def modeltest():
#上面函数为测试flask程序使用。
parser = argparse.ArgumentParser(description='Test')
parser.add_argument(
'--iter',
default='-1',
type=int,
help='iter: iteration of the checkpoint to load. Default: 80000')
parser.add_argument(
'--batch_size',
default='1',
type=int,
help='batch_size: batch size for parallel test. Default: 1')
parser.add_argument(
'--cache',
default=False,
type=boolean_string,
help='cache: if set as TRUE all the test data will be loaded at once'
' before the transforming start. Default: FALSE')
opt = parser.parse_args()
m = initialization(conf, test=opt.cache)[0]
# load model checkpoint of iteration opt.iter
print('Loading the model...')
m.load()
print('Transforming...')
time = datetime.now()
probe = m.transform('probe', opt.batch_size)
gallery = m.transform('gallery', opt.batch_size)
return evaluation(probe, gallery)
def modeltrain():
parser = argparse.ArgumentParser(description='Train')
parser.add_argument(
'--cache',
default=True,
type=boolean_string,
help='cache: if set as TRUE all the training data will be loaded at once'
' before the training start. Default: TRUE')
opt = parser.parse_args()
m = initialization(conf, train=opt.cache)[0]
print("Training START")
m.fit()
print("Training COMPLETE")
from model.initialization import initialization
from config import conf
import argparse
def boolean_string(s):
if s.upper() not in {'FALSE', 'TRUE'}:
raise ValueError('Not a valid boolean string')
return s.upper() == 'TRUE'
parser = argparse.ArgumentParser(description='Train')
parser.add_argument(
'--cache',
default=True,
type=boolean_string,
help='cache: if set as TRUE all the training data will be loaded at once'
' before the training start. Default: TRUE')
opt = parser.parse_args()
if __name__ == '__main__':
m = initialization(conf, train=opt.cache)[0]
print("Training START")
m.fit()
print("Training COMPLETE")
parser.add_argument(
'--iter',
default='-1',
type=int,
help='iter: iteration of the checkpoint to load. Default: 80000')
parser.add_argument(
'--batch_size',
default='1',
type=int,
help='batch_size: batch size for parallel test. Default: 1')
parser.add_argument(
'--cache',
default=False,
type=boolean_string,
help='cache: if set as TRUE all the test data will be loaded at once'
' before the transforming start. Default: FALSE')
opt = parser.parse_args()
m = initialization(conf, test=opt.cache)[0]
# load model checkpoint of iteration opt.iter
print('Loading the model...')
m.load()
print('Transforming...')
time = datetime.now()
probe = m.transform('probe', opt.batch_size)
gallery = m.transform('gallery', opt.batch_size)
evaluation(probe, gallery)
'--batch_size',
default='1',
type=int,
help='batch_size: batch size for parallel test. Default: 1')
opt = parser.parse_args()
# Exclude identical-view cases
def de_diag(acc, each_angle=False):
result = np.sum(acc - np.diag(np.diag(acc)), 1) / 10.0
if not each_angle:
result = np.mean(result)
return result
m = initialization(conf, test=True)[0]
# load model checkpoint of iteration opt.iter
print('Loading the model of iteration %d...' % opt.iter)
m.load(opt.iter)
print('Transforming...')
time = datetime.now()
test = m.transform('test', opt.batch_size)
print('Evaluating...')
acc = evaluation(test, conf['data'])
print('Evaluation complete. Cost:', datetime.now() - time)
for i in range(1):
print('===Rank-%d (Include identical-view cases)===' % (i + 1))
print('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (np.mean(
acc[0, :, :, i]), np.mean(acc[1, :, :, i]), np.mean(acc[2, :, :, i])))
