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
|
"""Time-Depth Separable Convolutions.
References:
https://arxiv.org/abs/1904.02619
https://arxiv.org/pdf/2010.01003.pdf
Code stolen from:
https://github.com/facebookresearch/gtn_applications
"""
from typing import List, Tuple
from einops import rearrange
import gtn
import numpy as np
import torch
from torch import nn
from torch import Tensor
class TDSBlock2d(nn.Module):
"""Internal block of a 2D TDSC network."""
def __init__(
self,
in_channels: int,
img_depth: int,
kernel_size: Tuple[int],
dropout_rate: float,
) -> None:
super().__init__()
self.in_channels = in_channels
self.img_depth = img_depth
self.kernel_size = kernel_size
self.dropout_rate = dropout_rate
self.fc_dim = in_channels * img_depth
# Network placeholders.
self.conv = None
self.mlp = None
self.instance_norm = None
self._build_block()
def _build_block(self) -> None:
# Convolutional block.
self.conv = nn.Sequential(
nn.Conv3d(
in_channels=self.in_channels,
out_channels=self.in_channels,
kernel_size=(1, self.kernel_size[0], self.kernel_size[1]),
padding=(0, self.kernel_size[0] // 2, self.kernel_size[1] // 2),
),
nn.ReLU(inplace=True),
nn.Dropout(self.dropout_rate),
)
# MLP block.
self.mlp = nn.Sequential(
nn.Linear(self.fc_dim, self.fc_dim),
nn.ReLU(inplace=True),
nn.Dropout(self.dropout_rate),
nn.Linear(self.fc_dim, self.fc_dim),
nn.Dropout(self.dropout_rate),
)
# Instance norm.
self.instance_norm = nn.ModuleList(
[
nn.InstanceNorm2d(self.fc_dim, affine=True),
nn.InstanceNorm2d(self.fc_dim, affine=True),
]
)
def forward(self, x: Tensor) -> Tensor:
"""Forward pass.
Args:
x (Tensor): Input tensor.
Shape:
- x: :math: `(B, CD, H, W)`
Returns:
Tensor: Output tensor.
"""
B, CD, H, W = x.shape
C, D = self.in_channels, self.img_depth
residual = x
x = rearrange(x, "b (c d) h w -> b c d h w", c=C, d=D)
x = self.conv(x)
x = rearrange(x, "b c d h w -> b (c d) h w")
x += residual
x = self.instance_norm[0](x)
x = self.mlp(x.transpose(1, 3)).transpose(1, 3) + x
x + self.instance_norm[1](x)
# Output shape: [B, CD, H, W]
return x
class TDS2d(nn.Module):
"""TDS Netowrk.
Structure is the following:
Downsample layer -> TDS2d group -> ... -> Linear output layer
"""
def __init__(
self,
input_dim: int,
output_dim: int,
depth: int,
tds_groups: Tuple[int],
kernel_size: Tuple[int],
dropout_rate: float,
in_channels: int = 1,
) -> None:
super().__init__()
self.in_channels = in_channels
self.input_dim = input_dim
self.output_dim = output_dim
self.depth = depth
self.tds_groups = tds_groups
self.kernel_size = kernel_size
self.dropout_rate = dropout_rate
self.tds = None
self.fc = None
def _build_network(self) -> None:
modules = []
stride_h = np.prod([grp["stride"][0] for grp in self.tds_groups])
if self.input_dim % stride_h:
raise RuntimeError(
f"Image height not divisible by total stride {stride_h}."
)
for tds_group in self.tds_groups:
# Add downsample layer.
out_channels = self.depth * tds_group["channels"]
modules.extend(
[
nn.Conv2d(
in_channels=self.in_channels,
out_channels=out_channels,
kernel_size=self.kernel_size,
padding=(self.kernel_size[0] // 2, self.kernel_size[1] // 2),
stride=tds_group["stride"],
),
nn.ReLU(inplace=True),
nn.Dropout(self.dropout_rate),
nn.InstanceNorm2d(out_channels, affine=True),
]
)
for _ in range(tds_group["num_blocks"]):
modules.append(
TDSBlock2d(
tds_group["channels"],
self.depth,
self.kernel_size,
self.dropout_rate,
)
)
self.in_channels = out_channels
self.tds = nn.Sequential(*modules)
self.fc = nn.Linear(
self.in_channels * self.input_dim // stride_h, self.output_dim
)
def forward(self, x: Tensor) -> Tensor:
"""Forward pass.
Args:
x (Tensor): Input tensor.
Shape:
- x: :math: `(B, H, W)`
Returns:
Tensor: Output tensor.
"""
B, H, W = x.shape
x = rearrange(
x, "b (h1 h2) w -> b h1 h2 w", h1=self.in_channels, h2=H // self.in_channels
)
x = self.tds(x)
# x shape: [B, C, H, W]
x = rearrange(x, "b c h w -> b w (c h)")
return self.fc(x)
|
