def main(config):
# init loaders and base
loaders = ReIDLoaders(config)
base = Base(config)
# make directions
make_dirs(base.output_path)
# init logger
logger = Logger(os.path.join(config.output_path, 'log.txt'))
logger(config)
assert config.mode in ['train', 'test', 'visualize']
if config.mode == 'train': # train mode
# automatically resume model from the latest one
if config.auto_resume_training_from_lastest_steps:
start_train_epoch = base.resume_last_model()
# main loop
for current_epoch in range(start_train_epoch,
config.total_train_epochs):
# save model
base.save_model(current_epoch)
# train
_, results = train_an_epoch(config, base, loaders, current_epoch)
logger('Time: {}; Epoch: {}; {}'.format(time_now(),
current_epoch, results))
# test
base.save_model(config.total_train_epochs)
mAP, CMC, pres, recalls, thresholds = test(config, base, loaders)
logger('Time: {}; Test Dataset: {}, \nmAP: {} \nRank: {}'.format(
time_now(), config.test_dataset, mAP, CMC))
plot_prerecall_curve(config, pres, recalls, thresholds, mAP, CMC,
'none')
elif config.mode == 'test': # test mode
base.resume_from_model(config.resume_test_model)
mAP, CMC, pres, recalls, thresholds = test(config, base, loaders)
logger('Time: {}; Test Dataset: {}, \nmAP: {} \nRank: {}'.format(
time_now(), config.test_dataset, mAP, CMC))
logger(
'Time: {}; Test Dataset: {}, \nprecision: {} \nrecall: {}\nthresholds: {}'
.format(time_now(), config.test_dataset, mAP, CMC, pres, recalls,
thresholds))
plot_prerecall_curve(config, pres, recalls, thresholds, mAP, CMC,
'none')
elif config.mode == 'visualize': # visualization mode
base.resume_from_model(config.resume_visualize_model)
visualize(config, base, loaders)
def main(config):
# init loaders and base
loaders = ReIDLoaders(config)
base = Base(config)
# make directions
make_dirs(base.output_path)
# init logger
logger = Logger(os.path.join(config.output_path, 'log.txt'))
logger(config)
assert config.mode in ['train', 'test', 'visualize']
if config.mode == 'train': # train mode
# automatically resume model from the latest one
if config.auto_resume_training_from_lastest_steps:
print('resume', base.output_path)
start_train_epoch = base.resume_last_model()
#start_train_epoch = 0
# main loop
for current_epoch in range(start_train_epoch,
config.total_train_epochs + 1):
# save model
base.save_model(current_epoch)
# train
base.lr_scheduler.step(current_epoch)
_, results = train_an_epoch(config, base, loaders)
logger('Time: {}; Epoch: {}; {}'.format(time_now(),
current_epoch, results))
# test
base.save_model(config.total_train_epochs)
mAP, CMC = test(config, base, loaders)
logger('Time: {}; Test Dataset: {}, \nmAP: {} \nRank: {}'.format(
time_now(), config.test_dataset, mAP, CMC))
elif config.mode == 'test': # test mode
base.resume_from_model(config.resume_test_model)
mAP, CMC = test(config, base, loaders)
logger('Time: {}; Test Dataset: {}, \nmAP: {} \nRank: {} with len {}'.
format(time_now(), config.test_dataset, mAP, CMC, len(CMC)))
elif config.mode == 'visualize': # visualization mode
base.resume_from_model(config.resume_visualize_model)
visualize(config, base, loaders)