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)
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)
optimizer_D = torch.optim.Adam(filter(lambda p: p.requires_grad, netD.parameters()), 0.0004)
print()
for i in range(opt.start_training_step, 4):
opt.nEpochs = training_settings[i-1]['nEpochs']
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 = Net()
mkdir_steptraing()
model = model.to(device)
criterion = torch.nn.MSELoss(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])
print()
for i in range(opt.start_training_step, 4):
opt.nEpochs = training_settings[i - 1]['nEpochs']
opt.lr = training_settings[i - 1]['lr']
opt.step = training_settings[i - 1]['step']
torch.manual_seed(opt.seed)
torch.cuda.manual_seed(opt.seed)
train_dir = opt.dataset
train_sets = [x for x in sorted(os.listdir(train_dir)) if is_hdf5_file(x)]
print("===> Loading model and criterion")
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 = Net()
mkdir_steptraing()
model = model.to(device)
criterion = torch.nn.MSELoss(size_average=True)
criterion = criterion.to(device)
optimizer = optim.Adam(model.parameters(), lr=opt.lr)
print()
for i in range(opt.start_training_step, 4):
opt.nEpochs = training_settings[i-1]['nEpochs']
opt.lr = training_settings[i-1]['lr']
opt.step = training_settings[i-1]['step']
opt.lr_decay = training_settings[i-1]['lr_decay']
opt.lambda_db = training_settings[i-1]['lambda_db']
opt.gated = training_settings[i-1]['gated']