autocast.metrics.deterministic#
Deterministic metrics.
Power-spectrum RMSE utilities in this module based on the implementation from: - Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation (Rozet et al., 2024), https://arxiv.org/abs/2507.02608, PolymathicAI/lola - Specific code from:
- class BTSCMetric(reduce_all=True, dist_sync_on_step=False)[source]#
Bases:
BaseMetric[Float[Tensor, 'batch time spatial *spatial channel']|Float[Tensor, 'batch time spatial *spatial channel ensemble'],Float[Tensor, 'batch time spatial *spatial channel']]Base class for metrics that operate on spatial tensors.
Checks input types and shapes and converts to Tensor.
- class MSE(reduce_all=True, dist_sync_on_step=False)[source]#
Bases:
BTSCMetricMean Squared Error over spatial dims.
- class MAE(reduce_all=True, dist_sync_on_step=False)[source]#
Bases:
BTSCMetricMean Absolute Error over spatial dims.
- class NMAE(reduce_all=True, dist_sync_on_step=False, eps=1e-07)[source]#
Bases:
BTSCMetricNormalized Mean Absolute Error over spatial dims.
- class NMSE(reduce_all=True, dist_sync_on_step=False, eps=1e-07)[source]#
Bases:
BTSCMetricNormalized Mean Squared Error over spatial dims.
- class RMSE(reduce_all=True, dist_sync_on_step=False)[source]#
Bases:
BTSCMetricRoot Mean Squared Error over spatial dims.
- class NRMSE(eps=1e-07, reduce_all=True, dist_sync_on_step=False)[source]#
Bases:
BTSCMetricNormalized Root Mean Squared Error over spatial dims.
- class VMSE(eps=1e-07, reduce_all=True, dist_sync_on_step=False)[source]#
Bases:
BTSCMetricVariance Scaled Mean Squared Error over spatial dims.
- class VRMSE(reduce_all=True, dist_sync_on_step=False, eps=1e-07)[source]#
Bases:
BTSCMetricVariance-Scaled Root Mean Squared Error over spatial dims.
Computes VRMSE = RMSE / std(y_true), where std is computed over spatial dims.
- class LInfinity(reduce_all=True, dist_sync_on_step=False)[source]#
Bases:
BTSCMetricL-Infinity Norm over spatial dims.
- class PowerSpectrumRMSE(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
BTSCMetricAverage power spectrum RMSE across first three Lola eval bands.
- class PowerSpectrumRMSELow(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
PowerSpectrumRMSEPower spectrum RMSE in the low-frequency band.
- class PowerSpectrumRMSEMid(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
PowerSpectrumRMSEPower spectrum RMSE in the mid-frequency band.
- class PowerSpectrumRMSEHigh(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
PowerSpectrumRMSEPower spectrum RMSE in the high-frequency band.
- class PowerSpectrumRMSETail(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
PowerSpectrumRMSEPower spectrum RMSE in the Lola high-frequency tail band.
- class PowerSpectrumCCRMSE(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
BTSCMetricAverage cross-correlation RMSE across first three Lola eval bands.
- class PowerSpectrumCCRMSELow(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
PowerSpectrumCCRMSECross-correlation RMSE in the low-frequency band.
- class PowerSpectrumCCRMSEMid(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
PowerSpectrumCCRMSECross-correlation RMSE in the mid-frequency band.
- class PowerSpectrumCCRMSEHigh(reduce_all=True, dist_sync_on_step=False, eps=1e-06)[source]#
Bases:
PowerSpectrumCCRMSECross-correlation RMSE in the high-frequency band.