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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 pyarrow as pa
from .coco_utils import CocoEvaluator, get_coco_api_from_dataset
# 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:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> my_new_module = evaluate.load("my_new_module")
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'accuracy': 1.0}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
# lists - summarize long lists similarly to NumPy
# arrays/tensors - let the frameworks control formatting
def summarize_if_long_list(obj):
if not type(obj) == list or len(obj) <= 6:
return f"{obj}"
def format_chunk(chunk):
return ", ".join(repr(x) for x in chunk)
return f"[{format_chunk(obj[:3])}, ..., {format_chunk(obj[-3:])}]"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class COCOEvaluate(evaluate.Metric):
"""TODO: Short description of my evaluation module."""
def __init__(self, coco, iou_types=['bbox'], **kwargs):
super().__init__(**kwargs)
self.coco_evaluator = CocoEvaluator(coco, iou_types)
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.Features(
{
'scores': datasets.Sequence(datasets.Value("float")),
'labels': datasets.Sequence(datasets.Value("int64")),
'boxes': datasets.Sequence(datasets.Sequence(datasets.Value("float"))),
})
]
,
'references': [
datasets.Features(
{
'size': datasets.Sequence(datasets.Value("float")),
'image_id': datasets.Sequence(datasets.Value("int64")),
'boxes': datasets.Sequence(datasets.Sequence(datasets.Value("float"))),
'class_labels': datasets.Sequence(datasets.Value("int64")),
'iscrowd': datasets.Sequence(datasets.Value("int64")),
'orig_size': datasets.Sequence(datasets.Value("float")),
'area': 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 _preprocess(self, predictions):
"""Optional: preprocess the predictions and references before computing the scores"""
processed_predictions = []
for pred in predictions:
processed_pred = {}
for key in pred.keys():
processed_pred[key] = pred[key].detach().cpu().tolist()
processed_predictions.append(processed_pred)
return processed_predictions
def add(self, *, prediction=None, reference=None, **kwargs):
"""Preprocesses the predictions and references and calls the function of the parent class."""
if prediction is not None:
prediction = self._preprocess(prediction)
if reference is not None:
reference = self._preprocess(reference)
super().add(prediction=prediction, references=reference, **kwargs)
def _compute(self, predictions, references):
"""Returns the scores"""
for pred, ref in zip(predictions, references):
res = {}
for target, output in zip(ref, pred):
res[target["image_id"][0]] = output
self.coco_evaluator.update(res)
self.coco_evaluator.synchronize_between_processes()
self.coco_evaluator.accumulate()
self.coco_evaluator.summarize()
stats = self.coco_evaluator.get_results()
return stats |