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taichi_ngp.py
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import os
import time
import cv2
import wget
import platform
import argparse
import numpy as np
from typing import Tuple
from matplotlib import pyplot as plt
import taichi as ti
from taichi.math import uvec3, vec3, vec2
from camera import OrbitCamera
def depth2img(depth):
depth = (depth-depth.min())/(depth.max()-depth.min())
depth_img = cv2.applyColorMap((depth*255).astype(np.uint8),
cv2.COLORMAP_TURBO)
return depth_img
arch = ti.cuda if ti._lib.core.with_cuda() else ti.vulkan
if platform.system() == 'Darwin':
block_dim = 64
else:
block_dim = 128
data_type = ti.f16
np_type = np.float16
tf_vec3 = ti.types.vector(3, dtype=data_type)
tf_vec8 = ti.types.vector(8, dtype=data_type)
tf_vec16 = ti.types.vector(16, dtype=data_type)
tf_vec32 = ti.types.vector(32, dtype=data_type)
tf_vec1 = ti.types.vector(1, dtype=data_type)
tf_vec2 = ti.types.vector(2, dtype=data_type)
tf_mat1x3 = ti.types.matrix(1, 3, dtype=data_type)
tf_index_temp = ti.types.vector(8, dtype=ti.i32)
MAX_SAMPLES = 1024
NEAR_DISTANCE = 0.01
SQRT3 = 1.7320508075688772
SQRT3_MAX_SAMPLES = SQRT3/1024
SQRT3_2 = 1.7320508075688772*2
PRETRAINED_MODEL_URL = 'https://github.com/Linyou/taichi-ngp-renderer/releases/download/v0.2/{}.npy'
#<----------------- hash table util code ----------------->
@ti.func
def calc_dt(t, exp_step_factor, grid_size, scale):
return data_type(ti.math.clamp(t*exp_step_factor, SQRT3_MAX_SAMPLES, SQRT3_2*scale/grid_size))
@ti.func
def __expand_bits(v):
v = (v * ti.uint32(0x00010001)) & ti.uint32(0xFF0000FF)
v = (v * ti.uint32(0x00000101)) & ti.uint32(0x0F00F00F)
v = (v * ti.uint32(0x00000011)) & ti.uint32(0xC30C30C3)
v = (v * ti.uint32(0x00000005)) & ti.uint32(0x49249249)
return v
@ti.func
def __morton3D(xyz):
xyz = __expand_bits(xyz)
return xyz[0] | (xyz[1] << 1) | (xyz[2] << 2)
@ti.func
def fast_hash(pos_grid_local):
result = ti.uint32(0)
primes = uvec3(ti.uint32(1), ti.uint32(2654435761), ti.uint32(805459861))
for i in ti.static(range(3)):
result ^= ti.uint32(pos_grid_local[i]) * primes[i]
return result
@ti.func
def under_hash(pos_grid_local, resolution):
result = ti.uint32(0)
stride = ti.uint32(1)
for i in ti.static(range(3)):
result += ti.uint32(pos_grid_local[i] * stride)
stride *= resolution
return result
@ti.func
def grid_pos2hash_index(indicator, pos_grid_local, resolution, map_size):
hash_result = ti.uint32(0)
if indicator == 1:
hash_result = under_hash(pos_grid_local, resolution)
else:
hash_result = fast_hash(pos_grid_local)
return hash_result % map_size
@ti.func
def frexp_bit(x):
exponent = 0
if x != 0.0:
# frac = ti.abs(x)
bits = ti.bit_cast(x, ti.u32)
exponent = ti.i32((bits & ti.u32(0x7f800000)) >> 23) - 127
# exponent = (ti.i32(bits & ti.u32(0x7f800000)) >> 23) - 127
bits &= ti.u32(0x7fffff)
bits |= ti.u32(0x3f800000)
frac = ti.bit_cast(bits, ti.f32)
if frac < 0.5:
exponent -= 1
elif frac > 1.0:
exponent += 1
return exponent
@ti.func
def mip_from_pos(xyz, cascades):
mx = ti.abs(xyz).max()
# _, exponent = _frexp(mx)
exponent = frexp_bit(ti.f32(mx))
# frac, exponent = ti.frexp(ti.f32(mx))
return ti.min(cascades-1, ti.max(0, exponent+1))
@ti.func
def mip_from_dt(dt, grid_size, cascades):
# _, exponent = _frexp(dt*grid_size)
exponent = frexp_bit(ti.f32(dt*grid_size))
# frac, exponent = ti.frexp(ti.f32(dt*grid_size))
return ti.min(cascades-1, ti.max(0, exponent))
#<----------------- hash table util code ----------------->
@ti.func
def random_in_unit_disk():
theta = 2.0 * np.pi * ti.random()
return ti.Vector([ti.sin(theta), ti.cos(theta)])
@ti.func
def random_normal():
x = ti.random() * 2. - 1.
y = ti.random() * 2. - 1.
