def test_vib(model,
queryloader,
galleryloader,
use_gpu,
args,
writer,
epoch,
ranks=[1, 5, 10, 20],
return_distmat=False,
use_cosine=False,
draw_tsne=False,
tsne_clusters=3):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
q_pids, q_camids = [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf_stat.append(features)
q_pids.extend(pids)
q_camids.extend(camids)
qf_stat = torch.cat(qf_stat, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf_stat.size(0), qf_stat.size(1)))
g_pids, g_camids = [], [], []
end = time.time()
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
gf_stat.append(features)
g_pids.extend(pids)
g_camids.extend(camids)
gf_stat = torch.cat(gf_stat, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".
format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, args.test_batch))
pdb.set_trace()
m, n = qf_stat.size(0), gf_stat.size(0)
score_board = torch.zeros((m, n, n), dtype=torch.int16)
qf = torch.zeros(m, 512) #,torch.zeros(m,512)
for _ in range(args.sampling_count):
qf_sample = model.reparametrize_n(qf_stat[:, 0], qf_stat[:, 1])
qf = qf + qf_sample
qf = qf / args.sampling_count
for _ in range(args.sampling_count):
#qf = model.reparametrize_n(qf_stat[:,0],qf_stat[:,1])
gf = model.reparametrize_n(gf_stat[:, 0], gf_stat[:, 1])
if not use_cosine:
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.numpy()
else:
qf_norm = qf / qf.norm(dim=1)[:, None]
gf_norm = gf / gf.norm(dim=1)[:, None]
distmat = torch.addmm(1, torch.ones((m, n)), -1, qf_norm,
gf_norm.transpose(0, 1))
distmat = distmat.numpy()
indices = np.argsort(distmat, axis=1)
for indx in range(m):
score_board[m, indices[indx], list(range(n))] += 1
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=args.use_metric_cuhk03)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if draw_tsne:
drawTSNE(qf, gf, q_pids, g_pids, q_camids, g_camids, tsne_clusters,
args.save_dir)
if return_distmat:
return distmat
if writer != None:
writer.add_scalars('Testing',
dict(rank_1=cmc[0], rank_5=cmc[4], mAP=mAP),
epoch + 1)
return cmc[0]
def test(model,
queryloader,
galleryloader,
use_gpu,
args,
writer,
epoch,
ranks=[1, 5, 10, 20],
return_distmat=False,
tsne_clusters=3):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
q_imgPath = []
for batch_idx, (input) in enumerate(queryloader):
if not args.draw_tsne:
imgs, pids, camids = input
else:
imgs, pids, camids, img_path = input
q_imgPath.extend(img_path)
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
q_imgPath = np.asarray(q_imgPath)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
g_imgPath = []
end = time.time()
for batch_idx, (input) in enumerate(galleryloader):
if not args.draw_tsne:
imgs, pids, camids = input
else:
imgs, pids, camids, img_path = input
g_imgPath.extend(img_path)
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
g_imgPath = np.asarray(q_imgPath)
print("Extracted features for gallery set, obtained {}-by-{} matrix".
format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, args.test_batch))
if args.use_ecn:
distmat = (ECN_custom(qf,
gf,
k=25,
t=3,
q=8,
method='rankdist',
use_cosine=args.use_cosine)).transpose()
elif not args.use_cosine:
m, n = qf.size(0), gf.size(0)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.numpy()
if args.re_ranking:
distmat_q_q = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, m) + \
torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, m).t()
distmat_q_q.addmm_(1, -2, qf, qf.t())
distmat_q_q = distmat_q_q.numpy()
distmat_g_g = torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, n).t()
distmat_g_g.addmm_(1, -2, gf, gf.t())
distmat_g_g = distmat_g_g.numpy()
distmat = re_ranking(distmat,
distmat_q_q,
distmat_g_g,
k1=20,
k2=6,
lambda_value=0.3)
else:
m, n = qf.size(0), gf.size(0)
qf_norm = qf / qf.norm(dim=1)[:, None]
gf_norm = gf / gf.norm(dim=1)[:, None]
distmat = torch.addmm(1, torch.ones((m, n)), -1, qf_norm,
gf_norm.transpose(0, 1))
distmat = distmat.numpy()
if args.re_ranking:
distmat_q_q = torch.addmm(1, torch.ones((m, m)), -1, qf_norm,
qf_norm.transpose(0, 1))
distmat_q_q = distmat_q_q.numpy()
distmat_g_g = torch.addmm(1, torch.ones((n, n)), -1, gf_norm,
gf_norm.transpose(0, 1))
distmat_g_g = distmat_g_g.numpy()
distmat = re_ranking(distmat,
distmat_q_q,
distmat_g_g,
k1=20,
k2=6,
lambda_value=0.3)
