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| a | b/transforms.py | ||
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| 1 | from monai.transforms import ( |
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| 2 | Compose, |
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| 3 | ToTensord, |
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| 4 | RandFlipd, |
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| 5 | Spacingd, |
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| 6 | RandScaleIntensityd, |
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| 7 | RandShiftIntensityd, |
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| 8 | NormalizeIntensityd, |
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| 9 | AddChanneld, |
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| 10 | DivisiblePadd |
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| 11 | ) |
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| 12 | |||
| 13 | |||
| 14 | #Transforms to be applied on training instances |
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| 15 | train_transform = Compose( |
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| 16 | [ |
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| 17 | AddChanneld(keys=["image", "label"]), |
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| 18 | Spacingd(keys=['image', 'label'], pixdim=(1., 1., 1.), mode=("bilinear", "nearest")), |
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| 19 | RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0), |
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| 20 | RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1), |
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| 21 | RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2), |
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| 22 | NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), |
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| 23 | RandScaleIntensityd(keys='image', factors=0.1, prob=1.0), |
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| 24 | RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0), |
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| 25 | DivisiblePadd(k=16, keys=["image", "label"]), |
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| 26 | ToTensord(keys=['image', 'label']) |
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| 27 | ] |
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| 28 | ) |
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| 29 | |||
| 30 | #Cuda version of "train_transform" |
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| 31 | train_transform_cuda = Compose( |
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| 32 | [ |
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| 33 | AddChanneld(keys=["image", "label"]), |
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| 34 | Spacingd(keys=['image', 'label'], pixdim=(1., 1., 1.), mode=("bilinear", "nearest")), |
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| 35 | RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0), |
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| 36 | RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1), |
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| 37 | RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2), |
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| 38 | NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), |
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| 39 | RandScaleIntensityd(keys='image', factors=0.1, prob=1.0), |
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| 40 | RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0), |
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| 41 | DivisiblePadd(k=16, keys=["image", "label"]), |
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| 42 | ToTensord(keys=['image', 'label'], device='cuda') |
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| 43 | ] |
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| 44 | ) |
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| 45 | |||
| 46 | #Transforms to be applied on validation instances |
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| 47 | val_transform = Compose( |
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| 48 | [ |
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| 49 | AddChanneld(keys=["image", "label"]), |
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| 50 | NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), |
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| 51 | DivisiblePadd(k=16, keys=["image", "label"]), |
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| 52 | ToTensord(keys=['image', 'label']) |
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| 53 | ] |
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| 54 | ) |
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| 55 | |||
| 56 | #Cuda version of "val_transform" |
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| 57 | val_transform_cuda = Compose( |
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| 58 | [ |
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| 59 | AddChanneld(keys=["image", "label"]), |
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| 60 | NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), |
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| 61 | DivisiblePadd(k=16, keys=["image", "label"]), |
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| 62 | ToTensord(keys=['image', 'label'], device='cuda') |
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| 63 | ] |
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| 64 | ) |