def fetch_datasets(data_config_file, opt):
all_datasets = load_datasets(data_config_file)
dataset = all_datasets[opt.dataset.upper()]
if opt.test:
test_data = all_datasets[opt.test.upper()]
else:
test_data = dataset
if opt.infer:
infer_dir = Path(opt.infer)
if infer_dir.exists():
# infer files in this directory
if infer_dir.is_file():
images = [str(infer_dir)]
else:
images = list(infer_dir.glob('*'))
if not images:
images = infer_dir.iterdir()
infer_data = Dataset(infer=images,
mode='pil-image1',
modcrop=False)
else:
infer_data = all_datasets[opt.infer.upper()]
else:
infer_data = test_data
if opt.mode:
dataset.mode = opt.mode
test_data.mode = opt.mode
infer_data.mode = opt.mode
return dataset, test_data, infer_data
def test_no_shuffle(self):
d = Dataset('data/').include('*.png')
data = d.compile()
ld = Loader(data, data, threads=4)
ld.cropper(CenterCrop(1))
itr1 = ld.make_one_shot_iterator([1, 3, 16, 16], -1, False)
ret1 = list(itr1)
self.assertEqual(len(ret1), 16)
self.assert_psnr(ret1)
itr2 = ld.make_one_shot_iterator([1, 3, 16, 16], -1, False)
ret2 = list(itr2)
self.assertEqual(len(ret2), 16)
self.assert_psnr(ret2)
for x, y in zip(ret1, ret2):
self.assertTrue(np.all((x['hr'] - y['hr']) < 1e-4))
def test_auto_deduce_shape(self):
d = Dataset('data').include_reg('set5')
ld = Loader(d, scale=1)
itr = ld.make_one_shot_iterator([1, -1, -1, -1], -1)
ret = list(itr)
self.assertEqual(len(ret), 5)
self.assert_psnr(ret)
def test_memory_limit():
d = Dataset('data/')
d = d.include('*.png')
data = d.compile()
ld = Loader(data, data, threads=4)
ld.cropper(RandomCrop(1))
ld.image_augmentation()
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10, True,
data.capacity / 2)
ret = list(itr)
assert len(ret) is 10
assert_psnr(ret)
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10, True,
data.capacity / 2)
ret = list(itr)
assert len(ret) is 10
assert_psnr(ret)
def test_no_shuffle_limit(self):
d = Dataset('data/')
d = d.include('*.png')
data = d.compile()
ld = Loader(data, data, threads=4)
ld.cropper(RandomCrop(1))
ld.image_augmentation()
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10, False,
data.capacity / 2)
ret = list(itr)
self.assertEqual(len(ret), 10)
self.assert_psnr(ret)
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10, False,
data.capacity / 2)
ret = list(itr)
self.assertEqual(len(ret), 10)
self.assert_psnr(ret)
def fetch_datasets(data_config_file, opt):
all_datasets = load_datasets(data_config_file)
dataset = all_datasets[opt.dataset.upper()]
if opt.test:
test_data = all_datasets[opt.test.upper()]
else:
test_data = dataset
if opt.infer:
infer_dir = Path(opt.infer)
if infer_dir.exists():
# infer files in this directory
if infer_dir.is_file():
images = [str(infer_dir)]
else:
images = list(infer_dir.glob('*'))
if not images:
images = infer_dir.iterdir()
infer_data = Dataset(infer=images,
mode='pil-image1',
modcrop=False)
else:
infer_data = all_datasets[opt.infer.upper()]
else:
infer_data = test_data
# TODO temp use, delete if not need
if opt.cifar:
cifar_data, cifar_test = tf.keras.datasets.cifar10.load_data()
dataset = Dataset(**dataset)
dataset.mode = 'numpy'
dataset.train = [cifar_data[0]]
dataset.val = [cifar_test[0]]
dataset.test = [cifar_test[0]]
return dataset, dataset, infer_data
return dataset, test_data, infer_data
def test_load_empty_data():
d = Dataset('not-found')
ld = Loader(d, scale=1)
itr = ld.make_one_shot_iterator([1, -1, -1, -1], -1)
assert len(list(itr)) is 0
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10)
ret = list(itr)
assert len(ret) is 10
assert not ret[0]['hr']
assert not ret[0]['lr']
assert not ret[0]['name']
def test_load_empty_data(self):
