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())
distmat = distmat.numpy()
logger.info("Computing APs for all query images ...")
cmc, all_ap, all_inp = evaluate_rank(distmat, q_pids, g_pids, q_camids,
g_camids)
logger.info("Finish computing APs for all query images!")
visualizer = Visualizer(test_loader.dataset)
visualizer.get_model_output(all_ap, distmat, q_pids, g_pids, q_camids,
g_camids)
logger.info("Start saving ROC curve ...")
fpr, tpr, pos, neg = visualizer.vis_roc_curve(args.output)
visualizer.save_roc_info(args.output, fpr, tpr, pos, neg)
logger.info("Finish saving ROC curve!")
logger.info("Saving rank list result ...")
query_indices = visualizer.vis_rank_list(args.output, args.vis_label,
args.num_vis, args.rank_sort,
args.label_sort, args.max_rank)
logger.info("Finish saving rank list results!")
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()
logger.info("Computing APs for all query images ...")
cmc, all_ap, all_inp = evaluate_rank(1 - distmat, q_pids, g_pids, q_camids,
g_camids)
visualizer = Visualizer(test_loader.loader.dataset)
visualizer.get_model_output(all_ap, distmat, q_pids, g_pids, q_camids,
g_camids)
logger.info("Saving rank list result ...")
query_indices = visualizer.vis_rank_list(args.output, args.vis_label,
args.num_vis, args.rank_sort,
args.label_sort, args.max_rank)
# encoding: utf-8 """ @author: xingyu liao @contact: [email protected] """ import matplotlib.pyplot as plt import sys sys.path.append('.') from fastreid.utils.visualizer import Visualizer if __name__ == "__main__": baseline_res = Visualizer.load_roc_info("logs/duke_vis/roc_info.pickle") mgn_res = Visualizer.load_roc_info("logs/mgn_duke_vis/roc_info.pickle") fig = Visualizer.plot_roc_curve(baseline_res['fpr'], baseline_res['tpr'], name='baseline') Visualizer.plot_roc_curve(mgn_res['fpr'], mgn_res['tpr'], name='mgn', fig=fig) plt.savefig('roc.jpg') fig = Visualizer.plot_distribution(baseline_res['pos'], baseline_res['neg'], name='baseline') Visualizer.plot_distribution(mgn_res['pos'], mgn_res['neg'], name='mgn', fig=fig) plt.savefig('dist.jpg')
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()
logger.info("Computing APs for all query images ...")
cmc, all_ap, all_inp = evaluate_rank(1-distmat, q_pids, g_pids, q_camids, g_camids)
visualizer = Visualizer(test_loader.loader.dataset)
visualizer.get_model_output(all_ap, distmat, q_pids, g_pids, q_camids, g_camids)
logger.info("Saving ranking list result ...")
visualizer.vis_ranking_list(args.output, args.num_vis, rank_sort=args.rank_sort, max_rank=args.max_rank)