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network_cc.py
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network_cc.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from encoding import get_encoder
from activation import trunc_exp
from nerf.renderer import NeRFRenderer
import raymarching
class NeRFNetwork(NeRFRenderer):
def __init__(self,
resolution=[128] * 3,
degree=4,
# rank_vec_density=[64],
# rank_mat_density=[16],
# rank_vec=[64],
# rank_mat=[64],
rank_vec_density=[64, 64, 64, 64, 64],
rank_mat_density=[0, 4, 8, 12, 16],
rank_vec=[64, 64, 64, 64, 64],
rank_mat=[0, 4, 16, 32, 64],
bg_resolution=[512, 512],
bg_rank=8,
bound=1,
**kwargs
):
super().__init__(bound, **kwargs)
self.resolution = resolution
self.degree = degree
self.encoder_dir, self.enc_dir_dim = get_encoder('sphere_harmonics', degree=self.degree)
self.out_dim = 3 * self.enc_dir_dim # only color dim
# group list in list for composition
self.rank_vec_density = [rank_vec_density]
self.rank_mat_density = [rank_mat_density]
self.rank_vec = [rank_vec]
self.rank_mat = [rank_mat]
# all components are divided into K groups
assert len(rank_vec) == len(rank_mat) == len(rank_vec_density) == len(rank_mat_density)
self.K = [len(rank_vec)]
# utility
self.group_vec_density = [np.diff(rank_vec_density, prepend=0)]
self.group_mat_density = [np.diff(rank_mat_density, prepend=0)]
self.group_vec = [np.diff(rank_vec, prepend=0)]
self.group_mat = [np.diff(rank_mat, prepend=0)]
self.mat_ids = [[0, 1], [0, 2], [1, 2]]
self.vec_ids = [2, 1, 0]
# allocate params
self.U_vec_density = nn.ParameterList()
self.S_vec_density = nn.ParameterList()
for k in range(self.K[0]):
if self.group_vec_density[0][k] > 0:
for i in range(3):
vec_id = self.vec_ids[i]
w = torch.randn(self.group_vec_density[0][k], self.resolution[vec_id]) * 0.2 # [R, H]
self.U_vec_density.append(nn.Parameter(w.view(1, self.group_vec_density[0][k], self.resolution[vec_id], 1))) # [1, R, H, 1]
w = torch.ones(1, self.group_vec_density[0][k])
torch.nn.init.kaiming_normal_(w)
self.S_vec_density.append(nn.Parameter(w))
self.U_mat_density = nn.ParameterList()
self.S_mat_density = nn.ParameterList()
for k in range(self.K[0]):
if self.group_mat_density[0][k] > 0:
for i in range(3):
mat_id_0, mat_id_1 = self.mat_ids[i]
w = torch.randn(self.group_mat_density[0][k], self.resolution[mat_id_1] * self.resolution[mat_id_0]) * 0.2 # [R, HW]
self.U_mat_density.append(nn.Parameter(w.view(1, self.group_mat_density[0][k], self.resolution[mat_id_1], self.resolution[mat_id_0]))) # [1, R, H, W]
w = torch.ones(1, self.group_mat_density[0][k])
torch.nn.init.kaiming_normal_(w)
self.S_mat_density.append(nn.Parameter(w))
self.U_vec = nn.ParameterList()
self.S_vec = nn.ParameterList()
for k in range(self.K[0]):
if self.group_vec[0][k] > 0:
for i in range(3):
vec_id = self.vec_ids[i]
w = torch.randn(self.group_vec[0][k], self.resolution[vec_id]) * 0.2 # [R, H]
