def evaluate(self):
features = torch.cat(self.features, dim=0)
spatial_features = np.vstack(self.spatial_features)
scores = torch.cat(self.scores, dim=0)
# query feature, person ids and camera ids
query_features = features[:self._num_query]
query_pids = np.asarray(self.pids[:self._num_query])
query_camids = np.asarray(self.camids[:self._num_query])
# gallery features, person ids and camera ids
gallery_features = features[self._num_query:]
gallery_pids = np.asarray(self.pids[self._num_query:])
gallery_camids = np.asarray(self.camids[self._num_query:])
dist = 1 - torch.mm(query_features, gallery_features.t()).numpy()
logger.info("Testing without DSR setting")
self._results = OrderedDict()
if self.cfg.TEST.DSR.ENABLED:
topk = self.cfg.TEST.DSR.TOPK
dist = compute_dsr_dist(spatial_features[:self._num_query],
spatial_features[self._num_query:], dist,
scores[:self._num_query], topk)
logger.info("Testing with DSR setting")
cmc, all_AP, all_INP = evaluate_rank(dist, query_pids, gallery_pids,
query_camids, gallery_camids)
mAP = np.mean(all_AP)
mINP = np.mean(all_INP)
self._results['R-1'] = cmc[0]
self._results['mAP'] = mAP
self._results['mINP'] = mINP
return copy.deepcopy(self._results)
def evaluate(self):
features = torch.cat(self.features, dim=0)
spatial_features = np.vstack(self.spatial_features)
scores = torch.cat(self.scores, dim=0)
# query feature, person ids and camera ids
query_features = features[:self._num_query]
query_pids = np.asarray(self.pids[:self._num_query])
query_camids = np.asarray(self.camids[:self._num_query])
# gallery features, person ids and camera ids
gallery_features = features[self._num_query:]
gallery_pids = np.asarray(self.pids[self._num_query:])
gallery_camids = np.asarray(self.camids[self._num_query:])
self._results = OrderedDict()
query_features = F.normalize(query_features, dim=1)
gallery_features = F.normalize(gallery_features, dim=1)
dist = 1 - torch.mm(query_features, gallery_features.t()).numpy()
if self.cfg.TEST.DSR.ENABLED:
dsr_dist = get_dsr_dist(spatial_features[:self._num_query],
spatial_features[self._num_query:], dist,
scores[:self._num_query])
logger.info("Test with DSR setting")
lamb = self.cfg.TEST.DSR.LAMB
dist = (1 - lamb) * dist + lamb * dsr_dist
cmc, all_AP, all_INP = evaluate_rank(dist, query_pids, gallery_pids,
query_camids, gallery_camids)
mAP = np.mean(all_AP)
mINP = np.mean(all_INP)
for r in [1, 5, 10]:
self._results['Rank-{}'.format(r)] = cmc[r - 1]
self._results['mAP'] = mAP
self._results['mINP'] = mINP
tprs = evaluate_roc(dist, query_pids, gallery_pids, query_camids,
gallery_camids)
fprs = [1e-4, 1e-3, 1e-2]
for i in range(len(fprs)):
self._results["TPR@FPR={}".format(fprs[i])] = tprs[i]
return copy.deepcopy(self._results)
def evaluate(self):
if comm.get_world_size() > 1:
comm.synchronize()
features = comm.gather(self.features)
features = sum(features, [])
spatial_features = comm.gather(self.spatial_features)
spatial_features = sum(spatial_features, [])
scores = comm.gather(self.scores)
scores = sum(scores, [])
pids = comm.gather(self.pids)
pids = sum(pids, [])
camids = comm.gather(self.camids)
camids = sum(camids, [])
# fmt: off
if not comm.is_main_process(): return {}
# fmt: on
else:
features = self.features
spatial_features = self.spatial_features
scores = self.scores
pids = self.pids
camids = self.camids
features = torch.cat(features, dim=0)
spatial_features = torch.cat(spatial_features, dim=0).numpy()
scores = torch.cat(scores, dim=0)
# query feature, person ids and camera ids
query_features = features[:self._num_query]
query_pids = np.asarray(pids[:self._num_query])
query_camids = np.asarray(camids[:self._num_query])
# gallery features, person ids and camera ids
gallery_features = features[self._num_query:]
gallery_pids = np.asarray(pids[self._num_query:])
gallery_camids = np.asarray(camids[self._num_query:])
if self.cfg.TEST.METRIC == "cosine":
query_features = F.normalize(query_features, dim=1)
gallery_features = F.normalize(gallery_features, dim=1)
dist = 1 - torch.mm(query_features, gallery_features.t()).numpy()
self._results = OrderedDict()
query_features = query_features.numpy()
gallery_features = gallery_features.numpy()
if self.cfg.TEST.DSR.ENABLED:
logger.info("Testing with DSR setting")
dsr_dist = compute_dsr_dist(spatial_features[:self._num_query],
spatial_features[self._num_query:],
dist, scores[:self._num_query])
max_value = 0
k = 0
for i in range(0, 101):
lamb = 0.01 * i
dist1 = (1 - lamb) * dist + lamb * dsr_dist
