def __init__(
self,
data,
batch_size=128,
dtype=None,
broadcastable=None,
name="Minibatch",
random_seed=42,
update_shared_f=None,
in_memory_size=None,
):
if dtype is None:
data = pm.smartfloatX(np.asarray(data))
else:
data = np.asarray(data, dtype)
in_memory_slc = self.make_static_slices(in_memory_size)
self.shared = aesara.shared(data[in_memory_slc])
self.update_shared_f = update_shared_f
self.random_slc = self.make_random_slices(self.shared.shape, batch_size, random_seed)
minibatch = self.shared[self.random_slc]
if broadcastable is None:
broadcastable = (False,) * minibatch.ndim
minibatch = aet.patternbroadcast(minibatch, broadcastable)
self.minibatch = minibatch
super().__init__(self.minibatch.type, None, None, name=name)
Apply(aesara.compile.view_op, inputs=[self.minibatch], outputs=[self])
self.tag.test_value = copy(self.minibatch.tag.test_value)
def convert_variable(self, var):
vt = var.type
if (isinstance(vt, type(self)) and self.typecode == vt.typecode
and self.ndim == vt.ndim
and self.context_name == vt.context_name and all(
sb == ob or ob
for sb, ob in zip(self.broadcastable, vt.broadcastable))):
return at.patternbroadcast(var, self.broadcastable)
def test_local_dimshuffle_subtensor():
dimshuffle_subtensor = out2in(local_dimshuffle_subtensor)
x = tensor.dtensor4("x")
x = tensor.patternbroadcast(x, (False, True, False, False))
i = tensor.iscalar("i")
out = x[:, :, 10:30, ::i].dimshuffle(0, 2, 3)
g = FunctionGraph([x, i], [out])
dimshuffle_subtensor(g)
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
# Test dimshuffle remove dimensions the subtensor don't "see".
x = tensor.tensor(broadcastable=(False, True, False), dtype="float64")
out = x[i].dimshuffle(1)
g = FunctionGraph([x, i], [out])
dimshuffle_subtensor(g)
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
# Test dimshuffle remove dimensions the subtensor don't "see" but
# have in between dimensions.
x = tensor.tensor(broadcastable=(False, True, False, True),
dtype="float64")
out = x[i].dimshuffle(1)
f = aesara.function([x, i], out)
topo = f.maker.fgraph.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
assert f(np.random.rand(5, 1, 4, 1), 2).shape == (4, )
# Test a corner case that had Aesara return a bug.
x = tensor.dtensor4("x")
x = tensor.patternbroadcast(x, (False, True, False, False))
assert x[:, :, 0:3, ::-1].dimshuffle(0, 2, 3).eval({
x: np.ones((5, 1, 6, 7))
}).shape == (5, 3, 7)