def test_max_pool_3d_3D_deprecated_interface(self):
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1, 1), (3, 2, 1))
imval = rng.rand(4, 5, 6)
images = dtensor3()
for maxpoolshp, ignore_border, mode in product(
maxpoolshps,
[True, False],
["max", "sum", "average_inc_pad", "average_exc_pad"],
):
# print 'maxpoolshp =', maxpoolshp
# print 'ignore_border =', ignore_border
numpy_output_val = self.numpy_max_pool_nd(imval,
maxpoolshp,
ignore_border,
mode=mode)
output = pool_3d(
input=images,
ds=maxpoolshp,
ignore_border=ignore_border,
st=maxpoolshp,
padding=(0, 0, 0),
mode=mode,
)
output_val = function([images], output)(imval)
utt.assert_allclose(output_val, numpy_output_val)
def mp(input):
return pool_3d(input, maxpoolshp, ignore_border, mode=mode)
def test_DownsampleFactorMax(self):
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, input size
examples = (
((2, ), (16, )),
(
(2, ),
(
4,
16,
),
),
(
(2, ),
(
4,
2,
16,
),
),
((1, 1), (4, 2, 16, 16)),
((2, 2), (4, 2, 16, 16)),
((3, 3), (4, 2, 16, 16)),
((3, 2), (4, 2, 16, 16)),
((3, 2, 2), (3, 2, 16, 16, 16)),
((2, 2, 3, 2), (3, 2, 6, 6, 6, 5)),
)
for example, ignore_border, mode in product(
examples,
[True, False],
["max", "sum", "average_inc_pad", "average_exc_pad"],
):
(maxpoolshp, inputsize) = example
imval = rng.rand(*inputsize)
images = aesara.shared(imval)
# Pure Numpy computation
numpy_output_val = self.numpy_max_pool_nd(imval,
maxpoolshp,
ignore_border,
mode=mode)
# The pool_2d or pool_3d helper methods
if len(maxpoolshp) == 2:
output = pool_2d(images, maxpoolshp, ignore_border, mode=mode)
f = function(
[],
[
output,
],
)
output_val = f()
utt.assert_allclose(output_val, numpy_output_val)
elif len(maxpoolshp) == 3:
output = pool_3d(images, maxpoolshp, ignore_border, mode=mode)
f = function(
[],
[
output,
],
)
output_val = f()
utt.assert_allclose(output_val, numpy_output_val)
# Pool op
maxpool_op = Pool(ndim=len(maxpoolshp),
ignore_border=ignore_border,
mode=mode)(images, maxpoolshp)
output_shape = Pool.out_shape(
imval.shape,
maxpoolshp,
ndim=len(maxpoolshp),
ignore_border=ignore_border,
)
utt.assert_allclose(np.asarray(output_shape),
numpy_output_val.shape)
f = function([], maxpool_op)
output_val = f()
utt.assert_allclose(output_val, numpy_output_val)
def mp(input):
return pool_3d(input, maxpoolshp, ignore_border, mode=mode)