def test_load_celebA():
print 'testing data loader'
BATCH_SIZE = 5
trainDataset = CELEBA(root=ROOT,
train=True,
transform=transforms.ToTensor())
trainLoader = torch.utils.data.DataLoader(trainDataset,
batch_size=BATCH_SIZE,
shuffle=True)
(x, y) = iter(trainLoader).next()
assert x.size() == (5, 3, 64, 64)
assert y.size() == (5, )
f.close()
return svm
if __name__ == '__main__':
opts = get_args()
#Load data
print 'Prepare data loaders...'
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.RandomHorizontalFlip()])
trainDataset = CELEBA(root=opts.root,
train=True,
transform=transforms.ToTensor())
trainLoader = torch.utils.data.DataLoader(trainDataset,
batch_size=opts.batchSize,
shuffle=True)
testDataset = CELEBA(root=opts.root,
train=False,
transform=transforms.ToTensor())
testLoader = torch.utils.data.DataLoader(testDataset,
batch_size=opts.batchSize,
shuffle=False)
print 'Data loaders ready.'
#Create model
dae = DAE(nz=opts.nz,
print 'Outputs will be saved to:', exDir
save_input_args(exDir, opts)
# Load data (glasses and male labels)
IM_SIZE = opts.imSize
print 'Prepare data loader...'
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
testDataset = CELEBA(
root=opts.root,
train=False,
labels=opts.labels,
transform=transform,
Ntest=1000
) #most models trained with Ntest=1000, but using 100 to prevent memory errors
testLoader = torch.utils.data.DataLoader(testDataset,
batch_size=opts.batchSize,
shuffle=False)
print 'Data loader ready.'
#Load model
gen = GEN(imSize=IM_SIZE, nz=opts.nz, fSize=opts.fSize)
if gen.useCUDA:
torch.cuda.set_device(opts.gpuNo)
gen.cuda()
gen.load_params(opts.exDir, gpuNo=opts.gpuNo)
gen.eval()
####### Save params #######
gen.save_params(exDir)
dis.save_params(exDir)
return gen, dis
if __name__ == '__main__':
opts = get_args()
####### Data set #######
print 'Prepare data loaders...'
transform = transforms.Compose([transforms.ToPILImage(), transforms.RandomHorizontalFlip(),\
transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainDataset = CELEBA(root=opts.root, train=True, transform=transform)
trainLoader = torch.utils.data.DataLoader(trainDataset,
batch_size=opts.batchSize,
shuffle=True)
transform = transforms.Compose([ transforms.ToPILImage(), \
transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
testDataset = CELEBA(root=opts.root, train=False, transform=transform)
testLoader = torch.utils.data.DataLoader(testDataset,
batch_size=opts.batchSize,
shuffle=False)
print 'Data loaders ready.'
###### Create model #####
IM_SIZE = 64