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
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
|
# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# The MIT License (MIT)
#
# Copyright (c) 2018-2021 www.open3d.org
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
# ----------------------------------------------------------------------------
import tensorflow as tf
import open3d.ml.tf as ml3d
import numpy as np
class MyParticleNetwork(tf.keras.Model):
def __init__(
self,
kernel_size=[4, 4, 4],
radius_scale=1.5,
coordinate_mapping='ball_to_cube_volume_preserving',
interpolation='linear',
use_window=True,
particle_radius=0.025,
timestep=1 / 50,
):
super().__init__(name=type(self).__name__)
self.layer_channels = [32, 64, 64, 3]
self.kernel_size = kernel_size
self.radius_scale = radius_scale
self.coordinate_mapping = coordinate_mapping
self.interpolation = interpolation
self.use_window = use_window
self.particle_radius = particle_radius
self.filter_extent = np.float32(self.radius_scale * 6 *
self.particle_radius)
self.timestep = timestep
self._all_convs = []
def window_poly6(r_sqr):
return tf.clip_by_value((1 - r_sqr)**3, 0, 1)
def Conv(name, activation=None, **kwargs):
conv_fn = ml3d.layers.ContinuousConv
window_fn = None
if self.use_window == True:
window_fn = window_poly6
conv = conv_fn(name=name,
kernel_size=self.kernel_size,
activation=activation,
align_corners=True,
interpolation=self.interpolation,
coordinate_mapping=self.coordinate_mapping,
normalize=False,
window_function=window_fn,
radius_search_ignore_query_points=True,
**kwargs)
self._all_convs.append((name, conv))
return conv
self.conv0_fluid = Conv(name="conv0_fluid",
filters=self.layer_channels[0],
activation=None)
self.conv0_obstacle = Conv(name="conv0_obstacle",
filters=self.layer_channels[0],
activation=None)
self.dense0_fluid = tf.keras.layers.Dense(name="dense0_fluid",
units=self.layer_channels[0],
activation=None)
self.convs = []
self.denses = []
for i in range(1, len(self.layer_channels)):
ch = self.layer_channels[i]
dense = tf.keras.layers.Dense(units=ch,
name="dense{0}".format(i),
activation=None)
conv = Conv(name='conv{0}'.format(i), filters=ch, activation=None)
self.denses.append(dense)
self.convs.append(conv)
def integrate_pos_vel(self, pos1, vel1):
"""Apply gravity and integrate position and velocity"""
dt = self.timestep
vel2 = vel1 + dt * tf.constant([0, -9.81, 0])
pos2 = pos1 + dt * (vel2 + vel1) / 2
return pos2, vel2
def compute_new_pos_vel(self, pos1, vel1, pos2, vel2, pos_correction):
"""Apply the correction
pos1,vel1 are the positions and velocities from the previous timestep
pos2,vel2 are the positions after applying gravity and the integration step
"""
dt = self.timestep
pos = pos2 + pos_correction
vel = (pos - pos1) / dt
return pos, vel
def compute_correction(self,
pos,
vel,
other_feats,
box,
box_feats,
fixed_radius_search_hash_table=None):
"""Expects that the pos and vel has already been updated with gravity and velocity"""
# compute the extent of the filters (the diameter)
filter_extent = tf.constant(self.filter_extent)
fluid_feats = [tf.ones_like(pos[:, 0:1]), vel]
if not other_feats is None:
fluid_feats.append(other_feats)
fluid_feats = tf.concat(fluid_feats, axis=-1)
self.ans_conv0_fluid = self.conv0_fluid(fluid_feats, pos, pos,
filter_extent)
self.ans_dense0_fluid = self.dense0_fluid(fluid_feats)
self.ans_conv0_obstacle = self.conv0_obstacle(box_feats, box, pos,
filter_extent)
feats = tf.concat([
self.ans_conv0_obstacle, self.ans_conv0_fluid, self.ans_dense0_fluid
],
axis=-1)
self.ans_convs = [feats]
for conv, dense in zip(self.convs, self.denses):
inp_feats = tf.keras.activations.relu(self.ans_convs[-1])
ans_conv = conv(inp_feats, pos, pos, filter_extent)
ans_dense = dense(inp_feats)
if ans_dense.shape[-1] == self.ans_convs[-1].shape[-1]:
ans = ans_conv + ans_dense + self.ans_convs[-1]
else:
ans = ans_conv + ans_dense
self.ans_convs.append(ans)
# compute the number of fluid neighbors.
# this info is used in the loss function during training.
self.num_fluid_neighbors = ml3d.ops.reduce_subarrays_sum(
tf.ones_like(self.conv0_fluid.nns.neighbors_index,
dtype=tf.float32),
self.conv0_fluid.nns.neighbors_row_splits)
self.last_features = self.ans_convs[-2]
# scale to better match the scale of the output distribution
self.pos_correction = (1.0 / 128) * self.ans_convs[-1]
return self.pos_correction
def call(self, inputs, fixed_radius_search_hash_table=None):
"""computes 1 simulation timestep
inputs: list or tuple with (pos,vel,feats,box,box_feats)
pos and vel are the positions and velocities of the fluid particles.
feats is reserved for passing additional features, use None here.
box are the positions of the static particles and box_feats are the
normals of the static particles.
"""
pos, vel, feats, box, box_feats = inputs
pos2, vel2 = self.integrate_pos_vel(pos, vel)
pos_correction = self.compute_correction(
pos2, vel2, feats, box, box_feats, fixed_radius_search_hash_table)
pos2_corrected, vel2_corrected = self.compute_new_pos_vel(
pos, vel, pos2, vel2, pos_correction)
return pos2_corrected, vel2_corrected
def init(self, feats_shape=None):
"""Runs the network with dummy data to initialize the shape of all variables"""
pos = np.zeros(shape=(1, 3), dtype=np.float32)
vel = np.zeros(shape=(1, 3), dtype=np.float32)
if feats_shape is None:
feats = None
else:
feats = np.zeros(shape=feats_shape, dtype=np.float32)
box = np.zeros(shape=(1, 3), dtype=np.float32)
box_feats = np.zeros(shape=(1, 3), dtype=np.float32)
_ = self.__call__((pos, vel, feats, box, box_feats))
|
