update ssl configures

update ssl configures

Author: taigw

seg_ssl/ACDC/README.md

@@ -86,7 +86,7 @@ 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 a combinatin of DiceLoss and CrossEntropyLoss, and train the network by the   `Adam` optimizer. The maximal iteration is 30k, and the training is early stopped if there is not performance improvement on the validation set for 10k iteratins. The learning rate scheduler is `ReduceLROnPlateau`. The corresponding configuration is:
@@ -185,13 +185,16 @@ pymic_run test config/unet2d_uamt.cfg
 The configuration file for UPRC is `config/unet2d_urpc.cfg`. This method requires deep supervision and pyramid prediction of a network. The network setting is:
 
 ```bash 
+[network]
 net_type      = UNet2D
 class_num     = 4
 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= True
+multiscale_pred = True
+[training]
+deep_supervise = True
 ```
 
 The setting for URPC training is:

seg_ssl/ACDC/config/unet2d_baseline.cfg

@@ -77,8 +77,6 @@ early_stop_patience = 10000
 
 ckpt_save_dir    = model/unet2d_baseline
 
-# start iter
-iter_start = 0
 iter_max   = 30000
 iter_valid = 100
 iter_save  = 30000

seg_ssl/ACDC/config/unet2d_cct.cfg

@@ -58,6 +58,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0.0, 0.0, 0.0, 0.5, 0.5]
 bilinear      = True
+multiscale_pred = False
 
 # parameters specific to CCT
 VAT_it = 2
@@ -67,7 +68,7 @@ Uniform_range = 0.3
 
 [training]
 # list of gpus
-gpus       = [1]
+gpus       = [0]
 
 loss_type     = [DiceLoss, CrossEntropyLoss]
 loss_weight   = [0.5, 0.5]
@@ -86,8 +87,6 @@ early_stop_patience = 10000
 
 ckpt_save_dir    = model/unet2d_cct
 
-# start iter
-iter_start = 0
 iter_max   = 30000
 iter_valid = 100
 iter_save  = [1000, 30000]

seg_ssl/ACDC/config/unet2d_cps.cfg

@@ -62,7 +62,7 @@ multiscale_pred = False
 
 [training]
 # list of gpus
-gpus       = [0]
+gpus       = [1]
 
 loss_type     = [DiceLoss, CrossEntropyLoss]
 loss_weight   = [0.5, 0.5]
@@ -81,8 +81,6 @@ early_stop_patience = 10000
 
 ckpt_save_dir    = model/unet2d_cps
 
-# start iter
-iter_start = 0
 iter_max   = 30000
 iter_valid = 100
 iter_save  = [1000, 30000]

seg_ssl/ACDC/config/unet2d_em.cfg

@@ -81,8 +81,6 @@ early_stop_patience = 10000
 
 ckpt_save_dir    = model/unet2d_em
 
-# start iter
-iter_start = 0
 iter_max   = 30000
 iter_valid = 100
 iter_save  = [1000, 30000]

seg_ssl/ACDC/config/unet2d_uamt.cfg

@@ -81,8 +81,6 @@ early_stop_patience = 10000
 
 ckpt_save_dir    = model/unet2d_uamt
 
-# start iter
-iter_start = 0
 iter_max   = 30000
 iter_valid = 100
 iter_save  = [1000,30000]

seg_ssl/ACDC/config/unet2d_urpc.cfg

@@ -83,8 +83,6 @@ early_stop_patience = 10000
 ckpt_save_dir    = model/unet2d_urpc
 ckpt_prefix      = unet2d_urpc
 
-# start iter
-iter_start = 0
 iter_max   = 30000
 iter_valid = 100
 iter_save  = [1000, 30000]