From 54c2783479c9514190af956b6e9dac25221a64c1 Mon Sep 17 00:00:00 2001
From: pengsongyou <psy920710@gmail.com>
Date: Mon, 8 Nov 2021 11:16:33 +0100
Subject: [PATCH] init commit

---
 src/dpsr.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/src/dpsr.py b/src/dpsr.py
index cbb5c9b..ff65287 100644
--- a/src/dpsr.py
+++ b/src/dpsr.py
@@ -17,7 +17,6 @@ class DPSR(nn.Module):
         self.dim = len(res)
         self.denom = np.prod(res)
         G = spec_gaussian_filter(res=res, sig=sig).float()
-        G = G
         # self.G.requires_grad = False # True, if we also make sig a learnable parameter
         self.omega = fftfreqs(res, dtype=torch.float32)
         self.scale = scale
@@ -33,11 +32,6 @@ class DPSR(nn.Module):
         assert(V.shape == N.shape) # [b, nv, ndims]
         ras_p = point_rasterize(V, N, self.res)  # [b, n_dim, dim0, dim1, dim2]
         
-        #!!! OLD
-        # ras_s = torch.rfft(ras_p, signal_ndim=self.dim)  # [b, n_dim, dim0, dim1, dim2/2+1, 2]
-        # ras_s = ras_s.permute(*tuple([0]+list(range(2, self.dim+2))+[1, self.dim+2]))
-        # N_ = (ras_s * self.G) # [b, n_dim, dim0, dim1, dim2/2+1, 2]
-
         ras_s = torch.fft.rfftn(ras_p, dim=(2,3,4))
         ras_s = ras_s.permute(*tuple([0]+list(range(2, self.dim+1))+[self.dim+1, 1]))
         N_ = ras_s[..., None] * self.G # [b, dim0, dim1, dim2/2+1, n_dim, 1]
@@ -46,7 +40,6 @@ class DPSR(nn.Module):
         omega *= 2 * np.pi  # normalize frequencies
         omega = omega.to(V.device)
         
-        # DivN = torch.sum(-img(N_) * omega, dim=-2) #!!! OLD [b, dim0, dim1, dim2/2+1, 2]
         DivN = torch.sum(-img(torch.view_as_real(N_[..., 0])) * omega, dim=-2)
         
         Lap = -torch.sum(omega**2, -2) # [dim0, dim1, dim2/2+1, 1]
@@ -55,7 +48,6 @@ class DPSR(nn.Module):
         Phi[tuple([0] * self.dim)] = 0
         Phi = Phi.permute(*tuple([[self.dim+1] + list(range(self.dim+1))]))  # [b, dim0, dim1, dim2/2+1, 2]
         
-        # phi = torch.irfft(Phi, signal_ndim=self.dim, signal_sizes=self.res)#!!! OLD [b, dim0, dim1, dim2]
         phi = torch.fft.irfftn(torch.view_as_complex(Phi), s=self.res, dim=(1,2,3))
         
         if self.shift or self.scale:
@@ -70,6 +62,5 @@ class DPSR(nn.Module):
             phi = phi.permute(*tuple([[self.dim] + list(range(self.dim))]))
             
             if self.scale:
-                # phi = phi / fv0.view(*tuple([-1] + [1] * self.dim)) * 0.5
                 phi = -phi / torch.abs(fv0.view(*tuple([-1]+[1] * self.dim))) *0.5
         return phi
\ No newline at end of file