def grad(self, inputs, grads):
v, x = inputs
(gz, ) = grads
return [
grad_not_implemented(self, 0, v),
gz * (iv(v - 1, x) + iv(v + 1, x)) / 2.0,
]
def test_unimplemented_grad_func(self):
# tests that function compilation catches unimplemented grads
# in the graph
a = vector()
b = grad_not_implemented(add, 0, a)
with pytest.raises(TypeError):
aesara.function([a], b, on_unused_input="ignore")
def grad(self, inputs, output_grads):
return [grad_not_implemented(self, 0, inputs[0])]
def grad(self, inp, grads):
x, neib_shape, neib_step = inp
(gz, ) = grads
if self.mode in ("valid", "ignore_borders"):
if (neib_shape is neib_step or neib_shape == neib_step or
# Aesara Constant == do not compare the data
# the equals function do that.
(hasattr(neib_shape, "equals") and neib_shape.equals(neib_step)
)):
return [
neibs2images(gz, neib_shape, x.shape, mode=self.mode),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step),
]
if self.mode in ["valid"]:
# Iterate over neighborhood positions, summing contributions.
def pos2map(pidx, pgz, prior_result, neib_shape, neib_step):
"""
Helper function that adds gradient contribution from a single
neighborhood position i,j.
pidx = Index of position within neighborhood.
pgz = Gradient of shape (batch_size*num_channels*neibs)
prior_result = Shape (batch_size, num_channnels, rows, cols)
neib_shape = Number of rows, cols in a neighborhood.
neib_step = Step sizes from image2neibs.
"""
nrows, ncols = neib_shape
rstep, cstep = neib_step
batch_size, num_channels, rows, cols = prior_result.shape
i = pidx // ncols
j = pidx - (i * ncols)
# This position does not touch some img pixels in valid mode.
result_indices = prior_result[:, :,
i:(rows - nrows + i + 1):rstep,
j:(cols - ncols + j + 1):cstep, ]
newshape = ((batch_size, num_channels) +
((rows - nrows) // rstep + 1, ) +
((cols - ncols) // cstep + 1, ))
return inc_subtensor(result_indices, pgz.reshape(newshape))
indices = arange(neib_shape[0] * neib_shape[1])
pgzs = gz.dimshuffle((1, 0))
result, _ = aesara.scan(
fn=pos2map,
sequences=[indices, pgzs],
outputs_info=zeros(x.shape),
non_sequences=[neib_shape, neib_step],
)
grad_input = result[-1]
return [
grad_input,
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step),
]
return [
grad_not_implemented(self, 0, x),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step),
]
