# Copyright (c) OpenMMLab. All rights reserved.
import itertools
import os
from collections import defaultdict
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from mmdet.core import INSTANCE_OFFSET
from .api_wrappers import COCO, pq_compute_multi_core
from .builder import DATASETS
from .coco import CocoDataset
try:
import panopticapi
from panopticapi.evaluation import VOID
from panopticapi.utils import id2rgb
except ImportError:
panopticapi = None
id2rgb = None
VOID = None
__all__ = ['CocoPanopticDataset']
class COCOPanoptic(COCO):
"""This wrapper is for loading the panoptic style annotation file.
The format is shown in the CocoPanopticDataset class.
Args:
annotation_file (str): Path of annotation file.
"""
def __init__(self, annotation_file=None):
if panopticapi is None:
raise RuntimeError(
'panopticapi is not installed, please install it by: '
'pip install git+https://github.com/cocodataset/'
'panopticapi.git.')
super(COCOPanoptic, self).__init__(annotation_file)
def createIndex(self):
# create index
print('creating index...')
# anns stores 'segment_id -> annotation'
anns, cats, imgs = {}, {}, {}
img_to_anns, cat_to_imgs = defaultdict(list), defaultdict(list)
if 'annotations' in self.dataset:
for ann, img_info in zip(self.dataset['annotations'],
self.dataset['images']):
img_info['segm_file'] = ann['file_name']
for seg_ann in ann['segments_info']:
# to match with instance.json
seg_ann['image_id'] = ann['image_id']
seg_ann['height'] = img_info['height']
seg_ann['width'] = img_info['width']
img_to_anns[ann['image_id']].append(seg_ann)
# segment_id is not unique in coco dataset orz...
if seg_ann['id'] in anns.keys():
anns[seg_ann['id']].append(seg_ann)
else:
anns[seg_ann['id']] = [seg_ann]
if 'images' in self.dataset:
for img in self.dataset['images']:
imgs[img['id']] = img
if 'categories' in self.dataset:
for cat in self.dataset['categories']:
cats[cat['id']] = cat
if 'annotations' in self.dataset and 'categories' in self.dataset:
for ann in self.dataset['annotations']:
for seg_ann in ann['segments_info']:
cat_to_imgs[seg_ann['category_id']].append(ann['image_id'])
print('index created!')
self.anns = anns
self.imgToAnns = img_to_anns
self.catToImgs = cat_to_imgs
self.imgs = imgs
self.cats = cats
def load_anns(self, ids=[]):
"""Load anns with the specified ids.
self.anns is a list of annotation lists instead of a
list of annotations.
Args:
ids (int array): integer ids specifying anns
Returns:
anns (object array): loaded ann objects
"""
anns = []
if hasattr(ids, '__iter__') and hasattr(ids, '__len__'):
# self.anns is a list of annotation lists instead of
# a list of annotations
for id in ids:
anns += self.anns[id]
return anns
elif type(ids) == int:
return self.anns[ids]
@DATASETS.register_module()
class CocoPanopticDataset(CocoDataset):
"""Coco dataset for Panoptic segmentation.
The annotation format is shown as follows. The `ann` field is optional
for testing.
.. code-block:: none
[
{
'filename': f'{image_id:012}.png',
'image_id':9
'segments_info': {
[
{
'id': 8345037, (segment_id in panoptic png,
convert from rgb)
'category_id': 51,
'iscrowd': 0,
'bbox': (x1, y1, w, h),
'area': 24315,
'segmentation': list,(coded mask)
},
...
}
}
},
...
]
Args:
ann_file (str): Panoptic segmentation annotation file path.
pipeline (list[dict]): Processing pipeline.
ins_ann_file (str): Instance segmentation annotation file path.
Defaults to None.
classes (str | Sequence[str], optional): Specify classes to load.
If is None, ``cls.CLASSES`` will be used. Defaults to None.
data_root (str, optional): Data root for ``ann_file``,
``ins_ann_file`` ``img_prefix``, ``seg_prefix``, ``proposal_file``
if specified. Defaults to None.
img_prefix (str, optional): Prefix of path to images. Defaults to ''.
seg_prefix (str, optional): Prefix of path to segmentation files.
Defaults to None.
proposal_file (str, optional): Path to proposal file. Defaults to None.
test_mode (bool, optional): If set True, annotation will not be loaded.
