def _evaluate(self,
epoch,
dataset_name='',
queryloader=None,
galleryloader=None,
dist_metric='euclidean',
visrank=False,
visrank_topk=20,
save_dir='',
use_metric_cuhk03=False,
ranks=[1, 5, 10, 20]):
batch_time = AverageMeter()
self.model.eval()
print('Extracting features from query set ...')
qf, q_pids, q_camids = [], [], []
for batch_idx, data in enumerate(queryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(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('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set ...')
gf, g_pids, g_camids = [], [], []
end = time.time()
for batch_idx, data in enumerate(galleryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(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('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=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]))
if visrank:
visualize_ranked_results(
distmat,
self.datamanager.return_testdataset_by_name(dataset_name),
save_dir=osp.join(save_dir, 'visrank-' + str(epoch + 1),
dataset_name),
topk=visrank_topk)
return cmc[0]
def _evaluate(self, epoch, dataset_name='', queryloader=None, galleryloader=None,
dist_metric='euclidean', normalize_feature=False, visrank=False,
visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=(1, 5, 10, 20),
rerank=False, iteration=0):
batch_time = AverageMeter()
print('Extracting features from query set...')
qf, q_pids, q_camids = [], [], [] # query features, query person IDs and query camera IDs
for batch_idx, data in tqdm(enumerate(queryloader), 'Processing query...'):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(imgs, data[3])
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('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set...')
gf, g_pids, g_camids = [], [], [] # gallery features, gallery person IDs and gallery camera IDs
for batch_idx, data in tqdm(enumerate(galleryloader), 'Processing gallery...'):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(imgs, data[3])
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('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
if normalize_feature:
print('Normalizing features with L2 norm...')
qf = F.normalize(qf, p=2, dim=1)
gf = F.normalize(gf, p=2, dim=1)
print('Computing distance matrix with metric={}...'.format(dist_metric))
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
if rerank:
print('Applying person re-ranking ...')
distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
distmat = re_ranking(distmat, distmat_qq, distmat_gg)
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(
distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=use_metric_cuhk03
)
if self.writer is not None:
self.writer.add_scalar('Val/{}/mAP'.format(dataset_name), mAP, epoch + 1)
for r in ranks:
self.writer.add_scalar('Val/{}/Rank-{}'.format(dataset_name, r), cmc[r - 1], epoch + 1)
print('** Results **')
print('mAP: {:.2%}'.format(mAP))
print('CMC curve')
for r in ranks:
print('Rank-{:<3}: {:.2%}'.format(r, cmc[r-1]))
if visrank:
visualize_ranked_results(
distmat,
self.datamanager.return_testdataset_by_name(dataset_name),
self.datamanager.data_type,
width=self.datamanager.width,
height=self.datamanager.height,
save_dir=osp.join(save_dir, 'visrank_' + dataset_name),
topk=visrank_topk
)
return cmc[0]
def _evaluate(
self,
dataset_name='',
query_loader=None,
gallery_loader=None,
dist_metric='euclidean',
normalize_feature=False,
visrank=False,
visrank_topk=10,
save_dir='',
use_metric_cuhk03=False,
ranks=[1, 5, 10, 20],
rerank=False
):
batch_time = AverageMeter()
def _feature_extraction(data_loader):
f_, pids_, camids_ = [], [], []
for batch_idx, data in enumerate(data_loader):
imgs, pids, camids = self.parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self.extract_features(imgs)
batch_time.update(time.time() - end)
features = features.cpu().clone()
f_.append(features)
pids_.extend(pids)
camids_.extend(camids)
f_ = torch.cat(f_, 0)
pids_ = np.asarray(pids_)
camids_ = np.asarray(camids_)
return f_, pids_, camids_
print('Extracting features from query set ...')
qf, q_pids, q_camids = _feature_extraction(query_loader)
print(qf.shape)
print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set ...')
gf, g_pids, g_camids = _feature_extraction(gallery_loader)
print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
if normalize_feature:
print('Normalzing features with L2 norm ...')
qf = F.normalize(qf, p=2, dim=1)
gf = F.normalize(gf, p=2, dim=1)
print(
'Computing distance matrix with metric={} ...'.format(dist_metric)
)
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
if rerank:
print('Applying person re-ranking ...')
distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
distmat = re_ranking(distmat, distmat_qq, distmat_gg)
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(
distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=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]))
if visrank:
visualize_ranked_results(
distmat,
self.datamanager.fetch_test_loaders(dataset_name),
self.datamanager.data_type,
width=self.datamanager.width,
height=self.datamanager.height,
save_dir=osp.join(save_dir, 'visrank_' + dataset_name),
topk=visrank_topk
)
return cmc[0], mAP
def _evaluate_reid(self,
model,
epoch,
dataset_name='',
query_loader=None,
gallery_loader=None,
dist_metric='euclidean',
normalize_feature=False,
visrank=False,
visrank_topk=10,
save_dir='',
use_metric_cuhk03=False,
ranks=(1, 5, 10, 20),
rerank=False,
model_name='',
lr_finder=False):
def _feature_extraction(data_loader):
f_, pids_, camids_ = [], [], []
for _, data in enumerate(data_loader):
imgs, pids, camids = self.parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
features = model(imgs),
features = features.data.cpu()
f_.append(features)
pids_.extend(pids)
camids_.extend(camids)
f_ = torch.cat(f_, 0)
pids_ = np.asarray(pids_)
camids_ = np.asarray(camids_)
return f_, pids_, camids_
qf, q_pids, q_camids = _feature_extraction(query_loader)
gf, g_pids, g_camids = _feature_extraction(gallery_loader)
if normalize_feature:
qf = F.normalize(qf, p=2, dim=1)
gf = F.normalize(gf, p=2, dim=1)
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
if rerank:
distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
distmat = re_ranking(distmat, distmat_qq, distmat_gg)
cmc, mAP = metrics.evaluate_rank(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=use_metric_cuhk03)
if self.writer is not None and not lr_finder:
self.writer.add_scalar(
'Val/{}/{}/mAP'.format(dataset_name, model_name), mAP,
epoch + 1)
for r in ranks:
self.writer.add_scalar(
'Val/{}/{}/Rank-{}'.format(dataset_name, model_name, r),
cmc[r - 1], epoch + 1)
if not lr_finder:
print('** Results ({}) **'.format(model_name))
print('mAP: {:.2%}'.format(mAP))
print('CMC curve')
for r in ranks:
print('Rank-{:<3}: {:.2%}'.format(r, cmc[r - 1]))
if visrank and not lr_finder:
visualize_ranked_results(
distmat,
self.datamanager.fetch_test_loaders(dataset_name),
self.datamanager.data_type,
width=self.datamanager.width,
height=self.datamanager.height,
save_dir=osp.join(save_dir, 'visrank_' + dataset_name),
topk=visrank_topk)
return cmc[0]
def _evaluate(self, arch, epoch, dataset_name='', queryloader=None, galleryloader=None,
dist_metric='euclidean', normalize_feature=False, visrank=False,
visrank_topk=20, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20],
rerank=False, viscam=False, viscam_num=10, viscam_only=False):
with self.experiment.test():
if not viscam_only:
batch_time = AverageMeter()
combine_time = AverageMeter()
self.model.eval()
print('Extracting features from query set ...')
qf, q_pids, q_camids = [], [], [] # query features, query person IDs and query camera IDs
for batch_idx, data in enumerate(queryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(imgs)
batch_time.update(time.time() - end, len(pids), True)
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('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set ...')
gf, g_pids, g_camids = [], [], [] # gallery features, gallery person IDs and gallery camera IDs
end = time.time()
for batch_idx, data in enumerate(galleryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(imgs)
batch_time.update(time.time() - end, len(pids), True)
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)
end = time.time()
num_images = len(g_pids)
self.combine_fn.train()
gf, g_pids = self.combine_fn(gf, g_pids, g_camids)
if self.save_embed:
assert osp.isdir(self.save_embed)
path = osp.realpath(self.save_embed)
np.save(path + '/gf-' + self.combine_method + '.npy', gf)
np.save(path + '/g_pids-' + self.combine_method + '.npy', g_pids)
combine_time.update(time.time() - end, num_images, True)
time.time() - end
gf = torch.tensor(gf, dtype=torch.float)
print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
print('Speed: {:.4f} sec/image'.format(batch_time.avg + combine_time.avg))
if normalize_feature:
print('Normalzing features with L2 norm ...')
