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def forwardEnv(self, diffusePred, normalPred, roughPred, envmap):
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envR, envC = envmap.size(2), envmap.size(3)
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bn = diffusePred.size(0)
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diffusePred = F.adaptive_avg_pool2d(diffusePred, (envR, envC) )
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normalPred = F.adaptive_avg_pool2d(normalPred, (envR, envC) )
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normalPred = normalPred / torch.sqrt( torch.clamp(
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torch.sum(normalPred * normalPred, dim=1 ), 1e-6, 1).unsqueeze(1) )
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roughPred = F.adaptive_avg_pool2d(roughPred, (envR, envC ) )
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temp = Variable(torch.FloatTensor(1, 1, 1, 1,1) )
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if self.isCuda:
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temp = temp.cuda()
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ldirections = self.ls.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
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camyProj = torch.einsum('b,abcd->acd',(self.up, normalPred)).unsqueeze(1).expand_as(normalPred) * normalPred
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camy = F.normalize(self.up.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).expand_as(camyProj) - camyProj, dim=1)
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camx = -F.normalize(torch.cross(camy, normalPred,dim=1), dim=1)
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l = ldirections[:, :, 0:1, :, :] * camx.unsqueeze(1) \
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+ ldirections[:, :, 1:2, :, :] * camy.unsqueeze(1) \
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+ ldirections[:, :, 2:3, :, :] * normalPred.unsqueeze(1)
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h = (self.v.unsqueeze(1) + l) / 2;
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h = h / torch.sqrt(torch.clamp(torch.sum(h*h, dim=2), min = 1e-6).unsqueeze(2) )
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vdh = torch.sum( (self.v * h), dim = 2).unsqueeze(2)
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temp.data[0] = 2.0
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frac0 = self.F0 + (1-self.F0) * torch.pow(temp.expand_as(vdh), (-5.55472*vdh-6.98316)*vdh)
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diffuseBatch = (diffusePred )/ np.pi
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roughBatch = (roughPred + 1.0)/2.0
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k = (roughBatch + 1) * (roughBatch + 1) / 8.0
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alpha = roughBatch * roughBatch
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alpha2 = alpha * alpha
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ndv = torch.clamp(torch.sum(normalPred * self.v.expand_as(normalPred), dim = 1), 0, 1).unsqueeze(1).unsqueeze(2)
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ndh = torch.clamp(torch.sum(normalPred.unsqueeze(1) * h, dim = 2), 0, 1).unsqueeze(2)
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ndl = torch.clamp(torch.sum(normalPred.unsqueeze(1) * l, dim = 2), 0, 1).unsqueeze(2)
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frac = alpha2.unsqueeze(1).expand_as(frac0) * frac0
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nom0 = ndh * ndh * (alpha2.unsqueeze(1).expand_as(ndh) - 1) + 1
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nom1 = ndv * (1 - k.unsqueeze(1).expand_as(ndh) ) + k.unsqueeze(1).expand_as(ndh)
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nom2 = ndl * (1 - k.unsqueeze(1).expand_as(ndh) ) + k.unsqueeze(1).expand_as(ndh)
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nom = torch.clamp(4*np.pi*nom0*nom0*nom1*nom2, 1e-6, 4*np.pi)
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specPred = frac / nom
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envmap = envmap.view([bn, 3, envR, envC, self.envWidth * self.envHeight ] )
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envmap = envmap.permute([0, 4, 1, 2, 3] )
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brdfDiffuse = diffuseBatch.unsqueeze(1).expand([bn, self.envWidth * self.envHeight, 3, envR, envC] ) * \
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ndl.expand([bn, self.envWidth * self.envHeight, 3, envR, envC] )
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colorDiffuse = torch.sum(brdfDiffuse * envmap * self.envWeight.expand_as(brdfDiffuse), dim=1)
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brdfSpec = specPred.expand([bn, self.envWidth * self.envHeight, 3, envR, envC ] ) * \
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ndl.expand([bn, self.envWidth * self.envHeight, 3, envR, envC] )
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colorSpec = torch.sum(brdfSpec * envmap * self.envWeight.expand_as(brdfSpec), dim=1)
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return colorDiffuse, colorSpec
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def BatchRankingLoss(albedoPred, eqPoint, eqWeight, darkerPoint, darkerWeight):
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tau = 0.5
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height, width = albedoPred.size(1), albedoPred.size(2)
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reflectance = torch.mean(albedoPred, dim=0)
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reflectLog = torch.log(reflectance + 0.001)
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reflectLog = reflectLog.view(-1)
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eqPoint = Variable(torch.from_numpy(eqPoint ).long() ).cuda( )
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eqWeight = Variable(torch.from_numpy(eqWeight ).float() ).cuda( )
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darkerPoint = Variable(torch.from_numpy(darkerPoint ).long() ).cuda( )
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darkerWeight = Variable(torch.from_numpy(darkerWeight ).float() ).cuda( )
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# compute the eq loss
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r1, c1, r2, c2 = torch.split(eqPoint, 1, dim=1)
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p1 = (r1 * width + c1).view(-1)
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p1.requires_grad = False
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p2 = (r2 * width + c2).view(-1)
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p2.requires_grad = False
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rf1 = torch.index_select(reflectLog, 0, p1)
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rf2 = torch.index_select(reflectLog, 0, p2)
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eqWeight = eqWeight.view(-1)
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eqLoss = torch.mean(eqWeight * torch.pow(rf1 - rf2, 2) )
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# compute the darker loss
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r1, c1, r2, c2 = torch.split(darkerPoint, 1, dim=1)
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p1 = (r1 * width + c1).view(-1)
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p1.requires_grad = False
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p2 = (r2 * width + c2).view(-1)
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p2.requires_grad = False
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rf1 = torch.index_select(reflectLog, 0, p1)
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rf2 = torch.index_select(reflectLog, 0, p2)
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darkerWeight = darkerWeight.view(-1)
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