return tf_vec2(x, y)
@ti.func
def dir_encode_func(dir_):
input = tf_vec32(0.0)
dir = dir_/dir_.norm()
x = dir[0]; y = dir[1]; z = dir[2]
xy= x*y; xz= x*z; yz= y*z; x2= x*x; y2= y*y; z2= z*z
temp = 0.28209479177387814
input[0] = data_type(temp)
input[1] = data_type(-0.48860251190291987*y)
input[2] = data_type(0.48860251190291987*z)
input[3] = data_type(-0.48860251190291987*x)
input[4] = data_type(1.0925484305920792*xy)
input[5] = data_type(-1.0925484305920792*yz)
input[6] = data_type(0.94617469575755997*z2 - 0.31539156525251999)
input[7] = data_type(-1.0925484305920792*xz)
input[8] = data_type(0.54627421529603959*x2 - 0.54627421529603959*y2)
input[9] = data_type(0.59004358992664352*y*(-3.0*x2 + y2))
input[10] = data_type(2.8906114426405538*xy*z)
input[11] = data_type(0.45704579946446572*y*(1.0 - 5.0*z2))
input[12] = data_type(0.3731763325901154*z*(5.0*z2 - 3.0))
input[13] = data_type(0.45704579946446572*x*(1.0 - 5.0*z2))
input[14] = data_type(1.4453057213202769*z*(x2 - y2))
input[15] = data_type(0.59004358992664352*x*(-x2 + 3.0*y2))
return input
@ti.data_oriented
class NGP_fw:
def __init__(self, grid_size, base_res, log2_T, res, level, exp_step_factor):
self.res = res
self.N_rays = res[0] * res[1]
self.grid_size = grid_size
self.grid_size3 = grid_size * grid_size * grid_size
# self.exp_step_factor = exp_step_factor
# self.scale = scale
# rays intersection parameters
# t1, t2 need to be initialized to -1.0
self.hits_t = ti.Vector.field(n=2, dtype=data_type, shape=(self.N_rays))
self.hits_t.fill(-1.0)
# self.center = tf_vec3(0.0, 0.0, 0.0)
self.center = ti.Vector.field(n=3, dtype=ti.f32, shape=())
# self.xyz_min = -tf_vec3(scale, scale, scale)
# self.xyz_max = tf_vec3(scale, scale, scale)
# self.half_size = (self.xyz_max - self.xyz_min) / 2
self.noise_buffer = ti.Vector.field(2, dtype=data_type, shape=(self.N_rays))
self.gen_noise_buffer()
self.rays_o = ti.Vector.field(n=3, dtype=data_type, shape=(self.N_rays))
self.rays_d = ti.Vector.field(n=3, dtype=data_type, shape=(self.N_rays))
# count the number of rays that still alive
self.counter = ti.field(ti.i32, shape=())
self.counter[None] = self.N_rays
# current alive buffer index
self.current_index = ti.field(ti.i32, shape=())
self.current_index[None] = 0
# how many samples that need to run the model
self.model_launch = ti.field(ti.i32, shape=())
# buffer for the alive rays
self.alive_indices = ti.field(ti.i32, shape=(2*self.N_rays,))
# padd the thread to the factor of block size (thread per block)
self.padd_block_network = ti.field(ti.i32, shape=())
self.padd_block_composite = ti.field(ti.i32, shape=())
# hash table variables
# self.min_samples = 1 if exp_step_factor==0 else 4
# self.per_level_scales = 1.3195079565048218 # hard coded, otherwise it will be have lower percision
self.base_res = base_res
self.max_params = 2**log2_T
self.level = level
# hash table fields
# self.offsets = ti.field(ti.i32, shape=(16,))
# self.hash_map_sizes = ti.field(ti.uint32, shape=(16,))
# self.hash_map_indicator = ti.field(ti.i32, shape=(16,))
self.offsets = ti.types.vector(16, dtype=ti.i32)(0)
self.hash_map_sizes = ti.types.vector(16, dtype=ti.uint32)(0)
self.hash_map_indicator = ti.types.vector(16, dtype=ti.i32)(0)
# buffers that used for points sampling
self.max_samples_per_rays = 1 if exp_step_factor==0. else 4
self.max_samples_shape = self.N_rays * self.max_samples_per_rays
self.xyzs = ti.Vector.field(3, dtype=data_type, shape=(self.max_samples_shape,))
self.dirs = ti.Vector.field(3, dtype=data_type, shape=(self.max_samples_shape,))
