def test_coco_ids_consisitent_with_name():
names1 = ('person', 'bicycle', 'car')
names2 = ('bicycle', 'car', 'person')
tmp_dir = tempfile.TemporaryDirectory()
three_class_json_file = osp.join(tmp_dir.name, 'three_class.json')
_create_three_class(three_class_json_file)
cat_names1 = CocoDataset(
ann_file=three_class_json_file, classes=names1, pipeline=[])
cat_names2 = CocoDataset(
ann_file=three_class_json_file, classes=names2, pipeline=[])
assert cat_names1.cat_ids == [0, 1, 2]
assert cat_names2.cat_ids == [1, 2, 0]
def test_coco_annotation_ids_unique():
tmp_dir = tempfile.TemporaryDirectory()
fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
_create_ids_error_coco_json(fake_json_file)
# test annotation ids not unique error
with pytest.raises(AssertionError):
CocoDataset(ann_file=fake_json_file, classes=('car', ), pipeline=[])
def test_dataset_evaluation():
tmp_dir = tempfile.TemporaryDirectory()
# create dummy data
fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
_create_dummy_coco_json(fake_json_file)
# test single coco dataset evaluation
coco_dataset = CocoDataset(
ann_file=fake_json_file, classes=('car', ), pipeline=[])
fake_results = _create_dummy_results()
eval_results = coco_dataset.evaluate(fake_results, classwise=True)
assert eval_results['bbox_mAP'] == 1
assert eval_results['bbox_mAP_50'] == 1
assert eval_results['bbox_mAP_75'] == 1
# test concat dataset evaluation
fake_concat_results = _create_dummy_results() + _create_dummy_results()
# build concat dataset through two config dict
coco_cfg = dict(
type='CocoDataset',
ann_file=fake_json_file,
classes=('car', ),
pipeline=[])
concat_cfgs = [coco_cfg, coco_cfg]
concat_dataset = build_dataset(concat_cfgs)
eval_results = concat_dataset.evaluate(fake_concat_results)
assert eval_results['0_bbox_mAP'] == 1
assert eval_results['0_bbox_mAP_50'] == 1
assert eval_results['0_bbox_mAP_75'] == 1
assert eval_results['1_bbox_mAP'] == 1
assert eval_results['1_bbox_mAP_50'] == 1
assert eval_results['1_bbox_mAP_75'] == 1
# build concat dataset through concatenated ann_file
coco_cfg = dict(
type='CocoDataset',
ann_file=[fake_json_file, fake_json_file],
classes=('car', ),
pipeline=[])
concat_dataset = build_dataset(coco_cfg)
eval_results = concat_dataset.evaluate(fake_concat_results)
assert eval_results['0_bbox_mAP'] == 1
assert eval_results['0_bbox_mAP_50'] == 1
assert eval_results['0_bbox_mAP_75'] == 1
assert eval_results['1_bbox_mAP'] == 1
assert eval_results['1_bbox_mAP_50'] == 1
assert eval_results['1_bbox_mAP_75'] == 1
# create dummy data
fake_pkl_file = osp.join(tmp_dir.name, 'fake_data.pkl')
_create_dummy_custom_pkl(fake_pkl_file)
# test single custom dataset evaluation
custom_dataset = CustomDataset(
ann_file=fake_pkl_file, classes=('car', ), pipeline=[])
fake_results = _create_dummy_results()
eval_results = custom_dataset.evaluate(fake_results)
assert eval_results['mAP'] == 1
# test concat dataset evaluation
fake_concat_results = _create_dummy_results() + _create_dummy_results()
# build concat dataset through two config dict
custom_cfg = dict(
type='CustomDataset',
ann_file=fake_pkl_file,
classes=('car', ),
pipeline=[])
concat_cfgs = [custom_cfg, custom_cfg]
concat_dataset = build_dataset(concat_cfgs)
eval_results = concat_dataset.evaluate(fake_concat_results)
assert eval_results['0_mAP'] == 1
assert eval_results['1_mAP'] == 1
# build concat dataset through concatenated ann_file
concat_cfg = dict(
type='CustomDataset',
ann_file=[fake_pkl_file, fake_pkl_file],
classes=('car', ),
pipeline=[])
concat_dataset = build_dataset(concat_cfg)
eval_results = concat_dataset.evaluate(fake_concat_results)
assert eval_results['0_mAP'] == 1
assert eval_results['1_mAP'] == 1
# build concat dataset through explicit type
concat_cfg = dict(
type='ConcatDataset',
datasets=[custom_cfg, custom_cfg],
separate_eval=False)
concat_dataset = build_dataset(concat_cfg)
eval_results = concat_dataset.evaluate(fake_concat_results, metric='mAP')
assert eval_results['mAP'] == 1
assert len(concat_dataset.datasets[0].data_infos) == \
len(concat_dataset.datasets[1].data_infos)
assert len(concat_dataset.datasets[0].data_infos) == 1
tmp_dir.cleanup()
parser.add_argument('--gpus', type=str, default='0', help='gpus, default is 0')
args = parser.parse_args()
args.model_save_path = '%s/%s/' % \
(config.model_save_path, time.strftime('%Y-%m-%d', time.localtime(time.time())))
if not os.path.exists(args.model_save_path):
_logger.warn("{} not exists, create it".format(args.model_save_path))
os.makedirs(args.model_save_path)
_set_file(args.model_save_path + 'log.log')
# Build model
from ..models.fbnet_faster_rcnn import FBNetCustomFasterRCNN
from ..search.detection_searcher import DetectionSearcher
from mmdet.datasets import CocoDataset, build_dataloader
coco_dataset = CocoDataset(**data_cfg['train'])
coco_dataset = build_dataloader(coco_dataset,
imgs_per_gpu=config.imgs_per_gpu,
workers_per_gpu=config.imgs_per_gpu,
dist=False,
num_gpus=len(args.gpus.split(',')))
model = FBNetCustomFasterRCNN(cfg=mmcv_config(model_cfg),
train_cfg=mmcv_config(train_cfg),
test_cfg=mmcv_config(test_cfg),
channels=model_cfg['neck']['in_channels'])
model.speed_test(torch.randn((1, 3, 224, 224)),
verbose=False,
device='cuda:' + args.gpus[0])
searcher = DetectionSearcher(model=model,