mmagic.evaluation.metrics.gradient_error

Module Contents

Classes

GradientError

Gradient error for evaluating alpha matte prediction.

class mmagic.evaluation.metrics.gradient_error.GradientError(sigma=1.4, norm_constant=1000, **kwargs)

Bases: mmagic.evaluation.metrics.base_sample_wise_metric.BaseSampleWiseMetric

Gradient error for evaluating alpha matte prediction.

备注

Current implementation assume image / alpha / trimap array in numpy format and with pixel value ranging from 0 to 255.

备注

pred_alpha should be masked by trimap before passing into this metric

参数

• sigma (float) – Standard deviation of the gaussian kernel. Defaults to 1.4 .

• norm_const (int) – Divide the result to reduce its magnitude. Defaults to 1000 .

Default prefix: ‘’

Metrics:

• GradientError (float): Gradient Error

metric = 'GradientError'

prepare(module: torch.nn.Module, dataloader: torch.utils.data.dataloader.DataLoader)

process(data_batch: Sequence[dict], data_samples: Sequence[dict]) → None

Process one batch of data samples and predictions. The processed results should be stored in self.results, which will be used to compute the metrics when all batches have been processed.

参数

• data_batch (Sequence[dict]) – A batch of data from the dataloader.

• predictions (Sequence[dict]) – A batch of outputs from the model.

compute_metrics(results: List)

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