self.U_vec.append(nn.Parameter(w.view(1, self.group_vec[0][k], self.resolution[vec_id], 1))) # [1, R, H, 1]
w = torch.ones(self.out_dim, self.group_vec[0][k])
torch.nn.init.kaiming_normal_(w)
self.S_vec.append(nn.Parameter(w))
self.U_mat = nn.ParameterList()
self.S_mat = nn.ParameterList()
for k in range(self.K[0]):
if self.group_mat[0][k] > 0:
for i in range(3):
mat_id_0, mat_id_1 = self.mat_ids[i]
w = torch.randn(self.group_mat[0][k], self.resolution[mat_id_1] * self.resolution[mat_id_0]) * 0.2 # [R, HW]
self.U_mat.append(nn.Parameter(w.view(1, self.group_mat[0][k], self.resolution[mat_id_1], self.resolution[mat_id_0]))) # [1, R, H, W]
w = torch.ones(self.out_dim, self.group_mat[0][k])
torch.nn.init.kaiming_normal_(w)
self.S_mat.append(nn.Parameter(w))
# flag
self.finalized = False if self.K[0] != 1 else True
# background model
if self.bg_radius > 0:
self.bg_resolution = bg_resolution
self.bg_rank = bg_rank
self.bg_mat = nn.Parameter(0.2 * torch.randn((1, bg_rank, bg_resolution[0], bg_resolution[1]))) # [1, R, H, W]
w = torch.ones(self.out_dim, bg_rank) # just color
torch.nn.init.kaiming_normal_(w)
self.bg_S = nn.Parameter(w)
def compute_features_density(self, x, K=-1, residual=False, oid=0):
# x: [N, 3], in [-1, 1]
# return: [K, N, out_dim]
prefix = x.shape[:-1]
N = np.prod(prefix)
vec_coord = torch.stack((x[..., self.vec_ids[0]], x[..., self.vec_ids[1]], x[..., self.vec_ids[2]]))
vec_coord = torch.stack((torch.zeros_like(vec_coord), vec_coord), dim=-1).view(3, -1, 1, 2)
mat_coord = torch.stack((x[..., self.mat_ids[0]], x[..., self.mat_ids[1]], x[..., self.mat_ids[2]])).view(3, -1, 1, 2) # [3, N, 1, 2]
# calculate first K blocks
if K <= 0:
K = self.K[oid]
# loop all blocks
if residual:
outputs = []
last_y = None
offset_vec = oid
offset_mat = oid
for k in range(K):
y = 0
if self.group_vec_density[oid][k]:
vec_feat = F.grid_sample(self.U_vec_density[3 * offset_vec + 0], vec_coord[[0]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_vec_density[3 * offset_vec + 1], vec_coord[[1]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_vec_density[3 * offset_vec + 2], vec_coord[[2]], align_corners=False).view(-1, N) # [r, N]
y = y + (self.S_vec_density[offset_vec] @ vec_feat)
offset_vec += 1
if self.group_mat_density[oid][k]:
mat_feat = F.grid_sample(self.U_mat_density[3 * offset_mat + 0], mat_coord[[0]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_mat_density[3 * offset_mat + 1], mat_coord[[1]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_mat_density[3 * offset_mat + 2], mat_coord[[2]], align_corners=False).view(-1, N) # [r, N]
y = y + (self.S_mat_density[offset_mat] @ mat_feat) # [out_dim, N]
offset_mat += 1
if last_y is not None:
y = y + last_y
if residual:
outputs.append(y)
last_y = y
if residual:
outputs = torch.stack(outputs, dim=0).permute(0, 2, 1).contiguous().view(K, *prefix, -1) # [K, out_dim, N] --> [K, N, out_dim]
else:
