Open borisandeva opened 8 months ago
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
Hi, I was trying to follow the semi-supervised example (dataset AtriaSeg). When I ran the following command I got an error.
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