self.deltas = ti.field(data_type, shape=(self.max_samples_shape,))
self.ts = ti.field(data_type, shape=(self.max_samples_shape,))
# buffers that store the info of sampled points
self.run_model_ind = ti.field(ti.int32, shape=(self.max_samples_shape,))
self.N_eff_samples = ti.field(ti.int32, shape=(self.N_rays,))
# intermediate buffers for network
self.out_3 = ti.field(data_type, shape=(self.max_samples_shape, 3))
self.out_1 = ti.field(data_type, shape=(self.max_samples_shape,))
self.temp_hit = ti.field(ti.i32, shape=(self.max_samples_shape,))
# results buffers
self.opacity = ti.field(ti.f32, shape=(self.N_rays,))
self.depth = ti.field(ti.f32, shape=(self.N_rays))
self.rgb = ti.Vector.field(3, dtype=ti.f32, shape=(self.N_rays,))
# GUI render buffer (data type must be float32)
self.render_buffer = ti.Vector.field(3, dtype=ti.f32, shape=(res[1], res[0],))
# camera parameters
self.lookat = np.array([0.0, 0.0, -1.0])
self.lookat_change = np.zeros((3,))
self.lookup = np.array([0.0, -1.0, 0.0])
def hash_table_init(self):
print('----GridEncoding----')
print(f'base_resolution: {self.base_res}')
print(f'log_scale:{self.per_level_scales:.5f}')
print(f'feature_per_level: {self.numof_hash_feet} logT: {self.max_params}')
print(f'level: {self.level}')
offset = 0
for i in range(self.level):
resolution = int(np.ceil(self.base_res * np.exp(i*np.log(self.per_level_scales)) - 1.0)) + 1
params_in_level = resolution ** 3
params_in_level = int(resolution ** 3) if params_in_level % 8 == 0 else int((params_in_level + 8 - 1) / 8) * 8
params_in_level = min(self.max_params, params_in_level)
self.offsets[i] = offset
self.hash_map_sizes[i] = params_in_level
self.hash_map_indicator[i] = 1 if resolution ** 3 <= params_in_level else 0
offset += params_in_level
offset *= self.numof_hash_feet
error_mesg = f"hash shape don't match offset: {offset}, hash shape: {self.hash_embedding.shape[0]}, final res: {resolution}"
assert offset == self.hash_embedding.shape[0], error_mesg
def get_direction(self):
w, h = int(self.res[1]), int(self.res[0])
fx = self.K[None][0, 0]
fy = self.K[None][1, 1]
cx, cy = 0.5*w, 0.5*h
x, y = np.meshgrid(
np.arange(w, dtype=np.float32)+ 0.5,
np.arange(h, dtype=np.float32)+ 0.5,
indexing='xy'
)
directions = np.stack([(x-cx)/fx, (y-cy)/fy, np.ones_like(x)], -1)
return directions.reshape(-1, 3)
@ti.kernel
def init_direction(self, w: ti.i32, h: ti.i32):
for i, j in ti.ndrange(w, h):
ori_w = 2*self.K[None][0, 2]
# ori_h = self.K[None][1, 2] * 2,
scale = w / ori_w
# scale_h = h / ori_h
fx = self.cam_fov_scale[None]*self.K[None][0, 0]*scale
fy = self.cam_fov_scale[None]*self.K[None][1, 1]*scale
cx, cy = 0.5*w, 0.5*h
uni_dir = ti.Matrix([[(i+0.5-cx)/fx, (j+0.5-cy)/fy, 1.]], dt=data_type)
self.directions[j*w+i] = uni_dir
def load_model(self, model_path):
print('Loading model from {}'.format(model_path))
model = np.load(model_path, allow_pickle=True).item()
np_hash = model['model.hash_encoder.params'].astype(np_type)
np_sigma = model['model.xyz_encoder.params'].astype(np_type)
np_rgb = model['model.rgb_net.params'].astype(np_type)
np_bitfield = model['model.density_bitfield']
self.rgb_depth = model['model.rgb_depth']
self.cascades = model['model.cascade']
self.numof_hash_feet = 2
self.scale = float(model['model.box_scale'])
self.xyz_min = -tf_vec3(self.scale)
self.xyz_max = tf_vec3(self.scale)
self.xyz_delta = self.xyz_max - self.xyz_min
# self.half_size = self.xyz_delta / 2
self.half_size = ti.Vector.field(3, dtype=ti.f32, shape=())
self.half_size[None] = self.xyz_delta / 2
self.exp_step_factor = 1/256 if self.scale > 0.5 else 0.
