def testSigmoid(self):
m1 = fnn.FlowSequential(fnn.Sigmoid())
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'Sigmoid Det is not zero.')
self.assertTrue((x - z).abs().max() < EPS, 'Sigmoid is wrong.')
def testInv(self):
m1 = fnn.FlowSequential(fnn.InvertibleMM(NUM_INPUTS))
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'InvMM Det is not zero.')
self.assertTrue((x - z).abs().max() < EPS, 'InvMM is wrong.')
def testCoupling(self):
m1 = fnn.FlowSequential(fnn.CouplingLayer(NUM_INPUTS, NUM_HIDDEN,
mask))
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'CouplingLayer Det is not zero.')
self.assertTrue((x - z).abs().max() < EPS, 'CouplingLayer is wrong')
def testBatchNorm(self):
m1 = fnn.FlowSequential(fnn.BatchNormFlow(NUM_INPUTS))
m1.train()
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'BatchNorm Det is not zero.')
self.assertTrue((x - z).abs().max() < EPS, 'BatchNorm is wrong.')
# Second run.
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'BatchNorm Det is not zero for the second run.')
self.assertTrue((x - z).abs().max() < EPS,
'BatchNorm is wrong for the second run.')
m1.eval()
m1 = fnn.FlowSequential(fnn.BatchNormFlow(NUM_INPUTS))
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'BatchNorm Det is not zero in eval.')
self.assertTrue((x - z).abs().max() < EPS,
'BatchNorm is wrong in eval.')
def testSequentialBN(self):
m1 = fnn.FlowSequential(
fnn.BatchNormFlow(NUM_INPUTS), fnn.InvertibleMM(NUM_INPUTS),
fnn.CouplingLayer(NUM_INPUTS, NUM_HIDDEN, mask))
m1.train()
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'Sequential BN Det is not zero.')
self.assertTrue((x - z).abs().max() < EPS, 'Sequential BN is wrong.')
# Second run.
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'Sequential BN Det is not zero for the second run.')
self.assertTrue((x - z).abs().max() < EPS,
'Sequential BN is wrong for the second run.')
m1.eval()
# Eval run.
x = torch.randn(BATCH_SIZE, NUM_INPUTS)
y, logdets = m1(x)
z, inv_logdets = m1(y, mode='inverse')
self.assertTrue((logdets + inv_logdets).abs().max() < EPS,
'Sequential BN Det is not zero for the eval run.')
self.assertTrue((x - z).abs().max() < EPS,
'Sequential is wrong for the eval run.')