def calmap(probeFeat, probeLabel, galleryFeat, galleryLabel, norm_flag=True, rerank=False):
if norm_flag:
probeFeat = pre.normalize(probeFeat,axis=1)
galleryFeat = pre.normalize(galleryFeat,axis=1)
if rerank:
dist = re_ranking(probeFeat,galleryFeat,k1=10,k2=6,lambda_value=0.3)
else:
dist = cdist(probeFeat, galleryFeat)
ap = []
for i in range(len(dist)):
a = dist[i]
rank = []
ind = list(np.where(galleryLabel == probeLabel[i])[0])
dp = a[ind]
a.sort()
for j in dp:
rank.append(list(a).index(j)+1)
rank.sort()
thisap = 0.
for k in range(len(ind)):
thisap = thisap + float((k+1))/rank[k]
ap.append(thisap/len(ind))
ap = np.array(ap)
map = np.mean(ap)
return map
def calacc(probeFeat, probeLabel, galleryFeat, galleryLabel, rerank=False, top_num=[1,5,10,20,30]):
if rerank:
dist = re_ranking(probeFeat,galleryFeat,k1=10,k2=6,lambda_value=0.3)
else:
dist = cdist(probeFeat, galleryFeat)
index = []
for i in range(len(dist)):
a = dist[i]
ind = np.where(galleryLabel == probeLabel[i])
dp = a[ind]
a.sort()
index.append(list(a).index(dp))
index = np.array(index)
cmc = lambda top,index : float(len(np.where(index<top)[0]))/len(probeLabel)
cmc_curve = [cmc(top, index) for top in top_num]
return cmc_curve
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckpt.write_log('\n[INFO] Test:')
self.model.eval()
self.ckpt.add_log(torch.zeros(1, 5))
qf = self.extract_feature(self.query_loader).numpy()
gf = self.extract_feature(self.test_loader).numpy()
if self.args.re_rank:
q_g_dist = np.dot(qf, np.transpose(gf))
q_q_dist = np.dot(qf, np.transpose(qf))
g_g_dist = np.dot(gf, np.transpose(gf))
dist = re_ranking(q_g_dist, q_q_dist, g_g_dist)
else:
dist = cdist(qf, gf)
r = cmc(dist,
self.queryset.ids,
self.testset.ids,
self.queryset.cameras,
self.testset.cameras,
separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)
m_ap = mean_ap(dist, self.queryset.ids, self.testset.ids,
self.queryset.cameras, self.testset.cameras)
self.ckpt.log[-1, 0] = m_ap
self.ckpt.log[-1, 1] = r[0]
self.ckpt.log[-1, 2] = r[2]
self.ckpt.log[-1, 3] = r[4]
self.ckpt.log[-1, 4] = r[9]
best = self.ckpt.log.max(0)
self.ckpt.write_log(
'[INFO] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f} (Best: {:.4f} @epoch {})'
.format(m_ap, r[0], r[2], r[4], r[9], best[0][0],
(best[1][0] + 1) * self.args.test_every))
if not self.args.test_only:
self.ckpt.save(self,
epoch,
is_best=((best[1][0] + 1) *
self.args.test_every == epoch))
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, q_pids, q_camids, lqf = [], [], [], []
for batch_idx, (imgs, pids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
# qf.append(extract_feature(
# model, imgs, requires_norm=True, vectorize=True,use_pcb=args.use_pcb).cpu().data)
features,local_features = model(imgs)
# print('lqf shape',local_features.shape)
# print('qf shape',features.shape)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
q_pids.extend(pids)
# q_camids.extend(camids)
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf,0)
print('qf shape',qf.shape)
print('lqf shape',lqf.shape)
q_pids = np.asarray(q_pids)
#q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids, lgf = [], [], [], []
end = time.time()
for batch_idx, (imgs, pids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
# features, local_features = model(imgs)
# gf.append(extract_feature(
# model, imgs, requires_norm=True, vectorize=True,use_pcb=args.use_pcb).cpu().data)
features,local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
g_pids.extend(pids)
# g_camids.extend(camids)
gf = torch.cat(gf, 0)
# lgf = torch.cat(lgf,0)
print('gf shape',gf.shape)
# print('lgf shape',lgf.shape)
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))
# feature normlization
qf = 1. * qf / (torch.norm(qf, 2, dim = -1, keepdim=True).expand_as(qf) + 1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim = -1, keepdim=True).expand_as(gf) + 1e-12)
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()
print('distmat shape1',distmat.shape)
distmat.addmm_(1, -2, qf, gf.t())
print('distmat shape2',distmat.shape)
distmat = distmat.cpu().numpy()
# args.test_distance = 'global'(default)
if not args.test_distance== 'global':
print("Only using global branch")
from util.distance import low_memory_local_dist
#embed()
lqf = lqf.permute(0,2,1)
lgf = lgf.permute(0,2,1)
local_distmat = low_memory_local_dist(lqf.numpy(),lgf.numpy(),aligned= not args.unaligned)
if args.test_distance== 'local':
print("Only using local branch")
distmat = local_distmat
if args.test_distance == 'global_local':
