content: # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  # SPDX-License-Identifier: LicenseRef-NvidiaProprietary
  #
  # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
  # property and proprietary rights in and to this material, related
  # documentation and any modifications thereto. Any use, reproduction,
  # disclosure or distribution of this material and related documentation
  # without an express license agreement from NVIDIA CORPORATION or
  # its affiliates is strictly prohibited.
  #
  # Modified by Zexin He
  # The modifications are subject to the same license as the original.
  
  
  """
  The ray marcher takes the raw output of the implicit representation and uses the volume rendering equation to produce composited colors and depths.
  Based off of the implementation in MipNeRF (this one doesn't do any cone tracing though!)
  """
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class LearnedVariance(nn.Module):
      def __init__(self, init_val):
          super(LearnedVariance, self).__init__()
          self.register_parameter("_inv_std", nn.Parameter(torch.tensor(init_val)))
  
      @property
      def inv_std(self):
          val = torch.exp(self._inv_std * 10.0)
          return val
  
      def forward(self, x):
          return torch.ones_like(x) * self.inv_std.clamp(1.0e-6, 1.0e6)
  
  
  class MipRayMarcher2(nn.Module):
      def __init__(self, activation_factory):
          super().__init__()
          self.activation_factory = activation_factory
          self.variance = LearnedVariance(0.3)
          self.cos_anneal_ratio = 1.0
      def get_alpha(self, sdf, normal, dirs, dists):
          # sdf: [N 1]  normal: [N 3]   dirs: [N 3] dists: [N 1]
          # import ipdb; ipdb.set_trace()
  
          inv_std = self.variance(sdf)
  
          true_cos = (dirs * normal).sum(-1, keepdim=True)
          # "cos_anneal_ratio" grows from 0 to 1 in the beginning training iterations. The anneal strategy below makes
          # the cos value "not dead" at the beginning training iterations, for better convergence.
          iter_cos = -(
              F.relu(-true_cos * 0.5 + 0.5) * (1.0 - self.cos_anneal_ratio)
              + F.relu(-true_cos) * self.cos_anneal_ratio
          )  # always non-positive
  
          # Estimate signed distances at section points
          estimated_next_sdf = sdf + iter_cos * dists * 0.5
          estimated_prev_sdf = sdf - iter_cos * dists * 0.5
  
          prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_std)
          next_cdf = torch.sigmoid(estimated_next_sdf * inv_std)
  
          p = prev_cdf - next_cdf
          c = prev_cdf
  
          alpha = ((p + 1e-5) / (c + 1e-5)).clip(0.0, 1.0)
          return alpha
      
      def run_forward(self, colors, sdfs, depths, normals, ray_directions, rendering_options, bgcolor=None, real_normals=None):
          # depths: [B N_ray*N_sample 1] 
          # sdfs: [B, N_ray, N_sample 1]
          # import ipdb; ipdb.set_trace() 
  
          deltas = depths[:, :, 1:] - depths[:, :, :-1]
          colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2
          sdfs_mid = (sdfs[:, :, :-1] + sdfs[:, :, 1:]) / 2
          depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2
          normals_mid = (normals[:, :, :-1] + normals[:, :, 1:]) / 2
  
          # zhaohx add for normal :
          real_normals_mid = (real_normals[:, :, :-1] + real_normals[:, :, 1:]) / 2
  
          # # using factory mode for better usability
          # densities_mid = self.activation_factory(rendering_options)(densities_mid)
  
          # density_delta = densities_mid * deltas
  
          # alpha = 1 - torch.exp(-density_delta)
  
          # import ipdb; ipdb.set_trace()
          dirs = ray_directions.unsqueeze(2).expand(-1, -1, sdfs_mid.shape[-2], -1)
          B, N_ray, N_sample, _ = sdfs_mid.shape
          alpha = self.get_alpha(sdfs_mid.reshape(-1, 1), normals_mid.reshape(-1, 3), dirs.reshape(-1, 3), deltas.reshape(-1, 1))
          alpha = alpha.reshape(B, N_ray, N_sample, -1)
          
          alpha_shifted = torch.cat([torch.ones_like(alpha[:, :, :1]), 1-alpha + 1e-10], -2)
          weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1]
  
          composite_rgb = torch.sum(weights * colors_mid, -2)
          weight_total = weights.sum(2)
          composite_depth = torch.sum(weights * depths_mid, -2) / weight_total
  
          # clip the composite to min/max range of depths
          composite_depth = torch.nan_to_num(composite_depth, float('inf'))
          composite_depth = torch.clamp(composite_depth, torch.min(depths), torch.max(depths))
          # import pdb; pdb.set_trace()
  
          # zhaohx add for normal :
          composite_normal = torch.sum(weights * real_normals_mid, -2) / weight_total
          composite_normal = torch.nan_to_num(composite_normal, float('inf'))
          composite_normal = torch.clamp(composite_normal, torch.min(real_normals), torch.max(real_normals))
  
          if rendering_options.get('white_back', False):
              # composite_rgb = composite_rgb + 1 - weight_total
              # weight_total[weight_total < 0.5] = 0
              # composite_rgb = composite_rgb * weight_total + 1 - weight_total
              # now is this
              if bgcolor is None:
                  composite_rgb = composite_rgb + 1 - weight_total
                  # composite_rgb = composite_rgb * weight_total + 1 - weight_total
              else:
                  # import pdb; pdb.set_trace()
                  bgcolor = bgcolor.permute(0, 2, 3, 1).contiguous().view(composite_rgb.shape[0], -1, composite_rgb.shape[-1])
                  composite_rgb = composite_rgb + (1 - weight_total) * bgcolor
                  # composite_rgb = composite_rgb * weight_total + (1 - weight_total) * bgcolor
              # composite_rgb = composite_rgb
              # print('new white_back')
  
          # rendered value scale is 0-1, comment out original mipnerf scaling
          # composite_rgb = composite_rgb * 2 - 1 # Scale to (-1, 1)
  
          return composite_rgb, composite_depth, weights, composite_normal
  
  
      def forward(self,  colors, sdfs, depths, normals, ray_directions, rendering_options, bgcolor=None, real_normals=None):
          composite_rgb, composite_depth, weights, composite_normal = self.run_forward(colors, sdfs, depths, normals, ray_directions, rendering_options, bgcolor, real_normals)
  
          return composite_rgb, composite_depth, weights, composite_normal
  