forked from bmild/nerf
-
Notifications
You must be signed in to change notification settings - Fork 0
/
run_nerf_helpers.py
185 lines (130 loc) · 5.77 KB
/
run_nerf_helpers.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
import os, sys
import tensorflow as tf
import numpy as np
import imageio
import json
# Misc utils
img2mse = lambda x, y : tf.reduce_mean(tf.square(x - y))
mse2psnr = lambda x : -10.*tf.log(x)/tf.log(10.)
to8b = lambda x : (255*np.clip(x,0,1)).astype(np.uint8)
# Positional encoding
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']
N_freqs = self.kwargs['num_freqs']
if self.kwargs['log_sampling']:
freq_bands = 2.**tf.linspace(0., max_freq, N_freqs)
else:
freq_bands = tf.linspace(2.**0., 2.**max_freq, N_freqs)
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 tf.concat([fn(inputs) for fn in self.embed_fns], -1)
def get_embedder(multires, i=0):
if i == -1:
return tf.identity, 3
embed_kwargs = {
'include_input' : True,
'input_dims' : 3,
'max_freq_log2' : multires-1,
'num_freqs' : multires,
'log_sampling' : True,
'periodic_fns' : [tf.math.sin, tf.math.cos],
}
embedder_obj = Embedder(**embed_kwargs)
embed = lambda x, eo=embedder_obj : eo.embed(x)
return embed, embedder_obj.out_dim
# Model architecture
def init_nerf_model(D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False):
relu = tf.keras.layers.ReLU()
dense = lambda W, act=relu : tf.keras.layers.Dense(W, activation=act)
print('MODEL', input_ch, input_ch_views, type(input_ch), type(input_ch_views), use_viewdirs)
input_ch = int(input_ch)
input_ch_views = int(input_ch_views)
inputs = tf.keras.Input(shape=(input_ch + input_ch_views))
inputs_pts, inputs_views = tf.split(inputs, [input_ch, input_ch_views], -1)
inputs_pts.set_shape([None, input_ch])
inputs_views.set_shape([None, input_ch_views])
print(inputs.shape, inputs_pts.shape, inputs_views.shape)
outputs = inputs_pts
for i in range(D):
outputs = dense(W)(outputs)
if i in skips:
outputs = tf.concat([inputs_pts, outputs], -1)
if use_viewdirs:
alpha_out = dense(1, act=None)(outputs)
bottleneck = dense(256, act=None)(outputs)
inputs_viewdirs = tf.concat([bottleneck, inputs_views], -1) # concat viewdirs
for i in range(D//2):
outputs = dense(W//2)(inputs_viewdirs)
outputs = dense(3, act=None)(outputs)
outputs = tf.concat([outputs, alpha_out], -1)
else:
outputs = dense(output_ch, act=None)(outputs)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
return model
# Ray helpers
def get_rays(H, W, focal, c2w):
i, j = tf.meshgrid(tf.range(W, dtype=tf.float32), tf.range(H, dtype=tf.float32), indexing='xy')
dirs = tf.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -tf.ones_like(i)], -1)
rays_d = tf.reduce_sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)
rays_o = tf.broadcast_to(c2w[:3,-1], tf.shape(rays_d))
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)
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 ndc_rays(H, W, focal, near, rays_o, rays_d):
# Shift ray origins to near plane
t = -(near + rays_o[...,2]) / rays_d[...,2]
rays_o = rays_o + t[...,None] * rays_d
# Projection
o0 = -1./(W/(2.*focal)) * rays_o[...,0] / rays_o[...,2]
o1 = -1./(H/(2.*focal)) * rays_o[...,1] / rays_o[...,2]
o2 = 1. + 2. * near / rays_o[...,2]
d0 = -1./(W/(2.*focal)) * (rays_d[...,0]/rays_d[...,2] - rays_o[...,0]/rays_o[...,2])
d1 = -1./(H/(2.*focal)) * (rays_d[...,1]/rays_d[...,2] - rays_o[...,1]/rays_o[...,2])
d2 = -2. * near / rays_o[...,2]
rays_o = tf.stack([o0,o1,o2], -1)
rays_d = tf.stack([d0,d1,d2], -1)
return rays_o, rays_d
# Hierarchical sampling helper
def sample_pdf(bins, weights, N_samples, det=False):
# Get pdf
weights += 1e-5 # prevent nans
pdf = weights / tf.reduce_sum(weights, -1, keepdims=True)
cdf = tf.cumsum(pdf, -1)
cdf = tf.concat([tf.zeros_like(cdf[...,:1]), cdf], -1)
# Take uniform samples
if det:
u = tf.linspace(0., 1., N_samples)
u = tf.broadcast_to(u, list(cdf.shape[:-1]) + [N_samples])
else:
u = tf.random.uniform(list(cdf.shape[:-1]) + [N_samples])
# Invert CDF
inds = tf.searchsorted(cdf, u, side='right')
below = tf.maximum(0, inds-1)
above = tf.minimum(cdf.shape[-1]-1, inds)
inds_g = tf.stack([below, above], -1)
cdf_g = tf.gather(cdf, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2)
bins_g = tf.gather(bins, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2)
denom = (cdf_g[...,1]-cdf_g[...,0])
denom = tf.where(denom<1e-5, tf.ones_like(denom), denom)
t = (u-cdf_g[...,0])/denom
samples = bins_g[...,0] + t * (bins_g[...,1]-bins_g[...,0])
return samples