outputs = last_y.permute(1, 0).contiguous().view(*prefix, -1) # [out_dim, N] --> [N, out_dim]
return outputs
def compute_features(self, x, K=-1, residual=False, oid=0):
# x: [N, 3], in [-1, 1]
# return: [K, N, out_dim]
prefix = x.shape[:-1]
N = np.prod(prefix)
vec_coord = torch.stack((x[..., self.vec_ids[0]], x[..., self.vec_ids[1]], x[..., self.vec_ids[2]]))
vec_coord = torch.stack((torch.zeros_like(vec_coord), vec_coord), dim=-1).view(3, -1, 1, 2)
mat_coord = torch.stack((x[..., self.mat_ids[0]], x[..., self.mat_ids[1]], x[..., self.mat_ids[2]])).view(3, -1, 1, 2) # [3, N, 1, 2]
# calculate first K blocks
if K <= 0:
K = self.K[oid]
# loop all blocks
if residual:
outputs = []
last_y = None
offset_vec = oid
offset_mat = oid
for k in range(K):
y = 0
if self.group_vec[oid][k]:
vec_feat = F.grid_sample(self.U_vec[3 * offset_vec + 0], vec_coord[[0]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_vec[3 * offset_vec + 1], vec_coord[[1]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_vec[3 * offset_vec + 2], vec_coord[[2]], align_corners=False).view(-1, N) # [r, N]
y = y + (self.S_vec[offset_vec] @ vec_feat)
offset_vec += 1
if self.group_mat[oid][k]:
mat_feat = F.grid_sample(self.U_mat[3 * offset_mat + 0], mat_coord[[0]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_mat[3 * offset_mat + 1], mat_coord[[1]], align_corners=False).view(-1, N) * \
F.grid_sample(self.U_mat[3 * offset_mat + 2], mat_coord[[2]], align_corners=False).view(-1, N) # [r, N]
y = y + (self.S_mat[offset_mat] @ mat_feat) # [out_dim, N]
offset_mat += 1
if last_y is not None:
y = y + last_y
if residual:
outputs.append(y)
last_y = y
if residual:
outputs = torch.stack(outputs, dim=0).permute(0, 2, 1).contiguous().view(K, *prefix, -1) # [K, out_dim, N] --> [K, N, out_dim]
else:
outputs = last_y.permute(1, 0).contiguous().view(*prefix, -1) # [out_dim, N] --> [N, out_dim]
return outputs
def normalize_coord(self, x, oid=0):
if oid == 0:
aabb = self.aabb_train
else:
tr = getattr(self, f'T_{oid}') # [4, 4] transformation matrix
x = torch.cat([x, torch.ones_like(x[:, :1])], dim=1) # to homo
x = (x @ tr.T)[:, :3] # [N, 4] --> [N, 3]
aabb = getattr(self, f'aabb_{oid}')
return 2 * (x - aabb[:3]) / (aabb[3:] - aabb[:3]) - 1 # [-1, 1] in bbox
def normalize_dir(self, d, oid=0):
if oid != 0:
tr = getattr(self, f'R_{oid}') # [3, 3] rotation matrix
d = d @ tr.T
return d
def forward(self, x, d, K=-1):
# x: [N, 3], in [-bound, bound]
# d: [N, 3], nomalized in [-1, 1]
N = x.shape[0]
# single object
if len(self.K) == 1:
x_model = self.normalize_coord(x)
feats_density = self.compute_features_density(x_model, K, residual=self.training) # [K, N, 1]
sigma = trunc_exp(feats_density).squeeze(-1) # [K, N]
enc_d = self.encoder_dir(d) # [N, C]
h = self.compute_features(x_model, K, residual=self.training) # [K, N, 3C]
h = h.view(K, N, 3, self.degree ** 2) # [K, N, 3, C]
h = (h * enc_d.unsqueeze(1)).sum(-1) # [K, N, 3]
rgb = torch.sigmoid(h) # [K, N, 3]
