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"""Mobile inverted residual block."""
from typing import Optional, Tuple
import torch
from torch import nn, Tensor
from torch.nn import functional as F
from .utils import calculate_output_image_size, drop_connection, get_same_padding_conv2d
class MBConvBlock(nn.Module):
"""Mobile Inverted Residual Bottleneck block."""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
bn_momentum: float,
bn_eps: float,
se_ratio: float,
id_skip: bool,
expand_ratio: int,
image_size: Optional[Tuple[int, int]],
) -> None:
super().__init__()
self.kernel_size = kernel_size
self.bn_momentum = bn_momentum
self.bn_eps = bn_eps
self.id_skip = id_skip
self.in_channels = self.in_channels
self.out_channels = out_channels
# Placeholders for layers.
self._inverted_bottleneck: nn.Sequential = None
self._depthwise: nn.Sequential = None
self._squeeze_excite: nn.Sequential = None
self._pointwise: nn.Sequential = None
self._build(
image_size=image_size,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
expand_ratio=expand_ratio,
se_ratio=se_ratio,
)
def _build(
self,
image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
expand_ratio: int,
se_ratio: float,
) -> None:
has_se = se_ratio is not None and 0.0 < se_ratio < 1.0
inner_channels = in_channels * expand_ratio
self._inverted_bottleneck = (
self._configure_inverted_bottleneck(
image_size=image_size,
in_channels=in_channels,
out_channels=inner_channels,
)
if expand_ratio != 1
else None
)
self._depthwise = self._configure_depthwise(
image_size=image_size,
in_channels=inner_channels,
out_channels=inner_channels,
groups=inner_channels,
kernel_size=kernel_size,
stride=stride,
)
image_size = calculate_output_image_size(image_size, stride)
self._squeeze_excite = (
self._configure_squeeze_excite(
in_channels=inner_channels,
out_channels=inner_channels,
se_ratio=se_ratio,
)
if has_se
else None
)
self._pointwise = self._configure_pointwise(
image_size=image_size, in_channels=inner_channels, out_channels=out_channels
)
def _configure_inverted_bottleneck(
self,
image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
) -> nn.Sequential:
"""Expansion phase."""
Conv2d = get_same_padding_conv2d(image_size=image_size)
return nn.Sequential(
Conv2d(
in_channels=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,
image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
groups: int,
kernel_size: int,
stride: int,
) -> nn.Sequential:
Conv2d = get_same_padding_conv2d(image_size=image_size)
return nn.Sequential(
Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=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, se_ratio: float
) -> nn.Sequential:
Conv2d = get_same_padding_conv2d(image_size=(1, 1))
num_squeezed_channels = max(1, int(in_channels * se_ratio))
return nn.Sequential(
Conv2d(
in_channels=in_channels,
out_channels=num_squeezed_channels,
kernel_size=1,
),
nn.Mish(inplace=True),
Conv2d(
in_channels=num_squeezed_channels,
out_channels=out_channels,
kernel_size=1,
),
)
def _configure_pointwise(
self, image_size: Optional[Tuple[int, int]], in_channels: int, out_channels: int
) -> nn.Sequential:
Conv2d = get_same_padding_conv2d(image_size=image_size)
return nn.Sequential(
Conv2d(
in_channels=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
),
)
def _stochastic_depth(
self, x: Tensor, residual: Tensor, drop_connection_rate: Optional[float]
) -> Tensor:
if self.id_skip and self.stride == 1 and self.in_channels == self.out_channels:
if drop_connection_rate:
x = drop_connection(x, p=drop_connection_rate, training=self.training)
x += residual
return x
def forward(
self, x: Tensor, drop_connection_rate: Optional[float] = None
) -> Tensor:
residual = x
if self._inverted_bottleneck is not None:
x = self._inverted_bottleneck(x)
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, drop_connection_rate)
return x
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