def test_sum(self):
for shape, tiles_count in self.data:
tiler = Tiler(shape, tiles_count, outlier=False, supersampling=4)
ones = np.ones(tiler.tile_shape, dtype=cfg.PRECISION.np_float)
ones_gpu = cl_array.to_device(cfg.OPENCL.queue, ones)
out = tiler.average(ones_gpu).get()
np.testing.assert_almost_equal(
out, ones[::tiler.supersampling, ::tiler.supersampling])
def test_tile_shape(self):
for size, tiles_count in self.data:
tiler = Tiler(size, tiles_count, True)
self.assertEqual(2 * size[0] / tiles_count[0], tiler.tile_shape[0])
self.assertEqual(2 * size[1] / tiles_count[1], tiler.tile_shape[1])
tiler = Tiler(size, tiles_count, False)
self.assertEqual(size[0] / tiles_count[0], tiler.tile_shape[0])
self.assertEqual(size[1] / tiles_count[1], tiler.tile_shape[1])
def test_tile_indices(self):
sizes = [256, 1024]
tile_counts = [2, 4]
for j in range(len(sizes)):
for i in range(len(sizes)):
size = sizes[j], sizes[i]
for t_j in range(len(tile_counts)):
for t_i in range(len(tile_counts)):
tiles_count = tile_counts[t_j], tile_counts[t_i]
outlier = True
tiler = Tiler(size, tiles_count, outlier)
self._check_tiles(tiler.tile_indices, size,
tiles_count, outlier)
outlier = False
tiler = Tiler(size, tiles_count, outlier)
self._check_tiles(tiler.tile_indices, size,
tiles_count, outlier)
def test_insert(self):
shape = 16, 32
tiles_count = 4, 2
tile_shape = [shape[i] / tiles_count[i] for i in range(len(shape))]
tilers = [
Tiler(shape, tiles_count, outlier=True, supersampling=1),
Tiler(shape, tiles_count, outlier=False, supersampling=1),
Tiler(shape, tiles_count, outlier=True, supersampling=2),
Tiler(shape, tiles_count, outlier=False, supersampling=2),
Tiler(shape, tiles_count, outlier=True, supersampling=4),
Tiler(shape, tiles_count, outlier=False, supersampling=4)
]
for tiler in tilers:
self.assertEqual(tiler.overall_image.shape, shape)
for j in range(tiles_count[0]):
for i in range(tiles_count[1]):
tile = np.random.random(tile_shape).\
astype(tiler.overall_image.dtype)
tiler.insert(tile, (j, i))
np.testing.assert_equal(
tiler.overall_image[j * tile_shape[0]:tile_shape[0] *
(j + 1),
i * tile_shape[1]:tile_shape[1] *
(i + 1)], tile)
def test_result_tile_shape(self):
for shape, tiles_count in self.data:
tiler = Tiler(shape, tiles_count, outlier=False, supersampling=1)
ground_truth = tiler.tile_shape
self.assertEqual(tiler.result_tile_shape, ground_truth)
tiler = Tiler(shape, tiles_count, outlier=False, supersampling=2)
ground_truth = tuple(
[dim / tiler.supersampling for dim in tiler.tile_shape])
self.assertEqual(tiler.result_tile_shape, ground_truth)
tiler = Tiler(shape, tiles_count, outlier=True, supersampling=1)
ground_truth = tuple([dim / 2 for dim in tiler.tile_shape])
self.assertEqual(tiler.result_tile_shape, ground_truth)
tiler = Tiler(shape, tiles_count, outlier=True, supersampling=2)
ground_truth = tuple(
[dim / 2 / tiler.supersampling for dim in tiler.tile_shape])
self.assertEqual(tiler.result_tile_shape, ground_truth)
tiler = Tiler(shape, tiles_count, outlier=True, supersampling=4)
ground_truth = tuple(
[dim / 2 / tiler.supersampling for dim in tiler.tile_shape])
self.assertEqual(tiler.result_tile_shape, ground_truth)