def validation_end(self, outputs):
feats,pids,camids = [],[],[]
for o in outputs:
feats.append(o['feats'])
pids.extend(o['pids'])
camids.extend(o['camids'])
feats = torch.cat(feats, dim=0)
if self.cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:self.num_query]
q_pids = np.asarray(pids[:self.num_query])
q_camids = np.asarray(camids[:self.num_query])
# gallery
gf = feats[self.num_query:]
g_pids = np.asarray(pids[self.num_query:])
g_camids = np.asarray(camids[self.num_query:])
m, n = qf.shape[0], gf.shape[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.cpu().numpy()
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
self.logger.info(f"Test Results - Epoch: {self.current_epoch + 1}")
self.logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
self.logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
tqdm_dic = {'rank1': cmc[0], 'mAP': mAP}
return tqdm_dic
def on_epoch_end(self, epoch, **kwargs: Any):
# test model performance
if (epoch + 1) % self._eval_period == 0:
self._logger.info('Testing ...')
feats, pids, camids = [], [], []
self.learn.model.eval()
with torch.no_grad():
for imgs, _ in self._test_dl:
feat = self.learn.model(imgs)
feats.append(feat)
feats = torch.cat(feats, dim=0)
if self._norm:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:self._num_query]
# gallery
gf = feats[self._num_query:]
m, n = qf.shape[0], gf.shape[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 = to_np(distmat)
cmc, mAP = evaluate(distmat, self.q_pids, self.g_pids,
self.q_camids, self.g_camids)
self._logger.info(f"Test Results - Epoch: {epoch+1}")
self._logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
self._logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r-1]:.1%}")
self.learn.save("model_{}".format(epoch))
def inference_with_distmat(
cfg,
test_dataloader,
num_query,
distmat,
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
pids, camids = [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
# query
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def test(self):
# convert to eval mode
self.model.eval()
metric_dict = list()
for val_dataset_name, val_dataloader, num_query in zip(
self.cfg.DATASETS.TEST_NAMES, self.val_dataloader_collection,
self.num_query_len_collection):
feats, pids, camids = [], [], []
val_prefetcher = data_prefetcher_mask(val_dataloader)
batch = val_prefetcher.next()
while batch[0] is not None:
img, mask, pid, camid = batch
adj_batch = self.adj.repeat(img.size(0), 1, 1)
with torch.no_grad():
output = self.model(img, img, mask, adj_batch)
# feat = output[1]
feat = torch.cat([output[1], output[3]], dim=1)
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
feats = torch.cat(feats, dim=0)
if self.cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# m, n = qf.shape[0], gf.shape[0]
distmat = torch.mm(qf, gf.t()).cpu().numpy()
# 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()
cmc, mAP = evaluate(-distmat, q_pids, g_pids, q_camids, g_camids)
self.logger.info(
f"Test Results on {val_dataset_name} - Epoch: {self.current_epoch}"
)
self.logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
self.logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
self.writer.add_scalar('rank1', cmc[0], self.global_step)
self.writer.add_scalar('mAP', mAP, self.global_step)
metric_dict.append({'rank1': cmc[0], 'mAP': mAP})
# convert to train mode
self.model.train()
return metric_dict[0]
def test(self):
# convert to eval mode
self.model.eval()
feats, pids, camids = [], [], []
val_prefetcher = data_prefetcher(self.val_dataloader, self.cfg)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = self.model(img)
if isinstance(feat, tuple):
feats.append(feat[0])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
####
feats = torch.cat(feats, dim=0)
if self.cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:self.num_query]
q_pids = np.asarray(pids[:self.num_query])
q_camids = np.asarray(camids[:self.num_query])
# gallery
gf = feats[self.num_query:]
g_pids = np.asarray(pids[self.num_query:])
g_camids = np.asarray(camids[self.num_query:])
# TODO: 添加rerank的测评结果
# m, n = qf.shape[0], gf.shape[0]
distmat = -torch.mm(qf, gf.t()).cpu().numpy()
