signal_to_reconstruction_error.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""Signal-to-Reconstruction Error (SRE) metric."""

import evaluate
import datasets
import numpy as np


_DESCRIPTION = """\
Compute the Signal-to-Reconstruction Error (SRE) metric. This metric is commonly used to
asses the performance of denoising, super-resolution and style transfer algorithms in
audio and image processing.
"""


_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""


_KWARGS_DESCRIPTION = """
Args:
    predictions (`list` of `np.array`): Predicted labels.
    references (`list` of `np.array`): Ground truth labels.
    sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
    sre (`float`): Signal-to-Reconstruction Error (SRE) metric. The SRE values are
positive and they are expressed in decibels (dB). The higher the SRE value, the better.
Examples:
    Example 1-A simple example
        >>> sre = evaluate.load("jpxkqx/signal_to_reconstruction_error")
        >>> results = sre.compute(references=[[0, 0], [-1, -1]], predictions=[[0, 1], [0, 0]])
        >>> print(results)
        {"Signal-to-Reconstruction Error": 23.01}
"""


def signal_reconstruction_error(y_true: np.array, y_hat: np.array) -> np.array:
    return 10 * np.log10(np.sum(y_true ** 2) / np.sum((y_true - y_hat) ** 2))
    

@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class SignaltoReconstrutionError(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features(self._get_feature_types()),
            homepage="https://huggingface.co/spaces/jpxkqx/signal_to_reconstrution_error",
        )

    def _get_feature_types(self):
        if self.config_name == "multilist":
            return {
                # 1st Seq - num_samples, 2nd Seq - Height, 3rd Seq - Width
                "predictions": datasets.Sequence(
                    datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
                ),
                "references": datasets.Sequence(
                    datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
                ),
            }
        else:
            return {
                # 1st Seq - Height, 2rd Seq - Width
                "predictions": datasets.Sequence(
                    datasets.Sequence(datasets.Value("float32"))
                ),
                "references": datasets.Sequence(
                    datasets.Sequence(datasets.Value("float32"))
                ),
            }

    def _compute(self, predictions, references, sample_weight=None):
        """Returns the scores"""
        samples = zip(np.array(references), np.array(predictions))
        psnrs = list(map(lambda args: signal_reconstruction_error(*args), samples))
        return {
            "Signal-to-Reconstruction Error": np.average(psnrs, weights=sample_weight)
        }