def construct_model(model_path, device, nframes):
from rbpn import Net as RBPN
model = RBPN(num_channels=3,
base_filter=256,
feat=64,
num_stages=3,
n_resblock=5,
nFrames=nframes,
scale_factor=4)
ckpt = torch.load(model_path, map_location='cuda:0')
new_ckpt = {}
for key in ckpt:
if key.startswith('module'):
new_key = key[7:]
else:
new_key = key
new_ckpt[new_key] = ckpt[key]
model = model.to(device)
model.load_state_dict(new_ckpt)
model.eval()
return model
def main():
""" Lets begin the training process! """
args = parser.parse_args()
# Initialize Logger
logger.initLogger(args.debug)
# Load dataset
logger.info('==> Loading datasets')
# print(args.file_list)
# sys.exit()
train_set = get_training_set(args.data_dir, args.nFrames,
args.upscale_factor, args.data_augmentation,
args.file_list, args.other_dataset,
args.patch_size, args.future_frame)
training_data_loader = DataLoader(dataset=train_set,
num_workers=args.threads,
batch_size=args.batchSize,
shuffle=True)
# Use generator as RBPN
netG = RBPN(num_channels=3,
base_filter=256,
feat=64,
num_stages=3,
n_resblock=5,
nFrames=args.nFrames,
scale_factor=args.upscale_factor)
logger.info('# of Generator parameters: %s',
sum(param.numel() for param in netG.parameters()))
# Use DataParallel?
if args.useDataParallel:
gpus_list = range(args.gpus)
netG = torch.nn.DataParallel(netG, device_ids=gpus_list)
# Use discriminator from SRGAN
netD = Discriminator()
logger.info('# of Discriminator parameters: %s',
sum(param.numel() for param in netD.parameters()))
# Generator loss
generatorCriterion = nn.L1Loss() if not args.APITLoss else GeneratorLoss()
# Specify device
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.gpu_mode else "cpu")
if args.gpu_mode and torch.cuda.is_available():
utils.printCUDAStats()
netG.cuda()
netD.cuda()
netG.to(device)
netD.to(device)
generatorCriterion.cuda()
# Use Adam optimizer
optimizerG = optim.Adam(netG.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-8)
optimizerD = optim.Adam(netD.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-8)
if args.APITLoss:
logger.info(
"Generator Loss: Adversarial Loss + Perception Loss + Image Loss + TV Loss"
)
else:
logger.info("Generator Loss: L1 Loss")
# print iSeeBetter architecture
utils.printNetworkArch(netG, netD)
if args.pretrained:
modelPath = os.path.join(args.save_folder + args.pretrained_sr)
utils.loadPreTrainedModel(gpuMode=args.gpu_mode,
model=netG,
modelPath=modelPath)
# sys.exit()
for epoch in range(args.start_epoch, args.nEpochs + 1):
runningResults = trainModel(epoch, training_data_loader, netG, netD,
optimizerD, optimizerG, generatorCriterion,
device, args)
if (epoch + 1) % (args.snapshots) == 0:
saveModelParams(epoch, runningResults, netG, netD)
shuffle=True,
num_workers=args.workers,
pin_memory=True)
device = torch.device("cuda:0")
print("constructing model ....")
model = RBPN(num_channels=3,
base_filter=256,
feat=64,
num_stages=3,
n_resblock=5,
nFrames=args.nframes,
scale_factor=4)
model = nn.DataParallel(model.to(device), gpuids)
if args.resume:
ckpt = torch.load(args.model_path)
new_ckpt = {}
for key in ckpt:
if not key.startswith('module'):
new_key = 'module.' + key
else:
new_key = key
new_ckpt[new_key] = ckpt[key]
model.load_state_dict(new_ckpt, strict=False)
print("model constructed")
# for key, value in model.named_parameters():
# if not ('pre_deblur' in key):