# 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()
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
self.logger.info(f"Test Results - Epoch: {self.current_epoch}")
self.logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
self.logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
self.writer.add_scalar('rank1', cmc[0], self.global_step)
self.writer.add_scalar('mAP', mAP, self.global_step)
metric_dict = {'rank1': cmc[0], 'mAP': mAP}
# convert to train mode
self.model.train()
return metric_dict
def inference_no_rerank(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
#feat = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
#local_feats.append(feat[1])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
distmat = -torch.mm(qf, gf.t()).cpu().numpy()
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
index = np.argsort(distmat, axis=1) # from small to large
new_gallery_index = np.unique(index[:, :top_k].reshape(-1))
print('new_gallery_index', len(new_gallery_index))
return new_gallery_index
def inference(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
distmat = torch.mm(qf, gf.t()).cpu().numpy()
# euclidean distance
# 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()
cmc, mAP = evaluate(-distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
def inference(cfg, model, data_bunch, test_labels, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
pids = []
camids = []
for p, c in test_labels:
pids.append(p)
camids.append(c)
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:])
feats = []
model.eval()
for imgs, _ in data_bunch.test_dl:
with torch.no_grad():
feat = model(imgs)
feats.append(feat)
feats = torch.cat(feats, dim=0)
qf = feats[:num_query]
gf = feats[num_query:]
m, n = qf.shape[0], gf.shape[0]
# Cosine distance
distmat = torch.mm(F.normalize(qf), F.normalize(gf).t())
# Euclid distance
# 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 = to_np(distmat)
# Compute CMC and mAP.
cmc, mAP = evaluate(-distmat, q_pids, g_pids, q_camids, g_camids)
logger.info('Compute CMC Curve')
logger.info("mAP: {:.1%}".format(mAP))
for r in [1, 5, 10]:
logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(r, cmc[r - 1]))
def inference_aligned_flipped(cfg, model, test_dataloader, num_query,
use_local_feature, use_rerank,
use_cross_feature):
"""
inference an aligned net with flipping and two pairs of global feature and local feature
:param cfg:
:param model:
:param test_dataloader:
:param num_query:
:return:
"""
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing aligned with flipping")
model.eval()
pids, camids = [], []
gfs, bn_gfs, lfs, bn_lfs = [], [], [], []
gfs_flipped, bn_gfs_flipped, lfs_flipped, bn_lfs_flipped = [], [], [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
gf, bn_gf, lf, bn_lf = model(img)
gff, bn_gff, lff, bn_lff = model(torch.flip(img, [3]))
# 4 features
gfs.append(gf.cpu())
bn_gfs.append(bn_gf.cpu())
if use_local_feature:
lfs.append(lf.cpu())
bn_lfs.append(bn_lf.cpu())
# 4 features flipped
gfs_flipped.append(gff.cpu())
bn_gfs_flipped.append(bn_gff.cpu())
if use_local_feature:
lfs_flipped.append(lff.cpu())
bn_lfs_flipped.append(bn_lff.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
logger.info(
f"use_cross_feature = {use_cross_feature}, use_local_feature = {use_local_feature}, use_rerank = {use_rerank}"
)
if use_cross_feature:
logger.info("Computing distmat with bn_gf (+ lf)")
distmat2 = compute_distmat(cfg,
num_query,
bn_gfs,
bn_gfs_flipped,
lfs,
lfs_flipped,
theta=0.45,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
distmat = distmat2
#distmat = (distmat1 + distmat2) / 2
else:
logger.info("Computing distmat with gf + bn_lf")
distmat1 = compute_distmat(cfg,
num_query,
gfs,
gfs_flipped,
bn_lfs,
bn_lfs_flipped,
theta=0.95,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
distmat = distmat1
#distmat1 = None
#distmat2 = None
#distmat = original_distmat
#distmat[:, new_gallery_index] = distmat1 - 100
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference_aligned(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
local_feats = []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
