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Experiment with redistributeseg augmentation #40

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2 changes: 2 additions & 0 deletions auglab/configs/transform_params_gpu.json
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@
"RedistributeSegTransform": {
"in_seg": 0.25,
"retain_stats": true,
"std_noise_range": [0.1, 0.3],
"dilation_iterations_range": [1, 5],
"probability": 0.4
},
"GaussianNoiseTransform": {
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10 changes: 8 additions & 2 deletions auglab/transforms/gpu/fromSeg.py
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Expand Up @@ -29,12 +29,16 @@ def __init__(
same_on_batch: bool = False,
p: float = 1.0,
keepdim: bool = True,
std_noise_range: list[float] = [0.1, 0.3],
dilation_iterations_range: list[int] = [1, 3],
**kwargs,
) -> None:
super().__init__(p=p, same_on_batch=same_on_batch, keepdim=keepdim)
self.in_seg = in_seg
self.apply_to_channel = apply_to_channel
self.retain_stats = retain_stats
self.std_noise_range = std_noise_range
self.dilation_iterations_range = dilation_iterations_range

@torch.no_grad()
def apply_transform(
Expand Down Expand Up @@ -96,7 +100,8 @@ def apply_transform(

# Vectorized dilation for all regions (3 iterations)
dilated = masks.float()
for _ in range(3):
dilation_iterations = torch.randint(self.dilation_iterations_range[0], self.dilation_iterations_range[1]+1, (1,), device=input.device)[0].item()
for _ in range(dilation_iterations):
if spatial_dims == 3:
dilated = F.max_pool3d(dilated.unsqueeze(0), 3, 1, 1).squeeze(0)
else:
Expand Down Expand Up @@ -125,8 +130,9 @@ def apply_transform(
dil_stds = dil_vars.sqrt()

# redist_std per region
std_noise_range = torch.rand(1, device=input.device)[0] * (self.std_noise_range[1] - self.std_noise_range[0]) + self.std_noise_range[0]
redist_std = torch.maximum(
torch.rand(R, device=input.device) * 0.2 + 0.4 * torch.abs((means - dil_means) * stds / (dil_stds + 1e-6)),
torch.rand(R, device=input.device) * std_noise_range + 0.4 * torch.abs((means - dil_means) * stds / (dil_stds + 1e-6)),
torch.full((R,), 0.01, device=input.device, dtype=input.dtype)
)

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10 changes: 6 additions & 4 deletions auglab/transforms/gpu/transforms.py
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Expand Up @@ -93,6 +93,8 @@ def _build_transforms(self) -> list[nn.Module]:
in_seg=redistribute_params.get('in_seg', 0.2),
retain_stats=redistribute_params.get('retain_stats', False),
p=redistribute_params.get('probability', 0),
std_noise_range=redistribute_params.get('std_noise_range', [0.1, 0.3]),
dilation_iterations_range=redistribute_params.get('dilation_iterations_range', [1, 3]),
))

# Scharr filter
Expand Down Expand Up @@ -395,22 +397,22 @@ def pad_numpy_array(arr, shape):
augmentor = AugTransformsGPU(json_path)

# Load images and masks tensors
img_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/data-multi-subject/sub-amu02/anat/sub-amu02_T1w.nii.gz'
img_path = '/home/ge.polymtl.ca/p118739/data/datasets/data-multi-subject/sub-amu02/anat/sub-amu02_T1w.nii.gz'
img = Image(img_path).change_orientation('RSP')
img = resample_nib(img, new_size=[1,1,1], new_size_type='mm', interpolation='linear')
img_tensor = torch.from_numpy(img.data.copy()).to(torch.float32)

seg_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/data-multi-subject/derivatives/labels/sub-amu02/anat/sub-amu02_T1w_label-spine_dseg.nii.gz'
seg_path = '/home/ge.polymtl.ca/p118739/data/datasets/data-multi-subject/derivatives/labels/sub-amu02/anat/sub-amu02_T1w_label-spine_dseg.nii.gz'
seg = Image(seg_path).change_orientation('RSP')
seg = resample_nib(seg, new_size=[1,1,1], new_size_type='mm', interpolation='nn')
seg_tensor_all = torch.from_numpy(seg.data.copy())

img2_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/spider-challenge-2023/sub-002/anat/sub-002_acq-lowresSag_T2w.nii.gz'
img2_path = '/home/ge.polymtl.ca/p118739/data/datasets/spider-challenge-2023/sub-002/anat/sub-002_acq-lowresSag_T2w.nii.gz'
img2 = Image(img2_path).change_orientation('RSP')
img2 = resample_nib(img2, new_size=[1,1,1], new_size_type='mm', interpolation='linear')
img2_tensor = torch.from_numpy(img2.data.copy()).to(torch.float32)

seg2_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/spider-challenge-2023/derivatives/labels/sub-002/anat/sub-002_acq-lowresSag_T2w_label-spine_dseg.nii.gz'
seg2_path = '/home/ge.polymtl.ca/p118739/data/datasets/spider-challenge-2023/derivatives/labels/sub-002/anat/sub-002_acq-lowresSag_T2w_label-spine_dseg.nii.gz'
seg2 = Image(seg2_path).change_orientation('RSP')
seg2 = resample_nib(seg2, new_size=[1,1,1], new_size_type='mm', interpolation='nn')
seg2_tensor_all = torch.from_numpy(seg2.data.copy())
Expand Down