"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# need initialize!!
G_xvz = _G_xvz()
G_vzx = _G_vzx()
D_xvs = _D_xvs()
G_xvz.apply(weights_init)
G_vzx.apply(weights_init)
D_xvs.apply(weights_init)
train_list = args.data_list
train_loader = torch.utils.data.DataLoader(data_loader.ImageList(
train_list,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
def L1_loss(x, y):
return torch.mean(torch.sum(torch.abs(x - y), 1))
v_siz = 9
z_siz = 128 - v_siz
x1 = torch.FloatTensor(args.batch_size, 3, 128, 128)
# 3 networks have been inherits from nn.Module
G_xvz = _G_xvz()
G_vzx = _G_vzx()
D_xvs = _D_xvs()
# Initialize weights for networks
G_xvz.apply(weights_init)
G_vzx.apply(weights_init)
D_xvs.apply(weights_init)
train_list = args.data_list # Path to image list for training
# Dataloader is used to wrap outside of torch.utils.data.Dataset (or our custormized dataset - ImageList) for loading data efficiently
train_loader = torch.utils.data.DataLoader(
data_loader.ImageList(
train_list,
transform=transforms.Compose([
transforms.ToTensor(), # to range (0,1)
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
def L1_loss(x, y):
return torch.mean(torch.sum(torch.abs(x - y), 1))
v_siz = 9
z_siz = 128 - v_siz
x1 = torch.FloatTensor(args.batch_size, 3, 128, 128)