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"""Mobile inverted residual block."""
from typing import Optional, Tuple, Union
import attr
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from text_recognizer.networks.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 BaseModule(nn.Module):
"""Base sub module class."""
bn_momentum: float = attr.ib()
bn_eps: float = attr.ib()
block: nn.Sequential = attr.ib(init=False)
def __attrs_pre_init__(self) -> None:
super().__init__()
def __attrs_post_init__(self) -> None:
self._build()
def _build(self) -> None:
pass
def forward(self, x: Tensor) -> Tensor:
"""Forward pass."""
return self.block(x)
@attr.s(auto_attribs=True, eq=False)
class InvertedBottleneck(BaseModule):
"""Inverted bottleneck module."""
in_channels: int = attr.ib()
out_channels: int = attr.ib()
def _build(self) -> None:
self.block = nn.Sequential(
nn.Conv2d(
in_channels=self.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,
),
nn.Mish(inplace=True),
)
@attr.s(auto_attribs=True, eq=False)
class Depthwise(BaseModule):
"""Depthwise convolution module."""
channels: int = attr.ib()
kernel_size: int = attr.ib()
stride: int = attr.ib()
def _build(self) -> None:
self.block = nn.Sequential(
nn.Conv2d(
in_channels=self.channels,
out_channels=self.channels,
kernel_size=self.kernel_size,
stride=self.stride,
groups=self.channels,
bias=False,
),
nn.BatchNorm2d(
num_features=self.channels, momentum=self.bn_momentum, eps=self.bn_eps
),
nn.Mish(inplace=True),
)
@attr.s(auto_attribs=True, eq=False)
class SqueezeAndExcite(BaseModule):
"""Sequeeze and excite module."""
in_channels: int = attr.ib()
channels: int = attr.ib()
se_ratio: float = attr.ib()
def _build(self) -> None:
num_squeezed_channels = max(1, int(self.in_channels * self.se_ratio))
self.block = nn.Sequential(
nn.Conv2d(
in_channels=self.channels,
out_channels=num_squeezed_channels,
kernel_size=1,
),
nn.Mish(inplace=True),
nn.Conv2d(
in_channels=num_squeezed_channels,
out_channels=self.channels,
kernel_size=1,
),
)
@attr.s(auto_attribs=True, eq=False)
class Pointwise(BaseModule):
"""Pointwise module."""
in_channels: int = attr.ib()
out_channels: int = attr.ib()
def _build(self) -> None:
self.block = nn.Sequential(
nn.Conv2d(
in_channels=self.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,
),
)
@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: Optional[InvertedBottleneck] = 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 = (
InvertedBottleneck(
in_channels=self.in_channels,
out_channels=inner_channels,
bn_momentum=self.bn_momentum,
bn_eps=self.bn_eps,
)
if self.expand_ratio != 1
else None
)
self._depthwise = Depthwise(
channels=inner_channels,
kernel_size=self.kernel_size,
stride=self.stride,
bn_momentum=self.bn_momentum,
bn_eps=self.bn_eps,
)
self._squeeze_excite = (
SqueezeAndExcite(
in_channels=self.in_channels,
channels=inner_channels,
se_ratio=self.se_ratio,
bn_momentum=self.bn_momentum,
bn_eps=self.bn_eps,
)
if has_se
else None
)
self._pointwise = Pointwise(
in_channels=inner_channels,
out_channels=self.out_channels,
bn_momentum=self.bn_momentum,
bn_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:
"""Forward pass."""
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
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