398 lines
15 KiB
Python
398 lines
15 KiB
Python
import numpy as np
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import time
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import torch
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import torch.nn as nn
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from torch.autograd import Function
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from torch.cuda.amp import custom_bwd, custom_fwd
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# lazy building:
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# `import raymarching` will not immediately build the extension, only if you actually call any functions.
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BACKEND = None
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def get_backend():
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global BACKEND
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if BACKEND is None:
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try:
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import _raymarching as _backend
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except ImportError:
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from .backend import _backend
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BACKEND = _backend
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return BACKEND
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# ----------------------------------------
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# utils
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# ----------------------------------------
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class _near_far_from_aabb(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, rays_o, rays_d, aabb, min_near=0.2):
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''' near_far_from_aabb, CUDA implementation
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Calculate rays' intersection time (near and far) with aabb
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Args:
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rays_o: float, [N, 3]
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rays_d: float, [N, 3]
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aabb: float, [6], (xmin, ymin, zmin, xmax, ymax, zmax)
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min_near: float, scalar
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Returns:
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nears: float, [N]
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fars: float, [N]
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'''
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if not rays_o.is_cuda: rays_o = rays_o.cuda()
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if not rays_d.is_cuda: rays_d = rays_d.cuda()
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rays_o = rays_o.contiguous().view(-1, 3)
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rays_d = rays_d.contiguous().view(-1, 3)
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N = rays_o.shape[0] # num rays
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nears = torch.empty(N, dtype=rays_o.dtype, device=rays_o.device)
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fars = torch.empty(N, dtype=rays_o.dtype, device=rays_o.device)
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get_backend().near_far_from_aabb(rays_o, rays_d, aabb, N, min_near, nears, fars)
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return nears, fars
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near_far_from_aabb = _near_far_from_aabb.apply
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class _sph_from_ray(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, rays_o, rays_d, radius):
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''' sph_from_ray, CUDA implementation
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get spherical coordinate on the background sphere from rays.
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Assume rays_o are inside the Sphere(radius).
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Args:
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rays_o: [N, 3]
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rays_d: [N, 3]
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radius: scalar, float
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Return:
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coords: [N, 2], in [-1, 1], theta and phi on a sphere. (further-surface)
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'''
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if not rays_o.is_cuda: rays_o = rays_o.cuda()
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if not rays_d.is_cuda: rays_d = rays_d.cuda()
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rays_o = rays_o.contiguous().view(-1, 3)
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rays_d = rays_d.contiguous().view(-1, 3)
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N = rays_o.shape[0] # num rays
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coords = torch.empty(N, 2, dtype=rays_o.dtype, device=rays_o.device)
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get_backend().sph_from_ray(rays_o, rays_d, radius, N, coords)
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return coords
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sph_from_ray = _sph_from_ray.apply
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class _morton3D(Function):
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@staticmethod
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def forward(ctx, coords):
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''' morton3D, CUDA implementation
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Args:
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coords: [N, 3], int32, in [0, 128) (for some reason there is no uint32 tensor in torch...)
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TODO: check if the coord range is valid! (current 128 is safe)
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Returns:
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indices: [N], int32, in [0, 128^3)
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'''
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if not coords.is_cuda: coords = coords.cuda()
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N = coords.shape[0]
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indices = torch.empty(N, dtype=torch.int32, device=coords.device)
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get_backend().morton3D(coords.int(), N, indices)
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return indices
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morton3D = _morton3D.apply
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class _morton3D_invert(Function):
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@staticmethod
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def forward(ctx, indices):
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''' morton3D_invert, CUDA implementation
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Args:
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indices: [N], int32, in [0, 128^3)
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Returns:
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coords: [N, 3], int32, in [0, 128)
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'''
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if not indices.is_cuda: indices = indices.cuda()
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N = indices.shape[0]
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coords = torch.empty(N, 3, dtype=torch.int32, device=indices.device)
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get_backend().morton3D_invert(indices.int(), N, coords)
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return coords
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morton3D_invert = _morton3D_invert.apply
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class _packbits(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, grid, thresh, bitfield=None):
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''' packbits, CUDA implementation
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Pack up the density grid into a bit field to accelerate ray marching.
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Args:
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grid: float, [C, H * H * H], assume H % 2 == 0
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thresh: float, threshold
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Returns:
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bitfield: uint8, [C, H * H * H / 8]
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'''
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if not grid.is_cuda: grid = grid.cuda()
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grid = grid.contiguous()
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C = grid.shape[0]
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H3 = grid.shape[1]
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N = C * H3 // 8
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if bitfield is None:
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bitfield = torch.empty(N, dtype=torch.uint8, device=grid.device)
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get_backend().packbits(grid, N, thresh, bitfield)
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return bitfield
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packbits = _packbits.apply
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class _flatten_rays(Function):
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@staticmethod
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def forward(ctx, rays, M):
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''' flatten rays
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Args:
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rays: [N, 2], all rays' (point_offset, point_count),
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M: scalar, int, count of points (we cannot get this info from rays unfortunately...)
