dose_prediction, nvidia docker初体验

part 1 : --preprocess using kubeflow, transform dicom to nii
python3 run_preprocessing.py --workflow-id="199" --config-file="input" --training-record-sets="chw_total_training" --test-record-sets="chw_total_training" --varian-manager=" http://172.17.2.159:8000/api" --data-input-folder="/home/yifeng/RT_dose_jin/test_data" --resolution=[2.0,2.0,2.0] --input-image-size=[256,256,256] --padding=[128,128,128] --experiment-name="RTDose_preprocess" --experiment-run="run2_setdirection"

part 2: --transform nii to mat, including dose and ct
import SimpleITK as sitk
import scipy.io as sio
t1_fn = './nifti/img0.nii.gz'
sitk_t1 = sitk.ReadImage(t1_fn)
t1 = sitk.GetArrayFromImage(sitk_t1)
t2_fn = './nifti/rtdose0.nii.gz'
sitk_t2 = sitk.ReadImage(t2_fn)
t2 = sitk.GetArrayFromImage(sitk_t2)
sio.savemat('/home/yifeng/Desktop/gancer/gancer/datasets/voxels/train/patient1.mat',{'ct_imgs':t1, 'dose_vals':t2})

t1_fn = './nifti/img1.nii.gz'
si