return sigma, rgb
# multi-object (composed scene), do not support rank-residual training for now.
else:
sigma_list = []
h_list = []
sigma_all = 0
rgb_all = 0
for oid in range(1, len(self.K)):
x_model = self.normalize_coord(x, oid=oid)
feats_density = self.compute_features_density(x_model, -1, residual=False, oid=oid) # [N, 1]
sigma = trunc_exp(feats_density).squeeze(-1) # [N]
sigma_list.append(sigma.detach().clone())
sigma_all += sigma
d_model = self.normalize_dir(d, oid=oid)
enc_d = self.encoder_dir(d_model) # [N, C]
h = self.compute_features(x_model, -1, residual=False, oid=oid) # [N, 3C]
h = h.view(N, 3, self.degree ** 2)
h = (h * enc_d.unsqueeze(1)).sum(-1) # [N, 3]
h_list.append(h)
ws = torch.stack(sigma_list, dim=0) # [O, N]
ws = F.softmax(ws, dim=0)
for oid in range(1, len(self.K)):
rgb_all += h_list[oid - 1] * ws[oid - 1].unsqueeze(-1)
rgb_all = torch.sigmoid(rgb_all)
return sigma_all, rgb_all
def density(self, x, K=-1):
# x: [N, 3], in [-bound, bound]
if len(self.K) == 1:
x_model = self.normalize_coord(x)
feats_density = self.compute_features_density(x_model, K, residual=False) # [N, 1 + 3C]
sigma = trunc_exp(feats_density).squeeze(-1) # [N]
return {
'sigma': sigma,
}
else:
sigma_all = 0
for oid in range(1, len(self.K)):
x_model = self.normalize_coord(x, oid=oid)
feats_density = self.compute_features_density(x_model, -1, residual=False, oid=oid) # [N, 1]
sigma = trunc_exp(feats_density).squeeze(-1) # [N]
sigma_all += sigma
return {
'sigma': sigma_all,
}
def background(self, x, d):
# x: [N, 2] in [-1, 1]
N = x.shape[0]
h = F.grid_sample(self.bg_mat, x.view(1, N, 1, 2), align_corners=False).view(-1, N) # [R, N]
h = (self.bg_S @ h).T.contiguous() # [3C, N] --> [N, 3C]
enc_d = self.encoder_dir(d)
h = h.view(N, 3, -1)
h = (h * enc_d.unsqueeze(1)).sum(-1) # [N, 3]
# sigmoid activation for rgb
rgb = torch.sigmoid(h)
return rgb
# L1 penalty for loss
def density_loss(self):
loss = 0
for i in range(len(self.U_vec_density)):
loss = loss + torch.mean(torch.abs(self.U_vec_density[i]))
for i in range(len(self.U_mat_density)):
loss = loss + torch.mean(torch.abs(self.U_mat_density[i]))
return loss
# upsample utils
@torch.no_grad()
def upsample_model(self, resolution):
for i in range(len(self.U_vec_density)):
vec_id = self.vec_ids[i % 3]
self.U_vec_density[i] = nn.Parameter(F.interpolate(self.U_vec_density[i].data, size=(resolution[vec_id], 1), mode='bilinear', align_corners=False))
for i in range(len(self.U_mat_density)):
mat_id_0, mat_id_1 = self.mat_ids[i % 3]
self.U_mat_density[i] = nn.Parameter(F.interpolate(self.U_mat_density[i].data, size=(resolution[mat_id_1], resolution[mat_id_0]), mode='bilinear', align_corners=False))
for i in range(len(self.U_vec)):
vec_id = self.vec_ids[i % 3]
self.U_vec[i] = nn.Parameter(F.interpolate(self.U_vec[i].data, size=(resolution[vec_id], 1), mode='bilinear', align_corners=False))
for i in range(len(self.U_mat)):
mat_id_0, mat_id_1 = self.mat_ids[i % 3]
self.U_mat[i] = nn.Parameter(F.interpolate(self.U_mat[i].data, size=(resolution[mat_id_1], resolution[mat_id_0]), mode='bilinear', align_corners=False))
self.resolution = resolution
print(f'[INFO] upsampled to {resolution}')
@torch.no_grad()
def shrink_model(self):
# shrink aabb_train and the model so it only represents the space inside aabb_train.
half_grid_size = self.bound / self.grid_size
thresh = min(self.density_thresh, self.mean_density)
