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Inoutseg fix #25

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Inoutseg fix #25

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7f72308
fixed _apply_region_mode logic by cloning the segmentation so as not …
Hendrik-code Jan 14, 2026
95a2a7c
fixed ordering of mix_prob and added it to inverse and histogram equa…
Hendrik-code Jan 14, 2026
d721980
embedded new arguments
Hendrik-code Jan 14, 2026
4fa9840
updated config file to newest observations
Hendrik-code Jan 14, 2026
20c93e8
add RandomChooseXTransformsGPU
NathanMolinier Jan 15, 2026
859c229
removed zscorenormalization as training and validation transform at t…
Hendrik-code Jan 21, 2026
2f1165e
Merge branch 'inoutseg_fix' of github.com:neuropoly/AugLab into inout…
Hendrik-code Jan 21, 2026
cd4330b
Account for transfrom probability in RandomChooseXTransformsGPU
NathanMolinier Jan 21, 2026
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114 changes: 53 additions & 61 deletions auglab/configs/transform_params_gpu.json
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,9 @@
"kernel_type": "Scharr",
"absolute": true,
"retain_stats": true,
"mix_prob": 0.90,
"in_seg": 0.5,
"out_seg": 0.5,
"mix_prob": 0.50,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.15
},
Expand All @@ -14,18 +14,18 @@
"sigma": 1.0,
"retain_stats": false,
"mix_prob": 0.50,
"in_seg": 0.5,
"out_seg": 0.5,
"mix_in_out": false,
"probability": 0.20
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.15
},
"UnsharpMaskTransform": {
"kernel_type": "UnsharpMask",
"sigma": 1.0,
"unsharp_amount": 1.5,
"retain_stats": false,
"in_seg": 0.5,
"out_seg": 0.5,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"mix_prob": 0.50,
"probability": 0.10
Expand All @@ -34,83 +34,85 @@
"kernel_type": "RandConv",
"kernel_sizes": [3,5,7],
"retain_stats": true,
"in_seg": 0.5,
"out_seg": 0.5,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"mix_prob": 0.50,
"probability": 0.10
},
"RedistributeSegTransform": {
"in_seg": 0.2,
"in_seg": 0.25,
"retain_stats": true,
"probability": 0.5
"probability": 0.1
},
"GaussianNoiseTransform": {
"mean": 0.0,
"std": 0.1,
"std": 1.0,
"in_seg": 0.5,
"out_seg": 0.5,
"mix_in_out": true,
"probability": 0.10
"probability": 0.15
},
"ClampTransform": {
"max_clamp_amount": 0.2,
"retain_stats": false,
"in_seg": 0.5,
"out_seg": 0.5,
"retain_stats": true,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.40
"probability": 0.05
},
"BrightnessTransform": {
"brightness_range": [0.75, 1.25],
"in_seg": 0.0,
"out_seg": 0.0,
"mix_in_out": false,
"probability": 0.15
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.5
},
"GammaTransform": {
"gamma_range": [0.7, 1.5],
"retain_stats": true,
"in_seg": 0.5,
"out_seg": 0.5,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.30
"probability": 0.50
},
"InvGammaTransform": {
"gamma_range": [0.7, 1.5],
"retain_stats": true,
"in_seg": 0.5,
"out_seg": 0.5,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.10
"probability": 0.30
},
"ContrastTransform": {
"contrast_range": [0.75, 1.25],
"retain_stats": false,
"in_seg": 0.0,
"out_seg": 0.0,
"mix_in_out": false,
"probability": 0.15
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.5
},
"FunctionTransform": {
"retain_stats": true,
"in_seg": 0.5,
"out_seg": 0.5,
"mix_in_out": true,
"probability": 0.05
"in_seg": 0,
"out_seg": 0,
Comment on lines +97 to +98
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Copilot AI Jan 14, 2026

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The in_seg and out_seg values are set to 0 (integer) instead of 0.0 (float) for FunctionTransform, while all other transforms use float notation (0.0, 0.1, etc.). For consistency and to avoid potential type issues, use 0.0 for float values throughout the configuration.

