Skip to content

Wrapped flag attribute as scalar tensor #7

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
prclibo wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Wrapped flag attribute as scalar tensor #7

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions normflow/flows/normalization.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,8 @@ class ActNorm(AffineConstFlow):

def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.data_dep_init_done_cpu = torch.tensor(0.)
self.register_buffer('data_dep_init_done', self.data_dep_init_done_cpu)
# self.data_dep_init_done_cpu = torch.tensor(0.)
self.register_buffer('data_dep_init_done', torch.tensor(0.))

def forward(self, z):
# first batch is used for initialization, c.f. batchnorm
Expand All @@ -24,7 +24,7 @@ def forward(self, z):
s_init = -torch.log(z.std(dim=self.batch_dims, keepdim=True) + 1e-6)
self.s.data = s_init.data
self.t.data = (-z.mean(dim=self.batch_dims, keepdim=True) * torch.exp(self.s)).data
self.data_dep_init_done = torch.tensor(1.)
self.data_dep_init_done[...] = 1.
return super().forward(z)

def inverse(self, z):
Expand All @@ -34,7 +34,7 @@ def inverse(self, z):
s_init = torch.log(z.std(dim=self.batch_dims, keepdim=True) + 1e-6)
self.s.data = s_init.data
self.t.data = z.mean(dim=self.batch_dims, keepdim=True).data
self.data_dep_init_done = torch.tensor(1.)
self.data_dep_init_done[...] = 1.
return super().inverse(z)


Expand All @@ -55,4 +55,4 @@ def forward(self, z):
std = torch.std(z, dim=0, keepdims=True)
z_ = (z - mean) / torch.sqrt(std ** 2 + self.eps)
log_det = torch.log(1 / torch.prod(torch.sqrt(std ** 2 + self.eps))).repeat(z.size()[0])
return z_, log_det
return z_, log_det
6 changes: 3 additions & 3 deletions normflow/flows/reshape.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -101,17 +101,17 @@ def __init__(self):
super().__init__()

def forward(self, z):
log_det = 0
log_det = z.new_tensor(0)
s = z.size()
z = z.view(s[0], s[1] // 4, 2, 2, s[2], s[3])
z = z.permute(0, 1, 4, 2, 5, 3).contiguous()
z = z.view(s[0], s[1] // 4, 2 * s[2], 2 * s[3])
return z, log_det

def inverse(self, z):
log_det = 0
log_det = z.new_tensor(0)
s = z.size()
z = z.view(*s[:2], s[2] // 2, 2, s[3] // 2, 2)
z = z.permute(0, 1, 3, 5, 2, 4).contiguous()
z = z.view(s[0], 4 * s[1], s[2] // 2, s[3] // 2)
return z, log_det
return z, log_det