# get new aabb from the coarsest density grid (TODO: from the finest that covers current aabb?)
valid_grid = self.density_grid[self.cascade - 1] > thresh # [N]
valid_pos = raymarching.morton3D_invert(torch.nonzero(valid_grid)) # [Nz] --> [Nz, 3], in [0, H - 1]
#plot_pointcloud(valid_pos.detach().cpu().numpy())
valid_pos = (2 * valid_pos / (self.grid_size - 1) - 1) * (self.bound - half_grid_size) # [Nz, 3], in [-b+hgs, b-hgs]
min_pos = valid_pos.amin(0) - half_grid_size # [3]
max_pos = valid_pos.amax(0) + half_grid_size # [3]
# shrink model
reso = torch.LongTensor(self.resolution).to(self.aabb_train.device)
units = (self.aabb_train[3:] - self.aabb_train[:3]) / reso
tl = (min_pos - self.aabb_train[:3]) / units
br = (max_pos - self.aabb_train[:3]) / units
tl = torch.round(tl).long().clamp(min=0)
br = torch.minimum(torch.round(br).long(), reso)
for i in range(len(self.U_vec_density)):
vec_id = self.vec_ids[i % 3]
self.U_vec_density[i] = nn.Parameter(self.U_vec_density[i].data[..., tl[vec_id]:br[vec_id], :])
for i in range(len(self.U_mat_density)):
mat_id_0, mat_id_1 = self.mat_ids[i % 3]
self.U_mat_density[i] = nn.Parameter(self.U_mat_density[i].data[..., tl[mat_id_1]:br[mat_id_1], tl[mat_id_0]:br[mat_id_0]])
for i in range(len(self.U_vec)):
vec_id = self.vec_ids[i % 3]
self.U_vec[i] = nn.Parameter(self.U_vec[i].data[..., tl[vec_id]:br[vec_id], :])
for i in range(len(self.U_mat)):
mat_id_0, mat_id_1 = self.mat_ids[i % 3]
self.U_mat[i] = nn.Parameter(self.U_mat[i].data[..., tl[mat_id_1]:br[mat_id_1], tl[mat_id_0]:br[mat_id_0]])
self.aabb_train = torch.cat([min_pos, max_pos], dim=0) # [6]
print(f'[INFO] shrink slice: {tl.cpu().numpy().tolist()} - {br.cpu().numpy().tolist()}')
print(f'[INFO] new aabb: {self.aabb_train.cpu().numpy().tolist()}')
@torch.no_grad()
def finalize_group(self, U, S):
if len(U) == 0 or len(S) == 0:
return nn.ParameterList(), nn.ParameterList()
# sort rank inside each group
for i in range(len(S)):
importance = S[i].abs().sum(0) # [C, R] --> [R]
for j in range(3):
importance *= U[3 * i + j].view(importance.shape[0], -1).norm(dim=-1) # [R, H] --> [R]
inds = torch.argsort(importance, descending=True) # important first
S[i] = nn.Parameter(S[i].data[:, inds])
for j in range(3):
U[3 * i + j] = nn.Parameter(U[3 * i + j].data[:, inds])
# fuse rank across all groups
S = nn.ParameterList([
nn.Parameter(torch.cat([s.data for s in S], dim=1))
])
U = nn.ParameterList([
nn.Parameter(torch.cat([v.data for v in U[0::3]], dim=1)),
nn.Parameter(torch.cat([v.data for v in U[1::3]], dim=1)),
nn.Parameter(torch.cat([v.data for v in U[2::3]], dim=1)),
])
return U, S
# finalize model parameters (fuse all groups) for faster inference, but no longer allow rank-residual training.
@torch.no_grad()
def finalize(self):
self.U_vec_density, self.S_vec_density = self.finalize_group(self.U_vec_density, self.S_vec_density)
self.U_mat_density, self.S_mat_density = self.finalize_group(self.U_mat_density, self.S_mat_density)
self.U_vec, self.S_vec = self.finalize_group(self.U_vec, self.S_vec)
self.U_mat, self.S_mat = self.finalize_group(self.U_mat, self.S_mat)
# update states
self.rank_vec_density[0] = [self.rank_vec_density[0][-1]]
self.rank_mat_density[0] = [self.rank_mat_density[0][-1]]
self.rank_vec[0] = [self.rank_vec[0][-1]]
self.rank_mat[0] = [self.rank_mat[0][-1]]
self.group_vec_density[0] = self.rank_vec_density[0]
self.group_mat_density[0] = self.rank_mat_density[0]
self.group_vec[0] = self.rank_vec[0]
self.group_mat[0] = self.rank_mat[0]
self.K[0] = 1
self.finalized = True
# assume finalized (sorted), simply slicing!
@torch.no_grad()
def compress_group(self, U, S, rank):
if rank == 0:
return nn.ParameterList(), nn.ParameterList()
S[0] = nn.Parameter(S[0].data[:, :rank].clone()) # clone is necessary, slicing won't change storage!