self.min_samples = 1 if self.exp_step_factor==0. else 4
self.per_level_scales = model['model.per_level_scale']
self.net_width = model['model.n_neurons']
self.sigma_n_input = model['model.sigma_n_input']
# self.sigma_n_input = 16
self.sigma_n_output = model['model.sigma_n_output']
self.rgb_n_input = model['model.rgb_n_input']
self.rgb_n_output = model['model.rgb_n_output']
# sigma_sm_per_weight = int(self.layer1_base / 128)
# rgb_sm_per_weight = int(self.layer2_base / 128)
self.sigma_model_size = np_sigma.shape[0]
self.rgb_model_size = np_rgb.shape[0]
self.sigma_sm_preload = int(self.sigma_model_size/block_dim)
self.rgb_sm_preload = int(self.rgb_model_size/block_dim)
self.sigma_layer1_base = self.net_width*self.sigma_n_input
self.rgb_layer1_base = self.net_width*self.rgb_n_input
self.rgb_layer2_base = self.rgb_layer1_base+self.net_width*self.net_width
self.hash_embedding= ti.field(dtype=data_type, shape=(np_hash.shape[0],))
self.sigma_weights= ti.field(dtype=data_type, shape=(self.sigma_model_size,))
self.rgb_weights= ti.field(dtype=data_type, shape=(self.rgb_model_size,))
# density_bitfield is used for point sampling
self.density_bitfield = ti.field(ti.uint8, shape=(self.cascades*(self.grid_size**3)//8))
self.hash_embedding.from_numpy(np_hash)
self.sigma_weights.from_numpy(np_sigma)
self.rgb_weights.from_numpy(np_rgb)
self.density_bitfield.from_numpy(np_bitfield)
self.xyzs_embedding = ti.field(data_type, shape=(self.max_samples_shape, self.sigma_n_input))
self.final_embedding = ti.field(data_type, shape=(self.max_samples_shape, self.sigma_n_output))
# use the pre-compute direction and scene pose
self.directions = ti.Matrix.field(n=1, m=3, dtype=data_type, shape=(self.N_rays,))
self.pose = ti.Matrix.field(n=4, m=4, dtype=data_type, shape=())
self.K = ti.Matrix.field(n=3, m=3, dtype=data_type, shape=())
self.cam_fov_scale = ti.field(dtype=data_type, shape=())
self.cam_fov_scale[None] = 1.0
self.K.from_numpy(model['K'].astype(np_type))
self.default_rot = model['poses'][20].astype(np_type)[:3, :3]
self.cam = OrbitCamera(self.default_rot, r=1.5)
# self.pose.from_numpy(model['poses'][20].astype(np_type))
# if self.res[0] != 800 or self.res[1] != 800:
# directions = self.get_direction(model['camera_angle_x'])[:, None, :].astype(np_type)
# else:
# directions = model['directions'][:, None, :].astype(np_type)
# self.directions.from_numpy(directions)
self.init_direction(self.res[1], self.res[0])
print('----model loaded----')
print('K: ')
print(self.K)
print('pose: ')
print(self.pose)
print("half size: ", self.half_size[None])
print('rgb_depth: ', self.rgb_depth)
print('cascades: ', self.cascades)
print('scale: ', self.scale)
print('per_level_scales: ', self.per_level_scales)
print('net_width: ', self.net_width)
print('sigma_n_input: ', self.sigma_n_input)
print('sigma_n_output: ', self.sigma_n_output)
print('rgb_n_input: ', self.rgb_n_input)
print('rgb_n_output: ', self.rgb_n_output)
print('sigma_model_size: ', self.sigma_model_size)
print('rgb_model_size: ', self.rgb_model_size)
print('sigma_sm_preload: ', self.sigma_sm_preload)
print('rgb_sm_preload: ', self.rgb_sm_preload)
@staticmethod
def taichi_init(kernel_profiler):
ti.init(
arch=arch,
offline_cache=True,
kernel_profiler=kernel_profiler,
enable_fallback=False,
)
@staticmethod
def taichi_print_profiler():
ti.profiler.print_kernel_profiler_info()
@ti.kernel
def reset(self):
self.depth.fill(0.0)
self.opacity.fill(0.0)
self.counter[None] = self.N_rays
for i, j in ti.ndrange(self.N_rays, 2):
self.alive_indices[i*2+j] = i
@ti.func
def _ray_aabb_intersec(self, ray_o, ray_d):
inv_d = 1.0 / ray_d
# half_size = tf_vec3(self.half_size[None])
t_min = (self.center[None]-self.half_size[None]-ray_o)*inv_d
t_max = (self.center[None]+self.half_size[None]-ray_o)*inv_d
_t1 = ti.min(t_min, t_max)
_t2 = ti.max(t_min, t_max)
t1 = _t1.max()
t2 = _t2.min()
return tf_vec2(t1, t2)
@ti.kernel
def gen_noise_buffer(self):
for i in range(self.N_rays):