Defaults to False.
filter_empty_gt (bool, optional): If set true, images without bounding
boxes of the dataset's classes will be filtered out. This option
only works when `test_mode=False`, i.e., we never filter images
during tests. Defaults to True.
file_client_args (:obj:`mmcv.ConfigDict` | dict): file client args.
Defaults to dict(backend='disk').
"""
CLASSES = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
' truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',
'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff',
'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light',
'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield',
'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow',
'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile',
'wall-wood', 'water-other', 'window-blind', 'window-other',
'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
'cabinet-merged', 'table-merged', 'floor-other-merged',
'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged',
'paper-merged', 'food-other-merged', 'building-other-merged',
'rock-merged', 'wall-other-merged', 'rug-merged'
]
THING_CLASSES = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
STUFF_CLASSES = [
'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain',
'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house',
'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield',
'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow',
'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile',
'wall-wood', 'water-other', 'window-blind', 'window-other',
'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
'cabinet-merged', 'table-merged', 'floor-other-merged',
'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged',
'paper-merged', 'food-other-merged', 'building-other-merged',
'rock-merged', 'wall-other-merged', 'rug-merged'
]
PALETTE = [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
(0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),
(175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255),
(0, 226, 252), (182, 182, 255), (0, 82, 0), (120, 166, 157),
(110, 76, 0), (174, 57, 255), (199, 100, 0), (72, 0, 118),
(255, 179, 240), (0, 125, 92), (209, 0, 151), (188, 208, 182),
(0, 220, 176), (255, 99, 164), (92, 0, 73), (133, 129, 255),
(78, 180, 255), (0, 228, 0), (174, 255, 243), (45, 89, 255),
(134, 134, 103), (145, 148, 174), (255, 208, 186),
(197, 226, 255), (171, 134, 1), (109, 63, 54), (207, 138, 255),
(151, 0, 95), (9, 80, 61), (84, 105, 51), (74, 65, 105),
(166, 196, 102), (208, 195, 210), (255, 109, 65), (0, 143, 149),
(179, 0, 194), (209, 99, 106), (5, 121, 0), (227, 255, 205),
(147, 186, 208), (153, 69, 1), (3, 95, 161), (163, 255, 0),
(119, 0, 170), (0, 182, 199), (0, 165, 120), (183, 130, 88),
(95, 32, 0), (130, 114, 135), (110, 129, 133), (166, 74, 118),
(219, 142, 185), (79, 210, 114), (178, 90, 62), (65, 70, 15),
(127, 167, 115), (59, 105, 106), (142, 108, 45), (196, 172, 0),
(95, 54, 80), (128, 76, 255), (201, 57, 1), (246, 0, 122),
(191, 162, 208), (255, 255, 128), (147, 211, 203),
(150, 100, 100), (168, 171, 172), (146, 112, 198),
(210, 170, 100), (92, 136, 89), (218, 88, 184), (241, 129, 0),
(217, 17, 255), (124, 74, 181), (70, 70, 70), (255, 228, 255),
(154, 208, 0), (193, 0, 92), (76, 91, 113), (255, 180, 195),
(106, 154, 176),
(230, 150, 140), (60, 143, 255), (128, 64, 128), (92, 82, 55),
(254, 212, 124), (73, 77, 174), (255, 160, 98), (255, 255, 255),
(104, 84, 109), (169, 164, 131), (225, 199, 255), (137, 54, 74),
(135, 158, 223), (7, 246, 231), (107, 255, 200), (58, 41, 149),
(183, 121, 142), (255, 73, 97), (107, 142, 35), (190, 153, 153),
(146, 139, 141),
(70, 130, 180), (134, 199, 156), (209, 226, 140), (96, 36, 108),
(96, 96, 96), (64, 170, 64), (152, 251, 152), (208, 229, 228),
(206, 186, 171), (152, 161, 64), (116, 112, 0), (0, 114, 143),
(102, 102, 156), (250, 141, 255)]
def __init__(self,
ann_file,
pipeline,
ins_ann_file=None,
classes=None,
data_root=None,
img_prefix='',
seg_prefix=None,
proposal_file=None,
test_mode=False,
filter_empty_gt=True,
file_client_args=dict(backend='disk')):
super().__init__(
ann_file,
pipeline,
classes=classes,
data_root=data_root,
img_prefix=img_prefix,
seg_prefix=seg_prefix,
proposal_file=proposal_file,
test_mode=test_mode,
filter_empty_gt=filter_empty_gt,
file_client_args=file_client_args)
self.ins_ann_file = ins_ann_file
def load_annotations(self, ann_file):
"""Load annotation from COCO Panoptic style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation info from COCO api.
