def with_linker(self, linker):
for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
((1, 2, 3), (1, 2), (2, 3)),
((1, 2, 1, 3), (1, 3), (2, 3)),
((2, 3, 4), (2, 1, 0), (4, 3, 2)),
((2, 3, 4), ('x', 2, 1, 0, 'x'),
(1, 4, 3, 2, 1)),
((1, 4, 3, 2, 1), (3, 2, 1), (2, 3, 4)),
((1, 1, 4), (1, 2), (1, 4)),
((1, 1, 1), (), ()),
((1,), ('x', 'x'), (1, 1))]:
ib = [(entry == 1) for entry in xsh]
x = TensorType('float64', ib)('x')
e = DimShuffle(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
assert f(numpy.ones(xsh)).shape == zsh
#test that DimShuffle.infer_shape work correctly
x = TensorType('float64', ib)('x')
e = DimShuffle(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x], [e.
shape])).make_function()
assert all(f(numpy.ones(xsh))) == all(zsh)
# Test when we drop a axis that is not broadcastable
ib = [False, True, False]
x = TensorType('float64', ib)('x')
self.assertRaises(ValueError, DimShuffle, ib, shuffle)
# Test when we drop a axis that don't have shape 1
ib = [True, True, False]
x = TensorType('float64', ib)('x')
e = DimShuffle(ib, (1, 2))(x)
f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
self.assertRaises(TypeError, f, numpy.ones((2, 1, 4)))
# Test that we can't take a dimensions multiple time
xsh, shuffle, zsh = ((1, 1, 4), (0, 1, 2, 0), (1, 4))
ib = [False, True, False]
x = TensorType('float64', ib)('x')
self.assertRaises(ValueError, DimShuffle, ib, shuffle)
def test_infer_shape(self):
for xsh, shuffle in [((2, 3), (1, 'x', 0)), ((1, 2, 3), (1, 2)),
((1, 2, 1, 3), (1, 3)), ((2, 3, 4), (2, 1, 0)),
((2, 3, 4), ('x', 2, 1, 0, 'x')),
((1, 4, 3, 2, 1), (3, 2, 1)), ((1, 1, 4), (1, 2)),
((1, 1, 1), ()), ((1, ), ('x', 'x'))]:
ib = [(entry == 1) for entry in xsh]
adtens = TensorType('float64', ib)('x')
adtens_val = numpy.ones(xsh)
self._compile_and_check([adtens],
[DimShuffle(ib, shuffle)(adtens)],
[adtens_val], DimShuffle)
def make_node(self, input):
res = DimShuffle.make_node(self, input)
otype = GpuArrayType(dtype=res.outputs[0].type.dtype,
broadcastable=res.outputs[0].type.broadcastable)
input = as_gpuarray_variable(input)
return Apply(self, [input], [otype()])
def make_node(self, input):
res = DimShuffle.make_node(self, input)
otype = GpuArrayType(dtype=res.outputs[0].type.dtype,
broadcastable=res.outputs[0].type.broadcastable)
input = as_gpuarray_variable(input)
return Apply(self, [input], [otype()])
def transpose_inplace(x, **kwargs):
"Perform a transpose on a tensor without copying the underlying storage"
dims = list(range(x.ndim - 1, -1, -1))
return DimShuffle(x.broadcastable, dims, inplace=True)(x)