diff --git a/src/data/dataset.py b/src/data/dataset.py index 3874640..a9e77b7 100644 --- a/src/data/dataset.py +++ b/src/data/dataset.py @@ -30,10 +30,10 @@ class InpaintingData(Dataset): transforms.ColorJitter(0.05, 0.05, 0.05, 0.05), transforms.ToTensor()]) self.mask_trans = transforms.Compose([ - transforms.Resize(args.image_size, interpolation=transforms.InterpolationMode.NEAREST), + transforms.Resize(args.image_size), transforms.RandomHorizontalFlip(), transforms.RandomRotation( - (0, 45), interpolation=transforms.InterpolationMode.NEAREST), + (0, 45)), ]) @@ -77,4 +77,4 @@ if __name__ == '__main__': data = InpaintingData(args) print(len(data), len(data.mask_path)) img, mask, filename = data[0] - print(img.size(), mask.size(), filename) \ No newline at end of file + print(img.size(), mask.size(), filename) diff --git a/src/test.py b/src/test.py index 872493b..486d000 100644 --- a/src/test.py +++ b/src/test.py @@ -33,9 +33,9 @@ def main_worker(args, use_gpu=True): # prepare dataset image_paths = [] for ext in ['.jpg', '.png']: - image_paths.extend(glob(os.path.join(args.dir_image, '*'+ext))) + image_paths.extend(glob(os.path.join(args.dir_test, '*'+ext))) image_paths.sort() - mask_paths = sorted(glob(os.path.join(args.dir_mask, '*.png'))) + mask_paths = sorted(glob(os.path.join(args.dir_mask,args.mask_type,'*.png'))) os.makedirs(args.outputs, exist_ok=True) # iteration through datasets diff --git a/src/utils/option.py b/src/utils/option.py index f71d55c..276f5b0 100644 --- a/src/utils/option.py +++ b/src/utils/option.py @@ -9,8 +9,8 @@ parser.add_argument('--dir_mask', type=str, default='../../dataset', help='mask dataset directory') parser.add_argument('--data_train', type=str, default='places2', help='dataname used for training') -parser.add_argument('--data_test', type=str, default='places2', - help='dataname used for testing') +parser.add_argument('--dir_test', type=str, default='../datasets/test_imgs/', + help='test image dataset directory') parser.add_argument('--image_size', type=int, default=512, help='image size used during training') parser.add_argument('--mask_type', type=str, default='pconv', @@ -93,4 +93,4 @@ losses = list(args.rec_loss.split('+')) args.rec_loss = {} for l in losses: weight, name = l.split('*') - args.rec_loss[name] = float(weight) \ No newline at end of file + args.rec_loss[name] = float(weight)