-
Notifications
You must be signed in to change notification settings - Fork 85
/
Copy pathmodel.py
140 lines (120 loc) · 5.05 KB
/
model.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
import torch
import torch.nn as nn
import torch.nn.functional as F
from util import sample_and_group
class Local_op(nn.Module):
def __init__(self, in_channels, out_channels):
super(Local_op, self).__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(out_channels)
self.bn2 = nn.BatchNorm1d(out_channels)
def forward(self, x):
b, n, s, d = x.size() # torch.Size([32, 512, 32, 6])
x = x.permute(0, 1, 3, 2)
x = x.reshape(-1, d, s)
batch_size, _, N = x.size()
x = F.relu(self.bn1(self.conv1(x))) # B, D, N
x = F.relu(self.bn2(self.conv2(x))) # B, D, N
x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
x = x.reshape(b, n, -1).permute(0, 2, 1)
return x
class Pct(nn.Module):
def __init__(self, args, output_channels=40):
super(Pct, self).__init__()
self.args = args
self.conv1 = nn.Conv1d(3, 64, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(64, 64, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(64)
self.gather_local_0 = Local_op(in_channels=128, out_channels=128)
self.gather_local_1 = Local_op(in_channels=256, out_channels=256)
self.pt_last = Point_Transformer_Last(args)
self.conv_fuse = nn.Sequential(nn.Conv1d(1280, 1024, kernel_size=1, bias=False),
nn.BatchNorm1d(1024),
nn.LeakyReLU(negative_slope=0.2))
self.linear1 = nn.Linear(1024, 512, bias=False)
self.bn6 = nn.BatchNorm1d(512)
self.dp1 = nn.Dropout(p=args.dropout)
self.linear2 = nn.Linear(512, 256)
self.bn7 = nn.BatchNorm1d(256)
self.dp2 = nn.Dropout(p=args.dropout)
self.linear3 = nn.Linear(256, output_channels)
def forward(self, x):
xyz = x.permute(0, 2, 1)
batch_size, _, _ = x.size()
# B, D, N
x = F.relu(self.bn1(self.conv1(x)))
# B, D, N
x = F.relu(self.bn2(self.conv2(x)))
x = x.permute(0, 2, 1)
new_xyz, new_feature = sample_and_group(npoint=512, radius=0.15, nsample=32, xyz=xyz, points=x)
feature_0 = self.gather_local_0(new_feature)
feature = feature_0.permute(0, 2, 1)
new_xyz, new_feature = sample_and_group(npoint=256, radius=0.2, nsample=32, xyz=new_xyz, points=feature)
feature_1 = self.gather_local_1(new_feature)
x = self.pt_last(feature_1)
x = torch.cat([x, feature_1], dim=1)
x = self.conv_fuse(x)
x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2)
x = self.dp1(x)
x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2)
x = self.dp2(x)
x = self.linear3(x)
return x
class Point_Transformer_Last(nn.Module):
def __init__(self, args, channels=256):
super(Point_Transformer_Last, self).__init__()
self.args = args
self.conv1 = nn.Conv1d(channels, channels, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(channels, channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(channels)
self.bn2 = nn.BatchNorm1d(channels)
self.sa1 = SA_Layer(channels)
self.sa2 = SA_Layer(channels)
self.sa3 = SA_Layer(channels)
self.sa4 = SA_Layer(channels)
def forward(self, x):
#
# b, 3, npoint, nsample
# conv2d 3 -> 128 channels 1, 1
# b * npoint, c, nsample
# permute reshape
batch_size, _, N = x.size()
# B, D, N
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x1 = self.sa1(x)
x2 = self.sa2(x1)
x3 = self.sa3(x2)
x4 = self.sa4(x3)
x = torch.cat((x1, x2, x3, x4), dim=1)
return x
class SA_Layer(nn.Module):
def __init__(self, channels):
super(SA_Layer, self).__init__()
self.q_conv = nn.Conv1d(channels, channels // 4, 1, bias=False)
self.k_conv = nn.Conv1d(channels, channels // 4, 1, bias=False)
self.q_conv.weight = self.k_conv.weight
self.q_conv.bias = self.k_conv.bias
self.v_conv = nn.Conv1d(channels, channels, 1)
self.trans_conv = nn.Conv1d(channels, channels, 1)
self.after_norm = nn.BatchNorm1d(channels)
self.act = nn.ReLU()
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
# b, n, c
x_q = self.q_conv(x).permute(0, 2, 1)
# b, c, n
x_k = self.k_conv(x)
x_v = self.v_conv(x)
# b, n, n
energy = torch.bmm(x_q, x_k)
attention = self.softmax(energy)
attention = attention / (1e-9 + attention.sum(dim=1, keepdim=True))
# b, c, n
x_r = torch.bmm(x_v, attention)
x_r = self.act(self.after_norm(self.trans_conv(x - x_r)))
x = x + x_r
return x