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
|
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.layers import VGGConv2d, DCGANConvTranspose2d, BasicLinear
class Encoder(nn.Module):
"""Squeeze input feature map to lower dimension"""
def __init__(
self,
in_channels: int = 3,
frame_size: Tuple[int, int] = (64, 48),
feature_channels: int = 64,
output_dims: Tuple[int, int, int] = (128, 128, 64)
):
super().__init__()
self.feature_channels = feature_channels
h_0, w_0 = frame_size
h_1, w_1 = h_0 // 2, w_0 // 2
h_2, w_2 = h_1 // 2, w_1 // 2
self.feature_size = self.h_3, self.w_3 = h_2 // 4, w_2 // 4
# Appearance features, canonical features, pose features
(self.f_a_dim, self.f_c_dim, self.f_p_dim) = output_dims
# Conv1 in_channels x H x W
# -> feature_map_size x H x W
self.conv1 = VGGConv2d(in_channels, feature_channels)
# MaxPool1 feature_map_size x H x W
# -> feature_map_size x H//2 x W//2
self.max_pool1 = nn.AdaptiveMaxPool2d((h_1, w_1))
# Conv2 feature_map_size x H//2 x W//2
# -> feature_map_size*4 x H//2 x W//2
self.conv2 = VGGConv2d(feature_channels, feature_channels * 4)
# MaxPool2 feature_map_size*4 x H//2 x W//2
# -> feature_map_size*4 x H//4 x W//4
self.max_pool2 = nn.AdaptiveMaxPool2d((h_2, w_2))
# Conv3 feature_map_size*4 x H//4 x W//4
# -> feature_map_size*8 x H//4 x W//4
self.conv3 = VGGConv2d(feature_channels * 4, feature_channels * 8)
# Conv4 feature_map_size*8 x H//4 x W//4
# -> feature_map_size*8 x H//4 x W//4 (for large dataset)
self.conv4 = VGGConv2d(feature_channels * 8, feature_channels * 8)
# MaxPool3 feature_map_size*8 x H//4 x W//4
# -> feature_map_size*8 x H//16 x W//16
self.max_pool3 = nn.AdaptiveMaxPool2d(self.feature_size)
embedding_dim = sum(output_dims)
# FC feature_map_size*8 * H//16 * W//16 -> embedding_dim
self.fc = BasicLinear(
(feature_channels * 8) * self.h_3 * self.w_3, embedding_dim
)
def forward(self, x):
x = self.conv1(x)
x = self.max_pool1(x)
x = self.conv2(x)
x = self.max_pool2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.max_pool3(x)
x = x.view(-1, (self.feature_channels * 8) * self.h_3 * self.w_3)
embedding = self.fc(x)
f_appearance, f_canonical, f_pose = embedding.split(
(self.f_a_dim, self.f_c_dim, self.f_p_dim), dim=1
)
return f_appearance, f_canonical, f_pose
class Decoder(nn.Module):
"""Upscale embedding to original image"""
def __init__(
self,
input_dims: Tuple[int, int, int] = (128, 128, 64),
feature_channels: int = 64,
feature_size: Tuple[int, int] = (4, 3),
out_channels: int = 3,
):
super().__init__()
self.feature_channels = feature_channels
self.h_0, self.w_0 = feature_size
embedding_dim = sum(input_dims)
# FC 320 -> feature_map_size*8 * H * W
self.fc = BasicLinear(
embedding_dim, (feature_channels * 8) * self.h_0 * self.w_0
)
# TransConv1 feature_map_size*8 x H x W
# -> feature_map_size*4 x H*2 x W*2
self.trans_conv1 = DCGANConvTranspose2d(feature_channels * 8,
feature_channels * 4)
# TransConv2 feature_map_size*4 x H*2 x W*2
# -> feature_map_size*2 x H*4 x W*4
self.trans_conv2 = DCGANConvTranspose2d(feature_channels * 4,
feature_channels * 2)
# TransConv3 feature_map_size*2 x H*4 x W*4
# -> feature_map_size x H*8 x W*8
self.trans_conv3 = DCGANConvTranspose2d(feature_channels * 2,
feature_channels)
# TransConv4 feature_map_size x H*8 x W*8
# -> in_channels x H*16 x W*16
self.trans_conv4 = DCGANConvTranspose2d(feature_channels, out_channels,
is_last_layer=True)
def forward(self, f_appearance, f_canonical, f_pose):
x = torch.cat((f_appearance, f_canonical, f_pose), dim=1)
x = self.fc(x)
x = x.view(-1, self.feature_channels * 8, self.h_0, self.w_0)
x = F.relu(x, inplace=True)
x = self.trans_conv1(x)
x = self.trans_conv2(x)
x = self.trans_conv3(x)
x = torch.sigmoid(self.trans_conv4(x))
return x
class AutoEncoder(nn.Module):
def __init__(
self,
channels: int = 3,
frame_size: Tuple[int, int] = (64, 48),
feature_channels: int = 64,
embedding_dims: Tuple[int, int, int] = (128, 128, 64)
):
super().__init__()
self.encoder = Encoder(channels, frame_size,
feature_channels, embedding_dims)
self.decoder = Decoder(embedding_dims, feature_channels,
self.encoder.feature_size, channels)
def forward(self, x_c1_t2, x_c1_t1=None, x_c2_t2=None):
n, t, c, h, w = x_c1_t2.size()
# x_c1_t2 is the frame for later module
x_c1_t2_ = x_c1_t2.view(n * t, c, h, w)
(f_a_c1_t2_, f_c_c1_t2_, f_p_c1_t2_) = self.encoder(x_c1_t2_)
if self.training:
# t1 is random time step, c2 is another condition
x_c1_t1 = x_c1_t1.view(n * t, c, h, w)
(f_a_c1_t1_, f_c_c1_t1_, _) = self.encoder(x_c1_t1)
x_c2_t2 = x_c2_t2.view(n * t, c, h, w)
(_, f_c_c2_t2_, f_p_c2_t2_) = self.encoder(x_c2_t2)
x_c1_t2_pred_ = self.decoder(f_a_c1_t1_, f_c_c1_t1_, f_p_c1_t2_)
x_c1_t2_pred = x_c1_t2_pred_.view(n, t, c, h, w)
xrecon_loss = torch.stack([
F.mse_loss(x_c1_t2[:, i, :, :, :], x_c1_t2_pred[:, i, :, :, :])
for i in range(t)
]).sum()
f_c_c1_t1 = f_c_c1_t1_.view(n, t, -1)
f_c_c1_t2 = f_c_c1_t2_.view(n, t, -1)
f_c_c2_t2 = f_c_c2_t2_.view(n, t, -1)
cano_cons_loss = torch.stack([
F.mse_loss(f_c_c1_t1[:, i, :], f_c_c1_t2[:, i, :])
+ F.mse_loss(f_c_c1_t2[:, i, :], f_c_c2_t2[:, i, :])
for i in range(t)
]).mean()
f_p_c1_t2 = f_p_c1_t2_.view(n, t, -1)
f_p_c2_t2 = f_p_c2_t2_.view(n, t, -1)
pose_sim_loss = F.mse_loss(f_p_c1_t2.mean(1), f_p_c2_t2.mean(1))
return (
(f_a_c1_t2_, f_c_c1_t2_, f_p_c1_t2_),
(xrecon_loss, cano_cons_loss, pose_sim_loss * 10)
)
else: # evaluating
return f_c_c1_t2_, f_p_c1_t2_
|
