renderer.py

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# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# 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
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# 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 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 torch
import torch.nn as nn
import torch.nn.functional as F

from .ray_marcher import MipRayMarcher2

# Copied from .math_utils.transform_vectors
def transform_vectors(matrix: torch.Tensor, vectors4: torch.Tensor) -> torch.Tensor:
    """
    Left-multiplies MxM @ NxM. Returns NxM.
    """
    res = torch.matmul(vectors4, matrix.T)
    return res

# Copied from .math_utils.normalize_vecs
def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor:
    """
    Normalize vector lengths.
    """
    return vectors / (torch.norm(vectors, dim=-1, keepdim=True))

# Copied from .math_utils.torch_dot
def torch_dot(x: torch.Tensor, y: torch.Tensor):
    """
    Dot product of two tensors.
    """
    return (x * y).sum(-1)

# Copied from .math_utils.get_ray_limits_box
def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor, box_side_length):
    """
    Author: Petr Kellnhofer
    Intersects rays with the [-1, 1] NDC volume.
    Returns min and max distance of entry.
    Returns -1 for no intersection.
    https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection
    """
    o_shape = rays_o.shape
    rays_o = rays_o.detach().reshape(-1, 3)
    rays_d = rays_d.detach().reshape(-1, 3)


    bb_min = [-1*(box_side_length/2), -1*(box_side_length/2), -1*(box_side_length/2)]
    bb_max = [1*(box_side_length/2), 1*(box_side_length/2), 1*(box_side_length/2)]
    bounds = torch.tensor([bb_min, bb_max], dtype=rays_o.dtype, device=rays_o.device)
    is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device)

    # Precompute inverse for stability.
    invdir = 1 / rays_d
    sign = (invdir < 0).long()

    # Intersect with YZ plane.
    tmin = (bounds.index_select(0, sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]
    tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]

    # Intersect with XZ plane.
    tymin = (bounds.index_select(0, sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]
    tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]

    # Resolve parallel rays.
    is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False

    # Use the shortest intersection.
    tmin = torch.max(tmin, tymin)
    tmax = torch.min(tmax, tymax)

    # Intersect with XY plane.
    tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]
    tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]

    # Resolve parallel rays.
    is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False

    # Use the shortest intersection.
    tmin = torch.max(tmin, tzmin)
    tmax = torch.min(tmax, tzmax)

    # Mark invalid.
    tmin[torch.logical_not(is_valid)] = -1
    tmax[torch.logical_not(is_valid)] = -2

    return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1)

# Copied from .math_utils.linspace
def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):
    """
    Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
    Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
    """
    # create a tensor of 'num' steps from 0 to 1
    steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)

    # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
    # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
    #   "cannot statically infer the expected size of a list in this contex", hence the code below
    for i in range(start.ndim):
        steps = steps.unsqueeze(-1)

    # the output starts at 'start' and increments until 'stop' in each dimension
    out = start[None] + steps * (stop - start)[None]

    return out

# Copied from .math_utils.generate_planes
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.

    Bugfix reference: https://github.com/NVlabs/eg3d/issues/67
    """
    return torch.tensor([[[1, 0, 0],
                            [0, 1, 0],
                            [0, 0, 1]],
                            [[1, 0, 0],
                            [0, 0, 1],
                            [0, 1, 0]],
                            [[0, 0, 1],
                            [0, 1, 0],
                            [1, 0, 0]]], dtype=torch.float32)

# Copied from .math_utils.project_onto_planes
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)
    coordinates = coordinates.to(inv_planes.device)
    projections = torch.bmm(coordinates, inv_planes)
    return projections[..., :2]

# Copied from .math_utils.sample_from_planes
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.view(N*n_planes, C, H, W)

    coordinates = (2/box_warp) * coordinates # add specific box bounds
    # half added here
    projected_coordinates = project_onto_planes(plane_axes, coordinates).unsqueeze(1)
    # removed float from projected_coordinates
    output_features = torch.nn.functional.grid_sample(plane_features.float(), 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

# Copied from .math_utils.sample_from_3dgrid
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):
    """
    Modified original version to filter out-of-box samples as TensoRF does.
    