d = Dataset('not-found')
ld = Loader(d, scale=1)
itr = ld.make_one_shot_iterator([1, -1, -1, -1], -1)
self.assertEqual(len(list(itr)), 0)
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10)
ret = list(itr)
self.assertEqual(len(ret), 10)
self.assertFalse(ret[0]['hr'])
self.assertFalse(ret[0]['lr'])
self.assertFalse(ret[0]['name'])
def load_folder(path):
"""loading `path` into a Dataset"""
if not Path(path).exists():
raise ValueError("--input_dir can't be found")
images = list(Path(path).glob('*'))
images.sort()
if not images:
images = list(Path(path).iterdir())
D = Dataset(test=images)
return D
def test_simplest_loader(self):
d = Dataset('data/set5_x2')
ld = Loader(d, scale=2, threads=4)
itr = ld.make_one_shot_iterator([4, 3, 4, 4], 10, True)
self.assertEqual(len(itr), 10)
ret = list(itr)
self.assertEqual(len(ret), 10)
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10, True)
self.assertEqual(len(itr), 10)
ret = list(itr)
self.assertEqual(len(ret), 10)
self.assert_psnr(ret)
def test_simplest_loader():
d = Dataset('data/set5_x2')
ld = Loader(d, scale=2, threads=4)
itr = ld.make_one_shot_iterator([4, 3, 4, 4], 10, True)
assert len(itr) is 10
ret = list(itr)
assert len(ret) is 10
itr = ld.make_one_shot_iterator([4, 3, 16, 16], 10, True)
assert len(itr) is 10
ret = list(itr)
assert len(ret) is 10
assert_psnr(ret)
def _handle_path(path, sess):
if path.endswith('.npz'):
f = np.load(path)
m, s = f['mu'][:], f['sigma'][:]
f.close()
else:
path = pathlib.Path(path)
files = list(path.glob('*.jpg')) + list(path.glob('*.png')) + list(
path.glob('*.bmp'))
images = Dataset(train=files, patch_size=48, strides=48)
loader = BatchLoader(50, images, 'train', 1, convert_to='RGB')
x = np.concatenate([img[0] for img in loader])
m, s = calculate_activation_statistics(x, sess)
return m, s
def test_complex_loader(self):
d = Dataset('data').use_like_video().include_reg('hr/xiuxian')
hr = d.compile()
d = Dataset('data').use_like_video().include_reg('lr/xiuxian')
lr = d.compile()
ld = Loader(hr, lr, threads=4)
ld.image_augmentation()
ld.cropper(RandomCrop(2))
itr = ld.make_one_shot_iterator([4, 3, 3, 16, 16], 10, shuffle=True)
ret = list(itr)
self.assertEqual(len(ret), 10)
self.assert_psnr(ret)
def predict(self, files, mode='pil-image1', depth=1, **kwargs):
r"""Predict output for frames
Args:
files: a list of frames as inputs
mode: specify file format. `pil-image1` for PIL supported images, or `NV12/YV12/RGB` for raw data
depth: specify length of sequence of images. 1 for images, >1 for videos
"""
sess = tf.get_default_session()
ckpt_last = self._restore_model(sess)
files = [Path(file) for file in to_list(files)]
data = Dataset(test=files,
mode=mode,
depth=depth,
modcrop=False,
**kwargs)
loader = QuickLoader(1,
data,
'test',
self.model.scale,
-1,
crop=None,
**kwargs)
it = loader.make_one_shot_iterator()
if len(it):
print('===================================')
print(f'Predicting model: {self.model.name} by {ckpt_last}')
print('===================================')
else:
return
for img in tqdm.tqdm(it, 'Infer', ascii=True):
feature, label, name = img[self.fi], img[self.li], img[-1]
tf.logging.debug('output: ' + str(name))
for fn in self.feature_callbacks:
feature = fn(feature, name=name)
outputs = self.model.test_batch(feature, None)
for fn in self.output_callbacks:
outputs = fn(outputs,
input=img[self.fi],
label=img[self.li],
mode=loader.color_format,
name=name)
def main():
flags, args = parser.parse_known_args()
opt = Config()
for pair in flags._get_kwargs():
opt.setdefault(*pair)
data_config_file = Path(flags.data_config)
if not data_config_file.exists():
raise RuntimeError("dataset config file doesn't exist!")
