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Sanghyun Son authoredSanghyun Son authored
srdata.py 6.88 KiB
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_lr = self._scan()
if args.ext.find('bin') >= 0:
# Binary files are stored in 'bin' folder
# If the binary file exists, load it. If not, make it.
list_hr, list_lr = self._scan()
self.images_hr = self._check_and_load(
args.ext, list_hr, self._name_hrbin()
)
self.images_lr = [
self._check_and_load(args.ext, l, self._name_lrbin(s)) \
for s, l in zip(self.scale, list_lr)
]
else:
if args.ext.find('img') >= 0 or benchmark:
self.images_hr, self.images_lr = list_hr, list_lr
elif args.ext.find('sep') >= 0:
os.makedirs(
self.dir_hr.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_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, load=False
)
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, load=False
)
if train:
n_patches = args.batch_size * args.test_every
n_images = len(args.data_train) * len(self.images_hr)
self.repeat = max(n_patches // n_images, 1)
# Below functions as used to prepare images
def _scan(self):
names_hr = sorted(
glob.glob(os.path.join(self.dir_hr, '*' + 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{}/{}x{}{}'.format(
s, filename, s, self.ext[1]
)
))
return names_hr, 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_lr = os.path.join(self.apath, 'LR_bicubic')
if self.input_large: self.dir_lr += 'L'
self.ext = ('.png', '.png')
def _name_hrbin(self):
return os.path.join(
self.apath,
'bin',
'{}_bin_HR.pt'.format(self.split)
)
def _name_lrbin(self, scale):
return os.path.join(
self.apath,
'bin',
'{}_bin_LR_X{}.pt'.format(self.split, scale)
)
def _check_and_load(self, ext, l, f, verbose=True, load=True):
if os.path.isfile(f) and ext.find('reset') < 0:
if load:
if verbose: print('Loading {}...'.format(f))
with open(f, 'rb') as _f: ret = pickle.load(_f)
return ret
else:
return None
else:
if verbose:
if ext.find('reset') >= 0:
print('Making a new binary: {}'.format(f))
else:
print('{} does not exist. Now making binary...'.format(f))
b = [{
'name': os.path.splitext(os.path.basename(_l))[0],
'image': imageio.imread(_l)
} for _l in l]
with open(f, 'wb') as _f: pickle.dump(b, _f)
return b
def __getitem__(self, idx):
lr, hr, filename = self._load_file(idx)
pair = self.get_patch(lr, hr)
pair = common.set_channel(*pair, n_channels=self.args.n_colors)
pair_t = common.np2Tensor(*pair, rgb_range=self.args.rgb_range)
return pair_t[0], pair_t[1], 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_lr = self.images_lr[self.idx_scale][idx]
if self.args.ext.find('bin') >= 0:
filename = f_hr['name']
hr = f_hr['image']
lr = f_lr['image']
else:
filename, _ = os.path.splitext(os.path.basename(f_hr))
if self.args.ext == 'img' or self.benchmark:
hr = imageio.imread(f_hr)
lr = imageio.imread(f_lr)
elif self.args.ext.find('sep') >= 0:
with open(f_hr, 'rb') as _f: hr = pickle.load(_f)[0]['image']
with open(f_lr, 'rb') as _f: lr = pickle.load(_f)[0]['image']
return lr, hr, filename
def get_patch(self, lr, hr):
scale = self.scale[self.idx_scale]
if self.train:
lr, hr = common.get_patch(
lr, 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, hr = common.augment(lr, hr)
else:
ih, iw = lr.shape[:2]
hr = hr[0:ih * scale, 0:iw * scale]
return lr, 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)