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)
# 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
args.model_weights = './models/lr_0.005_cqlambda_0_alpha_0.5_bias_0.0_gamma_20_dataset_vehicleID_hashbit_512.npy'
#maps = model.validation(database_img, query_img, args)
cmc, mAP = model.validation(database_img, query_img, args)
print(
'The cmc: Rank1:{},Rank2:{},Rank3:{},Rank4:{} Rank5:{},Rank6:{},Rank7:{},Rank8:{},Rank9:{}, Rank10:{}'
'Rank11:{},Rank12:{},Rank13:{},Rank14{},Rank15:{},Rank16:{},Rank17:{},Rank18:{},Rank19:{},Rank20:{},mAP is {}'
.format(cmc[0], cmc[1], cmc[2], cmc[3], cmc[4], cmc[5], cmc[6], cmc[7],
cmc[8], cmc[9], cmc[10], cmc[11], cmc[12], cmc[13], cmc[14],
cmc[15], cmc[16], cmc[17], cmc[18], cmc[19], mAP))
results = [item for item in cmc[:20]] + [mAP]
model_name = 'DCH-{}'.format(args.output_dim)
results_to_excel(results, model_name, args.dataset)
# 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))
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))