def test_neibs(self):
for shape, pshape in [
((10, 7, 18, 18), (2, 2)),
((10, 7, 6, 18), (3, 2)),
((5, 7, 66, 66), (33, 33)),
((5, 7, 68, 66), (34, 33)),
]:
for border in ["valid", "ignore_borders"]:
for dtype in self.dtypes:
images = shared(
np.arange(np.prod(shape), dtype=dtype).reshape(shape))
neib_shape = tt.as_tensor_variable(pshape)
f = function(
[],
images2neibs(images, neib_shape, mode=border),
mode=self.mode,
)
# print images.get_value(borrow=True)
neibs = f()
# print neibs
g = function(
[],
neibs2images(neibs, neib_shape, images.shape),
mode=self.mode,
)
assert any([
isinstance(node.op, self.op)
for node in f.maker.fgraph.toposort()
])
# print g()
assert np.allclose(images.get_value(borrow=True), g())
def test_neibs_full_with_inconsistent_borders(self):
shape = (2, 3, 5, 5)
images = tt.dtensor4()
images_val = np.arange(np.prod(shape), dtype="float32").reshape(shape)
f = aesara.function([images],
tt.sqr(images2neibs(images, (2, 2), mode="full")),
mode=self.mode)
with pytest.raises(TypeError):
f(images_val)
def speed_neibs(self):
shape = (100, 40, 18, 18)
images = shared(
np.arange(np.prod(shape), dtype="float32").reshape(shape))
neib_shape = tt.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape), mode=self.mode)
for i in range(1000):
f()
def test_neibs_half_step_by_valid(self):
neib_shapes = ((3, 3), (3, 5), (5, 3))
for shp_idx, (shape, neib_step) in enumerate([
[(7, 8, 5, 5), (1, 1)],
[(7, 8, 5, 5), (2, 2)],
[(7, 8, 5, 5), (4, 4)],
[(7, 8, 5, 5), (1, 4)],
[(7, 8, 5, 5), (4, 1)],
[(80, 90, 5, 5), (1, 2)],
[(1025, 9, 5, 5), (2, 1)],
[(1, 1, 5, 1037), (2, 4)],
[(1, 1, 1045, 5), (4, 2)],
]):
for neib_shape in neib_shapes:
for dtype in self.dtypes:
x = aesara.shared(
np.random.standard_normal(shape).astype(dtype))
extra = (neib_shape[0] // 2, neib_shape[1] // 2)
padded_shape = (
x.shape[0],
x.shape[1],
x.shape[2] + 2 * extra[0],
x.shape[3] + 2 * extra[1],
)
padded_x = at.zeros(padded_shape)
padded_x = at.set_subtensor(
padded_x[:, :, extra[0]:-extra[0], extra[1]:-extra[1]],
x)
x_using_valid = images2neibs(padded_x,
neib_shape,
neib_step,
mode="valid")
x_using_half = images2neibs(x,
neib_shape,
neib_step,
mode="half")
f_valid = aesara.function([],
x_using_valid,
mode="FAST_RUN")
f_half = aesara.function([], x_using_half, mode=self.mode)
unittest_tools.assert_allclose(f_valid(), f_half())
def test_neibs_manual(self):
shape = (2, 3, 4, 4)
for dtype in self.dtypes:
images = shared(
np.arange(np.prod(shape), dtype=dtype).reshape(shape))
neib_shape = tt.as_tensor_variable((2, 2))
for border in ["valid", "ignore_borders"]:
f = function([],
images2neibs(images, neib_shape, mode=border),
mode=self.mode)
assert any([
isinstance(node.op, self.op)
for node in f.maker.fgraph.toposort()
])
# print images.get_value(borrow=True)
neibs = f()
# print neibs
assert np.allclose(
neibs,
[
[0, 1, 4, 5],
[2, 3, 6, 7],
[8, 9, 12, 13],
[10, 11, 14, 15],
[16, 17, 20, 21],
[18, 19, 22, 23],
[24, 25, 28, 29],
[26, 27, 30, 31],
[32, 33, 36, 37],
[34, 35, 38, 39],
[40, 41, 44, 45],
[42, 43, 46, 47],
[48, 49, 52, 53],
[50, 51, 54, 55],
[56, 57, 60, 61],
[58, 59, 62, 63],
[64, 65, 68, 69],
[66, 67, 70, 71],
[72, 73, 76, 77],
[74, 75, 78, 79],
[80, 81, 84, 85],
[82, 83, 86, 87],
[88, 89, 92, 93],
[90, 91, 94, 95],
],
)
g = function([],
neibs2images(neibs, neib_shape, images.shape),
mode=self.mode)
assert np.allclose(images.get_value(borrow=True), g())
def test_neibs_full_step_by_valid(self):
for shp_idx, (shape, neib_step, neib_shapes) in enumerate([
[(7, 8, 5, 5), (1, 1), ((3, 3), (3, 5), (5, 3))],
[(7, 8, 5, 5), (2, 2), ((3, 3), (3, 5), (5, 3))],
[(7, 8, 6, 6), (3, 3), ((2, 2), (2, 5), (5, 2))],
