# Initialize Network
encoder = models.encoder(isAddCostVolume=opt.isAddCostVolume)
for param in encoder.parameters():
param.requires_grad = False
encoder.load_state_dict(
torch.load('{0}/encoder_{1}.pth'.format(opt.experiment, opt.nepoch - 1),
map_location={'cuda:0': 'cuda:{0}'.format(opt.gpuId)}))
decoder = models.decoder(isAddVisualHull=opt.isAddVisualHull)
for param in decoder.parameters():
param.requires_grad = False
decoder.load_state_dict(
torch.load('{0}/decoder_{1}.pth'.format(opt.experiment, opt.nepoch - 1),
map_location={'cuda:0': 'cuda:{0}'.format(opt.gpuId)}))
normalFeature = models.normalFeature()
for param in normalFeature.parameters():
param.requires_grad = False
normalFeature.load_state_dict(
torch.load('{0}/normalFeature_{1}.pth'.format(opt.experiment,
opt.nepoch - 1),
map_location={'cuda:0': 'cuda:{0}'.format(opt.gpuId)}))
normalPool = Variable(
torch.ones([1, angleNum * angleNum, 1, 1, 1], dtype=torch.float32))
normalPool.requires_grad = False
if opt.isAddCostVolume and opt.poolingMode == 2:
normalPool.data.copy_(
torch.load('{0}/normalPool_{1}.pth'.format(opt.experiment,
opt.nepoch - 1),
map_location={'cuda:0': 'cuda:{0}'.format(opt.gpuId)}))
opt.seed = 0
print("Random Seed: ", opt.seed)
random.seed(opt.seed)
torch.manual_seed(opt.seed)
if torch.cuda.is_available() and not opt.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
####################################
# Initialize Network
encoder = nn.DataParallel(models.encoder(isAddCostVolume=opt.isAddCostVolume),
device_ids=opt.deviceIds)
decoder = nn.DataParallel(models.decoder(), device_ids=opt.deviceIds)
normalFeature = nn.DataParallel(models.normalFeature(),
device_ids=opt.deviceIds)
normalPool = Variable(
torch.ones([1, angleNum * angleNum, 1, 1, 1], dtype=torch.float32))
############## ######################
# Send things into GPU
if opt.cuda:
encoder = encoder.cuda()
decoder = decoder.cuda()
normalFeature = normalFeature.cuda()
normalPool = normalPool.cuda()
####################################
# Other modules
renderer = models.renderer(eta1=opt.eta1,