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run_nerf_helpers.py
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run_nerf_helpers.py
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import torch
torch.autograd.set_detect_anomaly(True)
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torchvision
import math
# Misc
img2mse = lambda x, y : torch.mean((x - y) ** 2)
mse2psnr = lambda x : -10. * torch.log(x) / torch.log(torch.Tensor([10.]))
to8b = lambda x : (255*np.clip(x,0,1)).astype(np.uint8)
class Embedder:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs['input_dims']
out_dim = 0
if self.kwargs['include_input']:
embed_fns.append(lambda x : x)
out_dim += d
max_freq = self.kwargs['max_freq_log2']
min_freq = self.kwargs['min_freq_log2']
N_freqs = self.kwargs['num_freqs']
open_res = self.kwargs['open_res']
if self.kwargs['log_sampling']:
freq_bands = 2.**torch.linspace(min_freq, max_freq, steps=N_freqs)
else:
freq_bands = torch.linspace(2.**min_freq, 2.**max_freq, steps=N_freqs)
effective_freq = 0
for freq in freq_bands:
for p_fn in self.kwargs['periodic_fns']:
embed_fns.append(lambda x, p_fn=p_fn, freq=freq : p_fn(x * freq))
out_dim += d
self.embed_fns = embed_fns
self.out_dim = out_dim
def embed(self, inputs):
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
class MipEmbedder:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs['input_dims']
out_dim = 0
if self.kwargs['include_input']:
embed_fns.append(lambda x : x[:,:d])
out_dim += d
max_freq = self.kwargs['max_freq_log2']
min_freq = self.kwargs['min_freq_log2']
N_freqs = self.kwargs['num_freqs']
if self.kwargs['log_sampling']:
freq_bands_y = 2.**torch.linspace(min_freq, max_freq, steps=N_freqs)
freq_bands_w = 4.**torch.linspace(min_freq, max_freq, steps=N_freqs)
else:
freq_bands_y = torch.linspace(2.**min_freq, 2.**max_freq, steps=N_freqs)
freq_bands_w = torch.linspace(4.**min_freq, 4.**max_freq, steps=N_freqs)
for ctr in range(len(freq_bands_y)):
for p_fn in self.kwargs['periodic_fns']:
embed_fns.append(lambda inputs, p_fn=p_fn, freq_y=freq_bands_y[ctr], freq_w=freq_bands_w[ctr] : p_fn(inputs[:,:d] * freq_y) * torch.exp((-0.5) * freq_w * inputs[:,d:]))
out_dim += d
self.embed_fns = embed_fns
self.out_dim = out_dim
def embed(self, inputs):
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
import math
def integrated_pos_enc(x_coord, min_freq, max_freq, N_freqs):
x, x_cov_diag = x_coord[:,:3], x_coord[:,3:]
scales = 2.**torch.linspace(min_freq, max_freq, steps=N_freqs)
shape = list(x.shape[:-1]) + [-1]
y = torch.reshape(x[..., None, :] * scales[:, None], shape)
y_var = torch.reshape(x_cov_diag[..., None, :] * scales[:, None]**2, shape)
embedding = expected_sin(
torch.cat([y, y + 0.5 * math.pi], axis=-1),
torch.cat([y_var] * 2, axis=-1))[0]
return embedding
def expected_sin(x, x_var):
y = torch.exp(-0.5 * x_var) * torch.sin(x)
y_var = torch.maximum(
torch.tensor(0), 0.5 * (1 - torch.exp(-2 * x_var) * torch.cos(2 * x)) - y**2)
return y, y_var
def get_embedder(multires, open_res, min_multires=0, i=0, input_dims=3):
if i == -1:
return nn.Identity(), 3
embed_kwargs = {
'include_input' : True,
'input_dims' : input_dims,
'min_freq_log2': min_multires,
'max_freq_log2' : multires-1,
'num_freqs' : multires,
'log_sampling' : True,
'periodic_fns' : [torch.sin, torch.cos],
'open_res' : open_res,
}
embedder_obj = Embedder(**embed_kwargs)
embed = lambda x, eo=embedder_obj : eo.embed(x)
return embed, embedder_obj.out_dim
def get_mip_embedder(multires, min_multires=0, i=0, include_input=True, log_sampling=True):
if i == -1:
return nn.Identity(), 3
embed_kwargs = {
'include_input' : include_input,
'input_dims' : 3,
'min_freq_log2': min_multires,
'max_freq_log2' : multires-1,
'num_freqs' : multires,
'log_sampling' : log_sampling,
'periodic_fns' : [torch.sin, torch.cos],
}
embedder_obj = MipEmbedder(**embed_kwargs)
embed = lambda inputs, eo=embedder_obj : eo.embed(inputs)
return embed, embedder_obj.out_dim
class Bungee_NeRF_baseblock(nn.Module):
def __init__(self, net_width=256, input_ch=3, input_ch_views=3):
super(Bungee_NeRF_baseblock, self).__init__()
self.pts_linears = nn.ModuleList([nn.Linear(input_ch, net_width)] + [nn.Linear(net_width, net_width) for _ in range(3)])
self.views_linear = nn.Linear(input_ch_views + net_width, net_width//2)
self.feature_linear = nn.Linear(net_width, net_width)
self.alpha_linear = nn.Linear(net_width, 1)
self.rgb_linear = nn.Linear(net_width//2, 3)
def forward(self, input_pts, input_views):
h = input_pts
for i, l in enumerate(self.pts_linears):
h = self.pts_linears[i](h)
h = F.relu(h)
alpha = self.alpha_linear(h)
feature0 = self.feature_linear(h)