print("Using global and local branches")
distmat = local_distmat+distmat
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("------------------")
# args.reranking = false(default)
if args.reranking:
from util.re_ranking import re_ranking
if args.test_distance == 'global':
print("Only using global branch for reranking")
distmat = re_ranking(qf,gf,k1=20, k2=6, lambda_value=0.3)
else:
local_qq_distmat = low_memory_local_dist(lqf.numpy(), lqf.numpy(),aligned= not args.unaligned)
local_gg_distmat = low_memory_local_dist(lgf.numpy(), lgf.numpy(),aligned= not args.unaligned)
local_dist = np.concatenate(
[np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)],
axis=0)
if args.test_distance == 'local':
print("Only using local branch for reranking")
distmat = re_ranking(qf,gf,k1=20,k2=6,lambda_value=0.3,local_distmat=local_dist,only_local=True)
elif args.test_distance == 'global_local':
print("Using global and local branches for reranking")
distmat = re_ranking(qf,gf,k1=20,k2=6,lambda_value=0.3,local_distmat=local_dist,only_local=False)
print("Computing CMC and mAP for re_ranking")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=args.use_metric_cuhk03)
print("Results ----------")
print("mAP(RK): {:.1%}".format(mAP))
print("CMC curve(RK)")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
return mAP
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, 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.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)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
gf, 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.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)
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))
# feature normlization
qf = 1. * qf / (torch.norm(qf, 2, dim=-1, keepdim=True).expand_as(qf) +
1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim=-1, keepdim=True).expand_as(gf) +
1e-12)
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()
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.reranking:
from util.re_ranking import re_ranking
distmat = re_ranking(qf, gf, k1=20, k2=6, lambda_value=0.3)
print("Computing CMC and mAP for re_ranking")
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=args.use_metric_cuhk03)
print("Results ----------")
print("mAP(RK): {:.1%}".format(mAP))
print("CMC curve(RK)")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
return cmc[0]
def test(model, queryloader, galleryloader, use_gpu, dataset_q,dataset_g,track_id_tmp=None,rank=100):
batch_time = AverageMeter()
#embed()
model.eval()
with torch.no_grad():
qf, lqf, q_imgs = [], [], []
for q_idx in range(len(dataset_q)):
q_img = int(dataset_q[q_idx].split('/')[-1].strip('.jpg'))
q_imgs.append(q_img)
for batch_idx, (imgs) in enumerate(queryloader):
#embed()
if use_gpu: imgs = imgs.cuda()
end = time.time()
features,local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf,0)
print('lqf shape',lqf.shape)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, lgf, g_imgs = [], [], []
for g_idx in range(len(dataset_g)):
g_img = int(dataset_g[g_idx].split('/')[-1].strip('.jpg'))
g_imgs.append(g_img)
end = time.time()
# embed()
#obtain the track infoi
for batch_idx, (imgs) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features,local_features = model(imgs)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
#embed()
gf = torch.cat(gf, 0)
lgf = torch.cat(lgf,0)
print('lgf shape',lgf.shape)
gt_f,_ = track_info_average(track_id_tmp,gf,lgf)
embed()
print('len of gimgs',len(g_imgs))
print('Extracted features for gallery_track set,obtained {}-by-{} matrix'.format(gt_f.size(0),gt_f.size(1)))
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
#embed()
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch))
# feature normlization
qf = 1. * qf / (torch.norm(qf, 2, dim = -1, keepdim=True).expand_as(qf) + 1e-12)
#gf = 1. * gf / (torch.norm(gf, 2, dim = -1, keepdim=True).expand_as(gf) + 1e-12)
gt_f = 1. * gt_f / (torch.norm(gt_f, 2, dim = -1, keepdim=True).expand_as(gt_f) + 1e-12)
m, n = qf.size(0), gt_f.size(0)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gt_f, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gt_f.t())
distmat = distmat.numpy()
#embed()
print("------------------")
if args.reranking:
from util.re_ranking import re_ranking
if args.test_distance == 'global':
print("Only using global branch for reranking")
distmat = re_ranking(qf,gt_f,k1=20, k2=6, lambda_value=0.3)
else:
local_qq_distmat = low_memory_local_dist(lqf.numpy(), lqf.numpy(),aligned= not args.unaligned)
local_gg_distmat = low_memory_local_dist(lgf.numpy(), lgf.numpy(),aligned= not args.unaligned)
local_dist = np.concatenate(
[np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)],
axis=0)
if args.test_distance == 'local':
print("Only using local branch for reranking")
distmat = re_ranking(qf,gf,k1=20,k2=6,lambda_value=0.3,local_distmat=local_dist,only_local=True)
elif args.test_distance == 'global_local':
print("Using global and local branches for reranking")
distmat = re_ranking(qf,gf,k1=20,k2=6,lambda_value=0.3,local_distmat=local_dist,only_local=False)
#embed()
print("Computing CMC and mAP for re_ranking")
print("==> Test aicity dataset and write to csv")
test_rank_result = test_rank100_aicity(distmat,q_imgs,g_imgs,track_id_tmp,use_track_info=True)
# test_rank_result is a dict, use pandas to convert
# embed()
test_rank_result_df = pd.DataFrame(list(test_rank_result.items()),columns=['query_ids','gallery_ids'])
test_result_df = test_rank_result_df.sort_values('query_ids')
# write to csvi
embed()
with open('aic_res_'+args.result_dir+'.txt','w') as f:
for idx in range(len(test_result_df)):
sep_c = ' '
row_ranks = []
idx_row = test_result_df.iloc[idx]['gallery_ids'][:100]
#embed()
for item in idx_row:
row_rank = str(item[0])
row_ranks.append(row_rank)
sep_c = sep_c.join(row_ranks)
#embed()
sep_c = sep_c+'\n'
#embed()
f.write(sep_c)
f.close()
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
print('------start testing------')
cmc1 = []
cmc2 = []
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, q_pids, q_camids, lqf = [], [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids, lgf = [], [], [], []
end = time.time()
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
lgf = torch.cat(lgf, 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))
# feature normlization
qf = 1. * qf / (torch.norm(qf, 2, dim=-1, keepdim=True).expand_as(qf) +
1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim=-1, keepdim=True).expand_as(gf) +
1e-12)
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 not args.test_distance == 'global':
print("Only using global branch")
from util.distance import low_memory_local_dist
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=not args.unaligned)
if args.test_distance == 'local':
print("Only using local branch")
distmat = local_distmat
if args.test_distance == 'global_local':
print("Using global and local branches")
distmat = local_distmat + distmat
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("cms's shape: ", cmc.shape)
print("cms's type: ", cmc.dtype)
# cmc1 = []
# print("cmc2's shape: ",cmc2.shape)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
cmc1.append(cmc[r - 1])
print("------------------")
# test matplot
# x = np.linspace(0,2*np.pi,50)
# y = np.sin(x)
# print("cmc2's shape: ",cmc2.shape)
# print(cmc2)
if args.reranking:
from util.re_ranking import re_ranking
if args.test_distance == 'global':
print("Only using global branch for reranking")
distmat = re_ranking(qf, gf, k1=20, k2=6, lambda_value=0.3)
else:
local_qq_distmat = low_memory_local_dist(
lqf.numpy(), lqf.numpy(), aligned=not args.unaligned)
local_gg_distmat = low_memory_local_dist(
lgf.numpy(), lgf.numpy(), aligned=not args.unaligned)
local_dist = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
if args.test_distance == 'local':
print("Only using local branch for reranking")
distmat = re_ranking(qf,
gf,
k1=20,
k2=6,
lambda_value=0.3,
local_distmat=local_dist,
only_local=True)
elif args.test_distance == 'global_local':
print("Using global and local branches for reranking")
distmat = re_ranking(qf,
gf,
k1=20,
k2=6,
lambda_value=0.3,
local_distmat=local_dist,
only_local=False)
print("Computing CMC and mAP for re_ranking")
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=args.use_metric_cuhk03)
# cmc2 = []
print("Results ----------")
print("mAP(RK): {:.1%}".format(mAP))
print("CMC curve(RK)")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
cmc2.append(cmc[r - 1])
print("------------------")
# matlpot
# print("----cmc2----",cmc2)
# print("cmc1's value------")
# print(cmc1)
plt.plot(ranks,
cmc1,
label='ranking',
color='red',
marker='o',
markersize=5)
plt.plot(ranks,
cmc2,
label='re-ranking',
color='blue',
marker='o',
markersize=5)
plt.ylabel('Accuracy')
plt.xlabel('Rank_num')
plt.title('Result of Ranking and Re-ranking(query_tank_cam=5)')
plt.legend()
plt.savefig('/home/gaoziqiang/tempt/tank_cam5.png')
plt.show()
return cmc[0]