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