[(7, 8, 6, 6), (1, 3), ((2, 2), (2, 5), (5, 2))],
[(7, 8, 6, 6), (3, 1), ((2, 2), (2, 5), (5, 2))],
[(80, 90, 5, 5), (1, 2), ((3, 3), (3, 5), (5, 3))],
[(1025, 9, 5, 5), (2, 1), ((3, 3), (3, 5), (5, 3))],
[(1, 1, 11, 1037), (2, 3), ((3, 3), (5, 3))],
[(1, 1, 1043, 11), (3, 2), ((3, 3), (3, 5))],
]):
for neib_shape in neib_shapes:
for dtype in self.dtypes:
x = aesara.shared(
np.random.standard_normal(shape).astype(dtype))
extra = (neib_shape[0] - 1, neib_shape[1] - 1)
padded_shape = (
x.shape[0],
x.shape[1],
x.shape[2] + 2 * extra[0],
x.shape[3] + 2 * extra[1],
)
padded_x = at.zeros(padded_shape)
padded_x = at.set_subtensor(
padded_x[:, :, extra[0]:-extra[0], extra[1]:-extra[1]],
x)
x_using_valid = images2neibs(padded_x,
neib_shape,
neib_step,
mode="valid")
x_using_full = images2neibs(x,
neib_shape,
neib_step,
mode="full")
f_valid = aesara.function([],
x_using_valid,
mode="FAST_RUN")
f_full = aesara.function([], x_using_full, mode=self.mode)
unittest_tools.assert_allclose(f_valid(), f_full())
def test_neibs_bad_shape_wrap_centered(self):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(
np.arange(np.prod(shape), dtype=dtype).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = tt.as_tensor_variable(neib_shape)
f = function(
[],
images2neibs(images, neib_shape, mode="wrap_centered"),
mode=self.mode,
)
with pytest.raises(TypeError):
f()
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
images = shared(np.arange(np.prod(shape)).reshape(shape))
neib_shape = tt.as_tensor_variable((3, 3))
f = function(
[],
images2neibs(images, neib_shape, mode="wrap_centered"),
mode=self.mode,
)
with pytest.raises(TypeError):
f()
# Test a valid shapes
shape = (2, 3, 3, 3)
images = shared(np.arange(np.prod(shape)).reshape(shape))
neib_shape = tt.as_tensor_variable((3, 3))
f = function(
[],
images2neibs(images, neib_shape, mode="wrap_centered"),
mode=self.mode,
)
f()
def test_neibs_manual_step(self):
shape = (2, 3, 5, 5)
for dtype in self.dtypes:
images = shared(
np.asarray(np.arange(np.prod(shape)).reshape(shape),
dtype=dtype))
neib_shape = tt.as_tensor_variable((3, 3))
neib_step = tt.as_tensor_variable((2, 2))
for border in ["valid", "ignore_borders"]:
f = function(
[],
images2neibs(images, neib_shape, neib_step, mode=border),
mode=self.mode,
)
neibs = f()
assert self.op in [
type(node.op) for node in f.maker.fgraph.toposort()
]
assert np.allclose(
neibs,
[
[0, 1, 2, 5, 6, 7, 10, 11, 12],
[2, 3, 4, 7, 8, 9, 12, 13, 14],
[10, 11, 12, 15, 16, 17, 20, 21, 22],
[12, 13, 14, 17, 18, 19, 22, 23, 24],
[25, 26, 27, 30, 31, 32, 35, 36, 37],
[27, 28, 29, 32, 33, 34, 37, 38, 39],
[35, 36, 37, 40, 41, 42, 45, 46, 47],
[37, 38, 39, 42, 43, 44, 47, 48, 49],
[50, 51, 52, 55, 56, 57, 60, 61, 62],
[52, 53, 54, 57, 58, 59, 62, 63, 64],
[60, 61, 62, 65, 66, 67, 70, 71, 72],
[62, 63, 64, 67, 68, 69, 72, 73, 74],
[75, 76, 77, 80, 81, 82, 85, 86, 87],
[77, 78, 79, 82, 83, 84, 87, 88, 89],
[85, 86, 87, 90, 91, 92, 95, 96, 97],
[87, 88, 89, 92, 93, 94, 97, 98, 99],
[100, 101, 102, 105, 106, 107, 110, 111, 112],
[102, 103, 104, 107, 108, 109, 112, 113, 114],
[110, 111, 112, 115, 116, 117, 120, 121, 122],
[112, 113, 114, 117, 118, 119, 122, 123, 124],
[125, 126, 127, 130, 131, 132, 135, 136, 137],
[127, 128, 129, 132, 133, 134, 137, 138, 139],
[135, 136, 137, 140, 141, 142, 145, 146, 147],
[137, 138, 139, 142, 143, 144, 147, 148, 149],
],
)
def test_neibs_bad_shape(self):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(
np.arange(np.prod(shape), dtype=dtype).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = tt.as_tensor_variable(neib_shape)
f = function([],
images2neibs(images, neib_shape),
mode=self.mode)
with pytest.raises(TypeError):
f()
