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_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_workmode2():
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, mode=2)
z = realNVP.prior(10)
x = realNVP.generate(z, sliceDim=0)
zp = realNVP.inference(x, sliceDim=0)
assert_array_almost_equal(z.data.numpy(), zp.data.numpy())
saveDict = realNVP.saveModel({})
torch.save(saveDict, './saveNet.testSave')
# realNVP.loadModel({})
sListp = [MLP(1, 10), MLP(1, 10), MLP(1, 10), MLP(1, 10)]
tListp = [MLP(1, 10), MLP(1, 10), MLP(1, 10), MLP(1, 10)]
realNVPp = RealNVP([2], sListp, tListp, gaussian)
saveDictp = torch.load('./saveNet.testSave')
realNVPp.loadModel(saveDictp)
xx = realNVP.generate(z, sliceDim=0)
print("Forward after restore")
assert_array_almost_equal(xx.data.numpy(), x.data.numpy())
def test_invertible():
print("test realNVP")
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)
print(realNVP.mask)
print(realNVP.mask_)
z = realNVP.prior(10)
#mask = realNVP.createMask()
assert realNVP.mask.shape[0] == 4
assert realNVP.mask.shape[1] == 2
print("original")
#print(x)
x = realNVP.generate(z)
print("Forward")
#print(z)
zp = realNVP.inference(x)
print("Backward")
#print(zp)
assert_array_almost_equal(z.data.numpy(), zp.data.numpy())
saveDict = realNVP.saveModel({})
torch.save(saveDict, './saveNet.testSave')
# realNVP.loadModel({})
sListp = [MLP(2, 10), MLP(2, 10), MLP(2, 10), MLP(2, 10)]
tListp = [MLP(2, 10), MLP(2, 10), MLP(2, 10), MLP(2, 10)]
realNVPp = RealNVP([2], sListp, tListp, gaussian)
saveDictp = torch.load('./saveNet.testSave')
realNVPp.loadModel(saveDictp)
xx = realNVP.generate(z)
print("Forward after restore")
assert_array_almost_equal(xx.data.numpy(), x.data.numpy())