self.noise_buffer[i] = random_normal()
# self.noise_buffer[i] = random_in_unit_disk()
@ti.kernel
def ray_intersect_dof(self, dist_to_focus: float, len_dis: float):
ti.block_local(self.pose)
for i in self.directions:
c2w = self.pose[None]
dir_ori = self.directions[i]
offset = len_dis*self.noise_buffer[i]
offset_m = tf_mat1x3(
[[
offset[0],
offset[1],
0.0,
]]
)
c2w_dir = c2w[:3, :3].transpose()
offset_w = offset_m @ c2w_dir
mat_result = (dir_ori*dist_to_focus) @ c2w_dir - offset_w
ray_d = tf_vec3(mat_result[0, 0], mat_result[0, 1],mat_result[0, 2])
ray_o = c2w[:3, 3] + tf_vec3(offset_w[0, 0], offset_w[0, 1],offset_w[0, 2])
t1t2 = self._ray_aabb_intersec(ray_o, ray_d)
if t1t2[1] > 0.0:
self.hits_t[i][0] = data_type(ti.max(t1t2[0], NEAR_DISTANCE))
self.hits_t[i][1] = t1t2[1]
self.rays_o[i] = ray_o
self.rays_d[i] = ray_d
@ti.kernel
def ray_intersect(self):
ti.block_local(self.pose)
for i in self.directions:
c2w = self.pose[None]
mat_result = self.directions[i] @ c2w[:3, :3].transpose()
ray_d = tf_vec3(mat_result[0, 0], mat_result[0, 1],mat_result[0, 2])
ray_o = c2w[:3, 3]
t1t2 = self._ray_aabb_intersec(ray_o, ray_d)
if t1t2[1] > 0.0:
self.hits_t[i][0] = data_type(ti.max(t1t2[0], NEAR_DISTANCE))
self.hits_t[i][1] = t1t2[1]
self.rays_o[i] = ray_o
self.rays_d[i] = ray_d
@ti.kernel
def raymarching_test_kernel(self, N_samples: int):
self.run_model_ind.fill(0)
for n in ti.ndrange(self.counter[None]):
c_index = self.current_index[None]
r = self.alive_indices[n*2+c_index]
grid_size_inv = 1.0/self.grid_size
ray_o = self.rays_o[r]
ray_d = self.rays_d[r]
t1t2 = self.hits_t[r]
d_inv = 1.0/ray_d
t = t1t2[0]
t2 = t1t2[1]
s = 0
start_idx = n * N_samples
while (t<t2) & (s<N_samples):
# xyz = ray_o + t*ray_d
xyz = ray_o + t*ray_d
dt = calc_dt(t, self.exp_step_factor, self.grid_size, self.scale)
mip = ti.max(mip_from_pos(xyz, self.cascades),
mip_from_dt(dt, self.grid_size, self.cascades))
mip_bound = ti.min(ti.pow(2., mip-1), self.scale)
# mip = 0
# mip_bound = 0.5
mip_bound_inv = 1/mip_bound
nxyz = ti.math.clamp(0.5*(xyz*mip_bound_inv+1)*self.grid_size, 0.0, self.grid_size-1.0)
# nxyz = ti.ceil(nxyz)
idx = mip*self.grid_size3 + __morton3D(ti.cast(nxyz, ti.u32))
# occ = density_grid_taichi[idx] > 5.912066756501768
occ = self.density_bitfield[ti.u32(idx//8)] & (1 << ti.u32(idx%8))
if occ:
sn = start_idx + s
for p in ti.static(range(3)):
self.xyzs[sn][p] = xyz[p]
self.dirs[sn][p] = ray_d[p]
self.run_model_ind[sn] = 1
self.ts[sn] = t
self.deltas[sn] = dt
t += dt
self.hits_t[r][0] = t
s += 1
else:
txyz = (((nxyz+0.5+0.5*ti.math.sign(ray_d))*grid_size_inv*2-1)*mip_bound-xyz)*d_inv
t_target = t + ti.max(0, txyz.min())
t += calc_dt(t, self.exp_step_factor, self.grid_size, self.scale)
while t < t_target:
t += calc_dt(t, self.exp_step_factor, self.grid_size, self.scale)
self.N_eff_samples[n] = s
if s == 0:
self.alive_indices[n*2+c_index] = -1
@ti.kernel
def rearange_index(self, B: ti.i32):
self.model_launch[None] = 0
for i in ti.ndrange(B):
if self.run_model_ind[i]:
index = ti.atomic_add(self.model_launch[None], 1)
self.temp_hit[index] = i
self.model_launch[None] += 1
self.padd_block_network[None] = ((self.model_launch[None]+ block_dim - 1)// block_dim) * block_dim
# self.padd_block_composite[None] = ((self.counter[None]+ 128 - 1)// 128) *128
@ti.kernel
def hash_encode(self):
# get hash table embedding
# ti.loop_config(block_dim=16)
# for level in ti.static(range(16)):
for sn in ti.ndrange(self.model_launch[None]):
for level in ti.static(range(self.level)):
# normalize to [0, 1], before is [-0.5, 0.5]
# xyz = self.xyzs[self.temp_hit[sn]] + 0.5
xyz = (self.xyzs[self.temp_hit[sn]] - self.xyz_min) / (self.xyz_delta)
offset = self.offsets[level] * self.numof_hash_feet
indicator = self.hash_map_indicator[level]
map_size = self.hash_map_sizes[level]
init_val0 = tf_vec1(0.0)
init_val1 = tf_vec1(1.0)
local_feature_0 = init_val0[0]
local_feature_1 = init_val0[0]
index_temp = tf_index_temp(0)
