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# 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
# limitations under the License.
"""TODO: Add a description here."""
import evaluate
import datasets
import motmetrics as mm
import numpy as np
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Returns:
accuracy: description of the first score,
another_score: description of the second score,
Examples:
>>> import numpy as np
>>> mean_iou = evaluate.load("mean_iou")
>>> # suppose one has 3 different segmentation maps predicted
>>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
>>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])
>>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
>>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])
>>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
>>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])
>>> predicted = [predicted_1, predicted_2, predicted_3]
>>> ground_truth = [actual_1, actual_2, actual_3]
>>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), 'per_category_accuracy': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class MyMetricv2(evaluate.Metric):
"""TODO: Short description of my evaluation module."""
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
"predictions": datasets.Sequence(
datasets.Sequence(datasets.Value("float"))
),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("float"))
)
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=["http://path.to.reference.url/new_module"]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
def _compute(self, predictions, references):
"""Returns the scores"""
# TODO: Compute the different scores of the module
return calculate(predictions, references)
def calculate(predictions, references, max_iou: float = 0.5):
"""Returns the scores"""
try:
np_predictions = np.array(predictions)
except:
raise ValueError("The predictions should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height, confidence]")
try:
np_references = np.array(references)
except:
raise ValueError("The references should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height]")
if np_predictions.shape[1] != 7:
raise ValueError("The predictions should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height, confidence]")
if np_references.shape[1] != 6:
raise ValueError("The references should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height]")
if np_predictions[:, 0].min() <= 0:
raise ValueError("The frame number in the predictions should be a positive integer")
if np_references[:, 0].min() <= 0:
raise ValueError("The frame number in the references should be a positive integer")
num_frames = max(np_references[:, 0].max(), np_predictions[:, 0].max())
acc = mm.MOTAccumulator(auto_id=True)
for i in range(1, num_frames+1):
preds = np_predictions[np_predictions[:, 0] == i, 1:6]
refs = np_references[np_references[:, 0] == i, 1:6]
C = mm.distances.iou_matrix(refs[:,1:], preds[:,1:], max_iou = max_iou)
acc.update(refs[:,0].astype('int').tolist(), preds[:,0].astype('int').tolist(), C)
mh = mm.metrics.create()
summary = mh.compute(acc)
return summary
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