def reverse_flow(self, estimator, flow_embed, multgate, i_flow, input,
logdet):
assert input.size(1) % 2 == 0
flow_embed = flow_embed.expand(
(input.shape[0], flow_embed.shape[1], flow_embed.shape[2],
flow_embed.shape[3]))
flow_embed, _ = self.actnorm_embed(
flow_embed,
reverse=False) # NOTE: reverse=False is the correct usage for this
# 1.coupling
z1, z2 = split_feature(input, "split")
if self.flow_coupling == "additive":
out = estimator(flow_embed, multgate, z1)
out = self.conv_proj(out)
z2 = z2 - out
elif self.flow_coupling == "affine":
# h = self.block(z1)
h = estimator(flow_embed, multgate, z1)
h = self.conv_proj(h)
shift, scale = split_feature(h, "cross")
scale = torch.sigmoid(scale + 2.)
z2 = z2 / scale
z2 = z2 - shift
logdet = -torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
z = torch.cat((z1, z2), dim=1)
# 2. permute
z, logdet = self.flow_permutation(z, logdet, True)
# 3. actnorm
z, logdet = self.actnorm(z, logdet=logdet, reverse=True)
return z, logdet
def normal_flow(self, estimator, flow_embed, multgate, i_flow, input,
logdet):
assert input.size(1) % 2 == 0
# 1. actnorm
z, logdet = self.actnorm(input, logdet=logdet, reverse=False)
# 1.1 also actnorm embed
flow_embed = flow_embed.expand(
(z.shape[0], flow_embed.shape[1], flow_embed.shape[2],
flow_embed.shape[3]))
flow_embed, _ = self.actnorm_embed(flow_embed)
# 2. permute
z, logdet = self.flow_permutation(z, logdet, False)
# 3. coupling
z1, z2 = split_feature(z, "split")
if self.flow_coupling == "additive":
z1_out = estimator(flow_embed, multgate, z1)
z1_out = self.conv_proj(z1_out)
z2 = z2 + z1_out
elif self.flow_coupling == "affine":
h = estimator(flow_embed, multgate, z1)
h = self.conv_proj(h)
shift, scale = split_feature(h, "cross")
scale = torch.sigmoid(scale + 2.)
z2 = z2 + shift
z2 = z2 * scale
logdet = torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
z = torch.cat((z1, z2), dim=1)
return z, logdet
def normal_flow(self, input, y_onehot, logdet):
assert input.size(1) % 2 == 0
# 1. actnorm
z, logdet = self.actnorm(input, logdet=logdet, reverse=False)
# 2. permute
z, logdet = self.flow_permutation(z, logdet, False)
# 3. coupling
z1, z2 = split_feature(z, "split")
if self.flow_coupling == "additive":
if self.extra_condition:
z2 = z2 + self.block((z1, y_onehot))
else:
z2 = z2 + self.block(z1)
elif self.flow_coupling == "affine":
if self.extra_condition:
h = self.block((z1, y_onehot))
else:
h = self.block(z1)
shift, scale = split_feature(h, "cross")
scale = torch.sigmoid(scale + 2.)
z2 = z2 + shift
z2 = z2 * scale
logdet = torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
z = torch.cat((z1, z2), dim=1)
return z, logdet
def normal_flow(self, x, y_onehot):
b, c, h, w = x.shape
x, logdet = uniform_binning_correction(x)
z, objective = self.flow(x, logdet=logdet, reverse=False) # z_size = C
mean, logs = self.prior(
x, y_onehot) # if condition, mean_size = C//2; else, mean_size = C
if self.y_condition:
z_y, z_n = split_feature(z, "split")
self.mean_normal = torch.zeros(z_y.shape).cuda()
self.logs_normal = torch.ones(z_y.shape).cuda()
y_logits = self.project_class(z_y.mean(2).mean(2))
objective += gaussian_likelihood(
mean, logs, z_y) + gaussian_likelihood(self.mean_normal,
self.logs_normal, z_n)
else:
objective += gaussian_likelihood(mean, logs, z)
y_logits = None
# Full objective - converted to bits per dimension
bpd = (-objective) / (math.log(2.) * c * h * w)
return z, bpd, y_logits
def prior(self, data, y_onehot=None, batch_size=32):
if data is not None:
h = self.prior_h.repeat(data.shape[0], 1, 1, 1)
else:
# Hardcoded a batch size of 32 here
h = self.prior_h.repeat(batch_size, 1, 1, 1)
channels = h.size(1)
if self.learn_top:
h = self.learn_top_fn(h)
if self.y_condition:
assert y_onehot is not None
yp = self.project_ycond(y_onehot)
if data is not None:
h += yp.view(data.shape[0], channels, 1, 1)
else:
print("no data")
h += yp.view(batch_size, channels, 1, 1)
if self.yd_condition:
assert y_onehot is not None
yp = self.project_ycond(y_onehot)
if data is not None:
h += yp.view(data.shape[0], channels, 1, 1)
else:
print("no data")
h += yp.view(batch_size, channels, 1, 1)
return split_feature(h, "split")
def normal_flow(self, input, logdet):
assert input.size(1) % 2 == 0
# 1. actnorm
if not self.no_actnorm:
z, logdet = self.actnorm(input, logdet=logdet, reverse=False)
else:
z = input
# 2. permute
z, logdet = self.flow_permutation(z, logdet, False)
# 3. coupling
z1, z2 = split_feature(z, "split")
if self.flow_coupling == "additive":
z2 = z2 + self.block(z1)
elif self.flow_coupling == "affine":
h = self.block(z1)
shift, scale = split_feature(h, "cross")
scale = torch.sigmoid(scale +
self.affine_scale_eps) + self.affine_eps
z2 = z2 + shift
z2 = z2 * scale
logdet = torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
elif self.flow_coupling == "gaffine":
h = self.block(z1)
shift, scale = split_feature(h, "cross")
scale = torch.exp(np.log(self.max_scale) * torch.tanh(scale))
self.last_scale = scale
z2 = z2 + shift
z2 = z2 * scale
logdet = torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
elif self.flow_coupling == "naffine":
h = self.block(z1)
shift, scale = split_feature(h, "cross")
eps = self.affine_eps
scale = (2 * torch.sigmoid(scale) - 1) * (1 - eps) + 1
z2 = z2 + shift
z2 = z2 * scale
logdet = torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
z = torch.cat((z1, z2), dim=1)
return z, logdet
def forward(self, input, logdet=0., reverse=False, temperature=None):
if reverse:
z1 = input
mean, logs = self.split2d_prior(z1)
z2 = gaussian_sample(mean, logs, temperature)
z = torch.cat((z1, z2), dim=1)
return z, logdet
else:
z1, z2 = split_feature(input, "split")
mean, logs = self.split2d_prior(z1)
logdet = gaussian_likelihood(mean, logs, z2) + logdet
return z1, logdet
def reverse_flow(self, input, logdet):
assert input.size(1) % 2 == 0
# 1.coupling
z1, z2 = split_feature(input, "split")
if self.flow_coupling == "additive":
z2 = z2 - self.block(z1)
elif self.flow_coupling == "affine":
h = self.block(z1)
shift, scale = split_feature(h, "cross")
scale = torch.sigmoid(scale + 2.)
z2 = z2 / scale
z2 = z2 - shift
logdet = -torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
z = torch.cat((z1, z2), dim=1)
# 2. permute
z, logdet = self.flow_permutation(z, logdet, True)
# 3. actnorm
z, logdet = self.actnorm(z, logdet=logdet, reverse=True)
return z, logdet
def split2d_prior(self, z, condition):
if self.sp_condition is False:
# print('no split prior')
h = self.conv(z)
else:
print('split prior')
cond = self.cond_fc(condition)
cond = cond.view(z.size())
cond = self.cond_conv(cond)
cond = F.relu(cond, inplace=False)
z = torch.cat([z, cond], dim=1)
h = self.conv(z)
return split_feature(h, "cross")
def normal_flow(self, estimator, flow_embed, i_flow, input, logdet):
assert input.size(1) % 2 == 0
# 1. actnorm
z, logdet = self.actnorm(input, logdet=logdet, reverse=False)
# 2. permute
z, logdet = self.flow_permutation(z, logdet, False)
# 3. coupling
z1, z2 = split_feature(z, "split")
if self.flow_coupling == "additive":
z1_out = estimator(flow_embed, z1)
z2 = z2 + z1_out
elif self.flow_coupling == "affine":
h = estimator(flow_embed, z1)
shift, scale = split_feature(h, "cross")
scale = torch.sigmoid(scale + 2.)
z2 = z2 + shift
z2 = z2 * scale
logdet = torch.sum(torch.log(scale), dim=[1, 2, 3]) + logdet
z = torch.cat((z1, z2), dim=1)
return z, logdet
def forward(self, input, zn, logdet=0., reverse=False, temperature=None):
if reverse:
# z1 = input
z1 = input
z2 = zn.pop()
mean, logs = self.split2d_prior(z1)
# z2 = gaussian_sample(mean, logs, temperature)
z = torch.cat((z1, z2), dim=1)
return z, logdet, zn
else:
z1, z2 = split_feature(input, "split")
zn.append(z2.clone())
mean, logs = self.split2d_prior(z1)
logdet = gaussian_likelihood(mean, logs, z2) + logdet
return z1, logdet, zn
def prior(self, data, y_onehot=None):
if data is not None:
h = self.prior_h.repeat(data.shape[0], 1, 1, 1)
else:
# Hardcoded a batch size of 32 here
h = self.prior_h.repeat(32, 1, 1, 1)
channels = h.size(1)
if self.learn_top:
# import pdb
# pdb.set_trace()
h = self.learn_top_fn(h)
if self.y_condition:
assert y_onehot is not None
y_onehot = y_onehot.float()
yp = self.project_ycond(y_onehot)
h += yp.view(data.shape[0], channels, 1, 1)
return split_feature(h, "split")
def forward(self, input, logdet=0., reverse=False, temperature=None):
if reverse:
z1 = input
mean, logs = self.split2d_prior(z1)
if self.use_last:
self._last_z2.requires_grad_()
z2 = self._last_z2
self.use_last = False
else:
z2 = gaussian_sample(mean, logs, temperature)
self._last_z2 = z2#.clone()
z = torch.cat((z1, z2), dim=1)
return z, logdet
else:
z1, z2 = split_feature(input, "split")
self._last_z2 = z2.clone()
mean, logs = self.split2d_prior(z1)
d = gaussian_likelihood(mean, logs, z2)
logdet = d + logdet
self._last_logdet = d
return z1, logdet
def split2d_prior(self, z):
h = self.conv(z)
return split_feature(h, "cross")
