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Fix domino data factors logic #1221

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Fix domino data factors logic #1221

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76 changes: 47 additions & 29 deletions examples/cfd/external_aerodynamics/domino/src/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -336,40 +336,58 @@ def load_scaling_factors(
)

if cfg.model.normalization == "min_max_scaling":
vol_factors = np.asarray(
[
scaling_factors.max_val["volume_fields"],
scaling_factors.min_val["volume_fields"],
]
)
surf_factors = np.asarray(
[
scaling_factors.max_val["surface_fields"],
scaling_factors.min_val["surface_fields"],
]
)
if cfg.model.model_type == "volume" or cfg.model.model_type == "combined":
vol_factors = np.asarray(
[
scaling_factors.max_val["volume_fields"],
scaling_factors.min_val["volume_fields"],
]
)
else:
vol_factors = None
if cfg.model.model_type == "surface" or cfg.model.model_type == "combined":
surf_factors = np.asarray(
[
scaling_factors.max_val["surface_fields"],
scaling_factors.min_val["surface_fields"],
]
)
else:
surf_factors = None
elif cfg.model.normalization == "mean_std_scaling":
vol_factors = np.asarray(
[
scaling_factors.mean["volume_fields"],
scaling_factors.std["volume_fields"],
]
)
surf_factors = np.asarray(
[
scaling_factors.mean["surface_fields"],
scaling_factors.std["surface_fields"],
]
)
if cfg.model.model_type == "volume" or cfg.model.model_type == "combined":
vol_factors = np.asarray(
[
scaling_factors.mean["volume_fields"],
scaling_factors.std["volume_fields"],
]
)
else:
vol_factors = None
if cfg.model.model_type == "surface" or cfg.model.model_type == "combined":
surf_factors = np.asarray(
[
scaling_factors.mean["surface_fields"],
scaling_factors.std["surface_fields"],
]
)
else:
surf_factors = None
else:
raise ValueError(f"Invalid normalization mode: {cfg.model.normalization}")

vol_factors_tensor = torch.from_numpy(vol_factors)
surf_factors_tensor = torch.from_numpy(surf_factors)

dm = DistributedManager()
vol_factors_tensor = vol_factors_tensor.to(dm.device, dtype=torch.float32)
surf_factors_tensor = surf_factors_tensor.to(dm.device, dtype=torch.float32)

if cfg.model.model_type == "volume" or cfg.model.model_type == "combined":
vol_factors_tensor = torch.from_numpy(vol_factors)
vol_factors_tensor = vol_factors_tensor.to(dm.device, dtype=torch.float32)
else:
vol_factors_tensor = None
if cfg.model.model_type == "surface" or cfg.model.model_type == "combined":
surf_factors_tensor = torch.from_numpy(surf_factors)
surf_factors_tensor = surf_factors_tensor.to(dm.device, dtype=torch.float32)
else:
surf_factors_tensor = None

return vol_factors_tensor, surf_factors_tensor

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