def test_fail(self):
# Test that conv2d fails for dimensions other than 2 or 3.
with pytest.raises(Exception):
conv.conv2d(tt.dtensor4(), tt.dtensor3())
with pytest.raises(Exception):
conv.conv2d(tt.dtensor3(), tt.dvector())
def test_wrong_input(self):
# Make sure errors are raised when image and kernel are not 4D tensors
with pytest.raises(Exception):
self.validate((3, 2, 8, 8), (4, 2, 5, 5),
"valid",
input=tt.dmatrix())
with pytest.raises(Exception):
self.validate((3, 2, 8, 8), (4, 2, 5, 5),
"valid",
filters=tt.dvector())
with pytest.raises(Exception):
self.validate((3, 2, 8, 8), (4, 2, 5, 5),
"valid",
input=tt.dtensor3())
def test_simple_3d(self):
# Increments or sets part of a tensor by a scalar using full slice and
# a partial slice depending on a scalar.
a = tt.dtensor3()
increment = tt.dscalar()
sl1 = slice(None)
sl2_end = tt.lscalar()
sl2 = slice(sl2_end)
sl3 = 2
val_a = np.ones((5, 3, 4))
val_inc = 2.3
val_sl2_end = 2
for method in [tt.set_subtensor, tt.inc_subtensor]:
print("MethodSet", method)
resut = method(a[sl1, sl3, sl2], increment)
f = aesara.function([a, increment, sl2_end], resut)
expected_result = np.copy(val_a)
result = f(val_a, val_inc, val_sl2_end)
if method is tt.set_subtensor:
expected_result[:, sl3, :val_sl2_end] = val_inc
else:
expected_result[:, sl3, :val_sl2_end] += val_inc
utt.assert_allclose(result, expected_result)
# Test when we broadcast the result
resut = method(a[sl1, sl2], increment)
f = aesara.function([a, increment, sl2_end], resut)
expected_result = np.copy(val_a)
result = f(val_a, val_inc, val_sl2_end)
if method is tt.set_subtensor:
expected_result[:, :val_sl2_end] = val_inc
else:
expected_result[:, :val_sl2_end] += val_inc
utt.assert_allclose(result, expected_result)
def test_infer_shape(self):
z = tt.dtensor3()
x = tt.dmatrix()
y = tt.dscalar()
self._compile_and_check(
[x, y],
[self.op(x, y)],
[np.random.rand(8, 5), np.random.rand()],
self.op_class,
)
self._compile_and_check(
[z, y],
[self.op(z, y)],
# must be square when nd>2
[np.random.rand(8, 8, 8),
np.random.rand()],
self.op_class,
warn=False,
)
def test_multinomial_tensor3_b(self):
# Test the examples given in the multinomial documentation regarding
# tensor3 objects
rng_R = random_state_type()
n = 9
pvals = tensor.dtensor3()
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(10, 1, -1))
assert out.ndim == 4
assert out.broadcastable == (False, True, False, False)
f = compile.function([rng_R, pvals], [post_r, out],
accept_inplace=True)
rng = np.random.RandomState(utt.fetch_seed())
pvals_val = np.asarray([[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]]])
assert pvals_val.shape == (1, 3, 2)
out_rng, draw = f(rng, pvals_val)
assert draw.shape == (10, 1, 3, 2)
assert np.allclose(draw.sum(axis=3), 9)