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import os
import os.path as osp
import tempfile
import unittest
from copy import deepcopy
import mmcv
import numpy as np
import torch
from mmengine.fileio import dump
from mmdet.evaluation import INSTANCE_OFFSET, CocoPanopticMetric
try:
import panopticapi
except ImportError:
panopticapi = None
class TestCocoPanopticMetric(unittest.TestCase):
def _create_panoptic_gt_annotations(self, ann_file, seg_map_dir):
categories = [{
'id': 0,
'name': 'person',
'supercategory': 'person',
'isthing': 1
}, {
'id': 1,
'name': 'cat',
'supercategory': 'cat',
'isthing': 1
}, {
'id': 2,
'name': 'dog',
'supercategory': 'dog',
'isthing': 1
}, {
'id': 3,
'name': 'wall',
'supercategory': 'wall',
'isthing': 0
}]
images = [{
'id': 0,
'width': 80,
'height': 60,
'file_name': 'fake_name1.jpg',
}]
annotations = [{
'segments_info': [{
'id': 1,
'category_id': 0,
'area': 400,
'bbox': [10, 10, 10, 40],
'iscrowd': 0
}, {
'id': 2,
'category_id': 0,
'area': 400,
'bbox': [30, 10, 10, 40],
'iscrowd': 0
}, {
'id': 3,
'category_id': 2,
'iscrowd': 0,
'bbox': [50, 10, 10, 5],
'area': 50
}, {
'id': 4,
'category_id': 3,
'iscrowd': 0,
'bbox': [0, 0, 80, 60],
'area': 3950
}],
'file_name':
'fake_name1.png',
'image_id':
0
}]
gt_json = {
'images': images,
'annotations': annotations,
'categories': categories
}
# 4 is the id of the background class annotation.
gt = np.zeros((60, 80), dtype=np.int64) + 4
gt_bboxes = np.array(
[[10, 10, 10, 40], [30, 10, 10, 40], [50, 10, 10, 5]],
dtype=np.int64)
for i in range(3):
x, y, w, h = gt_bboxes[i]
gt[y:y + h, x:x + w] = i + 1 # id starts from 1
rgb_gt_seg_map = np.zeros(gt.shape + (3, ), dtype=np.uint8)
rgb_gt_seg_map[:, :, 2] = gt // (256 * 256)
rgb_gt_seg_map[:, :, 1] = gt % (256 * 256) // 256
rgb_gt_seg_map[:, :, 0] = gt % 256
img_path = osp.join(seg_map_dir, 'fake_name1.png')
mmcv.imwrite(rgb_gt_seg_map[:, :, ::-1], img_path)
dump(gt_json, ann_file)
return gt_json
def _create_panoptic_data_samples(self):
# predictions
# TP for background class, IoU=3576/4324=0.827
# 2 the category id of the background class
pred = np.zeros((60, 80), dtype=np.int64) + 2
pred_bboxes = np.array(
[
[11, 11, 10, 40], # TP IoU=351/449=0.78
[38, 10, 10, 40], # FP
[51, 10, 10, 5] # TP IoU=45/55=0.818
],
dtype=np.int64)
pred_labels = np.array([0, 0, 1], dtype=np.int64)
for i in range(3):
x, y, w, h = pred_bboxes[i]
pred[y:y + h, x:x + w] = (i + 1) * INSTANCE_OFFSET + pred_labels[i]
data_samples = [{
'img_id':
0,
'ori_shape': (60, 80),
'img_path':
'xxx/fake_name1.jpg',
'segments_info': [{
'id': 1,
'category': 0,
'is_thing': 1
}, {
'id': 2,
'category': 0,
'is_thing': 1
}, {
'id': 3,
'category': 1,
'is_thing': 1
}, {
'id': 4,
'category': 2,
'is_thing': 0
}],
'seg_map_path':
osp.join(self.gt_seg_dir, 'fake_name1.png'),
'pred_panoptic_seg': {
'sem_seg': torch.from_numpy(pred).unsqueeze(0)
},
}]
return data_samples
def setUp(self):
self.tmp_dir = tempfile.TemporaryDirectory()
self.gt_json_path = osp.join(self.tmp_dir.name, 'gt.json')
self.gt_seg_dir = osp.join(self.tmp_dir.name, 'gt_seg')
os.mkdir(self.gt_seg_dir)
self._create_panoptic_gt_annotations(self.gt_json_path,
self.gt_seg_dir)
self.dataset_meta = {
'classes': ('person', 'dog', 'wall'),
'thing_classes': ('person', 'dog'),
'stuff_classes': ('wall', )
}
self.target = {
'coco_panoptic/PQ': 67.86874803219071,
