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Wrapped flag attribute as scalar tensor #7

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Wrapped flag attribute as scalar tensor #7

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10 changes: 5 additions & 5 deletions normflow/flows/normalization.py
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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
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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