w_temp = tf_vec8(0.0)
hash_temp_1 = tf_vec8(0.0)
hash_temp_2 = tf_vec8(0.0)
scale = self.base_res * ti.exp(level*ti.log(self.per_level_scales)) - 1.0
resolution = ti.cast(ti.ceil(scale), ti.uint32) + 1
pos = xyz * scale + 0.5
pos_grid_uint = ti.cast(ti.floor(pos), ti.uint32)
pos -= pos_grid_uint
# pos_grid_uint = ti.cast(pos_grid, ti.uint32)
for idx in ti.static(range(8)):
# idx_uint = ti.cast(idx, ti.uint32)
w = init_val1[0]
pos_grid_local = uvec3(0)
for d in ti.static(range(3)):
if (idx & (1 << d)) == 0:
pos_grid_local[d] = pos_grid_uint[d]
w *= data_type(1 - pos[d])
else:
pos_grid_local[d] = pos_grid_uint[d] + 1
w *= data_type(pos[d])
index = ti.int32(grid_pos2hash_index(indicator, pos_grid_local, resolution, map_size))
index_temp[idx] = offset+index*self.numof_hash_feet
w_temp[idx] = w
# local_feature_0 += data_type(w * self.hash_embedding[offset+index*2])
# local_feature_1 += data_type(w * self.hash_embedding[offset+index*2+1])
for idx in ti.static(range(8)):
hash_temp_1[idx] = self.hash_embedding[index_temp[idx]]
hash_temp_2[idx] = self.hash_embedding[index_temp[idx]+1]
for idx in ti.static(range(8)):
local_feature_0 += data_type(w_temp[idx] * hash_temp_1[idx])
local_feature_1 += data_type(w_temp[idx] * hash_temp_2[idx])
self.xyzs_embedding[sn, level*self.numof_hash_feet] = local_feature_0
self.xyzs_embedding[sn, level*self.numof_hash_feet+1] = local_feature_1
@ti.kernel
def sigma_layer(self):
ti.loop_config(block_dim=block_dim)
for sn in ti.ndrange(self.padd_block_network[None]):
tid = sn % block_dim
did_launch_num = self.model_launch[None]
init_val = tf_vec1(0.0)
input_val = ti.simt.block.SharedArray((self.sigma_n_input, block_dim), data_type)
weight = ti.simt.block.SharedArray((self.sigma_model_size,), data_type)
hid1 = ti.simt.block.SharedArray((self.net_width, block_dim), data_type)
hid2 = ti.simt.block.SharedArray((self.net_width, block_dim), data_type)
for i in ti.static(range(self.sigma_sm_preload)):
k = tid*self.sigma_sm_preload+i
weight[k] = self.sigma_weights[k]
for i in ti.static(range(self.sigma_n_input)):
input_val[i, tid] = self.xyzs_embedding[sn, i]
ti.simt.block.sync()
if sn < did_launch_num:
# input_val = tf_vec32(0.0)
for i in range(self.net_width):
temp = init_val[0]
for j in ti.static(range(self.sigma_n_input)):
temp += input_val[j, tid] * weight[i*self.sigma_n_input+j]
hid1[i, tid] = temp
# ti.simt.block.sync()
for i in range(self.sigma_n_output):
temp = init_val[0]
for j in ti.static(range(self.net_width)):
temp += data_type(ti.max(0.0, hid1[j, tid])) * weight[self.sigma_layer1_base+i*self.net_width+j]
hid2[i, tid] = temp
# ti.simt.block.sync()
self.out_1[self.temp_hit[sn]] = data_type(ti.exp(hid2[0, tid]))
for i in ti.static(range(self.sigma_n_output)):
self.final_embedding[sn, i] = hid2[i, tid]
# ti.simt.block.sync()
@ti.kernel
def rgb_layer(self):
ti.loop_config(block_dim=block_dim)
for sn in ti.ndrange(self.padd_block_network[None]):
ray_id = self.temp_hit[sn]
tid = sn % block_dim
did_launch_num = self.model_launch[None]
init_val = tf_vec1(0.0)
weight = ti.simt.block.SharedArray((self.rgb_model_size,), data_type)
hid1 = ti.simt.block.SharedArray((self.net_width, block_dim), data_type)
hid2 = ti.simt.block.SharedArray((self.net_width, block_dim), data_type)
for i in ti.static(range(self.rgb_sm_preload)):
k = tid*self.rgb_sm_preload+i
weight[k] = self.rgb_weights[k]
ti.simt.block.sync()
if sn < did_launch_num:
dir_ = self.dirs[ray_id]
input = dir_encode_func(dir_)
for i in ti.static(range(16)):
input[16+i] = self.final_embedding[sn, i]
for i in range(self.net_width):
temp = init_val[0]
for j in ti.static(range(self.rgb_n_input)):
temp += input[j] * weight[i*self.rgb_n_input+j]
hid1[i, tid] = temp
if ti.static(self.rgb_depth == 2):
for i in range(self.net_width):
temp = init_val[0]
for j in ti.static(range(self.net_width)):
temp += data_type(ti.max(0.0, hid1[j, tid])) * weight[self.rgb_layer1_base+i*self.net_width+j]
hid2[i, tid] = temp
