-
Notifications
You must be signed in to change notification settings - Fork 0
/
pcpnet.py
298 lines (246 loc) · 10.3 KB
/
pcpnet.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
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
import utils
class STN(nn.Module):
def __init__(self, num_scales=1, num_points=500, dim=3, sym_op='max'):
super(STN, self).__init__()
self.dim = dim
self.sym_op = sym_op
self.num_scales = num_scales
self.num_points = num_points
self.conv1 = torch.nn.Conv1d(self.dim, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.mp1 = torch.nn.MaxPool1d(num_points)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, self.dim*self.dim)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.bn4 = nn.BatchNorm1d(512)
self.bn5 = nn.BatchNorm1d(256)
if self.num_scales > 1:
self.fc0 = nn.Linear(1024*self.num_scales, 1024)
self.bn0 = nn.BatchNorm1d(1024)
def forward(self, x):
batchsize = x.size()[0]
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
# symmetric operation over all points
if self.num_scales == 1:
x = self.mp1(x)
else:
x_scales = x.new_empty(x.size(0), 1024*self.num_scales, 1)
for s in range(self.num_scales):
x_scales[:, s*1024:(s+1)*1024, :] = self.mp1(x[:, :, s*self.num_points:(s+1)*self.num_points])
x = x_scales
x = x.view(-1, 1024*self.num_scales)
if self.num_scales > 1:
x = F.relu(self.bn0(self.fc0(x)))
x = F.relu(self.bn4(self.fc1(x)))
x = F.relu(self.bn5(self.fc2(x)))
x = self.fc3(x)
iden = torch.eye(self.dim, dtype=x.dtype, device=x.device).view(1, self.dim*self.dim).repeat(batchsize, 1)
x = x + iden
x = x.view(-1, self.dim, self.dim)
return x
class QSTN(nn.Module):
def __init__(self, num_scales=1, num_points=500, dim=3, sym_op='max'):
super(QSTN, self).__init__()
self.dim = dim
self.sym_op = sym_op
self.num_scales = num_scales
self.num_points = num_points
self.conv1 = torch.nn.Conv1d(self.dim, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.mp1 = torch.nn.MaxPool1d(num_points)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 4)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.bn4 = nn.BatchNorm1d(512)
self.bn5 = nn.BatchNorm1d(256)
if self.num_scales > 1:
self.fc0 = nn.Linear(1024*self.num_scales, 1024)
self.bn0 = nn.BatchNorm1d(1024)
def forward(self, x):
batchsize = x.size()[0]
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
# symmetric operation over all points
if self.num_scales == 1:
x = self.mp1(x)
else:
x_scales = x.new_empty(x.size(0), 1024*self.num_scales, 1)
for s in range(self.num_scales):
x_scales[:, s*1024:(s+1)*1024, :] = self.mp1(x[:, :, s*self.num_points:(s+1)*self.num_points])
x = x_scales
x = x.view(-1, 1024*self.num_scales)
if self.num_scales > 1:
x = F.relu(self.bn0(self.fc0(x)))
x = F.relu(self.bn4(self.fc1(x)))
x = F.relu(self.bn5(self.fc2(x)))
x = self.fc3(x)
# add identity quaternion (so the network can output 0 to leave the point cloud identical)
iden = x.new_tensor([1, 0, 0, 0])
x = x + iden
# convert quaternion to rotation matrix
x = utils.batch_quat_to_rotmat(x)
return x
class PointNetfeat(nn.Module):
def __init__(self, num_scales=1, num_points=500, use_point_stn=True, use_feat_stn=True, sym_op='max', get_pointfvals=False, point_tuple=1):
super(PointNetfeat, self).__init__()
self.num_points = num_points
self.num_scales = num_scales
self.use_point_stn = use_point_stn
self.use_feat_stn = use_feat_stn
self.sym_op = sym_op
self.get_pointfvals = get_pointfvals
self.point_tuple = point_tuple
if self.use_point_stn:
# self.stn1 = STN(num_scales=self.num_scales, num_points=num_points, dim=3, sym_op=self.sym_op)
self.stn1 = QSTN(num_scales=self.num_scales, num_points=num_points*self.point_tuple, dim=3, sym_op=self.sym_op)
if self.use_feat_stn:
self.stn2 = STN(num_scales=self.num_scales, num_points=num_points, dim=64, sym_op=self.sym_op)
self.conv0a = torch.nn.Conv1d(3*self.point_tuple, 64, 1)
self.conv0b = torch.nn.Conv1d(64, 64, 1)
self.bn0a = nn.BatchNorm1d(64)
