KeyError: 'gaussiannoise_channels'

[borisandeva]

Hi, I was trying to follow the semi-supervised example (dataset AtriaSeg). When I ran the following command I got an error.

 pymic_train config/unet3d_r10_em.cfg

torch.backends.cuda.matmul.allow_tf32 = True by default.
  This value defaults to True when PyTorch version in [1.7, 1.11] and may affect precision.
  See https://docs.monai.io/en/latest/precision_accelerating.html#precision-and-accelerating
dataset tensor_type float
dataset task_type seg
dataset supervise_type semi_sup
dataset root_dir ../../PyMIC_data/AtriaSeg/TrainingSet_crop/
dataset train_csv config/data/image_train_r10_lab.csv
dataset train_csv_unlab config/data/image_train_r10_unlab.csv
dataset valid_csv config/data/image_valid.csv
dataset test_csv config/data/image_test.csv
dataset train_batch_size 2
dataset train_batch_size_unlab 2
dataset train_transform ['RandomCrop', 'RandomFlip', 'NormalizeWithMeanStd', 'GammaCorrection', 'GaussianNoise', 'LabelToProbability']

dataset train_transform_unlab ['RandomCrop', 'RandomFlip', 'NormalizeWithMeanStd', 'GammaCorrection', 'GaussianNoise']
dataset valid_transform ['NormalizeWithMeanStd', 'LabelToProbability']
dataset test_transform ['NormalizeWithMeanStd']
dataset randomcrop_output_size [72, 96, 112]
dataset randomcrop_foreground_focus False
dataset randomcrop_foreground_ratio None
dataset randomcrop_mask_label None
dataset randomflip_flip_depth False
dataset randomflip_flip_height True
dataset randomflip_flip_width True
dataset normalizewithmeanstd_channels [0]
dataset gammacorrection_channels [0]
dataset gammacorrection_gamma_min 0.7
dataset gammacorrection_gamma_max 1.5
network net_type UNet3D
network class_num 2
network in_chns 1
network feature_chns [32, 64, 128, 256]
network dropout [0.0, 0.0, 0.5, 0.5]
network trilinear True
network multiscale_pred False
training gpus [1]
training loss_type ['DiceLoss', 'CrossEntropyLoss']
training loss_weight [0.5, 0.5]
training optimizer Adam
training learning_rate 0.001
training momentum 0.9
training weight_decay 1e-05
training lr_scheduler ReduceLROnPlateau
training lr_gamma 0.5
training reducelronplateau_patience 2000
training early_stop_patience 5000
training ckpt_save_dir model/unet3d_r10_em
training iter_max 20000
training iter_valid 100
training iter_save [1000, 20000]
semi_supervised_learning method_name EntropyMinimization
semi_supervised_learning regularize_w 0.1
semi_supervised_learning rampup_start 1000
semi_supervised_learning rampup_end 15000
testing gpus [1]
testing ckpt_mode 1
testing output_dir result/unet3d_r10_em
testing post_process None
testing sliding_window_enable False
dataset tensor_type = float
dataset task_type = seg
dataset supervise_type = semi_sup
dataset root_dir = ../../PyMIC_data/AtriaSeg/TrainingSet_crop/
dataset train_csv = config/data/image_train_r10_lab.csv
dataset train_csv_unlab = config/data/image_train_r10_unlab.csv
dataset valid_csv = config/data/image_valid.csv
dataset test_csv = config/data/image_test.csv
dataset train_batch_size = 2
dataset train_batch_size_unlab = 2
dataset train_transform = ['RandomCrop', 'RandomFlip', 'NormalizeWithMeanStd', 'GammaCorrection', 'GaussianNoise', 'LabelToProbability']
dataset train_transform_unlab = ['RandomCrop', 'RandomFlip', 'NormalizeWithMeanStd', 'GammaCorrection', 'GaussianNoise']
dataset valid_transform = ['NormalizeWithMeanStd', 'LabelToProbability']
dataset test_transform = ['NormalizeWithMeanStd']
dataset randomcrop_output_size = [72, 96, 112]
dataset randomcrop_foreground_focus = False
dataset randomcrop_foreground_ratio = None
dataset randomcrop_mask_label = None
dataset randomflip_flip_depth = False
dataset randomflip_flip_height = True
dataset randomflip_flip_width = True
dataset normalizewithmeanstd_channels = [0]
dataset gammacorrection_channels = [0]
dataset gammacorrection_gamma_min = 0.7
dataset gammacorrection_gamma_max = 1.5
dataset labeltoprobability_class_num = 2
network net_type = UNet3D
network class_num = 2
network in_chns = 1
network feature_chns = [32, 64, 128, 256]
network dropout = [0.0, 0.0, 0.5, 0.5]
network trilinear = True
network multiscale_pred = False
training gpus = [1]
training loss_type = ['DiceLoss', 'CrossEntropyLoss']
training loss_weight = [0.5, 0.5]
training optimizer = Adam
training learning_rate = 0.001
training momentum = 0.9
training weight_decay = 1e-05
training lr_scheduler = ReduceLROnPlateau
training lr_gamma = 0.5
training reducelronplateau_patience = 2000
training early_stop_patience = 5000
training ckpt_save_dir = model/unet3d_r10_em
training iter_max = 20000
training iter_valid = 100
training iter_save = [1000, 20000]
semi_supervised_learning method_name = EntropyMinimization
semi_supervised_learning regularize_w = 0.1
semi_supervised_learning rampup_start = 1000
semi_supervised_learning rampup_end = 15000
testing gpus = [1]
testing ckpt_mode = 1
testing output_dir = result/unet3d_r10_em
testing post_process = None
testing sliding_window_enable = False

********** Semi Supervised Learning **********

deterministric is true
Traceback (most recent call last):
  File "/gpu_home/bori/miniconda3/envs/pymic3/bin/pymic_train", line 8, in <module>
    sys.exit(main())
  File "/gpu_home/bori/miniconda3/envs/pymic3/lib/python3.9/site-packages/pymic/net_run/train.py", line 95, in main
    agent.run()
  File "/gpu_home/bori/miniconda3/envs/pymic3/lib/python3.9/site-packages/pymic/net_run/agent_abstract.py", line 311, in run
    self.create_dataset()
  File "/gpu_home/bori/miniconda3/envs/pymic3/lib/python3.9/site-packages/pymic/net_run/semi_sup/ssl_abstract.py", line 64, in create_dataset
    super(SSLSegAgent, self).create_dataset()
  File "/gpu_home/bori/miniconda3/envs/pymic3/lib/python3.9/site-packages/pymic/net_run/agent_abstract.py", line 247, in create_dataset
    self.train_set = self.get_stage_dataset_from_config('train')
  File "/gpu_home/bori/miniconda3/envs/pymic3/lib/python3.9/site-packages/pymic/net_run/agent_seg.py", line 61, in get_stage_dataset_from_config
    one_transform = self.transform_dict[name](transform_param)
  File "/gpu_home/bori/miniconda3/envs/pymic3/lib/python3.9/site-packages/pymic/transform/intensity.py", line 103, in __init__
    self.channels = params['GaussianNoise_channels'.lower()]
KeyError: 'gaussiannoise_channels'

I had to updgrade pytorch to a newer one due to my cuda version 11.6. Not it is:
torch==1.11.0+cu113
torchvision==0.12.0+cu113
pymic==0.4.0

and Python is 3.9