#feat = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
local_feats.append(feat[1])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if len(local_feats) > 0:
local_feats = torch.cat(local_feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# 局部特征是三维的，不做归一化 (对结果没影响)
#if len(local_feats) > 0:
# local_feats = F.normalize(local_feats, p=2, dim=1)
# query
qf = feats[:num_query]
if len(local_feats) > 0:
lqf = local_feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
if len(local_feats) > 0:
lgf = local_feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
if len(local_feats) > 0:
#if True:
# calculate the local distance
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_qg_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lqf.cpu().numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_distmat = np.concatenate([
np.concatenate([local_qq_distmat, local_qg_distmat], axis=1),
np.concatenate([local_qg_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
else:
local_distmat = None
# use reranking
logger.info("use reranking")
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
search_param = False
search_theta = True
if search_param:
best_score = 0
best_param = []
for k1 in range(5, 9):
for k2 in range(1, k1):
for l in np.linspace(0, 0.5, 11):
distmat = re_ranking(qf, gf, k1=k1, k2=k2, lambda_value=l)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids,
g_camids)
score = (cmc[0] + mAP) / 2
#logger.info(f"mAP: {mAP:.1%}")
print('k1, k2, l', k1, k2, np.around(l, 2),
'r1, mAP, score', np.around(cmc[0], 4),
np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = [k1, k2, l]
print('Best Param', best_param)
distmat = re_ranking(qf,
gf,
k1=best_param[0],
k2=best_param[1],
lambda_value=best_param[2],
local_distmat=local_distmat,
only_local=False)
elif search_theta:
best_score = 0
for theta in np.linspace(0, 1.0, 11):
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=theta,
only_local=False) # (current best)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
score = (cmc[0] + mAP) / 2
print('theta', theta, 'r1, mAP, score', np.around(cmc[0], 4),
np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = theta
print('Best Param', best_param)
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=best_param,
only_local=False) # (current best)
else:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
only_local=False,
theta_value=0.9) #(current best)
#distmat = re_ranking(qf, gf, k1=6, k2=2, lambda_value=0.4) # try
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference_aligned_flipped(cfg, model, test_dataloader, num_query,
use_local_feature, use_rerank,
use_cross_feature):
"""
inference an aligned net with flipping and two pairs of global feature and local feature
:param cfg:
:param model:
:param test_dataloader:
:param num_query:
:return:
"""
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing aligned with flipping")
model.eval()
pids, camids = [], []
gfs, bn_gfs, lfs, bn_lfs = [], [], [], []
gfs_flipped, bn_gfs_flipped, lfs_flipped, bn_lfs_flipped = [], [], [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
ret = model(img)
ret_flip = model(torch.flip(img, [3]))
if len(ret) == 4:
gf, bn_gf, lf, bn_lf = ret
gff, bn_gff, lff, bn_lff = ret_flip
elif len(ret) == 2:
gf, bn_gf = ret
gff, bn_gff = ret_flip
lf, bn_lf = None, None
lff, bn_lff = None, None
elif ret is not tuple:
bn_gf = ret[:, :2048]
gf = ret[:, 2048:]
gff = ret_flip[:, :2048]
bn_gff = ret_flip[:, 2048:]
lf, bn_lf = None, None
lff, bn_lff = None, None
else:
# print('ret', ret.size())
raise Exception("Unknown model returns, length = ", len(ret))
# 4 features
gfs.append(gf.cpu())
bn_gfs.append(bn_gf.cpu())
if use_local_feature:
if use_cross_feature:
lfs.append(lf.cpu())
else:
bn_lfs.append(bn_lf.cpu())
# 4 features flipped
gfs_flipped.append(gff.cpu())
bn_gfs_flipped.append(bn_gff.cpu())
if use_local_feature:
if use_cross_feature:
lfs_flipped.append(lff.cpu())
else:
bn_lfs_flipped.append(bn_lff.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
logger.info(
f"use_local_feature = {use_local_feature}, use_rerank = {use_rerank}")
logger.info("Computing distmat with bn_gf")
distmat2 = compute_distmat(cfg,
