batch_size = {"train": 36, "val": 36, "test": 4}
for i in range(10):
batch_size["val" + str(i)] = 4
if visda == False:
data_transforms = {
'train': tran.transform_train(resize_size=28, crop_size=28),
'val': tran.transform_train(resize_size=28, crop_size=28),
}
data_transforms = tran.transform_test(data_transforms=data_transforms,
resize_size=28,
crop_size=28)
dsets = {
"train":
ImageList(open(src).readlines(), transform=data_transforms["train"]),
"val":
ImageList(open(tgt).readlines(), transform=data_transforms["val"]),
"test":
ImageList(open(tgt).readlines(), transform=data_transforms["val"])
}
dset_loaders = {
x: torch.utils.data.DataLoader(dsets[x],
batch_size=batch_size[x],
shuffle=True,
num_workers=4)
for x in ['train', 'val']
}
dset_loaders["test"] = torch.utils.data.DataLoader(
dsets["test"],
batch_size=batch_size["test"],
rl="rr-real.txt"
if args.src =='rl':
source_path = rl
elif args.src =='rc':
source_path = rc
if args.tgt =='rl':
target_path = rl
elif args.tgt =='rc':
target_path = rc
dsets = {"train": ImageList(open(source_path).readlines(), transform=data_transforms["train"]),
"val": ImageList(open(target_path).readlines(),transform=data_transforms["val"]),
"test": ImageList(open(target_path).readlines(),transform=data_transforms["test"])}
dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=batch_size[x],
shuffle=True, num_workers=0)
for x in ['train', 'val']}
dset_loaders["test"] = torch.utils.data.DataLoader(dsets["test"], batch_size=batch_size["test"],
shuffle=False, num_workers=64)
dset_sizes = {x: len(dsets[x]) for x in ['train', 'val','test']}
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
def Regression_test(loader, model):
MSE=0
MAE=0
number = 0