deeplearning/modulus/modulus-core-v010/_modules/modulus/metrics/climate/acc.html

Core v0.1.0

Source code for modulus.metrics.climate.acc

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the “License”); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an “AS IS” BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO(Dallas) Introduce Distributed Class for computation. import torch from modulus.metrics.climate.reduction import _compute_lat_weights Tensor = torch.Tensor 
[docs]def acc(pred: Tensor, target: Tensor, climatology: Tensor, lat: Tensor) -> Tensor: """Calculates the Anomaly Correlation Coefficient Parameters ---------- pred : Tensor [..., H, W] Predicted tensor on a lat/long grid target : Tensor [..., H, W] Target tensor on a lat/long grid climatology : Tensor [..., H, W] climatology tensor lat : Tensor [H] latitude tensor Returns ------- Tensor ACC values for each field Note ---- Reference: https://www.atmos.albany.edu/daes/atmclasses/atm401/spring_2016/ppts_pdfs/ECMWF_ACC_definition.pdf """ assert ( pred.ndim > 2 ), “Expected predictions to have at least two dimensions (lat, lon)” assert ( target.ndim > 2 ), “Expected targets to have at least two dimensions (lat, lon)” assert ( climatology.ndim > 2 ), “Expected climatology to have at least two dimensions (lat, lon)” # subtract climate means pred_hat = pred - climatology target_hat = target - climatology # Get aggregator lat_weight = _compute_lat_weights(lat) # Weighted mean pred_bar = torch.sum( lat_weight[:, None] * pred_hat, dim=(-2, -1), keepdim=True ) / torch.sum( lat_weight[:, None] * torch.ones_like(pred_hat), dim=(-2, -1), keepdim=True ) target_bar = torch.sum( lat_weight[:, None] * target_hat, dim=(-2, -1), keepdim=True ) / torch.sum( lat_weight[:, None] * torch.ones_like(target_hat), dim=(-2, -1), keepdim=True ) pred_diff = pred_hat - pred_bar target_diff = target_hat - target_bar p1 = torch.sum(lat_weight[:, None] * pred_diff * target_diff, dim=(-2, -1)) p2 = torch.sum(lat_weight[:, None] * pred_diff * pred_diff, dim=(-2, -1)) p3 = torch.sum(lat_weight[:, None] * target_diff * target_diff, dim=(-2, -1)) m = p1 / torch.sqrt(p2 * p3) return m
© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.