num_query,
bn_gfs,
bn_gfs_flipped,
lfs,
lfs_flipped,
theta=0.45,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
logger.info("Computing distmat with gf + bn_lf")
distmat1 = compute_distmat(cfg,
num_query,
gfs,
gfs_flipped,
bn_lfs,
bn_lfs_flipped,
theta=0.95,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
for theta in np.linspace(0, 1, 21):
#theta = 0.55
distmat = distmat1 * (1 - theta) + distmat2 * theta
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"theta: {theta:.2%} mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference(
cfg,
model,
test_dataloader_collection,
num_query_collection,
is_vis=False,
test_collection=None,
use_mask=True,
num_parts=10,
mask_image=False
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
adj = torch.from_numpy(coarse_adj_npy).float()
idx = -1
for test_dataset_name, test_dataloader, num_query in zip(cfg.DATASETS.TEST_NAMES, test_dataloader_collection, num_query_collection):
idx += 1
feats, pids, camids = [], [], []
if use_mask:
test_prefetcher = data_prefetcher_mask(test_dataloader)
else:
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
if use_mask:
img, mask, pid, camid = batch
adj_batch = adj.repeat(img.size(0), 1, 1)
with torch.no_grad():
output = model(img, img, mask, adj_batch)
# feat = output[1]
# feat = output[3]
feat = torch.cat([output[1], output[3]], dim=1)
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
distmat = torch.mm(qf, gf.t()).cpu().numpy()
# euclidean distance
# 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()
cmc, mAP = evaluate(-distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"Results on {test_dataset_name} : ")
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
if is_vis:
query_rand = 10
topK = 10
is_save_all = True
query_rand_idx = range(0, num_query) if is_save_all else random.sample(range(0, num_query), query_rand)
print(f'|-------- Randomly saving top-{topK} results of {len(query_rand_idx)} queries for {test_dataset_name} --------|')
qf_rand = qf[query_rand_idx]
q_pids_rand = q_pids[query_rand_idx]
q_camids_rand = q_camids[query_rand_idx]
q_items = test_collection[idx][:num_query]
q_items_rand = list()
for i in query_rand_idx:
q_items_rand.append(q_items[i])
g_items = test_collection[idx][num_query:]
distmat_rand = torch.mm(qf_rand, gf.t()).cpu().numpy()
distmat_rand = -distmat_rand
indices = np.argsort(distmat_rand, axis=1)
matches = (g_pids[indices] == q_pids_rand[:, np.newaxis]).astype(np.int32)
save_img_size = (256, 256)
if test_dataset_name == 'market1501':
save_img_size = (128, 256)
for q_idx in range(len(query_rand_idx)):
savefilename = ''
# get query pid and camid
q_path = q_items_rand[q_idx][0]
q_pid = q_items_rand[q_idx][1]
q_camid = q_items_rand[q_idx][2]
savefilename += 'q-'+q_path.split('/')[-1]+'_g'
# remove gallery samples that have the same pid and camid with query
order = indices[q_idx]
remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
keep = np.invert(remove)
print('Query Path : ', q_path)
print('Result idx : ', order[:topK])
img_list = list()
q_img = cv2.imread(q_path)
q_img = cv2.resize(q_img, save_img_size)
cv2.rectangle(q_img, (0,0), save_img_size, (255,0,0), 4)
img_list.append(q_img)
for g_idx in order[:topK]:
g_img = cv2.imread(g_items[g_idx][0])
g_img = cv2.resize(g_img, save_img_size)
if q_pid == g_items[g_idx][1] and q_camid == g_items[g_idx][2]:
cv2.rectangle(g_img, (0,0), save_img_size, (255,255,0), 4)
elif q_pid == g_items[g_idx][1] and q_camid != g_items[g_idx][2]:
cv2.rectangle(g_img, (0,0), save_img_size, (0,255,0), 4)
else:
cv2.rectangle(g_img, (0,0), save_img_size, (0,0,255), 4)
img_list.append(g_img)
savefilename += '-'+str(g_items[g_idx][1])
pic = np.concatenate(img_list, 1)
picsavedir = os.path.join(cfg.OUTPUT_DIR, '-'.join(cfg.DATASETS.TEST_NAMES), 'examples', test_dataset_name)
if not os.path.exists(picsavedir): os.makedirs(picsavedir)
savefilepath = os.path.join(picsavedir, savefilename+'.jpg')
cv2.imwrite(savefilepath, pic)
print('Save example picture to ', savefilepath)
def inference(cfg, model, train_dataloader, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
logger.info("compute precise batchnorm ...")
