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mmagic.evaluation.metrics.sad 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Sequence

import numpy as np
import torch.nn as nn
from mmengine.model import is_model_wrapper
from torch.utils.data.dataloader import DataLoader

from mmagic.registry import METRICS
from .base_sample_wise_metric import BaseSampleWiseMetric
from .metrics_utils import _fetch_data_and_check, average


@METRICS.register_module()
[文档]class SAD(BaseSampleWiseMetric): """Sum of Absolute Differences metric for image matting. This metric compute per-pixel absolute difference and sum across all pixels. i.e. sum(abs(a-b)) / norm_const .. note:: Current implementation assume image / alpha / trimap array in numpy format and with pixel value ranging from 0 to 255. .. note:: pred_alpha should be masked by trimap before passing into this metric Default prefix: '' Args: norm_const (int): Divide the result to reduce its magnitude. Default to 1000. Metrics: - SAD (float): Sum of Absolute Differences """
[文档] default_prefix = ''
[文档] metric = 'SAD'
def __init__( self, norm_const=1000, **kwargs, ) -> None: self.norm_const = norm_const super().__init__(**kwargs)
[文档] def prepare(self, module: nn.Module, dataloader: DataLoader): self.size = len(dataloader.dataset) if is_model_wrapper(module): module = module.module self.data_preprocessor = module.data_preprocessor
[文档] def process(self, data_batch: Sequence[dict], data_samples: Sequence[dict]) -> None: """Process one batch of data and predictions. Args: data_batch (Sequence[Tuple[Any, dict]]): A batch of data from the dataloader. predictions (Sequence[dict]): A batch of outputs from the model. """ for data_sample in data_samples: pred_alpha, gt_alpha, _ = _fetch_data_and_check(data_sample) # divide by 1000 to reduce the magnitude of the result sad_sum = np.abs(pred_alpha - gt_alpha).sum() / self.norm_const result = {'sad': sad_sum} self.results.append(result)
[文档] def compute_metrics(self, results: List): """Compute the metrics from processed results. Args: results (dict): The processed results of each batch. Returns: Dict: The computed metrics. The keys are the names of the metrics, and the values are corresponding results. """ sad = average(results, 'sad') return {'SAD': sad}
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