Spaces:
Sleeping
Sleeping
File size: 9,410 Bytes
9d444ef 9add542 9d444ef b23c6fe 5b34f1e 9d444ef 5b34f1e 9d444ef 5b34f1e 9d444ef 5b34f1e 9d444ef 9add542 de1f95e 9d444ef 73dd662 9d444ef 73dd662 9d444ef 44bdae7 9d444ef de1f95e 9d444ef de1f95e 9d444ef de1f95e 0bb094e de1f95e 2b772af 9d444ef de1f95e 2b772af de1f95e 2b24bc8 de1f95e 5b34f1e 9d444ef 193d419 9d444ef 44bdae7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
# 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 Dict, List
import evaluate
import datasets
import numpy as np
from seametrics.panoptic import PanopticQuality
from seametrics.payload import Payload
_CITATION = """\
@inproceedings{DBLP:conf/cvpr/KirillovHGRD19,
author = {Alexander Kirillov and
Kaiming He and
Ross B. Girshick and
Carsten Rother and
Piotr Doll{\'{a}}r},
title = {Panoptic Segmentation},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
2019, Long Beach, CA, USA, June 16-20, 2019},
pages = {9404--9413},
publisher = {Computer Vision Foundation / {IEEE}},
year = {2019},
url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Kirillov\_Panoptic\_Segmentation\_CVPR\_2019\_paper.html
}
"""
_DESCRIPTION = """\
This evaluation metric calculates Panoptic Quality (PQ) for panoptic segmentation masks.
"""
_KWARGS_DESCRIPTION = """
Calculates PQ-score given predicted and ground truth panoptic segmentation masks.
Args:
predictions: a 4-d array of shape (batch_size, img_height, img_width, 2).
The last dimension should hold the category index at position 0, and
the instance ID at position 1.
references: a 4-d array of shape (batch_size, img_height, img_width, 2).
The last dimension should hold the category index at position 0, and
the instance ID at position 1.
Returns:
A single float number in range [0, 1] that represents the PQ score.
1 is perfect panoptic segmentation, 0 is worst possible panoptic segmentation.
Examples:
>>> import evaluate
>>> from seametrics.payload.processor import PayloadProcessor
>>> MODEL_FIELD = ["maskformer-27k-100ep"]
>>> payload = PayloadProcessor("SAILING_PANOPTIC_DATASET_QA",
>>> gt_field="ground_truth_det",
>>> models=MODEL_FIELD,
>>> sequence_list=["Trip_55_Seq_2", "Trip_197_Seq_1", "Trip_197_Seq_68"],
>>> excluded_classes=[""]).payload
>>> module = evaluate.load("SEA-AI/PanopticQuality")
>>> module.add_payload(payload, model_name=MODEL_FIELD[0])
>>> module.compute()
100%|██████████| 3/3 [00:03<00:00, 1.30s/it]
Added data ...
Start computing ...
Finished!
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class PQMetric(evaluate.Metric):
def __init__(
self,
label2id: Dict[str, int] = None,
stuff: List[str] = None,
per_class: bool = True,
split_sq_rq: bool = True,
**kwargs
):
super().__init__(**kwargs)
DEFAULT_LABEL2ID = {'WATER': 0,
'SKY': 1,
'LAND': 2,
'MOTORBOAT': 3,
'FAR_AWAY_OBJECT': 4,
'SAILING_BOAT_WITH_CLOSED_SAILS': 5,
'SHIP': 6,
'WATERCRAFT': 7,
'SPHERICAL_BUOY': 8,
'CONSTRUCTION': 9,
'FLOTSAM': 10,
'SAILING_BOAT_WITH_OPEN_SAILS': 11,
'CONTAINER': 12,
'PILLAR_BUOY': 13,
'AERIAL_ANIMAL': 14,
'HUMAN_IN_WATER': 15,
'OWN_BOAT': 16,
'WOODEN_LOG': 17,
'MARITIME_ANIMAL': 18}
DEFAULT_STUFF = ["WATER", "SKY", "LAND", "CONSTRUCTION", "ICE", "OWN_BOAT"]
self.label2id = label2id if label2id is not None else DEFAULT_LABEL2ID
self.stuff = stuff if stuff is not None else DEFAULT_STUFF
self.per_class = per_class
self.split_sq_rq = split_sq_rq
self.pq_metric = PanopticQuality(
things=set([self.label2id[label] for label in self.label2id.keys() if label not in self.stuff]),
stuffs=set([self.label2id[label] for label in self.label2id.keys() if label in self.stuff]),
return_per_class=per_class,
return_sq_and_rq=split_sq_rq
)
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(
datasets.Sequence(
datasets.Sequence(
datasets.Sequence(datasets.Value("float"))
)
),
),
"references": datasets.Sequence( # batch
datasets.Sequence( # img height
datasets.Sequence( # img width
datasets.Sequence(datasets.Value("float")) # 2
)
),
),
}
),
# Additional links to the codebase or references
codebase_urls=[
"https://lightning.ai/docs/torchmetrics/stable/detection/panoptic_quality.html"
],
)
def add(self, *, prediction, reference, **kwargs):
"""Adds a batch of predictions and references to the metric"""
# in case the inputs are lists, convert them to numpy arrays
self.pq_metric.update(prediction, reference)
# does not impact the metric, but is required for the interface x_x
super(evaluate.Metric, self).add(
prediction=self._postprocess(prediction),
references=self._postprocess(reference),
**kwargs
)
def _compute(self, *, predictions, references, **kwargs):
"""Called within the evaluate.Metric.compute() method"""
tp = self.pq_metric.metric.true_positives.clone()
fp = self.pq_metric.metric.false_positives.clone()
fn = self.pq_metric.metric.false_negatives.clone()
iou = self.pq_metric.metric.iou_sum.clone()
id2label = {id: label for label, id in self.label2id.items()}
things_stuffs = sorted(self.pq_metric.things) + sorted(self.pq_metric.stuffs)
# compute scores
result = self.pq_metric.compute() # shape : (n_classes (sorted things + sorted stuffs), scores (pq, sq, rq))
result_dict = dict()
if self.per_class:
if not self.split_sq_rq:
result = result.T
result_dict["scores"] = {id2label[numeric_label]: result[i].tolist() \
for i, numeric_label in enumerate(things_stuffs)}
result_dict["scores"].update({"overall": result.mean(axis=0).tolist()})
result_dict["numbers"] = {id2label[numeric_label]: [tp[i].item(), fp[i].item(), fn[i].item(), iou[i].item()] \
for i, numeric_label in enumerate(things_stuffs)}
result_dict["numbers"].update({"overall": [tp.sum().item(), fp.sum().item(), fn.sum().item(), iou.sum().item()]})
else:
result_dict["scores"] = {"overall": result.tolist() if self.split_sq_rq else [result.tolist()]}
result_dict["numbers"] = {"overall": [tp.sum().item(), fp.sum().item(), fn.sum().item(), iou.sum().item()]}
return result_dict
def add_payload(self, payload: Payload, model_name: str = None):
"""Converts the payload to the format expected by the metric"""
# import only if needed since fiftyone is not a direct dependency
from seametrics.panoptic.utils import payload_to_seg_metric
predictions, references, label2id = payload_to_seg_metric(payload, model_name, self.label2id)
self.label2id = label2id
self.add(prediction=predictions, reference=references)
def _postprocess(self, np_array):
"""Converts the numpy arrays to lists for type checking"""
# add fake data to avoid out of memory problem
# only reuqired for interface, not used by metric anyway
return np.zeros((1,1,1,1)).tolist()
|