def data_load(args):
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size // 2
dsets["target_l"] = ImageList_idx(open(args.tl_dset_path).readlines(),
transform=image_train())
dsets["target_u"] = ImageList_idx(open(args.tu_dset_path).readlines(),
transform=image_train())
dsets["test"] = ImageList_idx(open(args.tu_dset_path).readlines(),
transform=image_test())
dset_loaders["target_l"] = DataLoader(dsets["target_l"],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dset_loaders["target_u"] = DataLoader(dsets["target_u"],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dset_loaders["test"] = DataLoader(dsets["test"],
batch_size=train_bs * 3,
shuffle=False,
num_workers=args.worker,
drop_last=False)
return dset_loaders
def data_load(args):
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_tar = open(args.t_dset_path).readlines()
txt_test = open(args.test_dset_path).readlines()
if not args.da == 'uda':
label_map_s = {}
for i in range(len(args.src_classes)):
label_map_s[args.src_classes[i]] = i
new_tar = []
for i in range(len(txt_tar)):
rec = txt_tar[i]
reci = rec.strip().split(' ')
if int(reci[1]) in args.tar_classes:
if int(reci[1]) in args.src_classes:
line = reci[0] + ' ' + str(label_map_s[int(reci[1])]) + '\n'
new_tar.append(line)
else:
line = reci[0] + ' ' + str(len(label_map_s)) + '\n'
new_tar.append(line)
txt_tar = new_tar.copy()
txt_test = txt_tar.copy()
dsets["target"] = ImageList_idx(txt_tar, transform=image_train())
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets["test"] = ImageList_idx(txt_test, transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=train_bs*3, shuffle=False, num_workers=args.worker, drop_last=False)
return dset_loaders
def data_load(args):
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_tar = open(args.t_dset_path).readlines()
txt_test = open(args.test_dset_path).readlines()
dsets['target'] = ImageList_idx(txt_tar, transform=image_train())
dset_loaders['target'] = DataLoader(dsets['target'], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets['test'] = ImageList_idx(txt_test, transform=image_test())
dset_loaders['test'] = DataLoader(dsets['test'], batch_size=train_bs*3, shuffle=False, num_workers=args.worker, drop_last=False)
return dset_loaders
def data_load(args):
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_src = open(args.s_dset_path).readlines()
txt_tar = open(args.t_dset_path).readlines()
txt_test = open(args.test_dset_path).readlines()
if args.trte == "val":
dsize = len(txt_src)
tr_size = int(0.7*dsize)
print(dsize, tr_size, dsize - tr_size)
tr_txt, te_txt = torch.utils.data.random_split(txt_src, [tr_size, dsize - tr_size])
else:
tr_txt = txt_src
te_txt = txt_src
dsets["source_tr"] = ImageList(tr_txt, transform=image_train())
dset_loaders["source_tr"] = DataLoader(dsets["source_tr"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets["source_te"] = ImageList(te_txt, transform=image_test())
dset_loaders["source_te"] = DataLoader(dsets["source_te"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets["target"] = ImageList_idx(txt_tar, transform=image_train())
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets["test"] = ImageList(txt_test, transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=train_bs*3, shuffle=False, num_workers=args.worker, drop_last=False)
return dset_loaders
def data_load(args):
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_src = open(args.s_dset_path).readlines()
txt_tar = []
for i in range(len(args.t_dset_path)):
tmp = open(args.t_dset_path[i]).readlines()
txt_tar.extend(tmp)
txt_test = txt_tar.copy()
if args.trte == 'val':
dsize = len(txt_src)
tr_size = int(0.9*dsize)
print(dsize, tr_size, dsize - tr_size)
tr_txt, te_txt = torch.utils.data.random_split(txt_src, [tr_size, dsize - tr_size])
else:
tr_txt = txt_src
te_txt = txt_src
dsets['source_tr'] = ImageList(tr_txt, transform=image_train())
dset_loaders['source_tr'] = DataLoader(dsets['source_tr'], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets['source_te'] = ImageList(te_txt, transform=image_test())
dset_loaders['source_te'] = DataLoader(dsets['source_te'], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets['target'] = ImageList_idx(txt_tar, transform=image_train())
