def compute(self):
feats = torch.cat(self.feats, dim=0)
if self.feat_norm == 'yes':
#print("The test feature is normalized")
feats = torch.nn.functional.normalize(feats, dim=1, p=2)
# query
qf = feats[:self.num_query]
q_pids = np.asarray(self.pids[:self.num_query])
q_camids = np.asarray(self.camids[:self.num_query])
# gallery
gf = feats[self.num_query:]
g_pids = np.asarray(self.pids[self.num_query:])
g_camids = np.asarray(self.camids[self.num_query:])
distmat = euclidean_dist(qf, gf).cpu().numpy()
if self.validation_flag:
cmc, mAP = eval_func(distmat, q_pids, g_pids, q_camids, g_camids)
else:
cmc, mAP = np.zeros((self.max_rank,)), 0.0
if self.output_flag:
indices = np.argsort(distmat, axis=1)
np.savetxt("result.txt", indices[:, :100], fmt="%05d")
return cmc, mAP
def compute(self):
feats = torch.cat(self.feats, dim=0)
camfeats = torch.cat(self.camfeats, dim=0)
if self.feat_norm == 'yes':
#print("The test feature is normalized")
feats = torch.nn.functional.normalize(feats, dim=1, p=2)
camfeats = torch.nn.functional.normalize(camfeats, dim=1, p=2)
feats = feats.cpu().numpy().astype(np.float16)
# merge by trkid
self.pids = np.asarray(self.pids)
trkids = list(set(self.trkids))
merged_feats = np.zeros((len(trkids),feats.shape[1]))
merged_labels = np.zeros(len(trkids)) - 1
for i in range(len(trkids)):
tmpidxs = np.where(self.trkids==trkids[i])
tmpfeats = feats[tmpidxs]
merged_feats[i] = np.mean(tmpfeats,axis=0)
merged_labels[i] = self.pids[tmpidxs][0]
feats = torch.from_numpy(merged_feats).cuda()
distmat = re_ranking(feats, feats, 20, 6, 0.3)
if self.finetune:
camdistmat = euclidean_dist(camfeats, camfeats).cpu().numpy()
distmat -= camdistmat * 0.1
pos_bool = (distmat < 0)
distmat[pos_bool] = 0
eps = self.dist_thrd
cluster = DBSCAN(eps=eps,min_samples=4, metric='precomputed', n_jobs=2)
ret = cluster.fit_predict(distmat.astype(np.double))
# most count
labelset = Counter(ret[ret>=0])
labelset = labelset.most_common()
labelset = [i[0] for i in labelset]
labelset = labelset[:self.topk]
idxs = np.where(np.in1d(ret,labelset))[0]
psolabels = ret[idxs]
pids = merged_labels[idxs]
acc = sklearn.metrics.cluster.normalized_mutual_info_score(psolabels,pids)
# back to train images
outret = np.zeros(len(self.trkids)) - 1
for i in range(len(trkids)):
if ret[i] >= 0:
outret[self.trkids==trkids[i]] = ret[i]
# relabel
outret_set = set(outret)
outret_set_len = len(labelset) - 1
outliers = 0
pid2label = {}
for pid in outret_set:
if pid in labelset:
pid2label[pid] = labelset.index(pid)
elif self.finetune and outliers <= 200:
pid2label[pid] = outret_set_len + outliers
outliers += 1
else:
pid2label[pid] = -1
outret = np.asarray([pid2label[i] for i in outret])
return outret,acc,len(outret[outret!=-1])
def compute(self):
feats = torch.cat(self.feats, dim=0)
camera_feats = torch.cat(self.camera_feats, dim=0)
if self.feat_norm == 'yes':
#print("The test feature is normalized")
feats = torch.nn.functional.normalize(feats, dim=1, p=2)
camera_feats = torch.nn.functional.normalize(camera_feats, dim=1, p=2)
# query
qf = feats[:self.num_query]
q_campred = np.asarray(self.camera_scores[:self.num_query], np.int)
qcf = camera_feats[:self.num_query]
q_pids = np.asarray(self.pids[:self.num_query])
q_camids = np.asarray(self.camids[:self.num_query])
# gallery
gf = feats[self.num_query:]
g_campred = np.asarray(self.camera_scores[self.num_query:], np.int)
gcf = camera_feats[self.num_query:]
g_pids = np.asarray(self.pids[self.num_query:])
g_camids = np.asarray(self.camids[self.num_query:])
qf = qf.cpu().numpy().astype(np.float16)
gf = gf.cpu().numpy().astype(np.float16)
P, neg_vec = compute_P2(qf, gf, g_campred, 0.02)
qf = meanfeat_sub(P, neg_vec, qf, q_campred)
gf = meanfeat_sub(P, neg_vec, gf, g_campred)
gf_new = gf.copy()
for _ in range(3):
gf_new = mergesetfeat(gf_new, g_campred, gf, g_campred, 0.03, 50)
qf_new = qf.copy()
for _ in range(3):
qf_new = mergesetfeat(qf_new, q_campred, gf_new, g_campred, 0.03, 50)
qf = torch.from_numpy(qf_new).cuda()
gf = torch.from_numpy(gf_new).cuda()
distmat = re_ranking(qf, gf, k1=30, k2=8, lambda_value=0.3)
camdistmat = euclidean_dist(qcf, gcf).cpu().numpy()
if self.output_flag:
np.save(os.path.join(self.output_dir, 'distmat.npy'), distmat)
np.save(os.path.join(self.output_dir, 'q_pred.npy'), q_campred)
np.save(os.path.join(self.output_dir, 'g_pred.npy'), g_campred)
np.save(os.path.join(self.output_dir, 'camdistmat.npy'), camdistmat)
distmat -= camdistmat * 0.1
campredmat = np.equal(q_campred.reshape(-1,1), g_campred.T)
distmat += campredmat * 1.0
distmat = add_space(distmat, q_campred, g_campred)
if self.validation_flag:
cmc, mAP = eval_func(distmat, q_pids, g_pids, q_camids, g_camids)
else:
cmc, mAP = np.zeros((self.max_rank,)), 0.0
if self.output_flag:
indices = np.argsort(distmat, axis=1)
np.savetxt("result.txt", indices[:, :100], fmt="%05d")
return cmc, mAP