self.bn0b = nn.BatchNorm1d(64)
self.conv1 = torch.nn.Conv1d(64, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
if self.num_scales > 1:
self.conv4 = torch.nn.Conv1d(1024, 1024*self.num_scales, 1)
self.bn4 = nn.BatchNorm1d(1024*self.num_scales)
if self.sym_op == 'max':
self.mp1 = torch.nn.MaxPool1d(num_points)
elif self.sym_op == 'sum':
self.mp1 = None
else:
raise ValueError('Unsupported symmetric operation: %s' % (self.sym_op))
def forward(self, x):
# input transform
if self.use_point_stn:
# from tuples to list of single points
x = x.view(x.size(0), 3, -1)
trans = self.stn1(x)
x = x.transpose(2, 1)
x = torch.bmm(x, trans)
x = x.transpose(2, 1)
x = x.contiguous().view(x.size(0), 3*self.point_tuple, -1)
else:
trans = None
# mlp (64,64)
x = F.relu(self.bn0a(self.conv0a(x)))
x = F.relu(self.bn0b(self.conv0b(x)))
# feature transform
if self.use_feat_stn:
trans2 = self.stn2(x)
x = x.transpose(2, 1)
x = torch.bmm(x, trans2)
x = x.transpose(2, 1)
else:
trans2 = None
# mlp (64,128,1024)
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
# mlp (1024,1024*num_scales)
if self.num_scales > 1:
x = self.bn4(self.conv4(F.relu(x)))
if self.get_pointfvals:
pointfvals = x
else:
pointfvals = None # so the intermediate result can be forgotten if it is not needed
# symmetric max operation over all points
if self.num_scales == 1:
if self.sym_op == 'max':
x = self.mp1(x)
elif self.sym_op == 'sum':
x = torch.sum(x, 2, keepdim=True)
else:
raise ValueError('Unsupported symmetric operation: %s' % (self.sym_op))
else:
x_scales = x.new_empty(x.size(0), 1024*self.num_scales**2, 1)
if self.sym_op == 'max':
for s in range(self.num_scales):
x_scales[:, s*self.num_scales*1024:(s+1)*self.num_scales*1024, :] = self.mp1(x[:, :, s*self.num_points:(s+1)*self.num_points])
elif self.sym_op == 'sum':
for s in range(self.num_scales):
x_scales[:, s*self.num_scales*1024:(s+1)*self.num_scales*1024, :] = torch.sum(x[:, :, s*self.num_points:(s+1)*self.num_points], 2, keepdim=True)
else:
raise ValueError('Unsupported symmetric operation: %s' % (self.sym_op))
x = x_scales
x = x.view(-1, 1024*self.num_scales**2)
return x, trans, trans2, pointfvals
class PCPNet(nn.Module):
def __init__(self, num_points=500, output_dim=3, use_point_stn=True, use_feat_stn=True, sym_op='max', get_pointfvals=False, point_tuple=1):
super(PCPNet, self).__init__()
self.num_points = num_points
self.feat = PointNetfeat(
num_points=num_points,
num_scales=1,
use_point_stn=use_point_stn,
use_feat_stn=use_feat_stn,
sym_op=sym_op,
get_pointfvals=get_pointfvals,
point_tuple=point_tuple)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, output_dim)
self.bn1 = nn.BatchNorm1d(512)
self.bn2 = nn.BatchNorm1d(256)
self.do1 = nn.Dropout(p=0.3)
self.do2 = nn.Dropout(p=0.3)
def forward(self, x):
x, trans, trans2, pointfvals = self.feat(x)
x = F.relu(self.bn1(self.fc1(x)))
x = self.do1(x)
x = F.relu(self.bn2(self.fc2(x)))
x = self.do2(x)
x = self.fc3(x)
return x, trans, trans2, pointfvals
class MSPCPNet(nn.Module):
def __init__(self, num_scales=2, num_points=500, output_dim=3, use_point_stn=True, use_feat_stn=True, sym_op='max', get_pointfvals=False, point_tuple=1):
super(MSPCPNet, self).__init__()
self.num_points = num_points
self.feat = PointNetfeat(
num_points=num_points,
num_scales=num_scales,
use_point_stn=use_point_stn,
use_feat_stn=use_feat_stn,
sym_op=sym_op,
get_pointfvals=get_pointfvals,
point_tuple=point_tuple)
self.fc0 = nn.Linear(1024*num_scales**2, 1024)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, output_dim)
self.bn0 = nn.BatchNorm1d(1024)
self.bn1 = nn.BatchNorm1d(512)
self.bn2 = nn.BatchNorm1d(256)
self.do1 = nn.Dropout(p=0.3)
self.do2 = nn.Dropout(p=0.3)
def forward(self, x):
x, trans, trans2, pointfvals = self.feat(x)
x = F.relu(self.bn0(self.fc0(x)))
x = F.relu(self.bn1(self.fc1(x)))
x = self.do1(x)
x = F.relu(self.bn2(self.fc2(x)))
x = self.do2(x)
x = self.fc3(x)
return x, trans, trans2, pointfvals