def construct_model():
if args.nf:
chain = []
for i in range(args.depth):
chain.append(layers.PlanarFlow(2))
return layers.SequentialFlow(chain)
else:
chain = []
for i in range(args.depth):
if args.glow: chain.append(layers.BruteForceLayer(2))
chain.append(layers.CouplingLayer(2, swap=i % 2 == 0))
return layers.SequentialFlow(chain)
def build_model_tabular(args, dims, regularization_fns=[]):
hidden_dims = tuple(map(int, args.dims.split("-")))
def build_cnf():
diffeq = layers.ODEnet(
hidden_dims=hidden_dims,
input_shape=(dims, ),
strides=None,
conv=False,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=args.divergence_fn,
rademacher=args.rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
train_T=args.train_T,
regularization_fns=regularization_fns,
solver=args.solver,
)
return cnf
chain = [build_cnf() for _ in range(args.num_blocks)]
model = layers.SequentialFlow(chain)
return model
def create_cnf_model(args, data_shape, regularization_fns):
hidden_dims = tuple(map(int, args.dims.split(",")))
strides = tuple(map(int, args.strides.split(",")))
def build_cnf():
diffeq = layers.ODEnet(
hidden_dims=hidden_dims,
input_shape=data_shape,
strides=strides,
conv=args.conv,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
train_T=args.train_T,
regularization_fns=regularization_fns,
solver=args.solver,
)
return cnf
chain = [layers.LogitTransform(alpha=args.cnf_alpha)
] if args.cnf_alpha > 0 else [layers.ZeroMeanTransform()]
chain = chain + [build_cnf() for _ in range(args.num_blocks)]
if args.batch_norm:
chain.append(layers.MovingBatchNorm2d(data_shape[0]))
model = layers.SequentialFlow(chain)
return model
def build_model_tabular(
dims=2,
condition_dim=2,
layer_type='concatsquash',
nonlinearity='relu',
residual=False,
rademacher=False,
train_T=True,
solver='dopri5',
time_length=0.1,
divergence_fn='brute_force', # ["brute_force", "approximate"]
hidden_dims=(32, 32),
num_blocks=1,
batch_norm=False,
bn_lag=0,
regularization_fns=None):
def build_cnf():
diffeq = layers.ODEnet(
hidden_dims=hidden_dims,
input_shape=(dims, ),
condition_dim=condition_dim,
strides=None,
conv=False,
layer_type=layer_type,
nonlinearity=nonlinearity,
)
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=divergence_fn,
residual=residual,
rademacher=rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=time_length,
train_T=train_T,
regularization_fns=regularization_fns,
solver=solver,
)
return cnf
chain = [build_cnf() for _ in range(num_blocks)]
if batch_norm:
bn_layers = [
layers.MovingBatchNorm1d(dims, bn_lag=bn_lag)
for _ in range(num_blocks)
]
bn_chain = [layers.MovingBatchNorm1d(dims, bn_lag=bn_lag)]
for a, b in zip(chain, bn_layers):
bn_chain.append(a)
bn_chain.append(b)
chain = bn_chain
model = layers.SequentialFlow(chain)
set_cnf_options(model, solver, rademacher, residual)
return model
def build_model_tabular(args, dims, regularization_fns=None):
hidden_dims = tuple(map(int, args.dims.split("-")))
def build_cnf():
diffeq = layers.ODEnet(
hidden_dims=hidden_dims,
input_shape=(dims,),
strides=None,
conv=False,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
train_T=args.train_T,
solver=args.solver,
atol = args.atol,
rtol = args.rtol,
test_atol = args.test_atol,
test_rtol = args.test_rtol,
poly_num_sample=args.poly_num_sample,
poly_order=args.poly_order,
adjoint=args.adjoint,
)
return cnf
chain = [build_cnf() for _ in range(args.num_blocks)]
if args.batch_norm:
bn_layers = [layers.MovingBatchNorm1d(dims, bn_lag=args.bn_lag) for _ in range(args.num_blocks)]
bn_chain = [layers.MovingBatchNorm1d(dims, bn_lag=args.bn_lag)]
