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Source code for mmedit.evaluation.metrics.mae

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

import numpy as np

from mmedit.registry import METRICS
from .base_sample_wise_metric import BaseSampleWiseMetric


@METRICS.register_module()
[docs]class MAE(BaseSampleWiseMetric): """Mean Absolute Error metric for image. mean(abs(a-b)) 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: - MAE (float): Mean of Absolute Error """
[docs] metric = 'MAE'
[docs] def process_image(self, gt, pred, mask): """Process an image. Args: gt (Tensor | np.ndarray): GT image. pred (Tensor | np.ndarray): Pred image. mask (Tensor | np.ndarray): Mask of evaluation. Returns: result (np.ndarray): MAE result. """ gt = gt / 255. pred = pred / 255. diff = gt - pred diff = abs(diff) if self.mask_key is not None: diff *= mask # broadcast for channel dimension scale = np.prod(diff.shape) / np.prod(mask.shape) result = diff.sum() / (mask.sum() * scale + 1e-12) else: result = diff.mean() return result
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