)
return parser
if __name__ == '__main__':
args = get_parser().parse_args()
cfg = setup_cfg(args)
test_loader, num_query = build_reid_test_loader(
cfg, dataset_name=args.dataset_name)
demo = FeatureExtractionDemo(cfg, parallel=args.parallel)
logger.info("Start extracting image features")
feats = []
pids = []
camids = []
for (feat, pid, camid) in tqdm.tqdm(demo.run_on_loader(test_loader),
total=len(test_loader)):
feats.append(feat)
pids.extend(pid)
camids.extend(camid)
feats = torch.cat(feats, dim=0)
q_feat = feats[:num_query]
g_feat = feats[num_query:]
q_pids = np.asarray(pids[:num_query])
g_pids = np.asarray(pids[num_query:])
q_camids = np.asarray(camids[:num_query])
g_camids = np.asarray(camids[num_query:])
# compute cosine distance
distmat = 1 - torch.mm(q_feat, g_feat.t())
)
return parser
if __name__ == '__main__':
args = get_parser().parse_args()
logger = setup_logger()
cfg = setup_cfg(args)
test_loader, num_query = build_reid_test_loader(cfg, args.dataset_name)
demo = FeatureExtractionDemo(cfg, device=args.device, parallel=args.parallel)
logger.info("Start extracting image features")
feats = []
pids = []
camids = []
for (feat, pid, camid) in tqdm.tqdm(demo.run_on_loader(test_loader), total=len(test_loader.loader)):
feats.append(feat)
pids.extend(pid)
camids.extend(camid)
feats = torch.cat(feats, dim=0)
q_feat = feats[:num_query]
g_feat = feats[num_query:]
q_pids = np.asarray(pids[:num_query])
g_pids = np.asarray(pids[num_query:])
q_camids = np.asarray(camids[:num_query])
g_camids = np.asarray(camids[num_query:])
# compute cosine distance
distmat = torch.mm(q_feat, g_feat.t())
distmat = distmat.numpy()