| | import os |
| | import glob |
| | import random |
| | import pickle |
| |
|
| | from data import common |
| |
|
| | import numpy as np |
| | import imageio |
| | import torch |
| | import torch.utils.data as data |
| |
|
| | class SRData(data.Dataset): |
| | def __init__(self, args, name='', train=True, benchmark=False): |
| | self.args = args |
| | self.name = name |
| | self.train = train |
| | self.split = 'train' if train else 'test' |
| | self.do_eval = True |
| | self.benchmark = benchmark |
| | self.input_large = (args.model == 'VDSR') |
| | self.scale = args.scale |
| | self.idx_scale = 0 |
| | |
| | self._set_filesystem(args.dir_data) |
| | if args.ext.find('img') < 0: |
| | path_bin = os.path.join(self.apath, 'bin') |
| | os.makedirs(path_bin, exist_ok=True) |
| |
|
| | list_hr, list_edge, list_lr = self._scan() |
| | if args.ext.find('img') >= 0 or benchmark: |
| | self.images_hr, self.images_edge, self.images_lr = list_hr, list_edge, list_lr |
| | elif args.ext.find('sep') >= 0: |
| | os.makedirs( |
| | self.dir_hr.replace(self.apath, path_bin), |
| | exist_ok=True |
| | ) |
| | os.makedirs( |
| | self.dir_edge.replace(self.apath, path_bin), |
| | exist_ok=True |
| | ) |
| | for s in self.scale: |
| | os.makedirs( |
| | os.path.join( |
| | self.dir_lr.replace(self.apath, path_bin), |
| | 'X{}'.format(s) |
| | ), |
| | exist_ok=True |
| | ) |
| | |
| | self.images_hr, self.images_edge, self.images_lr = [], [], [[] for _ in self.scale] |
| | for h in list_hr: |
| | b = h.replace(self.apath, path_bin) |
| | b = b.replace(self.ext[0], '.pt') |
| | self.images_hr.append(b) |
| | self._check_and_load(args.ext, h, b, verbose=True) |
| |
|
| | for e in list_edge: |
| | g = e.replace(self.apath, path_bin) |
| | g = g.replace(self.ext[0], '.pt') |
| | self.images_edge.append(g) |
| | self._check_and_load( |
| | args.ext, e, g, verbose=True) |
| |
|
| | for i, ll in enumerate(list_lr): |
| | for l in ll: |
| | b = l.replace(self.apath, path_bin) |
| | b = b.replace(self.ext[1], '.pt') |
| | self.images_lr[i].append(b) |
| | self._check_and_load(args.ext, l, b, verbose=True) |
| | if train: |
| | n_patches = args.batch_size * args.test_every |
| | n_images = len(args.data_train) * len(self.images_hr) |
| | if n_images == 0: |
| | self.repeat = 0 |
| | else: |
| | self.repeat = max(n_patches // n_images, 1) |
| |
|
| | |
| | def _scan(self): |
| | names_hr = sorted( |
| | glob.glob(os.path.join(self.dir_hr, '*' + self.ext[0])) |
| | ) |
| | names_edge = sorted( |
| | glob.glob(os.path.join(self.dir_edge, '*' + self.ext[0])) |
| | ) |
| | names_lr = [[] for _ in self.scale] |
| | for f in names_hr: |
| | filename, _ = os.path.splitext(os.path.basename(f)) |
| | for si, s in enumerate(self.scale): |
| | names_lr[si].append(os.path.join( |
| | self.dir_lr, 'X{}/{}{}'.format( |
| | s, filename, self.ext[1] |
| | ) |
| | )) |
| |
|
| | return names_hr, names_edge, names_lr |
| |
|
| | def _set_filesystem(self, dir_data): |
| | self.apath = os.path.join(dir_data, self.name) |
| | self.dir_hr = os.path.join(self.apath, 'HR') |
| | self.dir_edge = os.path.join(self.apath, 'EDGE') |
| | self.dir_lr = os.path.join(self.apath, 'LR_bicubic') |
| | if self.input_large: self.dir_lr += 'L' |
| | self.ext = ('.jpg', '.jpg') |
| | |
| |
|
| | def _check_and_load(self, ext, img, f, verbose=True): |
| | if not os.path.isfile(f) or ext.find('reset') >= 0: |
| | if verbose: |
| | print('Making a binary: {}'.format(f)) |
| | with open(f, 'wb') as _f: |
| | pickle.dump(imageio.imread(img), _f) |
| |
|
| | def __getitem__(self, idx): |
| | lr, edge, hr, filename = self._load_file(idx) |
| | lr, edge, hr = self.get_patch(lr, edge, hr) |
| | lr, edge, hr = common.set_channel(lr, edge, hr, n_channels=self.args.n_colors) |
| | lr_tensor, edge_tensor, hr_tensor = common.np2Tensor(lr, edge, hr, rgb_range=self.args.rgb_range) |
| |
|
| | return lr_tensor, edge_tensor, hr_tensor, filename |
| |
|
| | def __len__(self): |
| | if self.train: |
| | return len(self.images_hr) * self.repeat |
| | else: |
| | return len(self.images_hr) |
| |
|
| | def _get_index(self, idx): |
| | if self.train: |
| | return idx % len(self.images_hr) |
| | else: |
| | return idx |
| |
|
| | def _load_file(self, idx): |
| | idx = self._get_index(idx) |
| | f_hr = self.images_hr[idx] |
| | f_edge = self.images_edge[idx] |
| | f_lr = self.images_lr[self.idx_scale][idx] |
| |
|
| | filename, _ = os.path.splitext(os.path.basename(f_hr)) |
| | if self.args.ext == 'img' or self.benchmark: |
| | hr = imageio.imread(f_hr) |
| | edge = imageio.imread(f_edge) |
| | lr = imageio.imread(f_lr) |
| | elif self.args.ext.find('sep') >= 0: |
| | with open(f_hr, 'rb') as _f: |
| | hr = pickle.load(_f) |
| | with open(f_edge, 'rb') as _f: |
| | edge = pickle.load(_f) |
| | with open(f_lr, 'rb') as _f: |
| | lr = pickle.load(_f) |
| |
|
| | return lr, edge, hr, filename |
| |
|
| | def get_patch(self, lr, edge, hr): |
| | scale = self.scale[self.idx_scale] |
| | if self.train: |
| | lr, edge, hr = common.get_patch( |
| | lr, edge, hr, |
| | patch_size=self.args.patch_size, |
| | scale=scale, |
| | multi=(len(self.scale) > 1), |
| | input_large=self.input_large |
| | ) |
| | if not self.args.no_augment: |
| | lr, edge, hr = common.augment(lr, edge, hr) |
| | else: |
| | ih, iw = lr.shape[:2] |
| | hr = hr[0:ih * scale, 0:iw * scale] |
| | edge = edge[0:ih * scale, 0:iw * scale] |
| |
|
| | return lr, edge, hr |
| |
|
| | def set_scale(self, idx_scale): |
| | if not self.input_large: |
| | self.idx_scale = idx_scale |
| | else: |
| | self.idx_scale = random.randint(0, len(self.scale) - 1) |
| |
|
| |
|
