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())
netD = torch.load(opt.resumeD)
netD.load_state_dict(netD.state_dict())
opt.start_training_step, opt.start_epoch = which_trainingstep_epoch(
opt.resume)
else:
model = Net()
netD = Discriminator()
mkdir_steptraing()
# model = torch.load('models/1/GFN_epoch_1.pkl')
# model.load_state_dict(model.state_dict())
# netD = torch.load('models/1/GFN_D_epoch_1.pkl')
# netD.load_state_dict(netD.state_dict())
model = model.to(device)
netD = netD.to(device)
criterion = torch.nn.MSELoss(size_average=True)
criterion = criterion.to(device)
cri_perception = VGGFeatureExtractor().to(device)
cri_gan = GANLoss('vanilla', 1.0, 0.0).to(device)
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())
netD = torch.load(opt.resumeD)
netD.load_state_dict(netD.state_dict())
opt.start_training_step, opt.start_epoch = which_trainingstep_epoch(
opt.resume)
else:
model = Net()
netD = Discriminator()
mkdir_steptraing()
model = model.to(device)
netD = netD.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,
[0.9, 0.999])
optimizer_D = torch.optim.Adam(
filter(lambda p: p.requires_grad, netD.parameters()), 0.0002, [0.9, 0.999])
print()
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())
# netD = torch.load(opt.resumeD)
# netD.load_state_dict(netD.state_dict())
opt.start_training_step, opt.start_epoch = which_trainingstep_epoch(opt.resume)
else:
model = Net()
# netD = Discriminator()
mkdir_steptraing()
model = model.to(device)
# netD = netD.to(device)
criterion = torch.nn.MSELoss(size_average=True)
criterion = criterion.to(device)
cri_perception = VGGFeatureExtractor().to(device)
# textual init
vgg19 = cri_perception.vggNet
loss_layer = [1, 6, 11, 20]
loss_fns = [GramMSELoss()] * len(loss_layer)
if torch.cuda.is_available():
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())
netD = torch.load(opt.resumeD)
netD.load_state_dict(netD.state_dict())
opt.start_training_step, opt.start_epoch = which_trainingstep_epoch(opt.resume)
else:
model = Net()
netD = Discriminator()
mkdir_steptraing()
# model = torch.load('models/1/GFN_epoch_1.pkl')
# model.load_state_dict(model.state_dict())
# netD = torch.load('models/1/GFN_D_epoch_1.pkl')
# netD.load_state_dict(netD.state_dict())
model = model.to(device)
netD = netD.to(device)
criterion = torch.nn.L1Loss(size_average=True)
criterion = criterion.to(device)
cri_perception = VGGFeatureExtractor().to(device)
cri_gan = GANLoss('vanilla', 1.0, 0.0).to(device)