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"""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(16, 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),
)
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)
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