## TODOs
- [x] Paper released
- [x] Code released
- [x] Slide-SAM-B weights released
- [x] Slide-SAM-H weights released
## Models
### Large scale Medical Image Pretrained Weights
| Name | Resolution | Prompt | Weights |
| :---------: | :---------: | :---------: | :----------------------------------------------------------: |
| Slide-SAM-B | 1024 x 1024 | box & point | [Google Drive](https://drive.google.com/file/d/1YJ7deylSXfEwRfOAc72zBJE1xGlxSnp0/view?usp=drive_link) \| [Baidu Disk (7be9)](https://pan.baidu.com/s/1jvJ2W4MK24JdpZLwPqMIfA) |
| Slide-SAM-H | 1024 x 1024 | box & point | [Google Drive](https://drive.google.com/file/d/1KnSL5mgAdUDsktvrwcYp_9IJeqfFWhIN/view?usp=drive_link) \| [Baidu Disk (05dy)](https://pan.baidu.com/s/1jnOwyWd-M1fBIauNi3IA4w) |
## Getting Started
### Install tutils tools
```
pip install trans-utils
```
### 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](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/dataset_format.md) or ```organize_by_names``` to prepare your custom datasets.
Then run :
```python
python -m datasets.generate_txt
```
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 volume 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
```
## Configs Settings
important settings
```yaml
base:
base_dir: "../runs/sam/" # logging dir
dataset:
types: ['3d'] # ['3d', '2d']
split: 'train'
data_root_path: '../datasets/'
dataset_list: ["pancreas"]
data_txt_path: './datasets/dataset_list/'
dataset2d_path: "../08_AbdomenCT-1K/"
cache_data_path: '../cached_dataset2/'
cache_prefix: ['6016'] # cache prefix of cached dataset for training
# For example: ['07',] for 07_WORD
```
## Start Training from scratch (SAM)
Run training on multi-gpu
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m core.ddp --tag debug
```
Run training on single-gpu
```
CUDA_VISIBLE_DEVICES=0 python -m core.ddp --tag debug
```
## Sliding Inference and Test
```
python -m core.volume_predictor
```
## Testset Validation
```python
EX_CONFIG = {
'dataset':{
'prompt': 'box', # prompt type: box or point
'dataset_list': ['word'], # dataset_list name
'label_idx': 2, # label index for inference,
},
"pth": "./model.pth"
}
```
```
python -m test.volume_eval
```
## Finetuning (Recommended)
```yaml
training:
breakpoint_path: "./model.pth" # pretrained weight path
```
```
python -m core.ddp_sub --tag run
```
## Validation with Finetuned Weights
```
python -m test.volume_eval_sublora
```
```python
EX_CONFIG = {
'dataset':{
'prompt': 'box', # prompt type: box or point
'dataset_list': ['word'], # dataset_list name
'label_idx': 2, # label index for inference,
},
"pth": "./model_finetuned.pth"
}
```
