batch_size=args.batch_size,
shuffle=False)
BIAS_TRAIN = (train_data.dataset.__len__() - 1) / (args.batch_size - 1)
BIAS_TEST = (test_data.dataset.__len__() - 1) / (args.batch_size - 1)
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
encA = EncoderA(args.wseed, zPrivate_dim=args.n_private, num_pixels=NUM_PIXELS)
decA = DecoderA(args.wseed, zPrivate_dim=args.n_private, num_pixels=NUM_PIXELS)
encB = EncoderB(args.wseed, zPrivate_dim=args.n_private, num_pixels=NUM_PIXELS)
decB = DecoderB(args.wseed, zPrivate_dim=args.n_private, num_pixels=NUM_PIXELS)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
cuda_tensors(encB)
cuda_tensors(decB)
optimizer = torch.optim.Adam(list(encB.parameters()) +
list(decB.parameters()) +
transform=transforms.ToTensor()),
batch_size=args.batch_size, shuffle=True)
print('>>> data loaded')
print('train: ', len(train_data.dataset))
print('test: ', len(test_data.dataset))
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
encA = EncoderA(args.wseed, zShared_dim=args.n_shared)
decA = DecoderA(args.wseed, zShared_dim=args.n_shared)
encB = EncoderB(args.wseed, zShared_dim=args.n_shared)
decB = DecoderB(args.wseed, zShared_dim=args.n_shared)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
cuda_tensors(encB)
cuda_tensors(decB)
optimizer = torch.optim.Adam(
list(encB.parameters()) + list(decB.parameters()) + list(encA.parameters()) + list(decA.parameters()),
lr=args.lr)
shuffle=True,
num_workers=len(GPU))
BIAS_TRAIN = (train_data.dataset.__len__() - 1) / (args.batch_size - 1)
BIAS_TEST = (test_data.dataset.__len__() - 1) / (args.batch_size - 1)
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
encA = EncoderA(args.wseed, zPrivate_dim=args.n_privateA, zSharedAttr_dim=ATTR_DIM)
decA = DecoderA(args.wseed, zPrivate_dim=args.n_privateA, zSharedAttr_dim=ATTR_DIM)
encB = EncoderB(args.wseed, zSharedAttr_dim=ATTR_DIM)
decB = DecoderB(args.wseed, zSharedAttr_dim=ATTR_DIM)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
cuda_tensors(encB)
cuda_tensors(decB)
if len(args.gpu) > 2:
print('multi: ' + args.gpu)
encA = nn.DataParallel(encA)
BIAS_TEST = (test_data.dataset.__len__() - 1) / (args.batch_size - 1)
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
encA = EncoderA(args.wseed,
zPrivate_dim=args.n_privateA,
zShared_dim=args.n_shared,
num_hidden=args.num_hidden)
decA = DecoderA(args.wseed,
zPrivate_dim=args.n_privateA,
zShared_dim=args.n_shared,
num_hidden=args.num_hidden)
encB = EncoderB(args.wseed,
zShared_dim=args.n_shared,
num_hidden=args.num_hidden)
decB = DecoderB(args.wseed,
zShared_dim=args.n_shared,
num_hidden=args.num_hidden)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
shuffle=True,
num_workers=len(GPU))
BIAS_TRAIN = (train_data.dataset.__len__() - 1) / (args.batch_size - 1)
BIAS_TEST = (test_data.dataset.__len__() - 1) / (args.batch_size - 1)
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
encA = EncoderA(args.wseed)
decA = DecoderA(args.wseed)
if CUDA:
encA.cuda()
decA.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
if len(args.gpu) > 2:
print('multi: ' + args.gpu)
encA = nn.DataParallel(encA)
decA = nn.DataParallel(decA)
optimizer = torch.optim.Adam(list(encA.parameters()) + list(decA.parameters()),
lr=args.lr)
BIAS_TEST = (test_data.dataset.__len__() - 1) / (args.batch_size - 1)
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
encA = EncoderA(args.wseed,
zPrivate_dim=args.n_privateA,
zSharedAttr_dim=ATTR_DIM,
zSharedLabel_dim=N_CLASSES)
decA = DecoderA(args.wseed,
zPrivate_dim=args.n_privateA,
zSharedAttr_dim=ATTR_DIM,
zSharedLabel_dim=N_CLASSES)
encB = EncoderB(args.wseed,
zSharedAttr_dim=ATTR_DIM,
zSharedLabel_dim=N_CLASSES)
decB = DecoderB(args.wseed,
zSharedAttr_dim=ATTR_DIM,
zSharedLabel_dim=N_CLASSES)
encC = EncoderC(args.wseed, zSharedLabel_dim=N_CLASSES)
decC = DecoderC(args.wseed, zSharedLabel_dim=N_CLASSES)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
viz_port = args.viz_port # port number, eg, 8097
VIZ = visdom.Visdom(port=args.viz_port)
viz_init()
viz_ll_iter = args.viz_ll_iter
viz_la_iter = args.viz_la_iter
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
encA = EncoderA(num_pixels=4096, num_hidden=512, zPrivate_dim=args.n_private, zShared_dim=args.n_shared)
decA = DecoderA(num_pixels=4096, num_hidden=512, zPrivate_dim=args.n_private, zShared_dim=args.n_shared)
encB = EncoderB(num_pixels=4096, num_hidden=512, zPrivate_dim=args.n_private, zShared_dim=args.n_shared)
decB = DecoderB(num_pixels=4096, num_hidden=512, zPrivate_dim=args.n_private, zShared_dim=args.n_shared)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
cuda_tensors(encB)
cuda_tensors(decB)
optimizer = torch.optim.Adam(list(encB.parameters())+list(decB.parameters())+list(encA.parameters())+list(decA.parameters()),
lr=args.lr)
