def test_tempalte_contraction_mlp():
gaussian = Gaussian([2])
sList = [MLP(1, 10), MLP(1, 10), MLP(1, 10), MLP(1, 10)]
tList = [MLP(1, 10), MLP(1, 10), MLP(1, 10), MLP(1, 10)]
realNVP = RealNVP([2], sList, tList, gaussian)
x = realNVP.prior(10)
mask = realNVP.createMask(["channel"] * 4, ifByte=1)
print("original")
#print(x)
z = realNVP._generateWithContraction(x, realNVP.mask, realNVP.mask_, 0,
True)
print("Forward")
#print(z)
zp = realNVP._inferenceWithContraction(z, realNVP.mask, realNVP.mask_, 0,
True)
print("Backward")
#print(zp)
assert_array_almost_equal(realNVP._generateLogjac.data.numpy(),
-realNVP._inferenceLogjac.data.numpy())
x_data = realNVP.prior(10)
y_data = realNVP.prior.logProbability(x_data)
print("logProbability")
'''
def test_contraction_cuda_withDifferentMasks():
gaussian3d = Gaussian([2, 4, 4])
x = gaussian3d(3).cuda()
#z3dp = z3d[:,0,:,:].view(10,-1,4,4)
#print(z3dp)
#print(x)
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0],
[1, 2, 1, 0]] # [channel, filter_size, stride, padding]
sList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
tList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
realNVP = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d)
realNVP = realNVP.cuda()
mask = realNVP.createMask(
["channel", "checkerboard", "channel", "checkerboard"], 1, cuda=0)
z = realNVP._generateWithContraction(x, realNVP.mask, realNVP.mask_, 2,
True)
print(
realNVP._logProbabilityWithContraction(z, realNVP.mask, realNVP.mask_,
2))
zz = realNVP._inferenceWithContraction(z, realNVP.mask, realNVP.mask_, 2,
True)
assert_array_almost_equal(x.cpu().data.numpy(), zz.cpu().data.numpy())
assert_array_almost_equal(realNVP._generateLogjac.data.cpu().numpy(),
-realNVP._inferenceLogjac.data.cpu().numpy())
def test_multiplyMask_generateWithContraction_CNN():
gaussian3d = Gaussian([2, 4, 4])
x = gaussian3d(3)
#z3dp = z3d[:,0,:,:].view(10,-1,4,4)
#print(z3dp)
#print(x)
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0],
[1, 2, 1, 0]] # [channel, filter_size, stride, padding]
sList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
tList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
realNVP = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d)
mask = realNVP.createMask(
["channel", "checkerboard", "channel", "checkerboard"], ifByte=1)
z = realNVP._generateWithContraction(x, realNVP.mask, realNVP.mask_, 2,
True)
#print(z)
zz = realNVP._inferenceWithContraction(z, realNVP.mask, realNVP.mask_, 2,
True)
#print(zz)
assert_array_almost_equal(x.data.numpy(), zz.data.numpy())
assert_array_almost_equal(realNVP._generateLogjac.data.numpy(),
-realNVP._inferenceLogjac.data.numpy())