modules/eg3ds/volumetric_rendering/renderer.py

# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
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# documentation and any modifications thereto. Any use, reproduction,
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# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.

"""
The renderer is a module that takes in rays, decides where to sample along each
ray, and computes pixel colors using the volume rendering equation.
"""
import copy
import math
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange

from modules.eg3ds.volumetric_rendering.ray_marcher import MipRayMarcher2
from modules.eg3ds.volumetric_rendering import math_utils
from utils.commons.tensor_utils import convert_like
from utils.commons.hparams import hparams
import copy


def generate_planes():
    """
    Defines planes by the three vectors that form the "axes" of the
    plane. Should work with arbitrary number of planes and planes of
    arbitrary orientation.
    the acutally used axes is the inv_planes (transpose)
    """
    return torch.tensor([[[1, 0, 0],
                        [0, 1, 0],
                        [0, 0, 1]], # xyz

                        [[1, 0, 0],
                        [0, 0, 1],
                        [0, 1, 0]], # xzy

                        [[0, 0, 1],
                        [1, 0, 0], # after transpose, is yzx
                        [0, 1, 0]]], dtype=torch.float32)
    
def project_onto_planes(planes, coordinates):
    """
    Does a projection of a 3D point onto a batch of 2D planes,
    returning 2D plane coordinates.

    Takes plane axes of shape n_planes, 3, 3
    # Takes coordinates of shape N, M, 3
    # returns projections of shape N*n_planes, M, 2
    """
    N, M, C = coordinates.shape
    n_planes, _, _ = planes.shape
    coordinates = coordinates.unsqueeze(1).expand(-1, n_planes, -1, -1).reshape(N*n_planes, M, 3)
    inv_planes = torch.linalg.inv(planes).unsqueeze(0).expand(N, -1, -1, -1).reshape(N*n_planes, 3, 3)
    projections = torch.bmm(coordinates, inv_planes) # 
    return projections

def sample_from_planes(plane_axes, plane_features, coordinates, mode='bilinear', padding_mode='zeros', box_warp=None):
    assert padding_mode == 'zeros'
    N, n_planes, C, H, W = plane_features.shape
    _, M, _ = coordinates.shape
    plane_features = plane_features.reshape(N*n_planes, C, H, W)

    coordinates = (2/box_warp) * coordinates # TODO: add specific box bounds

    projected_coordinates = project_onto_planes(plane_axes, coordinates).unsqueeze(1)[..., :2]
    output_features = torch.nn.functional.grid_sample(plane_features, projected_coordinates.float(), mode=mode, padding_mode=padding_mode, align_corners=False).permute(0, 3, 2, 1).reshape(N, n_planes, M, C)
    return output_features


def sample_from_trigrids(plane_axes, plane_features, coordinates, mode='bilinear', padding_mode='zeros', box_warp=None, triplane_depth=1):
    assert padding_mode == 'zeros'
    N, n_planes, CD, H, W = plane_features.shape
    _, M, _ = coordinates.shape
    C, D = CD // triplane_depth, triplane_depth
    plane_features = plane_features.view(N*n_planes, C, D, H, W)

    coordinates = (2/box_warp) * coordinates # TODO: add specific box bounds

    projected_coordinates = project_onto_planes(plane_axes, coordinates).unsqueeze(1).unsqueeze(2) # (N x n_planes) x 1 x 1 x M x 3
    output_features = torch.nn.functional.grid_sample(plane_features, projected_coordinates.float(), mode=mode, padding_mode=padding_mode, align_corners=False).permute(0, 4, 3, 2, 1).reshape(N, n_planes, M, C)
    return output_features

def sample_from_3dgrid(grid, coordinates):
    """
    Expects coordinates in shape (batch_size, num_points_per_batch, 3)
    Expects grid in shape (1, channels, H, W, D)
    (Also works if grid has batch size)
    Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels)
    """
    batch_size, n_coords, n_dims = coordinates.shape
    sampled_features = torch.nn.functional.grid_sample(grid.expand(batch_size, -1, -1, -1, -1),
                                                       coordinates.reshape(batch_size, 1, 1, -1, n_dims),
                                                       mode='bilinear', padding_mode='zeros', align_corners=False)
    N, C, H, W, D = sampled_features.shape
    sampled_features = sampled_features.permute(0, 4, 3, 2, 1).reshape(N, H*W*D, C)
    return sampled_features


class ImportanceRenderer(torch.nn.Module):
    def __init__(self, hp=None):
        super().__init__()
        global hparams
        self.hparams = copy.copy(hparams) if hp is None else copy.copy(hp)
        hparams = self.hparams

        self.ray_marcher = MipRayMarcher2()
        self.plane_axes = generate_planes()
        self.triplane_feature_type = hparams.get("triplane_feature_type", "triplane")