"""
self.coco = COCOPanoptic(ann_file)
self.cat_ids = self.coco.get_cat_ids()
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.categories = self.coco.cats
self.img_ids = self.coco.get_img_ids()
data_infos = []
for i in self.img_ids:
info = self.coco.load_imgs([i])[0]
info['filename'] = info['file_name']
info['segm_file'] = info['filename'].replace('jpg', 'png')
data_infos.append(info)
return data_infos
def get_ann_info(self, idx):
"""Get COCO annotation by index.
Args:
idx (int): Index of data.
Returns:
dict: Annotation info of specified index.
"""
img_id = self.data_infos[idx]['id']
ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
ann_info = self.coco.load_anns(ann_ids)
# filter out unmatched images
ann_info = [i for i in ann_info if i['image_id'] == img_id]
return self._parse_ann_info(self.data_infos[idx], ann_info)
def _parse_ann_info(self, img_info, ann_info):
"""Parse annotations and load panoptic ground truths.
Args:
img_info (int): Image info of an image.
ann_info (list[dict]): Annotation info of an image.
Returns:
dict: A dict containing the following keys: bboxes, bboxes_ignore,
labels, masks, seg_map.
"""
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
gt_mask_infos = []
for i, ann in enumerate(ann_info):
x1, y1, w, h = ann['bbox']
if ann['area'] <= 0 or w < 1 or h < 1:
continue
bbox = [x1, y1, x1 + w, y1 + h]
category_id = ann['category_id']
contiguous_cat_id = self.cat2label[category_id]
is_thing = self.coco.load_cats(ids=category_id)[0]['isthing']
if is_thing:
is_crowd = ann.get('iscrowd', False)
if not is_crowd:
gt_bboxes.append(bbox)
gt_labels.append(contiguous_cat_id)
else:
gt_bboxes_ignore.append(bbox)
is_thing = False
mask_info = {
'id': ann['id'],
'category': contiguous_cat_id,
'is_thing': is_thing
}
gt_mask_infos.append(mask_info)
if gt_bboxes:
gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
gt_labels = np.array(gt_labels, dtype=np.int64)
else:
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
gt_labels = np.array([], dtype=np.int64)
if gt_bboxes_ignore:
gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
else:
gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
ann = dict(
bboxes=gt_bboxes,
labels=gt_labels,
bboxes_ignore=gt_bboxes_ignore,
masks=gt_mask_infos,
seg_map=img_info['segm_file'])
return ann
def _filter_imgs(self, min_size=32):
"""Filter images too small or without ground truths."""
ids_with_ann = []
# check whether images have legal thing annotations.
for lists in self.coco.anns.values():
for item in lists:
category_id = item['category_id']
is_thing = self.coco.load_cats(ids=category_id)[0]['isthing']
if not is_thing:
continue
ids_with_ann.append(item['image_id'])
ids_with_ann = set(ids_with_ann)
valid_inds = []
valid_img_ids = []
for i, img_info in enumerate(self.data_infos):
img_id = self.img_ids[i]
if self.filter_empty_gt and img_id not in ids_with_ann:
continue
if min(img_info['width'], img_info['height']) >= min_size:
valid_inds.append(i)
valid_img_ids.append(img_id)
self.img_ids = valid_img_ids
return valid_inds
def _pan2json(self, results, outfile_prefix):
"""Convert panoptic results to COCO panoptic json style."""
label2cat = dict((v, k) for (k, v) in self.cat2label.items())
pred_annotations = []
outdir = os.path.join(os.path.dirname(outfile_prefix), 'panoptic')
for idx in range(len(self)):
img_id = self.img_ids[idx]
segm_file = self.data_infos[idx]['segm_file']
pan = results[idx]
pan_labels = np.unique(pan)
segm_info = []
for pan_label in pan_labels:
sem_label = pan_label % INSTANCE_OFFSET
# We reserve the length of self.CLASSES for VOID label
if sem_label == len(self.CLASSES):
continue
# convert sem_label to json label
cat_id = label2cat[sem_label]
is_thing = self.categories[cat_id]['isthing']
mask = pan == pan_label
area = mask.sum()
segm_info.append({
'id': int(pan_label),
'category_id': cat_id,
'isthing': is_thing,
'area': int(area)
})
# evaluation script uses 0 for VOID label.
pan[pan % INSTANCE_OFFSET == len(self.CLASSES)] = VOID
pan = id2rgb(pan).astype(np.uint8)
mmcv.imwrite(pan[:, :, ::-1], os.path.join(outdir, segm_file))
record = {
'image_id': img_id,
'segments_info': segm_info,
'file_name': segm_file
}
pred_annotations.append(record)
pan_json_results = dict(annotations=pred_annotations)
return pan_json_results
def results2json(self, results, outfile_prefix):
"""Dump the results to a COCO style json file.
