def test_conv_works_with_npmat(plot=False):
# use the same data for both experiments:
from pylab import randn
color = 0
image_size = img_size = 10
filter_size = 2
num_groups = 2
num_images_per_group = 3
num_filters_per_group = 2
iorder = 1
color_mult = [1, 3][color]
images = rand(num_groups, num_images_per_group, color_mult, image_size, image_size) < 0.1
filters = randn(num_groups, num_filters_per_group, color_mult, filter_size, filter_size)
filters[:, 0] = 1
ans = [None] * 2
print "a"
for (i, gx) in enumerate([g, gc]):
conv.g = gx
conv._cu = gx._cudamat
# I don't get it. And I really need to get if I'm to apply it anywhere at all.
# How are these things working?
ans[i] = conv.conv(gx.garray(images), gx.garray(filters), targets=None, iorder=iorder).asarray()
print "b"
print abs(ans[0] - ans[1]).max()
# quite successful, indeed. It's weird as hell. The strangest
# thing is that it used to work.
if plot:
from pylab import show, subplot
subplot(221)
show(images[1, 1, 0, :, :])
subplot(223)
show(ans[0][1, 1, 0, :, :])
subplot(224)
show(ans[1][1, 1, 0, :, :])
return ans
def test_conv(plot=False, iorder=1, color=1, g=1, c=None, f=1, i=1):
# use the same data for both experiments:
from pylab import randn
image_size = img_size = 10
filter_size = 2
num_groups = 4
num_images_per_group = 3
num_filters_per_group = 6
color_mult = [1, 3][color]
images = rand(num_groups, num_images_per_group, color_mult, image_size, image_size) < 0.02
filters = randn(num_groups, num_filters_per_group, color_mult, filter_size, filter_size)
filters[:, 0, :, :, :] = 1
if color_mult == 3:
filters[:, :, 0, :, :] = 0 # to have only one color filter, or else we won't have
filters[:, :, 2, :, :] = 0 # pretty responses.
ans = [None] * 2
print "a"
for (i, gx) in enumerate([gnumpy, gc]):
conv.g = gx
conv._cu = gx._cudamat
# I don't get it. And I really need to get if I'm to apply it anywhere at all.
# How are these things working?
ans[i] = conv.conv(gx.garray(images), gx.garray(filters), targets=None, iorder=iorder).asarray()
print "b"
print abs(ans[0] - ans[1]).max()
# quite successful, indeed. It's weird as hell. The strangest
# thing is that it used to work.
if plot:
if c is None:
c = [0, 1][color]
from pylab import show, subplot
if iorder == 1:
subplot(221)
show(images[g, i, c, :, :])
subplot(223)
show(ans[0][g, i, f, :, :])
subplot(224)
show(ans[1][g, i, f, :, :])
else:
subplot(221)
show(images[g, i, c, :, :])
subplot(223)
show(ans[0][g, f, i, :, :])
subplot(224)
show(ans[1][g, f, i, :, :])
# subplot(224); show(ans[0][1,1,1,:,:])
return ans
