def mp(input, grad):
out = DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border,
st=stride)(input)
grad_op = MaxPoolGrad(maxpoolshp,
ignore_border=ignore_border,
st=stride)
return grad_op(input, out, grad)
def test_infer_shape(self):
image = tensor.dtensor4()
maxout = tensor.dtensor4()
gz = tensor.dtensor4()
rng = numpy.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2))
image_val = rng.rand(4, 6, 7, 9)
out_shapes = [[[[4, 6, 7, 9], [4, 6, 7, 9]],
[[4, 6, 3, 4], [4, 6, 4, 5]],
[[4, 6, 2, 3], [4, 6, 3, 3]],
[[4, 6, 3, 3], [4, 6, 4, 3]],
[[4, 6, 2, 4], [4, 6, 3, 5]]],
[[None, None],
[[4, 6, 4, 5], None],
[[4, 6, 3, 3], None],
[[4, 6, 4, 3], None],
[[4, 6, 3, 5], None]],
[[None, None],
[None, None],
[[4, 6, 3, 4], None],
[[4, 6, 4, 4], None],
[None, None]]]
for i, maxpoolshp in enumerate(maxpoolshps):
for j, ignore_border in enumerate([True, False]):
for k, padding in enumerate([(0, 0), (1, 1), (1, 2)]):
if out_shapes[k][i][j] is None:
continue
# checking shapes generated by DownsampleFactorMax
self._compile_and_check([image],
[DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border,
padding=padding)(image)],
[image_val], DownsampleFactorMax)
# checking shapes generated by MaxPoolGrad
maxout_val = rng.rand(*out_shapes[k][i][j])
gz_val = rng.rand(*out_shapes[k][i][j])
self._compile_and_check([image, maxout, gz],
[MaxPoolGrad(maxpoolshp,
ignore_border=ignore_border,
padding=padding)
(image, maxout, gz)],
[image_val, maxout_val, gz_val],
MaxPoolGrad,
warn=False)
# checking with broadcastable input
image = tensor.tensor(dtype='float64',
broadcastable=(False, False, True, True))
image_val = rng.rand(4, 6, 1, 1)
self._compile_and_check(
[image],
[DownsampleFactorMax((2, 2),
ignore_border=True,
padding=(0, 0))(image)],
[image_val], DownsampleFactorMax)
def mp(input, grad):
out = DownsampleFactorMax(
maxpoolsize, ignore_border=True,
st=stridesize,
padding=paddingsize,
)(input)
grad_op = MaxPoolGrad(maxpoolsize, ignore_border=True,
st=stridesize, padding=paddingsize)
return grad_op(input, out, grad)
