def create_base_transform(i):
if args.base_transform_type == 'rq':
return transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=utils.create_alternating_binary_mask(features=dim,
even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
num_blocks=args.num_transform_blocks,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm),
num_bins=args.num_bins,
apply_unconditional_transform=False,
)
elif args.base_transform_type == 'affine':
return transforms.AffineCouplingTransform(
mask=utils.create_alternating_binary_mask(features=dim,
even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
num_blocks=args.num_transform_blocks,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm))
else:
raise ValueError
def create_base_transform(i, context_features=None):
if args.prior_type == 'affine-coupling':
return transforms.AffineCouplingTransform(
mask=utils.create_alternating_binary_mask(
features=args.latent_features, even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_blocks=args.num_transform_blocks,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm))
elif args.prior_type == 'rq-coupling':
return transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=utils.create_alternating_binary_mask(
features=args.latent_features, even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_blocks=args.num_transform_blocks,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm),
num_bins=args.num_bins,
tails='linear',
tail_bound=args.tail_bound,
apply_unconditional_transform=args.
apply_unconditional_transform,
)
elif args.prior_type == 'affine-autoregressive':
return transforms.MaskedAffineAutoregressiveTransform(
features=args.latent_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_blocks=args.num_transform_blocks,
use_residual_blocks=True,
random_mask=False,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm)
elif args.prior_type == 'rq-autoregressive':
return transforms.MaskedPiecewiseRationalQuadraticAutoregressiveTransform(
features=args.latent_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_bins=args.num_bins,
tails='linear',
tail_bound=args.tail_bound,
num_blocks=args.num_transform_blocks,
use_residual_blocks=True,
random_mask=False,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm)
else:
raise ValueError
def create_base_transform(i):
if args.base_transform_type == 'affine':
return transforms.AffineCouplingTransform(
mask=utils.create_alternating_binary_mask(features=dim,
even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=32,
num_blocks=2,
use_batch_norm=True))
else:
return transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=utils.create_alternating_binary_mask(features=dim,
even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=32,
num_blocks=2,
use_batch_norm=True),
tails='linear',
tail_bound=5,
num_bins=args.num_bins,
apply_unconditional_transform=False)
def create_transform_step(num_channels, hidden_channels, actnorm,
coupling_layer_type, spline_params, use_resnet,
num_res_blocks, resnet_batchnorm, dropout_prob):
if use_resnet:
def create_convnet(in_channels, out_channels):
net = nn_.ConvResidualNet(in_channels=in_channels,
out_channels=out_channels,
hidden_channels=hidden_channels,
num_blocks=num_res_blocks,
use_batch_norm=resnet_batchnorm,
dropout_probability=dropout_prob)
return net
else:
if dropout_prob != 0.:
raise ValueError()
def create_convnet(in_channels, out_channels):
return ConvNet(in_channels, hidden_channels, out_channels)
mask = utils.create_mid_split_binary_mask(num_channels)
if coupling_layer_type == 'cubic_spline':
coupling_layer = transforms.PiecewiseCubicCouplingTransform(
mask=mask,
transform_net_create_fn=create_convnet,
tails='linear',
tail_bound=spline_params['tail_bound'],
num_bins=spline_params['num_bins'],
apply_unconditional_transform=spline_params[
'apply_unconditional_transform'],
min_bin_width=spline_params['min_bin_width'],
min_bin_height=spline_params['min_bin_height'])
elif coupling_layer_type == 'quadratic_spline':
coupling_layer = transforms.PiecewiseQuadraticCouplingTransform(
mask=mask,
transform_net_create_fn=create_convnet,
tails='linear',
tail_bound=spline_params['tail_bound'],
num_bins=spline_params['num_bins'],
apply_unconditional_transform=spline_params[
'apply_unconditional_transform'],
min_bin_width=spline_params['min_bin_width'],
min_bin_height=spline_params['min_bin_height'])
elif coupling_layer_type == 'rational_quadratic_spline':
coupling_layer = transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=mask,
transform_net_create_fn=create_convnet,
tails='linear',
tail_bound=spline_params['tail_bound'],
num_bins=spline_params['num_bins'],
apply_unconditional_transform=spline_params[
'apply_unconditional_transform'],
min_bin_width=spline_params['min_bin_width'],
min_bin_height=spline_params['min_bin_height'],
min_derivative=spline_params['min_derivative'])
elif coupling_layer_type == 'affine':
coupling_layer = transforms.AffineCouplingTransform(
mask=mask, transform_net_create_fn=create_convnet)
elif coupling_layer_type == 'additive':
coupling_layer = transforms.AdditiveCouplingTransform(
mask=mask, transform_net_create_fn=create_convnet)
else:
raise RuntimeError('Unknown coupling_layer_type')
step_transforms = []
if actnorm:
step_transforms.append(transforms.ActNorm(num_channels))
step_transforms.extend(
[transforms.OneByOneConvolution(num_channels), coupling_layer])
return transforms.CompositeTransform(step_transforms)
distribution = distributions.TweakedUniform(
low=torch.zeros(dim),
high=torch.ones(dim)
)
transform = transforms.CompositeTransform([
transforms.CompositeTransform([
transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=utils.create_alternating_binary_mask(
features=dim,
even=(i % 2 == 0)
),
transform_net_create_fn=lambda in_features, out_features:
nn_.ResidualNet(
in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
num_blocks=args.num_transform_blocks,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm
),
num_bins=args.num_bins,
tails=None,
tail_bound=1,
# apply_unconditional_transform=args.apply_unconditional_transform,
min_bin_width=args.min_bin_width
),
]) for i in range(args.num_flow_steps)
])
flow = flows.Flow(transform, distribution).to(device)
path = os.path.join(cutils.get_final_root(), '{}-final.t'.format(dataset_name))
state_dict = torch.load(path)
