diff --git a/_modules/simlearner3d/models/generic_model.html b/_modules/simlearner3d/models/generic_model.html index 28535c4..3370974 100644 --- a/_modules/simlearner3d/models/generic_model.html +++ b/_modules/simlearner3d/models/generic_model.html @@ -247,12 +247,14 @@

Source code for simlearner3d.models.generic_model

# Forward FeatsL=self.feature(x0) FeatsR=self.feature(x1) + Offset_pos=- (0.5) * torch.rand(dispnoc0.size(),device=device) + (0.5) Offset_neg=((self.false1 - self.false2) * torch.rand(dispnoc0.size(),device=device) + self.false2) RandSens=torch.rand(dispnoc0.size(),device=device) RandSens=((RandSens < 0.5).float()+(RandSens >= 0.5).float()*(-1.0)) + Offset_pos=Offset_pos*RandSens Offset_neg=Offset_neg*RandSens #dispnoc0=torch.nan_to_num(dispnoc0, nan=0.0) - D_pos=dispnoc0 + D_pos=dispnoc0+Offset_pos D_neg=dispnoc0+Offset_neg Index_X=torch.arange(0,dispnoc0.size()[-1],device=device) Index_X=Index_X.expand(dispnoc0.size()[-2],dispnoc0.size()[-1]).unsqueeze(0).unsqueeze(0).repeat_interleave(x0.size()[0],0) @@ -318,12 +320,14 @@

Source code for simlearner3d.models.generic_model

device='cuda' if x0.is_cuda else 'cpu' FeatsL=self.feature(x0) FeatsR=self.feature(x1) + Offset_pos=- (0.5) * torch.rand(dispnoc0.size(),device=device) + (0.5) Offset_neg=((self.false1 - self.false2) * torch.rand(dispnoc0.size(),device=device) + self.false2) RandSens=torch.rand(dispnoc0.size(),device=device) RandSens=((RandSens < 0.5).float()+(RandSens >= 0.5).float()*(-1.0)) + Offset_pos=Offset_pos*RandSens Offset_neg=Offset_neg*RandSens #dispnoc0=torch.nan_to_num(dispnoc0, nan=0.0) - D_pos=dispnoc0 + D_pos=dispnoc0+Offset_pos D_neg=dispnoc0+Offset_neg Index_X=torch.arange(0,dispnoc0.size()[-1],device=device) Index_X=Index_X.expand(dispnoc0.size()[-2],dispnoc0.size()[-1]).unsqueeze(0).unsqueeze(0).repeat_interleave(x0.size()[0],0) @@ -389,12 +393,14 @@

Source code for simlearner3d.models.generic_model

device='cuda' if x0.is_cuda else 'cpu' FeatsL=self.feature(x0) FeatsR=self.feature(x1) + Offset_pos=- (0.5) * torch.rand(dispnoc0.size(),device=device) + (0.5) Offset_neg=((self.false1 - self.false2) * torch.rand(dispnoc0.size(),device=device) + self.false2) RandSens=torch.rand(dispnoc0.size(),device=device) RandSens=((RandSens < 0.5).float()+(RandSens >= 0.5).float()*(-1.0)) + Offset_pos=Offset_pos*RandSens Offset_neg=Offset_neg*RandSens #dispnoc0=torch.nan_to_num(dispnoc0, nan=0.0) - D_pos=dispnoc0 + D_pos=dispnoc0+Offset_pos D_neg=dispnoc0+Offset_neg Index_X=torch.arange(0,dispnoc0.size()[-1],device=device) Index_X=Index_X.expand(dispnoc0.size()[-2],dispnoc0.size()[-1]).unsqueeze(0).unsqueeze(0).repeat_interleave(x0.size()[0],0)