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=args.use_metric_cuhk03)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if args.draw_tsne:
drawTSNE(qf, gf, q_pids, g_pids, q_camids, g_camids, q_imgPath,
g_imgPath, tsne_clusters, args.save_dir)
if return_distmat:
return distmat
if writer != None:
writer.add_scalars('Testing',
dict(rank_1=cmc[0], rank_5=cmc[4], mAP=mAP),
epoch + 1)
return cmc[0]
def test(model, queryloader, galleryloader, use_gpu, args,writer,epoch, ranks=[1, 5, 10, 20], return_distmat=False,use_cosine = False,draw_tsne=False,tsne_clusters=3):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
qf_std = []
q_imgPath = []
for batch_idx, (input) in enumerate(queryloader):
if not args.draw_tsne:
imgs, pids, camids = input
else:
imgs, pids, camids,img_path = input
q_imgPath.extend(img_path)
if use_gpu: imgs = imgs.cuda()
end = time.time()
features,std = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
std = std.data.cpu()
qf.append(features)
qf_std.append(std)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
qf_std = torch.cat(qf_std, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
q_imgPath = np.asarray(q_imgPath)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
#gf_std = []
g_imgPath = []
end = time.time()
for batch_idx, (input) in enumerate(galleryloader):
if not args.draw_tsne:
imgs, pids, camids = input
else:
imgs, pids, camids,img_path = input
g_imgPath.extend(img_path)
if use_gpu: imgs = imgs.cuda()
end = time.time()
features,_ = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
#std = std.data.cpu()
gf.append(features)
#gf_std.append(std)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
#gf_std = torch.cat(gf_std, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
g_imgPath = np.asarray(q_imgPath)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch))
m, n = qf.size(0), gf.size(0)
if args.use_ecn:
distmat= (ECN(qf.numpy(),gf.numpy(),k=25,t=3,q=8,method='rankdist')).transpose()
elif args.mahalanobis:
print("Using STD for Mahalanobis distance")
distmat = torch.zeros((m,n))
# #pdb.set_trace()
# qf = qf.data.numpy()
# gf= gf.data.numpy()
# qf_std = qf_std.data.numpy()
# for q_indx in range(int(m)):
# distmat[q_indx]= pw(np.expand_dims(qf[q_indx],axis=0),gf,metric='mahalanobis',n_jobs=8, VI=(np.eye(qf_std[q_indx].shape[0])*(1/qf_std[q_indx]).transpose()))
# print(q_indx)
# pdb.set_trace()
qf = qf / qf_std
for q_indx in range(int(m)):
gf_norm = gf * 1/qf_std[q_indx]
distmat[q_indx] = torch.pow(qf[q_indx], 2).sum(dim=0, keepdim=True).expand(n) + \
torch.pow(gf_norm, 2).sum(dim=1, keepdim=True).squeeze()
distmat[q_indx].unsqueeze(0).addmm_(1, -2, qf[q_indx].unsqueeze(0), gf_norm.t())
distmat = distmat.numpy()
elif not (use_cosine or args.use_cosine):
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.numpy()
if args.re_ranking:
distmat_q_q = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, m) + \
torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, m).t()
distmat_q_q.addmm_(1, -2, qf, qf.t())
distmat_q_q = distmat_q_q.numpy()
distmat_g_g = torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, n).t()
distmat_g_g.addmm_(1, -2, gf, gf.t())
distmat_g_g = distmat_g_g.numpy()
print("Normal Re-Ranking")
distmat = re_ranking(distmat, distmat_q_q, distmat_g_g, k1=20, k2=6, lambda_value=0.3)
else:
qf_norm = qf/qf.norm(dim=1)[:,None]
gf_norm = gf/gf.norm(dim=1)[:,None]
distmat = torch.addmm(1,torch.ones((m,n)),-1,qf_norm,gf_norm.transpose(0,1))
distmat = distmat.numpy()
if args.re_ranking:
distmat_q_q = torch.addmm(1,torch.ones((m,m)),-1,qf_norm,qf_norm.transpose(0,1))
distmat_q_q = distmat_q_q.numpy()
distmat_g_g = torch.addmm(1,torch.ones((n,n)),-1,gf_norm,gf_norm.transpose(0,1))
distmat_g_g = distmat_g_g.numpy()
print("Re-Ranking with Cosine")
distmat = re_ranking(distmat, distmat_q_q, distmat_g_g, k1=20, k2=6, lambda_value=0.3)
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=args.use_metric_cuhk03)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1]))
print("------------------")
if draw_tsne:
drawTSNE(qf,gf,q_pids, g_pids, q_camids, g_camids,q_imgPath, g_imgPath,tsne_clusters,args.save_dir)
if return_distmat:
return distmat
if writer != None:
writer.add_scalars(
'Testing',
dict(rank_1=cmc[0],
rank_5 = cmc[4],
mAP=mAP),
epoch + 1)
return cmc[0]