CorruptionError¶
- class mmcls.evaluation.CorruptionError(topk=(1,), thrs=0.0, collect_device='cpu', prefix=None, ann_file=None)[源代码]¶
Mean Corruption Error (mCE) metric.
The mCE metric is proposed in Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.
- 参数
topk (int | Sequence[int]) – If the ground truth label matches one of the best k predictions, the sample will be regard as a positive prediction. If the parameter is a tuple, all of top-k accuracy will be calculated and outputted together. Defaults to 1.
thrs (Sequence[float | None] | float | None) – If a float, predictions with score lower than the threshold will be regard as the negative prediction. If None, not apply threshold. If the parameter is a tuple, accuracy based on all thresholds will be calculated and outputted together. Defaults to 0.
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. Defaults to None.
ano_file (str, optional) – The path of the annotation file. This file will be used in evaluating the fine-tuned model on OOD dataset, e.g. ImageNet-A. Defaults to None.
- compute_metrics(results)[源代码]¶
Compute the metrics from processed results.
- 参数
results (dict) – The processed results of each batch.
- 返回
The computed metrics. The keys are the names of the metrics, and the values are corresponding results.
- 返回类型
Dict
- process(data_batch, data_samples)[源代码]¶
Process one batch of data samples.
The processed results should be stored in
self.results
, which will be used to computed the metrics when all batches have been processed. The difference between this method andprocess
inAccuracy
is that theimg_path
is extracted from thedata_batch
and stored in theself.results
.- 参数
data_batch – A batch of data from the dataloader.
data_samples (Sequence[dict]) – A batch of outputs from the model.