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_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 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_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 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 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 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(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))