'recon_poeA', 'recon_poeB',
'recon_crA', 'recon_crB',
'total_loss', 'test_total_loss',
'test_acc', 'val_total_loss',
'val_acc', 'test_f1', 'val_f1')
VIZ = visdom.Visdom(port=args.viz_port)
viz_init()
preprocess_data = transforms.Compose([
transforms.CenterCrop((168, 178)),
transforms.Resize((64, 64)),
transforms.ToTensor(),
])
train_data = torch.utils.data.DataLoader(datasets(
partition='train',
data_dir='../../data/celeba2',
image_transform=preprocess_data),
batch_size=args.batch_size,
shuffle=True)
test_data = torch.utils.data.DataLoader(datasets(
partition='test',
data_dir='../../data/celeba2',
image_transform=preprocess_data),
batch_size=args.batch_size,
shuffle=False)
val_data = torch.utils.data.DataLoader(datasets(
partition='val',
data_dir='../../data/celeba2',
image_transform=preprocess_data),
batch_size=args.batch_size,
if args.viz_on:
WIN_ID = dict(
llA='win_llA', llB='win_llB',
total_losses='win_total_losses',
llB_test='win_llB_test', llA_test='win_llA_test'
)
LINE_GATHER = probtorch.util.DataGather(
'epoch', 'recon_A', 'recon_B',
'total_loss', 'test_total_loss', 'recon_A_test', 'recon_B_test'
)
VIZ = visdom.Visdom(port=args.viz_port)
viz_init()
train_data = torch.utils.data.DataLoader(datasets(path, ATTR_IDX, train=True, crop=1.2), batch_size=args.batch_size,
shuffle=True,
num_workers=len(GPU))
test_data = torch.utils.data.DataLoader(datasets(path, ATTR_IDX, train=False, crop=1.2), batch_size=args.batch_size,
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())
llB_test='win_llB_test',
llA_test='win_llA_test',
llB_val='win_llB_val',
llA_val='win_llA_val',
acc='win_acc')
LINE_GATHER = probtorch.util.DataGather('epoch', 'recon_A', 'recon_B',
'total_loss', 'test_total_loss',
'recon_A_test', 'recon_B_test',
'val_total_loss', 'recon_A_val',
'recon_B_val', 'tr_acc', 'te_acc',
'val_acc')
VIZ = visdom.Visdom(port=args.viz_port)
viz_init()
train_data = torch.utils.data.DataLoader(datasets(path,
ATTR_IDX,
train=True,
crop=1.2),
batch_size=args.batch_size,
shuffle=True,
num_workers=len(GPU))
test_data = torch.utils.data.DataLoader(datasets(path,
ATTR_IDX,
train=False,
crop=1.2),
batch_size=args.batch_size,
shuffle=True,
num_workers=len(GPU))
val_data = torch.utils.data.DataLoader(datasets(path,
ATTR_IDX,
train=True,
'test_acc')
VIZ = visdom.Visdom(port=args.viz_port)
viz_init()
path = '../../data/awa/Animals_with_Attributes2/'
test_classes = np.genfromtxt(path + 'testclasses.txt',
delimiter='\n',
dtype=str)
class_meta = np.genfromtxt(path + 'classes.txt', delimiter='\n', dtype=str)
test_labels = []
for test_class in test_classes:
for i in range(len(class_meta)):
if test_class in class_meta[i]:
test_labels.append(i)
train_data = torch.utils.data.DataLoader(datasets(train=True),
batch_size=args.batch_size,
shuffle=True)
test_data = torch.utils.data.DataLoader(datasets(train=False),
batch_size=args.batch_size,
shuffle=True)
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())
win=WIN_ID['total_losses'],
update='append',
opts=dict(xlabel='epoch',
ylabel='loss',
title='Total Loss',
legend=['train_loss', 'test_loss']))
if args.viz_on:
WIN_ID = dict(total_losses='win_total_losses')
LINE_GATHER = probtorch.util.DataGather('epoch', 'total_loss',
'test_total_loss')
VIZ = visdom.Visdom(port=args.viz_port)
viz_init()
train_data = torch.utils.data.DataLoader(datasets(path, train=True, crop=1.2),
batch_size=args.batch_size,
shuffle=True,
num_workers=len(GPU))
test_data = torch.utils.data.DataLoader(datasets(path, train=False, crop=1.2),
batch_size=args.batch_size,
shuffle=True,
num_workers=len(GPU))
def cuda_tensors(obj):
for attr in dir(obj):
value = getattr(obj, attr)
if isinstance(value, torch.Tensor):
setattr(obj, attr, value.cuda())
acc='win_acc', total_losses='win_total_losses', f1='win_f1'
)
LINE_GATHER = probtorch.util.DataGather(
'epoch',
'total_loss', 'test_acc', 'test_f1'
)
VIZ = visdom.Visdom(port=args.viz_port)
viz_init()
preprocess_data = transforms.Compose([
transforms.CenterCrop((168, 178)),
transforms.Resize((64, 64)),
transforms.ToTensor(),
])
train_data = torch.utils.data.DataLoader(datasets(partition='train', data_dir='../../data/celeba2',
image_transform=preprocess_data), batch_size=args.batch_size,
shuffle=True)
test_data = torch.utils.data.DataLoader(datasets(partition='test', data_dir='../../data/celeba2',
image_transform=preprocess_data), batch_size=args.batch_size,
shuffle=False)
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, n_attr=N_ATTR)
if CUDA:
