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,
num_steps=args.num_steps,
adjoint=args.adjoint,
)
return cnf
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
def create_cnf(diffeq, regularization_fns=None):
# inlined args default values
solver = "dopri5"
divergence_function = "approximate" #"brute_force" # TODO?
residual = False
rademacher = False
time_length = 1.0
train_T = True # TODO
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=divergence_function,
residual=residual,
rademacher=rademacher,
)
cnf = layers.CNF(
odefunc=odefunc,
T=time_length,
train_T=train_T,
regularization_fns=regularization_fns,
solver=solver,
)
set_cnf_options(cnf)
return cnf
def _make_odefunc(size):
net = ODEnet(idims,
size,
strides,
True,
layer_type="concat",
nonlinearity=nonlinearity)
f = layers.ODEfunc(net)
return f
def _make_odefunc(size):
net = ODEnet(idims,
size,
strides,
True,
layer_type=layer_type,
nonlinearity=nonlinearity,
zero_last_weight=zero_last)
f = layers.ODEfunc(net, div_samples=div_samples)
return f
def _make_odefunc():
nets = [
ODEnet(idims,
get_size(scale),
strides,
True,
layer_type="concat",
num_squeeze=scale) for scale in range(scales)
]
net = ParallelSumModules(nets)
f = layers.ODEfunc(net)
return f
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
def construct_amortized_odefunc(args, z_dim, amortization_type="bias"):
hidden_dims = get_hidden_dims(args)
if amortization_type == "bias":
diffeq = AmortizedBiasODEnet(
hidden_dims=hidden_dims,
input_dim=z_dim,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
elif amortization_type == "hyper":
diffeq = HyperODEnet(
hidden_dims=hidden_dims,
input_dim=z_dim,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
elif amortization_type == "lyper":
diffeq = LyperODEnet(
hidden_dims=hidden_dims,
input_dim=z_dim,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
)
elif amortization_type == "low_rank":
diffeq = AmortizedLowRankODEnet(
hidden_dims=hidden_dims,
input_dim=z_dim,
layer_type=args.layer_type,
nonlinearity=args.nonlinearity,
rank=args.rank,
)
odefunc = layers.ODEfunc(
diffeq=diffeq,
divergence_fn=args.divergence_fn,
residual=args.residual,
rademacher=args.rademacher,
)
return odefunc
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
