def test_local_reshape_dimshuffle():
reshape_dimshuffle = out2in(local_reshape_dimshuffle)
x = tensor.matrix('x')
y = x.dimshuffle('x', 0, 'x', 1)
out = tensor.reshape(y, (1, x.shape[0] * x.shape[1], 1))
g = FunctionGraph([x], [out])
reshape_dimshuffle(g)
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
def test_local_reshape_dimshuffle():
reshape_dimshuffle = out2in(local_reshape_dimshuffle)
x = tensor.matrix("x")
y = x.dimshuffle("x", 0, "x", 1)
out = tensor.reshape(y, (1, x.shape[0] * x.shape[1], 1))
g = FunctionGraph([x], [out])
reshape_dimshuffle(g)
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
def test_local_alloc_dimshuffle():
alloc_dimshuffle = out2in(local_alloc_dimshuffle)
x = tensor.vector('x')
m = tensor.iscalar('m')
y = x.dimshuffle('x', 0)
out = tensor.alloc(y, m, 1, x.shape[0])
g = FunctionGraph([x, m], [out])
alloc_dimshuffle(g)
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
def test_local_alloc_dimshuffle():
alloc_dimshuffle = out2in(local_alloc_dimshuffle)
x = tensor.vector("x")
m = tensor.iscalar("m")
y = x.dimshuffle("x", 0)
out = tensor.alloc(y, m, 1, x.shape[0])
g = FunctionGraph([x, m], [out])
alloc_dimshuffle(g)
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
def test_local_dimshuffle_subtensor():
dimshuffle_subtensor = out2in(local_dimshuffle_subtensor)
x = tensor.tensor4('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])
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 = theano.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 Theano 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)
def test_local_dimshuffle_alloc():
reshape_dimshuffle = out2in(local_dimshuffle_alloc)
x = tensor.vector('x')
out = tensor.alloc(x, 3, 2).dimshuffle('x', 'x', 0, 1)
g = FunctionGraph([x], [out])
reshape_dimshuffle(g)
l = theano.gof.PerformLinker()
l.accept(g)
f = l.make_function()
assert f([3, 4]).ndim == 4
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
def test_local_dimshuffle_alloc():
reshape_dimshuffle = out2in(local_dimshuffle_alloc)
x = tensor.vector("x")
out = tensor.alloc(x, 3, 2).dimshuffle("x", "x", 0, 1)
g = FunctionGraph([x], [out])
reshape_dimshuffle(g)
l = PerformLinker()
l.accept(g)
f = l.make_function()
assert f([3, 4]).ndim == 4
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
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 = theano.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 Theano 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)
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 = theano.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, )
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 = theano.function([x, i], out)
topo = f.maker.fgraph.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
assert f(numpy.random.rand(5, 1, 4, 1), 2).shape == (4,)
