def test_tempalte_invertibleMLP():
print("test mlp")
gaussian = Gaussian([2])
sList = [MLP(2, 10), MLP(2, 10), MLP(2, 10), MLP(2, 10)]
tList = [MLP(2, 10), MLP(2, 10), MLP(2, 10), MLP(2, 10)]
realNVP = RealNVP([2], sList, tList, gaussian)
x = realNVP.prior(10)
mask = realNVP.createMask(["channel"] * 4, ifByte=0)
print("original")
#print(x)
z = realNVP._generate(x, realNVP.mask, realNVP.mask_, True)
print("Forward")
#print(z)
zp = realNVP._inference(z, realNVP.mask, realNVP.mask_, True)
print("Backward")
#print(zp)
assert_array_almost_equal(realNVP._generateLogjac.data.numpy(),
-realNVP._inferenceLogjac.data.numpy())
print("logProbability")
print(realNVP._logProbability(z, realNVP.mask, realNVP.mask_))
assert_array_almost_equal(x.data.numpy(), zp.data.numpy())
def test_tempalte_contractionCNN_checkerboard_cuda():
gaussian3d = Gaussian([2, 4, 4])
x3d = gaussian3d(3).cuda()
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0], [2, 2, 1, 0]]
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)
]
realNVP3d = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d)
realNVP3d = realNVP3d.cuda()
mask3d = realNVP3d.createMask(["checkerboard"] * 4, ifByte=0, cuda=0)
z3d = realNVP3d._generate(x3d, realNVP3d.mask, realNVP3d.mask_, True)
zp3d = realNVP3d._inference(z3d, realNVP3d.mask, realNVP3d.mask_, True)
print(realNVP3d._logProbability(z3d, realNVP3d.mask, realNVP3d.mask_))
assert_array_almost_equal(x3d.cpu().data.numpy(), zp3d.cpu().data.numpy())
assert_array_almost_equal(realNVP3d._generateLogjac.data.cpu().numpy(),
-realNVP3d._inferenceLogjac.data.cpu().numpy())
def test_tempalte_invertibleCNN():
gaussian3d = Gaussian([2, 4, 4])
x3d = gaussian3d(3)
#z3dp = z3d[:,0,:,:].view(10,-1,4,4)
#print(z3dp)
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0],
[2, 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)
]
realNVP3d = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d)
mask3d = realNVP3d.createMask(["channel"] * 4, ifByte=0)
print("Testing 3d")
print("3d original:")
#print(x3d)
z3d = realNVP3d._generate(x3d, realNVP3d.mask, realNVP3d.mask_, True)
print("3d forward:")
#print(z3d)
zp3d = realNVP3d._inference(z3d, realNVP3d.mask, realNVP3d.mask_, True)
print("Backward")
#print(zp3d)
assert_array_almost_equal(realNVP3d._generateLogjac.data.numpy(),
-realNVP3d._inferenceLogjac.data.numpy())
print("3d logProbability")
print(realNVP3d._logProbability(z3d, realNVP3d.mask, realNVP3d.mask_))
saveDict3d = realNVP3d.saveModel({})
torch.save(saveDict3d, './saveNet3d.testSave')
# realNVP.loadModel({})
sListp3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
tListp3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
realNVPp3d = RealNVP([2, 4, 4], sListp3d, tListp3d, gaussian3d)
saveDictp3d = torch.load('./saveNet3d.testSave')
realNVPp3d.loadModel(saveDictp3d)
zz3d = realNVPp3d._generate(x3d, realNVPp3d.mask, realNVPp3d.mask_)
print("3d Forward after restore")
#print(zz3d)
assert_array_almost_equal(x3d.data.numpy(), zp3d.data.numpy())
assert_array_almost_equal(zz3d.data.numpy(), z3d.data.numpy())