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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, opts, transform=None, preprocess=None): | |
self.paths = sorted(data_utils.make_dataset(root)) | |
self.transform = transform | |
self.preprocess = preprocess | |
self.opts = opts | |
def __len__(self): | |
return len(self.paths) | |
def __getitem__(self, index): | |
from_path = self.paths[index] | |
if self.preprocess is not None: | |
from_im = self.preprocess(from_path) | |
else: | |
from_im = Image.open(from_path).convert('RGB') | |
if self.transform: | |
from_im = self.transform(from_im) | |
return from_im | |