135 lines
5.1 KiB
Python
135 lines
5.1 KiB
Python
""" Squeeze-and-Excitation Channel Attention
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An SE implementation originally based on PyTorch SE-Net impl.
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Has since evolved with additional functionality / configuration.
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Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
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Also included is Effective Squeeze-Excitation (ESE).
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Paper: `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
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Hacked together by / Copyright 2021 Ross Wightman
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"""
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from typing import Optional, Tuple, Type, Union
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from torch import nn as nn
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from .create_act import create_act_layer
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from .helpers import make_divisible
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class SEModule(nn.Module):
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""" SE Module as defined in original SE-Nets with a few additions
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Additions include:
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* divisor can be specified to keep channels % div == 0 (default: 8)
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* reduction channels can be specified directly by arg (if rd_channels is set)
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* reduction channels can be specified by float rd_ratio (default: 1/16)
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* global max pooling can be added to the squeeze aggregation
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* customizable activation, normalization, and gate layer
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"""
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def __init__(
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self,
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channels: int,
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rd_ratio: float = 1. / 16,
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rd_channels: Optional[int] = None,
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rd_divisor: int = 8,
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add_maxpool: bool = False,
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bias: bool = True,
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act_layer: Type[nn.Module] = nn.ReLU,
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norm_layer: Optional[Type[nn.Module]] = None,
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gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
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device=None,
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dtype=None,
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):
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dd = {'device': device, 'dtype': dtype}
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super().__init__()
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self.add_maxpool = add_maxpool
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if not rd_channels:
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rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
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self.fc1 = nn.Conv2d(channels, rd_channels, kernel_size=1, bias=bias, **dd)
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self.bn = norm_layer(rd_channels, **dd) if norm_layer else nn.Identity()
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self.act = create_act_layer(act_layer, inplace=True)
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self.fc2 = nn.Conv2d(rd_channels, channels, kernel_size=1, bias=bias, **dd)
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self.gate = create_act_layer(gate_layer)
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def forward(self, x):
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x_se = x.mean((2, 3), keepdim=True)
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if self.add_maxpool:
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# experimental codepath, may remove or change
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x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
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x_se = self.fc1(x_se)
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x_se = self.act(self.bn(x_se))
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x_se = self.fc2(x_se)
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return x * self.gate(x_se)
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SqueezeExcite = SEModule # alias
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class EffectiveSEModule(nn.Module):
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""" 'Effective Squeeze-Excitation
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From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
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"""
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def __init__(
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self,
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channels: int,
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add_maxpool: bool = False,
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gate_layer: Union[str, Type[nn.Module]] = 'hard_sigmoid',
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device=None,
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dtype=None,
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**_,
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):
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dd = {'device': device, 'dtype': dtype}
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super().__init__()
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self.add_maxpool = add_maxpool
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self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0, device=device, dtype=dtype)
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self.gate = create_act_layer(gate_layer)
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def forward(self, x):
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x_se = x.mean((2, 3), keepdim=True)
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if self.add_maxpool:
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# experimental codepath, may remove or change
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x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
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x_se = self.fc(x_se)
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return x * self.gate(x_se)
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EffectiveSqueezeExcite = EffectiveSEModule # alias
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class SqueezeExciteCl(nn.Module):
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""" SE Module as defined in original SE-Nets with a few additions
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Additions include:
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* divisor can be specified to keep channels % div == 0 (default: 8)
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* reduction channels can be specified directly by arg (if rd_channels is set)
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* reduction channels can be specified by float rd_ratio (default: 1/16)
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* global max pooling can be added to the squeeze aggregation
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* customizable activation, normalization, and gate layer
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"""
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def __init__(
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self,
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channels: int,
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rd_ratio: float = 1. / 16,
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rd_channels: Optional[int] = None,
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rd_divisor: int = 8,
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bias: bool = True,
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act_layer: Type[nn.Module] = nn.ReLU,
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gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
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device=None,
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dtype=None,
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):
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dd = {'device': device, 'dtype': dtype}
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super().__init__()
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if not rd_channels:
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rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
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self.fc1 = nn.Linear(channels, rd_channels, bias=bias, **dd)
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self.act = create_act_layer(act_layer, inplace=True)
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self.fc2 = nn.Linear(rd_channels, channels, bias=bias, **dd)
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self.gate = create_act_layer(gate_layer)
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def forward(self, x):
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x_se = x.mean((1, 2), keepdims=True) # FIXME avg dim [1:n-1], don't assume 2D NHWC
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x_se = self.fc1(x_se)
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x_se = self.act(x_se)
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x_se = self.fc2(x_se)
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return x * self.gate(x_se) |