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51 lines
2.2 KiB
51 lines
2.2 KiB
from torch import nn as nn
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import torch.nn.functional as F
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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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* min_channels can be specified to keep reduced channel count at a minimum (default: 8)
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* divisor can be specified to keep channels rounded to specified values (default: 1)
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* reduction channels can be specified directly by arg (if reduction_channels is set)
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* reduction channels can be specified by float ratio (if reduction_ratio is set)
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"""
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def __init__(self, channels, reduction=16, act_layer=nn.ReLU, gate_layer='sigmoid',
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reduction_ratio=None, reduction_channels=None, min_channels=8, divisor=1):
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super(SEModule, self).__init__()
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if reduction_channels is not None:
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reduction_channels = reduction_channels # direct specification highest priority, no rounding/min done
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elif reduction_ratio is not None:
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reduction_channels = make_divisible(channels * reduction_ratio, divisor, min_channels)
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else:
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reduction_channels = make_divisible(channels // reduction, divisor, min_channels)
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self.fc1 = nn.Conv2d(channels, reduction_channels, kernel_size=1, bias=True)
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self.act = act_layer(inplace=True)
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self.fc2 = nn.Conv2d(reduction_channels, channels, kernel_size=1, bias=True)
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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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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)
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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__(self, channels, gate_layer='hard_sigmoid'):
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super(EffectiveSEModule, self).__init__()
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self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
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self.gate = create_act_layer(gate_layer, inplace=True)
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def forward(self, x):
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x_se = x.mean((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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