There are 4 types of results: proposals, bbox predictions, mask
predictions, panoptic segmentation predictions, and they have
different data types. This method will automatically recognize
the type, and dump them to json files.
.. code-block:: none
[
{
'pan_results': np.array, # shape (h, w)
# ins_results which includes bboxes and RLE encoded masks
# is optional.
'ins_results': (list[np.array], list[list[str]])
},
...
]
Args:
results (list[dict]): Testing results of the dataset.
outfile_prefix (str): The filename prefix of the json files. If the
prefix is "somepath/xxx", the json files will be named
"somepath/xxx.panoptic.json", "somepath/xxx.bbox.json",
"somepath/xxx.segm.json"
Returns:
dict[str: str]: Possible keys are "panoptic", "bbox", "segm", \
"proposal", and values are corresponding filenames.
"""
result_files = dict()
# panoptic segmentation results
if 'pan_results' in results[0]:
pan_results = [result['pan_results'] for result in results]
pan_json_results = self._pan2json(pan_results, outfile_prefix)
result_files['panoptic'] = f'{outfile_prefix}.panoptic.json'
mmcv.dump(pan_json_results, result_files['panoptic'])
# instance segmentation results
if 'ins_results' in results[0]:
ins_results = [result['ins_results'] for result in results]
bbox_json_results, segm_json_results = self._segm2json(ins_results)
result_files['bbox'] = f'{outfile_prefix}.bbox.json'
result_files['proposal'] = f'{outfile_prefix}.bbox.json'
result_files['segm'] = f'{outfile_prefix}.segm.json'
mmcv.dump(bbox_json_results, result_files['bbox'])
mmcv.dump(segm_json_results, result_files['segm'])
return result_files
def evaluate_pan_json(self,
result_files,
outfile_prefix,
logger=None,
classwise=False,
nproc=32):
"""Evaluate PQ according to the panoptic results json file."""
imgs = self.coco.imgs
gt_json = self.coco.img_ann_map # image to annotations
gt_json = [{
'image_id': k,
'segments_info': v,
'file_name': imgs[k]['segm_file']
} for k, v in gt_json.items()]
pred_json = mmcv.load(result_files['panoptic'])
pred_json = dict(
(el['image_id'], el) for el in pred_json['annotations'])
# match the gt_anns and pred_anns in the same image
matched_annotations_list = []
for gt_ann in gt_json:
img_id = gt_ann['image_id']
if img_id not in pred_json.keys():
raise Exception('no prediction for the image'
' with id: {}'.format(img_id))
matched_annotations_list.append((gt_ann, pred_json[img_id]))
gt_folder = self.seg_prefix
pred_folder = os.path.join(os.path.dirname(outfile_prefix), 'panoptic')
pq_stat = pq_compute_multi_core(
matched_annotations_list,
gt_folder,
pred_folder,
self.categories,
self.file_client,
nproc=nproc)
metrics = [('All', None), ('Things', True), ('Stuff', False)]
pq_results = {}
for name, isthing in metrics:
pq_results[name], classwise_results = pq_stat.pq_average(
self.categories, isthing=isthing)
if name == 'All':
pq_results['classwise'] = classwise_results
classwise_results = None
if classwise:
classwise_results = {
k: v
for k, v in zip(self.CLASSES, pq_results['classwise'].values())
}
print_panoptic_table(pq_results, classwise_results, logger=logger)
results = parse_pq_results(pq_results)
results['PQ_copypaste'] = (
f'{results["PQ"]:.3f} {results["SQ"]:.3f} '
f'{results["RQ"]:.3f} '
f'{results["PQ_th"]:.3f} {results["SQ_th"]:.3f} '
f'{results["RQ_th"]:.3f} '
f'{results["PQ_st"]:.3f} {results["SQ_st"]:.3f} '
f'{results["RQ_st"]:.3f}')
return results
def evaluate(self,
results,
metric='PQ',
logger=None,
jsonfile_prefix=None,
classwise=False,
nproc=32,
**kwargs):
"""Evaluation in COCO Panoptic protocol.
