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@ -1,17 +1,17 @@
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'''
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ECA module from ECAnet
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ECA module from ECAnet
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original paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
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https://arxiv.org/abs/1910.03151
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https://github.com/BangguWu/ECANet
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original ECA model borrowed from original github
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modified circular ECA implementation and
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modified circular ECA implementation and
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adoptation for use in pytorch image models package
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by Chris Ha https://github.com/VRandme
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MIT License
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Copyright (c) 2019 BangguWu, Qilong Wang
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Copyright (c) 2019 BangguWu, Qilong Wang
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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@ -31,10 +31,8 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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'''
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import torch
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from torch import nn
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from torch.nn.parameter import Parameter
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import torch.nn.functional as F
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class EcaModule(nn.Module):
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"""Constructs a ECA module.
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@ -47,7 +45,7 @@ class EcaModule(nn.Module):
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super(EcaModule, self).__init__()
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assert k_size % 2 == 1
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
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self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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# feature descriptor on the global spatial information
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@ -82,11 +80,11 @@ class EcaModule(nn.Module):
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class CecaModule(nn.Module):
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"""Constructs a circular ECA module.
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the primary difference is that the conv uses a circular padding rather than zero padding.
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This is because unlike images, the channels themselves do not have inherent ordering nor
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This is because unlike images, the channels themselves do not have inherent ordering nor
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locality. Although this module in essence, applies such an assumption, it is unnecessary
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to limit the channels on either "edge" from being circularly adapted to each other.
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This will fundamentally increase connectivity and possibly increase performance metrics
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(accuracy, robustness), without signficantly impacting resource metrics
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(accuracy, robustness), without signficantly impacting resource metrics
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(parameter size, throughput,latency, etc)
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Args:
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@ -100,16 +98,16 @@ class CecaModule(nn.Module):
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#pytorch circular padding mode is bugged as of pytorch 1.4
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# see https://github.com/pytorch/pytorch/pull/17240
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#implement manual circular padding
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self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding = 0, bias=False)
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self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=0, bias=False)
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self.padding = (k_size - 1) // 2
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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# feature descriptor on the global spatial information
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y = self.avg_pool(x)
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#manually implement circular padding, F.pad does not seemed to be bugged
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y = F.pad(y.view(x.shape[0],1,-1),(self.padding,self.padding),mode='circular')
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y = F.pad(y.view(x.shape[0], 1, -1), (self.padding, self.padding), mode='circular')
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# Two different branches of ECA module
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y = self.conv(y)
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