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
|
"""Efficientnet backbone."""
from typing import Tuple
from torch import nn, Tensor
from text_recognizer.networks.efficientnet.mbconv import MBConvBlock
from text_recognizer.networks.efficientnet.utils import (
block_args,
round_filters,
round_repeats,
)
class EfficientNet(nn.Module):
"""Efficientnet without classification head."""
archs = {
# width, depth, dropout
"b0": (1.0, 1.0, 0.2),
"b1": (1.0, 1.1, 0.2),
"b2": (1.1, 1.2, 0.3),
"b3": (1.2, 1.4, 0.3),
"b4": (1.4, 1.8, 0.4),
"b5": (1.6, 2.2, 0.4),
"b6": (1.8, 2.6, 0.5),
"b7": (2.0, 3.1, 0.5),
"b8": (2.2, 3.6, 0.5),
"l2": (4.3, 5.3, 0.5),
}
def __init__(
self,
arch: str,
stochastic_dropout_rate: float = 0.2,
bn_momentum: float = 0.99,
bn_eps: float = 1.0e-3,
depth: int = 7,
out_channels: int = 1280,
) -> None:
super().__init__()
self.params = self._get_arch_params(arch)
self.stochastic_dropout_rate = stochastic_dropout_rate
self.bn_momentum = bn_momentum
self.bn_eps = bn_eps
self.depth = depth
self.out_channels: int = out_channels
self._conv_stem: nn.Sequential
self._blocks: nn.ModuleList
self._conv_head: nn.Sequential
self._build()
def _get_arch_params(self, value: str) -> Tuple[float, float, float]:
"""Validates the efficientnet architecure."""
if value not in self.archs:
raise ValueError(f"{value} not a valid architecure.")
return self.archs[value]
def _build(self) -> None:
"""Builds the efficientnet backbone."""
_block_args = block_args()[: self.depth]
in_channels = 1 # BW
out_channels = round_filters(32, self.params)
self._conv_stem = nn.Sequential(
nn.ZeroPad2d((0, 1, 0, 1)),
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=2,
bias=False,
),
nn.BatchNorm2d(
num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps
),
nn.Mish(inplace=True),
nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=2,
bias=False,
),
nn.BatchNorm2d(
num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps
),
nn.Mish(inplace=True),
)
self._blocks = nn.ModuleList([])
for args in _block_args:
args.in_channels = round_filters(args.in_channels, self.params)
args.out_channels = round_filters(args.out_channels, self.params)
num_repeats = round_repeats(args.num_repeats, self.params)
del args.num_repeats
for _ in range(num_repeats):
self._blocks.append(
MBConvBlock(
**args,
bn_momentum=self.bn_momentum,
bn_eps=self.bn_eps,
)
)
args.in_channels = args.out_channels
args.stride = 1
in_channels = round_filters(_block_args[-1].out_channels, self.params)
self._conv_head = nn.Sequential(
nn.Conv2d(
in_channels,
self.out_channels,
kernel_size=1,
stride=1,
bias=False,
),
nn.BatchNorm2d(
num_features=self.out_channels,
momentum=self.bn_momentum,
eps=self.bn_eps,
),
nn.Dropout(p=self.params[-1]),
)
def extract_features(self, x: Tensor) -> Tensor:
"""Extracts the final feature map layer."""
x = self._conv_stem(x)
for i, block in enumerate(self._blocks):
stochastic_dropout_rate = self.stochastic_dropout_rate
if self.stochastic_dropout_rate:
stochastic_dropout_rate *= i / len(self._blocks)
x = block(x, stochastic_dropout_rate=stochastic_dropout_rate)
x = self._conv_head(x)
return x
def forward(self, x: Tensor) -> Tensor:
"""Returns efficientnet image features."""
return self.extract_features(x)
|