cmc, all_AP, all_INP = evaluate_rank(dist1, query_pids,
gallery_pids,
query_camids,
gallery_camids)
if (cmc[0] > max_value):
k = lamb
max_value = cmc[0]
dist1 = (1 - k) * dist + k * dsr_dist
cmc, all_AP, all_INP = evaluate_rank(dist1, query_pids,
gallery_pids, query_camids,
gallery_camids)
else:
cmc, all_AP, all_INP = evaluate_rank(dist, query_pids,
gallery_pids, query_camids,
gallery_camids)
mAP = np.mean(all_AP)
mINP = np.mean(all_INP)
for r in [1, 5, 10]:
self._results['Rank-{}'.format(r)] = cmc[r - 1] * 100
self._results['mAP'] = mAP * 100
self._results['mINP'] = mINP * 100
return copy.deepcopy(self._results)
def evaluate(self):
if comm.get_world_size() > 1:
comm.synchronize()
features = comm.gather(self.features)
features = sum(features, [])
spatial_features = comm.gather(self.spatial_features)
spatial_features = sum(spatial_features, [])
scores = comm.gather(self.scores)
scores = sum(scores, [])
pids = comm.gather(self.pids)
pids = sum(pids, [])
camids = comm.gather(self.camids)
camids = sum(camids, [])
# fmt: off
if not comm.is_main_process(): return {}
# fmt: on
else:
features = self.features
spatial_features = self.spatial_features
scores = self.scores
pids = self.pids
camids = self.camids
features = torch.cat(features, dim=0)
spatial_features = torch.cat(spatial_features, dim=0).numpy()
scores = torch.cat(scores, dim=0)
# query feature, person ids and camera ids
query_features = features[:self._num_query]
query_pids = np.asarray(pids[:self._num_query])
query_camids = np.asarray(camids[:self._num_query])
# gallery features, person ids and camera ids
gallery_features = features[self._num_query:]
gallery_pids = np.asarray(pids[self._num_query:])
gallery_camids = np.asarray(camids[self._num_query:])
if self.cfg.TEST.METRIC == "cosine":
query_features = F.normalize(query_features, dim=1)
gallery_features = F.normalize(gallery_features, dim=1)
dist = 1 - torch.mm(query_features, gallery_features.t()).numpy()
self._results = OrderedDict()
query_features = query_features.numpy()
gallery_features = gallery_features.numpy()
if self.cfg.TEST.DSR.ENABLED:
logger.info("Testing with DSR setting")
dist = compute_dsr_dist(spatial_features[:self._num_query],
spatial_features[self._num_query:], dist,
scores[:self._num_query])
cmc, all_AP, all_INP = evaluate_rank(dist,
query_features,
gallery_features,
query_pids,
gallery_pids,
query_camids,
gallery_camids,
use_distmat=True)
else:
cmc, all_AP, all_INP = evaluate_rank(dist,
query_features,
gallery_features,
query_pids,
gallery_pids,
query_camids,
gallery_camids,
use_distmat=False)
mAP = np.mean(all_AP)
mINP = np.mean(all_INP)
for r in [1, 5, 10]:
self._results['Rank-{}'.format(r)] = cmc[r - 1]
self._results['mAP'] = mAP
self._results['mINP'] = mINP
if self.cfg.TEST.ROC_ENABLED:
scores, labels = evaluate_roc(dist, query_features,
gallery_features, query_pids,
gallery_pids, query_camids,
gallery_camids)
fprs, tprs, thres = metrics.roc_curve(labels, scores)
for fpr in [1e-4, 1e-3, 1e-2]:
ind = np.argmin(np.abs(fprs - fpr))
self._results["TPR@FPR={:.0e}".format(fpr)] = tprs[ind]
return copy.deepcopy(self._results)
print("=> Check precision")
num_q = 30
num_g = 300
dim = 512
max_rank = 5
q_feats = np.random.rand(num_q, dim).astype(np.float32) * 20
q_feats = q_feats / np.linalg.norm(q_feats, ord=2, axis=1, keepdims=True)
g_feats = np.random.rand(num_g, dim).astype(np.float32) * 20
g_feats = g_feats / np.linalg.norm(g_feats, ord=2, axis=1, keepdims=True)
distmat = 1 - np.dot(q_feats, g_feats.transpose())
q_pids = np.random.randint(0, num_q, size=num_q)
g_pids = np.random.randint(0, num_g, size=num_g)
q_camids = np.random.randint(0, 5, size=num_q)
g_camids = np.random.randint(0, 5, size=num_g)
cmc_py, mAP_py, mINP_py = evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)
cmc_cy, mAP_cy, mINP_cy = evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)
np.testing.assert_allclose(cmc_py, cmc_cy, rtol=1e-3, atol=1e-6)
np.testing.assert_allclose(mAP_py, mAP_cy, rtol=1e-3, atol=1e-6)
np.testing.assert_allclose(mINP_py, mINP_cy, rtol=1e-3, atol=1e-6)
print('Rank results between python and cython are the same!')
scores_cy, labels_cy = evaluate_roc(distmat, q_pids, g_pids, q_camids, g_camids, use_cython=True)
scores_py, labels_py = evaluate_roc(distmat, q_pids, g_pids, q_camids, g_camids, use_cython=False)
np.testing.assert_allclose(scores_cy, scores_py, rtol=1e-3, atol=1e-6)
np.testing.assert_allclose(labels_cy, labels_py, rtol=1e-3, atol=1e-6)
print('ROC results between python and cython are the same!\n')