000
+iter_max   = 30000
 iter_valid = 100
-iter_save  = [2000, 20000]
+iter_save  = [2000, 30000]
 
 [weakly_supervised_learning]
 wsl_method     = USTM