# Test that ignore border work in that case.
f = function(
[],
images2neibs(images, neib_shape, mode="ignore_borders"),
mode=self.mode,
)
assert self.op in [
type(node.op) for node in f.maker.fgraph.toposort()
]
f()
def fn(images):
return images2neibs(images, (2, 2), mode="ignore_borders")
def fn(images):
return images2neibs(images, (1, 2), (5, 2))
def fn(images):
return images2neibs(images, (2, 2))
def fn(images):
return images2neibs(images, (3, 3), mode="full")
def fn(images):
return images2neibs(images, (3, 3), mode="wrap_centered")
def test_neibs_wrap_centered_step_manual(self):
expected1 = [
[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[9, 5, 6, 14, 10, 11, 19, 15, 16],
[6, 7, 8, 11, 12, 13, 16, 17, 18],
[8, 9, 5, 13, 14, 10, 18, 19, 15],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[16, 17, 18, 21, 22, 23, 1, 2, 3],
[18, 19, 15, 23, 24, 20, 3, 4, 0],
]
expected2 = [
[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[12, 13, 14, 17, 18, 19, 22, 23, 24],
]
expected3 = [
[19, 15, 16, 24, 20, 21, 4, 0, 1, 9, 5, 6, 14, 10, 11],
[17, 18, 19, 22, 23, 24, 2, 3, 4, 7, 8, 9, 12, 13, 14],
[9, 5, 6, 14, 10, 11, 19, 15, 16, 24, 20, 21, 4, 0, 1],
[7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24, 2, 3, 4],
]
expected4 = [
[23, 24, 20, 21, 22, 3, 4, 0, 1, 2, 8, 9, 5, 6, 7],
[21, 22, 23, 24, 20, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
[13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
[11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20],
]
expected5 = [
[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[9, 5, 6, 14, 10, 11, 19, 15, 16],
[7, 8, 9, 12, 13, 14, 17, 18, 19],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[17, 18, 19, 22, 23, 24, 2, 3, 4],
]
expected6 = [
[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[11, 12, 13, 16, 17, 18, 21, 22, 23],
[13, 14, 10, 18, 19, 15, 23, 24, 20],
]
# TODO test discontinous image
for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate([
[(7, 8, 5, 5), (3, 3), (2, 2), expected1],
[(7, 8, 5, 5), (3, 3), (3, 3), expected2],
[(7, 8, 5, 5), (5, 3), (3, 3), expected3],
[(7, 8, 5, 5), (3, 5), (3, 3), expected4],
[(80, 90, 5, 5), (3, 3), (2, 3), expected5],
[(1025, 9, 5, 5), (3, 3), (3, 2), expected6],
[(1, 1, 5, 1035), (3, 3), (3, 3), None],
[(1, 1, 1045, 5), (3, 3), (3, 3), None],
]):
for dtype in self.dtypes:
images = shared(
np.asarray(np.arange(np.prod(shape)).reshape(shape),
dtype=dtype))
neib_shape = tt.as_tensor_variable(neib_shape)
neib_step = tt.as_tensor_variable(neib_step)
expected = np.asarray(expected)
f = function(
[],
images2neibs(images,
neib_shape,
neib_step,
mode="wrap_centered"),
mode=self.mode,
)
neibs = f()
if expected.size > 1:
for i in range(shape[0] * shape[1]):
assert np.allclose(
neibs[i * expected.shape[0]:(i + 1) *
expected.shape[0], :],
expected + 25 * i,
), "wrap_centered"
assert self.op in [
type(node.op) for node in f.maker.fgraph.toposort()
]
def test_infer_shape(self):
shape = (100, 40, 6, 3)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="valid")],
[images],
Images2Neibs,
)
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="valid")],
[images],
Images2Neibs,
)
shape = (100, 40, 5, 4)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="ignore_borders")],
[images],
Images2Neibs,
)
shape = (100, 40, 5, 3)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="ignore_borders")],
[images],
Images2Neibs,
)
shape = (100, 40, 6, 7)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 2), mode="ignore_borders")],
[images],
Images2Neibs,
)
shape = (100, 40, 5, 10)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(3, 3), mode="wrap_centered")],
[images],
Images2Neibs,
)
shape = (100, 40, 6, 4)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="half")],
[images],
Images2Neibs,
)
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="half")],
[images],
Images2Neibs,
)
shape = (100, 40, 6, 5)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="full")],
[images],
Images2Neibs,
)
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="full")],
[images],
Images2Neibs,
)
def fn(images):
return images2neibs(images, (3, 3), mode="half")