Inference expected files

[Santiago-Quinteros]

Hi I'm very new in the Deep Learning world and I am having some troubles in running inference. I am trying to test my first training with only one fold (and 1 thread because there was some problems with that). In my output folder only 3 files have been generated "dataset.json", "plans.json" and "predict_from_raw_data_args.json" but not the segmentation file "CT_001.nii.gz" I expected. Am I doing something wrong?

I have been waiting for hours and nothing, I have 16 gb of ram.

OMP_NUM_THREADS=1 nnUNetv2_predict -i ./imagesTs -o ./predictedTs3dNosaveprob -d 223 -c 3d_fullres -f 0 --verbose -npp 1 -nps 1 --disable_tta

#######################################################################
Please cite the following paper when using nnU-Net:
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
#######################################################################

There are 1 cases in the source folder
I am process 0 out of 1 (max process ID is 0, we start counting with 0!)
There are 1 cases that I would like to predict

Predicting CT_001:
perform_everything_on_gpu: True
Input shape: torch.Size([1, 558, 691, 558])
step_size: 0.5
mirror_axes: None
n_steps 640, image size is torch.Size([558, 691, 558]), tile_size [128, 128, 128], tile_step_size 0.5
steps:
[[0, 61, 123, 184, 246, 307, 369, 430], [0, 63, 125, 188, 250, 313, 375, 438, 500, 563], [0, 61, 123, 184, 246, 307, 369, 430]]
preallocating arrays
running prediction
100%|█████████████████████████████████████████| 640/640 [02:39<00:00, 4.02it/s]
Prediction done, transferring to CPU if needed
sending off prediction to background worker for resampling and export
done with CT_001

[Santiago-Quinteros]

We reduced the size of the dataset and it worked fine