for _ext in ('json', 'yaml', 'yml'): # for compat
# apply a 2-stage (or master-slave) configuration, master can be
# override by slave
model_config_root = Path('Parameters/root.{}'.format(_ext))
if opt.p:
model_config_file = Path(opt.p)
else:
model_config_file = Path('Parameters/{}.{}'.format(opt.model, _ext))
if model_config_root.exists():
opt.update(Config(str(model_config_root)))
if model_config_file.exists():
opt.update(Config(str(model_config_file)))
model_params = opt.get(opt.model, {})
suppress_opt_by_args(model_params, *args)
opt.update(model_params)
model = get_model(flags.model)(**model_params)
if flags.cuda:
model.cuda()
root = f'{flags.save_dir}/{flags.model}'
if flags.comment:
root += '_' + flags.comment
verbosity = logging.DEBUG if flags.verbose else logging.INFO
trainer = model.trainer
datasets = load_datasets(data_config_file)
try:
test_datas = [datasets[t.upper()] for t in flags.test]
run_benchmark = True
except KeyError:
test_datas = []
for pattern in flags.test:
test_data = Dataset(test=_glob_absolute_pattern(pattern),
mode='pil-image1', modcrop=False)
father = Path(flags.test)
while not father.is_dir():
if father.parent == father:
break
father = father.parent
test_data.name = father.stem
test_datas.append(test_data)
run_benchmark = False
if opt.verbose:
dump(opt)
for test_data in test_datas:
loader_config = Config(convert_to='rgb',
feature_callbacks=[], label_callbacks=[],
output_callbacks=[], **opt)
loader_config.batch = 1
loader_config.subdir = test_data.name
loader_config.output_callbacks += [
save_image(root, flags.output_index, flags.auto_rename)]
if opt.channel == 1:
loader_config.convert_to = 'gray'
with trainer(model, root, verbosity, flags.pth) as t:
if flags.seed is not None:
t.set_seed(flags.seed)
loader = QuickLoader(test_data, 'test', loader_config,
n_threads=flags.thread)
loader_config.epoch = flags.epoch
if run_benchmark:
t.benchmark(loader, loader_config)
else:
t.infer(loader, loader_config)
def test_image_data():
d = Dataset('data/set5_x2')
data = d.compile()
assert len(data) is 5
assert data.capacity == 983040
def test_include_exclude():
d = Dataset('data')
d.include_('xiuxian*')
data1 = d.compile()
d = Dataset('data')
d.include_reg_('set5')
data2 = d.compile()
d = Dataset('data').include_reg('set5').exclude('png')
data3 = d.compile()
assert len(data1) is 6
assert len(data2) is 5
assert len(data3) is 0
def test_video_data(self):
d = Dataset('data/video/custom_pair').use_like_video()
data = d.compile()
self.assertEqual(len(data), 2)
def test_video_data():
d = Dataset('data/video/custom_pair').use_like_video()
data = d.compile()
assert len(data) is 2
def test_include_exclude(self):
d = Dataset('data')
d.include_('xiuxian*')
data1 = d.compile()
d = Dataset('data')
d.include_reg_('set5')
data2 = d.compile()
d = Dataset('data').include_reg('set5').exclude('png')
data3 = d.compile()
self.assertEqual(len(data1), 6)
self.assertEqual(len(data2), 5)
self.assertEqual(len(data3), 0)
def test_multi_url(self):
d = Dataset('data/set5_x2', 'data/kitti_car')
data = d.compile()
self.assertEqual(len(data), 8)
def test_multi_url():
d = Dataset('data/set5_x2', 'data/kitti_car')
data = d.compile()
assert len(data) is 8
def test_image_data(self):
d = Dataset('data/set5_x2')
data = d.compile()
self.assertEqual(len(data), 5)
self.assertEqual(data.capacity, 983040)
