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dataset.py
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dataset.py
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import os
import glob
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision.io import read_image
from torchvision import transforms
# import torchvision.transforms.functional as FT
class SRDataset(Dataset):
def __init__(self, root='', is_train=True, downsampling_method=transforms.InterpolationMode.BILINEAR):
self.data_root = root
prefix = 'train' if is_train else 'eval'
self.data_path = os.path.join(root, prefix, '*')
self.downsampling_method = downsampling_method
self.images = sorted(glob.glob(self.data_path))
self.crop_size = [64, 64]
if is_train:
self.transform = transforms.Compose([
transforms.RandomCrop(self.crop_size),
transforms.ColorJitter(0.2, 0.2, 0.2, 0.2)])
else:
self.transform = transforms.Compose([])
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
file_name = self.images[idx]
data = read_image(file_name).float()
data = (data - data.min()) / (data.max() - data.min()) # normalize
data = self.transform(data)
_, h, w = data.shape
lr_size = [h // 2, w // 2]
lr_image = transforms.Resize(lr_size, self.downsampling_method)(data)
return lr_image, data