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Returns:
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res: [M], flattened ray index.
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'''
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if not rays.is_cuda: rays = rays.cuda()
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rays = rays.contiguous()
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N = rays.shape[0]
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res = torch.zeros(M, dtype=torch.int, device=rays.device)
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get_backend().flatten_rays(rays, N, M, res)
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return res
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flatten_rays = _flatten_rays.apply
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# ----------------------------------------
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# train functions
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# ----------------------------------------
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class _march_rays_train(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, rays_o, rays_d, bound, density_bitfield, C, H, nears, fars, perturb=False, dt_gamma=0, max_steps=1024, contract=False):
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''' march rays to generate points (forward only)
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Args:
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rays_o/d: float, [N, 3]
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bound: float, scalar
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density_bitfield: uint8: [CHHH // 8]
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C: int
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H: int
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nears/fars: float, [N]
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step_counter: int32, (2), used to count the actual number of generated points.
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mean_count: int32, estimated mean steps to accelerate training. (but will randomly drop rays if the actual point count exceeded this threshold.)
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perturb: bool
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align: int, pad output so its size is dividable by align, set to -1 to disable.
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force_all_rays: bool, ignore step_counter and mean_count, always calculate all rays. Useful if rendering the whole image, instead of some rays.
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dt_gamma: float, called cone_angle in instant-ngp, exponentially accelerate ray marching if > 0. (very significant effect, but generally lead to worse performance)
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max_steps: int, max number of sampled points along each ray, also affect min_stepsize.
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Returns:
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xyzs: float, [M, 3], all generated points' coords. (all rays concated, need to use `rays` to extract points belonging to each ray)
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dirs: float, [M, 3], all generated points' view dirs.
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ts: float, [M, 2], all generated points' ts.
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rays: int32, [N, 2], all rays' (point_offset, point_count), e.g., xyzs[rays[i, 0]:(rays[i, 0] + rays[i, 1])] --> points belonging to rays[i, 0]
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'''
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if not rays_o.is_cuda: rays_o = rays_o.cuda()
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if not rays_d.is_cuda: rays_d = rays_d.cuda()
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if not density_bitfield.is_cuda: density_bitfield = density_bitfield.cuda()
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rays_o = rays_o.float().contiguous().view(-1, 3)
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rays_d = rays_d.float().contiguous().view(-1, 3)
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density_bitfield = density_bitfield.contiguous()
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N = rays_o.shape[0] # num rays
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step_counter = torch.zeros(1, dtype=torch.int32, device=rays_o.device) # point counter, ray counter
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if perturb:
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noises = torch.rand(N, dtype=rays_o.dtype, device=rays_o.device)
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else:
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noises = torch.zeros(N, dtype=rays_o.dtype, device=rays_o.device)
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# first pass: write rays, get total number of points M to render
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rays = torch.empty(N, 2, dtype=torch.int32, device=rays_o.device) # id, offset, num_steps
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get_backend().march_rays_train(rays_o, rays_d, density_bitfield, bound, contract, dt_gamma, max_steps, N, C, H, nears, fars, None, None, None, rays, step_counter, noises)
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# allocate based on M
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M = step_counter.item()
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# print(M, N)
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# print(rays[:, 0].max())
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xyzs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device)
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dirs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device)
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ts = torch.zeros(M, 2, dtype=rays_o.dtype, device=rays_o.device)
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# second pass: write outputs
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get_backend().march_rays_train(rays_o, rays_d, density_bitfield, bound, contract, dt_gamma, max_steps, N, C, H, nears, fars, xyzs, dirs, ts, rays, step_counter, noises)
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return xyzs, dirs, ts, rays
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march_rays_train = _march_rays_train.apply
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class _composite_rays_train(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, sigmas, rgbs, ts, rays, T_thresh=1e-4, binarize=False):
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''' composite rays' rgbs, according to the ray marching formula.
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Args:
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rgbs: float, [M, 3]
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sigmas: float, [M,]
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ts: float, [M, 2]
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rays: int32, [N, 3]
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Returns:
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weights: float, [M]
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weights_sum: float, [N,], the alpha channel
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depth: float, [N, ], the Depth
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image: float, [N, 3], the RGB channel (after multiplying alpha!)