dset_loaders['target'] = DataLoader(dsets['target'], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets['test'] = ImageList(txt_test, transform=image_test())
dset_loaders['test'] = DataLoader(dsets['test'], batch_size=train_bs*2, shuffle=False, num_workers=args.worker, drop_last=False)
return dset_loaders
def data_load(args):
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_src = open(args.s_dset_path).readlines()
txt_tar = open(args.t_dset_path).readlines()
txt_test = open(args.test_dset_path).readlines()
count = np.zeros(args.class_num)
tr_txt = []
te_txt = []
for i in range(len(txt_src)):
line = txt_src[i]
reci = line.strip().split(' ')
if count[int(reci[1])] < 3:
count[int(reci[1])] += 1
te_txt.append(line)
else:
tr_txt.append(line)
dsets["source_tr"] = ImageList(tr_txt, transform=image_train())
dset_loaders["source_tr"] = DataLoader(dsets["source_tr"],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dsets["source_te"] = ImageList(te_txt, transform=image_test())
dset_loaders["source_te"] = DataLoader(dsets["source_te"],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dsets["target"] = ImageList_idx(txt_tar, transform=image_train())
dset_loaders["target"] = DataLoader(dsets["target"],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dsets["target_te"] = ImageList(txt_tar, transform=image_test())
dset_loaders["target_te"] = DataLoader(dsets["target_te"],
batch_size=train_bs,
shuffle=False,
num_workers=args.worker,
drop_last=False)
dsets["test"] = ImageList(txt_test, transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"],
batch_size=train_bs * 2,
shuffle=False,
num_workers=args.worker,
drop_last=False)
return dset_loaders
def data_load(args):
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_src = open(args.s_dset_path).readlines()
txt_tar = []
for i in range(len(args.t_dset_path)):
tmp = open(args.t_dset_path[i]).readlines()
txt_tar.extend(tmp)
txt_test = txt_tar.copy()
dsets["target"] = ImageList_idx(txt_tar, transform=image_train())
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
dsets["test"] = ImageList(txt_test, transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=train_bs*2, shuffle=False, num_workers=args.worker, drop_last=False)
return dset_loaders
def data_load(args):
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_src = open(args.s_dset_path).readlines()
txt_tar = open(args.t_dset_path).readlines()
txt_test = open(args.test_dset_path).readlines()
if not args.da == 'uda':
label_map_s = {}
for i in range(len(args.src_classes)):
label_map_s[args.src_classes[i]] = i
new_src = []
for i in range(len(txt_src)):
rec = txt_src[i]
reci = rec.strip().split(' ')
if int(reci[1]) in args.src_classes:
line = reci[0] + ' ' + str(label_map_s[int(reci[1])]) + '\n'
new_src.append(line)
txt_src = new_src.copy()
new_tar = []
for i in range(len(txt_tar)):
rec = txt_tar[i]
reci = rec.strip().split(' ')
if int(reci[1]) in args.tar_classes:
if int(reci[1]) in args.src_classes:
line = reci[0] + ' ' + str(label_map_s[int(
reci[1])]) + '\n'
new_tar.append(line)
else:
line = reci[0] + ' ' + str(len(label_map_s)) + '\n'
new_tar.append(line)
txt_tar = new_tar.copy()
txt_test = txt_tar.copy()
if args.trte == 'val':
dsize = len(txt_src)
tr_size = int(0.9 * dsize)
print(dsize, tr_size, dsize - tr_size)
tr_txt, te_txt = torch.utils.data.random_split(
txt_src, [tr_size, dsize - tr_size])
else:
tr_txt = txt_src
te_txt = txt_src
dsets['source_tr'] = ImageList(tr_txt, transform=image_train())
dset_loaders['source_tr'] = DataLoader(dsets['source_tr'],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dsets['source_te'] = ImageList(te_txt, transform=image_test())
dset_loaders['source_te'] = DataLoader(dsets['source_te'],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dsets['target'] = ImageList_idx(txt_tar, transform=image_train())
dset_loaders['target'] = DataLoader(dsets['target'],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False)
dsets['test'] = ImageList_idx(txt_test, transform=image_test())
dset_loaders['test'] = DataLoader(dsets['test'],
batch_size=train_bs * 3,
shuffle=False,
num_workers=args.worker,
drop_last=False)
return dset_loaders
