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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, Dict, Tuple
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import random
from dva.mvp.extensions.mvpraymarch.mvpraymarch import mvpraymarch
from dva.mvp.extensions.utils.utils import compute_raydirs
import logging
logger = logging.getLogger(__name__)
def convert_camera_parameters(Rt, K):
R = Rt[:, :3, :3]
t = -R.permute(0, 2, 1).bmm(Rt[:, :3, 3].unsqueeze(2)).squeeze(2)
return dict(
campos=t,
camrot=R,
focal=K[:, :2, :2],
princpt=K[:, :2, 2],
)
def subsample_pixel_coords(
pixel_coords: th.Tensor, batch_size: int, ray_subsample_factor: int = 4
):
H, W = pixel_coords.shape[:2]
SW = W // ray_subsample_factor
SH = H // ray_subsample_factor
all_coords = []
for _ in range(batch_size):
# TODO: this is ugly, switch to pytorch?
x0 = th.randint(0, ray_subsample_factor - 1, size=())
y0 = th.randint(0, ray_subsample_factor - 1, size=())
dx = ray_subsample_factor
dy = ray_subsample_factor
x1 = x0 + dx * SW
y1 = y0 + dy * SH
all_coords.append(pixel_coords[y0:y1:dy, x0:x1:dx, :])
all_coords = th.stack(all_coords, dim=0)
return all_coords
def resize_pixel_coords(
pixel_coords: th.Tensor, batch_size: int, ray_subsample_factor: int = 4
):
H, W = pixel_coords.shape[:2]
SW = W // ray_subsample_factor
SH = H // ray_subsample_factor
all_coords = []
for _ in range(batch_size):
# TODO: this is ugly, switch to pytorch?
x0, y0 = ray_subsample_factor // 2, ray_subsample_factor // 2
dx = ray_subsample_factor
dy = ray_subsample_factor
x1 = x0 + dx * SW
y1 = y0 + dy * SH
all_coords.append(pixel_coords[y0:y1:dy, x0:x1:dx, :])
all_coords = th.stack(all_coords, dim=0)
return all_coords
class RayMarcher(nn.Module):
def __init__(
self,
image_height,
image_width,
volradius,
fadescale=8.0,
fadeexp=8.0,
dt=1.0,
ray_subsample_factor=1,
accum=2,
termthresh=0.99,
blocksize=None,
with_t_img=True,
chlast=False,
assets=None,
):
super().__init__()
# TODO: add config?
self.image_height = image_height
self.image_width = image_width
self.volradius = volradius
self.dt = dt
self.fadescale = fadescale
self.fadeexp = fadeexp
# NOTE: this seems to not work for other configs?
if blocksize is None:
blocksize = (8, 16)
self.blocksize = blocksize
self.with_t_img = with_t_img
self.chlast = chlast
self.accum = accum
self.termthresh = termthresh
base_pixel_coords = th.stack(
th.meshgrid(
th.arange(self.image_height, dtype=th.float32),
th.arange(self.image_width, dtype=th.float32),
)[::-1],
dim=-1,
)
self.register_buffer("base_pixel_coords", base_pixel_coords, persistent=False)
self.fixed_bvh_cache = {-1: (th.empty(0), th.empty(0), th.empty(0))}
self.ray_subsample_factor = ray_subsample_factor
def _set_pix_coords(self):
dev = self.base_pixel_coords.device
self.base_pixel_coords = th.stack(
th.meshgrid(
th.arange(self.image_height, dtype=th.float32, device=dev),
th.arange(self.image_width, dtype=th.float32, device=dev),
)[::-1],
dim=-1,
)
def resize(self, h: int, w: int):
self.image_height = h
self.image_width = w
self._set_pix_coords()
def forward(
self,
prim_rgba: th.Tensor,
prim_pos: th.Tensor,
prim_rot: th.Tensor,
prim_scale: th.Tensor,
K: th.Tensor,
RT: th.Tensor,
ray_subsample_factor: Optional[int] = None,
):
"""
Args:
prim_rgba: primitive payload [B, K, 4, S, S, S],
K - # of primitives, S - primitive size
prim_pos: locations [B, K, 3]
prim_rot: rotations [B, K, 3, 3]
prim_scale: scales [B, K, 3]
K: intrinsics [B, 3, 3]
RT: extrinsics [B, 3, 4]
Returns:
a dict of tensors
"""