for a, b in zip(chain, bn_layers):
bn_chain.append(a)
bn_chain.append(b)
chain = bn_chain
model = layers.SequentialFlow(chain)
set_cnf_options(args, model)
return model
def build_model():
activation_fn = ACTIVATION_FNS[args.act]
dims = [data_dim] + list(map(int, args.dims.split('-'))) + [data_dim]
blocks = []
for _ in range(args.nblocks):
blocks.append(
layers.imBlock(
build_nnet(dims, activation_fn),
# ACTIVATION_FNS['zero'](),
build_nnet(dims, activation_fn),
n_dist=args.n_dist,
n_power_series=args.n_power_series,
exact_trace=False,
brute_force=args.brute_force,
n_samples=args.n_samples,
n_exact_terms=args.n_exact_terms,
neumann_grad=False,
grad_in_forward=False, # toy data needn't save memory
eps_forward=args.epsf))
model = layers.SequentialFlow(blocks).to(device)
return model
for _ in range(args.nblocks):
blocks.append(
layers.iResBlock(
build_nnet(dims, activation_fn),
n_dist=args.n_dist,
n_power_series=args.n_power_series,
exact_trace=args.exact_trace,
brute_force=args.brute_force,
n_samples=args.n_samples,
neumann_grad=False,
grad_in_forward=False,
)
)
if args.actnorm: blocks.append(layers.ActNorm1d(2))
if args.batchnorm: blocks.append(layers.MovingBatchNorm1d(2))
model = layers.SequentialFlow(blocks).to(device)
elif args.arch == 'realnvp':
blocks = []
for _ in range(args.nblocks):
blocks.append(layers.CouplingLayer(2, swap=False))
blocks.append(layers.CouplingLayer(2, swap=True))
if args.actnorm: blocks.append(layers.ActNorm1d(2))
if args.batchnorm: blocks.append(layers.MovingBatchNorm1d(2))
model = layers.SequentialFlow(blocks).to(device)
logger.info(model)
logger.info("Number of trainable parameters: {}".format(count_parameters(model)))
#genGen = Generator(16)
#genGen = Generator(128)
def create_model(args, data_shape, regularization_fns):
hidden_dims = tuple(map(int, args.dims.split(",")))
strides = tuple(map(int, args.strides.split(",")))
if args.multiscale:
model = odenvp.ODENVP(
(args.batch_size, *data_shape),
n_blocks=args.num_blocks,
intermediate_dims=hidden_dims,
nonlinearity=args.nonlinearity,
alpha=args.alpha,
cnf_kwargs={
"T": args.time_length,
"train_T": args.train_T,
"regularization_fns": regularization_fns
},
)
elif args.parallel:
model = multiscale_parallel.MultiscaleParallelCNF(
(args.batch_size, *data_shape),
n_blocks=args.num_blocks,
intermediate_dims=hidden_dims,
alpha=args.alpha,
time_length=args.time_length,
)
else:
if args.autoencode:
def build_cnf():
autoencoder_diffeq = layers.AutoencoderDiffEqNet(
hidden_dims=hidden_dims,
input_shape=data_shape,
strides=strides,
conv=args.conv,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
odefunc = layers.AutoencoderODEfunc(
autoencoder_diffeq=autoencoder_diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
regularization_fns=regularization_fns,
solver=args.solver,
)
return cnf
else:
def build_cnf():
diffeq = layers.ODEnet(
hidden_dims=hidden_dims,
input_shape=data_shape,
strides=strides,
conv=args.conv,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
train_T=args.train_T,
regularization_fns=regularization_fns,
solver=args.solver,
)
return cnf
chain = [layers.LogitTransform(alpha=args.alpha)
] if args.alpha > 0 else [layers.ZeroMeanTransform()]
chain = chain + [build_cnf() for _ in range(args.num_blocks)]
if args.batch_norm:
chain.append(layers.MovingBatchNorm2d(data_shape[0]))
model = layers.SequentialFlow(chain)
return model
if args.actnorm: blocks.append(layers.ActNorm1d(2))
for _ in range(args.nblocks):
blocks.append(
layers.iResBlock(
build_nnet(dims, activation_fn),
n_dist=args.n_dist,
n_power_series=args.n_power_series,
exact_trace=args.exact_trace,
