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"""Mobile inverted residual block."""
from typing import Optional, Sequence, Union, Tuple
import attr
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from text_recognizer.networks.encoders.efficientnet.utils import stochastic_depth
def _convert_stride(stride: Union[Tuple[int, int], int]) -> Tuple[int, int]:
"""Converts int to tuple."""
return (stride,) * 2 if isinstance(stride, int) else stride
@attr.s(eq=False)
class MBConvBlock(nn.Module):
"""Mobile Inverted Residual Bottleneck block."""
def __attrs_pre_init__(self) -> None:
super().__init__()
in_channels: int = attr.ib()
out_channels: int = attr.ib()
kernel_size: Tuple[int, int] = attr.ib()
stride: Tuple[int, int] = attr.ib(converter=_convert_stride)
bn_momentum: float = attr.ib()
bn_eps: float = attr.ib()
se_ratio: float = attr.ib()
expand_ratio: int = attr.ib()
pad: Tuple[int, int, int, int] = attr.ib(init=False)
_inverted_bottleneck: nn.Sequential = attr.ib(init=False)
_depthwise: nn.Sequential = attr.ib(init=False)
_squeeze_excite: nn.Sequential = attr.ib(init=False)
_pointwise: nn.Sequential = attr.ib(init=False)
@pad.default
def _configure_padding(self) -> Tuple[int, int, int, int]:
"""Set padding for convolutional layers."""
if self.stride == (2, 2):
return ((self.kernel_size - 1) // 2 - 1, (self.kernel_size - 1) // 2,) * 2
return ((self.kernel_size - 1) // 2,) * 4
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
self._build()
def _build(self) -> None:
has_se = self.se_ratio is not None and 0.0 < self.se_ratio < 1.0
inner_channels = self.in_channels * self.expand_ratio
self._inverted_bottleneck = (
self._configure_inverted_bottleneck(out_channels=inner_channels)
if self.expand_ratio != 1
else None
)
self._depthwise = self._configure_depthwise(
in_channels=inner_channels,
out_channels=inner_channels,
groups=inner_channels,
)
self._squeeze_excite = (
self._configure_squeeze_excite(
in_channels=inner_channels, out_channels=inner_channels,
)
if has_se
else None
)
self._pointwise = self._configure_pointwise(in_channels=inner_channels)
def _configure_inverted_bottleneck(self, out_channels: int) -> nn.Sequential:
"""Expansion phase."""
return nn.Sequential(
nn.Conv2d(
in_channels=self.in_channels,
out_channels=out_channels,
kernel_size=1,
bias=False,
),
nn.BatchNorm2d(
num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps
),
nn.Mish(inplace=True),
)
def _configure_depthwise(
self, in_channels: int, out_channels: int, groups: int,
) -> nn.Sequential:
return nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=self.kernel_size,
stride=self.stride,
groups=groups,
bias=False,
),
nn.BatchNorm2d(
num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps
),
nn.Mish(inplace=True),
)
def _configure_squeeze_excite(
self, in_channels: int, out_channels: int
) -> nn.Sequential:
num_squeezed_channels = max(1, int(in_channels * self.se_ratio))
return nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=num_squeezed_channels,
kernel_size=1,
),
nn.Mish(inplace=True),
nn.Conv2d(
in_channels=num_squeezed_channels,
out_channels=out_channels,
kernel_size=1,
),
)
def _configure_pointwise(self, in_channels: int) -> nn.Sequential:
return nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=self.out_channels,
kernel_size=1,
bias=False,
),
nn.BatchNorm2d(
num_features=self.out_channels,
momentum=self.bn_momentum,
eps=self.bn_eps,
),
)
def _stochastic_depth(
self, x: Tensor, residual: Tensor, stochastic_dropout_rate: Optional[float]
) -> Tensor:
if self.stride == (1, 1) and self.in_channels == self.out_channels:
if stochastic_dropout_rate:
x = stochastic_depth(
x, p=stochastic_dropout_rate, training=self.training
)
x += residual
return x
def forward(
self, x: Tensor, stochastic_dropout_rate: Optional[float] = None
) -> Tensor:
residual = x
if self._inverted_bottleneck is not None:
x = self._inverted_bottleneck(x)
x = F.pad(x, self.pad)
x = self._depthwise(x)
if self._squeeze_excite is not None:
x_squeezed = F.adaptive_avg_pool2d(x, 1)
x_squeezed = self._squeeze_excite(x)
x = torch.tanh(F.softplus(x_squeezed)) * x
x = self._pointwise(x)
# Stochastic depth
x = self._stochastic_depth(x, residual, stochastic_dropout_rate)
return x
|