qf = F.normalize(qf, p=2, dim=1)
gf = F.normalize(gf, p=2, dim=1)
print('Computing distance matrix with metric={} ...'.format(dist_metric))
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
if rerank:
print('Applying person re-ranking ...')
distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
distmat = re_ranking(distmat, distmat_qq, distmat_gg)
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(
distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=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]))
# write to Tensorboard and comet.ml
if not self.test_only:
rs = {'eval-rank-{:<3}'.format(r):cmc[r-1] for r in ranks}
self.writer.add_scalars('eval/ranks',rs,epoch)
self.experiment.log_metrics(rs,step=epoch)
self.writer.add_scalar('eval/mAP',mAP,epoch)
self.experiment.log_metric('eval-mAP',mAP,step=epoch)
print('Results written to tensorboard and comet.ml.')
if visrank:
visualize_ranked_results(
distmat,
self.datamanager.return_testdataset_by_name(dataset_name),
save_dir=osp.join(save_dir, 'visrank-'+str(epoch+1), dataset_name),
topk=visrank_topk
)
if viscam:
if arch == 'osnet_x1_0' or arch == 'osnet_custom':
# print(self.model)
visualize_cam(
model=self.model,
finalconv='conv5', # for OSNet
dataset=self.datamanager.return_testdataset_by_name(dataset_name),
save_dir=osp.join(save_dir, 'viscam-'+str(epoch+1), dataset_name),
num=viscam_num
)
elif arch == 'resnext50_32x4d':
# print(self.model)
visualize_cam(
model=self.model,
finalconv='layer4', # for resnext50
dataset=self.datamanager.return_testdataset_by_name(dataset_name),
save_dir=osp.join(save_dir, 'viscam-'+str(epoch+1), dataset_name),
num=viscam_num
)
if viscam_only:
raise RuntimeError('Stop exec because `viscam_only` is set to true.')
return cmc[0]
def _evaluate(self,
epoch,
dataset_name='',
queryloader=None,
galleryloader=None,
dist_metric='euclidean',
normalize_feature=False,
visrank=False,
visrankactiv=False,
visrank_topk=10,
save_dir='',
use_metric_cuhk03=False,
ranks=[1, 5, 10, 20],
rerank=False,
visrankactivthr=False,
maskthr=0.7,
visdrop=False,
visdroptype='random'):
batch_time = AverageMeter()
print('Extracting features from query set ...')
qf, qa, q_pids, q_camids, qm = [], [], [], [], [
] # query features, query activations, query person IDs, query camera IDs and image drop masks
for _, data in enumerate(queryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(imgs)
activations = self._extract_activations(imgs)
dropmask = self._extract_drop_masks(imgs, visdrop, visdroptype)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
qa.append(torch.Tensor(activations))
qm.append(torch.Tensor(dropmask))
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
qm = torch.cat(qm, 0)
qa = torch.cat(qa, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set ...')
gf, ga, g_pids, g_camids, gm = [], [], [], [], [
] # gallery features, gallery activations, gallery person IDs, gallery camera IDs and image drop masks
end = time.time()
for _, data in enumerate(galleryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
features = self._extract_features(imgs)
activations = self._extract_activations(imgs)
dropmask = self._extract_drop_masks(imgs, visdrop, visdroptype)
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
ga.append(torch.Tensor(activations))
gm.append(torch.Tensor(dropmask))
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
gm = torch.cat(gm, 0)
ga = torch.cat(ga, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
if normalize_feature:
print('Normalzing features with L2 norm ...')
qf = F.normalize(qf, p=2, dim=1)
gf = F.normalize(gf, p=2, dim=1)
print(
'Computing distance matrix with metric={} ...'.format(dist_metric))
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
#always show results without re-ranking first
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=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]))
if rerank:
print('Applying person re-ranking ...')
distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
distmat = re_ranking(distmat, distmat_qq, distmat_gg)
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(
distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=use_metric_cuhk03)
print('** Results with Re-Ranking**')
print('mAP: {:.1%}'.format(mAP))
print('CMC curve')
for r in ranks:
print('Rank-{:<3}: {:.1%}'.format(r, cmc[r - 1]))
if visrank:
visualize_ranked_results(
distmat,
self.datamanager.return_testdataset_by_name(dataset_name),
self.datamanager.data_type,
width=self.datamanager.width,
height=self.datamanager.height,
save_dir=osp.join(save_dir, 'visrank_' + dataset_name),
topk=visrank_topk)
if visrankactiv:
visualize_ranked_activation_results(
distmat,
qa,
ga,
self.datamanager.return_testdataset_by_name(dataset_name),
self.datamanager.data_type,
width=self.datamanager.width,
height=self.datamanager.height,
save_dir=osp.join(save_dir, 'visrankactiv_' + dataset_name),
topk=visrank_topk)
if visrankactivthr:
visualize_ranked_threshold_activation_results(
distmat,
qa,
ga,
self.datamanager.return_testdataset_by_name(dataset_name),
self.datamanager.data_type,
width=self.datamanager.width,
height=self.datamanager.height,
save_dir=osp.join(save_dir, 'visrankactivthr_' + dataset_name),
topk=visrank_topk,
threshold=maskthr)
if visdrop:
visualize_ranked_mask_activation_results(
distmat,
qa,
ga,
qm,
gm,
self.datamanager.return_testdataset_by_name(dataset_name),
self.datamanager.data_type,
width=self.datamanager.width,
height=self.datamanager.height,
save_dir=osp.join(
save_dir, 'visdrop_{}_{}'.format(visdroptype,
dataset_name)),
topk=visrank_topk)
return cmc[0]
def _evaluate(self,
epoch,
dataset_name='',
queryloader=None,
galleryloader=None,
dist_metric='euclidean',
normalize_feature=False,
visrank=False,
visrank_topk=20,
save_dir='',
use_metric_cuhk03=False,
ranks=[1, 5, 10, 20],
rerank=False,
load_pose=False,
part_score=False):
batch_time = AverageMeter()
self.model.eval()
print('Extracting features from query set ...')
qf, q_pids, q_camids = [], [], [
] # query features, query person IDs and query camera IDs
q_score = []
for batch_idx, data in enumerate(queryloader):
if load_pose:
imgs, pids, camids, pose = self._parse_data_for_eval(data)
else:
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
if load_pose:
if part_score:
features, score = self._extract_features(imgs, pose)
score = score.data.cpu()
q_score.append(score)
else:
features = self._extract_features(imgs, pose)
else:
features = self._extract_features(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)
if part_score:
q_score = torch.cat(q_score)
print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set ...')
gf, g_pids, g_camids = [], [], [
] # gallery features, gallery person IDs and gallery camera IDs
g_score = []
end = time.time()
for batch_idx, data in enumerate(galleryloader):
if load_pose:
imgs, pids, camids, pose = self._parse_data_for_eval(data)
else:
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
imgs = imgs.cuda()
end = time.time()
if load_pose:
# if part_score:
if part_score:
features, score = self._extract_features(imgs, pose)
score = score.data.cpu()
g_score.append(score)
else:
features = self._extract_features(imgs, pose)
else:
features = self._extract_features(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)
if part_score:
g_score = torch.cat(g_score)
print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
if normalize_feature:
print('Normalzing features with L2 norm ...')
qf = F.normalize(qf, p=2, dim=1)
gf = F.normalize(gf, p=2, dim=1)
print(
'Computing distance matrix with metric={} ...'.format(dist_metric))
if part_score:
distmat = metrics.compute_weight_distance_matrix(
qf, gf, q_score, g_score, dist_metric)
else:
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
if rerank:
print('Applying person re-ranking ...')
distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
distmat = re_ranking(distmat, distmat_qq, distmat_gg)
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=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]))
if visrank:
visualize_ranked_results(
distmat,
self.datamanager.return_testdataset_by_name(dataset_name),
save_dir=osp.join(save_dir, 'visrank-' + str(epoch + 1),
dataset_name),
topk=visrank_topk)
return cmc[0]