'coco_panoptic/SQ': 80.89770126158936,
'coco_panoptic/RQ': 83.33333333333334,
'coco_panoptic/PQ_th': 60.45252075318891,
'coco_panoptic/SQ_th': 79.9959505972869,
'coco_panoptic/RQ_th': 75.0,
'coco_panoptic/PQ_st': 82.70120259019427,
'coco_panoptic/SQ_st': 82.70120259019427,
'coco_panoptic/RQ_st': 100.0
}
self.data_samples = self._create_panoptic_data_samples()
def tearDown(self):
self.tmp_dir.cleanup()
@unittest.skipIf(panopticapi is not None, 'panopticapi is installed')
def test_init(self):
with self.assertRaises(RuntimeError):
CocoPanopticMetric()
@unittest.skipIf(panopticapi is None, 'panopticapi is not installed')
def test_evaluate_without_json(self):
# with tmpfile, without json
metric = CocoPanopticMetric(
ann_file=None,
seg_prefix=self.gt_seg_dir,
classwise=False,
nproc=1,
outfile_prefix=None)
metric.dataset_meta = self.dataset_meta
metric.process({}, deepcopy(self.data_samples))
eval_results = metric.evaluate(size=1)
self.assertDictEqual(eval_results, self.target)
# without tmpfile and json
outfile_prefix = f'{self.tmp_dir.name}/test'
metric = CocoPanopticMetric(
ann_file=None,
seg_prefix=self.gt_seg_dir,
classwise=False,
nproc=1,
outfile_prefix=outfile_prefix)
metric.dataset_meta = self.dataset_meta
metric.process({}, deepcopy(self.data_samples))
eval_results = metric.evaluate(size=1)
self.assertDictEqual(eval_results, self.target)
@unittest.skipIf(panopticapi is None, 'panopticapi is not installed')
def test_evaluate_with_json(self):
# with tmpfile and json
metric = CocoPanopticMetric(
ann_file=self.gt_json_path,
seg_prefix=self.gt_seg_dir,
classwise=False,
nproc=1,
outfile_prefix=None)
metric.dataset_meta = self.dataset_meta
metric.process({}, deepcopy(self.data_samples))
eval_results = metric.evaluate(size=1)
self.assertDictEqual(eval_results, self.target)
# classwise
metric = CocoPanopticMetric(
ann_file=self.gt_json_path,
seg_prefix=self.gt_seg_dir,
classwise=True,
nproc=1,
outfile_prefix=None)
metric.dataset_meta = self.dataset_meta
metric.process({}, deepcopy(self.data_samples))
eval_results = metric.evaluate(size=1)
self.assertDictEqual(eval_results, self.target)
# without tmpfile, with json
outfile_prefix = f'{self.tmp_dir.name}/test1'
metric = CocoPanopticMetric(
ann_file=self.gt_json_path,
seg_prefix=self.gt_seg_dir,
classwise=False,
nproc=1,
outfile_prefix=outfile_prefix)
metric.dataset_meta = self.dataset_meta
metric.process({}, deepcopy(self.data_samples))
eval_results = metric.evaluate(size=1)
self.assertDictEqual(eval_results, self.target)
@unittest.skipIf(panopticapi is None, 'panopticapi is not installed')
def test_format_only(self):
with self.assertRaises(AssertionError):
metric = CocoPanopticMetric(
ann_file=self.gt_json_path,
seg_prefix=self.gt_seg_dir,
classwise=False,
nproc=1,
format_only=True,
outfile_prefix=None)
outfile_prefix = f'{self.tmp_dir.name}/test'
metric = CocoPanopticMetric(
ann_file=self.gt_json_path,
seg_prefix=self.gt_seg_dir,
classwise=False,
nproc=1,
format_only=True,
outfile_prefix=outfile_prefix)
metric.dataset_meta = self.dataset_meta
metric.process({}, deepcopy(self.data_samples))
eval_results = metric.evaluate(size=1)
self.assertDictEqual(eval_results, dict())
self.assertTrue(osp.exists(f'{self.tmp_dir.name}/test.panoptic'))
self.assertTrue(osp.exists(f'{self.tmp_dir.name}/test.panoptic.json'))
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