for i in range(3):
U[i] = nn.Parameter(U[i].data[:, :rank].clone())
return U, S
@torch.no_grad()
def compress(self, ranks):
# ranks: (density_vec, density_mat, color_vec, color_mat)
if not self.finalized:
self.finalize()
self.U_vec_density, self.S_vec_density = self.compress_group(self.U_vec_density, self.S_vec_density, ranks[0])
self.U_mat_density, self.S_mat_density = self.compress_group(self.U_mat_density, self.S_mat_density, ranks[1])
self.U_vec, self.S_vec = self.compress_group(self.U_vec, self.S_vec, ranks[2])
self.U_mat, self.S_mat = self.compress_group(self.U_mat, self.S_mat, ranks[3])
# update states
self.rank_vec_density[0] = [ranks[0]]
self.rank_mat_density[0] = [ranks[1]]
self.rank_vec[0] = [ranks[2]]
self.rank_mat[0] = [ranks[3]]
self.group_vec_density[0] = self.rank_vec_density[0]
self.group_mat_density[0] = self.rank_mat_density[0]
self.group_vec[0] = self.rank_vec[0]
self.group_mat[0] = self.rank_mat[0]
@torch.no_grad()
def compose(self, other, R=None, s=None, t=None):
if not self.finalized:
self.finalize()
if not other.finalized:
other.finalize()
# parameters
self.U_vec_density.extend(other.U_vec_density)
self.S_vec_density.extend(other.S_vec_density)
self.U_mat_density.extend(other.U_mat_density)
self.S_mat_density.extend(other.S_mat_density)
self.U_vec.extend(other.U_vec)
self.S_vec.extend(other.S_vec)
self.U_mat.extend(other.U_mat)
self.S_mat.extend(other.S_mat)
# states
self.rank_vec_density.extend(other.rank_vec_density)
self.rank_mat_density.extend(other.rank_mat_density)
self.rank_vec.extend(other.rank_vec)
self.rank_mat.extend(other.rank_mat)
self.group_vec_density.extend(other.group_vec_density)
self.group_mat_density.extend(other.group_mat_density)
self.group_vec.extend(other.group_vec)
self.group_mat.extend(other.group_mat)
self.K.extend(other.K)
# transforms
oid = len(self.K) - 1
# R: a [3, 3] rotation matrix in SO(3)
if R is None:
R = torch.eye(3, dtype=torch.float32)
elif isinstance(R, np.ndarray):
R = torch.from_numpy(R.astype(np.float32))
else: # tensor
R = R.float()
# s is a scalar scaling factor
if s is None:
s = 1
# t is a [3] translation vector
if t is None:
t = torch.zeros(3, dtype=torch.float32)
elif isinstance(t, np.ndarray):
t = torch.from_numpy(t.astype(np.float32))
else: # tensor
t = t.float()
# T: the [4, 4] transformation matrix
# first scale & rotate, then translate.
T = torch.eye(4, dtype=torch.float32)
T[:3, :3] = R * s
T[:3, 3] = t
# T is the model matrix, but we want the matrix to transform rays, i.e., the inversion.
T = torch.inverse(T).to(self.aabb_train.device)
R = R.T.to(self.aabb_train.device)
self.register_buffer(f'T_{oid}', T)
self.register_buffer(f'R_{oid}', R)
self.register_buffer(f'aabb_{oid}', other.aabb_train)
# update density grid multiple times to make sure it is accurate
# TODO: 3 is very empirical...
for _ in range(3):
self.update_extra_state()
# optimizer utils
def get_params(self, lr1, lr2):
params = [
{'params': self.U_vec_density, 'lr': lr1},
{'params': self.S_vec_density, 'lr': lr2},
{'params': self.U_mat_density, 'lr': lr1},
{'params': self.S_mat_density, 'lr': lr2},
{'params': self.U_vec, 'lr': lr1},
{'params': self.S_vec, 'lr': lr2},
{'params': self.U_mat, 'lr': lr1},
{'params': self.S_mat, 'lr': lr2},
]
if self.bg_radius > 0:
params.append({'params': self.bg_mat, 'lr': lr1})
params.append({'params': self.bg_S, 'lr': lr2})
return params