def __init__(self):
super(Net, self).__init__()
self.deblurMoudle = self._make_net(_DeblurringMoudle)
# self.deblurMoudle = SRNDeblurNet()
# self.srMoudle = self._make_net(_SRMoudle)
# self.srMoudle = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, norm_type=None)
self.srMoudle = MSRN()
self.geteMoudle = self._make_net(_GateMoudle)
self.reconstructMoudle = self._make_net(_ReconstructMoudle)
opt = parser.parse_args()
opt.seed = random.randint(1, 1200)
torch.manual_seed(opt.seed)
torch.cuda.manual_seed(opt.seed)
if opt.resume:
if os.path.isfile(opt.resume):
print("Loading from checkpoint {}".format(opt.resume))
model = torch.load(opt.resume)
model.load_state_dict(model.state_dict())
opt.start_training_step, opt.start_epoch = which_trainingstep_epoch(
opt.resume)
else:
model = MSRN()
model = model.to(device)
criterion = torch.nn.L1Loss(size_average=True)
criterion = criterion.to(device)
cri_perception = VGGFeatureExtractor().to(device)
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()), 0.0001)
print('# GFN_deblur parameters:',
sum(param.numel() for param in model.parameters()))
print()
opt.start_epoch = 1
opt.nEpochs = 1000
for epoch in range(opt.start_epoch, opt.nEpochs + 1):
trainloader = CreateDataLoader(opt)
def __init__(self):
super(Net, self).__init__()
self.deblurMoudle = _DeblurringMoudle()
self.srMoudle = MSRN()
self.reconstructMoudle = self._make_net(_ReconstructMoudle)