    def forward(self, planes, decoder, ray_origins, ray_directions, rendering_options):
        self.plane_axes = self.plane_axes.to(ray_origins.device)

        if rendering_options['ray_start'] == rendering_options['ray_end'] == 'auto':
            ray_start, ray_end = math_utils.get_ray_limits_box(ray_origins, ray_directions, box_side_length=rendering_options['box_warp']) # 根据ndc world bbox的大小（默认-1，1），自动计算near和far
            is_ray_valid = ray_end > ray_start
            if torch.any(is_ray_valid).item():
                ray_start[~is_ray_valid] = ray_start[is_ray_valid].min()
                ray_end[~is_ray_valid] = ray_start[is_ray_valid].max()
        else: # 如果bbox没有被限定在-1，1的bbox里面，使用自行设定的near far
            # Create stratified depth samples
            ray_start, ray_end = rendering_options['ray_start'], rendering_options['ray_end']

        depths_coarse = self.sample_stratified(ray_origins, ray_start, ray_end, rendering_options['depth_resolution'], rendering_options['disparity_space_sampling'])
        batch_size, num_rays, samples_per_ray, _ = depths_coarse.shape

        # Coarse Pass
        sample_coordinates = (ray_origins.unsqueeze(-2) + depths_coarse * ray_directions.unsqueeze(-2)).reshape(batch_size, -1, 3)
        sample_directions = ray_directions.unsqueeze(-2).expand(-1, -1, samples_per_ray, -1).reshape(batch_size, -1, 3)
        
        out = self.run_model(planes, decoder, sample_coordinates, sample_directions, rendering_options)
        colors_coarse = out['rgb']
        densities_coarse = out['sigma']
        colors_coarse = colors_coarse.reshape(batch_size, num_rays, samples_per_ray, colors_coarse.shape[-1])
        densities_coarse = densities_coarse.reshape(batch_size, num_rays, samples_per_ray, 1)

        # Fine Pass
        N_importance = rendering_options['depth_resolution_importance']
        if N_importance > 0:
            _, _, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options)

            depths_fine = self.sample_importance(depths_coarse, weights, N_importance)
            sample_directions = ray_directions.unsqueeze(-2).expand(-1, -1, N_importance, -1).reshape(batch_size, -1, 3)
            sample_coordinates = (ray_origins.unsqueeze(-2) + depths_fine * ray_directions.unsqueeze(-2)).reshape(batch_size, -1, 3)

            out = self.run_model(planes, decoder, sample_coordinates, sample_directions, rendering_options)
            colors_fine = out['rgb']
            densities_fine = out['sigma']
            colors_fine = colors_fine.reshape(batch_size, num_rays, N_importance, colors_fine.shape[-1])
            densities_fine = densities_fine.reshape(batch_size, num_rays, N_importance, 1)

            all_depths, all_colors, all_densities = self.unify_samples(depths_coarse, colors_coarse, densities_coarse,
                                                                  depths_fine, colors_fine, densities_fine)

            # Aggregate
            rgb_final, depth_final, weights = self.ray_marcher(all_colors, all_densities, all_depths, rendering_options)
        else:
            rgb_final, depth_final, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options)

        return rgb_final, depth_final, weights.sum(2), is_ray_valid

    def run_model(self, planes, decoder, sample_coordinates, sample_directions, options):
        hparams = self.hparams
        if hparams['enable_rescale_plane_regulation'] and self.training:
            target_size = random.randint(int(256 * hparams.get("min_rescale_factor", 0.5)), 256)
            planes = rearrange(planes, "n k c h w -> n (k c) h w")
            planes = F.interpolate(planes, (target_size, target_size), mode='bilinear', align_corners=False, antialias=False)
            planes = rearrange(planes, "n (k c) h w -> n k c h w", k=3)
        
        self.plane_axes = self.plane_axes.to(planes.device)
        if self.triplane_feature_type in ["triplane"]:
            sampled_features = sample_from_planes(self.plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=options['box_warp'])
        elif self.triplane_feature_type in ["trigrid", 'trigrid_v2']:
            sampled_features = sample_from_trigrids(self.plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=options['box_warp'], triplane_depth=hparams.get("triplane_depth", 1))
        elif self.triplane_feature_type == "3dgrid":
            sampled_features = sample_from_3dgrid(planes, sample_coordinates)

        out = decoder(sampled_features, sample_coordinates)
        if options.get('density_noise', 0) > 0:
            out['sigma'] += torch.randn_like(out['sigma']) * options['density_noise']
        return out

    def sort_samples(self, all_depths, all_colors, all_densities):
        _, indices = torch.sort(all_depths, dim=-2)
        all_depths = torch.gather(all_depths, -2, indices)
        all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
        all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1))
        return all_depths, all_colors, all_densities

    def unify_samples(self, depths1, colors1, densities1, depths2, colors2, densities2):
        all_depths = torch.cat([depths1, depths2], dim = -2)
        all_colors = torch.cat([colors1, colors2], dim = -2)
        all_densities = torch.cat([densities1, densities2], dim = -2)