Args:
results (list[dict]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated. 'PQ', 'bbox',
'segm', 'proposal' are supported. 'pq' will be regarded as 'PQ.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
jsonfile_prefix (str | None): The prefix of json files. It includes
the file path and the prefix of filename, e.g., "a/b/prefix".
If not specified, a temp file will be created. Default: None.
classwise (bool): Whether to print classwise evaluation results.
Default: False.
nproc (int): Number of processes for panoptic quality computing.
Defaults to 32. When `nproc` exceeds the number of cpu cores,
the number of cpu cores is used.
Returns:
dict[str, float]: COCO Panoptic style evaluation metric.
"""
metrics = metric if isinstance(metric, list) else [metric]
# Compatible with lowercase 'pq'
metrics = ['PQ' if metric == 'pq' else metric for metric in metrics]
allowed_metrics = ['PQ', 'bbox', 'segm', 'proposal']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
eval_results = {}
outfile_prefix = os.path.join(tmp_dir.name, 'results') \
if tmp_dir is not None else jsonfile_prefix
if 'PQ' in metrics:
eval_pan_results = self.evaluate_pan_json(
result_files, outfile_prefix, logger, classwise, nproc=nproc)
eval_results.update(eval_pan_results)
metrics.remove('PQ')
if (('bbox' in metrics) or ('segm' in metrics)
or ('proposal' in metrics)):
assert 'ins_results' in results[0], 'instance segmentation' \
'results are absent from results'
assert self.ins_ann_file is not None, 'Annotation '\
'file for instance segmentation or object detection ' \
'shuold not be None'
coco_gt = COCO(self.ins_ann_file)
panoptic_cat_ids = self.cat_ids
self.cat_ids = coco_gt.get_cat_ids(cat_names=self.THING_CLASSES)
eval_ins_results = self.evaluate_det_segm(results, result_files,
coco_gt, metrics, logger,
classwise, **kwargs)
self.cat_ids = panoptic_cat_ids
eval_results.update(eval_ins_results)
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results
def parse_pq_results(pq_results):
"""Parse the Panoptic Quality results."""
result = dict()
result['PQ'] = 100 * pq_results['All']['pq']
result['SQ'] = 100 * pq_results['All']['sq']
result['RQ'] = 100 * pq_results['All']['rq']
result['PQ_th'] = 100 * pq_results['Things']['pq']
result['SQ_th'] = 100 * pq_results['Things']['sq']
result['RQ_th'] = 100 * pq_results['Things']['rq']
result['PQ_st'] = 100 * pq_results['Stuff']['pq']
result['SQ_st'] = 100 * pq_results['Stuff']['sq']
result['RQ_st'] = 100 * pq_results['Stuff']['rq']
return result
def print_panoptic_table(pq_results, classwise_results=None, logger=None):
"""Print the panoptic evaluation results table.
Args:
pq_results(dict): The Panoptic Quality results.
classwise_results(dict | None): The classwise Panoptic Quality results.
The keys are class names and the values are metrics.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
"""
headers = ['', 'PQ', 'SQ', 'RQ', 'categories']
data = [headers]
for name in ['All', 'Things', 'Stuff']:
numbers = [
f'{(pq_results[name][k] * 100):0.3f}' for k in ['pq', 'sq', 'rq']
]
row = [name] + numbers + [pq_results[name]['n']]
data.append(row)
table = AsciiTable(data)
print_log('Panoptic Evaluation Results:\n' + table.table, logger=logger)
if classwise_results is not None:
class_metrics = [(name, ) + tuple(f'{(metrics[k] * 100):0.3f}'
for k in ['pq', 'sq', 'rq'])
for name, metrics in classwise_results.items()]
num_columns = min(8, len(class_metrics) * 4)
results_flatten = list(itertools.chain(*class_metrics))
headers = ['category', 'PQ', 'SQ', 'RQ'] * (num_columns // 4)
results_2d = itertools.zip_longest(
*[results_flatten[i::num_columns] for i in range(num_columns)])
data = [headers]
data += [result for result in results_2d]
table = AsciiTable(data)
print_log(
'Classwise Panoptic Evaluation Results:\n' + table.table,
logger=logger)