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code：7be9
## Before Training
install tutils
### install tutils
```
pip install trans-utils
```
prepare datasets
### prepare datasets
We recommend you to convert the dataset into the nnUNet format.
```
00_custom_dataset
imagesTr
xxx_0000.nii.gz
...
labelsTr
xxx.nii.gz
...
```
try to use the function ```organize_in_nnunet_style``` or ```organize_by_names``` to prepare your custom datasets.
Then run
```
python -m datasets.generate_txt
```
cache 3d data into slices
A ```[example]_train.txt``` will be generated in ```./datasets/dataset_list/```
The content should be like below
```
01_BCV-Abdomen/Training/img/img0001.nii.gz 01_BCV-Abdomen/Training/label/label0001.nii.gz
01_BCV-Abdomen/Training/img/img0002.nii.gz 01_BCV-Abdomen/Training/label/label0002.nii.gz
01_BCV-Abdomen/Training/img/img0003.nii.gz 01_BCV-Abdomen/Training/label/label0003.nii.gz
```
### cache 3d data into slices
After generating the ```[example]_train.txt``` file, check the config file ```configs/vit_b.yaml```.
Update the params in ```dataset``` by yours. And the ```dataset_list``` should be the name of the generated txt file ```[example]```.
Then run
```
python -m datasets.cache_dataset3d
```