def demo(config):
# init loaders and base
loaders = CustomedLoaders(config)
base = DemoBase(config)
# visualization
base.resume_from_model(config.resume_visualize_model)
make_dirs(config.visualize_output_path)
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:
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)
def main(config):
# loaders and base
loaders = Loaders(config)
base = Base(config, loaders)
# make dirs
make_dirs(config.save_images_path)
make_dirs(config.save_models_path)
make_dirs(config.save_features_path)
# logger
logger = Logger(os.path.join(config.output_path, 'log.txt'))
logger(config)
if config.mode == 'train':
# automatically resume model from the latest one
start_train_epoch = 0
root, _, files = os_walk(config.save_models_path)
if len(files) > 0:
# get indexes of saved models
indexes = []
for file in files:
indexes.append(int(file.replace('.pkl', '').split('_')[-1]))
# remove the bad-case and get available indexes
model_num = len(base.model_list)
available_indexes = copy.deepcopy(indexes)
for element in indexes:
if indexes.count(element) < model_num:
available_indexes.remove(element)
available_indexes = sorted(list(set(available_indexes)),
reverse=True)
unavailable_indexes = list(
set(indexes).difference(set(available_indexes)))
if len(available_indexes
) > 0: # resume model from the latest model
base.resume_model(available_indexes[0])
start_train_epoch = available_indexes[0] + 1
logger(
'Time: {}, automatically resume training from the latest step (model {})'
.format(time_now(), available_indexes[0]))
else: #
logger('Time: {}, there are no available models')
# main loop
for current_epoch in range(
start_train_epoch, config.warmup_reid_epoches +
config.warmup_gan_epoches + config.train_epoches):
# test
if current_epoch % 10 == 0 and current_epoch > config.warmup_reid_epoches + config.warmup_gan_epoches:
results = test(config, base, loaders, brief=True)
for key in results.keys():
logger('Time: {}\n Setting: {}\n {}'.format(
time_now(), key, results[key]))
# visualize generated images
if current_epoch % 10 == 0 or current_epoch <= 10:
visualize(config, loaders, base, current_epoch)
# train
if current_epoch < config.warmup_reid_epoches: # warmup reid model
results = train_an_epoch(config,
loaders,
base,
current_epoch,
train_gan=True,
train_reid=True,
train_pixel=False,
optimize_sl_enc=True)
elif current_epoch < config.warmup_reid_epoches + config.warmup_gan_epoches: # warmup GAN model
results = train_an_epoch(config,
loaders,
base,
current_epoch,
train_gan=True,
train_reid=False,
train_pixel=False,
optimize_sl_enc=False)
else: # joint train
results = train_an_epoch(config,
loaders,
base,
current_epoch,
train_gan=True,
train_reid=True,
train_pixel=True,
optimize_sl_enc=True)
logger('Time: {}; Epoch: {}; {}'.format(time_now(),
current_epoch, results))
# save model
base.save_model(current_epoch)
# test
results = test(config, base, loaders, brief=False)
for key in results.keys():
logger('Time: {}\n Setting: {}\n {}'.format(
time_now(), key, results[key]))
elif config.mode == 'test':
# resume from pre-trained model and test
base.resume_model_from_path(config.pretrained_model_path,
config.pretrained_model_epoch)
results = test(config, base, loaders, brief=False)
for key in results.keys():
logger('Time: {}\n Setting: {}\n {}'.format(
time_now(), key, results[key]))
def main(config):
# loaders and base
loaders = Loaders(config)
base = Base(config, loaders)
# make dirs
make_dirs(config.save_images_path)
make_dirs(config.save_wp_models_path)
make_dirs(config.save_st_models_path)
make_dirs(config.save_features_path)
logger = setup_logger('adaptation_reid', config.output_path, if_train=True)
if config.mode == 'train':
if config.resume:
# automatically resume model from the latest one
if config.resume_epoch_num == 0:
start_train_epoch = 0
root, _, files = os_walk(config.save_models_path)
if len(files) > 0:
# get indexes of saved models
indexes = []
for file in files:
indexes.append(
int(file.replace('.pkl', '').split('_')[-1]))
# remove the bad-case and get available indexes
model_num = len(base.model_list)
available_indexes = copy.deepcopy(indexes)
for element in indexes:
if indexes.count(element) < model_num:
available_indexes.remove(element)
available_indexes = sorted(list(set(available_indexes)),
reverse=True)
unavailable_indexes = list(
set(indexes).difference(set(available_indexes)))
if len(available_indexes
) > 0: # resume model from the latest model
base.resume_model(available_indexes[0])
start_train_epoch = available_indexes[0] + 1
logger.info(
'Time: {}, automatically resume training from the latest step (model {})'
.format(time_now(), available_indexes[0]))
else: #
logger.info('Time: {}, there are no available models')
else:
start_train_epoch = config.resume_epoch_num
else:
start_train_epoch = 0
# main loop
for current_epoch in range(
start_train_epoch, config.warmup_reid_epoches +
config.warmup_gan_epoches + config.warmup_adaptation_epoches):
# train
if current_epoch < config.warmup_reid_epoches: # warmup reid model
results = train_an_epoch(config,
0,
loaders,
base,
current_epoch,
train_gan=True,
train_reid=True,
self_training=False,
optimize_sl_enc=True,
train_adaptation=False)
elif current_epoch < config.warmup_reid_epoches + config.warmup_gan_epoches: # warmup GAN model
results = train_an_epoch(config,
0,
loaders,
base,
current_epoch,
train_gan=True,
train_reid=False,
self_training=False,
optimize_sl_enc=False,
train_adaptation=False) # joint train
elif current_epoch < config.warmup_reid_epoches + config.warmup_gan_epoches + config.warmup_adaptation_epoches: #warmup adaptation
results = train_an_epoch(config,
0,
loaders,
base,
current_epoch,
train_gan=True,
train_reid=False,
self_training=False,
optimize_sl_enc=False,
train_adaptation=True)
print("another epoch")
logger.info('Time: {}; Epoch: {}; {}'.format(
time_now(), current_epoch, results))
# save model
if current_epoch % config.save_model_interval == 0:
base.save_model(current_epoch, True)
if current_epoch % config.test_model_interval == 0:
visualize(config, loaders, base, current_epoch)
test(config, base, loaders, epoch=0, brief=False)
total_wp_epoches = config.warmup_reid_epoches + config.warmup_gan_epoches
for iter_n in range(config.iteration_number):
src_dataset, src_dataloader, trg_dataset, trg_dataloader = loaders.get_self_train_loaders(
)
trg_labeled_dataloader = generate_labeled_dataset(
base, iter_n, src_dataset, src_dataloader, trg_dataset,
trg_dataloader)
for epoch in range(total_wp_epoches + 1, config.self_train_epoch):
results = train_an_epoch(
config,
iter_n,
loaders,
base,
epoch,
train_gan=True,
train_reid=False,
self_training=True,
optimize_sl_enc=True,
trg_labeled_loader=trg_labeled_dataloader)
logger.info('Time: {}; Epoch: {}; {}'.format(
time_now(), current_epoch, results))
if epoch % config.save_model_interval == 0:
base.save_model(iter_n * config.self_train_epoch + epoch,
False)
elif config.mode == 'test':
# resume from pre-trained model and test
base.resume_model_from_path(config.pretrained_model_path,
config.pretrained_model_epoch)
cmc, map = test(config, base, loaders, epoch=100, brief=False)