def test_cppn_xor(network, conv_layer, channel):
optvis = OptVis(network, xor_objective)
for _ in range(3): # 3 attempts
cppn = CPPNParam(64)
optvis.vis(cppn,
thresh=(50, ),
lr=0.01,
transform=False,
verbose=False)
if check_channel(torch.sigmoid(cppn())):
return
raise RuntimeError('CPPN failed xor after 3 tries.')
def test_neuron_fail(network, linear_layer, channel, n_classes):
with pytest.raises(AssertionError):
optvis = OptVis.from_layer(network,
linear_layer,
channel=channel,
neuron=n_classes - 1)
optvis.vis(verbose=False)
def test_cppn_loss(network, conv_layer, channel):
cppn = CPPNParam(64)
optvis = OptVis.from_layer(network, conv_layer, channel=channel)
assert_loss_decreases(optvis,
img_param=cppn,
thresh=(50, ),
lr=0.01,
transform=False)
def test_shortcut(network, imsize, conv_layer, channel, n_steps):
def was_called(h, m, i, o):
raise Exception
with Hook(network['layer4/0/conv1'], was_called):
optvis = OptVis.from_layer(network,
conv_layer,
channel=channel,
neuron=6,
shortcut=True)
assert_loss_decreases(optvis, thresh=n_steps)
def test_image_params(network, imsize, conv_layer, channel, n_steps):
optvis = OptVis.from_layer(network, conv_layer, channel=channel)
# with fft and decorrelate
img_param = ImageParam(imsize, fft=True, decorrelate=True)
assert_loss_decreases(optvis, img_param=img_param, thresh=n_steps)
# with fft and without decorrelate
img_param = ImageParam(imsize, fft=True, decorrelate=True)
assert_loss_decreases(optvis, img_param=img_param, thresh=n_steps)
# without fft and with decorrelate
img_param = ImageParam(imsize, fft=True, decorrelate=True)
assert_loss_decreases(optvis, img_param=img_param, thresh=n_steps)
# without fft and decorrelate
img_param = ImageParam(imsize, fft=False, decorrelate=False)
assert_loss_decreases(optvis, img_param=img_param, thresh=n_steps)
def test_neuron(network, imsize, linear_layer, neuron, n_steps):
optvis = OptVis.from_layer(network, linear_layer, neuron=neuron)
assert_loss_decreases(optvis, thresh=n_steps)
def test_channel(network, imsize, conv_layer, channel, n_steps):
optvis = OptVis.from_layer(network, conv_layer, channel=channel, neuron=6)
assert_loss_decreases(optvis, thresh=n_steps)
def test_objective_operators(network, conv_layer, channels, n_steps):
objective1 = LayerObjective(network, conv_layer, channel=channels[0])
objective2 = LayerObjective(network, conv_layer, channel=channels[1])
# Sum
optvis = OptVis(network, objective1 + objective2)
assert_loss_decreases(optvis, thresh=n_steps)
# Sub
optvis = OptVis(network, objective1 - 1)
assert_loss_decreases(optvis, thresh=n_steps)
optvis = OptVis(network, 1 - objective1)
assert_loss_decreases(optvis, thresh=n_steps)
# Neg
optvis = OptVis(network, -objective1)
assert_loss_decreases(optvis, thresh=n_steps)
# Product
with pytest.raises(TypeError):
optvis = OptVis(network, objective1 * objective2)
optvis.vis(thresh=(1, ), verbose=False)
optvis = OptVis(network, 2 * objective1)
assert_loss_decreases(optvis, thresh=n_steps)
optvis = OptVis(network, objective1 * 2)
assert_loss_decreases(optvis, thresh=n_steps)
def test_dream(network, imsize, conv_layer, n_steps):
optvis = OptVis.from_dream(network, conv_layer)
img_param = ImageFile("test/fixtures/dog.jpg", imsize)
optvis.vis(img_param, thresh=n_steps, transform=False, verbose=False)
assert_loss_decreases(optvis, train=False)