images, id_labels, pose_labels, Nd, Np, Nz, channel_num = create_randomdata(
)
else:
print('n\Loading data from [%s]...' % args.data_place)
# try:
images, id_labels, pose_labels, Nd, Np, Nz, channel_num = FetchData(
args.data_place)
# except:
# print("Sorry, failed to load data")
# model
if args.snapshot is None:
if not (args.multi_DRGAN):
D = single_model.Discriminator(Nd, Np, channel_num)
G = single_model.Generator(Np, Nz, channel_num)
else:
if args.images_perID == 0:
print(
"Please specify -images-perID of your data to input to multi_DRGAN"
)
exit()
else:
D = multi_model.Discriminator(Nd, Np, channel_num)
G = multi_model.Generator(Np, Nz, channel_num,
args.images_perID)
else:
print('\nLoading model from [%s]...' % args.snapshot)
try:
D = torch.load('{}_D.pt'.format(args.snapshot))
G = torch.load('{}_G.pt'.format(args.snapshot))
images, id_labels, pose_labels, Nd, Ni, Nz, channel_num = create_randomdata()
else:
print('\n Loading data from [%s]...' % args.data_place)
try:
train_img_path_list, id_labels, pose_labels, Nd, Ni, Nz, channel_num = DataLoader2(args.data_place)
except:
print("Sorry, failed to load data")
test_img_path_list = train_img_path_list[-NUM_TEST_IMG:]
train_img_path_list = train_img_path_list[:-NUM_TEST_IMG]
# model
if args.snapshot is None:
if not(args.multi_DRGAN):
D = single_model.Discriminator(Nd, Ni, channel_num, args)
G = single_model.Generator(Ni, Nz, channel_num, args)
start_epoch = 1
else:
if args.images_perID==0:
print("Please specify -images-perID of your data to input to multi_DRGAN")
exit()
# else:
# D = multi_model.Discriminator(Nd, Ni, channel_num)
# G = multi_model.Generator(Ni, Nz, channel_num, args.images_perID)
else:
print('\n Loading model from [%s]...' % args.snapshot)
try:
D = torch.load('{}_D.pt'.format(args.snapshot))
G = torch.load('{}_G.pt'.format(args.snapshot))
start_epoch = int(args.snapshot.split('/')[-1][5:])
except: