update config for nll examples

update config for nll examples

Author: taigw

seg_nll/JSRT/README.md

@@ -71,6 +71,22 @@ pymic_run train config/unet_gce.cfg
 pymic_run test config/unet_gce.cfg
 ```
 
+### CLSLSR
+The CLSLSR method estimates errors in the original noisy label and obtains pixel-level weight maps based on an intial model, and then uses the weight maps to suppress noises in  a standard supervised learning procedure. Assume that the initial model is the baseline method, run the following command to obtain the weight maps:
+
+```bash
+python clslsr_get_condience config/unet_ce.cfg
+```
+
+The weight maps will be saved in `$root_dir/slsr_conf`. Then train the new model and do inference by:
+
+```bash
+pymic_run train config/unet_clslsr.cfg
+pymic_run test config/unet_clslsr.cfg
+```
+
+Note that the weight maps for training images are specified in the configuration file `train_csv = config/data/jsrt_train_mix_clslsr.csv`.
+
 ### Co-Teaching
 The configuration file for Co-Teaching is `config/unet2d_cot.cfg`. The corresponding setting is:
 

seg_nll/JSRT/config/unet_ce.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0,  0,  0.3, 0.4, 0.5]
 bilinear      = False
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_nll/JSRT/config/unet_clslsr.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0,  0,  0.3, 0.4, 0.5]
 bilinear      = False
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_nll/JSRT/config/unet_cot.cfg

@@ -42,11 +42,11 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0,  0,  0.3, 0.4, 0.5]
 bilinear      = False
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus
-gpus = [1]
+gpus = [0]
 
 loss_type     = CrossEntropyLoss
 
@@ -63,8 +63,6 @@ ReduceLROnPlateau_patience = 2000
 
 ckpt_save_dir    = model/unet_cot
 
-# start iter
-iter_start = 0
 iter_max   = 10000
 iter_valid = 100
 iter_save  = [10000]

seg_nll/JSRT/config/unet_dast.cfg

@@ -44,12 +44,11 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0,  0,  0.3, 0.4, 0.5]
 bilinear      = False
-deep_supervise= False
 output_mode   = first
 
 [training]
 # list of gpus
-gpus = [1]
+gpus = [0]
 
 loss_type     = CrossEntropyLoss
 
@@ -66,8 +65,6 @@ ReduceLROnPlateau_patience = 2000
 
 ckpt_save_dir    = model/unet_dast
 
-# start iter
-iter_start = 0
 iter_max   = 10000
 iter_valid = 100
 iter_save  = [10000]

seg_nll/JSRT/config/unet_gce.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0,  0,  0.3, 0.4, 0.5]
 bilinear      = False
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus

seg_nll/JSRT/config/unet_trinet.cfg

@@ -42,7 +42,7 @@ in_chns       = 1
 feature_chns  = [16, 32, 64, 128, 256]
 dropout       = [0,  0,  0.3, 0.4, 0.5]
 bilinear      = False
-deep_supervise= False
+multiscale_pred = False
 
 [training]
 # list of gpus