# The detail network setting
opt = parser.parse_args()
print(opt)
# Save all the codes
os.system('mkdir %s' % opt.experiment)
os.system('cp *.py %s' % opt.experiment)
if torch.cuda.is_available() and opt.noCuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# Initialize network
net = faceNet.faceNet(m=opt.marginFactor, feature=False)
lossLayer = faceNet.CustomLoss(s=opt.scaleFactor)
# Move network and containers to gpu
if not opt.noCuda:
net = net.cuda(opt.gpuId)
# Initialize optimizer
optimizer = optim.SGD(net.parameters(),
lr=opt.initLR,
momentum=0.9,
weight_decay=5e-4)
# Initialize dataLoader
faceDataset = dataLoader.BatchLoader(imageRoot=opt.imageRoot,
alignmentRoot=opt.alignmentRoot,
cropSize=(opt.imWidth, opt.imHeight))
# The detail network setting
opt = parser.parse_args()
print(opt)
# Save all the codes
os.system('mkdir %s' % opt.experiment)
os.system('cp *.py %s' % opt.experiment)
if torch.cuda.is_available() and opt.noCuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# Initialize network
net = faceNet.faceNet(m=opt.marginFactor, feature=False)
lossLayer = faceNet.CustomLoss()
# Move network and containers to gpu
if not opt.noCuda:
net = net.cuda(opt.gpuId)
# Initialize optimizer
optimizer = optim.SGD(net.parameters(),
lr=opt.initLR,
momentum=0.9,
weight_decay=5e-4)
# Initialize dataLoader
faceDataset = dataLoader.BatchLoader(imageRoot=opt.imageRoot,
alignmentRoot=opt.alignmentRoot,
cropSize=(opt.imWidth, opt.imHeight))
