Spaces:
Running
Running
File size: 7,661 Bytes
f965db0 3359d6e f965db0 e599283 2931c23 f965db0 3359d6e f965db0 3359d6e f965db0 3359d6e f965db0 3359d6e 2931c23 3359d6e f965db0 2931c23 f965db0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
# 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."""
from typing import List, Tuple, Dict, Literal
import evaluate
import datasets
import numpy as np
from seametrics.detection import PrecisionRecallF1Support
from seametrics.fo_utils.utils import _fo_dets_to_metrics_dict
from seametrics.fo_utils.utils import _add_batch
_CITATION = """\
@InProceedings{coco:2020,
title = {Microsoft {COCO:} Common Objects in Context},
authors={Tsung{-}Yi Lin and
Michael Maire and
Serge J. Belongie and
James Hays and
Pietro Perona and
Deva Ramanan and
Piotr Dollar and
C. Lawrence Zitnick},
booktitle = {Computer Vision - {ECCV} 2014 - 13th European Conference, Zurich,
Switzerland, September 6-12, 2014, Proceedings, Part {V}},
series = {Lecture Notes in Computer Science},
volume = {8693},
pages = {740--755},
publisher = {Springer},
year={2014}
}
"""
_DESCRIPTION = """\
This evaluation metric is designed to give provide object detection metrics at different object size levels.
It is based on a modified version of the commonly used COCO-evaluation metrics.
"""
_KWARGS_DESCRIPTION = """
Calculates object detection metrics given predicted and ground truth bounding boxes for a single image.
Args:
predictions: list of predictions to score. Each prediction should
be a list containing the four co-ordinates that specify the bounding box.
Co-ordinate format is as defined when instantiating the metric
(parameter: bbox_type, defaults to xywh).
references: list of reference for each prediction. Each prediction should
be a list containing the four co-ordinates that specify the bounding box.
Bounding box format should be the same as for the predictions.
Returns:
dict containing dicts for each specified area range with following items:
'range': specified area with [max_px_area, max_px_area]
'iouThr': min. IOU-threshold of a prediction with a ground truth box
to be considered a correct prediction
'maxDets': maximum number of detections
'tp': number of true positive (correct) predictions
'fp': number of false positive (incorrect) predictions
'fn': number of false negative (missed) predictions
'duplicates': number of duplicate predictions
'precision': best possible score = 1, worst possible score = 0
large if few false positive predictions
formula: tp/(fp+tp)
'recall' best possible score = 1, worst possible score = 0
large if few missed predictions
formula: tp/(tp+fn)
'f1': best possible score = 1, worst possible score = 0
trades off precision and recall
formula: 2*(precision*recall)/(precision+recall)
'support': number of ground truth bounding boxes considered in the evaluation,
'fpi': number of images with no ground truth but false positive predictions,
'nImgs': number of images considered in evaluation
Examples:
>>> import evaluate
>>> from seametrics.fo_to_payload.utils import fo_to_payload
>>> payload = fo_to_payload(..., models=model_list)
>>> for model in payload["models"]:
>>> module = evaluate.load("./detection_metric.py", iou_thresholds=0.9)
>>> module.add_batch(payload)
>>> result = module.compute()
>>> print(result)
{'all': {
'range': [0, 10000000000.0],
'iouThr': '0.00',
'maxDets': 100,
'tp': 1,
'fp': 3,
'fn': 1,
'duplicates': 0,
'precision': 0.25,
'recall': 0.5,
'f1': 0.3333333333333333,
'support': 2,
'fpi': 0,
'nImgs': 2
}
}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class DetectionMetric(evaluate.Metric):
def __init__(
self,
area_ranges_tuples: List[Tuple[str, List[int]]] = [("all", [0, 1e5 ** 2])],
iou_threshold: float = 1e-10,
class_agnostic: bool = True,
bbox_format: str = "xywh",
iou_type: Literal["bbox", "segm"] = "bbox",
**kwargs
):
super().__init__(**kwargs)
area_ranges = [v for _, v in area_ranges_tuples]
area_ranges_labels = [k for k, _ in area_ranges_tuples]
metric_params = dict(
iou_thresholds=[iou_threshold],
area_ranges=area_ranges,
area_ranges_labels=area_ranges_labels,
class_agnostic=class_agnostic,
iou_type=iou_type,
box_format=bbox_format
)
self.coco_metric = PrecisionRecallF1Support(**metric_params)
def _info(self):
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(feature=datasets.Sequence(datasets.Value("float"))),
'references': datasets.Sequence(feature=datasets.Sequence(datasets.Value("float"))),
}
),
# Additional links to the codebase or references
codebase_urls=["https://github.com/SEA-AI/metrics/tree/main",
"https://github.com/cocodataset/cocoapi/tree/master"]
)
def add_batch(
self,
data: dict,
model: str = None
):
"""Add predictions and ground truths of a single image to update the metric.
Args:
data (dict): containing standard payload of data that should be evaluated
format should be as returned by function `fo_to_payload()` in seametrics library
model (str): should be one out of values given in data["models"]
if not defined, defaults to data["models"][0], as only one model can be evaluated a time.
"""
# populate two empty lists in format suitable for hugging face metric
# nothing is computed based on them but prevents huggingface error
self, predictions,references = _add_batch(self, data, model)
# prevents hugging face error, doesn't do a lot
super(evaluate.Metric, self).add_batch(
predictions=predictions,
references=references
)
def _compute(
self,
predictions,
references
):
"""Returns the scores"""
result = self.coco_metric.compute()["metrics"]
return result
|