# dataset_train = DIV2K(args.root, args.mask_root, img_tf, mask_tf, 'train')
# dataset_val = DIV2K(args.root, args.mask_root, img_tf, mask_tf, 'val')
iterator_train = iter(
data.DataLoader(dataset_train,
batch_size=args.batch_size,
sampler=InfiniteSampler(len(dataset_train)),
num_workers=args.n_threads))
print(len(dataset_train))
model = PConvUNet()
if torch.cuda.device_count() > 1:
print("Use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
model = model.to(device)
if args.finetune:
lr = args.lr_finetune
model.freeze_enc_bn = True
else:
lr = args.lr
start_iter = 0
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr)
criterion = InpaintingLoss(VGG16FeatureExtractor()).to(device)
if args.resume:
start_iter = load_ckpt(args.resume, [('model', model)],
type=str,
default='../posewarp/data/hunter_256.jpeg')
parser.add_argument('--maskPath',
type=str,
default='../posewarp/data/hunter_256_IUV.npy')
#parser.add_argument('--maskPath', type=str, default='/home/cloudlet/vlr_824/project/backgroundInpainting/mask/003328.jpg')
parser.add_argument('--modelPath', type=str, default='./pretrainedModel.pth')
parser.add_argument('--image_size', type=int, default=256)
args = parser.parse_args()
device = torch.device('cuda')
size = (args.image_size, args.image_size)
img_transform = transforms.Compose([
transforms.Resize(size=size),
transforms.ToTensor(),
transforms.Normalize(mean=opt.MEAN, std=opt.STD)
])
mask_transform = transforms.Compose(
[transforms.Resize(size=size),
transforms.ToTensor()])
dataset_val = customData(args.imPath, args.maskPath, img_transform,
mask_transform, 'val')
model = PConvUNet()
load_ckpt(args.modelPath, [('model', model)])
model.to(device)
model.eval()
evaluateCustom(model, dataset_val, device, 'result.jpg')