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"mix_in_out": false,
"probability": 0.025
},
"InverseTransform": {
"retain_stats": true,
"in_seg": 0.5,
"out_seg": 0.5,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.05
"mix_prob": 0.50,
"probability": 0.1
},
"HistogramEqualizationTransform": {
"retain_stats": false,
"in_seg": 0.5,
"out_seg": 0.5,
"retain_stats": true,
"in_seg": 0,
"out_seg": 0,
"mix_in_out": true,
"mix_prob": 0.80,
"probability": 0.10
},
"SimulateLowResTransform": {
Expand All @@ -123,14 +125,14 @@
"scale": [0.6, 1.0],
"crop": [1.0, 1.0],
"same_on_batch": false,
"probability": 0.40
"probability": 0.05
},
"BiasFieldTransform": {
"retain_stats": false,
"coefficients": 0.5,
"in_seg": 0.5,
"out_seg": 0.5,
"mix_in_out": false,
"retain_stats": true,
"coefficients": 0.2,
"in_seg": 0.1,
"out_seg": 0.1,
"mix_in_out": true,
"probability": 0.10
},
"FlipTransform": {
Expand All @@ -145,19 +147,9 @@
"scale": [0.7, 1.4],
"shear": [-5, 5, -5, 5, -5, 5],
"resample": "bilinear",
"probability": 0
},
"nnUNetSpatialTransform": {
"patch_center_dist_from_border": 80,
"random_crop": true,
"p_elastic_deform": 0.2,
"p_rotation": 0.5,
"p_scaling": 0.5,
"scaling": [0.7, 1.4],
"p_synchronize_scaling_across_axes": 1,
"bg_style_seg_sampling": false
"probability": 0.5
},
"ZscoreNormalizationTransform": {
"probability": 0.3
"probability": 0.0
}
}
19 changes: 9 additions & 10 deletions auglab/trainers/nnUNetTrainerDAExt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,7 +210,7 @@ def get_training_transforms(
else:
patch_size_spatial = patch_size
ignore_axes = None

if 'nnUNetSpatialTransform' in config:
spatial_params = config['nnUNetSpatialTransform']
else:
Expand All @@ -234,7 +234,7 @@ def get_training_transforms(

if do_dummy_2d_data_aug:
transforms.append(Convert2DTo3DTransform())

if use_mask_for_norm is not None and any(use_mask_for_norm):
transforms.append(MaskImageTransform(
apply_to_channels=[i for i in range(len(use_mask_for_norm)) if use_mask_for_norm[i]],
Expand Down Expand Up @@ -284,8 +284,8 @@ def get_training_transforms(
channel_in_seg=0
)
)
transforms.append(ZscoreNormalization())

# transforms.append(ZscoreNormalization())

# NOTE: DownsampleSegForDSTransform is now handled in train_step for GPU augmentations
# if deep_supervision_scales is not None:
Expand Down Expand Up @@ -323,13 +323,13 @@ def get_validation_transforms(
channel_in_seg=0
)
)
transforms.append(ZscoreNormalization())

# transforms.append(ZscoreNormalization())

if deep_supervision_scales is not None:
transforms.append(DownsampleSegForDSTransform(ds_scales=deep_supervision_scales))
return ComposeTransforms(transforms)

def train_step(self, batch: dict) -> dict:
data = batch['data']
target = batch['target']
Expand All @@ -350,13 +350,13 @@ def train_step(self, batch: dict) -> dict:
with autocast(self.device.type, enabled=True) if self.device.type == 'cuda' else dummy_context():
# Apply GPU augmentations to full-resolution data/target
data, target = self.transforms(data, target)

# Create multi-scale targets for deep supervision after augmentation
deep_supervision_scales = self._get_deep_supervision_scales()
if deep_supervision_scales is not None:
ds_transform = DownsampleSegForDSTransformCustom(ds_scales=deep_supervision_scales)
target = ds_transform(target)

output = self.network(data)
# del data
l = self.loss(output, target)
Expand Down Expand Up @@ -519,4 +519,3 @@ def train_step(self, batch: dict) -> dict:
torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)
self.optimizer.step()
return {'loss': l.detach().cpu().numpy()}

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