--- a
+++ b/transforms.py
@@ -0,0 +1,64 @@
+from monai.transforms import (
+    Compose,
+    ToTensord,
+    RandFlipd,
+    Spacingd,
+    RandScaleIntensityd,
+    RandShiftIntensityd,
+    NormalizeIntensityd,
+    AddChanneld,
+    DivisiblePadd
+)
+
+
+#Transforms to be applied on training instances
+train_transform = Compose(
+    [   
+        AddChanneld(keys=["image", "label"]),
+        Spacingd(keys=['image', 'label'], pixdim=(1., 1., 1.), mode=("bilinear", "nearest")),
+        RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0),
+        RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1),
+        RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2),
+        NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True),
+        RandScaleIntensityd(keys='image', factors=0.1, prob=1.0),
+        RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0),
+        DivisiblePadd(k=16, keys=["image", "label"]),
+        ToTensord(keys=['image', 'label'])
+    ]
+)
+
+#Cuda version of "train_transform"
+train_transform_cuda = Compose(
+    [   
+        AddChanneld(keys=["image", "label"]),
+        Spacingd(keys=['image', 'label'], pixdim=(1., 1., 1.), mode=("bilinear", "nearest")),
+        RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0),
+        RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1),
+        RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2),
+        NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True),
+        RandScaleIntensityd(keys='image', factors=0.1, prob=1.0),
+        RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0),
+        DivisiblePadd(k=16, keys=["image", "label"]),
+        ToTensord(keys=['image', 'label'], device='cuda')
+    ]
+)
+
+#Transforms to be applied on validation instances
+val_transform = Compose(
+    [   
+        AddChanneld(keys=["image", "label"]),
+        NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True),
+        DivisiblePadd(k=16, keys=["image", "label"]),
+        ToTensord(keys=['image', 'label'])
+    ]
+)
+
+#Cuda version of "val_transform"
+val_transform_cuda = Compose(
+    [   
+        AddChanneld(keys=["image", "label"]),
+        NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True),
+        DivisiblePadd(k=16, keys=["image", "label"]),
+        ToTensord(keys=['image', 'label'], device='cuda')
+    ]
+)
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