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
|
import torch
import torch.nn as nn
from models.auto_encoder import AutoEncoder
from models.hpm import HorizontalPyramidMatching
from models.part_net import PartNet
from utils.triplet_loss import BatchAllTripletLoss
class RGBPartNet(nn.Module):
def __init__(
self,
ae_in_channels: int = 3,
ae_in_size: tuple[int, int] = (64, 48),
ae_feature_channels: int = 64,
f_a_c_p_dims: tuple[int, int, int] = (128, 128, 64),
hpm_use_1x1conv: bool = False,
hpm_scales: tuple[int, ...] = (1, 2, 4),
hpm_use_avg_pool: bool = True,
hpm_use_max_pool: bool = True,
fpfe_feature_channels: int = 32,
fpfe_kernel_sizes: tuple[tuple, ...] = ((5, 3), (3, 3), (3, 3)),
fpfe_paddings: tuple[tuple, ...] = ((2, 1), (1, 1), (1, 1)),
fpfe_halving: tuple[int, ...] = (0, 2, 3),
tfa_squeeze_ratio: int = 4,
tfa_num_parts: int = 16,
embedding_dims: int = 256,
triplet_margins: tuple[float, float] = (0.2, 0.2),
image_log_on: bool = False
):
super().__init__()
(self.f_a_dim, self.f_c_dim, self.f_p_dim) = f_a_c_p_dims
self.hpm_num_parts = sum(hpm_scales)
self.image_log_on = image_log_on
self.ae = AutoEncoder(
ae_in_channels, ae_in_size, ae_feature_channels, f_a_c_p_dims
)
self.pn = PartNet(
ae_in_channels, fpfe_feature_channels, fpfe_kernel_sizes,
fpfe_paddings, fpfe_halving, tfa_squeeze_ratio, tfa_num_parts
)
out_channels = self.pn.tfa_in_channels
self.hpm = HorizontalPyramidMatching(
ae_feature_channels * 2, out_channels, hpm_use_1x1conv,
hpm_scales, hpm_use_avg_pool, hpm_use_max_pool
)
self.num_total_parts = self.hpm_num_parts + tfa_num_parts
empty_fc = torch.empty(self.num_total_parts,
out_channels, embedding_dims)
self.fc_mat = nn.Parameter(empty_fc)
(hpm_margin, pn_margin) = triplet_margins
self.hpm_ba_trip = BatchAllTripletLoss(hpm_margin)
self.pn_ba_trip = BatchAllTripletLoss(pn_margin)
def fc(self, x):
return x @ self.fc_mat
def forward(self, x_c1, x_c2=None, y=None):
# Step 1: Disentanglement
# n, t, c, h, w
((x_c, x_p), losses, images) = self._disentangle(x_c1, x_c2)
# Step 2.a: Static Gait Feature Aggregation & HPM
# n, c, h, w
x_c = self.hpm(x_c)
# p, n, c
# Step 2.b: FPFE & TFA (Dynamic Gait Feature Aggregation)
# n, t, c, h, w
x_p = self.pn(x_p)
# p, n, c
# Step 3: Cat feature map together and fc
x = torch.cat((x_c, x_p))
x = self.fc(x)
if self.training:
y = y.T
hpm_ba_trip = self.hpm_ba_trip(
x[:self.hpm_num_parts], y[:self.hpm_num_parts]
)
pn_ba_trip = self.pn_ba_trip(
x[self.hpm_num_parts:], y[self.hpm_num_parts:]
)
losses = torch.stack((*losses, hpm_ba_trip, pn_ba_trip))
return losses, images
else:
return x.unsqueeze(1).view(-1)
def _disentangle(self, x_c1_t2, x_c2_t2=None):
n, t, c, h, w = x_c1_t2.size()
device = x_c1_t2.device
x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :]
if self.training:
((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2)
# Decode features
with torch.no_grad():
x_c = self._decode_cano_feature(f_c_, n, t, device)
x_p = self._decode_pose_feature(f_p_, n, t, c, h, w, device)
i_a, i_c, i_p = None, None, None
if self.image_log_on:
i_a = self._decode_appr_feature(f_a_, n, t, device)
# Continue decoding canonical features
i_c = self.ae.decoder.trans_conv3(x_c)
i_c = torch.sigmoid(self.ae.decoder.trans_conv4(i_c))
i_p = x_p
return (x_c, x_p), losses, (i_a, i_c, i_p)
else: # evaluating
f_c_, f_p_ = self.ae(x_c1_t2)
x_c = self._decode_cano_feature(f_c_, n, t, device)
x_p = self._decode_pose_feature(f_p_, n, t, c, h, w, device)
return (x_c, x_p), None, None
def _decode_appr_feature(self, f_a_, n, t, device):
# Decode appearance features
f_a = f_a_.view(n, t, -1)
x_a = self.ae.decoder(
f_a.mean(1),
torch.zeros((n, self.f_c_dim), device=device),
torch.zeros((n, self.f_p_dim), device=device)
)
return x_a
def _decode_cano_feature(self, f_c_, n, t, device):
# Decode average canonical features to higher dimension
f_c = f_c_.view(n, t, -1)
x_c = self.ae.decoder(
torch.zeros((n, self.f_a_dim), device=device),
f_c.mean(1),
torch.zeros((n, self.f_p_dim), device=device),
cano_only=True
)
return x_c
def _decode_pose_feature(self, f_p_, n, t, c, h, w, device):
# Decode pose features to images
x_p_ = self.ae.decoder(
torch.zeros((n * t, self.f_a_dim), device=device),
torch.zeros((n * t, self.f_c_dim), device=device),
f_p_
)
x_p = x_p_.view(n, t, c, h, w)
return x_p
|
