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Fix Division by Zero and Deprecated Function Bugs #120

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Fix Division by Zero and Deprecated Function Bugs #120

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2 changes: 1 addition & 1 deletion DeepLense_Physics_Informed_Super_Resolution_Anirudh_Shankar/fGAN/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,5 +33,5 @@ def __getitem__(self, index):
class_index = index%self.num_samples
image = torch.tensor(np.array([np.load(self.directory+selected_class+'/%s'%selected_class+self.aux+'%d.npy'%(class_index))]))
if self.aux == '_sim_':
image = (image - torch.min(image))/(torch.max(image)-torch.min(image))
image = (image - torch.min(image))/(torch.max(image)-torch.min(image) + 1e-8)
return image
12 changes: 6 additions & 6 deletions DeepLense_Physics_Informed_Super_Resolution_Anirudh_Shankar/fGAN/models.py
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Expand Up @@ -101,7 +101,7 @@ def create_sersic(self, source_profile_regenerated, resolution, R_sersic):
x_center_LR, y_center_LR = self.origin_shift(x_center_LR, x/2)*resolution, self.origin_shift(y_center_LR, y/2)*resolution

S_lens = self.sersic_law(theta_x_source.view(1,-1).repeat(B,1), theta_y_source.view(1,-1).repeat(B,1), x_center_LR.view(B,-1), y_center_LR.view(B,-1), R_sersic) #no gradient
S_lens = (S_lens - torch.min(S_lens, dim=-1)[0].view(B,1))/(torch.max(S_lens, dim=-1)[0].view(B,1)-torch.min(S_lens, dim=-1)[0].view(B,1))
S_lens = (S_lens - torch.min(S_lens, dim=-1)[0].view(B,1))/(torch.max(S_lens, dim=-1)[0].view(B,1)-torch.min(S_lens, dim=-1)[0].view(B,1) + 1e-8)
return S_lens

def create_lensing(self, LR, alpha, alpha_interpolated, resolution, resolution_, magnification, R_sersic = None):
Expand Down Expand Up @@ -179,11 +179,11 @@ def create_lensing(self, LR, alpha, alpha_interpolated, resolution, resolution_,
I_lens = self.sersic_law(beta_x_source.view(B,-1), beta_y_source.view(B,-1), x_center_LR.view(B,1), y_center_LR.view(B,1), R_sersic) #gradient flow
S_lens = self.sersic_law(theta_x_source.reshape(1,-1).repeat(B,1), theta_y_source.reshape(1,-1).repeat(B,1), x_center_LR.view(B,-1), y_center_LR.view(B,-1), R_sersic) #no gradient

I_lens_LR = (I_lens_LR - torch.min(I_lens_LR, dim=-1)[0].view(B,1))/(torch.max(I_lens_LR, dim=-1)[0].view(B,1)-torch.min(I_lens_LR, dim=-1)[0].view(B,1))
S_lens = (S_lens - torch.min(S_lens, dim=-1)[0].view(B,1))/(torch.max(S_lens, dim=-1)[0].view(B,1)-torch.min(S_lens, dim=-1)[0].view(B,1))
I_lens = (I_lens - torch.min(I_lens, dim=-1)[0].view(B,1))/(torch.max(I_lens, dim=-1)[0].view(B,1)-torch.min(I_lens, dim=-1)[0].view(B,1))
I_lens_LR = (I_lens_LR - torch.min(I_lens_LR, dim=-1)[0].view(B,1))/(torch.max(I_lens_LR, dim=-1)[0].view(B,1)-torch.min(I_lens_LR, dim=-1)[0].view(B,1) + 1e-8)
S_lens = (S_lens - torch.min(S_lens, dim=-1)[0].view(B,1))/(torch.max(S_lens, dim=-1)[0].view(B,1)-torch.min(S_lens, dim=-1)[0].view(B,1) + 1e-8)
I_lens = (I_lens - torch.min(I_lens, dim=-1)[0].view(B,1))/(torch.max(I_lens, dim=-1)[0].view(B,1)-torch.min(I_lens, dim=-1)[0].view(B,1) + 1e-8)
source_profile_regenerated = source_profile_regenerated.view(B,-1)
source_profile_regenerated = (source_profile_regenerated - torch.min(source_profile_regenerated, dim=-1)[0].view(B,1))/(torch.max(source_profile_regenerated, dim=-1)[0].view(B,1)-torch.min(source_profile_regenerated, dim=-1)[0].view(B,1))
source_profile_regenerated = (source_profile_regenerated - torch.min(source_profile_regenerated, dim=-1)[0].view(B,1))/(torch.max(source_profile_regenerated, dim=-1)[0].view(B,1)-torch.min(source_profile_regenerated, dim=-1)[0].view(B,1) + 1e-8)


I_lens_LR = I_lens_LR.view(B,1,x,y)
Expand Down Expand Up @@ -422,4 +422,4 @@ def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
# x = self.fc2(x)
return torch.nn.functional.sigmoid(x)
return torch.sigmoid(x)