def test_net_on_dataset(args,
dataset_name,
proposal_file,
output_dir,
multi_gpu=False,
gpu_id=0,
include_feat=False):
"""Run inference on a dataset."""
dataset = JsonDataset(dataset_name)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb(gt=args.do_val))
all_results = multi_gpu_test_net_on_dataset(args,
dataset_name,
proposal_file,
num_images,
output_dir,
include_feat=include_feat)
else:
all_results = test_net(args,
dataset_name,
proposal_file,
output_dir,
gpu_id=gpu_id,
include_feat=include_feat)
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(
test_timer.average_time))
# logger.info('Starting evaluation now...')
# task_evaluation.eval_rel_results(all_results, output_dir, args.do_val)
return all_results
def get_roidb_and_dataset(dataset_name, proposal_file, ind_range, do_val=True):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
dataset = JsonDataset(dataset_name)
roidb = dataset.get_roidb(gt=do_val)
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, dataset, start, end, total_num_images