# model.train()
# update_bn_stats(model, train_dataloader, num_iters=300)
model.eval()
cat_feats, feats, pids, camids = [], [], [], []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
cat_feat, feat = model(img)
cat_feats.append(cat_feat.cpu())
feats.append(feat.cpu())
pids.extend(np.asarray(pid.cpu().numpy()))
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
cat_feats = torch.cat(cat_feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
cat_feats = F.normalize(cat_feats, p=2, dim=1)
# query
cat_qf = cat_feats[:num_query]
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
cat_gf = cat_feats[num_query:]
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
cat_dist = torch.mm(cat_qf, cat_gf.t())
distmat = torch.mm(qf, gf.t())
# IIA post fusion strategy for all query and gallery
# qf = qf
# gf = gf
# m = qf.shape[0]
# n = gf.shape[0]
# distmat = torch.zeros((m, n)).to(qf)
# for i, q_f in enumerate(qf):
# print(i)
# D = torch.cat([q_f[None, :], gf], dim=0) # [1+g, 2048]
# S = torch.mm(D, D.t()) # [1+g, 1+g]
# for _ in range(5):
# S = S - torch.eye(S.shape[0]).to(S)
# s_v, s_i = torch.topk(S, 10, dim=1)
# s_v = F.softmax(s_v, dim=1)
# s = torch.zeros((S.size()[0], S.size()[0])).to(qf) # [1+g, 1+g]
# for j in range(s_i.shape[0]):
# s[j, s_i[j]] = s_v[j]
# u = 0.8 * torch.eye(S.size()[0]).to(s) + 0.2 * s
# D = torch.mm(u, D)
# S = torch.mm(D, D.t())
# distmat[i] = S[0][1:]
cmc, mAP = evaluate(1 - distmat.numpy(), q_pids, g_pids, q_camids,
g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
cmc, mAP = evaluate(1 - cat_dist.numpy(), q_pids, g_pids, q_camids,
g_camids)
logger.info('cat feature')
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
def inference_aligned(
cfg,
model,
test_dataloader,
num_query,
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
g_feats, l_feats, pids, camids = [], [], [], []
val_prefetcher = data_prefetcher(test_dataloader)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
g_feat, l_feat = model(img)
#g_feat, l_feat = model(torch.flip(img, [3])) # better
g_feats.append(g_feat.data.cpu())
l_feats.append(l_feat.data.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
g_feats = torch.cat(g_feats, dim=0)
l_feats = torch.cat(l_feats, dim=0)
if cfg.TEST.NORM:
g_feats = F.normalize(g_feats, p=2, dim=1)
# query
qf = g_feats[:num_query]
lqf = l_feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = g_feats[num_query:]
lgf = l_feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# calculate the global distance
if True:
logger.info("--------use re-ranking--------")
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.numpy(),
lqf.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.numpy(),
lgf.numpy(),
aligned=True)
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)
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist,
theta_value=0.5,
only_local=False)
