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])))