brute_force=args.brute_force,
n_samples=args.n_samples,
neumann_grad=False,
grad_in_forward=False,
))
if args.actnorm: blocks.append(layers.ActNorm1d(2))
if args.batchnorm: blocks.append(layers.MovingBatchNorm1d(2))
model = layers.SequentialFlow(blocks).to(device)
elif args.arch == 'implicit':
dims = [2] + list(map(int, args.dims.split('-'))) + [2]
blocks = []
if args.actnorm: blocks.append(layers.ActNorm1d(2))
for _ in range(args.nblocks):
blocks.append(
layers.imBlock(
build_nnet(dims, activation_fn),
build_nnet(dims, activation_fn),
n_dist=args.n_dist,
n_power_series=args.n_power_series,
exact_trace=args.exact_trace,
brute_force=args.brute_force,
n_samples=args.n_samples,
neumann_grad=False,
def build_model(args, state_dict):
# load dataset
train_loader, test_loader, data_shape = get_dataset(args)
hidden_dims = tuple(map(int, args.dims.split(",")))
strides = tuple(map(int, args.strides.split(",")))
# neural net that parameterizes the velocity field
if args.autoencode:
def build_cnf():
autoencoder_diffeq = layers.AutoencoderDiffEqNet(
hidden_dims=hidden_dims,
input_shape=data_shape,
strides=strides,
conv=args.conv,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
odefunc = layers.AutoencoderODEfunc(
autoencoder_diffeq=autoencoder_diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
solver=args.solver,
)
return cnf
else:
def build_cnf():
diffeq = layers.ODEnet(
hidden_dims=hidden_dims,
input_shape=data_shape,
strides=strides,
conv=args.conv,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
solver=args.solver,
)
return cnf
chain = [layers.LogitTransform(alpha=args.alpha), build_cnf()]
if args.batch_norm:
chain.append(layers.MovingBatchNorm2d(data_shape[0]))
model = layers.SequentialFlow(chain)
if args.spectral_norm:
add_spectral_norm(model)
model.load_state_dict(state_dict)
return model, test_loader.dataset
def build_augmented_model_tabular(args, dims, regularization_fns=None):
"""
The function used for creating conditional Continuous Normlizing Flow
with augmented neural ODE
Parameters:
args: arguments used to create conditional CNF. Check args parser for details.
dims: dimension of the input. Currently only allow 1-d input.
regularization_fns: regularizations applied to the ODE function
Returns:
a ctfp model based on augmened neural ode
"""
hidden_dims = tuple(map(int, args.dims.split(",")))
if args.aug_hidden_dims is not None:
aug_hidden_dims = tuple(map(int, args.aug_hidden_dims.split(",")))
else:
aug_hidden_dims = None
def build_cnf():
diffeq = layers.AugODEnet(
hidden_dims=hidden_dims,
input_shape=(dims, ),
effective_shape=args.effective_shape,
strides=None,
conv=False,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
aug_dim=args.aug_dim,
aug_mapping=args.aug_mapping,
aug_hidden_dims=args.aug_hidden_dims,
)
odefunc = layers.AugODEfunc(
diffeq=diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
effective_shape=args.effective_shape,
)
cnf = layers.CNF(
odefunc=odefunc,
T=args.time_length,
train_T=args.train_T,
regularization_fns=regularization_fns,
solver=args.solver,
rtol=args.rtol,
atol=args.atol,
)
return cnf
chain = [build_cnf() for _ in range(args.num_blocks)]
if args.batch_norm:
bn_layers = [
layers.MovingBatchNorm1d(dims,
bn_lag=args.bn_lag,
effective_shape=args.effective_shape)
for _ in range(args.num_blocks)
]
bn_chain = [
layers.MovingBatchNorm1d(dims,
bn_lag=args.bn_lag,
effective_shape=args.effective_shape)
]
for a, b in zip(chain, bn_layers):
bn_chain.append(a)
bn_chain.append(b)
chain = bn_chain
model = layers.SequentialFlow(chain)
set_cnf_options(args, model)
return model