## theta hyer-patameters
# for theta in np.arange(0,1.1,0.1):
# distmat = re_ranking(qf,gf,k1=6,k2=2,lambda_value=0.3,local_distmat=local_dist,theta=theta,only_local=False)
# cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
# logger.info(f"mAP: {mAP:.1%}")
# for r in [1, 5, 10]:
# logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
# logger.info("Theta:{}; Score: {}".format(theta, (mAP+cmc[0])/2.))
#score = distmat
#index = np.argsort(score, axis=1) # from small to large
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
print('Cython is {} times faster than python\n'.format(pytime / cytime))
print("=> Check precision")
num_q = 30
num_g = 300
max_rank = 5
distmat = np.random.rand(num_q, num_g) * 20
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, t_cmc, mAP, t_mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
max_rank,
use_cython=False)
print("Python:\nmAP = {} \ncmc = {} \nt_cmc={} \nt_mAP={}\n".format(
mAP, cmc, t_cmc, t_mAP))
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
max_rank,
use_cython=True)
print("Cython:\nmAP = {} \ncmc = {}\n".format(mAP, cmc))
def inference_flipped(cfg,
model,
test_dataloader,
num_query,
use_re_ranking=True,
distance_metric='global_local'):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
g_feats, l_feats, gf_feats, lf_feats, pids, camids = [], [], [], [], [], []
val_prefetcher = data_prefetcher(test_dataloader)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
g_feat, l_feat = model(img)
gf_feat, lf_feat = model(torch.flip(img, [3]))
g_feats.append(g_feat.data.cpu())
l_feats.append(l_feat.data.cpu())
gf_feats.append(gf_feat.data.cpu())
lf_feats.append(lf_feat.data.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
g_feats = torch.cat(g_feats, dim=0)
l_feats = torch.cat(l_feats, dim=0)
gf_feats = torch.cat(gf_feats, dim=0)
lf_feats = torch.cat(lf_feats, dim=0)
if cfg.TEST.NORM:
g_feats = F.normalize(g_feats, p=2, dim=1)
gf_feats = F.normalize(gf_feats, p=2, dim=1)
# query
qf = g_feats[:num_query]
lqf = l_feats[:num_query]
qff = gf_feats[:num_query]
lqff = lf_feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = g_feats[num_query:]
lgf = l_feats[num_query:]
gff = gf_feats[num_query:]
lgff = lf_feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# calculate the global distance
if not use_re_ranking:
m, n = qf.shape[0], gf.shape[0]
global_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()
global_distmat.addmm_(1, -2, qf, gf.t())
global_distmat = global_distmat.numpy()
# calculate the local distance
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
if distance_metric == 'global':
logger.info("--------use global features--------")
distmat = global_distmat
elif distance_metric == 'local':
logger.info("--------use local features--------")
distmat = local_distmat
elif distance_metric == 'global_local':
logger.info("--------use global and local features--------")
distmat = global_distmat + local_distmat
else:
logger.info("--------use re-ranking--------")
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.numpy(),
lqf.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.numpy(),
lgf.numpy(),
aligned=True)
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)
logger.info("--------use re-ranking flipped--------")
lqff = lqff.permute(0, 2, 1)
lgff = lgff.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqff.numpy(),
lgff.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqff.numpy(),
lqff.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgff.numpy(),
lgff.numpy(),
aligned=True)
local_dist_flipped = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
# for theta in np.arange(0.0,1.0,0.05):
# distmat = re_ranking(qf,gf,k1=6,k2=2,lambda_value=0.3,local_distmat=local_dist,theta=theta,only_local=False)
# distmat_flip = re_ranking(qff,gff,k1=6,k2=2,lambda_value=0.3,local_distmat=local_dist_flipped,theta=theta,only_local=False)
