print("Total of classes : %d" % CIFARNCLASS)
print("Train info")
print("Image : {}({}), Label : {}".format(train_image.shape, train_image.dtype,
train_label.shape))
print("Val info")
print("Image : {}({}), Label : {}".format(val_image.shape, val_image.dtype,
val_label.shape))
print("Test info")
print("Image : {}({}), Label : {}".format(test_image.shape, test_image.dtype,
test_label.shape))
train_class_count = label_count(train_label, CIFARNCLASS)
val_class_count = label_count(val_label, CIFARNCLASS)
test_class_count = label_count(test_label, CIFARNCLASS)
print("Train class mean : {}, std : {}".format(np.mean(train_class_count),
np.std(train_class_count)))
print("Val class mean : {}, std : {}".format(np.mean(val_class_count),
np.std(val_class_count)))
print("Test class mean : {}, std : {}".format(np.mean(test_class_count),
np.std(test_class_count)))
create_muldir(CIFARPROCESSED)
write_npy(train_image, CIFARPROCESSED + 'train_image.npy')
write_npy(train_label, CIFARPROCESSED + 'train_label.npy')
write_npy(val_image, CIFARPROCESSED + 'val_image.npy')
write_npy(val_label, CIFARPROCESSED + 'val_label.npy')
write_npy(test_image, CIFARPROCESSED + 'test_image.npy')
write_npy(test_label, CIFARPROCESSED + 'test_label.npy')
if args.ltype == 'npair': args.nsclass = args.nbatch // 2
#for args.lamb in [0.001, 0.003, 0.01]
#for args.ma in [0.3, 1.0]:
METRIC_PARAM = args.lamb if args.ltype == 'npair' else args.ma
FILE_ID = params2id(args.dataset, args.nbatch, args.nsclass, args.conv,
args.ltype, METRIC_PARAM, args.m, args.dtype,
args.dptype)
SAVE_DIR = RESULT_DIR + 'metric/save/%s/' % FILE_ID
LOG_DIR = RESULT_DIR + 'metric/log/'
CSV_DIR = RESULT_DIR + 'metric/csv/'
PKL_DIR = RESULT_DIR + 'metric/pkl/'
BOARD_DIR = RESULT_DIR + 'metric/board/%s/' % FILE_ID
create_muldir(SAVE_DIR, BOARD_DIR, LOG_DIR, CSV_DIR, PKL_DIR)
# load data
datasetmanager = DATASETMANAGER_DICT[args.dataset]
dm_train, dm_val, dm_test = datasetmanager(args.ltype,
nsclass=args.nsclass)
for v in [dm_train, dm_val, dm_test]:
v.print_shape()
model = DeepMetric(dm_train, dm_val, dm_test, LOG_DIR + FILE_ID + '.log',
args)
model.build()
model.set_up_train()
try:
model.restore(save_dir=SAVE_DIR)
except AttributeError:
type=str)
args = parser.parse_args()
if __name__ == '__main__':
args.d = args.m
FILE_ID = params2id(args.dataset, args.conv, args.ltype, args.m)
QUERY_FILE_ID = params2id(args.dataset, '*', '*', args.conv, args.ltype,
'*', args.m, '*', '*')
CSV_DIR = RESULT_DIR + 'metric/csv/'
PKL_DIR = RESULT_DIR + 'metric/pkl/'
LOG_DIR = RESULT_DIR + 'metric/log/'
SAVE_DIR = RESULT_DIR + 'metric/save/%s/' % FILE_ID
create_muldir(PKL_DIR, CSV_DIR, LOG_DIR)
copy_dst_csv = CSV_DIR + FILE_ID + '.csv'
copy_dst_pkl = PKL_DIR + FILE_ID + '.pkl'
if os.path.exists(SAVE_DIR): remove_dir(SAVE_DIR)
if os.path.exists(copy_dst_csv): remove_file(copy_dst_csv)
if os.path.exists(copy_dst_pkl): remove_file(copy_dst_pkl)
pkl_files = glob.glob(PKL_DIR + QUERY_FILE_ID + '.pkl')
print(pkl_files)
if len(pkl_files) == 0:
print("No such pkl files")
sys.exit()
for i in range(len(val_label)):
val_label_set[val_label[i]].append(i)
new_val_idx = list()
for i in range(1000):
new_val_idx.extend(val_label_set[i][:50])
new_val_idx = np.array(new_val_idx)
new_val_img = val_img[new_val_idx]
new_val_label = val_label[new_val_idx]
print("Train Image : {}({}, max={}, min={}), Label : {}".format(
train_img.shape, train_img.dtype, np.max(train_img), np.min(train_img),
train_label.shape))
print("Val Image : {}({}, max={}, min={}), Label : {}".format(
new_val_img.shape, new_val_img.dtype, np.max(new_val_img),
np.min(new_val_img), new_val_label.shape))
print("Test Image : {}({}, max={}, min={}), Label : {}".format(
test_img.shape, test_img.dtype, np.max(test_img), np.min(test_img),
test_label.shape))
create_muldir(IMAGENET32PROCESSED)
write_npy(train_img, IMAGENET32PROCESSED + 'train_img.npy')
write_npy(train_label, IMAGENET32PROCESSED + 'train_label.npy')
write_npy(new_val_img, IMAGENET32PROCESSED + 'val_img.npy')
write_npy(new_val_label, IMAGENET32PROCESSED + 'val_label.npy')
write_npy(test_img, IMAGENET32PROCESSED + 'test_img.npy')
write_npy(test_label, IMAGENET32PROCESSED + 'test_label.npy')