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121 lines
4.6 KiB
121 lines
4.6 KiB
'''
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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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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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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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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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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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class eca_layer(nn.Module):
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"""Constructs a ECA module.
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Args:
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channel: Number of channels of the input feature map
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k_size: Adaptive selection of kernel size
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"""
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def __init__(self, channel, k_size=3):
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super(eca_layer, 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.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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# reshape for convolution
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y = y.view(x.shape[0], 1, -1)
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# Two different branches of ECA module
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y = self.conv(y)
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# Multi-scale information fusion
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y = self.sigmoid(y.view(x.shape[0], -1, 1, 1))
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return x * y.expand_as(x)
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'''original implementation
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def forward(self, x):
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# x: input features with shape [b, c, h, w]
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b, c, h, w = x.size()
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# feature descriptor on the global spatial information
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y = self.avg_pool(x)
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# Two different branches of ECA module
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y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
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# Multi-scale information fusion
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y = self.sigmoid(y)
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return x * y.expand_as(x)
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'''
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class ceca_layer(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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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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(parameter size, throughput,latency, etc)
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Args:
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channel: Number of channels of the input feature map
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k_size: Adaptive selection of kernel size
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"""
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def __init__(self, channel, k_size=3):
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super(ceca_layer, 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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#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.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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# Two different branches of ECA module
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y = self.conv(y)
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# Multi-scale information fusion
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y = self.sigmoid(y.view(x.shape[0], -1, 1, 1))
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return x * y.expand_as(x)
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