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"""Efficientnet backbone."""
from typing import Tuple
import attr
from torch import nn, Tensor
from .mbconv import MBConvBlock
from .utils import (
block_args,
round_filters,
round_repeats,
)
@attr.s(eq=False)
class EfficientNet(nn.Module):
"""Efficientnet without classification head."""
def __attrs_pre_init__(self) -> None:
super().__init__()
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),
}
arch: str = attr.ib()
params: Tuple[float, float, float] = attr.ib(default=None, init=False)
stochastic_dropout_rate: float = attr.ib(default=0.2)
bn_momentum: float = attr.ib(default=0.99)
bn_eps: float = attr.ib(default=1.0e-3)
out_channels: int = attr.ib(default=None, init=False)
_conv_stem: nn.Sequential = attr.ib(default=None, init=False)
_blocks: nn.ModuleList = attr.ib(default=None, init=False)
_conv_head: nn.Sequential = attr.ib(default=None, init=False)
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
self._build()
@arch.validator
def check_arch(self, attribute: attr._make.Attribute, value: str) -> None:
"""Validates the efficientnet architecure."""
if value not in self.archs:
raise ValueError(f"{value} not a valid architecure.")
self.params = self.archs[value]
def _build(self) -> None:
"""Builds the efficientnet backbone."""
_block_args = block_args()
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, 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(320, self.params)
self.out_channels = round_filters(1280, 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,
),
)
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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