Source code for mmagic.evaluation.metrics.mse

# Copyright (c) OpenMMLab. All rights reserved.
"""Evaluation metrics based on pixels."""

from mmagic.registry import METRICS
from .base_sample_wise_metric import BaseSampleWiseMetric

[docs]class MSE(BaseSampleWiseMetric): """Mean Squared Error metric for image. mean((a-b)^2) Args: gt_key (str): Key of ground-truth. Default: 'gt_img' pred_key (str): Key of prediction. Default: 'pred_img' mask_key (str, optional): Key of mask, if mask_key is None, calculate all regions. Default: None collect_device (str): Device name used for collecting results from different ranks during distributed training. Must be 'cpu' or 'gpu'. Defaults to 'cpu'. prefix (str, optional): The prefix that will be added in the metric names to disambiguate homonymous metrics of different evaluators. If prefix is not provided in the argument, self.default_prefix will be used instead. Default: None Metrics: - MSE (float): Mean of Squared Error """
[docs] metric = 'MSE'
[docs] def process_image(self, gt, pred, mask): """Process an image. Args: gt (Torch | np.ndarray): GT image. pred (Torch | np.ndarray): Pred image. mask (Torch | np.ndarray): Mask of evaluation. Returns: result (np.ndarray): MSE result. """ gt = gt / 255. pred = pred / 255. diff = gt - pred diff *= diff if self.mask_key is not None: diff *= mask result = diff.sum() / mask.sum() else: result = diff.mean() return result
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