def main(config):
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpu_id
if not os.path.exists(config.save_dir):
os.makedirs(config.save_dir)
sys.stdout = Logger(os.path.join(config.save_dir, 'train.log'))
pprint(vars(config))
data_root = os.path.join('../../data', config.dataset)
config.wordvec_dict = f'{data_root}/wordvec.txt'
img_tr = f'{data_root}/train.txt'
img_te = f'{data_root}/test.txt'
img_db = f'{data_root}/database.txt'
if config.test == True:
config.network_weights = os.path.join(config.save_dir, 'network_weights.npy')
else:
train_img = dataset.import_train(data_root, img_tr)
network_weights = model.train(train_img, config)
config.network_weights = network_weights
query_img, database_img = dataset.import_validation(data_root, img_te, img_db)
maps = model.validation(database_img, query_img, config)
for key in maps:
print(f"{key}: {maps[key]}")
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
label_dims = {'cifar10': 10, 'cub': 200, 'nuswide_81': 81, 'coco': 80}
Rs = {'cifar10': 54000, 'nuswide_81': 5000, 'coco': 5000}
args.R = Rs[args.dataset]
args.label_dim = label_dims[args.dataset]
args.img_tr = os.path.join(args.data_dir, args.dataset, "train.txt")
args.img_te = os.path.join(args.data_dir, args.dataset, "test.txt")
args.img_db = os.path.join(args.data_dir, args.dataset, "database.txt")
pprint(vars(args))
data_root = os.path.join(args.data_dir, args.dataset)
query_img, database_img = dataset.import_validation(
data_root, args.img_te, args.img_db) # test_image, database_image
if not args.evaluate:
train_img = dataset.import_train(data_root, args.img_tr)
model_weights = model.train(train_img, database_img, query_img, args)
args.model_weights = model_weights
maps = model.validation(database_img, query_img, args)
for key in maps:
print(("{}\t{}".format(key, maps[key])))
pprint(vars(args))
parser.add_argument('--finetune-all', default=True, type=bool)
parser.add_argument('--save-dir', default="./models/", type=str)
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
label_dims = {'cifar10': 10, 'cub': 200, 'nuswide_81': 81, 'coco': 80}
Rs = {'cifar10': 54000, 'nuswide_81': 5000, 'coco': 5000}
args.R = Rs[args.dataset]
args.label_dim = label_dims[args.dataset]
args.img_tr = "/home/caoyue/data/{}/train.txt".format(args.dataset)
args.img_te = "/home/caoyue/data/{}/test.txt".format(args.dataset)
args.img_db = "/home/caoyue/data/{}/database.txt".format(args.dataset)
pprint(vars(args))
query_img, database_img = dataset.import_validation(args.img_te, args.img_db)
if not args.evaluate:
train_img = dataset.import_train(args.img_tr)
model_weights = model.train(train_img, database_img, query_img, args)
args.model_weights = model_weights
maps = model.validation(database_img, query_img, args)
for key in maps:
print(("{}\t{}".format(key, maps[key])))
pprint(vars(args))
# CQ params
'max_iter_update_b': 3,
'max_iter_update_Cb': 1,
'cq_lambda': cq_lambda,
'code_batch_size': 500,
'n_subspace': subspace_num,
'n_subcenter': 256,
'label_dim': label_dims[_dataset],
'img_tr': "/home/caoyue/data/{}/train.txt".format(_dataset),
'img_te': "/home/caoyue/data/{}/test.txt".format(_dataset),
'img_db': "/home/caoyue/data/{}/database.txt".format(_dataset),
'save_dir': "./models/",
'log_dir': log_dir,
'dataset': _dataset
}
pprint(config)
train_img = dataset.import_train(config['img_tr'])
model_weights = model.train(train_img, config)
config['model_weights'] = model_weights
query_img, database_img = dataset.import_validation(config['img_te'], config['img_db'])
maps = model.validation(database_img, query_img, config)
for key in maps:
print(("{}: {}".format(key, maps[key])))
pprint(config)
# CQ params
'max_iter_update_b': 3,
'max_iter_update_Cb': 1,
'cq_lambda': cq_lambda,
'code_batch_size': 500,
'n_subspace': subspace_num,
'n_subcenter': 256,
'label_dim': label_dims[_dataset],
'img_tr': "{}/train.txt".format(data_root),
'img_te': "{}/test.txt".format(data_root),
'img_db': "{}/database.txt".format(data_root),
'save_dir': "./models/",
'log_dir': log_dir,
'dataset': _dataset
}
pprint(config)
train_img = dataset.import_train(data_root, config['img_tr'])
model_weights = model.train(train_img, config)
config['model_weights'] = model_weights
query_img, database_img = dataset.import_validation(data_root,
config['img_te'],
config['img_db'])
maps = model.validation(database_img, query_img, config)
for key in maps:
print(("{}\t{}".format(key, maps[key])))
pprint(config)
parser.add_argument('--save-dir', default="./models/", type=str)
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true')
parser.add_argument('--val-freq', default=1, type=int)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
label_dims = {'cifar10': 10, 'nuswide_81': 81, 'coco': 80, 'imagenet': 100}
Rs = {'cifar10': 54000, 'nuswide_81': 5000, 'coco': 5000, 'imagenet': 5000}
args.R = Rs[args.dataset]
args.label_dim = label_dims[args.dataset]
args.img_tr = "/home/caoyue/data/{}/train.txt".format(args.dataset)
args.img_te = "/home/caoyue/data/{}/test.txt".format(args.dataset)
args.img_db = "/home/caoyue/data/{}/database.txt".format(args.dataset)
pprint(vars(args))
query_img, database_img = dataset.import_validation(args.img_te, args.img_db)
if not args.evaluate:
train_img = dataset.import_train(args.img_tr)
model_weights = model.train(train_img, database_img, query_img, args)
args.model_weights = model_weights
else:
maps = model.validation(database_img, query_img, args)
for key in maps:
print(("{}\t{}".format(key, maps[key])))
pprint(vars(args))
