def test_maxpool(self):
# generate flatted images
maxpoolshps = ((2, 2), (3, 3), (4, 4), (5, 5), (6, 6))
imval = np.random.rand(4, 5, 10, 10)
images = dmatrix()
for maxpoolshp in maxpoolshps:
# symbolic stuff
output, outshp = sp.max_pool(images, imval.shape[1:], maxpoolshp)
f = function(
[
images,
],
[
output,
],
)
output_val = f(imval.reshape(imval.shape[0], -1))
# numeric verification
my_output_val = np.zeros(
(
imval.shape[0],
imval.shape[1],
imval.shape[2] // maxpoolshp[0],
imval.shape[3] // maxpoolshp[1],
)
)
assert np.prod(my_output_val.shape[1:]) == np.prod(
np.r_[imval.shape[1], outshp]
)
for n in range(imval.shape[0]):
for k in range(imval.shape[1]):
for i in range(imval.shape[2] // maxpoolshp[0]):
for j in range(imval.shape[3] // maxpoolshp[1]):
ii, jj = i * maxpoolshp[0], j * maxpoolshp[1]
patch = imval[
n, k, ii : ii + maxpoolshp[0], jj : jj + maxpoolshp[1]
]
my_output_val[n, k, i, j] = np.max(patch)
my_output_val = my_output_val.reshape(imval.shape[0], -1)
assert np.all(output_val == my_output_val)
def mp(input):
output, outshp = sp.max_pool(input, imval.shape[1:], maxpoolshp)
return output
utt.verify_grad(mp, [imval.reshape(imval.shape[0], -1)])
def mp(input):
output, outshp = sp.max_pool(input, imval.shape[1:],
maxpoolshp)
return output