        _, indices = torch.sort(all_depths, dim=-2)
        all_depths = torch.gather(all_depths, -2, indices)
        all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
        all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1))

        return all_depths, all_colors, all_densities

    def sample_stratified(self, ray_origins, ray_start, ray_end, depth_resolution, disparity_space_sampling=False):
        """
        Return depths of approximately uniformly spaced samples along rays.
        """
        N, M, _ = ray_origins.shape
        if disparity_space_sampling:
            depths_coarse = torch.linspace(0,
                                    1,
                                    depth_resolution,
                                    device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1)
            depth_delta = 1/(depth_resolution - 1)
            depths_coarse += torch.rand_like(depths_coarse) * depth_delta
            depths_coarse = 1./(1./ray_start * (1. - depths_coarse) + 1./ray_end * depths_coarse)
        else:
            if type(ray_start) == torch.Tensor:
                depths_coarse = math_utils.linspace(ray_start, ray_end, depth_resolution).permute(1,2,0,3)
                depth_delta = (ray_end - ray_start) / (depth_resolution - 1)
                depths_coarse += torch.rand_like(depths_coarse) * depth_delta[..., None]
            else:
                depths_coarse = torch.linspace(ray_start, ray_end, depth_resolution, device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1)
                depth_delta = (ray_end - ray_start)/(depth_resolution - 1)
                depths_coarse += torch.rand_like(depths_coarse) * depth_delta

        return depths_coarse

    def sample_importance(self, z_vals, weights, N_importance):
        """
        Return depths of importance sampled points along rays. See NeRF importance sampling for more.
        """
        with torch.no_grad():
            batch_size, num_rays, samples_per_ray, _ = z_vals.shape

            z_vals = z_vals.reshape(batch_size * num_rays, samples_per_ray)
            weights = weights.reshape(batch_size * num_rays, -1) # -1 to account for loss of 1 sample in MipRayMarcher

            # smooth weights
            weights = torch.nn.functional.max_pool1d(weights.unsqueeze(1).float(), 2, 1, padding=1)
            weights = torch.nn.functional.avg_pool1d(weights, 2, 1).squeeze()
            weights = weights + 0.01

            z_vals_mid = 0.5 * (z_vals[: ,:-1] + z_vals[: ,1:])
            importance_z_vals = self.sample_pdf(z_vals_mid, weights[:, 1:-1],
                                             N_importance).detach().reshape(batch_size, num_rays, N_importance, 1)
        return importance_z_vals

    def sample_pdf(self, bins, weights, N_importance, det=False, eps=1e-5):
        """
        Sample @N_importance samples from @bins with distribution defined by @weights.
        Inputs:
            bins: (N_rays, N_samples_+1) where N_samples_ is "the number of coarse samples per ray - 2"
            weights: (N_rays, N_samples_)
            N_importance: the number of samples to draw from the distribution
            det: deterministic or not
            eps: a small number to prevent division by zero
        Outputs:
            samples: the sampled samples
        """
        N_rays, N_samples_ = weights.shape
        if isinstance(N_samples_, torch.Tensor):
            N_samples_ = N_samples_.to(device=weights.device)
        if isinstance(N_rays, torch.Tensor):
            N_rays = N_rays.to(device=weights.device)
        weights = weights + eps # prevent division by zero (don't do inplace op!)
        pdf = weights / torch.sum(weights, -1, keepdim=True) # (N_rays, N_samples_)
        cdf = torch.cumsum(pdf, -1) # (N_rays, N_samples), cumulative distribution function
        cdf = torch.cat([torch.zeros_like(cdf[: ,:1]), cdf], -1)  # (N_rays, N_samples_+1)
                                                                   # padded to 0~1 inclusive

        if det:
            u = torch.linspace(0, 1, N_importance, device=bins.device)
            u = u.expand(N_rays, N_importance)
        else:
            u = torch.rand(N_rays, N_importance, device=bins.device)
        u = u.contiguous()

        inds = torch.searchsorted(cdf, u, right=True)
        below = torch.clamp_min(inds-1, 0)
        above = torch.clamp_max(inds, N_samples_)

        inds_sampled = torch.stack([below, above], -1).view(N_rays, 2*N_importance)
        cdf_g = torch.gather(cdf, 1, inds_sampled).view(N_rays, N_importance, 2)
        bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, N_importance, 2)

        denom = cdf_g[...,1]-cdf_g[...,0]
        denom[denom<eps] = 1 # denom equals 0 means a bin has weight 0, in which case it will not be sampled
                             # anyway, therefore any value for it is fine (set to 1 here)

        samples = bins_g[...,0] + (u-cdf_g[...,0])/denom * (bins_g[...,1]-bins_g[...,0])
        return samples