RuntimeError

[Zhenxiaoan]

(LLM) root@node1:~/Desktop/LLM/OpenMED/MIS-FM# python train.py demo/pctnet_scratch.cfg
dataset tensor_type float
dataset task_type seg
dataset root_dir /home/x/projects/PyMIC_project/PyMIC_examples/PyMIC_data/AtriaSeg/TrainingSet_crop
dataset train_csv demo/data/image_train.csv
dataset valid_csv demo/data/image_valid.csv
dataset test_csv demo/data/image_test.csv
dataset modal_num 1
dataset train_batch_size 2
dataset valid_batch_size 1
dataset patch_size [64, 128, 128]
dataset train_transform ['RandomCrop', 'NormalizeWithMeanStd', 'RandomFlip', 'LabelToProbability']
dataset valid_transform ['NormalizeWithMeanStd', 'LabelToProbability']
dataset test_transform ['NormalizeWithMeanStd']
dataset normalizewithmeanstd_channels [0]
dataset randomcrop_foreground_focus False
dataset randomcrop_foreground_ratio None
dataset randomcrop_mask_label None
dataset randomcrop_inverse False
dataset randomflip_flip_depth True
dataset randomflip_flip_height True
dataset randomflip_flip_width True
dataset randomflip_inverse False
dataset labeltoprobability_class_num 2
dataset labeltoprobability_inverse False
dataset randomcrop_output_size [64, 128, 128]
network net_type PCTNet
network class_num 2
network in_chns 1
network input_size [64, 128, 128]
network feature_chns [24, 48, 128, 256, 512]
network dropout [0, 0, 0.2, 0.2, 0.2]
network resolution_mode 1
network multiscale_pred True
training gpus [0]
training deep_supervise True
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 StepLR
training lr_gamma 0.5
training lr_step 4000
training early_stop_patience 10000
training ckpt_save_dir demo/model/pctnet_scratch
training iter_start 0
training iter_max 10000
training iter_valid 500
training iter_save 10000
testing gpus [0]
testing ckpt_mode 1
testing output_dir demo/result/pctnet_scratch
testing sliding_window_enable True
testing sliding_window_batch 8
testing sliding_window_size [64, 128, 128]
testing sliding_window_stride [32, 64, 64]
dataset tensor_type = float
dataset task_type = seg
dataset root_dir = /home/x/projects/PyMIC_project/PyMIC_examples/PyMIC_data/AtriaSeg/TrainingSet_crop
dataset train_csv = demo/data/image_train.csv
dataset valid_csv = demo/data/image_valid.csv
dataset test_csv = demo/data/image_test.csv
dataset modal_num = 1
dataset train_batch_size = 2
dataset valid_batch_size = 1
dataset patch_size = [64, 128, 128]
dataset train_transform = ['RandomCrop', 'NormalizeWithMeanStd', 'RandomFlip', 'LabelToProbability']
dataset valid_transform = ['NormalizeWithMeanStd', 'LabelToProbability']
dataset test_transform = ['NormalizeWithMeanStd']
dataset normalizewithmeanstd_channels = [0]
dataset randomcrop_foreground_focus = False
dataset randomcrop_foreground_ratio = None
dataset randomcrop_mask_label = None
dataset randomcrop_inverse = False
dataset randomflip_flip_depth = True
dataset randomflip_flip_height = True
dataset randomflip_flip_width = True
dataset randomflip_inverse = False
dataset labeltoprobability_class_num = 2
dataset labeltoprobability_inverse = False
dataset randomcrop_output_size = [64, 128, 128]
network net_type = PCTNet
network class_num = 2
network in_chns = 1
network input_size = [64, 128, 128]
network feature_chns = [24, 48, 128, 256, 512]
network dropout = [0, 0, 0.2, 0.2, 0.2]
network resolution_mode = 1
network multiscale_pred = True
training gpus = [0]
training deep_supervise = True
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 = StepLR
training lr_gamma = 0.5
training lr_step = 4000
training early_stop_patience = 10000
training ckpt_save_dir = demo/model/pctnet_scratch
training iter_start = 0
training iter_max = 10000
training iter_valid = 500
training iter_save = 10000
testing gpus = [0]
testing ckpt_mode = 1
testing output_dir = demo/result/pctnet_scratch
testing sliding_window_enable = True
testing sliding_window_batch = 8
testing sliding_window_size = [64, 128, 128]
testing sliding_window_stride = [32, 64, 64]
deterministric is true
/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/functional.py:507: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1711403392949/work/aten/src/ATen/native/TensorShape.cpp:3549.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
parameter number 45287730
2024-04-22 12:50:54 training start
Traceback (most recent call last):
File "/root/Desktop/LLM/OpenMED/MIS-FM/train.py", line 46, in
main()
File "/root/Desktop/LLM/OpenMED/MIS-FM/train.py", line 43, in main
agent.run()
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/pymic/net_run/agent_abstract.py", line 314, in run
self.train_valid()
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/pymic/net_run/agent_seg.py", line 314, in train_valid
train_scalars = self.training()
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/pymic/net_run/agent_seg.py", line 137, in training
data = next(self.trainIter)
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 631, in next
data = self._next_data()
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1346, in _next_data
return self._process_data(data)
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1372, in _process_data
data.reraise()
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/_utils.py", line 722, in reraise
raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 51, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/pymic/io/nifty_dataset.py", line 65, in getitem
image_dict = load_image_as_nd_array(image_full_name)
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/pymic/io/image_read_write.py", line 79, in load_image_as_nd_array
image_dict = load_nifty_volume_as_4d_array(image_name)
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/pymic/io/image_read_write.py", line 20, in load_nifty_volume_as_4d_array
img_obj = sitk.ReadImage(filename)
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/SimpleITK/extra.py", line 375, in ReadImage
return reader.Execute()
File "/root/miniconda3/envs/LLM/lib/python3.9/site-packages/SimpleITK/SimpleITK.py", line 8430, in Execute
return _SimpleITK.ImageFileReader_Execute(self)
RuntimeError: Exception thrown in SimpleITK ImageFileReader_Execute: /tmp/SimpleITK/Code/IO/src/sitkImageReaderBase.cxx:97:
sitk::ERROR: The file "/home/x/projects/PyMIC_project/PyMIC_examples/PyMIC_data/AtriaSeg/TrainingSet_crop/BYSRSI3H4YTWKMM3MADP.nii.gz" does not exist.
How to fix it?

[taigw]

From your error information, it seems that you have not set the path for training images correctly. Please see the latest readme file. You need to set train_dir in the configuration file correctly based on your own machine.