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from torch.utils.data import Dataset
from PIL import Image
from utils import data_utils
class InferenceDataset(Dataset):
def __init__(self, root=None, paths_list=None, opts=None, transform=None, return_path=False):
if paths_list is None:
self.paths = sorted(data_utils.make_dataset(root))
else:
self.paths = data_utils.make_dataset_from_paths_list(paths_list)
self.transform = transform
self.opts = opts
self.return_path = return_path
def __len__(self):
return len(self.paths)
def __getitem__(self, index):
from_path = self.paths[index]
from_im = Image.open(from_path)
from_im = from_im.convert('RGB') if self.opts.label_nc == 0 else from_im.convert('L')
if self.transform:
from_im = self.transform(from_im)
if self.return_path:
return from_im, from_path
else:
return from_im
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