    Reference:
    TensoRF: https://github.com/apchenstu/TensoRF/blob/main/models/tensorBase.py#L277
    """
    def __init__(self):
        super().__init__()
        self.activation_factory = self._build_activation_factory()
        self.ray_marcher = MipRayMarcher2(self.activation_factory)
        self.plane_axes = generate_planes()

    def _build_activation_factory(self):
        def activation_factory(options: dict):
            if options['clamp_mode'] == 'softplus':
                return lambda x: F.softplus(x - 1)  # activation bias of -1 makes things initialize better
            else:
                assert False, "Renderer only supports `clamp_mode`=`softplus`!"
        return activation_factory

    def _forward_pass(self, depths: torch.Tensor, ray_directions: torch.Tensor, ray_origins: torch.Tensor,
                        planes: torch.Tensor, decoder: nn.Module, rendering_options: dict):
        """
        Additional filtering is applied to filter out-of-box samples.
        Modifications made by Zexin He.
        """

        # context related variables
        batch_size, num_rays, samples_per_ray, _ = depths.shape
        device = planes.device
        depths = depths.to(device)
        ray_directions = ray_directions.to(device)
        ray_origins = ray_origins.to(device)
        # define sample points with depths
        sample_directions = ray_directions.unsqueeze(-2).expand(-1, -1, samples_per_ray, -1).reshape(batch_size, -1, 3)
        sample_coordinates = (ray_origins.unsqueeze(-2) + depths * ray_directions.unsqueeze(-2)).reshape(batch_size, -1, 3)

        # filter out-of-box samples
        mask_inbox = \
            (rendering_options['sampler_bbox_min'] <= sample_coordinates) & \
                (sample_coordinates <= rendering_options['sampler_bbox_max'])
        mask_inbox = mask_inbox.all(-1)

        # forward model according to all samples
        _out = self.run_model(planes, decoder, sample_coordinates, sample_directions, rendering_options)

        # set out-of-box samples to zeros(rgb) & -inf(sigma)
        SAFE_GUARD = 3
        DATA_TYPE = _out['sigma'].dtype
        colors_pass = torch.zeros(batch_size, num_rays * samples_per_ray, 3, device=device, dtype=DATA_TYPE)
        densities_pass = torch.nan_to_num(torch.full((batch_size, num_rays * samples_per_ray, 1), -float('inf'), device=device, dtype=DATA_TYPE)) / SAFE_GUARD
        colors_pass[mask_inbox], densities_pass[mask_inbox] = _out['rgb'][mask_inbox], _out['sigma'][mask_inbox]

        # reshape back
        colors_pass = colors_pass.reshape(batch_size, num_rays, samples_per_ray, colors_pass.shape[-1])
        densities_pass = densities_pass.reshape(batch_size, num_rays, samples_per_ray, densities_pass.shape[-1])

        return colors_pass, densities_pass

    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 = get_ray_limits_box(ray_origins, ray_directions, box_side_length=rendering_options['box_warp'])
            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()
            depths_coarse = self.sample_stratified(ray_origins, ray_start, ray_end, rendering_options['depth_resolution'], rendering_options['disparity_space_sampling'])
        else:
            # Create stratified depth samples
            depths_coarse = self.sample_stratified(ray_origins, rendering_options['ray_start'], rendering_options['ray_end'], rendering_options['depth_resolution'], rendering_options['disparity_space_sampling'])
        
        depths_coarse =  depths_coarse.to(planes.device)

        # Coarse Pass
        colors_coarse, densities_coarse = self._forward_pass(
            depths=depths_coarse, ray_directions=ray_directions, ray_origins=ray_origins,
            planes=planes, decoder=decoder, rendering_options=rendering_options)

        # 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)

            colors_fine, densities_fine = self._forward_pass(
                depths=depths_fine, ray_directions=ray_directions, ray_origins=ray_origins,
                planes=planes, decoder=decoder, rendering_options=rendering_options)

            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)

    def run_model(self, planes, decoder, sample_coordinates, sample_directions, options):
        plane_axes = self.plane_axes.to(planes.device)
        sampled_features = sample_from_planes(plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=options['box_warp'])

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

    def run_model_activated(self, planes, decoder, sample_coordinates, sample_directions, options):
        out = self.run_model(planes, decoder, sample_coordinates, sample_directions, options)
        out['sigma'] = self.activation_factory(options)(out['sigma'])
        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 = 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
        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