def evaluate(self):
self.model.eval()
print('extract features, this may take a few minutes')
qf = extract_feature(self.model, tqdm(self.query_loader)).numpy()
gf = extract_feature(self.model, tqdm(self.test_loader)).numpy()
def rank(dist):
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)
return r, m_ap
######################### 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)
r, m_ap = rank(dist)
print(
'[With Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
.format(m_ap, r[0], r[2], r[4], r[9]))
#########################no re rank##########################
dist = cdist(qf, gf)
r, m_ap = rank(dist)
print(
'[Without Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
.format(m_ap, r[0], r[2], r[4], r[9]))
def evaluate(self):
self.model.eval()
qf = extract_feature(self.model, tqdm(self.query_loader, desc='Extracting query features')).numpy()
gf = extract_feature(self.model, tqdm(self.test_loader, desc='Extracting test features')).numpy()
def rank(dist):
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)
return r, m_ap
######################### 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)
r, m_ap = rank(dist)
tqdm.write('[With Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
.format(m_ap, r[0], r[2], r[4], r[9]))
retval = {
're-ranking': (m_ap, r[0], r[2], r[4], r[9])
}
#########################no re rank##########################
dist = cdist(qf, gf)
r, m_ap = rank(dist)
tqdm.write('[Without Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
.format(m_ap, r[0], r[2], r[4], r[9]))
retval['without'] = (m_ap, r[0], r[2], r[4], r[9])
return retval
def evaluate_ai(self):
self.model.load_state_dict(torch.load('weights/AI_mgn/pyramid_v4.pth'))
self.model.eval()
print('extract features, this may take a few minutes')
qf = extract_feature(self.model, tqdm(self.query_loader)).numpy()
gf = extract_feature(self.model, tqdm(self.test_loader)).numpy()
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)
for i in range(dist.shape[0]):
txt_list = np.zeros((100))
for j in range(txt_list.shape[0]):
txt_list[j] = int(np.argmin(dist[i])) + 1
dist[i][int(txt_list[j]) - 1] = 100000
txt_list = pd.DataFrame(txt_list, dtype=np.int32)
txt_list.to_csv('save/{:0>6d}.txt'.format(i + 1),
header=0,
index=0)
def evaluate(self):
self.model.eval()
print('extract features, this may take a few minutes')
qf = extract_feature(self.model, tqdm(self.query_loader)).numpy()
gf = extract_feature(self.model, tqdm(self.test_loader)).numpy()
######################### 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)
queryset = self.queryset
testset = self.testset
m_ap = mean_ap(dist, queryset, testset)
print('[With Re-Ranking] mAP: {:.4f}'.format(m_ap))
#########################no re rank##########################
dist = cdist(qf, gf)
m_ap = mean_ap(dist, queryset, testset)
print('[Without Re-Ranking] mAP: {:.4f}'.format(m_ap))
def evaluate(self, epoch):
self.model.eval()
print('extract features, this may take a few minutes')
qf = extract_feature(self.model, tqdm(self.query_loader)).numpy()
gf = extract_feature(self.model, tqdm(self.test_loader)).numpy()
epoch_json = 'metric/metric_epoch' + str(epoch)
os.makedirs(epoch_json)
def result(distmat,
query_ids=None,
gallery_ids=None,
query_cams=None,
gallery_cams=None,
title=None):
m, n = distmat.shape
# Fill up default values
if query_ids is None:
query_ids = np.arange(m)
if gallery_ids is None:
gallery_ids = np.arange(n)
if query_cams is None:
query_cams = np.zeros(m).astype(np.int32)
if gallery_cams is None:
gallery_cams = np.ones(n).astype(np.int32)
# Ensure numpy array
query_ids = np.asarray(query_ids)
gallery_ids = np.asarray(gallery_ids)
query_cams = np.asarray(query_cams)
gallery_cams = np.asarray(gallery_cams)
# Sort and find correct matches
indices = np.argsort(distmat, axis=1)
dd = []
for i in range(m):
# Filter out the same id and same camera
d = {}
d['query_id'] = query_ids[i].astype(np.int32).tolist()
valid = ((gallery_ids[indices[i]] != query_ids[i]) &
(gallery_cams[indices[i]] != query_cams[i]))
ans_ids = gallery_ids[indices[i]][valid]
d['ans_ids'] = ans_ids.astype(np.int32).tolist()
dd.append(d)
with open(epoch_json + '/' + title + '.json',
'w',
encoding='utf-8') as json_file:
json.dump(dd, json_file, ensure_ascii=False)
print('json finished')
#########################no re rank##########################
dist = cdist(qf, gf)
result(dist,
self.queryset.ids,
self.testset.ids,
title='without rerank')
######################### 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)
result(dist, self.queryset.ids, self.testset.ids, title='rerank')
######################### query expansion##########################
qf_new = []
T = 9
for i in range(len(dist)):
indice = np.argsort(dist[i])[:T]
temp = np.concatenate((qf[i][np.newaxis, :], gf[indice]), axis=0)
qf_new.append(np.mean(temp, axis=0, keepdims=True))
qf = np.squeeze(np.array(qf_new))
# feature norm
q_n = np.linalg.norm(qf, axis=1, keepdims=True)
qf = qf / q_n
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)
result(dist,
self.queryset.ids,
self.testset.ids,
title='query_expansion',
epoch=epoch)
# feature norm
g_n = np.linalg.norm(gf, axis=1, keepdims=True)
gf = gf / g_n
epoch_json = 'metric_final'
os.makedirs(epoch_json)
#########################no re rank##########################
dist = cdist(qf, gf)
result(dist, data_jitter.queryset.ids, data_jitter.testset.ids, title='without rerank')
######################### 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)
result(dist, data_jitter.queryset.ids, data_jitter.testset.ids, title='rerank')
######################### query expansion##########################
qf_new = []
T = 9
for i in range(len(dist)):
indice = np.argsort(dist[i])[:T]
temp = np.concatenate((qf[i][np.newaxis, :], gf[indice]), axis=0)
qf_new.append(np.mean(temp, axis=0, keepdims=True))
qf = np.squeeze(np.array(qf_new))
# feature norm
q_n = np.linalg.norm(qf, axis=1, keepdims=True)
qf = qf / q_n