for i in ti.static(range(self.rgb_n_output)):
temp = init_val[0]
for j in ti.static(range(self.net_width)):
temp += data_type(ti.max(0.0, hid2[j, tid])) * weight[self.rgb_layer2_base+i*self.net_width+j]
hid1[i, tid] = temp
for i in range(self.rgb_n_output):
self.out_3[self.temp_hit[sn], i] = data_type(1 / (1 + ti.exp(-hid1[i, tid])))
else:
for i in range(self.rgb_n_output):
temp = init_val[0]
for j in ti.static(range(self.net_width)):
temp += data_type(ti.max(0.0, hid1[j, tid])) * weight[self.net_width*self.rgb_n_input+i*self.net_width+j]
hid2[i, tid] = temp
# ti.simt.block.sync()
for i in ti.static(range(self.rgb_n_output)):
self.out_3[self.temp_hit[sn], i] = data_type(1 / (1 + ti.exp(-hid2[i, tid])))
# ti.simt.block.sync()
@ti.kernel
def composite_test(self, max_samples: ti.i32, T_threshold: data_type):
for n in ti.ndrange(self.counter[None]):
N_samples = self.N_eff_samples[n]
if N_samples != 0:
c_index = self.current_index[None]
r = self.alive_indices[n*2+c_index]
T = data_type(1.0 - self.opacity[r])
start_idx = n * max_samples
rgb_temp = tf_vec3(0.0)
depth_temp = tf_vec1(0.0)
opacity_temp = tf_vec1(0.0)
out_3_temp = tf_vec3(0.0)
for s in range(N_samples):
sn = start_idx + s
a = data_type(1.0 - ti.exp(-self.out_1[sn]*self.deltas[sn]))
w = a * T
for i in ti.static(range(3)):
out_3_temp[i] = self.out_3[sn, i]
rgb_temp += w * out_3_temp
depth_temp[0] += w * self.ts[sn]
opacity_temp[0] += w
T *= data_type(1.0 - a)
if T <= T_threshold:
self.alive_indices[n*2+c_index] = -1
break
self.rgb[r] += rgb_temp
self.depth[r] += depth_temp[0]
self.opacity[r] += opacity_temp[0]
@ti.kernel
def re_order(self, B: ti.i32):
self.counter[None] = 0
c_index = self.current_index[None]
n_index = (c_index + 1) % 2
self.current_index[None] = n_index
for i in ti.ndrange(B):
alive_temp = self.alive_indices[i*2+c_index]
if alive_temp >= 0:
index = ti.atomic_add(self.counter[None], 1)
self.alive_indices[index*2+n_index] = alive_temp
def write_image(self):
rgb_np = self.rgb.to_numpy().reshape(self.res[0], self.res[1], 3)
depth_np = self.depth.to_numpy().reshape(self.res[0], self.res[1])
plt.imsave('taichi_ngp.png', (rgb_np*255).astype(np.uint8))
plt.imsave('taichi_ngp_depth.png', depth2img(depth_np))
def render(self, max_samples, T_threshold, use_dof=False, dist_to_focus=0.8, len_dis=0.0, clear_rgb=True) -> Tuple[float, int, int]:
samples = 0
if clear_rgb:
self.rgb.fill(0.0)
self.reset()
self.gen_noise_buffer()
if use_dof:
self.ray_intersect_dof(dist_to_focus, len_dis)
else:
self.ray_intersect()
while samples < max_samples:
N_alive = self.counter[None]
if N_alive == 0: break
# how many more samples the number of samples add for each ray
N_samples = max(min(self.N_rays//N_alive, 64), self.min_samples)
samples += N_samples
launch_model_total = N_alive * N_samples
# print(f"samples: {samples}, N_alive: {N_alive}, N_samples: {N_samples}")
self.raymarching_test_kernel(N_samples)
self.rearange_index(launch_model_total)
# self.dir_encode()
self.hash_encode()
self.sigma_layer()
self.rgb_layer()
# self.FullyFusedMLP()
self.composite_test(N_samples, T_threshold)
self.re_order(N_alive)
return samples, N_alive, N_samples
def render_frame(self, n=1):
t = time.time()
for _ in range(n):
samples, N_alive, N_samples = self.render(max_samples=100, T_threshold=1e-2, clear_rgb=True)
ti.sync()
print(f"samples: {samples}, N_alive: {N_alive}, N_samples: {N_samples}")
print(f'Render time: {1000*(time.time()-t)/n:.2f} ms')
self.write_image()
@ti.kernel
def rgb_to_render_buffer(self, frame: ti.i32):
for i, j in self.render_buffer:
rgb = self.rgb[(self.res[0]-j)*self.res[1]+i]
self.render_buffer[i, j] = rgb / frame
@ti.kernel
def depth_max(self) -> vec2:
max_v = self.depth[0]
min_v = self.depth[0]
for i in ti.ndrange(self.N_rays):
ti.atomic_max(max_v, self.depth[i])
ti.atomic_min(min_v, self.depth[i])
return vec2(max_v, min_v)
@ti.kernel
def depth_to_render_buffer(self, max_min: vec2):
for i, j in self.render_buffer:
