def get_mean_and_var(self):
imgs = []
for t in self.train:
imgs.extend(t[0])
channel = 3
x_tot = np.zeros(channel)
x2_tot = np.zeros(channel)
for img in tqdm(imgs):
x = T.ToTensor()(read_image(img)).view(3, -1)
x_tot += x.mean(dim=1).numpy()
x2_tot += (x**2).mean(dim=1).numpy()
channel_avr = x_tot / len(imgs)
channel_std = np.sqrt(x2_tot / len(imgs) - channel_avr**2)
print(channel_avr, channel_std)
def attr_predict(model, transform_test, use_gpu):
import psycopg2
from PIL import Image
# set-up a postgres connection
conn = psycopg2.connect(database='ng',
user='******',
password='******',
host='146.148.89.5',
port=5432)
dbcur = conn.cursor()
print("connection successful")
try:
sql = dbcur.mogrify("""
select mgid, ctype, pid, camid, imagepaths from mmir_ground
where ctype='image' or ctype='video'
""")
dbcur.execute(sql)
with torch.no_grad():
model.eval()
rows = dbcur.fetchall()
for row in rows:
mgid, ctype, pid, camid, img_paths = row
imgs = []
sz = 0
for img_path in img_paths:
img = read_image(
"/homes/ksolaima/scratch1/yuange250_video_pedestrian_attributes_recognition-master"
+ img_path[1:])
img = transform_test(img)
img = img.unsqueeze(0)
imgs.append(img)
imgs = torch.cat(imgs, dim=0)
imgs = imgs.unsqueeze(
0) # to match the group testing of theirs -_-
if use_gpu:
imgs = imgs.cuda()
outputs = model(imgs)
# outputs = [torch.mean(out, 0).view(1, -1) for out in outputs] # no need
predicted_attrs = []
for i in range(len(outputs)):
outs = outputs[i].cpu().numpy()
# print(outs.shape)
predicted_attrs.append(
np.argmax(outs, 1)[0]
) # [0] because of the unsqueezing of grouping testing
print(predicted_attrs)
sql = dbcur.mogrify(
"""
INSERT INTO mmir_predicted (
mgid,
ctype,
pid,
camid,
ubc,
lbc,
gender,
imagepaths
) VALUES (%s, %s,%s,%s,%s,%s,%s,%s) ON CONFLICT DO NOTHING;""",
(str(mgid), ctype, str(pid), str(camid),
str(predicted_attrs[0]), str(predicted_attrs[1]),
str(predicted_attrs[2]), img_paths))
dbcur.execute(sql)
conn.commit()
except (Exception, psycopg2.Error) as error:
print(error)
finally:
#closing database connection.
if (conn):
dbcur.close()
conn.close()
print("PostgreSQL connection is closed")