def infer_shape(self, fgraph, node, in_shapes):
shape_a = in_shapes[0]
n = node.inputs[1]
axis = node.inputs[2]
if len(shape_a) == 1:
return [(n, )]
elif isinstance(axis, TensorConstant):
out_shape = (list(shape_a[0:axis.data.item()]) + [n] +
list(shape_a[axis.data + 1:]))
else:
l = len(shape_a)
shape_a = stack(shape_a)
out_shape = concatenate((shape_a[0:axis], [n], shape_a[axis + 1:]))
n_splits = [1] * l
out_shape = split(out_shape, n_splits, l)
out_shape = [a[0] for a in out_shape]
return [out_shape]
def kron(a, b):
"""Kronecker product.
Same as scipy.linalg.kron(a, b).
Parameters
----------
a: array_like
b: array_like
Returns
-------
array_like with a.ndim + b.ndim - 2 dimensions
Notes
-----
numpy.kron(a, b) != scipy.linalg.kron(a, b)!
They don't have the same shape and order when
a.ndim != b.ndim != 2.
"""
a = as_tensor_variable(a)
b = as_tensor_variable(b)
if a.ndim + b.ndim <= 2:
raise TypeError(
"kron: inputs dimensions must sum to 3 or more. "
f"You passed {int(a.ndim)} and {int(b.ndim)}."
)
o = atm.outer(a, b)
o = o.reshape(at.concatenate((a.shape, b.shape)), a.ndim + b.ndim)
shf = o.dimshuffle(0, 2, 1, *list(range(3, o.ndim)))
if shf.ndim == 3:
shf = o.dimshuffle(1, 0, 2)
o = shf.flatten()
else:
o = shf.reshape(
(o.shape[0] * o.shape[2], o.shape[1] * o.shape[3])
+ tuple(o.shape[i] for i in range(4, o.ndim))
)
return o
def neibs2images(neibs, neib_shape, original_shape, mode="valid"):
"""
Function :func:`neibs2images <aesara.sandbox.neighbours.neibs2images>`
performs the inverse operation of
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>`. It inputs
the output of :func:`images2neibs <aesara.sandbox.neighbours.neibs2images>`
and reconstructs its input.
Parameters
----------
neibs : 2d tensor
Like the one obtained by
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>`.
neib_shape
`neib_shape` that was used in
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>`.
original_shape
Original shape of the 4d tensor given to
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>`
Returns
-------
object
Reconstructs the input of
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>`,
a 4d tensor of shape `original_shape`.
Notes
-----
Currently, the function doesn't support tensors created with
`neib_step` different from default value. This means that it may be
impossible to compute the gradient of a variable gained by
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>` w.r.t.
its inputs in this case, because it uses
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>` for
gradient computation.
Examples
--------
Example, which uses a tensor gained in example for
:func:`images2neibs <aesara.sandbox.neighbours.neibs2images>`:
.. code-block:: python
im_new = neibs2images(neibs, (5, 5), im_val.shape)
# Aesara function definition
inv_window = aesara.function([neibs], im_new)
# Function application
im_new_val = inv_window(neibs_val)
.. note:: The code will output the initial image array.
"""
neibs = as_tensor_variable(neibs)
neib_shape = as_tensor_variable(neib_shape)
original_shape = as_tensor_variable(original_shape)
new_neib_shape = stack(
[original_shape[-1] // neib_shape[1], neib_shape[1]])
output_2d = images2neibs(neibs.dimshuffle("x", "x", 0, 1),
new_neib_shape,
mode=mode)
if mode == "ignore_borders":
# We use set_subtensor to accept original_shape we can't infer
# the shape and still raise error when it don't have the right
# shape.
valid_shape = original_shape
valid_shape = set_subtensor(
valid_shape[2], (valid_shape[2] // neib_shape[0]) * neib_shape[0])
valid_shape = set_subtensor(
valid_shape[3], (valid_shape[3] // neib_shape[1]) * neib_shape[1])
output_4d = output_2d.reshape(valid_shape, ndim=4)
# padding the borders with zeros
for d in (2, 3):
pad_shape = list(output_4d.shape)
pad_shape[d] = original_shape[d] - valid_shape[d]
output_4d = concatenate([output_4d, zeros(pad_shape)], axis=d)
elif mode == "valid":
# TODO: we do not implement all mode with this code.
# Add a check for the good cases.
output_4d = output_2d.reshape(original_shape, ndim=4)
else:
raise NotImplementedError(f"neibs2images do not support mode={mode}")
return output_4d
def _grad_helper(z):
pre = aet.concatenate([[0.0], z])
app = aet.concatenate([z, [0.0]])
return pre - app
