add validation transform

Author: taigw

segmentation/JSRT/config/train_test.cfg

@@ -16,6 +16,7 @@ modal_num = 1
 
 # data transforms
 train_transform = [NormalizeWithMeanStd, LabelConvert, RandomCrop, LabelToProbability]
+valid_transform = [NormalizeWithMeanStd, LabelConvert, LabelToProbability]
 test_transform  = [NormalizeWithMeanStd]
 
 NormalizeWithMeanStd_channels = [0]

segmentation/JSRT2/config/train_test.cfg

@@ -16,6 +16,7 @@ modal_num = 1
 
 # data transforms
 train_transform = [NormalizeWithMeanStd, LabelConvert, RandomCrop, LabelToProbability]
+valid_transform = [NormalizeWithMeanStd, LabelConvert, LabelToProbability]
 test_transform  = [NormalizeWithMeanStd]
 
 NormalizeWithMeanStd_channels = [0]
@@ -50,7 +51,7 @@ class_num     = 2
 in_chns       = 1
 feature_chns  = [4, 16, 24, 32, 48]
 dropout       = [0.0, 0.0, 0.3, 0.3, 0.4, 0.5]
-bilinear     = True
+bilinear      = True
 
 [training]
 # list of gpus

segmentation/fetal_hc/config/train_test.cfg

@@ -16,6 +16,7 @@ modal_num = 1
 
 # data transforms
 train_transform = [Rescale, RandomCrop, RandomFlip, NormalizeWithMeanStd, LabelConvert, LabelToProbability]
+valid_transform  = [Rescale, NormalizeWithMeanStd, LabelToProbability]
 test_transform  = [Rescale, NormalizeWithMeanStd]
 
 Rescale_output_size = [256, 384]

segmentation/prostate/README.md

@@ -36,5 +36,5 @@ pymic_net_run test config/train_test.cfg
 pymic_evaluate_seg config/evaluation.cfg
 ```
 
-The obtained average Dice score by default setting should be close to 87.63%. You can set `metric = assd` in `config/evaluation.cfg` and run the evaluation command again. You will get Average Symmetric Surface Distance (ASSD) evaluation results. You can try your efforts to improve the performance with different networks or training strategies by changing the configuration file `config/train_test.cfg`.
+The obtained average Dice score by default setting should be close to 86.38%. You can set `metric = assd` in `config/evaluation.cfg` and run the evaluation command again. You will get Average Symmetric Surface Distance (ASSD) evaluation results. You can try your efforts to improve the performance with different networks or training strategies by changing the configuration file `config/train_test.cfg`.
 

segmentation/prostate/config/evaluation.cfg

@@ -3,11 +3,7 @@ metric = dice
 label_list = [1]
 organ_name = prostate
 
-ground_truth_folder_root  = /home/disk2t/data/prostate/promise12/preprocess/train
+ground_truth_folder_root  = /home/disk2t/data/Abdomen/prostate/promise12/preprocess/train
 segmentation_folder_root  = result
 evaluation_image_pair     = ./config/data/image_test_gt_seg.csv
 
-ground_truth_label_convert_source = None
-ground_truth_label_convert_target = None
-segmentation_label_convert_source = None
-segmentation_label_convert_target = None

segmentation/prostate/config/train_test.cfg

@@ -3,7 +3,7 @@
 tensor_type = float
 
 task_type = seg
-root_dir  = /home/disk2t/data/prostate/promise12/preprocess/train
+root_dir  = /home/disk2t/data/Abdomen/prostate/promise12/preprocess/train
 train_csv = config/data/image_train.csv
 valid_csv = config/data/image_valid.csv
 test_csv  = config/data/image_test.csv
@@ -16,6 +16,7 @@ modal_num = 1
 
 # data transforms
 train_transform = [NormalizeWithMeanStd, RandomFlip,  RandomCrop, LabelToProbability]
+valid_transform = [NormalizeWithMeanStd, Pad, LabelToProbability]
 test_transform  = [NormalizeWithMeanStd, Pad]
 
 
@@ -96,11 +97,11 @@ evaluation_mode   = True
 test_time_dropout = False
 
 # use test time augmentation
-tta_mode = 1
+tta_mode = 0
 
-infer_sliding_window  = 1
-sliding_window_size   = [272, 480]
-sliding_window_stride = [272, 480]
+sliding_window_enable = True
+sliding_window_size   = [96, 96, 96]
+sliding_window_stride = [96, 96, 96]