--- a
+++ b/data_3d.py
@@ -0,0 +1,94 @@
+import os
+import monai.transforms as mt
+import numpy as np
+from pathlib import Path
+from torch.utils.data.dataset import Dataset
+
+
+def normalize(data):
+    data = (data - data.mean()) / data.std()
+    return data
+
+
+class ACDC_3D(Dataset):
+    def __init__(self, source, ind, Transform=None):
+        # basic transforms
+        self.loader = mt.LoadImaged(keys=["image", "mask"])
+        self.add_channel = mt.AddChanneld(keys=["image", "mask"])
+        self.spatial_pad = mt.SpatialPadD(keys=["image", "mask"], spatial_size=tar_shape)
+        self.spacing = mt.Spacingd(keys=["image", "mask"], pixdim=(1.25, 1.25, 10.0), mode=("nearest", "nearest"))
+        # index
+        self.ind = ind
+        # transform
+        if Transform is not None:
+            self.transform = Transform
+        else:
+            self.transform = mt.Compose([
+                mt.SpatialPadD(keys=["image", "mask"], spatial_size=tar_shape),
+                mt.ToTensorD(keys=["image", "mask"], allow_missing_keys=False)
+            ])
+
+        # take the images
+        source = Path(source)
+        dirs = os.listdir(str(source))  # stores patient name
+        all_data_ed = []
+        all_data_ed_mask = []
+        all_data_es = []
+        all_data_es_mask = []
+        for filenames in dirs:
+            patient_path = Path(str(source), filenames)  # individual patient path
+            patient_info = str(patient_path) + "/Info.cfg"  # patient information
+            file = open(patient_info, 'r').readlines()
+            ED_frame = int(file[0].split(":")[1])
+            ES_frame = int(file[1].split(":")[1])
+            ED = Path(str(patient_path), filenames + "_frame{:02d}.nii.gz".format(ED_frame))
+            ES = Path(str(patient_path), filenames + "_frame{:02d}.nii.gz".format(ES_frame))
+            ED_gt = Path(str(patient_path), filenames + "_frame{:02d}_gt.nii.gz".format(ED_frame))
+            ES_gt = Path(str(patient_path), filenames + "_frame{:02d}_gt.nii.gz".format(ES_frame))
+            all_data_ed.append(ED)
+            all_data_ed_mask.append(ED_gt)
+            all_data_es.append(ES)
+            all_data_es_mask.append(ES_gt)
+
+        if self.ind is not None:
+            all_data_ed = [all_data_ed[i] for i in self.ind]
+            all_data_ed_mask = [all_data_ed_mask[i] for i in self.ind]
+            all_data_es = [all_data_es[i] for i in self.ind]
+            all_data_es_mask = [all_data_es_mask[i] for i in self.ind]
+
+        self.data = [all_data_ed, all_data_ed_mask, all_data_es, all_data_es_mask]
+
+    def __len__(self):
+        return len(self.data[0])
+
+    def __getitem__(self, idx):
+        ED_img, ED_mask, ES_img, ES_mask = self.data[0][idx], self.data[1][idx], self.data[2][idx], self.data[3][idx]
+        # data dict
+        ED_data_dict = {"image": ED_img,
+                        "mask": ED_mask}
+        ES_data_dict = {"image": ES_img,
+                        "mask": ES_mask}
+        # instead of returning both ED and ES, I have to return just a random choice between ED and ES(image and mask)
+        datalist = [ED_data_dict, ES_data_dict]
+        data_return = np.random.choice(datalist)
+        data_return = self.loader(data_return)
+        data_return = self.add_channel(data_return)
+        data_return = self.spacing(data_return)
+        data_return["image"] = normalize(data_return["image"])
+        data_return = self.transform(data_return)
+        return data_return
+
+
+# target/crop shape for the images and masks when training
+tar_shape = [300, 300, 10]
+crop_shape = [224, 224, 10]
+
+
+def train_loader_ACDC(train_index, data_path="../training/training", transform=None):
+    train_loader = ACDC_3D(source=data_path, Transform=transform, ind=train_index)
+    return train_loader
+
+
+def val_loader_ACDC(val_index, data_path=r"../training/training", transform=None):
+    val_loader = ACDC_3D(source=data_path, Transform=transform, ind=val_index)
+    return val_loader