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 testSequentialBN(self):
m1 = fnn.FlowSequential(fnn.BatchNormFlow(NUM_INPUTS),
fnn.InvertibleMM(NUM_INPUTS),
fnn.CouplingLayer(NUM_INPUTS, NUM_HIDDEN))
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 BN is wrong for the eval run.')
def testSequential(self):
m1 = fnn.FlowSequential(
fnn.ActNorm(NUM_INPUTS), fnn.InvertibleMM(NUM_INPUTS),
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,
'Sequential Det is not zero.')
self.assertTrue((x - z).abs().max() < EPS, 'Sequential 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 Det is not zero for the second run.')
self.assertTrue((x - z).abs().max() < EPS,
'Sequential is wrong for the second run.')
num_cond_inputs,
s_act='tanh',
t_act='relu'),
fnn.BatchNormFlow(num_inputs),
fnn.Reverse(num_inputs)
]
elif args.flow == 'maf-split-glow':
for _ in range(args.num_blocks):
modules += [
fnn.MADESplit(num_inputs,
num_hidden,
num_cond_inputs,
s_act='tanh',
t_act='relu'),
fnn.BatchNormFlow(num_inputs),
fnn.InvertibleMM(num_inputs)
]
model = fnn.FlowSequential(*modules)
for module in model.modules():
if isinstance(module, nn.Linear):
nn.init.orthogonal_(module.weight)
if hasattr(module, 'bias') and module.bias is not None:
module.bias.data.fill_(0)
model.to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-6)
writer = SummaryWriter(comment=args.flow + "_" + args.dataset)