# TODO: maybe we can re-use mvpraymarcher?
B = prim_rgba.shape[0]
device = prim_rgba.device
# TODO: this should return focal 2x2?
camera = convert_camera_parameters(RT, K)
camera = {k: v.contiguous() for k, v in camera.items()}
dt = self.dt / self.volradius
if ray_subsample_factor is None:
ray_subsample_factor = self.ray_subsample_factor
if ray_subsample_factor > 1 and self.training:
pixel_coords = subsample_pixel_coords(
self.base_pixel_coords, int(B), ray_subsample_factor
)
elif ray_subsample_factor > 1:
pixel_coords = resize_pixel_coords(
self.base_pixel_coords,
int(B),
ray_subsample_factor,
)
else:
pixel_coords = (
self.base_pixel_coords[np.newaxis].expand(B, -1, -1, -1).contiguous()
)
prim_pos = prim_pos / self.volradius
focal = th.diagonal(camera["focal"], dim1=1, dim2=2).contiguous()
# TODO: port this?
raypos, raydir, tminmax = compute_raydirs(
viewpos=camera["campos"],
viewrot=camera["camrot"],
focal=focal,
princpt=camera["princpt"],
pixelcoords=pixel_coords,
volradius=self.volradius,
)
rgba = mvpraymarch(
raypos,
raydir,
stepsize=dt,
tminmax=tminmax,
algo=0,
template=prim_rgba.permute(0, 1, 3, 4, 5, 2).contiguous(),
warp=None,
termthresh=self.termthresh,
primtransf=(prim_pos, prim_rot, prim_scale),
fadescale=self.fadescale,
fadeexp=self.fadeexp,
usebvh="fixedorder",
chlast=True,
)
rgba = rgba.permute(0, 3, 1, 2)
preds = {
"rgba_image": rgba,
"pixel_coords": pixel_coords,
}
return preds
def generate_colored_boxes(template, prim_rot, alpha=10000.0, seed=123456):
B = template.shape[0]
output = template.clone()
device = template.device
lightdir = -3 * th.ones([B, 3], dtype=th.float32, device=device)
lightdir = lightdir / th.norm(lightdir, p=2, dim=1, keepdim=True)
zz, yy, xx = th.meshgrid(
th.linspace(-1.0, 1.0, template.size(-1), device=device),
th.linspace(-1.0, 1.0, template.size(-1), device=device),
th.linspace(-1.0, 1.0, template.size(-1), device=device),
)
primnormalx = th.where(
(th.abs(xx) >= th.abs(yy)) & (th.abs(xx) >= th.abs(zz)),
th.sign(xx) * th.ones_like(xx),
th.zeros_like(xx),
)
primnormaly = th.where(
(th.abs(yy) >= th.abs(xx)) & (th.abs(yy) >= th.abs(zz)),
th.sign(yy) * th.ones_like(xx),
th.zeros_like(xx),
)
primnormalz = th.where(
(th.abs(zz) >= th.abs(xx)) & (th.abs(zz) >= th.abs(yy)),
th.sign(zz) * th.ones_like(xx),
th.zeros_like(xx),
)
primnormal = th.stack([primnormalx, -primnormaly, -primnormalz], dim=-1)
primnormal = primnormal / th.sqrt(th.sum(primnormal**2, dim=-1, keepdim=True))
output[:, :, 3, :, :, :] = alpha
np.random.seed(seed)
for i in range(template.size(1)):
# generating a random color
output[:, i, 0, :, :, :] = np.random.rand() * 255.0
output[:, i, 1, :, :, :] = np.random.rand() * 255.0
output[:, i, 2, :, :, :] = np.random.rand() * 255.0
# get light direction in local coordinate system?
lightdir0 = lightdir
mult = th.sum(
lightdir0[:, None, None, None, :] * primnormal[np.newaxis], dim=-1
)[:, np.newaxis, :, :, :].clamp(min=0.2)
output[:, i, :3, :, :, :] *= 1.4 * mult
return output
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