def main():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
cfg.model.pretrained = None
dataset = utils.get_dataset(cfg.data.val)
class_names = cfg.data.val.class_names
if args.gpus == 1:
model = build_detector(
cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
#load_checkpoint(model, args.checkpoint)
model = MMDataParallel(model, device_ids=[0])
load_params_from_file(model, args.checkpoint)
data_loader = build_dataloader(
dataset,
1,
cfg.data.workers_per_gpu,
num_gpus=1,
shuffle=False,
dist=False)
outputs = single_test(model, data_loader, args.out, class_names)
else:
NotImplementedError
# kitti evaluation
gt_annos = kitti.get_label_annos(dataset.label_prefix, dataset.sample_ids)
result = get_official_eval_result(gt_annos, outputs, current_classes=class_names)
print(result)
def evaluate(self, runner, results):
gt_annos = kitti.get_label_annos(self.dataset.label_prefix, self.dataset.sample_ids)
result = get_official_eval_result(gt_annos, results, current_classes=0)
runner.logger.info(result)
runner.log_buffer.ready = True
def main():
args = parse_args()
# if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
# raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
cfg.model.pretrained = None
dataset = utils.get_dataset(cfg.data.val)
class_names = cfg.data.val.class_names
if args.gpus == 1:
model = build_detector(cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint)
model = MMDataParallel(model, device_ids=[0])
data_loader = build_dataloader(
dataset,
1,
cfg.data.workers_per_gpu,
num_gpus=1,
#collate_fn= cfg.data.collate_fn,
shuffle=False,
dist=False)
outputs = single_test(model, data_loader, args.out, class_names)
else:
NotImplementedError
# kitti evaluation
gt_annos = kitti.get_label_annos(dataset.label_prefix, dataset.sample_ids)
result = get_official_eval_result(gt_annos,
outputs,
current_classes=class_names)
print(result)
if args.out:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_file = args.out + '.json'
results2json(dataset, outputs, result_file)
coco_eval(result_file, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}.json'.format(name)
results2json(dataset, outputs_, result_file)
coco_eval(result_file, eval_types, dataset.coco)