# distmat = distmat + distmat_flip
# cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
# logger.info(f"mAP: {mAP:.1%}")
# for r in [1, 5, 10]:
# logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
# logger.info("Theta:{}; Score: {}".format(theta, (mAP+cmc[0])/2.))
theta = 0.45
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist,
theta_value=theta,
only_local=False)
distmat_flip = re_ranking(qff,
gff,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist_flipped,
theta_value=theta,
only_local=False)
distmat = (distmat + distmat_flip) / 2
score = distmat
index = np.argsort(score, axis=1) # from small to large
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
#local_feats = []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
#feat = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
#local_feats.append(feat[1])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
local_distmat = None
# use reranking
logger.info("use reranking")
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
search_param = False
if search_param:
best_score = 0
best_param = []
for k1 in range(5, 9):
for k2 in range(1, k1):
for l in np.linspace(0, 0.5, 11):
distmat = re_ranking(qf, gf, k1=k1, k2=k2, lambda_value=l)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids,
g_camids)
score = (cmc[0] + mAP) / 2
#logger.info(f"mAP: {mAP:.1%}")
print('k1, k2, l', k1, k2, np.around(l, 2),
'r1, mAP, score', np.around(cmc[0], 4),
np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = [k1, k2, l]
print('Best Param', best_param)
distmat = re_ranking(qf,
gf,
k1=best_param[0],
k2=best_param[1],
lambda_value=best_param[2],
local_distmat=local_distmat,
only_local=False)
else:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
only_local=False,
theta_value=0.9) #(current best)
#distmat = re_ranking(qf, gf, k1=6, k2=2, lambda_value=0.4) # try
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference_flipped(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, feats_flipped, pids, camids = [], [], [], []
local_feats, local_feats_flipped = [], []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
feat_flipped = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
local_feats.append(feat[1])
feats_flipped.append(feat_flipped[0])
local_feats_flipped.append(feat_flipped[1])
else:
feats.append(feat)
feats_flipped.append(feat_flipped)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
feats_flipped = torch.cat(feats_flipped, dim=0)
if len(local_feats) > 0:
local_feats = torch.cat(local_feats, dim=0)
local_feats_flipped = torch.cat(local_feats_flipped, dim=0)
#print('feats_flipped', len(feats_flipped), feats_flipped[0])
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
feats_flipped = F.normalize(feats_flipped, p=2, dim=1)
# query
qf = feats[:num_query]
qf_flipped = feats_flipped[:num_query]
if len(local_feats) > 0:
lqf = local_feats[:num_query]
lqf_flipped = local_feats_flipped[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
gf_flipped = feats_flipped[num_query:]
if len(local_feats) > 0:
lgf = local_feats[num_query:]
lgf_flipped = local_feats_flipped[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
#distmat = torch.mm(qf, gf.t()).cpu().numpy()
if len(local_feats) > 0:
#if True:
# calculate the local distance
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_qg_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lqf.cpu().numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_distmat = np.concatenate([
np.concatenate([local_qq_distmat, local_qg_distmat], axis=1),
np.concatenate([local_qg_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
# flipped
lqf = lqf_flipped.permute(0, 2, 1)
lgf = lgf_flipped.permute(0, 2, 1)
local_qg_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lqf.cpu().numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_distmat_flipped = np.concatenate([
np.concatenate([local_qq_distmat, local_qg_distmat], axis=1),
np.concatenate([local_qg_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
else:
local_distmat = None
# use reranking
logger.info("use reranking")
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
search_theta = True
if search_theta:
best_score = 0
#for theta in np.linspace(0.9, 1.0, 11):
for theta in np.linspace(0, 1.0, 21):
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=theta,
only_local=False) # (current best)
distmat_flipped = re_ranking(qf_flipped,
gf_flipped,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat_flipped,
theta_value=theta,
only_local=False) # (current best)
distmat = (distmat + distmat_flipped) / 2
#cmc, mAP = evaluate(distmat + distmat_flipped, q_pids, g_pids, q_camids, g_camids)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
score = (cmc[0] + mAP) / 2
print('theta', np.around(theta, 2), 'r1, mAP, score',
np.around(cmc[0], 4), np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = theta
best_distmat = distmat
# saving
strtime = time.strftime("%Y%m%d_%H%M%S", time.localtime())
if abs(theta - 0.95) < 1e-4:
# saving dist_mats
f = h5py.File(
'dist_mats/val_%s_%s_t0.95_flip.h5' %
(cfg.MODEL.NAME, strtime), 'w')
f.create_dataset('dist_mat', data=distmat, compression='gzip')
f.close()
print('Best Param', best_param)
distmat = best_distmat
else:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
only_local=False,
theta_value=0.95) #(current best)
distmat_flipped = re_ranking(qf_flipped,
gf_flipped,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat_flipped,
theta_value=0.95,
only_local=False) # (current best)
distmat = (distmat + distmat_flipped) / 2
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