max_v = max_min[0]
min_v = max_min[1]
depth = self.depth[(self.res[0]-j)*self.res[1]+i]
pixel = (vec3(depth)-min_v)/(max_v-min_v)
self.render_buffer[i, j] = pixel
def init_cam(self):
self.lookat = self.lookat @ self.pose.to_numpy()[:, :3].T
def render_gui(self):
video_manager = None
# check if the export file exists for snapshot and video
export_dir = './export/'
if not os.path.exists(export_dir):
os.mkdir(export_dir)
H, W = self.res
window = ti.ui.Window('Taichi NGP', (W, H))
canvas = window.get_canvas()
gui = window.get_gui()
last_orbit_x = None
last_orbit_y = None
rotate_speed = 50
movement_speed = 0.03
max_samples_for_rendering = 100
render_time = 0
# white_bg = False
recording = False
show_depth = False
use_dof = False
last_use_dof = False
frame = 0
T_threshold = 1e-2
dist_to_focus = 1.2
len_dis=0.04
self.init_cam()
last_pose = self.pose.to_numpy()
total_frame = 0
last_dist_to_focus = dist_to_focus
last_len_dis = len_dis
box_size_x = self.half_size[None][0]
box_size_y = self.half_size[None][1]
box_size_z = self.half_size[None][2]
last_box_size_x = box_size_x
last_box_size_y = box_size_y
last_box_size_z = box_size_z
center_x = self.center[None][0]
center_y = self.center[None][1]
center_z = self.center[None][2]
last_center_x = center_x
last_center_y = center_y
last_center_z = center_z
cam_fov_scale = 1.0
last_cam_fov_scale = cam_fov_scale
while window.running:
# TODO: make it more efficient
total_frame+=1
if window.is_pressed(ti.ui.RMB):
curr_mouse_x, curr_mouse_y = window.get_cursor_pos()
if last_orbit_x is None or last_orbit_y is None:
last_orbit_x, last_orbit_y = curr_mouse_x, curr_mouse_y
else:
dx = curr_mouse_x - last_orbit_x
dy = curr_mouse_y - last_orbit_y
self.cam.orbit(dx, -dy)
last_orbit_x, last_orbit_y = curr_mouse_x, curr_mouse_y
else:
last_orbit_x = None
last_orbit_y = None
if window.is_pressed('w'):
self.cam.scale(0.2)
if window.is_pressed('s'):
self.cam.scale(-0.2)
if window.is_pressed('a'):
self.cam.pan(100, 0.)
if window.is_pressed('d'):
self.cam.pan(-100, 0.)
if window.is_pressed('e'):
self.cam.pan(0., -100)
if window.is_pressed('q'):
self.cam.pan(0., 100)
if self.cam.params_changed:
self.rgb.fill(0.0)
total_frame = 1
self.cam.params_changed = False
self.pose.from_numpy(self.cam.pose.astype(np_type))
with gui.sub_window("Options", 0.05, 0.05, 0.68, 0.3) as w:
w.text(f'General')
box_size_x = w.slider_float('box size_x', box_size_x, 0.01, 64.0)
box_size_y = w.slider_float('box size_y', box_size_y, 0.01, 64.0)
box_size_z = w.slider_float('box size_z', box_size_z, 0.01, 64.0)
center_x = w.slider_float('center_x', center_x, -64.0, 64.0)
center_y = w.slider_float('center_y', center_y, -64.0, 64.0)
center_z = w.slider_float('center_z', center_z, -64.0, 64.0)
cam_fov_scale = w.slider_float('camera fov', cam_fov_scale, 0.01, 2.0)
T_threshold = w.slider_float('transparency threshold', T_threshold, 0., 1.)
max_samples_for_rendering = w.slider_float("max samples", max_samples_for_rendering, 1, 100)
show_depth = w.checkbox("show depth", show_depth)
# white_bg = w.checkbox("white background", white_bg)
if last_box_size_x != box_size_x or last_box_size_y != box_size_y or last_box_size_z != box_size_z:
last_box_size_x = box_size_x
last_box_size_y = box_size_y
last_box_size_z = box_size_z
self.half_size[None] = vec3(box_size_x, box_size_y, box_size_z)
self.rgb.fill(0.0)
total_frame = 1
if last_center_x != center_x or last_center_y != center_y or last_center_z != center_z:
last_center_x = center_x
last_center_y = center_y
last_center_z = center_z
self.center[None] = vec3(center_x, center_y, center_z)
self.rgb.fill(0.0)
total_frame = 1
if last_cam_fov_scale != cam_fov_scale:
last_cam_fov_scale = cam_fov_scale
self.cam_fov_scale[None] = cam_fov_scale
self.init_direction(self.res[1], self.res[0])
self.rgb.fill(0.0)
total_frame = 1
w.text(f'Camera')
use_dof = w.checkbox("apply depth of field", use_dof)
dist_to_focus = w.slider_float("focus distance", dist_to_focus, 0.8, 3.)