Initial commit.

[pytorch.git] / hallu.py

diff --git a/hallu.py b/hallu.py
index 6b0b303..b738b52 100755 (executable)
--- a/hallu.py
+++ b/hallu.py
@@ -1,5 +1,10 @@
 #!/usr/bin/env python
 
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
 # ImageMagick's montage to make the mosaic
 #
 # montage hallu-*.png -tile 5x6 -geometry +1+1 result.png
@@ -12,7 +17,7 @@ class MultiScaleEdgeEnergy(torch.nn.Module):
         super(MultiScaleEdgeEnergy, self).__init__()
         k = torch.exp(- torch.tensor([[-2., -1., 0., 1., 2.]])**2 / 2)
         k = (k.t() @ k).view(1, 1, 5, 5)
-        self.register_buffer('gaussian_5x5', k / k.sum())
+        self.gaussian_5x5 = torch.nn.Parameter(k / k.sum()).requires_grad_(False)
 
     def forward(self, x):
         u = x.view(-1, 1, x.size(2), x.size(3))
@@ -43,7 +48,7 @@ for l in [ 5, 7, 12, 17, 21, 28 ]:
     ref_output = model(ref_input).detach()
 
     for n in range(5):
-        input = ref_input.new_empty(ref_input.size()).uniform_(-0.01, 0.01).requires_grad_()
+        input = torch.empty_like(ref_input).uniform_(-0.01, 0.01).requires_grad_()
         optimizer = torch.optim.Adam( [ input ], lr = 1e-2)
         for k in range(1000):
             output = model(input)