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'''
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sigmas = sigmas.float().contiguous()
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rgbs = rgbs.float().contiguous()
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M = sigmas.shape[0]
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N = rays.shape[0]
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weights = torch.zeros(M, dtype=sigmas.dtype, device=sigmas.device) # may leave unmodified, so init with 0
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weights_sum = torch.empty(N, dtype=sigmas.dtype, device=sigmas.device)
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depth = torch.empty(N, dtype=sigmas.dtype, device=sigmas.device)
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image = torch.empty(N, 3, dtype=sigmas.dtype, device=sigmas.device)
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get_backend().composite_rays_train_forward(sigmas, rgbs, ts, rays, M, N, T_thresh, binarize, weights, weights_sum, depth, image)
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ctx.save_for_backward(sigmas, rgbs, ts, rays, weights_sum, depth, image)
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ctx.dims = [M, N, T_thresh, binarize]
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return weights, weights_sum, depth, image
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_weights, grad_weights_sum, grad_depth, grad_image):
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grad_weights = grad_weights.contiguous()
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grad_weights_sum = grad_weights_sum.contiguous()
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grad_depth = grad_depth.contiguous()
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grad_image = grad_image.contiguous()
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sigmas, rgbs, ts, rays, weights_sum, depth, image = ctx.saved_tensors
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M, N, T_thresh, binarize = ctx.dims
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grad_sigmas = torch.zeros_like(sigmas)
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grad_rgbs = torch.zeros_like(rgbs)
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get_backend().composite_rays_train_backward(grad_weights, grad_weights_sum, grad_depth, grad_image, sigmas, rgbs, ts, rays, weights_sum, depth, image, M, N, T_thresh, binarize, grad_sigmas, grad_rgbs)
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return grad_sigmas, grad_rgbs, None, None, None, None
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composite_rays_train = _composite_rays_train.apply
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# ----------------------------------------
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# infer functions
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# ----------------------------------------
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class _march_rays(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, bound, density_bitfield, C, H, near, far, perturb=False, dt_gamma=0, max_steps=1024, contract=False):
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''' march rays to generate points (forward only, for inference)
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Args:
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n_alive: int, number of alive rays
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n_step: int, how many steps we march
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rays_alive: int, [N], the alive rays' IDs in N (N >= n_alive, but we only use first n_alive)
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rays_t: float, [N], the alive rays' time, we only use the first n_alive.
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rays_o/d: float, [N, 3]
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bound: float, scalar
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density_bitfield: uint8: [CHHH // 8]
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C: int
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H: int
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nears/fars: float, [N]
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align: int, pad output so its size is dividable by align, set to -1 to disable.
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perturb: bool/int, int > 0 is used as the random seed.
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dt_gamma: float, called cone_angle in instant-ngp, exponentially accelerate ray marching if > 0. (very significant effect, but generally lead to worse performance)
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max_steps: int, max number of sampled points along each ray, also affect min_stepsize.
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Returns:
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xyzs: float, [n_alive * n_step, 3], all generated points' coords
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dirs: float, [n_alive * n_step, 3], all generated points' view dirs.
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ts: float, [n_alive * n_step, 2], all generated points' ts
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'''
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if not rays_o.is_cuda: rays_o = rays_o.cuda()
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if not rays_d.is_cuda: rays_d = rays_d.cuda()
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rays_o = rays_o.float().contiguous().view(-1, 3)
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rays_d = rays_d.float().contiguous().view(-1, 3)
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M = n_alive * n_step
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xyzs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device)
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dirs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device)
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ts = torch.zeros(M, 2, dtype=rays_o.dtype, device=rays_o.device) # 2 vals, one for rgb, one for depth
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if perturb:
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# torch.manual_seed(perturb) # test_gui uses spp index as seed
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noises = torch.rand(n_alive, dtype=rays_o.dtype, device=rays_o.device)
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else:
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noises = torch.zeros(n_alive, dtype=rays_o.dtype, device=rays_o.device)
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get_backend().march_rays(n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, bound, contract, dt_gamma, max_steps, C, H, density_bitfield, near, far, xyzs, dirs, ts, noises)
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return xyzs, dirs, ts
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march_rays = _march_rays.apply
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class _composite_rays(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32) # need to cast sigmas & rgbs to float
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def forward(ctx, n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh=1e-2, binarize=False):
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''' composite rays' rgbs, according to the ray marching formula. (for inference)
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Args:
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n_alive: int, number of alive rays
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n_step: int, how many steps we march
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rays_alive: int, [n_alive], the alive rays' IDs in N (N >= n_alive)
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rays_t: float, [N], the alive rays' time
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sigmas: float, [n_alive * n_step,]
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rgbs: float, [n_alive * n_step, 3]
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ts: float, [n_alive * n_step, 2]
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In-place Outputs:
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weights_sum: float, [N,], the alpha channel
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depth: float, [N,], the depth value
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image: float, [N, 3], the RGB channel (after multiplying alpha!)
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'''
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sigmas = sigmas.float().contiguous()
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rgbs = rgbs.float().contiguous()
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get_backend().composite_rays(n_alive, n_step, T_thresh, binarize, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image)
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return tuple()
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composite_rays = _composite_rays.apply |