3DUnet_Like/dataset/utils.py

import json
import os 
from sklearn.model_selection import train_test_split

from monai.data import DataLoader, Dataset
from monai import transforms

def datafold_read(datalist, basedir, fold=0, key="training"):
    with open(datalist) as f:
        json_data = json.load(f)

    json_data = json_data[key]

    for d in json_data:
        for k in d:
            if isinstance(d[k], list):
                d[k] = [os.path.join(basedir, iv) for iv in d[k]]
            elif isinstance(d[k], str):
                d[k] = os.path.join(basedir, d[k]) if len(d[k]) > 0 else d[k]

    tr = []
    val = []
    for d in json_data:
        if "fold" in d and d["fold"] == fold:
            val.append(d)
        else:
            tr.append(d)

    return tr, val


def split_train_test(datalist, basedir, fold,test_size = 0.2, volume : float = None) :
    train_files, _ = datafold_read(datalist=datalist, basedir=basedir, fold=fold)
    if volume != None :
        train_files, _ = train_test_split(train_files,test_size=volume,random_state=42)
        
    train_files,validation_files = train_test_split(train_files,test_size=test_size, random_state=42)
    
    validation_files,test_files = train_test_split(validation_files,test_size=test_size, random_state=42)
    return train_files, validation_files, test_files


def get_loader(batch_size, data_dir, json_list, fold, roi,volume :float = None,test_size = 0.2):
    train_files,validation_files,test_files = split_train_test(datalist = json_list,basedir = data_dir,test_size=test_size,fold = fold,volume= volume)
    
    train_transform = transforms.Compose(
        [
            transforms.LoadImaged(keys=["image", "label"]),
            transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys="label"),
            transforms.CropForegroundd(
                keys=["image", "label"],
                source_key="image",
                k_divisible=[roi[0], roi[1], roi[2]],
            ),
            transforms.RandSpatialCropd(
                keys=["image", "label"],
                roi_size=[roi[0], roi[1], roi[2]],
                random_size=False,
            ),
            transforms.RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=0),
            transforms.RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=1),
            transforms.RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=2),
            transforms.NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
            transforms.RandScaleIntensityd(keys="image", factors=0.1, prob=1.0),
            transforms.RandShiftIntensityd(keys="image", offsets=0.1, prob=1.0),
        ]
    )
    val_transform = transforms.Compose(
        [
            transforms.LoadImaged(keys=["image", "label"]),
            transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys="label"),
            transforms.NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
        ]
    )

    train_ds = Dataset(data=train_files, transform=train_transform)
    train_loader = DataLoader(
        train_ds,
        batch_size=batch_size,
        shuffle=True,
        num_workers=2,
        pin_memory=True,
    )
    val_ds = Dataset(data=validation_files, transform=val_transform)
    val_loader = DataLoader(
        val_ds,
        batch_size=1,
        shuffle=False,
        num_workers=2,
        pin_memory=True,
    )
    test_ds = Dataset(data=test_files, transform=val_transform)
    test_loader = DataLoader(
        test_ds,
        batch_size=1,
        shuffle=False,
        num_workers=2,
        pin_memory=True,
    )
    return train_loader, val_loader,test_loader