h0 = torch.cat([feature0, input_views], -1)
h0 = self.views_linear(h0)
h0 = F.relu(h0)
rgb = self.rgb_linear(h0)
return rgb, alpha, h
class Bungee_NeRF_resblock(nn.Module):
def __init__(self, net_width=256, input_ch=3, input_ch_views=3):
super(Bungee_NeRF_resblock, self).__init__()
self.pts_linears = nn.ModuleList([nn.Linear(input_ch+net_width, net_width), nn.Linear(net_width, net_width)])
self.views_linear = nn.Linear(input_ch_views + net_width, net_width//2)
self.feature_linear = nn.Linear(net_width, net_width)
self.alpha_linear = nn.Linear(net_width, 1)
self.rgb_linear = nn.Linear(net_width//2, 3)
def forward(self, input_pts, input_views, h):
h = torch.cat([input_pts, h], -1)
for i, l in enumerate(self.pts_linears):
h = self.pts_linears[i](h)
h = F.relu(h)
alpha = self.alpha_linear(h)
feature0 = self.feature_linear(h)
h0 = torch.cat([feature0, input_views], -1)
h0 = self.views_linear(h0)
h0 = F.relu(h0)
rgb = self.rgb_linear(h0)
return rgb, alpha, h
class Bungee_NeRF_block(nn.Module):
def __init__(self, num_resblocks=3, net_width=256, input_ch=3, input_ch_views=3):
super(Bungee_NeRF_block, self).__init__()
self.input_ch = input_ch
self.input_ch_views = input_ch_views
self.num_resblocks = num_resblocks
self.baseblock = Bungee_NeRF_baseblock(net_width=net_width, input_ch=input_ch, input_ch_views=input_ch_views)
self.resblocks = nn.ModuleList([
Bungee_NeRF_resblock(net_width=net_width, input_ch=input_ch, input_ch_views=input_ch_views) for _ in range(num_resblocks)
])
def forward(self, x):
input_pts, input_views = torch.split(x, [self.input_ch, self.input_ch_views], dim=-1)
alphas = []
rgbs = []
base_rgb, base_alpha, h = self.baseblock(input_pts, input_views)
alphas.append(base_alpha)
rgbs.append(base_rgb)
for i in range(self.num_resblocks):
res_rgb, res_alpha, h = self.resblocks[i](input_pts, input_views, h)
alphas.append(res_alpha)
rgbs.append(res_rgb)
output = torch.cat([torch.stack(rgbs,1),torch.stack(alphas,1)],-1)
return output
def get_rays(H, W, focal, c2w):
i, j = torch.meshgrid(torch.linspace(0, W-1, W), torch.linspace(0, H-1, H))
i = i.t()
j = j.t()
dirs = torch.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -torch.ones_like(i)], -1)
dirs = dirs/torch.norm(dirs, dim=-1)[...,None]
rays_d = torch.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)
rays_o = c2w[:3,-1].expand(rays_d.shape)
return rays_o, rays_d
def get_rays_np(H, W, focal, c2w):
i, j = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy')
dirs = np.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -np.ones_like(i)], -1)
dirs = dirs/np.linalg.norm(dirs, axis=-1)[..., None]
rays_d = np.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)
rays_o = np.broadcast_to(c2w[:3,-1], np.shape(rays_d))
return rays_o, rays_d
def get_radii_for_test(H, W, focal, c2w):
i, j = torch.meshgrid(torch.linspace(0, W-1, W), torch.linspace(0, H-1, H))
i = i.t()
j = j.t()
dirs = torch.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -torch.ones_like(i)], -1)
rays_d = torch.sum(dirs[np.newaxis, ..., np.newaxis, :] * c2w[:, np.newaxis, np.newaxis, :3,:3], -1)
dx = torch.sqrt(
torch.sum((rays_d[:, :-1, :, :] - rays_d[:, 1:, :, :])**2, -1))
dx = torch.cat([dx, dx[:, -2:-1, :]], 1)
radii = dx[..., None] * 2 / np.sqrt(12)
return radii
def sorted_piecewise_constant_pdf(bins, weights, num_samples, randomized):
eps = 1e-5
weight_sum = torch.sum(weights, axis=-1, keepdims=True)
padding = torch.maximum(torch.tensor(0), eps - weight_sum)
weights += padding / weights.shape[-1]
weight_sum += padding
pdf = weights / weight_sum
cdf = torch.minimum(torch.tensor(1), torch.cumsum(pdf[..., :-1], axis=-1))
cdf = torch.cat([
torch.zeros(list(cdf.shape[:-1]) + [1]), cdf,
torch.ones(list(cdf.shape[:-1]) + [1])
], axis=-1)
if randomized:
s = 1 / num_samples
u = np.arange(num_samples) * s
u = np.broadcast_to(u, list(cdf.shape[:-1]) + [num_samples])
jitter = np.random.uniform(high=s - np.finfo('float32').eps, size=list(cdf.shape[:-1]) + [num_samples])
u = u + jitter
u = np.minimum(u, 1. - np.finfo('float32').eps)
else:
u = np.linspace(0., 1. - np.finfo('float32').eps, num_samples)
u = np.broadcast_to(u, list(cdf.shape[:-1]) + [num_samples])
u = torch.from_numpy(u).to(cdf)
mask = u[..., None, :] >= cdf[..., :, None]
def find_interval(x):
x0 = torch.max(torch.where(mask, x[..., None], x[..., :1, None]), dim=-2)[0]
x1 = torch.min(torch.where(~mask, x[..., None], x[..., -1:, None]), dim=-2)[0]
return x0, x1
bins_g0, bins_g1 = find_interval(bins)
cdf_g0, cdf_g1 = find_interval(cdf)
t = (u - cdf_g0) / (cdf_g1 - cdf_g0)
t[t != t] = 0
t = torch.clamp(t, 0, 1)
samples = bins_g0 + t * (bins_g1 - bins_g0)
return samples