def make_node(self, activations, labels, input_lengths):
t_activations = at.as_tensor_variable(activations)
# Ensure activations array is C-contiguous
t_activations = cpu_contiguous(t_activations)
t_labels = at.as_tensor_variable(labels)
t_input_lengths = at.as_tensor_variable(input_lengths)
if t_activations.type.dtype != "float32":
raise TypeError("activations must use the float32 type!")
if t_activations.ndim != 3:
raise ValueError("activations must have 3 dimensions.")
if t_labels.type.dtype != "int32":
raise TypeError("labels must use the int32 type!")
if t_labels.ndim != 2:
raise ValueError("labels must have 2 dimensions.")
if t_input_lengths.type.dtype != "int32":
raise TypeError("input_lengths must use the int32 type!")
if t_input_lengths.ndim != 1:
raise ValueError("input_lengths must have 1 dimension.")
costs = fvector(name="ctc_cost")
outputs = [costs]
if self.compute_grad:
gradients = ftensor3(name="ctc_grad")
outputs += [gradients]
return Apply(self,
inputs=[t_activations, t_labels, t_input_lengths],
outputs=outputs)
def test_cpu_contiguous():
a = fmatrix("a")
i = iscalar("i")
a_val = np.asarray(np.random.rand(4, 5), dtype="float32")
f = aesara.function([a, i], cpu_contiguous(a.reshape((5, 4))[::i]))
topo = f.maker.fgraph.toposort()
assert any([isinstance(node.op, CpuContiguous) for node in topo])
assert f(a_val, 1).flags["C_CONTIGUOUS"]
assert f(a_val, 2).flags["C_CONTIGUOUS"]
assert f(a_val, 3).flags["C_CONTIGUOUS"]
# Test the grad:
utt.verify_grad(cpu_contiguous, [np.random.rand(5, 7, 2)])